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03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 1
Atmospheric Composition Change: Ecosystems - Atmosphere interactions
D. Fowler1*, K. Pilegaard2, M.A. Sutton1, P. Ambus2, M. Raivonen3, J. Duyzer4, D. Simpson5, H.
Fagerli6, J.K. Schjoerring7, A. Neftel8, J. Burkhardt9, U. Daemmgen10, J. Neirynck11, E.
Personne12, R. Wichink-Kruit13, K. Butterbach-Bahl14, C. Flechard8, J.P. Tuovinen15, M. Coyle1,
G. Gerosa16, B. Loubet17, N. Altimir18, L. Gruenhage19, C. Ammann8, S. Cieslik20, E. Paoletti21,
T.N. Mikkelsen2, H. Ro-Poulsen22, P. Cellier17, J.N. Cape1, L. Horváth23, F. Loreto24, Ü.
Niinemets25, P. I. Palmer26, J. Rinne27, P. Misztal1, E. Nemitz1, D. Nilsson28, S. Pryor29, M.W.
Gallagher30, T. Vesala27, U. Skiba1, N. Brüeggemann14, S. Zechmeister-Boltenstern31, J.
Williams32, C. O’Dowd33, M. C. Facchini34, G. de Leeuw35, A. Flossman36, N. Chaumerliac36,
J.W. Erisman37
1Centre of Ecology and Hydrology, Bush Estate, Edinburgh, UK 2Risø National Laboratory, Technical University of Denmark, Frederiksborgvej 399, DK-4000 Roskilde, Denmark 3Department of Forest Ecology, University of Helsinki, Finland 4TNO Institute of Environmental Sciences, The Netherlands 5Chalmers University of Technology, Hörsalsvg, Gothenburg, Sweden 6Norwegian Meteorological Institute, P.O. Box 43, Blindern, 0313 Oslo, Norway 7Royal and Veterinary and Agricultural University, Bülowsvej 17, Frederiksberg C, Denmark 8Agroscope FAL Reckenholz, Federal Research Station for Agroecology and Agriculture, PO Box, CH 8046 Zurich, Switzerland 9University of Bonn, Institute of Crop Science and Resource Conservation - Plant Nutrition, Karlrobert-Kreiten-Straße 13, 53115 Bonn, Germany 10 Bundesforschungsanstalt für Landwirtschaft (FAL) Institut für Agrarökologie, Bundesallee 50, 38116 Braunschweig, Germany 11 Research Institute for Nature and Forest, Gaverstraat 4, B-9500 Geraardsbergen, Belgium 12 INRA, INA PG, UMR Environm & Grandes Cultures, F-78850 Thiverval Grignon, France 13Department of Meteorology and Air Quality, Wageningen University and Research Centre (WUR), Postbus 47, 6700 AA Wageningen, The Netherlands 14Institute of Meteorology and Climate Research, Atmos. Environ. Research (IMK-IFU), Research Centre Karlsruhe GmbH, Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany 15Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland 16Dipartimento di Biologia Vegetale, Università di Firenze, Piazzale Cascine 28, I-50144 Firenze, Italy 17 INRA Unité Mixte de Recherche, 78850 Thiverval-Grignon, France 18 Department of Forest Ecology, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland 19 Institute for Plant Ecology, Justus-Liebig-University of Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany 20Joint Research Centre, I-21027 Ispra, Italy 21IPP-CNR, Via Madonna del Piano 10, I-50019 Sesto Fiorentino, Firenze, Italy 22Botanical Institute, University of Copenhagen, Øster Farimagsgade 2D, 1353 Copenhagen K, Denmark 23Hungarian Meteorological Service, PO Box 39, H-1675 Budapest, Hungary 24Consiglio Nazionale delle Ricerche – Istituto di Biologia Agroambientale e Forestale, Via Salaria Km 29.300, 00015 Monterotondo Scalo, Italy 25Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 1, 51014 Tartu, Estonia 26School of GeoSciences, University of Edinburgh, King's Buildings, West Mains Road, Edinburgh, EH9 3JN, UK 27Department of Physical Sciences, University of Helsinki, Helsinki, Finland
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28 Stockholm Univ, Dept Appl Environm Sci, Atmospher Sci Unit, S-10691 Stockholm, Sweden 29Atmospheric Science Program, Dept of Geography, Indiana University, Bloomington, IN, USA 30School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Williamson Building, Oxford Road, Manchester, M13 9PL, UK 31Department of Forest Ecology, Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Seckendorff-Gudent-Weg 8, 1131 Vienna, Austria 32Max-Planck-Institut für Chemie, J.J.-Becher-Weg 27, D-55128 Mainz, Germany 33Department of Experimental Physics and Environmental Change Institute, National University of Ireland, Galway University Road, Galway, Ireland 34Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy 35Climate and Global Change Unit, Research and Development, Finnish Meteorological Institute, 00560 Helsinki, Finland 36Laboratoire de Météorologie Physique, Université Blaise Pascal-CNRS-OPGC, 24, avenue des Landais, F-63177 Aubière Cedex, France 37Energy Research Centre of The Netherlands, The Netherlands
* Author to whom correspondence addressed
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Table of Contents Page Abstract 6
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Introduction Scale Reactivity of natural surfaces Frameworks for analysis and interpretation of trace gas and aerosol exchange Bi-directional exchange Aerosols Ocean Atmosphere Exchange Wet deposition
7 9
10 10 11 12 12 13
2 2.1 2.2 2.3 2.4 2.5 2.6 2.7
Reactive Gaseous Nitrogen Compounds Introduction Emissions from soils Emissions of NOy from plant surfaces Canopy atmosphere interactions Models and measurements Exchange of HNO3, HONO, PAN Upscaling and regional and global trends
14 14 14 18 19 20 22 26
3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8
Reduced Nitrogen (NH3, amines) Introduction Advances in Measurement Methods Key controls on biosphere-atmosphere exchange of Ammonia Effects of ecosystem type on Ammonia biosphere-atmosphere exchange Modelling surface-atmosphere exchange of Ammonia Dynamic simulation ecosystem C-N cycling of Ammonia fluxes Integrating Ammonia exchange processes Future challenges for Ammonia exchange
28 28 29 34 35 39 42 42 44
4 4.1 4.2 4.2.1 4.2.1.1 4.2.1.2 4.2.1.3 4.2.2 4.2.3 4.2.3.1 4.2.3.2 4.3 4.4 4.5
Sulphur Dioxide Introduction Worldwide advances in SO2 flux monitoring & modelling Asia Sulphur dioxide deposition to soils Micrometeorological measurements over vegetated areas Long term deposition studies and inferential modelling North America Europe Long-term flux monitoring in the UK Other recent European datasets Control of surface uptake rates by leaf cuticular chemistry Advances in deposition modelling Future challenges
46 46 47 47 47 48 49 50 51 51 52 54 56 58
5 5.1 5.2 5.2.1 5.2.2 5.2.3 5.2.4 5.2.5 5.3 5.4 5.5 5.6
Ozone Introduction Deposition rates European Forests Crops Grasslands Other vegetated surfaces Non-vegetated surfaces Non-stomatal deposition processes Model development and validation Risk Assessment Methods Potential effects of climate change
59 59 61 61 63 64 65 66 67 69 70 72
6 6.1
Biogenic Volatile Organic Compounds (BVOC) Introduction
75 75
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6.1.1 6.1.2 6.2 6.2.1 6.2.2 6.2.3 6.2.4 6.2.5 6.3 6.4 6.4.1 6.4.2 6.4.3 6.4.4 6.4.5 6.4.6 6.4.7 6.5 6.6
Volatile Isoprenoids Oxygenated volatile compounds Environmental controls on BVOC emission Physiological and physico-chemical controls of emissions Physico-chemical controls of emission in species lacking specific storage structures Uptake and release of volatile compounds by vegetation CO2-dependence of emissions Induced emissions Contemporary difficulties in scaling BVOC emissions from leaf to ecosystem BVOC fluxes over Europe, by compound and in relation to the needs of photochemical oxidant models Flux measurement techniques Isoprene Monoterpenes Sesquiterpenes Methanol Acetone and Acetaldehyde Other compounds The EU large field campaigns in the Mediterranean area: from BEMA to ACCENT Remote sensing of BVOC
75 76 77 77 78
79 80 80 81 82
82 82 83 84 84 85 85 85 87
7 7.1 7.2 7.2.1 7.2.2 7.3 7.3.1 7.3.2 7.4 7.4.1 7.4.2 7.4.3 7.5 7.5.1 7.5.2 7.5.3 7.6 7.6.1 7.6.2 7.7
Aerosols Introduction Review of new measurement approaches and instrumentation Flux measurements of particle numbers (size-resolved or total), without information on chemical composition Flux measurements of individual aerosol chemical species Area sources of particles Resuspension Urban emissions of aerosols Dry deposition of particles Dry deposition rates to vegetation Parameterising and modelling deposition rates Dry deposition rates to urban areas Uncertainties Uncertainties in the application of micrometeorological flux measurement techniques for deriving the local flux Relating measured fluxes to surface exchange: flux divergence and the effect of chemical interactions Interpretation of measurements for model verification Future research needs Deposition measurements and reporting Deposition models Conclusions – Aerosols
91 91 92 92
94 95 95 96 99 99
104 104 105 105
106
109 109 109 111 113
8 8.1 8.2 8.3 8.3.1 8.3.2 8.3.3 8.3.4 8.3.4.1 8.3.4.2 8.4 8.4.1
Ecosystem-Atmosphere exchange of Radiatively Active Gases – N2O & CH4 Introduction Global budgets of N2O and CH4 Biological Sources of N2O and CH4 The biology of production and consumption of N2O and CH4 in soils and sediments Distribution of active microbial populations in soils N2O and CH4 fluxes from the main global ecosystems Plant mediated transport and production of N2O and CH4 Methane from vegetation Nitrous Oxide from vegetation New developments in measurements of N2O and CH4 and denitrification Flux Chambers
115 115 115 116 116
117 118 119 119 120 121 121
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8.4.2 8.4.3 8.4.4 8.5 8.6 8.7
Micrometeorological methods Comparison of eddy covariance with chamber methods Recent methodological advances in measurements of total denitrification rates Modelling of N2O and CH4 fluxes on site and regional scale: approaches, applications and uncertainties Validation of models by landscape and regional scale measurements Conclusions
122 122 122 124
127 128
9 9.1 9.1.1 9.1.2 9.2 9.2.1 9.2.2 9.2.3
Exchange of trace gases & aerosols over the oceans New trace gas interactions at the air-sea interface Introduction Case Studies Aerosols Primary Marine Aerosol (PMA) Source functions Chemical Composition of primary sea spray Secondary Aerosol Production
129 129 129 131 136 136 138 141
10 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8
Processes of wet scavenging of aerosols and trace gases from the atmosphere Introduction Nucleation scavenging of drops and ice crystals Impaction scavenging of aerosol particles Scavenging of gases Clouds Orographic precipitation Snow Chemistry Conclusions & some priority areas of future research
143 143 143 145 146 146 149 149 153
11 Ecosystem-Atmosphere exchange – Conclusions 154 12 Acknowledgements 157 13 References 158
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Abstract
Ecosystems and the atmosphere
This review describes the state of understanding the processes involved in the exchange of trace
gases and aerosols between the earth’s surface and the atmosphere. The gases covered include
NO, NO2, HONO, HNO3, NH3, SO2, DMS, Biogenic VOC, O3, CH4, N2O and aerosols in the
size range 1nm to 10um including organic and inorganic chemical species. The main focus of the
review is on the exchange between terrestrial ecosystems, both managed and natural and the
atmosphere, although ocean-atmosphere exchange is included. The material presented is biased
towards the last decade, but includes earlier work, where more recent developments are limited
or absent.
Methodology and new instrumentation have enabled, if not driven technical advances in
measurement, and these are described, including the application of new mass spectrometric
methods, such as AMS and PTRMS adapted for field measurement of vertical fluxes using
micrometeorological methods for chemically resolved aerosols and for a wide range of VOC
respectively. Also briefly described are advances in theory and techniques in micrometeorology.
For some of the compounds there have been paradigm shifts in approach and application of both
techniques and assessment. These include flux measurements over marine surfaces and urban
areas using micrometeorological methods and the up-scaling of flux measurements using aircraft
and satellite remote sensing. The application of a flux based approach in assessment of O3 effects
on vegetation at regional scales of ozone is an important policy linked development secured
through improved quantification of fluxes. The coupling of monitoring modelling and intensive
flux measurement at a continental scale within the NitroEurope network represents a quantum
development in the application of research teams to address the underpinning science of reactive
nitrogen in the cycling between ecosystems and the atmosphere in Europe.
Some important developments of the science have been applied to assist in addressing policy
questions, which have been the main driver of the research agenda, while other developments in
understanding have not been applied to their wider field especially in CTM models through
deficiencies in linking data to enable application or inertia within the modelling community. The
paper identifies some of these applications, gaps and research questions that have remained
intractable at least since 2000, within the specialized sections of the paper rather than collated
together within the conclusions and where possible these have been focussed on research
questions for the coming decade.
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1 Introduction
The composition of the earth’s atmosphere is unique in the solar system in being largely
determined by biological processes in soils, vegetation and the oceans interacting with physical
and chemical processes within the atmosphere. The surface–atmosphere exchange of most gases
contributing major and trace constituents of the atmosphere is coupled to biological production
processes and transferred through the surface–atmosphere interface. Thus, developing a
mechanistic understanding of the production and exchange processes is a core activity in
understanding the earth system. The subject of this review is much narrower than the scope of
these opening lines, and is restricted to the trace gases and aerosols exchanged between the
atmosphere and the earth’s surface. However, as is clear from much of the international
assessment of changes in atmospheric composition since the industrial revolution, these trace
atmospheric constituents are changing the earth’s climate (IPCC 2007), global biodiversity
(Millenium Ecosystem Assessment 2005) and the biogeochemical cycling of major nutrients
including nitrogen, carbon, and sulphur. The earth's surface is a sink for some atmospheric trace
gases and aerosols, and a source for many others and for most, the surface–atmosphere interface
represents a zone within which a substantial fraction of the overall control of fluxes occur. An
understanding of the rate controlling processes at this interface is therefore vital in describing the
overall exchange process and understanding the global biogeochemical cycles. Applications of
science in this field, in addition to their intrinsic value, are necessary to quantify and model
responses to human perturbation of many of the biogeochemical cycles (C, N, S, halogens and
metals to name but a few). These perturbations include changes in land use or emissions of trace
gases to the atmosphere, through combustion and industrial activities. Taking as an example the
global nitrogen cycle, human activity through combustion processes for oxidized nitrogen and
the Haber Bosch process for reduced nitrogen now dominates the cycling of reactive nitrogen
through the atmosphere and back to terrestrial and marine ecosystems (Galloway et al 2004). The
total emission of reactive nitrogen (Nr) from human activities at the end of the 20th century
exceeds that from natural processes by a factor of 4 (20.7 Tg of oxidized and reduced reactive
nitrogen Nr from natural sources within a total of 104 Tg-N in 1993, Galloway et al 2004). As
nitrogen is a limiting nutrient in many ecosystems, these modifications of the natural cycling
have profound effects on ecosystem function, biodiversity and atmospheric composition
(Erisman et al 2008). The human disturbance of the global carbon cycle is also extensive, and the
quantities involved are very large. Global emissions of CO2 from fossil fuels since 1700 amount
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 8
to approximately 600 Gt-C, which have increased the atmospheric CO2 mixing ratio from 280
ppm to 380 ppm in 2006, an increase of about 30%, (IPCC 2007).
These high level indicators of human influence provide essential context for this review paper,
but conceal the detailed changes taking place and the range of chemical species and interactions
involved. The subject area includes many different chemical species, and it is not possible to be
comprehensive in this review for all gases. In particular the subject of the global carbon cycle
and CO2 in particular are much too large to cover in this review. The focus of this review is on
the reactive trace gases and for the greenhouse gases, CH4 and N2O. The different gases are
associated with a range of biological sources and have varied chemical reactivity in the
atmosphere and at surfaces. These differences reveal the range of controls and temporal and
spatial variability in rates of exchange, which are the focus of the review. The review moves
through a wide range of chemical species, identifying the current state of knowledge and, where
possible the applications of the new developments in a policy context.
The gases emitted from terrestrial and ocean ecosystems include all of the major greenhouse
gases, H2O, CO2, CH4 and N2O, the nitrogen gases (both in reduced and oxidized forms), sulphur
compounds, volatile organic compounds (VOC) and halogens.
Quantifying the fluxes of these trace components of the atmosphere is clearly a prerequisite
within an assessmemnt process leading to the development of policy within the context of
climate change, eutrophication, acidification and photochemical oxidant formation. Many
research groups have become involved in the measurement and modelling of emission and
uptake (deposition) fluxes of trace gases and particles. The mechanistic understanding developed
mainly within the last 30 years from two different and quite narrow fields of study. The first was
concerned with the sources of atmospheric trace constitiuents, and the greenhouse gases were
among the first compounds for which surface fluxes were quantified directly by field
measurements. These included small scale (0.1 m to 0.5 m2) measurements of fluxes from soils
and vegetation using chamber methods for CO2, CO, CH4, N2O (Junge, 1963). The
measurements showed large spatial and temporal variability so that up-scaling to regions
generated very large uncertainties. The other development was mainly associated with the
atmospheric transport and deposition of pollutants, including nitrogen and sulphur compounds
and the photochemical oxidants in the 1960s and 1970s (Husar et al 1978). These early studies
were made to determine the importance of surface removal which is better known as dry
deposition (to distinguish the process from removal by precipitation) as a sink for reactive trace
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 9
gases (NO2, SO2, O3). Advances in understanding and computing resources have allowed more
sophisticated approaches to be adopted, in which the processes at the surface recognised
different sinks and interactions with other trace gases, allowing rates of dry deposition to change
with time and with surface characteristics.
1.1 Scale
Emission or deposition schemes to quantify trace gas fluxes operate at a range of scales
depending on the applications (Fig 1.1). For hourly integration the application is primarily for
research purposes and mechanistic study at the small scale (<102 m2). For landscape scale
measurements and for assessment of the fate of pollutants at the regional scale (106 km2) the
application has both research and policy application. At this scale spatial and temporal
integration provides robust parameterization. The application in global models to quantify
sources and sinks is restricted in spatial resolution, typically to 1o x 1o, and likewise has research
and policy application. For the landscape scale, flux measurements may be made directly, using
micrometeorological methods above canopies of vegetation, soil, or even ocean surfaces and
have become the method of choice for long term flux measurement. These techniques provide, in
addition to the target trace gas flux the turbulent exchange characteristics of the underlying
surface and the partitioning of available radient energy which enables the processes to be
investigated at a sufficiently large scale to integrate canopy scale fluxes over typically 105 m2.
Figure 1.1 A diagrammatic representation of the scales of measurement of trace gas fluxes for process studies (a
transverse section through a Phaseolus vulgaris leaf, showing the palisade and mesophyll cellsbounded by epidermal
cells and the air spaces for internal exchange between trace gases and intercellular fluid). The field scale at which
most of the micrometeorological flux measurements are made and the continental scale where models provide the
emission and deposition fluxes. In this case the emission fluxes of oxides of nitrogen over Europe are shown,
revealing the importance of international shipping to the continental fluxes.
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The sections focus mainly on individual trace gases or classes of atmospheric particles, and each
considers the surface-atmosphere exchange over specific ecosystems. The exceptions are the
ocean surfaces and wet deposition, within which a range of relevant compounds are considered.
1.2 Reactivity of natural surfaces
For many of the short lived gases (<2 days in the boundary layer) there are multiple sinks at the
surface and these include foliar surfaces and soil whose properties as sinks for a range of gases
vary with humidity and the presence of surface water and are influenced, sometimes strongly, by
the presence of other gases (Fig 1.2). The chemical and physical complexity of terrestrial
surfaces, illustrated in Figures 1.1 and 1.2 at the microscopic scale is greatly simplified in the
parameterisations used in models. This is necessary in part due to the nature of the flux
measuring systems, which integrate the net fluxes, and fail to reveal the microscopic scale of
variability of the true exchange.
Figure 1.2 Illustrating the importance of different sinks for reaction of trace gases at the terrestrial surfaces, notably
the external surfaces of vegetation often as in this case covered by complex layers of epicuticular wax and illustrated
in figure 1.1, the internal structure of leaves, following uptake through the stomatal apertures and soils greatly
simplified in this illustration
1.3 Frameworks for analysis and interpretation of trace gas and aerosol exchange
The measurements of surface-atmosphere exchange provide at the simplest level the mass
exchange per unit area of surface, which may be ground, water or leaf area, per unit time. To
extract useful information on the underlying processes it is necessary to quantify the
contributions each step in the transfer pathway makes to the overall exchange between defined
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 11
points, which in this scheme is simplified to vertical levels between a source and a sink. The
most widely applied transfer scheme is a resistance analogue (Monteith and Unsworth 2007), in
which the flux of trace gas or particle is treated as an analogue of electrical current in a simple
network of resistances, which may act in series if there is just one sink at the surface, such as a
pure water surface, or may have several sinks at the surface, acting in parallel, each representing
a distinct chemical component of the underlying surface. A simple resistance network
representing three different sinks at the surface, and the two atmospheric resistances (Ra and Rb,
respectively the turbulent transfer resistance and the leaf boundary layer resistance) are
illustrated in Figure 1.3.
⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜
⎝
⎛
++=
++=
c3c2c1
c
cba
R1
R1
R1
1R
RRRtR
χO3(z0’) = 0
χO3(z-d)
FO3
Rc3soil
RbO3
Rc2cuticle Rc1
stomata
Ra
Figure 1.3 A simple resistance analogy for a trace gas with sinks in stomata, on foliar surfaces and in soil.
The atmospheric resistances may be separated from the total resistance using independent
measures of the turbulence above the vegetation. The overall flux may be measured by a variety
of micrometeorological methods (Monteith 1975), and thus the total of the surface or canopy
resistances to transfer between the source and sink may be quantified as the residual, as shown in
Figure 1.3.
1.4 Bi-directional exchange
For many of the trace gases, regardless of their reactivity, the exchange fluxes may be shown to
vary in sign as well as magnitude, with emission and deposition being commonly observed. The
most widely known example of bidirectional exchanges is CO2, which exhibits both deposition
and emission fluxes due to photosynthesis and respiration respectively. In this case the concept
of compensation points as mixing ratios at which no net exchange takes place is now widely
recognised for a range of trace gases (NH3, NO, CO2).
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The recognition of bi-directional exchange requires modelling approaches to simulate the
process for application in surface-atmosphere exchange models, as illustrated for NH3 in Fig 1.4.
Figure 1.4. A diagrammatic representation of bi-directional exchange, for NH3 exchange between the atmosphere
and vegetation.
1.5 Aerosols
The understanding of deposition and emissions of aerosols over terrestrial surfaces has advanced
considerably in the last decade, after a long period in which application of a model developed by
Slinn (1984) has been a standard for many modelling approaches. Likewise, the emission of
aerosols by resuspension from terrestrial surfaces has advanced following innovative new
measurement approaches.
1.6 Ocean atmosphere exchange
For many years the ocean atmosphere exchange of trace gases has been treated in a more
simplistic way (Liss et al 1981), in part due to the relative simplicity of the ocean surface relative
to terrestrial surfaces, but also due to the difficulty in making measurements of fluxes of trace
gases over the open ocean and the focus on the more polluted regions. However there has been
an accelerating interest in ocean –atmosphere exchange as new techniques have become
available to make the flux measurements and as very new issues have been identified. Current
interest in ocean acidification and ocean eutrophication further raise the profile of ocean –
atmosphere exchange, and given that these surfaces cover 71% of the earth’s surface, the
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 13
relatively small section of this review paper on this topic belies its importance in understanding
atmospheric composition change.
1.7 Wet deposition
Understanding of underlying processes of precipitation scavenging has continued to develop,
with important improvements, justifying the inclusion of this important subject in the review.
The applications of wet deposition schemes are very important in the LRT models (EMEP 2007)
and increasingly in global chemistry-transport models (CTM) (Stevenson et al). These two
applications have very different demands on available knowledge and understanding. In the case
of LRT models in Europe (eg. EMEP), the applications form part of the integrated assessment
process and within the user community the pressure to provide ever finer spatial scale estimates
for the assessment of inputs presents challenges to both the scientific understanding but even
more to the capability of LRT models and the meteorological models on which they depend.
Current demand for assessments of effects at the 1km x 1km scale allows the scale of the input
estimate to approach the scale of an individual nature reserve, for example.
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2 Reactive gaseous nitrogen compounds – oxidized nitrogen 2.1 Introduction
Developments in understanding surface-atmosphere exchanges of NO and NO2 over the last
decade have focussed on three specific issues:- the long term emission of NO from soils; the
interaction of chemical processing of nitrogen oxides in and above plant canopies; and the
deposition of NO2 and HNO3 to foliar and soil surfaces. The measurements have been made over
different vegetation, but the recent focus has been on forests, in part because the interactions
between these processes are greatest for forests, but also because some of the measurements are
simpler to make and interpret for mature forests. This chapter outlines the developments in
understanding NOy exchange between terrestrial ecosystems and the atmosphere, concentrating
on developments during the last decade.
The consequence of soil emissions of NO and within canopy conversion of NO to NO2 by
reaction with O3, is that this produces a within canopy source of NO2. The within canopy source
of NO2 interacts with the NO2 from above the forest canopy to determine the net exchange above
the forest. At very small ambient NO2 concentrations, the forest may therefore be a source of
NOx to the lower atmosphere, as the emission of NO from soil and conversion to NO2 within
canopy exceeds the uptake by the canopy and NO2 is emitted by the forest. At larger ambient
NO2 concentrations, the forest becomes a net sink, as stomatal uptake of NO2 from above canopy
sources exceeds the NO emission from the soil.
2.2 Emissions from soils
Soil surface emissions of NO are the result of several biological and abiotic processes in the soil
producing and consuming NO. Production and consumption of NO occurs predominantly via the
biological nitrification and denitrification processes. Nitrification is the oxidation of soil NH4+ to
NO3-, and denitrification is the anaerobic reduction of soil NO3
- to N2O and N2. In nitrification
NO is formed as a by-product during the oxidation of NH4+ to NO2
- and possibly also as a result
of nitrifier reduction of NO2- leading to a NO production of 1-4% of the NH4
+ being oxidized
(Skiba et al., 1997). The NO produced may be transformed within the soil profile by oxidation to
NO3- or it may be released to the atmosphere following diffusion to the soil surface. In
denitrification, NO occurs as an intermediate in the cascade of reductive processes, and in the
soil profile NO reduction may contribute to the formation of N2O. Abiotic production of NO
occurs from oxidation of nitrous acid (HONO) that has been produced by protonation of
biologically formed NO2- (Venterea et al., 2005). Under certain conditions e.g. after application
of anhydrous ammonia to agricultural soils or acidic forest soils, the coupled biological-abiotic
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 15
production of NO may constitute the dominant process for soil NO emissions (Venterea and
Rolston, 2000; Gödde and Conrad, 1998). Factors that increase nitrification and denitrification,
e.g. substrate and O2 availability, temperature and pH are thus predicted to influence NO
formation. Likewise, factors affecting transport processes in the soil are predicted to regulate
emissions of NO (and other gases). It has been hypothesized (Davidson, 1991) that where WFPS
(water filled pore space) is less than 0.6, nitrification is the dominant process and relatively high
emissions of NO may be observed. Under more reducing conditions, 0.6<WFPS<0.9,
denitrification dominates which has a higher potential for NO production compared to
nitrification (Skiba et al., 1997); however under conditions where anoxic conditions are
generated by high soil water content or by compaction of fine textured soil the probability of NO
being re-consumed by the denitrifying community is greatly enhanced. Soil water may also play
a central role in mediating chemical processes leading to NO formation (Venterea et al., 2005).
Under most soil conditions, both nitrification and denitrification occur simultaneously and the
net flux of NO between soil and atmosphere is the result of both processes together. As current
views of controls over NO gas emissions are still incomplete and need revision e.g. with
emphasis on the role of abiotic formation (Venterea et al., 2005) there is a continuous need to
further develop and improve methodologies to identify and characterize the NO formation
processes. Gödde and Conrad (1998) achieved this by a combined modelling and experimental
approach to determine the net NO flux in relation to NO concentration in order to quantify
production and consumption rate constants and compensation concentration. Recent advances in
methodological approaches to deepen our understanding of soil based NO emissions have
include application of stable isotope techniques. Stark et al. (2002) applied a 15N-isotope pool
dilution method to obtain the simultaneous gross rates of NO forming processes combined with
soil emissions, and Russow et al. (2000) adopted a kinetic isotope method (KIM) to study the
complex N transformation processes involved in soil NO emissions.
NO and N2O emissions were measured continuously at 15 forest sites as part of the EU-funded
project NOFRETETE (Pilegaard et al., 2006) including coniferous and deciduous forests in
different European climates, ranging from boreal to temperate continental forests and from
Atlantic to Mediterranean forests. Furthermore the sites differ in atmospheric N-deposition
ranging from low deposition (0.2 g N m2 a-1) to high deposition (4 g N m-2 a-1).
The relationships of the emissions of NO and N2O, with the parameters nitrogen deposition,
forest type, age, C/N, pH, soil temperature and water-filled pore space (WFPS) were investigated
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 16
by means of stepwise multiple regression analysis. NO emission was dependent on forest type
and positively correlated with nitrogen deposition (Fig 2.1). WFPS was tested for curvature by
including a quadratic term, but this was not significant. Separately performed regression analyses
for deciduous and coniferous forests showed, however, that the relationship between nitrogen
deposition and NO emission was only significant for the coniferous forests: (NO (µg N m`2 h`1)
= `13.9 + 25.5 [N deposition (g m`2 a`1)], r2=0.82) The N2O emission was significantly
negatively correlated with both the C/N ratio and the age of the stands; a logarithmic
transformation of N2O emission improved the significance of the correlation.
Figure 2.1 Left: NO emission (µg N m`2 h`1) as a function of nitrogen deposition (g N m`2 a`1).
Regression lines (solid = significant, dashed = non significant) for coniferous and deciduous sites,
respectively. Right: N2O emission (µg N m`2 h`1) as a function of C/N ratio. The full line represents a
linear regression and the dotted line a logarithmic regression.
Soil temperature is a key variable affecting the emission rates of both gases (Fig 2.2). Emissions
of both NO (Slemr and Seiler, 1984) and N2O (Skiba et al., 1998) increase with rising soil
temperature due to the fact that rates of enzymatic processes generally increase with temperature
as long as other factors (e.g. substrate or moisture) are not limiting. Soil water acts as a transport
medium for NO3- and NH4
+ and influences the rate of O2 supply and thereby controls whether
aerobic processes such as nitrification or anaerobic processes such as denitrification dominate
within the soil. While N2O emissions are known to increase at higher water contents through
larger losses from denitrification (Papen and Butterbach-Bahl, 1999) the relationship between
the NO flux and the soil water is more complex. Due to limited substrate diffusion at very low
water content and limited gas diffusion at high water content, nitric oxide emissions are
suspected to have a maximum at low to medium soil water content.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 17
-5 0 5 10 15 200
100
200
300
400
NO
em
issi
on [µ
g N
O-N
m-2 h
-1]
Forest floor temperature [°C]
Figure 2.2 Relationship between daily mean forest floor temperature and daily mean NO emissions at the
Höglwald Forest (spruce, control) for the observation period January 1, 2004 – December 31, 2006. For
details on measurement and site characteristics see Gasche & Papen (1999)
The effects of soil moisture and temperature on NO and N2O emission were studied in a
laboratory experiment with soil cores from some of the NOFRETETE field sites (Schindlbacher
et al., 2004). Soil moisture and temperature explained most of the variations in NO (up to 74 %)
and N2O (up to 86 %) emissions for individual soils. NO and N2O were emitted from all soils
except from a boreal pine forest soil in Finland, where the laboratory experiment showed that
NO was consumed. NO emissions from a German spruce forest ranged from 1.3 to over 600 µg
NO-N m-2 h-1 and greatly exceeded emissions from other soils. Average N2O emissions from this
soil tended also to be highest (170 ± 40 µg N2O-N m-2 h-1), but did not differ significantly from
other soils. NO and N2O emissions showed a positive exponential relationship to soil
temperature.
The results from the annual averages of field data did not show any significant relationship with
soil temperature for either NO or for N2O emission. Schindlbacher et al. (2004) showed that N2O
emissions increased with increasing WFPS or decreasing water tension, respectively. Maximum
N2O emissions were measured between 80 and 95 % WFPS or 0 kPa water tension. The optimal
moisture for NO emission differed significantly between the soils, and ranged between 15 %
WFPS in sandy Italian floodplain soil and 65 % in loamy Austrian beech forest soils. For the
field data WFPS was not a significant parameter for N2O emission, but had a positive significant
effect on NO emission (Fig 2.3). The annual average WFPS in the field was higher than the
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 18
optima found for NO in the laboratory experiment, but since not all field sites were studied in the
laboratory it is difficult to provide a general conclusion. The interannual variation within single
sites clearly showed relationships to both temperature and soil moisture. An important factor for
N2O emission is freeze-thaw events which can produce a significant outburst of N2O [Kitzler et
al., 2006].
0 20 40 60 80 100
020
4060
8010
0
WFPS(%)
NO
em
issi
on (%
of m
axim
um)
Sandy loamSilty loamSandy clay loamLoam(1)Loam(2)
Figure 2.3 The relationship of NO emission and water filled pore space at different localities in the
NOFRETE project (based on data in Schindlbacher et al. 2004).
In general, relationships between nitrogen oxides emission and soil moisture and soil
temperature can be found within a single locality when studying short-term variations. However,
using the same parameters when comparing annual values from different localities within a large
region as in this study does not necessarily reveal comparable relationships since other factors
such as soil properties, stand age, and site hydrological conditions interfere.
2.3 Emissions of NOy from plant surfaces
Production of NOy on Scots pine branch surfaces by ultraviolet radiation has been observed in
Hyytiälä, southern Finland (Hari et al., 2003) (Fig 2.4). Other studies have shown that irradiance-
dependent NOy emissions from snow and different chamber surfaces have been observed to
originate from HNO3 or nitrate photolysis. In Hyytiälä, Raivonen et al (2006) investigated
whether the NOy emitted from pine shoots could originate from photolysis of HNO3 attached to
the needle surface. Field data of several years from Hyytiälä were used to test this hypothesis.
The HNO3 deposition, estimated for the Hyytiälä site, has been high enough to account for the
NOy emission rates observed from the chambers. The particular characteristics of the daily
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 19
pattern of CO2 exchange or stomatal control was not reflected in the NOy flux. When a pine
branch was rinsed, which reduced the amount of water-soluble nitrogen compounds (e.g., HNO3,
nitrates and HONO) from the needle surface, NOy emissions from that branch decreased
compared to another non-rinsed branch. Therefore, it was concluded that the results support the
hypothesis and that HNO3 photolysis on plant surfaces needs to be taken into account both from
air chemistry and plant sciences point of view.
Figure 2.4 Effect of UV radiation on the NOy concentration in a small Teflon chamber that enclosed a clean pine
branch or a branch that had been treated with NH4NO3 solution. The branches were dead and dry, cut from the tree.
UV wavelengths were filtered away using a Plexiglas plate (Raivonen et al., 2006).
2.4 Canopy atmosphere interactions
The interaction between chemical reactions of nitrogen oxides taking place in the canopy and
trunk space of a forest is a special case because in this area chemical and turbulent timescales
change substantially leading to a very complex situation in which even the direction of fluxes
may change (Duyzer et al., 1995) (Fig 2.5).
This makes it nearly impossible to interpret measurements of the turbulent fluxes of some
reactive trace species above the canopy from single point eddy covariance measurements.
Several models have been developed to simulate the overall exchange and show the magnitudes
of the different competing processes. These models describe the coupled processes of
atmospheric transport and chemical processes above and in canopies in detail. Over forests the
situation is even more complex. Flux measurements are usually carried out near the top of rough
canopies leading to potential inaccuracies in the K theory approximation. This theory is
relatively easy to combine with vertical atmospheric transport phenomena with fast chemical
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 20
reactions. A probably more realistic description of atmospheric transport, using Lagrangian
models, (Nemitz et al [date]) is much more difficult to combine with chemical reactions.
Figure 2.5. Complete schematic of the various canopy interactions in the case of the exchange of nitrogen oxides
with forests.
2.5 Models and measurements
Measurements of small fluxes of NO and NO2 have shown spurious results especially at low
concentrations due to a lack of specificity of monitors and a lack of instrument sensitivity, but
other problems may well have contributed, including violation of conditions under which such
fluxes may be measured above canopies and the complexity of interactions; soils and sunlight
driven reactions may both be sources of NOy and these interact with the stomatal sinks and the
chemical processing within the canopy trunk space.
As a result of these limitations there are only limited data available for verification of models.
Duyzer et al (2004) described the analyses of a data set acquired in the framework of a European
project from an experiment carried out in a 20 m high coniferous forest (Speulderbos, The
Netherlands). A 1D multilayer model of a forest canopy was used to analyze the field data. In
each layer vertical transport was described using K-theory; canopy uptake was described using a
resistance layer model. Simple chemical reactions between ozone and nitric oxide and photolysis
of nitrogen dioxide were described. The coupled differential equations were solved numerically.
Input to the model calculations were concentrations of nitrogen oxides and ozone at the highest
level above the forest, levels of radiation, temperature, humidity, wind speed, turbulence
parameters and an estimate of the emission of nitric oxide. Output of the model is the
concentration and fluxes of the relevant components at the height of each level in and above the
canopy. These may be compared with measured fluxes of these components at two levels above
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 21
and one below the canopy. It is fair to say that the comparison between measured and modelled
fluxes is not impressive. There are many possible explanations for this observation but no clear
single cause has been identified.
Depending on the magnitude of this soil flux the NO2 flux is either downward or upward. In the
case of the coniferous forest described here and the conditions during the experiment the result
was that when the NO flux from the soil exceeded 10 ng/m2/s, the NO2 emission was upward
(i.e. away from the forest).
At high concentrations the NO2 flux is directed towards the forest and at small concentrations the
flux is more likely to be directed towards the free atmosphere. This may be interpreted as a
compensation point above which the flux is directed towards the surface and below which the
flux is away from the surface.
In summary the flux of NO2 above a forest can be described with the following function:
where all variables have their common meaning and CNO2 denotes the concentration of nitrogen
dioxide above the canopy. This equation is rather qualitative but indicates the sensitivity of the
flux of NO2 above the canopy. More quantitative model runs are needed, but these require a
large amount of input data and the results are still uncertain.
A simple resistance model (Duyzer et al 2005) was tested in a deciduous forest (Sorø, Denmark)
and is illustrated in Figure 2.6. Generally the understanding of the various processes and their
interaction is increasing. Nevertheless many uncertainties remain and there is a need for further
improvement of models, especially for lagrangian models incorporating chemical reactions. On
the other hand, the accuracy of the results of field measurements has been rather low. It should
be noted that although the interaction between atmospheric chemical reactions and exchange
between the canopy and the atmosphere is easy to understand its importance may be limited. In
cases where fluxes of nitrogen oxides are small the corrections could be large in a relative sense
but still rather small in an absolute sense. The currently available models could very well be
capable of making estimates of the magnitude of these effects. In view of all the uncertainties
hindering improved estimates in testing of models the limited quality of the description of
atmospheric transport processes within the canopy may not be a serious problem here.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 22
Figure 2.6 Profiles of CO2, NO2, O3 and NO in a beech forest near Sorø, Denmark. The profiles clearly show the
effects of stable conditions during night and daytime turbulence mixing the full air column. CO2 is built up during
night due to soil respiration; O3 is depleted during night due to deposition and chemical reactions. At the soil surface
high concentrations of NO are seen due to emission from the soil.
2.6 Exchange of HNO3, HONO, PAN
The deposition of HNO3 to terrestrial surfaces has been shown to be primarily controlled by the
rates of turbulent exchange in the atmospheric boundary layer and the leaf boundary layer
(Huebert et al 1982). The highly reactive and soluble nature of gaseous HNO3 leads to large rates
of deposition, approaching the maximum rates of deposition limited by turbulent exchange when
each molecule arriving at terrestrial surfaces is immediately absorbed at the surface. In these
conditions the surface is considered to be acting as a perfect sink, canopy resistance is zero and
the numerical value for the deposition velocity becomes:
Vg(NHO3) =vmax=1/ra+rb
The values for deposition velocity in these conditions are very sensitive to wind velocity values
and approach several cm s-1 even over relatively short vegetation. The consequence of these
large rates of deposition are that even in areas with small HNO3 concentrations, dry deposition of
HNO3 contributes a substantial quantity if nitrogen. Taking an ambient concentration of 0.1 ppb
HNO3, the annual deposition of N for a forest would be of the order 3 kg N ha-1 annually from
HNO3 alone.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 23
The close coupling between rates of turbulent exchange and dry deposition rates for HNO3 also
generates substantial spatial variability in N deposition in the landscape, with hot spots for N
deposition being forests and especially forest edges, hedgerows and isolated, exposed hills,
where wind speeds are larger.
Several studies have recently attempted to measure total oxidized nitrogen (NOy) fluxes or even
total reactive nitrogen (Nr = NOy + NHx) to ecosystems (Turnipseed et al., 2006). These
approaches offer the prospect to apply eddy-covariance techniques for the robust and relatively
cost effective determination of total atmospheric N deposition, but they do not provide the
chemical speciation needed to further process understanding. There have, however, also been
advances in the understanding of individual N compounds other than NH3, NO and NO2:
A recent lab study (Sparks et al., 2003) has confirmed that PAN deposition through the stomata
can make a significant contribution to plant uptake of atmospheric N. In addition, recent
instrument developments in chemical ionization mass spectroscopy (CIMS) and thermal-
dissociation laser induced fluorescence (TD-LIF) have enabled the application of eddy-
covariance to the biosphere / atmosphere exchange of preoxy acyl nitrates (PANs such as PAN,
PPN and MPAN). Measurements were made over two contrasting US pine forests at Duke
Forest, North Carolina, (RH > 75%) and Blodgett Forest, California, (RH < 30 %) (Farmer et al.,
2006; Turnipseed et al., 2006; Wolfe et al., 2008).
At Duke Forest fluxes of PAN, PPN and MPAN were measured with a CIMS technique
(Turnipseed et al., 2006). There were no significant differences in the Vd of the three different
PAN compounds, but all three species deposited about four times faster than predicted by the
model of Wesely (1989) during the day, and nearly an order of magnitude faster during the night,
indicating that aqueous solubility considerations are insufficient to predict the behaviour of PAN
on surfaces. The average Vd was 2.5 mm s-1 during day and 8 mm s-1 during night. In contrast to
the considerations of Wesely (1989), wet surfaces showed a smaller non-stomatal resistance (Rns
= 125 s m-1) than dry surfaces (Rns = 250 s m-1).
At the much drier Blodgett forest site, the flux of the sum of all PANs was measured by TD-LIF,
based on thermal conversion and NO2 detection (Farmer et al., 2006). PAN was derived as the
difference between the ambient temperature and 180°C channel. They found upward fluxes in
summer and on average bi-directional exchange with afternoon deposition in winter, when noon-
time deposition velocities averaged 8 mm s-1. More recently, these measurements have been
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 24
repeated with the more selective CIMS technique (Wolfe et al., 2008). Here measurements
indicated larger average midday values of Vd for PPN (12 mm s-1) than for the PAN and MPAN
(4 mm s-1), while both compounds deposited slowly at night (Vd < 2 mm s-1). The authors of this
study attribute the difference in the Vd between compounds to MPAN and PAN production
inside the canopy and suggest that the PPN fluxes are a better descriptor of the surface
deposition. They suggest that the non-stomatal uptake is dominated, but not fully explained, by
thermochemical decomposition, and thus strongly linked to canopy temperature.
In summary, this recent measurement evidence suggests that deposition rates of PANs in warm
climates are at least a factor of 5 larger than predicted by commonly used models and non-
stomatal deposition is larger to wet and humid surfaces than to dry surfaces.
During the same TD-LIF study, Farmer et al. (2006) measured fluxes of total alkyl nitrates (gas
and aerosol phase), from the difference between the 180°C and 330°C channels. These
compounds showed large winter-time midday deposition velocities of 20 mm s-1, approaching
those of HNO3 (25 mm s-1) (Farmer and Cohen, 2008). Even higher Vd of 30 mm s-1 was derived
by Horii et al. (2005) for what they interpret as isoprene-derived hydroxyalkyl nitrates.
Nitric acid (HNO3) has traditionally been believed to deposit at the maximum rate possible
according to turbulence (Vmax) and its flux measurement continues to be used to derive quasi-
laminar boundary-layer resistances for vegetation (e.g. Pryor and Klemm, 2004). This view has
been challenged by recent measurements that indicated non-negligible canopy resistances in the
range of 50 to > 200 s m-1 during midday (Nemitz et al., 2004b; Wolff et al., 2007; Nemitz et al.,
2008). This has been attributed to non-zero chemical compensation points governed by the
thermodynamic equilibrium with NH4NO3 on leaf surfaces or fertilizer pellets. Figure 2.7 shows
an example of reduced deposition of HNO3 and HCl over a Dutch heathland.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 25
15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
F χ [n
g m
-2 s
-1]
-40
-30
-20
-10
0
10
15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
Rc [
s m
-1]
0
100
200
300
400
500
15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
u * [m
s-1
]
0.0
0.1
0.2
0.3
0.4
0.5
RH
(z0')
[%]
0
20
40
60
80
100
120
RH u*
15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
Vd [
mm
s-1
]
0
5
10
15
20
25
30
Vmax(HNO3)Vmax(HCl)Vd(HNO3)Vd(HCl)
HNO3
HCl
HNO3 HCl
Figure 2.7 Example time series of HNO3 and HCl exchange measured above a Dutch heathland with a denuder
gradient system with online analysis by ion chromatography. The panels show: (a) fluxes, (b) deposition velocities
of HNO3 and HCl in comparison with their maximum values and (c) Rc for HNO3 and HCl, (d) friction velocity (u*)
and relative humidity (h). Data represent 2.5 hr. running means of 30 min. Vd(HNO3 and Vd(HCl) are reduced
compared with their maximum values, presumably due to non-zero chemical compensation points originating from
deposited NH4+ salts. From Nemitz et al. (2004a).
The view that HNO3 normally deposits with a near zero canopy resistance still holds. There is an
increasing measurement database of HNO3 concentrations in national and regional networks
suitable for inferential modelling of HNO3 deposition (Tang et al., 2008), which now provides
independent confirmation from the model results, that HNO3 deposition makes a very significant
contribution to nitrogen deposition across Europe. In addition, Europe-wide monitoring activities
have produced the first hourly monitoring datasets of HNO3, which allows for a much more in-
depth assessment of the performance of oxidized nitrate chemistry in atmospheric transport
models (Tarrason and Nyiri, 2008).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 26
Much development has occurred in measurement techniques for nitrous acid (HONO), e.g. based
on long path absorption photometry (LOPAP) and differential optical absorption spectrometry
(DOAS). This has contributed to the improvement of process understanding of sources of HONO
in the atmosphere, e.g. revealing larger daytime sources than previously thought and identifying
NO2 reactions with humic acid as a novel production mechanism (Kleffmann et al., 2005;
Stemmler et al., 2006). By contrast, applications of these approaches to flux gradient
measurements are still rare, which nevertheless confirm surface sources of HONO (Vitel et al.,
2002; Kleffmann et al., 2003).
2.7 Upscaling and regional and global trends
The complexity of processes involved in NO emissions from soils has resulted in a significant
uncertainty in the regional and global source strength of soils for NO. However, different
methodologies have been developed, e.g. relatively simple statistical models as well as process
based model approaches, to cope with the problem of regionalisation of soil NO fluxes. The most
widely used approach for calculating regional NO emissions from soils is based on the work of
Yienger and Levy (1995). These authors consider land use and respective background emission
strengths, nitrogen fertilization rate (2.5% loss of applied nitrogen), temperature effects (three
classes: cold-linear, exponential and optima) as well as the pulsing of NO emissions following
prolonged dry periods (four classes, based on intensity of rainfall) to estimate soil NO emissions.
Yienger and Levy also provide a so called canopy reduction factor in order to consider chemical
conversion and re-deposition of NO as NO2 within the canopy. Compared to the methodology by
Yienger and Levy, the Skiba-EMEP/CORINAIR approach is more simplistic. Based on a
literature review by Skiba et al. (1997) this approach postulates that 0.3% of any form of
nitrogen is volatilized as NO, i.e. regardless whether it originates from inorganic or organic
fertilization or atmospheric N deposition. Furthermore, a background emission of 0.1 ng NO-N
m-2 s-1 (≈0.032 kg NO-N ha-1 a-1) was assumed (Simpson et al., 1999). In addition, EMEP/
CORINAIR also use a more detailed methodology (BEIS-2), which originates from the work of
Novak and Pierce (1993) and considers soil temperature as well as different land use classes. A
statistical summary model was developed by Stehfest and Bouwman (2006), which is based on
the most extensive literature review currently available. This methodology for calculating soil
NO emissions on global and regional scales considers land-use, N fertilization rate [Fertilizer],
soil N content (three different classes, estimated as 1:10 of soil organic carbon content) [SON]
and climatic regions. The methodology was recently adapted to calculate a European wide
inventory of NO emissions from forest soils (Kesik et al., 2006, 2007). Kesik et al. used the
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 27
process oriented ecosystem model Forest-DNDC. The model was extensively tested for its
performance to predict NO emissions at the various NOFRETETE field sites, which were
located across Europe and, thus, were covering different climatic conditions (Pilegaard et al.,
2006). Regionalization was finally achieved by linking the model to a detailed GIS database
holding all relevant information for initializing and driving the model such as data on vegetation
(e.g. forest type) and soil properties (e.g. texture, soil pH, organic C content) and climate (either
present day conditions or projected future climate predictions). This approach demonstrated for
the first time the huge regional differences in NO emissions from forest soils across Europe as
shown in Figure 2.8, to estimate its significance on a regional scale and to unravel the
importance of atmospheric N deposition for the magnitude of forest soil NO emissions.
Fig. 2.8 Importance of atmospheric N deposition for NO emissions from forests soils. Shown is the difference in NO
emissions for a scenario with zero atmospheric N deposition and present day atmospheric N deposition. In huge
parts of central Europe but also Scandinavia forest NO emissions are likely to decrease significantly if atmospheric
N deposition can be reduced to background levels.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 28
3 Biosphere atmosphere exchange of ammonia 3.1 Introduction
Substantial progress has been made during the last five years in understanding ammonia
biosphere-atmosphere exchange. Experimental studies have included controlled laboratory
analysis, while a series of micrometeorological studies have assessed net fluxes occurring under
field conditions. In particular, major advances have been made in modelling the different aspects
of ammonia exchange. This has included not just analysis of the drivers of the vertical flux
densities, but also a consideration of non-stationarities, such as advection effects and chemical
interactions. Traditionally, micrometeorological experiments were designed to avoid these
effects, focusing as far as possible on ‘ideal’ micrometeorological conditions, so as to better
quantify the vertical exchange processes, and develop parametrizations for ‘dry deposition
schemes’ in regional models (Fowler and Duyzer 1989; Fowler et al., 2001; Sutton et al. 1994;
Simpson et al. 2006). However, for ammonia, it has become increasingly clear that these non-
stationarities represent important effects that are widespread in the real environment and need to
be quantified (Sutton et al., 2007).
The developments in the last years have arisen from a wide range of national and international
projects. National studies have particularly addressed exchange with key ecosystems of regional
importance, such as ammonia losses from agricultural systems (e.g., Milford et al., 2001a;
Walker et al., 2006; Wichink Kruit et al., 2007) and the ammonia inputs into semi-natural
ecosystems of conservation value (e.g., Wyers and Erisman 1998; Neiyrink and Ceulemans,
2008). Collaborative international projects have sought to integrate and extend these interests,
making the comparison between ecosystem types and looking at the interactions (e.g., Sutton et
al., 2008d).
The first European collaborative project dedicated to ammonia exchange was ‘EXAMINE’.
Attention was given to quantifying ammonia exchange with a range of European ecosystems,
under both experimental and field conditions (e.g., Sutton et al. 1995; Schjoerring et al. 1998;
Neftel et al. 1998; Meixner et al. 1996; Nemitz et al., 2004), including analysis of the surface
gas-particle interactions between ammonia, nitric acid and hydrochloric acid (e.g., Nemitz et al.
1996; Nemitz and Sutton 2004). As part of EXAMINE a major collaborative analysis was made
in the North Berwick experiment, which provided a uniquely detailed examination of the
processes controlling ammonia exchange with an oilseed rape canopy (Husted et al. 2000;
Nemitz et al. 2000a,b; Sutton et al. 2000a,b).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 29
In the second major European collaboration dedicated to ammonia, the GRAMINAE project
analyzed the processes controlling ammonia exchange with grassland ecosystems across Europe
(Sutton et al. 2001). This included assessment of both the bi-directional fluxes of ammonia with
agricultural grassland – as these affect atmospheric ammonia balance (e.g., Milford et al. 2001a;
Mosquera et al. 2001), and with semi-natural grasslands as these are impacted by the atmosphere
(e.g., Horvath et al. 2005). These studies were complemented by the LIFE project, which added
long term ammonia flux data for a number of grassland, moorland and forest ecosystems (e.g.,
Flechard and Fowler 1998; Erisman et al. 2001; Spindler et al. 2001).
As understanding of ammonia exchange has improved and scientific ambition developed,
increased attention has been given to integrating the different drivers of ammonia exchange
processes. This has, for example, been reflected in the Braunschweig Integrated Experiment of
GRAMINAE (e.g., Sutton et al. 2002, 2008a,b), which linked a wide range of biospheric,
atmospheric and management interactions as these control ammonia exchange with managed
grassland. This integration has developed substantially under the NitroEurope Integrated Project
(Sutton et al. 2007), which is currently addressing how the different components of nitrogen
fluxes, including ammonia and oxidized nitrogen, integrate and interact to influence net
greenhouse gas balance. In parallel, major advances have been made in spatial modelling of
ammonia fluxes, from individual forest edges to global scales (Theobald et al. 2005; Dentener et
al. 2006; Hertel et al., 2006; Loubet et al., 2008a; Sutton et al. 2008c).
3.2 Advances in measurement methods
Before considering the developments outlined above in more detail, it is important to highlight
that the advances have been critically dependent on improvements in measurement technology
(See Table 3.1). At the start of the 1990s, ammonia flux measurements were still being made
using wet chemistry and manual batch sampling with time integration of typically 2 hours (e.g.,
Sutton et al. 1993; Duyzer 1994). The most important key advance has been the introduction of
continuous wet chemistry methods for measuring ammonia profiles, including the AMANDA
wet rotating denuder (Wyers et al. 1993) and the mini-Wet Effluent Diffusion Denuder (e.g.,
Blatter et al., 1993; Neftel et al., 1999). Although these techniques are liable to malfunction,
with effort and careful operation they have produced many key datasets over the last 15 years
(e.g., Erisman and Wyers, 1993; Sutton and Fowler 1993; Sutton et al., 1995; 1998; Fowler et al.
1998; Flechard and Fowler 1998; Neftel et al., 1998; Milford et al. 2001a,b; Nemitz et al., 2001b,
2004) and still represent the state-of-the-art as regards precise measurement of small ammonia
fluxes (Wichink Kruit et al. 2007; Neiyrink and Ceulemans, 2008; Sutton et al., 2008c).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 30
A unique inter-comparison of four continuous wet chemical systems was made at the
GRAMINAE Braunschweig Experiment (Sutton et al., 2002, 2007, 2008b; Milford et al., 2008),
which highlights the potential and limitations of the approach. Figure 3.1 shows the ammonia
flux measured before and after cutting of an agricultural grassland, as well as after subsequent
fertilization with calcium ammonium nitrate. The measurement systems were able to detect the
wide range of ammonia fluxes, but the degree of agreement varied greatly between days. This
was a result of varying performance of the different analyzers, highlighting the need for highly
intensive instrument maintenance.
06/06/00 07/06/00 08/06/00 09/06/00
NH
3 flu
x (n
g m
-2 s-1
)
0
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4000
600031/05/00 01/06/00 02/06/00 03/06/00
NH
3 flu
x (n
g m
-2 s-1
)
0
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22/05/00 23/05/00 24/05/00 25/05/00
NH
3 flu
x (n
g m
-2 s-1
)
-75
-50
-25
0
25
50CEHFRI
Pre-cut
Post-cut
Post-fert
CEH FRI FAL-D FAL-CH
CEH FRI FAL-D FAL-CH
Figure 3.1: Inter-comparison of continuous profile systems for measuring ammonia fluxes by the aerodynamic
gradient method (AGM), from the GRAMINAE Braunschweig Experiment. Although highly scattered, this flux
inter-comparison is unique and represents the current state-of-the-art in chemical detection systems for ammonia
fluxes. Increased emissions due to cutting of the underlying grass sward (29 May) and the effect of N fertilization
with (100 kg N ha-1, 5 June) are clearly shown (Sutton et al., 2002; 2007, 2008; Milford et al., 2008).
Despite the good improvements that have been made in the automation and reliability of the
continuous wet chemical gradient methods (e.g., Wichink Kruit et al. 2007; Flechard et al. 2007;
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 31
Sutton et al., 2007), their remain several limitations, which have encouraged researchers to seek
alternative ammonia flux measurement approaches. In principle, many refinements have allowed
the automated wet chemical methods to become more reliable and comprehensive (such as being
able to measure aerosol and acid gas gradients simultaneously, Oms et al. 1996; Trebs et al.,
2006). However, the use of many moving parts can be considered as inherently liable to faults.
Similarly, the response times of these instruments are typically >5 minutes, which means that
they are normally limited to the measurement of mean concentration differences and vertical
gradients.
The benefits of quantifying ammonia fluxes using the gradient technique have been clearly
demonstrated by the many papers published using this approach. In terms of informing our
understanding of ammonia exchange processes and model development, this has almost
exclusively been provided by measurements using the aerodynamic gradient method (90%), with
a few studies (in continental climates) applying the modified Bowen Ratio method (5%). By
contrast, the key disadvantage of this method is that it depends on good stationarity, with no
change in the vertical flux with height. However these methods are not suitable for the study of
exchange fluxes where advection of ammonia from local sources is of interest (e.g., Loubet et
al., 2001, 2006; Milford et al. 2001b) and where gas-particle ammonia - ammonium interactions
are significant (e.g., Brost et al., 1988; Nemitz et al. 1996; 2004).
To address some aspects of advection and air chemistry interactions, determination of fluxes at a
single height offers a way forward. If this can be achieved, in principle, deployment of replicate
measurement systems at several heights could then be able to determine vertical flux divergences
(Sutton et al., 2007, 2008a). Both the Eddy Covariance (EC) method and Relaxed Eddy
Accumulation (REA) allow fluxes to be determined from measurements at one height, and have
therefore been the subject of several recent studies. The advantage of REA is that slow response
ammonia measurements can be combined with fast response switching, as has recently been
demonstrated in an inter-comparison of 4 REA systems for ammonia (Hensen et al., 2008). A
further advantage is that programmed periods of random switching between air up- and down-
drafts allows automatic zero checks and the correction of any biases (Nemitz et al., 2001a;
Hensen et al. 2008). By contrast, the challenge for REA and ammonia is that the concentration
differential to be measured is typically much smaller than for the gradient method, which to a
large extent cancels out the precision benefit of the auto-referencing.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 32
Several recent studies have demonstrated the potential of fast response tunable diode laser
absorption spectroscopy (TDLAS) for measurement of ammonia fluxes by eddy covariance
(Shaw et al., 1998; Famulari et al., 2004; Whitehead et al., 2008). In principle, reliable flux
measurements can now be made for periods of large ammonia fluxes (e.g., after manured
application), as has recently been demonstrated in an intercomparison of two laser systems
(Figure 3.2). However, there was little correlation for fluxes <50 ng m-2 s-1, while the AMANDA
systems have been shown to be able to measure <10 ng m-2 s-1 (e.g., Sutton et al., 1995, 1998).
Table 3.1 provides an overview of these and other systems for measuring ammonia fluxes. In
principle, TDL and EC has the potential to be rated as high as the continuous gradient methods,
but this still needs to be demonstrated by a more substantial body of published measurements,
particularly over longer time periods and of a suitable quality for testing of models.
-200
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200
400
600
800
1000
1200
29/04/200514:24
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30/04/200502:24
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30/04/200514:24
30/04/200520:24
TDLASQC-TDLAS
Flux
NH
3(n
gm
-2s-1
)
Date, Time (GMT)
-200
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400
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30/04/200508:24
30/04/200514:24
30/04/200520:24
TDLASQC-TDLAS
Flux
NH
3(n
gm
-2s-1
)
Date, Time (GMT)
Figure 3.2: Fluxes of NH3 measured by eddy covariance over intensively managed grassland (Easter Bush,
Scotland) several days after the application of liquid manure to the grassland (Sutton et al. 2007; Whitehead et al.
2008)
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 33
Table 3.1: Practical suitability of systems to measure ammonia biosphere-atmosphere exchange.
Chemical approach
Advantage Disadvantage Application References
Box AGM REA EC Batch filterpacks
Simple, cheap, high air vol.
Uncertain gas – aerosol split, Batch
☺ Harrison et al. (1989); Sutton et al. (1993).
Batch denuders
Simple, cheap good gas-aerosol split
Low air volume Batch
☺ Duyzer (1994); Andersen et al. (1999).
Automated batch annular denuders
Automated in field, medium cost, Precise, High air volume
High laboratory processing cost, Only hourly, Need two syst-ems for fluxes.
☺ ☺ Keuken et al. (1988); Loubet et al. (2006, 2008b).
Continuous annular denuders
Automatic Sensitive, Precise, High air volume
Cost, Complexity, Fault liable, Gradient only
☺☺ ☺☺ Wyers et al. (1993); Erisman and Wyers (1993); Sutton et al. (1995, 2000b, 2001a); Nemitz et al. (2001b)
Continuous parallel plate denuders
Automatic, Sensitive, High air volume REA
Cost, Complexity, Fault liable
☺ ☺☺ Nemitz et al. (2001a); Hensen et al. (2008).
Continuous mini-WEDD
Automatic, Sensitive, Precise
Cost, Complexity, Fault liable
☺ ☺☺ ☺ Neftel et al. (1999); Hensen et al. (2008)
Continuous membrane denuder AIRmonia
Automatic, Sensitive, Precise, reliable
Cost Medium complexity
☺ ☺☺ ☺ Flechard et al. (2007); Hensen et al. (2008)
Photo-accoustic
Automatic, Sensitive, In principle reliable
Cost, Complexity, not reliable
☺☺ ☺ Whitehead et al. (2008)
Tunable Diode Laser
Automatic, Sensitive, Fast response (>10 Hz)
Very high cost Complexity, Maintenance
☺ ☺ ☺ Shaw et al. (1999) Famulari et al (2004) Twigg et al. (2005)Whitehead et al. (2008)
Notes: AGM: Aerodynamic Gradient Method; REA: Relexed Eddy Accumulation, EC: Eddy Covariance.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 34
3.3 Key controls on biosphere atmosphere exchange of ammonia
Figure 3.3 summarizes the main processes affecting the net exchange of ammonia with the
atmosphere (Sutton et al., 2007). The primary driver of ammonia exchange is the difference
between the atmospheric ammonia concentration and the average concentration in the ecosystem
canopy, both of which vary in time and in space (e.g., Sutton et al., 1995, Asman et al., 1998).
Within the canopy, several sources and sinks combine together to determine the average
ammonia concentration in the canopy, including exchange with plant tissues through stomata,
with leaf cuticles and with decomposing leaf litter and the soil surface (e.g., Denmead et al.,
1976; Sutton et al., 1993b, 1998). Ammonia within or immediately above the canopy air space
may undergo chemical reactions, for example forming particulate matter, while depletion of
gases within a plant canopy coupled with altered microclimate can lead to evaporation of
ammonium containing aerosol (Brost et al., 1988; Nemitz et al. 1996, 2004, 2008a). Finally, the
complex nature of ammonia sources and sinks in rural landscapes means that strong horizontal
gradients of ammonia occur. The result is that ammonia is not simply deposited from above, but
fluxes are often significantly influenced by advection effects, for example where advection from
a ground level source beneath a micrometeorological reference height adds substantially to a net
deposition flux (Loubet et al., 2001, 2008a,b, Milford et al., 2001b).
Advection from local sources
Within-canopy sources & sinks
Within-canopy chemistry
Above-canopychemistry
Ideal: flux measurements resolved with height, sufficiently accurate to quantify these effects
Figure 3.3. Summary of the key issues affecting the net land-atmosphere exchange of ammonia. Each of these
interactions can lead to ammonia fluxes changing with height above the ground. Ideally, flux measurements, based
on e.g. relaxed eddy accumulation or eddy covariance, made at several heights above the canopy would be used to
quantify these effects, though until now such assessments have had to focus on the use of vertical profiles in mean
ammonia concentration.
It is relevant to summarize the main influences on the primary drivers of exchange, the
atmospheric ammonia concentration and the mean concentration of ammonia within the canopy.
The first of these is influenced partly by dispersion from adjacent ammonia sources and partly by
exchange with the surface itself. Over a surface which acts as an ammonia sink, above-canopy
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 35
ammonia concentrations are depleted compared with background concentrations, while, the
above canopy concentrations may be significantly enhanced if the surface is a net source (e.g.,
Sutton et al., 1997, 2000a).
The mean ammonia concentration of the canopy itself results from the resolution of competing
emission and deposition processes with leaf cuticles, through stomata and with the ground
surface. The concept of ‘compensation point’ concentrations has often been used to describe
these relationships. The earliest view of a compensation point for ammonia related it to exchange
through plant stomata with the leaf apoplast (Lemon and van Houtte, 1980; Farquhar et al.,
1980). Under this interpretation, net ammonia fluxes would depend on the difference between
what has since been termed the ‘stomatal compensation point’ (χs) and the atmospheric
concentration (χa). By contrast, subsequent studies highlighted the fact that ammonia deposition
rates were often faster than feasible by stomatal uptake, demonstrating the importance of
ammonia deposition to leaf cuticles (e.g., Sutton et al., 1993a,b; Duyzer 1994). The resolution
between these positions was provided in the development of the concept of the ‘canopy
compensation point’ (χc), which accounts for both bi-directional stomatal exchange and
deposition to leaf cuticles (Sutton and Fowler, 1993; Sutton et al., 1995). Such canopy
compensation point concepts have since been further developed to include bi-directional
exchange with leaf surfaces and exchange with the ground surface under the canopy (e.g., Sutton
et al., 1998, Flechard et al., 1999; Nemitz et al., 2001b).
These inter-relationships are summarized in Figure 3.4. The figure illustrates the resistance
framework of the two-layer canopy compensation point approach (Nemitz et al., 2001b). One of
the key points to note about ammonia compensation points is that they depend on the net
solubility of ammonia in aqueous solution, which is largely dependent on its equililbrium with
ammonium ions. By combining the temperature dependence of the Henry equilibrium and the
ammonium dissociation equilibrium, the gaseous ammonia concentration can be compared with
a given ratio of [NH4+]/[H+], which has been termed Γ (Nemitz et al., 2000b, 2001a; Sutton et
al., 2000). On this basis, Γ can be used to provide temperature-normalized compensation points,
for example χs = f(T, Γs) where Γs = [NH4+]apoplast/[H+]apoplast.
3.4 Effects of ecosystem type on Ammonia biosphere-atmosphere exchange
It has long been established that ecosystem type affects net ammonia fluxes (cf., Denmead et al.,
1976; Horvath, 1983; Sutton et al., 1993b, 1995; Duyzer, 1994). Overall, unfertilized
ecosystems, such as forest and moorlands have mostly shown ammonia deposition, while
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 36
fertilized and grazed agricultural ecosystems tend to show bi-directional fluxes with some
periods of deposition and some periods of emission. Of course, the distinction is not absolute, as
smaller ammonia emissions may also occur from semi-natural ecosystems (e.g., Sutton et al.,
1995; Flechard and Fowler, 1998). However, such a general difference is clear, and can be
explained by the increase in χs and χground that occurs in fertilized and grazed ecosystems. Two
recently published examples of ammonia exchange provide a useful basis to highlight these
differences.
Neirynck et al. (2005) report ammonia flux measurements made using the AMANDA technique
(Wyers et al., 1993) over a coniferous forest in Belgium. Their forest site occurs in an area of
intensive livestock rearing, so that ammonia concentrations from some wind directions are very
large (5-25 ug m-3) while for other wind sectors ammonia concentrations were more moderate
(2-4 ug m-3). Even considering the effects of canopy wetness, in all conditions the mean diurnal
profiles show consistent net deposition to the forest canopy. Curiously, the largest deposition
fluxes occurred in dry conditions, which is counter intuitive, as Rw would be expected to be
smaller when the canopy is wet (Sutton et al., 1995; 1998; Nemitz et al., 2001b). Although this
difference is partly explained by different values of Fmax during conditions of different canopy
wetness, this appears not to fully explain the difference. Further analysis by Neiyrinck et al.
(2005) showed differences in the overall canopy resistance (Rc) for ammonia deposition with
different canopy wetness and temperature, and with larger values of Rc occurring at higher
ammonia concentrations.
Neirynck et al. (2005) did however find some periods of net ammonia emission from their forest
canopy (Figure 3.4). These were recorded during periods with winds from the high ammonia
wind sector and found to only happen at very large ammonia concentrations, which occurred
when air temperatures were larger than 15 ºC and relative humidity less than 60%. Figure 3.4
presents an intriguing result, since according to the concepts of ammonia compensation points a
different picture should emerge, namely that periods of ammonia emission occur when
atmospheric ammonia concentrations are small. By contrast, such a relationship is possible when
emissions from a canopy are strong (and not compensation point driven), so that it is the
emissions from the surface that generate increased ammonia air concentrations. For example,
this phenomenon was observed following harvest of an oilseed rape field (Sutton et al., 2000a,b).
However, in the present case, the air concentrations are extremely high (25-56 µg m-3),
combined with large ammonia fluxes (0.1 -1.5 µg m-2 s-1). For example, if Ra+Rb were 40 s m-1,
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 37
this would imply minimum emission potentials from the canopy (χ(zo’)) in the range 30 – 120 µg
m-3, which represent extremely high values for a forest canopy.
It is perhaps feasible that these emission periods represent events of desorption of previously
deposited ammonia occurring in dry conditions. Conversely, it is also feasible that they represent
apparent ‘emissions’, being an artefact whereby horizontal ammonia concentration gradients
away from an adjacent ground-based ammonia source (e.g. manure spreading, farms etc) lead to
an advection error. This would reduce the measured deposition rate and could explain apparent
ammonia upward fluxes in this context. This illustration emphasizes the complexity of
measuring ammonia exchange processes and highlights the need for further investigation of each
option.
Fig. 3.4. Dependence of ammonia flux on concentration in the high ammonia wind sector during warm daytime
conditions with dry canopy. (Neirynck et al., 2005, Reproduced by permission Elsevier). [Permission should be
sought]
The above example of mainly ammonia deposition to a forest ecosystem may be contrasted with
recently published measurements of ammonia fluxes over an intensively managed grassland in
the Netherlands (Wichink Kruit et al., 2007). The diurnal patterns in ammonia concentration and
net exchange flux are illustrated in Figure 3.5. Hourly ammonia concentrations in the air at this
site were again very large, 1-50 µg m-3, with an overall mean of around 10 µg m-3. In this case,
net emission occurred for around 40% of the diurnal period (10:00-20:00), with net deposition at
other times.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 38
Figure 3.5. NH3-concentration (upper panel) and NH3-flux (lower panel) measurements above managed grassland
in The Netherlands from 18 July until 15 August 2004 (summer period). The horizontal axis represents time of the
day (UTC). Local time is UTC+2. The vertical axis represents the NH3-concentration (µg m−3) or NH3-flux
(ng m−2 s−1). Diamonds are calculated values for the half-hourly NH3-concentration or NH3-flux; the solid line (—
—) (with vertical 25 and 75 percentile bars) is the median of all half-hourly fluxes for that time. The dashed line (- -
-) in the lower panel is the mean leaf wetness signal during this period (Wichink Kruit et al., 2007. Reproduced by
permission Elsevier). [Permission from Elsevier should be sought].
Wichink Kruit et al. (2007) also estimated the canopy compensation point (χc) based on profile
estimation of χ (zo’). They then combined this with estimates of surface temperature to estimate
Γ( zo’) or ‘Γc’ from the measurements (Figure 3.6). Estimated values of χc were in the range 1-
30 ug m-3, which is comparable with other studies for managed grassland (e.g., Milford et al.
2001a, Sutton et al., 2001; Loubet et al. 2006), and substantially smaller than the upper values
implied for the forest in Figure 2.4. Normalized for canopy temperature, the values of Γc were in
the range 200-11,000 through a period of May to October 2004, with a mean value of just over
2000. These values are comparable with other estimates which have elsewhere been shown to
vary substantially with grassland management practice (e.g. Milford et al., 2001b; Sutton et al.,
2001), and may be in the range 10000-30000 for some days after fertilization (Sutton et al.,
2001, 2008b). It must be remembered, however, that Γ(zo’) only represents a crude indicator of
the actual controlling values. During emission situations, in the presence of additional stomatal
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 39
or within canopy resistances, Γ(zo’) represents a minimum estimate, while during periods of
deposition it represents a maximum estimate of the actual mean value of Γ at the surface.
Fig. 3.6. Derived canopy compensation points (χc = χ(zo’)) (upper panel) and ratios between NH4+ and H+
concentration (Γc = Γ(zo’)) (lower panel) from the end of May until the end of October 2004 (diamonds) and the
constant value (2200) that is normally assumed for modeling (line). (Wichink Kruit et al., 2007. Reproduced by
permission Elsevier). [Permission from Elsevier should be sought].
3.5 Modelling surface –atmosphere exchange of ammonia
Over recent years, the canopy compensation point approach has become the standard basis to
model bi-directional ammonia surface atmosphere exchange. Starting with the 1-layer models
offsetting bi-directional stomatal exchange against deposition to leaf surfaces (Sutton and
Fowler, 1993; Sutton et al., 1995), subsequent models have developed in several directions. The
main subsequent developments can be summarized as follows:
Treatment of multiple canopy layers In addition to ammonia exchange with the top part of the
canopy, leaf litter and the soil surface have been shown to be important sources of ammonia
emission into the plant canopy (e.g., Nemitz et al. 2000a). For an oilseed rape canopy Nemitz et
al. (2000b) also highlighted the importance of an upper and lower part of the main foliage,
distinguishing the main foliage from an over canopy of oilseed ‘siliques’. In practice this three
layer model becomes complex to parametrize, and there has now developed consensus that a
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 40
two-layer model (as shown in Figure 3.4) represents an appropriate balance of realistic
description while avoiding too much complexity. A recent implementation of the 2-layer model
is that of Personne et al. (2008) for the GRAMINAE Integrated Experiment (Sutton et al.,
2008b). They used measured bioassay estimates of Γs and Γlitter (Mattsson et al., 2008a,b;
Herrmann et al., 2008) combined with an energy balance approach to calculate component
resistances, showing close agreement with measured ammonia fluxes (Figure 3.7).
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
22-May 29-May 05-Jun 12-Jun
NH
3 flu
xes
(ng
m-2
s-1)
Measured flux
Modelled flux (Gamma Litter)
Figure 3.7: Comparison of ammonia fluxes simulated by a two-layer canopy compensation point model
(SURFATM-NH3) with measured fluxes (Fmg) during the GRAMINAE Braunschweig Experiment. For this model
scenario, the ground emission is assumed to originate from leaf litter based on measured Γlitter (Personne et al., 2008;
Sutton et al., 2008b).
Treatment of cuticular fluxes. The initial parametrizations of the cuticular resistance (Rw)
allowed only for deposition, dependent on relative humidity (Sutton and Fowler, 1993; Sutton et
al., 1995) or vapour pressure deficit (Nemitz et al., 2000b, 2001b). As noted above for the forest
example, ammonia deposited to a canopy surface may also be re-emitted to the atmosphere,
particularly under drying conditions. A first approach to simulate this effect treated the leaf
surface as a humidity dependent capacitance (Qd), which would be in equilibrium with a non
zero leaf surface concentration (χd) (Sutton et al., 1998). In this case an adsorption/desorption
resistance (Rd) is also defined. This first dynamic approach had the advantage of being able to
simulate ammonia charging and discharging of the cuticle, but had the disadvantage that the leaf
surface pH needed to be specified as an input. The approach was further developed by Flechard
et al. (1999) who considered the full aqueous chemistry on leaf surfaces, dependent on multiple
air pollutant inputs and potential leaching of base cations from leaf surfaces. In this model, leaf
surface pH is solved by ion balance, and the model is able to take account of the effects of other
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 41
trace components (like SO2 concentrations) on ammonia fluxes. Burkhardt et al. (2008) have
recently extended this model to incorporate the two-layer approach with bi-directional exchange
for each of the leaf surface, stomata and ground surface. The model is able to qualitatively
reproduce the bidirectional fluxes during the pre-cut period of the GRAMINAE Braunschweig
Experiment, but at present overestimates the emission and deposition peaks. The cuticular
resistance clearly responds to the chemistry of the liquid film on vegetation and the combination
of reactive gases present (Flechard et al 1999). Even in the absence of additional reactive trace
gases, the cuticular resistance declines with increasing NH3 concentration. In a series of chamber
experiments Jones et al (2007) quantified the relationships between ambient NH3 concentration
and the bulk canopy resistance for a range of moorland vegetation as shown in figure 3.8 in
which the non-stomatal ‘cuticular’ resistance is seen to increase lineary with NH3 concentration,
leading to much smaller deposition at high concentrations than if deposition velocity remained
constant with concentration as is usually assumed.
Figure 3.8. Relationship between ammonia concentrations and resistances to deposition to moorland vegetation. A
significant difference was found between day and night for the bulk canopy resistance (Rc), which included both
stomatal uptake and deposition to the leaf surfaces. Once the effect of the stomatal resistance (Rs) was accounted for,
the cuticular resistance (Rw) was found to be not significantly different between day and night (Jones et al., 2007).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 42
3.6 Dynamic simulation of ecosystem C-N cycling and ammonia fluxes.
A disadvantage of the basic scheme for simulating ammonia fluxes outlined above is that
empirical values of Γ must be provided. The only way forward from this position is to develop
models of carbon-nitrogen cycling that can simulate Γvalues for the different pools based on an
understanding of the pool dynamics (cf. Massad et al., 2008). To date, the only such model to
attempt this coupling is the PaSim model of Riedo et al. (2002). To do this the model
distinguished plant nitrogen pools into structural nitrogen, substrate nitrogen and apoplastic
nitrogen (a sub-pool of substrate nitrogen), linking these with plant uptake and growth processes.
The model was parametrized based on measured fluxes for a Scottish grassland (Milford et al.,
2001b) and has recently been tested for the Braunschweig Experiment (Sutton et al., 2008b).
Overall, the model was able to simulate the larger net emissions that occurred after cutting and
after fertilization, as well as the decline in the 10 day period following fertilization. By contrast,
the component fluxes were less well described. Bioassays, chamber measurements and within-
canopy profiles during the Braunschweig Experiment (Mattsson et al., 2008a,b; Herrmann et al.,
2008; David et al., 2008a,b; Nemitz et al. 2008b, Sutton et al., 2008b) highlighted leaf litter as
being a key source of emission following cutting. This source is currently not simulated in
PaSim, which simulated that increased emissions after cutting were due to an increase in
apoplastic ammonium. The bioassays indicated that the foliage was more likely to be a sink of
soil/litter ammonia emissions, highlighting the need for improved ecosystem modelling of
ammonia exchange that accounts for litter decomposition processes (Sutton et al., 2008b).
3.7 Integrating ammonia exchange processes
The preceding sections have highlighted the many processes and interactions that define
ammonia fluxes between vegetation and the atmosphere. It thus becomes a major challenge to
integrated each of these processes to develop a holistic view. It is necessary to quantify the
interactions in each case in order that valid conclusions can be obtained. This creates a major
challenge for experimentalists to be able to address all the questions in the field. For example, in
the absence of measurements of horizontal concentration profiles, it is difficult to quantify the
potential for advection effects to have influenced the results presented in Figure 3.4. Similarly, it
remains an open question in most studies whether gas-particle interactions have a significant
influence on measured ammonia fluxes.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 43
It was with such interactions in mind that the GRAMINAE Integrated Experiment was designed.
A number of findings from this experiment have already been mentioned, but the experiment
demonstrates both the challenges and the power of developing an integrated approach. Figure 3.9
summarizes each of the issues and measurement methods that were investigated during this
experiment (Sutton et al., 2008a). The key conclusions have been summarized by Sutton et al.
(2008b) and include:
• That unreplicated ammonia flux measurements in most studies are highly uncertain and
need to be considered with great caution when being compared with model estimates.
• That advection effects can significantly influence measured ammonia fluxes, both due to
dispersion away from nearby point sources (correction for advection effects increases net
deposition) and due to emissions from an emitting field itself (correction for advection
increases net emission).
• That gas-particle interactions had a minor effect on measured ammonia fluxes, though the
relative effect on calculated aerosol deposition rates was significant (being the cause of
apparent aerosol emissions).
• That reasonable agreement can be found between relaxed eddy accumulation for
ammonia and the aerodynamic gradient method, though measurements are not
sufficiently precise to detect flux divergence (except for possible cases of extreme
advection errors).
• That net emissions from a grassland canopy are controlled by the recapture of leaf litter
ammonia emissions by overlying foliage and the interaction of cuticular exchange pools
with mainly stomatal uptake of ammonia from the leaf litter emissions. Net emissions
increase following cutting due to exposure of the litter and cutting induced senescence,
with a similar recapture process affecting net emission following fertilization.
• A range of models is able to simulate the dynamics of net ammonia exchange with the
managed grassland, but further attention is needed to develop dynamic treatments of
ammonia emissions from leaf litter decomposition.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 44
NH3 release from litter decomposition
Soil chemistry interactions with plant N uptake & NH3 fluxes
Advection of NH3 from nearby sources & effectson vertical fluxes
Interactions with acid gases and ammonium
particles & effectson net NH3 fluxes
Effects of cutting& N fertilization events & choices
Estimation offarm-scale NH3
emissionsfrom plume
measurements
NH3 compensationpoints of foliage
Continuous measurement of NH3fluxes by gradient
and REA approaches
Effects of leaf senescenceand plant species on
NH3 emission potential
Quantification of energy balance &
environ controls onNH3 exchange
Within-canopy cycling of NH3 fluxes
Determination ofwithin-canopy
turbulent exchange
Plant bioassay determination of
NH3 emission potential
Effects of dew& leaf surface
chemistryon NH3 fluxes
NH3 release from litter decomposition
Soil chemistry interactions with plant N uptake & NH3 fluxes
Advection of NH3 from nearby sources & effectson vertical fluxes
Interactions with acid gases and ammonium
particles & effectson net NH3 fluxes
Effects of cutting& N fertilization events & choices
Estimation offarm-scale NH3
emissionsfrom plume
measurements
NH3 compensationpoints of foliage
Continuous measurement of NH3fluxes by gradient
and REA approaches
Effects of leaf senescenceand plant species on
NH3 emission potential
Quantification of energy balance &
environ controls onNH3 exchange
Within-canopy cycling of NH3 fluxes
Determination ofwithin-canopy
turbulent exchange
Plant bioassay determination of
NH3 emission potential
Effects of dew& leaf surface
chemistryon NH3 fluxes
Figure 3.9 Overview of issues addressed by the GRAMINAE Integrated Experiment (Sutton et al. 2008a).
3.8 Future challenges for ammonia exchange
The results from the GRAMINAE Integrated Experiment provide a microcosm of some of the
key challenges to measure ammonia fluxes and model the process interactions. In a wider
perspective key challenges include the climate dependence of net ammonia emission and
deposition, and the characteristic fluxes of other ecosystems in the world.
In principle the models of ammonia exchange incorporate the main features of environmental
conditions and could therefore be applied in different climates. Here the limitations faced are
ones of lack of available data for empirical factors such as Γ values and on extrapolation to
conditions with rather different climates. Currently, the estimates of Γ have mainly been derived
for cool European conditions for a very limited number of ecosystems. Although there have
been many studies on ammonia emission rates from fertilized tropical systems, such as rice and
maize, there are hardly any published studies on ammonia fluxes over semi-natural unfertilized
tropical ecosystems. The rates of ammonia deposition/exchange in these situations are thus
highly uncertain. Given the differences in biology of these systems, there can be no substitute
for direct measurements.
A modest degree of climate change (e.g. + 2 ºC) is a much easier matter to simulate, for example
based on the analysis of temperature effects within existing datasets. The thermodynamics of
ammonia solubility and dissociation are rather straightforward, indicating for example a
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 45
doubling in χs every 5 ºC increase for a given value of Γ (Sutton et al., 2001). However, caution
is needed before making climate change simulations on this basis. Analysis of the PaSim model
under different temperature regimes showed that net ammonia fluxes for Easter Bush in Scotland
(cf to measured fluxes of Milford et al., 2001b) were rather insensitive to temperature. For
example, increased temperature (in the absence of moisture limitation) led to increased grass
growth which diluted available nitrogen pools, thereby reducing Γ values (Sutton and Milford,
unpublished simulations). Similarly, increases in wetness, while favouring smaller values of Rw
may also lead to increased rates of leaf litter decomposition, favouring ammonia emissions. To
take another example, in colder conditions, NH3 from manure application to the land surface
tends to be emitted at smaller rates, but the emission lasts longer, especially if a waterlogged or
frozen soil conditions prevent infiltration. With these illustrations in mind, it becomes a major
future challenge to generalize how ammonia fluxes might change in the future under different
climatic regimes.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 46
4 Sulphur Dioxide 4.1 Introduction
There are three very different spatial scales relevant to the exchange of SO2 at terrestrial
surfaces, first the micro-scale, at which the chemical and biological interactions occur (Figure
1.1). Second is the spatial scale at which most of measurement and interpretation takes place,
which is the field scale (103 to 105 m2) for measurements using micrometeorological methods.
Lastly, the application of knowledge of the surface exchange process is primarily at regional to
continental scales to characterise the fluxes and budgets within chemical transport models
(CTM) and comparisons with the concentration fields observable from satellites (Richter et al
[date]).
The measurements have primarily been at the field scale using micrometeorological methods,
although there have been some laboratory studies, mainly in the early days of SO2 dry deposition
research. The initial measurements were used primarily to estimate the regional scales of dry
deposition, often using a fixed deposition velocity as a key variable within long range transport
models (eg Fisher, 1978). With larger data sets of measurements covering a wide range of
conditions, it is clear that rates of dry deposition vary considerably in time and space (Fowler
and Unsworth, 1976) in particular because the sinks available at terrestrial surfaces, including the
stomata in vegetation, leaf surfaces and the presence of liquid water on vegetation from dew or
rain, present a variable absorbing surface. The data have shown the role of atmospheric
composition and surface leaf water chemistry in controlling canopy resistance.
Most dry deposition measurements of sulphur dioxide over the last 30 years have been made in
N. America and Europe, and have served as a basis for the parameterisation of dry deposition
models (Erisman, 1994; Smith et al., 2000; Zhang et al., 2002), which in turn have been applied
to ecosystems in different parts of the globe. However, most SO2 emission and deposition now
occurs outside N. America and Europe. Asia’s contribution in 1985 of 20 % to global
anthropogenic SO2 emissions has doubled since then, reaching 37% by the year 2000, of which
23% is emitted by China alone and 5% by India.
Southern China is one of the world’s most sulphur polluted areas. Paradoxically, in Northern
China, where ambient SO2 concentrations are very large, rainfall is generally alkaline, and the
areas polluted by acid rain do not necessarily correspond to the areas of high SO2 emissions. One
of the reasons for this discrepancy is the presence of alkaline soils (yellow sand) distributed over
the arid areas of N.W. China (e.g. the loess plateau and Gobi desert), the windborne erosion of
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 47
particles with high base cation concentrations can neutralize atmospheric acidity (Utiyama et al.,
2005). Loess soil, which covers vast areas of the Eurasian continent extending from N.E. China
to Central Asia, contains Ca in large quantities, and calcium carbonate (CaCO3) reacts with
atmospheric SO2, to form calcium sulphate (CaSO4). Thus, even bare soil without vegetation
may be a significant sink (Sorimachi et al., 2004), which may affect the regional SO2 budget if
the process is inadequately quantified in dry deposition models.
In this section we review research and monitoring from the last decade, including SO2 dry
deposition measurements from Asia, North America and Europe, as well as findings from long-
term flux monitoring experiments. The current state of knowledge concerning mechanisms of
SO2 dry removal from the atmosphere is reviewed, with consequences for temporal trends in
atmospheric concentration and deposition, and key future research areas are identified.
4.2 Worldwide advances in SO2 flux monitoring and modeling
4.2.1 Asia
Sulphur dioxide dry deposition to vegetated surfaces is largely controlled by non-stomatal
processes, but in many arid ecosystems and deserts of the world where vegetation is sparse, the
nature and pH of soils determine the sink strength. In Asia, substantial efforts have for example
gone into the characterization of SO2 uptake by loess soils, given their large geographical
representation in Northern China, their alkaline nature and their ability to neutralize atmospheric
acidity and to serve as an oxidation medium for SO2. Both micrometeorological and laboratory-
or field-based flow reactor methods were deployed. New micrometeorological measurements
over forests and short vegetation have also been reported over the last 10 years in the region,
reflecting the growing concern over increasing sulphur emissions and deposition to ecosystems.
4.2.1.1 Sulphur dioxide deposition to soils
Utiyama et al. (2005) measured dry deposition to loess soil and dead grass in Beijing using the
aerodynamic gradient method, though in neutral conditions 22% of the time. In stable or unstable
thermal stratification, they used a surface reaction concept for inferring dry deposition. Two
surface kinetics models were considered: either i) the reaction occurs in soil pores and SO2
molecules diffuse through porosity while reacting with alkaline sites on the pore surface; or ii)
the adsorption mechanism is of Langmuir-Hinshelwood type, where the partial pressure of SO2
and its desorption pressure from the site are in equilibrium. The model parameters are then fitted
so that the resulting (modelled) vertical SO2 concentration gradient matches the observations.
Measured deposition velocities (Vd) were in the range 1-12 mm s-1.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 48
Sorimachi et al. (2004) used a combination of laboratory flow-reactor methods and field-based
passive collectors to determine SO2 dry deposition to 6 Chinese loess soils originating from arid
areas in N. China such as the loess plateau and Gobi desert. They found that the uptake rate
increased with soil alkalinity and relative humidity (RH). Mean canopy resistances (Rc) of 160 (±
60) s m-1 for RH <10%, and Rc of 90 (± 50) s m-1 for RH = 60%, were measured. Likewise,
Sorimachi and Sakamoto (2007) conducted laboratory-based flow-reactor measurements of SO2
deposition to soil samples from 12 sites in the arid loess plateau and deserts of Northern China.
Canopy resistances in the range 28-650 s m-1 (with a mean of around 200 s m-1) were found to be
dependent on RH, as was S(IV) oxidation to S(VI). It was hypothesized that Northern China
soils, which are much more alkaline than in Southern China, are a greater sink for SO2 and a
neutralizing buffer for acidifying atmospheric deposition. By comparison, in modelling SO2
deposition to Asia, Xu and Carmichael (1998) used a fixed Rc for deserts of 500 s m-1, which is
clearly too high in the case of Northern China deserts. A flow-reactor was also used by
Sakamoto et al. (2004) to determine SO2 dry deposition to yellow sand and soil-mediated SO2
oxidation by O3. The deposition velocity for SO2 increased with RH due to the positive effect of
RH on the SO2 oxidation rate.
4.2.1.2 Micrometeorological measurements over vegetated areas
Matsuda et al. (2006) reported micrometeorological (aerodynamic gradient) flux measurements
of SO2 and O3 over a tropical (teak) forest in Northern Thailand in dry and wet seasons. The
deposition velocity for SO2 in the dry season was rather low, in the range 1-3.1 mm s-1 in
daytime and 0.8-1.1 mm s-1 in night-time. In the wet season, however, Vd was much higher due
to enhanced non-stomatal uptake in wet conditions, with values in the range 9.5-13.9 mm s-1 in
daytime and 2.6-4.2 mm s-1 in night-time. The data were compared with a recent non-stomatal
resistance scheme (Zhang et al., 2003a), and it was concluded that extended experimental SO2
dry deposition studies are needed in the tropics, while Zhang et al. (2003a) recommend more
studies to quantify the different effects of dew and rain on SO2 deposition.
Sulphur dioxide dry deposition was also measured by Matsuda et al. (2002) over a red pine
forest located in Oshiba Highland, Nagano, Japan, using a Bowen ratio technique. The median
daytime (12:00 to 14:00) deposition velocity was 9 mm s-1. Measurements compared favourably
with estimates by an inferential model for wet conditions, but for dry or mixed wet-dry surfaces
there were large differences between model and measurements. The authors ascribed the
discrepancy to a relative humidity threshold value used in the inferential scheme to characterize
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 49
canopy wetness, and pointed to the need for a refined parameterisation of the cuticle or external
leaf surface resistance.
In a study of SO2 and O3 dry deposition to short grassy vegetation over an alkaline soil (pH=9.2)
near Beijing, using the aerodynamic gradient method, Sorimachi et al. (2003) measured mean Vd
values of 2 (±1) mm s-1 and 4 (±2) mm s-1 in late summer and early winter, respectively.
Although the grass was lush and thick in the late summer, and senescent and leafless in the early
winter observation period, there was no difference in the mean Rc (180 ±270 s m-1 and 180 ±300
s m-1, respectively), but the uncertainties given reveal a large variability in measured Rc. The
difference in Vd stemmed from the higher aerodynamic (Ra) and quasi-laminar sub-layer (Rb)
resistances in late summer than in early winter. The absence of vegetation and stomatal uptake in
early winter, which might otherwise have reduced the SO2 sink strength, seems to have been
compensated for by the soil alkalinity. As the soil was more exposed and the in-canopy
aerodynamic resistance was reduced, the soil surface offered more adsorption and reaction sites
for SO2, with the result that the field was an equally efficient SO2 sink in early winter as in
summer.
The deposition velocity for SO2 was measured by Jitto et al. (2007) during a 1-year experiment
over a canopy of irrigated rice paddy in Thailand using the Bowen ratio technique. The
deposition velocity was highest around noon and lowest at night. Seasonally-averaged values of
Vd were 6.7, 12.5, and 15.1 mm s-1 in the winter, summer, and rainy seasons, respectively.
4.2.1.3 Long-term deposition studies and inferential modelling
As alternatives to costly and labour-intensive micrometeorological measurements of dry
deposition, several authors in Asia have estimated long-term SO2 deposition using monitored
concentration data and inferential models, or long-term artificial collection devices. The latter
can only provide crude estimates of deposition rates, as surrogate surfaces do not adequately
account for the complexity of natural surfaces, but they do allow continuous monitoring at a
number of sites and help to detect trends.
Ta et al. (2005) thus provided long-term sulphur dioxide dry deposition estimates across Gansu
Province, China, using K2CO3-coated surrogate sulfation plates. Samples were taken monthly for
11 years at 48 sites distributed across 11 cities in the province. The data showed that cumulative
SO2 dry deposition fluxes were closely related to local SO2 emissions, and had seasonal
variations with maxima in winter and minima during summer as a result of higher winter and
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 50
lower summer SO2 emissions and concentrations. Monthly average SO2 deposition velocities,
however, peaked in April-July at 11–27 mm s-1, and minimum values were observed in January
at 2-10 mm s-1.
Inferential models (Erisman, 1994; Smith et al., 2000; Zhang et al., 2002) may be used to
estimate dry deposition at observation sites, where single-height ambient concentration
measurements are available together with standard meteorological data. Model parameters,
however, have been largely derived from European and N. American studies and may not
necessarily be adequate for Asian vegetation and soils, and numerical evaluations need to be
carried out. Thus Takahashi et al. (2002) simulated the dry deposition of SO2 to a Japanese cedar
(Cryptomeria japonica) forest located in Gumma Prefecture, based on the results of 1-year’s
concentration measurements. The mean modelled Vd at this site was 8.8 mm s-1 (Takahashi et al.,
2001). The inferential estimate of the dry sulphur deposition flux was 11.1 mmol m-2 yr-1 (3.6 kg
S ha-1 yr-1), which compared well with the net throughfall flux (12.4mmol m-2 yr-1, or 4.0 kg S
ha-1 yr-1). Over a broadleaf forest on typical red soil of Southern China, Xu et al. (2004)
simulated Vd for SO2 and particulate SO42-, as well as their atmospheric deposition fluxes. The
simulations indicated that about 99% of the dry sulphur deposition flux in the forest resulted
from SO2, which contributed over 69% of the total (wet + dry) annual sulphur deposition.
By comparison, Wang et al. (2003) computed dry deposition fluxes of SO2 and SO42- for 1 year
to agricultural land over red soil (pH = 5.3 to 5.8) in the Jiangxi province of Central China. The
crops grown were rice paddies and oilseed rape. Sulphur dioxide concentrations were measured
8 times day-1, 7 days month-1, using a bubbler method. Annual mean modelled estimates of Vd
were 3.8 (± 0.16) mm s-1 for SO2 and 0.20 (± 0.12) mm s-1 for SO42-. Measured monthly mean
concentrations ranged from 9 to 163 µg S m-3 (6.7-121 ppb), with an annual mean of 64 µg S m-3
(47 ppb). Estimates of total monthly wet and dry deposition of SO2 and SO42- ranged from 2.2 to
20.3 kg S ha-1 with an annual total deposition of over 100 kg S ha-1, of which 83% was via dry
deposition, accounting for over 90% of total S input to farmland in this area.
4.2.2 North America
Few long-term datasets of SO2 dry deposition monitoring have emerged over the last 10 years,
reflecting the declining importance of SO2 as an acidifying input relative to NOy and NHx.
Advances have nonetheless been made in inferential modelling of SO2 uptake, especially
regarding the quantification of the non-stomatal (external) leaf surface resistance, which serve as
a basis for simulating regional patterns of SO2 deposition (Zhang et al., 2002, 2003b).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 51
Micrometeorological SO2 flux data from 5 sites (2 forests, a corn field, a soybean field and a
pasture) in eastern USA (Finkelstein et al., 2000; Meyers et al., 1998) were compared with
modelled data by Zhang et al. (2003a), with the specific objective of evaluating the new non-
stomatal resistance scheme of the new Canadian model (Zhang et al., 2002, 2003b). Over the
forest sites, Finkelstein et al. (2000) had noted that wetness tended to increase deposition
velocity, but that the nature of wetness (rain or dew) and its chemistry also controlled canopy
resistance. Non-stomatal surfaces like leaf surface, stem, trunk and ground were important sinks
for SO2, and the authors concluded that a better understanding of surface chemistry and water
film chemistry was needed.
Dew formation has long been recognised as an important sink for SO2 (Fowler et al, 1974). In
more recent work Meyers et al (1998) show that dew is the reason for the relatively high early
morning deposition rates at 2 of the 3 low vegetation sites studied in Eastern USA. Recognizing
the weakness of existing North American parameterisations (e.g. Meyers et al., 1998) in
predicting SO2 deposition rates to non-stomatal surfaces, especially in wet canopies, Zhang et al.
(2003a) demonstrate that the AURAMS scheme (Zhang et al., 2002) performed well at these 5
sites, using different resistance values for dew and rain. The revised non-stomatal resistance
scheme (Zhang et al., 2003b) includes a treatment of in-canopy transport, soil and cuticle terms,
and is a function of relative humidity, leaf area index and friction velocity. For wet canopies, the
cuticular resistance is treated differently for dew and rain.
4.2.3 Europe
4.2.3.1 Long-term flux monitoring in the UK
Sulphur dioxide fluxes have been monitored continuously since the mid 90’s at two rural sites in
the UK, over agricultural land at Sutton Bonnington in the English Midlands, and over moorland
at Auchencorth Moss in S. Scotland. The dry deposition measurements have continued to bring
surprises over the last 10 years. At Sutton Bonnington, the ambient concentrations have declined
from about 2.8 ppb in 1996 to current values close to 1.4 ppb and yet the deposition velocity
continues to increase due to continued reduction in the canopy resistance (Rc) (Fig. 4.1). Over
the monitoring period the canopy resistance has almost halved and is now about 70sm-1. The
consequence of the steady decline in the canopy resistance along with a decline in ambient
concentration is that the flux remains nearly constant. The measurements of the atmospheric
terms (Ra and Rb) show that the trend is not caused by changes in turbulence, and thus the
interpretation of cause in changes in the surface processing of the deposited SO2 is secure.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 52
These dry deposition measurements have proved valuable in explaining the consistently larger
decline in ambient SO2 concentration than in emissions in Europe. In the absence of these flux
measurements it would be a matter of speculation as to the underlying cause of the faster decline
in ambient concentration than emission. Even with these measurements there remains the
possibility that SO2 oxidation rates have increased due to the growing oxidizing capacity of the
atmosphere and have contributed to the relative changes in emission and deposition (non-
linearity). It will be necessary in the further analysis and interpretation of European pollution
climate data to carefully examine the relative importance of the different contributors to the
observed trends in concentration and deposition and quantify the relative importance of changes
in dry deposition and oxidation rates in the long term trends.
0123456789
10
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Mix
ing
ratio
(ppb
)
NH3SO2
0
0.1
0.2
0.3
0.4
0.5
0.6
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
SO2/N
H3 m
olar
rat
io (p
pb p
pb-1
)
0
20
40
60
80
100
120
140
Rc S
O2 (
s m-1
)SO2/NH3Rc (Wheat)
0123456789
10
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Mix
ing
ratio
(ppb
)
NH3SO2
0
0.1
0.2
0.3
0.4
0.5
0.6
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
SO2/N
H3 m
olar
rat
io (p
pb p
pb-1
)
0
20
40
60
80
100
120
140
Rc S
O2 (
s m-1
)SO2/NH3Rc (Wheat)
Figure 4.1 Changes in the mean concentrations (ppbV) and ratio of ammonia and sulphur dioxide and in
the May-July canopy resistance for SO2 deposition on Wheat at Sutton Bonnington between 1996 and
2003 .
4.2.3.2 Other recent European datasets
The SO2 flux-gradient data obtained over short vegetation by Feliciano et al. (2001), collected
over a period of 3 years in the mid-nineties at 3 different sites in Portugal, were important in
providing Rc estimates for the Mediterranean region of Southern Europe. The 3 sites had
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 53
contrasting pollution climates, with a coastal, oceanic, humid meadow in N. Portugal, a hot and
semi-arid pseudo-steppe and a site located in a mostly dry, intensive agricultural area, both in S.
Portugal. Median canopy resistances varied from 140 s m-1 to 200 s m-1 and although stomatal
uptake was important when vegetation was biologically active, the annual deposition was
dominated by non-stomatal mechanisms on wet surfaces. The night-time canopy resistance, a
proxy for the non-stomatal resistance, increased with decreasing relative humidity at all 3 sites.
A comparison of nocturnal Rc for the southern sites showed that, for a given level of relative
humidity, the Rc at the intensive agricultural site was systematically lower than at the pseudo-
steppe site, which is used more extensively for grazing and hay production. Although the authors
make no mention of NH3 being measured at these sites, it might be hypothesized that a higher
NH3 concentration at the intensive agricultural site may have been responsible for the observed
lower nocturnal Rc, compared with the extensively-managed, steppe-like grassland.
The development of low-cost systems for the long-term monitoring of SO2, NH3 and other trace
gas fluxes holds promise for widening the range of dry deposition datasets for comparison with
inferential models. Hole et al. (2008) present an 18-month dataset of SO2 fluxes acquired with a
conditional time-averaged gradient (COTAG) system (Fowler et al., 2001; Famulari et al., 2008)
in a semi-alpine ecosystem in Southern Norway. The mean annual SO2 deposition velocity was
4.0 mm s-1, although the dataset included some negative deposition velocities (upward fluxes),
and the annual mean Vd was 13.0 mm s-1 if only the positive values were included. The authors
report evidence of enhanced SO2 deposition rates during an episode in November 2005 when the
NH3/SO2 ratio was high, and conversely of decreased SO2 uptake and increased NH3 uptake in
November 2004 when the NH3/SO2 ratio was low. Comparison with the inferential model by
Zhang et al. (2002, 2003b) was satisfactory but the model could not reproduce the large observed
variability in exchange rates, which may result from NH3-SO2 co-deposition processes not being
included in their resistance scheme.
More experimental evidence of the mutual influences of NH3 and SO2 concentrations on their
deposition rates was obtained by Derome et al. (2004), though not by micrometeorological
measurements but using bulk precipitation collectors and throughfall measurements in Scots pine
canopies in SW Finland. The study was conducted over a 6-year period (1993-1998) in the
vicinity of a Cu-Ni smelter, which emitted large amounts of gaseous NH3. These emissions were
shown to have strongly enhanced the scavenging of atmospheric SO2 by the pine canopy,
resulting in increased levels of N and S deposition and increased foliar N and S concentrations.
In an NH3 fumigation experiment, Cape et al. (1998) had previously described similar findings
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 54
over a Scots pine forest in Central Scotland, with the canopy resistance for SO2 decreasing with
elevated NH3 concentration. Although NH3 concentrations were not measured in the Finnish
study (Derome et al., 2004), they were likely higher than normally encountered in the
countryside, except near animal housing in areas of intensive agriculture, where such processes
could be significant.
4.3 Control of surface uptake rates by leaf cuticular chemisty
A number of authors have addressed the issue of the chemical control of surface pollutant uptake
rates (eg. Flechard et al, 1999). The most important finding for SO2 deposition is that the rates of
deposition are controlled mainly by the chemistry at the vegetation-atmosphere interface, and
that as the surfaces are wet most of the time, the processes are regulated by the chemistry of the
thin film of moisture. In principle, many compounds influence the chemistry of this surface
layer, including plant exudates and soil derived compounds, but the key reactant for SO2 is NH3.
Thus the ambient concentrations of SO2 and NH3 essentially regulate the pH of the surface
moisture and thus control the uptake of SO2. The full surface chemistry of the process has been
incorporated into a dynamic mechanistic model shown in Fig. 4.2 (Flechard et al 1999). The
chemistry of the surface water film is initialised in the model using measured precipitation
chemistry, the model then simulates the dynamic responses of the net land-atmosphere exchange
of SO2 as the ambient concentrations of the reactive trace gases and meteorological conditions
change. The model has been shown to provide good agreement with observed 30 min average
fluxes for several days. An example is provided in Figure 4.3, for a five-day period at
Auchencorth Moss in the Scottish Borders. The general agreement between measured and
modelled fluxes is excellent during the three day period, 21st March to 23rd March 1995. From
the 24th March, the observed NH3 concentrations are increased to 2ug m-3 to provide sufficient
NH3 to neutralise the acidity from the ambient SO2 oxidation in solution. The consequence is to
decrease the canopy resistance for SO2 and increase the deposition rate of SO2 to the maximum
under the prevailing atmospheric conditions. Demonstrating a close link between SO2 deposition
and ambient NH3 is not new, as this was predicted in earlier work within the first phase of
BIATEX. However, this work quantified the process correctly for the first time, demonstrated
the effects in field conditions at ambient concentrations and provided a mechanistic model
incorporating the full chemical scheme.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 55
KhsKha KhhonoKhc
Kn1 Ks1Kc1Ka
NH3,aq CO2,aq HNO2,aq
NH3 CO2 HNO2 SO2
SO2,aq
CO32- SO3
2- SO42-
NH4+ HCO3
- NO2- HSO3
- HSO4-
Ks2Kc2 Ks3
Kw
H2O H++OH-
χd
Rd
AQUEOUS
GASEOUS
Kha
χs(NH3)
Rs
Rcut
Fcut
Rb
Ra{z-d}
χ{z-d}
χ{z0’}=χc
χ{z0}
WET DRY
HNO3
HCl
Cl-
NO3- H2O2
O3
APOPLAST
Ft
Fd
FsK+Mg2+
Na+ Ca2
+
K+Mg2+
Na+ Ca2
+
KaNH3,aqNH4
+
Kw
H2O H++OH-
Figure 4.2. A schematic representation of the dynamic canopy compensation pollution model for SO2 and NH3 exchange over vegetation (from Flechard et al 1999).
21/0
3/95
12:
00
22/0
3/95
00:
00
22/0
3/95
12:
00
23/0
3/95
00:
00
23/0
3/95
12:
00
24/0
3/95
00:
00
24/0
3/95
12:
00
25/0
3/95
00:
00
25/0
3/95
12:
00
26/0
3/95
00:
00
26/0
3/95
12:
00
SO2 f
lux
(ng
m-2
s-1 S
O2)
-60
-50
-40
-30
-20
-10
0
10
Meas. FSO2
FSO2, max
Mod. FSO2 (χNH3 = ambient)Mod. FSO2 (χNH3 = 2 µg m-3)
Figure 4.3. A comparison between measured and modelled SO2 fluxes at Auchencorth Moss over the
period 21-3-95 to 26-3-95 showing the influence of increasing ambient NH3 concentration on SO2 flux
(from Flechard et al., 1999).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 56
4.4 Advances in deposition modelling
The magnitude of dry deposition at the national and regional scales requires that process-based,
rather than empirical, parameterisations be implemented in atmospheric models, accounting for
variations in surface chemical characteristics driven by local pollution climates. The observation
of changes in deposition velocity from the European SO2 deposition studies of the 90’s is now
widely known and is being used by EMEP to explain growing discrepancies in the model-
measurement comparisons over Europe. The work has led to modifications of the EMEP model
(Simpson et al., 2003) to simulate the temporal trends, resulting in an increase in Vd for SO2 over
many parts of the continent, and driven by the long term, large scale decrease in the SO2/NH3
ratio (Fig. 4.4). The new scheme, for non-stomatal resistance of both NH3 and SO2, incorporates
an acidity to alkalinity (SO2/NH3) molar ratio as a scaling factor for resistances. For SO2, two
non stomatal resistances Rns,wet and Rns,dry are calculated as a function of the SO2/NH3 ratio, and a
function of relative humidity is used for the transition from dry to wet when the surface cannot
be considered fully wet nor fully dry.
.
Figure 4.4. Modelled (EMEP) dry deposition velocity of SO2 (cm s-1) over Europe for 1980 and 2000,
taking into account the effect of the change in the SO2/NH3 ratio on the canopy resistance (Ref to Fagerli
et al., in press).
The EMEP non-stomatal scheme has also been used in field-scale inferential modelling of N and
S dry deposition as part of the NitroEurope project, using low-cost, long-term atmospheric trace
gas and aerosol DELTA samplers (Tang et al. 2009). Another implementation of the
parameterisation was made by Zimmermann et al. (2006) for the simulation of atmospheric
deposition to Norway spruce, using the SPRUCEDEP SVAT model, and comparison with
throughfall measurements and a canopy base cation budget model. The agreement between
(inferential & canopy budget) modelling and observations was very good for S and oxidised N.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 57
Here, the contribution of dry to total (dry+wet) deposition was around 60% for S and for both
reduced and oxidised N.
The Dutch IDEM model (Bleeker et al., 2004; Erisman et al., 1994) also uses an NH3/SO2 molar
ratio as a proxy for surface acidity. For NH3, a range of default Rext values are used for 3 classes
of the N/S ratio (very low, low and high), depending on surface wetness, land-use and time of
day, while for SO2 the only effect implemented is to add 50 s m-1 to the non-stomatal resistance
when the N/S ratio is very low (<0.02).
The large reduction in European SO2 emissions and ambient concentrations over the last 25
years, and the relative stagnation in NH3 emissions and concentrations in Western Europe over
the same period has meant that the SO2/NH3 ratio has decreased dramatically, resulting in a
reduced Rc for SO2 (Figure 4.1). While ambient SO2 was a relatively good proxy for total
atmospheric and leaf surface acidity 15 or 25 years ago, the relative share of SO2 compared to
other inorganic atmospheric acids (e.g. HNO3 and HCl) is now much lower. The NitroEurope
network of 56 DELTA samplers across the European continent currently provides monthly mean
concentrations of HNO3 and HCl as well as SO2 and NH3 and aerosol NH4+, NO3
- and SO42-
(Tang et al., 2009), with a view to validating European concentration fields of concentration and
deposition for these species. The data show (Fig. 4.5) that the geometric mean mixing ratios of
SO2, HNO3 and HCl across the network are 0.4, 0.35 and 0.15 ppb, respectively, so that, on
average, SO2 makes up only about 40% of the sum of acids (SO2 + HNO3 + HCl). Further, the
data indicate that at some sites (e.g. most Danish, French and Italian sites), the acidity is largely
dominated by HNO3 and HCl, which are considered in most models (e.g. Simpson et al., 2003)
to be deposited at the maximum rates allowed by turbulence (Rc ~ 0 s m-1). Under such
conditions, the proxy (SO2+HNO3+HCl) / NH3 would seem more appropriate to quantify the
relative importance of surface acidity and alkalinity in model parameterisations, than the ratio of
SO2 alone to NH3. Clearly the surface affinity for SO2 uptake will depend whether fast-
depositing, strong acids are present, as the acidity is no longer SO2-dominated, and this needs to
be accounted for in extended surface resistance parameterisations.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 58
0
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Meh
Aci
d/N
H3 r
atio
(ppb
ppb
-1)
SO2/NH3(SO2+HNO3+HCl) / NH3
Fig. 4.5. Top: Annual mean concentrations of SO2, HNO3 and HCl across the NitroEurope network;
Bottom: Annual mean Acid/NH3 molar ratios calculated from SO2 alone or from SO2+HNO3+HCl.
4.5 Future challenges
The principal controls over SO2 deposition to terrestrial surfaces have been identified from field,
mainly micrometeorological measurements. These studies have enabled the controlling steps in
the deposition pathway to be separated and their response to environmental variables quantified.
In turn the data and responses have been used to develop process based models and applied to
quantify regional deposition budgets at country and continental scales. There have been
surprises, notably in the last decade. The largest surprise has been the recognition that long term
(~1 year) average deposition velocities change with time due to changes in the chemical
climatology at the regional scale. Thus a few measurements of SO2 deposition rates to
parameterise models will not necessarily be satisfactory in the long term. It is necessary to
underpin estimates of regional SO2 deposition with measured deposition fluxes and estimates of
the surface resistance to quantify the long term trends. The same logic means that deposition
velocities from one region will not necessarily apply elsewhere. The most important region
globally for sulphur emissions and deposition is currently East Asia, and China specifically.
While the methodologies developed in Europe and North America are applicable for
measurements of SO2 deposition, the use of parameters deduced in Europe or North America are
not applicable, and measures fluxes and surface resistances in these regions are necessary to
underpin the assessments.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 59
5. Ozone 5.1 Introduction
Ozone is a gaseous, phytotoxic secondary air pollutant with widespread effects on human health,
vegetation and materials. It is also a greenhouse gas (GHG), affecting the radiation balance of
the earth, and it interacts with other GHGs such as methane. Its deleterious effects on plants pose
a large-scale risk to crop production and forest vitality, which has been widely documented in
Europe and North America (e.g. Hayes et al., 2007; Karnosky et al., 2007), and there is also
evidence of ozone impacts in Asia, Africa and Latin America (e.g. Ashmore, 2005).
Ozone deposition to external surfaces of vegetation is important as a removal pathway for
ground level ozone but is of little consequence for plant effects. The primary potential for injury
to vegetation, requires stomatal uptake of ozone molecules (Fig. 5.1) followed by reaction with
the internal plant tissue generating highly reactive oxidants that interfere with physiological
processes (e.g. Matyssek et al., 2008). As ozone is a strong oxidant, it can also react with leaf
cuticles and other external plant surfaces or with volatile compounds emitted by vegetation and
non-stomatal ozone deposition is a substantial fraction of the total flux. In addition to vegetation,
ozone molecules may be deposited at any surface providing a chemical sink or acting as a
surface for heterogeneous decomposition (Cape et al 2009). Quantifying the stomatal uptake
rates is central to understanding the ozone-induced risk to vegetation, but the non-stomatal
deposition needs to be quantified to correctly partition the total deposition flux.
ATMOSPHERE
SOIL
CANOPY
turbulent transfer to the surface
stomatal uptake, ozone enters the plant through stomata and reacts with internal plant tissues and
fluids non-stomatal uptake to
leaf cuticles, stems, soil or
any other materials
In-canopy chemistry: reactions of ozone with plant VOCs or soil NO emissions
Fig 5.1 The sinks for ozone at terrestrial surfaces and process regulatory fluxes.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 60
Over terrestrial surfaces ozone, confined within the atmospheric boundary layer, has a relatively
short lifetime scale of the order of one day due to dry deposition at the surface (Wesely and
Hicks, 2000). Thus surface removal represents an important control on the near-surface ozone
concentrations, and is the main cause of diurnal variation in rural areas (Garland and Derwent,
1979). Dry deposition constitutes a major term in the global mass balance of tropospheric ozone,
the mean global dry deposition sink calculated with 20 chemistry-transport models (CTMs)
(1000 Tg yr-1) clearly exceeding the net stratospheric input (550 Tg yr-1) (Stevenson et al., 2006).
The first measurements of ozone deposition fluxes were made in the 1950s using the
micrometeorological gradient method (Regener, 1957). The earliest investigations were aimed at
quantifying the surface sink term of the tropospheric ozone budget. Based on these, Galbally
(1971) concluded that bulk surface resistance (Rc) of dry soil and short grass surfaces was
approximately 100 s m-1. These and other pioneering studies (e.g. Turner et al., 1974) showed
that vegetation and soil constitute important pathways by which ozone is removed from the
atmosphere, while water and snow surfaces are rather inefficient sinks. However, the early
studies provide rather little information about the processes that control ozone deposition to
terrestrial or marine surfaces.
There has also been an interest in measuring surface fluxes prompted by ecological concerns.
The importance of environmental conditions on the plant injury through the regulation of
stomatal uptake was recognised in 1960’s by Mukammal (1965), who observed that the presence
of high concentrations was not a sufficient condition for plant injury. Indeed, a few years later
the close coupling between ozone and water vapour fluxes was demonstrated by Rich et al.
(1970). However, while ozone effects on vegetation have been closely associated with stomatal
uptake for decades, only recently have practical risk assessment methods been formulated in
terms of stomatal uptake rather than ambient concentration (UNECE, 2004).
Micrometeorological techniques have been in use since the first flux measurements. In the late
1970s, Eastman and Stedman (1977) developed a fast-response ozone sensor that facilitated
direct ozone flux density measurements by the eddy covariance method. This resulted in a series
of measurement campaigns in the eastern United States, including various vegetated and other
surface types (Wesely, 1983). These studies improved the understanding of deposition processes
and formed the basis for the detailed surface resistance parameterisation of Wesely (1989). This
parameterisation has been implemented into numerous CTMs. In Europe, the eddy covariance
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 61
technique was adopted somewhat later, but rapidly became popular with the introduction of a
commercial fast-response ozone sensor (Güsten et al., 1992).
5.2 Deposition rates
Early estimates of ozone dry deposition were obtained from measurements of the diurnal cycle
of ozone in rural areas (Garland and Derwent, 1979). The first long-term measurements showed
a seasonal cycle over vegetated surfaces that followed the growing season of the plants (Colbeck
& Harrison, 1985), leading to the assumption that ozone deposition is mainly controlled by
stomatal uptake and deposition to non-vegetated surfaces is constant, only depending on the
material and surface area. More recent studies have shown that although deposition rates are
partly governed by stomatal uptake over a plant canopy, it only accounts for ca 40 to 60% of
total deposition on average and that the non-stomatal component is not constant (Coyle et al
2008b, Hogg et al 2007). These observations are described in more detail in the following
sections.
5.2.1 European forests
Ozone deposition fluxes to forests, as well as other vegetated surfaces, are largely controlled by
the physiological activity and associated gas exchange of the vegetation, with solar radiation, air
temperature, air humidity and soil moisture as the main controlling variables. Thus the
deposition velocities (Vd) observed above forests typically exhibit diurnal and seasonal cycles
that depend on the structure, physiological responses and phenological state of the trees.
According to present understanding, however, there are other significant processes in addition to
stomatal regulation of gas exchange that control the magnitude and variation of the ozone
deposition efficiency of forests (Altimir et al., 2006; Cieslik, 2004; Dorsey et al., 2004;
Goldstein et al., 2004; Hogg et al., 2007; Lamaud et al., 2002; Tuovinen et al., 2001; Zhang et
al., 2002).
The temporal patterns are clearly demonstrated by the long-term flux measurement data
available from a few sites, such as those reported for a temperate spruce forest by Mikkelsen et
al. (2004) and a boreal pine forest by Keronen et al. (2003) and Altimir et al. (2006). In the
boreal region in winter, the dormancy of vegetation and below-zero temperatures result in low
and relatively stable deposition rates (Vd ≈ 0.1 cm s-1, Figure 5.2a), while the mean diurnal cycle
at the temperate forest shows a weak midday enhancement related to gas exchange superimposed
on a relatively high and seasonally invariable base level (Vd ≈ 0.5 cm s-1). The decrease of Rc in
spring correlates with the onset of photosynthesis, and a well-defined, symmetrical diurnal cycle
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 62
ensues in summer, with a Vd maximum of 0.7–0.9 cm s-1 (Keronen et al., 2003; Mikkelsen et al.,
2004). However, Altimir et al. (2006) observed that the correlation with physiological activity is
poorer in autumn and concluded that the deposition rate is modified by the frequent wetting of
the forest canopy. Both Mikkelsen et al. (2004) and Altimir et al. (2006) attribute a major part of
the total annual ozone deposition to non-stomatal pathways.
Boreal Pine Forest
00:0
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Apr-SepOct-Mar
a
d
b
c
Figure 5.2 Median diurnal cycles in deposition velocity at (a) boreal scots pine forest, Hyytiala, Finland, 2002-2003 (Altimir et al 2006), (b) oak forest, Alice Holt, England, 16th July-05 – 18th August-05 (Coyle et al 2006), (c) potatoes, Gilchriston, Scotland, 9th July-06 – 3rd Aug-06, (Coyle et al 2008b) (d) intensively managed lolium perene grassland, Easter Bush Scotland, 2002-03 (Coyle 2005)
High non-stomatal fluxes are also observed in Mediterranean forests (Gerosa et al., 2005, 2008).
However, the diurnal and seasonal variations differ from those of the northern forests in many
aspects. The measurements above oak forests in central Italy (Gerosa et al., 2005, 2008; Cieslik,
2008) and south-eastern France (Michou et al., 2005) show that dry and hot conditions can
significantly affect the diurnal courses of Vd and Rc. On the other hand, in this region there is a
potential for high deposition rates throughout the year, and higher stomatal uptake may take
place during winter than summer months, in spite of the lower ozone concentrations in winter
(Cieslik, 2008).
During dry periods stomata are either almost completely closed or the cycle of stomatal
conductance (Gst = Rst-1) is less symmetrical, with a rapid increase from the nocturnal levels to a
maximum in the morning, rather than around noon, and a gradual decrease towards the evening.
The latter behaviour may take place in more northern forests as well, if leaf temperatures reach
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 63
sufficiently high levels (e.g. Dorsey et al., 2004; Tuovinen et al., 2001). At the French site, Vd
starts increasing very early (at 3–4 a.m. local time) and it was suggested that this could be related
to either stomatal response to blue light, resulting in uptake already in the predawn hours, or to
non-stomatal deposition enhanced by surface wetness (Michou et al., 2005). At the Italian site,
however, the maximum (in Gst) occurs later and seems to have a non-stomatal origin, possibly
due to the reaction with the NO accumulated within the forest canopy (Gerosa et al., 2005) or
leaf wetness (Gerosa et al., 2008). Similarly, the measurements taken by Coyle et al. (2006) in an
oak forest in England in summer show a steep increase to the maximum at 8 a.m. (Vd ≈ 1.0 cm s-
1) and a approximately linear decrease to a nocturnal level (Vd ≈ 0.1 cm s-1), Figure 5.2b.
In addition to the phenological development of plants, the seasonal cycle can be strongly
influenced by the soil moisture conditions, especially in southern Europe. Consequently, there
may be large annual variation in the ozone uptake rates depending on the occurrence and
persistence of drought. For example, in August 2003 the mean Gst derived for the Italian oak
forest by Gerosa et al. (2008) was only 35% of that in the cooler and wetter August of 2004, and
the effect of drought persisted even after soil water was replenished by rainfall.
5.2.2 Crops
For agricultural crops, ozone deposition rates exhibit pronounced seasonality that results from
the distinct phenological stages of the growing season. The flux measurements above an Italian
barley field by Gerosa et al. (2004) demonstrate how Vd increases during the first growth stages
(seedling growth, tillering, stem elongation). The maximum is reached soon after anthesis,
during the grain filling period, when photosynthetic activity is at its highest level. Gerosa et al.
(2004) observed an average Rc of about 75 s m-1 for this period, while the bulk stomatal
resistance (Rst) was about 150 s m-1. After that, deposition rates decrease gradually with ripening
of the crop and leaf senescence, as Rst increases, and are further reduced by harvest. A slightly
higher minimum Rc but a similar decrease was observed at the same site for wheat. In this case,
the decline was amplified by the rapid drying of soil (Gerosa et al., 2003).
For both barley and wheat, the diurnal cycles of Vd and surface conductances were symmetrical
during the photosynthetically active period with a maximum (Vd ≈ 0.7–0.9 cm s-1) around noon
(Gerosa et al., 2003, 2004). During the latter part of the growing season, the midday deposition
rates are strongly reduced, due to increased Rst, resulting in an earlier maximum and a diurnal
course that is skewed towards morning. In the afternoon, values comparable to those after
harvest were observed for the barley field (Gerosa et al., 2004).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 64
For other crops, data are rather limited. Michou et al. (2005) report a symmetrical diurnal cycle
for the Vd measured over a rapidly growing maize field, with a mean minimum of 0.05 cm s-1
and a mean maximum of 0.50 cm s-1. In an earlier North American study, a similar diurnal cycle,
with slightly higher values throughout the day, was observed over a maize field during the period
of most active growth (Meyers et al., 1998). During senescence, the hourly mean Vd only reached
0.2 cm s-1 in the morning. The patterns were also similar for soybean, but the Rc of soybean
seems to be significantly lower than that of maize, as the maximum mean hourly Vd was almost 1
cm s-1 during the active growth period (Meyers et al., 1998). Coyle et al. (2008b) reported an
asymmetrical diurnal cycle in Vd over a potato field during the phase of tuber initiation and
growth through to harvest, with a median value of 0.6 cm s-1, day-time values from 0.5 to 2 cm s-
1 decreasing to 0.4 cm s-1, or less, at night (Figure 5.2c).
As was the case with forests, stomatal uptake rates derived from the evapotranspiration fluxes
only explain a part of the total ozone flux and a significant proportion must be attributed to non-
stomatal sinks. Even during the active growth period, no more than 50–60% of the total flux to a
wheat field was stomatal, and this fraction decreased during the senescence (Gerosa et al., 2003).
The same was true for barley, and even though the day-to-day variation in the inferred non-
stomatal flux fraction is large, the corresponding non-stomatal surface conductance seems to
remain relatively stable throughout the summer (Gerosa et al., 2004). Similar results were
obtained for onion, but in this case the variation can be explained by irrigation that clearly
enhanced both stomatal and total fluxes (Cieslik, 2008). In the case of potatoes, the non-stomatal
component was only ~15% when the canopy was well-watered but increased to ~80% when the
crop dried out (Coyle et al., 2008b).
5.2.3 Grasslands
Grasslands can be used to describe a wide variety of habitats from intensively managed pastures
that are usually dominated by a single species, such as Lolium perenne, to natural grasslands
which contain a rich diversity of grasses, forbs and legumes and are often of high conservation
value. The canopies that have been studied to date all exhibit similar patterns of deposition as
forests and agricultural crops in that they have phenologically driven seasonal and diurnal cycles.
There are few long-term measurements of ozone deposition to grasslands reported in the
literature at present (Colbeck and Harrison, 1985; Grünhage et al., 1994; Pio et al., 2000). All
these studies measured during winter and summer so that the growing season and dormant
periods were observed. The results of several short-term campaigns over active and dormant or
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 65
dry grasslands have also been reported and are summarised below (Bassin et al., 2004; De
Miguel and Bilbao, 1999; Droppo 1985; Duyzer et al., 1983; Garland and Derwent, 1979;
Meyers et al., 1998; Pederson et al., 1995; Sorimachi et al., 2003).
Over active, green grasslands the daytime Vd is only 0.5 cm s-1 on average, decreasing to ~0.1-
0.2 cm s-1 at night, although peaks over 1 cm s-1 are often observed (Figure 5.2d). The diurnal
cycle is often symmetrical with a steady increase in the morning after sunrise and decrease is the
afternoon as light and temperatures decrease. However (Grünhage et al., 1994) reported an
asymetrical diurnal cycle in Vd with a steep increase after 6 a.m. until 9 a.m. when it steadily
declined. This is attributed to an increase in water vapour pressure deficit (VPD) at midday and
the afternoon causing stomata to close, as has been observed for many other canopies. Where
measurements have been made over dormant (i.e. during winter) or dry grasslands the diurnal
cycle is far less pronounced with daytime Vd only reaching ~0.2 cm s-1 although night-time
values are similar at all times of year (Figure 5.2d).
Coyle, (2005) showed that non-stomatal uptake was ~60% of the total budget over an improved
grassland in Central Scotland. Pleijel et al, (1995) found that the non-stomatal sink is enhanced
by surface wetness while the work of Coyle (2005) also demonstrated that the non-stomatal
component was not constant but varied with wetness, surface temperature, solar radiation and
wind speed as has been suggested for other canopies.
5.2.4 Other vegetated surfaces
Measurements have been made over a variety of other plant canopies from moorlands and
subartic mires to tropical forests. They all exhibit diurnal and seasonal cycles driven by
variations in stomatal activity and climate, as described for the previous canopies. For example
Fowler et al (2001) for moorland reported summer diurnal cycles ranging from ~0.3 cm s-1 at
night to ~0.6 cm s-1 during the day while in the winter the afternoon peak was only ~0.4 cm s-1
with night-time values also ~0.3 cm s-1. Tuovinen et al (1998) measured a small diurnal cycle
over flark fen, 300 km north of the Artic circle in the late summer, ranging from 0.1 to 0.15 cm s-
1 at night to only ~0.2 cm s-1 during the day. For tropical forests there are often two seasons, wet
and dry: During the wet season, Rummel et al (2007) reported diurnal cycles over Amazonian
rain forest ranging from ~0.4 cm s-1 during the night to ~1 cm s-1 at day, with a symmetrical
diurnal cycle while Matsuda et al (2006) reported values ranging from 0.26 to 0.63 cm s-1 for
night and day respectively over a deciduous forest (teak) in Thailand; during the dry season both
canopies exhibited asymmetrical diurnal cycles with a peak of ~1.5 cm s-1 at 4 am over the
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 66
Amazon rain forest, declining to ~0.4 cm s-1 at night, and ~0.5 cm s-1 at 9 am over Thai teak
forest, declining to ~0.1 cm s-1 at night which are attributed to the effect large afternoon vpd.
5.2.5 Non-vegetated surfaces
Deposition to the soil underlying the vegetation layer may significantly contribute to the vertical
ozone flux observed above the canopy (e.g. Dorsey et al., 2004). Especially in arid regions, the
surface resistance of soil (Rsoil) is the key determinant of the surface removal of ozone (e.g.
Michou et al., 2005). As the literature review by Massman (2004) and the more recent data of
Sorimachi and Sakamoto (2007) indicate, Rsoil is highly variable, ranging from 10 to >1000 s m-1.
It has been observed that Rsoil decreases with increasing organic content and porosity of soil.
Clearly, wet soils have a considerably higher Rsoil (~500 s m-1) than dry soils (~100 s m-1)
(Galbally and Roy, 1980; Massman, 2004), as increasing the moisture content of soil decreases
its porosity and thus reduces the area of reactive surface available to ozone molecules (Sorimachi
and Sakamoto, 2007). However, it is difficult to disentangle individual effects based on the
current data, and the situation is further complicated by biogenic NO emissions from soils.
Removal through the reaction with NO potentially constitutes a significant sink for ozone,
especially in forests at night (Pilegaard, 2001; Dorsey et al., 2004), even though this reaction
does not take place specifically at the air–soil interface.
Snow
The dry deposition velocity over snow- and ice-covered surfaces and water is known to be
relatively small. However, the measurement data are highly variable. It is possible that the
measured Vd is affected by chemical reactions taking place in the snowpack, which, combined
with dynamic transport processes, can result in the observed variability. Helmig et al. (2007)
modelled ozone concentrations in high northern latitudes with different values of Rc and
demonstrated how even small deposition rates, if effective over large areas, can significantly
affect the near-surface concentrations and that a high Rc is required for snow in order to
reproduce the observed concentrations; the best agreement was obtained when limiting Vd to
0.01 cm s-1.
Water
For water surfaces, the understanding of controlling processes is more coherent, involving both
turbulent and molecular mixing, and chemical reactions. It has been observed that wind-induced
turbulence greatly enhances deposition rates by increasing atmospheric mixing, surface
roughness, wave breaking and spray generation (e.g. Gallagher et al., 2001). . The observations
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 67
of Rc range from 1000 to 10,000 s m-1 (Gallagher et al., 2001). At low wind speeds, Vd
increasingly depends on the molecular gas transfer near the air–water interface. Even in the
absence of turbulence, significant deposition rates are possible, since the chemical reactions
taking place in the aqueous phase enable more efficient deposition than the solubility of ozone
alone would suggest. The modelling results of Fairall et al. (2007) show that the oceanic
turbulent mixing also plays an important role in enhancing ozone deposition by up to a factor of
three. Iodide (Chang et al., 2004) and chlorophyll (Clifford et al., 2008) have been suggested as
the main reagents controlling ozone destruction in the organic surface microlayer. As the
distribution of these compounds in water bodies is related to that of phytoplankton, the chemical
enhancement of ozone deposition can be expected to be highly variable both temporally and
spatially. Similarly, coastal ozone deposition to the iodide-rich macroalgae surfaces depends on
the tidal phase, and fluxes can be further enhanced by photochemical destruction of ozone during
the iodine-mediated particle formation events (Whitehead et al., 2008).
5.3 Non-stomatal deposition processes
Although the stomatal uptake of ozone is an important sink over vegetated surfaces it accounts
for only a fraction (typically 1/3 to 2/3) of the total deposition. In most studies it has been
assumed that the non-stomatal sink is constant, depending only on the surface material and area,
although it was expected that the presence of surface water would inhibit deposition as the
solubility of ozone is quite small. However, field measurements have indicated that surface
temperature, solar radiation, surface wetness and wind speed may all have an influence on the
magnitude of the non-stomatal flux. The influence of wind speed is simply explained, as when
wind speed increases more air will penetrate the canopy, increasing the surface area available for
deposition. The mechanisms that have been proposed for the other parameters are:
• Temperature: thermal decomposition ozone on plant surfaces, mediated by waxes and
other substances on the surface (Coyle et al 2008b, Hogg et al 2007, Fowler et al 2001,
Cape et al 2009)
• Solar radiation: ozone photolysis also mediated by the surface (Coyle et al 2008b, Hogg
et al 2007 and references therein) and reaction with VOCs emitted by vegetation (Hogg
et al 2007, Coyle 2005)
• Surface wetness: aqueous reactions in water-films on plant surfaces (Coyle et al 2008b,
Altimir et al)
Principal component analysis of data from several field campaigns indicated that parameters had
the following order of precedence: temperature, surface wetness (measured as vapour pressure),
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 68
solar radiation then wind speed although for forests temperature and wetness were of almost
equal importance (Coyle et al, 2008a).
Some preliminary work exposing wax coated and metal surfaces to ozone in controlled
environment chambers (Cape et al in press) showed that Vd is temperature dependent. An
Arrhenius type plot of the change in reaction rate with temperature is shown in Fig. 5.3 (the
natural logarithm of the surface resistance in s m-1 is taken as the inverse reaction rate). The
results for four vegetated canopies are also plotted in Fig. 5.3 and the slope of their linear
regression lines is of the same order of magnitude as the artificial surfaces. This shows that the
dependence on temperature and activation energies are similar for all surfaces but the absolute
reaction rates differ. The simplest conclusion is that heterogeneous decomposition of ozone to O2
is responsible; hence the similar activation energies in Fig. 5.3 and the variation in reaction rates
can be attributed to differences in effective surface area (Cape et al, 2009).
y = 40.1x - 3.28, R2 = 0.84
y = 24.9x + 0.14, R2 = 0.29y = 22.8x + 0.85, R2 = 0.18
y = 16.1x + 2.18, R2 = 0.44
4
6
8
10
12
14
0.39 0.4 0.41 0.42 0.43
1000/RT
ln(R
ns)
stainless steel
aluminium foil
paraffin wax
beeswax
moorland vegetation
Potatoes
Grassland
Forests
y = 36.4x - 9.62, R2 = 1
y = 49.9x - 14.84, R2 = 0.06
y = 52.6x - 16.26, R2 = 0.06
y = 56.2x - 16.56, R2 = 0.14
Figure 5.3 Arrenhius reaction rate plots for ozone deposition to various surfaces: stainless steel, Aluminium foil, paraffin wax
and beeswax from Cape et al in press; moorland Fowler et al 2001; potatoes Coyle et al 2008b; Grassland and Forests, Coyle et al
2008a.
Cape et al (2009) also tested the hypothesis that the surface reactivity of vegetation may be
enhanced by reaction biogenic volatile organic compounds (BVOCs) dissolved in cuticular
waxes and showed no enhancement due to surface reaction. However ozone does react in the
gase phase with BVOCs emitted by vegetation, with gas-phase reaction rate coefficients varying
between 10-18 to 10-16 cm3 molec-1 s-1, potentially leading to apparent non-stomatal deposition
velocities of similar magnitude to those measured in the field (Coyle 2005). The emission of
BVOCs by vegetation is also light and temperature dependent, increasing with both parameters
which may explain some of the variation in non-stomatal deposition. Nevertheless, most
measurements indicate that the concentrations or reactivity of the emitted BVOCs are not
sufficient to explain the magnitude or variation in non-stomatal deposition in all circumstances.
In can be concluded that although BVOCs may play a part in the non-stomatal deposition they
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 69
are only significant for species that emit currently unidentified compounds that react very rapidly
with ozone (Hogg et al, 2007).
Although some studies have shown surface water inhibits ozone deposition to a vegetated
canopy the consensus is now that it increases deposition (Hogg et al 2007, Coyle et al 2008a) in
most circumstances. Although Fuentes (1992) found more organic compounds in water from
maple leaves compared to poplar, specific compounds that may be responsible have not been
identified. Coyle et al (2008b) suggested that non-stomatal deposition is governed by three main
regimes: ozone deposition increasing as the temperature and solar radiation increases on a dry
surface due to thermal decomposition; decreased deposition on surfaces with a thin film of water
present as thermal decomposition is blocked by the water film; enhanced deposition on a fully
wetted surface due to aqueous reactions in the water.
5.4 Model development and validation
The recent European measurement data on ozone fluxes have been used for testing and
improving the DO3SE deposition module, which has been incorporated into the Unified EMEP
CTM developed for European policy-making applications (Tuovinen et al., 2001, 2004;
Emberson et al., 2007). DO3SE makes it possible to calculate stomatal and non-stomatal ozone
fluxes to different vegetation types, taking into account both plant phenological and
meteorological factors (irradiance, temperature, humidity, soil moisture) on an hourly basis
(Simpson et al., 2007; Emberson et al., 2000, 2007). Many of the validation studies have been
focussed on the leaf-scale stomatal conductance of a specific plant species only, since
parameterisations of this kind are needed to relate the ozone uptake to plant response (e.g. Pleijel
et al., 2007). There are also smaller-scale CTMs covering a part of Europe, which have been
used for high-resolution mapping of ozone fluxes (e.g. Lagzi et al., 2004), and local-scale soil–
vegetation–atmosphere–transfer models that include ozone (e.g. Grünhage and Haenel, 2008).
Inferential modelling (IM) has been used for calculating regional ozone budgets (Coyle et al.,
2003) and mapping exposures and doses on a high (1–2-km) spatial resolution for national-scale
risk assessment of ozone effects (e.g. Keller et al., 2007).
The global-scale CTMs (Stevenson et al., 2006), as well as many regional models (e.g. Vautard
et al., 2005), typically include a variant of the parameterisation of Wesely (1989). This
parameterisation does not include the stomatal effect of soil moisture conditions, which has been
shown to be a significant modifier of ozone fluxes, especially in the Mediterranean region
(Gerosa et al., 2008), yet proved challenging to model within CTMs (Emberson et al., 2007).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 70
Another key problem with large-scale CTMs is related to the aggregated land cover classes,
making it difficult to parameterise sub-grid processes. For example, the dry deposition module of
the recently developed global Modular Earth Submodel System (MESSy) does not differentiate
between different vegetation types, even though a soil moisture stress function is included
(Kerkeweg et al., 2006).
The non-stomatal deposition processes are parameterised in CTMs and IM systems in a much
cruder way than the stomatal component, typically by defining constant values for the relevant
resistances. However, a preliminary parameterisation has been developed for the EMEP CTM
for surface wetness effects for northern European coniferous forests (Tuovinen et al., 2008). This
parameterisation is derived from the observations of Altimir et al. (2006) and represents
enhancing surface sink with increasing surface wetness, parameterised as a function of relative
humidity. This results in higher and more variable ozone removal rates within the model. In the
future, integrated models are needed for coupling the surface exchange of energy, carbon and
trace gases. In particular, a multi-layer structure facilitating an explicit simulation of vertical
mixing and other in-canopy processes would be useful for interpreting flux measurements (e.g.
Duyzer et al., 2004; Simon et al., 2005).
5.5 Risk assessment methods
European abatement strategies are founded on effects-based approaches, which involves
different numerical indicators for different air pollution effects on vegetation and human health
(UNECE, 2004). For potential ozone effects on vegetation, the AOTX (Accumulated exposure
Over a Threshold of X ppb) exposure index replaced simpler concentration averages in the 1990s
(Fuhrer et al., 1997). Present definitions also include a metric based on the stomatal uptake flux,
AFstY (Accumulated stomatal flux Fst above a threshold of Y nmol m-2 s-1), which represents the
absorbed dose and is thus considered biologically more meaningful than the concentration-based
AOTX (UNECE, 2004). AFstY is more complex than AOTX in that it entails modelling the
stomatal conductance.
AOT40 is still the most common risk indicator used in Europe for setting environmental
objectives and defining the so-called critical levels. For a proper application of AOT40, ozone
concentration must be known at the height of the canopy top (UNECE, 2004). This means that
the concentration determined above this height (as is typically the case with measurements) must
be transformed to the correct reference height, because of the deposition-sink generated vertical
concentration gradient; a failure to correct for this gradient may seriously overestimate the risk
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 71
metrics (Tuovinen and Simpson, 2008). Even if the profile correction compensates for a
significant bias, the near-surface concentration may prove a poor surrogate for the effect-
inducing flux. High ozone concentrations are frequently connected with conditions that
potentially limit the stomatal uptake, such as high temperature and VPD (e.g. Solberg et al.,
2008). This co-variation means that the concentration at leaf surface is not necessarily connected
with a proportional stomatal uptake and therefore plant response (Cieslik, 2004). This is one
reason for the different accumulation rates of AOT40 and stomatal uptake observed in many
studies (Fig. 5.4).
Figure 5.4 Accumulation of AOT40 and stomatal dose over a Holm oak forest (6 August – 11 October) in 2003 and
2004 in Italy (Gerosa et al., 2008).
As a compromise between the less data-intense exposure indices and the more realistic dose
metrics, solutions based on an ‘effective’ concentration have been suggested (e.g. Gerosa et al.,
2004; Pihl Karlsson et al., 2004; Pleijel et al., 2004). Common to all these is the idea that the
concentrations for AOTX are first modified to accommodate environmental factors controlling
stomatal uptake, such as VPD in the definition of the modified AOT30 (Pihl Karlsson et al.,
2004). In some cases, all the main modifiers, such as those in the DO3SE model, are considered
(Gerosa et al., 2004; Pleijel et al., 2004). However, this approach is very close to actually
calculating the stomatal flux, as it involves a multiplicative stomatal conductance model. Related
to this observation, it is worth noticing that even the profile corrections required for AOTX are
based on flux–gradient relationships of ozone and thus entail deposition modelling (Tuovinen
and Simpson, 2008). From a micrometeorological point of view, it would thus appear natural to
aim at developing accurate parameterisations for partitioning the total ozone flux into the
stomatal and non-stomatal components and applying flux-based risk metrics because of their
superior biological basis.
The model calculations of Simpson et al. (2007) indicate that AOT40 and AFstY (for both crops
and forests) show very different regional patterns of exceedance of critical levels across Europe
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 72
with much smaller south–north gradients and larger exceedance area for AFstY (Fig. 5.5). Even
though there are still numerous uncertainties involved in this kind of modelling, there is evidence
that the flux-based risk maps better correlate with observed plant damage (Hayes et al., 2007).
Figure 5.5 Ratio of the AOT40 (left) and AFst1.6 (right) risk metric to the corresponding critical level for forests
(not shown for values < 1) (Simpson et al., 2007).
5.6 Potential effects of climate change
Changes in the climatic conditions and chemical composition of the atmosphere are expected to
have a wide range of effects on the interactions between tropospheric ozone, itself a GHG, and
the biosphere. Firstly, ozone exposures are changing globally due to changes in its precursor
emissions. Secondly, the long-term changes in meteorological conditions affect atmospheric
transport patterns and the rates of tropospheric chemical reactions and dry deposition processes,
and also modify plant phenology. In addition to the rising temperature and changes in
precipitation distribution, elevated CO2 and ozone concentrations may act as significant
modifiers to stomatal exchange (e.g. Ashmore, 2005). Finally, the characteristics of vegetation
cover and land use may be altered on various scales as a result of human activities, effects of
climate and ozone on plant species composition and ecosystem function, and natural
disturbances, all of which potentially generate feedbacks to the surface removal of ozone.
So far only a few studies have addressed the projected changes beyond the ozone precursor
emissions and atmospheric dynamics. In the multi-model ensemble simulations of future
concentrations by Stevenson et al. (2006), the projected Vd was only altered by the
meteorological responses, neglecting the effects of water stress and elevated CO2 concentrations,
for example. In some studies, individual, uncoupled effects have been included in the models.
For example, Sanderson et al. (2007) investigated the impact of the stomatal conductance
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 73
changes as directly induced by the rising CO2 levels. In this experiment, a global CTM was run
with an assumed reduction in Gst due to a doubling of CO2 concentrations, but with no changes
in the meteorological input data. This resulted in a decreased dry deposition sink, which
increased the near-surface ozone concentrations by 2–8 ppb on a seasonal basis. However, a
reduction in Gst, due to increased CO2 or any other effect, does not proportionally reduce the
stomatal uptake flux. This results from the effect of decreased surface removal on the overall
mass balance, with higher concentrations partly compensating for the suppressed Gst.
While it would seem plausible that in warm climates increasing temperatures reduce stomatal
exchange, the opposite is true for cooler regions. However, based on a modified version of the
DO3SE model, Karlsson et al. (2007) concluded that in northern Scandinavia the most significant
impact of the higher temperatures may be related to an earlier onset of the growing season and
the associated phenological development, rather than their direct enhancement of Gst. This
together with elevated and increasing ozone concentrations in spring may amplify the risk of
negative ozone effects on vegetation in these areas. On the other hand, there may be a
counteracting effect on the stomatal uptake mediated by the concurrent higher VPD (Karlsson et
al., 2007; Harmens et al., 2007).
The summer of 2003 was exceptionally warm in Europe, especially in the central part of the
continent, and may be taken as an analogue of the future summers to be expected in the latter
part of the 21st century. A series of heat waves produced meteorological conditions highly
favourable for the net formation of ozone and its build-up over large areas; indeed, record-high
near-surface concentrations were observed in many locations (Solberg et al., 2008). It is very
likely that reduced dry deposition played a significant role in the formation of the ozone episodes
in 2003. The high temperatures and soil moisture deficits (SMDs) most probably decreased the
Gst of vegetation and thus ozone removal from the atmosphere in central and southern Europe, as
indicated by the Italian eddy covariance measurements (Gerosa et al., 2008) discussed above.
Fig. 5.4 shows that the ozone dose absorbed by a Holm oak forest in August–September 2003
was less than 50% of that during the same period in 2004, in spite of the much higher
concentrations in 2003.
The sensitivity runs with a global CTM by Solberg et al. (2008) demonstrate the potentially large
effect of dry deposition on near-surface concentrations. Similarly, doubling of Rc within the
modelling study of Vautard et al. (2005), partly because of the expected increase in SMD which
is not taken into account in the deposition parameterisation, improved the model performance by
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 74
increasing the modelled concentrations. Considering the accumulation of ozone dose of plants
over the whole growing season, the significance of drought periods much depends on their
timing with respect to the phenological stage of plants and the occurrence of elevated ozone
concentrations (Harmens et al., 2007). In addition, prolonged drought stress may result in
sustained impairment of the hydraulic conductivity of plants, challenging the traditional dry
deposition models (Gerosa et al., 2008).
It has been estimated that exposure of plants to even the current levels of ozone may
significantly increase water use of forest trees (McLaughlin et al., 2007) and reduce plant
productivity in the most polluted areas of the world, with exacerbating effects projected for the
future (Felzer et al., 2005). With elevated ozone concentrations, the reductions in carbon
sequestration may also lower soil carbon formation rates and alter the below-ground carbon
cycling (Loya et al., 2003). Sitch et al. (2007) suggest that the ozone-induced suppression of the
global land-carbon sink gives rise to additional accumulation of anthropogenic CO2 emissions in
the atmosphere and thus should be considered an indirect radiative forcing, which could exceed
the direct radiative forcing due to ozone increases.
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6. Biogenic volatile organic compounds (BVOC) 6.1 Introduction
Biogenic volatile organic compounds (BVOC) are emitted by almost all plants. In higher plants,
emissions range from close to zero to 10-20% of the carbon fixed by photosynthesis. Global
emission is estimated at around 800 Tg C y-1 although this figure has been regularly revised
based on improvements in scaling up of laboratory results, spatial and temporal integrations, and
large scale monitoring at whole ecosystem levels (Lathiere et al., 2006; Arneth et al. 2008).
About half of the emissions are believed to be isoprene (Guenther et al. 2006). Monoterpenes,
the other large class of volatile isoprenoids, contribute another 10-15 % of the total BVOC
emissions. Sesquiterpenes, a third class of isoprenoids, are emitted in small quantities from non-
stressed vegetation, except from flowers. The remainder is emitted as oxygenated volatile
compounds, including alcohols, aldehydes and ketones, particularly during certain periods of
plant development or in response to environmental stress (Heiden et al., 2003; Seco et al., 2007).
6.1.1 Volatile isoprenoids
Volatile isoprenoids have been extensively studied compounds because of their relevant
functions in plant vs. environment interactions and their role in the atmosphere. Isoprene and
monoterpenes are formed in plastids via methylerythritol phosphate (MEP) pathway
(Lichtenthaler, 1999).
While the isoprenoid emissions mainly rely on newly synthesized photosynthetic metabolites in
chloroplasts, extra-chloroplastic sources can feed carbon to sustain isoprene or monoterpene
biosynthesis, including xylem-transported sugars and chloroplastic starch (Karl et al., 2002a;
Kreuzwieser et al., 2002;) as well as refixation of respired CO2 (Loreto et al., 2004). These
additional carbon sources of isoprenoid biosynthesis can become significant especially when
photosynthesis is constrained by environmental stresses. Under extreme stress conditions, such
as drought stress, the leaf carbon budget can become negative, as leaves release more carbon in
the form of isoprenoids and respiratory CO2 than they gain through photosynthesis (e.g. Brilli et
al., 2007).
Monoterpenes and sesquiterpenes are active compounds in plant interactions with other
organisms. Monoterpenes may either attract or deter herbivores or carnivores, and attract
pollinators (Gershenzon and Dudareva, 2007). Indirect evidence suggests that isoprene and
monoterpenes as lipid soluble molecules may protect plant membranes from thermal and
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 76
oxidative stress (reviewed by Sharkey and Yeh, 2001). This favourable action can assist in
adapting isoprenoid-emitters to increasing oxidative pressures (Lerdau, 2007).
6.1.2 Oxygenated volatile compounds
While the production and emission of volatile isoprenoids, in particular isoprene and
monoterpenes, is strongly species-specific, all plants emit oxygenated volatiles. At the global
scale, the emission source strength of these compounds is generally lower than that for volatile
isoprenoids, and many of these oxygenated compounds are less reactive than isoprenoids in the
atmosphere. However, the emissions of oxygenated compounds, which may be induced by
developmental and stress factors, may be large at certain periods of the year and by certain
vegetation types (see also flux measurements below).
Methanol, acetaldehyde and C-6 compounds are often emitted in large quantities, especially in
the presence of mechanical wounding or other stresses (Loreto et al. 2006). Methanol formation
is likely due to the demethylation of pectins in cell walls (Galbally and Kirstine, 2002) and does
not have any known protective role for plants. The release of methanol into the atmosphere is
therefore associated with cell wall damage occurring because of wounding (Karl et al., 2001a).
Methanol is also emitted by growing plant tissues (Nemecek-Marshall et al., 1995; Harley et al.,
2007; Hüve et al., 2007), and senescing tissues (Fall, 2003). Large fluxes of methanol could be
measured, as an example, from rapidly expanding leaves of the Mediterranean vegetation during
the spring ACCENT-VOCBAS 2007 campaign (see below).
Large fluxes of acetaldehyde have been observed in conditions of root anoxia such as under
waterlogging stress. Short-lived bursts of acetaldehyde are sometimes also observed from
darkened leaves (Karl et al., 2002b). Interestingly, acetaldehyde is also emitted following
wounding (Fall et al., 1999) and ozone stress episodes, and large fluxes of this compound can be
observed under natural conditions (Lathiere et al. 2006).
Finally, C-6 oxygenated compounds are emitted from leaves subject to various stresses such as
wounding, e.g. as a consequence of cutting hay, insect feeding, ozone stress and heat stress
(Hatanaka, 1993). Aldehydes, (Z)-3-hexenal, (E)-3-hexenal and (E)-2-hexenal with characteristic
green leaf (‘cut grass’) odor are formed first, and then transformed into corresponding alcohols
by alcohol dehydrogenases. Esters such as hexenolacetates can be further formed and emitted.
Proton-transfer reaction mass-spectrometry has provided detailed insight into the associated
time-sequence of events (Beauchamp et al., 2005).
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6.2 Environmental controls on BVOC emissions
6.2.1 Physiological and physico-chemical controls of emissions
Models of BVOC emissions consider that emissions are controlled either by physiological
factors (“isoprene” algorithm) or by physico-chemical factors (“monoterpene” algorithm)
(Guenther et al., 1993). By considering only physiological factors, the synthesized compounds
are immediately released from the foliage. In contrast, by considering only physico-chemical
factors, the compounds are emitted from specialized storage structures such as resin ducts
present in conifers, glandular trichomes in species from Lamiaceae (peppermint), and oil glands
in Rutaceae (lemon, orange) and Myrtaceae (eucalypts).
Physiological controls operate at the level of compound synthesis. Light and temperature, the
key environmental drivers, affect the rate of intermediate production; temperature also affects the
activity of flux controlling enzymes (Fig. 6.1, Niinemets et al., 1999;) Over longer term, the
synthesis and turnover of rate-limiting enzymes can control the flux rate (Monson et al., 1994;
Lehning et al., 2001; Fischbach et al., 2002). In species with large storage pools, the emissions
can be independent of the rate of compound synthesis, being controlled by temperature effects on
the evaporation and diffusion of compounds from the storage pools (Fig. 6.1, e.g. Tingey et al.,
1991).
Often there is no clear-cut separation between physiological and physico-chemical controls.
Although it is generally belived that only evaporation from pools controls the emissions in
species with specialized storage pools, there is increasing evidence that the emissions can be
partly controlled by physiological factors also in classical “storage” species. On the other hand,
physico-chemical controls on the emissions often interact with physiological controls in species
without specialized storage pools for BVOC (Fig. 6.1)
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Figure 6.1 The emission of volatile organic compounds from plants is limited by both physiological and physico-
chemical characteristics. Physiological factors control the rate of synthesis of VOC and operate at the level of
intermediate production and the activity of flux controlling enzymes such as isoprene synthase or terpene synthases
for volatile isoprenoids. Physico-chemical factors, in particular, low compound volatility and rate of diffusion, can
limit the release of synthesized VOC from the leaves and determine the degree to which the synthesized compound
can be stored in the leaves.
6.2.2 Physico-chemical controls of emission in species lacking specific storage structures
Every BVOC species can be non-specifically stored in leaf liquid and lipid phases, with the non-
specific storage capacity depending on the compound physico-chemical traits such as the
gas/liquid aqueous phase partition coefficient (Henry’s law constant, H) and lipid/liquid phase
partition coefficient (octanol/water partition coefficient, KOW). For instance, compounds that are
highly water-soluble such as methanol and ethanol can accumulate in leaf aqueous phase,
especially if gas-phase diffusion out of the leaves is hindered due to limited opening of stomatal
pores. Although the rate of compound synthesis may respond very quickly to environmental
perturbations, a build up of water-soluble compounds reduces the sensitivity of the emission
responses to variation in environmental factors.
Analyses of the emissions of strongly lipid-soluble BVOC species such as non-oxygenated
monoterpenes indicates that the non-specific storage of these compounds, mainly in leaf lipid-
phase, significantly alters the emission kinetics in species lacking specialized monoterpene
storage pools. In these species, to reach steady-state rates of monoterpene emission can take
from minutes to hours depending on monoterpene physico-chemical characteristics (Niinemets
and Reichstein, 2002; Noe et al., 2006).
The delayed emission responses due to non-specific storage can also result in modified
sensitivity of the emissions to environmental factors. For instance, a sigmoidal light-response
curve can result if non-specific storage pools have not yet reached a steady-state with each light
level. Under such experimental conditions, the emission rate recorded is lower than the
monoterpene synthesis rate. Given the long time-periods, often on the order of 1 h required to
reach the steady-state, non-steady-state conditions are common in monoterpene measurements.
In addition to the alteration of the immediate environmental controls, non-specific storage can
modify the emissions at daily and weekly time-scales. Another important implication of the non-
specific storage is significant nocturnal emissions, which was observed e.g. in the case of
monoterpenes (Loreto et al. 2000; Niinemets et al., 2002b). Simulation analyses indicate
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 79
substantial nocturnal emissions at the ecosystem scale (Fig. 6.2). Significant night-time BVOC
emissions from non-specific storage can have large influence on OH-radical concentration and
atmospheric reactivity in morning hours. Current steady-state models predict zero nightime
monoterpenes emissions for species lacking specialized storage structures.
Figure 6.2 Monoterpene emissions from Quercus ilex dominated forest in Castelporziano, Italy for six days in June
simulated using the standard Guenther et al. (1993) model and a model considering non-specific monoterpene
storage in leaf liquid and lipid pools (modified from Niinemets and Reichstein, 2002; Niinemets, 2008). The
standard emission model predicts that the emission rate responds immediately to changes in light and temperature,
but non-specific storage of lipid-soluble non-oxygenated and water-soluble oxygenated monoterpenes results in
time-lags between terpene synthesis and emission. As the result of these time-lags, the emissions are predicted to
continue also at night, although synthesis has ceased.
6.2.3 Uptake and release of volatile compounds by vegetation
A relevant implication of the non-specific storage is the uptake of volatile compounds from
ambient air when air concentrations are higher than those in equilibrium with plant liquid and
lipid phases. The compounds taken up during the periods with high atmospheric BVOC
concentrations may be released back into the ambient air when air concentrations are small if
they are not metabolized or translocated to the roots (Bimmeet et al. [date]). The uptake of
water-soluble compounds is expected to scale with leaf water content, while the uptake of lipid-
soluble compounds with leaf lipid content (Noe et al., 2008a). Thus, even species considered
“non-emitting” can emit several BVOCs at trace level from the non-specific storage built up
from ambient sources.
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6.2.4 CO2-dependence of emissions
The classical Guenther (1993) algorithm considers strong light and temperature dependencies of
isoprene and “non-stored” monoterpene emissions. CO2 was not considered as an important
factor in controlling the emission of volatile isoprenoids. More recent laboratory and field
studies have established that volatile isoprenoids are sensitive to ambient CO2 and that the
emissions decrease in plants grown at CO2 concentrations higher than ambient (Loreto et al.,
2001; Rosenstiel et al., 2003). This effect may be observed at CO2 concentrations that are likely
to be reached in the future (double or less than double the current concentrations) and should be
therefore addressed in future modeling efforts.
There is now sufficient information to believe that the negative effect of CO2 on isoprene
emission is ubiquitous and not species-specific. Arneth et al. (2008) included empirically CO2-
dependence into Niinemets et al. (1999) model, and predicted that CO2 reduction of isoprene
emission could partially compensate for the emission increases with rising temperature.
However, elevated CO2 will enhance photosynthesis and growth of plants, in particular
vegetation leaf area, which may in turn increase the emission of isoprene by vegetation. The net
effect of these interactions await experimental confirmation.
Monoterpene emissions are also likely to be influenced by CO2, but there is less experimental
evidence than for isoprene (e.g. Loreto et al., 2001). More studies are clearly needed to assess
whether the different physico-chemical controls (see above) and the presence of small internal
pools buffer the effect of CO2 and make monoterpenes less sensitive to CO2 control.
6.2.5 Induced emissions
In addition to the constitutive emissions, recent work demonstrates that synthesis of volatile
isoprenoids is induced in many species in response to biotic (e.g. attack of herbivores and
pathogens) and abiotic (e.g. ozone stress, heat stress) stresses (Beauchamp et al., 2005; Blande et
al., 2005; Loreto et al. 2006).
Previous work has shown that the emission of volatile organic compounds is induced in response
to stress practically in all plant species, also in those not emitting volatile isoprenoids under
optimal growth conditions (e.g. tobacco and sunflower Heiden et al., 1999; Beauchamp et al.,
2005). In constitutively emitting species, the volatile isoprenoid blend differs between induced
and constitutive emissions. For instance, European aspen (Populus tremula) emits isoprene as
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the main product in non-stressed conditions, but induced emissions are dominated by
monoterpenes limonene and α-pinene and sesquiterpenes (Blande et al., 2005). In non-stressed
conditions, the emissions of the monoterpene-emitting species Pinus pinea are dominated by
limonene, but the emissions in conditions of high temperature and low water availability are
dominated by linalool and ocimene; these emissions significantly exceed the emission in non-
stressed conditions (Staudt et al., 2000).
Current emission models do not consider “non-emitting” species, which, in addition to emissions
from non-specific storage, may have significant induced emissions. Furthermore, modification of
gene expression profiles in response to stress and upon adaptation to stress may in many cases
explain the modified emission compositions. Accordingly, understanding induction mechanisms
and consideration of induced emissions is crucial in explaining and predicting emission profiles.
6.3 Contemporary difficulties in scaling BVOC emissions from leaf to ecosystem
Parameterization of models at ecosystem scales is bound by a series of uncertainties. A key
uncertainty is currently the lack of reliable information of emission potentials (Arneth et al.,
2008). Although the available emission information has been recently collated, data for many
key emitting species are still lacking. Recent observations have indicated that several important
species such as cork oak (Quercus suber) (Staudt et al., 2004; Pio et al., 2005) and European
beech (Fagus sylvatica) (Moukhtar et al., 2005; Holzke et al., 2006) previously considered non-
emitters are moderate to strong emitters of monoterpenes.
The emission potentials of many ornamental, alien species used in urban landscaping and in
gardens are missing (Owen et al., 2003; Noe et al., 2008b). Understanding the emissions of
ornamental species is further important in light of potential changes in vegetation in urban
landscapes driven by global change and the growth of urban areas. Global warming and
associated increase of evergreen emitting exotic species in northern urban landscapes of northern
hemisphere can importantly enhance the winter emissions (Niinemets and Peñuelas, 2008).
In addition, stress- and time-dependent modifications of emission potentials are only partly
understood, but such adaptive responses can vastly affect ecosystem fluxes. Apart from gradual
changes in BVOC emission capacity in response to day-to-day and seasonal differences in
weather conditions (Guenther, 1999; Sharkey et al., 1999), emissions triggered by biotic stresses
such as herbivory or pathogen attack or by abiotic stress factor such as elevated ozone
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concentrations (e.g. Beauchamp et al., 2005), can potentially greatly influence whole ecosystem
fluxes.
6.4 BVOC fluxes over Europe, by compound and in relation to the needs of
photochemical oxidant models
6.4.1 Flux measurement techniques
The development of disjunct eddy covariance (DEC) methods (Rinne et al., 2001;) alongside the
development of proton transfer reaction – mass spectrometry (PTR-MS, Lindinger et al., 1998)
has enabled direct flux measurements of multiple VOC species simultaneously. The PTR-MS
has also been used in a more traditional continuously sampling eddy covariance technique, in
this mode the flux of just one VOC species can be measured at a time (Karl et al., 2001bc). The
DEC methods have been utilized in different European ecosystems (Rinne et al., 2007; Davidson
et al., 2008ab). Intercomparison experiments to validate these new techniques have been
conducted partly under ACCENT-BIAFLUX (Ammann et al., 2006; Neftel et al., 2007; Rinne et
al., 2008). For isoprene there also exists a fast isoprene sensor (FIS) based on
chemiluminescence enabling the application of eddy covariance measurements (Guenther and
Hills, 1998).
High reactivity of several plant BVOCs imposes significant difficulties in determining whole
canopy terpene emission fluxes by micrometeorological techniques. In particular, some
monoterpenes and most sesquiterpenes have atmospheric lifetimes on the order of minutes. High
reactivity of these compounds can imply that before reaching the BVOC detector, a significant
fraction of the emitted compounds has already reacted in the atmosphere, resulting in
underestimated emission fluxes (Rinne et al., 2007). To determine the emissions of reactive
terpenes, atmospheric chemistry models have been inverted (Bonn et al., 2007). However, the
lack of reactive rate coefficients for many terpenes and the dependencies of these on humidity
and temperature seriously hamper the overall assessment of the rates of emission and
contribution to atmospheric reactivity.
6.4.2 Isoprene
Isoprene is the most studied biogenic VOC. European isoprene emissions have been studied at
the ecosystem scale for various ecosystems. In some European ecosystems considerable isoprene
emission fluxes have been measured (Ciccioli et al., 1997; Davidson et al., 2008b). In contrast
European boreal coniferous forests are generally very low emitters (Rinne et al., 1999;). In
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measurements conducted above homogeneous conifer forest, the isoprene emissions from
isoprene emitters concentrated in e.g. ditches, lakeshores and roadsides, will not be observed.
Landscape scale emissions over southern Finland, estimated by a boundary layer budget method,
show much smaller isoprene than monoterpene emissions (Spirig et al., 2004). In general,
measuring forest sites, characterised by mixed composition, must account for the plant species
composition within the flux footprint. This has been shown by Spirig et al., (2005), who
observed the variation in normalized ecosystem scale isoprene emission from a central European
mixed broadleaf forest to be dependent on the abundance of Quercus robur, which is a high
isoprene emitter, in the flux footprint area.
Flux measurements of isoprene often correlate well with leaf-level measurements, reflecting the
relatively long atmospheric lifetime of isoprene (~1 hour) compared with other emitted BVOCs.
However, when the ecosystems are not homogenous, correspondence between emissions at plant
and ecosystem levels may not be straightforward. For example, the study conducted at Siikaneva
fen ecosystem using soil chambers (Hellén et al., 2006) and REA technique (Haapanala et al.,
2006) shows a considerable discrepancy between the isoprene emissions measured by these two
techniques. The fluxes measured by Haapanala et al. (2006) under low CTCL values were
typically lower than the model values, which may imply deeper penetration of PAR into the
moss carpet at high light conditions; a simple light penetration model slightly improved the
correlation (Haapanala et al. 2006).
6.4.3 Monoterpenes
Many European ecosystems emit considerable amounts of monoterpenes into the atmosphere and
thus many flux measurement experiments have been concentrated on these compounds. The
diurnal variations of the monoterpene fluxes above boreal coniferous forests appear to be
relatively well reproduced by the temperature dependent Tingey-Guenther emission algorithm
(Guenther et al., 1993). Monoterpene emissions from Mediterranean ecosystems are better
described by the light and temperature dependent isoprene emission algorithm (Seufert et al.,
1997; Schween et al., 1997), with important discrepancies likely reflecting non-specific storage
and stress-dependent changes in emissions (Niinemets et al., 2002a; b, see above).
At coniferous forest sites a diurnal concentration cycle with highest concentrations at night are
typical (e.g. Hakola et al., 2000; Steinbrecher et al., 2000; Rinne et al., 2005). This is due to the
emission continuing during night time, although at a lower rate than during the day, and to
considerably reduced turbulent mixing at night. On the contrary, in the Mediterranean region, as
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well as in Amazonian tropics, and in European mixed broadleaf forest, where the night time
monoterpene emission is practically zero due to the light dependence of the monoterpene
emission, daytime maxima are typical (Zimmermann et al., 1988; Schween et al., 1997; Rinne et
al., 2002, Spirig et al., 2005).
The major monoterpenes emitted by forest ecosystems in Europe seem to be α- and β-pinene and
∆3-carene. For example, monoterpene emissions from Norway spruce forest consist of α- and β-
pinene (Christensen et al., 2000; Gallagher et al., 2000). However, in some Mediterranean
ecosystems limonene can form a significant part of the monoterpene emission (Schween et al.,
1997) and, in the case of orange orchards, be the dominant monoterpene emitted (Christensen et
al., 2000; Darmais et al., 2000).
6.4.4 Sesquiterpenes
Due to the enhanced chemical reactivity of sesquiterpenes, the atmospheric concentrations of
these compounds are very low, making flux measurements extremely difficult. Enclosure
measurements at an orange orchard revealed substantial emission of β-caryophyllene, while the
flux measurements conducted by REA method show the fluxes to be close to zero indicating
rapid within and below canopy chemical degradation (Ciccioli et al., 1999).
6.4.5 Methanol
The development of the DEC-PTR-MS has recently enabled ecosystem scale flux measurements
of methanol leading to considerable progress in our knowledge of ecosystem scale emission of
this compound. Methanol seems to be emitted from all ecosystems and also from drying and
decaying plant material. Methanol is typically the second or third most abundantly emitted
biogenic VOC after isoprene and monoterpenes in most ecosystems.
The measured ecosystem scale methanol emissions have generally not been fitted to emission
algorithms, in the same way isoprene and monoterpene fluxes have been modelled. Only a recent
empirical temperature dependent formulation has been presented (Harley et al., 2007). Based on
a mechanistic understanding of the physico-chemical control (see above), methanol emission
should be regulated by stomata, in contrast to isoprene and monoterpene emission. In field flux
experiments, this kind of behaviour has not been generally observed. Brunner et al. (2007) have
observed the methanol emissions from agricultural grassland ecosystem to be relatively high in
the morning as compared to the emission in the evening. A similar observation was found in the
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recent ACCENT-VOCBAS campaign on the Mediterranean macchia of Castelporziano (Davison
et al. 2008b).
6.4.6 Acetone and acetaldehyde
Acetone and acetaldehyde in the atmosphere are the result of both primary emissions from
biogenic and anthropogenic sources and secondary formation from other gaseous precursors.
Fluxes of these carbonyls have been observed to be emitted from coniferous forests in Europe
(Rinne et al., 2007) as well as in the US (Schade and Goldstein, 2001;). Also the Mediterranean
macchia ecosystem is observed to emit these compounds (Davidson et al., 2008b). No emission
of these compounds from broadleaf deciduous forests has been reported. Anaerobic conditions in
root system have been observed to enhance the emission of acetaldehyde from plant foliage
(Kreuzwieser et al., 1999) as also mechanistically explained above. However, no ecosystem
scale flux measurement of this compound, in conditions where the root systems was anaerobic,
has been reported.
6.4.7 Other compounds
The emissions of many compounds, other than isoprene, monoterpenes, methanol, acetone and
acetaldehyde, have been too small to be measured by micrometeorological flux measurement
techniques. However, there are a few other compounds which have been observed to be emitted
by micrometeorological flux measurement techniques in certain ecosystems. Some western US
pine forests have been shown to emit 2-Methyl-3-buten-2-ol (MBO) in considerable amounts
(e.g. Baker et al., 1999; Schade et al., 2000). From European pines no significant emissions of
MBO have been observed (e.g. Hakola et al., 2006). It is noteworthy that drying hay was
observed to emit (Z)-3-hexenal and (Z)-3-hezenol and hexenyl acetates (Davidson et al., 2008a).
These compounds appear to be reliable markers of membrane damage and lipoxygenation, as
previously indicated (Loreto et al. 2006).
6.5 The EU large field campaigns in the Mediterranean area: from BEMA to ACCENT
Pioneering campaigns around the world (e.g. the SOS 1999 campaign in North America, the
LBA/CLAIRE 1999 campaign over the Amazon, the SAFARI 2000 campaign in Southern
Africa) highlighted the predominance of isoprene as the most commonly emitted VOC by
vegetation, over different environments, and by different ecosystems, spanning from tropical to
boreal forests. In Europe, however, a different picture emerged. The BIPHOREP 1996-1997
campaign in boreal Europe revealed the dominance of monoterpenes as the most abundantly
emitted VOC from European boreal ecosystems. In otherwise clean northerly air masses, at least
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the growth, if not the formation, of organic aerosol, seems to be heavily affected by the presence
of biogenic VOCs. The most likely candidates contributing to this aerosol growth are
monoterpene oxidation products (Lee et al. 2006). The BEMA (Biogenic Emission in the
Mediterranean Area) 1996 campaign highlighted the remarkable peculiarity of Mediterranean
coastal vegetation that is almost uniquely characterized by monoterpene-emitting species (Loreto
et al., 1998a). The reason for this distinctive trait of Mediterranean vegetation remains unknown.
A new campaign was organized during spring 2007 in the Mediterranean area, funded by the
European Commission project ACCENT and by the European Science Foundation programme
VOCBAS. The campaign was deliberately held on the same site of the BEMA campaign, the
large peri-urban natural preserved area of Castelporziano, in the conurbation of Roma. This site
has two main characteristics that make it an excellent case of study for biosphere-atmosphere
interactions. First, the 6000 ha wide preserved area of Castelporziano is a hot spot for
biodiversity in the Mediterranean, with more than 1000 plant species represented in the flora of
the area. The main ecosystems going toward the sea are characterized by oak (Quercus ilex,
Quercus suber, Quercus cerris) and pine (Pinus pinea) forests, often associated with a rich
understory vegetation. The part of the Estate facing the Tyrrhenian sea is characterized by sand
dunes and a humid retrodunal area, with a large and extremely well preserved area covered by
Mediterranean “macchia” vegetation, prevalently shrubs and small evergreen trees, such as
Juniperus communis, Quercus ilex, Phillyrea latifolia, Arbutus unedo, Rosmarinus officinale,
Erica arborea, and Cistus incanus. Second, the preserved area is only distant 25 km from the
centre of Roma in the S-E direction. It is exposed to a constant wind circulation that favours
transport of air masses from the city center during night-time, and from the sea during daytime.
This periodically exposes vegetation to urban pollutants and may trigger formation of secondary
pollutants that are contributed by BVOC precursors (Chameides et al., 1988; Di Carlo et al.,
2004).
This ACCENT-VOCBAS campaign (i) provided fluxes of BVOC from Mediterranean
vegetation, in a season during which plants are in optimal physiological conditions prior to
drought and heat stress conditions experienced later in the year, but during which BVOC
emisions are thought to be constrained by leaf development limitations; (ii) investigated, by
coupling concentration and flux measurements, the in situ extent of BVOC reactivity, and in
particular whether in situ BVOC oxidation could drive formation of secondary organic
compounds in the atmosphere; (iii) identified whether BVOCs can act as precursors of
photochemical smog; (iv) provided a second assessment, ten years after the campaign organized
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by the EC-BEMA project (Seufert et al. 1997), of BVOC emission in an area largely affected by
anthropogenic and climatic changes, thus creating the foundation for a historical series of
measurements which may be especially important in view of current and future climate change
factors, and, in particular, of the simultaneous and strong increase of temperature, drought and
pollutants in the Mediterranean area; and (v) fostered interdisciplinary collaboration between the
communities of biologists, atmospheric chemists and physicists, further catalyzing research on
the important roles of BVOCs in the environment.
The campaign was run in the macchia strip placed between the dunes and the main forested land
inside the Castelporziano Estate. The oak and pine forested area was extremely well
characterized during the BEMA campaign (1997) but the macchia vegetation received only
limited attention (Owen et al., 1997). The macchia strip is characterized by a modest roughness
of the terrain due to the presence of small sandy dunes, and by a gradient of soil humidity due to
the presence of freshwater sources. Both features were considered to be irrelevant for flux
measurements during the experimental period that was characterized by optimal water supply
conditions for the plants. The campaign was organized and coordinated by the Consiglio
Nazionale delle Ricerche of Italy (CNR) and was run by ten European groups with different
tasks, spanning from measurements of CO2, H2O and BVOC exchanges at leaf level, to flux
measurements of the same parameters and of pollutants above the entire ecosystem. To achieve
flux measurements, a series of scaffolds were erected, in the middle of the fetch used for flux
measurements, which were equipped with sensors and inlets for instrumentations that was
located on shelters at the bottom of the scaffolds.
6.6 Remote sensing of BVOC
Over the last decade space-based instrumentation, capable of probing the lower troposphere, has
reached the level of accuracy necessary to quantify surface sources and sinks of trace gases from
observed variations in trace gas concentrations (Palmer, 2008). The only non-methane BVOC
measured in the lower troposphere from space is formaldehyde (HCHO), a high yield product of
VOC oxidation that is measured from clear-sky backscattered solar radiation at ultraviolet
wavelengths. The main sinks of HCHO are oxidation by OH and photolysis leading to a
tropospheric lifetime of several hours. Figure 5.3 shows the global distribution of HCHO
columns observed by the Ozone Monitoring Instrument during August 2006.
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Figure 6.3 Formaldehyde columns (1016 molec cm-2) retrieved from the NASA Aura Ozone Monitoring Instrument
(OMI) for August 2006, courtesy of Thomas Kurosu, Harvard Smithsonian Center for Astrophysics.
Oxidation of methane (CH4) by OH, the largest global source of HCHO, provides a uniform
HCHO background of ~100 pptv, reflecting the 8-year lifetime of CH4. The limit of detection of
HCHO from current space-borne instrument is approximately 4x1015 molec cm-2 (Chance et al,
2001), which is close to the source of HCHO from CH4 oxidation. Over the continental boundary
layer, oxidation of anthropogenic and biogenic VOCs provide an additional source of HCHO
that can reach on a local scale up to several ppbv, equating to columns over an order of
magnitude determined by CH4 (Figure 6.3). Observed variations in HCHO, determined by the
oxidation of VOCs, therefore provide constraints on emissions of the parent VOCs. Horizontal
transport smears the local relationship between VOC emissions and HCHO columns, the extent
of which is determined by wind speed and the time-dependent yield of HCHO from the VOC
oxidation (Palmer et al, 2003). Over a number of global regions, variations in HCHO columns
are determined by isoprene (Palmer et al, 2006;), due to its rapid production and high molar yield
of HCHO (Palmer et al, 2006). Other reactive biogenic VOCs, such as monoterpenes, also have
short atmospheric lifetimes but they quickly produce acetone with a high yield that has an
atmospheric lifetime of weeks and consequently slows down the production of HCHO (Palmer et
al, 2006). Long-lived VOCs such as CH4 and CH3OH, while being the largest sources of HCHO,
only contribute to the slowly varying background of HCHO.
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Early work showed that the magnitude and distribution of GOME-derived isoprene emissions
based on HCHO measurements were more consistent with in situ measurements than either the
GEIA or BEIS2 isoprene inventories, based on Guenther et al, 1995 (Palmer et al, 2003).
Monthly mean distributions of HCHO, and inferred isoprene emissions, during summertime are
dominated by high values over the southeastern states of the USA (Chance et al, 2001) due to a
large density of isoprene-emitting oak trees over the Ozarks Plateau ( Wiedinmyer et al, 2005).
Examination of GOME orbital data revealed large variations in HCHO, explained by changes in
surface temperature, which led to inferred monthly mean isoprene emissions that were
significantly lower than those predicted by bottom-up models (Palmer et al, 2003). Later work
showed that the observed seasonal and year-to-year variability was consistent with the MEGAN
model (Guenther et al, 2006), but GOME-derived isoprene emissions were 25% higher (lower) at
the beginning (end) of the growing season (Abbot et al, 2003; Palmer et al, 2006). Both MEGAN
and GOME show a maximum over the Southeast US but disagree in the precise location, with
implications for modelling surface ozone (Fiore et al, 2005).
Isoprene emissions from tropical ecosystems have been estimated to contribute 75% of the
global isoprene budget, but are not well quantified. Over tropical South America, the widespread
extent of biomass burning in the dry season means that without high-resolution data the only
practical approach is to use data over west Amazonia, which is largely unaffected by fires
(Barkley et al, 2008). There is a strong seasonal cycle of GOME HCHO columns over this
region reproduced each year during 1996-2001, characterized by large values in the wet and dry
seasons, separated by low values in the wet-to-dry transition period (May-July); this is consistent
with in situ isoprene concentration measurements (Palmer et al, 2007). This large-scale reduction
in isoprene emissions suggests a major temporary shift in underlying meteorology or phenology,
but its origin remains unclear. This study found that MEGAN and GOME were in better
agreement in the dry season, when GOME isoprene emissions could not be explained by changes
in surface temperature. GOME isoprene emissions in the wet season could be not explained by
changes in surface temperature, precipitation or soil moisture, suggesting either an unexplained
process that determines isoprene during this season or noisy data (Barkley et al, 2008). These
substantial open questions will be readdressed with data from newer sensors and should be the
subject of extensive year-long ground-based measurement campaigns.
There are a number of uncertainties associated with the HCHO measurement and the approach
used to infer surface emissions of isoprene from these measurements (Palmer et al, 2003, Millet
et al, 2006), which lead to uncertainties that total more than 100% of estimated emissions
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(Palmer et al, 2006). Work related to the analysis of GOME data has suggested that HCHO
production predicted by chemical mechanisms typically used by large-scale chemistry transport
models are in error by more than 25% (Palmer et al, 2006).
Current studies that determine VOC emissions using HCHO column data are already focusing on
data from newer space-borne sensors (e.g., Aura OMI and MetOp GOME-2) that have better
spatial (100s km2) and daily temporal resolution, enabling more detailed testing of current
bottom-up inventories. The advances in resolution, in particular, improve: 1) the probability of
cloud-free scenes and consequently lead a greater number of useful data points; 2) spatial
disaggregation of different HCHO sources, e.g., biomass burning and biogenic VOC emissions
over tropical regions, which can lead a better description of land-surface processes; and 3) their
usefulness in planning and executing measurement campaigns. For example, analysis of GOME-
2 and OMI HCHO data, which have local overpass times of 09:30 and 13:30, will allow us to
develop a crude understanding of the diurnal cycle of BVOC emissions on continental scales.
Knowledge of the complex organic chemistry associated with BVOC oxidation will only
improve with a concerted effort to increase the number of laboratory and field measurements.
With the rapid increase in available remotely sensed datasets that could be brought to bear on
estimating BVOC emissions (e.g., HCHO, leaf phenology, land-cover) there should be scope to
develop new functional descriptions of isoprene emission that are independent of the
assumptions made in traditional bottom-up models derived from in situ measurements.
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7. Deposition and resuspension of aerosols onto and from terrestrial
surfaces 7.1 Introduction
Aerosols present a complex multi-variate and multi-scale problem to environmental research.
They have strong impacts on climate indeed they represent the largest uncertainties in our ability
to predict climate change (IPCC, 2007), human health and provide a means for pollutant
deposition to sensitive ecosystems (transporting e.g. nitrogen, sulphur and toxic metals). A vital
component of the global aerosol cycle is deposition of many critical secondary process-derived
condensed phase compounds as well as primary generated aerosols, both in the ultrafine and
coarse modes. The majority arise from the consequences of anthropogenic activities associated
with global industrialisation and urbanization where large emissions of reactive nitrogen species
lead to an increase in nitrogen aerosol formation which is eventually removed by wet or dry
deposition. Sulphate aerosol remains an important issue in the Eastern US and China. Over the
last 150 years the atmospheric particulate loading has changed from coal and other solid fuel
burning to modern combustion processes that liberate a greater preponderance of sub-micron,
ultrafine particles. This has initiated a paradigm shift in some quarters of the relative importance
of coarse mode as opposed to fine mode particulates and their behaviour for mainly health
related reasons. This shift will likely move into a third phase where emissions from nano-particle
technologies are starting to play an increasing role. In addition, it is becoming obvious that
particulate matter provides an area of policy conflict: efforts to curb PM concentrations to
protect human health are likely to reduce global dimming and thus further accelerate climate
change, although there are also components, such as soot, that have a negative impact on both
human health and the climate system (cf. Raes et al., this issue). The primary aims of research
into the biosphere / atmosphere exchange of particles are:
(a) to improve our estimates of primary aerosol emissions from diffuse sources and their
parametrisations,
(b) to measure directly the contribution of particle deposition to the deposition of compounds
that are detrimental to ecosystems or may accumulate in water, soils or crops (e.g. N and S
compounds, heavy metals and nano particles),
(c) to derive parametrisations of the deposition velocity (Vd) of particles, for inclusion into
CTMs e.g. aimed at predicting deposition, human health impacts and climate impacts.
(d) to study aerosol formation and dynamics in the atmosphere.
If the models predict the size distribution of the aerosol explicitly, Vd needs to be parametrised as
a function of particle diameter (Dp). By contrast, where the models only deal with the bulk
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species, Vd may be derived from measurements of the individual compounds (e.g. Ruijgrok et al.,
1997) or, more commonly, from a weighting of Vd(Dp) with a typical size distribution of the
aerosol component.
7.2 Review of New Measurement Approaches and Instrumentation
Progress in the quantification and parameterisation of surface/atmosphere exchange of particles
and aerosol compounds is closely related to developments in the measurement technology. In the
1970s and 80s measurements of aerosol dry deposition were made in the wind tunnel or using
surrogate surface collectors, such as knife-edge collectors, inverted frisbee type dust deposition
gauges and moss bags. Although these techniques are still being used, they have attracted serious
criticism as their aerodynamic properties are usually not representative for the surface for which
deposition is to be estimated. More recently, non-intrusive micrometeorological flux
measurement techniques have increasingly been extended to measure surface / atmosphere
exchange fluxes at the field scale. Measurements of particle fluxes fall into two categories:
particle number fluxes (total or size-segregated) and chemically resolved aerosol fluxes.
7.2.1 Flux measurements of particle numbers (size-resolved or total), without information on
chemical composition.
These measurements are usually used to derive (size-dependent) deposition velocities which can
be used in atmospheric transport and deposition models. Since the 1980s, fast optical particle
counters (e.g. OPCs such as ASASP-X/555X, Particle Measurement Systems; FAST, Droplet
Measurement Technologies) have been used for eddy-covariance (EC) measurements of size-
resolved aerosol number fluxes (e.g. Duan et al., 1988; Gallagher et al., 1997; Nemitz et al.,
2002a; Sievering, 1983; Vong et al., 2003). The measurements usually cover the size spectrum
between 0.1 to 0.5 µm, but even in this size range the particle statistics of these instruments have
often been marginal in deriving statistically significant fluxes. Several studies have attempted to
extend measurements to smaller and also to larger sizes. Particles significantly < 0.06 µm cannot
be sized with current optical techniques. Instead, recent studies have attempted to measure size-
segregated fluxes of smaller particles, either using the relaxed eddy accumulation (REA)
technique combined with size selection using a differential mobility analyser or interpreting total
particle number fluxes during periods where a certain size-range dominated the flux (Grönholm
et al., 2007; Pryor, 2006). Due to limited counting statistics, fluxes had to be averaged over many
days to obtain robust statistics and these measurement methods are therefore not yet suitable to
study short-term processes (Fig. 7.1). Variability between measurements is likely to be linked to
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 93
differences in turbulence, canopy morphology (including leaf area index) and surface roughness
length (cf. Section 7.4.1).
1
2
3
4
56789
10
2
3
Vd
[mm
/s]
7 8 910
2 3 4 5 6 7 8 9100
2
Dp [nm]
Beech forest (mean)dep. only, CPC (Pryor, 2006)
Beech forest (median), dep. only, CPC (Pryor, 2006)
Scots pine, CPC nucleation event (Gaman et al., 2004)
Scots pine, DMA REA (Gronholm et al., 2007)
Figure 7.1. Summary of measured size-segregated particle deposition velocities to forest for particles with
diameters < 100 nm.
Size-segregated EC fluxes of larger particles are only possible when these particles are abundant
and occur as the result of specific mechanisms, e.g. in dust storms, biomass burning or industrial
processes. High volume, closed path aerodynamic Mie scattering time of flight optical particle
counters, for sizes 0.5 < Dp < 20 µm, and open path forward scattering optical particle counters
have been used to measure deposition rates of super-micron particles and fog droplets (e.g.
Beswick et al., 1991; Burkhard et al., 2002; Klemm and Wrzesinski, 2007; Kowalski and Vong,
1999). As a result of current instrument limitations, measurement evidence is sparse on
deposition rates in the important accumulation mode (0.3 – 2 µm), which contains much of the
mass of sulphur, nitrogen and secondary organics. Recently developed mass-based flux
measurement approaches based on time-of-flight aerosol mass spectrometry (see below) may go
some way towards filling this gap in the future.
It is sometimes easier to measure super-micron emission fluxes. For example, Fratini et al.
(2007) reported measurements of desert dust resuspension, and Nemitz et al. (2000b) presented
urban EC flux measurements in the range 0.8 to 10 µm, made with aerodynamic particle sizers
(APS 3320, TSI Instruments), both made in conditions where fluxes were large.
In general, better counting statistics can be achieved when integrated particle number fluxes are
measured over larger size ranges. For example, condensation particle counters (CPCs) are now
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 94
more commonly used to measure total particle number fluxes with a lower cut-off between 2.5
and 20 nm over ice, sea water, short and tall vegetation canopies, as well as urban areas (e.g.
Buzorius et al., 1998; Buzorius et al., 2001; Dorsey et al., 2002; Held et al., 2006; Martensson et
al., 2002; Nemitz et al., 2002a; Nilsson and Rannik, 2001). CPC derived flux measurements are
dominated by particles in the range 10 to 100 nm, with smaller particles dominating during
nucleation events (Buzorius et al., 2001). Depending of the model used, CPCs have a response
time of around 1 s, which is sufficient to measure fluxes from taller towers, but requires flux
corrections for shorter vegetation. An alternative EC flux measurement approach that integrates
over all particles was implemented by Fontan et al. (1997) based on particle counting through a
combination of corona charging and detection by electrometers. Only very few gradient
measurements of small particle number fluxes have been reported in the literature (Hummelshoj,
1994) due to the large errors associated with these measurements. Work has started to extend EC
approaches to the measurement of particle fluxes from moving platforms such as aircraft (e.g.
Buzorius et al., 2006) and ships (Norris et al., 2008).
7.2.2 Flux measurements of individual aerosol chemical species
Measurements of chemically resolved aerosol fluxes can be used to quantify deposition inputs
directly, to investigate effects of aerosol composition on exchange rates and to understand
apparent emission fluxes. The number of studies that have applied micrometeorological
approaches to measure fluxes of aerosol compounds has been surprisingly limited. Up to the
1990s, the main option was gradient measurement with labour intensive manual sampling
techniques based on filter packs or denuder/filter combinations (Duyzer, 1994; Rattray and
Sievering, 2001; Wyers and Duyzer, 1997), where a key challenge is to achieve the precision
required to resolve the very small aerosol gradients, which are often < 3%. Manual sampling
techniques have also been used in REA approaches to measure fluxes, e.g. of sulphate to a maize
crop (Meyers et al., 2006) and of ions and heavy metal above a city (Nemitz et al., 2000c). A
family of automated real-time gradient monitors, based on gas and aerosol capture by
continuously flushed wet rotating denuders and steam jet aerosol collectors, respectively, and
online analysis by ion chromatography and/or flow injection analysis (for NH4+) (Thomas et al.,
2009) has been used in a number of studies to measure deposition of water soluble inorganic
aerosol components (Nemitz et al., 2004b; Nemitz et al., 2000a).
Eddy-covariance measurements of aerosol chemical species were first presented for SO42-
deposition to grassland, based on an analyser with thermal conversion to SO2 (Wesely et al.,
1985), with no further studies until the advent of aerosol mass spectrometry offered the prospect
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 95
for fast measurement of a number of aerosol compounds. Held et al. (2003) theoretically
investigated the suitability of aerosol mass spectrometers based on single-particle analysis by
laser ablation and ionisation for disjunct eddy-covariance flux measurements, dealing with the
limited counting statistics and quantification issues related to this instrument. An alternative
instrument, the Aerodyne Aerosol Mass Spectrometer (AMS), which is based on thermal
vapourisation coupled with electron impact ionisation, averages over a much larger aerosol
population and is quantitative for sub-micron aerosol components that are non-refractory, i.e.
volatilise at the vapouriser temperature of ~600 °C. An operational EC system using the
quadrupole-based AMS (Q-AMS) (Nemitz et al., 2008b) has been used to measure fluxes of
NO3-, SO4
2- and organic aerosol to urban areas and forests (Nemitz et al., 2008a; Phillips et al.,
2008; Thomas, 2007; Thomas et al., 2008; Thomas et al., 2007). The Q-AMS monitors only one
single mass/charge ratio (m/z) at a time. However, the quadrupole MS can be switched very
rapidly so that quasi-continuous time series of concentrations can be established at typically 10
different m/z at 10 Hz, similar to the use of the PTRMS for VOC measurements. Since >100
different m/z contribute to the organic mass spectrum, with the Q-AMS, the total organic aerosol
mass flux has to be estimated from fast response measurements at a few m/z. The arrival of the
next generation AMS based on a high-resolution time-of-flight mass spectrometers (HR-ToF-
AMS) (DeCarlo et al., 2006) provides the prospect of monitoring all m/z continuously at 10 Hz.
This should enable a fully quantitative flux measurement of the organic fraction and provide data
to apply statistical approaches, currently used to deconvolve the organic mass concentration in
different organic aerosol classes (e.g. Ulbrich et al., 2008), to the flux measurement.
7.3 Area sources of particles
7.3.1 Resuspension
Resuspension of particles has been studied extensively, both theoretically, in the wind tunnel or
through concentration measurements (Braaten and Paw U, 1992; Harrison et al., 2001;
Nicholson, 1988; Nicholson, 1993; Nicholson et al., 1989). A full review of the understanding of
this process is beyond the scope of this paper. Instead, we here focus on a recent development
involving the first application of micrometeorological flux measurement techniques to the direct
measurement of resuspension fluxes, and their potential to derive new parametrisations of the
process. Nemitz et al. (2009) measured super-micron size-segregated aerosol fluxes measured
with an Aerodynamic Particle Sizer (APS 3320, TSI Inc.) from a tower, some 65 m above the
city of Edinburgh, Scotland. The measurements, binned according to diameter and wind speed
lead to a parameterisation of the form: dFm(Dp)/dlog(Dp) = am(Dp) U b(Dp)
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 96
am(Dp) =exp(-68.69 + 73.39(1-exp(-0.730 Dp [µm]))) (1)
b(Dp) = 20.12 exp(-0.506 Dp [µm])
where U is wind speed measured at a height of 65 m.
The measurements show that above U(65 m) = 6 m s-1 resuspension becomes an important
particle source in the urban environment, with a mode centred around a Dp of 2.8 µm, which
increases with decreasing wind speed. An attempt to include traffic activity in the
parametrisation failed due to the overriding effect of wind speed. This suggests that, although
vehicle induced resuspension may be important to lift particles off the surface at street level,
high wind speeds are nevertheless required to flush these out of the street canyons. It should be
noted that the windy periods with largest coarse particle emissions did not result in the largest
concentrations, due to increased dispersion during these periods.
Similarly, Fratini et al. (2007) measured coarse aerosol fluxes during desert storms in the
Alashan desert in Nothern China, using an optical particle counter. The authors found that the
dependence of the resuspension flux (in µg cm-2 s-1) of PM1, PM2.5 and PM10 on u* (in m s-1)
could be described by the power relationships of F1 = 469 u*3.11, F2.5 = 6220 u*
3.34 and F10 =
47500 u*3.36, respectively. It should be noted that these two studies address different resuspension
processes, reflecting road dust resuspension with vehicle contribution, and natural saltation
processes, respectively.
7.3.2 Urban emissions of aerosols
In recent years, flux measurement techniques have been extended to the urban environment to
quantify emission fluxes of trace gases such as CO2, N2O, CO and VOCs (Nemitz et al., 2002b;
Velasco et al., 2005; Vesala et al., 2007), but also of particles. Total number fluxes have been
measured with condensation particle counters over several cities (Dorsey et al., 2002;
Martensson et al., 2006; Nemitz et al., 2008b). Martin et al. (2008) recently compared the pattern
of particle number fluxes measured at four different locations, three of which are shown in Fig.
7.2. Fluxes, typically covering the diameter range 0.01 (or 0.003) to 2 µm, ranged from 5000 to
70,000 # cm-2 s-1 and showed a clear dependence on traffic activity confirming the role of traffic
emissions as the major source of particles in the urban area. They derived a parametrisation for
the flux (FPred in cm-2 s-1) over each city based on friction velocity (u* in m s-1), sensible heat flux
(H in W m-2) and traffic activity (TA in veh s-1) of the form
[ ] 0trafficheat*frictionPred FTAEFHEFuEFCF −×+×+×= , (2)
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 97
which is shown in Fig. 6.3 for comparison. Here C is a site-specific factor in the range 0.12 to
0.55. The emission factors are EFfriction = 4500 cm-3, EFheat = 6.53 × 106 W-1 s-1 and EFtraffic lies
in the range 9000 to 12,600 veh-1 cm-2, depending on the location of the traffic census site. The
sink flux (F0) ranged from 13,000 to 57,000 cm-2 s-1.
Figure 7.2. Summary of diurnal patterns of measured particle number fluxes (dotted lines) and their
parametrisation (solid lines) for three UK cities: M – Manchester; L – London; E – Edinburgh; Win06 –
winter 2006 etc.
In the Edinburgh study of Nemitz et al. (2009) aerosol number fluxes were dominated by traffic
activity, while the aerosol mass emission fluxes were dominated by the wind-driven
resuspension described in the previous section This may be different for less windy locations as
demonstrated by Schmidt and Klemm (2008), who presented flux measurements of super-micron
particles made with a novel disjunct eddy covariance system, based on an Electronic Low
Pressure Impactor (ELPI, Dakati, Finland), indicating net coarse-mode deposition to the German
town of Münster. During the measurement periods, wind speed averaged 8.0 m s-1 in Edinburgh
and 4.4 m s-1 in Münster (O. Klemm, pers. communication). Donateo et al. (2006) reported flux
measurements of PM2.5 above an urban area made with an optical detector calibrated against
gravimetric PM2.5 measurements. These measurements indicated continuous net upward fluxes
and represent a combination of Aitken, accumulation mode particles and super-micron particles.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 98
Chemically resolved flux measurements using the AMS EC technique described above have now
been made over a range of cities (Boulder, US; Mexico City; Gothenburg, Sweden; Manchester,
Edinburgh and London, UK) (Grivicke et al., 2007; Nemitz et al., 2008b; Phillips et al., 2007;
Thomas, 2007). The AMS measures total organic aerosol mass contained in particles with 60 to
800 nm vacuum aerodynamic diameter and volatilizes at typically 600°C. More information on
the organic aerosol classes can be obtained from the organic mass spectrum, with statistical
techniques (e.g. Ulbrich et al., 2008), which can be used to separate the organic mass flux into
fluxes of (primary) hydrocarbon-like organic aerosol (HOA) and (secondary) oxygenated
organic aerosol (OOA), where the OOA can often be divided into a more (OOA-I) and a less
(OOA-II) oxidized component. The measurements to date show clear diurnal fluxes of HOA
reflecting the pattern of the surface sources (Fig 7.3). However, the flux ratios of HOA/CO and
HOA/CO2 vary over the day , indicating that either (a) some of the HOA evaporates at rates that
vary over the day (e.g. Robinson et al., 2007) or (b) that the fuel mix contributing to the
emissions of HOA and CO varies over the night. Measurements suggest that food cooking may
contribute to HOA emissions in the evening in London. Fluxes of OOA-I appear to be mainly
downwards consistent with its production during long-term transport, while small upward fluxes
of OOA-II were measured, indicating that some OOA-II formation occurs below the
measurement height of typically 30 to 200 m. The urban flux measurements indicate that SO42- is
deposited to most city centres. Apparently, with the introduction of ultra low sulphur fuels, there
are no primary sources of this compound. Fluxes of NO3- were more variable: in Gothenburg,
Edinburgh, London and Boulder, net emission was observed, but fluxes were dominated by
individual, often cool or foggy days, indicating urban NO3- formation under these conditions. By
contrast, above Manchester and Mexico City, the average flux was downwards.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 99
Figure 7.3. Averaged diurnal cycles of the fluxes of hydrocarbon like organic aerosol (HOA) and nitrate (NO3
-) over a range of cities as measured by eddy-covariance using an Aerodyne Aerosol Mass Spectrometer (data from Nemitz et al., 2008a (Boulder), Phillips et al., 2007 (Manchester, London) and Thomas, 2007 (Gothenburg)). The grey range indicates the 5th to 95th percentile.
7.4 Dry deposition of particles
7.4.1 Dry deposition rates to vegetation
Dry deposition of atmospheric particles can account for a large fraction, sometimes more than
half, of the total deposition of many important chemical compounds in the atmosphere, (e.g.
nitrate; Lovett, 1994), contributing significantly to global biogeochemical cycling.
Understanding atmospheric deposition processes in relation to the ever increasing sources of fine
particles in particular is therefore becoming a research priority. In general, both aerosol mass and
number, and, increasingly, surface area, must be determined to assess the environmental impacts
of anthropogenic activities. Mapping between number and mass fluxes however requires either a
detailed knowledge of the aerosol mass size distribution at relatively high temporal resolution or
by direct measurement of the aerosol deposition flux as a function of both size and chemical
composition. In this short summary we will critique the present level of understanding from an
observational perspective and identify the current gaps in our knowledge. There have been a
number of recent reviews on the subject of atmospheric aerosol deposition which have attempted
to collate the sparse experimental results available from the previous two and half decades.
These consist of many disparate and difficult to compare or even reconcile, methodologies. The
reviews, whilst achieving this difficult process in some respects, have succeeded mainly in
highlighting the general lack of a systematic approach towards improved understanding of
mechanistic deposition processes and, despite best efforts, have simply reinforced the view of
200
100
0
-100
24181260
1200
800
400
0
HOA flux [ng m
-2 s-1]
400300200100
0-100
HO
A flu
x [n
g m
-2 s
-1]
120
80
40
0
Manchester
London
Boulder
Gothenburg
40
20
0
24181260
200150100500
-50
NO
3 - flux [ng m-2 s
-1]
-40-20
02040
NO
3- flux
[ng
m-2
s-1
]
40
20
0
-20
Manchester
London
Boulder
Gothenburg
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 100
continued large uncertainties. Reviews have been very much focused on either a modeling
(Petroff et al., 2007b; Zhang and Vet, 2006) or a measurement perspective (Petroff et al., 2007a;
Pryor et al., 2007).
Many regional pollutant deposition models are currently struggling to correctly incorporate
physico-chemical properties of aerosols using realistic coupled sectional approaches, particularly
with respect to secondary organic aerosols. However, it could be stated that the scientific
community has not delivered any significant improvement in the accuracy of model predictive
capabilities for the atmospheric aerosol deposition pathway over the last two decades, (compare
for example one of the first reviews of model uncertainty, Ruijrok et al. (1997), with e.g. Petroff
et al. (2007b)). The aerosol modelling and composition community are pushing ahead with such
developments whilst seemingly unaware of the poor state of knowledge of deposition processes
and caution is required to avoid simply wasting research effort here. Whether the current level of
understanding and uncertainty is acceptable depends on the compound of interest and its position
with the aerosol mass size distribution prevalent in the atmosphere. Little in the way of detailed
sensitivity studies has been available since Ruijgrok et al. (1997). Feedback of such sensitivity
studies to the measurement community would also appear to be an area that requires
improvement (Zhang and Vet, 2006).
There has also been little in the way of any new laboratory investigation, at least for
atmospherically relevant conditions, that can usefully inform these communities on specific gaps
in knowledge that need to be pursued. While some progress is being made to highlight gaps in
knowledge with respect to models (Petroff et al., 2007b), these again show that different model
descriptions of atmospheric particle deposition, which rely on very limited semi-empirical data
and highly tunable, collection efficiency parameterisations, are as variable or more so than the
atmospheric observations that do exist (e.g. Zhang and Vet, 2006). Some improvements in
relating natural surface morphology descriptions to wind tunnel studies have been made.
In the following we explore the dependence of particle deposition velocity (Vd) on key
parameters, in comparison with measurements. Most of these measurements were made over
forests, where they are generally easier to obtain than over short vegetated surfaces for many
reasons related to the micrometeorological flux technique. The fluxes have been determined
using mainly but not exclusively direct micrometeorological techniques (cf. e.g. Pryor et al.,
2008b). The data must be treated with circumspection as (a) little account of particle composition
is provided in many of the studies reporting these data, (b) the sizes reported are an ad hoc
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 101
mixture of optical, electrical mobility, mass and aerodynamic diameters with little quantitative
information on the influence individual measurement techniques may have in altering actual
depositing particle size (which will be particle growth factor and hence composition dependent)
and (c) systematic detailed information on the morphology of the surfaces is not always reported
which often hinders useful model development.
Friction velocity (u*). Increased turbulence increases transport in the turbulent part of the
atmosphere, decreases the effective thickness of the quasi-laminar sub-layer and increases the
drag coefficient. It is therefore not surprising that Vd increases with increasing u*. Most modeling
approaches and measurements indicate a near-proportion relationship between Vd and u* for sub-
micron particles (Fig. 7.4).
Figure 7.4: Dependence of small particle deposition velocity on friction velocity (u*) for a range of surfaces; from
Pryor et al. (2008b).
Surface roughness length (z0) and canopy morphology. The effect of the surface roughness
length (for closed canopies: z0 ≈ 0.1 × canopy height,) extends beyond its effects on increasing
u*. Vd for forest tends to be by a factor of 5 to 10 larger than Vd for grass, due to the increased
height of the canopy and leaf area index and the enhanced turbulence induced by forest canopies.
Davidson et al. (1982) showed theoretically that even for the same vegetation type (grassland),
Vd may change within a factor of 5, depending on the exact morphology of the vegetation.
Similar results were more recently obtained by Petroff et al. (2007b) for forests, emphasizing the
influence of leaf dimensions and orientation on Vd. There is measurement based evidence as
well: Ould-dada (2002) used wind tunnel studies to investigate the dry deposition velocity of
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 102
sub-micron particles to model Norway spruce (Picea abies). A total canopy deposition velocity
of 5 mm s-1 was found which is in line with previous micrometeorological measurements to real
forest canopies reported in the literature. However, the deposition pattern was found to be a
highly complex function of height within the canopy. More studies such as these are needed to
improve model development and in-canopy gradient measurements are needed to validate multi-
layer deposition models.
Particle diameter (Dp). Both theoretical approaches and measurements show an effect of
particle size on Vd: the main deposition processes (Brownian diffusion, interception, impaction
and gravitational settling) are all size-dependent. Brownian diffusion is responsible for high Vd
for small particles (Dp < 100 nm), while gravitational settling is the dominant process for Dp > 5
µm. Interception and impaction are most effective in the intermediate size range, but less
effective than the other two processes. The resulting trough in Vd(Dp) (Fig. 7.5) is partially
responsible for the survival of the accumulation mode in the atmosphere.
Figure 7.5: Evolution of the deposition velocity Vd with the particle diameter Dp on grass and grass-like canopies
(lhs) and coniferous canopies (rhs) for friction velocity between 0.35 and 0.56 m s-1, as given by various
measurement campaigns and six existing models from the literature. Canopy characteristics used by models are hc =
0.07 m, z0 = 0.01 m, LAI = 4, dn = 3 mm, a = 1.78 for grass and h = 17 m, hc = 7 m, z0 = 1 m, LAI = 22, dn = 1 mm,
a = 3.81 for forest. Deposition velocities are recalculated at the same reference height zR = = 100z0. The parameters
of Slinn’s model (1982) are fIN = 0.01, dr = 20 mm, cv/cd = 1/3, b = 2. The model of Zhang et al. (2001) is applied on
Land Use Categories #6 (grass) and #1 (evergreen-needle-leaf trees), the corresponding parameters being,
respectively, fIM = 1.2 and 1, and fB = 0.52 and 0.56; from Petroff et al. (2007b).
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 103
When comparing measurements of Vd(Dp) with each other or with model results it needs to be
borne in mind that different instruments measure different types of diameter (i.e. geometric,
aerodynamic, vacuum-aerodynamic, electro-mobility and optical), which are often difficult to
compare without exact information on particle shape and composition. Some instruments dry out
the particles, while others measure the wet size. The wet size at the measurement height may not
reflect the size at which they impact with the surface, due to water uptake or release during the
deposition process, in a response to humidity gradients. Despite recent advances in measurement
technology, it is still not known how particle composition, particle shape and particle
hygroscopicity e.g. might influence microscale deposition mechanisms and collection
efficiencies onto and by different surface types with very different microstructures, which are
known to significantly influence e.g. aqueous phase aerosol contact angle and therefore the likely
collection efficiency.
Atmospheric stability (ζ = 1/L). There is strong evidence from a range of studies that Vd can be
greatly enhanced in unstable conditions. Figure 7.6 summarises the findings from several field
studies, by exploring the dependence of Vd, normalised by u*, on the inverse of the Monin-
Obukhov length (L), a standard measure of atmospheric stability. The cause for this enhancement
is not fully understood and therefore difficult to reproduce in numerical models.
0.04
0.03
0.02
0.01
0.00
Vd
/ u*
-0.04 -0.02 0.00 0.021/L [m-1]
unstable stable
grass; Wesely et al. (1985) forest; Gallagher et al. (1995) heathland; Nemitz et al. (2004) Dp = 0.1 µm 0.2 µm 0.3 µm 0.4 µm 0.5 µm
Figure 7.6: Summary of the dependence of aerosol deposition velocity on the Monin-Obukhov length (L),
indicating a sharp increase of normalized deposition velocity (Vd/u*) in unstable conditions; from Pryor et al.
(2008b), modified.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 104
7.4.2 Parameterising and modelling deposition rates
The first detailed sensitivity study of aerosol deposition models compared to field observations
was conducted by Ruijgrok et al. (1997). They reported a factor of 5 uncertainty in model
predictions for sub micron aerosol deposition velocities based on input uncertainty. Despite this
there was general consensus that dry deposition measurements (mainly by gradient filter pack
and throughfall techniques) yielded deposition rates significantly larger than analytical models
were predicting for forested surfaces compared to grasslands (Erisman, 1993), which led to some
improvement in aerosol collection efficiency descriptions in models. Later the first eddy
covariance measurements at the same locations tended to confirm this, notwithstanding the
sampling issues associated with aerosol growth factors mentioned below (Erisman et al., 1996).
The latest review (Petroff et al., 2007b) suggests this uncertainty has now been reduced, to
around a factor of 3. Efforts to reduce this uncertainty further are restricted by the quality of the
measurements (as discussed in Section 6.4 below) and the completeness in the metadata reported
with measurements (in particular on canopy structure). The high sensitivity of the models to the
canopy structure further adds uncertainty to the application of simple generalised
parametrisations at the regional scale, where input parameters are limited.
7.4.3 Dry deposition rates to urban areas
Very little is currently known about deposition rates to urban areas, despite their importance for
estimating the contribution of aerosol deposition to the soiling and weathering of buildings, and
for the atmospheric lifetime in the atmosphere (Pesava et al., 1999). Although aerosol deposition
measurements have been made in cities, these were usually made with surrogate collectors (e.g.
Yun et al., 2002), which are unlikely to be representative of the uptake by urban structures.
Alternatively, the soiling of building has been studied, which provides information of the aerosol
deposition to a particular receptor, but not on the net removal rate from the atmosphere to the
urban matrix (Horvath et al., 1996).
Application of micrometeorological flux measurement techniques to the urban environment has
now been demonstrated (see Section 7.2.2 above). However, the net flux of particles above urban
areas is dominated by emission sources from the city and deposition rates can only be applied if
a chemical aerosol species or size-class is found which is not emitted from the city. Nemitz et al.
(2000c) presented initial measurements of chemically resolved aerosol fluxes at a coastal site and
showed that chloride was deposited at low wind speeds, presumably reflecting deposition of sea
salt, while it was emitted during windy periods, probably reflecting wind-driven resuspension of
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 105
previously deposited material. Similarly, Nemitz et al. (2008b) measured SO42- deposition fluxes
to urban environment with the Q-AMS EC system. These measurements suggest deposition
velocities in the range of 2 to 6 mm s-1, but will need to be supported with data from other cities.
As mentioned above, Schmidt and Klemm (2008) detected net deposition of PM2.5 to a German
town, but these fluxes almost certainly contain an upward component.
7.5 Uncertainties
7.5.1 Uncertainties in the application of micrometeorological flux measurement techniques for
deriving the local flux
Technical challenges in applying micrometeorological techniques to the measurement of aerosol
fluxes go beyond those encountered for gas flux measurements. Aerosol fluxes are often small
and deposition rates slow, resulting in small concentration differences that need to be resolved
for gradient and REA measurements. Similarly, the relative corrections, e.g. for density
fluctuations (Webb et al., 1980), may become large and can easily result in reversal of the sign of
the flux. A continuing challenge of particle flux measurements is that, due to the limited
counting statistics of the measurement, standard data processing techniques, such as tests and
corrections based on co-spectral analysis, and non-stationarity tests are difficult to implement.
While most gas analysers respond to many thousands of molecules per tenth of a second, aerosol
counters may only detect tens of particles resulting in statistical uncertainties (Fairall, 1984).
Similarly, in mass-based measurements the contribution from a few large particles may greatly
affect gradients and EC results. Different aerosol measurement instruments respond to different
parameters, not all of which are conserved. For example, artificial fluxes may be introduced if
particles are sized according to their wet size which responds to humidity fluctuations
(Kowalski, 2001), and similar problems are caused by volatilisation or formation in the
atmosphere which is discussed in more detail below. The exact effect on the measurement
depends on the setup and requires careful consideration: most previously reported sub-micron
aerosol number fluxes have relied on optical particle counting techniques that use closed path,
high power active laser cavity scattering cells. Furthermore most of these, but not all, adopt
recycling dry particle free sheath air flow with significantly lower RH than the ambient aerosol
flow to minimise contamination of the instrument optics. These instruments therefore most likely
provide a measure of the dry or partially dry particle size (e.g. O’Dowd 1992) and not the
ambient particle size. Hence measurements of sub-micron particle fluxes reported in the
literature are likely representative of “dry or near dry aerosol deposition velocities” of optical
particle size and not the actual ambient deposition velocity, whereas most large particle fluxes
are likely a combination of both, some being derived from closed path and other open path
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 106
instruments. Flux measurements using photometric techniques to determine total PM2.5 and PM10
mass fluxes may not be subject to these effects and for routine measurement of total mass fluxes
e.g. for network applications may be the most appropriate when combined with additional
composition measurements.
Care needs to be taken that the inlet system does not respond in a way that may be correlated
with w. This could occur during non-isokinetic sampling of coarse particles or during fast
switching of inlet flows in REA systems. There is also evidence that non-stationarities may
affect aerosol exchange particularly often (Fontan et al., 1997).
7.5.2 Relating measured fluxes to surface exchange: flux divergence and the effect of chemical
interactions
A further uncertainty of the flux estimation with micrometerological techniques is that, although
the local flux at the measurement height may be correct, it may differ from the actual surface /
atmosphere exchange. The most commonly applied form of the scalar conservation equation is
(Pryor et al., 2008b):
( ) SCvz
Cx
DCux
Cuxt
Cg
ii
ii
i+
∂∂
−∂
∂=′′
∂∂
+∂∂
+∂∂
2
2
(1) (2) (3) (4) (5) (6)
(3)
Here term (1) is the local change in concentration, term (2) advection by the mean flow, term (3)
represents the divergence of the turbulent flux, term (4) vertical transport by diffusion, term (5)
vertical transport by sedimentation and term (6) concentration changes due to sources or sinks.
Methods of estimating particle (and other scalar) fluxes at the air-surface interface have typically
relied on the assumptions of horizontal homogeneity, steady state, the absence chemical source
or sink of the scalar, that the constant flux layer assumption applies to the lowest tens of meters
above the surface (Businger et al., 1971), and that the turbulence responsible for transporting the
scalar of interest is locally-induced (Monin and Obukhov, 1954; Monin and Zilitinkevich, 1974).
However, as described in this sub-section, there are multiple causes of flux-divergence (i.e. that
the flux observed at some height above the surface is not equal to that at the surface). Three
dominant sources of particle flux divergence are described below along with methods for their
identification and quantification:
Non-conservative behaviour of the scalar under study (i.e. particles) due to the interaction of
other particle dynamics processes with the vertical exchange (i.e. ( )0≠S ). The degree to which S
deviates from 0 (i.e. the magnitude of the vertical flux divergence due to phase transitions) is
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 107
determined by; the chemical climate (Nemitz and Sutton, 2004; Nemitz et al., 2004b; Sutton et
al., 2007), particle ensemble (Pryor and Binkowski, 2004), and specific aspect of the particle
ensemble being observed. If Eq. (3) is applied to consideration of the mass of the entire particle
ensemble, then only mass transfer (i.e. evaporation and/or condensation) can result in flux
divergence. While if Eq. (3) is applied to a size-resolved number particle ensemble for any given
particle diameter, 0=S could derive from concentration changes resulting from nucleation,
coagulation, and condensation/evaporation. If Eq. (3) is applied to a chemically resolved (but not
size resolved) particle ensemble, evaporation/condensation and/or heterogeneous chemistry in/on
particle surfaces could cause flux divergence. It can also modify fluxes if it leads to growth or
shrinkage across the cut-off size of the particle probe. Nemitz et al. (to be submitted, 2008)
observed apparent emission fluxes with a CPC setup above a grassland fertilised with NH4NO3,
attributed these fluxes to aerosol growth due to NH3 and HNO3 uptake and used the fluxes to
derive particle growth rates across the 11 nm cut-off of the CPC (Nemitz et al., 2008). This
demonstrates that flux measurements can be used to infer information on S and thus on aerosol
processing, if the true deposition rate can be estimated independently.
The partitioning of species between the gas and particle phase is also associated with gas flux
divergence (Soerensen et al., 2005) and can change the net rate of surface uptake of, for
example, nitrate if the deposition velocities of the gas and particle phase species differ
substantially (Pryor and Soerensen, 2000). The likelihood of flux contamination due to non-
conservative behaviour can be estimated using time-scale analysis (De Arellano and Duynkerke,
1992), and can be quantified by deploying eddy covariance measurement systems at multiple
heights.
(i) Horizontal advection, due to the presence of large spatial gradients in particle number, mass
and/or composition. The importance of horizontal advection has been extensively evaluated
in the carbon dioxide flux community (Baldocchi et al., 2001; Hong et al., 2008) and is likely
to be important to particle fluxes in regions close to large particle emissions, but with few
exceptions (Vong et al., 2003) the horizontal advection term has generally been neglected in
most particle flux studies. The potential influence of horizontal advection can be quantified
using a horizontally dispersed measurement array.
(ii) The influence of non-local or ‘top-down’ processes in dictating vertical exchange. Observed
scalar fluxes near to the ground are derived from two components: local surface-driven
turbulence, and non-local or ‘top-down’ processes such as entrainment of air from above the
mixed-layer which can cause fluxes that are counter to local gradients (Holtslag and Moeng,
1991). The importance of non-locally induced turbulence in dictating observed fluxes has
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 108
been documented in gas exchange studies (Gao et al., 1989), but received less attention in the
aerosol community, despite evidence that it plays a substantial role in dictating flux
magnitudes and may provide an explanation for upward fluxes in environments that have
traditionally been viewed as solely particle sinks (Pryor et al., 2008a). The potential
influence of ‘top-down’ processes on observed near-surface fluxes can be identified using
scalar correlations (Sempreviva and Gryning, 2000) and quadrant analysis.
With the advent of techniques to measure compound-resolved aerosol mass fluxes, there is
growing evidence that particle deposition velocities of NO3- and NH4
+ to semi-natural vegetation
tend to exceed those derived for SO42- or from particle number flux measurements (Nemitz et al.,
2004b; Thomas et al., 2008) (Fig. 7.7). The deposition rate of these compounds measured over
heathland and forest greatly exceed those predicted theoretically for short and tall vegetation,
respectively (Fig. 7.2). The likely cause is evaporation of NH4NO3 near the ground during the
deposition process, where thermodynamic equilibrium favours the gas phase, due to the
depletion of NH3 and HNO3 by deposition of these reactive gases to foliar surfaces and warm
surface temperatures. Thus, the flux measured well above the canopy is not limited by the
physical interaction of the particles with the vegetation surface, but reflects the evaporation sink
in the airspace above. This is supported by the fact that the relationship between Vd and u* does
not differ between surfaces (Fig. 7.7), which implies that turbulent transport (which scales with
u*) is the main constraint on the flux. Since NH3 and HNO3 deposited to semi-natural vegation
much more effectively than NH4NO3 aerosol, this shift to the gas phase increases the deposition
rate of total ammonium and total nitrate. Since this evaporation only occurs close to the canopy,
it represents a non-resolvable (subgrid) process in traditional transport models. Future
parameterisations of Vd should account for this additional sink for highly volatile aerosol
components.
40
30
20
10
0
Vd
[mm
/s]
1.00.80.60.40.20.0u* [m/s]
NH4+ to heathland (Nemitz et al., 2004)
NO3- to oak forest (Thomas, 2007)
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 109
Figure 7.7: Apparent nitrate and ammonium deposition velocities derived from chemically speciated
micrometeorological flux measurements. The large values are indicative of an additional loss of ammonium nitrate
near the surface, due to evaporation.
7.5.3 Interpretation of measurements for model verification
Deposition is effectively a number dominated process which, using current methods, is subject to
large uncertainties. Mapping from number to mass space requires detailed knowledge of
composition, size, shape and hygroscopicity. The hygroscopicity of aerosols can potentially
generate the largest uncertainty, not just in the measurement, but also in interpreting the
measurements. For example, the size at which a particle interacts with the vegetation surface
often differs from its actual size at the measurement height, which again may differ from the size
reported by a given instrument (dry vs. wet; geometric vs. optical diameter etc.). Most previous
studies of deposition have not been sufficiently complete to address any of these uncertainties
with respect to a complete closure of aerosol number and mass for model comparison through
use of growth factors. As a consequence, size-segregated and chemically speciated eddy
covariance (and related) aerosol flux measurement techniques cannot currently provide
unambiguous results of particle number or mass fluxes to surfaces as a consequence of
fluctuations in aerosol size distributions on time scales that can lead to sampling biases. This
sampling ambiguity and the potential for bi-directionality in aerosol fluxes limit the accuracy
with which Vd can be determined and hence will hamper improvement in model mechanistic
descriptions of particle deposition to natural vegetated surfaces.
7.6 Future research needs 7.6.1 Deposition measurements and reporting Standardisation of eddy-covariance approaches and data analysis procedures
Comparisons between different measurement systems are currently made difficult by the diverse
approach used to measure the fluxes as well as to analyse and present the data. For example,
some authors have derived parameterisations of Vd(Dp) averaging over the negative (deposition)
fluxes only, while other authors have averaged over the entire datasets. The difference can be
large (Nemitz et al., 2002a). Often important parameters (e.g. leaf dimensions) are not provided
in the scientific papers to provide the input parameters to apply the models to the measurement
datasets. Thus there is an urgent need to harmonise flux measurement approaches as much as
possible and to provide guidance for auxiliary parameters that should be measured and provided
with the measurement datasets to maximise their potential for model evaluation.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 110
Improved measurements in the accumulation mode
There are still significant gaps in observations, particularly with respect to the critical size range
associated with the transition between Brownian and turbulent impaction dominated particle
capture regimes. This size range is still poorly resolved by eddy covariance techniques (~ 0.5 <
Dp < 2 µm) as a consequence of few measurements being available by any suitably characterised
techniques and the large errors associated with these. The predicted minimum in Vd as a function
of size in this transition regime can vary widely between different models (Fig. 7.5) and this will
have significant consequences for long term integrated dry deposited mass fluxes. This is
therefore seen as a key area in need of attention by both models and field observations.
Understanding the effect of stability and leaf properties on deposition velocities
Another serious issue is the lack of any detailed testable hypothesis in models explaining the link
between increasing Vd and atmospheric stability and which most measurements have reported in
the literature for particle sizes Dp< 0.5 µm, most clearly seen for Aitken and small accumulation
mode sizes. So far there are no wind tunnel studies of aerosol deposition to vegetated surfaces
that take account of atmospheric stability and these are needed to allow further model
development. Studies of the hydrophobicity and anti-adhesion of non-smooth leaf surfaces show
that the morphology of plant epidermal cells and the morphology and distribution density of
epicuticular waxes significantly affect their hydrophobicity and anti-adhesion properties and
potentially the adhesion of aerosol particles following impaction and interception, Ren et al.
(2007). The microstructure of plant surfaces has been well documented but an interesting
phenomenon which might have potentially serious implications for some studies of dry and wet
deposition is the so-called self-cleaning mechanism of some leaf structures (referred to as the
“Lotus Effect”). Some plant leaves are completely lacking in microstructures while others can
have sunken or raised nervatures which as a consequence cause super hydrophobic behaviour.
This in turn leads to a remarkable self-cleaning process whereby fog droplets e.g. rolling down
the leaf surface pick up aerosols and remove them from the leaf surface. Experiments whereby
leaf surfaces have been artificially contaminated with radioactively tagged aerosols and then
subjected to artificial fog droplets have been used to determine the retention rate of aerosols to
plant surfaces and these can range from over 90% to less than 10% depending on the species
examined and which were linked to differences in leaf microstructure and orientation (Neinhuis
and Barthlott, 1997).
Considering the many inherent uncertainties in field flux measurements more wind tunnel
studies are needed under better controlled conditions of stability, surface morphology and
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 111
aerosol composition. The question is how detailed should a model be to describe adequately the
surface interactions with aerosols (whether they be “dry” or “wet” aerosols)? And more
importantly how can their importance be measured? What information should be reported on
surface morphology for future model development in order to attempt inclusion of these effects
in future? These questions are best tackled by revisiting wind tunnel studies coupled with
modern particle measurement techniques.
Filtering or accounting for chemical interactions and water uptake.
A particular aspect of the data processing is the correction for aerosol dynamics due to water
equilibration and / or chemical interactions. A growing number of datasets indicates that size-
segregated particle number fluxes are affected by chemical effects (Nemitz et al., 2004a; Nemitz
et al., 2004b) and this is confirmed by the first results from the chemically resolved mass fluxes
from the Q-AMS eddy covariance system, which indicates that often some chemical aerosol
components may be emitted at the same time as others are being deposited. Although some
modelling studies have been successful in qualitatively reproducing the observations both for
bulk chemical fluxes and size-segregated fluxes (e.g. Nemitz and Sutton, 2004; Van Oss et al.,
1998), standardized operational procedures for correction have not yet been developed. Indeed,
we do not currently have the strategies in place to test whether a particular dataset may be
affected by chemical interactions. It is unclear whether correction procedures will ever be
sufficiently accurate to fully correct for these effects, given the small value of the deposition
rates.
7.6.2 Deposition models
Migration to a probabilistic approach
Comparison of measured deposition velocities as a function of size with different regional scale
model descriptions show large differences. Hence, unacceptable errors will very likely be
incurred in annual cumulative mass deposition values. A detailed sensitivity analysis between
different deposition schemes used in current regional models with observations has not yet been
undertaken. Given the large apparent difference between measurements and model schemes, it
may be more appropriate to move towards a probabilistic approach in deriving deposition
estimates, by exploring a range of possible solutions, together with statements on their
probability. For this purpose, probability density function distributions for aerosol deposition
velocities need to be developed which can be used to test sensitivities to these factors in regional
transport and global climate models
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 112
Improvement of modelling approaches
New modelling approaches compare favourably with the available measurement database, with
the caveats on the data quality mentioned above. Figure 7.5 demonstrates that the model of
Petroff et al. (2007b) in particular appears to be successful, similar to earlier modelling results of
Davidson et al. (1982). Both models have in common that they include a detailed description of
the canopy morphology. This introduces additional requirements for input parameters and further
degrees of freedom for adjustments to make the model match the measurements. However, good
agreement is achieved with measured canopy characteristics, increasing the confidence in the
modelling approach. However, the model of Petroff et al. (2007b) needs to be simplified for
application in operational chemical transport models and standardized characterizations for the
different vegetation classes need to be developed.
Impact of surface anisotropy on suspension & deposition
Spatial organisation of vegetation on sub-grid scales can influence aerosol surface exchange
properties by introducing significant perturbations to mean wind flows by altering the probability
density functions for turbulence velocities above that surface which in turn can alter the
magnitudes of aerosol surface exchange fluxes. The impact of this surface anisotropy is often
seen in observations of dust suspension fluxes over surfaces where elongated regions may occur
which are free of vegetation (e.g. Gillette and Chen, 2001). As a result of this, neighbouring
surfaces, which have the identical vegetative indexes, can produce dust fluxes that differ from
one another by as much as a factor of 4-8 (Okin, 2005). Recently models have been developed
that capture and demonstrate the importance of sub-grid cell isotropic spatial variability however
these have focussed on dust suspension and the subsequent impact on both horizontal and
vertical dust mass fluxes, which can be considerable (Gillette and Chen, 2001).
As deposition mechanisms for sub micron aerosols are controlled by turbulent interaction with
surfaces, and are highly sensitive to particle size and micro-scale structures, it is likely that
deposition velocities too are also affected by surface anisotropy and the manner in which this
links to the microstructure. The notion of a mean aerosol deposition velocity in this context has
little value (much as it is now thought to be for suspension fluxes) (Okin, 2005) and a
probabilistic approach must be used. Unfortunately, unlike dust emission mass fluxes, there are
virtually no observations of the impact of sub-grid scale surface anisotropy on aerosol
deposition. Initially, a theoretical model study could explore the likely impact of anisotropy on
effective dry deposition rates, for example adopting the concept of a lateral cover parameter (λ)
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 113
as a measure of the vegetative canopy area intercepted by the wind and its contribution through
surface drag to the surface roughness, from which an effective aerodynamic roughness length for
the landscape can be calculated (1993). In parallel, as a first step to improving understanding in
this area (which has been relatively moribund for some considerable time) (Pryor et al., 2008b)
the community needs to collect high quality micrometeorological aerosol flux measurements
over a number of different surfaces with very different anisotropic variability. The observations
should focus on determining the frequency distribution function, f(Vd (Dp)) of aerosol deposition
velocities.
7.7 Conclusions – Aerosols
After little progress in the understanding of surface/atmosphere exchange of aerosols in the
1980s and early 1990s, the development of novel instrumentation suitable for flux measurements
has led to new investigations into the surface exchange, extending micrometeorological flux
measurements to the urban environment and the sea. For example, new developments in mass
spectrometry have enabled the first eddy-covariance flux measurements of aerosol components
(NO3-, SO4
2- and organics) above urban areas and vegetation, providing new information on
sources, sinks and chemical processing, together with deposition rates of the accumulation mode
and the potential of studying deposition rates in relation to particle composition. Furthermore,
size-segregated particle flux measurement approaches have now been extended to the sub-100
nm size range, providing the first data for model evaluation. The first long-term flux
measurements of total aerosol number provide increasingly robust datasets of removal rates.
As more detailed flux measurements as a function of size and composition have become
available, it is becoming clear that size-segregated particle number flux measurements are often
influenced by hygroscopic growth and chemical processing. This highlights the need to minimize
or to filter/correct for these effects when the data are used for model validation. Apparent
upward fluxes have been used to study the formation of NH4NO3 or biogenic SOA formation.
Measured effective deposition rates of NH4NO3 to semi-natural vegetation greatly exceed those
of other aerosol compounds, indicating that new sub-grid parameterizations need to be developed
to account of the additional deposition mediated through the evaporation of volatile aerosol
components (e.g. NH4NO3).
Theoretical developments demonstrate that models of dry deposition need to account for canopy
structure and small-scale morphology. Existing models are now capable of reproducing selected
measurements, but will need to be simplified for operational application in transport models and
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 114
incorporate effects of atmospheric stability. The data available for model validation are highly
disperse in terms of quality, approaches, diameter measured and auxiliary information provided.
Harmonised approaches in data processing and presentation are needed. More sensitivity studies
and probabilistic approaches are needed to explore the range of possible deposition estimates. In
addition, the potential of modelling concepts that account for small-scale spatial variability
(increasingly applied for resuspension) should be explored to estimate dry deposition. Here a
new series of targeted, high-quality wind-tunnel experiments, coupled with the improved
measurement technology would help decrease remaining uncertainties.
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8. Ecosystem-atmosphere exchange of the radiatively active gases - N2O and
CH4 8.1 Introduction
Atmospheric concentrations of the three main greenhouse gases CO2, CH4 and N2O have
increased since the industrial revolution in the 18th century due to anthropogenic activities.
Increased fossil fuel burning, land use change and the intensification of agriculture facilitated by
the manufacture of synthetic nitrogen and consequently population growth are the main causes.
Increased fossil fuel combustion is the main cause for rises in CO2, whereas microbial processes
in soils, sediments, and waters and rumens of animals, are responsible for the bulk of the
observed increased atmospheric CH4 and N2O concentrations. In this section current
understanding of CH4 and N2O, and especially of the biological processes, measurement
methodologies and models, are reviewed.
8.2 Global budgets of N2O and CH4
Atmospheric N2O and CH4 concentrations have risen from background levels prior
industrialisation from 270 to 320 ppb N2O and from 700 to 1782 ppb CH4 in 2006
(http://www.esrl.noaa.gov/gmd/aggi/). Nitrous oxide concentration has increased at a relatively
uniform rate, with a mean annual growth rate over the last 10 years of 0.76 ppb/year (Hirsch et
al., 2006). By contrast, the growth rate in CH4 concentration has changed considerably since the
early 1990s from a steady monotonic increase of approximately 15 ppb year-1 declining to some
years with no net change over the year, but with very large and unexplained inter-annual
variations in growth rate, (IPCC 2007).
(www.wmo.int/pages/prog/arep/gaw/ghg/documents/ghg-bulletin-3.pdf).
The global budget of N2O is constrained by the sink strength in the stratosphere and atmospheric
increase (http://www.esrl.noaa.gov/gmd/aggi); hence, the global source strength is 15. 8 – 16 Tg
N2O-N y-1 (Crutzen et al, 2008, Hirsh et al, 2006). There are some indications that also soils may
significantly act as sink for atmospheric N2O and that the soil N2O reduction has decreased
within the last decades (Chapuis-Lardy et al., 2007; Conen and Neftel, 2007). However, this is so
far not considered in any global estimate.
The atmospheric increase is largely attributed to agricultural activity (Table 8.1). Natural sources
of N2O, the oceans, tropical and temperate forests and grasslands/savannahs, are unlikely to have
changed much since pre-industrial times except where land use has changed significantly.
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 116
The CH4 budget is constrained by measurements of the major sources and δ13C signature to a
global total of 430 - 600 Tg y-1 (Wuebbles and Hayhoe, 2002, IPCC, 2007). Anthropogenic
sources contribute 70% of the total budget. Natural sources (wetlands, oceans, termites) are also
large and dominated global emissions until the 20th century (Table 8.1). Increased livestock
production and fossil fuel use are the main reasons for the atmospheric increase of CH4 (IPCC
2007). Soils are are a minor sink for CH4 and accounts for approximately 6% of the global
budget; the dominant removal process for atmospheric CH4 is oxidation by OH, mainly in the
troposphere.
8.3 Biological sources of N2O and CH4
8.3.1 The biology of production and consumption of N2O and CH4 in soils and sediments
Microorganims are the dominant sources and sinks of N2O and CH4 in the troposphere. A good
knowledge of the underlying processes and microbial community structure is essential for
improving global estimates of N2O and CH4. Fortunately the main microbial reactions involved
(nitrification, denitrification, methanogenesis and CH4 oxidation) are ubiquitous to all live
containing ecosystems and are all sensitive to anthropogenic activities (e.g. irrigation, drainage,
fertilisation) and climate (temperature and precipitation).
Table 8.1 Estimates of global N2O and CH4 budgets (Tg y-1)
N2O source a Tg N2O-N y-1 CH4 source b Tg CH4 y-1
Natural sources
Oceans 3.8 (1.8 – 5.8) Oceans 4 (0.2 – 20)
Atmosphere 0.6 (0.3 – 1.2) Termites 20 (2 – 22)
Soils 6.6 (3.3 – 9) Wetlands 100 (92 – 232)
Others c 21 (10.4 – 48.2)
Anthropogenic sources
Agriculture 2.8 (1.7 – 4.8) Rice cultivation 60 (25 – 90)
Biomass burning 0.7 (0.2 – 1) Biomass burning 50 (27 – 80)
Energy & Industry 0.7 (0.2 – 1.8) Energy d 106 (46 – 174)
Others e 2.5 (0.9 – 4.1) Ruminants 81 (65 – 100)
Waste disposal 61 (40 – 100)
Total sources 17.7 (8.5 – 27.7) 503 (410 - 660)
Sinks Stratosphere 12.5 (10-15) f Stratosphere 40 (32 – 48) Soils 1.5 – 3 g Soils 30 (15 – 45)
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 117
Tropospheric OH 445 (360 – 530) Total sinks 14 (11.5 – 18) 515 (430 – 600)
a sources are estimates for the 1990’s as provided by IPCC 2007, Table 8.7, b from Wuebbles and Hayhoe, 2002, c others = marine sediments, geological sources and wild fires, d energy = natural gas, coal mining and other fuel
related sources. e atmospheric deposition, aquatic systems, sewage, f Hirsh et al, 2006, g Cicerone et al., 1989.
Nitrous oxide is a by- product of aerobic nitrification and an obligate intermediate in the
denitrification pathway, and is emitted by both nitrifiers and denitrifiers. Production and
consumption of N2O is regulated by oxygen partial pressure; nitrification is additionally
controlled by the concentration of NH4+, while denitrification is also controlled by availability of
carbon and NO3- (Conrad 1996). Denitrification is the main biological process responsible for
returning fixed N to the atmosphere as N2, thus closing the N cycle (Philippot et al., 2008). This
reduction of soluble N to gaseous N is negative for agriculture, since it can deplete the soil of
NO3- , an essential plant nutrient. The denitrification N2O/N2 product ratio is variable, and N2O
may even be the dominant end product. However, denitrification also provides a valuable
ecosystem service by mediating N removal from NO3- polluted waters in sediments and
other water-saturated soils. Denitrifiers can be sinks for N2O. Sink activity appears to be
stimulated by low availability of mineral N (Capuis-Lardy et al., 2007, Conen and Neftel, 2007).
Methane is produced by methanogenic archaea in anaerobic soil (Philipot et al, 2008). The
organisms require low redox conditions as well as on the fermentative production of precursors
for the methanogens. The main terrestrial CH4 sources are wetland ecosystems, where both
methanogens and methanotrophs are present and active. Methane is consumed by methanotrophs
active in the aerobic layers of most soils; undisturbed soils are largest CH4 sinks.
8.3.2 Distribution of active microbial populations in soils
Although many different microbial species can produce and consume N2O and CH4, information
on the microbial biodiversity can provide useful insight into the health and functioning of the
soil. The development and recent automation of molecular methods have made it possible to
characterise the abundance and function of soil microbial populations relatively quickly. One of
these methods is the analysis of the Phospholipid Fatty Acids (PLFAs) composition of the
microbial membrane (Bach et al, 2008). This method, together with analysis of microbial
biomass carbon, Gram staining, N mineralisation rates, N2O, NO and CH4 fluxes, was applied to
soils from arable, grassland, wetland and forest ecosystems from the main climate zones in
Europe as part of the NitroEurope Project (http://www.nitroeurope.eu). The PLFA composition
provided an overview on the distribution of functional microbial groups in soils of different
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 118
landuses. There was a good separation between microbial communities from wetlands and
forests, but a closer similarity between microbes from grasslands and croplands (Fig. 8.1). The
ratio of two marker PLFAs, cyclic fatty acids and precursor monounsaturated fatty acids, is an
index of bacterial stress. In this study, the stress parameter correlated with soil NO emissions and
these were related to N-deposition rates and soil acidity (Pfeffer et al., pers com.). N2O
emissions correlated positively with the abundance of gram-negative bacteria, potential N-
mineralization rates and microbial biomass carbon. This can be explained by the fact that gram-
negative bacteria contain many microbial groups important for the N-cycle, such as nitrifiers,
free-living N2-fixers and several denitrifiers.
forestcroplandwetlandgrassland
UK-AMo
FI-Lom
FI-HyyDE-Hog
NL-SpeDK-Sor
IT-BCiDE-Geb
FR-Gri
IT-Cas
UK-Ebu
HU-Bug
CH-Oen
forestcroplandwetlandgrassland
UK-AMo
FI-Lom
FI-HyyDE-Hog
NL-SpeDK-Sor
IT-BCiDE-Geb
FR-Gri
IT-Cas
UK-Ebu
HU-Bug
CH-Oen
forestcroplandwetlandgrassland
forestcroplandwetlandgrassland
forestcroplandwetlandgrassland
forestcroplandwetlandgrassland
UK-AMo
FI-Lom
FI-HyyDE-Hog
NL-SpeDK-Sor
IT-BCiDE-Geb
FR-Gri
IT-Cas
UK-Ebu
HU-Bug
CH-Oen
Figure. 8.1 Principal component analysis of microbial communites, determined as PLFAs (nmol g-1 soil dry weight)
of 13 NitroEurope sites representing different landuses. Abbreviations: AM: arbuscular mycorhiza fungi; Sites:
Forests: FI-Hyy = Hyytiälä, FIN; DK-Sor = Sorø, DK; NL_Spe = Speulder Bos, NL; DE-Hog = Högelwald, DE;
grasslands: UK-Ebu = Easter Bush, UK; CH-Oen = Oensingen, CH; HU-Bug =Bugac, HU; croplands: DE-Geb =
Gebesee, DE; FR-Gri = Grignon, FR; IT-Cas = Castellaro, I; IT-BCi = Borgo Cioffi, I; wetlands: FI_Lom:
Lompolojänkkä, FIN; UK-Amo = Auchencorth Moss, UK; Figure provided by B.Kitzler and M.Pfeffer (BWF,
Austria).
8.3.3 N2O and CH4 fluxes from the main global ecosystems
Biosphere atmosphere exchange of N2O and CH4 has been studied for over 30 years. The data
available are biased towards the large N2O and CH4 emitting ecosystems in highly developed
countries, principally northern Europe, the USA, Canada and Japan. For N2O, studies on N
fertilised agricultural soils dominate and for CH4 studies on rice paddy fields and northern
wetlands. Studies from Asia, where N demand is increasing at a faster rate than elsewhere, are
now emerging. There are insufficient data from agricultural systems in Central and South
American and African countries, from new emerging cropping systems, especially biofuel crops,
and land use change in temperate as well as tropical countries to provide the detailed
understanding required for model validation and for inclusion in emission inventories.
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8.3.4 Plant mediated transport and production of N2O and CH4
The general view is that soil-based microbial production and consumption of CH4 and N2O are
considered to be the major processes involved in biosphere-atmosphere exchange of the two
greenhouse-gases. Based on this perception, and combined with the lack of appropriate
methodologies, our current knowledge about their exchange rates is almost exclusively based on
observations achieved using shallow, soil anchored enclosures. For many ecosystems such
enclosures may exclude the vegetation (e.g. tall crops and forests) and biases in emission
estimates according to contributions from vegetation may occur.
It is well documented that soil-atmosphere transport of both CH4 and N2O is mediated by
aerenchymatic wetland herbaceous species such as rice (e.g. Yan et al., 2000). Mangrove prop
roots and also wetland and flood-tolerant trees have been shown to mediate CH4 and N2O
transport from the soil to the atmosphere, e.g. through the bark of black alder or from hybrid
poplar seedlings (McBain et al., 2004), but only under conditions when the root zone was
exposed to above ambient concentrations of the gas.
The role of non-aerenchymatic plants and in particular trees in the exchange of CH4 and N2O
between the soil-plant system and the atmosphere has only been sparsely investigated. Recent
investigations, however, have emphasized a non-negligible role of vegetation in the biosphere-
atmosphere exchange of greenhouse gases.
8.3.4.1 Methane from vegetation
In 2006 Keppler et al reported a very surprising observation that higher plants had the capability
to emit CH4 under aerobic conditions with a mean emission rate of 374 ng CH4 g-1 dw h-1. From
their findings they calculated a global CH4 source strength of 62–236 Tg yr-1 for living plants
and 1–7 Tg y-1 for plant litter, the sum of which equals c. 10–40% of the total global CH4 source
strength. Methyl-ester groups of pectin, an abundant polysaccharide in cell walls of non-woody
plant tissue, served as a precursor for CH4 (Keppler et al., 2008). UV light appears to be
important in emissions of CH4 from plant material. Vigano et al., (2008) demonstrated that in
the absence of UV light CH4 was not produced until the temperature reached 70-80 oC; with UV
light emissions were significant already at room temperature with rates up to 67 ng CH4 g-1 dw h-
1. McLeod et al. (2008) provided further evidence that not only CH4 but also ethane, ethylene
and CO2 are produced from methyl-ester groups of pectin under UV irradiance, and that reactive
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oxygen species (ROS) arising from environmental stress may have a role in the formation of CH4
from pectin. By contrast, Dueck et al. (2007) observed no significant CH4 emissions from
photosynthesizing or dark respiring leaves, adding evidence to speculations that plant derived
CH4 originates from abiotic processes.
8.3.4.2 Nitrous oxide from vegetation
In a number of experiments, especially crops, plant mediated emission of N2O have been
observed. Chen et al. (1999) found N2O emissions up to 2.8 mg m-2 d-1 from the plants in a soil-
rye grass (Lolium perenne) system, and plant-mediated N2O emissions from maize, soybean and
wheat contributed up to 11, 16 and 62% to the total sum of N2O emissions, respectively (Zou et
al., 2005). In contrast, Müller (2003) found that presence of plants in old grassland could induce
N2O uptake. However, these studies did not identify the mechanisms underlying the plant based
N2O production or reduction processes. Chang et al., (1998) observed that barley (Hordeum
vulgare) and oil-seed rape (Brassica napus) emitted N2O from the shoots upon irrigation with
water containing N2O, and hypothesized that N2O was conveyed by the plants from the soil to
the atmosphere via the transpiration stream. In contrast, Smart and Bloom (2001) found that N2O
emissions from wheat (Triticum aestivum) leaves was correlated with leaf NO3- assimilation
activity. They found that N2O was formed during in vitro NO2--reductase activity of the leaves
and suggested that N2O formation during NO2- photo-assimilation could be an important global
biogenic N2O source. Conversion of 15NO3- to 15N2O in a range of aseptically grown plant
species was reported by Hakata et al. (2003), and increased N2O emission from soybean was
observed concomitant with an herbicide induced accumulation of plant NO2- (Zhang et al., 2000)
providing further evidence for in planta production of N2O.
Figure 7.2 Nitrous oxide emissions (µg N2O-N m-2 h-1) from beech (Fagus sylvatica) leaves after
exposing the beech roots to different concentrations of N2O in the root compartment solution. Bars
indicate average (+SE) of two different beech seedlings. Modified after Pihlatie et al. (2005).
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The potential for tree species to act as conduits for N2O emissions were demonstrated in the
work by Pihlatie et al. (2005). In a laboratory experiment with beech (Fagus sylvatica) seedlings
it was found that fertilization with 15N-ammonium-nitrate (15NH415NO3) induced foliage 15N2O
emissions and exposing the beech roots to elevated N2O concentrations induced significant
emissions of N2O from shoots and leaves (Figure 8.2). Pihlatie et al. also found that
concentrations of dissolved N2O in leaves in a beech forest canopy exceeded ambient
atmospheric concentrations, indicating a potential for canopy N2O emissions.
In summary, substantial evidence exist that plants contribute directly to the emission of CH4 and
N2O. Yet, most work is hitherto based on small-scale laboratory work and the scale of the fluxes
appears small. However, there is an urgent need for field based measurements and more detailed
explanation of the underlying processes.
8.4 New developments in measurements of N2O and CH4 and denitrification
N2O and CH4 fluxes are measured at scales ranging from a few grams of soil to several km. Each
scale and method has contributed to our current understanding of biosphere atmosphere
exchange of N2O and CH4 (Denmead, 2008). Our global understanding of N2O and CH4 fluxes
and their control by physical, chemical and microbial processes has largely arisen from flux
chamber measurements. Recent development of high frequency instruments, that detect very
small concentration changes, has improved our knowledge of N2O and CH4 biosphere
atmosphere exchange at the field/landscape scale and at a high temporal resolution.
8.4.1 Flux chambers
Usually closed (non steady state) chambers are used for N2O and CH4 flux measurements, e.g.
Butterbach-Bahl et al. (1997), Conen and Smith (1998). Advantages of chambers over
micrometeorological techniques are that chambers are low cost and can be used on small fields/
plots. Disadvantages include limited spatial averaging of a spatially variable quantity due to
small area (usually <1 m) of most of these enclosures. Recent developments in chamber
methodologies include 1) an intercomparison of the main chambers types employed for N2O and
CH4 chambers used within the European community (Philatie et al. pers. comm.), similar to the
intercomparison of soil respiration chambers (Pumpanen et al., 2004). ACCENT has contributed
towards the funding of this exercise; 2) the validity of the commonly used linear regression
equation to calculate fluxes from non steady state chambers was questioned, as it may
underestimate the true flux. An exponential approach may be more accurate (Kroon et al., 2008),
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3) development of the fast box method (Hensen et al., 2006) facilitates chamber measurements
from many spots within the field and establish a picture of the spatial heterogeneity of N2O and
CH4 emissions very quickly. This method requires combining manual chambers with sensitive
fast response analysis of N2O and CH4, for example tunable diode lasers.
8.4.2 Micrometeorological methods
The development of tunable diode lasers for CH4 and N2O provides a method of measuring N2O
and CH4 biosphere atmosphere exchange by micrometeorological methods at high temporal
frequency (30 min) over surfaces where fluxes are reasonably large (approx 20 ng m-2 s-1 of N2O
or CH4). This measurement approach is particularly valuable for heterogeneous ecosystems, i.e.
grazed grasslands, and soft surfaces, where compaction by walking to a flux chamber may
release gases into the chamber, e.g. peat wetlands or dung heaps. Eddy covariance
measurements of CH4 for example were made over northern wetlands in Finland (Rinne et al.,
2007) and of N2O for example over grazed grasslands in Scotland (Di Marco et al, 2004). For
well defined point sources, like dung heaps and landfill sites the Gaussian plume method has
been used to calculate the emission strength, by either walking or driving through the emission
plume (Skiba et al., 2006, Hensen et al., 2006).
8.4.3 Comparison of eddy covariance with chamber methods
Scaling up to the field and regional scale is usually based on data from small flux chambers.
Several studies have been conducted to establish the validity of this approach. It is interesting
that for N2O fluxes from grasslands and arable soils (Christensen et al., 1996) chambers
strategically placed within the footprint of the micrometeorological tower are in reasonable
agreement with eddy covariance. However for CH4 fluxes from rice paddies discrepancies of a
factor of 2 to 3 between chamber and micrometeorological method, the chambers giving lower
emissions, were reported by Kanemasu et al. (1995) from the Philippines and Hargreaves (pers.
com.) from a rice paddy field in the Po Valley, Italy. These different observations suggest that
more comparisons need to be carried out and that chambers may well be suitable for relatively
firm surfaces, but not those of low bulk density or complete water logged.
8.4.4 Recent methodological advances in measurements of total denitrification rates
For a full understanding of the processes and accurate simulation of observations using models,
we need to know the removal rate of N2O in the ecosystem. The only natural process of
permanent removal of excess N from ecosystems is denitrification to N2. The very high natural
background of atmospheric N2 hampers direct quantification of total denitrification. A wealth of
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methods has been developed in the past decades for quantification of total denitrification
(Groffman et al., 2006). Unfortunately, none is without drawbacks and even today, there is no
method that can be used at the field scale or at high temporal resolution. The most common
method is the acetylene inhibition method (Balderston et al., 1976), by which the terminal step of
denitrification, i.e. the reduction of N2O to N2 is inhibited by acetylene. Major drawbacks are
that it is not easy to achieve 100% diffusion of C2H2 to the active denitrification sites, that
nitrification is also inhibited by C2H2 and that C2H2 interacts with NO in oxic environments. To
overcome these problems a completely new concept of replacing the background N2 during soil
core incubations with a noble gas (e.g. with a He:O2 mixture) has been developed and facilitates
direct measurements of N2O and N2 (Scholefield et al., 1997, Butterbach-Bahl et al., 2002). The
major drawback is the high capital investment in equipment and the time-consuming flushing
procedure to remove N2.
The use of stable isotope analysis either in tracer studies with isotopically enriched tracer
compounds or at the natural abundance level offer promising alternatives, but very little progress
has been made in the last 5 years. Application of 15NO3- containing fertiliser and monitoring 15N-
labelled N2O and N2 provides a suitable tracer for denitrification to N2 for agricultural N
fertilised soils, but not in N-poor environments. For these 15N tracers can artificially stimulate N
turnover, microbial immobilisation or dissimilatory reduction of NO3- to NH4
+. For N poor
environments natural abundance of N and O isotopes may offer an alternative, as due to kinetic
isotope fractionation the intermediates and the end product of denitrification become
increasingly depleted in 15N, whereas the remaining soil NO3- becomes increasingly enriched in
15N and 18O. If substrate is not limiting, large kinetic N isotope fractionation factors of up to -
40‰ can be observed during denitrification (Groffman et al., 2006). However, if denitrification
is limiting or rates are small, as in the case for N poor ecosystems, the apparent N isotope
fractionation is too small to provide unambiguous interpretation of the data.
Dual-isotope labelling with 15N and 18O-enriched NO3- can identify nitrification or denitrification
as source of N2O; this information is desirable for the models (Wrage et al., 2005). However,
there are problems. At low pH NO2-, intermediate of nitrification and denitrification, rapidly
undergoes O-isotope exchange with water (Casciotti et al., 2007). This O-isotope exchange
might lead to misinterpretation of the results when stoichiometric relationships in the different
N2O formation pathways are assumed, and is very likely the cause of O-isotope exchange
between N2O and water, as reported by Kool et al. (2009).
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The most recent approach of quantifying denitrification rates and differentiating between
nitrification and denitrification as sources of N2O is the analysis of N2O isotopomers.
Intramolecular physico-chemical site differences between terminal and central N atom lead to
differences in N isotope ratios between the two positions during N2O formation and
consumption. Differences in this so-called 15N site preference have been attributed to N2O
production during nitrification and denitrification, respectively (Pérez et al., 2001), unfortunately
microbial populations have a larger impact on the isotopic and isotopomer signatures of N2O
than the production pathway itself (Sutka et al., 2003). Very recently, the 15N isotopic abundance
of soil-emitted NO was determined for the first time (Li & Wang, 2008). The authors found very 15N-depleted NO with δ15N values down to –50‰ and could identify both nitrification and
denitrification as sources of soil-emitted NO.
It is concluded that many challenges of quantifying total denitrification and differentiating
between N2O produced by nitrification or denitrification remain.
8.5 Modelling of N2O and CH4 fluxes on site and regional scales: approaches,
applications and uncertainties
Signatory states to the United Nations Framework on Climate Change (UNFCC) are required to
produce annual national inventories of greenhouse gas emissions from all anthropogenic sources,
including emissions from soils. With regard to CH4 and N2O, soils are the dominating sources in
the respective global atmospheric budgets of both trace gases. The IPCC (2006) recommends
three different approaches (Tier 1 to Tier 3) to provide emission inventories. Tier 1 represents
the simplest way to model or estimate GHG fluxes on site and regional scales. It is a purely
statistical approach, relating e.g. soil N2O emissions to the amount of applied fertilizer. In the
2007 IPPC reporting guidelines (IPCC, 2006) the default emission factor for direct N2O losses
from soils following N fertilisation is 1%. However, even if one assumes that this factor is
representative on a global scale, which has been questioned in the recent past (Crutzen et al.,
2008), a fixed emission factor cannot consider reported effects of climate, management or soil
properties on the magnitude of GHG exchange. Therefore, based on a detailed survey on
reported soil N2O emissions worldwide, Stehfest and Bouwman (2006) developed a more
detailed statistical approach for calculation of emission inventories, which also considers general
environmental factors such as climate, texture and soil organic carbon contents and management
related factors such as fertilisation rates and crop types. However, beside the fact that the
demand on required input information is much larger, this approach also has its weaknesses: a)
the high uncertainty of the developed statistical model, b) the rough classification scheme (which
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is due to the limited availability of field data sets describing GHG emissions for different
environmental conditions) and c) incomplete coverage of pulse events, which may dominate
annual site budgets.
To account for the huge spatial and temporal variability of GHG fluxes on site to global scales
the development and use of process-oriented models may at present be the most promising
approach (Butterbach-Bahl et al., 2004). These models simulate the GHG exchange at a given
site based on the underlying processes, i.e. by simulating the dominant physico-chemical, plant
and microbial processes involved in ecosystem C and N cycling and associated GHG exchange
(Li et al., 2000). As a general assumption, one defines that the controlling factors for e.g.
microbial C and N turnover such as temperature, moisture and substrate responses, are
comparable across different climatic zones and land uses and that by capturing the major
biogeochemical processes within an ecosystem it is possible to predict the temporal variability of
fluxes. Such models require a thorough process understanding of the coupled C and N (P) cycles,
even though the level of process description may vary between the models currently in use (e.g.
Li et al., 2000). However, these models do also have the drawback, that modelling of ecosystem
processes involves a huge data set of parameters needed to describe heat transfer, water
movement, plant and microbial growth or anthropogenic management. Since each parameter has
its specific, though often unknown uncertainty, the uncertainty of simulation results is often
significant, if at all measurable. In comparison to statistical approaches mechanistic models often
show an improved performance with regard to reproducing observed differences in GHG fluxes
between sites, seasons and management practices (e.g. Kesik et al., 2006).
However, the increasing use of mechanistic models also shows that we still need to improve our
process understanding, e.g. the regulation of microbial processes and its dependence on microsite
variability of environmental conditions such as redox potential or the feedback of temperature on
organic matter decomposition. A good example of the deficiencies in understanding N2 versus
N2O production during denitrification is provided by (Groffman et al., 2006). This gap in
knowledge also precludes the parameterisation of the denitrification process in biogeochemical
models such as DNDC or DayCent, which in consequence leads to a systematic underestimation
of N2 losses using either model.
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Figure 8.3: N2O emissions from agricultural soils in Europe using the GIS coupled DNDC model. For further details
on databases and methodology see Butterbach-Bahl et al., (2008)
Biogeochemical process models have recently been used in a number of studies for calculating
regional soil GHG emission inventories (Figure 8.3). Thereby regionalisation is achieved by
coupling of the models to GIS databases holding all the relevant information needed for
initialising (soil and vegetation properties, management) and driving (meteorological conditions)
the models (Kesik et al., 2006). Such an approach partly neglects landscape processes, such as
e.g. lateral flow and transport of nutrients and sediments via leaching or erosion, even though an
increasing number of groups are working on fully coupled landscape models, which do allow to
consider interactions between the biosphere, hydrosphere and atmosphere on landscape scales.
National inventories for N2O and/ or CH4 emissions from soils using DNDC or DayCent have
been calculated for US, UK, China, Germany, India or Europe. Even on a global scale, the GIS
coupled Forest-DNDC model was used to estimate N2O emissions from tropical rain forest soils
(Werner et al. 2007). Increasingly biogeochemical models have been used to study potential
strategies for mitigating GHG emissions from soils on site (Li et al., 2005) as well as on regional
scales (e.g. Li et al., 2006) or to improve our understanding how future changes in climate or
land use may feedback on biosphere-atmosphere exchange of GHG (Parton et al., 2007).
Uncertainty in such emission inventories and mitigation/ feedback studies is associated with the
uncertainties in input parameters as well as with the uncertainties in model parameters. However,
in present studies the uncertainties in input parameters have mainly been addressed, e.g. by use
of Monte Carlo techniques (Kesik et al., 2006; Werner et al., 2007), whereas model parametric
uncertainty is often neglected (Van Oijen et al., 2005). This is a shortcoming, which needs to be
properly addressed in coming studies and which is already in the focus of model development
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and application within the NitroEurope project (www.nitroeurope.eu). Nevertheless,
biogeochemical models offer a great chance to prove our understanding of ecosystem processes
and GHG exchange and they will play an important role in identifying and predicting
consequences and feedbacks of global changes (climate and land sue change) for ecosystem
functioning and biosphere-atmosphere trace gas exchange.
8.6 Validation of models by landscape and regional scale measurements
Developments in inverse modeling and direct large-scale measurements provide very powerful
tools to constrain and verify our bottom up models and inventories. For example, Bergamaschi et
al., (2005) compared inverse models with national bottom-up inventories for CH4. These
developments are taken further within the NitroEurope project (www.nitroeurope.eu).
The development of instruments sensitive enough to measure very small concentration
differences has made it possible to directly measure CH4 and N2O concentrations from aircraft
and satellites. For example, in the UK aircraft based N2O and CH4 concentrations measurements
downwind of the British coast have delivered unique measurements of CH4 and N2O at the
country scale and provided independent top-down estimates of UK emissions. Measurements
were interpreted by using a simple boundary-layer budget approach and the dispersion model
NAME. This approach suggests that the bottom up national emission inventory underestimates
CH4 emissions by a factor of two and N2O emissions by a factor of three (Poulsen et al.,
submitted). An underestimation of the UK national CH4 inventory was also reported by
Bergamaschi et al.’s (2005) comparison of bottom up and inverse modelling approaches.
Satellite-borne instruments, such as SCIAMACHY, are able to provide CH4 concentration
measurements at the global scale. SCIAMACHY can clearly detect spatial and temporal
variations in CH4 concentrations in the boundary layer, a considerable achievement given the
small enhancements in a large background signal. Using these methods emissions due to
coalfields, rice cultivation, ruminants and wetlands are visible for China and India and the Po
valley in Italy (Buchwitz et al., 2006). Comparisons between SCIAMACHY observations of CH4
concentrations and those derived from simple emission inventories revealed large regional and
seasonal differences, especially over tropical rainforests. To some extent these differences were
caused by overestimating CH4 concentrations when water vapour concentrations were high
(Frankenberg et al., 2008). In spite of this correction, SCIAMACHY still estimates a larger CH4
budget for the tropics than previously estimated. Unfortunately, validation of the global N2O
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budget using satellites is currently not possible as sufficiently accurate and precise N2O satellite
data with high sensitivity near the earth’s surface do not exist.
8.7 Conclusions
The key developments and gaps in knowledge are:
o New molecular tools are now available to link soil microbial biodiversity with soil
function and can provide an overview of the distribution of functional microbial groups
in soils of different landuses, and assign trace gas emissions to the active microbial
population.
o Instrument development has facilitated CH4 and N2O flux measurements at the field and
landscape scale and provides long-term measurements at large spatial scale and high
temporal resolution at key sites.
o New methods to study denitrification rates to N2 and isotope studies to elucidate the
microbial pathway responsible for N2O production and removal are being developed, but
none of these methods can currently be used at the fieldscale or high frequency temporal
scales.
o There are insufficient data to scale up CH4 and N2O emissions to the global scale or to
include ‘new’ crops, i.e. bioenergy crops.
o There is a gap in knowledge of the contribution and quantification of plants, especially
trees, in producing and transporting N2O, CH4 from soil to atmosphere.
Biogeochemical models have been developed and synthesize our understanding of ecosystem
processes and GHG exchange. They play an important role in identifying and predicting
consequences and feedbacks of global changes (climate and land use change) for ecosystem
functioning and biosphere-atmosphere trace gas exchange.
Inverse modelling, tower and aircraft based boundary layer budget studies have been developed
to validate bottom up models and inventories.
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9. Exchange of trace gases and aerosols over the oceans
9.1 New trace gas interactions at the air-sea interface
9.1.1 Introduction
Considering the size and potential importance of the air-ocean interface, it is surprisingly poorly
characterised for most organic trace gases. These organic species are known to play important
roles in the Earth’s atmosphere, impacting ozone chemistry and aerosol formation, thereby
influencing the Earth’s overall oxidation capacity and radiative budget (Williams 2004 and
references therein). It should be noted that the net primary production (NPP) of the ocean is
comparable in size to that of the terrestrial biosphere (ca. 45 PgC yr-1), even though there is
approximately 100 times less biomass in the ocean than on land. The relative paucity of ocean
based data compared to terrestrial sites is due partly to accessibility and partly to the high spatial
and temporal variation within the limited oceanic biomass. Moreover, there has been a
perception from earlier studies of oceanic alkanes and alkenes that the global ocean is a
relatively minor source term. Over the period of the ACCENT project, this view has changed
remarkably and recent studies are beginning to recognise the profound effects of the ocean-air
interface on global chemical budgets. For many important chemical species in the atmosphere
the role of the ocean remains the greatest uncertainty in the budget.
The sunlit regions of the oceans are home to a myriad tiny plant species and bacteria. These
organisms photosynthesise carbon dioxide (CO2) from the atmosphere into biomass, and a
fraction of the carbon “leaks” out into the surrounding seawater in the form of organic
compounds. Some small volatile species with low Henry´s Law coefficients are known to escape
directly to the atmosphere (e.g. dimethyl sulphide, DMS) while larger species will remain in the
water phase. Subsequent photo-oxidation in both air and seawater phases generates a multitude
of photochemical breakdown products. These compounds may affect the hygroscopicity and
reflectivity of the marine boundary layer aerosol, either by condensing onto existing aerosol
surfaces, or by being ejected directly with primary aerosol from the sea surface.
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Figure 9.1 A schematic diagram of the processes affecting organic species at the air-sea interface.
Several important organic emissions from the ocean have been identified previously. The best
known is dimethyl sulphide (DMS) which is produced biogenically in the ocean (e.g. Keller et
al., 1989; Liss et al., 1997, and references therein), and yields the inorganic aerosol component
sulphate upon complete oxidation in the atmosphere (Kiene and Bates, 1990). It is also well
established that organohalogens are emitted in various forms (e.g. methyl iodide, bromoform,
methyl bromide) from phytoplankton, bacteria, molluscs and worms (e.g. Gribble, 1992,
Carpenter et al. 2000). Following atmospheric oxidation, these can affect either tropospheric or
stratospheric ozone, depending on the lifetime of the species. However, over the period of the
ACCENT project (2003-2008), there has come a realisation that the surface ocean can play an
important role in the budgets of many more organic trace gases. For example, the surface ocean
has been shown recently to be a large reservoir for oxygenated organic species e.g. acetone
(Singh et al., 2003, Williams et al., 2004). The possible influence of oceanic isoprene on marine
clouds has also been hotly debated (Meskhidze and Nenes, 2006). Finally a surface ocean source
of methanol first speculated in mesocosm studies (Sinha et al. 2005) has been implemented in a
global model assessment of methanol, thereby generating an improved fit between model and
measurement data (Millet et al. 2008). Therefore this article has been focussed on the more
recent discoveries related to acetone (CH3COCH3), methanol (CH3OH), isoprene (C5H8),
monoterpenes (C10H16) and alkyl nitrates (RONO2) in order to highlight the new developments in
air/ocean interactions.
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9.1.2 Case studies
Acetone (Ocean uptake)
Over the past 5-6 years our understanding of the role of the ocean in the global acetone budget
has changed remarkably. Acetone is ubiquitous in the troposphere and found at relatively high
mixing ratios (ca. 200 ppt) even in the remote Pacific atmosphere (Singh et al. 2001). Since
acetone is recognised as an important precursor for PAN, ozone and HOx, especially in the cold,
dry, upper troposphere, there has been interest in determining the sources and sinks worldwide.
In 2002, Jacob et al. published a global budget of acetone (Jacob et al. 2002) which differed from
all previous budget attempts in that it considered the role of the ocean for the first time. Through
inverse modelling, they estimated that the ocean was an important net source of acetone. Indeed,
from the total global source of 95 Tg, some 25 Tg was estimated to originate from the ocean in
order to balance the known sources and sinks. This was pioneering work since at that time no
seawater acetone measurements, or air-sea fluxes were available. However, in the space of just 2
years this view changed drastically. In 2004 the model developed by Jacob et al. 2002 was tested
against measurements over the remote Pacific (Singh et al. 2004). It was found that the model
consistently overpredicted the measured acetone mixing ratios in the marine boundary layer and
the authors concluded that the ocean was a net global sink for 15 Tg, and that the sources and
sinks were not balanced. In 2004, the first open ocean measurements of acetone in air and
seawater were made (Williams et al. 2004). The interhemispheric gradients and depth profiles
shown by Williams et al. 2004 were consistent with uptake of acetone from the air to the sea and
a microbial sink in the seawater.
In 2004, two important new publications emerged concerning acetone. The first was a laboratory
based study which re-determined the photolysis quantum yield of acetone as a function of
temperature and pressure (Blitz et al. 2004). It was found that the accepted acetone photolysis
rates were significantly overestimated (factor 3-5), particularly for the cold, low pressure
conditions of the upper troposphere. Within the global budget of acetone this represented a
reduction in the effectiveness of the photolysis sink term (Arnold et al. 2004). In the same year, a
new shipborne measurement study was published in which the authors directly measured the flux
of acetone at the ocean surface for the first time using an eddy correlation method (Marandino et
al. 2005). Interestingly, the authors consistently found uptake fluxes (from the air to the ocean)
for acetone over the oligotrophic Pacific ocean which became stronger further from the equator.
For comparison Marandino et al. 2005 also determined the flux of acetone by making separate
measurements in the seawater (5m depth) and air (18m height). Similar to the results from the
Tropical Atlantic (Williams et al. 2004), these water and air measurements led to highly variable
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 132
flux results at the surface, whereas the direct flux measurement was more consistent. This
strongly suggested that for acetone, the actual air/ocean flux is being driven by processes in the
uppermost layer (0-5m).
Although these two new studies (Blitz et al. 2004 and Marandino et al. 2005) had a strong impact
on the original budget of Jacob et al. 2005,the overall the sources and and sinks for acetone were
still not balanced. To understand these processes better a new approach to investigate acetone
ocean fluxes was made using so-called “mesocosms” (Sinha et al. 2005). The mesocosms are
light permeable Teflon tent-like structures which float on the surface ocean enclosing a volume
of air near the surface, and with walls that extend some 20m below the surface to restrict the
advection of the water mass below. The airspace in the top of the mesocosm was continually
flushed with ambient air to give a residence time of approximately 3 hours in contact with the
water surface. By measuring at the inlet and outlet, the flux could be calculated while
phytoplankton in the water column were monitored. In the case of methanol a clear uptake flux
(from the air to the ocean) was observed throughout the experiment, whereas for DMS the flux
was always from the ocean to the air. Interestingly, for acetone the flux was found to be variable
but systematic. In strong daylight and in the presence of significant biological activity, acetone
was emitted from the ocean to the air. In low light or biologically poor regimes, however,
acetone was taken up by the water. These results are consistent with the results of Marandino et
al. 2005 and the ocean being a net sink for acetone on a global scale, since most of the ocean is
oligotrophic. However, biologically active regions (e.g. upwelling zones, ocean fronts, or large
natural phytoplankton blooms) can be strong sources in daylight and depending on their size
could to some extent offset the general sink. It is therefore important to investigate these
biological hotspots in future to better constrain the global budget.
Methanol (Ocean uptake)
In many respects the global methanol budget is similar to that of acetone discussed above. Plant
growth accounts for most of the estimated global source (40-80%) and again the role of the
ocean is one of the largest uncertainties in the budget. Studies of methanol have consistently
indicated an ocean uptake of methanol (Williams et al. 2004, Lewis et al. 2005, Sinha et al. 2007
and references therein.) Recently, a global 3-D chemical transport model (GEOS-Chem) was
used to integrate and interpret new aircraft, surface, and oceanic observations of methanol in
terms of the constraints that they place on the atmospheric methanol budget (Millet et al. 2008).
It was shown that for methanol, although overall the ocean represents a net sink, a separate light
dependent oceanic source needs to be introduced in order to correctly simulate regional
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distributions in the atmosphere. This in-water source has the effect of tempering the uptake flux
particularly in the Tropics. It was deduced that that the ocean contains a large primary source (85
Tg y-1) of methanol to the atmosphere and also a large sink (101 Tg y-1), comparable in
magnitude to atmospheric oxidation by OH (88 Tg y-1). Thus the ocean is a net sink overall, but
the in-water source term must be included to match with available atmospheric measurements
datasets.
Isoprene (Ocean emission)
Isoprene, the strongest terrestrial biogenic emission, has also been observed as an oceanic
emission (Bonsang et al., 1992) and in laboratory based studies of plankton (Shaw et al., 2003
and references therein). It has been suggested recently that marine isoprene emissions are the
cause of cloud droplet radius changes in marine clouds situated directly over phytoplankton
blooms (Meskhidze and Nenes, 2006). However, an impact of the isoprene on cloud properties
appears unlikely given that concentrations of isoprene measured over the Southern Oceans do
not impact the organic carbon aerosol concentrations significantly (Arnold et al. 2008). In the
aforementioned paper an aerosol production efficiency of 2% was assumed for isoprene, and the
modelled contribution of isoprene to organic carbon (OC) was found to be less than a 1%.
Moreover, since time is required to oxidise isoprene to nucleating products, a superpositioning of
a cloud effect over a bloom in a region of high wind speeds again appears unlikely. Typical
mixing ratios of isoprene over phytoplankton rich areas are 200-300 ppt, approximately an order
of magnitude less than over the rainforest (Williams et al. 2001). However, since isoprene reacts
rapidly in air, terrestrial emissions will not impact the open ocean. Marine isoprene emissions
could influence the local ozone production efficiency in regions where ship emissions of NOx
occur. This may be significant for fishing fleets, as the fish, and hence the fleet, follow the
isoprene producing phytoplankton.
Halogenated Organics (Ocean emission and uptake)
The ocean acts as a huge reservoir for chlorine, bromine and iodine and volatile organic halogen
species (e.g. halocarbons) provide a pathway to transport halogens from the water phase to the
atmosphere. Previous studies revealed that halocarbons like CH3Cl, CH3Br, CH3I, CHBr3 and
CH2Br2 are emitted from various marine organisms, especially macro- and microalgae (Ekdahl et
al., 1998, Scarratt and Moore, 1999 and references therein). The global sources of CH3I, CH2Br2
and CHBr3 are dominated by marine contributions. Algal emissions of halogenated compounds
vary considerably, not only from species to species, but also as a function of age, temperature,
time of day, nutrition, partial desiccation, grazing, light and tidal movement (Ekdahl et al.,
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 134
1998). Polybrominated species (e.g. bromoform) are primarily emitted by macroalgae which
occur only in coastal regions, whereas monohalgenated compounds can be produced from
various open ocean biomes.
Monoterpenes (Ocean emission)
Recent laboratory incubation experiments and shipboard measurements in the Southern Atlantic
Ocean have provided first evidence for marine production of monoterpenes (Yassaa et al. 2008).
Nine marine phytoplankton monocultures were investigated using a GC-MS equipped with an
enantiomerically selective column and found to emit at rates, expressed as nmol C10H16
(monoterpene). g [Chl_a]-1. day-1, from 0.3 nmol g [chl_a]-1 day -1 for Skeletonema costatum and
Emiliania huxleyi to 225.9 nmol g [chl_a]-1 day -1 for Dunaliella tertiolecta. Nine monoterpenes
were identified in the sample and not in the control, namely; (-)-/(+)-pinene, myrcene, (+)-
camphene, (-)-sabinene, (+)-3-carene, (-)-pinene, (-)-limonene and p-ocimene.
The laboratory measurements are also supported by shipboard measurements of monoterpenes in
air were made between January and March 2007, while crossing the South Atlantic Ocean, see
Figure 9.2. Monoterpenes were detected in air over high ocean chlorophyll regions sufficiently
far from land as to exclude influence from terrestrial sources. Maximum levels of 100-200 pptv
total monoterpenes were encountered when the ship crossed an active phytoplankton bloom,
whereas over the oligotrophic ocean monoterpenes were mostly below detection limit. The
monoterpenes/isoprene ratio reached 21% in laboratory experiments (the ratio between the
highest production rates of total monoterpenes and isoprene) and ranged between 7 to 60% in the
Southern Atlantic Ocean.
Figure 9.2 MODIS chlorophyll picture of the Southern Atlanic ocean in January, inset the Research vessel Marion
Dufresne.
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Alkyl nitrates (Ocean emission)
Alkyl nitrates were assumed until recently to be exclusively of anthropogenic origin, being
emitted directly from combustion or chemical processes (Simpson et al., 2002), or being
produced at low yield in the photooxidation of organic compounds in the presence of NOx via
the reaction of an organic peroxy radical (RO2) and NO (Roberts, 1990). However,
measurements of MeONO2 and EtONO2 both in equatorial air and seawater (Chuck et al., 2002)
have revealed positive saturation anomalies, and high levels of RONO2 which correlate strongly
with species of known marine origin such as bromoform (Blake et al., 1999). The mechanism of
formation of marine alkyl nitrates still remains somewhat unclear. Production in seawater
through aqueous phase photochemistry (Dahl et al., 2003) has been shown to occur via the
reaction of ROO + NO, where photolysis of coloured dissolved organic matter (CDOM)
generates the peroxy radicals and nitrate (NO2-) photolysis generates the NO. Interestingly, the
yield of the reaction ROO + NO in seawater was found to be significantly higher than in the gas
phase. Alternatively, alkyl nitrates may be emitted directly from marine biota, although direct
evidence for enzymatically mediated production has not yet been found. Using a chemical
transport model Neu et al. (2008) found the maximum impact of the oceanic alkyl nitrates to be
over the Western Pacific, where they were responsible for of increase of up to 20 % of the ozone
column.
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9.2 Aerosols
9.2.1 Primary Marine Aerosol (PMA) Source functions
Primary marine aerosol (PMA), or sea-spray aerosol is a major source of global natural aerosol
mass budgets and is important for global climate. Mass is dominated by the supermicron size
range and traditionally source functions have been derived in this size regime. The supermicon
size range also contributes significantly to aerosol scattering (Kleefeld et al., 2002) and optical
depth (Mulcahy et al., 2008) and thus the direct climate forcing effect. In terms of the submicron
size range, number concentration rather than mass becomes important in terms of the indirect
radiative forcing effect through the production of cloud nuclei (O’Dowd et al., 1999). Only quite
recently it has become accepted that submicron sea spray aerosol exists, and as a result,
submicron source functions are relatively new in terms of development.
The PMA source function describes the flux of sea spray aerosol, i.e. the number of droplets
produced per unit surface area and per unit of time, evaluated typically at 10 m above the ocean.
Hence the function describes an effective flux, parameterized in terms of ambient parameters
such as wind speed and water temperature. Measurements may provide total fluxes, i.e. the total
number of particles in a given size interval, or spectral fluxes. The latter are expressed in number
of droplets for a range of size intervals, i.e. µm-1 m-2 s-1. In this review, we focus on a selection
of recently developed or improved source functions which span both sub-micron and super-
micron sizes. Particular emphasis is focused on the submicron spray flux and chemical
characteristics. A comprehensive historical review, focused primarily on sea-salt aerosol
production, is provided by Lewis & Schwartz (2005) with some additions and description of
specific source function formulation in O’Dowd and de Leeuw (2007).
It is often assumed that the dependences on droplet size and environmental parameters can be
separated, i.e. the source function is presented as the product of a size dependent function, g(r),
and a function that describes the parameterization as function of of environmental parameters,
f(a,b,…), where r is the droplet size at a specified relative humidity (dry, RH=80%, or wet) and
a, b, … are, e.g., wind speed, water temperature, atmospheric stability, etc. Scaling arguments
show that droplet production varies approximately with the third power of the wind speed.
However, other types of paramterization have been proposed as well. Selected source functions
are shown in Figure 9.1 for a wind speed of 8 m s-1. There are two main developments to report
on: the first in terms of the supermicron sizes where the data in Figure 9.1 show that the
discrepancy between different formulations is much reduced with respect to the review situation
reported by Andreas (2002). With respect to Lewis and Schwartz (2004), the uncertainty has
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 137
been reduced by a factor of 2. For small particles a definite size dependence emerges varying
roughly as r80-1.5. The source functions shown in Figure 9.1 were obtained using different
methods and different physical principles but leading to consistent results.
The second main development is
the extension of the source
function well into the submicron
size range. The Mårtensson et al
(2003) laboratory based study
extended the size resolved source
function down to r80=20 nm and
found that the production as a
function of size was also
dependent on temperature.
Clarke et al. (2006) provide a
source function for particles
down to 10 nm. These studies
combine experiments in the laboratory or over the surf zone, to determine the spectral shape of
the flux, with whitecap coverage which in turn is paramterized as function of wind speed. Direct
and in-situ measurements of sea spray total number fluxes (D> 10 nm) are provided by the eddy
covariance (EC) method that was first attempted by Nilsson et al. (2001) in the Arctic Ocean.
The advantage of this method, as opposed to the whitecap method, is that all particles within the
detectable size range may be measured, and hence there is no restriction to bubble-mediated
production. The technique was also used at a coastal station over the North East Atlantic by
Geever et al., (2005), who quantified total number concentration over two size ranges covering
the Aitken mode (10-100 nm) and the Accumulation mode (100-500 nm), and by Norris et al.
(2008) who provided a size-segregated source function from eddy covariance measurements
aboard a ship at the North Atlantic.
Overall, the most recent schemes agree quite well (e.g. Clarke et al. surf zone study compares
very well to the Mårtensson et al. laboratory based parameterization), providing an improved
level of confidence in PMA source functions over sizes from 0.01 µm to ~10 µm. In addition, the
Mårtensson et al. (2003) parameterization provides a water temperature dependence that
compares favorably with independent measurements (e.g., Clarke et al. (2006) for 25oC, Vignati
et al (2001) for ca. 15 oC).
Radius (microns)
10-2 10-1 100 101 102
dF/d
Log(
r) m
-2 s
-1
100
101
102
103
104
105
106
107
108
Monahan et al 1986Monahan ExtrapolMartensson et al., JGR, 2003 Vignati et al., JGR, 2001Gong, JGR, 2003Clarke et al., JGR, 2006de Leeuw et al., JGR, 2000de Leeuw et al., AMS, 2003Reid et al., JGR, 2001,
Figure 9.1. Compilation of sea-spray source functions. Flux values
are for a wind speed of 8 m s-1.
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9.2.2 Chemical Composition of primary sea spray
Although the dominant mass fraction of sea-spray aerosol is sea-salt, organic matter also
contributes to the overall mass and it has long been known that marine aerosols contain organic
material (i.e. Blanchard, 1964). Field measurements suggested a significant biogenic primary
source of marine organic components (O’Dowd et al., 2004; Cavalli et al., 2004). In particular a
dominant water-insoluble organic fraction in fine marine aerosol collected during periods of
phytoplankton bloom in the North Atlantic was observed and it was hypothesized that these
insoluble organic components could have a mainly primary origin. Similar results supporting a
biologically driven oceanic OC source have been recently reported by Spracklen et al.,(2008)
Figure 9.2 (Left) Chemical and mass size distributions for
North Atlantic marine aerosol during periods of low
biological activity and high biological activity.
(Right) oceanic chlorophyll-a concentrations over the
North Atlantic for low and high biological activity
periods.
The most comprehensive study to date on the organic fraction of sea-spray aerosol has been
conducted by O’Dowd et al., (2004). They found a significant and dominating fraction of
organic matter in submicron sizes, while the supermicron size range was predominately
inorganic sea-salt. It should be noted, however, that the absolute magnitudes of organic mass in
the sub and super micron size ranges were equivalent (with one third of the total organic mass
residing in the coarse mode), and that it was their relative concentrations that differed
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 139
significantly. Figure 9.2 illustrates the chemical composition of clean marine aerosol over the
north east Atlantic during winter and summer periods, corresponding to low and high biological
activity periods (O’Dowd et al., 2004). Also shown is the distribution of chlorophyll-a derived
from the SeaWifs satellite. During periods of high biological activity, the organic fraction ranged
from 40-60% of the submicron mass, while during low biological activity periods, the fraction
reduced to about 10-15%. O’Dowd et al, (2004) argued that the water insoluble organic fraction,
dominating the organic composition in the fine size fraction, was likely to be derived from bubble
mediated production..
Fig 9.3. (Left) Average chemical composition relative
concentration from bubble bursting tank samples.
(Bottom) average ratio of WIOC to sea-salt from
atmospheric samples at Mace Head. Data for the 0.06 –
0.125 µm size range are not reported from the bubble
tank because the total carbon analyses in this stage were
below detection limit. The bars are the standard
deviation of the mean.
(Right) Visible satellite image of plankton bloom off the
west coast of Ireland during the MAP cruise June-July
2006. Bubble-bursting tank experiments were
conducted in and around the plankton bloom.
Later experiments, using the gradient technique to determine aerosol chemical fluxes at Mace
Head (Ceburnis et al., 2008) showed that the water-insoluble organic carbon (WIOC) mass
invariably had an upward mass flux associated with it and followed similar trends to sea-salt
gradients, while water-soluble organic carbon (WSOC) mass possessed a downward flux profile
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identical to nss-suphate. They concluded from the gradients that WSOC must be formed from
secondary aerosol formation processes while WIOC must be formed from primary production.
These conclusions were further supported by Facchini et al., (2008a) who conducted bubble-
bursting experiments amidst a plankton bloom over the NE Atlantic during the MAP (Marine
Aerosol Production) cruise in 2006 (Figure 9.3). During these experiments, it was found that
spray particles exhibited a progressive increase in the organic matter content from 3 ± 0.4% up to
77 ± 5% with decreasing particle diameter from 8 to 0.125 microns (Figure 9.3). Submicron
organic matter was almost entirely water insoluble (WIOM) and consisted of colloids and
aggregates exuded by phytoplankton. Facchini et al (2008a) found that the WIOC to sea-salt
mass ratio fingerprint as a function of particle size in the bubble tank experiments matched that
observed in atmospheric samples both at Mace Head (shown in Figure 9.3) and on the MAP
cruise over the open ocean. These results conclusively confirmed that the WIOC component
observed in marine air samples relate to primary aerosol production. Electron microscopy
observations of individual particles collected at the ocean surface in a number of sites support the
hypothesis that complex exopolimers and the microgels forming from these, produced by bacteria
and algae, are involved in bubble bursting processes (Bigg and Leck., 2008). Keene et al., (2007)
also measured sea salt and organic carbon in water extracts of nascent marine aerosol, showing a
strong enrichment of organic in all size fractions, with the highest enrichment in the smallest size
fractions. However, the authors did not distinguish between water soluble and insoluble organics.
These results indicate that a sea-spray source function should not only consider size resolved
mass, but also chemical composition. The first attempt at a combined sub-micron organic-
inorganic sea-spray source function, implemented in a regional climate mode, was produced by
O’Dowd et al (2008). They combined the Geever et al, (2005) accumulation mode number flux,
combined with the Yoon et al., (2007) seasonal modal diameter (minus secondary aerosol mass),
and integrated with the seasonal trend in WIOM/sea-salt ratios to produce a physico-chemical
flux function driven by wind speed and satellite-derived chlorophyll-a concentrations over the
North East Atlantic. The model predicted results compare well to seasonal observations at Mace
Head and are illustrated in Figure 9.4 for winter and summer seasons.
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Figure 9.4. (Top panel) Near surface sea-spray mass concentrations around the European regions and average wind
vectors. (Bottom Panel) Percentage primary organic contribution to sea-spray mass.
9.2.3 Secondary Aerosol Production
In recent years, significant effort has been made into the study of new particle formation in the
coastal zone in the hope that it would elucidate key processes associated with nucleation over the
open ocean. Most of these studies have focused on nucleation in coastal zones (e.g. at Mace
Head) and revealed regular particle bursts, with burst concentrations often exceeding 106 cm-3.
These events have been linked to release of biogenic iodine vapours from coastal algae followed
by the photochemical production of iodine oxide aerosols (O’Dowd et al., 2002; McFiggans et
al., 2005). A detailed review of studies into these processes is found in O’Dowd and Hoffmann
(2005). Further studies revealed that the nucleation mode particles could also contain some
organic aerosol mass suggesting that secondary organic aerosol production also occurs in marine
air and contributes to aerosol growth (Vaattovaara et al., 2006). The findings of O’Dowd et al
(2004) and Ceburnis et al., (2008) also point to significant contributions of secondary organic
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aerosol, manifesting itself in the WSOC component. The most relevant organic secondary
component (SOA) is methanesulphonic acid (MSA). Some dicarboxylic acids were associated to
secondary formation mechanisms in previous papers (i.e. Kawamura et al., 1999) but a relevant
fraction of the observed concentrations of oxidized organic matter in marine aerosol still remains
unaccounted. Modelling studies by Meskhidze and Nenes, (2006), proposed that isoprene
emissions from plankton were sufficient to produce enough water soluble organic aerosol to
significantly enhance CCN concentrations and cloud albedo; however, it was later revealed that
the isoprene fluxes were inadvertently overestimated by a factor of 100. Nevertheless, Zorn et
al., (2008) also confirmed the dominance of organic aerosol mass in air overlying plankton
blooms over the southern ocean but no detail on speciation was elucidated. Recently a new
secondary organic aerosol component, produced through the reaction of gaseous amines with
sulphuric acid has been found in marine aerosol over the North Atlantic.(Facchini et al., 2008b).
Dimethyl and diethyl ammonium salts (DMA+ and DEA+) are the most abundant organic species,
second only to MSA, detected in fine marine particles in North Atlantic and represent on average
11% of SOA and a dominant part (35% on average) of the aerosol water soluble organic nitrogen
(WSON). Several evidences support the hypothesis that DMA+ and DEA+
have a biogenic
oceanic source even if the formation mechanism of these biogenic amines remains unclear..
In conclusion, apart from MSA and a few dicarboxylic acids and amine salts, the vast majority of
secondary organic marine aerosol remains to be identified, suggesting that other formation
mechanisms and alternative SOA components should be studied.
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10. The processes of wet scavenging of aerosols and trace gases from the
atmosphere 10.1 Introduction
Precipitation or wet scavenging is an efficient cleaning mechanism of the atmosphere. It
combines all the in and below cloud processes that take up trace gases and particles into liquid
drops or crystals forming a cloud and deposits the material to terrestrial or marine surfaces in
rain or snow.
10.2 Nucleation scavenging of drops and ice crystals
Droplets form on a subset of the aerosol particles present in every air mass (CCN=cloud
condensation nuclei). This mechanism is probably the most important to incorporate pollutants
into the cloud phase. Depending on their size, chemical composition and the ambient relative
humidity, aerosol particles take up a certain amount of water (Pruppacher and Klett, 1997) and
when exceeding their critical size they activate to cloud droplets. In the classical Köhler theory,
only their composition with respect to insoluble material and inorganic salts is considered.
Recent studies (e.g., Anttila, and Kerminen, 2002; Sorjamaa et al, 2004; Romakkaniemi et al,
2005; Kokkola et al, 2006; Topping et al, 2007) have highlighted the importance of soluble trace
gases and partly soluble organic substances which often coat the surface of the particles for the
activation properties.
Even though our knowledge of the formation of droplets is now reasonably satisfactory, the
nucleation of ice crystals is a subject still quite poorly understood. In the atmosphere, significant
numbers of ice particles start to form only below −5 ◦C coexisting still with liquid drops.
Homogeneous freezing of liquid droplets depends on the size; large droplets can freeze
homogeneously at temperatures of around −33 ◦C, whereas at −40 ◦C even the smallest droplets
freeze homogeneously. New insight into the homogeneous nucleation of ice crystals under these
conditions which correspond to cirrus clouds was obtained in the AIDA chamber (Benz et al,
2005; Möhler et al, 2006). In the temperature range between −5 and −40 ◦C, the presence of
insoluble nuclei is necessary to initiate the formation of an ice crystal. These ice nuclei (IN) are
aerosol particles that can act in four main ways:
– Deposition mode: water is adsorbed directly from the vapor phase onto the surface of an
IN where it is transformed into ice
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– Condensation–freezing mode: this is a hybrid process that requires supersaturation with
respect to water. Here, the CCN that has formed the drop acts now as an IN. This process
seems far more effective than the deposition mode.
– Freezing mode: the IN, scavenged by the drop, initiates the ice phase from within a
supercooled water droplet
– Contact mode: the IN initiates the ice phase at the moment of contact with the
supercooled drop
The number of IN depends on the chemical properties of the aerosol particles. It has been found
that there exists a dependency on supersaturation (Meyers et al., 1992) and also on temperature
(Fletcher, 1962). In contrast to CCN, a good IN should be insoluble and have a crystalline-type
structure to facilitate the formation of the ice lattice (e.g. silicate).
Recently, the role of primary biological aerosols for nucleation of drops and ice crystals has been
highlighted (Deguillaume et al, 2008). These particles can be viable organisms capable of
metabolic reactions which can involve atmospheric organic compounds and oxidants (airborne
micro-organisms) (Ariya and Amyot, 2004; Sun and Ariya, 2006). They also comprise either
biological particles including alive, dead cells and cell fragments, capable of nucleating cloud
droplets and ice particles via physical processes (Möhler et al., 2007) or any kind of organic
substances deriving from biomolecules and contributing to aerosol masses. Airborne micro-
organisms are incorporated into cloud droplets and raindrops by nucleation scavenging as they
have CCN or IN potential (e.g., Lee et al., 2002; Bauer et al., 2003; Möhler et al., 2007) or by
washout processes. Some investigations clearly show that most of these micro-organisms are
able to develop at low temperatures (between −5 and 5°C) encountered in clouds. Furthermore,
measurements of concentrations of adenosine triphosphate (ATP) in cloud water indicate that
most micro-organisms are still metabolically active (Amato et al., 2007). For a comprehensive
review of the biogenic versus anthropogenic sources of IN, see also Szyrmer and Zawadzki
(1997).
Despite the presence of ice in many cloud systems, interactions between trace chemicals and ice
are not well understood (Abbatt, 2003). Chemical solutes originally dissolved in a supercooled
drop may be retained or expelled from the drop as it freezes. Non-volatile species, such as
sulfate, are efficiently retained during freezing but this retention process is not well characterized
for many soluble gases found in clouds (Voisin et al., 2000). Cloud modeling studies have found
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that partitioning of solutes during hydrometeor freezing may significantly affect chemical
distributions in the troposphere and deposition to the ground (Audiffren et al., 1999; Mari et al.,
2000; Crutzen and Lawrence, 2000; Yin et al., 2005, Kärcher and Basko, 2004). A better
understanding of the partitioning of volatile chemical solutes during freezing is needed to
quantify their effects on tropospheric gas-phase and precipitation chemistry.
Bacteria which have entered the liquid phase find therein a solution of organic compounds which
may serve as nutrients. Recent studies show that living and active microorganisms, including
bacteria, yeasts and fungi, are present in the atmospheric water phase (Alfreider et al., 1996;
Fuzzi et al., 1997; Skidmore et al., 2000; Sattler et al., 2001; Bauer et al., 2003; Segawa et al.,
2005; Amato et al., 2005; 2007a). These microorganisms could play an active role in chemistry
and microphysics of clouds as discussed by a growing number of scientists (Ariya and Amyot,
2004; Amato et al., 2005, 2007b; Morris et al., 2008a; Möhler et al., 2007; Deguillaume et al.,
2008). Indeed, living microorganisms are clearly biocatalysts which could transform organic
compounds as an alternative route to photochemistry. Many unresolved questions remain on this
topic and long term observations can be used to evaluate the diurnal and seasonal variations of
structure and activity of microorganisms as a function of environmental conditions (ie humidity,
light, temperature, pH...).
10.3 Impaction scavenging of aerosol particles
Inside the cloud unactivated aerosol particles remain between the nucleated drops as interstitial
aerosol. These particles can collide with the hydrometeors and become incorporated into the
cloud particles. However, due to the fact that already the main part of the particle mass was
scavenged by nucleation, inside cloud this process does not contribute significantly to the
pollution mass in precipitation (Flossmann, 1998a and b; Flossmann and Wobrock, 1996). An
importance can be attributed to this process in combination with the contact mode freezing of the
previous section. Once the hydrometeors fall and leave cloud base, on their way to the earth’s
surface they meet an unperturbed aerosol particles population. Here, the collision with aerosol
particles can contribute a significant portion to the aerosol particle loading of the rain on the
ground, depending also on the height of cloud base.
During the cloud lifetime, chemical processes lead to the formation of new chemical species with
relatively low volatility such as inorganic and organic acids, which can modify the physico-
chemical properties of aerosol particles after the cloud dissipates (Feingold and Kreidenweis,
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2002; Yin et al., 2005) and lead to secondary organic aerosols formation (Gelencsér and Varga,
2005). For some chemical species, aerosol particle dissolution is the only source in cloud
droplets; for instance, transition metal ions, and in particular iron, which is well known to play a
major role in the oxidizing capacity of clouds (Deguillaume et al. 2005). Study of such complex
interactions needs process modelling efforts integrating in-situ measurements.
10.4 Scavenging of gases
In addition to the particles, numerous trace gases are present in the atmosphere. Gases are taken
up into drops according to their solubility. The maximum amount of a gas that can be taken up
into water is a function of the Henry’s law coefficient. A comprehensive compilation of updated
Henry’s law coefficients is available at http://www2.mpch-
mainz.mpg.de/~sander/res/henry.html. Henry’s law describes the equilibrium between the
concentration in the air and the liquid, however, once in the liquid phase most gases are
destroyed by chemical reactions and, thus, an equilibrium will never be achieved. Consequently,
more and more gas can be taken up into the cloud drops. Only the droplet lifetime (max. 30 min)
will limit the gas scavenging. Recently, our knowledge of the uptake and reaction coefficients of
the ambient trace gases has significantly increased and quite complex aqueous phase reaction
schemes have become available (Hermann et al., 2005), including an extended reaction
mechanism for atmospherically important hydrocarbons containing more than two and up to six
carbon atoms.
The complexities of the cloud processes involved in pollutant scavenging have discouraged
investigators from simultaneously treating all aspects of multiphase chemistry and microphysics
with equal rigor. However, efforts made to develop sophisticated cloud models with complex
multiphase chemistry allow more detailed studies on the interaction between microphysical and
chemical multiphase processes (Leriche et al., 2007; Ervens et al., 2004). One important feature
lies in a detailed representation of the microphysical as well as multiphase chemical processes.
These developments really distance themselves from the other attempts of coupling multiphase
chemistry in 3D models, which are often restricted to the study of inorganic species and basic
organic species wet deposition (Tost et al., 2007).
10.5 Clouds
Clouds form when air ascends and following expansion and cooling the water vapour condenses.
The droplets grow further by condensation, then, collide and coalesce with each other, until they
become sufficiently heavy to fall against the updraft velocity that has suspended them until now.
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Depending on the height of cloud base and the temperature conditions they might reach the
ground as rain. If the temperature in the clouds reaches temperatures sufficiently below zero,
then ice crystals develop. They also grow by water vapour deposition, and collide with each
other. If they become sufficiently heavy, they fall to the ground in solid or liquid form, as a
function of the below-cloud temperature. During their entire life time, these cloud hydrometeors
(=drops or ice particles) take up pollution in particulate and gaseous form and deposit it on the
ground together with precipitation. A schematic display for liquid clouds is shown in Figure
10.1.
Figure 10.1: Schematic display of the microphysical and scavenging processes in all liquid clouds
Only few clouds form locally due to convection and, thus, have only a limited geographical
impact. Most clouds are embedded in large scale system and cover areas of several thousand
km2. They incorporate the local pollution when the droplets nucleate and then transport them
over larger distances, processing the material during transport..
The problem of correctly describing this process is coupled to the problem of scales. As shown
below, the nucleation of hydrometeors and all subsequent reactions take place on the scale of the
individual drop or ice crystal. The formation, transport and dissipation mechanisms of clouds,
however, act over a much larger region and require description on a synoptic or even
hemispheric scale. In the past, this fact imposed severe constraints in the accuracy of the
modelling of these processes and resulted either in highly parameterized dynamical models with
detailed treatments of the chemistry (Wolke et al., 2005, Sander et al., 2005) or in highly
simplified chemical schemes in sophisticated meteorological models (Mari et al.,2000). Only
recently have 3-D dynamic codes with detailed microphysical treatment and aerosol particles
(Leroy et al, 2008) and chemistry (Tost et al., 2007) become available due to developments in
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computers. These models obviously are restricted to rather limited modelling domains, however,
they highlight e.g. the importance of the background aerosol population for the development of
the cloud (Leroy et al, 2008).
Cloud drops :
0.01 g m-3
Raindrops : 1 g m-3
Ice crystals :
0.01 g m-3
Clean boundary layer:
NAP ≈ 400 cm-3
Cloud drops :
0.01 g m-3
Raindrops : 0.03 g m-3
Ice crystals :
0.01 g m-3
Polluted boundary layer:
NAP ≈ 6500 cm-3 Figure 10.2: Sensitivity study concerning the number concentration of boundary layer aerosol particles (Leroy et al,
2008) after 40 min of cloud development; the displayed domain is a 2-D cross section of the 3-D domain restricted
to 30km in the horizontal and 15km in the vertical; the envelopes of the different hydrometeors are specified for
each figure
Figure 10.2 displays the results of a sensitivity test for the CRYSTAL-FACE cloud (Leroy et al,
2008). The simulation shows a clean boundary layer in which rain develops readily while
precipitation formation is suppressed in the polluted case by the large population of aerosols
derived from air pollutants. Not only has the precipitation been suppressed but the horizontal and
vertical structure of the cloud is substantially modified.
By including as many as possible of these processes in larger scale models parameterisations
have been developed to yield the first reliable maps on critical loads (e.g. Hoose et al, 2008;
Pozzoli et al, 2008). One emphasis of these models has been the aspect of topography.
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10.6 Orographic precipitation
At mid-latitudes, mountainous terrain is commonly associated with high annual precipitation due
to the forced ascent of air resulting in cloud formation and precipitation. At a sub-grid scale,
however, there can be significant variations in pollutant deposition due to local emissions and
variation in topography and vegetation. A need, therefore, arises for fine scale process models to
investigate pollutant deposition at the kilometer scale (Dore et al., 2006).
The aerosol population (size distribution and composition) has a major influence on the
dynamics and microphysics of orographic cloud development. The cloud condensation nuclei
(CCN) population entering cloud base determines the extent and onset of warm rain produced by
collision coalescence. These, along with the presence of heterogeneous ice nuclei affect the onset
of the glaciation process and the efficiency of secondary ice processes such as the Hallett-
Mossop process of ice splintering. These in turn determine the release of latent heat of fusion in
the cloud, which has a major influence on the vigour and structure of the cloud dynamics. The
initiation and development of the ice phase is crucial to the precipitation formation and its
location within the cloud. More detailed process studies are needed to understand such complex
feedbacks. In the case of orographic clouds, it is shown that aerosol-cloud interactions may
cause a displacement of precipitation from the upslope side of a hill towards the downslope side
when the number of aerosols is increased (Mühlbauer and Lohmann, 2008). Inverse relations
between air pollution and orographic precipitation could be of major interest for weather
prediction and hydrological budget evaluation.
The initial physical and chemical state of aerosol entering the clouds is strongly influenced by
the prevailing oxidant climate and airmass history. The degree of in-particle oxidation and
resultant hygroscopic properties has been seen previously to be closely tied to the degree of gas
phase photochemical ageing (Cubison et al. 2006). The specificity, in terms of time since surface
emission and oxidative exposure, which can be made from direct aerosol measurements, is
however relatively poor. This may be inferred much more accurately however by making
coincident measurements of gas phase volatile organic tracers (VOCs).
10.7 Snow Chemistry
Snow lying on the Earth‘s surface has traditionally been viewed as a chemically inert medium,
whose influence on the overlying atmosphere was exerted through its albedo effect, and by
restricting exchange of gases between the air and land/sea surfaces. The a priori view was that
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the boundary layer and troposphere over Antarctica would be somewhat uninteresting, with low
concentrations of reactive radicals such as OH, HO2, NO and NO2, and a composition
dominated by longer-lived chemical species. The equivalent regions of the Arctic atmosphere
were assumed dominated by long-range transport of anthropogenic emissions from lower
latitudes. However, recent research has shown that this picture is far from the truth, and that
snow is a highly photochemically active medium (see Grannas et al., 2007 for a comprehensive
review). Snow-pack impurities, of which there are many, can be photolysed to release reactive
trace gases to the atmosphere. These processes are likely to be active anywhere that sunlight
irradiates snow. The importance of these processes to boundary layer composition varies with
geographical location; in regions with a high background of radicals, for example arising from
anthropogenic pollution, emissions from snow are of lesser importance. But in the remote polar
regions, emissions from snow can be the dominant source of reactive trace gases and have a
major influence on boundary layer chemical composition. This conclusion was first reached for
NOx (NO + NO2), which was measured in the boundary layer at Summit, Greenland at
surprisingly high concentrations, and with a ratio to NOy that suggested a local source (Honrath
et al., 1999). Measurements of NOx within the snow-pack interstitial air revealed concentrations
that were higher still, suggesting that the snow-pack itself was the source, with a gradient to the
atmosphere. Subsequent measurements made in Antarctica confirmed that NOx production
within the snow-pack was a feature of both polar regions (Jones et al., 2000). Additional
measurements confirmed that NOx generated within the snow-pack was released to the overlying
boundary layer (Jones et al., 2001), contributing to the higher than expected NOx concentrations
that were encountered. Production and emission of NOx from the snow-pack has now been
measured at many polar locations (Beine et al., 2002; Dibb et al., 2002; Honrath et al., 2002;
Oncley et al., 2004), and also during one study in the mid-latitudes of the US (Honrath et al.,
2000). The NOx is produced by the photolysis of nitrate impurities within the snow (Dubowski
et al., 2001; Jacobi and Hilker, 2007), and as nitrate is a ubiquitous snow-pack impurity, this
appears to be a process occurring wherever there is sunlight irradiating snow. The most extreme
case is that of South Pole, where NO mixing ratios exceeding 1000 pptv (parts per trillion by
volume) have been measured on occasions (Wang et al., 2008). This puts them in a league with a
polluted mid-latitude troposphere, rather than a remote clean atmosphere. The exceedingly high
mixing ratios are driven by numerous factors including a sometimes very shallow and stable
boundary layer into which emissions are concentrated, high concentrations of nitrate impurities
in the top layers of the snow-pack, intense solar radiation at such a high elevation (South Pole is
at 2835 m asl) and the fact that South Pole is downslope of the polar plateau, and therefore
receives air from a large snow-covered catchment (Neff et al., 2008). Not surprisingly, the effect
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of these emissions is measured in other trace gases whose chemistry is influenced by NOx. For
example, the OH:HO2 ratio is shifted towards OH as a result of the reaction NO + HO2 -> NO2
+ OH Another example is ozone, whose only chemical source in the troposphere is from the
photolysis of NO2: NO2 + hν -> NO + O (3P). O(3P) + O2 -> O3 Normally this suite of
reactions is associated with polluted regions, but mixing ratios of NO2 at South Pole are
sufficiently large for in situ production of ozone in the summertime boundary layer to occur
(Crawford et al., 2001; Helmig et al., 2008). Other trace gases are also produced by the action of
sunlight on snow, and their production and release into the boundary layer have been studied at a
number of locations. For example, HONO can be generated from the photolysis of nitrate
depending upon a number of factors including the snow pH (Beine et al., 2003, 2005; Amoroso
et al., 2005; Jacobi and Hilker, 2007). HCHO can be produced from snow, either through
photolysis of snow impurities (e.g. Sumner and Shepson, 1999; Sumner et al., 2002; Grannas et
al., 2002, 2004; Dassau et al., 2002) or by volatilitic release driven by changes in temperature
(e.g. Hutterli et al., 1999, 2002, 2003; Couch et al., 2000; Burkhart et al., 2002). Hydrogen
peroxide, H2O2, is similarly lost from the snow-pack as a result of physical processes (Hutterli
et al., 2003). These three trace gases are particularly interesting as they are all direct sources of
OH, so will influence the oxidative capacity of the atmosphere in that region. Indeed, as a result
of snow-pack emissions, OH at South Pole has been measured at concentrations of the order 106
(Mauldin et al., 2001), more typical of tropical regions. In addition to HCHO, there is evidence
that other organic trace gases also have a snow-pack source. For example, fluxes of carbonyls,
alkyl halides, alkenes and alkyl nitrates have been measured at various polar locations (Sumner
and Shepson, 1999; Grannas et al., 2002, 2004) Frozen surfaces are also a key source of
halogens. For example, elevated concentrations of reactive bromine compounds (BrO, Br2,
BrCl) have been observed in polar regions (e.g. Richter et al., 1998; Foster et al., 2001; Spicer et
al., 2002; Saiz-Lopez et al., 2007a) with a source associated with sea salt/sea ice/snow-pack. For
example, as new sea ice forms, sea salt is expelled from the ice lattice such that concentrations
can build up in brine pools and on the surface of associated frost flowers (Rankin et al., 2002;
Kaleschke et al., 2004; Jacobi et al., 2006). Sea salt aerosol, either suspended or deposited to the
snow surface, can also act as a source of bromine. Furthermore, recent measurements have
detected significant concentrations of iodine monoxide in the Antarctic boundary layer.
Measurements from a ground-based station (Saiz-Lopez et al., 2007a) as well as from satellites
(Saiz-Lopez et al., 2007b; Schönhardt et al., 2008) have revealed a seasonal maximum in spring,
but with higher than expected concentrations sustained into the summer months. Although the
source of iodine is not confirmed, it is postulated to originate from algae that colonises the
underside of sea ice. The presence of even relatively small concentrations of these reactive
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halogens has a marked effect on boundary layer chemistry. Bromine is primarily responsible for
the dramatic boundary layer ozone depletion events that are observed in coastal regions of both
the Arctic and Antarctica (Oltmans, 1981; Barrie et al., 1988; Helmig et al., 2007). It is also
instrumental in the oxidation of gaseous elemental mercury (Hg0) to forms of reactive gaseous
mercury (Schroeder et al., 1998; Steffen et al., 2007), as well as providing an additional, and
efficient, pathway for oxidation of dimethylsulphide (von Glasow and Crutzen, 2004). In
addition, bromine and iodine compounds in the summertime Antarctic boundary layer have been
shown to profoundly impact the baseline photochemistry, acting as the major sink of NOx, and
shifting the ratios of both NO2:NO and HO2:OH. A full review of halogen chemistry in polar
regions is provided by Simpson et al. (2007). So, rather than being chemically uninteresting,
snow at the Earth‘s surface is a source of reactive trace gases to the surrounding atmosphere,
either through photolytic or volatilitic processes. Whether this source is a significant influence
on boundary layer chemical composition depends greatly upon background concentrations.
Certainly in the remote polar regions, boundary layer composition has been found to be very
different from what was anticipated.
Organic N in air and rain
Better understanding of the processes that link the chemical and biological properties of aerosols
with cloud formation and droplet growth has indicated a need for better knowledge of the
organic components. Although transport or organic C, and deposition to the earth’s surface, has
not been regarded as quantitatively important for ecosystem health, organic N has the potential to
add to the known effects of inorganic N wet deposited from the atmosphere especially in remote
areas. Studies of precipitation chemistry have highlighted our lack of knowledge of the organic
nitrogen constituents (both gaseous and particulate) in the atmosphere. Recent reviews (Cornell
et al., 2003, Neff et al., 2002) have indicated that the contribution of water-soluble organic
nitrogen (WSON) in precipitation to wet deposition may be up to one-third of the total, yet little
is known about the chemical composition, form or sources of this material. Initial scepticism
about the nature of WSON has to some extent been dispelled (Cape et al., 2001), but the broad
range of possible composition and emission sources means that the transfer pathways are still
somewhat uncertain. It is known, for example, that biological processes interconvert inorganic
and organic nitrogen in forest canopies (Fang et al., 2008), but it is not clear how much
biological activity may occur in the atmosphere or on the surfaces of sampling equipment. The
presence of both gaseous and particulate WSON in the atmosphere implies that dry deposition is
an important but unquantified pathway for transfer of organic nitrogen to the earth’s surface.
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10.8 Conclusions and some priority areas of future research
Atmospheric pollution is made of aerosol particles and trace gases. Both are taken up into cloud
hydrometeors during the life time of a cloud, processed and either released during evaporation or
deposited onto the ground with the liquid or solid precipitation. Knowledge of the underlying
processes has greatly advanced concerning the liquid phase. Here, the gap concerning the role of
the organic material is almost closed now. The greatest uncertainty nowadays lies with the ice
phase. Generally, the ice phase is chemically less active than the liquid phase. Thus, the uptake
and processing is reduced. Furthermore, the role of the ice phase in the precipitation formation
and the deposition is not completely known and this factor still attaches a large uncertainty to the
values that are obtained by current models.
The second challenge in the modelling of wet deposition is linked to role that bacteria and other
living organic matter can play in the microphysics of a cloud and in atmospheric aqueous phase
chemistry. Thus, their impact on the oxidizing capacity of the atmosphere still needs to be
quantified.
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11. Ecosystem-Atmosphere exchange – Conclusions
This paper has reviewed the state of knowledge of atmosphere - surface interactions of a broad
range of trace gases and particles. Given the wide range of chemical species reviewed and the
reasonably self contained sections within the paper, this concluding section does not attempt to
provide a summary of the sections. The following commentary reflects on the overall direction
of the science which, like the subject material, is becoming ever more global and searching for
integrating mechanisms. There has been substantial progress over the last decade in process
understanding, field measurement and in modelling. Models have been developed incorporating
the process understanding to generalize the ecosystem - atmosphere exchange over regions and
are currently able to describe the fluxes with uncertainties of the order of 30% in wet deposition
and 50% in dry deposition. However, there is a lack of measurements to evaluate the models. For
the future, extensive measuring campaigns and monitoring are necessary to further develop this
important field, such as the development of super sites in the EMEP network with a full
spectrum of gas and aerosol phase trace atmospheric constituents and continuous measurements
of surface – atmospheric fluxes. Such long term flux measurements of reactive pollutants to test,
develop and validate models represent an important development, which, in turn will be
expanded regionally.
Policy needs
In the policy development there is a need to address environmental priority issues, among which
none are currently greater than climate change. However, human health, ecosystem quality and
the sustainability of land use uses are growing in importance.
There is a current tendency to group the environmental impacts because they are linked through
the actors and receptor which are often the same. New more integrated directions are Global
change, or reactive nitrogen, air quality and climate change, quality of life, bringing together a
range of related issues in the search for more sustainable solutions to the underlying problems.
This is however for from being implemented in policies because it involves a bigger scale and
therewith more actors (global), but aims are being developed, such as the Millennium Goals
(and/or the CO2 concentration in the atmosphere). In the end it will be the actors that will change
their activities and the receptors (humans, plants, animals and ecosystems) need to adapt. The
link between surface and atmosphere for the exchange persists to exist and the need for
quantification. The extension is the scale that needs to be linked from very local to global (Earth
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 155
system approach) and that different components of biogeochemical cycles need to be integrated
(Carbon, nitrogen, oxygen, hydrogen, phosphorus cycles).
Current understanding
It is questionable whether our current understanding of the atmosphere - surface exchange is
keeping pace with these more integrated needs. The processes are very variable and the
interactions of the different components are not yet studied mainly because of instrument and
resource limitations. In the 1950s the work started with single component fluxes to a few
ecosystems. Currently, as presented in this chapter, the state of knowledge is grouped into
families of components (VOC, reactive nitrogen, GHG, particles), with an extension of some
ecosystems (oceans). Within the area of reactive nitrogen it has been shown that there is a
dynamic exchange between the atmosphere and surface, regulated by the surface and stomatal
chemical interactions; deposition, re-emission and re-deposition processes; and by the exchange
of different forms of nitrogen in interaction with the status of the system (saturation, carbon,
phosphorus, water filled pore space, etc) (Sutton et al., 2008; Pilegaard et al., 2009). Sulphur has
a large impact on the uptake and release of ammonia at the surface. This in all is part of the
nitrogen cascade, where one molecule of reactive nitrogen that enters a system in oxidised or
reduced state is used and transformed in that system, can be leached to the groundwater as
nitrate, entering the river and in the estuaries where it can be emitted as N2O contributing to
climate change and into the stratosphere, where eventually it is broken down depleting the ozone
layer (Galloway et al., 2003). There is evidence that increased nitrogen deposition leads to NO
emissions from the soil. This links the nitrogen with the oxidants surface - atmosphere exchange.
Oxidants in their turn affect the ecosystem health and therewith the nitrogen uptake and use
efficiency. These are examples of the strong interaction between the components in the surface -
atmosphere exchange.
Future developments
Agriculture is a major source of emissions to the atmosphere, which relative to industry has been
regulated substantially less (Aneja et al., 2008). Until now policy has regarded agriculture as
necessary to produce our food and animal feed. More and more, however, it is recognized that
the production should be within the limits of sustainability, limiting losses of pollution to surface
or groundwater and emission to the atmosphere of reactive nitrogen compounds, greenhouse
gases, persistent organic pollutants, H2S and odour. Agriculture is a collection of diffuse sources
with many uncertainties in the emissions. Greenhouse gas emissions and ammonia are uncertain
because the emissions depend on farm management practices, soil type, fertilizer use, type of
03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 156
crop or animal breeding, size and location of the farm, etc. This makes quantification of the
sources and successful targeted measures and policies for control very difficult.
The next step will be to understand the strong interactions between the different
cycles/components and the regional interactions in urban and rural landscapes. Studying the
landscape interactions is a new topic, where just a few exercises have been done (Cellier et al., ..;
Nemitz et al., ..). The landscape scale, such as agricultural areas, complex terrain, urban areas,
etc. have their own dynamic and interactions which differ from the normally studied stationary
conditions. It is necessary to study this scale because of the need to determine the contribution of
the individual sources which need to be controlled. In rural, agriculture dominated areas there is
a large contribution of different reactive nitrogen sources, such as animals in- or outside housing
systems, application of fertilizers or manure, storage of manure, traffic and small industries.
Within such an area deposition and re-emission takes place with a high spatial and temporal
dynamic. Especially for nitrogen the use efficiency can be improved if the individual
contribution of the different sources and resulting losses can be quantified.
In urban areas there is a concentration of sources of gases and pollutants from industry, traffic
and households. More than 50% of people live in urban areas and therefore the emissions to the
atmosphere and the resulting exposure in these areas is a growing concern over the world. It is
necessary to understand the dynamics in emissions and deposition of primary and secondary
particles. Trees or treated surfaces might act as depositing surfaces improving air quality in cities
(REF) or decreasing the net ammonia emissions around farms. It is necessary to determine the
effect of these policies against other measures.
A diverse range of natural and anthropogenic particles are capable of initiating the ice phase, but
the most active naturally occurring ice nuclei (IN) are biological in origin and have the capacity
to catalyze freezing at temperatures near -2°C. Based on the ubiquitous distribution of biological
IN in snow and rain from global locations, they are likely to encounter the appropriate conditions
to affect atmospheric processes leading to precipitation. The cloud nucleation processes are
currently the most uncertain in the global climate change modelling and predictions. Surface
fluxes of biological components and atmosphere - surface exchange of primary and secondary
particles is a research area that needs to be taken forward quickly.
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Acknowledgements We gratefully acknowledge financial support from the European Commission for the ACCENT,
NitroEurope and GRAMINAE projects, from COST for ACTION 729 and the European Science
Foundation for the Nitrogen in Europe (NinE) program. This work was partly supported by the
UK Department for Environment Food and Rural Affairs and the NERC Centre for Ecology and
Hydrology.
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