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Defra Project: WQ0111 Final Report

Updating Previous Estimates of the Load and Source Apportionment of Nitrogen to

Waters in the UK

Defra Project: WQ0111

Prepared for: Soheila Amin-HanjaniDefra AEQ

Prepared by: ADAS UK LtdENSIS Ltd

Greg Hughes, Eunice Lord, Lucy Wilson, Richard Gooday, Steven Anthony (ADAS UK Ltd)

Chris Curtis, Gavin Simpson (ENSIS Ltd)

Date: October 2008

Report Status: Final


Executive Summary

This report summarises technical work for Defra’s AEQ division on updating the N loads emitted to water within the UK paying attention to source apportionment. The project aims to build on previous studies taking advantage of changes in available data and methodologies. In particular, attention was paid to the atmospheric deposition component of the N load and the sources from which this arises, e.g. agriculture; imported emissions.

This N apportionment study centred on data for 2004 confirms that the dominant source of N in UK waters is agriculture (~52% plus a further ~10% from woodland, rough grazing and similar land use) followed by wastewater from sewers and septic tanks (~26%) – see Table 10-1

o The apportionment varies both regionally and by river basin district, depending largely on the balance between agricultural activity and population density

The estimates of N inputs to water, corrected for basin retention, correlate well with estimates of N export to the seas around the UK collated for OSPARCOM reporting purposes – Table 10-8 and Figure 10-3

The estimates are in line with the results from similar studies throughout northern Europe, for similar areas

The results of this study are broadly similar to those of a previous study for England and Wales centred on 1995/2000 (Hunt et al., 2004).

o Direct comparison of agricultural contributions between the current study and Hunt et al., 2004 is difficult as they included rough grazing in this category while the current study has included this rough grazing loss in another category, namely loss from “woodland, rough grazing and other natural areas”, where atmospheric deposition is a dominant input.

o Accounting for this difference still suggests that inputs from agricultural land have fallen slightly, due partly to a reduction in certain livestock numbers

o Inputs from sewers appear to have fallen, which may be due to a combination of more detailed information and reduced P content in detergents

o Sediment loss from land, not included in the previous study, was estimated to contribute ~7% of the final load – Table 10-1

Direct atmospheric deposition to water contributed ~1% of the load (Table 10-1). This value is greater than in the previous study and is because of the inclusion of the surface area of streams and rivers.

A novel aspect of the work was the attempt to apportion the effect of atmospheric deposition on nitrate loss from the land.

o The method assumed that the current practices, being based on experience under ambient conditions, already in effect adjust for the fertiliser-equivalent value of average deposition rates. If deposition were reduced to zero, fertiliser inputs would increase, other factors being equal.

o The negative effect of deposition relates to the timing of deposition and the fact that it occurs on all land including unfertilised land (uplands)

o Novel methods of calculation were developed to take account of the effects of atmospheric deposition on diffuse rural N losses

o The net effect of deposition, relative to equivalent yields and management in the absence of deposition, contributed ~4% of the loss from managed agricultural land (~2% of total) – Tables 5-3 and 10-6

o Woodland, upland and similar unfertilised areas which derive the great majority of their N input from atmospheric deposition contributed 67 kT N (~10% of total loss).


Another novel aspect of the work was the assessment of the sources from which the deposited atmospheric deposition arose (Table 10-7). These results suggest that agricultural, imported, road transport and non-agricultural land emissions dominate. They also allow one to assess the “recycled” effects of a source on itself, for example agriculture itself is the source of almost 50% of the atmospheric deposition to agricultural land.

Dissolved organic N (DON) is excluded from most studies, or included only for some components e.g. urban runoff. DON is considered potentially important especially in relation to eutrophication.

o A scoping study concluded that there were now sufficient data (recent and prospective) to give confidence that a sound estimate could be constructed for diffuse DON losses, building on experience with related pollutants such as P.

o Data on DON in wastewater are not routinely collected, and a separate study would be required to estimate these from such limited data as exist

o A preliminary estimate suggests that DON could add 5-15% to the N export to waters calculated in this study

A range of further refinements could be undertakeno Explore the inclusion of DON furthero Compare the point source contributions in the light of recent research

comparing Environment Agency compliance monitoring data with water company data e.g. Page et al., 2008; WHS, 2008.

o Include updated modelling of leaching from natural areas being developed under the Freshwater Umbrella contract by Ensis Ltd once available

o Include further minor sources e.g. sewage sludge and other organic waste spreading to land; Aquaculture

o Explore the uncertainty in the modelling that underlies this assessment

Report Citation:


Lord, E.I., Hughes, G.O., Wilson, L., Gooday, R., Anthony, S.A., Curtis, C. and Simpson, G., 2008. Updating Previous Estimates of the Load and Source Apportionment of Nitrogen to Waters in the UK. Final Report for Defra Project WQ0111, 104pp.


Table of Contents

1 Introduction.......................................................................................................................11.1 Background..............................................................................................................11.2 Objectives.................................................................................................................2

2 Review of methods of apportionment of sources of N in waters.......................................32.1 Recent studies of N source apportionment methods...............................................3

2.1.1 EEA Review..........................................................................................................32.1.2 EUROHARP review..............................................................................................62.1.3 Comparative study: Nordic countries....................................................................82.1.4 Comparative study: Rhine....................................................................................9

2.2 Previous England and Wales study: WRC, Hunt et al. (2004).................................92.3 Other potentially relevant studies of the UK...........................................................11

2.3.1 Nitrogen in the UK: HRI study...........................................................................112.3.2 River basin characterisation method, England and Wales.................................11

2.4 Statistical source apportionment for part of East Anglia.........................................122.5 Attempting a European method: JRC/GREEN.......................................................122.6 Commentary on components of the apportionment process..................................14

2.6.1 Estimation of point source inputs.......................................................................142.6.2 Estimation of diffuse source inputs.....................................................................142.6.3 Estimation of other diffuse sources of N.............................................................152.6.4 Retention............................................................................................................152.6.5 Accounting for atmospheric inputs.....................................................................162.6.6 Partitioning by hydrological pathway..................................................................17

2.7 Summary................................................................................................................172.8 Conclusions............................................................................................................19

3 General Methodology......................................................................................................204 Atmospheric Deposition..................................................................................................21

4.1 Methodology...........................................................................................................214.2 Environment Data...................................................................................................214.3 Results....................................................................................................................24

5 Agriculture.......................................................................................................................305.1 Methodology...........................................................................................................30

5.1.1 NEAP-N..............................................................................................................305.1.2 Effect of Atmospheric N on nitrate leaching.......................................................31

5.2 Datasets.................................................................................................................335.2.1 Climate Data.......................................................................................................335.2.2 Soils....................................................................................................................335.2.3 Census Data.......................................................................................................345.2.4 CORINE Land Cover Data.................................................................................34

5.3 Results....................................................................................................................346 Woodland and Natural Areas..........................................................................................39

6.1 Methodology...........................................................................................................396.1.1 CATCHMENT MODELS OF N LEACHING........................................................406.1.2 Development of a Multivariate Adaptive Regression Spline Model....................41

6.2 Datasets.................................................................................................................436.2.1 Catchment attribute data....................................................................................436.2.2 Data screening...................................................................................................446.2.3 Datasets used for prediction...............................................................................46

6.3 Results....................................................................................................................467 Urban Areas and Roads.................................................................................................52

7.1 Methodology for hard surface runoff......................................................................527.2 Methodology for urban leaching.............................................................................537.3 Results....................................................................................................................53


8 Sewage and Industrial Discharges.................................................................................568.1 Methodology for Point Sources..............................................................................568.2 Methodology for Septic Tanks................................................................................578.3 Results....................................................................................................................57

9 Particulate Sources.........................................................................................................599.1 Methodology...........................................................................................................599.2 Datasets.................................................................................................................599.3 Results....................................................................................................................60

10 Compiling the UK Load and Source Apportionment..................................................6410.1 Total N load and Sources.......................................................................................6410.2 Comparison with OSPAR Commission data..........................................................73

10.2.1 UK OSPAR data collection and calculation....................................................7310.2.2 Catchment Retention......................................................................................7310.2.3 Results............................................................................................................74

10.3 Comparison with results of other N source apportionment studies........................7610.3.1 Previous England and Wales study 2004.......................................................7610.3.2 Other European studies: EEA review.............................................................7710.3.3 JRC study.......................................................................................................8010.3.4 Conclusion......................................................................................................80

11 Review of evidence for non-nitrate soluble N as diffuse pollutant..............................8111.1 Evidence on organic N content of waters...............................................................81

11.1.1 Agricultural catchments..................................................................................8111.1.2 Upland catchments.........................................................................................82

11.2 Sources of soluble organic N from agricultural land...............................................8311.2.1 Soil..................................................................................................................8311.2.2 Manure...........................................................................................................8311.2.3 Farm hard standings.......................................................................................85

11.3 Other Sources of soluble organic N.......................................................................8611.4 Relevant modelling approaches.............................................................................86

11.4.1 Summary of PSYCHIC methodology..............................................................8611.4.2 Manure-related data used by PSYCHIC.........................................................87

11.5 Conclusions............................................................................................................8712 Discussion..................................................................................................................89

12.1 Agriculture..............................................................................................................8912.2 Woodland and natural areas..................................................................................8912.3 Particulate N losses................................................................................................8912.4 Sewage and industrial N........................................................................................8912.5 Urban runoff and leaching......................................................................................9012.6 Atmospheric deposition..........................................................................................9012.7 Dissolved organic N...............................................................................................9012.8 Ecological impacts on water...................................................................................9012.9 Impacts on water quality assessments...................................................................91

13 Summary and Conclusions........................................................................................9114 References.................................................................................................................93


List of Figures

Figure 2-1: Types of models and output.............................................................................7Figure 4-1: Average annual total atmospheric deposition of inorganic N (NHx and NOy) in

kg for each 1km grid cell calibrated to the 2001-2003 period as simulated using the FRAME model.................................................................................23

Figure 4-2: Surface water area (ha/km2) per grid cell for the UK......................................25Figure 4-3: Atmospheric deposition of inorganic N (kg) directly to surface water for each

1km grid cell...................................................................................................26Figure 4-4: Atmospheric deposition of inorganic N (kg) to agricultural land in each 1km

grid cell...........................................................................................................28Figure 4-5: Atmospheric deposition of inorganic N (kg) to woodland and natural areas in

each 1km grid cell...........................................................................................29Figure 5-1: Leached N from arable agriculture and managed grass (kg) as simulated by

the modified NEAP-N model for each 1km grid cell.......................................36Figure 5-2: Proportion of N leached from arable agriculture and managed grass

attributable to atmospheric deposition within each 1km grid cell...................37Figure 6-1: Examples of piecewise linear splines over the range (0,1) with a single knot

located at x = 0.5............................................................................................42Figure 6-2: Location of the 780 sites used to build the MARS model...............................45Figure 6-3: Proportion (%) of atmospheric N that is leached from woodland and natural

areas as predicted by the derived MARS model............................................48Figure 6-4: N Load (kg) that is leached from woodland and natural areas.......................49Figure 7-1: N Load (kg) that is leached or runs off from urban and road areas per 1km

grid cell...........................................................................................................55Figure 8-1: N Load (kg) that is derived from Sewage and Industrial discharges for each

1km grid cell...................................................................................................58Figure 9-1: Sediment yield (kg/ha/yr) according to the model of Cooper et al., 2006 for

each 1km grid cell...........................................................................................61Figure 9-2: Sediment yield class model results presented alongside the catchments on

which it was trained (after Cooper et al., 2006)..............................................62Figure 9-3: N Load (kg) that is derived from particulate sources for each 1km grid cell...63Figure 10-1: N Load (kg) that arises from all sources within each 1km grid cell................65Figure 10-2: River basin districts of the UK (UKTAG, 2008)...............................................68Figure 10-3: Comparison between the OSPARCOM loads and the loads derived by this

study (kT) for each of the years 2002 to 2004 for each of the RID maritime zones..............................................................................................................75

Figure 10-4: OSPARCOM zones used to summarise the N load data (kg)........................75Figure 10-5: N source apportionment to waters in England and Wales by region. Hunt et

al., 2004..........................................................................................................76Figure 10-6: Source apportionment of nitrogen load in selected regions and catchments

(Bøgestrand et al., 2005)................................................................................77Figure 10-7: (B) relative and (C) area-specific nitrogen source apportionments for

European catchments (by a source-oriented approach) (Bøgestrand et al., 2005)..............................................................................................................78

Figure 10-8: Source apportionment of N loads to the North Sea in 2000 by a source-oriented approach. Point sources green, above diffuse sources orange). (Bøgestrand et al., 2005 from OSPARCOM, 2003).......................................79

Figure 10-9: Source apportionment and N fertiliser use (Bøgestrand et al., 2005)............80Figure 10-10: Source contribution to the in-river nitrate loads according to the JRC model

estimates (Grizzetti & Bourouai, 2005)...........................................................80Figure 10-11: Nitrogen sources apportionment 1995-2202 (Grizzetti & Bourouai, 2005).....80Figure 11-1: Nitrate, Ammonium and SON concentrations in drainage water from

grassland and the effect of addition of slurry at sampling occasion 29 (Defra


project ES0106, Plot 1 , 2005-6)....................................................................84Figure 11-2: Soluble non-reactive P and DON in drainage water from grassland showing

the effect of slurry application in winter at sampling occasion 29 on a clay site (Defra project ES0106, Plot 1, 2005-6)..........................................................84

List of Tables

Table 2-1: Overview of European river catchment source apportionment studies: EEA review...............................................................................................................4

Table 2-2: Models explored within EUROHARP................................................................7Table 2-3: Assumed nitrogen per capita loads for STW source estimation.....................10Table 4-1: Atmospheric deposition broad footprint classes (Bealey, Pers. Comm.).......21Table 4-2: Regional and National summaries of the atmospheric deposition directly to

water (kT), broken down by broad atmospheric deposition footprint type (%)........................................................................................................................27

Table 5-1: Adjust fertiliser rates (kg ha-1 N) for different atmospheric deposition rates...31Table 5-2: Increase in annual leaching (kg ha-1) with increasing atmospheric deposition

under varying climates as simulated using NIPPER (Gooday et al., 2007; Lord et al., 2007).....................................................................................................33

Table 5-3: Regional and National summaries of the contribution (kT) of N from different agricultural sources........................................................................................35

Table 5-4: Regional and National summaries of the N atmospheric deposition emitted by agriculture to water broken down (%) by broad atmospheric deposition footprint type...................................................................................................38

Table 6-1: LCM2000 Classification and range of values in total FAB dataset (n=1722); shading indicates LCM classes used for screening “non-agricultural” sites...44

Table 6-2: Summary statistics of the variables used in the development of the MARS model..............................................................................................................46

Table 6-3: Terms included in the MARS model fit plus information on the piecewise linear splines representing each term.............................................................47

Table 6-4: Regional and National summaries of the contribution (kT) of N from different non-agricultural natural sectors......................................................................50

Table 6-5: Regional and National summaries of the N atmospheric deposition emitted by non-agricultural natural areas to water broken down (%) by broad atmospheric deposition footprint type.............................................................51

Table 7-1: Approximate run-off coefficients for different types of urban land use (modified after Debo and Reese, 1995; and Ellis, 1986)................................................52

Table 7-2: Urban and Road Specific EMC’s (Mitchell, 2005)..........................................52Table 7-3: Summary of the per capita export co-efficient’s derived for the various urban

sources of leaching (after Wakida and Lerner, 2005)....................................53Table 7-4: Regional and National summaries of the contribution (kT) of N from the

different urban sectors....................................................................................54Table 8-1: Environment Agency Water Management Region summaries of the

contribution (kT) of N from sewage and industrial discharges.......................56Table 8-2: Regional and National summaries of the contribution (kT) of N from sewage

and industrial discharges................................................................................57Table 9-1: Catchment types according to Cooper et al., 2006 along with the median

sediment yield for catchments of each type...................................................59Table 9-2: Regional and National summaries of the contribution (kT) of N from

particulate sources.........................................................................................60Table 10-1: Regional and National summaries of the contribution (a in kT; b in %) from all

sources of N...................................................................................................66Table 10-2: Regional and National summaries of the contribution (kg/ha) from all sources

of N.................................................................................................................67Table 10-3: River Basin District summaries of the contribution (kT) from all sources of N69


Table 10-4: River Basin District summaries of the contribution (kg/ha) from all sources of N.....................................................................................................................69


Table 10-5: Regional and National summaries of the contribution (kT) from all sources of N via surface and ground water pathways.....................................................70

Table 10-6: Regional and National summaries of the N load emitted to water (B) that arises from atmospheric deposition (C and E) broken down by each of the sources (D).....................................................................................................71

Table 10-7: Regional and National summaries of the N atmospheric deposition emitted by all sources to water (kT) broken down (%) by broad atmospheric deposition footprint type...................................................................................................72

Table 10-8: Comparison of the maritime area summaries of the OSPAR Commission RID - Riverine and Direct discharges (kT) from the UK for the years 2001 through 2004 with the contribution from all sources of N (kT) accounting for catchment/basin retention..............................................................................72

Table 10-9: N source apportionment to waters in England and Wales (calculation basis 1), Hunt et al., 2004.............................................................................................76


1 Introduction

This report summarises technical work for Defra’s AEQ division on updating the N loads emitted to water within the UK paying attention to source apportionment. The project aims to build on previous studies (e.g. Hunt et al., 2004) taking advantage of changes in available data and methodologies. In particular, attention will be paid to the atmospheric deposition component of the N load and the sources from which this arises, e.g. agriculture; imported emissions.

In order to define the methodology for this project a review of previous approaches to quantifying the N load and source apportionment was undertaken focussing on European countries with similar climates and agricultural systems (See Section 2). A review of the ability to include a key source of N missing from previous studies, namely organic N, was also undertaken (See Section 11) although inclusion of this source in this project was beyond the scope of the project. Section 3 outlines the general approach that has been used in this project with the detailed methodologies, data requirements and results from the modelling of each source described in Sections 4 through 9. The compilation of the total N load emitted to UK waters along with an exploration of the National, regional and river basin district contributions are outlined in Section 10.1. The evaluation of the results through comparison with OSPARCOM results and the results of previous projects both in the UK and within the EU is presented in Sections 10.2 and 10.3. The report concludes with a discussion of the results highlighting caveats and dependencies as well as future improvements.

1.1 Background

Excessive nitrogen in waters can lead to eutrophication and acidification of aquatic and terrestrial ecosystems. With the challenge of the Water Framework Directive (WFD), and the need therefore to address not only pollution from point sources but also those from diffuse sources (including those from agriculture and urban sources), it is important to ensure that the most up to date and accurate apportionment data is utilised. Consideration is needed of all sources of N, the different forms in which N can be lost or deposited as well as all modes by which N can enter water courses – through run off, direct discharge as well as by direct deposition from air.

The purpose of source apportionment studies is to compare the relative contributions of various sources of N in waters, as a basis for guiding policy decisions. Source apportionment studies are widely used in monitoring progress with regard to emissions to the marine environment, especially where a number of countries are involved. In these cases, they are often fairly simple in structure. More detailed attribution of sources is possible however, allowing more complex issues of source, speciation and secondary impacts to be addressed.

Nitrogen can enter water courses through a variety of pathways ranging from direct discharge (e.g. from wastewater treatment plants) to direct deposition from air. Nitrogen loss to water can be in different forms – nitrates, ammonium, sediment-bound or organic N in water from the land or direct discharge; ammonia or nitrogen dioxide from air deposition. All of these forms are at least partly bio-available and can therefore affect the ecology of inland and marine waters.

A review of current evidence on the impact of agriculture on water quality was published in June 2004 (Defra 2004), in parallel with the joint Defra-HMT consultation, which put forward a number of approaches to tackling Diffuse Water Pollution from Agriculture (DWPA). This paper, and most publications, apportioned 60 % of N loading in UK waters to agriculture,

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deriving this estimate from a study carried out by WRc in 2004 (Hunt et al., 2004). This study looked at the contributions from agriculture, Sewage Treatment Works (STWs) and from smaller but significant sources such as septic tanks and from non-agricultural land management. Consideration was given to direct deposition from air. Estimates of inputs were made to both inland and marine waters and for England and Wales as a whole as well as on a regionalised basis.

This work sets out to update that study, using updated data and methods, and adding more detail to the calculation. Particular concerns which are relatively novel in this type of study are to address the full range of N sources and forms, and to look at the indirect contribution of atmospheric deposition to N loss to water. This complements studies which address the contribution of nitrogen in waters to atmospheric N emissions.

1.2 Objectives

The overall objectives of the project are summarised below:

1. Update previous work to account for recent changes in the N inputs and source apportionment

2. Expand previous work to include the whole of the UK3. Explore the role of atmospheric deposition in these N loads in more detail, especially

with respect to the various industrial sectors/sources from which this atmospheric source arises

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2 Review of methods of apportionment of sources of N in waters

Most systems for estimating N loss to waters could in theory be classed as N apportionment methods, since a budget of N losses is created. In order to limit the field of study, this review concentrates on recent studies where N apportionment was a key objective, working at a reasonably large scale (major catchment to national) and using statistical data (rather than detailed local information) as input. This approach excludes a number of highly detailed models of diffuse N losses which require high-quality local data as input. Some of the models listed below are borderline for inclusion, in that their development history focussed on small scale modelling of loss of nitrate from land, but they have been included because they have been used in N apportionment studies.

Sources of N loss to water are conventionally divided into two or three classes:

1. point sources Urban wastewater treatment plants / sewage Industrial discharges

2. diffuse sources Agriculture Scattered dwellings Urban losses other than sewage Atmospheric deposition to water bodies Sometimes including “background losses” as below

3. background losses “Natural” land not receiving fertilisers (mountain, moorland, forest)

Point sources are defined as relatively few stationary locations or fixed facilities from which pollutants are discharged, in relatively large quantities, such that measurement of the pollution from individual sources is conceptually feasible. In-river or closely linked activities such as fish farms may also be included in this category.

Diffuse losses are pollution from widespread activities with no specific point of discharge, such as losses from natural areas and agricultural land, urban areas and amenity land. Waste water-related losses from scattered dwellings are typically included here because of the number of, and difficulty in quantifying, the individual inputs.

Results of source apportionment studies can be presented in three ways:

1. The absolute loads (mass) emitted by different sources2. The relative contribution or the percentage share of different sources 3. The area-specific load from different sources calculated as absolute amount in

weight emitted by the different sources divided by the area of the catchment (t/km2)

The EEA Review recommends that the area-specific loads are often the most useful statistics for comparing different studies.

2.1 Recent studies of N source apportionment methods

2.1.1 EEA Review

The European Environment Agency commissioned a report on source apportionment (Bøgestrand et al., 2005). This report compiled results from existing large-scale source apportionment studies throughout Europe.

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2.1.1.1 EEA review: methods considered and overview

Methods reported are shown in the table below:

Table 2-1: Overview of European river catchment source apportionment studies: EEA review

Model system and catchments studied References

Moneris methodology— EuroCat (project)— Danube (project)— Nutrient emissions into surface waters of Germany Rivers covered: Axios; Danube; Daugava; Elbe; Ems; Humber; Odra; Po; Povadijska; Rhine; Vistula; Weser

Behrendt/EuroCat (2004); Schreiber et al. (2003); Behrendt et al. (2003)

Euroharp — 'Towards European harmonised procedures for quantification of nutrient losses from diffuse sources', EU fifth framework programme research project. 17 European catchments

Schoumans & Silgram (2003)

Bernet — Baltic eutrophication regional network. 7 catchments (Bernet 2004)— Denmark National source apportionments split by sub-catchments— 9 coastal area catchments and subdivision of these

Annual reportingBøgestrand (2004 and 1999)

— Italy (rivers: Po, Adige, Piave, Serchio) ANPA (2001)— Sweden — 119 coastal catchment areas and > 1 000 sub-catchments Brand and Ejhed (2002)— Norway — 6 sea catchments and 247 river catchments Selvik et al. (2004)Large rivers— Danube — 388 sub-catchments— Odra — 45 sub-catchments— Po — 33 sub-catchments— Vistula — 47 sub-catchments

Schreiber et al. (2003)Behrendt et al. (2002)Palmeri et al. (2005)Kowalkowski and Buszewski (2004)

Large riversAxios (Nikolaidis et al., 2004); Danube (Somlyódy et al., 1997; Schreiber et al., 2003); Daugava (Behrendt/EuroCat, 2004); Elbe (De Wit et al., 2001; Behrendt et al., 2003); Ems (Behrendt et al., 2003); Odra (Behrendt et al., 2002); Po (De Wit et al., 2001; Palmeri et al., 2005); Rhine (IKSR, 1996; Dijk et al., 1997; De Wit et al., 2001; Behrendt et al., 2003); Vistula (Kowalkowski and Buszewski, 2004); Weser (Behrendt et al., 2003)European lakes— Peipsi (Vassiljev and Stålnacke, 2003); Mjøsa (Nashoug, 1999);

Reports generally apportioned emissions into: Point sources Agriculture Background, comprising non-agricultural rural land.

Studies used two main calculation approaches: A load-based approach, in which the measured nitrogen load discharging to sea was

taken as the basic total, and calculable contributions to this (eg point sources), together with estimates of retention, are used to arrive at the ‘unknown’ contributions by difference (e.g. diffuse losses).

A source-oriented approach, in which all contributions are calculated, combined, and modified by retention factors. Here the estimate of diffuse source contributions are made explicitly and takes account of the type of activity on land in the catchment/region.

The authors made the point that in comparing source apportionment results calculated by different methods, factors which must be considered include:

Flow: o Larger rivers have larger loads. The report concludes that re-expression in

terms of load per unit contributing area is most often helpful.o Year-to-year variation in flow can make a great difference to loads

Method: Different models/approaches give different results. Percentage contribution is affected by the size of other contributions in the

catchment. Comparison of contributions to inland waters only will be affected by proportion of

sewage discharge direct to sea, which can be very high in countries with dominantly coastal cities (DK, Italy).

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2.1.1.2 EEA review: commentary on methods

The report does not go into great detail on methodology, but makes the following points.

Several countries produce regular (annual) source apportionment reports The marine conventions Helcom and OSPAR have produced source apportionment results for their respective international seas, i.e. the Baltic and the North Sea. Helcom and OSPAR source apportionments are generally based on pollution load estimates and source apportionment results based on harmonised methodologies (HARP, 2000 and HELCOM 2004) as reported by member countries. There are, however, some differences between the countries, because they may choose from a number of options for the estimation of, for example, retention.

Diffuse sources: agricultureThe methods used differed considerably in their data requirements, time required to run (from hours or days to months per catchment) and capability to carry out scenario analyses. The simplest models were load-oriented, especially if it is assumed that sewage input data are known. The most complex models dealt with the whole soil-plant-atmosphere-drainage system, sometimes on daily or even finer timescales. Intermediate were variations on the theme of export coefficients. Diffuse pollution modelling is dealt with in more detail under the EUROHARP project below.

Point sources: sewage and waste waterThere was general agreement on approach here, but variation in detail (for example lower limits on the size of sources included).

Atmospheric N depositionSome methods (especially those including large lakes) calculated direct deposition to waters. Atmospheric deposition to land was generally not considered separately as a contributory factor.

RetentionRetention was considered by many methods. In some cases it was calculated as the difference between estimated losses and measured emissions to the sea. In the case of the load-based approach, retention is calculated independently and diffuse losses are calculated by subtraction of point sources (adjusted for retention) from measured emissions to the sea. The method of calculating retention can affect results and comparability between studies.

2.1.1.3 EEA review: conclusions

The reviewers concluded that it would be desirable to develop databases and methodology to allow a regular updating of a Europe-wide source apportionment. To achieve this would require consolidation and improvement of data sources, and development of statistical models for different European regions of the relationship between such data and river nutrient fluxes. Data requirements identified were:

Point sources:Data making it possible to quantify annual nutrient discharges from various point sources (sewage treatment plants, industrial plants, scattered dwellings, fish farms, urban stormwater run-off, etc.)

Retention in riparian areas:Data making it possible to quantify annual nutrient retention in streams, lakes, reservoirs and inundated riparian wetlands utilising a harmonised method

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Groundwater delay/retention:Information that enables a calculation of average groundwater residence times in European hydrogeological regions and the potential degradation of nitrogen in groundwater aquifers

Agriculture and diffuse losses:Information on agricultural practices (nutrient surplus), soil types, geology, land use, topography and climate for the selected Eionet-water stations that makes it possible to develop statistical models for diffuse nutrient losses.

2.1.2 EUROHARP review

The OSPAR “HARP” (harmonised reporting) guidelines for reporting of nutrient emissions to the North sea (HARP 2000) give guidance on estimating the contribution of nutrient sources to surface waters. The guidelines for estimation of point sources are relatively uncontentious, although countries vary in the accuracy and completeness of data. However, no single method could be agreed for estimating diffuse losses from agricultural land to surface waters or in stream retention of nutrients because of fundamental differences in the methodologies used in individual countries. In order to compare these different approaches, the EUROHARP project was initiated in 2002, at OSPAR’s request. Methods for N and P estimation put forward by the collaborators from each country were tested by their ‘owners’ on a wide variety of catchments to investigate to what extent methods were transferable outside their zone of development.

The EUROHARP project reviewed and tested European methods for estimating N and P in surface waters (Schoumans & Silgram, 2003). The models varied widely in how detailed and mechanistic the process calculations were; whether they were ‘lumped’ or spatially disaggregated; the spatial and temporal resolution of input data required (e.g. climate versus daily weather data) and the processes to which they gave emphasis. Most did not consider urban or non-agricultural rural land uses in great detail. The different models not only gave different results, but also different responses to mitigation measures.

These problems will apply to varying degrees to all aspects of the total N emission calculations, and hence to the derived source apportionment. We can only strive to arrive at a method which appears reasonable both scientifically and in relation to the data requirements; and in which as far as possible the major components can be independently tested for appropriate behaviour.

Nine quantification tools were involved in this study, as listed below. References to the source models are given, but in some cases the method has evolved from that in previous publications, and reference should be made to the EUROHARP reports.

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Table 2-2: Models explored within EUROHARP

Model system ReferenceNL-CAT (a combination of the models ANIMO/SWAP/SWQN/SWQL)

Groenendijk and Kroes, 1999; Kroes et al, 2000.

N-LES CAT Simmelsgaard et al, 2000MONERIS Behrendt et al., 1999, 2000, 2002TRK (a combination of the models SOILNDB/HBV-N)

Johnsson et al., 2002 Johnsson & Mårtensson, 2002 Brandt and Ejhed 2002

SWAT Neitsch et al., 2001a, 2001bEveNFlow Silgram et al., 2008;Lord & Anthony

2000; NOPOLU Campling et al, 2005. Source Apportionment (SA) OSPAR 2000

2.1.2.1 EUROHARP review: methods

The models ranged from the simplest source apportionment method (SA, agricultural N losses were assumed to account for all losses not attributed to point sources and background losses) to highly detailed daily process-based models of the agricultural system. The simplest source apportionment method could be run within less than a week, excluding time for data collation. The most complex models would take 2 months or more to setup and run for a new catchment. The NL-CAT, TRK and SWAT systems were identified as the most data hungry and highly process-oriented, and requiring the greatest time input per catchment to produce results.

Figure 2-1: Types of models and output

Many of the models produce additional information, for example time course of N concentrations in rivers, which is policy-relevant but not explicitly required for N apportionment studies.

Models also differed in the emphasis they gave to different processes, for example Nordic models deal with snow melt, while central and southern European models generally do not. Some models partition N losses into ground and surface waters.

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2.1.2.2 EUROHARP review: experiences with using models

The 9 models and calculation systems were applied to 14 catchments across Europe. It was found that there were frequent problems with data availability and formats. For example models which estimate impact on the basis of animal numbers of different sizes experienced difficulties adapting to a different classification system for livestock or indeed to different livestock management systems in other countries. The quantity and quality of crucial model input data was in many catchments not sufficient especially for the detailed models. This concerned especially hydrological data (weather, flow etc), nutrient monitoring data, point source input data and detailed spatial and temporal information on agricultural practices, the latter being often only available from general statistics at the regional level.

Water balance was the most accurately modelled output, estimates being generally within about 30% of measured values. The modelling of annual diffuse N losses had a CV of 40% between the models tested. There was no evidence that more complex models were more accurate than simpler models in estimating annual loads. Some of the more complex models required measured data on flows and N concentrations as inputs, which limits their general applicability for national use or in catchments where the measured N load is not already known.

The reviewers concluded that no single modelling tool appeared suitable for use across all European catchments. All models have strengths and weaknesses. They warned that due to the different methodologies results of source apportionment studies are not always fully comparable. For example the activities included under a given heading might vary (agriculture might include or exclude marginal lands); and where estimates were arrived at by difference (e.g. the ‘SA’ Source Apportionment method) all errors and unconsidered sources would be attributed to diffuse losses.

The reviewers commented that when judging the cost-benefit of applying different models several different factors have to be taken into account:

The ability of the model to simulate catchment hydrology. The ability of the models to simulate different nutrient fractions The spatial and temporal resolution of the model. The ability of the model to be used for scenario analysis. The performance of the model in different catchment types. The aim of the work to be done

2.1.3 Comparative study: Nordic countries.

Venohr et al. (2005) compared the MONERIS model (Behrendt et al., 1999, 2000, 2002) and the Swedish model HBV-N, linked to SOIL-NDB (Johnsson et al., 2002 Johnsson & Mårtensson, 2002 Brandt and Ejhed 2002). MONERIS is an annual model (Germany) based on relating pressures (e.g. N surplus on agricultural land) to measured losses. It assumes flow data are provided. HBV-N is a dynamic model which uses modelled estimates of diffuse loss and flow. SOIL-NDB is a detailed mechanistic soil/crop/water model. Thus for MONERIS the agricultural input was N surplus (including atmospheric deposition) mapped to county level, while for HBV-N it was modelled N leaching based on known land use and typical rotations, climate and soils. Despite the difference in philosophy and complexity, there was no clear difference in performance between the models. Most estimates by both models were within 30% of measured concentration or load, and there was some evidence of correlation between measured and predicted loads. A major cause of uncertainty in loads for HBV-N was identified as coarse (and hence potentially inaccurate) rainfall data.

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The MONERIS output apportioned results by pathway (groundwater, surface water) rather than sector (arable, forest etc) as in HBV-N. The latter is more useful for most policy purposes. The collaborators comment that the MONERIS model is simple for the novice to use, and produces results in a form which can be traced back. Unfortunately the derivation of N surplus data for individual catchments can be difficult (more difficult than providing land use and livestock numbers); and the method depends on having good average measured flow data. The HBV-N model system, on the other hand, requires expert knowledge to run, but is more versatile. The writers felt that in practice the data requirements for this more complex model were no more difficult to satisfy than those of MONERIS.

2.1.4 Comparative study: Rhine.

Two approaches to estimating source apportionment on the River Rhine, which crosses several countries, were compared by Dijk et al (1997). The first (riverine method) measured actual loads, and the relationships between flow, concentration and load. On this basis, the load was partitioned by source, on the basis that diffuse losses increase with flow, whereas point source losses do not. This method is attractive in its simplicity, so long as detailed measured data are available, since no other information is required. However it gives no further breakdown of diffuse sources, and therefore no information to guide policy action. In order to estimate actual loads as emitted, a reverse-retention calculation was applied to the measured loads, based on temperature.

The second method classified catchments on the basis of their characteristics. In the preliminary analysis, these were population density (as an indicator of sewage inputs) and specific runoff (as an indicator of diffuse loads). A regression of measured loads on the catchment characteristics was then developed. Additional detail was added in subsequent iterations. For example it was found that percentage of arable land was a useful statistical predictor of diffuse emissions.

The critical objective of these studies was to find ways to apportion the measured load between the point and diffuse sources, and spatially (i.e. between subcatchments and countries). The method is therefore very tightly linked to detailed measured data for all subcatchments studied. By this method, the sources are constrained (after retention calculations) to sum to the measured loads. Both methods of source apportionment gave similar estimates of emissions (22 to 23 kg/ha N of which 47 to 48% is from diffuse sources) but for individual catchments the estimates could differ significantly. Estimated average diffuse losses were 29 kg/ha/year from arable land, and 6 from ‘other’ land.

2.2 Previous England and Wales study: WRC, Hunt et al. (2004)

Hunt et al. (2004) based at the Water Research Centre in England prepared a source apportionment study for England and Wales, updating the Royal Society estimate of N sources, and taking account of approaches elsewhere in Europe. The calculation year was 2000/2001, and the project applied to England and Wales only. Both budget and time scales were very tight, limiting the level of detail possible especially for the relatively minor sources. Several of the data sources used are available in different forms for NI and Scotland compared to those used by this report for England and Wales. Extension of the method to the UK as a whole would necessitate some modifications to the calculations. The overall approach for diffuse losses (non-agricultural rural, urban and other land) was based on the NEAP-N model (Lord and Anthony, 2000; broadly an export coefficient modified by climate and soil factors), using spatial data on cropping, livestock numbers, soils and climate at 1 km resolution from the MAGPIE database. This methodology had been

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shown to give good predictions of both the quantity of nitrate loss and the spatial patterns of variation across England and Wales. This diffuse input was calculated by subcontractors who had developed the model used and the spatial data required. An updated version of this method is used in the present study.

The method did not take explicit and separate account of nitrogen inputs to agricultural land by aerial deposition, but implicitly included such deposition, inasmuch as the coefficients had been developed from measurements obtained under prevailing deposition conditions.

Losses from urban land were estimated by summing contributions from a number of identified sources, including atmospheric deposition, leaf fall, animal excreta, and making allowance for the fraction of runoff passing to sewers and therefore separately accounted for. Estimates for non-agricultural land were considered simplistic by the authors themselves.

Point source inputs from sewers were derived using Population Equivalent (PE) data provided by the Water Companies. In addition, allowance was made for varying degrees of N removal according to the different types of treatment process, as shown in Table 2-3. This approach incorporated some industrial discharges to sewer, as these are included in the PE data. The authors recommended regular review of these data as the extent of treatment was increasing. An attempt was made to estimate the proportion of point source inputs discharged direct to the marine environment rather than to rivers, so as to facilitate comparison of river loads with measured data at tidal limits.

Table 2-3: Assumed nitrogen per capita loads for STW source estimation

Medium g N/head/day CommentCrude sewage 12 Ammonia plus organic NSettled sewage 11 Ammonia plus organic N in settled sewage SSBiological Filter effluent 8 N removed by assimilation to form biomass

Activated Sludge Plant effluent 7 Allows for extra removal of N at nitrifying sites with an anoxic zone

Tertiary Biological Filter effluent 7.2 Assume that N is reduced by 10%Tertiary Activated Sludge Plant effluent 6.3 Assume that N is reduced by 10%

Septic tanks and drainage fields that discharge treated effluent to ground water were estimated by calculated the total number of “unsewered” individuals in England and Wales combined with an estimate of the N load per person – assumed, conservatively, to be the same as that for crude sewage.

Additional inputs included an estimate of combined sewer overflows; direct industrial discharges (the authors noting that the database available to them was not complete) and direct atmospheric deposition to waters.

Results were compared with measured in-river loads, and the discrepancy attributed to retention (largely by denitrification). The in-river-loss coefficients showed a negative correlation with specific runoff, which was in line with that reported for numerous mainland European catchments by Behrendt and Opitz, (1999).

The authors noted various limitations of the work possible within this project, and made recommendations for future work. These included:

more detailed assessment of STWs (point sources) including updated allowance for treatment

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investigation of how to better assess in-river loss coefficients creation of a source-apportionment database which would facilitate updating of the

calculation in future, as a major part of the work was collation of the wide variety of data sets and varying formats.

Assessment of uncertainties in more detail

2.3 Other potentially relevant studies of the UK

2.3.1 Nitrogen in the UK: HRI study

The report titled Nitrogen in the UK (Lillywhite & Rahn, 2005) draws together the elements of a nitrogen mass balance for the UK, including industrial and social as well as land use issues, and documenting inputs and outputs using a range of existing studies and data sources. Figures given for nitrogen export to water predate the calculations in Hunt et al. (2004) but are based upon the same method. Because of the scope of the study, results were taken from previous summarised work rather than recalculated or updated.

2.3.2 River basin characterisation method, England and Wales

The River Basin Characterisation method 2 for N in waters (Environment Agency 2007; “RBC2”) was developed as a joint methodology for identification of polluted water under both the Nitrate Directive and the Water Framework Directive. It built on work carried out as part of the review of the designation of Nitrate Vulnerable Zones (Defra, 2007). The method assumed that agricultural land use, consented sewage treatment works, and areas of urban land use, were the principal sources of nitrate loading to rivers. Non-consented discharges, including septic tanks, were not considered.

Datasets on agricultural and urban land-use, point source pollution, groundwater concentrations and baseflow indices were used to derive catchment specific predictors of surface water nitrate concentration. Pressure data representing agricultural land use were calculated using agricultural census, soil, climate and the NEAP-N model, broadly as in Hunt et al., 2004 and as in the current project. Indicators of point source inputs were also included. The regression between pressures and measured concentrations was used to predict likely concentrations in those few areas where measurement data were missing or inadequate. In effect the approach interpolates spatially between measured data locations, using ancillary information.

While this method will give some indication of the relative importance of point and diffuse sources in a catchment, it is not a quantitative source apportionment, for the following reasons:

Not all potential factors are included, which means that the agricultural component, for example, could include other contributions (e.g. septic tanks).

The approach is a regression method, in which the proportion of variance explained by each pressure factor is determined. This is not the same as determining the proportion which each factor actually contributes because:

o There can be confounding, e.g. all rural sources can be partly predicted in terms of the agricultural pressure

o Regression coefficients are mathematically distinct from proportions obtained by summation, and a bias is introduced by the uncertainties in the regression.

The 'result' being explained is the lci on the upper 95th percentile N concentration, whereas most source apportionment studies (including this one) seek to apportion load. The results will differ because:

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o Highest concentrations typically occur during winter, when the diffuse contribution is greatest. Therefore the method will tend to over-estimate the diffuse contribution to loads.

o Greatest concentrations will occur in dry areas, while loads are more evenly distributed and can be greater in wet areas. Since intensity of land use and population density are both negatively correlated with rainfall in the UK, the weighting accorded to point versus diffuse factors could vary depending on whether concentration or load is considered.

The method contains some of the elements of a source apportionment, but is mathematically and practically distinct. The pressure information which was used is included within the present project as part of the total calculation. The calculation approach does not use any additional data sets not considered in the current project.

2.4 Statistical source apportionment for part of East Anglia

Grizzetti et al. (2005) developed a non-linear regression model to determine the contribution of each source of N (punctual and diffuse) to the river mouth transport for an area of 8913 km2 which partially covers the river basins of the Great Ouse, Nene, Welland and Witham. The model relates river nitrate load to the sum of the different nitrogen sources reduced by the retention processes occurring in soils and water. Nitrogen sources considered fertilizer (artificial and manure), atmospheric deposition and point sources. While this approach will give some indication of the relative importance of the different sources used in its construction it is limited by the number of sources accounted for. The calculation approach does not use any additional data sets not considered in the current project.

2.5 Attempting a European method: JRC/GREEN

Grizzetti and Bouraoui (2006) carried out a European scale study for the JRC (Institute for Environment and Sustainability, Rural, Water and Ecosystem Resources Unit) of the European Union, using statistical tools to identify catchments with high nutrient losses. This approach is of interest because it sets out to develop a regression method applicable across Europe, including tackling the difficulty of inadequate and non-uniform data sets and varying land use systems.

Sources of N were classed as either point or diffuse. Both were estimated, and retention coefficients applied. Diffuse sources were estimated on the basis of inputs (applied fertilizer, manure, atmospheric deposition, septic tanks), summed for each sub-basin. These data were estimated from Corine land cover and NUTS2 region data on inputs. A ‘retention’ factor was applied to account for soil processes, offtake and volatilization, which was inversely related to rainfall (on the basis that there would be less leaching in drier areas). Point sources were then added, and a river retention factor applied to the total which was a positive function of reach length (as an indicator of residence time). Sub-catchments were accumulated in downstream sequence, with retention applied in each reach. Urban areas (permeable and impervious) were included, as well as direct atmospheric deposition to lakes. Data on point sources were found to be incomplete, and the authors put considerable effort into unbiased estimation of missing data.

The method gave results comparable with other studies reviewed, mainly in France and Germany. Diffuse sources were deemed to account for between 50 and 86% of load, compared with 52 to 90 in the other studies.

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The method is possibly excessively simple except as a first screen. For example the ratio between N input (as manure plus fertilizer) and N loss to water is typically very different in livestock-dominated catchments compared to arable catchments, for a given rainfall (Lord et al., 2002), which could provide misleading data for individual catchments. Adding manure to fertilizer input exacerbates the problem. The rainfall factor could make matters even worse, since it will indicate greater proportional losses in wetter areas where grassland is more common. The regression approach inevitably introduces confounding factors e.g. of rainfall and land use factors. The retention factors take no account of variation in soil types.

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2.6 Commentary on components of the apportionment process

2.6.1 Estimation of point source inputs

Point source inputs may be estimated from population equivalents, from detailed measurements of combined flow and quality, of from some combination of these types of information. Often measured data are absent or incomplete, and even data on licensed discharges may not be complete.

HARP guideline 4 (HARP, 2000) sets out a methodology for harmonised reporting of N and P from sewage discharges, to include:

Discharges of nitrogen and phosphorus by combined sewer systems; Discharges of nitrogen and phosphorus by separate sewer systems; Discharges of nitrogen and phosphorus by sewer systems that are not connected to

a waste water treatment plants, and Households within the agglomeration which are not connected to a public sewer

system, but that are expected to be connected in the near future.

Discharges from septic tanks are covered in Guideline 5. The recommended methods are based on either monitoring of discharges (recommended for large discharges) or for small plants (less than 2000 population equivalent, on the basis of population equivalent. Estimates are also required for non-measured losses such as leakage and overflow.

All source apportionment methods considered here included waste-water treatment works as the main point source, or have been linked to methods of estimating these. All use approaches similar to those in the HARP guidelines, the main differences being due to differences in quality of input data. Some also include other industrial effluents including fish farms. Not all calculations include discharges from small rural populations. TRK is one of the most detailed in this respect (see Schoumans & Silgram, 2003) as it includes the position of wastewater treatment works etc. as point source discharges with measured data values, the percentage of separate sewers for paved surfaces and the position of rural households and their discharge data. The most detailed models have the purpose of estimating the time series of nitrate and flow for different reaches of rivers, which is not necessary for source apportionment of N loads at regional or national level.

The JRC European model based its estimates on the population density map and the statistics on population connected to waste water treatment plants and type of treatment, because the necessary data for the more detailed calculation were not consistently available for all countries considered. The approach depends on the availability of reasonable estimates of the level of treatment of the waste water and per capita excretion.

The actual method used to interpolate for missing data varies between countries, depending on the data available. The important issue is to ensure that the values reflect the best estimate of actual as opposed to licensed or monitored discharge. Population equivalents are frequently used to interpolate for missing data (e.g. Grizzetti & Bouraoui, 2006).

2.6.2 Estimation of diffuse source inputs

Diffuse sources may be assessed in one of two basic ways: load-oriented and source-oriented.

Load-oriented approach - diffuse loss is estimated as the difference between the total load measured at a river monitoring station and the measured or calculated

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emissions from point sources upstream of the monitoring station. Estimates of retention and losses in the river system are added to calculate the losses at source (before retention). For this method, no data are needed on land use or management. However measured water quality data are required in the river. The method is not considered further here, since it depends solely on calculations of point sources (discussed above) and in-stream retention (discussed below).

Source-oriented approach - diffuse losses are estimated in their own right. Estimates of retention and losses in the river system can be subtracted to calculate the total load at the river mouth (after retention).

Models for estimation of diffuse losses range from export coefficients based on land use and livestock numbers, to detailed daily calculations of the whole soil-plan-water system.

Process-orientated dynamic quantification tools normally require large amounts of input data at a very detailed temporal and spatial scale. For use at national scale, such detailed data will not be available, and assumptions or default values must be developed. This can effectively reduce the range of response of the model. It also means that the quality of the result may depend on the extent to which the chosen ‘scenarios’ represent the national situation. For example, modeling based on averaged input data may not give the same result as modeling of the full range of situations which gave rise to the average data.

These considerations mean that for modeling at national scale, even complex models are likely to require some degree of calibration, or at least evaluation against measured data for a range of catchment types. This is in line with the findings of the EUROHARP project (Schoumans & Silgram, 2003), that more complex models did not perform better than simpler models in estimating diffuse losses for ‘unknown’ catchments.

Despite the excellent scientific and validation pedigrees of many of the models, the long setup and run times, as reported by the EUROHARP project (Schoumans & Silgram, 2003) and the detailed local data requirements, made them unsuitable for use at national scale.

2.6.3 Estimation of other diffuse sources of N

Models varied greatly in the extent to which they took account of other diffuse sources of N, especially urban. NL-CAT calculates the N losses from areas such as forests, wetlands, urban paved areas etc., but most models use standard input figures for these areas.

Many studies calculated losses for non-agricultural areas by analogy with similar catchments where losses had been measured. The result was often labelled as ‘background N loss’.

2.6.4 Retention

The meaning of ‘retention’ varied between studies. Some workers expressed the relationship between N inputs to soil systems or soil N surplus, and N outputs to water, in terms of ‘soil retention’ (e.g. Grizzetti & Bouraoui, 2006; Behrendt et al., 2002). Other models directly calculate emissions from soil, and refer to ‘retention’ solely in relation to surface and groundwater processes. Delay in groundwater is sometimes included in the ‘retention’ factor.

In-stream nitrogen retention processes are processes which reduce the quantity of N reaching the measurement point, or the sea, relative to the amount entering the water system from all sources. Biological and chemical denitrification of nitrate, nutrient uptake and deposition of particulate bound nitrogen is the major mechanisms removing or storage.

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Biological uptake is normally not included in calculations at annual level because the N is released during the annual cycling of biota.

Retention in groundwaters may be due to denitrification, but if calculated by comparison with measured nitrate exports in surface waters, could be confounded with the effects of delays as polluted water passes through the aquifer and emerges to feed the surface water decades later.

Retention below the root zone was estimated in several models (e.g. NL-CAT, Kroes et al., 2000) as is in-stream retention (e.g. EveNFlow; Silgram et al., 2008). NL-CAT had one of the most comprehensive approaches to retention, dealing with potentially very complex ground and surface water flows.

The JRC system (Grizzetti & Bouraoui, 2006) used ‘retention’ to cover the widest range of processes. It takes the N input to land as its starting point. This N input includes manures as well as fertiliser, which means N inputs to grazing systems are effectively counted twice. It then applies a soil retention coefficient to this surplus, which has to take account of crop uptake, volatilisation and all other system processes. After addition of point sources, a river retention coefficient is applied. The values are derived by regressions on measured data, with rainfall and basin size as the main explanatory variables. The MONERIS system is slightly more sophisticated, in that the input is the N surplus (offtake is subtracted) and flow is partitioned into ground and surface waters. A groundwater retention coefficient is applied, then ground water, surface water loss from land, and point sources are added together and a surface water retention coefficient is applied. Both of these coefficients therefore implicitly account for soil processes and volatilisation as well as in-river and groundwater processes. They are derived from ‘similar’ catchments by regression of the N surplus on the measured N export in surface waters. Both of these systems have the advantage that they are designed for use with large river systems. Their disadvantage is that there is no means of testing the results at field scale – a huge range of processes is lumped into a single coefficient, and there is no guarantee that ‘similar’ catchments will behave in a similar way with respect to all of these.

The EUROHARP project developed a manual of peer-reviewed methods developed and implemented by the consortium for estimating nutrient retention in surface waters (Kronvang et al., 2004). This gave methods applicable to streams and rivers, lakes and reservoirs, and riparian zones. These methods are intended as first-pass estimates, to be validated where possible by mass balance calculations comparing inputs to outputs. Guidance is given on use of the system, and where it could give misleading results. For rivers, the method is based on catchment size and total length of river network. The retention is an annual figure, with no seasonality or temperature dependence.

2.6.5 Accounting for atmospheric inputs

Atmospheric input values across Europe may be obtained from sources such as the EMEP Unified model (EMEP 2005) and may be modified according to the local land surface cover (crop type or water).

The input from atmospheric deposition of N was taken into account in some detailed models (including NL-Cat/ANIMO). In other models (e.g. EveNFlow) the coefficients used to estimate N loss were taken from field data, such that typical levels of atmospheric deposition are implicit in the estimates.

Many of the methods, especially those studying lake areas, took account of direct atmospheric deposition to water bodies. Some methods also took account of direct deposition to paved urban areas (e.g. Hunt et al., 2004).

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None of the source apportionment methods reviewed attempted to estimate the impact of atmospheric deposition on N losses from agricultural land. N losses from upland or non-agricultural areas (where the dominant N input is atmospheric deposition) were generally estimated from measurement of similar catchments, and were sometimes designated as ‘background, non-anthropogenic’ load despite the potential contribution of anthropogenic atmospheric inputs. (e.g. Kronvang et al., 2005; Salo et al., 1997) . Methods of relating atmospheric inputs to N loss have been explored in other studies, and are reviewed in the relevant section of this report.

2.6.6 Partitioning by hydrological pathway

In general, the purpose of partition of losses by hydrological pathway is to make due allowance for differential losses (surface runoff versus leaching); retention and delay (groundwater compared with streams) and to better simulate the day to day variation in N concentrations in rivers. The partitioning between surface runoff, drain flow and leaching or deep seepage is less critical for N than for pollutants such as P, sediment and pesticides, because the proportion retained is generally small (unless there is substantial denitrification due to shallow water tables). Retention (in this sense of denitrification) is rare in UK groundwaters. Taking account of delay may improve simulation of river N concentrations, but in terms of policy decisions may be less relevant, since high N concentrations in groundwater will eventually reach rivers. The final purpose is important for validation, but not essential to source apportionment studies.

2.7 Summary

Calculation of N source apportionment at regional/national scale requires methods which are adapted to the data available at that scale. The methods reviewed here have used two basic approaches to the problem: simplify the model, or adapt the way in which a more complex model is used.

Point sourcesThere is general agreement on the approach for calculating point sources, and guidance is given by HARP (2000). The main difficulties arise in obtaining sufficiently accurate data, especially on smaller sources, or where treatment methods are changing. Care is needed in accounting for sources other than sewage which may discharge through sewers (e.g. urban runoff, industrial sources) to ensure there is no double counting. While location of discharges, and even licensed conditions, may be available, detailed information to enable load estimation is often incomplete or difficult to extract. In such cases, and especially at large scale, population equivalents may give a more robust estimate (e.g. Grizzetti & Bouraoui, 2006).

Urban diffuseMethods for calculating urban runoff and leaching are varied and generally relatively simple. Many use atmospheric inputs as a component.

Rural diffuseDiffuse losses from agricultural land are the dominant source of nitrate in most catchments studied, and their calculation methods range from the immensely detailed (e.g. Groenendijk and Roelsma, 2002 ) to simple calculation by difference (as in the Source Apportionment guidelines, HARP 2000).

The simplest method, by subtraction of point sources, is quick to use. However it requires estimates of river N load, point source inputs, and in-river retention. All of these are

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uncertain which means all errors accumulate in the diffuse estimate. Furthermore river N loads, especially accurate means over several years, are not available sufficiently universally to provide national coverage. Finally this method gives no means of attributing cause, notably for this project the impact of atmospheric deposition on diffuse losses.

The next tier of models takes statistical data as indicators of loss. This method effectively uses N surplus or input to spatially disaggregate the measured river loads. However there appears to be no recognition in terms of classification of catchments, of the fact that the ratio loss/surplus is much smaller where livestock are involved than in arable, livestock-free systems. Given the systematic regional variation in livestock farming, this could introduce errors and incorrect attribution of cause.

The next level of complexity is to use export coefficients The system used by Hunt et al. (2004) was based on export coefficients, modified by a leaching function dependent on climate and soil, and calibrated against measured data (Lord & Anthony, 2000).

The more complex models take two approaches: mechanistic and semi-empirical informed by scientific understanding. Use of complex mechanistic models in large catchments, where detailed local management data are not available, means that many parameters must be estimated or an average value used. This often creates a constraint on the subtlety and complexity of the response in practice, so that the complexity of the model is not fully exploited.

However complex the model, there are uncertainties in our biological understanding and input data which mean predictions cannot be perfect. Since the objective in this case is to estimate diffuse losses as accurately as possible, calibration of results against measured data is important. The approach taken by systems such as SOILNDB (Johnsson et al., 2002) is to determine N loss in a mechanistic way, but for a long sequence of weather data, and calibrated against measured data.

On the basis of experience within the EUROHARP project (Kronvang et al., 2007) and other comparative exercises, a vital factor would appear to be that the method used gives results which are correct on average for the area under study, and preferably correctly show the effects of variation in land use, climate, soil. This is most likely to occur with a model which has been developed locally. In practical terms, the model should be simple to use, and have data requirements which can readily be met from statistical sources available for the whole country.

N deposition impactsAs regards N deposition, the more complex process-based models and the models based on N surplus take this into account explicitly. Many methods also take account of direct N deposition to surface waters and in some cases to urban hard surfaces. None of the studies attempted to disentangle the net impact of N deposition on N loss from diffuse sources.

RetentionRetention in rivers (i.e. the difference between estimated N inputs to rivers and measured loads at the interface with the sea) can be estimated by difference between calculated and measured load, although this method accumulates all errors into the retention factor. Use of this approach to estimate losses from agriculture (effectively, to estimate ‘soil retention’ ) appears to us to be more crude than necessary, since high quality data are available relating land use and management to nitrate loss at field scale. These data can, at their simplest, be used to generate ‘export coefficients’ as a function of land use. In-river retention is difficult to estimate with any accuracy, but a standardised methodology has been developed (Kronvang et al., 2004) which provides a reasonable estimate of the likely range of values.

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Non-nitrate forms of NFew methods deal with these sources explicitly or in any detail. The MONERIS model includes N on eroded soil, but the method of predicting erosion was simpler than methods currently available at national scale in the UK. No model was found which gives a detailed estimate of soluble organic N as a function of livestock numbers.

2.8 Conclusions

Point sources: estimation is simple in principle, but collation of reliable data is a constraint. Values need to take account variation in levels of treatment. Interpolation can be done using population equivalents, calibrated against areas where good data exist. Septic tanks and small sources need to be accounted for, as do industrial discharges via sewers. Direct discharges to sea will not affect measured river loads but will affect the marine environment.

Agriculture: The critical issue is that there should be some calibration, or evidence that the estimates are broadly correct for the area to which they are applied. Estimation of diffuse sources by a method which can be checked and calibrated against measured data at field scale, seems to us preferable to coarser-scale regressions of land use against river data, because the former method can be shown to be appropriate to local conditions; avoids the risk of confounding effects; allows partitioning within agricultural systems in greater detail; and holds out the potential for exploring mitigation options.

Other diffuse rural sources: Some methods considered emissions from non-agricultural rural land to be ‘background, non-anthropogenic’ but this distinction does not appear to be very useful in the UK, where all locations are affected by anthropogenic atmospheric deposition. In general estimation of these sources has been by analogy with catchments where measured data were available. Methods exist in the literature for more explicit calculation, but have not been widely used in source apportionment studies.

Minor N sources: Great variation exists here, in part driven by local availability of input data and measurements. Urban estimates were generally based on some form of extrapolation from available measured data.

N deposition: Many methods calculated direct deposition to waters but none of the methods considered explicitly the impact of N deposition on diffuse losses, although some of the models contained calculations which could have been used in this way.

Overall we consider that the data available to us for nitrogen source apportionment to waters in the UK is as good as that available to most of these studies, and better in some areas.

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3 General Methodology

The methodology adopted in this project was designed to:

Exploit models that are fairly well established and where possible have been designed specifically for or used for water quality policy compliance/development in previous projects. As such these have a degree of acceptance and have been reviewed, their strengths and weaknesses identified and in some instances actually validated. This project drew extensively on modelling methodologies employed within Water Framework Directive (WFD) and Nitrate Vulnerable Zone (NVZ) policy work undertaken by the Defra, EA, SEPA and EHS-NI (e.g. Anthony et al., 2005b; Hughes et al., 2006; Anthony et al., 2008).

Have an appropriate level of complexity for regional/national scale assessments. Using simple modelling approaches over data hungry sophisticated process-based models is advantageous when undertaking policy type work (Heathwaite, 2003). Where possible approaches strongly based on available measured data were favoured. Typically, the methodologies employed were relatively simple, worked at a coarse time-step and at best estimated the pollutant load to water bodies, without consideration of any subsequent transport, retention and bio-chemical cycling/processing.

The methodologies were developed to work with roughly equivalent environmental and agricultural data that were available for the different countries in the UK. We chose to build on previous ADAS national scale modeling approaches drawing on the MAGPIE (Lord and Anthony, 2000), ADAS Land Use (Comber et al., 2007) and Manure Management (Comber et al., In Press) databases and the NSRI Landis database which are all available at 1km grid cell resolution. The 1km environment database of input data requirements for the models was then coupled to these models and where appropriate model output at this 1km grid cell resolution stored in the database.

Methodologies for a range of N sources were adopted, namely:

1. Agriculture, including arable and managed grass2. Woodland/Forests and other natural areas, including rough grass3. Point sources, including sewage treatment works as well industrial sources4. Urban hard surface runoff as well as leaching5. Particulates arising largely from sources 1 and 26. Direct deposition to water

These methodologies are detailed in the ensuing section (Sections 4 through 9) along with their data requirements, where these data for each country were sourced along with a summary and description of the outputs. Where appropriate, the weaknesses of the modelling methodologies are also highlighted. Limited validation of the results was undertaken through comparison with measured and/or modelled results from other studies or regulatory reports (Sections 10.2 and 10.3).

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4 Atmospheric Deposition

Atmospheric deposition is an important part of any N load and apportionment project. In this project direct deposition data are used directly in three sources, namely direct deposition to water (Section 4.3), to agriculture (Section 5) and woodland and natural areas (Section 6).

4.1 Methodology

The load of atmospheric deposition to the various land uses was calculated by combining the appropriate wet and dry deposition rates with an estimate of the extent of the land use as has been done in previous studies (e.g. Bealey et al., 2007), for example combining the forest wet and dry deposition rates (keq/ha/yr) with the area of forest/woodland (ha).

4.2 Environment Data

Atmospheric deposition data were provided by CEH for a recent and typical emissions year, namely 2003 (Bealey, Pers. Comm.). These data were simulated using the FRAME (Fine Resolution Atmospheric Multi-pollutant Exchange) model which is a Lagrangian atmospheric transport model used to assess the long-term annual mean deposition of reduced and oxidised nitrogen and sulphur over the United Kingdom (Dore et al., 2007). A detailed description of the FRAME model is contained in Singles et al. (1998). These deposition data are supplied at a 5km grid cell resolution and are broken down into a range of sources as well as summarised into a range of broad “footprint” categories, e.g. livestock emissions. Within this report the broad footprint categories, summarised in Table 4-4, will be used when subtotals by source are provided. The total atmospheric deposition (kg) in each grid cell is illustrated in Figure 4-2.

Table 4-4: Atmospheric deposition broad footprint classes (Bealey, Pers. Comm.)

Footprint Category

Description of sources Pollutants

Other Point Sources

All other (smaller) point sources SOx, NOy

Combustion in Energy Production & Transformation

Electricity and heat production; Petroleum refining; Manufacture of Solid Fuels and Other Energy Industries; Coal mining, oil / gas extraction.

SOx, NOy

Combustion in Commercial, Institutional & Residential

Non-industrial plants, but includes Commercial and Institutional plants; Residential plants; Plants in agriculture, forestry and aquaculture

SOx, NOy

Combustion in Industry

Combustion in the manufacturing industry SOx, NOy

Production Processes

Production processes in petroleum industries; Iron and steel industries and collieries; Organic/inorganic chemical industries; Wood, paper pulp, food, drink and other industries; Production of halocarbons and sulphur hexafluoride.

SOx, NOy

Production of Fossil Fuel and Distribution

Extraction of solid/liquid/gaseous fossil fuels; Petrol distribution; Gas distribution networks, Geothermal

SOx, NOy

Solvent Use Paint application (car manufacturing); Degreasing, dry cleaning and electronics; Chemical products manufacturing or processing; Other use of solvents and related activities

SOx, NOy

Road Transport Passenger cars; Light-duty vehicles; Heavy-duty SOx, NOy

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vehicles; MotorcyclesOther Transport Railways; Military; Maritime activities; Inland Water

Ways; Air traffic; Agriculture, Forestry and Domestic.SOx, NOy

Livestock emissions

Manure management regarding Nitrogen compounds NHx

Fertilizers, crops and grass

Fertilised agricultural land NHx

Non-agricultural emissions

Sewage; Catalytic converters; Wild animals; Seabirds and Industrial Processes.

NHx

Imported Emissions

Emissions from outside the UK SOx, NOy

The area of the agricultural, woodland and natural areas was defined using the CEH 0.25ha resolution Land Cover Map 2000 (LCM2000) summarised per 1km grid cell. For the purposes of this study when calculating the direct deposition to inland water bodies, these were defined to include rivers/streams and lakes. Owing to the scale of the LCM2000 dataset the rivers/streams categories are under-represented except in the case where rivers exceed 25m in width. As such a new 1km grid cell version of water surface area was created by combining lake and stream/river surface area estimates. Lake surface areas in each grid cell were derived from the UK Lakes database (Hughes et al., 2004) as well as a comparable lakes coverage for Northern Ireland supplied by the Department of the Environment Northern Ireland (DOENI). River/stream length (km/km2) per grid cell was derived from the following sources: for England and Wales from a Countryside Information System product that summarises the CEH 1:50 000 rivers coverage (Moore, R.V. et al., 1994); for Scotland a bespoke summary of the OS Mastermap rivers theme (1:2500 scale) was undertaken as Defra do not license the CEH rivers network product for Scotland and the purchase of the data was beyond the project budget; for Northern Ireland a bespoke summary of a comparable WFD rivers coverage supplied by DOENI was undertaken. Every effort was made to use comparable datasets and while the use of different datasets may introduce some additional uncertainty into the results, given the small contribution that direct deposition to water makes to the overall UK N loads to water this should not prove to be problematic. The surface area of the rivers and streams was determined by multiplying the river/stream length per grid cell by their average width as determined from the Environment Agencies River Habitat Survey (RHS) (Environment Agency, 2003) for a range of Strahler stream orders. This is likely to be a conservative estimate of water surface area as these RHS surveys are carried out in summer when stream widths are at a low.

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Figure 4-2: Average annual total atmospheric deposition of inorganic N (NHx and NOy) in kg for each 1km grid cell calibrated to the 2001-2003 period as simulated using the FRAME model

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4.3 Results

The resulting surface water areas are illustrated in Figure 4-3 where the lakes in Cumbria and the lochs/loughs of Scotland and Northern Ireland are quite apparent. The load (kg) of N deposited directly into water bodies across the UK is illustrated in Figure 4-4 and displays similar patterns to the water body area map.

The direct atmospheric deposition to surface water bodies results have been summarised into regional and national totals which are provided in Table 4-5. This source of N contributes 8.59 kT of N to surface waters with Scotland and England contributing the most to this total, being 3.80 and 3.48 kT respectively. Table 4-5 also provides a breakdown of these direct atmospheric contributions by broad footprint classes where it will be noted that livestock, imported and road transport emissions dominate.

The load of atmospheric deposition received by agriculture (Section 5) and woodland and natural areas (Section 6) are illustrated in Figure 4-5 and Figure 4-6, respectively. The loads broadly reflect the extent of the different land uses and the spatial distribution of the atmospheric deposition loads. These loads were used as inputs to the models used to simulate N leaching from agriculture (Section 5.1.2) and woodland/natural areas (Section 6.2.1).

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Figure 4-3: Surface water area (ha/km2) per grid cell for the UK

25


Figure 4-4: Atmospheric deposition of inorganic N (kg) directly to surface water for each 1km grid cell

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Table 4-5: Regional and National summaries of the atmospheric deposition directly to water (kT), broken down by broad atmospheric deposition footprint type (%)

Government Office Region

N from direct

Atmospheric Deposition to surface water (kT)

Combustion in

Commercial, Institutional

& Residential

Combustion in Energy

Production & Transformation


Fertilizers, crops and

grass

Production of Fossil Fuel and

Distribution

Imported Emissions

Livestock emissions


Other Point

SourcesOther

TransportProduction Processes

Road Transport

Solvent Use

East of England 0.49 2.69 0.24 1.78 7.08 0.00 17.38 31.40 16.24 1.81 3.62 0.00 17.76 0.00

East Midlands 0.49 2.76 0.25 1.92 6.16 0.00 16.22 36.47 13.42 2.28 3.55 0.00 16.95 0.00

London 0.07 3.05 0.21 1.48 2.00 0.00 13.76 18.05 44.27 1.11 3.78 0.00 12.29 0.00

North East 0.21 3.17 0.44 1.63 4.71 0.02 19.49 40.66 10.00 2.18 4.29 0.00 13.40 0.00

North West 0.51 3.87 0.39 1.66 3.33 0.01 16.47 44.16 10.13 2.46 3.95 0.00 13.58 0.00

South East 0.44 2.45 0.20 1.48 4.65 0.00 21.61 33.66 15.83 1.30 4.53 0.00 14.29 0.00

South West 0.43 1.88 0.15 1.13 4.88 0.00 19.66 49.81 8.01 1.18 3.21 0.00 10.10 0.00

West Midlands 0.32 2.57 0.20 1.63 5.03 0.00 15.31 46.18 11.46 1.72 2.76 0.00 13.14 0.00

Yorkshire and The Humber 0.52 2.79 0.32 1.75 4.38 0.01 17.18 40.72 11.84 2.86 3.54 0.00 14.61 0.00

England 3.48 2.78 0.27 1.63 4.99 0.00 17.73 39.57 12.98 1.98 3.68 0.00 14.38 0.00

Wales 0.54 3.14 0.26 1.54 4.23 0.00 21.71 43.51 7.38 2.26 3.24 0.00 12.73 0.00

Scotland 3.80 3.54 0.93 1.57 3.33 0.02 25.72 35.67 8.94 2.03 5.03 0.00 13.23 0.00

Northern Ireland 0.77 3.69 0.23 1.04 3.31 0.01 15.99 58.53 5.96 1.33 2.63 0.00 7.26 0.00

UK 8.59 3.22 0.56 1.54 4.05 0.01 21.36 39.79 10.21 1.96 4.15 0.00 13.13 0.00

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Figure 4-5: Atmospheric deposition of inorganic N (kg) to agricultural land in each 1km grid cell

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Figure 4-6: Atmospheric deposition of inorganic N (kg) to woodland and natural areas in each 1km grid cell

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5 Agriculture

Agriculture has been identified in previous studies (e.g. Hunt et al., 2004) as a major source of inorganic N, typically dominating the total loads. Similar to the work undertaken by Hunt et al. (2004) this source of N was assessed using the NEAP-N model which has a strong track record for policy level assessments (e.g. Anthony, 2005).

5.1 Methodology

Calculation of national baseline N loss is carried out by a simplified model widely used for policy purposes (NEAP-N), which gives a baseline nitrate loss as a function of cropping, livestock numbers, climate and soils. Superimposed on this we calculate the proportion of the nitrate loss which is directly attributable to atmospheric deposition, as a function of local deposition, land use, climate and soil. The relationships required are calculated by application of a more detailed daily time-step soil-crop model (NIPPER) to a range of field-scale scenarios.

5.1.1 NEAP-N

The national NEAP-N nitrate leaching model is part of the MAGPIE nitrate leaching decision support system (Lord and Anthony, 2000) used in support of government policy on the control of nitrate leaching. A detailed description of NEAP-N is given by Anthony et al., (1996); Lord and Anthony, (2000) and Silgram et al., (2001). The model is designed to give rapid national predictions of N loss and its spatial variation as a function of land use, climate and soils, for any location in the UK. In summary, NEAP-N considers a single maximum potential nitrate loss coefficient for individual crop and livestock types, which is modified (reduced) in accordance with local information on soil type and hydrologically effective rainfall. NEAP-N baseline values for nitrate leached under different UK arable crops fertilised in accordance with DEFRA guidelines (MAFF, 2000) are derived from Lord (1992), with revisions based on the results of recent research (Lord et al., 1995; Shepherd and Lord, 1996; Lord and Mitchell, 1998). Losses modelled using this approach have been found to compare favourably with independent field measurements using porous pots in fields within the Nitrate Sensitive Areas (NSA) scheme (Lord, 1992; Anthony et al., 1996) and with stream nitrate fluxes measured in several contrasting catchments (Lord et al., 1995).

Data for grassland systems are derived from research under-pinning the N-CYCLE model developed by IGER in the UK (Scholefield et al., 1991; Scholefield and Rodda, 1992). The coefficients take account of the whole grassland system (cutting and grazing plus longterm organic matter buildup) as well as the leaching arising from applications of manure under typical timing and management (Lord et al., 1995; Smith et al., 2001). The coefficients for grassland and housed livestock are expressed in terms of numbers of stock.

The NEAP-N simulation of nitrate leaching requires an estimate of hydrologically effective rainfall (HER). This is calculated for each km square from a series of regressions summarising MORECS (Hough and Jones, 1997) calculations of annual crop water balances in terms of crop, soil type, annual rainfall and annual potential evapotranspiration. For this purpose, the dominant soil series within each spatial calculation unit is placed into one of three classes based on Available Water Capacity (AWC), the water held between field capacity and permanent wilting point. Using this pragmatic approach, Barrie et al. (1994) explained over 95% of the variance in HER in long-term data from 67 weather stations in lowland England from 1961-80, while Anthony et al. (1996) accounted for over 90% of the variation for 31 weather stations across England and Wales.

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The leaching function used to estimate actual N loss as a function of HER and soil water content at field capacity is similarly a simplification of the SLIM model developed by Addiscott and Whitmore (1991), derived from multiple model runs and tested against measured field data (Anthony et al., 1996).

5.1.2 Effect of Atmospheric N on nitrate leaching

To assess the contribution of atmospheric N to leaching from agricultural land, a more detailed model was required, which could calculate crop and leaching responses to both quantity and timing of atmospheric N deposition. The objective was to estimate the proportion of such deposited N which leached.

The underlying hypothesis, which differs from many previous studies (Goulding et al., 1998; Anthony, 2005) is that atmospheric N has long been a part of the UK environment, and is implicitly taken into account in fertiliser recommendation systems and farmer decisions, since the relevant data and judgement were all developed in the presence of atmospheric N deposition. In effect therefore we are seeking to quantify the difference in leaching between two parallel universes, identical except for the fact that one of them has no (or less) atmospheric deposition.

In order to explore this net effect of atmospheric deposition, a range of scenarios have been modelled with a sophisticated process based model of nitrate leaching (Gooday et al. 2007; Lord et al., 2007). The outputs of these scenarios were summarised in relationships describing the change in leaching with varying rates of atmospheric deposition and for various climates. The scenarios were based around typical rotations for UK soils, including a mix of common crops (both autumn and winter sown) and grassland fertilised at different rates (to represent the differing intensity of dairy and beef / sheep farming). The use of rotations ensured that legacy effects on crop growth from deposition and management in previous years were accounted for. The mix of spring and winter crops was set to be close to the mix recorded by census data. Atmospheric nitrogen deposition, like fertiliser, can contribute to crop growth and yield (Hatch et al., 2002). Average fertiliser rates taken from the British Survey of Fertiliser Practice (Goodlass and Welch, 2005) were used with an average UK deposition rate (16 kg N ha-1 yr-1) to determine a baseline level of N uptake and growth for each of the crops being modelled. Assuming that farmers already take account of local N deposition rates (implicitly through experience), for the 3 further rates of atmospheric deposition being considered (0%, 50% and 200% of the UK average value), fertiliser rates were adjusted for the crops being modelled until the levels of crop growth and uptake were equal to those under the average deposition rates (Table 5-6). The contribution of atmospheric deposition to leaching could thus be determined by difference between equivalent scenarios under the different deposition rates.

For grassland and autumn sown crops, modelled fertiliser rates required to maintain production are almost equal to the change in deposition, i.e. deposited N is almost as efficient as fertiliser. For spring sown crops, fertiliser changes are smaller (60%-80% of the deposition change) as the crop is in the ground for a shorter amount of time and so is not able to utilise the entire annual atmospheric deposition.

Table 5-6: Adjust fertiliser rates (kg ha-1 N) for different atmospheric deposition rates

Crop/Land use Atmospheric Deposition (kg N ha-1 yr-1)0 8 16 32

Winter Wheat 213 205 197 181

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Winter Barley 151 145 139 128Spring Barley 116 111 105 94Winter OSR 225 217 209 193Peas 0 0 0 0Sugar Beet 108 102 95 83Grass (Beef / Sheep) 146 138 130 114Grass (Dairy) 66 58 50 34

The results of the modelled scenarios are shown in Table 5-7. The increase in leaching as a proportion of N deposition is smaller for grassland than for arable. This reflects that fact that at a given level of N input from all sources, nitrate leaching under grassland is normally less than under arable. Grassland is also typically present all year, continually taking up some N from the soil, so small amounts of N deposition during the winter can be taken up into biomass. For arable systems, the ground is typically bare over winter before a spring sown crop, so N deposited between senescence or harvest of the previous crop up to about mid winter is likely to be at least partly lost by leaching. Even autumn sown crops have limited ground cover before mid winter, and their N uptake capacity is typically smaller than the sum of soil mineralisation and N deposition since senescence of the previous crop. Under the wettest climate considered, approximately 18% of the annual N deposition is leached in an arable system compared with only 3% in a grassland system (after adjustment of fertiliser inputs). These results contrast with those of Anthony (2005), where atmospheric N inputs were assumed to be in addition to accurate fertilisation and so were inefficiently used. The results of Anthony (2005) were that 30% and 20% of atmospheric deposition could be leached in a wet climate for arable and grassland respectively. These effectively represent a maximum value for potential leaching of atmospheric N, compared with the values produced in this work where it is assumed farmers take some account of deposition in their fertiliser applications and that typical rates of deposition are built in to fertiliser recommendations.

Adjusting the results for soil type and drainage resulted in coefficients representing the net impact under high rainfall conditions, which could then be incorporated within the national NEAP-N model, framework. The potential additional nitrate leaching due to deposition was 18% or 3% of the total for arable and grassland respectively.

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Table 5-7: Increase in annual leaching (kg ha-1) with increasing atmospheric deposition under varying climates as simulated using NIPPER (Gooday et al., 2007; Lord et al., 2007)

Land Use

Deposition (kg N ha-1 yr-1)

Annual Average Rainfall (mm)

600 750 900Arable

8 0.9 0.9 1.316 1.9 2.1 2.832 4.2 4.5 6.3

Grass

8 0.1 0.1 0.216 0.1 0.2 0.532 0.3 0.4 1.0

5.2 Datasets

The datasets required to run the NEAP-N model are described in the ensuing sub-sections. These datasets are also used for a number of the other modelling approaches and as such these will reference to this section rather than repeat the information provided here.

5.2.1 Climate Data

The NEAP-N model is driven by long-term average annual total rainfall. For this study, this was defined was the 1971-2000 period. However, the NEAP-N climate database is for the 1961-1990 period. This concerns reference annual potential evapotranspiration (PETG) and rainfall (AAR). The PETG data is derived from the MORECS model (Hough et al., 1997) at a spatial resolution of 40by40km2. This data was not modified for this study. The AAR data is held at a spatial resolution of 1by1km2 and captures the fine-scale effect of topography on rainfall. This dataset was derived by spatial interpolation from over 10,000 recording stations (Spackman, 1993). The most recent available AAR data for the 1971-2000 period is from the UK-CIP archive and is at a spatial resolution of only 5by5km2. It does not represent the fine scale variation, and is derived from fewer recording stations. Therefore, we scaled the 1961-90 data in proportion to the ratio of the UK-CIP 1971-2000 and 1961-1990 averages for the 5by5km2 cells in which each 1by1km2 cell is located. This process is described in more detail in Anthony et al. (2005b).

5.2.2 Soils

England and Wales: Attributes of the dominant soil series within each 1km2 cell were obtained from the NSRI database and the Hydrology of Soil Types (HOST) classification (Boorman et al., 1995). Key soil attributes were the total and plant available water content at field capacity, and the soil texture and drainage class according to the N-Cycle model descriptions. The latter were determined from the soil particle size distribution and HOST class according to rules provided by NSRI (Hollis, pers. comm.).

Scotland: Defra do not license the Scottish Soils Database and the purchase of this dataset was beyond the scope of this projects budget. Given the relatively simple soils classes used in NEAP-N the Soils Geographic Database of Europe was used. Host classes were derived using a suite of pedotransfer functions derived by John Hollis for the FOOTPRINT project (Hollis, pers. comm.).

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Northern Ireland: A soils data license for Northern Ireland was provided by DOENI. This data had been manipulated into 1km grid cell database in a previous project (Anthony et al., 2005b) and was used within this project.

5.2.3 Census Data

Estimates of the numbers of livestock and crop areas for each 1km2 cell for the UK were obtained from the ADAS National Land Use database. This dataset incorporates the ADAS National Land Cover Map and collated Defra/Welsh Assembly Government/Scottish Government/Northern Ireland Government agricultural census data for the year 2004. To improve the spatial precision and accuracy of the agricultural census dataset, ADAS have integrated the census with additional information on the location of non-agricultural and agricultural land, derived from remote sensing and Ordnance Survey mapping. A detailed description of the derivation of this ADAS National Land Use database can be found in the final reports for Defra (formerly MAFF) projects NT2206 and NT2203.

5.2.4 CORINE Land Cover Data

The CORINE Land Cover map for 2000 (CLC2000) was produced by CEH by generalisation of the more detailed Land Cover Map 2000 (LCM2000). The map generalisation process used a cross tabulation of thematic classes, a semi-automated spatial generalisation procedure and visual satellite image comparison using computer assisted image interpretation tools. The data are designed to be used at a scale of 1:100,000 and has a minimum mapped unit of 25 ha. It records 44 land cover and land use classes which represent the major surface types across Europe. Unlike the LCM2000, the CLC2000 separately identifies urban land cover types, including industrial units, parks, leisure facilities, and construction activity. The dataset was obtained directly from the European Environment Agency by ADAS at a spatial resolution of 100 by 100 m2. The data were projected onto the OS National Grid and relevant items extracted to support the urban nitrate leaching model.

5.3 Results

The results from the NEAP-N simulation are illustrated in Figure 5-7. The results highlight the key arable areas, for example parts of East Anglia and the east coast, as well as high rainfall intensively stocked grassland areas, for example the South West Region. Interrogation of the proportion of N leached from agriculture that may be attributed to atmospheric deposition, as illustrated in Figure 5-8, suggests that for managed grass areas this proportion is typically less than 6% while for arable areas it is typically up to 10%. Small pockets of higher proportions are also evident and these are an artefact of the land cover dataset identifying managed grass in areas where few or no animals are present and as such atmospheric deposition is the dominant N source. This occurs in so few 1km grid cells that it does not affect the national and regional summaries which are presented in Table 5-8. In all regions the N from fertiliser applications and soils as well as excretal returns from grazing animals dominate, with atmospheric sources comprising just ~4% of the total. Investigation of the dominant footprint sources contributing to the atmospheric input, as summarised in Table 5-9, indicate that agriculture itself contributes almost 50% of this atmospheric deposition along with imported emissions, road transport and other point sources.

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Table 5-8: Regional and National summaries of the contribution (kT) of N from different agricultural sources


N from all Agricultural Sources (kT)

Proportion (%) fromAtmospheric deposition*

Manure - housed animals

Grassland systems$ Arable #

East of England 34.44 6.25 12.06 4.03 77.67

East Midlands 30.64 5.52 8.41 15.43 70.64London 0.21 5.82 16.33 17.57 60.28North East 8.57 4.44 3.40 27.69 64.48North West 20.41 2.75 6.19 65.27 25.79South East 28.44 5.35 7.02 20.24 67.40South West 56.29 3.67 6.33 53.12 36.87West Midlands 27.62 3.84 9.42 36.53 50.21Yorkshire and The Humber 28.20 5.38 12.45 19.43 62.74

England 234.81 4.67 8.51 31.12 55.70Wales 31.90 1.89 3.32 85.72 9.06Scotland 63.37 2.81 4.78 47.22 45.18Northern Ireland 22.66 2.01 10.44 70.44 17.10

UK 352.75 3.91 7.50 41.48 47.11* Atmospheric deposition to arable land and managed grass only – deposition to rough grazing is covered under natural areas$ Excretal returns plus fertiliser where it is applied# Fertiliser and soil for arable systems

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Figure 5-7: Leached N from arable agriculture and managed grass (kg) as simulated by the modified NEAP-N model for each 1km grid cell

36


Figure 5-8: Proportion of N leached from arable agriculture and managed grass attributable to atmospheric deposition within each 1km grid cell

37


Table 5-9: Regional and National summaries of the N atmospheric deposition emitted by agriculture to water broken down (%) by broad atmospheric deposition footprint type


N from Agricultural Atmospheric Deposition

(kT)

Combustion in


& Residential





grass


Distribution

Imported Emissions

Livestock emissions


Other Point

SourcesOther


Road Transport

Solvent Use

Total Agriculture*

East of England 2.15 2.54 0.23 1.70 6.84 0.00 17.33 34.73 14.23 1.75 3.55 0.00 17.09 0.00 41.54

East Midlands 1.68 2.68 0.26 1.88 6.56 0.00 16.38 36.54 12.82 2.26 3.42 0.00 17.19 0.00 43.38

London 0.01 2.99 0.24 1.59 2.39 0.00 15.70 19.04 37.09 1.33 4.81 0.00 14.84 0.00 21.54

North East 0.38 2.76 0.39 1.66 6.75 0.02 15.66 41.20 12.65 1.90 3.95 0.00 13.06 0.00 48.15

North West 0.56 2.85 0.27 1.46 3.86 0.01 12.91 49.32 12.64 1.78 3.18 0.00 11.73 0.00 53.27

South East 1.52 2.33 0.20 1.49 5.53 0.00 21.85 36.38 12.99 1.32 3.51 0.00 14.39 0.00 41.63

South West 2.06 1.91 0.15 1.17 5.19 0.00 19.57 49.69 8.08 1.16 2.84 0.00 10.24 0.00 55.12

West Midlands 1.06 2.48 0.20 1.59 5.38 0.00 15.36 47.44 10.60 1.63 2.63 0.00 12.67 0.00 53.24

Yorkshire and The Humber 1.51 2.32 0.29 1.62 5.46 0.01 15.70 43.37 11.08 2.37 3.55 0.00 14.23 0.00 48.56

England 10.94 2.40 0.23 1.56 5.81 0.00 17.53 41.44 11.78 1.74 3.30 0.00 14.20 0.00 48.19

Wales 0.60 2.67 0.22 1.38 4.87 0.00 19.88 47.81 6.95 1.83 3.13 0.00 11.25 0.00 52.31

Scotland 1.79 3.17 0.66 1.72 6.05 0.03 16.22 43.72 10.33 1.60 4.19 0.00 12.30 0.00 49.93

Northern Ireland 0.45 4.27 0.22 0.97 3.59 0.01 14.94 59.69 6.20 1.19 2.41 0.00 6.51 0.00 63.18

UK 13.78 2.57 0.28 1.56 5.73 0.01 17.38 42.61 11.20 1.71 3.38 0.00 13.57 0.00 49.55

* Total agriculture value = Fertilizers, crops and grass + Livestock emissions

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6 Woodland and Natural Areas

As part of the Freshwater Umbrella contract to DEFRA AEQ, staff at ENSIS Ltd. are developing empirical and semi-empirical models to estimate leaching losses of nitrate from uplands, extensive grasslands, woodlands and other non-agricultural, unfertilised land to surface waters (lakes and streams). The basic premise of this work is that any measurable nitrate in these surface waters derives originally from atmospheric N deposition because in pristine, unpolluted water bodies nitrate concentrations are generally extremely low and close to detection limits. N deposition is the only identifiable source of anthropogenic N inputs to these systems.

Nitrate leaching attributable to N deposition is of particular policy relevance to DEFRA AEQ because of impacts on surface water quality in terms of acidification and nutrient enrichment (eutrophication). Much of the work under the Freshwater Umbrella programme is process-based experimental work to determine which biogeochemical processes regulate the retention and leaching of inorganic N deposition. In the UK uplands inorganic N leaching is heavily dominated by nitrate rather than reduced forms of N (ammonium) and a major focus under the Freshwater Umbrella is to determine the relative importance of direct leaching of atmospherically deposited nitrate (termed “hydrological nitrate”) and microbially produced nitrate which may have originated as either oxidised or reduced forms of N in atmospheric deposition. This distinction is key to understanding biogeochemical controls and timescales of N saturation, nitrate leaching and hence impacts of N deposition. However, regardless of whether observed nitrate is “hydrological” or microbially produced it is still assumed that all nitrate leaching from non-agricultural catchments is a direct result of anthropogenic N deposition.

The scope of this report is therefore not to attempt to apportion the sources of nitrate losses to surface waters since we are assuming a priori that we have restricted our study sites to those where N deposition is the only source. Instead, our remit is to provide a method to determine what proportion of N deposition is leached from these sites according to current knowledge. A prerequisite is to employ catchment scale predictors of the proportion of N deposition leached which may be upscaled to any areas of the UK where N deposition is the dominant source of nitrate leaching. The relevance of this to the wider N Source Apportionment project is to provide a basis for estimating the contribution of N deposition sources in any region of the UK, through direct estimates of leaching fluxes of N from upstream source regions to downstream modelling units.

6.1 Methodology

Definition of catchments impacted mainly by atmospheric deposition. The first requirement of this task is the definition of appropriate study areas – in fact our specific remit is to model the proportion of N deposition leached from any catchment where atmospheric deposition is the only or overwhelming source of leached nitrate. Most lowland catchments are highly impacted by agriculture (e.g. arable crops, fertilizer additions, intensive livestock) and urban or industrial activities (sewage, storm runoff, industrial effluents etc.) whereas most upland catchments have relatively little direct disturbance other than low intensity stocking of sheep or cattle. However, there are some lowland catchments in protected areas which are not disturbed and the only pollutant inputs do come from the atmosphere. Our first task is therefore to define catchments impacted primarily by atmospheric pollutants. This has previously been done using land cover data (e.g. LCM1990, LCM2000) whereby a cutoff value for the proportion of arable land or urban/developed land is defined (e.g. land with >5% arable may be excluded). If we wished to restrict our analysis to upland areas only, an additional criterion based on altitude could be employed, but given our wider remit this is not appropriate for the present study. For brevity we will use the term "non-agricultural" here to

39


cover any catchment where atmospheric pollutants are likely to be the dominant input, i.e. the term includes urban and industrial impacts as well as agricultural.

Identifying key determinants of nitrate leaching – which catchments leach a large proportion of deposited N as nitrate? In pristine, unpolluted lakes and streams (of which there are probably no examples in the UK) the dominant form of leached N is dissolved organic N (DON). Mineral (inorganic) forms of N (ammonium and nitrate) are generally assumed to be negligible. For the purposes of critical loads modelling in the UK it is therefore assumed that any observed inorganic N in "upland" water bodies originates directly or indirectly from the atmospheric deposition of anthropogenic, inorganic N. Even this assumption is complicated by the fact that terrestrial ecosystems store most deposited N to be leached in mineral form (via mineralization and nitrification cycles) at a later date. Understanding and modelling these processes is a key focus of the current DEFRA Freshwater Umbrella contract, using stable isotopes to differentiate direct leaching of atmospheric nitrate and microbially produced nitrate (potentially "old" or "stored" atmospheric nitrate or ammonium). Leaching of ammonium is generally assumed to be negligible although there are regions where measurable ammonium leaching does occur, for example in Pennine streams draining peatlands.

The approach here is therefore to use water chemistry data with estimates of deposition and runoff to determine the proportion of N deposition that is leached as nitrate from all upland catchments. The challenge then is to produce a model which best predicts the proportion of deposition leached (or the nitrate leaching flux for a given level of N deposition – essentially the same calculation) using nationally available datasets to allow upscaling to the whole country within the framework of a wider ADAS model for N Source Apportionment (which will include all other major anthropogenic N sources). The choice of key catchment attributes will be guided by previous studies conducted by ENSIS Ltd. and a brief review of the current scientific literature.

6.1.1 CATCHMENT MODELS OF N LEACHING

Links between catchment characteristics and lake or stream water chemistry have been explored in various studies over the last 15 years, for example in mountain lakes (Kamenik et al., 2001; Kernan et al., 2002) and moorland streams (Smart et al., 2005) where links between nitrate concentrations, catchment slope and the proportion of bare rock in catchments were found. Several recent papers have been published specifically on linking catchment characteristics with NO3

- leaching fluxes (e.g. Kernan & Allott, 1999; Chapman et al., 2001; Helliwell et al., 2001; Maberly et al., 2003). Kopácek et al. (2005) found that catchment weighted mean carbon pools in soil, gradient and maximum altitude were all important explanatory variables for NO3

- leaching in the Tatra Mountains. A model which also incorporated precipitation, N deposition and soil C:N ratios explained 80% of NO3

- variability over 60 years. Helliwell et al. (2007) found that N deposition could be linked to inorganic N concentrations and DOC/DON ratios in four upland regions of the UK, with key catchment controls linked to altitude, steepness, soil thickness and carbon pools. Forest cover and age are also important determinants of N leaching with greater uptake in younger, aggrading forest (Emmett et al., 1993, 1998; Reynolds et al., 1994). Simple mass balance models such as FAB (Posch et al., 1997) have also been used to predict nitrate leaching based on published values for sustainable sinks for deposited N in soils, lake sediments and vegetation (Curtis et al., 1998; Kaste et al., 2002).

Conceptually, the high retention of deposited N in catchment soils and vegetation must eventually lead to “N saturation” as assimilation leads to increasing concentrations of N in plant tissues and soil organic matter, and a corresponding decline in C : N ratios if carbon sequestration is not increased. In several studies, the susceptibility of catchments to nitrate leaching under chronic N deposition inputs has therefore been linked to forest floor, soil or

40


litter layer C:N ratios (Dise et al., 1998; Emmett et al., 1998; Gundersen et al., 1998; MacDonald et al., 2002; Curtis et al., 2004; Pilkington et al., 2005) or total catchment carbon pools (Evans et al., 2006) for which streamwater dissolved organic carbon (DOC) may act as a surrogate (Harriman et al., 1998). Vegetation type (acid grassland, heathland, deciduous woodland, coniferous woodland) has been found to moderate the relationship between soil C:N ratio and nitrate leaching (Rowe et al., 2006), while biomass of bryophytes and lichens has been linked to the proportion of inorganic N leached by Curtis et al. (2005a). Under the DEFRA funded Freshwater Umbrella programme, Curtis et al. (2007) produced a model of nitrate leaching that employs N deposition and mean biomass of bryophytes and lichens to explain 61% of the variance in 16 unafforested catchments from the UK Acid Waters Monitoring Network.

It has been argued that elevated N deposition does not necessarily lead to a decline in soil C:N ratio and subsequent nitrate leaching. White et al. (1996) found that at low levels of N inputs (<9 kgN ha-1 yr-1) to Calluna moorland on peaty podsol soils, total soil N content and C:N ratio increased almost linearly with N deposition, presumably due to the effect of acidification on organic matter accumulation rate, such that C accumulated faster than N. At higher total N deposition rates the C:N ratio declined with increasing deposition. Hence there may be a lower threshold of N deposition for certain soils, below which C:N ratio does not decline and no leaching of NO3

- occurs (e.g. Dise & Wright, 1995). A similar threshold deposition of c. 10 kgN ha-1 yr-1 was also observed by Gundersen (1995) and Curtis et al. (1998).

Evans et al. (2004) proposed a conceptual model whereby nitrate leaching is regulated by areas of mineral soils draining directly to streams while inorganic N is strongly retained in deep organic soils (peats) and wetland areas or exported as organic N. Soil type and hydrological flowpaths would therefore be the critical determinants of N leaching.

While soil carbon pools, flowpaths, vegetation biomass and C:N ratios are undoubtedly important factors in catchment susceptibility to nitrate leaching under chronic N deposition, such data are rarely available and therefore may not be appropriate for producing empirical models which may be applied at the national scale. Several studies have highlighted the importance of vegetation type and proportion of bare rock in determining nitrate leaching and these data are available nationally in the LCM2000 dataset. For the current study, we therefore restrict our modelling efforts to the use of nationally available datasets including landcover (broad vegetation types in LCM2000), deposition, runoff and altitude.

6.1.2 Development of a Multivariate Adaptive Regression Spline Model

The first step in the analysis was the use of land cover data (LCM2000) to both screen our database to identify "non-agricultural" catchments and then to predict the proportion of N deposition that is leached. Our initial modelling attempts were conducted using statistical data mining tools, in particular regression trees. Regression trees are simple but flexible, non-parametric statistical models, which are especially useful in situations where the correct form for the statistical model one wishes to fit is not immediately obvious, and where there are a large number of potential predictor variables from which to select. Regression tree model results are also easily incorporated in GIS or other software thus enabling predictions to be produced outside of the statistical software used to fit the tree. However, initial work suggested that more flexible techniques were required, such as multivariate adaptive regression splines (MARS), so the latter technique was adopted.

The proportion of N deposition leached as nitrate was used as the response variable to be predicted in the following modelling exercise. Given the large number of potential predictors and little theoretical information as to which set of variables would be the best predictor of the proportion of N leached, we selected a modelling tool that is derived from the field of

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statistical machine learning, Multivariate Adaptive Regression Splines (MARS). At a basic level MARS is very similar to the regression tree methodology which was assessed first in the current study, but achieves a more flexible, and hence better, fit through the use of piecewise linear functions as opposed to the piecewise constant functions of regression trees.

Piecewise linear splines of a predictor are fitted to a response variable using a single knot. Figure 1 shows examples of piecewise linear splines of the form used. The upper panels show a right hand spline and the lower panels a left hand spline. The left hand panels show the effect of a positive least squares coefficient in the MARS routine, and the right hand panels the result of a negative least squares coefficient. More than one basis function for a single predictor variable can be combined to provide a very flexible fit, using simple, inexpensive splines such as these.

Figure 6-9: Examples of piecewise linear splines over the range (0,1) with a single knot located at x = 0.5

The MARS fitting procedure proceeds iteratively, starting from a model with no basis functions. The fitting procedure searches over all predictor variables and within each predictor variable searches for all possible locations to place a knot. For a continuous variable with n unique values, there are n – 1 possible places for the knot. Each predictor variable and knot location forms a candidate spline and the spline that best fits the data is selected and enters the model. Fitting proceeds by finding the next best candidate spline, given that the first spline is already included. This process is repeated until one of a range of stopping criteria is reached. This forward stage is likely to have produced a model that is grossly over fitted to the original data. Therefore a backwards pruning step is used to progressively remove splines that entered the model at later stages that do not contribute significantly to the fit. Interaction terms were allowed, where splines are fitted to two predictor variables and both splines enter the model in a single step.

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6.2 Datasets

The basis of this study is the UK freshwater critical loads dataset, compiled since 1991 by ENSIS Ltd under contract to DEFRA. These data are used for application of the First-order Acidity Balance (FAB) model of Posch et al. (1997) to generate critical loads of acidity for freshwaters which are submitted to the international integrated assessment, mapping and modelling programme co-ordinated by the UNECE Co-ordination Centre for Effects (CCE). The dataset is hereafter referred to as the “FAB mapping dataset” and comprises catchment attribute and water chemistry data for 1722 lakes, streams and reservoirs in the UK.

Water chemistry data (including nitrate concentration) vary from one-off dip samples to annual mean monthly data. The original mapping survey was carried out between 1991 and 1994 but supplementary datasets have been added up to 2002. The updating process is ongoing and a new FAB mapping dataset is currently being generated to incorporate a regional dataset from the North Yorkshire Moors for March 2005. The strategy for the original survey employed a 10km grid and the freshwater sensitivity map of Hornung et al. (1995) to select the most acid-sensitive water body within each grid square, at the highest altitude. This strategy deliberately biased the dataset towards upland, non-agricultural catchments since the soils best suited to intensive agriculture are inherently insensitive to acidification. The resulting dataset represented a “worst-case” scenario in terms of acidification and provided appropriate data for focussing on the effects of atmospheric pollutants on surface waters. This dataset is therefore highly suited to the present study.

The original grid-based survey was subsequently expanded by the addition of regional datasets from many areas of the UK including random surveys in Wales and Scotland and comprehensive lake surveys in acid-sensitive regions like the Lake District, Galloway, Cairngorms and the Pennines (see Curtis et al., 2005b for a summary). The current FAB mapping dataset of 1722 sites is therefore a non-statistical, non-random composite of many datasets which is biased towards the acid sensitive upland regions of the UK. Lowland and agricultural catchments are however still represented because of the grid-based sampling strategy of the original survey dataset across the whole of the UK.

6.2.1 Catchment attribute data

Application of the FAB critical load model requires certain catchment attribute data which are also useful for the derivation of empirical relationships between deposition, catchment predictors and water chemistry. Catchment-weighted data are obtained by GIS overlay of catchment boundaries (generated either by automated procedures using digital terrain models or by manual digitising from Ordnance Survey maps) onto vector or raster (gridded) datasets. For the present study the following data were used:

Total (wet+dry, NOx+NHy) N deposition estimates modelled at a 5km grid resolution, with annual mean estimates for two three-year periods 1995-1997 and 1998-2000 by CEH Edinburgh (keq ha-1 yr-1);

Long-term mean runoff data (MORECS) at a 1km grid resolution with annual mean estimates for 1941-70 for Great Britain and 1961-90 for Northern Ireland (Hough, M.N. and Jones, R.J.A., 1997);

Lake to catchment area ratio (zero for stream catchments) to provide lake surface area as a proportion of the total catchment;

Land-cover data based on the LCM2000 classification (Table 1); Site altitude (at the lake or stream sampling point) – not available for all sites; Nitrate leaching flux as a proportion (%) of deposition inputs, calculated using water

chemistry and runoff data to provide an estimate of annual mean flux; calculated for both 1995-97 and 1998-2000 deposition datasets.

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Summary statistics for the catchment attribute data in the complete mapping dataset are presented in Table 6-10.

Table 6-10: LCM2000 Classification and range of values in total FAB dataset (n=1722); shading indicates LCM classes used for screening “non-agricultural” sites

LCM Class Description Minimum (%) Mean (%) Maximum (%)

gb1 Sea / Estuary 0 0.1 29.9gb2 Water (inland) 0 2.3 27.8gb3 Littoral rock 0 0.0 1.7gb4 Littoral sediment 0 0.0 10.2gb5 Saltmarsh 0 0.0 5.7gb6 Supra-littoral rock 0 0.0 2.8gb7 Supra-littoral sediment 0 0.0 2.4gb8 Bog (deep peat) 0 5.8 97.9gb9 Dense dwarf shrub heath 0 8.6 91.8gb10 Open dwarf shrub heath 0 19.0 100.0gb11 Montane habitats 0 6.2 100.0gb12 Broad-leaved / mixed woodland 0 3.3 63.9gb13 Coniferous woodland 0 10.4 99.8gb14 Improved grassland 0 7.1 91.9gb15 Neutral grass 0 6.1 88.4gb16 Setaside grass 0 0.1 15.8gb17 Bracken 0 2.5 74.4gb18 Calcareous grass 0 1.1 55.5gb19 Acid grassland 0 20.4 98.0gb20 Fen, marsh, swamp 0 0.1 12.6gb21 Arable cereals 0 1.2 69.5gb22 Arable horticulture 0 2.3 84.9gb23 Arable non-rotational 0 0.1 8.9gb24 Suburban / rural development 0 0.9 57.3gb25 Continuous urban 0 0.3 43.0gb26 Inland bare ground 0 2.0 94.7gb27 Unclassified 0 0.0 0.0

Other catchment attribute data collated for the FAB mapping programme include soils (from 1:250,000 soil survey maps) and geology. While these data are undoubtedly significant for understanding relationships between N deposition, N retention and nitrate leaching, it was decided on the basis of previous modelling experience to exclude these data from the current study to restrict the number of catchment predictors to a manageable number. As Table 6-10 shows, there are still 27 LCM2000 classes plus other catchment attributes utilised in this study.

6.2.2 Data screening

Previous attempts to generate empirical catchment models for predicting nitrate leaching have shown that the complex spatial variation in N sources from agricultural and urban areas to semi-natural upland catchment is a major obstacle. However, with our present remit to provide a model only for semi-natural, non-agricultural catchments we are able to greatly restrict our modelling on the basis of the available catchment attribute data outlined above.

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The following screening criteria were applied to remove catchments from the modelling dataset that could have non-atmospheric sources of N inputs:

1. Remove sites with nitrate leaching fluxes >100% of modelled deposition inputs as this suggests non-atmospheric N sources;

2. Remove sites with any agricultural land (LCM2000) within the catchment boundary (arable classes gb21+gb22+gb23 >0%);

3. Remove sites which have a high proportion of managed grass (gb14 > 5%) as this class will be accounted for the in the NEAP-N runs (See Section 5). This removed only 3 sites.

4. Remove sites with any urban, suburban, industrial, developed or unclassified land (LCM2000) within the catchment boundary (gb24+gb25+gb27 >0%);

5. Remove catchments with sea or estuary (LCM2000) within the catchment as this is indicative of inaccurate catchment boundaries (gb1>0%).

Site altitude was not used for screening since our target dataset incorporates both lowland and upland non-agricultural catchments. Data screening reduced the original dataset of 1722 sites to 780 non-agricultural sites (see Figure 6-10) and the number of possible LCM2000 predictors from 27 to 20. Summary statistics for the catchment attributes of the screened dataset are given in Table 6-11. Deposition and % leaching for the period 1998-2000 were selected for the modelling exercise as the most recent available data. Following the removal of catchments with any agricultural or urban influence, the proportion of N deposition leached varies between 0-96% with a median of 3.3% and a mean of 7.5%. The third quartile value is 9.9% showing that for three quarters of screened catchments less than 10% of N deposition is leached as nitrate. Note that it is assumed that 100% of this nitrate is derived from N deposition sources. The most important landcover classes in terms of proportional area within the screened catchments are gb10 (open dwarf shrub heath; mean 27.5%), gb19 (acid grassland; mean 21.0%), gb11 (montane habitats; mean 10.6%), gb9 (dense dwarf shrub heath; mean 9.9%), gb8 (bog – deep peat; mean 8.7%) and gb13 (coniferous woodland; mean 8.5%). The only landcover classes for which cover reaches 100% are gb10 (open dwarf shrub heath) and gb11 (montane habitats) so it is not possible to determine the range of % N leaching by each exclusive LCM2000 class.

Figure 6-10: Location of the 780 sites used to build the MARS model

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Table 6-11: Summary statistics of the variables used in the development of the MARS model

Variable Min. 1st

Quartile Median 3rd

Quartile Max. Mean

N deposition 1998-2000 0.30 0.57 0.87 1.36 2.83 1.00Lake:catchment area ratio 0.00 0.02 0.06 0.13 0.60 0.09Long-term mean runoff (mm) 353 953 1348 1801 3875 1434Proportion of NO3

- leached (%) 0.0 0.0 3.3 9.9 96.4 7.5gb2 0.0 0.0 1.2 3.7 27.8 2.6gb3 0.0 0.0 0.0 0.0 0.0 0.0gb4 0.0 0.0 0.0 0.0 0.0 0.0gb5 0.0 0.0 0.0 0.0 1.8 0.0gb6 0.0 0.0 0.0 0.0 2.8 0.0gb7 0.0 0.0 0.0 0.0 0.0 0.0gb8 0.0 0.0 0.0 3.4 97.9 8.7gb9 0.0 0.0 2.0 10.3 97.5 9.9gb10 0.0 3.6 18.5 46.5 100.0 27.5gb11 0.0 0.0 0.0 0.0 100.0 10.6gb12 0.0 0.0 0.0 0.2 33.8 0.7gb13 0.0 0.0 0.1 5.0 99.8 8.5gb15 0.0 0.0 0.0 0.8 84.3 4.5gb16 0.0 0.0 0.0 0.0 4.9 0.0gb17 0.0 0.0 0.0 0.4 74.4 2.5gb18 0.0 0.0 0.0 0.0 31.3 0.4gb19 0.0 1.1 10.2 33.7 97.5 21.0gb20 0.0 0.0 0.0 0.0 12.6 0.0gb26 0.0 0.0 0.0 1.1 94.7 2.1

6.2.3 Datasets used for prediction

The MARS model developed required the following inputs:

1. N Deposition2. MORECS Runoff3. LCM2000 proportions for 19 land covers

While the model was built using N Deposition values from the 1998-2000 period, which was consistent with all of the other datasets used in the model construction, for predictive purposes these were replaced with N deposition data from the 2001-2003 period supplied by CEH (See Section 1). MORECS runoff was simulated using the NEAP-N model which contains a statistical mimic of the MORECS model (Anthony et al., 1996). The LCM2000 land cover proportions were derived from the LCM2000 dataset. In order to apply the model to 1km grid cells that do not contain only the 19 land covers used in the model construction, e.g. woodland in a dominantly agricultural grid square – the proportions were re-proportioned assuming that the 19 land covers covered 100% of the land area.

6.3 Results

The fitted MARS model contains 12 predictor variables for a total of 22 piecewise linear splines, plus the model intercept. The terms in the MARS model and information on the piecewise linear functions are shown in Table 6-12. The majority of the terms are second order interactions. This model has an R2

adjusted of 49.8 and an apparent root mean square error of prediction of ±8.16.

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Table 6-12: Terms included in the MARS model fit plus information on the piecewise linear splines representing each term

Term Variable Knot Left/Right Sign Coefficient1 Intercept N/A N/A + 2.08E+012 gb19 67.15 left - -3.62E-013 gb19 * gb26 67.15; 2.44 right/right + 1.09E+004 gb5 * gb19 67.15 right/right - -7.63E+005 runoff * gb19 1164; 67.15 right/right + 1.30E-036 gb26 8.89 right + 1.26E+007 gb26 8.89 left - -9.31E-018 gb10 * gb26 17.48; 8.89 left/left + 2.18E-029 gb19 * gb26 67.15; 0.46 left/right - -1.71E-0210 gb12 * gb19 0.89; 67.15 right/left + 2.62E-0211 gb17 * gb19 2.23; 67.15 right/left + 4.08E-0312 totn9800 * gb2 1.97; 11.04 left/right + 1.98E+0013 gb2 * gb26 10.28; 8.89 right/left - -2.22E-0114 gb2 * gb26 10.28; 8.89 left/left - -3.94E-0215 gb13 * gb18 51.88; 0.93 right/right + 4.06E+0116 totn9800 * gb15 1.97; 24.55 left/left - -1.66E-0117 gb13 * gb19 51.88; 10.13 left/right - -4.97E-0318 gb13 * gb19 51.88; 10.13 left/left + 7.68E-0319 gb12 3.88 left + 1.56E+0020 lc * gb12 3.88 right/right - -1.38E+0121 gb2 * gb12 0.58; 3.8 right/right + 4.85E-0122 runoff * gb19 587.8; 67.15 right/left + 1.17E-0423 runoff 2775 left + 5.36E-03

The 1km grid square predictions of the proportion of atmospheric deposition that is leached from woodland and natural areas are illustrated in Figure 6-11. These proportions were coupled with the actual loads of atmospheric deposition (See Figure 4-6) and loads of leached N calculated. These loads of N leached are illustrated in Figure 6-12. High leaching areas are located in many parts of the UK with many lowland areas apparent and may reflect an application of the model beyond the conditions on which it was trained (See Figure 6-10). Also evident is one of the shortcomings of the model with a number of the large inland water bodies, like lough Erne clearly visible. Owing to the catchments used to train the model containing lakes the inland water land cover category (gb2) was used in and selected in the MARS model. Hence a small degree of overlap with the direct atmospheric deposition to water totals produced in Section 1 may occur although this is negligible as only large inland water bodies larger than 1 to 2 hectares would be affected. These large water bodies were removed from the regional and national totals which are summarised in Table 6-13. The results indicate that as much as 67 kT of N arise from these areas with woodlands accounting for the bulk of this (36 kT) while other natural areas, including rough grazing, are slightly less (31 kT). This pattern varies at a regional level and is a function of the prevalence of woodlands. The bulk of this atmospheric deposition that leaches arises from agriculture and to a lesser extent imported, road transport and non-agricultural emissions (See Table 6-14).

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Figure 6-11: Proportion (%) of atmospheric N that is leached from woodland and natural areas as predicted by the derived MARS model

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Figure 6-12: N Load (kg) that is leached from woodland and natural areas

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Table 6-13: Regional and National summaries of the contribution (kT) of N from different non-agricultural natural sectors


N from Woodland and Natural Area Atmospheric Deposition (kT) Woodland Other incl.

rough grassEast of England 5.91 3.53 2.38East Midlands 4.31 2.36 1.94London 0.62 0.36 0.26North East 2.00 0.99 1.00North West 5.06 2.09 2.97South East 10.21 6.93 3.28South West 8.75 6.00 2.75West Midlands 4.54 2.63 1.90Yorkshire and The Humber 5.68 2.80 2.87England 47.08 27.70 19.38Wales 6.74 3.35 3.38Scotland 11.73 4.61 7.12Northern Ireland 1.55 0.42 1.13UK 67.10 36.09 31.00

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Table 6-14: Regional and National summaries of the N atmospheric deposition emitted by non-agricultural natural areas to water broken down (%) by broad atmospheric deposition footprint type


N from Woodland

and Natural Area

Atmospheric Deposition

(kT)

Combustion in


& Residential





grass


Distribution

Imported Emissions

Livestock emissions


Other Point

SourcesOther


Road Transport

Solvent Use

East of England 5.91 3.15 0.24 1.80 6.11 0.00 15.15 31.66 15.10 1.64 4.56 0.00 20.58 0.00

East Midlands 4.31 3.32 0.25 2.04 5.51 0.00 14.24 34.92 12.24 2.29 4.74 0.00 20.46 0.00

London 0.62 3.35 0.21 1.45 2.06 0.00 12.12 17.54 44.44 1.03 3.65 0.00 14.15 0.00

North East 2.00 3.04 0.44 1.63 5.39 0.02 16.44 40.24 10.98 2.03 4.68 0.00 15.10 0.00

North West 5.06 3.73 0.32 1.70 3.39 0.01 14.17 43.74 11.39 2.22 4.12 0.00 15.23 0.00

South East 10.21 2.92 0.20 1.53 4.76 0.00 19.05 33.01 14.16 1.24 4.90 0.00 18.24 0.00

South West 8.75 2.07 0.15 1.21 4.91 0.00 17.81 47.50 7.78 1.12 4.54 0.00 12.93 0.00

West Midlands 4.54 2.79 0.21 1.64 5.28 0.00 13.46 44.28 11.71 1.61 3.23 0.00 15.79 0.00


England 47.08 2.94 0.24 1.61 4.85 0.00 16.20 38.88 12.36 1.72 4.41 0.00 16.79 0.00

Wales 6.74 3.10 0.25 1.52 4.48 0.00 19.74 43.15 7.45 2.01 3.86 0.00 14.44 0.00

Scotland 11.73 3.77 0.78 1.70 3.88 0.02 20.72 37.82 9.61 1.88 5.30 0.00 14.52 0.00


UK 67.10 3.13 0.33 1.61 4.61 0.01 17.34 39.53 11.26 1.77 4.47 0.00 15.94 0.00

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7 Urban Areas and Roads

7.1 Methodology for hard surface runoff

For this project we adapted the general methodology of Mitchell et al. (2001) for the calculation of runoff from roads and urban areas, and integration with literature derived Event Mean Concentrations (EMCs) of pollutants, to calculate the annual pollutant load delivered to surface waters. The annual average runoff L (mm) from the urban land area was calculated using a modified version of the Wallingford Procedure:

where P is the proportion of the land area that is impermeable, R is the annual average rainfall (mm), SPR is the standard percentage runoff and replaces the winter rainfall acceptance value of the local soil type in the Wallingford Procedure as suggested by Boorman et al.(1995), and U is the urban catchment wetness index that is determined from annual average rainfall (Mitchell et al., 2001).

The EMC model requires input data on the area and type of roads and urban development, and mean annual rainfall. The area and type of urban development was defined by the CORINE land cover dataset for each 1km2 in the reference grids. The surface areas of roads were calculated using a vector road network, which was buffered according to standard assumptions about width for each type of road. Long-term mean annual rainfall was taken from the UKCIP02 baseline dataset. The proportion of land that is impermeable was determined from the population density (assuming 3 people per household and assumptions used in sewer modelling systems (Ellis, 1986).

Table 7-15: Approximate run-off coefficients for different types of urban land use (modified after Debo and Reese, 1995; and Ellis, 1986).

Urban land use Run-off coefficientResidential Population Density 0.1 – 0.8

21000/km2 0.818000/ km2 0.715000/ km2 0.612000/ km2 0.54500-12000/ km2 0.43000-4500/ km2 0.3

Industrial/commercial 0.4 – 1AA Roads and Highways 0.5Urban green space 0.1

The runoff coefficients listed in Table 7-15 were modified from permeability values for different types of urban areas published by Ellis (1986). Run-off coefficients for roads and industrial areas were suggested by Mitchell et al. (2001). EMC’s were collated from the literature and are summarised in Table 7-16.

Table 7-16: Urban and Road Specific EMC’s (Mitchell, 2005)

Land cover Event Mean Concentration of TN (mg/L)Urban Open 1.68Industrial 1.52Residential 2.85Highway 2.37

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7.2 Methodology for urban leaching

The urban leaching methodology is based on the compartment model of Lerner (2000) as updated in a recent publication (Wakida and Lerner, 2005). Considering the complexity of the wide range of pollutant sources and recharge in urban areas the authors determined the total recharge and the associated average concentration of N to the aquifer below Nottingham and then subdivided the N load between the various sources. They identified the following sources, namely, leakage from water mains, leakage from sewers, release of N during house building, leaching from parks, open spaces and gardens as well as contaminated land (including landfills, industrial land and chemical spills. Through the use of empirical data from research conducted around Nottingham the load from the first four sources was quantified by Wakida and Lerner. They then attributed the difference between the total load and these four sources to the contaminated land source. These loads were then re-expressed per capita for this project (See Table 7-17). These per capita export coefficients were used with the 2001 population census data supplied by the ONS and worked up into a 1km grid cell dataset by ADAS for MAGPIE (Lord and Anthony, 2000).

7.3 Results

The per capita export coefficients derived from the work of Wakida and Lerner (2005) are summarised in Table 7-17. While this approach meets our overall modelling philosophy being empirical and simple it is weakened by the fact that it is based on a single city in the UK which is underlain by a sandstone aquifer which may not be wholly appropriate for other cities in the UK that are underlain by chalk aquifers or indeed have no underlying aquifer.

Table 7-17: Summary of the per capita export co-efficient’s derived for the various urban sources of leaching (after Wakida and Lerner, 2005)

Source kg N/ha/yr kg N/person/yearWater Mains 7.7 0.2089Sewers 2.7 0.0733Open Spaces 1.9 0.0515Construction 0.7 0.0190Contaminated Land 8 0.2170

The N load from urban and road surfaces as well as urban leaching is illustrated in Figure 7-13. It should be noted that these values exclude that which arises from leaking sewers as this is incorporated into the sewage totals given in Section 8. The distribution of the loads highlights the urban centres as would be expected with the London and Birmingham conurbations clearly visible. The contribution of the various sources to these loads at a regional and national scale are summarised in Table 7-18. The UK totals are comprised largely of contributions from contaminated land and water mains with England contributing the most to these totals reflecting the per capita based approach.

53


Table 7-18: Regional and National summaries of the contribution (kT) of N from the different urban sectors


Total Urban

Urban and

RoadsWater Mains

Open Spaces Construction Contaminated

LandAtmospheric Deposition

Component*East of England 3.05 0.37 1.13 0.28 0.10 1.17 0.52East Midlands 2.42 0.34 0.87 0.22 0.08 0.91 0.46London 4.06 0.49 1.50 0.37 0.14 1.56 0.70North East 1.47 0.22 0.53 0.13 0.05 0.55 0.29North West 4.12 0.78 1.41 0.35 0.13 1.46 0.97South East 4.60 0.63 1.67 0.41 0.15 1.74 0.85South West 2.90 0.46 1.03 0.25 0.09 1.07 0.60West Midlands 3.12 0.51 1.10 0.27 0.10 1.14 0.66Yorkshire and The Humber 2.90 0.44 1.04 0.26 0.09 1.08 0.57

England 28.63 4.24 10.27 2.53 0.93 10.66 5.62Wales 1.82 0.38 0.61 0.15 0.06 0.63 0.46Scotland 3.21 0.70 1.05 0.26 0.10 1.09 0.85Northern Ireland 0.98 0.17 0.34 0.08 0.03 0.36 0.21

UK 34.63 5.48 12.27 3.02 1.12 12.74 7.14*Calculated through: (1 * Urban and Roads) + (0.4 * Open Spaces) + (0.4 * Construction)

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Figure 7-13: N Load (kg) that is leached or runs off from urban and road areas per 1km grid cell

55


8 Sewage and Industrial Discharges

The N load that arises from sewage and industrial sources was assessed using three different approaches to account for three different sources, namely point source discharges like sewage treatment works, septic tanks and leakage from sewers. The first two are detailed in the following sub-sections while the approach used to derive the leakage from sewers is described in Section 7.

8.1 Methodology for Point Sources

Nitrogen in sewage effluent and industrial discharges was calculated using data provided by the Environment Agency (EA). The database had been developed by the Water Research Centre (WRc) for the Agency as part of a national water quality model framework (Kelly et al., 2006). The Environment Agency database provided estimates of consented annual average flows for c. 3,550 effluent discharges. This database included only discharges with a population equivalent greater than 250 and that discharge to surface water and not directly to sea. Previously, Anthony and Lyons (2006) constructed a database of only sewage effluent discharges for England and Wales using the Environment Agency consents register as of 2003. This database did not include any discharge threshold. The effluent discharge contributed by the smaller works was less than 5% of the total for England and Wales. Coastal discharges within the Anthony and Lyons (2006) dataset were added to the Environment Agency dataset to create a complete database of all sewage effluent and industrial discharges.

The database also provided estimates of annual average N as ammonia, nitrate and nitrite concentrations in the effluent based on compliance monitoring. These data were not available for every discharge. Missing concentration data were therefore replaced with averages, derived separately for each Environment Agency region and for effluent discharges in three flow bands. The flow bands were centred on population equivalents in the ranges 0 to 2,000; 2,000 to 10,000; and greater than 10,000. The annual load from each point source was calculated by integrating the annual average concentrations in the effluent with the dry weather flow for each point source. These total loads for each source were totalled for each EA region and are summarised in Table 8-19. The total load compares well with estimates from previous studies, e.g. Hunt et al. (2004) who estimated the total load using the value of 7g N/person/day which equates to 132.96 kT. It also compares well with the Urban Waste Water Treatment (UWWT) Directive (91/271/EEC) Group 3 countries water quality target of 2.7 kg N/person/day (EEA, 2004), is higher than the calculated UWWT Group 1 emission of 2.3 kg N/person/day and the value of 2.41 kg N/person/day derived by Anthony et al. (2005b).

Table 8-19: Environment Agency Water Management Region summaries of the contribution (kT) of N from sewage and industrial discharges

EA WM Region N Load (kT) kg N/person/year g N/person/dayAnglian Region Total 14.58 2.54 6.96EA Wales Total 9.48 3.01 8.23Midlands Region Total 22.10 2.60 7.12North East Region Total 22.55 3.16 8.65North West Region Total 22.05 3.28 8.99South West Region Total 17.15 4.10 11.23Southern Region Total 10.89 2.36 6.47Thames Region Total 24.02 2.00 5.49Total England and Wales 142.89 2.75 7.52

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The equivalent data to that supplied by the EA was requested from SEPA and DOENI. However, following inspection it was found that the datasets were not sufficiently complete that the same analysis could be undertaken for these countries. As such regional per capita export coefficients from similar EA regions were selected, in this case the average of both the North East and North West regions was used, notably 3.21 kg N/person/year.

8.2 Methodology for Septic Tanks

Nitrogen losses from households not connected to mains sewerage were included as a source in this project. The proportion of the population in each 1km grid cell that is connected to the mains sewerage was taken from previous studies for Scotland, Northern Ireland (Anthony et al., 2005b) and Wales (Anthony et al., 2008). For England data on the number of properties connected for sewerage in 2001 were collated for each sewerage company area from annual reports made to the Office of Water Services (OFWAT, 2001). These were compared to the total number of properties in each service area, estimated from Ordnance Survey Address Point data for 2001. The number of unconnected properties in each area was calculated by difference, and was found to be in the range 0.5 to 12%. The unconnected properties were expected to be concentrated in rural areas and as such a simple urban/rural cut-off was used to ensure that the proportion of the population that was not connected to the sewage mains for each sewerage company area were dominantly rural. The per capita export coefficient used to derive the N load from septic tanks was 1.64 kg N/person/year (Anthony et al., 2005b; Anthony et al., 2008).

8.3 Results

All loads of N derived from sewage and industrial sources of N were totalled and are illustrated at 1km grid cell scale in Figure 8-14. The map clearly highlights urban areas as would be expected with the highest loads in major conurbations reflecting the population density. The regional and national summaries are provided in Table 8-20 with the national contribution of these sources totalling 176.9 kT with the major contributor to this being that from sewage and industrial point sources.

Table 8-20: Regional and National summaries of the contribution (kT) of N from sewage and industrial discharges

Government Office RegionTotal

Sewage and

Industrial

STW and Industrial

Point Sources

Leakage from

Urban Sewers

Leakage from

Septic Tanks

East of England 14.45 13.68 0.39 0.38East Midlands 11.40 10.87 0.31 0.23London 15.05 14.39 0.53 0.13North East 8.17 7.94 0.18 0.04North West 22.80 22.09 0.49 0.21South East 20.86 18.88 0.59 1.40South West 21.48 20.18 0.36 0.94West Midlands 14.46 13.69 0.39 0.39Yorkshire and The Humber 16.04 15.66 0.36 0.02England 144.70 137.37 3.60 3.74Wales 9.34 8.72 0.21 0.41Scotland 17.13 16.24 0.37 0.53Northern Ireland 5.72 5.30 0.12 0.30UK 176.90 167.63 4.30 4.97

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Figure 8-14: N Load (kg) that is derived from Sewage and Industrial discharges for each 1km grid cell

58


9 Particulate Sources

The N loads derived from agriculture (Section 5) and woodland/natural areas (Section 6) are as a result of leaching. As such we need to account for N that is associated with particulates, mostly sediment, that are derived from the erosion of land surfaces, stream banks and the remobilisation of stream bed sediments.

9.1 Methodology

In order to do this we need to model the sediment yield (that which is not only mobilised within the catchment but actually delivered to its exit) and then estimate the likely N content of these sediments based on their source to calculate the particulate N load. The simple sediment yield approach according to Cooper et al., 2006 was implemented to calculate the sediment yields. While this approach is quite simple it is based on empirical data and has the benefit of being actual sediment yield as opposed to the erosion estimates provided by other models. This model classifies landscapes/catchments into 5 classes based on their geology, wetness and altitude and then accords these a median yield based on empirical datasets. These classes and the median yield are summarised in Table 16. Where there was an under-representation within some of the sediment yield catchment types, for example “High Dry Other” and “Low Dry Peat” which only have 2 sites these were combined with another appropriate class. While the Cooper model was constructed at a catchment scale, they also applied it at a 1km grid cell scale. We have implemented the model at both a catchment and 1km grid cell scale and have retained the 1km results as we believe these capture more of the variation within UK landscapes.

Table 9-21: Catchment types according to Cooper et al., 2006 along with the median sediment yield for catchments of each type

Altitude Wetness Geology Median Sediment Yield (t/ha/yr)

Low Dry & Wet Chalk 4.50Low Dry Other 31.26Low Wet Other 59.60High & Low Wet Peat 107.35High Dry & Wet Other 16.00Low/High – Altitude <330m/ Altitude >330mWet/Dry – SPR > 40/ SPR < 40Geology – HOST = 1 for Chalk; HOST = 29 for PEAT; HOST = 2 to 28 for Other

9.2 Datasets

Average altitude for each 1km grid cell was determined using an OS 50m digital elevation model (DEM) covering the UK. This DEM was filled first using ESRI ArcGIS to remove any spurious pits or spikes.

The wetness factor uses standard percentage runoff (SPR) in its construction. For all regions these SPR values were taken from the HOST manual (Boorman et al., 1995) where the SPR for each HOST class is provided. These SPR values were linked to the HOST class of each dominant soil in each 1km grid cell. Similarly, the geology factor utilises HOST class in its construction. The HOST class of each dominant soil in each 1km grid cell was sourced from the soils datasets described in Section5.

Soil-derived sediment transported to rivers contains associated organic matter, which itself contains a proportion of nitrogen. In order to estimate the nitrogen content of suspended sediments, topsoil samples from grassland, arable land and woodland were taken from a

59


wide range of catchments. Total N content was measured on the <63 µm fraction, which characterises the particle size in suspended sediment mobilised and delivered to watercourses (Motha et al., 2002). Data were taken from a number of published studies (including Collins 2008a,b) supplemented by detailed data from previously published work (A. Collins, 2008; pers. comm.) The values for woodland, grassland and arable averaged 4964 (+/- 318), 5016 (+/- 216) and 3040 (+/- 157) mg N per kg sediment dry matter respectively. Within 17 rural, dominantly grassland catchments the mean N content of sediment was 3206 (+/- 289) mg/kg. In 6 subcatchments of the Herefordshire Wye, which is mixed arable and grassland, the measured N content of suspended sediment in streams was in the range 3000-5000 mg/kg. However a few catchments, notably chalk catchments in the south-east such as the Hampshire Avon, showed much greater N content of sediments, averaging up to 13 000 mg/kg despite similar N contents in the <63 fraction of the local topsoil. These catchments are known to have a large sewage input and a relatively small sediment load, and it is suspected that the sediment may absorb some of the organic N contributed by sewage. For this reason, the N content of sediment was based on the values derived from fine soil particles, and the low-population catchments, and taken as 5000 mg/kg for sediment from uncultivated land (grassland and forest) and 3000 mg/kg for sediment from cultivated (arable) land.

9.3 Results

The sediment yield derived for each 1km grid cell is illustrated in Figure 9-15. These results suggest that the peaty lowlands and uplands in Scotland are a large source of particulate N. However, a limitation of this approach may be that the model is being applied beyond the conditions on which it was trained and also subject to the limitation of lumping the two peat classes owing to limited data on lowland peats (See Figure 9-16). The N load from particulates is illustrated in Figure 9-17 and largely reflects the distribution of the sediment yield classes with minor variations introduced by the difference in N content from the different land covers. The regional and national summaries provided in Table 9-22 indicate that the total UK load is 45.2 kT with the majority of this load coming from rough grass areas reflecting the higher N content of sediment derived from this land cover.

Table 9-22: Regional and National summaries of the contribution (kT) of N from particulate sources

Government Office Region Total Particulate Arable Managed

GrassRough Grass Woodland

East of England 2.26 1.38 0.38 0.32 0.17East Midlands 1.99 0.95 0.65 0.21 0.18London 0.08 0.01 0.01 0.06 0.01North East 1.51 0.18 0.44 0.67 0.23North West 2.31 0.12 1.03 0.96 0.20South East 2.58 0.62 0.89 0.46 0.60South West 3.38 0.53 2.01 0.41 0.44West Midlands 1.88 0.53 0.99 0.16 0.20Yorkshire and The Humber 2.24 0.57 0.66 0.81 0.20England 18.23 4.89 7.06 4.05 2.23Wales 3.24 0.08 1.88 0.68 0.60Scotland 20.97 0.75 2.24 14.54 3.44Northern Ireland 2.78 0.11 1.40 1.05 0.24UK 45.22 5.83 12.57 20.32 6.50

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Figure 9-15: Sediment yield (kg/ha/yr) according to the model of Cooper et al., 2006 for each 1km grid cell

61


Figure 9-16: Sediment yield class model results presented alongside the catchments on which it was trained (after Cooper et al., 2006)

62


Figure 9-17: N Load (kg) that is derived from particulate sources for each 1km grid cell

63


10 Compiling the UK Load and Source Apportionment

The results from each of the individual sources were complied into national and regional budgets. The budgets were also subdivided by source in various ways to elucidate issues of source and impact.

10.1 Total N load and Sources

The total N load for each of the 1km grid cells in the UK is illustrated in Figure 10-18 where it will be noted that the urban areas with the combination or urban hard surface runoff as well as urban leaching and the direct contributions from point sources produce the highest loads on a 1km grid cell basis. Regional and national summaries of the total N load expressed in kT and kg/ha are provided in Table 10-23 and Table 10-24 while the river basin district summaries (See Figure 10-19) are provided in Table 10-25 and Table 10-26. These results suggest that sewage and industrial sources along with agriculture are the dominant sources of N emitted to water in the UK and across most regions. Comparison between the national figures may be done using total load or an area normalise load (kg/ha). On a national basis (kg/ha) relative importance of the loads varies, for example Scotland have the highest load from particulates and the lowest from Sewage and Industrial sources.

While the individual models employed were not able to provide detailed information on the exposure pathways the project was asked to provide a simple breakdown of that which might be associated with runoff and that which might travel via groundwater. This was undertaken by assessing each source and whether it could contribute to groundwater, e.g. urban hard surface runoff and point source discharges are direct to surface water. For all other sources the Base Flow Index (BFI) for the dominant soil HOST class within each 1km grid cell was used to partition the loads into that which would potentially travel via surface or groundwater pathways. This is a very simplistic approach and we acknowledge that there are a range of weaknesses associated with the approach. As such the results provided in Table 10-27 should be viewed as indicative. The results indicate a clear regional variation which is in part driven by geology and in part driven by urbanisation and population numbers and suggests the potential contribution to ground water may be large.

In order to assess the total contribution of atmospheric deposition from all sources (See Table 10-28) a number of assumptions had to be made. All N arising from urban and woodland/natural areas was assumed to be from atmospheric deposition even though we know that, for example, urban sources will include particulate and organic matter. The proportion of particulate N arising from atmospheric sources was set to 4% (See Section 5.3). The results indicate that the contribution to total N load ranges between 7.3 and 30.3%. Exploration of the atmospheric deposition “footprint” indicates that agricultural and imported emissions dominate.

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Figure 10-18: N Load (kg) that arises from all sources within each 1km grid cell

65


Table 10-23: Regional and National summaries of the contribution (a in kT; b in %) from all sources of N

(a) kT

Government Office RegionDirect

Deposition to Water

Urban Runoff

and Leaching

Sewage and

IndustrialAgriculture

Woodland and

Natural Areas

Particulate Total

East of England 0.49 3.05 14.45 34.44 5.91 2.26 60.6East Midlands 0.49 2.42 11.40 30.64 4.31 1.99 51.25London 0.07 4.06 15.05 0.21 0.62 0.08 20.09North East 0.21 1.47 8.17 8.57 2.00 1.51 21.93North West 0.51 4.12 22.80 20.41 5.06 2.31 55.21South East 0.44 4.60 20.86 28.44 10.21 2.58 67.13South West 0.43 2.90 21.48 56.29 8.75 3.38 93.23West Midlands 0.32 3.12 14.46 27.62 4.54 1.88 51.94Yorkshire and The Humber 0.52 2.90 16.04 28.20 5.68 2.24 55.58England 3.48 28.63 144.70 234.81 47.08 18.23 476.93Wales 0.54 1.82 9.34 31.90 6.74 3.24 53.58Scotland 3.80 3.21 17.13 63.37 11.73 20.97 120.21Northern Ireland 0.77 0.98 5.72 22.66 1.55 2.78 34.46

UK 8.59 34.63 176.90 352.75 67.10 45.22 685.19

(b) %


Deposition to Water

Urban Runoff

and Leaching

Sewage and


Woodland and

Natural Areas

Particulate

East of England 0.8 5.0 23.8 56.8 9.8 3.7East Midlands 1.0 4.7 22.2 59.8 8.4 3.9London 0.3 20.2 74.9 1.0 3.1 0.4North East 1.0 6.7 37.3 39.1 9.1 6.9North West 0.9 7.5 41.3 37.0 9.2 4.2South East 0.7 6.9 31.1 42.4 15.2 3.8South West 0.5 3.1 23.0 60.4 9.4 3.6West Midlands 0.6 6.0 27.8 53.2 8.7 3.6Yorkshire and The Humber 0.9 5.2 28.9 50.7 10.2 4.0England 0.7 6.0 30.3 49.2 9.9 3.8Wales 1.0 3.4 17.4 59.5 12.6 6.0Scotland 3.2 2.7 14.3 52.7 9.8 17.4Northern Ireland 2.2 2.8 16.6 65.8 4.5 8.1UK 1.3 5.1 25.8 51.5 9.8 6.6

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Table 10-24: Regional and National summaries of the contribution (kg/ha) from all sources of N


Deposition to Water

Urban Runoff

and Leaching

Sewage and


Woodland and

Natural Areas

Particulate Area (km2)*

East of England 0.26 7.61 7.21 18.14 3.11 1.19 18981.68East Midlands 0.31 7.31 6.97 19.64 2.76 1.27 15595.98London 0.45 95.31 91.14 1.30 3.94 0.53 1578.98North East 0.25 9.54 9.27 10.01 2.33 1.76 8561.46North West 0.36 16.29 15.79 14.59 3.62 1.65 13992.15South East 0.23 11.00 9.96 15.00 5.39 1.36 18958.83South West 0.18 9.08 8.53 23.80 3.70 1.43 23650.02West Midlands 0.25 11.12 10.53 21.24 3.49 1.44 13003.00Yorkshire and The Humber 0.34 10.44 10.19 18.35 3.69 1.46 15370.26England 0.27 11.16 10.59 18.11 3.63 1.41 129692.35Wales 0.26 4.54 4.23 15.49 3.27 1.57 20597.72Scotland 0.51 2.32 2.20 8.58 1.59 2.84 73871.09Northern Ireland 0.57 4.25 3.94 16.84 1.15 2.07 13456.17

UK 0.36 7.44 7.05 14.85 2.82 1.90 237617.33* as defined by the land cover dataset

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Figure 10-19: River basin districts of the UK (UKTAG, 2008)

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Table 10-25: River Basin District summaries of the contribution (kT) from all sources of N

River Basin District AgricultureWoodland

and Natural Areas

ParticulateSewage

and Industrial

Urban Runoff and Leaching

Direct Deposition

to WaterRBD

Totals

Anglian 48.33 7.15 2.97 14.00 2.98 0.69 76.11Dee 2.90 0.72 0.30 1.85 0.34 0.09 6.20Humber 46.83 8.62 3.45 31.86 6.34 0.85 97.96Neagh Bann 11.73 0.57 1.07 1.95 0.32 0.48 16.12North Eastern 5.74 0.43 0.59 2.74 0.50 0.04 10.04North West 15.49 4.44 1.77 21.67 3.93 0.41 47.71North Western 5.20 0.55 1.12 1.03 0.16 0.25 8.31Northumbia 8.30 2.03 1.53 8.17 1.47 0.23 21.74Scotland 47.62 9.56 18.35 16.19 3.04 3.35 98.11Severn 40.34 8.24 3.24 17.82 3.14 0.53 73.31Solway Tweed 22.04 3.14 3.34 1.58 0.27 0.55 30.92South East 13.81 4.20 1.05 8.40 1.89 0.14 29.49South West 42.79 5.94 2.48 12.57 1.69 0.30 65.77Thames 21.34 8.23 2.01 32.58 7.69 0.43 72.29Western Wales 20.30 3.28 1.96 4.48 0.85 0.24 31.11

UK Total 352.75 67.10 45.22 176.90 34.63 8.59 685.19

Table 10-26: River Basin District summaries of the contribution (kg/ha) from all sources of N

River Basin District AgricultureWoodland

and Natural Areas

ParticulateSewage

and Industrial

Urban Runoff and Leaching

Direct Deposition

to WaterRBD Area

(km2)

Anglian 19.35 2.86 1.19 5.60 1.19 0.28 24978.96Dee 13.40 3.34 1.40 8.55 1.57 0.42 2161.09Humber 18.47 3.40 1.36 12.57 2.50 0.34 25353.36Neagh Bann 19.63 0.95 1.79 3.27 0.54 0.81 5973.76North Eastern 20.25 1.51 2.09 9.68 1.75 0.13 2835.32North West 13.86 3.97 1.59 19.39 3.52 0.37 11178.21North Western 11.19 1.19 2.42 2.21 0.35 0.53 4644.44Northumbia 9.82 2.40 1.81 9.67 1.74 0.27 8456.53Scotland 7.58 1.52 2.92 2.58 0.48 0.53 62832.06Severn 19.22 3.93 1.54 8.49 1.50 0.25 20981.50Solway Tweed 14.88 2.12 2.25 1.06 0.18 0.37 14817.52South East 16.86 5.12 1.28 10.24 2.31 0.17 8195.39South West 24.51 3.40 1.42 7.20 0.97 0.17 17462.24Thames 13.57 5.24 1.28 20.73 4.89 0.28 15721.94Western Wales 16.88 2.73 1.63 3.73 0.71 0.20 12022.36

UK Total 14.85 2.82 1.90 7.44 1.46 0.36 237617.33

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Table 10-27: Regional and National summaries of the contribution (kT) from all sources of N via surface and ground water pathways

Government Office Region Total N Load

Load (kT) Load (%)Ground Water

Surface Water

Ground Water

Surface Water

East of England 60.60 27.54 33.06 45.44 54.56East Midlands 51.25 25.11 26.14 49.00 51.00London 20.09 2.91 17.18 14.49 85.51North East 21.93 6.19 15.74 28.22 71.78North West 55.21 18.21 37.00 32.98 67.02South East 67.13 30.99 36.14 46.16 53.84South West 93.23 49.12 44.11 52.68 47.32West Midlands 51.94 21.56 30.38 41.52 58.48Yorkshire and The Humber 55.58 25.27 30.31 45.46 54.54England 476.93 206.89 270.04 43.38 56.62Wales 53.58 25.51 28.07 47.61 52.39Scotland 120.21 48.84 71.37 40.63 59.37Northern Ireland 34.46 13.62 20.84 39.53 60.47UK Total 685.19 294.87 390.32 43.03 56.97

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Table 10-28: Regional and National summaries of the N load emitted to water (B) that arises from atmospheric deposition (C and E) broken down by each of the sources (D)

A B C D E


Total N Load (kT)

Total N Load from

Atmospheric Deposition (kT)

N Loads from Atmospheric Deposition (%) Total N Load from

Atmospheric Deposition (%)

AgricultureWoodland

and Natural Areas

Urban ParticulateDirect

Deposition to Water

East of England 60.60 9.75 22.04 60.59 5.33 7.01 5.02 16.10East Midlands 51.25 7.51 22.38 57.42 6.13 7.54 6.53 14.65London 20.09 1.47 0.68 42.11 47.55 4.90 4.75 7.33North East 21.93 3.85 9.87 51.97 7.54 25.16 5.46 17.55North West 55.21 8.39 6.68 60.34 11.57 15.34 6.08 15.19South East 67.13 14.25 10.67 71.67 5.97 8.61 3.09 21.22South West 93.23 12.97 15.88 67.47 4.63 8.71 3.32 13.91West Midlands 51.94 7.11 14.91 63.88 9.29 7.42 4.50 13.68Yorkshire and The Humber 55.58 9.43 16.02 60.26 6.05 12.15 5.52 16.96England 476.93 74.71 14.64 63.01 7.52 10.16 4.66 15.67Wales 53.58 9.84 6.10 68.53 4.68 15.21 5.49 18.36Scotland 120.21 36.48 4.91 32.16 2.33 50.19 10.42 30.35Northern Ireland 34.46 4.44 10.14 34.94 4.73 32.82 17.36 12.87UK 685.19 125.45 10.98 53.49 5.69 22.99 6.85 18.31

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Table 10-29: Regional and National summaries of the N atmospheric deposition emitted by all sources to water (kT) broken down (%) by broad atmospheric deposition footprint type


N ATM Agric (kT)

Combustion in


& Residential





grass


Distribution

Imported Emissions

Livestock emissions


Other Point

SourcesOther


Road Transport

Solvent Use

East of England 9.75 2.59 0.23 1.73 6.75 0.00 17.39 33.58 14.93 1.76 3.67 0.00 17.37 0.00

East Midlands 7.51 2.81 0.25 1.92 6.05 0.00 16.30 36.90 12.65 2.37 3.69 0.00 17.05 0.00

London 1.47 2.99 0.20 1.43 1.91 0.00 13.53 17.42 46.32 1.10 3.19 0.00 11.90 0.00

North East 3.85 3.13 0.46 1.68 5.10 0.02 18.46 40.72 10.34 2.20 4.15 0.00 13.73 0.00

North West 8.39 3.62 0.33 1.62 3.46 0.01 14.92 45.90 10.57 2.27 3.81 0.00 13.50 0.00

South East 14.25 2.41 0.20 1.50 5.05 0.00 22.19 34.96 13.96 1.33 3.75 0.00 14.64 0.00

South West 12.97 1.91 0.15 1.15 4.88 0.00 20.27 49.34 7.78 1.18 3.12 0.00 10.22 0.00

West Midlands 7.11 2.58 0.20 1.62 5.14 0.00 15.44 46.32 11.30 1.76 2.70 0.00 12.92 0.00


England 74.71 2.67 0.25 1.60 5.10 0.00 17.87 40.93 11.94 1.91 3.54 0.00 14.20 0.00

Wales 9.84 3.06 0.25 1.50 4.46 0.00 21.45 44.62 6.95 2.11 3.21 0.00 12.38 0.00

Scotland 36.48 3.82 0.86 1.66 3.67 0.02 23.20 37.62 8.93 1.90 4.87 0.00 13.43 0.00


UK 125.45 3.08 0.40 1.57 4.60 0.01 19.46 41.41 10.37 1.89 3.78 0.00 13.43 0.00

Table 10-30: Comparison of the maritime area summaries of the OSPAR Commission RID - Riverine and Direct discharges (kT) from the UK for the years 2001 through 2004 with the contribution from all sources of N (kT) accounting for catchment/basin retention

Maritime Area 2001 2002 2003 2004 Minimum 01/04 Maximum 01/04 ADASNO3-N + NH4-N NO3-N + NH4-N NO3-N + NH4-N NO3-N + NH4-N NO3-N + NH4-N NO3-N + NH4-N NO3-N + NH4-N

north North 221* 55 44 55 44 55 28.92south North * 125 97 89 89 125 96.71Channel 39.4 43.3 28.3 31.8 28.3 43.3 34.21Celtic 55.4 55.4 43.2 29.7 29.7 55.4 49.84Irish 48 49 34 47 34 49 45.80Atlantic 18.3 22.7 14 20.4 14 22.7 16.16

* Prior to 2002 the North Sea maritime area was reported as a single area.

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The model was validated using two approaches. The first was to compare the results against the data that the UK supplies to the OSPAR Commission each year on riverine and direct inputs to each of the marine water bodies surrounding the UK. The second was to compare the results with those compiled by other EU countries with similar climates.

10.2 Comparison with OSPAR Commission data

The OSPARCOM reports of riverine and direct (RID) discharges for 2001 through 2004 were sourced from their website (OSPARCOM, 2006; 2005; 2004; 2003). Estimates of NH4-N and NO3-N in kT were extracted from these annual reports and are tabulated in Table 10-30.

10.2.1 UK OSPAR data collection and calculation

The manner in which the UK collects and calculates the data submitted to the OSPAR Commission is described in the annual RID reports (OSPARCOM, 2004) from which this précis was extracted. The responsible agencies in each of the four home nations executed the water quality surveys, namely the Environment Agency in England and Wales, the Scottish Environment Protection Agency in Scotland and the Environment and Heritage Service in Northern Ireland. Methods used vary between the home nations but were subjected to formal analytical quality assurance procedures. Generally, all the main river systems were sampled at an approximately monthly interval at a sampling point close to but upstream of the tidal limit. All significant “direct” discharges of industrial or sewage effluent downstream of the riverine sampling points (i.e. those emissions direct to estuaries and coastal waters) were also sampled.

Both of the formulae recommended by the OSPAR Commission RID were used for calculating loads from monthly samples. The first formula which requires the mean annual flow rate for a river was used in some parts of Scotland where continuous flow records were available. In England and Wales, western Scotland and Northern Ireland, the second formula was used. The best available estimates for flow for some smaller rivers with no gauging stations were used. The aim of the survey has been to achieve at least 90% coverage of the overall inputs from the UK with the total inputs reported not being proportioned up to give a 100% estimated value.

10.2.2 Catchment Retention

Nitrogen and other pollutants that enter watercourses will be affected by instream retention processes such as sedimentation and denitrification. This will result in a discrepancy between the losses from diffuse and point sources in catchments and what can be measured at monitoring stations at the outlet of the catchments.

To account for retention in river systems the gross loads of nitrogen were reduced using the method developed by Behrendt and Opitz (2000). This method can be used for large river basins and as such was only applied to surface water and coastal catchments. Retention factors were calculated based on catchment hydraulic load, where hydraulic load is defined as the annual runoff divided by the water surface area in the catchment using equation 10.1 as given by Behrendt and Opitz (2000).

10.1

Where LN,P is the net loss of nitrogen or phosphorus and DN,P is the gross loss. The constants a and b were set to 1.9 and –0.49 for total nitrogen. The hydraulic load was calculated from

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the annual runoff (See Section 6.2.1) and the surface water area calculated using the following empirical relationship in equation 10.2:

10.2

Where ALAKE (km2) is the surface area of the lakes within the catchment and A is the catchment area (km2) (Behrendt and Opitz, 2000). The surface area of the lakes was extracted from the data compiled for the direct deposition to water (See Section 4.2). The catchment areas for complete surface water basins for England, Wales and Northern Ireland were created from the data supplied to this project (See Section 4.2). Catchment retention figures for Scotland were taken from a previous project (Anthony et al., 2005b). The OSPARCOM zone boundaries were constructed from the JRC CCM catchment boundary dataset (Vogt et al., 2003).

10.2.3 Results

These indicate that the results from this study compare favourably (See Figure 10-20) with the annual results reported to the OSPAR Commission with all but one of our results falling within the inter annual variation reported over a time period relevant to the datasets utilised within our study, e.g. 2001-2003 for atmospheric deposition data, 2004 for agricultural census statistics and 2001 for population census statistics. The most notable exception is the north North Sea maritime area which is 15kT lower than those figures reported. This is most likely a function of three factors:

1. Different datasets used in the construction of the Scotland loads – possible point sources not captured by using per capita export coefficients derived for northern England. Point sources account for 25.8% of the N load emitted to water and as such this variation in the point source datasets may account for a portion of this discrepancy.

2. Different datasets used in the construction of the Scotland loads – using the European soils dataset which owing to its limited texture classes may result in heavier soils and lower N mineralisation/leaching. Agriculture accounts for 51.5% of the UK N load emitted to water and as such this variation in the soils dataset may account for a portion of this discrepancy.

3. The OSPARCOM data used in the comparison, although based on the harmonised monitoring system datasets, is also modelled. As such uncertainty will be apparent in this dataset, especially for this zone that spans two agencies.

Comparison between the results is possible as the OSPARCOM data extracted is for estimates of NH4-N and NO3-N, excluding organic N, and are thus similar to the results from this study which focuses on NH4-N and NO3-N as well (apart from the particulates and urban hard surface runoff which use TN). These results suggest that the modelling approach used in this project has provided realistic and robust estimates of the N loads emitted to surface waters in the UK.

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N Load Comparison y = 1.2673xR2 = 0.919

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120

ADAS (kT)

OSP

ARC

OM

(kT)

2002 2003 2004 Linear (2002)

Figure 10-20: Comparison between the OSPARCOM loads and the loads derived by this study (kT) for each of the years 2002 to 2004 for each of the RID maritime zones.

Figure 10-21: OSPARCOM zones used to summarise the N load data (kg)

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10.3 Comparison with results of other N source apportionment studies

The results from the previous section expressed in Kt (e.g. Table 10-23), percent (e.g. Table 10-23 b) and kg/ha (e.g. Table 10-24) were compared with the results from a range of previous studies (See Section 2).

10.3.1 Previous England and Wales study 2004

Hunt et al. (2004) found total emissions (Table 10-31) to be of a similar order to but slightly greater than our estimate (544 v 531 kT N per annum). They found that 330 kT N (61%) derived from agricultural land, including rough grazing compared to 321 kT N by our estimate from managed agricultural land, including the 54 kT N from woodland, rough grazing and similar areas. The small reduction in agricultural inputs is consistent with a small reduction in livestock numbers and associated fertiliser N use on grassland over this period.

The loss attributed to sewage, septic tanks and industrial sources was 175 kT, compared with 145 by our method, which is consistent with improvements in sewage treatment, and may also be accounted for in part by more detailed data available to us. Direct N deposition was estimated as 4 kT N by us, and 2 by Hunt et al., probably due chiefly to different estimates of the surface area of streams. Urban sources were the major component of ‘Other Land’ in the 2004 report, contributing 23 kT N, whereas our estimate is substantially greater, at 30 kT N. The previous study did not estimate N content of particulate matter (sediment), which account for 21 kT N (4%) of our estimated inputs to waters.

Table 10-31: N source apportionment to waters in England and Wales (calculation basis 1), Hunt et al., 2004.

Source (kt N/yr) %Agricultural Land 330 60.6Sewage Treatment Works 175 32.1Other Land 23 4.1Direct Industrial 9.5 1.8Septic Tanks 3.9 0.7Direct Deposition 2.1 0.4Combined Storm Overflows 1.9 0.3Totals 544 100

Figure 10-22: N source apportionment to waters in England and Wales by region. Hunt et al., 2004.

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As would be expected, the proportion of N in waters which is attributed to agricultural land is greater in rural regions such as Wales (See Figure 10-22), and smaller in regions with large population concentrations such as Thames. This finding was echoed in our own study.

Overall, allowing for differences in the classification of sources, our results describe a similar picture to those of Hunt et al. (2004).

10.3.2 Other European studies: EEA review

The EEA review of N and P source apportionment studies (Bøgestrand et al., 2005) covers the great majority of studies of interest. Their collated data and comments form a useful basis for assessment of our results (See Figure 10-23).

The EEA review reported that agricultural land the single dominating source of nitrogen pollution and typically contributed 50 – 80% of the total calculated loads, increasing to > 60% if ‘background’ diffuse losses (losses from non-agricultural unfertilised rural land such as uplands) are included. The results from both this and the previous report place England at the lower end of this range, consistent with a relatively high population density overall, while Wales is closer to the upper end of the range.

The authors found that the total load (expressed as kg N per ha total land) varied over a 5-fold range, and the main determinant of it was intensity of agricultural activity. Calculated N loss was strongly correlated with both N fertiliser use and N surplus in the studies reviewed.

Figure 10-23: Source apportionment of nitrogen load in selected regions and catchments (Bøgestrand et al., 2005).

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Figure 10-24: (B) relative and (C) area-specific nitrogen source apportionments for European catchments (by a source-oriented approach) (Bøgestrand et al., 2005)

Within western Europe (other than areas dominated by mountain and forest), the source apportionment between point sources and diffuse is relatively consistent, as illustrated by the Elbe, Rhine and Po (See Figure 10-24).

For the North Sea, in the year 2000, the estimated contribution of ‘background’ sources was 10%, while the ‘anthropogenic N’ was broken down between agricultural diffuse sources (64%) and point sources (36%). (See Figure 10-25, 10-9). Within the North Sea area, there are great differences between the northern, mountainous countries (Norway, Sweden) and the lowland, farmed countries. The figures for the UK are closest to the Netherlands and Germany. Our estimate of ca. 10% of the load coming from uplands, woodland and other rural unfertilised land, is broadly in line with estimates of ‘background’ N loss from other countries. As indicated earlier, the definition of background losses is variable between studies.

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Figure 10-25: Source apportionment of N loads to the North Sea in 2000 by a source-oriented approach. Point sources green, above diffuse sources orange). (Bøgestrand et al., 2005 from OSPARCOM, 2003)

The authors compared a number of studies with the average N fertiliser input to the area (See Figure 10-26). A good correlation between N fertiliser use and total N load was observed, with data for England and Wales (and for UK from this report) conforming to the general trend. OSPAR (2003) estimated that for the North Sea in 2000, 75% of N from point sources was associated with sewage discharges, 15% with other industrial discharges, 10% with septic tanks and 1% with aquaculture.

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Figure 10-26: Source apportionment and N fertiliser use (Bøgestrand et al., 2005).10.3.3 JRC study

Broadly the results from the JRC estimates (See Figure 10-27 and Figure 10-28) for northern Europe (e.g. Meuse, Weser, Seine, Rhone, Elbe) are in line with those summarised by the EEA, and with our results. The waste water treatment inputs to the Rhine are above average, perhaps reflecting high population densities in the lower reaches, while those for the Danube are lower.

Figure 10-27: Source contribution to the in-river nitrate loads according to the JRC model estimates (Grizzetti & Bourouai, 2005).

Figure 10-28: Nitrogen sources apportionment 1995-2202 (Grizzetti & Bourouai, 2005).

10.3.4 Conclusion

The source attribution between diffuse and point sources from this study is in line with estimates from other European studies, and with the previous report for England and Wales (Hunt et al., 2004). However, it contains detailed additional estimates, for example with

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respect to woodland and natural areas and for atmospheric deposition through breaking these figures down by the “Footprint”.

11 Review of evidence for non-nitrate soluble N as diffuse pollutant

Since implementation of the Nitrates Directive, attention has been drawn to sources of nitrate in waters and how these can be reduced. In particular, measures have been taken to reduce the nitrate arising from both agricultural land and sewage treatment works.

Other forms of N, although generally less important in quantity, also enter water and may have adverse effects. Ammonium and nitrite are both highly toxic to fish and other biota. Both, and especially nitrite, are relatively short-lived, making accounting for them in any budgeting exercise somewhat problematic. Indeed the Environment Agency prefers to include these pollutants within the total estimate of nitrate in waters when reporting surface water quality.

Losses of organic forms of N have been relatively little explored until recent years. They could in principle act as both a source of inorganic N (eventually nitrate) by nitrification and a sink by immobilisation or by denitrification, fuelled by the accompanying organic carbon. Seitzinger & Saunders (1997) found that organic N in rivers could act as a feedstock for microbial biomass, as well as mineralising to nitrate. It could therefore contribute significantly, and to a variable extent depending on its characteristics, to the risk of estuarine eutrophication. Stepanauskas et al. (2002) found that about 30% of dissolved organic N and 70% of dissolved organic P was potentially available to the biomass of the Baltic.

Organic N, being necessarily part of organic matter, effectively provides a wide range of nutrients to fuel plant growth, so that the contribution of soluble organic matter to eutrophication risk may be relatively independent of whether local biomass growth is, for example, N or P limited.

11.1 Evidence on organic N content of waters

11.1.1 Agricultural catchments

Soluble organic N was found to be a significant component (40%) of total N load in subsurface flow derived from grazed grassland, but less so in surface runoff (Heathwaite & Johnes, 1996, building on the work of Johnes, 1990). Anthony (1994) found that albuminoid N (organic N) in a Norfolk catchment varied seasonally, peaking in late summer when it contributed 20-40% of TN, while the discharge weighted contribution to the annual total nitrogen flux was no more than ~10%. . In a study of a mixed agricultural lowland catchment in Nottinghamshire, England, with grass and arable cropping (Defra project NT1856), dissolved organic N (DON) accounted for between 10% and 30% of the total dissolved N load in the headwater catchments. Agricultural land was found to be a more important source of N than the sewage inputs in the catchment. Particulate organic N (PON) was also found to be an important component of the total N load at times, ranging from 6 to 21% of total. Dissolved organic N losses were 2.1 to 4.7 kg/ha N, compared with 5.8 (dry year) to 30.9 kg/ha for nitrate N. Losses of soluble organic P, as represented by (TDP – SRP) represented about 5-20% of TP losses. Typical flow-weighted mean DON concentrations were 1.2 to 1.7 mg/l N compared with 9-12 mg/l nitrate-N.

In these data, DON loads and concentrations were about 6 times TDP and about 15 times (TDP-SRP). Assuming TDP – SRP represents dissolved organic P, this implies a P:N ratio

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in dissolved organic matter of about 15. DON loads and concentrations were typically similar to or slightly higher than TDP, on a weight basis. Interestingly, one of these catchments contained a sewage works. While this impacted on nitrate loss, the impact on DON and DOP was less apparent.

Organic matter in rivers may originate from biological activity in the river especially during summer. For example chlorophyll levels typically peak during summer, and in low flow periods are correlated with concentrations of SRP, particulate N, organic C and suspended sediments (as well as with catchment area and flow, which are indicative or residence time) (Neal et al., 2006). Seitinger & Saunders (1997) considered that the N derived from in-river activity may be more biologically active than that derived from soil organic matter.

Edwards et al. (2000) found that in a mixed agricultural catchment (the Dee, north-east Scotland) the source of soluble organic N varied seasonally, being largely due to in-stream vegetation during summer, but originating from soil during winter. The winter flows will dominate load, but summer flows may be more critical for ecological impacts. Soluble organic N was more important as a fraction of the (low) total N load in upland areas, while in intensively farmed areas nitrate was relatively more important. 11.1.2 Upland catchments

N content of upland streams tend to be low. Nitrate is normally the dominant form of N (Reynolds and Edwards, 1995) and is typically <0.5 mg/l N. Chapman et al. (2001) found that DON concentrations were relatively constant, and were correlated with DOC. Surprisingly they were not correlated with the area of peat-covered land in 4 streams studied. DON concentrations were greatest in summer, and accounted for 25-30% of TN (which was less than 1 mg/l N). Nitrate concentrations were more variable, and were correlated with the proportion of improved grassland and forest soils. Particulate N in these catchments accounted for <7% of TN. Dissolved organic carbon (DOC) concentrations in UK upland waters are reported to be increasing (Evans et al., 2005) by an average of 91% during the last 15 years in 22 studied catchments. The authors suggested but could not prove that the increase may be a response to a combination of declining acid deposition and rising temperatures.

DOC fluxes from upland peat catchments in Northern England were predicted to average 250 kg/ha organic C by 2010, (Worrall & Burt, 2005) giving a total flux for the UK from upland peats approaching 1 Mt. For a C:N ratio of 10 to 25, the corresponding flux of organic N would be 25 to 10 kg/ha N per annum.

Helland et al., (2003) report that the C:N mole ratio of upland streams in Norway was 20 implying a C:N ratio of 25 by weight). It was unaffected by flow in mountain catchments but increased with flow in forest areas. They reported that the small proportion of the material which could be removed by flocculation with salt (<10%) had a C:N mole ratio of 7-12, more typical of soil organic matter. This implied that much of the organic matter in upland catchments may be of plant origin.

In a study of Danish catchments, Kronvang et al. (1995) found that the inorganic fraction of nitrogen represented 50% of the concentration of total N in streams draining natural catchments (largely heathland and forest, rather than upland) and 92% in those draining agricultural catchments, while dissolved reactive phosphorus accounted for 48% and 42% of the concentration of total phosphorus in streams draining natural and agricultural catchments respectively.

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11.2 Sources of soluble organic N from agricultural land

Data on the soluble organic matter component of losses to water from agricultural land is limited, and refers to rather few sites. The measurements recently authorised on several sites within 8 micro-catchments in the NVZ Impacts Assessment project NIT18, complemented by data on P and nitrate loss, will provide a valuable addition.

11.2.1 Soil

Long term applications of organic manures has been shown to cause an increase in the amount of soluble organic N (SON) in the soil and cause leaching of DON in lysimeters, (Shepherd & Bennett, 1998). It is not clear to what depth the DON will leach, and in many aquifers concentrations are likely to be very small by the time the saturated zone is reached. However, presumably any surface runoff would be correspondingly enriched in soluble organic matter.

Losses of dissolved organic N in drainflow from long term arable clay land in Oxfordshire (amounting to 1.6 kg/ha N) accounted for about 9% of total N loss, most of the remainder being nitrate. Average losses from unmanured grassland (i.e. on plots receiving manure in spring, taking only losses up to date of application), were 0.5 kg/ha DON, representing 36% of TN loss (nitrate losses were unusually low on this grassland, possibly because it was newly established on previously long-term arable land). DON losses increased markedly where manure was applied in autumn (0.66) and especially winter (1.46 kg/ha DON) representing 24 and 30% of total N loss. (Defra project ES0106; some data given in Williams et al., 2006).

11.2.2 Manure

While several studies have measured nitrate, ammonium, P and sediment losses associated with manure application, rather fewer have data on soluble organic N losses. .

Following manure (FYM, slurry) applications to grassland hillslopes, organic N represented 60-70% of the additional N loss in surface runoff (Heathwaite et al., 1998).

Concentrations of soluble organic N on the clay site in Oxfordshire (Defra project ES0206 as cited under the soil section above; Williams et al, 2006) increased following manure application, as did concentrations of P and ammonium. DON loss therefore increased where manures were applied to arable land in autumn (2.2) or winter (2.4 kg/ha N), but as a proportion of total N, DON was not much affected by manure application, since both nitrate and DON increased. On grassland plots, DON losses also increased where manures were applied in autumn (to 0.66) and especially winter (1.46 k/ha DON), accounting for 24 and 30% of total N loss. This proportion is smaller than for the spring-manured plots, since nitrate losses increased even more than DON losses.

Over the course of the winter, the proportion of soluble N which was organic was often low, prior to manure application. Following manure application in winter, the proportion generally increased to 30-60%, and often remained high for some weeks (Figure 1). The timecourse of DON concentrations was more closely linked to that of ammonium and dissolved P than to nitrate.

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0

2

4

6

8

10

12

0 10 20 30 40 50 60

Sample No

mg/

l N

Nitrate

Ammonium

Soluble Organic N

Figure 11-29: Nitrate, Ammonium and SON concentrations in drainage water from grassland and the effect of addition of slurry at sampling occasion 29 (Defra project ES0106, Plot 1 , 2005-6).

0

2

4

6

8

10

0 10 20 30 40 50 60

Sample No

TDP-

MR

P m

g/l

00.2

0.40.60.8

11.2

DON mg/l

DONTDP-MRP

.

Figure 11-30: Soluble non-reactive P and DON in drainage water from grassland showing the effect of slurry application in winter at sampling occasion 29 on a clay site (Defra project ES0106, Plot 1, 2005-6).

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Dissolved organic N concentrations were correlated with total dissolved non-reactive P (TDP - SRP) concentrations, and followed the same pattern of behaviour after the addition of manure (Figure 11-30). This represents soluble organic P. For the plot shown, the relationship was:

DON = 7.9 * (TDP – MRP) r2 = 0.93.

Preliminary analysis of the detailed data from these experiments shows a clear distinction in DON in drain flow, between periods when DON concentrations (like DOP, i.e. (TDP-SRP) losses, are low and relatively invariant; and periods following manure application when they may rise sharply and in concert, decline rapidly, then enter a phase of much slower decline towards the baseline level. The ratio DON:DOP was greater during the pre-manure phase (10 to 40) than it was following manure application (ca. 8), indicating a difference in composition of the organic matter. It is likely that the DON losses in the absence of manure are of a different character than those following manure application, and it is possible that they have different C:N ratio and bioavailability. The contrast may vary with the composition of the manure applied.

This data set provides a powerful basis for developing a prediction method for soluble organic N and relating it to emissions of other manure-derived pollutants.

11.2.3 Farm hard standings

Runoff from hardstandings can be rich in organic matter, some of it soluble. Runoff measured from several farms during summer storms was found to contain organic forms of N and P (Edwards et al., 2008). Nitrate represented typically < 15% of N. These concentrations were considered by the authors to be consistent with manure being the major contaminant. TN:TP ratio was 12 (range 2 – 60), while DON:DOP ratio averaged 26, again with a wide range. The dominant form of soluble P was SRP (average 68%), and soluble organic P accounted for just over half of SRP. Organic N accounted for about 45% of total dissolved N.

Concentrations of DON averaged 37 mg/l (median 4 mg/l) and of DOP averaged 1.4 (median 0.5) mg/l. Concentrations of ammonium N were often high, averaging 70 mg/l (median 8 mg/l). The C:N ratio of dissolved organic matter was 9.5, similar to or a little smaller than that in soil organic matter.

A rapid decline in DON occurred after the onset of a storm event, followed by a recovery as flow declined towards the end of a storm. A broad inverse relationship between flow and concentration was found for DON, TDP and DOC could be inferred using the timing of when samples were collected during the storm events. The concentrations of pollutants in hard standing runoff declined relatively little during the duration of a storm, in contrast to concentrations in roof runoff. Cleaning of the yards and absence of stock was found not to eliminate pollutants in hard standing runoff (although clearly it could reduce it).

The authors measured definite downstream impacts of farmyards on water quality, and commented also on the contribution made by overflowing stores and septic tanks during heavy storms. Median concentrations of DOC in runoff were in the range 80 to 670 mg/l.

Concentrations of DON, DOP and DOC were 75, 5 and 21 times as great in hardstanding runoff as in roof runoff. Roof runoff contributes substantial quantities of water, but less pollution than hard standings.

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11.3 Other Sources of soluble organic N

The urban N emission estimates in this project are for total N, and therefore include soluble organic N.

Sewage is a significant source of N as nitrate and ammonium. Data on the content of other forms of N are not routinely collected. Data on other relevant parameters could be potential predictors of dissolved organic N (subject to calibration for different circumstances). These include Biological Oxygen Demand (BOD) which is routinely measured on both wastewater effluent and river water monitoring samples, since there are standards relating to these. A study of available data in EA archives is required to develop an estimate of DON from this source.

The generation of DON in waters as a result of biological activity during summer also requires investigation and quantification. This process is in part a transformation of N from inorganic to organic form, and therefore could be treated as a subset of the in-river retention process. Its importance is unclear, and may lie mainly in effects of the timecourse of biomass growth and decay, and hence release of soluble organic material.

11.4 Relevant modelling approaches

This review shows that much of the DON in water originates from soil and manures, and that the pattern of concentration variation as a function of management and rainfall patterns reflects that of soluble P (especially (TDP-MRP) and ammonium loss.

Dissolved organic N may be derived from soils or manures. It is readily absorbed onto soil and therefore moves dominantly by surface runoff or bypass flow pathways. Organic matter will contain a range of elements, and models which estimate dissolved organic carbon, phosphorus or nitrogen are all effectively targeting the same mix of molecules.

The methods and data used by models aiming to estimate impact of farming practice on P loss could therefore inform development of a DON model. The PSYCHIC model was developed to allow estimation of the impacts of management practices on P loss, and has a level of complexity and responsiveness which may lend itself to adaptation for the purpose of estimation of dissolved organic N. A broad overview of the model logic and data requirements therefore follows.

Although ammonium derives from manure, and has similar transport characteristics to dissolved organic N, there is no sufficiently detailed model of ammonium loss to waters available for adaptation.

11.4.1 Summary of PSYCHIC methodology

PSYCHIC was developed by a consortium of experts on P loss to waters (Defra project PE0202), Phosphorus and sediment yield characterization in catchments (PSYCHIC); Davison et al., 2008), for the purpose of providing a tool for assessment of sources of P in waters and the impact of mitigation options.

P losses are calculated in terms of sediment loss by direct disturbance of soil (erosion) and dissolution. It is the latter which is of most interest in relation to DON. Dissolution from soil or from freshly applied manure or fertiliser is calculated in terms of P content of the material and rainfall quantity. For fresh additions of manure or fertiliser, the total quantity available is also taken into account (by a decay process), and dissolution is terminated once the material is incorporated in the soil.

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Once P has been dissolved, the proportion of rainfall which moves as surface runoff is estimated, on the basis of factors such as soil type and rainfall intensity data. The proportion moving via drains is calculated on the basis of soil hydrology as expressed in its HOST class, modified by an estimate of the likelihood that drains have been installed. For each pathway, delivery to streams is then assessed in terms of the connectivity of the landscape. For the surface runoff pathway, infiltration is assumed to reduce delivery to stream according to stream density. Finally allowance is made for adsorption of P on subsoil (for drain flow) and on the surface soil during transfer to water (for surface runoff).

PSYCHIC also makes a simple estimate of the risk of P loss from hard standings and from deposition on roads, chiefly under dairy systems. This estimate takes account of periods for which cows are on roads or tracks, and the estimated connectivity of the track to streams. The same logic would hold for organic N, and recent data (e.g. Edwards et al., 2007) plus more detailed survey information could support refinement of this calculation.

The PSYCHIC algorithms for dissolution from soil are probably not especially helpful for organic N. There may however be useful calibration data in the DESPRAL project (Withers et al., 2007) which measured dissolution of N and P from soils under laboratory and field conditions, as well as from Defra ES0206 (Williams et al., 2006) and from the newly initiated measurements within Nitrate Vulnerable Zones.(Defra project NIT18 continuation).

11.4.2 Manure-related data used by PSYCHIC

DEFRA agricultural census statistics, spatially interpolated

Parish-level results of the DEFRA agricultural census have been spatially interpolated and integrated with an ADAS-developed dataset of land cover at a 1km2 resolution, to produce a national dataset of agricultural land use at 1km2 resolution (Lord & Anthony 2000 and updates). The data include cropping and detailed breakdowns of different livestock types. This database is used within the present project for estimation of nitrate loss from agricultural land.

Regional estimates of types of manure production dates spread, and land types on which spread.

The current Defra project WQ0103 (The National Inventory and Map of Livestock Manure Loadings to Agricultural Land: MANURES-GIS) will develop a mapped inventory of manure production (based on animal numbers), composition; and the quantities applied to different land uses each month. These data will be calculated by interpolation from national or regional survey information regarding types of manure, housing of stock, manure storage, and dates of spreading. A previous version of this database was used within PSYCHIC and a number of other water quality projects.

11.5 Conclusions

There is limited high quality data on losses of dissolved organic N (DON) to waters, but recent and ongoing projects are improving the situation.o In arable systems DON may represent 1-2 kg/ha N or ca 10% of TN loss.o In grass systems DON losses may be similar or smaller, but represent a greater

proportion of losso DON loss does not appear to be sensitive to atmospheric deposition or

inorganic fertiliser use

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o Following application of manures, concentrations of DON, (TDP-MRP) and ammonium N all increase in concert. All these pollutants are derived from manure and have similar transport behaviours in soils. The DON and (TDP-MRP) may represent the same soluble organic matter components

o Effects of manure on nitrate loss are to a different time scale than DON, ammonium and TDP, with less close relationships between the quantities lost.

In uplands, N losses are very small, but DON may be a relatively large proportion. DON does not appear to be affected by atmospheric deposition, and was not readily predicted on the basis of soil type, but may be increasing.

Losses of DON from urban land are in principle already included in estimates of total N loss in runoff from urban land.

On the basis of this review, and taking account of filtration of DON by passage through groundwaters, inclusion of DON in emissions of N to waters would be expected to increase these numbers by 5-15% for the UK.

The commonality of sources, and the similarity in transport pathways, suggest that

prediction of DON loss from agricultural land could be based on methods developed for P. o The PSYCHIC model appears the best starting point for development of DON

estimation at present. The model is undergoing review under Defra project NIT18 Phase 2 (Monitoring the effectiveness of NVZ Action Programme measures: continuation and development of existing effectiveness evaluation strategy).

Recent data which provide strong underpinning for model development include:o Data from the Defra project ES0206, especially valuable since it contains

simultaneous data on nitrate, ammonium, and P species losses; the effects of manure; and contrasting grassland and arable plots.

o This project has been recently extended under WQ0118, but unfortunately measurement of DON has not been funded.

o NIT18 Phase 2 project. (Monitoring of impacts of the NVZ Action Proramme) New measurements across a wide range of agricultural land uses are about to start within this project, with simultaneous measurements of nitrate, ammonium, SON, and P species. These will complement the ES0206 data.

Recommendation

- A model of DON loss to waters could be developed from existing data and understanding of related pollutants

- The approach for diffuse sources should build on understanding of P losses as developed for example in the PSYCHIC model

- The data from Defra projects ES0206 (4 years data) and NIT18 (data collection about to start) should underpin the development of the agricultural model, exploiting the added information content given by the measurement of forms of N and simultaneously

- Consideration should be given to funding analysis of a subset of samples from project WQ0118 for Dissolved Organic N, to complement the other pollutants measured.

- For point sources, a review of available data from wastewater monitoring and surface water monitoring (Environment Agency) is required.

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12 Discussion

This study confirms that agricultural activity and wastewater/sewage discharges are the major contributors to nitrogen in UK waters. In this it confirms many previous studies of similar areas.

12.1 Agriculture

The N loss from agricultural land has been attributed in the ratio 41% from grassland systems (including manures from the livestock they support), 47% from arable systems excluding manures they receive, and a further 8% from pigs and poultry. Finally, 4% of the loss from managed agricultural land has been estimated to be directly attributable to the fact that the agricultural land receives atmospheric deposition. Most of this is from arable land.

The estimate of loss attributable to atmospheric deposition is somewhat lower than some previous estimates (eg Goulding et al., 1998) and smaller than the proportion of agricultural N inputs contributed by atmospheric deposition. This is because the basis of the calculation is to give guidance as to how leaching would change if there were no atmospheric input. Some of the atmospheric N is deposited at times when the crop cannot use it – especially on arable land – and is therefore at risk of leaching. Some however effectively contributes to crop growth. If atmospheric deposition ceased, responses to fertiliser N would increase and recommendations as well as farming practice would eventually respond to this change, as they respond to evolution of varieties and economic change. Previous estimates have essentially tested the effect of deposited N as if N fertiliser applications were immutable, and atmospheric N was additional to this. There is an additional effect due to the spatial and temporal variability in N deposition, which farmers cannot readily take into account. However this variance is small (SE < 5 kg/ha N) compared to the many other uncertainties in the N response which are present at the time of fertilisation, and was ignored in this work.

12.2 Woodland and natural areas

The estimate of N loss from other rural land uses (mainly forestry and rough grazing) was newly developed for this project, with the purpose of taking account of variation in atmospheric deposition, the dominant N input to such land. The final estimate, of ca 10% of the total load, is in line with estimates from other countries. The proportion is greatest in Scotland and the South East region, and least in Northern Ireland (and of course London).

12.3 Particulate N losses

Particulate N losses derive largely from rural land. The estimate of N loss in sediment accounts for 7% of total loss The estimate is crude at this stage, but does indicate that sediment is a relatively minor source of N. It will be even less important in terms of ecological impact, since the mineralisation rate for soil-derived organic matter is typically under 5% per year, and a substantial proportion of the organic matter in sediment will be recalcitrant, with mineralisation half-life of centuries. Greater ecological impact is to be expected from the sediment itself, than from its N content.

12.4 Sewage and industrial N

Sewage and industrial N sources account for 30% of inputs in England, but only 14% in Scotland, with a UK average of 26% overall. Direct inputs from sewers dominate this category of loss. Septic tanks contribute about 3% and leakage from urban sewers is estimated as a similar amount. In rural areas, septic tanks may be relatively more important, contributing about 5% in Scotland and Northern Ireland.

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12.5 Urban runoff and leaching

Urban diffuse losses account for 5% of the N loss overall, with a pattern similar to that for sewage, reflecting population density.

12.6 Atmospheric deposition

Direct atmospheric deposition to inland surface waters is a minor contributor to the budget (1.3%), reflecting the relatively small area of open water in the UK. Its relative importance is greatest in Scotland and least in England, reflecting both water areas and the greater inputs from other sources within England.

The contribution from woodland and natural areas, which in the long term could be attributed to atmospheric inputs, adds a further 10%. While the evidence indicates that N loss is correlated with atmospheric deposition, it is debatable whether the whole of this should be attributed to atmospheric deposition, since some N fixation is likely to occur albeit at low rates. Reduction of N deposition will, on the evidence, reduce N loss from these areas, but probably not to zero within a finite time scale.

The estimated net effect of atmospheric deposition to agricultural land is estimated to contribute 2% of the total UK N loss budget. That is, if atmospheric deposition is reduced by 50%, this would be expected to reduce total N loss to waters by 1%. In the short term the benefit might be slightly greater, if fertiliser inputs took a while to adjust to the change.

12.7 Dissolved organic N

Dissolved organic N (DON) is contributed from agricultural land, woodland and natural areas, urban land and point sources. It appears to be a minor but potentially significant source (estimated tentatively as 5 -15% of budget). Evidence suggests that the contribution from soil is not closely related to atmospheric or fertiliser inputs, suggesting that it could be relatively more important as these sources are controlled. It contributes a greater proportion of total N loss from woodland and natural areas, where N losses are small, than from agricultural land (especially arable land in the absence of manures).

Runoff and drainage following manure applications can contain substantial quantities of DON, along with related pollutants, and this material is more nutrient rich and likely to be more reactive than DON derived from soil.

Particulate pollutants, organic matter and pollutants such as P may persist longer in rivers, because they settle out or bind to river bed sediments. The ecological effects of DON and particulate matter may therefore persist longer, after removal of the source, than the effects of nitrate loss to water.

12.8 Ecological impacts on water

The time course of loss of point and diffuse sources differs (e.g. Dijk et al., 1997 as discussed above). Point sources typically discharge about the same quantity of pollutants all year while diffuse sources generally discharge when there is an excess of rainfall over evapotranspiration, i.e. mainly in the winter period. Ecological impacts on rivers are sensitive to the variation in water quality at different times of year, especially when considering a pollutant such as nitrate which is highly soluble and quickly washed down-river. River water quality during the summer is dominated by the combination of sewage discharge and groundwater flows. This means that point source discharges, such as

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sewage, will have greater impact on ecological status than their absolute contribution to the budget implies. The ecological impacts are also a function of the N:P ratio (e.g. James et al., 2005).

As indicated above, different forms of N have different bioavailability. Nitrate and ammonium are highly bio-available, as are the forms of N in atmospheric deposition. Ammonium is toxic to many forms of animal life including fish. Sediment-bound N is likely to be the least bio-available of all sources considered here. Dissolved organic N (DON) appears to be intermediate, being partly bio-available. It is likely that the DON derived from fresh applications of manure will be more bio-available than that derived by passage of water over and through soil.

12.9 Impacts on water quality assessments

Under the Nitrates Directive, the designation of NVZs has been based upon an estimate of the risk of exceedence of 50 mg/l nitrate in surface and ground waters. The high nitrate concentrations which could trigger designation are typically measured during winter, when the contribution from diffuse sources is at its greatest. The importance of diffuse contributions in determining whether a surface water catchment is designated is therefore greater than the simple mass balance would indicate.

The RBC2 methodology (Environment Agency, 2007) assesses the risk of exceedence of the 50 mg/l nitrate standard, based on the ‘lower 95% confidence band of the upper 95the percentile concentration’. A regression between pressure indicators representing diffuse losses, point sources and other factors, and the above statistic, is used to predict the risk of exceedence in unmonitored catchments. This contribution of agriculture to this risk of exceedence is therefore likely to exceed the budgetary contribution from agriculture based on kT N exported to water.

13 Summary and Conclusions

This N apportionment study centred on data for 2004 confirms that the dominant source of N in UK waters is agriculture (~52% plus a further ~10% from woodland, rough grazing and similar land use) followed by wastewater from sewers and septic tanks (~26%) – see Table 10-1

o The apportionment varies both regionally and by river basin district, depending largely on the balance between agricultural activity and population density

The estimates of N inputs to water, corrected for basin retention, correlate well with estimates of N export to the seas around the UK collated for OSPARCOM reporting purposes – see Table 10-8 and Figure 10-3

The estimates are in line with the results from similar studies throughout northern Europe, for similar areas

The results of this study are broadly similar to those of a previous study for England and Wales centred on 1995/2000 (Hunt et al., 2004).

o Direct comparison of agricultural contributions between the current study and Hunt et al., 2004 is difficult as they included rough grazing in this category while the current study has included this rough grazing loss in another category, namely loss from “woodland, rough grazing and other natural areas”, where atmospheric deposition is a dominant input.

o Accounting for this difference still suggests that inputs from agricultural land have fallen slightly, due partly to a reduction in certain livestock numbers

o Inputs from sewers appear to have fallen, which may be due to a combination of more detailed information and reduced P content in detergents

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o Sediment loss from land, not included in the previous study, was estimated to contribute ~7% of the final load – see Table 10-1

Direct atmospheric deposition to water contributed ~1% of the load (Table 10-1). This value is greater than in the previous study and is because of the inclusion of the surface area of streams and rivers.

A novel aspect of the work was the attempt to apportion the effect of atmospheric deposition on nitrate loss from the land.

o The method assumed that the current practices, being based on experience under ambient conditions, already in effect adjust for the fertiliser-equivalent value of average deposition rates. If deposition were reduced to zero, fertiliser inputs would increase, other factors being equal.

o The negative effect of deposition relates to the timing of deposition and the fact that it occurs on all land including unfertilised land (uplands)

o Novel methods of calculation were developed to take account of the effects of atmospheric deposition on diffuse rural N losses

o The net effect of deposition, relative to equivalent yields and management in the absence of deposition, contributed ~4% of the loss from managed agricultural land (~2% of total) – see Tables 5-3 and 10-6

o Woodland, upland and similar unfertilised areas which derive the great majority of their N input from atmospheric deposition contributed 67 kT N (~10% of total loss).

Another novel aspect of the work was the assessment of the sources from which the deposited atmospheric deposition arose (Table 10-7). These results suggest that agricultural, imported, road transport and non-agricultural land emissions dominate. They also allow one to assess the “recycled” effects of a source on itself, for example agriculture itself is the source of almost 50% of the atmospheric deposition to agricultural land.

Dissolved organic N (DON) is excluded from most studies, or included only for some components e.g. urban runoff. DON is considered potentially important especially in relation to eutrophication.

o A scoping study concluded that there were now sufficient data (recent and prospective) to give confidence that a sound estimate could be constructed for diffuse DON losses, building on experience with related pollutants such as P.

o Data on DON in wastewater are not routinely collected, and a separate study would be required to estimate these from such limited data as exist

o A preliminary estimate suggests that DON could add 5-15% to the N export to waters calculated in this study

A range of further refinements could be undertakeno Explore the inclusion of DON furthero Compare the point source contributions in the light of recent research

comparing Environment Agency compliance monitoring data with water company data e.g. Page et al., 2008; WHS, 2008.

o Include updated modelling of leaching from natural areas being developed under the Freshwater Umbrella contract by Ensis Ltd once available

o Include further minor sources e.g. sewage sludge and other organic waste spreading to land; Aquaculture

o Explore the uncertainty in the modelling that underlies this assessment

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