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Atmospheric Environment 39 (2005) 3089–3101

www.elsevier.com/locate/atmosenv

Long-term simulations of photo oxidant pollution overPortugal using the CHIMERE model

A. Monteiroa,�, R. Vautardb, C. Borregoa, A.I. Mirandaa

aDepartamento de Ambiente e Ordenamento, Universidade de Aveiro, Aveiro 3810-193, PortugalbLaboratoire de Meteorologie Dynamique, Ecole Polytechnique, F-91128 Palaiseau cedex, France

Received 5 August 2004; received in revised form 12 January 2005; accepted 19 January 2005

Abstract

This work examines the performance of the CHIMERE photochemical model in simulating ozone and nitrogen

dioxide in Portugal over a long-term summer period. The analysis focuses on comparisons against the available

measurements during the 2001 summer season. The meteorological forcing of the model is given by the European

Centre for Medium-Range Weather Forecasts (ECMWF), and the emission inventory used was obtained with a top-

down methodology updated for the simulation year.

Despite the coarse resolution of the meteorological data, the complex topography and coastal location of Portugal,

the results obtained show that the modelling system is able to reproduce the nitrogen dioxide and ozone episodes that

occurred during the simulated summer period. Mean error and correlation improve when considering the sum of photo

oxidant instead of individual pollutants, indicating that a significant part of the model error is due to either the lack of

representativeness of monitoring stations or to inaccuracies in the emission inventory.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Regional; Modelling; Validation; Ozone; Nitrogen dioxide

1. Introduction

Measured concentrations available at given monitor-

ing sites are generally not numerous enough to describe

the spatial distribution of ozone and other pollutants

over wide areas, whereas this information is a crucial

factor to evaluate the impact of photochemical pollution

on human health and natural ecosystems. Numerical

modelling systems can represent suitable tools for these

purposes, allowing both the study of photochemical

pollution with an adequate spatial detail and the

assessment of appropriate emission reduction strategies.

e front matter r 2005 Elsevier Ltd. All rights reserve

mosenv.2005.01.045

ing author. Tel.: +351 234370200;

29290.

ess: alexandra@dao.ua.pt (A. Monteiro).

In order to assess the comprehensive effects of

photochemical pollution, not only ozone peak concen-

trations need to be examined, but also ozone exposure

on the ‘‘seasonal’’ scale needs to be quantified. Recent

works (Sistla et al., 2001) point out the importance to

perform policies analysis on a ‘‘climatological’’ basis

rather than focusing on a single critical episode; this

approach allows to better evaluate model performances

(Hogrefe et al., 2001) and to quantify policies effects

with respect to long-term air quality standards. Several

ozone modelling studies were already performed for

Portugal, for b-mesoscale domains and during some

specific and episode days (Barros et al., 2003; Borrego et

al., 2000, 2002). Large scale simulations including

Portugal have also been done, but with a coarse grid

resolution. The present work represents an important air

d.

ARTICLE IN PRESSA. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–31013090

quality modelling study for Portugal, since it aims to

assess the quality of photo oxidant pollution simulations

over the whole continental region of Portugal and

during a long time period, the summer season of 2001.

The paper is organized as follows: Section 2 is devoted

to a brief and general model description. Section 3

describes the model experiment performed over Portu-

gal. Section 4 contains the results, and Section 5 a

conclusive summary.

2. Model description

CHIMERE is a three-dimensional chemistry-trans-

port model (CTM), based on the integration of the mass

continuity equation for the concentrations of several

chemical species in each cell of a given grid (Schmidt et

al., 2001). CHIMERE has been used for several research

applications, among which include sensitivity to anthro-

pogenic or biogenic emissions (Beekmann and Derog-

nat, 2003; Menut, 2003; Sillman et al., 2003; Derognat et

al., 2003; Schmidt and Martin, 2003), emission diag-

nostics (Vautard et al., 2003) or photo-oxidant forecast-

ing over Europe and the Paris region (Vautard et al.,

2001). From the year 2000–2003, a web server displayed

these real-time forecasts together with surface observa-

tions for verification (http://euler.lmd.polytechnique.fr/

pioneer). Results showed reasonable skill for ozone daily

maxima forecasts with average root mean square (RMS)

error of about 10 ppb and 0.8 of correlation, which is in

agreement with the ozone forecast model intercompar-

ison experiment described by Tilmes et al. (2002).

Let us first briefly recall the physical and chemical

processes included in the model. The model version used

here is primarily described by Schmidt et al. (2001), and

further updates, especially for the smaller-scale version,

can be found in the work by Vautard et al. (2003) and on

the web site http://euler.lmd.polytechnique.fr/chimere,

where the model can be downloaded with some

documentation. Horizontal transport is taken into

account by calculating pollutant flux convergence from

the boundaries of each model cell. The numerical

scheme used to interpolate fluxes at cell boundaries is

the Parabolic Piecewise Method (Collela and Wood-

ward, 1984), for slow species (ozone and precursors),

and a first-order scheme is used for the other species.

Vertical transport is assumed to balance horizontal mass

divergence/convergence. Turbulent mixing is calculated

using a Kz formulation, following Troen and Mahrt

(1986) without a counter gradient term for chemical

species.

The chemical mechanism uses 44 species and 116

reactions and was derived from the original complete

scheme MELCHIOR (Lattuati, 1997). In order to

reduce the computing time it follows the formalism of

‘‘chemical operators’’ (Aumont et al., 1997). The

hydrocarbon degradation is fairly similar to the EMEP

gas phase mechanism (Simpson, 1993). Adaptations are

made in particular for low NOx conditions and NO3

nitrate chemistry. All rate constants are updated

according to Atkinson et al. (1997) and DeMore et al.

(1997). The impact of cloud on photolytic reaction rates

is included in a highly parameterized fashion: in the

presence of clouds a radiation attenuation coefficient is

taken into account throughout the model layers. Dry

deposition is parameterized as a downward flux from the

lowest model layer. The deposition velocity is described

through a resistance analogy (Wesely and Hicks, 1977).

The numerical method for the time solution of the stiff

system of partial differential equations is the second-

order TWOSTEP algorithm originally proposed by

Verwer (1994).

3. Model application

3.1. Simulation domain and resolution

The model is applied to the Continental region of

Portugal, as shown in Fig. 1, with a horizontal domain

of 290� 580 km and a 10 km horizontal resolution, and

during the entire ozone season of 2001, from 1 May to

31 August. Vertical grid consists in six hybrid sigma-

pressure layers with a model top at 700 hPa. The top

altitudes of the layers vary with time, but their

approximate values are, from bottom to top: 50, 250,

600, 1200, 2000 and 3000m.

An attempt is made to differentiate concentrations

near the emission sources (like for traffic monitoring

station) and ‘‘background’’ concentrations. The model

surface layer (the lowest layer), in each horizontal cell, is

divided into two disjoint volumes: an emission volume

and a background volume. The ‘‘emission volume’’ is

assumed to occupy 10% of the total volume of the total

surface cell. All surface emissions are sent into the

emission volume while no emission is sent in the

background volume. Emissions from high (450m)

point sources are sent into Layer 2 of the model. The

emission volume exchanges air and pollutants with the

background volume, with a constant mixing time scale

of 15min, which enables the mixing of fresh emissions

with background air. This time scale was empirically

tuned over the Paris area in a previous study, in such a

way that the model average concentrations over traffic

monitoring stations match the observed ones. In the

emission layer no horizontal transport takes place. This

parameterization of the exchange is rather crude but

allows a clear distinction between background and near-

source concentrations. Sensitivity tests showed that

background concentrations are not very sensitive to

the mixing time scale. By contrast, concentrations in the

emission layer are sensitive to the mixing time scale,

ARTICLE IN PRESS

Fig. 1. Simulated domain for Continental Portugal (290� 580km) and the location of the air quality stations with available data for

the simulated time period, with a zoom into the Lisbon and Porto urban areas.

A. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–3101 3091

since mixing is the only process driving dispersion in this

volume (no transport assumed).

3.2. Meteorological data

The meteorological input variables are taken, as by

Schmidt et al. (2001), from ECMWF 3-hourly short-

term forecasts: 3D-dimensional fields of horizontal wind

(u and v), temperature, specific humidity, cloud liquid

water content, and two-dimensional fields of surface

pressure, heat fluxes, 2m temperature and cloud cover.

The ECMWF operational short-term forecasts are

extracted on a 14degree grid but have a T511 resolution,

that is, structures with total spherical wavenumbers

larger than 511 are not represented (effective resolution

of about 12degree). Forecast fields are linearly inter-

polated to the CHIMERE grid and linear time inter-

polation is also applied to obtain hourly values.

3.3. Boundary and initial concentrations

The Portugal model run is nested within a continen-

tal-scale run, using the same physics and a simple one-

way technique: the coarse grid long-term simulation is

performed with CHIMERE over a regional area from

10.5 W to 22.5 E and from 35 N to 57.5 N. Monthly

climatologies of O3, NO2, CO, PAN, CH4, C2H6,

HCHO and HNO3 taken from the MOZART second-

generation model (Horowitz et al., 2003) are forcing this

continental-scale simulation at the lateral and top

boundaries. The comparison between the monitoring

background data and the climatotological MOZART

values, shows that these values used as boundary

conditions are on average 1.6 times superior to the

observed ozone data and 3% of the NO2 monitored

data, which indicates the importance of the emissions

input and the dispersion photochemical processes

simulated by the CTM. Therefore, the large-scale run

concentrations of 22 slow species including ozone and

precursors (VOC and reactive nitrogen species) are

passed as lateral boundary values to the small-scale run.

As the Portugal model top is as high as the continental

version, its top boundary concentrations used are also

the MOZART monthly climatological values. The

simulation is continuous in time and a spin-up period

of 10 days is considered before any statistics on the

results are calculated. The spin-up period is also

initialized with the MOZART climatological values.

ARTICLE IN PRESS

Fig. 2. Spatial distribution of NMVOC industrial and NOx

traffic emissions, respectively, resulting from the top-down

methodology applied to the national EMEP inventory.

A. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–31013092

3.4. Emission data

The CHIMERE model requires the input of emission

for 15 primary compounds: NO, NO2, HONO, SO2,

CO, Ethane, n-Butane, Ethene, Propene, Isoprene, a-pinene, o-xylene, Formaldehyde, Acetaldehyde, Methyl

ethyl Ketone. At European scale, emissions were derived

from the annual totals of the EMEP database for 1999,

through a methodology similar to that described by

Schmidt et al. (2001). Over the Portuguese domain, area-

source annual emission data for the four anthropogenic

species NOx, SO2, CO and NMVOC are obtained from

the Portuguese EMEP database for the reference

simulation year (2001) for each pollutant activity

(traffic, solvents, industrial and residential combustion

and others) and then spatially disaggregated in order to

obtain the resolution required by the small-scale model.

The disaggregation is made in two steps. First, emissions

are estimated at municipality level using adequate

statistical indicators for each pollutant activity (types

of fuel consumption) and then processed applying

Census data in order to obtain sub-municipal resolution

(Borrego et al., 2002). Examples of the emissions spatial

distribution estimated from this approach are showed in

Fig. 2.

Both maps show that most of the emissions are

concentrated along the western coast of Portugal, where

the two main urban centres of Lisbon and Porto are

easily located. The traffic sector represents, compared to

industrial activities, a major source of air pollution

(Borrego et al., 2002).

The annual emissions of large point sources were

obtained directly from the available monitoring data of

each industrial plant.

Time disaggregation is obtained by the application of

monthly, weekly and hourly profiles from the University

of Stuttgart (GENEMIS, 1994). The NMVOCs are

disaggregated into 227 individual VOCs according to the

UK speciation (Passant, 2000) for each activity sector

above referred, before being grouped into the model

VOCs using the standard procedure of Middleton et al.

(1990).

The methodology for biogenic emissions of isoprene

and terpenes is described by Schmidt et al. (2001). The

land use database comes from the Global Land Cover

Facility (Hansen et al., 2000), which after some

processing provides the cell by cell coverage of

coniferous and broadleaf forests. The spatial distribu-

tion of tree species within these classes is established

following the methodology outlined by Simpson et al.

(1999). The Stohl et al. (1996) methodology is used for

biogenic emissions of NO from fertilized soils.

3.5. Observations

The Portuguese national air quality network includes

32 stations, of which 14 are background, 13 traffic and

five are industrial stations, according to the European

Environment Agency criteria. However, for the simu-

lated period, only 21 stations had uninterrupted

monitored data. These stations are mainly concentrated

in the two main coastal urban areas (Fig. 1): Lisbon

(eight stations) and Porto (six stations).

4. Results and discussion

As in previous ozone model evaluations (Simpson,

1993), daily maximum ozone values were used as the

basis of statistical evaluation. The comparison is made

using time series, scatter plots and the calculation of

RMS errors and correlation coefficients for each

observation site.

Besides that, the original time series was split into

different spectral components allowing the identification

of the time scales that are responsible for the correlation

structure in the original data, determining which

processes are poorly represented and helping further

model developments. The observed O3 hourly excee-

dances (to the standards settled in the 2002/3/EC

Daughter Directive) were also compared to the model

predictions in order to identify and analyze the origin of

these ozone episodes. During Summer 2001, the thresh-

old for public information (180mgm�3 hourly average)

that can cause health risks for short time exposure of

groups particularity sensitive, was exceeded 64 times.

Also the threshold for public alert (240mgm�3 for three

consecutive hourly averages), above which health risks

for short time exposure of the population are expected

was exceeded two times at Teixugueira station.

ARTICLE IN PRESS

Table 1

Mean values, RMS error and the correlation coefficient for each station, for the NO2, O3 and Ox species

Station Name Type Mean values (mgm�3) RMS error (mgm�3) r correlation

O3 NO2 Ox O3 NO2 Ox O3 NO2 Ox

Lavradio Industrial 46.2 30.8 39.2 27.9 34.7 17.9 0.6 0.4 0.6

Teixugueira Industrial 49.0 13.4 31.5 33.0 31.4 18.0 0.8 0.1 0.8

Monte chaos Industrial 43.8 6.4 25.3 36.4 11.0 21.3 0.5 0.2 0.5

Santiago cacem Industrial 59.5 3.1 31.4 39.1 13.8 21.6 0.5 0.0 0.6

Lec-a Rural 45.0 21.4 34.2 18.7 43.2 16.2 0.8 0.4 0.7

Avanca Rural 52.0 13.9 33.3 23.1 15.5 10.9 0.8 0.2 0.8

Monte velho Rural 75.5 3.6 39.1 32.4 12.3 16.8 0.5 0.1 0.5

Ermesinde Suburban 50.7 28.8 40.4 21.2 27.0 13.0 0.8 0.6 0.8

Vila nova Telha Suburban 45.7 16.7 31.0 23.1 36.9 16.2 0.7 0.6 0.8

Beato Suburban 61.3 14.3 38.2 26.9 55.4 16.4 0.8 0.7 0.8

Custoias Suburban 37.1 19.3 27.5 29.0 48.7 25.6 0.7 0.6 0.8

Reboleira Suburban 65.4 14.1 39.4 33.6 54.1 14.3 0.7 0.8 0.8

Alfragide Suburban 71.0 33.6 53.2 37.7 37.6 19.0 0.6 0.7 0.7

Loures Urban 59.7 8.0 34.1 18.1 31.3 10.4 0.8 0.7 0.8

Paio pires Urban 56.1 20.0 38.4 23.0 41.7 14.4 0.8 0.6 0.8

Laranjeiro Urban 53.0 29.6 42.0 30.4 41.1 15.9 0.7 0.6 0.7

Entrecampos Traffic 36.1 22.0 29.6 13.2 42.7 22.4 0.8 0.7 0.8

Hospital velho Traffic 39.8 22.1 31.6 15.4 61.4 29.2 0.8 0.0 0.3

Feup Traffic 33.5 n.a. n.a. 19.4 n.a. n.a. 0.6 n.a. n.a.

Formosa Traffic 38.6 42.1 40.8 21.5 34.4 17.7 0.7 0.6 0.8

Coimbra Traffic 27.8 50.7 38.9 45.2 56.9 14.6 0.5 0.1 0.7

Average 54.4 17.3 36.1 28.3 33.5 16.7 0.7 0.5 0.7

n.a.:not available.

0

0.2

0.4

0.6

0.8

1

0 30

Nor

mal

ized

err

or 12/07

13/0729/07

27/08

days1209060

Average Error for all the stations

07/08

Fig. 3. Normalized absolute bias for the daily ozone peak,

average for all the stations, during the simulated period.

A. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–3101 3093

4.1. Error statistics

The RMS error and the correlation coefficients (r-

values), calculated from the daily maximum ozone

concentrations, for each station and for NO2, O3 and

Ox (sum of O3 and NO2) species are listed in Table 1.

Despite the RMS error quite high for some stations,

the simulation exhibits a correlation between 0.5 and 0.8

for O3 and Ox at all monitoring sites. Both the RMS

error and correlation coefficient indicate a better model

performance for O3 than for NO2, since this latter

pollutant is generally more sensitive to errors in

emissions and meteorology, especially under stagnant

conditions. Statistical scores are significantly improved

if Ox is considered instead of each pollutant separately.

In fact, Ox is much less sensitive to emissions and their

uncertainties because it is not affected by the fast

photostationary equilibrium between NO, NO2 and O3.

Thus, an error in NO emissions can dramatically change

individual compounds concentrations but not Ox.

Another type of difficulty that can arise is related to

the representativeness of monitoring locations: if for

instance, a station is located close to a NOx source not

representative of the model grid cell, ozone concentra-

tion could be overestimated and NO2 concentration

could be underestimated by the model while Ox should

not be affected by this problem.

The analysis by station type reveals that relatively

large errors exist for industrial stations (namely Monte

Chaos and Santiago Cacem) perhaps due to an incorrect

estimation of point sources emissions or to representa-

tiveness problems. The RMS error is much lower when

considering Ox instead of ozone. However, the correla-

tion is poor even for Ox for three of these four stations.

Traffic stations values are compared with the model’s

emission sub-layer (polluted layer) results. They exhibit

reasonable scores for ozone, both in terms of RMS and

ARTICLE IN PRESSA. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–31013094

correlation, but NO2 is poorly simulated. For two of

these stations the Ox scores are lower than the ozone

scores, mostly because the Ox peak variability is

dominated by NO2 variability. By contrast, the station

of ‘‘Coimbra’’ has the highest RMS error and lowest

correlation for ozone and NO2, but Ox is fairly well

simulated. This station is located on the main avenue of

the City of Coimbra and is affected by the local titration

of ozone by NO emissions. Still the Ox peak variability is

largely due to ozone, hence the behaviour is different to

the above two stations.

There should not be any representativeness problem

for suburban stations. However, large RMS errors for

O3 are found for Reboleira and Alfragide (33.6 and

37.7mgm�3, respectively). These statistics are difficult to

understand and cannot be interpreted without further

information on the observation sites. For these two

stations the Ox simulation has RMS below 20 mgm�3

and reasonable correlations, indicating possible

influences of local NOx sources. These stations also

exhibit a large variability due to occurrences of intense

ozone plumes. When such variability exists, a low

ozone RMS is difficult to obtain because errors are

also large. The complex nature of the flow in

coastal areas, which cannot be taken into account

by the coarse meteorological model grid, could also be

at least in part responsible for the particularly

high RMS. The model performs reasonably well

with ozone and Ox peaks at the three urban stations

and two of the rural stations. For the last rural

station (Monte Velho), the RMS of the Ox errors

is much smaller than the RMS of the ozone errors,

again indicating a possible representativeness problem.

The poor correlations show that this cannot be the

only problem. Again, ozone simulation at this

coastal station could suffer from insufficiently resolved

winds.

In order to analyze the global performance of the

model, the mean normalized absolute bias error

(1=NPN

1¼1jCobs � Csimj=Cobs; considering the N stations

and daily peak ozone concentrations) is plotted versus

time, in Fig. 3. During most of the days, this normalized

error remains below 0.4. However, there are 5 days with

high errors, identified in the graph. A meteorological

analysis shows similar synoptic conditions in all these

days: a low thermal pressure at surface together with an

anticyclone at 500 hPa covering the Iberian Peninsula.

Backtrajectories analysis shows that in all these 5 days

the air was coming from the Atlantic, in agreement with

the air quality background conditions given to the

model. This suggests that the meteorological model

could not simulate correctly these specific conditions or

an insufficient vertical resolution of the CHIMERE

model exists. The use of a more detailed mesoscale

meteorological model will be the focus of future

developments.

In addition to the RMS error and correlation

coefficient other statistical parameters are recommended

by the US Environment Protection Agency (USEPA,

1996) in order to evaluate the performance of photo-

chemical models:

�

Unpaired highest-prediction accuracy (Au )

Au ¼coð:; :Þ � cpð:; :Þ

coð:; :Þ100%,

�

Normalized bias test (Dk)

Dk ¼1

N t

XN

i¼1

XHi

j¼1

coði; jÞ � cpði; jÞ

coði; jÞ;

�

Gross error of all pairs 4120 mgm�3(Ed)

Ed ¼1

N t

XN

i¼1

XHi

j¼1

coði; jÞ � cpði; jÞ

coði; jÞ,

where Coð:; :Þ is the maximum 1-h observed concentra-

tion over all hours and monitoring stations; Cpð:; :Þ is

maximum 1-h predicted concentration over all hours

and surface grid plumes; Coði; jÞ is observed value at

monitoring station i for hour j; Cpði; jÞ is predicted value

at monitoring station i for hour j; N is number of

monitoring stations; Hi is number of hourly predictio-

n–observation pairs for monitoring station i; N t is total

number of station hours.

When considering all stations and simulated hours, Au

has a value of 19.8, Dk a value of �15.7 and E a value of

8.2. Tesche et al. (1990) state that, based on past

photochemical model evaluations acceptable models

produce peak (unpaired) prediction accuracy, overall

bias, and gross error statistics in the approximate ranges

of 715–20, 75–15 and 730–35%, respectively. The

estimated performance indices fall within these ranges

showing that the model behaviour is acceptable for air

quality management. The lower value of the E indice

reveals that the model has a particularly good ability to

predict higher and peak ozone concentrations.

4.2. Time series and scatter plots

Time series and scatter plots belong to the standard

methods for the comparison of model results with

observations. Time series for a selection of four

‘‘representative’’ sites, chosen to cover the different type

of monitoring stations, are presented in Fig. 4. The

model is in general able to simulate the daily maximum

ozone variability. It reproduces the episodic events of

high ozone mixing ratios. This holds in particular for the

remote sites with rural and suburban background

stations like Avanca and Ermesinde, and also for traffic

ARTICLE IN PRESS

FORMOSA(traffic)

0 30 60 90 1200

50

100

150

200

250

0

50

100

150

200

250observedmodeled

LAVRADIO(industrial) observed

modeled

AVANCA(rural background)

0

50

100

150

200

250

0

50

100

150

200

250observedmodeled

ERMESINDE(suburban background)

observedmodeled

NO

2 (�

g. m

-3)

NO

2 (�

g. m

-3)

days0 30 60 90 120

0 30 60 90 120 0 30 60 90 120

days

Fig. 5. Observed and modelled maximum daily NO2 concentration for different types of monitoring stations.

AVANCA(rural background)

0 30 60 90 120

0 30 60 90 120

0 30 60 90 1200

50

100

150

200

250observedsimulated

ERMESINDE(suburban background) observed

simulated

FORMOSA(traffic)

0

50

100

150

200

250observedsimulated

LAVRADIO(industrial)

0

50

100

150

200

250

0

50

100

150

200

250

observedsimulated

O3

(�g.

m-3

)O

3 (�

g. m

-3)

days0 30 60 90 120

days

Fig. 4. Observed and modelled maximum daily ozone concentration for different types of monitoring stations.

A. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–3101 3095

stations located in urban areas with lower ozone

concentrations. Monitoring sites close to larger indus-

trial sites like Lavradio exhibit larger differences

between observed and modelled ozone peak values.

The time series point out differences in the model

performance for each episode. During the two large

episodes around 29 May and 1 August, there is an

overall tendency to overestimate the ozone peak.

Conversely, around 20 June the model underestimates

the O3 episode values.

In Fig. 5 the same analysis for the NO2 species is

presented. Simulations show a reasonable agreement

with the observed data for all the different station types.

The mean, variability, and the differences of variability

ARTICLE IN PRESS

FORMOSA(traffic)

0 30 60 900

50

100

150

200

250

0

50

100

150

200

250

0

50

100

150

200

250

observedmodeled

LAVRADIO(industrial) observed

modeled

AVANCA(rural background) observed

modeled

ERMESINDE(suburban background) observed

modeled

Ox

(ppb

)

0

50

100

150

200

250

Ox

(ppb

)

days

120 0 30 60 90days

120

0 30 60 90 120 0 30 60 90 120

Fig. 6. Observed and modelled maximum daily Ox concentration for different types of monitoring stations.

A. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–31013096

of simulated maxima are coherent with that observed,

even though there may be strong errors in individual

daily maxima simulated amplitudes. As in the previous

ozone plots, the industrial site of Lavradio exhibits the

highest departures between observed and modelled

maximum NO2 concentration values.

Fig. 6 presents the time series plots for the Ox daily

maxima. The improvement of the results is clear, as

compared to the O3 and NO2 daily maxima plots. The

correspondence between observed and modelled values

is much closer even for the industrial site, where the O3

and NO2 plots show significant errors. It is interesting to

notice that, in the Ox time series, the relatively large

errors on the ozone ‘‘background level’’ given by the

lowest daily maxima, disappear. This could be an

indication of the lack of representativeness of the

station, which may be influenced by local sources.

The scatter plots for some of the ‘‘representative’’

stations (Fig. 7) reveal perhaps the most important

feature of model errors: despite the spread of points

around the diagonal, there is a general tendency to

overestimate the ozone concentrations, which could be

due to a model bias or an overestimation of boundary

concentrations. However, an opposite behaviour exists

for O3 at the urban and traffic stations, like Beato and

Formosa. The corresponding scatter plots show a

general slight underestimation of the ozone values

mainly for the extreme values, so the model variability

is smaller than the observed one. This can be due to the

horizontal model grid size, insufficient to resolve city

plumes. Once again, the scattered plots examples of Fig.

7 confirm the clear improvement of the model perfor-

mance when considering Ox correlation results ðr40:70Þinstead of ozone or nitrogen dioxide individually. The

worst performance is obtained for NO2 ðr40:41Þ; for allmonitoring stations types, where in general NO2 is

overestimated.

4.3. Time series spectral decomposition

Since this is a longer-term modelling application, it is

advisable to split and evaluate the model performance

(accuracy) at different time scales with specific frequen-

cies ranges and fluctuations. Following the methodology

presented by Hogrefe et al. (2001), ozone hourly time

series were spectrally decomposed into four specific time

series periods with characteristic processes affecting

ozone concentration, namely intra-day component

(ID), diurnal component (DI), synoptic component

(SY) and the longer-term (baseline) component (BL),

which are related to the log-transformation (P) of the

original time series by: PðtÞ ¼ eIDðtÞeDIðtÞeSYðtÞeBLðtÞ:InTable 2, the distribution of the variance between the

time series components for both observation and model

results (considering all the monitoring stations) are

presented. The model predicts a variance distribution

that is very close to the observations. In both cases the

DI is the largest contributor to the overall variance (as it

was expected, since this DI includes the day and

nighttime differences), followed by the synoptic, baseline

ARTICLE IN PRESS

O3BEATO

(suburban background)r = 0.76

NO2BEATOr = 0.73

OxBEATOr = 0.75

O3FORMOSA

r = 0.69

NO2FORMOSA

0

50

100

150

200

250

r = 0.58

OxFORMOSA

0

50

100

150

200

250

r = 0.79

O3LAVRADIO(industrial)r = 0.59

NO2LAVRADIO

0 50 100 150 200 2500 50 100 150 200 250 0 50 100 150 200 250

0 50 100 150 200 250

0 50 100 150 200 2500 50 100 150 200 2500 50 100 150 200 250

0 50 100 150 200 2500 50 100 150 200 250

0

50

100

150

200

250

r = 0.41

OxLAVRADIO

0

50

100

150

200

250

r = 0.63

O3LEÇA

(rural background)

0

50

100

150

200

250

r = 0.78

NO2LEÇA

0

50

100

150

200

250

r = 0.42

OxLEÇA

0 50 100 250

Mod

eled

dat

a

Mod

eled

dat

a

Mod

eled

dat

a

0

50

100

150

200

250

Mod

eled

dat

a

0

50

100

150

200

250

Mod

eled

dat

a

0

50

100

150

200

250

Mod

eled

dat

a

0

50

100

150

200

250

Mod

eled

dat

a

0

50

100

150

200

250

Mod

eled

dat

a

Mod

eled

dat

a

Mod

eled

dat

a

Mod

eled

dat

a

Mod

eled

dat

a

0

50

100

150

200

250

r = 0.73

Observed data150 2000 50 100 250

Observed data150 2000 50 100 250

Observed data150 200

(traffic)

Fig. 7. Scatter plots for simulated versus observed daily maxima of O3, NO2 and Ox mixing ratios in mgm�3 for the period from 1 May

to 30 August 2001.

Table 2

Relative contribution (%) of the components time series

variances to the total variance for observations and model

predictions, the sum of the components variances and the

original time series variance

Observations Model prediction

ID DI SY BL Sum Original ID DI SY BL Sum Original

10 46 27 17 0.99 0.82 8 51 26 14 1.25 1.00

Table 3

Correlation coefficient and ratio of variances (modelled to

observed) for the different time series components (original

time series and intra-day, diurnal, synoptic and baseline

components)

Correlation coefficient Ratio of variances (model/observed)

Original ID DI SY BL Original ID DI SY BL

0.60 0.38 0.69 0.55 0.59 1.23 0.96 1.42 1.29 1.02

A. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–3101 3097

ARTICLE IN PRESSA. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–31013098

and ID. However, there is a slight underestimation of

the ID and a more significant overestimation of the

diurnal time scales.

Table 3 lists the correlation coefficient and ratios of

the variances of the modelled to observed for each time

series component (for all the monitoring stations and for

the entire simulation length), in order to compare the

absolute amount of energy at the different time scales

between observations and model predictions. It can be

seen that the model overestimates the variance of the

original, diurnal and synoptic time series, but slightly

underestimates the intra-day time scale. The baseline

variance (its amount of energy) is the best captured by

the model. The variability of the DI (the largest

contributor to the overall variance) is the most over-

Fig. 8. Ozone concentration fields for the episode days occurred dur

name, the time and the registered exceedance value.

estimated, suggesting that the model does not properly

capture all processes that characterize the diurnal cycle

and that are associated with the diurnal variation of the

solar flux (differences between daytime photochemical

production and nightime consumption of ozone, as well

as the diurnal cycle of boundary layer evolution and

decay).

The correlation for the ID is less than 0.4, suggesting

that despite the good variance agreement, the processes

contributing to the high-frequency fluctuations (hori-

zontal turbulence and vertical mixing, local titration by

fresh emissions of NO and ozone response to fast-

changing emission patterns during the rush traffic hours)

in the ID are not captured by the model. The high

correlations for the DI are due to the inherent diurnal

ing the simulated period. In each map are indicate the station

ARTICLE IN PRESSA. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–3101 3099

cycle caused by the day and night differences. However,

when the night and day period are evaluated separately,

the correlation factor is significantly low for the night

period (0.43 for the night and 0.73 for the day). The

synoptic and baseline components display similar

correlations higher than 0.55. These results combined

with the variance analysis suggest that the longer time

scales are represented quite well by the model, which

means that the model is able to reproduce the changing

synoptic conditions and capture the seasonal variation

of the solar flux, changing large-scale flow patterns and

changes in vegetation coverage and biogenic emissions.

4.4. Concentration fields patterns

In Fig. 8 is displayed the spatial ozone concentration

fields at some specific hours of episodic days where

ozone exceedances occurred during the simulated

period. In almost all cases the ozone exceedances are

well reproduced by the model. However, there are large

areas of inland Portugal where the simulated ozone

exceedances cannot be verified due to the absence of

monitoring sites. The concentration patterns show that

there is a potential advection and transport of the

pollutants emitted from the major urban centres (Porto

and Lisbon) to the coast and to some inland parts of

Portugal. This can be confirmed and more deeply

analyzed with mesoscale meteorological simulations

performed in this specific regions (Barros et al., 2003;

Borrego et al., 2000).

5. Summary and conclusions

The results of a long-term simulation of photo

oxidant pollution over Continental Portugal are pre-

sented. The CHIMERE CTM is used and forced by

ECMWF weather forecasts, which is a considerable

simplification with respect to using a mesoscale meteor-

ological model, especially for the Portuguese area where

topographic and coastal effects are important.

The validation procedure consists in comparing the

simulation results with observation values obtained

from ground-based monitoring stations over the whole

summer season of 2001, from 1 May to 31 August, using

error statistics such as root mean square error, correla-

tion coefficients and the EPA statistical parameters, and

qualitative comparisons with time series and scatter

plots. Both the RMS error and correlation coefficients

indicate that the differences between observed and

modelled values are considerably lower when analysing

the sum of photo-oxidants, Ox, than when analysing O3

and NO2. This indicates that a large part of the error in

ozone simulation could be due to the representativeness

of stations in the model grid cells or to emissions

estimation errors. In all cases, the errors deal with the

rapid conversion between ozone and NO2. The ozone

RMS errors range from 13.2 to 45.2mgm�3, with an

average value of 28mgm�3, which is translatable in 60%

of representative stations. Nevertheless the remaining

uncertainties regarding the representativeness of stations

and inhomogeneous data, this preliminary long-term

modelling approach seems to have acceptable results,

given the coarse resolution grid of the meteorological

data used (that should be, at least in part, responsible for

some errors). The background ozone prediction could

also be another factor of bias. Almost all the excee-

dances are well simulated, but the model also predicts

ozone standard exceedances in large areas of Portugal,

located mostly in the inland part of the country.

In addition, the original season time series was

spectrally decomposed into intra-day, diurnal, synoptic

and baseline time scales. This analysis reveal that the

model approximately captures the relative contributions

of all components, with a slightly underestimation of the

ID variability and a small overestimation of the relative

strength of the diurnal fluctuations. Correlations be-

tween model predictions and observations are weak for

the ID and high for the inherent diurnal cycle and long-

term components. The poor performance on the intra-

day time scale is at least partially related to the

horizontal grid resolution dimension used in the model

and to the inability of the input fields (meteorology,

emissions, landuse, etc) to capture this scale in time and

space. The better performance of the model on longer

time scales suggest that modelling periods should be

longer than the duration of a single episode to increase

confidence in the regulatory modelling applications.

Nevertheless, the results of this validation experiment

calls for improving the modelling system in several

aspects. Future model developments would have to

focus on the intra-day and diurnal cycle processes,

improving vertical mixing and dry deposition, which are

only very crudely represented. The meteorological

parameters need to be better simulated, with a numerical

model able to simulate the mesoscale thermal circula-

tions, like sea and valley breezes.

Ozone concentration predictions are the main subject

discussed in this work, but the model also simulates

nitrogen dioxide, which can be especially in winter, of

major concern for the air quality managers in urban

areas. During Summer 2001, the NO2 concentrations

were also modelled and a fair correspondence between

observed and simulated peaks was found. However, the

concentrations are often overestimated. The experiment

is being carried on for the winter season, and will be

discussed in a subsequent paper. Another future task

will be the evaluation of this modelling system in

forecast mode, one of the requirements of the new Air

Quality Framework Directive (96/62/CE), and a reliable

tool for the air quality management in Portugal, in

reducing and protecting the population exposure more

ARTICLE IN PRESSA. Monteiro et al. / Atmospheric Environment 39 (2005) 3089–31013100

efficiently and enabling real-time emission abatement

measures.

Acknowledgements

This work is partially financed by the Portuguese

Environment Institute and CRUP. Additional support is

provided by the Portuguese Ministerio da Ciencia,

Inovac- ao e Ensino Superior, for the PhD Grant of A.

Monteiro (SFRH/BD/10922/2002). The authors would

like to thank R. Friedrich and B. Wickert (University of

Stuttgart) for making available VOC profiles and the

temporal variability of annual emissions. The authors

are also grateful to ACCENT European Network of

Excellence.

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