Article 1 : Modélisation des aérosols en Ile-de-France

Atmospheric Environment 39 (2005) 5851–5864

Long-term urban aerosol simulation versus routine particulate

matter observations

A. Hodzic

a,

_{, R. Vautard}

a

, B. Bessagnet

b

, M. Lattuati

c

, F. Moreto

c

a_{Laboratoire de Me´te´orologie Dynamique, Institut Pierre-Simon Laplace, 91128 Palaiseau Cedex 1, France} b_{Institut National de l’Environnement Industriel et des Risques, INERIS, Verneuil en Halatte, France}

c_{AIRPARIF, 75004 Paris, France}

Received 6 January 2005; received in revised form 2 June 2005; accepted 13 June 2005

Abstract

The ability of chemistry-transport models (CTMs) to accurately simulate particulate matter in urban areas is still to be demonstrated. This study presents a statistical evaluation of the performances of a mesoscale aerosol CTM over the Paris area, calculated over a long time period. Model simulations are compared to measured particulate matter PM10

and PM2.5levels at monitoring ground stations. In summer, the PM10daily mean levels are fairly well predicted by the

model at all stations with correlation coefficients exceeding 0.67, relatively low biases (o2.5 mg m
3) and normalized errors (o27%). The relatively uniform negative biases suggest that the background PM10levels are underestimated. In

winter, discrepancies between the model and observations are more important, in particular at urban sites where several erroneous peaks are simulated. Consequently, the correlation coefficient drops down to 0.59 at urban sites and PM10

values are overestimated by about 10 mg m
3_{with normalized errors exceeding 55%. We assume that discrepancies}

between simulated and observed PM levels are due to (i) TEOM (tapered element oscillating microbalance) measurement underestimation (35% in winter) caused by the evaporation of ammonium-nitrate, (ii) the under- prediction of the model vertical mixing over the urban heat island and (iii) possible overestimation of local PM emissions. We use corrections for the urban boundary layer height and we subtract ammonium-nitrate from model PM10concentrations. These modifications significantly improve the comparison statistics at urban sites in winter: the

mean bias (o2 mg m
3_{) and normalized error (o30%) are reduced, while the correlation coefficient increased to 0.64.}

However, the overestimation at urban sites is inconsistent with the underestimation of PM10 background

concentrations. The analysis of the total model biases at urban sites reveals that the underprediction of

PM10 background levels is largely compensated by their local overprediction due to the overestimation of

anthropogenic emissions.

r2005 Elsevier Ltd. All rights reserved.

Keywords: Aerosol modeling; PM10; Model error statistics; Urban aerosol; Model skill

1. Introduction

Atmospheric aerosols are of major scientific interest due to their demonstrated role in climate change and their effect on human health and local visibility. The impact of atmospheric aerosols on the Earth’s radiative

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86 Évaluation de la distribution spatiale des PM au sol

balance is of comparable magnitude to the greenhouse gases’ effects (Anderson et al., 2003). Indeed, the overall cooling effect of aerosols is estimated to be up to
2.5 W m
2(IPCC, 2001), while the increase of green- house gases produces a warming of 2.4 Wm
2_{. During}

pollution episodes, anthropogenic aerosols also have a strong optical signature and significantly contribute to atmospheric scattering properties at urban scale. Be- sides, recent epidemiological studies (Pope, 2000;

Moshammer and Neuberger, 2003;Hauck et al., 2004) have established a strong link between aerosol concen- trations and significant adverse health effects. The aerosol parameters driving health effects are not yet clearly identified, but aerosol toxicity seems to be related to the mass and the number concentrations of fine particles rather than to their chemical composition. In order to control the particulate matter (PM) levels, the EU and US legislations established target values for the

annual and daily mean PM10 and PM2.5mass concen-

trations (particles below 10 and 2.5 mm in diameter, respectively). The EU annual average PM10 standards

are fixed to 40 mg m
3for 2005 and to 20 mg m
3for 2010 and the daily mean value of 50 mg m
3 _{should not be}

exceeded 35 times yr
1in 2005 and 7 times yr
1in 2010. In order to investigate the PM pollution in Europe, monitoring networks have been deployed. According to

Van Dingenen et al. (2004)andPutaud et al. (2004), the

background annual average PM10 and PM2.5 mass

concentrations for continental Europe, derived from 31 European air quality monitoring ground stations, have been 7.074.1 and 4.872.4 mg m
3_{, respectively, over the}

past decade. In the observed aerosol composition at the surface, organic matter was found to be the major component of PM10and PM2.5, except at natural and

rural sites where sulfate contributions prevailed. How- ever, a large variability of aerosol concentrations and characteristics was found among different European locations, showing the importance of local aerosol characterization, especially in large cities where aerosols may result both from continental transport and local pollution sources.

In order to test the current knowledge about the atmospheric aerosol physics and chemistry, and in order to predict aerosol concentrations during pollution events, scientists have developed three-dimensional chemistry-transport models (CTMs) including sophisti- cated aerosol parameterizations (Seigneur, 2001; Hass et al., 2003). Current CTMs need to be evaluated against observations to assess their accuracy to reproduce PM concentrations. Up to now, model evaluations based on statistical comparisons with long-term sets of measure- ments, have been rarely reported in the literature and most of them deal with the regional scale (Ackermann et al., 1998;Mebust et al., 2003;Van Loon et al., 2003).

Seigneur (2001) reviews the state-of-the-art of several aerosol models of different complexity and evaluates

their performances during pollution events in Los Angeles. The daily mean PM2.5concentrations appear

to be predicted within 50% normalized errors for urban- scale models, with however, compensating errors among individual particulate species. In Europe, an exhaustive evaluation of secondary aerosols has been carried out by

Hass et al. (2003) comparing simulations from six models to surface measurements of inorganic ions provided by the EMEP and national air quality networks during April to September 1995. For sulfate and ammonium particles, most of the comparisons for station averages fall within a factor of two without any significant systematic bias. Nitrate is systematically overestimated by models. Another model intercompar- ison study has been recently performed in the frame-

work of the evaluation of the EMEP model (Van Loon

et al., 2004). It revealed, in particular, the large gap between simulated and observed PM10mass concentra-

tions. PM10model underestimations could be due to the

lack of biogenic sources (Aeolian dust and resuspen- sion), as suggested byVautard et al. (2005). The poorly known secondary formation of organic matter also could explain part of these discrepancies. Recently, the continental-scale version of the CHIMERE model (Schmidt et al., 2001), used in this study, was evaluated using EMEP background stations for 1999 (Bessagnet et al., 2004). Correlation coefficients calculated on PM10

vary between 0.3 and 0.7 with normalized errors between 30% and 80%. Like other European models CHIMERE underestimates PM concentrations particu- larly in dry regions (lack of dust emission and transport from Saharan regions) and coastal sites (no sea salt).

While large-scale models are useful tools to study the continental transport of pollutants and to evaluate emission reduction policies, a higher spatial resolution is required to evaluate the exposure of the population to PM pollution in urban areas (Jacobson, 1997;Pai et al., 2000). Long-term aerosol model evaluation studies at urban scale applied to different geographical locations are needed. The aim of this article is to evaluate the skill of the urban-scale version of the CHIMERE model in simulating fine particle mass concentration (PM10 and

PM2.5). The city of Paris is chosen as the application

area, because (i) it is one of the largest cities in Europe and, due to its geographical situation, (ii) the flow does not undergo complex-terrain effects such as breezes and (iii) the local pollution signal is easy to distinguish from the background (Vautard et al., 2001). Simulated mass concentrations are compared, in a statistical manner, to routine measurements provided by AIRPARIF air quality monitoring network over the period running from 1 April 2003 to 31 March 2004.

In Section 2, the CHIMERE model and its aerosol module are briefly described. In Section 3, the AIR- PARIF monitoring network is presented and the possible measurement errors are discussed. In Section 4, the

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4.3 Évaluation statistique du modèle CHIMERE en Ile-de-France 87

evaluation of the model is performed by means of statistical indices for the two main calendar seasons. Sensitivity to the vertical mixing and secondary organic aerosols is discussed. The contribution of locally emitted and transported PM10 levels to statistical scores is also

evaluated. Section 5 contains a conclusion.

2. Model formulation and forcing 2.1. General model configuration

CHIMERE is a three-dimensional CTM that simu- lates gas-phase chemistry (Schmidt et al., 2001;Vautard et al., 2001), aerosol formation, transport and deposi- tion (Bessagnet et al., 2004;Hodzic et al., 2004;Vautard et al., 2005) at European and urban scales. It has been designed with the aim of both performing episodic and long-term simulations at various spatial scales ranging from local to regional scale, on a personal computer or a workstation. The latest versions of the model and their documentation are available for download on the web sitehttp://euler.lmd.polytechnique.fr/chimere. The gen- eral performances of the model for the simulation of ozone and aerosols can be found in the previous references. In the present application, the model is run at an urban scale over a domain covering the greater Paris metropolitan area. The model domain approxi- mately spans from 1.21E to 3.61E and from 47.91N to 49.51N with a6 km grid size resolution. The vertical resolution consists of eight vertical layers of various thickness extending from ground to 500 hPa. The first layer is 50 m deep and subsequent layer depths increase with height. The upper layer is 2 km thick and extends to about 5.5 km. Boundary conditions are provided by a prior regional, large-scale simulation, covering Western Europe with a1/21 resolution, using theVautard et al. (2005) version of the model. Boundary conditions of regional simulations are taken from climatologies of the

MOZART global CTM (Horowitz, 2003). For aerosol

species, concentrations issued from monthly means of the GOCART model (Ginoux et al., 2001) are used, as inVautard et al. (2005).

The model simulates the concentration of 44 gaseous species and six aerosol chemical compounds. The gas-

phase chemistry scheme (Lattuati, 1997) has been

extended to include sulfur aqueous chemistry, secondary organic chemistry and heterogeneous chemistry of HONO (Aumont et al., 2003) and nitrate (Jacob, 2000). 2.2. Aerosol formulation

The population of aerosol particles is represented by a sectional formulation, assuming discrete aerosol size sections and considering the particles of a given section to be internally mixed. Six diameter bins ranging

between 10 nm and 40 mm, with ageometric increase of bin bounds, are used. The aerosol module accounts for both inorganic and organic species, of primary or secondary origin, such as primary particulate matter (PPM), sulfates, nitrates, ammonium, secondary organic species (SOA) and water. PPM is composed of primary anthropogenic species such as elemental and organic carbon, and mineral materials.

Sulfate is produced from gaseous and aqueous oxidation of SO2(Berge, 1993). Nitric acid is produced

in the gas phase by NOx oxidation, and also by

heterogeneous reaction of N2O5on the aerosol surface

(Jacob, 2000). Issued directly from primary emissions, ammonia is converted into aerosol phase (mainly ammonium-nitrate and ammonium-sulfate) by neutrali- zation with nitric and sulfuric acids. Secondary organic aerosols are formed by condensation of biogenic and anthropogenic hydrocarbon oxidation products; they are partitioned between the aerosol and gas phase through a temperature-dependent partition coefficient (Pankow, 1994). A look-up table method, set up from the ISORROPIA equilibrium model (Nenes et al., 1998, 1999), is used to calculate concentrations at equilibrium for inorganic aerosols composed of sulfate, nitrate, ammonium and water. Dynamical processes influencing aerosol population are also described. New particles are formed by nucleation of H2SO4(Kulmala et al., 1998)

and grow due to the coagulation and condensation of semi-volatile species. The coagulation process applied for a multicomponent system is calculated as inGelbard and Seinfeld, (1980). Aerosols can be removed by dry deposition (Seinfeld and Pandis, 1998) and wet removal (Guelle et al., 1998; Tsyro 2002). Particles can be scavenged either by coagulation with cloud droplets or by precipitating drops. Further description of the parameterization used for different processes can be found inBessagnet et al., (2004).

Transport of Saharan dust from the GOCART boundary conditions, as well as within-domain erosion are considered, using the formulation ofVautard et al. (2005). Resuspension of material other than soil particles is not taken into account because of the large uncertainty.

2.3. Meteorological input

CHIMERE requires several meteorological variables as input data, such as wind, temperature, mixing ratio for water vapor and liquid water in clouds, 2-m temperature, surface heat and moisture fluxes and precipitation. As in Hodzic et al. (2004), the meteor- ological fields for both urban and regional CHIMERE domains were generated using the NCAR mesoscale modeling system MM5 (Dudhia, 1993). Meteorological simulations are performed with a two-way nesting procedure with two domains of respective resolutions

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88 Évaluation de la distribution spatiale des PM au sol

15 and 5 km and 25 vertical levels. The smallest meteorological domain encompasses the CHIMERE domain. The meteorological variables are linearly interpolated on the CHIMERE grid. Regional CHI- MERE simulations are forced by MM5 simulations over Europe with a36-km resolution. As shown byLiu et al. (2004), in the MM5 V3.6.2 version, friction velocity can be largely overestimated, due to the simplified estima- tion of the convective velocity scale. Here the calculation of friction velocity uses the classical formula proposed byBeljaars (1994), with afixed height scale Zi¼1500 m.

2.4. Emission data base

The model requires hourly spatially resolved emis- sions for the main anthropogenic gas and aerosol species. For the large-scale simulations, the anthropo- genic emissions for NOx, CO, SO2, NMVOC and NH3

gas-phase species, and for PM2.5and PM10are provided

by EMEP (Vestreng, 2003) with a spatial resolution of 50 km. For the urban-scale simulations around Paris, the new AIRPARIF emission inventory is used. This inventory includes the emission estimates of NOx, CO,

SO2, NMVOC, NH3, PM10and PM2.5for the year 2000.

It extends over a441  468 km wide area around Paris with a spatial resolution of 1 km. The emissions are computed for three typical days in July and December (weekday, Saturday and Sunday) and hourly distrib- uted. The inventory takes into account emissions from line sources (streets and highways), area sources (local heating) and large point sources. The general methodol- ogy used for emission inventory, called the ‘‘bottom-up approach’’, consists in coupling European emission factors (CORINAIR, TNO) with statistical information describing sources’ activity. For large point sources, emissions are calculated using the tax on polluting activities or energetic consummation. Emissions for road transport are based on real flux and average speed measured on Ile de France for 2000 and on COPERT III emission factors (AIRPARIF, 2004). The NOx emis-

sions are partitioned into NO (90%), NO2(9.2%) and

HONO (0.8%). The NMVOC speciation into appro- priate classes for the chemical mechanism is carried out according to IER methodology (Institut fu¨r Energie- wirtschaft und Rationelle Energieanwendung). The PM emissions are partitioned into three size bins: PM2.5,

particles between PM2.5and PM10 and particles larger

than PM10. There is no chemical speciation for PM

emissions. Most of the urban PM emissions come from road transport (46%), due for a half, to diesel combustion and for a half to brake, tyre wear and road erosion. Other important sources of PM are production processes (23%) and the residential combustion (17%). The uncertainties in the emission estimates are an important issue in the aerosol modeling. The compar- ison of total PM emissions over the Paris region

indicates that the local emission estimates used in this study are a factor 2–3 lower than the regional ones (EMEP): e.g. the PM10 annual primary emission mass

over the Paris area is close to 69 kT year
1_{in the EMEP}

database compared to 22 kT year
1in the local inven- tory. The inconsistency of local versus regional EMEP estimates probably results from the methodology applied to distribute the total national emissions (provided by different countries) over the EMEP grid. As the gridded emissions have not been reported for France, the total emissions have been distributed according to the population density over the EMEP grid (Vestreng, 2003). Therefore, in the EMEP inven- tory, the PM emissions over the Ile-de-France area account for 20% of the total national emissions, compared to 5–10% in the AIRPARIF inventory. Similar results are observed for other primary pollutants indicating that the regional EMEP emissions are over- estimated over the Paris region. The methodology used to construct the AIRPARIF local inventory is expected to provide the emission estimates closer to the reality. 2.5. Model simulations

In this study, the model is run from 27 March 2003 to 31 March 2004 for both regional- and urban-scale versions. The simulations are performed in time slices of 5 consecutive days, each new period being initialized by the previous one, so that the concentrations are continuous in time. The first spin-up run of 5 days is used to initialize the model.

3. PM observations

3.1. The AIRPARIF network

In order to evaluate the atmospheric CTM, simulated concentrations are compared with the AIRPARIF air quality monitoring network observations. Routine measurements of PM10and PM2.5mass concentrations

over the Paris agglomeration have been performed since 1996 and are available, respectively, at 14 and 5 ground

stations during our study period. Fig. 1 shows the

location of aerosol measurement sites.

Fig. 2shows the evolution of the annual mean PM10

and PM2.5 concentrations between 1998 and 2003

observed at different AIRPARIF measurement sites. At urban and near-city stations, the PM10 concentra-

tions range from 22 to 24 mg m
3 _{with no significant}

inter-annual trend, while at traffic stations the PM10

values decrease from 50 mg m
3_{in 1998 to 43 mg m}
3_in

2000. At the only background station, data are available since 2002 and the concentrations are about 15 mg m
3. The EU annual PM10standard of 40 mg m
3expected in

2005 is exceeded at traffic stations, while urban and

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4.3 Évaluation statistique du modèle CHIMERE en Ile-de-France 89

near-city sites are above the PM10standard of 20 mg m
3

targeted for 2010. The PM2.5concentrations have been

measured at four urban and one traffic stations since

1999. At background urban sites, PM2.5concentrations

lie close to 15 mg m
3, while at the traffic site, they reach about 30 mg m
3_{. The order of magnitudes of}

concentrations encountered in and near Paris is similar to that observed at other European measurement sites (Van Dingenen et al., 2004).

3.2. Uncertainty of observations

One major difficulty encountered when trying to evaluate the performances of the aerosol model using ground observations is in quantifying the uncertainties in measurements. The mass concentration of the PM fraction is determined continuously using a Tapered Element Oscillating Microbalance (TEOM). All stations of the network are equipped with the same type of instruments, providing a homogeneous set of data. The principle of the TEOM measurement is based on the frequency of mechanical oscillation of a tapered glass tube which is directly proportional to its mass. Changes in the effective mass of the tube, due to the deposition of particles, lead to a change in the resonance frequency. In order to minimize errors due to the condensation of the water on the filter and to remove water in aerosol particles, a routine TEOM instrument dries the sampled air stream by heating the inlet at 50 1C. This could lead

Dans le document Modélisation des aérosols de pollution en Ile-de-France. (Page 86-100)