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The impact of thermodynamic module in the CTM performances
Atmospheric Environment 61 (2012) 652e660
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
The impact of thermodynamic module in the CTM performances C. Carnevale a, *, G. Finzi a, E. Pisoni a, P. Thunis b, M. Volta a a b
Department of Information Engineering, Faculty of Engineering, University of Brescia, Italy European commission, DG JRC, Institute for Environment and Sustainability, TP 483, I-21020 Ispra, Italy
h i g h l i g h t s < The impact of the thermodynamic module in a Chemical Transport Model has been evaluated. < The role of secondary inorganic fraction on PM10 levels has been assessed. < The impact of the different chemical species in aerosol performances has been shown.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 February 2012 Received in revised form 19 June 2012 Accepted 21 June 2012
This work investigates the impact of the inorganic thermodynamic module on Chemical Transport Model simulations. The study has been focused on Northern Italy, one of the most polluted area in Europe, where high PM10 levels are reached despite the Regional and National Authority efforts to limit emission in the atmosphere. Two different configurations of the Chemical Transport Model TCAM have been tested performing yearly simulation over the selected domain for 2005. In the first configuration, the SCAPE2 thermodynamic module has been implemented, while in the second one, ISORROPIA-II has been integrated into the system. An exhaustive evaluation of the performances is presented for total PM10 with respect to its daily values, and the analysis of the impact of the two modules on PM2.5 and on aerosol chemical components has been performed. The ISORROPIA-II module configuration shows better overall performances both in terms of correlation and Normalized Mean Absolute Error for PM10 and PM2.5. The evaluation of the inorganic ions performances shows a quite different behavior for the two configurations, with ISORROPIA-II providing better results for both Normalized Mean Absolute Error and Normalized Mean Error. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Chemical Transport Model Thermodynamic module PM10 Northern Italy Aerosol speciation
1. Introduction Aerosol pollutants, due to their impact on human health and ecosystems, have been the focus of Research Institutes and Environmental Agencies studies in the last 10 years. Aerosol impact ranges from visibility reduction (Altshuller, 1984) to heavy effects on human health as respiratory and cardiovascular diseases, that are strictly related to the toxicity of the inorganic fraction of the aerosol (Zanobetti et al., 2000; Lee et al., 2007). Moreover, due to different origins of the aerosol chemical compounds, the definition and implementation of air quality strategies for PM10 is a very challenging task (Thunis et al., 2007; Carnevale et al., 2008a,b, De Meij et al., 2009).
* Corresponding author. E-mail addresses: [email protected], (C. Carnevale).
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1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.06.058
The problem of aerosol chemical characterization is particularly true in areas like Northern Italy, where high PM10 levels and inorganic fraction ranging from 30% to 50% are measured during both summer and winter months (Lonati et al., 2005; Carnevale et al., 2010). Due to the point-wise nature of measurements collected by monitoring network and to the costs and difficulties involved in the chemical analysis of the aerosol components, the availability of modeling systems allowing to correctly reproduce (i.e. with a certain level of performances) the aerosol levels and their chemical composition is crucial to (1) evaluate the impact of aerosol on human/ecosystem health, (2) provide Regional Authorities with tools to evaluate the priorities in air quality control plan and (3) define effective emission control policies over a domain (Fountoukis and Nenes, 2007). An extended discussion about the issues related to the use of CTM for aerosol emission control strategies selection can be found in (Carnevale et al., 2008a) and (Pisoni et al., 2009). To compute aerosol chemical composition and phase, Chemical Transport Models (CTMs) require information about the
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Fig. 1. The GAMES modeling system configuration.
thermodynamic equilibrium state, driving the mass transfer between the gas and aerosol phases. Thermodynamic modules are devoted to this computation, which represents the starting point for the evaluation of the concentrations of the gas (nitrogen oxides, ammonia, sulfur oxides) and aerosol (nitrates, ammonium and sulphates) species subject to mass transfer between the two phases in atmosphere. The development of this kind of modules is a very hard task for modeling groups due to (1) the lack of knowledge in the involved physical/chemical processes and (2) the nonlinearity of the relationships, that can lead to a numerically complex differential equation system to be solved. A large number of inorganic equilibrium modules have been implemented in the Chemical Transport Model in the last decades (Clegg and Pfizer, 1992; Kim et al., 1993; Jacobson et al., 1996; Nenes et al., 1998; Fountoukis and Nenes, 2007; Metzger et al., 2002; Cheng et al., 2010). These modules may differ in terms of treated chemical species, of their level of detail in the representation of the phenomena and of implemented numerical schemes. The use of CTM model to evaluate aerosol chemical composition by means of these thermodynamic modules has been extensively presented in literature, in particular in recent years (Makar et al., 2003; Carnevale et al., 2010; Karydis et al., 2010; Pay et al., 2012). In the literature, modelemodel comparisons between thermodynamic modules, usually in a box model configuration, have been presented (Zhang et al., 2000; Fountoukis and Nenes, 2007) to evaluate the discrepancies over a large spread of benchmark conditions (NOx, NH3, SO2 levels, temperature, relative humidity). In Moya et al. (2001) a comparison between measured aerosol components and the output of thermodynamic modules driven by measured concentration of inorganic species precursors and meteorological variables has been presented, showing a major ¼ range of variability in NHþ 4 and SO4 concentrations, with respect to . In Hayami et al. (2008) an evaluation of the performances of NO 3 different thermodynamic module has been presented over a continental domain, with a relatively coarse resolution (0.5 ). In this work, the impact of the thermodynamic equilibrium module on CTMs has been studied over mesoscale domain, with an horizontal resolution of 6 km. At this scale, the heavy nonlinearity involved in the formation/accumulation/removal of secondary
inorganic aerosol can produce large differences between performances, and consequently in the information Regional Authorities can use to define air quality control plans. In particular, SCAPE2 (Kim et al., 1993; Kim and Seinfeld, 1995) and ISORROPIA-II (Nenes et al., 1998; Fountoukis and Nenes, 2007; Fountoukis et al., 2009) have been integrated in the TCAM (Transport and Chemical Aerosol Model) (Carnevale et al., 2008c) model and the resulting configurations have been tested over a Northern Italy domain. This area, including high industrial and urban areas as well as the rural area of the Po’ Valley, is characterized by high aerosol levels with different chemical compositions. Moreover this region is one of the most polluted areas in Europe, with concentration of PM10 often over European directives threshold, with a high contribution of inorganic species to PM10. Finally, the peculiarity of its meteorological conditions (high frequencies of low wind speed simulation and frequent stagnating conditions) and the high spatial gradient in the emissions, make this region a challenging benchmark for modelers (Cuvelier et al., 2007; De Meij et al., 2009). The presented simulations have been performed in the frame of the POMI exercise (http://aqm.jrc.ec.europa.eu/pomi). 2. Modeling system configuration The simulations have been performed by means of the Gas Aerosol Modeling Evaluation System (GAMES, Fig. 1) (Volta and
Table 1 Comparison between the two tested thermodynamic modules. Feature Chemical species Activity coefficients
Temperature dependance Solution of thermodynamic equilibrium
SCAPE2
ISORROPIA-II
¼ þ þ NO 3 ; NH4 ; SO4 , Na , Cl, Caþ, Kþ, Mg Pitzer (multicomponent) Zdanovskii, Robinson and Stokes (water) Equilibrium constants, DRHs Bisectional
¼ þ þ NO 3 ; NH4 ; SO4 , Na , Cl, Caþ, Kþ, Mg Bromley/precomputed table
Equilibrium constants, DRHs, activity coefficients Analytical/bisectional
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Fig. 2. The Po Valley model domain orography with PM10 (crosses) and PARFIL monitoring campaign (circles) monitoring sites.
Finzi, 2006). In the standard configuration, the modeling system includes: the TCAM Chemical Transport Model (Carnevale et al., 2008c); the meteorological pre-processor PROMETEO (Scire et al., 1990); the emission processor POEM-PM (Carnevale et al., 2006); and a pre-processor computing the boundary conditions on the basis of continental scale simulation results. The modeling system has been widely used and validated in the frame of a number of national and international projects,
showing very good performances for gas phase pollutants and performances comparable to that of other literature models for PM10 (Cuvelier et al., 2007; Carnevale et al., 2008c; Di Nicolantonio et al., 2009). Two different configurations of TCAM have been tested, differing in the modeling of inorganic thermodynamic equilibrium: in the first configuration (TCAM_SCAPE2), the SCAPE2 (Kim et al., 1993) thermodynamic module has been implemented, while in
Fig. 3. PM10 precursor emission maps (ton/year/cell).
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Fig. 4. Mean PM10 concentration (mg m3) computed by TCAM with the two different thermodynamic modules.
Fig. 5. Normalized Mean Absolute Error computed for TCAM simulations with the two different thermodynamic modules.
Fig. 6. Correlation coefficient computed for TCAM simulation with the two different thermodynamic modules.
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Fig. 7. Monthly mean values (mg m3) computed by TCAM using the two different thermodynamic modules.
the second one (TCAM_ISO), ISORROPIA-II (Nenes et al., 1998; Fountoukis and Nenes, 2007; Fountoukis et al., 2009; http://nenes. eas.gatech.edu/ISORROPIA) has been integrated in the system. 2.1. TCAM model TCAM is a 3-D multiphase Eulerian model solving, for each time step, a Partial Differential Equation system which describes the horizontal/vertical transport, the multiphase chemical reactions and the gas to particle conversion phenomena using a splitting operator technique (Marchuk, 1975). The horizontal transport is solved by a Chapeau function approximation (Pepper et al., 1979) and the non linear Forester filter (Forester, 1977), while the vertical transport PDE system is solved by a hybrid implicit/explicit scheme. The gas chemistry is described by a modified version of SAPRC97 scheme (Carter et al., 1997). The Ordinary Differential Equation chemical kinetic system is solved by means of the ImpliciteExplicit Hybrid (IEH) solver (Chock et al., 1994), which splits the species in fast and slow ones, according to their reaction rates. The system of fast species is solved by means of the implicit Livermore Solver for Ordinary Differential Equations (LSODE) (Hindmarsh, 1975) implementing an Adams predictor/corrector method in the nonstiff case and the Backward Differentiation Formula method in the stiff case (Wille, 1994). The slow species system is solved by the AdamseBashfort method (Wille, 1994). The aerosol module describes the dynamics of 21 chemical compounds: twelve inorþ þ þ ganic species (H2O, SO¼ 4 , NH4 , Cl , NO3 , Na , H , SO2 (aq), H2O2
(aq), O3 (aq), elemental carbon and other), and 9 organics, namely a generic primary organic species and 8 classes of secondary organic species. Chemical species are split in n (namely n ¼ 10) size bins. Particles in each size bin are described by means of a fixedmoving approach. A generic particle is represented with an internal core containing the non-volatile material, like elemental carbon, crustal and dust. The core dimension of each size class is established at the beginning of the simulation and is held constant during all computations. The volatile material is supposed to reside in the outer shell of the particle, whose dimension is evaluated by the module at each time step on the basis of the total mass and of the total number of suspended particles. The inorganic species thermodynamic equilibrium is solved by means of either SCAPE2 (Kim et al., 1993) or ISORROPIA-II (meta-stable mode, Nenes et al., 1998; Fountoukis and Nenes, 2007; http://nenes.eas.gatech.edu/ ISORROPIA) modules. 2.2. Thermodynamic module comparison Table 1 presents the major characteristics of the two implemented thermodynamic modules. They consider the same chemical species but they have different computations for the activity coefficients and different solution scheme for the related nonlinear equation system. In SCAPE2 activity coefficients are computed by means of Pfizer and Zdanovskii and Robinson and Stokes methods (Kim et al., 1993), while ISORROPIA-II applies the Bromley method (Nenes et al., 1998) taking into account the temperature effect as well. In terms of solution method, SCAPE2 solves the whole thermodynamic equation system by means of a bisectional approach that in some cases could cause numerical instability; in contrast, ISORROPIA-II solves analytically as many equations as possible by means of variable substitutions, and use a numerical approach only for the remaining equations. In terms of simplifications taken during the solution procedure, the main aspect to be considered is that the ISORROPIA-II algorithm starts with the assumption of fully dry aerosols, while SCAPE2 assumes that all salts are dissolved. 3. The case study The impact of thermodynamic module on TCAM performances has been evaluated in the frame of the POMI exercise (http://aqm. jrc.ec.europa.eu/pomi/). In this modeling exercise, meteorological,
Fig. 8. Taylor plot comparison between the two configuration results.
C. Carnevale et al. / Atmospheric Environment 61 (2012) 652e660 Table 2 Data availability for the PARFIL monitoring campaign stations selected for the evaluation of PM2.5 concentration and aerosol chemical speciation. Station
Inorganic ions
PM2.5
# Year # Summer # Winter # Year # Summer # Winter Alpe S. Colombano Bosco Fontana Brescia Cantu Lodi Mantova Milano Saronno Varese
243 221 240 289 283 174 109 e 343
107 105 111 161 151 37 63 e 177
136 116 129 138 132 137 46 e 166
e 41 43 e e e e 52 56
e 27 26 e e e e 25 24
e 14 17 e e e e 27 32
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emission and boundary conditions input have been shared by JRCIES for a domain including all the Northern Italy (Fig. 2). Due to high emission levels, frequent stagnating conditions and low wind speed, this area is affected by high levels of PM10, often exceeding the European standard limits, in particular in the urban and most populated areas of the Po Valley. The meteorological fields have been computed by means of the MM5 model (Grell et al., 1993) and the boundary conditions have been generated by CHIMERE model continental simulation (Schmidt et al., 2001; De Meij et al., 2009). A detailed description of the meteorological regimes of the area and the validation of meteorological driver used in the frame of POMI exercise can be found in (Pernigotti et al., 2012).
Fig. 9. PM2.5 mean [mg m3], (left, aecee) and PM2.5 and PM10 Normalized Mean Absolute Error (right, bedef) computed for TCAM simulations with the two different thermodynamic modules.
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Fig. 10. Soccer plot of NO 3 performances in the 4 monitoring stations used for the inorganic ion validation.
Emission data have been collected for 2005 by JRC-IES starting from national/regional/local data. No resuspension or dust intrusion phenomena have been considered in these inventories. Fig. 3 presents the maps of the main PM10 precursors (primary PM10, nitrogen oxides, ammonia and organic compound) over the domain. The spatial distribution is very similar for all pollutants, with urban centers and main roads clearly highlighted. The only exception is related to ammonia emissions, that are more intense in the rural part of Po Valley between Milano and Cremona cities. The time period selected for the intercomparison exercise is the entire 2005 year. The horizontal resolution adopted for the simulation is 6 6 km2, with meteorological/emissive input provided at the same resolution. The CTM domain has been split in 11 vertical layer, ranging exponentially from 20 m (first layer) to 4900 m above ground level. 4. Results and discussion The validation of the two configurations (TCAM_SCAPE2 and TCAM_ISO in the following) has been performed using the PM10 daily mean time series collected at 70 stations selected in order to
be representative of emission and meteorological regimes in the domain (Fig. 2). In order to better appreciate the differences between the two configurations, the evaluation has been performed for the entire year and divided into summer and winter months, using a subset of the statistical indicators suggested in the frame of the FAIRMODE forum (Thunis, 2012). Fig. 4 presents the comparison in terms of box plots between observed and simulated PM10 concentrations. The results are very consistent in the 2 cases, with a quite large underestimation of PM10 levels in both configurations. This behavior, noticed for all the models participating to the exercise (Thunis et al., submitted for publication), can be explained by different factors, ranging from meteorology (MM5 run overestimation of wind speed in the areas, Pernigotti et al., 2012), to emissions (general underestimation of primary organic compounds) to phenomena modeling (in particular related to SOA formation). It is important to note that in general the results computed with SCAPE2 are a little higher in winter with respect to those computed in the ISORROPIA-II configuration, while in the summer the
Fig. 11. Soccer plot of NHþ 4 performances in the 4 monitoring stations used for the inorganic ion validation.
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Fig. 12. Soccer plot of SO¼ 4 performances in the 4 monitoring stations used for the inorganic ions validation.
behavior seems to be opposite, even if differences are lower. The spread of values computed by TCAM_ISO is lower (the 1st and 3rd quartile are very close), suggesting that TCAM_SCAPE2 is more able to reproduce the standard deviation of the observation time series. The differences between the two configurations are better identified with the normalized mean absolute error P (NMAE ¼ 1=N jMod Obsj=Obs, Fig. 5) and the correlation coefficient (Fig. 6). It can be easily shown that TCAM_ISO presents lower NMAE, in particular during the summer with a median that drops from 0.55 to 0.35, and a higher correlation both in summer and winter months with a difference of 0.2 between the two configurations. The monthly mean values computed for the meta-station (Fig. 7) show that the winter/summer gradient is quite correctly reproduced by the two configurations, even if the underestimation, in particular in winter months, is noticeable. It can be highlighted that in the modeled monthly time series the differences are more stressed in winter/summer interface months, as April and October, where TCAM_SCAPE2 shows a peculiar behavior (in particular in October, with an increase of concentrations and with a subsequent decrease in November and December not shown by measurements and by TCAM_ISO). This is due to the numerical instability of SCAPE2 (in particular related to NHþ 4 formation, see Section 4.2) only partially limited decreasing the simulation time-step in the interface between the hot/cold seasons. The meta-station Taylor plots (Taylor, 2001) (Fig. 8) allow to resume the performances for PM10 in terms of standard deviations, correlation and root mean square error, with TCAM_ISO presenting a good agreement in terms of correlation and error, showing therefore lower variability with respect to observations (as stated by the discrepancies in the standard deviation computed for simulated and observed time series). 4.1. PM2.5 performances The performances of the model with respect to PM2.5 have been evaluated using data collected in 8 stations during the PARFIL campaign (Fig. 2) and shared during the POMI project. Table 2 presents the data availability in the selected stations. Fig. 9 presents the comparison between modeled and observed mean PM2.5 values and Normalised Mean Absolute error. The results show how the underestimation of the finer fraction is limited and that the performances are in general consistently better than PM10 ones, as stated by lower value of NMAE for the selected stations.
4.2. Impact on ions performances The impact of the thermodynamic module on inorganic ion performances has been evaluated at 4 stations collecting data in different summer/winter period during the PARFIL campaign (Fig. 2, Table 2). The data have been shared by JRC-IES during the POMI simulation exercise. Despite the limited number of the data, P Normalized Mean Error ðNME ¼ 1=N Mod Obs=ObsÞ, and þ Normalized Mean Absolute Error for daily time series of NO 3 , NH4 and SO¼ have been computed and presented using a version of 4 soccer plots (Appel et al., 2011), in Figs. 10e12. With this kind of representation, a value over the bisecting line means that the bias between model and measurement is only due to the overestimation tendency of the model. The TCAM_ISO configuration shows better performances, as highlighted in particular by the NMAE value. The behavior in terms of NME does not show a specific trend, with some changes in the sign of the indicator particularly for NHþ 4 , and TCAM_ISO showing performances consistently better than TCAM_SCAPE2, which has the tendency to overestimate this fraction also at low value of humidity. The performances of NO 3 ion show the mutual impact between the photochemistry and the NO 3 production. In fact, during winter the performances of the two configurations are very similar, while during summer, when photochemistry starts, the differences become more significant due mainly to the impact of chemistry on NO 3 formation/removal (and less to the meteorological effects driving the condensation phenomena). 5. Conclusions In this work the impact of the implementation of different thermodynamic module on Chemical Transport Model performances is investigated. The TCAM model has been applied, in the frame of the POMI exercise to a Northern Italy domain in two different configurations, using either the SCAPE2 or the ISORROPIAII modules, for the computation of the inorganic thermodynamic equilibrium. Performances have been evaluated both in terms of PM10 levels and chemical speciation, using state-of-the-art statistical indicators. The validation shows that, in terms of total PM10, both configurations underestimate total PM10 concentrations, especially during the cold months. Even if the underestimation is lower with the TCAM_SCAPE configuration, the overall performances for TCAM_ISO are generally better, as stated by the
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median of the correlation that increases by 0.2, and by the lower values of the normalized mean absolute error. This is also confirmed by the analysis of PM2.5 and of the single inorganic ions, with TCAM_SCAPE showing a quite large overestimation of NHþ 4 that can explain the higher simulated PM10. The differences in performances for the inorganic ions are significant both in terms of normalized mean absolute error and normalized mean error, showing a completely different behavior for the two configurations. Future works will focus on (1) a more consistent and statistically sounding evaluation of inorganic ions, that can only be performed when more continuous and spatial distributed speciation measured data will be available, (2) filling the gap between simulated PM10 levels and measurement, investigating new modeling techniques for SOA production, as well as the availability of detailed emission inventory and (3) evaluating the impact of the thermodynamic module on the assessment of emission reduction scenarios. Acknowledgments The authors kindly acknowledge JRC-IES and all the groups involved in the frame of the POMI project. This work has been supported by Regione Lombardia and CILEA Consortium through a LISA Initiative (Laboratory for Interdisciplinary Advanced Simulation) 2010 grant [link: http://lisa.cilea.it] References Altshuller, A.P., 1984. Atmospheric particle sulfur and sulfur dioxide relationships at urban and nonurban locations. Atmospheric Environment 18, 1421e1431. Appel, K.W., Gilliam, R.C., Davis, N., Zubrov, A., Howard, S.C., 2011. Overview of the atmospheric model evaluation toll (AMET) v1.1 for evaluating meteorological and air quality models. Environmental Modeling and Software 26, 434e443. Carnevale, C., Gabusi, V., Volta, M., 2006. POEM-PM: an emission model for secondary pollution control scenarios. Environmental Modelling and Software 21, 320e329. Carnevale, C., Finzi, G., Pisoni, E., Volta, M., 2008a. Modelling assessment of PM10 exposure control policies in Northern Italy. Ecological Modelling 217, 219e229. Carnevale, C., Pisoni, E., Volta, M., 2008b. A multi-objective nonlinear optimization approach to designing effective air quality control policies. Automatica 44, 1632e1641. Carnevale, C., Decanini, E., Volta, M., 2008c. Design and validation of a multiphase 3D model to simulate tropospheric pollution. Science of the Total Environmnet 390, 166e176. Carnevale, C., Finzi, G., Pisoni, E., Volta, M., 2010. A non-linear analysis to detect the origin of PM10 concentrations in Northern Italy. Science of the Total Environment 409, 182e191. Carter, W., Luo, D., Malkina, I., 1997. Environmental Chamber Studies for Development of an Updated Photochemical Mechanism for VOC Reactivity Assessment. Tech. Rep., California Air Resources Board, Sacramento (CA), Final Report. Cheng, Y., Liang, D., Wang, W., Gong, S., Xue, M., 2010. An efficient approach of aerosol thermodynamic equilibrium predictions by the HDMR method. Atmospheric Environment 44, 1321e1330. Chock, D., Winkler, S., Sun, P., 1994. A comparison of stiff chemistry solvers for air quality modeling. Air & Waste Management Association 87th Annual Meeting. Clegg, S.L., Pfizer, K.S., 1992. Thermodynamics of multicomponents, miscible, ionic solutions: generalized equations for symmetrical electrolytes. Journal of Physical Chemistry 96, 3513e3520. Cuvelier, C., Thunis, P., Vautard, R., Amann, M., Bessagnet, B., Bedogni, M., Berkowicz, R., Brandt, J., Brocheton, F., Builtjes, P., Carnavale, C., Denby, B., Douros, J., Graf, A., Hellmuth, O., Hodzic, A., Honore, C., Jonson, J., Kerschbaumer, A., de Leeuw, F., Minguzzi, E., Moussiopoulos, N., Pertot, C., Peuch, V.H., Pirovano, G., Rouil, L., Sauter, F., Schaap, M., Stern, R., Tarrason, L., Vignati, E., Volta, M., White, L., Wind, P., Zuber, A., 2007. CityDelta: a model intercomparison study to explore the impact of emission reductions in European cities in 2010. Atmospheric Environment 41 (1), 189e207. De Meij, A., Thunis, P., Bessagnet, B., Cuvelier, C., 2009. The sensitivity of the CHIMERE model to emissions reduction scenarios on air quality in Northern Italy. Atmospheric Environment 43 (11), 1897e1907. Di Nicolantonio, W., Cacciari, A., Petritoli, A., Carnevale, C., Pisoni, E., Volta, M., Stocchi, P., Curci, G., Bolzacchini, E., Ferrero, L., Ananasso, C., Tomasi, C., 2009. MODIS and OMI satellite observations supporting air quality monitoring. Radiation Protection Dosimetry. http://dx.doi.org/10.1093/rpd/ncp231. Forester, C., 1977. Higher order monotonic convection difference schemes. Journal of Computational Physics 23, 1e22.
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Thunis, P., Rouil, L., Cuvelier, C., Stern, R., Kerschbaumer, A., Bessagnet, B., Schaap, M., Builtjes, P., Tarrason, L., Douros, J., Moussiopoulos, N., Pirovano, G., Bedogni, M., 2007. Analysis of model responses to emission-reduction scenarios within the CityDelta project. Atmospheric Environment 41, 208e220. Volta, M., Finzi, G., 2006. GAMES, a comprehensive gas aerosol modelling evaluation system. Environmental Modelling and Software 21, 587e594. Wille, D., 1994. New Stepsize Estimators for Linear Multistep Methods. Numerical Analysis Report 247, inst-MCCM. Zanobetti, A., Schwartz, J., Dockery, D.W., 2000. Airborne particles are a risk factor for hospital admissions for heart and lung disease. Environmental Health Perspective 108, 1071e1082. Zhang, Y., Seigneur, C., Seinfeld, J.H., Jacobson, M., Clegg, S.L., Binkowski, F.S., 2000. A comparative review of inorganic aerosol thermodynamic equilibrium modules: similarities, differences and their likely causes. Atmospheric Environment 34, 117e137.
Contents lists available at SciVerse ScienceDirect
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The impact of thermodynamic module in the CTM performances C. Carnevale a, *, G. Finzi a, E. Pisoni a, P. Thunis b, M. Volta a a b
Department of Information Engineering, Faculty of Engineering, University of Brescia, Italy European commission, DG JRC, Institute for Environment and Sustainability, TP 483, I-21020 Ispra, Italy
h i g h l i g h t s < The impact of the thermodynamic module in a Chemical Transport Model has been evaluated. < The role of secondary inorganic fraction on PM10 levels has been assessed. < The impact of the different chemical species in aerosol performances has been shown.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 February 2012 Received in revised form 19 June 2012 Accepted 21 June 2012
This work investigates the impact of the inorganic thermodynamic module on Chemical Transport Model simulations. The study has been focused on Northern Italy, one of the most polluted area in Europe, where high PM10 levels are reached despite the Regional and National Authority efforts to limit emission in the atmosphere. Two different configurations of the Chemical Transport Model TCAM have been tested performing yearly simulation over the selected domain for 2005. In the first configuration, the SCAPE2 thermodynamic module has been implemented, while in the second one, ISORROPIA-II has been integrated into the system. An exhaustive evaluation of the performances is presented for total PM10 with respect to its daily values, and the analysis of the impact of the two modules on PM2.5 and on aerosol chemical components has been performed. The ISORROPIA-II module configuration shows better overall performances both in terms of correlation and Normalized Mean Absolute Error for PM10 and PM2.5. The evaluation of the inorganic ions performances shows a quite different behavior for the two configurations, with ISORROPIA-II providing better results for both Normalized Mean Absolute Error and Normalized Mean Error. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Chemical Transport Model Thermodynamic module PM10 Northern Italy Aerosol speciation
1. Introduction Aerosol pollutants, due to their impact on human health and ecosystems, have been the focus of Research Institutes and Environmental Agencies studies in the last 10 years. Aerosol impact ranges from visibility reduction (Altshuller, 1984) to heavy effects on human health as respiratory and cardiovascular diseases, that are strictly related to the toxicity of the inorganic fraction of the aerosol (Zanobetti et al., 2000; Lee et al., 2007). Moreover, due to different origins of the aerosol chemical compounds, the definition and implementation of air quality strategies for PM10 is a very challenging task (Thunis et al., 2007; Carnevale et al., 2008a,b, De Meij et al., 2009).
* Corresponding author. E-mail addresses: [email protected], (C. Carnevale).
[email protected]
1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.06.058
The problem of aerosol chemical characterization is particularly true in areas like Northern Italy, where high PM10 levels and inorganic fraction ranging from 30% to 50% are measured during both summer and winter months (Lonati et al., 2005; Carnevale et al., 2010). Due to the point-wise nature of measurements collected by monitoring network and to the costs and difficulties involved in the chemical analysis of the aerosol components, the availability of modeling systems allowing to correctly reproduce (i.e. with a certain level of performances) the aerosol levels and their chemical composition is crucial to (1) evaluate the impact of aerosol on human/ecosystem health, (2) provide Regional Authorities with tools to evaluate the priorities in air quality control plan and (3) define effective emission control policies over a domain (Fountoukis and Nenes, 2007). An extended discussion about the issues related to the use of CTM for aerosol emission control strategies selection can be found in (Carnevale et al., 2008a) and (Pisoni et al., 2009). To compute aerosol chemical composition and phase, Chemical Transport Models (CTMs) require information about the
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Fig. 1. The GAMES modeling system configuration.
thermodynamic equilibrium state, driving the mass transfer between the gas and aerosol phases. Thermodynamic modules are devoted to this computation, which represents the starting point for the evaluation of the concentrations of the gas (nitrogen oxides, ammonia, sulfur oxides) and aerosol (nitrates, ammonium and sulphates) species subject to mass transfer between the two phases in atmosphere. The development of this kind of modules is a very hard task for modeling groups due to (1) the lack of knowledge in the involved physical/chemical processes and (2) the nonlinearity of the relationships, that can lead to a numerically complex differential equation system to be solved. A large number of inorganic equilibrium modules have been implemented in the Chemical Transport Model in the last decades (Clegg and Pfizer, 1992; Kim et al., 1993; Jacobson et al., 1996; Nenes et al., 1998; Fountoukis and Nenes, 2007; Metzger et al., 2002; Cheng et al., 2010). These modules may differ in terms of treated chemical species, of their level of detail in the representation of the phenomena and of implemented numerical schemes. The use of CTM model to evaluate aerosol chemical composition by means of these thermodynamic modules has been extensively presented in literature, in particular in recent years (Makar et al., 2003; Carnevale et al., 2010; Karydis et al., 2010; Pay et al., 2012). In the literature, modelemodel comparisons between thermodynamic modules, usually in a box model configuration, have been presented (Zhang et al., 2000; Fountoukis and Nenes, 2007) to evaluate the discrepancies over a large spread of benchmark conditions (NOx, NH3, SO2 levels, temperature, relative humidity). In Moya et al. (2001) a comparison between measured aerosol components and the output of thermodynamic modules driven by measured concentration of inorganic species precursors and meteorological variables has been presented, showing a major ¼ range of variability in NHþ 4 and SO4 concentrations, with respect to . In Hayami et al. (2008) an evaluation of the performances of NO 3 different thermodynamic module has been presented over a continental domain, with a relatively coarse resolution (0.5 ). In this work, the impact of the thermodynamic equilibrium module on CTMs has been studied over mesoscale domain, with an horizontal resolution of 6 km. At this scale, the heavy nonlinearity involved in the formation/accumulation/removal of secondary
inorganic aerosol can produce large differences between performances, and consequently in the information Regional Authorities can use to define air quality control plans. In particular, SCAPE2 (Kim et al., 1993; Kim and Seinfeld, 1995) and ISORROPIA-II (Nenes et al., 1998; Fountoukis and Nenes, 2007; Fountoukis et al., 2009) have been integrated in the TCAM (Transport and Chemical Aerosol Model) (Carnevale et al., 2008c) model and the resulting configurations have been tested over a Northern Italy domain. This area, including high industrial and urban areas as well as the rural area of the Po’ Valley, is characterized by high aerosol levels with different chemical compositions. Moreover this region is one of the most polluted areas in Europe, with concentration of PM10 often over European directives threshold, with a high contribution of inorganic species to PM10. Finally, the peculiarity of its meteorological conditions (high frequencies of low wind speed simulation and frequent stagnating conditions) and the high spatial gradient in the emissions, make this region a challenging benchmark for modelers (Cuvelier et al., 2007; De Meij et al., 2009). The presented simulations have been performed in the frame of the POMI exercise (http://aqm.jrc.ec.europa.eu/pomi). 2. Modeling system configuration The simulations have been performed by means of the Gas Aerosol Modeling Evaluation System (GAMES, Fig. 1) (Volta and
Table 1 Comparison between the two tested thermodynamic modules. Feature Chemical species Activity coefficients
Temperature dependance Solution of thermodynamic equilibrium
SCAPE2
ISORROPIA-II
¼ þ þ NO 3 ; NH4 ; SO4 , Na , Cl, Caþ, Kþ, Mg Pitzer (multicomponent) Zdanovskii, Robinson and Stokes (water) Equilibrium constants, DRHs Bisectional
¼ þ þ NO 3 ; NH4 ; SO4 , Na , Cl, Caþ, Kþ, Mg Bromley/precomputed table
Equilibrium constants, DRHs, activity coefficients Analytical/bisectional
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Fig. 2. The Po Valley model domain orography with PM10 (crosses) and PARFIL monitoring campaign (circles) monitoring sites.
Finzi, 2006). In the standard configuration, the modeling system includes: the TCAM Chemical Transport Model (Carnevale et al., 2008c); the meteorological pre-processor PROMETEO (Scire et al., 1990); the emission processor POEM-PM (Carnevale et al., 2006); and a pre-processor computing the boundary conditions on the basis of continental scale simulation results. The modeling system has been widely used and validated in the frame of a number of national and international projects,
showing very good performances for gas phase pollutants and performances comparable to that of other literature models for PM10 (Cuvelier et al., 2007; Carnevale et al., 2008c; Di Nicolantonio et al., 2009). Two different configurations of TCAM have been tested, differing in the modeling of inorganic thermodynamic equilibrium: in the first configuration (TCAM_SCAPE2), the SCAPE2 (Kim et al., 1993) thermodynamic module has been implemented, while in
Fig. 3. PM10 precursor emission maps (ton/year/cell).
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Fig. 4. Mean PM10 concentration (mg m3) computed by TCAM with the two different thermodynamic modules.
Fig. 5. Normalized Mean Absolute Error computed for TCAM simulations with the two different thermodynamic modules.
Fig. 6. Correlation coefficient computed for TCAM simulation with the two different thermodynamic modules.
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Fig. 7. Monthly mean values (mg m3) computed by TCAM using the two different thermodynamic modules.
the second one (TCAM_ISO), ISORROPIA-II (Nenes et al., 1998; Fountoukis and Nenes, 2007; Fountoukis et al., 2009; http://nenes. eas.gatech.edu/ISORROPIA) has been integrated in the system. 2.1. TCAM model TCAM is a 3-D multiphase Eulerian model solving, for each time step, a Partial Differential Equation system which describes the horizontal/vertical transport, the multiphase chemical reactions and the gas to particle conversion phenomena using a splitting operator technique (Marchuk, 1975). The horizontal transport is solved by a Chapeau function approximation (Pepper et al., 1979) and the non linear Forester filter (Forester, 1977), while the vertical transport PDE system is solved by a hybrid implicit/explicit scheme. The gas chemistry is described by a modified version of SAPRC97 scheme (Carter et al., 1997). The Ordinary Differential Equation chemical kinetic system is solved by means of the ImpliciteExplicit Hybrid (IEH) solver (Chock et al., 1994), which splits the species in fast and slow ones, according to their reaction rates. The system of fast species is solved by means of the implicit Livermore Solver for Ordinary Differential Equations (LSODE) (Hindmarsh, 1975) implementing an Adams predictor/corrector method in the nonstiff case and the Backward Differentiation Formula method in the stiff case (Wille, 1994). The slow species system is solved by the AdamseBashfort method (Wille, 1994). The aerosol module describes the dynamics of 21 chemical compounds: twelve inorþ þ þ ganic species (H2O, SO¼ 4 , NH4 , Cl , NO3 , Na , H , SO2 (aq), H2O2
(aq), O3 (aq), elemental carbon and other), and 9 organics, namely a generic primary organic species and 8 classes of secondary organic species. Chemical species are split in n (namely n ¼ 10) size bins. Particles in each size bin are described by means of a fixedmoving approach. A generic particle is represented with an internal core containing the non-volatile material, like elemental carbon, crustal and dust. The core dimension of each size class is established at the beginning of the simulation and is held constant during all computations. The volatile material is supposed to reside in the outer shell of the particle, whose dimension is evaluated by the module at each time step on the basis of the total mass and of the total number of suspended particles. The inorganic species thermodynamic equilibrium is solved by means of either SCAPE2 (Kim et al., 1993) or ISORROPIA-II (meta-stable mode, Nenes et al., 1998; Fountoukis and Nenes, 2007; http://nenes.eas.gatech.edu/ ISORROPIA) modules. 2.2. Thermodynamic module comparison Table 1 presents the major characteristics of the two implemented thermodynamic modules. They consider the same chemical species but they have different computations for the activity coefficients and different solution scheme for the related nonlinear equation system. In SCAPE2 activity coefficients are computed by means of Pfizer and Zdanovskii and Robinson and Stokes methods (Kim et al., 1993), while ISORROPIA-II applies the Bromley method (Nenes et al., 1998) taking into account the temperature effect as well. In terms of solution method, SCAPE2 solves the whole thermodynamic equation system by means of a bisectional approach that in some cases could cause numerical instability; in contrast, ISORROPIA-II solves analytically as many equations as possible by means of variable substitutions, and use a numerical approach only for the remaining equations. In terms of simplifications taken during the solution procedure, the main aspect to be considered is that the ISORROPIA-II algorithm starts with the assumption of fully dry aerosols, while SCAPE2 assumes that all salts are dissolved. 3. The case study The impact of thermodynamic module on TCAM performances has been evaluated in the frame of the POMI exercise (http://aqm. jrc.ec.europa.eu/pomi/). In this modeling exercise, meteorological,
Fig. 8. Taylor plot comparison between the two configuration results.
C. Carnevale et al. / Atmospheric Environment 61 (2012) 652e660 Table 2 Data availability for the PARFIL monitoring campaign stations selected for the evaluation of PM2.5 concentration and aerosol chemical speciation. Station
Inorganic ions
PM2.5
# Year # Summer # Winter # Year # Summer # Winter Alpe S. Colombano Bosco Fontana Brescia Cantu Lodi Mantova Milano Saronno Varese
243 221 240 289 283 174 109 e 343
107 105 111 161 151 37 63 e 177
136 116 129 138 132 137 46 e 166
e 41 43 e e e e 52 56
e 27 26 e e e e 25 24
e 14 17 e e e e 27 32
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emission and boundary conditions input have been shared by JRCIES for a domain including all the Northern Italy (Fig. 2). Due to high emission levels, frequent stagnating conditions and low wind speed, this area is affected by high levels of PM10, often exceeding the European standard limits, in particular in the urban and most populated areas of the Po Valley. The meteorological fields have been computed by means of the MM5 model (Grell et al., 1993) and the boundary conditions have been generated by CHIMERE model continental simulation (Schmidt et al., 2001; De Meij et al., 2009). A detailed description of the meteorological regimes of the area and the validation of meteorological driver used in the frame of POMI exercise can be found in (Pernigotti et al., 2012).
Fig. 9. PM2.5 mean [mg m3], (left, aecee) and PM2.5 and PM10 Normalized Mean Absolute Error (right, bedef) computed for TCAM simulations with the two different thermodynamic modules.
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Fig. 10. Soccer plot of NO 3 performances in the 4 monitoring stations used for the inorganic ion validation.
Emission data have been collected for 2005 by JRC-IES starting from national/regional/local data. No resuspension or dust intrusion phenomena have been considered in these inventories. Fig. 3 presents the maps of the main PM10 precursors (primary PM10, nitrogen oxides, ammonia and organic compound) over the domain. The spatial distribution is very similar for all pollutants, with urban centers and main roads clearly highlighted. The only exception is related to ammonia emissions, that are more intense in the rural part of Po Valley between Milano and Cremona cities. The time period selected for the intercomparison exercise is the entire 2005 year. The horizontal resolution adopted for the simulation is 6 6 km2, with meteorological/emissive input provided at the same resolution. The CTM domain has been split in 11 vertical layer, ranging exponentially from 20 m (first layer) to 4900 m above ground level. 4. Results and discussion The validation of the two configurations (TCAM_SCAPE2 and TCAM_ISO in the following) has been performed using the PM10 daily mean time series collected at 70 stations selected in order to
be representative of emission and meteorological regimes in the domain (Fig. 2). In order to better appreciate the differences between the two configurations, the evaluation has been performed for the entire year and divided into summer and winter months, using a subset of the statistical indicators suggested in the frame of the FAIRMODE forum (Thunis, 2012). Fig. 4 presents the comparison in terms of box plots between observed and simulated PM10 concentrations. The results are very consistent in the 2 cases, with a quite large underestimation of PM10 levels in both configurations. This behavior, noticed for all the models participating to the exercise (Thunis et al., submitted for publication), can be explained by different factors, ranging from meteorology (MM5 run overestimation of wind speed in the areas, Pernigotti et al., 2012), to emissions (general underestimation of primary organic compounds) to phenomena modeling (in particular related to SOA formation). It is important to note that in general the results computed with SCAPE2 are a little higher in winter with respect to those computed in the ISORROPIA-II configuration, while in the summer the
Fig. 11. Soccer plot of NHþ 4 performances in the 4 monitoring stations used for the inorganic ion validation.
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Fig. 12. Soccer plot of SO¼ 4 performances in the 4 monitoring stations used for the inorganic ions validation.
behavior seems to be opposite, even if differences are lower. The spread of values computed by TCAM_ISO is lower (the 1st and 3rd quartile are very close), suggesting that TCAM_SCAPE2 is more able to reproduce the standard deviation of the observation time series. The differences between the two configurations are better identified with the normalized mean absolute error P (NMAE ¼ 1=N jMod Obsj=Obs, Fig. 5) and the correlation coefficient (Fig. 6). It can be easily shown that TCAM_ISO presents lower NMAE, in particular during the summer with a median that drops from 0.55 to 0.35, and a higher correlation both in summer and winter months with a difference of 0.2 between the two configurations. The monthly mean values computed for the meta-station (Fig. 7) show that the winter/summer gradient is quite correctly reproduced by the two configurations, even if the underestimation, in particular in winter months, is noticeable. It can be highlighted that in the modeled monthly time series the differences are more stressed in winter/summer interface months, as April and October, where TCAM_SCAPE2 shows a peculiar behavior (in particular in October, with an increase of concentrations and with a subsequent decrease in November and December not shown by measurements and by TCAM_ISO). This is due to the numerical instability of SCAPE2 (in particular related to NHþ 4 formation, see Section 4.2) only partially limited decreasing the simulation time-step in the interface between the hot/cold seasons. The meta-station Taylor plots (Taylor, 2001) (Fig. 8) allow to resume the performances for PM10 in terms of standard deviations, correlation and root mean square error, with TCAM_ISO presenting a good agreement in terms of correlation and error, showing therefore lower variability with respect to observations (as stated by the discrepancies in the standard deviation computed for simulated and observed time series). 4.1. PM2.5 performances The performances of the model with respect to PM2.5 have been evaluated using data collected in 8 stations during the PARFIL campaign (Fig. 2) and shared during the POMI project. Table 2 presents the data availability in the selected stations. Fig. 9 presents the comparison between modeled and observed mean PM2.5 values and Normalised Mean Absolute error. The results show how the underestimation of the finer fraction is limited and that the performances are in general consistently better than PM10 ones, as stated by lower value of NMAE for the selected stations.
4.2. Impact on ions performances The impact of the thermodynamic module on inorganic ion performances has been evaluated at 4 stations collecting data in different summer/winter period during the PARFIL campaign (Fig. 2, Table 2). The data have been shared by JRC-IES during the POMI simulation exercise. Despite the limited number of the data, P Normalized Mean Error ðNME ¼ 1=N Mod Obs=ObsÞ, and þ Normalized Mean Absolute Error for daily time series of NO 3 , NH4 and SO¼ have been computed and presented using a version of 4 soccer plots (Appel et al., 2011), in Figs. 10e12. With this kind of representation, a value over the bisecting line means that the bias between model and measurement is only due to the overestimation tendency of the model. The TCAM_ISO configuration shows better performances, as highlighted in particular by the NMAE value. The behavior in terms of NME does not show a specific trend, with some changes in the sign of the indicator particularly for NHþ 4 , and TCAM_ISO showing performances consistently better than TCAM_SCAPE2, which has the tendency to overestimate this fraction also at low value of humidity. The performances of NO 3 ion show the mutual impact between the photochemistry and the NO 3 production. In fact, during winter the performances of the two configurations are very similar, while during summer, when photochemistry starts, the differences become more significant due mainly to the impact of chemistry on NO 3 formation/removal (and less to the meteorological effects driving the condensation phenomena). 5. Conclusions In this work the impact of the implementation of different thermodynamic module on Chemical Transport Model performances is investigated. The TCAM model has been applied, in the frame of the POMI exercise to a Northern Italy domain in two different configurations, using either the SCAPE2 or the ISORROPIAII modules, for the computation of the inorganic thermodynamic equilibrium. Performances have been evaluated both in terms of PM10 levels and chemical speciation, using state-of-the-art statistical indicators. The validation shows that, in terms of total PM10, both configurations underestimate total PM10 concentrations, especially during the cold months. Even if the underestimation is lower with the TCAM_SCAPE configuration, the overall performances for TCAM_ISO are generally better, as stated by the
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median of the correlation that increases by 0.2, and by the lower values of the normalized mean absolute error. This is also confirmed by the analysis of PM2.5 and of the single inorganic ions, with TCAM_SCAPE showing a quite large overestimation of NHþ 4 that can explain the higher simulated PM10. The differences in performances for the inorganic ions are significant both in terms of normalized mean absolute error and normalized mean error, showing a completely different behavior for the two configurations. Future works will focus on (1) a more consistent and statistically sounding evaluation of inorganic ions, that can only be performed when more continuous and spatial distributed speciation measured data will be available, (2) filling the gap between simulated PM10 levels and measurement, investigating new modeling techniques for SOA production, as well as the availability of detailed emission inventory and (3) evaluating the impact of the thermodynamic module on the assessment of emission reduction scenarios. Acknowledgments The authors kindly acknowledge JRC-IES and all the groups involved in the frame of the POMI project. This work has been supported by Regione Lombardia and CILEA Consortium through a LISA Initiative (Laboratory for Interdisciplinary Advanced Simulation) 2010 grant [link: http://lisa.cilea.it] References Altshuller, A.P., 1984. Atmospheric particle sulfur and sulfur dioxide relationships at urban and nonurban locations. Atmospheric Environment 18, 1421e1431. Appel, K.W., Gilliam, R.C., Davis, N., Zubrov, A., Howard, S.C., 2011. Overview of the atmospheric model evaluation toll (AMET) v1.1 for evaluating meteorological and air quality models. Environmental Modeling and Software 26, 434e443. Carnevale, C., Gabusi, V., Volta, M., 2006. POEM-PM: an emission model for secondary pollution control scenarios. Environmental Modelling and Software 21, 320e329. Carnevale, C., Finzi, G., Pisoni, E., Volta, M., 2008a. Modelling assessment of PM10 exposure control policies in Northern Italy. Ecological Modelling 217, 219e229. Carnevale, C., Pisoni, E., Volta, M., 2008b. A multi-objective nonlinear optimization approach to designing effective air quality control policies. Automatica 44, 1632e1641. Carnevale, C., Decanini, E., Volta, M., 2008c. Design and validation of a multiphase 3D model to simulate tropospheric pollution. Science of the Total Environmnet 390, 166e176. Carnevale, C., Finzi, G., Pisoni, E., Volta, M., 2010. A non-linear analysis to detect the origin of PM10 concentrations in Northern Italy. Science of the Total Environment 409, 182e191. Carter, W., Luo, D., Malkina, I., 1997. Environmental Chamber Studies for Development of an Updated Photochemical Mechanism for VOC Reactivity Assessment. Tech. Rep., California Air Resources Board, Sacramento (CA), Final Report. Cheng, Y., Liang, D., Wang, W., Gong, S., Xue, M., 2010. An efficient approach of aerosol thermodynamic equilibrium predictions by the HDMR method. Atmospheric Environment 44, 1321e1330. Chock, D., Winkler, S., Sun, P., 1994. A comparison of stiff chemistry solvers for air quality modeling. Air & Waste Management Association 87th Annual Meeting. Clegg, S.L., Pfizer, K.S., 1992. Thermodynamics of multicomponents, miscible, ionic solutions: generalized equations for symmetrical electrolytes. Journal of Physical Chemistry 96, 3513e3520. Cuvelier, C., Thunis, P., Vautard, R., Amann, M., Bessagnet, B., Bedogni, M., Berkowicz, R., Brandt, J., Brocheton, F., Builtjes, P., Carnavale, C., Denby, B., Douros, J., Graf, A., Hellmuth, O., Hodzic, A., Honore, C., Jonson, J., Kerschbaumer, A., de Leeuw, F., Minguzzi, E., Moussiopoulos, N., Pertot, C., Peuch, V.H., Pirovano, G., Rouil, L., Sauter, F., Schaap, M., Stern, R., Tarrason, L., Vignati, E., Volta, M., White, L., Wind, P., Zuber, A., 2007. CityDelta: a model intercomparison study to explore the impact of emission reductions in European cities in 2010. Atmospheric Environment 41 (1), 189e207. De Meij, A., Thunis, P., Bessagnet, B., Cuvelier, C., 2009. The sensitivity of the CHIMERE model to emissions reduction scenarios on air quality in Northern Italy. Atmospheric Environment 43 (11), 1897e1907. Di Nicolantonio, W., Cacciari, A., Petritoli, A., Carnevale, C., Pisoni, E., Volta, M., Stocchi, P., Curci, G., Bolzacchini, E., Ferrero, L., Ananasso, C., Tomasi, C., 2009. MODIS and OMI satellite observations supporting air quality monitoring. Radiation Protection Dosimetry. http://dx.doi.org/10.1093/rpd/ncp231. Forester, C., 1977. Higher order monotonic convection difference schemes. Journal of Computational Physics 23, 1e22.
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