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Introduction In chemical fate and transport modeling, geographic information systems (GIS) have been used so far mainly as a pre- and post-processor. However, in other domains of environmental science many examples have appeared in the literature of spatially explicit models able to capture the fundamental spatial patterns of phenomena without use of complex numerical models. This is achieved by through the built-in analytical capabilities of GIS, and particularly map algebra. In this contribution we illustrate the use of GIS calculations for chemical fate and transport modeling. Materials and methodsPistocchi, 2008, describes a method to describe the fate and transport of pollutants using GIS operations only. The model computes atmospheric concentration from emissions using the ADEPT model (Roemer et al., 2005) and subsequently concentrations in soil, inland and marine waters using map algebraic operations. Direct emissions to soil and water, along with atmospheric deposition, are considered. The mass of chemicals in soils and water is a function of emission and deposition rates, and removal rates including volatilization, advective transport and degradation. Figure 1 shows an example of the maps obtained through the modeling process. Environmental variables required as input to the above equations are parameterized as described in Pistocchi et al., 2006. Emission to the soil and seawater used to compute soil mass balance according to equation (1) are given by the sum of direct emissions and deposition from the atmosphere. The model has been compared with the results of the well known EMEP MSCE-POP model (Gusev et al., 2005) and with monitoring data, for PCBs and PCDD/Fs (Pistocchi, 2007). Results and discussionThe model was used to compute maps of atmospheric, soil and seawater concentration of PCBs and PCDD/Fs, using emission estimates from EMEP for the years 1998 and 2001 respectively. The results indicate a relatively good agreement both in terms of orders of magnitude (represented by the ratio of the average values) and spatial pattern (represented by the coefficient of correlation) as shown in table 1. For soils, the MSCE-POP model provides PCB concentrations about 2 orders of magnitude higher than MAPPE, mainly due to the assumptions on the soil chemical profile (Pistocchi, 2008). Both the MSCE-POP model results and the ones from MAPPE were compared to existing measurements available across Europe, particularly in soil (Meijer et al., 2003) and in air passive samplers (Jaward et al., 2004) for PCBs. In the first case, the MSCE-POP model results were less correlated to observations than the MAPPE ones (Pistocchi, 2007) while in the second case they behaved in a similar way. Predictions on PCDD/Fs were compared to available data from the literature (Buckley-Golder et al., 1999; Schmid et al., 2005) and did not highlight relevant discrepancies between MAPPE and MSCE-POP; orders of magnitude were correctly captured but spatial patterns were only weakly reproduced (Pistocchi, 2008). ConclusionThe proposed model allows reproducing the spatial patterns of complex numerical models such as MSCE-POP, with far simpler computational effort and higher flexibility and transparency. Comparison with measurements did not indicate better performance of such more complex models. Although for mechanistic description of chemical fate and transport processes in space and time traditional models represent the most appropriate tool, a GIS-based approach can be recommended for screening level applications, especially when dealing with chemicals having high uncertainties in emissions and physico-chemical properties.

References[1] Pistocchi, A., A GIS-based approach for modeling the fate and transport of pollutants in

Europe, submitted to Environmental Science and Technology, 42, 3640-3647, 2008[2] Pistocchi, A., Vizcaino Martinez, M.P., Pennington, D.W., Analysis of Landscape and

Climate Parameters for Continental Scale Assessment of the Fate of Pollutants; Office for Official Publications of the European Communities, EUR 22624 EN, Luxembourg: 2006

[3] A.Gusev, E.Mantseva, V.Shatalov, B.Strukov, Regional Multicompartment Model MSCE-POP. EMEP/MSC-E Technical Report 5/2005, www.emep.int

[4] Mejier, S.N., Ockenden, W.A., Sweetman, A., Breivik, K., Grimalt, J.O., Jones, K., Global distribution and budget of PCBs and HCB in background surface soils: implications for sources of Environmental processes, Environ.Sci. Technol., 2003, 37,667-672

[5] Jaward F.M., Farrar, N.J, Harner, T., Sweetman, A.J., Jones, K.C., Passive Air sampling of PCBs, PBDEs, and Organochlorine Pesticides Across Europe, Environ. Sci. Technol. 2004, 38, 34-41

[6] Buckley-Golder, D., Fiedler, H., Woodfield, M., Compilation of EU Dioxin Exposure and Health Data, Task 2 – Environmental Levels, prepared for EC DG ENV by UK DETR, UK, October 1999 http://ec.europa.eu/environment/dioxin/pdf/task2.pdf

[7] Schmid, P., Erika Gujer, Markus Zennegg , Thomas D. Bucheli , Andre´ Desaules, Correlation of PCDD/F and PCB concentrations in soil samples from the Swiss soil monitoring network (NABO) to specific parameters of the observation sites, Chemosphere , 2005, 58 227–234

Acknowledgement - The research was partly funded by the European Commission FP6 contract no. 003956 (NoMiracle IP: http://nomiracle.jrc.it ).

Fate and transport of pollutants at the continental scale – insights from the MAPPE

model for POPsA.Pistocchi, G.Bidoglio, D.W.Pennington, M. P.Vizcaino Martinez

ContactA PistocchiEuropean Commission • DG Joint Research CentreInstitute for Environment and SustainabilityTel. +39 0332785591• Fax + 39 0332785601E-mail: alberto.pistocchi@jrc.it

Table 1 - correlation and average ratios between MAPPE and MSCE-POP model results. From Pistocchi, 2008.

Figure 1:

atmospheric concentration (a), deposition rate (b), soil and sea concentration (c) and deposition fluxes (d) for PCBs in 1998, as predicted by the MAPPE model.

This research was financially partly supported by FP6 Contract No. 003956.

Quantity R2 for PCBs

(Average MAPPE) / (average MSCE-

POP) for PCBs

R2 for dioxins/furans

(Average MAPPE) / (average MSCE-

POP) for dioxins/furans

Deposition, land 0.62 0.57 0.69 2.96

Deposition, seawater 0.39 2.02 0.62 2.10

Concentration, air 0.88 0.58 0.74 0.85

Concentration, soil 0.41 0.01 0.63 0.36

Concentration, seawater 0.39 1.21 0.52 0.55

a b 1

c d 2