The MAPPE Strategy for GIS-Based Fate and Transport Modeling of Chemicals

1 11A GIS-based approach for modeling the fate and 2 transport of pollutants in Europe3A.Pistocchi4EC, DG JRC, IES, via E.Fermi, 1, 21020 Ispra (VA) Italy[email protected] tel +390332785591 fax +390332785601.6Abstract7 This paper presents an approach to estimate chemical concentration in multiple8environmental media (soil, water, and the atmosphere) with the sole use of basic9geographical information system (GIS) operations, and particularly map algebra. This10allows solving mass balance equations in a different way from the traditional methods11involving numerical or analytical solution of systems of equations, producing maps of12chemical fluxes and concentrations only through combinations of maps of emissions and13environmental removal or transfer rates.14 Benchmarking with the well-established EMEP MSCE-POP model shows that the15method provides consistent results with this more detailed description. When available,16experimental evidence equally supports the proposed method in relation to the more17complex approaches. 18 Thanks to the use of GIS calculations, the results can be obtained with a spatial19resolution limited only by input data; the use of map algebra warrants flexible20modification of the model algorithms, for e.g. partitioning, degradation, and inter-media21transfer. 1 21 The management of data directly in GIS, with no need for model input and output2processing, stimulates the adoption of up-to-date representations of landscape and climate3variables nowadays more and more frequently available from remote sensing acquisitions4and sectoral studies. 5 The method is particularly suited for a preliminary assessment of the spatial6distribution of chemicals especially under high uncertainty and when many chemicals7and their synergy need to be investigated, prior to dipping into more specialized and8computation-intensive numerical models. 9Introduction 10 In the last years, researchers have spent efforts in developing spatially distributed11fate and transport models of chemicals, i.e. models allowing spatially explicit12representations (maps) of contaminants from a given spatial distribution of sources [1, 2,133, 4, 5, 6, 7, 13, 28], as well as model intercomparison exercises [14, 15, 47]. 14 Existing spatially explicit models provide a valuable analytical tool in order to15understand the mechanics of pollution; yet they tend to be rather complex when spatial16resolution increases, requiring high computation time that makes them impractical17outside of specific specialized studies.18 At the same time, increasingly detailed spatial data on environmental processes19and chemical emissions are becoming available in formats easy to process using20geographic information systems (GIS). In chemical fate and transport modeling, GIS has21been used so far mainly as a pre- and post-processor, although many examples appeared22in the literature of spatially explicit models able to capture the fundamental spatial23patterns of phenomena with no use of complex numerical models, capitalizing on the 1 31built-in analytical capabilities of GIS [27, 8, 9, 10, 26, 44]. By expanding the concepts2already used in such approaches, and in many other areas of environmental and earth3sciences, we aim at demonstrating the use of GIS calculations for chemical fate and4transport assessment, as initially suggested in [23] and [25]. 5Materials and methods6Map-algebraic formulation of the fate and transport equations7In this paper, we solve the mass balance equation directly in GIS in terms of map algebra8(e.g. [29, 30] among many others – see Figure 1 for a general scheme of the calculation).9This is a standard technique by which gridcell-based GIS software manipulate maps, by10applying algebraic operations on a cell-by-cell basis. Using analysis capabilities built in11GIS allows a very simple set up of calculations, with great flexibility in the choice of12algorithms, and with a straightforward control on the calculation steps for error tracking.13Moreover, model resolution is only limited by the availability of data with no need of14complex processing of model input.15In the paper, we will refer to soil, air and seawater compartments only. The case of inland16waters can be treated in map-algebraic terms as discussed in a separate paper [43] and in17[23]. For soils, during a period of constant E0 and Koverall a solution of the mass balance18equation is:E0 19M = M0 exp(K overallt)  (1  exp(K overallt)) (1) K overall20Where M0 is an appropriate initial distribution of mass, and K overall is the overall removal21rate, E0 is a map of chemical emissions to soil, while t is time. 1 41Equation (1) holds for cases where advection from the surrounding cells is negligible.2Such is the case, for instance, of soil when lateral exchanges (e.g. re-deposition of3contaminated sediments eroded upslope; re-infiltration of contaminated water from4upstream; subsurface lateral fluxes) can be neglected. In such a case, E0 is the sum of5local mass discharge and atmospheric deposition. 6Under steady state conditions, equation (1) becomes:E 7M= 0 (1a). K overall8Seawater can be treated in this way, assuming negligible lateral transport due to currents9and dispersion (“water column approach”) as discussed in [22]. 10The atmospheric compartment is described with the ADEPT model approach [31]. The11concentration of a generic, reactive chemical in the atmosphere within the mixed layer at12a generic point (x,y) is computed as: n13Catmo   EiSRi (x, y)exp(KΤti (x, y)) (2) i 114where Ei for i = 1, …, n is the emission at any of the n locations from where advective-15dispersive fluxes enter the control volume. The maps SRi and Tti respectively represent a16“source-receptor term” accounting for dilution and advective transport, and a “time of17travel” of the contaminants, and K is the overall decay rate to which a chemical in subject18throughout the pathway from the generic location i-th and the control volume boundary.19As in the atmosphere advection and dilution largely dominate over other processes, a20single K value for the whole Europe is normally acceptable [38]. The SRi and Tt maps21used in this paper represent concentration in Europe (ug/m3) deriving from the emission22of 1 Mt/y of a conservative contaminant in the generic i-th country, assuming emissions 1 51are distributed within the country according to population density. The ADEPT model2(2) is evaluated and extended to a generic distribution of sources in a separate paper [21].3 Atmospheric deposition is the product of a concentration map and a deposition4velocity map: 5 Dep = Kdep Catmo (3)6where Kdep is a map representing deposition velocities and is given by: P  K  (v  wP)  (1  )v   dep dep  diff K  7  aw  (3a) 8where P is precipitation, w is a scavenging factor, vdep is particle deposition velocity, vdiff9is velocity of diffusion across the air-surface interface, Kaw is the air-water partitioning10coefficient, and  is the fraction of chemical attached to the aerosol phase. 11 Deposition from the atmosphere sums to direct emission to the soil to compute12soil mass balance according to equation (3), and the same for seawater. 13 The map Koverall in soil is given by: E Q VOL 14 K overall     K deg Z (4) R s R L RG15 where E is soil erosion rate, Q is water throughflow, VOL is volatilization rate16from soil, and RS, RL, RG are coefficients that account for the partitioning of the substance17in solid, liquid and gas phase in soils, whereas Kdeg is the degradation rate in soils, and18 Z is the soil compartment bulk thickness. 19 The map Koverall in seawater is given by: SETTL VOL 20 K overall    K deg Z (5) R sed Rdiss 1 61 where SETTL is the sediment settling velocity in seawater, VOL is volatilization2rate from seawater, RSed, Rdiss are coefficients that account for the partitioning of the3substance in sediment-attached and dissolved phase in soils, whereas Kdeg is the4degradation rate in seawater, and Z is the seawater compartment mixing depth.5 Further details and discussion on the computation of the different parameters in6equations (3) to (5) can be found in [23, 38]. Calculation can be iterative, as7volatilisation from soil and water provides additional input to the atmosphere, hence new8depositions and so on. However, [11] showed that these feedback mass fluxes are often9not relevant for most chemicals. A discussion of the model input landscape and climate10parameters is in [24]. 11The main practical strength of a map algebraic approach is the possibility to replace 12individual algorithms and input data for the calculation of Kdep or Koverall, simply by 13modifying individual input terms in map algebra expressions, with no need for re-coding 14numerical models. Moreover, input of individual model parameters is in the form of 15maps, which allows quick visual data control. 16Model implementation and benchmarking 17The equations above described can be easily implemented in any GIS software. The18model has been named Multimedia Assessment of Pollutant Pathways in Europe or19MAPPE, the Italian word to denote maps. Model assumptions, algorithms and a software20developed to run the model in the popular ArcGIS software are presented in [23, 37].21 To evaluate the above proposed method, we performed a benchmarking exercise22with the EMEP MSCE-POP model ([13]). The evaluation was done using23polychlorobiphenyls (PCBs) and polychlorodibenzodioxins/furans (PCDD/Fs), in that 1 71they are relatively well studied, representative persistent organic pollutants (POPs)2fulfilling the criteria of [12]. Calculations were performed under steady state3assumptions. 4 The EMEP calculation results for PCBs appear to be quite significantly correlated5(Figure 1 Supporting Information (S.I.)). In particular, atmospheric deposition is highly6correlated to ocean concentration (94% explained variance), whereas atmospheric7concentration is less correlated to deposition (80% explained variance). This suggests that8spatial variation in modeled atmosphere deposition rates play a bigger role than variation9in modeled ocean removal rate. The soil compartment shows a remarkably lower10correlation with the air compartment than ocean, which consistently corresponds to a11higher importance of the past history of emissions, and the spatial variation of removal12rates in soils. 13In general, the “water column” model approach used for ocean in the present study does14not introduce appreciable errors with regard to the MSCE-POP model, as lateral transfer15does not appear important at the working scale of the model. 16 Table 1 (S.I.) reports the physico-chemical properties used for the chemicals.17Table 2 (S.I.) provides the atmospheric emission totals per country, assumed as the only18source of emission [18]. Chemical properties are the ones in [13] for PCB 153, and for192,3,4, 7,8Cl5DF. The properties of the former have proven to represent reasonably well20the behaviour of the sum of PCBs ([17]), while the ones of the latter have been used to21describe the total concentration of dioxins and furans as a mixture in terms of toxic22equivalents (TEQ) ([20]). 23Evaluation with monitoring data 1 81 The experimental data to be used for the evaluation of spatially distributed models2should be as consistent and homogeneous as possible. Measurements can be quite3sensitive to experimental conditions both when sampling in the field, and when4performing analyses in the laboratory. In general, it would be preferable to refer to a5homogeneous measurement campaign having sufficient representativeness of spatial6patterns. Data sets having such features could be found in the case of PCBs for soils [33]7and for air [32]. In the case of air passive sampling, it is worth mentioning that the data8do not allow a direct comparison with atmospheric concentration as they provide values9of chemical mass collected per sample during the measurement period. Nevertheless,10there is a correlation between samples mass and atmospheric concentration in the gas11phase ([36]), which allows considering the chemical mass per sample as a good proxy of12total atmospheric concentration, at least in terms of general spatial trends. Despite being13a widely studied class of chemicals, to our knowledge dioxins and furans have not yet14been subject, as PCBs, to studies about their spatial distribution yielding georeferenced15monitoring data. A compilation of monitoring data was available from [35], while for16Swiss soils we referred to the data of [34]. Additionally, a preliminary model evaluation17has been performed on the basis of dairy product lipid monitoring. Fatty dairy product18samples are easy to collect and handle, and are promising as integrative passive samplers19[48], although existing data are still insufficient for extensive evaluation of models. The20results of this preliminary evaluation are presented and discussed in the Supporting21Information. 1 91Results2PCBs3Atmospheric concentration (Figure 2 a) follows from the assumption of emissions4proportional to national totals and population density, intrinsic in the ADEPT model [31],5as clearly shown by some hot-spots that can be immediately linked to large urban areas.6A large area with relatively high and uniform concentration is observed in Central7Europe, while more peripheral areas show less relevant pollution. Deposition rates8(Figure 2 b) follow precipitation, wind, and temperature (determining the air-water9partition coefficient according to the exponential law illustrated in [13]), and they10correspond to high latitudes and elevations. Areas with reduced air turbulence such as the11Po plain in Italy, or Hungary, tend to have lower deposition rates. Deposition fluxes12(Figure 2 c) follow atmospheric concentration, although in areas of strong variation for13deposition rates, such as the Alps or Great Britain, patterns show some differentiation.14The same considerations apply for soil and ocean concentrations (Figure 2 d); locally,15variations in soil properties and climate (hence removal rates) may affect the spatial16pattern, but the dominant shape of the spatial distribution originates from deposition17fluxes. 18MAPPE and MSCE-POP model results correlation coefficients, and the ratio between19mean predicted values of concentrations and deposition fluxes, are reported in Table 320(S.I.). Atmospheric concentration is predicted with relatively good consistency between21the MAPPE and MSCE-POP models. MAPPE predicts lower concentrations as about2258% of the ones predicted by MSCE-POP (Figure 2 S.I.). The MAPPE model explains2388% of the variance produced by the MSCE-POP model. MAPPE predicts also lower 1 101deposition to land surface as about 57% (Figure 2 S.I.). It is to mention that this holds2when comparing total (gas + particle phase) deposition of MAPPE with gas phase3particle deposition only in MSCE-POP (as this is the result made available by EMEP). As4gas phase to particle phase deposition rates ratios in MAPPE are usually in the range of 25to 5, atmospheric particle deposition in MAPPE is consequently lower than 57% of the6one in MSCE-POP. 7The total deposition to the sea predicted by MAPPE is on average about twice as much as8particle phase deposition in MSCE-POP (Figure 2 S.I.). According to the same9considerations as before, it can be said that atmospheric particle phase deposition to the10sea is lower than the one in MSCE-POP. 11Spatial trends of soil concentration predicted by the MAPPE model are reasonably12consistent with the MSCE-POP model (about 40% variance explained), but MAPPE13underestimates concentrations of a factor higher than 100 (Figure 3 S.I.), apart from the14range of lower concentration values which are within less than one order of magnitude.15For sea concentrations, the two models provide a consistent estimate of orders of16magnitude, MAPPE predicting higher by about 20% (Figure 3 S.I.), but the correlation17between the two models weakens slightly. 18 Neither the MSCE-POP nor the MAPPE model provide satisfactory correlation with the19passive sampler mass distribution (see Figure 4 S.I. for spatial distribution of samples),20although both capture a general trend in concentrations (Figure 3) as testified by the least21square regression line shown in the graph. Determination coefficients are as low as 0.1722for the MSCE-POP and 0.14 for the MAPPE model. The MSCE-POP model, though, is23known to predict air concentration reasonably well [18, 19]. 1 111If one considers soil concentrations (see Figure 4 S.I. for spatial distribution of samples),2the behaviour of the two models is rather different (Figure 4): the MSCE-POP model3shows a very high dispersion of the output values with respect to monitoring data,4whereas MAPPE seems to capture trends in a much more consistent way. At the same5time, monitoring data suggest that correct soil concentration values should be somewhere6in between the ones predicted by MSCE-POP (most of the times overestimating the7measurements) and MAPPE (systematically underestimating them above values of about81 ng/g, while keeping on the 1:1 line below; this behaviour suggests that for9“background” sites the MAPPE model might be unbiased). 10Dioxins and Furans 11 Atmospheric concentration (Figure 5 S.I.) closely follows emissions, as in the case of12PCBs. Two areas of high atmospheric concentration are highlighted, one corresponding13to the big western conurbation spanning from London to Milan, and the other In central14Europe. Also Bulgaria is predicted as a hot spot area for atmospheric concentration.15Deposition rates (Figure 5 S.I.) follow similar patterns to the ones for PCBs. Deposition16fluxes (Figure 5 S.I.) suggest hot spots in Switzerland, Belgium, Czech Republic, and in17many large urban areas due to high air concentration. Soil and ocean concentrations18follow the same pattern as deposition fluxes (Figure 5 S.I.). 19Correlation coefficients and the ratio between mean predicted values of concentrations20and deposition fluxes are also reported in Table 3 S.I.. Atmospheric concentration is21predicted with relatively good consistency between the MAPPE and MSCE-POP models.22Although the scatter of the values is slightly wider than for PCBs (R2=0.74), there is no23systematic underestimation (Figure 6 S.I.). In this case, however, MAPPE estimates 1 121deposition to both land surface and ocean, on average higher of a factor between 2 and 3,2slightly higher for land surface (Figure 6 S.I.). 3Spatial trends of soil concentration predicted by the MAPPE model are reasonably4consistent with the MSCE-POP model, MAPPE estimating concentrations a factor of5about 2 lower (Figure 7 S.I.). For sea concentrations, the two models provide a consistent6estimate in absolute values, with higher correlation between the estimates than in the case7of soils (Figure 7 S.I.). 8With reference to both the compilation of European monitoring [35], for concentration in9soils and the atmosphere, and the more recent survey on Swiss soils [34], MSCE-POP10and MAPPE are consistently underestimating air and soil concentration of a factor not11less than 10. The spatial trends of concentrations are also showing poor correspondence12between monitoring data and model results (Figure 5). 13Discussion14PCB 15The lower predictions of the MAPPE model with respect to MSCE-POP can be explained16in terms of missing sources (such as extra-continental emissions, volatilization from17soils). This reason can well account for a difference of about 40% in emissions, hence18concentrations [38]. In general, there is no evidence that one of the two patterns is better19than the other. From the passive sampler results (Figure 3) it appears that the two scatters20are very similar to each other. 21The two models provide comparable orders of magnitude also of atmospheric deposition,22but significant discrepancies may arise when separating particle phase and gas phase. 1 131This critically depends on the fraction of the chemical that the model predicts as being2attached to aerosol. Differences up to a factor of 10, depending on the equations used and3the value of the parameters, were observed in other model intercomparisons [14]. 4The large underestimation of soil concentration in MAPPE with respect to the MSCE-5POP values can be due to a combination of the following factors: 6 1) the assumed exponential soil chemical profile of MSCE-POP, results being7 referred to the first layer of soil (1 mm); average concentrations in soil can be as8 low as 5 to 10% than the one in the top mm of soil [38]; this leads also to higher9 soil volatilization, hence atmospheric emissions not accounted for in MAPPE;10 2) the effect of past emission history: the transient effects due to the history of past11 emissions highlight that soil masses at present days can be as high as a factor of 512 than the ones predicted by steady state balance from present emissions [38];13 3) from Figure 3 S.I. total deposition in MAPPE is lower than particle phase14 deposition in MSCE-POP on land; this means that a fortiori total deposition is15 estimated lower by a factor >2.16 The product of the three factors of underestimation due to the reasons discussed17above is between 10 x 5 x 2 = 100 and 20 x 5 x 2 = 200, which can justify the18discrepancy. It is worth stressing that experimental evidence is not clear about the19applicability of an exponential soil concentration profile as suggested in [39], due to the20effects of disturbances such as bioturbation, ploughing in agricultural soils, and other21factors which tend to homogenize concentrations in topsoil (e.g. [40, 41, 46]). 1 141 The MAPPE model captures a general spatial trend, and the order of magnitude of2concentrations, also with respect to measurements in fatty dairy products, as discussed in3the S.I.4Dioxins and Furans 5MAPPE and MSCE-POP provide consistent estimates, with no appreciable discrepancy.6However, both models produce the same type of underestimation of the monitoring data,7about a factor of 10. Part of the underestimation can be linked to emission inventories,8which are apparently low. In fact, estimates issued by EMEP while preparing the material9for this paper ([19]) showed an increase of emissions by a factor 3 with respect to the10ones in [18]. Another issue to address is the time frame of the monitoring data: the data11compiled in [35] refer to years from the 1980’s to mid 1990’s, while the model results are12obtained with emissions of the year 2001. However, according to EMEP ([18], [19]),13during years from 1990 to 2004 the reduction in emissions over Europe was estimated as14only 35 %. Other comparisons with model applications show that the trend in15underestimation is a common problem. For instance, the EMEP MSCE-POP model16updated in 2006 ([19]) still confirms a generally light underestimation, and an inspection17of Figure 4 in [11] also suggests that predictions tend to lay towards the lower limit of18monitored values, compatibly with an underestimation of a factor of 3 approximately. It19is also to be considered that many of the data used for comparison refer to urban20environments, where concentrations tend to be significantly higher (up to a factor of 5)21than in background locations ([19]). 22Soil concentration is slightly underestimated by the MAPPE model with respect to23MSCE-POP, but still in the same order of magnitude. Unlike for PCBs, the results of the 1 151EMEP model are provided as averages over the top 5 cm of soil. This reduces the effect2of the exponential profile already discussed for PCBs. The transient effect in dioxin3emissions from 1990 reported in [18], can account for a factor of about 2 [38]. Ocean4concentrations appear unbiased and largely dominated by atmospheric deposition. For the5case of soils, we observe the same trend in underestimation as for the atmosphere. It is6interesting to notice that more recent samples, as in [34], are less underestimated. This7supports the conjecture that part of the underestimation on the data of [35] is due to the8time period of the samples. 9 The MAPPE model reproduces a weak spatial trend, as from Figure 5, showing10that predictions are within a factor of 10 from observations. The MAPPE model captures11the order of magnitude of concentrations with respect to measurements in lipids, as12discussed in the S.I., but not the spatial pattern.13Perspectives and conclusions14 The paper demonstrates the use of the novel MAPPE approach to describe the fate and15transport of contaminants in the environment, using GIS analysis only with no need for16specialized model codes. The approach has a number of practical advantages, among17which virtual independence on resolution (only limited by the available input data),18generally low computation time requirements compared to other models, easy19identification of the calculation steps that contribute the most to discrepancies between20observations and predictions, thanks to the simplicity of algorithms and the possibility of21visually inspecting maps of all model parameters. Moreover, model algorithms can be22adjusted quickly without any code modification as required instead in traditional models.23We show that the model provides results which are consistent with the ones of the much 1 161more sophisticated and comprehensive MSCE-POP model, and we explain discrepancies2on the basis of model assumptions adopted for the present study, which may be anyway3modified upon strong monitoring evidence. Comparisons with monitoring data, however,4highlight that the proposed approach does not perform less accurately, and sometimes can5be regarded as preferable, with respect to the MSCE-POP one. The proposed method6aims at providing a synergic, and not an alternative tool to the more comprehensive7models, that provide insights on more detailed aspects of the mechanics of pollution but8may be surrogated by the proposed approach for the purpose of mapping long term9averaged spatial distributions of pollutants, integrating monitoring, modeling and10emission inventories as suggested in [40]. 11Acknowledgements12The research was partly funded by the European Commission FP6 contract no. 003956 13(NoMiracle IP: http://nomiracle.jrc.it ). I thank gratefully V.Shatalov and E.Mantseva 14from the EMEP MSCE-POP modeling team for providing data, reports and discussion, 15and colleagues D.Pennington, G.Umlauf, I. 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Santillo, D., Fernandes, A., Stringer, R., Alcock, R., Rose, M., White, S., Jones,2 K., Johnston, P., Butter as an indicator of regional persistent organic pollutant3 contamination: further development of the approach using PCDD/Fs and PCBs,4 Food Additives and Contaminants, 2003, vol. 20, no. 3, 281-290. 1 251Emissions air Overall average (national totals) air removal rate for Europe Source-receptor maps Air concentration mapTime-of-travel mapsLandscape and climate K map maps dep Atmospheric K map, overall deposition map water Scalar physico- chemical properties: K , K , K map, soil ow, aw, overall molecular weight , , degradation rate, air , , degradation rate, soil , Soil Water , concentration concentration degradation rate, map map water.Emissions soil Emissions water2Figure 1 – logics of the map calculations. In grey input data (grey boxes are maps, grey text scalars); 3in black boxes, output maps. 1 261a b2c d34Figure 2 – atmospheric concentration (a), deposition rate (b), soil and sea concentration (c) and 5deposition fluxes (d) for PCBs, as predicted by the MAPPE model.6 1 11E+03 g n s s 1E+02 a m r e l p m a s e v 1E+01 i s s a p1E+00 1E-03 1E-02 1E-01 1E+00 predicted concentration ng m-3 2 A1E+03 g n s s 1E+02 a m r e l p m a s e v 1E+01 i s s a p1E+00 1E-03 1E-02 1E-01 1E+00 predicted concentration ng m-3 3 B2 1 281Figure 3 – model evaluation for PCBs with air passive samplers: (A) MSCE-POP model; (B) MAPPE2model 1 291 MAPPE100010010 l e d o m g / g nC 1 0.1 1 10 1000.10.01 C ng/g monitoring 2 1 30MSCE-POP100010010 l e d o m g / g nC 1 0.1 1 10 1000.10.01 C ng/g monitoring 12Figure 4– model evaluation for PCBs with soil samples. Lines 1:1 and a factor 10 interval are 3displayed. 1 311 soil 1:1 obs / 10 air soil (Schmid et al., 2005) obs X 10 100001000 n o i 100 t a r t n e c n o 10 c d e t u p m 1 o c0.10.01 0.01 0.1 1 10 100 1000 observed concentration 23Figure 5 – scatter diagram of observations and calculation results for dioxins. Values are in ng I-4TEQ /Kg dm for soils and fg I-TEq / m3 for air. Data refer to the MAPPE model prediction, while 5the MSCE-POP ones are very similar and not reported for simplicity.6

The MAPPE Strategy for GIS-Based Fate and Transport Modeling of Chemicals

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