The MAPPE Strategy for GIS-Based Fate and Transport Modeling of Chemicals
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1A GIS-based approach for modeling the fate and
2 transport of pollutants in Europe
3A.Pistocchi
4EC, DG JRC, IES, via E.Fermi, 1, 21020 Ispra (VA) Italy
[email protected] tel +390332785591 fax +390332785601.
6Abstract
7 This paper presents an approach to estimate chemical concentration in multiple
8environmental media (soil, water, and the atmosphere) with the sole use of basic
9geographical information system (GIS) operations, and particularly map algebra. This
10allows solving mass balance equations in a different way from the traditional methods
11involving numerical or analytical solution of systems of equations, producing maps of
12chemical fluxes and concentrations only through combinations of maps of emissions and
13environmental removal or transfer rates.
14 Benchmarking with the well-established EMEP MSCE-POP model shows that the
15method provides consistent results with this more detailed description. When available,
16experimental evidence equally supports the proposed method in relation to the more
17complex approaches.
18 Thanks to the use of GIS calculations, the results can be obtained with a spatial
19resolution limited only by input data; the use of map algebra warrants flexible
20modification of the model algorithms, for e.g. partitioning, degradation, and inter-media
21transfer. 1 2
1 The management of data directly in GIS, with no need for model input and output
2processing, stimulates the adoption of up-to-date representations of landscape and climate
3variables nowadays more and more frequently available from remote sensing acquisitions
4and sectoral studies.
5 The method is particularly suited for a preliminary assessment of the spatial
6distribution of chemicals especially under high uncertainty and when many chemicals
7and their synergy need to be investigated, prior to dipping into more specialized and
8computation-intensive numerical models.
9Introduction
10 In the last years, researchers have spent efforts in developing spatially distributed
11fate and transport models of chemicals, i.e. models allowing spatially explicit
12representations (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 to
15understand the mechanics of pollution; yet they tend to be rather complex when spatial
16resolution increases, requiring high computation time that makes them impractical
17outside of specific specialized studies.
18 At the same time, increasingly detailed spatial data on environmental processes
19and chemical emissions are becoming available in formats easy to process using
20geographic information systems (GIS). In chemical fate and transport modeling, GIS has
21been used so far mainly as a pre- and post-processor, although many examples appeared
22in the literature of spatially explicit models able to capture the fundamental spatial
23patterns of phenomena with no use of complex numerical models, capitalizing on the 1 3
1built-in analytical capabilities of GIS [27, 8, 9, 10, 26, 44]. By expanding the concepts
2already used in such approaches, and in many other areas of environmental and earth
3sciences, we aim at demonstrating the use of GIS calculations for chemical fate and
4transport assessment, as initially suggested in [23] and [25].
5Materials and methods
6Map-algebraic formulation of the fate and transport equations
7In this paper, we solve the mass balance equation directly in GIS in terms of map algebra
8(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, by
10applying algebraic operations on a cell-by-cell basis. Using analysis capabilities built in
11GIS allows a very simple set up of calculations, with great flexibility in the choice of
12algorithms, 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 of
14complex processing of model input.
15In the paper, we will refer to soil, air and seawater compartments only. The case of inland
16waters can be treated in map-algebraic terms as discussed in a separate paper [43] and in
17[23]. For soils, during a period of constant E0 and Koverall a solution of the mass balance
18equation is:
E0 19M = M0 exp(K overallt) (1 exp(K overallt)) (1) K overall
20Where M0 is an appropriate initial distribution of mass, and K overall is the overall removal
21rate, E0 is a map of chemical emissions to soil, while t is time. 1 4
1Equation (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 of
3contaminated sediments eroded upslope; re-infiltration of contaminated water from
4upstream; subsurface lateral fluxes) can be neglected. In such a case, E0 is the sum of
5local mass discharge and atmospheric deposition.
6Under steady state conditions, equation (1) becomes:
E 7M= 0 (1a). K overall
8Seawater can be treated in this way, assuming negligible lateral transport due to currents
9and dispersion (“water column approach”) as discussed in [22].
10The atmospheric compartment is described with the ADEPT model approach [31]. The
11concentration of a generic, reactive chemical in the atmosphere within the mixed layer at
12a generic point (x,y) is computed as:
n
13Catmo EiSRi (x, y)exp(KΤti (x, y)) (2) i 1
14where 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 a
16“source-receptor term” accounting for dilution and advective transport, and a “time of
17travel” of the contaminants, and K is the overall decay rate to which a chemical in subject
18throughout 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, a
20single K value for the whole Europe is normally acceptable [38]. The SRi and Tt maps
21used in this paper represent concentration in Europe (ug/m3) deriving from the emission
22of 1 Mt/y of a conservative contaminant in the generic i-th country, assuming emissions 1 5
1are distributed within the country according to population density. The ADEPT model
2(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 deposition
4velocity 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, vdiff
9is velocity of diffusion across the air-surface interface, Kaw is the air-water partitioning
10coefficient, and is the fraction of chemical attached to the aerosol phase.
11 Deposition from the atmosphere sums to direct emission to the soil to compute
12soil 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 RG
15 where E is soil erosion rate, Q is water throughflow, VOL is volatilization rate
16from soil, and RS, RL, RG are coefficients that account for the partitioning of the substance
17in solid, liquid and gas phase in soils, whereas Kdeg is the degradation rate in soils, and
18 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 6
1 where SETTL is the sediment settling velocity in seawater, VOL is volatilization
2rate from seawater, RSed, Rdiss are coefficients that account for the partitioning of the
3substance in sediment-attached and dissolved phase in soils, whereas Kdeg is the
4degradation rate in seawater, and Z is the seawater compartment mixing depth.
5 Further details and discussion on the computation of the different parameters in
6equations (3) to (5) can be found in [23, 38]. Calculation can be iterative, as
7volatilisation from soil and water provides additional input to the atmosphere, hence new
8depositions and so on. However, [11] showed that these feedback mass fluxes are often
9not relevant for most chemicals. A discussion of the model input landscape and climate
10parameters 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. The
18model has been named Multimedia Assessment of Pollutant Pathways in Europe or
19MAPPE, the Italian word to denote maps. Model assumptions, algorithms and a software
20developed 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 exercise
22with the EMEP MSCE-POP model ([13]). The evaluation was done using
23polychlorobiphenyls (PCBs) and polychlorodibenzodioxins/furans (PCDD/Fs), in that 1 7
1they are relatively well studied, representative persistent organic pollutants (POPs)
2fulfilling the criteria of [12]. Calculations were performed under steady state
3assumptions.
4 The EMEP calculation results for PCBs appear to be quite significantly correlated
5(Figure 1 Supporting Information (S.I.)). In particular, atmospheric deposition is highly
6correlated to ocean concentration (94% explained variance), whereas atmospheric
7concentration is less correlated to deposition (80% explained variance). This suggests that
8spatial variation in modeled atmosphere deposition rates play a bigger role than variation
9in modeled ocean removal rate. The soil compartment shows a remarkably lower
10correlation with the air compartment than ocean, which consistently corresponds to a
11higher importance of the past history of emissions, and the spatial variation of removal
12rates in soils.
13In general, the “water column” model approach used for ocean in the present study does
14not introduce appreciable errors with regard to the MSCE-POP model, as lateral transfer
15does 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 only
18source of emission [18]. Chemical properties are the ones in [13] for PCB 153, and for
192,3,4, 7,8Cl5DF. The properties of the former have proven to represent reasonably well
20the behaviour of the sum of PCBs ([17]), while the ones of the latter have been used to
21describe the total concentration of dioxins and furans as a mixture in terms of toxic
22equivalents (TEQ) ([20]).
23Evaluation with monitoring data 1 8
1 The experimental data to be used for the evaluation of spatially distributed models
2should be as consistent and homogeneous as possible. Measurements can be quite
3sensitive to experimental conditions both when sampling in the field, and when
4performing analyses in the laboratory. In general, it would be preferable to refer to a
5homogeneous measurement campaign having sufficient representativeness of spatial
6patterns. 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 data
8do not allow a direct comparison with atmospheric concentration as they provide values
9of chemical mass collected per sample during the measurement period. Nevertheless,
10there is a correlation between samples mass and atmospheric concentration in the gas
11phase ([36]), which allows considering the chemical mass per sample as a good proxy of
12total atmospheric concentration, at least in terms of general spatial trends. Despite being
13a widely studied class of chemicals, to our knowledge dioxins and furans have not yet
14been subject, as PCBs, to studies about their spatial distribution yielding georeferenced
15monitoring data. A compilation of monitoring data was available from [35], while for
16Swiss soils we referred to the data of [34]. Additionally, a preliminary model evaluation
17has been performed on the basis of dairy product lipid monitoring. Fatty dairy product
18samples are easy to collect and handle, and are promising as integrative passive samplers
19[48], although existing data are still insufficient for extensive evaluation of models. The
20results of this preliminary evaluation are presented and discussed in the Supporting
21Information. 1 9
1Results
2PCBs
3Atmospheric concentration (Figure 2 a) follows from the assumption of emissions
4proportional 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 Central
7Europe, while more peripheral areas show less relevant pollution. Deposition rates
8(Figure 2 b) follow precipitation, wind, and temperature (determining the air-water
9partition coefficient according to the exponential law illustrated in [13]), and they
10correspond to high latitudes and elevations. Areas with reduced air turbulence such as the
11Po plain in Italy, or Hungary, tend to have lower deposition rates. Deposition fluxes
12(Figure 2 c) follow atmospheric concentration, although in areas of strong variation for
13deposition 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 spatial
16pattern, but the dominant shape of the spatial distribution originates from deposition
17fluxes.
18MAPPE and MSCE-POP model results correlation coefficients, and the ratio between
19mean predicted values of concentrations and deposition fluxes, are reported in Table 3
20(S.I.). Atmospheric concentration is predicted with relatively good consistency between
21the MAPPE and MSCE-POP models. MAPPE predicts lower concentrations as about
2258% of the ones predicted by MSCE-POP (Figure 2 S.I.). The MAPPE model explains
2388% of the variance produced by the MSCE-POP model. MAPPE predicts also lower 1 10
1deposition to land surface as about 57% (Figure 2 S.I.). It is to mention that this holds
2when comparing total (gas + particle phase) deposition of MAPPE with gas phase
3particle deposition only in MSCE-POP (as this is the result made available by EMEP). As
4gas phase to particle phase deposition rates ratios in MAPPE are usually in the range of 2
5to 5, atmospheric particle deposition in MAPPE is consequently lower than 57% of the
6one in MSCE-POP.
7The total deposition to the sea predicted by MAPPE is on average about twice as much as
8particle phase deposition in MSCE-POP (Figure 2 S.I.). According to the same
9considerations as before, it can be said that atmospheric particle phase deposition to the
10sea is lower than the one in MSCE-POP.
11Spatial trends of soil concentration predicted by the MAPPE model are reasonably
12consistent with the MSCE-POP model (about 40% variance explained), but MAPPE
13underestimates concentrations of a factor higher than 100 (Figure 3 S.I.), apart from the
14range 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 of
16magnitude, MAPPE predicting higher by about 20% (Figure 3 S.I.), but the correlation
17between the two models weakens slightly.
18 Neither the MSCE-POP nor the MAPPE model provide satisfactory correlation with the
19passive 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 least
21square regression line shown in the graph. Determination coefficients are as low as 0.17
22for the MSCE-POP and 0.14 for the MAPPE model. The MSCE-POP model, though, is
23known to predict air concentration reasonably well [18, 19]. 1 11
1If 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 model
3shows 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 same
5time, monitoring data suggest that correct soil concentration values should be somewhere
6in between the ones predicted by MSCE-POP (most of the times overestimating the
7measurements) and MAPPE (systematically underestimating them above values of about
81 ng/g, while keeping on the 1:1 line below; this behaviour suggests that for
9“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 of
12PCBs. Two areas of high atmospheric concentration are highlighted, one corresponding
13to the big western conurbation spanning from London to Milan, and the other In central
14Europe. 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. Deposition
16fluxes (Figure 5 S.I.) suggest hot spots in Switzerland, Belgium, Czech Republic, and in
17many large urban areas due to high air concentration. Soil and ocean concentrations
18follow the same pattern as deposition fluxes (Figure 5 S.I.).
19Correlation coefficients and the ratio between mean predicted values of concentrations
20and deposition fluxes are also reported in Table 3 S.I.. Atmospheric concentration is
21predicted 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 no
23systematic underestimation (Figure 6 S.I.). In this case, however, MAPPE estimates 1 12
1deposition 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 reasonably
4consistent with the MSCE-POP model, MAPPE estimating concentrations a factor of
5about 2 lower (Figure 7 S.I.). For sea concentrations, the two models provide a consistent
6estimate in absolute values, with higher correlation between the estimates than in the case
7of soils (Figure 7 S.I.).
8With reference to both the compilation of European monitoring [35], for concentration in
9soils and the atmosphere, and the more recent survey on Swiss soils [34], MSCE-POP
10and MAPPE are consistently underestimating air and soil concentration of a factor not
11less than 10. The spatial trends of concentrations are also showing poor correspondence
12between monitoring data and model results (Figure 5).
13Discussion
14PCB
15The lower predictions of the MAPPE model with respect to MSCE-POP can be explained
16in terms of missing sources (such as extra-continental emissions, volatilization from
17soils). This reason can well account for a difference of about 40% in emissions, hence
18concentrations [38]. In general, there is no evidence that one of the two patterns is better
19than the other. From the passive sampler results (Figure 3) it appears that the two scatters
20are 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 13
1This critically depends on the fraction of the chemical that the model predicts as being
2attached to aerosol. Differences up to a factor of 10, depending on the equations used and
3the 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 being
7 referred to the first layer of soil (1 mm); average concentrations in soil can be as
8 low as 5 to 10% than the one in the top mm of soil [38]; this leads also to higher
9 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 past
11 emissions highlight that soil masses at present days can be as high as a factor of 5
12 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 phase
14 deposition in MSCE-POP on land; this means that a fortiori total deposition is
15 estimated lower by a factor >2.
16 The product of the three factors of underestimation due to the reasons discussed
17above is between 10 x 5 x 2 = 100 and 20 x 5 x 2 = 200, which can justify the
18discrepancy. It is worth stressing that experimental evidence is not clear about the
19applicability of an exponential soil concentration profile as suggested in [39], due to the
20effects of disturbances such as bioturbation, ploughing in agricultural soils, and other
21factors which tend to homogenize concentrations in topsoil (e.g. [40, 41, 46]). 1 14
1 The MAPPE model captures a general spatial trend, and the order of magnitude of
2concentrations, also with respect to measurements in fatty dairy products, as discussed in
3the 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 material
9for this paper ([19]) showed an increase of emissions by a factor 3 with respect to the
10ones in [18]. Another issue to address is the time frame of the monitoring data: the data
11compiled in [35] refer to years from the 1980’s to mid 1990’s, while the model results are
12obtained 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 as
14only 35 %. Other comparisons with model applications show that the trend in
15underestimation is a common problem. For instance, the EMEP MSCE-POP model
16updated in 2006 ([19]) still confirms a generally light underestimation, and an inspection
17of Figure 4 in [11] also suggests that predictions tend to lay towards the lower limit of
18monitored values, compatibly with an underestimation of a factor of 3 approximately. It
19is also to be considered that many of the data used for comparison refer to urban
20environments, 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 to
23MSCE-POP, but still in the same order of magnitude. Unlike for PCBs, the results of the 1 15
1EMEP model are provided as averages over the top 5 cm of soil. This reduces the effect
2of the exponential profile already discussed for PCBs. The transient effect in dioxin
3emissions from 1990 reported in [18], can account for a factor of about 2 [38]. Ocean
4concentrations appear unbiased and largely dominated by atmospheric deposition. For the
5case of soils, we observe the same trend in underestimation as for the atmosphere. It is
6interesting to notice that more recent samples, as in [34], are less underestimated. This
7supports the conjecture that part of the underestimation on the data of [35] is due to the
8time period of the samples.
9 The MAPPE model reproduces a weak spatial trend, as from Figure 5, showing
10that predictions are within a factor of 10 from observations. The MAPPE model captures
11the order of magnitude of concentrations with respect to measurements in lipids, as
12discussed in the S.I., but not the spatial pattern.
13Perspectives and conclusions
14 The paper demonstrates the use of the novel MAPPE approach to describe the fate and
15transport of contaminants in the environment, using GIS analysis only with no need for
16specialized model codes. The approach has a number of practical advantages, among
17which virtual independence on resolution (only limited by the available input data),
18generally low computation time requirements compared to other models, easy
19identification of the calculation steps that contribute the most to discrepancies between
20observations and predictions, thanks to the simplicity of algorithms and the possibility of
21visually inspecting maps of all model parameters. Moreover, model algorithms can be
22adjusted 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 16
1more sophisticated and comprehensive MSCE-POP model, and we explain discrepancies
2on the basis of model assumptions adopted for the present study, which may be anyway
3modified upon strong monitoring evidence. Comparisons with monitoring data, however,
4highlight that the proposed approach does not perform less accurately, and sometimes can
5be regarded as preferable, with respect to the MSCE-POP one. The proposed method
6aims at providing a synergic, and not an alternative tool to the more comprehensive
7models, that provide insights on more detailed aspects of the mechanics of pollution but
8may be surrogated by the proposed approach for the purpose of mapping long term
9averaged spatial distributions of pollutants, integrating monitoring, modeling and
10emission inventories as suggested in [40].
11Acknowledgements
12The 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. Vives Rubio, and M.P.Vizcaino Martinez at
16the IES of EC DG JRC for their critical reading of versions of the manuscript, and
17valuable comments and suggestions.
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1
Emissions air Overall average (national totals) air removal rate for Europe
Source-receptor maps Air concentration map
Time-of-travel maps
Landscape 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 water
2Figure 1 – logics of the map calculations. In grey input data (grey boxes are maps, grey text scalars);
3in black boxes, output maps. 1 26
1a b
2c d
3
4Figure 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 1
1E+03 g n
s
s 1E+02 a m
r e l p m a s
e
v 1E+01 i s s a p
1E+00 1E-03 1E-02 1E-01 1E+00 predicted concentration ng m-3 2 A
1E+03 g n
s
s 1E+02 a m
r e l p m a s
e
v 1E+01 i s s a p
1E+00 1E-03 1E-02 1E-01 1E+00 predicted concentration ng m-3 3 B
2 1 28
1Figure 3 – model evaluation for PCBs with air passive samplers: (A) MSCE-POP model; (B) MAPPE
2model 1 29
1
MAPPE
1000
100
10 l e d o m
g / g n
C 1 0.1 1 10 100
0.1
0.01 C ng/g monitoring 2 1 30
MSCE-POP
1000
100
10 l e d o m
g / g n
C 1 0.1 1 10 100
0.1
0.01 C ng/g monitoring 1
2Figure 4– model evaluation for PCBs with soil samples. Lines 1:1 and a factor 10 interval are
3displayed. 1 31
1
soil 1:1 obs / 10 air soil (Schmid et al., 2005) obs X 10
10000
1000 n o
i 100 t a r t n e c n
o 10 c
d e t u p
m 1 o c
0.1
0.01 0.01 0.1 1 10 100 1000 observed concentration 2
3Figure 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