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A GIS-based approach for modeling the fate and
transport of pollutants in Europe
A.Pistocchi
EC, DG JRC, IES, via E.Fermi, 1, 21020 Ispra (VA) Italy
[email protected] tel +390332785591 fax +390332785601.
Abstract
This paper presents an approach to estimate chemical concentration in multiple
environmental media (soil, water, and the atmosphere) with the sole use of basic
geographical information system (GIS) operations, and particularly map algebra. This
allows solving mass balance equations in a different way from the traditional methods
involving numerical or analytical solution of systems of equations, producing maps of
chemical fluxes and concentrations only through combinations of maps of emissions and
environmental removal or transfer rates.
Benchmarking with the well-established EMEP MSCE-POP model shows that the
method provides consistent results with this more detailed description. When available,
experimental evidence equally supports the proposed method in relation to the more
complex approaches.
Thanks to the use of GIS calculations, the results can be obtained with a spatial
resolution limited only by input data; the use of map algebra warrants flexible
modification of the model algorithms, for e.g. partitioning, degradation, and inter-media
transfer.
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The management of data directly in GIS, with no need for model input and output
processing, stimulates the adoption of up-to-date representations of landscape and climate
variables nowadays more and more frequently available from remote sensing acquisitions
and sectoral studies.
The method is particularly suited for a preliminary assessment of the spatial
distribution of chemicals especially under high uncertainty and when many chemicals
and their synergy need to be investigated, prior to dipping into more specialized and
computation-intensive numerical models.
Introduction
In the last years, researchers have spent efforts in developing spatially distributed
fate and transport models of chemicals, i.e. models allowing spatially explicit
representations (maps) of contaminants from a given spatial distribution of sources [1, 2,
3, 4, 5, 6, 7, 13, 28], as well as model intercomparison exercises [14, 15, 47].
Existing spatially explicit models provide a valuable analytical tool in order to
understand the mechanics of pollution; yet they tend to be rather complex when spatial
resolution increases, requiring high computation time that makes them impractical
outside of specific specialized studies.
At the same time, increasingly detailed spatial data on environmental processes
and chemical emissions are becoming available in formats easy to process using
geographic information systems (GIS). In chemical fate and transport modeling, GIS has
been used so far mainly as a pre- and post-processor, although many examples appeared
in the literature of spatially explicit models able to capture the fundamental spatial
patterns of phenomena with no use of complex numerical models, capitalizing on the
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built-in analytical capabilities of GIS [27, 8, 9, 10, 26, 44]. By expanding the concepts
already used in such approaches, and in many other areas of environmental and earth
sciences, we aim at demonstrating the use of GIS calculations for chemical fate and
transport assessment, as initially suggested in [23] and [25].
Materials and methods
Map-algebraic formulation of the fate and transport equations
In this paper, we solve the mass balance equation directly in GIS in terms of map algebra
(e.g. [29, 30] among many others – see Figure 1 for a general scheme of the calculation).
This is a standard technique by which gridcell-based GIS software manipulate maps, by
applying algebraic operations on a cell-by-cell basis. Using analysis capabilities built in
GIS allows a very simple set up of calculations, with great flexibility in the choice of
algorithms, and with a straightforward control on the calculation steps for error tracking.
Moreover, model resolution is only limited by the availability of data with no need of
complex processing of model input.
In the paper, we will refer to soil, air and seawater compartments only. The case of inland
waters can be treated in map-algebraic terms as discussed in a separate paper [43] and in
[23]. For soils, during a period of constant E0 and Koverall a solution of the mass balance
equation is:
M = (1)
Where M0 is an appropriate initial distribution of mass, and is the overall removal
rate, is a map of chemical emissions to soil, while t is time.
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Equation (1) holds for cases where advection from the surrounding cells is negligible.
Such is the case, for instance, of soil when lateral exchanges (e.g. re-deposition of
contaminated sediments eroded upslope; re-infiltration of contaminated water from
upstream; subsurface lateral fluxes) can be neglected. In such a case, E0 is the sum of
local mass discharge and atmospheric deposition.
Under steady state conditions, equation (1) becomes:
M= (1a).
Seawater can be treated in this way, assuming negligible lateral transport due to currents
and dispersion (“water column approach”) as discussed in [22].
The atmospheric compartment is described with the ADEPT model approach [31]. The
concentration of a generic, reactive chemical in the atmosphere within the mixed layer at
a generic point (x,y) is computed as:
(2)
where Ei for i = 1, …, n is the emission at any of the n locations from where advective-
dispersive fluxes enter the control volume. The maps SRi and Tti respectively represent a
“source-receptor term” accounting for dilution and advective transport, and a “time of
travel” of the contaminants, and K is the overall decay rate to which a chemical in subject
throughout the pathway from the generic location i-th and the control volume boundary.
As in the atmosphere advection and dilution largely dominate over other processes, a
single K value for the whole Europe is normally acceptable [38]. The SRi and Tt maps
used in this paper represent concentration in Europe (ug/m3) deriving from the emission
of 1 Mt/y of a conservative contaminant in the generic i-th country, assuming emissions
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are distributed within the country according to population density. The ADEPT model
(2) is evaluated and extended to a generic distribution of sources in a separate paper [21].
Atmospheric deposition is the product of a concentration map and a deposition
velocity map:
Dep = Kdep Catmo (3)
where Kdep is a map representing deposition velocities and is given by:
(3a)
where P is precipitation, w is a scavenging factor, vdep is particle deposition velocity, vdiff
is velocity of diffusion across the air-surface interface, Kaw is the air-water partitioning
coefficient, and is the fraction of chemical attached to the aerosol phase.
Deposition from the atmosphere sums to direct emission to the soil to compute
soil mass balance according to equation (3), and the same for seawater.
The map Koverall in soil is given by:
(4)
where E is soil erosion rate, Q is water throughflow, VOL is volatilization rate
from soil, and RS, RL, RG are coefficients that account for the partitioning of the substance
in solid, liquid and gas phase in soils, whereas Kdeg is the degradation rate in soils, and
is the soil compartment bulk thickness.
The map Koverall in seawater is given by:
(5)
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where SETTL is the sediment settling velocity in seawater, VOL is volatilization
rate from seawater, RSed, are coefficients that account for the partitioning of the
substance in sediment-attached and dissolved phase in soils, whereas Kdeg is the
degradation rate in seawater, and is the seawater compartment mixing depth.
Further details and discussion on the computation of the different parameters in
equations (3) to (5) can be found in [23, 38]. Calculation can be iterative, as
volatilisation from soil and water provides additional input to the atmosphere, hence new
depositions and so on. However, [11] showed that these feedback mass fluxes are often
not relevant for most chemicals. A discussion of the model input landscape and climate
parameters is in [24].
The main practical strength of a map algebraic approach is the possibility to replace
individual algorithms and input data for the calculation of Kdep or Koverall, simply by
modifying individual input terms in map algebra expressions, with no need for re-coding
numerical models. Moreover, input of individual model parameters is in the form of
maps, which allows quick visual data control.
Model implementation and benchmarking
The equations above described can be easily implemented in any GIS software. The
model has been named Multimedia Assessment of Pollutant Pathways in Europe or
MAPPE, the Italian word to denote maps. Model assumptions, algorithms and a software
developed to run the model in the popular ArcGIS software are presented in [23, 37].
To evaluate the above proposed method, we performed a benchmarking exercise
with the EMEP MSCE-POP model ([13]). The evaluation was done using
polychlorobiphenyls (PCBs) and polychlorodibenzodioxins/furans (PCDD/Fs), in that
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they are relatively well studied, representative persistent organic pollutants (POPs)
fulfilling the criteria of [12]. Calculations were performed under steady state
assumptions.
The EMEP calculation results for PCBs appear to be quite significantly correlated
(Figure 1 Supporting Information (S.I.)). In particular, atmospheric deposition is highly
correlated to ocean concentration (94% explained variance), whereas atmospheric
concentration is less correlated to deposition (80% explained variance). This suggests that
spatial variation in modeled atmosphere deposition rates play a bigger role than variation
in modeled ocean removal rate. The soil compartment shows a remarkably lower
correlation with the air compartment than ocean, which consistently corresponds to a
higher importance of the past history of emissions, and the spatial variation of removal
rates in soils.
In general, the “water column” model approach used for ocean in the present study does
not introduce appreciable errors with regard to the MSCE-POP model, as lateral transfer
does not appear important at the working scale of the model.
Table 1 (S.I.) reports the physico-chemical properties used for the chemicals.
Table 2 (S.I.) provides the atmospheric emission totals per country, assumed as the only
source of emission [18]. Chemical properties are the ones in [13] for PCB 153, and for
2,3,4, 7,8Cl5DF. The properties of the former have proven to represent reasonably well
the behaviour of the sum of PCBs ([17]), while the ones of the latter have been used to
describe the total concentration of dioxins and furans as a mixture in terms of toxic
equivalents (TEQ) ([20]).
Evaluation with monitoring data
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The experimental data to be used for the evaluation of spatially distributed models
should be as consistent and homogeneous as possible. Measurements can be quite
sensitive to experimental conditions both when sampling in the field, and when
performing analyses in the laboratory. In general, it would be preferable to refer to a
homogeneous measurement campaign having sufficient representativeness of spatial
patterns. Data sets having such features could be found in the case of PCBs for soils [33]
and for air [32]. In the case of air passive sampling, it is worth mentioning that the data
do not allow a direct comparison with atmospheric concentration as they provide values
of chemical mass collected per sample during the measurement period. Nevertheless,
there is a correlation between samples mass and atmospheric concentration in the gas
phase ([36]), which allows considering the chemical mass per sample as a good proxy of
total atmospheric concentration, at least in terms of general spatial trends. Despite being
a widely studied class of chemicals, to our knowledge dioxins and furans have not yet
been subject, as PCBs, to studies about their spatial distribution yielding georeferenced
monitoring data. A compilation of monitoring data was available from [35], while for
Swiss soils we referred to the data of [34]. Additionally, a preliminary model evaluation
has been performed on the basis of dairy product lipid monitoring. Fatty dairy product
samples are easy to collect and handle, and are promising as integrative passive samplers
[48], although existing data are still insufficient for extensive evaluation of models. The
results of this preliminary evaluation are presented and discussed in the Supporting
Information.
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Results
PCBs
Atmospheric concentration (Figure 2 a) follows from the assumption of emissions
proportional to national totals and population density, intrinsic in the ADEPT model [31],
as clearly shown by some hot-spots that can be immediately linked to large urban areas.
A large area with relatively high and uniform concentration is observed in Central
Europe, while more peripheral areas show less relevant pollution. Deposition rates
(Figure 2 b) follow precipitation, wind, and temperature (determining the air-water
partition coefficient according to the exponential law illustrated in [13]), and they
correspond to high latitudes and elevations. Areas with reduced air turbulence such as the
Po plain in Italy, or Hungary, tend to have lower deposition rates. Deposition fluxes
(Figure 2 c) follow atmospheric concentration, although in areas of strong variation for
deposition rates, such as the Alps or Great Britain, patterns show some differentiation.
The same considerations apply for soil and ocean concentrations (Figure 2 d); locally,
variations in soil properties and climate (hence removal rates) may affect the spatial
pattern, but the dominant shape of the spatial distribution originates from deposition
fluxes.
MAPPE and MSCE-POP model results correlation coefficients, and the ratio between
mean predicted values of concentrations and deposition fluxes, are reported in Table 3
(S.I.). Atmospheric concentration is predicted with relatively good consistency between
the MAPPE and MSCE-POP models. MAPPE predicts lower concentrations as about
58% of the ones predicted by MSCE-POP (Figure 2 S.I.). The MAPPE model explains
88% of the variance produced by the MSCE-POP model. MAPPE predicts also lower
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deposition to land surface as about 57% (Figure 2 S.I.). It is to mention that this holds
when comparing total (gas + particle phase) deposition of MAPPE with gas phase
particle deposition only in MSCE-POP (as this is the result made available by EMEP). As
gas phase to particle phase deposition rates ratios in MAPPE are usually in the range of 2
to 5, atmospheric particle deposition in MAPPE is consequently lower than 57% of the
one in MSCE-POP.
The total deposition to the sea predicted by MAPPE is on average about twice as much as
particle phase deposition in MSCE-POP (Figure 2 S.I.). According to the same
considerations as before, it can be said that atmospheric particle phase deposition to the
sea is lower than the one in MSCE-POP.
Spatial trends of soil concentration predicted by the MAPPE model are reasonably
consistent with the MSCE-POP model (about 40% variance explained), but MAPPE
underestimates concentrations of a factor higher than 100 (Figure 3 S.I.), apart from the
range of lower concentration values which are within less than one order of magnitude.
For sea concentrations, the two models provide a consistent estimate of orders of
magnitude, MAPPE predicting higher by about 20% (Figure 3 S.I.), but the correlation
between the two models weakens slightly.
Neither the MSCE-POP nor the MAPPE model provide satisfactory correlation with the
passive sampler mass distribution (see Figure 4 S.I. for spatial distribution of samples),
although both capture a general trend in concentrations (Figure 3) as testified by the least
square regression line shown in the graph. Determination coefficients are as low as 0.17
for the MSCE-POP and 0.14 for the MAPPE model. The MSCE-POP model, though, is
known to predict air concentration reasonably well [18, 19].
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If one considers soil concentrations (see Figure 4 S.I. for spatial distribution of samples),
the behaviour of the two models is rather different (Figure 4): the MSCE-POP model
shows a very high dispersion of the output values with respect to monitoring data,
whereas MAPPE seems to capture trends in a much more consistent way. At the same
time, monitoring data suggest that correct soil concentration values should be somewhere
in between the ones predicted by MSCE-POP (most of the times overestimating the
measurements) and MAPPE (systematically underestimating them above values of about
1 ng/g, while keeping on the 1:1 line below; this behaviour suggests that for
“background” sites the MAPPE model might be unbiased).
Dioxins and Furans
Atmospheric concentration (Figure 5 S.I.) closely follows emissions, as in the case of
PCBs. Two areas of high atmospheric concentration are highlighted, one corresponding
to the big western conurbation spanning from London to Milan, and the other In central
Europe. Also Bulgaria is predicted as a hot spot area for atmospheric concentration.
Deposition rates (Figure 5 S.I.) follow similar patterns to the ones for PCBs. Deposition
fluxes (Figure 5 S.I.) suggest hot spots in Switzerland, Belgium, Czech Republic, and in
many large urban areas due to high air concentration. Soil and ocean concentrations
follow the same pattern as deposition fluxes (Figure 5 S.I.).
Correlation coefficients and the ratio between mean predicted values of concentrations
and deposition fluxes are also reported in Table 3 S.I.. Atmospheric concentration is
predicted with relatively good consistency between the MAPPE and MSCE-POP models.
Although the scatter of the values is slightly wider than for PCBs (R2=0.74), there is no
systematic underestimation (Figure 6 S.I.). In this case, however, MAPPE estimates
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deposition to both land surface and ocean, on average higher of a factor between 2 and 3,
slightly higher for land surface (Figure 6 S.I.).
Spatial trends of soil concentration predicted by the MAPPE model are reasonably
consistent with the MSCE-POP model, MAPPE estimating concentrations a factor of
about 2 lower (Figure 7 S.I.). For sea concentrations, the two models provide a consistent
estimate in absolute values, with higher correlation between the estimates than in the case
of soils (Figure 7 S.I.).
With reference to both the compilation of European monitoring [35], for concentration in
soils and the atmosphere, and the more recent survey on Swiss soils [34], MSCE-POP
and MAPPE are consistently underestimating air and soil concentration of a factor not
less than 10. The spatial trends of concentrations are also showing poor correspondence
between monitoring data and model results (Figure 5).
Discussion
PCB
The lower predictions of the MAPPE model with respect to MSCE-POP can be explained
in terms of missing sources (such as extra-continental emissions, volatilization from
soils). This reason can well account for a difference of about 40% in emissions, hence
concentrations [38]. In general, there is no evidence that one of the two patterns is better
than the other. From the passive sampler results (Figure 3) it appears that the two scatters
are very similar to each other.
The two models provide comparable orders of magnitude also of atmospheric deposition,
but significant discrepancies may arise when separating particle phase and gas phase.
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This critically depends on the fraction of the chemical that the model predicts as being
attached to aerosol. Differences up to a factor of 10, depending on the equations used and
the value of the parameters, were observed in other model intercomparisons [14].
The large underestimation of soil concentration in MAPPE with respect to the MSCE-
POP values can be due to a combination of the following factors:
1) the assumed exponential soil chemical profile of MSCE-POP, results being
referred to the first layer of soil (1 mm); average concentrations in soil can be as
low as 5 to 10% than the one in the top mm of soil [38]; this leads also to higher
soil volatilization, hence atmospheric emissions not accounted for in MAPPE;
2) the effect of past emission history: the transient effects due to the history of past
emissions highlight that soil masses at present days can be as high as a factor of 5
than the ones predicted by steady state balance from present emissions [38];
3) from Figure 3 S.I. total deposition in MAPPE is lower than particle phase
deposition in MSCE-POP on land; this means that a fortiori total deposition is
estimated lower by a factor >2.
The product of the three factors of underestimation due to the reasons discussed
above is between 10 x 5 x 2 = 100 and 20 x 5 x 2 = 200, which can justify the
discrepancy. It is worth stressing that experimental evidence is not clear about the
applicability of an exponential soil concentration profile as suggested in [39], due to the
effects of disturbances such as bioturbation, ploughing in agricultural soils, and other
factors which tend to homogenize concentrations in topsoil (e.g. [40, 41, 46]).
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The MAPPE model captures a general spatial trend, and the order of magnitude of
concentrations, also with respect to measurements in fatty dairy products, as discussed in
the S.I.
Dioxins and Furans
MAPPE and MSCE-POP provide consistent estimates, with no appreciable discrepancy.
However, both models produce the same type of underestimation of the monitoring data,
about a factor of 10. Part of the underestimation can be linked to emission inventories,
which are apparently low. In fact, estimates issued by EMEP while preparing the material
for this paper ([19]) showed an increase of emissions by a factor 3 with respect to the
ones in [18]. Another issue to address is the time frame of the monitoring data: the data
compiled in [35] refer to years from the 1980’s to mid 1990’s, while the model results are
obtained with emissions of the year 2001. However, according to EMEP ([18], [19]),
during years from 1990 to 2004 the reduction in emissions over Europe was estimated as
only 35 %. Other comparisons with model applications show that the trend in
underestimation is a common problem. For instance, the EMEP MSCE-POP model
updated in 2006 ([19]) still confirms a generally light underestimation, and an inspection
of Figure 4 in [11] also suggests that predictions tend to lay towards the lower limit of
monitored values, compatibly with an underestimation of a factor of 3 approximately. It
is also to be considered that many of the data used for comparison refer to urban
environments, where concentrations tend to be significantly higher (up to a factor of 5)
than in background locations ([19]).
Soil concentration is slightly underestimated by the MAPPE model with respect to
MSCE-POP, but still in the same order of magnitude. Unlike for PCBs, the results of the
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EMEP model are provided as averages over the top 5 cm of soil. This reduces the effect
of the exponential profile already discussed for PCBs. The transient effect in dioxin
emissions from 1990 reported in [18], can account for a factor of about 2 [38]. Ocean
concentrations appear unbiased and largely dominated by atmospheric deposition. For the
case of soils, we observe the same trend in underestimation as for the atmosphere. It is
interesting to notice that more recent samples, as in [34], are less underestimated. This
supports the conjecture that part of the underestimation on the data of [35] is due to the
time period of the samples.
The MAPPE model reproduces a weak spatial trend, as from Figure 5, showing
that predictions are within a factor of 10 from observations. The MAPPE model captures
the order of magnitude of concentrations with respect to measurements in lipids, as
discussed in the S.I., but not the spatial pattern.
Perspectives and conclusions
The paper demonstrates the use of the novel MAPPE approach to describe the fate and
transport of contaminants in the environment, using GIS analysis only with no need for
specialized model codes. The approach has a number of practical advantages, among
which virtual independence on resolution (only limited by the available input data),
generally low computation time requirements compared to other models, easy
identification of the calculation steps that contribute the most to discrepancies between
observations and predictions, thanks to the simplicity of algorithms and the possibility of
visually inspecting maps of all model parameters. Moreover, model algorithms can be
adjusted quickly without any code modification as required instead in traditional models.
We show that the model provides results which are consistent with the ones of the much
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more sophisticated and comprehensive MSCE-POP model, and we explain discrepancies
on the basis of model assumptions adopted for the present study, which may be anyway
modified upon strong monitoring evidence. Comparisons with monitoring data, however,
highlight that the proposed approach does not perform less accurately, and sometimes can
be regarded as preferable, with respect to the MSCE-POP one. The proposed method
aims at providing a synergic, and not an alternative tool to the more comprehensive
models, that provide insights on more detailed aspects of the mechanics of pollution but
may be surrogated by the proposed approach for the purpose of mapping long term
averaged spatial distributions of pollutants, integrating monitoring, modeling and
emission inventories as suggested in [40].
Acknowledgements
The research was partly funded by the European Commission FP6 contract no. 003956
(NoMiracle IP: http://nomiracle.jrc.it ). I thank gratefully V.Shatalov and E.Mantseva
from the EMEP MSCE-POP modeling team for providing data, reports and discussion,
and colleagues D.Pennington, G.Umlauf, I. Vives Rubio, and M.P.Vizcaino Martinez at
the IES of EC DG JRC for their critical reading of versions of the manuscript, and
valuable comments and suggestions.
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Figure 1 – logics of the map calculations. In grey input data (grey boxes are maps, grey text scalars);
in black boxes, output maps.
Landscape and climate
maps Kdep map
Scalar physico-chemical properties:
Kow,, Kaw,,molecular weight,,
degradation rate, air,,degradation rate,
soil,,degradation rate,
water.
Emissions soil Emissions water
Emissions air(national totals)
Koverall map, water
Overall average air removal rate for Europe
Air concentration mapSource-receptor maps
Time-of-travel maps
Atmospheric deposition map
Water concentration map
Soilconcentration map
Koverall map, soil
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3
26
a b
c d
Figure 2 – atmospheric concentration (a), deposition rate (b), soil and sea concentration (c) and
deposition fluxes (d) for PCBs, as predicted by the MAPPE model.
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1
2
3
4
5
6
1E+00
1E+01
1E+02
1E+03
1E-03 1E-02 1E-01 1E+00
predicted concentration ng m-3
pass
ive
sam
pler
mas
s ng
A
1E+00
1E+01
1E+02
1E+03
1E-03 1E-02 1E-01 1E+00
predicted concentration ng m-3
pass
ive
sam
pler
mas
s ng
B
Figure 3 – model evaluation for PCBs with air passive samplers: (A) MSCE-POP model; (B) MAPPE
model
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3
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5
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MAPPE
0.01
0.1
1
10
100
1000
0.1 1 10 100
C ng/g monitoring
C n
g/g
mod
el
MSCE-POP
0.01
0.1
1
10
100
1000
0.1 1 10 100
C ng/g monitoring
C n
g/g
mod
el
Figure 4– model evaluation for PCBs with soil samples. Lines 1:1 and a factor 10 interval are
displayed.
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0.01
0.1
1
10
100
1000
10000
0.01 0.1 1 10 100 1000
observed concentration
com
pute
d co
ncen
trat
ion
soil 1:1 obs / 10 air soil (Schmid et al., 2005) obs X 10
Figure 5 – scatter diagram of observations and calculation results for dioxins. Values are in ng I-
TEQ /Kg dm for soils and fg I-TEq / m3 for air. Data refer to the MAPPE model prediction, while
the MSCE-POP ones are very similar and not reported for simplicity.
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6
