Stochastic modeling of atmospheric pollution a spatial time series framework. Part I methodology
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Stochastic modeling of atmospheric pollution a spatial time series framework. Part I methodology


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realizations of a series of 1D processes, the
standardized residual process \ufffdRK (u, t), t3¹\ufffd at each loca-
tion u, using the corresponding covariance model
C
\ufffdK
(\ufffd; \ufffd(u)). Any stochastic simulation algorithm can be
used for simulating these residual pro"les, e.g., sequential
simulation or simulation via Cholesky decomposition of
the covariance matrix (Deutsch and Journel, 1998).
Simulated realizations of the spatiotemporal residual
"eld RK (u, t) exhibit both temporal and spatial correla-
tion. Temporal correlation is imposed via the (location-
speci"c) temporal covariance model C
\ufffdK
(\ufffd; \ufffd*(u)). Spatial
correlation is induced via the spatial correlation of the
entries of the parameter vector \ufffd*(u), i.e., via the
covariance models C
\ufffd
(h) and C
\ufffd\ufffd
(h) of the sill and range
values of each component TS \ufffdRK
\ufffd
(u, t), t3¹\ufffd. A non-
conditional realization \ufffdr( \ufffd\ufffd\ufffd(u, t), t3¹\ufffd of standardized
residual pro"le at any location u, however, cannot be
generated independently from a realization \ufffdr( \ufffd\ufffd\ufffd(u\ufffd, t),
t3¹\ufffd at another location u\ufffd, even if the corresponding
two parameter vectors \ufffdH(u) and \ufffdH(u\ufffd) are spatially corre-
lated; the reason is that the random numbers used for
simulating the two TS (e.g., Chat"eld, 1996) at locations
u and u\ufffd should not be drawn independently at these
locations. Additional spatial correlation is induced by an
unobservable random "eld of innovations, which is spa-
tially correlated and serially uncorrelated (Wikle and
Cressie, 1997; Wilks, 1998). If sequential Gaussian
simulation, i.e., autoregression, is used for simulating
P.C. Kyriakidis, A.G. Journel / Atmospheric Environment 35 (2001) 2331}2337 2335
standardized residual r( -pro"les at each unmonitored lo-
cation u, such innovation values are spatially colored and
temporally white standard normal deviates; their spatial
covariance could be identi"ed to that of the sample
standardized r( -residuals after the latter are transformed
to a Gaussian distribution.
The above innovations "eld is also used for indirectly
conditioning simulated r( -pro"les to sample residual r( -
data measured at nearby monitoring stations. When
simulating a non-conditional realization \ufffdr( \ufffd\ufffd\ufffd(u, t
\ufffd
), i"
1,2,¹\ufffd of the standardized residual TS \ufffdRK (u, t), t3¹\ufffd at
location u, sample residual r( -data available at nearby
monitoring stations are not taken into account; only
previously simulated r( -values at u. Consequently, a high
simulated residual value r( \ufffd\ufffd\ufffd(u, t
\ufffd
) can be generated next to
a low sample residual r( (u\ufffd , t\ufffd ) measured at the same time
instant t
\ufffd
at an adjacent station location u\ufffd . In the con-
text of sequential Gaussian simulation, indirect condi-
tioning to contemporaneous r( -data can be achieved by
conditioning innovation realizations to the normal
scores (quantiles of a Gaussian distribution) of the con-
temporaneous sample r( -data. Such a conditioning step
results into simulating high innovation values, and con-
sequently high r( -values, nearby high sample r( -data
(Kyriakidis, 1999).
Simulated realizations \ufffdz\ufffd\ufffd\ufffd(u, t), t3¹\ufffd of concentra-
tion pro"les are "nally built by adding the simulated
trend \ufffdm\ufffd\ufffd\ufffd(u, t), t3¹\ufffd and residual \ufffdr\ufffd\ufffd\ufffd(u, t), t3¹\ufffd
pro"les. Since the spatiotemporal trend and residual
components are assumed uncorrelated, see de"nition (1),
simulation of the trend "eld is performed independently
from that of the residual "eld. Often, concentration re-
cords \ufffdz(u\ufffd , t\ufffd ), i"1,2,¹\ufffd\ufffd at the n monitoring station
locations u\ufffd , \ufffd3(n) are not complete. In this case, the
missing values of the station pro"les are in-"lled by
stochastic simulation. The set of S alternative, equally
probable, realizations \ufffdz\ufffd\ufffd\ufffd(u, t), u3D, t3¹\ufffd provide a
model of uncertainty for the unknown process levels in
both space and time, which can be used for risk assess-
ment studies or for deciding on additional sampling
(Haas, 1998).
3. Discussion
A framework for stochastic spatiotemporal modeling
of atmospheric pollution has been presented in this pa-
per. Observed concentration levels are viewed as a joint
realization of a collection of spatially correlated time
series, thus capitalizing on the typically better informed
time domain. The stochastic spatiotemporal "eld is de-
composed into a stochastic trend and a stochastic resid-
ual component. Parametric temporal trend models are
established at all monitoring stations, independently
from one location to another, and their parameters are
coregionalized in space. This amounts to mapping the
task of simulating a space}time trend "eld to that of
generating realizations of a set of cross-correlated RFs
modeling the joint spatial distribution of the temporal
trend parameters. Joint conditional simulation of such
parameters ensures reproduction of important cross-cor-
relations between them. Simulated realizations of the
spatiotemporal residual component are "nally generated
via a series of TS realizations, whose parameters are
correlated in space.
The proposed methodology is a (distributed para-
meter) spatial time series framework merging modeling
approaches typically applied independently in a spatial
and temporal context; this comprehensive framework
allows a consistent modeling of spatial and temporal
variability through stochastic simulation. Concentration
pro"les obtained from mechanistic model predictions at
each location could alternatively be viewed as local trend
components (Venkatram, 1988), and the spatiotemporal
distribution of the resulting residual pro"les could then
be modeled along the lines proposed in this work. The set
of alternative concentration realizations provides
a model of uncertainty regarding the unknown concen-
tration levels in both space and time. Such an uncertainty
model could be used in a risk analysis context for study-
ing the impact of emission control policies or for design-
ing monitoring networks.
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