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1601701 
Journal Article 
BME representation of particulate matter distributions in the state of California on the basis of uncertain measurements 
Christakos, G; Serre, ML; Kovitz, JL 
2001 
Yes 
Journal of Geophysical Research: Atmospheres
ISSN: 2169-897X
EISSN: 2169-8996 
106 
D9 
9717-9731 
WOS:000168731900007 
Maps of temporal and spatial values of annual averages of
daily particulate matter (PM10) concentrations were generated throughout the state of California
using uncertain forms of physical data. The PM10 estimates were derived in an integrated
space/time domain using the Bayesian maximum entropy (BME) mapping approach of modern
spatiotemporal geostatistics. The approach possesses some interesting features which allow an
insightful analysis of the PM10 space/time distribution. A complete stochastic characterization
of the pollutant involves the probability density function of the PM10 map, which is the result
of a rigorous knowledge-integration process. This process is considerably flexible, it can
account for several physical knowledge bases and sources of uncertainty, and it may involve
Bayesian or material conditionalization rules. Taking advantage of BME's flexibility, PM10
estimates were chosen which offered an appropriate representation of the real distribution in
space/time, and a meaningful assessment of the representation accuracy was derived. Depending on
the space scales/timescales considered, the PM,, distributions depicted considerable levels of
variability, which may be associated with topographic features, climatic changes, seasonal
patterns, and random fluctuations. The importance of integrating soft information available at
surrounding sites as well as at the estimation points themselves was discussed. Comparisons were
designed which demonstrated the usefulness of the BME-based maps to represent PM10 distributions
in space/time. Areas were identified where the annual PM10 geometric mean reached or exceeded the
California standard, which is valuable information for regulatory purposes.