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 …

Spatiotemporal maps of particulate matter (PM) concentrations contribute considerably to
the understanding of the underlying natural processes and the adequate assessment of the …

The space/time distribution of PM10 in Thailand is modeled using the Bayesian maximum
entropy (BME) method of modern spatiotemporal geostatistics. Three kinds of BME …

Geostatistical methods are widely used in estimating long-term exposures for
epidemiological studies on air pollution, despite their limited capabilities to handle spatial …

The present paper reviews the conceptual framework and development of the Bayesian
Maximum Entropy (BME) approach. BME has been considered as a significant breakthrough …

Understanding the limitations of environmental data is important for managing
environmental systems effectively and for encouraging the responsible use of uncertain …

Space-time data analysis and assimilation techniques in atmospheric sciences typically
consider input from monitoring measurements. The input is often processed in a manner that …

In the US, particulate matter (PM10) is considered an important criteria air pollutant and it is
monitored throughout the country by means of a considerably dense network of stations …

Abstract Bayesian Maximum Entropy (BME) has been successfully used in geostatistics to
calculate predictions of spatial variables given some general knowledge base and sets of …

Geostatistics should not be considered merely as a set of mathematical techniques for
semivariogram calculation, spatial estimation and simulation, on the basis of a (usually …