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 …

China experiences severe particulate matter (PM) pollution problems closely linked to its
rapid economic growth. Advancing the understanding and characterization of …

The accurate and informative space-time mapping of air pollutants is a crucial component of
many human exposure studies. In the present work, space-time maps of daily distributions of …

Numerous studies have shown that fine airborne particulate matter particles (PM2. 5) are
more dangerous to human health than coarse particles, eg PM10. The assessment of the …

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

Fine airborne particulate matter (PM2. 5) has adverse effects on human health. Assessing
the long-term effects of PM2. 5 exposure on human health and ecology is often limited by a …

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 …

Understanding the daily changes in ambient air quality concentrations is important to the
assessing human exposure and environmental health. However, the fine temporal scales …

This work proposes a space/time estimation method for atmospheric PM 2.5 components by
modelling the mass fraction at a selection of space/time locations where the component is …

Air pollution has become more serious in many developing countries. Heavy particulate
matter (PM) air pollution is a major threat to people's respiratory and cardiopulmonary …