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Several epidemiological studies have confirmed the associations and estimated the risk of cardiovascular and pulmonary mortality due to short term exposure to PM10. However the errors introduced by techniques previously used to interpolate PM10 in characterizing the strength of the associations remain in question. Therefore we incorporate the Bayesian Maximum Entropy (BME) method of modern spatiotemporal Geostatistics in the exposure assessment to investigate the effect that daily PM10 has on cardiovascular and respiratory mortality in different regions of Thailand. The BME maps show that the PM10 field across Thailand exhibits considerable space/time variability. These maps suggest that the PM10 daily average concentration did not comply with the PM10 standard, which is supported by the maps of PM10 daily maximum concentration, and confirmed by BME maps of non-attainment areas. Furthermore, the BME analysis targeted districts in the Northeast region as sites where new monitoring stations should be added in order to improve the Thailand's existing monitoring network. These maps provided the most accurate estimate of PM10 exposure obtained to date for each district location and day for which cardiovascular and respiratory mortality was reported in Thailand during 1998-2003. The strength of the associations was then investigated through an analysis using a case-crossover design. The observed associations were stronger for pulmonary mortality than for cardiovascular mortality. The high odds ratios observed in Bangkok and the central region were possibly due to industrialization, construction, and traffic emission sources while positive associations found in other regions could be related to sources from agricultural biomass burning, forest fire, windblown dust, and sea spray. Furthermore, a holistochastic human exposure analysis propagated mapping and epidemiologic uncertainty to obtain the lower-bound and upper-bound estimates of the number of deaths from cardiovascular and respiratory causes that resulted from acute health response to short term exposure to PM10 across Thailand. An Elasticity uncertainty analysis suggests that the uncertainty in the assessment of the number of deaths caused by PM10 can be improved by adding monitoring stations in the Northeastern region, and by improving the epidemiologic study in the Northern region.