Mapping exposure to particulate pollution during severe haze episode using improved MODIS AOT-PM10 regression model with synoptic meteorology classification

Abstract

Severe smoke haze from biomass burning is frequently observed in Northern Thailand during dry months of February–April. Sparsely located monitoring stations operated in this vast mountainous region could not provide sufficient particulate matter (PM) data for exposure risk assessment. Satellite aerosol optical thickness (AOT) data could be used, but their reliable relationship with ground-based PM data should be first established. This study aimed to improve the regression model between PM10 and Moderate Resolution Imaging Spectroradiometer AOT with consideration of synoptic patterns to better assess the exposure risk in the area. Among four synoptic patterns, each representing the totality of meteorology governing Northern Thailand on a given day, most severe haze days belonged to pattern 2 that featured conditions of clear sky, stagnant air, and high PM10 levels. AOT-24 h PM10 regression model for pattern 2 had coefficient of determination improved to 0.51 from 0.39 of combined case. Daily exposure maps to PM10 in most severe haze period of February–April 2007 were produced for Chiangmai, the largest and most populated province in Northern Thailand. Regression model for pattern 2 was used to convert 24 h PM10 ranges of modified risk scale to corresponding AOT ranges, and the mapping was done using spatially continuous AOT values. The highest exposure risk to PM10 was shown in urban populated areas. Larger numbers of forest fire hot spots and more calm winds were observed on the days of higher exposure risk. Early warning and adequate health care plan are necessary to reduce exposure risk to future haze episodes in the area.