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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.

SUBMITTER: Leelasakultum K

PROVIDER: S-EPMC7067214 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Mapping exposure to particulate pollution during severe haze episode using improved MODIS AOT-PM<sub>10</sub> regression model with synoptic meteorology classification.

Leelasakultum Ketsiri K Kim Oanh Nguyen Thi NT

GeoHealth 20170614 4

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 PM<sub>1 ...[more]

PMID: 32190788

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Project description:This present work investigates several local and synoptic meteorological aspects associated with two wintertime haze episodes in Greater Bangkok using observational data, covering synoptic patterns evolution, day-to-day and diurnal variation, dynamic stability, temperature inversion, and back-trajectories. The episodes include an elevated haze event of 16 days (14-29 January 2015) for the first episode and 8 days (19-26 December 2017) for the second episode, together with some days before and after the haze event. Daily PM2.5 was found to be 50 µg m-3 or higher over most of the days during both haze events. These haze events commonly have cold surges as the background synoptic feature to initiate or trigger haze evolution. A cold surge reached the study area before the start of each haze event, causing temperature and relative humidity to drop abruptly initially but then gradually increased as the cold surge weakened or dissipated. Wind speed was relatively high when the cold surge was active. Global radiation was generally modulated by cloud cover, which turns relatively high during each haze event because cold surge induces less cloud. Daytime dynamic stability was generally unstable along the course of each haze event, except being stable at the ending of the second haze event due to a tropical depression. In each haze event, low-level temperature inversion existed, with multiple layers seen in the beginning, effectively suppressing atmospheric dilution. Large-scale subsidence inversion aloft was also persistently present. In both episodes, PM2.5 showed stronger diurnality during the time of elevated haze, as compared to the pre- and post-haze periods. During the first episode, an apparent contrast of PM2.5 diurnality was seen between the first and second parts of the haze event with relatively low afternoon PM2.5 over its first part, but relatively high afternoon PM2.5 over its second part, possibly due to the role of secondary aerosols. PM2.5/PM10 ratio was relatively lower in the first episode because of more impact of biomass burning, which was in general agreement with back-trajectories and active fire hotspots. The second haze event, with little biomass burning in the region, was likely to be caused mainly by local anthropogenic emissions. These findings suggest a need for haze-related policymaking with an integrated approach that accounts for all important emission sectors for both particulate and gaseous precursors of secondary aerosols. Given that cold surges induce an abrupt change in local meteorology, the time window to apply control measures for haze is limited, emphasizing the need for readiness in mitigation responses and early public warning.

Dataset Information

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

Publications

Mapping exposure to particulate pollution during severe haze episode using improved MODIS AOT-PM<sub>10</sub> regression model with synoptic meteorology classification.

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