泰國曼谷地區之空氣污染暴露評估

Abstract

The severe air pollution problem in Bangkok (BKK) is an issue of great concern in Thailand. This study aims to examine the effect of meteorological factors (ambient temperature, pressure, wind speed and wind direction) on air pollutants (PM10, CO8hr, O3_1hr, NO2, and SO2). The temporal and spatial trends of the air pollutants, particularly particulate matter (PM2.5 and PM10), were illustrated to understand their patterns. The PM2.5/PM10 ratios were characterized for industrial, residential, roadside and background sites in wet and dry seasons. The variation of particulate matter during the “lockdown” period for COVID-19 control in Bangkok was evaluated to describe the pollutant behaviour. The datasets of air pollutants and meteorological parameters in Bangkok used in this study were obtained from the Pollution Control Department of the Ministry of Natural Resources and Environment, Thailand. Long–term data from 1996-2009 were analysed to identify relationships between the air pollutants and meteorological factors. A stepwise multiple linear regression model was applied to find significant meteorological factors affecting the air pollutants. A stepwise multiple linear regression model was used to analyse the significant factors affecting PM10, CO8hr, O3_1hr, NO2, and SO2 levels at two groups of sites; results showed a decreased association with meteorological parameters and an increased association with the type of studied area and season. In contrast, O3_1hr levels exhibited a decreased association with the area studied. The coefficients of determination (r2) for PM10, CO8hr, O3_1hr, NO2, and SO2 regression models were 0.54, 0.68, 0.5, 0.64, and 0.22, respectively. In terms of particulate matter, short term datasets from 2018-2019 were examined using ordinary kriging and ANOVA. The monthly mean PM2.5 concentrations was highest at roadsides in the dry season (35.2 ± 19.1μg /m3), and lowest at industrial sites in the wet season (17.5 ± 10.5 μg /m3); similar trends occurred for seasonal variation in PM10 concentrations. The PM2.5/PM10 ratios ranged from 0.52 to 0.68; the highest value was detected in the dry season, while the lowest was in the wet season. It was concluded that the particulate matter concentrations significantly changed in both time and space. Transportation and transformation of pollutants were the primary causes of PM2.5 and PM10 concentrations in Bangkok. Values exceeding safe levels occurred during the northeast monsoon season because of dry weather and the northeast wind direction, transporting the PM2.5 created from open burning of biomass in agriculture areas. Remarkably, a combination of low temperature, high pressure and stagnant wind can lead to the high PM2.5 levels. The ordinary kriging results showed that during the COVID-19 lockdown period (April–May 2020), the PM2.5 and PM10 dramatically decreased compared with values for the same months of 2019. This was attributed to the work-from-home policy and other lifestyle changes that decreased particulate matter sources. In conclusion, this study characterized the variation of air pollutants in Bangkok as spatio-temporally heterogeneous. Traffic emissions are the main cause of spatial variation of air pollutants, and the meteorological parameters might be important factors affecting temporal variation.