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Abstract
Ambient PM2.5 data in the Central Business District (CBD) of Bangkok monitored by Pollution Control Department and Bangkok Metropolitan Administration were collected over three years in Bangkok from 2015 to 2017. The other air pollutions data were used as the dependent variables to develop mathematic models with statistical distribution technique. Multiple linear regression technique was selected as the main statistical distribution methodology for estimating PM2.5 concentrations in non-monitored areas. The predicted PM2.5 concentrations were validated against the measured PM2.5 concentrations by various statistical techniques. The validation found that the model had strong significant correlations for ambient and roadside area with R2 = 0.88 and 0.96, respectively. The non-carcinogenic health risk assessment of PM2.5 was quantified as the hazard quotient (HQ) from both the measured and predicted data. The risk areas and HQ were compared using the inverse distance weighting interpolation technique and illustrated as GIS-based maps. During December to February, the HQ values of PM2.5 were exceed 1 (HQs > 1) at all area of CBD; however, the highest HQ was found in the southern part of CBD. The finding could be used for residential health awareness in that area.
Acknowledgments
The authors deeply appreciate the Pollution Control Department, Thailand, and Bangkok Metropolitan Administration for air pollution and meteorological information.
Disclosure statement
No potential conflict of interest was reported by the authors.
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.