Influence of Land Use and Meteorological Factors on PM 2.5 and PM 10 Concentrations in Bangkok, Thailand

Pannee Cheewinsiriwat, Chanita Duangyiwa, Manlika Sukitpaneenit and Marc E. J. Stettler
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Pannee Cheewinsiriwat: Geography and Geoinformation Research Unit, Faculty of Arts, Chulalongkorn University, Bangkok 10330, Thailand
Chanita Duangyiwa: Geography and Geoinformation Research Unit, Faculty of Arts, Chulalongkorn University, Bangkok 10330, Thailand
Manlika Sukitpaneenit: Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
Marc E. J. Stettler: Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK

Sustainability, 2022, vol. 14, issue 9, 1-12

Abstract: Particulate matter (PM) is regarded a major problem worldwide because of the harm it causes to human health. Concentrations of PM with particle diameter less than 2.5 µm (PM 2.5 ) and with particle diameter less than 10 µm (PM 10 ) are based on various emission sources as well as meteorological factors. In Bangkok, where the PM 2.5 and PM 10 monitoring stations are few, the ability to estimate concentrations at any location based on its environment will benefit healthcare policymakers. This research aimed to study the influence of land use, traffic load, and meteorological factors on the PM 2.5 and PM 10 concentrations in Bangkok using a land-use regression (LUR) approach. The backward stepwise selection method was applied to select the significant variables to be included in the resultant models. Results showed that the adjusted coefficient of determination of the PM 2.5 and PM 10 LUR models were 0.58 and 0.57, respectively, which are in the same range as reported in the previous studies. The meteorological variables included in both models were rainfall and air pressure; wind speed contributed to only the PM 2.5 LUR model. Further, the land-use types selected in the PM 2.5 LUR model were industrial and transportation areas. The PM 10 LUR model included residential, commercial, industrial, and agricultural areas. Traffic load was excluded from both models. The root mean squared error obtained by 10-fold cross validation was 9.77 and 16.95 for the PM 2.5 and PM 10 LUR models, respectively.

Keywords: land use regression model; PM 2.5; PM 10; meteorological factors; Bangkok (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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