Influence of Land Use and Meteorological Factors on PM2.5 and PM10 Concentrations in Bangkok, Thailand

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 (PM2.5) and with particle diameter less than 10 µm (PM10) are based on various emission sources as well as meteorological factors. In Bangkok, where the PM2.5 and PM10 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 PM2.5 and PM10 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 PM2.5 and PM10 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 PM2.5 LUR model. Further, the land-use types selected in the PM2.5 LUR model were industrial and transportation areas. The PM10 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 PM2.5 and PM10 LUR models, respectively.

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

“…Third, we did not include meteorological variables in the development of LUR models for the cities of Bucaramanga and Barranquilla due to limited number of meteorological stations and data to produce a valid estimated surface. Although the models´ performance for PM 2.5 were good particularly for Bucaramanga, including meteorological variables might have increased the models´ performance as they have been reported as important predictors for intraurban variations in other countries (Cheewinsiriwat et al, 2022;Olvera Alvarez et al, 2018). Another limitation of our study is that we did not include local emission sources and regional sources (such as forest res) in the prediction models.…”

“…(4.86-32.69) for Cali,13.89 𝛍g/m 3 (4.39-25.52) for Bogotá, and 12.93 𝛍g/m 3 (4.90-32.23) for Bucaramanga.For NO 2 sampling, 17 out of the 240 tubes deployed were removed due to vandalism or invalid measurements, leaving 223 observations for the analyses. The mean NO 2 concentrations during the dry season were slightly higher than those in the rainy season (see supplementary material TableS1).…”

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

“…Third, we did not include meteorological variables in the development of LUR models for the cities of Bucaramanga and Barranquilla due to limited number of meteorological stations and data to produce a valid estimated surface. Although the models' performance for PM 2.5 were good particularly for Bucaramanga, including meteorological variables might have increased the models' performance as they have been reported as important predictors for intraurban variations in other countries (Cheewinsiriwat et al 2022;Olvera Alvarez et al 2018). Another limitation of our study is that we did not include local emission sources and regional sources (such as forest fires) in the prediction models.…”