Combined use of principal component analysis/multiple linear regression analysis and artificial neural network to assess the impact of meteorological parameters on fluctuation of selected PM2.5-bound elements

PLoS One. 2024 Mar 20;19(3):e0287187. doi: 10.1371/journal.pone.0287187. eCollection 2024.

Abstract

Based on the data of the State of Global Air (2020), air quality deterioration in Thailand has caused ~32,000 premature deaths, while the World Health Organization evaluated that air pollutants can decrease the life expectancy in the country by two years. PM2.5 was collected at three air quality observatory sites in Chiang-Mai, Bangkok, and Phuket, Thailand, from July 2020 to June 2021. The concentrations of 25 elements (Na, Mg, Al, Si, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Sr, Ba, and Pb) were quantitatively characterised using energy-dispersive X-ray fluorescence spectrometry. Potential adverse health impacts of some element exposures from inhaling PM2.5 were estimated by employing the hazard quotient and excess lifetime cancer risk. Higher cancer risks were detected in PM2.5 samples collected at the sampling site in Bangkok, indicating that vehicle exhaust adversely impacts human health. Principal component analysis suggests that traffic emissions, crustal inputs coupled with maritime aerosols, and construction dust were the three main potential sources of PM2.5. Artificial neural networks underlined agricultural waste burning and relative humidity as two major factors controlling the air quality of Thailand.

MeSH terms

  • Air Pollutants* / analysis
  • Dust / analysis
  • Environmental Monitoring
  • Humans
  • Neoplasms*
  • Particulate Matter / analysis
  • Principal Component Analysis
  • Regression Analysis
  • Thailand

Substances

  • Air Pollutants
  • Dust
  • Particulate Matter

Grants and funding

The authors acknowledge funding from ‘the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB40000000)’ and Thailand Science Research and Innovation (TSRI).