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Investigating the association between air pollutants' concentration and meteorological parameters in a rapidly growing urban center of West Bengal, India: a statistical modeling-based approach.

Arghadeep BoseIndrajit Roy Chowdhury
Published in: Modeling earth systems and environment (2023)
The ambient air quality in a city is heavily influenced by meteorological conditions. The city of Siliguri, known as the "Gateway of Northeast India", is a major hotspot of air pollution in the Indian state of West Bengal. Yet almost no research has been done on the possible impacts of meteorological factors on criterion air pollutants in this rapidly growing urban area. From March 2018 to September 2022, the present study aimed to determine the correlations between meteorological factors, including daily mean temperature (℃), relative humidity (%), rainfall (mm), wind speed (m/s) with the concentration of criterion air pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , CO, O 3 , and NH 3 ). For this research, the trend of all air pollutants over time was also investigated. The Spearman correlation approach was used to correlate the concentration of air pollutants with the effect of meteorological variables on these pollutants. Comparing the multiple linear regression (MLR) and non-linear regression (MLNR) models permitted to examine the potential influence of meteorological factors on concentrations of air pollutants. According to the trend analysis, the concentration of NH3 in the air of Siliguri is rising, while the concentration of other pollutants is declining. Most pollutants showed a negative correlation with meteorological variables; however, the seasons impacted on how they responded. The comparative regression research results showed that although the linear and non-linear models performed well in predicting particulate matter concentrations, they performed poorly in predicting gaseous contaminants. When considering seasonal fluctuations and meteorological parameters, the results of this research will definitely help to increase the accuracy of air pollution forecasting near future.
Keyphrases
  • air pollution
  • particulate matter
  • lung function
  • heavy metals
  • risk assessment
  • physical activity
  • mass spectrometry
  • cystic fibrosis
  • room temperature
  • chronic obstructive pulmonary disease
  • human health