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Scientific journal publication

Long-term meteorology-adjusted and unadjusted trends of PM2.5 using the AirGAM model over Delhi, 2007–2022

Chetna, NN; Dhaka, Surendra K.; Walker, Sam-Erik; Rawat, Vikas; Singh, Narendra

Publication details

Journal: Atmospheric Environment: X, vol. 22, 100255, 2024

Doi: doi.org/10.1016/j.aeaoa.2024.100255

Summary:
This study investigates the impact of meteorological variations on the long-term patterns of PM2.5 in Delhi from 2007 to 2022 using the AirGAM 2022r1 model. Generalized Additive Modeling was employed to analyze meteorology-adjusted (removing the influence of inter-annual variations in meteorology) and unadjusted trends (trends without considering meteorology) while addressing auto-correlation. PM2.5 levels showed a modest decline of 14 μg m−3 unadjusted and 18 μg m−3 meteorology-adjusted over the study period. Meteorological conditions and time factors significantly influenced trends. Temperature, wind speed, wind direction, humidity, boundary layer height, medium-height cloud cover, precipitation, and time variables including day-of-week, day-of-year, and overall time, were used as GAM model inputs. The model accounted for 55% of PM2.5 variability (adjusted R-squared = 0.55). Day-of-week and medium-height cloud cover were non-significant, while other covariates were significant (p