Aerosol dynamics in semi-arid cities are key to understanding air quality and climate interactions.This study examines the spatiotemporal variability of Aerosol Optical Depth(AOD)over Jaipur,India,from 2018 to 2024 us...Aerosol dynamics in semi-arid cities are key to understanding air quality and climate interactions.This study examines the spatiotemporal variability of Aerosol Optical Depth(AOD)over Jaipur,India,from 2018 to 2024 using MODIS observations at 470,500,and 550 nm,combined with meteorological data and ground-based air quality records.The Mann–Kendall test identified a statistically significant decreasing trend at 500 nm(slope=–2.07,p<0.05),while 470 and 550 nm showed weak,nonsignificant declines.AOD peaked in April–June,declined during the monsoon,and rose again in October–November due to burning and festivals.Correlation analysis demonstrated strong positive associations with PM_(2.5),PM_(10),and temperature,with minimum temperature emerging as the most influential predictor,whereas relative humidity showed weak or negative relationships.Anomaly detection confirmed episodic high-AOD events during dust storms,winter inversions,and agricultural burning.Predictive modelling using Multiple Linear Regression(MLR)and Random Forest highlighted the complementary roles of linear drivers.Nonlinear dynamics,with Random Forest achieving high predictive accuracy(R^(2)=0.892 for training,0.588 for testing).These findings demonstrate that aerosol variability in Jaipur is governed by a dual influence of natural dust and anthropogenic emissions,with wavelength-specific responses.The results provide scientific evidence for integrating satellite monitoring,ground observations,and predictive models into urban air quality management and climate adaptation strategies in semi-arid regions.展开更多
文摘Aerosol dynamics in semi-arid cities are key to understanding air quality and climate interactions.This study examines the spatiotemporal variability of Aerosol Optical Depth(AOD)over Jaipur,India,from 2018 to 2024 using MODIS observations at 470,500,and 550 nm,combined with meteorological data and ground-based air quality records.The Mann–Kendall test identified a statistically significant decreasing trend at 500 nm(slope=–2.07,p<0.05),while 470 and 550 nm showed weak,nonsignificant declines.AOD peaked in April–June,declined during the monsoon,and rose again in October–November due to burning and festivals.Correlation analysis demonstrated strong positive associations with PM_(2.5),PM_(10),and temperature,with minimum temperature emerging as the most influential predictor,whereas relative humidity showed weak or negative relationships.Anomaly detection confirmed episodic high-AOD events during dust storms,winter inversions,and agricultural burning.Predictive modelling using Multiple Linear Regression(MLR)and Random Forest highlighted the complementary roles of linear drivers.Nonlinear dynamics,with Random Forest achieving high predictive accuracy(R^(2)=0.892 for training,0.588 for testing).These findings demonstrate that aerosol variability in Jaipur is governed by a dual influence of natural dust and anthropogenic emissions,with wavelength-specific responses.The results provide scientific evidence for integrating satellite monitoring,ground observations,and predictive models into urban air quality management and climate adaptation strategies in semi-arid regions.
