Exploring association of aerosols based on meteorological factors over mega city Lahore (Pakistan) and central place of Indo-Gangetic basin

Abstract

Urban air pollution poses a significant challenge, negatively affecting visibility, agriculture, health, and transportation. This research focused on exploring the variability of aerosols using the autoregressive distributed lag (ARDL) approach. To achieve this, monthly aerosol data were obtained from the Aura satellite’s Ozone Monitoring Instrument (OMI) at a distance of 500 nautical miles. In addition, meteorological factors such as Cloud Fraction (CF), Relative Humidity (RH), Tropopause Height (TH), Total Column Water Vapor (TCWV), Water Vapor Mass Mixing Ratio (WVMMR), Surface Skin Temperature (SST), Surface Air Temperature (SAT), and Geopotential Height (GH) were gathered from the atmospheric infrared sounder (AIRS) onboard the AQUA satellite. The MERRA-2 model provided the Total Surface Precipitation (TSP) and Surface Wind Speed (SWS). To assess the short- and long-term relationship between aerosols and meteorological parameters, the ARDL bounds testing technique was applied. The study found evidence of a long-term relationship and co-integration between the variables of interest and aerosols when aerosols were the dependent variable. Particularly, GH, SST, and SWS exhibited both long-term and short-term impacts on aerosol variability. SWS, in particular, was found to have a significant influence on aerosol variability. Conversely, CF, TSP, and WVMMR were found to have no significant impact on aerosol variability. To ensure the stability of the model, the CUSUM test was employed, confirming its stability. Furthermore, the prediction model demonstrated a good fit, bolstering the reliability of our findings.