Driving forces of PM2.5 concentrations in megacity Lahore: application of geographically and temporally weighted regression model to city-level

BACKGROUND AND AIM: Fine particulate matter (PM 2.5µm) is the main indicator for air pollution which is closely linked to human health and much wider impact on environment and considered an important impact pathway of global climate change. Accurately identifying the main drivers of PM2.5 in megacities is of great significance, as cities are the main hub of air pollution. OBJECTIVE: The temporal and spatial heterogeneity of PM2.5 and its drivers, considering both climate change and socioeconomic developmental conditions, has not been explored in the previous studies especially at the city-level. METHOD: We adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of climate change and socioeconomic variables on the concentration of PM2.5 in one of the highest polluted megacity Lahore, covering the period 1998 to 2020. RESULTS: PM2.5 concentration steadily increased from an average of 53 to 88µg/m3 during last 20 years in Lahore city. Windspeed, relative humidity, temperature and precipitation reduce PM2.5 concentrations, with strongest to weakest influence in that order. Eastern part of the city more strongly favorably influenced than western part of the city by the climate variables leading to higher PM2.5 concentrations in eastern part of the city. Foreign direct investment (FDI) very strongly and GDP moderately intensified PM2.5 concentrations. Population density strongly intensifies PM2.5 throughout the city, while road density and land use intensify the concentrations at specific locations within the city. Less dense areas of road and land use inversely favorably influence PM2.5 in the city. CONCLUSIONS: In the city of Lahore, PM2.5 concentrations showed strong spatial variances and correspond to a series of phases. Both the sources of PM2.5 and its concentration are spatially heterogeneous. This information at the sub-districts scale within a city may help in formulating targeted interventions for air pollution mitigation.