Long-term surface monitoring of hourly <2.5 μm diameter particulate matter (PM2.5) concentrations in megacities provides an essential record to estimate adverse health effects experienced by many people. In addition, hidden from immediate view, the time series of measurements contains a wealth of information on the types of sources and the impact of meteorology. A generalized additive model (GAM) was developed to analyze multiple years of hourly data in order to distinguish the sources leading to adverse air quality and the response of the concentrations to boundary layer mixing, wind transport, and precipitation. The model estimates the impact of long-range transport by using clusters of back-trajectories and a trajectory cluster contribution function. Estimates of emissions from wildfires and agricultural biomass burning based on multisatellite remote sensing were combined with back-trajectories to estimate the impacts at the measurement sites. The method was applied to Dhaka, Bangladesh, and Kolkata, India, for the dry season from November to March for up to 7 years of data. Weak winds and low mixing heights lead to very low ventilation coefficients, which contribute to extreme air pollution events. The contribution of local emissions was inferred from the variability in the measured concentrations that could be attributed to the boundary layer height, the local winds, and the diurnal cycle. By this method, local emissions were estimated to contribute between 57% and 67% of the aerosol concentrations. Long-range air mass transport from the Indo-Gangetic plain was associated with concentrations that were 40% higher than when the winds blew from the east. Wildfires and agricultural biomass burning were estimated to increase PM2.5 by 7–8% over the long-term baseline. Overall, regional factors were estimated to contribute between 31% and 37% of the variability. Weekdays and holidays were found to have very limited impacts on the PM2.5 concentrations. Model residuals in Dhaka highlight the need to measure more accurately the low wind speeds and shallow mixing heights in order to improve the characterization of emission sources. By integrating back-trajectories and fire emission estimates into the GAM simulations, this study provides estimates of local versus regional pollution and quantifies the impact of meteorology on air quality in the region. Furthermore, the approach can be readily adapted to many locations with limited air quality management infrastructure.