Predicting fine-scale daily NO₂ over Mexico City using an ensemble modeling approach

Abstract

Background and Aim: In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe, with substantially less work conducted in low- and middle-income countries. Furthermore, the availability of new satellite instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. Methods: We estimate daily ground-level nitrogen dioxide (NO₂) concentrations in the Mexico City Metropolitan Area at 1-km² grids from 2005 to 2019 using a multi-stage approach. In stage 1, we impute missing satellite NO₂ column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach based on estimates from atmospheric ensemble models. In stage 2, we calibrate the association of column NO₂ to ground-level NO₂ using ground monitors and meteorological features using linear mixed-effects (LME) and RF models. In stage 3, we predict the stage 2 model over each 1-km² grid in our study area, then ensemble the results using a generalized additive model (GAM). In stage 4, we used RF to model the local component at the 200-m² scale by explaining the residual between predicted NO₂ from stage 3 and measured NO₂ at the monitoring stations. Results: The cross-validated R² of the LME and RF models in stage 2 were 0.69 and 0.75 respectively, and 0.83 for the ensembled GAM. Cross-validated root-mean-squared prediction error (RMSPE) of the GAM was 4.41 µg/m³. The RF model in stage 4 further explained on average 54% of the variation in the residual NO₂ concentrations. Conclusions: Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO₂ estimates for further epidemiologic studies in Mexico City.