Vol. 37 No. 1 (2023) | DOI: https://doi.org/10.20937/ATM.53110

Autores: Cervantes-Martínez, K., Riojas-Rodríguez, H., Díaz Avalos, C., Moreno-Macías, H., López-Ridaura, R., Stern, D., Acosta-Montes, J. O. and Texcalac-Sangrador, J. L.

Abstract

pidemiological studies on air pollution in Mexico often use the environmental concentrations of pollutants as measured by monitors closest to the home of participants as exposure proxies, yet this approach does not account for the space gradients of pollutants and ignores intra-city human mobility. This study aimed to develop high-resolution spatial and temporal models for predicting long-term exposure to PM_2.5 and NO₂ in ~16 500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants, and used secondary source information on geographical and meteorological variables as well as other pollutants to fit two generalized additive models capable of predicting monthly PM_2.5 and NO₂ concentrations during the 2004-2019 period. Both models were evaluated through 10-fold cross-validation, and showed high predictive accuracy with out-of-sample data and no overfitting (CV-RMSE = 0.102 for PM_2.5 and CV-RMSE = 4.497 for NO₂). Participants were exposed to a monthly average of 24.38 (6.78) µg m^–3 of PM_2.5 and 28.21 (8.00) ppb of NO₂ during the study period. These models offer a promising alternative for estimating PM_2.5 and NO₂ exposure with high spatiotemporal resolution for epidemiological studies in the Mexico City Metropolitan Area.