Resumen

CERVANTES-MARTINEZ, Karla et al. Geocoding and spatiotemporal modeling of long-term PM_2.5 and NO₂ exposure in the Mexican Teachers’ Cohort. Atmósfera [online]. 2023, vol.37, e53110.  Epub 17-Abr-2023. ISSN 0187-6236.  https://doi.org/10.20937/atm.53110.

Epidemiological 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 PM2.5 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 PM2.5 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 PM2.5 and CV-RMSE = 4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) µg m-3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a promising alternative for estimating PM2.5 and NO2 exposure with high spatiotemporal resolution for epidemiological studies in the Mexico City Metropolitan Area.

Palabras llave : air pollution; generalized additive models; exposure assessment.

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