Objectives To estimate avoidable mortality, potential years of life lost and economic costs associated with particulate matter PM 2.5 exposure for 2 years (2013 and 2015) in Mexico using two scenarios of reduced concentrations (i.e., mean annual PM 2.5 concentration \ 12 lg/m 3 and mean annual PM 2.5 concentration \ 10 lg/m 3 ). Methods The health impact assessment method was followed. This method consists of: identification of health effects, selection of concentration-response functions, estimation of exposure, quantification of impacts quantification and economic assessment using the willingness to pay and human capital approaches. Results For 2013, we included data from 62 monitoring sites in ten cities, (113 municipalities) where 36,486,201 live. In 2015, we included 71 monitoring sites from fifteen cities (121 municipalities) and 40,479,629 inhabitants. It was observed that reduction in the annual PM 2.5 average to 10 lg/would have prevented 14,666 deaths and 150,771 potential years of life lost in 2015, with estimated costs of 64,164 and 5434 million dollars, respectively. Conclusions Reducing PM 2.5 concentration in the Mexican cities studied would reduce mortality by all causes by 8.1%, representing important public health benefits.
Epidemiological studies on the effects of air pollution in Mexico often use the environmental concentrations of monitors closest to the home as exposure proxies, yet this approach disregards the space gradients of pollutants and assumes that individuals have no intra-city mobility. Our aim was to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in a population of ~ 16 500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants. Using information from secondary sources on geographic and meteorological variables as well as other pollutants, we fitted two generalized additive models to predict monthly PM2.5 and NO2 concentrations in the 2004–2019 period. The models were evaluated through 10-fold cross validation. Both 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/m3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a solid alternative for estimating PM2.5 and NO2 exposure with high spatio-temporal resolution for epidemiological studies in the Valle de México region.
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