2.2. Exposure Measures

A land-use regression (LUR) model was performed to predict the monthly concentrations of PM_2.5. Particulars can be accessed elsewhere [27]. In a nutshell, data on PM_2.5 and meteorological monitoring from January 2013 to December 2017 were extracted from monitoring stations with technical assistance from the Qingyue environmental protection information technology service center (http://data.epmap.org/) and the Chinese Ecology and Environment Ministry. The spatial–temporal fluctuation in PM_2.5 was interpreted by the generalized additive model (GAM). The proximity to roads, land cover (percentage of land cover in different buffers), latitude, longitude, altitude, population density, and meteorological data were all considered as potential land-use predictors. We utilized 10-fold cross-validation to evaluate the LUR model, and the R² value was 0.75 (for more details, see the Supplementary Materials). Then, we converted each participant’s residential address into a standard format recognized by the Geographic Information System (GIS) and determined exposure by averaging PM_2.5 concentrations from 2 years before enrollment for each participant.