Estimating historical PM₂.₅ exposures for three decades (1987–2016) in Japan using measurements of associated air pollutants and land use regression

Abstract

Accurate estimation of historical PM₂.₅ exposures for epidemiological studies is challenging when extensive monitoring data are limited in duration. Here, we develop a national-scale PM₂.₅ exposure model for Japan using measurements recorded between 2014 and 2016 to estimate monthly means for 1987 through 2016. Our objective is to obtain accurate PM₂.₅ estimates for years prior to implementation of extensive PM₂.₅ monitoring, using observations from a limited period. We utilize a neural network to convey the non-linear relationship between the target pollutant and predictors, while incorporating the associated air pollutants. We obtain high R² values of 0.76 and 0.73 through spatial and temporal cross validation, respectively. We evaluate estimation accuracy using an independent data set and achieve an R² of 0.75. Moreover, monthly variations for 2000–2013 are well reproduced with correlation coefficients of greater than 0.78, obtained through a comparison with observations. We estimate monthly means at 1 × 1 km resolution from 1987 through 2016. The estimates show decreases in the area and population weighted means beginning in the 1990s. We successfully estimate monthly mean PM₂.₅ concentrations over three decades with outstanding predictive accuracy. Our findings illustrate that the presented approach achieves accurate long-term historical estimations using observations limited in duration.