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.
Keywords: Air pollution; Machine learning; Spatial distribution; Temporal trend.
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