教員業績データベース |
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論文種別 | 原著 |
言語種別 | 英語 |
査読の有無 | 査読あり |
表題 | Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan. |
掲載誌名 | 正式名:The Science of the total environment 略 称:Sci Total Environ ISSNコード:00489697 |
掲載区分 | 国外 |
巻・号・頁 | 634,pp.1269-1277 |
著者・共著者 | Araki Shin, Shima Masayuki, Yamamoto Kouhei. |
発行年月 | 2018/09 |
概要 | Adequate spatial and temporal estimates of NO2 concentrations are essential for proper prenatal exposure assessment. Here, we develop a spatiotemporal land use random forest (LURF) model of the monthly mean NO2 over four years in a metropolitan area of Japan. The overall objective is to obtain accurate NO2 estimates for use in prenatal exposure assessments. We use random forests to convey the non-linear relationship between NO2 concentrations and predictor variables, and compare the prediction accuracy with that of a linear regression. In addition, we include the distance decay effect of emission sources on NO2 concentrations for more efficient model construction. The prediction accuracy of the LURF model is evaluated through a leave-one-monitor-out cross validation. We obtain a high R2 value of 0.79, which is better than that of the conventional land use regression model using linear regression (R2 of 0.73). We also evaluate the LURF model via a temporal and overall cross validation and obtain R2 values of 0.84 and 0.92, respectively. We successfully integrate temporal and spatial components into our model, which exhibits higher accuracy than spatial models constructed individually for each month. Our findings illustrate the advantage of using a LURF to model the spatiotemporal variability of NO2 concentrations. |
DOI | 10.1016/j.scitotenv.2018.03.324 |
PMID | 29710628 |