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• W2801493073 endingPage "1277" @default.
• W2801493073 startingPage "1269" @default.
• W2801493073 abstract "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." @default.
• W2801493073 created "2018-05-17" @default.
• W2801493073 creator A5009629262 @default.
• W2801493073 creator A5036543952 @default.
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• W2801493073 date "2018-09-01" @default.
• W2801493073 modified "2023-10-18" @default.
• W2801493073 title "Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan" @default.
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• W2801493073 doi "https://doi.org/10.1016/j.scitotenv.2018.03.324" @default.
• W2801493073 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29710628" @default.
• W2801493073 hasPublicationYear "2018" @default.
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