气候变化领域集成服务门户(CCPortal): An integrated model combining random forests and WRF/CMAQ model for high accuracy spatiotemporal PM2.5 predictions in the Kansai region of Japan

CCPortal

DOI	10.1016/j.atmosenv.2021.118620
	An integrated model combining random forests and WRF/CMAQ model for high accuracy spatiotemporal PM2.5 predictions in the Kansai region of Japan
	Thongthammachart T.; Araki S.; Shimadera H.; Eto S.; Matsuo T.; Kondo A.
发表日期	2021
ISSN	1352-2310
卷号	262
英文摘要	Accurate spatial and temporal prediction of PM2.5 ambient concentration is crucial to appropriate exposure assessment. We develop a spatiotemporal land use regression model by integrating a random forests (RF) technique and the Community Multiscale Air Quality (CMAQ) modeling system to accurately estimate daily PM2.5 levels in the Kansai region of Japan, which is affected by long-range transport in the Asian continent and by local pollution. The most important advantage of RF is that it captures nonlinearity among the target air pollutants and the predictor variables including land-use variables, meteorological variables, and CMAQ-estimated PM2.5 concentration. We compare the predicting performances of the land use random forests (LURF) models with and without CMAQ variables to determine their effectiveness. A cross-validation (CV) technique that calculates the coefficient of determination (R2) and root mean square error (RMSE) is performed to evaluate their prediction performances through spatial and temporal CVs. The performance of the with-CMAQ LURF model was superior to that of the without-CMAQ LURF model. Moreover, we evaluated the PM2.5 prediction performances of the with-CMAQ LURF and the with-CMAQ land use linear regression (LULR) models via CV to determine the efficiency of the non-linear model. Accordingly, the with-CMAQ LURF model is preferable for PM2.5 estimation compared to that of the with-CMAQ LULR model. In addition, the with-CMAQ LURF model exhibits higher PM2.5 predictability than the CMAQ model, as indicated by the higher model-R2 and lower model-RMSE values. Our findings demonstrate that the CMAQ-simulated PM2.5 level integrated into the LURF is advantageous in accurately estimating PM2.5 concentration, which is influenced by long-range transport and local pollution. © 2021 Elsevier Ltd
关键词	Air pollution Chemical transport model Land use regression Random forests
语种	英语
scopus关键词	Decision trees; Forecasting; Land use; Mean square error; Random forests; Regression analysis; Chemical transport models; Community multi-scale air qualities; Community multi-scale air quality models; Land use regression; Local pollutions; Long range transport; Performance; PM$-2.5$; Random forest modeling; Random forests; Air quality; air quality; ambient air; land use; meteorology; nonlinearity; particulate matter; regression analysis; spatiotemporal analysis; air quality; article; cross validation; Japan; land use; linear regression analysis; nonlinear system; particulate matter 2.5; prediction; predictor variable; random forest; simulation; Japan
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248291
作者单位	Graduate School of Engineering, Osaka University, Suita, Japan
推荐引用方式 GB/T 7714	Thongthammachart T.,Araki S.,Shimadera H.,et al. An integrated model combining random forests and WRF/CMAQ model for high accuracy spatiotemporal PM2.5 predictions in the Kansai region of Japan[J],2021,262.
APA	Thongthammachart T.,Araki S.,Shimadera H.,Eto S.,Matsuo T.,&Kondo A..(2021).An integrated model combining random forests and WRF/CMAQ model for high accuracy spatiotemporal PM2.5 predictions in the Kansai region of Japan.ATMOSPHERIC ENVIRONMENT,262.
MLA	Thongthammachart T.,et al."An integrated model combining random forests and WRF/CMAQ model for high accuracy spatiotemporal PM2.5 predictions in the Kansai region of Japan".ATMOSPHERIC ENVIRONMENT 262(2021).