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| Estimating aerosol optical extinction across eastern China in winter during 2014–2019 using the random forest approach 期刊论文
ATMOSPHERIC ENVIRONMENT, 2022, 卷号: 269
作者: Chen X.; Li X.; Li X.; Liang J.; Li J.; Chen G.; Pei Z.; Wei X.; Zeng G.
  收藏 | 浏览/下载：38/0 | 提交时间：2022/01/18
Haze pollution Optical extinction PM2.5 Random forests Slower decline trend |
| Deriving hourly full-coverage PM2.5 concentrations across China's Sichuan Basin by fusing multisource satellite retrievals: A machine-learning approach 期刊论文
ATMOSPHERIC ENVIRONMENT, 2022, 卷号: 271
作者: Liu Y.; Li C.; Liu D.; Tang Y.; Seyler B.C.; Zhou Z.; Hu X.; Yang F.; Zhan Y.
收藏 | 浏览/下载：31/0 | 提交时间：2022/01/18
Aerosol optical depth Data fusion Fine particulate matter Hourly PM2.5 Machine learning Sichuan basin |
| Combined land-use and street view image model for estimating black carbon concentrations in urban areas 期刊论文
ATMOSPHERIC ENVIRONMENT, 2021, 卷号: 265
作者: Liu X.; Hadiatullah H.; Zhang X.; Schnelle-Kreis J.; Zhang X.; Lin X.; Cao X.; Zimmermann R.
收藏 | 浏览/下载：26/0 | 提交时间：2022/01/18
Black carbon Land-use Random forest Street view images |
| Gaussian Markov random fields improve ensemble predictions of daily 1 km PM2.5 and PM10 across France 期刊论文
ATMOSPHERIC ENVIRONMENT, 2021, 卷号: 264
作者: Hough I.; Sarafian R.; Shtein A.; Zhou B.; Lepeule J.; Kloog I.
收藏 | 浏览/下载：112/0 | 提交时间：2022/01/18
Aerosol optical depth Ensemble model Epidemiology Exposure assessment Particulate matter |
| Declining dry deposition of NO2 and SO2 with diverse spatiotemporal patterns in China from 2013 to 2018 期刊论文
ATMOSPHERIC ENVIRONMENT, 2021, 卷号: 262
作者: Zhou K.; Zhao Y.; Zhang L.; Xi M.
收藏 | 浏览/下载：16/0 | 提交时间：2022/01/18
Air pollution control Dry deposition Random forest Spatiotemporal pattern |
| An integrated model combining random forests and WRF/CMAQ model for high accuracy spatiotemporal PM2.5 predictions in the Kansai region of Japan 期刊论文
ATMOSPHERIC ENVIRONMENT, 2021, 卷号: 262
作者: Thongthammachart T.; Araki S.; Shimadera H.; Eto S.; Matsuo T.; Kondo A.
收藏 | 浏览/下载：23/0 | 提交时间：2022/01/18
Air pollution Chemical transport model Land use regression Random forests |
| Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach 期刊论文
ATMOSPHERIC ENVIRONMENT, 2020, 卷号: 230
作者: Zheng T.; Bergin M.H.; Hu S.; Miller J.; Carlson D.E.
收藏 | 浏览/下载：19/0 | 提交时间：2022/01/18
CNN Computer vision Convolutional neural network Fine particulate matter (PM2.5) prediction Random forest RF Satellite imagery |
| Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data 期刊论文
ATMOSPHERIC ENVIRONMENT, 2020, 卷号: 224
作者: Qin K.; Han X.; Li D.; Xu J.; Li D.; Loyola D.; Zhou X.; Xue Y.; Zhang K.; Yuan L.
收藏 | 浏览/下载：17/0 | 提交时间：2022/01/18
Extra trees NO2 OMI OMNO2d POMINO Random forest |
| A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration 期刊论文
ATMOSPHERIC ENVIRONMENT, 2020, 卷号: 222
作者: Berrocal V.J.; Guan Y.; Muyskens A.; Wang H.; Reich B.J.; Mulholland J.A.; Chang H.H.
收藏 | 浏览/下载：99/0 | 提交时间：2022/01/18 |
| Roles of RH, aerosol pH and sources in concentrations of secondary inorganic aerosols, during different pollution periods 期刊论文
ATMOSPHERIC ENVIRONMENT, 2020, 卷号: 241
作者: Gao J.; Wei Y.; Shi G.; Yu H.; Zhang Z.; Song S.; Wang W.; Liang D.; Feng Y.
收藏 | 浏览/下载：23/0 | 提交时间：2022/01/18
Aerosol pH Gaseous precursors Machine learning RH Secondary inorganic aerosols Source apportionment |
