Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning-Reference-Cited by-同舟云学术

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Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning

Published:2021-06 Issue: Volume:254 Page:105516
ISSN:0169-8095
Container-title:Atmospheric Research
language:en
Short-container-title:Atmospheric Research

Author:

Zeng Zhaoliang,Gui Ke,Wang Zemin,Luo Ming,Geng Hong,Ge Erjia,An Jiachun,Song Xiangyu,Ning Guicai,Zhai Shixian,Liu Haizhi

Funder

National Natural Science Foundation of China

Guangdong Provincial Pearl River Talents Program

Publisher

Elsevier BV

Subject

Atmospheric Science

Reference50 articles.

1. Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms;Barzegar;Sci. Total Environ.,2018

2. Large contribution of meteorological factors to inter-decadal changes in regional aerosol optical depth;Che;Atmos. Chem. Phys.,2019

3. Stacking machine learning model for estimating hourly PM2.5 in China based on Himawari 8 aerosol optical depth data;Chen;Sci. Total Environ.,2019

4. LightGBM-PPI: predicting protein-protein interactions through LightGBM with multi-information fusion;Chen;Chemom. Intell. Lab. Syst.,2019

5. Extreme gradient boosting model to estimate PM 2.5 concentrations with missing-filled satellite data in China;Chen;Atmos. Environ.,2019

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