Using machine learning to quantify drivers of aerosol pollution trend in China from 2015 to 2022-Reference-Cited by-同舟云学术

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Using machine learning to quantify drivers of aerosol pollution trend in China from 2015 to 2022

Published:2023-04 Issue: Volume:151 Page:105614
ISSN:0883-2927
Container-title:Applied Geochemistry
language:en
Short-container-title:Applied Geochemistry

Author:

Ji Yao,Zhang Yunjiang,Liu Diwen,Zhang Kexin,Cai Pingping,Zhu Baizhen,Zhang Binqian,Xian Jiukun,Wang Hongli,Ge Xinlei

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Pollution,Environmental Chemistry

Reference49 articles.

1. Severe haze in northern China: a synergy of anthropogenic emissions and atmospheric processes;An;Proc. Natl. Acad. Sci. U.S.A.,2019

2. Associations between short-term exposure to PM2.5 and stroke incidence and mortality in China: a case-crossover study and estimation of the burden;Ban;Environ. Pollut.,2021

3. Random forests;Breiman;Mach. Learn.,2001

4. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter;Burnett;Proc. Natl. Acad. Sci. U.S.A.,2018

5. Retrospective analysis of 2015–2017 wintertime PM2.5 in China: response to emission regulations and the role of meteorology;Chen;Atmos. Chem. Phys.,2019

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4. Ada-XG-CatBoost: A Combined Forecasting Model for Gross Ecosystem Product (GEP) Prediction;Sustainability;2024-08-22

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