High-resolution downscaling of source resolved PM2.5 predictions using machine learning models-Reference-Cited by-同舟云学术

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High-resolution downscaling of source resolved PM2.5 predictions using machine learning models

Published:2023-10 Issue: Volume:310 Page:119967
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Dinkelacker Brian T.,Garcia Rivera Pablo^ORCID,Marshall Julian D.,Adams Peter J.,Pandis Spyros N.^ORCID

Publisher

Elsevier BV

Subject

Atmospheric Science,General Environmental Science

Reference30 articles.

1. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems;Abadi,2015

2. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions;Bai;Atmos. Pollut. Res.,2016

3. Environmental justice: the economics of race, place, and pollution;Banzhaf;JEP (J. Environ. Psychol.),2019

4. Classification and Regression Trees;Breiman,1984

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

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High-Resolution Spatiotemporal Modeling for PM_2.5 Major Components in the Pearl River Delta and Its Implications for Epidemiological Studies;Environmental Science & Technology;2024-06-11

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