A novel machine learning method for evaluating the impact of emission sources on ozone formation

Autor:	Yong Cheng, Xiao-Feng Huang, Yan Peng, Meng-Xue Tang, Bo Zhu, Shi-Yong Xia, Ling-Yan He
Rok vydání:	2023
Předmět:	Machine Learning Volatile Organic Compounds Ozone Air Pollution Health Toxicology and Mutagenesis General Medicine Environmental Pollution Toxicology Pollution
Zdroj:	Environmental Pollution. 316:120685
ISSN:	0269-7491
DOI:	10.1016/j.envpol.2022.120685
Popis:	Ambient ozone air pollution is one of the most important environmental challenges in China today, and it is particularly significant to identify pollution sources and formulate control strategies. In present study, we proposed a novel method of positive matrix factorization-SHapley Additive explanation (PMF-SHAP) for evaluating the impact of emission sources on ozone formation, which can quantify the main emission sources of ozone pollution. In this method, we first used the PMF model to identify the source of volatile organic compounds (VOCs), and then quantified various emission sources using a combination of machine learning (ML) models and the SHAP algorithm. The R
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00ede962fbe5817ba639720250242d96 https://doi.org/10.1016/j.envpol.2022.120685 Zobrazit plný text záznamu Full Text from ScienceDirect