基于机器学习的深圳大气颗粒物消光来源识别

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Abstract High time resolution observations of scattering coefficient of particulate matter (PM) were conducted using turbidimeter, while PM_2.5 and its chemical compositions were analysed using others corresponding online instruments from November 2021 to December 2021 in Shenzhen, China. Receptor model and interpretable machine learning models were employed to identify the major sources of PM scattering coefficient. The average PM scattering coefficient at 525nm was 119.6 1/Mm during the observation period in Shenzhen. Correlation analysis revealed that PM scattering coefficient had low correlations with relative humidity (RH) and high correlations with PM_2.5 and its components, such as NH₄⁺, NO₃^-, m/z 44, OM, SO₄^2-, and black carbon, indicating the important influence of secondary components and black carbon in PM_2.5 on PM scattering coefficient in Shenzhen. The XGBoost model was employed to predict the PM scattering coefficient based on RH and PM_2.5 sources identified by the receptor model, and then the SHAP model was used to identify the major factors of PM scattering coefficient. The results indicated that secondary nitrate, secondary sulfate, vehicle emissions, and secondary organic aerosols were the major sources of PM scattering coefficient in Shenzhen during fall and winter, followed by coal combustion and RH. Biomass burning, ship emissions, industrial emissions, fugitive dust and construction dust had small contributions.

Key words： PM_2.5 scattering coefficient source apportionment PMF model machine learning

Received: 16 October 2023

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X513

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	Articles by authors
	LI Shi-ping
	PENG Xing
	YAO Pei-ting
	LIN Xiao-yu
	YUN Long
	HUANG Xiao-feng
	HE Ling-yan

Cite this article:

LI Shi-ping,PENG Xing,YAO Pei-ting等. Source apportionment of extinction of ambient particulate matter in Shenzhen using machine learning[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(6): 2977-2984.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I6/2977

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