气候变化领域集成服务门户(CCPortal): The impacts of transported wildfire smoke aerosols on surface air quality in New York State: A multi-year study using machine learning

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DOI	10.1016/j.atmosenv.2021.118513
	The impacts of transported wildfire smoke aerosols on surface air quality in New York State: A multi-year study using machine learning
	Hung W.-T.; Lu C.-H.S.; Alessandrini S.; Kumar R.; Lin C.-A.
发表日期	2021
ISSN	1352-2310
卷号	259
英文摘要	Smoke aerosols emitted from wildfires can transport across long distances and affect the local air quality in downwind regions. In New York State (NYS), the local air quality has significantly improved due to reductions in anthropogenic emission over the past decades. As the intensity and frequency of wildfires are continuously increasing under changing climate, smoke aerosols are predicted to become the dominant source of fine particulate matter (PM2.5) concentration in NYS in the future. In this study, smoke and non-smoke cases in NYS during the summer seasons of 2012–2019 were identified using satellite measurements and aerosol reanalysis products. Overall, smoke cases showed higher PM2.5 concentrations than non-smoke cases with average PM2.5 concentrations of 11.5 ± 5.9 μg m−3 and 6.6 ± 4.6 μg m−3, respectively. PM2.5 concentrations exceeding 20 μg m−3 mainly occurred during smoke cases. In addition, an artificial neural network (ANN) algorithm was used to estimate surface PM2.5 mass concentrations at 21 air quality monitoring sites in NYS. Results showed that, for smoke cases, the application of predictors designed as indicators of vertical transport mechanisms and smoke inflow from the fire source regions generally improved the model performance by reducing the model errors. Also, analysis of the variable correlations and variable importance indicated that synoptic subsidence, entrainment process, and turbulent mixing within PBL collectively contributed to PM2.5 concentrations for smoke cases. Machine learning techniques showed the capabilities of learning the general air quality features, characterizing the key contributors to PM2.5 concentrations, and distinguishing the vertical transport processes of smoke aerosols. © 2021 The Authors
关键词	Air quality Machine learning PM2.5 Smoke aerosol Vertical mixing
语种	英语
scopus关键词	Aerosols; Air entrainment; Air quality; Atmospheric movements; Fires; Machine learning; Neural networks; Smoke; % reductions; Anthropogenic emissions; Changing climate; Fine particulate matter; Machine-learning; New York State; PM$-2.5$; Smoke aerosols; Vertical mixing; Wildfire smoke; Mixing; air quality; anthropogenic source; artificial neural network; atmospheric pollution; concentration (composition); machine learning; particulate matter; smoke; spatiotemporal analysis; turbulent mixing; wildfire; aerosol; air monitoring; article; artificial neural network; machine learning; New York; particulate matter 2.5; smoke; summer; wildfire; New York [New York (STT)]; New York [United States]; United States
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248375
作者单位	Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, United States; University Corporation for Atmospheric Research, Boulder, CO, United States; Joint Center for Satellite Data Assimilation, Boulder, CO, United States; Research Applications Laboratory, National Center of Atmospheric Research, Boulder, CO, United States
推荐引用方式 GB/T 7714	Hung W.-T.,Lu C.-H.S.,Alessandrini S.,et al. The impacts of transported wildfire smoke aerosols on surface air quality in New York State: A multi-year study using machine learning[J],2021,259.
APA	Hung W.-T.,Lu C.-H.S.,Alessandrini S.,Kumar R.,&Lin C.-A..(2021).The impacts of transported wildfire smoke aerosols on surface air quality in New York State: A multi-year study using machine learning.ATMOSPHERIC ENVIRONMENT,259.
MLA	Hung W.-T.,et al."The impacts of transported wildfire smoke aerosols on surface air quality in New York State: A multi-year study using machine learning".ATMOSPHERIC ENVIRONMENT 259(2021).