干旱区生态环境知识资源中心(Arid): Estimation of fine particulate matter in an arid area from visibility based on machine learning

Arid

DOI	10.1038/s41370-022-00480-3
	Estimation of fine particulate matter in an arid area from visibility based on machine learning
	Li, Jing; Kang, Choong-Min; Wolfson, Jack M.; Alahmad, Barrak; Al-Hemoud, Ali; Garshick, Eric; Koutrakis, Petros
通讯作者	Li, J
来源期刊	JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY
ISSN	1559-0631
EISSN	1559-064X
出版年	2022
卷号	32 期号:6 页码:926-931
英文摘要	Background The absence of air pollution monitoring networks makes it difficult to assess historical fine particulate matter (PM2.5) exposures for countries in the areas, such as Kuwait, which are severe impacted by desert dust and anthropogenic pollution. Objective We constructed an ensemble machine learning model to predict daily PM2.5 concentrations for regions lack of PM2.5 observations. Methods The model was constructed based on daily PM2.5, visibility, and other meteorological data collected at two sites in Kuwait. Then, our model was applied to predict the daily level of PM2.5 concentrations for eight airports located in Kuwait and Iraq from 2013 to 2020. Results As compared to traditional statistic models, the proposed machine learning methods improved the accuracy in using visibility to predict daily PM2.5 concentrations with a cross-validation R-2 of 0.68. The predicted level of daily PM2.5 concentrations were consistent with previous measurements. The predicted average yearly PM2.5 concentration for the eight stations is 50.65 mu g/m(3). For all stations, the monthly average PM2.5 concentrations reached their maximum in July and their minimum in November. Significance These findings make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in regions with few particulate matter monitoring stations. Impact statement The scarcity of air pollution ground monitoring networks makes it difficult to assess historical fine particulate matter exposures for countries in arid areas such as Kuwait. Visibility is closely related to atmospheric particulate matter concentrations and historical airport visibility records are commonly available in most countries. Our model make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in arid regions with few particulate matter ground monitoring stations. The product of such models can be critical for environmental risk assessments and population health studies.
英文关键词	Air Pollution Environmental Monitoring Exposure Modeling
类型	Article
语种	英语
开放获取类型	Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000860933300001
WOS关键词	SOUTHWEST ASIA ; VISUAL RANGE ; EXPOSURES ; PM2.5 ; MORTALITY
WOS类目	Environmental Sciences ; Public, Environmental & Occupational Health ; Toxicology
WOS研究方向	Environmental Sciences & Ecology ; Public, Environmental & Occupational Health ; Toxicology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393430
推荐引用方式 GB/T 7714	Li, Jing,Kang, Choong-Min,Wolfson, Jack M.,et al. Estimation of fine particulate matter in an arid area from visibility based on machine learning[J],2022,32(6):926-931.
APA	Li, Jing.,Kang, Choong-Min.,Wolfson, Jack M..,Alahmad, Barrak.,Al-Hemoud, Ali.,...&Koutrakis, Petros.(2022).Estimation of fine particulate matter in an arid area from visibility based on machine learning.JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY,32(6),926-931.
MLA	Li, Jing,et al."Estimation of fine particulate matter in an arid area from visibility based on machine learning".JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY 32.6(2022):926-931.