干旱区生态环境知识资源中心(Arid): Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing

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DOI	10.1016/j.envint.2021.106445
	Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
	Li, Jing; Garshick, Eric; Hart, Jaime E.; Li, Longxiang; Shi, Liuhua; Al-Hemoud, Ali; Huang, Shaodan; Koutrakis, Petros
通讯作者	Huang, SD (corresponding author), Harvard TH Chan Sch Publ Hlth, Dept Environm Hlth, Boston, MA 02115 USA.
来源期刊	ENVIRONMENT INTERNATIONAL
ISSN	0160-4120
EISSN	1873-6750
出版年	2021
卷号	151
英文摘要	Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM2.5) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM2.5 concentrations have limited the assessment of the health impact of these exposures, both in the native populations and previously deployed military personnel. As part of a Department of Veterans Affairs Cooperative Studies Program health study of land-based U.S. military personnel who were previously deployed to these countries, we developed a novel approach to estimate spatially and temporarily resolved daily PM2.5 exposures 2001-2018. Since visibility is proportional to ground-level particulate matter concentrations, we were able to take advantage of extensive airport visibility data that became available as a result of regional military operations over this time period. First, we combined a random forest machine learning and a generalized additive mixed model to estimate daily high resolution (1 km x 1 km) visibility over the region using satellite-based aerosol optical depth (AOD) and airport visibility data. The spatially and temporarily resolved visibility data were then used to estimate PM2.5 concentrations from 2001 to 2018 by converting visibility to PM2.5 using empirical relationships derived from available regional PM2.5 monitoring stations. We adjusted for spatially resolved meteorological parameters, land use variables, including the Normalized Difference Vegetation Index, and satellite-derived estimates of surface dust as a measure of sandstorm activity. Cross validation indicated good model predictive ability (R-2 = 0.71), and there were considerable spatial and temporal differences in PM2.5 across the region. Annual average PM2.5 predictions for Iraq and Kuwait were 37 and 41 mu g/m(3), respectively, which are greater than current U.S. and WHO standards. PM2.5 concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM2.5 concentrations above 45 mu g/m(3) with weekly averages as high as 150 mu g/m(3) depending on calendar year. The highest annual PM2.5 concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM2.5 values were observed in 2014. On average, July had the highest concentrations, and November had the lowest values, consistent with seasonal patterns of air pollution in this region. This is the first study that estimates long-term PM2.5 exposures in Iraq and Kuwait at a high resolution based on measurements data that will allow the study of health effects and contribute to the development of regional environmental policies. The novel approach demonstrated may be used in other parts of the world with limited monitoring networks.
英文关键词	PM2.5 Aerosol optical depth (AOD) Visibility High resolution Exposure
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000632313500005
WOS关键词	AEROSOL OPTICAL DEPTH ; PARTICULATE MATTER ; RESPIRATORY HEALTH ; UNITED-STATES ; SOUTHWEST ASIA ; VISUAL RANGE ; SATELLITE ; AFGHANISTAN ; VISIBILITY ; DEPLOYMENT
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350090
作者单位	[Li, Jing; Hart, Jaime E.; Li, Longxiang; Shi, Liuhua; Huang, Shaodan; Koutrakis, Petros] Harvard TH Chan Sch Publ Hlth, Dept Environm Hlth, Boston, MA 02115 USA; [Garshick, Eric] VA Boston Healthcare Syst, Pulm Allergy Sleep & Crit Care Med Sect, Med Serv, Boston, MA 02132 USA; [Garshick, Eric; Hart, Jaime E.] Brigham & Womens Hosp, Dept Med, Channing Div Network Med, 75 Francis St, Boston, MA 02115 USA; [Garshick, Eric; Hart, Jaime E.] Harvard Med Sch, Boston, MA 02115 USA; [Shi, Liuhua] Emory Univ, Rollins Sch Publ Hlth, Dept Environm Hlth, Atlanta, GA 30322 USA; [Al-Hemoud, Ali] Kuwait Inst Sci Res, Environm & Life Sci Res Ctr, Crisis Decis Support Program, Safat 13109, Kuwait
推荐引用方式 GB/T 7714	Li, Jing,Garshick, Eric,Hart, Jaime E.,et al. Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing[J],2021,151.
APA	Li, Jing.,Garshick, Eric.,Hart, Jaime E..,Li, Longxiang.,Shi, Liuhua.,...&Koutrakis, Petros.(2021).Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing.ENVIRONMENT INTERNATIONAL,151.
MLA	Li, Jing,et al."Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing".ENVIRONMENT INTERNATIONAL 151(2021).