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Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing

Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM(2.5)) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM(2.5) concentration...

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Autores principales: Li, Jing, Garshick, Eric, Hart, Jaime E., Li, Longxiang, Shi, Liuhua, Al-Hemoud, Ali, Huang, Shaodan, Koutrakis, Petros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023768/
https://www.ncbi.nlm.nih.gov/pubmed/33618328
http://dx.doi.org/10.1016/j.envint.2021.106445
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author Li, Jing
Garshick, Eric
Hart, Jaime E.
Li, Longxiang
Shi, Liuhua
Al-Hemoud, Ali
Huang, Shaodan
Koutrakis, Petros
author_facet Li, Jing
Garshick, Eric
Hart, Jaime E.
Li, Longxiang
Shi, Liuhua
Al-Hemoud, Ali
Huang, Shaodan
Koutrakis, Petros
author_sort Li, Jing
collection PubMed
description Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM(2.5)) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM(2.5) 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 PM(2.5) 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 × 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 PM(2.5) concentrations from 2001 to 2018 by converting visibility to PM(2.5) using empirical relationships derived from available regional PM(2.5) 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 PM(2.5) across the region. Annual average PM(2.5) predictions for Iraq and Kuwait were 37 and 41 μg/m(3), respectively, which are greater than current U.S. and WHO standards. PM(2.5) concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM(2.5) concentrations above 45 μg/m(3) with weekly averages as high as 150 μg/m(3) depending on calendar year. The highest annual PM(2.5) concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM(2.5) 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 PM(2.5) 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.
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spelling pubmed-80237682022-06-01 Estimation of ambient PM(2.5) 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 Environ Int Article Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM(2.5)) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM(2.5) 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 PM(2.5) 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 × 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 PM(2.5) concentrations from 2001 to 2018 by converting visibility to PM(2.5) using empirical relationships derived from available regional PM(2.5) 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 PM(2.5) across the region. Annual average PM(2.5) predictions for Iraq and Kuwait were 37 and 41 μg/m(3), respectively, which are greater than current U.S. and WHO standards. PM(2.5) concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM(2.5) concentrations above 45 μg/m(3) with weekly averages as high as 150 μg/m(3) depending on calendar year. The highest annual PM(2.5) concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM(2.5) 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 PM(2.5) 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. 2021-02-19 2021-06 /pmc/articles/PMC8023768/ /pubmed/33618328 http://dx.doi.org/10.1016/j.envint.2021.106445 Text en This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Jing
Garshick, Eric
Hart, Jaime E.
Li, Longxiang
Shi, Liuhua
Al-Hemoud, Ali
Huang, Shaodan
Koutrakis, Petros
Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
title Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
title_full Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
title_fullStr Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
title_full_unstemmed Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
title_short Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
title_sort estimation of ambient pm(2.5) in iraq and kuwait from 2001 to 2018 using machine learning and remote sensing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023768/
https://www.ncbi.nlm.nih.gov/pubmed/33618328
http://dx.doi.org/10.1016/j.envint.2021.106445
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