Impacts of estimated plume rise on PM.sub.2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes
Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality...
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Veröffentlicht in: | Atmospheric chemistry and physics 2023-03, Vol.23 (5), p.3083 |
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creator | Li, Yunyao Tong, Daniel Ma, Siqi Freitas, Saulo R Ahmadov, Ravan Sofiev, Mikhail Zhang, Xiaoyang Kondragunta, Shobha Kahn, Ralph Tang, Youhua Baker, Barry Campbell, Patrick Saylor, Rick Grell, Georg Li, Fangjun |
description | Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM.sub.2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM.sub.2.5 concentrations near the source region but higher AOD and PM.sub.2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %-30 % near the source regions and 5 %-10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM.sub.2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (35 µg m.sup.-3) predictions. Higher plume heights generally produce larger downwind PM.sub.2.5 exceedance areas. The PM.sub.2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM.sub.2.5 concentration and predicted PM.sub.2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM.sub.2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire event |
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In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM.sub.2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM.sub.2.5 concentrations near the source region but higher AOD and PM.sub.2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %-30 % near the source regions and 5 %-10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM.sub.2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (35 µg m.sup.-3) predictions. Higher plume heights generally produce larger downwind PM.sub.2.5 exceedance areas. The PM.sub.2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM.sub.2.5 concentration and predicted PM.sub.2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM.sub.2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.</description><identifier>ISSN: 1680-7316</identifier><language>eng</language><publisher>Copernicus GmbH</publisher><subject>Air pollution ; Air quality ; Comparative analysis ; Remote sensing ; Wildfires</subject><ispartof>Atmospheric chemistry and physics, 2023-03, Vol.23 (5), p.3083</ispartof><rights>COPYRIGHT 2023 Copernicus GmbH</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>Li, Yunyao</creatorcontrib><creatorcontrib>Tong, Daniel</creatorcontrib><creatorcontrib>Ma, Siqi</creatorcontrib><creatorcontrib>Freitas, Saulo R</creatorcontrib><creatorcontrib>Ahmadov, Ravan</creatorcontrib><creatorcontrib>Sofiev, Mikhail</creatorcontrib><creatorcontrib>Zhang, Xiaoyang</creatorcontrib><creatorcontrib>Kondragunta, Shobha</creatorcontrib><creatorcontrib>Kahn, Ralph</creatorcontrib><creatorcontrib>Tang, Youhua</creatorcontrib><creatorcontrib>Baker, Barry</creatorcontrib><creatorcontrib>Campbell, Patrick</creatorcontrib><creatorcontrib>Saylor, Rick</creatorcontrib><creatorcontrib>Grell, Georg</creatorcontrib><creatorcontrib>Li, Fangjun</creatorcontrib><title>Impacts of estimated plume rise on PM.sub.2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes</title><title>Atmospheric chemistry and physics</title><description>Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM.sub.2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM.sub.2.5 concentrations near the source region but higher AOD and PM.sub.2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %-30 % near the source regions and 5 %-10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM.sub.2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (35 µg m.sup.-3) predictions. Higher plume heights generally produce larger downwind PM.sub.2.5 exceedance areas. The PM.sub.2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM.sub.2.5 concentration and predicted PM.sub.2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM.sub.2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.</description><subject>Air pollution</subject><subject>Air quality</subject><subject>Comparative analysis</subject><subject>Remote sensing</subject><subject>Wildfires</subject><issn>1680-7316</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNptkEtLxDAUhbNQcKz-h4ArFy1J06atu2HwMTCi-FgPaXLbifRFbqpu_ecT0YUDchcX7vnOOXCPyILLksWF4PKEnCK-MZbmjGcL8rXuJ6U90rGhgN72yoOhUzf3QJ1FoONAH-8TnOskTXIKnxrAqEEDnRwYq70NgJmdHdogegfB92E701gHFN5h8HhFFdVjqAl5AQ5FfucAKOpdoPGMHDeqQzj_3RF5vbl-Wd3Fm4fb9Wq5iVvOuIy5qOvcZKAKoUopWFUZkQsGpeRSMsmN-VZqZlQuIBN1VRpelVypukozKAoRkYuf3FZ1sLVDM3qndG9Rb5dFxlJZyJAbkeQfKoyB3upxgMaG-4Hh8sAQGB8e0aoZcbt-fvrL7gHF0HiQ</recordid><startdate>20230309</startdate><enddate>20230309</enddate><creator>Li, Yunyao</creator><creator>Tong, Daniel</creator><creator>Ma, Siqi</creator><creator>Freitas, Saulo R</creator><creator>Ahmadov, Ravan</creator><creator>Sofiev, Mikhail</creator><creator>Zhang, Xiaoyang</creator><creator>Kondragunta, Shobha</creator><creator>Kahn, Ralph</creator><creator>Tang, Youhua</creator><creator>Baker, Barry</creator><creator>Campbell, Patrick</creator><creator>Saylor, Rick</creator><creator>Grell, Georg</creator><creator>Li, Fangjun</creator><general>Copernicus GmbH</general><scope>ISR</scope></search><sort><creationdate>20230309</creationdate><title>Impacts of estimated plume rise on PM.sub.2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes</title><author>Li, Yunyao ; Tong, Daniel ; Ma, Siqi ; Freitas, Saulo R ; Ahmadov, Ravan ; Sofiev, Mikhail ; Zhang, Xiaoyang ; Kondragunta, Shobha ; Kahn, Ralph ; Tang, Youhua ; Baker, Barry ; Campbell, Patrick ; Saylor, Rick ; Grell, Georg ; Li, Fangjun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-g1016-13bb5d4ea73a863099d3530e86166061dda73ab0da53e43b98d1981aab924e773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Air pollution</topic><topic>Air quality</topic><topic>Comparative analysis</topic><topic>Remote sensing</topic><topic>Wildfires</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Yunyao</creatorcontrib><creatorcontrib>Tong, Daniel</creatorcontrib><creatorcontrib>Ma, Siqi</creatorcontrib><creatorcontrib>Freitas, Saulo R</creatorcontrib><creatorcontrib>Ahmadov, Ravan</creatorcontrib><creatorcontrib>Sofiev, Mikhail</creatorcontrib><creatorcontrib>Zhang, Xiaoyang</creatorcontrib><creatorcontrib>Kondragunta, Shobha</creatorcontrib><creatorcontrib>Kahn, Ralph</creatorcontrib><creatorcontrib>Tang, Youhua</creatorcontrib><creatorcontrib>Baker, Barry</creatorcontrib><creatorcontrib>Campbell, Patrick</creatorcontrib><creatorcontrib>Saylor, Rick</creatorcontrib><creatorcontrib>Grell, Georg</creatorcontrib><creatorcontrib>Li, Fangjun</creatorcontrib><collection>Gale In Context: Science</collection><jtitle>Atmospheric chemistry and physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Yunyao</au><au>Tong, Daniel</au><au>Ma, Siqi</au><au>Freitas, Saulo R</au><au>Ahmadov, Ravan</au><au>Sofiev, Mikhail</au><au>Zhang, Xiaoyang</au><au>Kondragunta, Shobha</au><au>Kahn, Ralph</au><au>Tang, Youhua</au><au>Baker, Barry</au><au>Campbell, Patrick</au><au>Saylor, Rick</au><au>Grell, Georg</au><au>Li, Fangjun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impacts of estimated plume rise on PM.sub.2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes</atitle><jtitle>Atmospheric chemistry and physics</jtitle><date>2023-03-09</date><risdate>2023</risdate><volume>23</volume><issue>5</issue><spage>3083</spage><pages>3083-</pages><issn>1680-7316</issn><abstract>Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM.sub.2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM.sub.2.5 concentrations near the source region but higher AOD and PM.sub.2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %-30 % near the source regions and 5 %-10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM.sub.2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (35 µg m.sup.-3) predictions. Higher plume heights generally produce larger downwind PM.sub.2.5 exceedance areas. The PM.sub.2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM.sub.2.5 concentration and predicted PM.sub.2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM.sub.2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.</abstract><pub>Copernicus GmbH</pub><tpages>3083</tpages></addata></record> |
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subjects | Air pollution Air quality Comparative analysis Remote sensing Wildfires |
title | Impacts of estimated plume rise on PM.sub.2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes |
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