Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors
Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up...
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description | Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. Applying the statistical model predicts that prescribed burns of 1000 ha can be expected to cause air quality exceedances over an area of ~3500 ha. Cool weather that reduces the risk of fire escape, has the highest potential for polluting nearby communities, and fires that burn into the night are particularly bad. |
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Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. Applying the statistical model predicts that prescribed burns of 1000 ha can be expected to cause air quality exceedances over an area of ~3500 ha. Cool weather that reduces the risk of fire escape, has the highest potential for polluting nearby communities, and fires that burn into the night are particularly bad.</description><identifier>ISSN: 2073-4433</identifier><identifier>EISSN: 2073-4433</identifier><identifier>DOI: 10.3390/atmos12111389</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Air ; air pollution ; Air quality ; Airborne particulates ; Burns ; Chronic obstructive pulmonary disease ; Distance ; Fires ; Forest & brush fires ; Mathematical models ; Particulate matter ; Plumes ; PM2.5 ; Pollutants ; Pollution ; Prescribed fire ; Risk reduction ; Sensors ; Smoke ; smoke dispersion ; smoke exposure ; smoke plume ; Smoke plumes ; Statistical models ; Weather ; Weather conditions</subject><ispartof>Atmosphere, 2021-11, Vol.12 (11), p.1389</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c370t-adf0333f380643b38c0d146e9b183cf6b4523ab814fed9363401a195617fcd653</citedby><cites>FETCH-LOGICAL-c370t-adf0333f380643b38c0d146e9b183cf6b4523ab814fed9363401a195617fcd653</cites><orcidid>0000-0001-5327-568X ; 0000-0001-7035-2894</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,2096,27901,27902</link.rule.ids></links><search><creatorcontrib>Price, Owen Francis</creatorcontrib><creatorcontrib>Forehead, Hugh</creatorcontrib><title>Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors</title><title>Atmosphere</title><description>Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. Applying the statistical model predicts that prescribed burns of 1000 ha can be expected to cause air quality exceedances over an area of ~3500 ha. Cool weather that reduces the risk of fire escape, has the highest potential for polluting nearby communities, and fires that burn into the night are particularly bad.</description><subject>Air</subject><subject>air pollution</subject><subject>Air quality</subject><subject>Airborne particulates</subject><subject>Burns</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Distance</subject><subject>Fires</subject><subject>Forest & brush fires</subject><subject>Mathematical models</subject><subject>Particulate matter</subject><subject>Plumes</subject><subject>PM2.5</subject><subject>Pollutants</subject><subject>Pollution</subject><subject>Prescribed fire</subject><subject>Risk reduction</subject><subject>Sensors</subject><subject>Smoke</subject><subject>smoke dispersion</subject><subject>smoke exposure</subject><subject>smoke plume</subject><subject>Smoke plumes</subject><subject>Statistical models</subject><subject>Weather</subject><subject>Weather conditions</subject><issn>2073-4433</issn><issn>2073-4433</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNpVkU1LAzEQhhdRsFSP3gNeXU120nRzlNJqoUVBPYdJNpGt201Nskj_vdGK6FzmZT6emWGK4oLRawBJbzBtfWQVYwxqeVSMKjqFknOA4z_6tDiPcUOzcQkV8FERn7b-zZJHTMmGPhIMfugb8hhsNKHVtiGLNmvS9uR2iClg12JP5oPBbr9LZOFzMsUrgpGsLcYh5A69Jyv_Uc58TBkcUmuGDpMla9-3yYd4Vpw47KI9__Hj4mUxf57dl6uHu-XsdlUamNJUYuMoADioqeCgoTa0YVxYqVkNxgnNJxWgrhl3tpEggFOGTE4EmzrTiAmMi-WB23jcqF1otxj2ymOrvgM-vKrv7TqrBNdYZaZwmmcpUXAUE-qwwlpzbTLr8sDaBf8-5JvVxg-hz-urSlAmZV1RkavKQ5UJPsZg3e9URtXXm9S_N8EnZVGGfg</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Price, Owen Francis</creator><creator>Forehead, Hugh</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>PCBAR</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>SOI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-5327-568X</orcidid><orcidid>https://orcid.org/0000-0001-7035-2894</orcidid></search><sort><creationdate>20211101</creationdate><title>Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors</title><author>Price, Owen Francis ; Forehead, Hugh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c370t-adf0333f380643b38c0d146e9b183cf6b4523ab814fed9363401a195617fcd653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Air</topic><topic>air pollution</topic><topic>Air quality</topic><topic>Airborne particulates</topic><topic>Burns</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Distance</topic><topic>Fires</topic><topic>Forest & brush fires</topic><topic>Mathematical models</topic><topic>Particulate matter</topic><topic>Plumes</topic><topic>PM2.5</topic><topic>Pollutants</topic><topic>Pollution</topic><topic>Prescribed fire</topic><topic>Risk reduction</topic><topic>Sensors</topic><topic>Smoke</topic><topic>smoke dispersion</topic><topic>smoke exposure</topic><topic>smoke plume</topic><topic>Smoke plumes</topic><topic>Statistical models</topic><topic>Weather</topic><topic>Weather conditions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Price, Owen Francis</creatorcontrib><creatorcontrib>Forehead, Hugh</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection (ProQuest)</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environment Abstracts</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Atmosphere</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Price, Owen Francis</au><au>Forehead, Hugh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors</atitle><jtitle>Atmosphere</jtitle><date>2021-11-01</date><risdate>2021</risdate><volume>12</volume><issue>11</issue><spage>1389</spage><pages>1389-</pages><issn>2073-4433</issn><eissn>2073-4433</eissn><abstract>Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. 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subjects | Air air pollution Air quality Airborne particulates Burns Chronic obstructive pulmonary disease Distance Fires Forest & brush fires Mathematical models Particulate matter Plumes PM2.5 Pollutants Pollution Prescribed fire Risk reduction Sensors Smoke smoke dispersion smoke exposure smoke plume Smoke plumes Statistical models Weather Weather conditions |
title | Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors |
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