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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Autores principales:	Owen Francis Price, Hugh Forehead
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	smoke plume smoke exposure PM<sub>2.5</sub> smoke dispersion air quality air pollution Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/64ba2cf66fb44ba9a64a650fa2a8b4bc
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spelling	oai:doaj.org-article:64ba2cf66fb44ba9a64a650fa2a8b4bc2021-11-25T16:44:00ZSmoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors10.3390/atmos121113892073-4433https://doaj.org/article/64ba2cf66fb44ba9a64a650fa2a8b4bc2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1389https://doaj.org/toc/2073-4433Prescribed 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 PM<sub>2.5</sub> concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM<sub>2.5</sub> 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. PM<sub>2.5</sub> concentrations were higher in still, cool weather and with an unstable atmosphere. PM<sub>2.5</sub> 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.Owen Francis PriceHugh ForeheadMDPI AGarticlesmoke plumesmoke exposurePM<sub>2.5</sub>smoke dispersionair qualityair pollutionMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1389, p 1389 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smoke plume smoke exposure PM<sub>2.5</sub> smoke dispersion air quality air pollution Meteorology. Climatology QC851-999
spellingShingle	smoke plume smoke exposure PM<sub>2.5</sub> smoke dispersion air quality air pollution Meteorology. Climatology QC851-999 Owen Francis Price Hugh Forehead Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors
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 PM<sub>2.5</sub> concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM<sub>2.5</sub> 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. PM<sub>2.5</sub> concentrations were higher in still, cool weather and with an unstable atmosphere. PM<sub>2.5</sub> 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.
format	article
author	Owen Francis Price Hugh Forehead
author_facet	Owen Francis Price Hugh Forehead
author_sort	Owen Francis Price
title	Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors
title_short	Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors
title_full	Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors
title_fullStr	Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors
title_full_unstemmed	Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors
title_sort	smoke patterns around prescribed fires in australian eucalypt forests, as measured by low-cost particulate monitors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/64ba2cf66fb44ba9a64a650fa2a8b4bc
work_keys_str_mv	AT owenfrancisprice smokepatternsaroundprescribedfiresinaustralianeucalyptforestsasmeasuredbylowcostparticulatemonitors AT hughforehead smokepatternsaroundprescribedfiresinaustralianeucalyptforestsasmeasuredbylowcostparticulatemonitors
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Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors

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