Biomass burning emissions in north Australia during the early dry season: an overview of the 2014 SAFIRED campaign

Mallet, Marc; Desservettaz, Maximilien; Miljevic, Branka; Milić, Anđelija; Ristovski, Zoran; Alroe, Joel; Cravigan, Luke T.; Jayaratne, E.R.; Paton-Walsh, Clare; Griffith, David; Wilson, Stephen R.; Kettlewell, Graham; Schoot, Marcel V. van der; Selleck, Paul; Reisen, Fabienne; Lawson, Sarah; Ward, Jason; Harnwell, James; Cheng, Min; Gillett, Rob; Molloy, Suzie; Howard, Dean; Nelson, Peter F.; Morrison, A. B.; Edwards, Grant C.; Williams, Alastair G.; Chambers, Scott D.; Werczynski, Sylvester; Williams, Leah R.; Winton, V. Holly L.; Atkinson, Brad; Wang, Xianyu; Keywood, Melita

doi:10.5194/acp-17-13681-2017

Cited by 25 publications

(31 citation statements)

References 62 publications

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“…A detailed description of the campaign, the site, and all the instruments deployed is available in the overview manuscript by Mallet et al []. Components of interest with regard to this manuscript are presented below:…”

Section: The Safired Campaign and Instrumentationmentioning

confidence: 99%

“…Figures and show the time series of trace gases and Aitken and accumulation aerosol size modes recorded during the campaign. Those figures also denote the four biomass burning periods (BBP) identified by Mallet et al [] and nine strong smoke events (A to I) identified as part of this study (see Table ).…”

Section: Atmospheric Composition and Meteorology During The Safired Cmentioning

confidence: 99%

“…Distance from site was estimated using MODIS hot spot locations and HYSPLIT back trajectories. See Mallet et al [] and Milic et al [] for more details.…”

Section: Atmospheric Composition and Meteorology During The Safired Cmentioning

confidence: 99%

“…Note the breaks in the y axis to better represent the range of values recorded during the campaign. BBP1–4 represent the burning periods 1 to 4 as introduced by Mallet et al [], and nine events are identified A to I.…”

Section: Atmospheric Composition and Meteorology During The Safired Cmentioning

confidence: 99%

“…The particle size distributions measured by the SMPS were converted to particle mass distributions by assuming a constant density of 1.2 g cm −3 across all submicron particles. This value for density was calculated based on the average contributions to the submicron mass of organic carbon (84%, 1.1 g cm −3 ), inorganics (8%, 1.8 g cm −3 ), and elemental carbon (8%, 1.8 g cm −3 ), as measured by the cTOF‐AMS and filter measurements [ Mallet et al , ; Schkolnik et al , ].…”

Section: Atmospheric Composition and Meteorology During The Safired Cmentioning

confidence: 99%

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Emission factors of trace gases and particles from tropical savanna fires in Australia

et al. 2017

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Savanna fires contribute significantly to global aerosol loading and hence to the Earth's radiative budget. Modeling of the climatic impact of these aerosols is made difficult due to a lack of knowledge of their size distribution. Australia is the third largest source of global carbon emissions from biomass burning, with emissions dominated by tropical savanna fires. Despite this, only a few previous studies have reported emission factors of trace gases from this important ecosystem and there are no previous published emission factors for the aerosol properties reported here for Australian savanna fires. In June 2014, the SAFIRED campaign (Savanna Fires in the Early Dry season) took place in the Northern Territory of Australia, with the purpose of investigating emissions and aging of aerosols from Australian savanna fires. This paper presents observed enhancement ratios and inferred emission factors of trace gases (CO2, CO, CH4, N2O, and gaseous elemental mercury), particles over different size modes (Aitken and accumulation), and speciated aerosols components (organics, sulfate, nitrate, ammonium, and chloride). Nine smoke events were identified from the data using large enhancements in CO and/or aerosol data to indicate biomass burning event. The results reported in this paper include the first emission factors for Aitken and accumulation mode aerosols from savanna fires, providing useful size information to enable better modeling of the climatic impact of this important source of global aerosols.

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Section: The Safired Campaign and Instrumentationmentioning

confidence: 99%

Section: Atmospheric Composition and Meteorology During The Safired Cmentioning

confidence: 99%

“…Distance from site was estimated using MODIS hot spot locations and HYSPLIT back trajectories. See Mallet et al [] and Milic et al [] for more details.…”

Section: Atmospheric Composition and Meteorology During The Safired Cmentioning

confidence: 99%

Section: Atmospheric Composition and Meteorology During The Safired Cmentioning

confidence: 99%

Section: Atmospheric Composition and Meteorology During The Safired Cmentioning

confidence: 99%

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Emission factors of trace gases and particles from tropical savanna fires in Australia

et al. 2017

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Remote sensing of aerosols due to biomass burning over Kanpur, Sao-Paulo, Ilorin and Canberra

et al. 2022

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Meteorological normalisation of PM10 using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah

Mallet

2021

Atmospheric Pollution Research

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The impacts of poor air quality on human health are becoming more apparent. Businesses and governments are implementing technologies and policies in order to improve air quality. Despite this the PM 10 air quality in the mining town of Moranbah, Australia, has worsened since measurements commenced in 2011. The annual average PM 10 concentrations during 2012, 2017, 2018 and 2019 have all exceeded the Australian National Environmental Protection Measure's standard, and there has been an increase in the frequency of exceedances of the daily standard. The average annual increase in PM 10 was 1.2 0.5 μg per year between 2011 and 2019 and has been 2.5 1.2 μg per year since 2014. The cause of this has not previously been established. Here, two machine learning algorithms (gradient boosted regression and random forest) have been implemented to model and then meteorologically normalise PM 10 mass concentrations measured in Moranbah. The best performing model, using the random forest algorithm, was able to explain 59% of the variance in PM 10 using a range of meteorological, environmental and temporal variables as predictors. An increasing trend after normalising for these factors was found of 0.6 0.5 μg per year since 2011 and 1.7 0.3 μg per year since 2014. These results indicate that more than half of the increase in PM 10 is due to a rise in local emissions in the region. The remainder of the rise in PM 10 was found to be due to a decrease of soil water content in the surrounding region, which can facilitate higher dust emissions. Whether the presence of open-cut coal mines exacerbated the role of soil water content is unclear. Although fires can have drastic effects on the local air quality, changes in fire patterns are not responsible for the rising trend. PM 10 composition measurements or more detailed data relating to local sources is still needed to better isolate these emissions. Nonetheless, this study highlights the need and potential for action by industry and government to improve the air quality and reduce health risks for the nearby population.

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Biomass burning emissions in north Australia during the early dry season: an overview of the 2014 SAFIRED campaign

Cited by 25 publications

References 62 publications

Emission factors of trace gases and particles from tropical savanna fires in Australia

Emission factors of trace gases and particles from tropical savanna fires in Australia

Remote sensing of aerosols due to biomass burning over Kanpur, Sao-Paulo, Ilorin and Canberra

Meteorological normalisation of PM10 using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah

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