Mallet, MD, Meteorological normalisation of PM10 using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah, Atmospheric Pollution Research, 12, (1) pp. 23-35. ISSN 1309-1042 (2021) [Refereed Article]
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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 PM10 air quality in the mining town of Moranbah, Australia, has worsened since measurements commenced in 2011. The annual average PM10 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 PM10 was 1.2 ±0.5 μg m3 per year between 2011 and 2019 and has been 2.5 ±1.2 μg m3 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 PM10 mass concentrations measured in Moranbah. The best performing model, using the random forest algorithm, was able to explain 59% of the variance in PM10 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 m3 per year since 2011 and 1.7 ± 0.3 μg m3 per year since 2014.per year since 2014. These results indicate that more than half of the increase in PM10 is due to a rise in local emissions in the region. The remainder of the rise in PM10 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. PM10 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.
|Item Type:||Refereed Article|
|Keywords:||meteorological normalisation, PM10, air quality, mining activities, machine learning|
|Research Division:||Earth Sciences|
|Research Group:||Atmospheric sciences|
|Research Field:||Air pollution processes and air quality measurement|
|Objective Division:||Environmental Management|
|Objective Group:||Air quality, atmosphere and weather|
|Objective Field:||Air quality|
|UTAS Author:||Mallet, MD (Dr Marc Mallet)|
|Web of Science® Times Cited:||4|
|Deposited By:||Oceans and Cryosphere|
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