Meteorological normalisation of PM(10) using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah

利用机器学习对PM(10)进行气象标准化处理,结果显示澳大利亚矿业小镇莫兰巴附近污染源的排放量明显增加。

阅读:2

Abstract

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 m-3 per year between 2011 and 2019 and has been 2.5 ± 1.2 μg m-3 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 m-3 per year since 2011 and 1.7 ± 0.3 μg m-3 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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。