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Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 by Binqian Zhang; Yunjiang Zhang; Kexin Zhang; Yichen Zhang; Yao Ji; Baizhen Zhu; Zeye Liang; Hongli Wang; Xinlei Ge is a Environmental Science article available to read on EtoBox.
What is Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 about?
The Tibetan Plateau (known as the Earth's Third Pole) has significant impact on climate. Fine particulate matter (PM2.5) is an important air pollutant in this region and has significant impact on health and climate. To mitigate PM2.5 air pollution over China, a series of clean air actions has been implemented. However, interannual trends in particulate air pollution and its response to anthropogenic emissions in the Tibetan Plateau are poorly understood. Here, we applied a random forest (RF) algorithm to quantify drivers of PM2.5 trends in six cities of the Tibetan Plateau from 2015 to 2022. The decreasing trends (−5.31 to −0.73 μg m−3 a−1) in PM2.5 during 2015–2022 were observed in all cities. The RF weather-normalized PM2.5 trends – which were driven by anthropogenic emissions – were −4.19 to −0.56 μg m−3 a−1, resulting in dominant contributions (65 %–83 %) to the observed PM2.5 trends. Relative to 2015, such anthropogenic emission driver was estimated to contribute −27.12 to −3.16 μg m−3 to declines in PM2.5 concentrations in 2022. However, the interannual changes in meteorological conditions only made a small contribution to the trends in PM2.5 concentrations. Potential source
Who reads Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Binqian Zhang; Yunjiang Zhang; Kexin Zhang; Yichen Zhang; Yao Ji; Baizhen Zhu; Zeye Liang; Hongli Wang; Xinlei Ge
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Environmental Science (Physical Sciences)