气候变化领域集成服务门户(CCPortal): A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019

CCPortal

DOI	10.5194/acp-22-8385-2022
	A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019
	Weng, Xiang; Forster, Grant L.; Nowack, Peer
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	8385
结束页码	8402
卷号	22 期号:12 页码:18
英文摘要	Surface ozone concentrations increased in many regions of China from 2015 to 2019. While the central role of meteorology in modulating ozone pollution is widely acknowledged, its quantitative contribution remains highly uncertain. Here, we use a data-driven machine learning approach to assess the impacts of meteorology on surface ozone variations in China for the period 2015-2019, considering the months of highest ozone pollution from April to October. To quantify the importance of various meteorological driver variables, we apply nonlinear random forest regression (RFR) and linear ridge regression (RR) to learn about the relationship between meteorological variability and surface ozone in China, and contrast the results to those obtained with the widely used multiple linear regression (MLR) and stepwise MLR. We show that RFR outperforms the three linear methods when predicting ozone using local meteorological predictor variables, as evident from its higher coefficients of determination (R-2) with observations (0.5-0.6 across China) when compared to the linear methods (typically R-2 = 0.4-0.5). This refers to the importance of nonlinear relationships between local meteorological factors and ozone, which are not captured by linear regression algorithms. In addition, we find that including nonlocal meteorological predictors can further improve the modelling skill of RR, particularly for southern China where the averaged R-2 increases from 0.47 to 0.6. Moreover, this improved RR shows a higher averaged meteorological contribution to the increased trend of ozone pollution in that region, pointing towards an elevated importance of large-scale meteorological phenomena for ozone pollution in southern China. Overall, RFR and RR are in close agreement concerning the leading meteorological drivers behind regional ozone pollution. In line with expectations, our analysis underlines that hot and dry weather conditions with high sunlight intensity are strongly related to high ozone pollution across China, thus further validating our novel approach. In contrast to previous studies, we also highlight surface solar radiation as a key meteorological variable to be considered in future analyses. By comparing our meteorology based predictions with observed ozone values between 2015 and 2019, we estimate that almost half of the 2015-2019 ozone trends across China might have been caused by meteorological variability. These insights are of particular importance given possible increases in the frequency and intensity of weather extremes such as heatwaves under climate change.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000818960500001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273646
作者单位	Imperial College London; Imperial College London; Imperial College London
推荐引用方式 GB/T 7714	Weng, Xiang,Forster, Grant L.,Nowack, Peer. A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019[J],2022,22(12):18.
APA	Weng, Xiang,Forster, Grant L.,&Nowack, Peer.(2022).A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(12),18.
MLA	Weng, Xiang,et al."A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.12(2022):18.