气候变化领域集成服务门户(CCPortal): Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

CCPortal

DOI	10.5194/acp-22-10551-2022
	Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions
	Qiu, Minghao; Zigler, Corwin; Selin, Noelle E.
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	10551
结束页码	10566
卷号	22 期号:16 页码:16
英文摘要	Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O-3, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30 %-42 % using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.
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:000841772800001
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/273175
作者单位	Massachusetts Institute of Technology (MIT); University of Texas System; University of Texas Austin; Massachusetts Institute of Technology (MIT); Stanford University
推荐引用方式 GB/T 7714	Qiu, Minghao,Zigler, Corwin,Selin, Noelle E.. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions[J],2022,22(16):16.
APA	Qiu, Minghao,Zigler, Corwin,&Selin, Noelle E..(2022).Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(16),16.
MLA	Qiu, Minghao,et al."Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.16(2022):16.