气候变化领域集成服务门户(CCPortal): Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model

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DOI	10.1016/j.scitotenv.2024.170235
	Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model
	Dai, Tianjiao; Dai, Qili; Yin, Jingchen; Chen, Jiajia; Liu, Baoshuang; Bi, Xiaohui; Wu, Jianhui; Zhang, Yufen; Feng, Yinchang
发表日期	2024
ISSN	0048-9697
EISSN	1879-1026
起始页码	917
卷号	917
英文摘要	Ambient particulate matter (PM2.5 and PM10), has been extensively monitored in numerous urban areas across the globe. Over the past decade, there has been a significant improvement in PM2.5 air quality, while improvements in PM10 levels have been comparatively modest, primarily due to the limited reduction in coarse particle (PM2.5-10) pollution. Unlike PM2.5, PM2.5-10 predominantly originates from local emissions and is often characterized by pronounced spatial heterogeneity. In this study, we utilized over one million data points on PM concentrations, collected from >100 monitoring sites within a Chinese megacity, to perform spatial source apportionment of PM2.5-10. Despite the widespread availability of such data, it has rarely been employed for this purpose. We employed an enhanced positive matrix factorization approach, capable of handling large datasets, in conjunction with a Bayesian multivariate receptor model to deduce spatial source impacts. Four primary sources were successfully identified and interpreted, including residential burning, industrial processes, road dust, and meteorology-related sources. This interpretation was supported by a considerable body of prior knowledge concerning emission sources, which is usually unavailable in most cases. The methodology proposed in this study demonstrates significant potential for generalization to other regions, thereby contributing to the development of air quality management strategies.
英文关键词	Spatial source apportionment; Positive matrix factorization; Bayesian multivariate receptor model; Big data
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001176358200001
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295317
作者单位	Nankai University; Nankai University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
推荐引用方式 GB/T 7714	Dai, Tianjiao,Dai, Qili,Yin, Jingchen,et al. Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model[J],2024,917.
APA	Dai, Tianjiao.,Dai, Qili.,Yin, Jingchen.,Chen, Jiajia.,Liu, Baoshuang.,...&Feng, Yinchang.(2024).Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model.SCIENCE OF THE TOTAL ENVIRONMENT,917.
MLA	Dai, Tianjiao,et al."Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model".SCIENCE OF THE TOTAL ENVIRONMENT 917(2024).