出版日期：2020-07-10 00:00:00

著者：Jia-Hong Tang; Shih-Chun Candice Lung; Jing-Shiang Hwang

著錄名稱、卷期、頁數：Atmospheric Pollution Research 11(10), p. 1715-1727

摘要：Identifying realistic pollution source profiles and quantifying the contributions of atmospheric particulate matter are crucial for the development of pollution mitigation strategies to protect public health. In this paper, we proposed a multivariate source apportionment model by using a Bayesian framework for latent source profiles to incorporate expert knowledge regarding emissions that can facilitate source profile estimation, and atmospheric effects, such as meteorological conditions, can improve source concentration estimations. This approach can maintain positivity and summation constraints for source contributions and profiles. Furthermore, available expert knowledge regarding source profiles is incorporated as prior knowledge to avoid restrictive assumptions regarding the presence or absence of chemical constituent tracers in source profile modeling. We used long-term PM2.5 measurements collected from two locations with different environmental characteristics in northern Taiwan to demonstrate the feasibility of the proposed model and evaluated its performance by using simulated data.

關鍵字：Air pollution; Bayesian methods; Particulate matter; Source apportionment

語言：en_US

ISSN：1309-1042

期刊性質：國外

收錄於：SCI Scopus

通訊作者：Jing-Shiang Hwang

審稿制度：是

國別：NLD

出版型式：電子版