Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model

被引：3

作者：

Liao, Kezheng ^{[1
]}

Zhang, Jie ^{[2
]}

Chen, Yiang ^{[3
]}

Lu, Xingcheng ^{[4
]}

Fung, Jimmy C. H. ^{[3
,5
]}

Ying, Qi ^{[2
]}

Yu, Jian Zhen ^{[1
,3
]}

机构：

[1] Hong Kong Univ Sci & Technol, Dept Chem, Clear Water Bay, Hong Kong 999077, Peoples R China

[2] Texas A&M Univ, Zachry Dept Civil & Environm Engn, College Stn, TX 77843 USA

[3] Hong Kong Univ Sci & Technol, Div Environm & Sustainabil, Clear Water Bay, Hong Kong 999077, Peoples R China

[4] Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong 999077, Peoples R China

[5] Hong Kong Univ Sci & Technol, Dept Math, Clear Water Bay, Hong Kong 999077, Peoples R China

来源：

NPJ CLIMATE AND ATMOSPHERIC SCIENCE | 2023年 / 6卷 / 01期

基金：

美国国家卫生研究院;

关键词：

PEARL RIVER DELTA; SECONDARY ORGANIC AEROSOL; POSITIVE MATRIX FACTORIZATION; CHEMICAL-TRANSPORT; ANTHROPOGENIC EMISSIONS; PM2.5; CONCENTRATION; HEAVY-METALS; CHINA; CARBON; INVENTORY;

D O I：

10.1038/s41612-023-00393-4

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

Trace metals in fine particulate matter (PM2.5) are of significant concern in environmental chemistry due to their toxicity and catalytic capability. An observation-constrained hybrid model is developed to resolve regional source contributions to trace metals and other primary species in PM2.5. In this method, source contributions to primary PM2.5 (PPM2.5) from the Community Multiscale Air Quality (CMAQ) Model at each monitoring location are improved to align better with the observation data by applying source-specific scaling factors estimated from a unique regression model. The adjusted PPM2.5 predictions and chemical speciation data are then used to generate observation-constrained source profiles of primary species. Finally, spatial distributions of their source contributions are produced by multiplying the improved CMAQ PPM2.5 contributions with the deduced source profiles. The model is applied to the Pearl River Delta (PRD) region, China using daily observation data collected at multiple stations in 2015 to resolve source contributions to 8 trace metals, elemental carbon, primary organic carbon, and 10 other primary species. Compared to three previous methods, the new model predicts 13 species with smaller model errors and 16 species with less model biases in 10-fold cross validation analysis. The source profiles determined in this study also show good agreement with those collected from the literature. The new model shows that during 2015 in the PRD region, Cu is mainly from the area sources (31%), industry sector (27%), and power generation (20%), with an annual average concentration as high as 50 ng m(-3) in some districts. Meanwhile, major contributors to Mn are area sources (40%), emission from outside PRD (23%) and power generation (17%), leading to a mean level of around 10 ng m(-3). Such information is essential in assessing the epidemiological impacts of trace metals as well as formulating effective control measures to protect public health.

引用

页数：11