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
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
收藏
页数:11
相关论文
共 50 条
  • [31] Chemical speciation and source apportionment of fine particulate matter in Santiago, Chile, 2013
    Villalobos, Ana M.
    Barraza, Francisco
    Jorquera, Hector
    Schauer, James J.
    SCIENCE OF THE TOTAL ENVIRONMENT, 2015, 512 : 133 - 142
  • [32] Development and evaluation of a daily temporal interpolation model for fine particulate matter species concentrations and source apportionment
    Redman, Jeremiah D.
    Holmes, Heather A.
    Balachandran, Sivaraman
    Maier, Marissa L.
    Zhai, Xinxin
    Ivey, Cesunica
    Digby, Kyle
    Mulholland, James A.
    Russell, Armistead G.
    ATMOSPHERIC ENVIRONMENT, 2016, 140 : 529 - 538
  • [33] Improve regional distribution and source apportionment of PM2.5 trace elements in China using inventory-observation constrained emission factors
    Ying, Qi
    Feng, Miao
    Song, Danlin
    Wu, Li
    Hu, Jianlin
    Zhang, Hongliang
    Kleeman, Michael J.
    Li, Xinghua
    SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 624 : 355 - 365
  • [34] Pollution Characteristics and Source Apportionment of Fine Particulate Matter in Autumn and Winter in Puyang, China
    Chen C.
    Wang T.-J.
    Li Y.-H.
    Ma H.-L.
    Chen P.-L.
    Wang D.-Y.
    Zhang Y.-X.
    Qiao Q.
    Li G.-M.
    Wang W.-H.
    Huanjing Kexue/Environmental Science, 2019, 40 (08): : 3421 - 3430
  • [35] Spatial characteristics of fine particulate matter in subway stations: Source apportionment and health risks
    Ji, Wenjing
    Zhao, Kaijia
    Liu, Chenghao
    Li, Xiaofeng
    ENVIRONMENTAL POLLUTION, 2022, 305
  • [36] Concentrations and source insights for trace elements in fine and coarse particulate matter
    Clements, Nicholas
    Eav, Jenny
    Xie, Mingjie
    Hannigan, Michael P.
    Miller, Shelly L.
    Navidi, William
    Peel, Jennifer L.
    Schauer, James J.
    Shafer, Martin M.
    Milford, Jana B.
    ATMOSPHERIC ENVIRONMENT, 2014, 89 : 373 - 381
  • [37] Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China
    Zhang, Mei
    Jia, Jia
    Wang, Bo
    Zhang, Weihong
    Gu, Chenming
    Zhang, Xiaochen
    Zhao, Yuanhao
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (12)
  • [38] Source apportionment of fine particulate matter by clustering single-particle data: Tests of receptor model accuracy
    Bhave, PV
    Fergenson, DP
    Prather, KA
    Cass, GR
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2001, 35 (10) : 2060 - 2072
  • [39] Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia
    Friend, Adrian J.
    Ayoko, Godwin A.
    ENVIRONMENTAL CHEMISTRY, 2009, 6 (05) : 398 - 406
  • [40] Research status and prospects on source apportionment of atmospheric fine particulate matter in Shandong Province
    Zhou, Rui-Zhi
    Yan, Cai-Qing
    Cui, Min
    Xu, Min
    Liu, Wei-Jian
    Chen, Hai-Biao
    Zhou, Tao-Meizi
    Zheng, Mei
    Zhongguo Huanjing Kexue/China Environmental Science, 2021, 41 (07): : 3029 - 3042