Characteristics, sources, and health risks of PM2.5-bound trace metals in northern Zhejiang Province: The effects of meteorological variables based on machine learning

被引:6
作者
Zhang, Fei [1 ]
Shen, Yemin [3 ]
Xu, Bingye [3 ]
Shen, Jiasi [3 ]
Jin, Lingling [3 ]
Yao, Lan [4 ,5 ]
Kuang, Binyu [1 ]
Xu, Zhengning [1 ]
Pei, Xiangyu [1 ]
Tang, Qian [3 ]
Tian, Xudong [3 ]
Wang, Zhibin [1 ,2 ]
机构
[1] Zhejiang Univ, Coll Environm & Resource Sci, Zhejiang Prov Key Lab Organ Pollut Proc & Control, Hangzhou 310058, Zhejiang, Peoples R China
[2] ZJU Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 311200, Peoples R China
[3] Zhejiang Ecol & Environm Monitoring Ctr, Hangzhou 310012, Peoples R China
[4] Shanghai Normal Univ, Sch Environm & Geol Sci, Yangtze River Delta Urban Wetland Ecosyst Natl Fie, Shanghai 200234, Peoples R China
[5] Shanghai Acad Environm Sci, State Environm Protect Key Lab Format & Prevent Ur, Shanghai 200233, Peoples R China
基金
中国国家自然科学基金;
关键词
PM2.5-bound metal; Random forest; Meteorological variables; Source apportionment; Health risk; Machine learning; SOURCE APPORTIONMENT; CHEMICAL CHARACTERISTICS; SOURCE IDENTIFICATION; HEAVY-METALS; LONG-TERM; ELEMENTS; CHINA; EMISSIONS; PM2.5; SPECIATION;
D O I
10.1016/j.jclepro.2024.142089
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Atmospheric metals have recently attracted the attention of governments recently due to their significant health risks. In retrospect, a five-year (2017 - 2021) continuous measurement of airborne metals (13 elements) was conducted in northern Zhejiang Province. A declining trend of total metals (22%) was found from 2017 to 2021, with K, Fe, Zn, Ca, and Mn being its predominant compositions (95.3%). The Random Forest model simulation demonstrated the important roles of meteorological conditions in the trace metal reduction, such as Ca, As, K, Cr, and Zn. In addition, the emission controls contributed more than 50% to most of the remaining metals, especially for Cu (92.6%) and V (85.9%), which demonstrated the effectiveness of the government policies. The relative humidity, wind speed and wind direction were found to be the most important variables affecting the ambient concentration of trace metals. Meteorology was demonstrated to be the worst for trace metal diffusion in 2018. The primary noncarcinogenic metal was Mn (85.9%), while the major carcinogenic metal was Cd (63.4%). Six sources were resolved by positive matrix factorization (PMF). Anthropogenic source emission was largest in winter, followed by spring, summer, and autumn. The reduction of ferrous metal smelting and coal combustion emissions might be the major goal for reducing the public health risk of trace metals in the future.
引用
收藏
页数:10
相关论文
共 74 条
[1]   Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes [J].
An, Zhisheng ;
Huang, Ru-Jin ;
Zhang, Renyi ;
Tie, Xuexi ;
Li, Guohui ;
Cao, Junji ;
Zhou, Weijian ;
Shi, Zhengguo ;
Han, Yongming ;
Gu, Zhaolin ;
Ji, Yuemeng .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2019, 116 (18) :8657-8666
[2]  
[曹宗元 Cao Zongyuan], 2023, [海洋预报, Marine Forecasts], V40, P89
[3]   First long-term and near real-time measurement of trace elements in China's urban atmosphere: temporal variability, source apportionment and precipitation effect [J].
Chang, Yunhua ;
Huang, Kan ;
Xie, Mingjie ;
Deng, Congrui ;
Zou, Zhong ;
Liu, Shoudong ;
Zhang, Yanlin .
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2018, 18 (16) :11793-11812
[4]   VOC characteristics and source apportionment at a PAMS site near an industrial complex in central Taiwan [J].
Chen, Chun-Hao ;
Chuang, Yen-Chang ;
Hsieh, Chu-Chin ;
Lee, Chih-Sheng .
ATMOSPHERIC POLLUTION RESEARCH, 2019, 10 (04) :1060-1074
[5]   A review of biomass burning: Emissions and impacts on air quality, health and climate in China [J].
Chen, Jianmin ;
Li, Chunlin ;
Ristovski, Zoran ;
Milic, Andelija ;
Gu, Yuantong ;
Islam, Mohammad S. ;
Wang, Shuxiao ;
Hao, Jiming ;
Zhang, Hefeng ;
He, Congrong ;
Guo, Hai ;
Fu, Hongbo ;
Miljevic, Branka ;
Morawska, Lidia ;
Phong Thai ;
Lam, Yun Fat ;
Pereira, Gavin ;
Ding, Aijun ;
Huang, Xin ;
Dumka, Umesh C. .
SCIENCE OF THE TOTAL ENVIRONMENT, 2017, 579 :1000-1034
[6]   Sources and uncertainties of health risks for PM2.5-bound heavy metals based on synchronous online and offline filter-based measurements in a Chinese megacity [J].
Chen, Rui ;
Zhao, Yehui ;
Tian, Yingze ;
Feng, Xin ;
Feng, Yinchang .
ENVIRONMENT INTERNATIONAL, 2022, 164
[7]   Characteristics and sources of PM2.5-bound elements in Shanghai during autumn and winter of 2019: Insight into the development of pollution episodes [J].
Chen, Yanan ;
Ye, Xingnan ;
Yao, Yinghui ;
Lv, Zhixiao ;
Fu, Zhenghang ;
Huang, Cheng ;
Wang, Ruoyan ;
Chen, Jianmin .
SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 881
[8]   Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover [J].
Chen, Ying ;
Haywood, Jim ;
Wang, Yu ;
Malavelle, Florent ;
Jordan, George ;
Partridge, Daniel ;
Fieldsend, Jonathan ;
De Leeuw, Johannes ;
Schmidt, Anja ;
Cho, Nayeong ;
Oreopoulos, Lazaros ;
Platnick, Steven ;
Grosvenor, Daniel ;
Field, Paul ;
Lohmann, Ulrike .
NATURE GEOSCIENCE, 2022, 15 (08) :609-+
[9]   Atmospheric Emission Characteristics and Control Policies of Five Precedent-Controlled Toxic Heavy Metals from Anthropogenic Sources in China [J].
Cheng, Ke ;
Wang, Yan ;
Tian, Hezhong ;
Gao, Xiang ;
Zhang, Yongxin ;
Wu, Xuecheng ;
Zhu, Chuanyong ;
Gao, Jiajia .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2015, 49 (02) :1206-1214
[10]   In situ continuous observation of hourly elements in PM2.5 in urban beijing, China: Occurrence levels, temporal variation, potential source regions and health risks [J].
Cui, Yang ;
Ji, Dongsheng ;
He, Jun ;
Kong, Shaofei ;
Wang, Yuesi .
ATMOSPHERIC ENVIRONMENT, 2020, 222