Literature DB >> 32311623

Chemical characteristics and source apportionment of PM2.5 using PMF modelling coupled with 1-hr resolution online air pollutant dataset for Linfen, China.

Yafei Li1, Baoshuang Liu2, Zhigang Xue3, Yufen Zhang1, Xiaoyun Sun1, Congbo Song4, Qili Dai1, Ruichen Fu5, Yonggang Tai5, Jinyu Gao5, Yajun Zheng5, Yinchang Feng1.   

Abstract

The chemical species in PM2.5 and air pollutant concentration data with 1-hr resolution were monitored synchronously between 15 November 2018 and 20 January 2019 in Linfen, China, which were analysed for multiple temporal patterns, and PM2.5 source apportionment using positive matrix factorisation (PMF) modelling coupled with online chemical species data was conducted to obtain the apportionment results of distinct temporal patterns. The mean concentration of PM2.5 was 124 μg/m3 during the heating period, and NO3- and organic carbon (OC) were the dominant species. The concentrations and percentages of NO3-, SO42-, and OC increased notably during the growth periods of haze events, thereby indicating secondary particle formation. Six factors were identified by the PMF model during the heating period, including vehicular emissions (VE: 26.5%), secondary nitrate (SN: 16.5%), coal combustion and industrial emissions (CC&IE: 25.7%), secondary sulfate and secondary organic carbon (SS&SOC: 24.4%), biomass burning (BB: 1.0%), and crustal dust (CD: 5.9%). The primary sources of PM2.5 on clean days were CD (33.3%), VE (23.1%), and SS&SOC (20.6%), while they were CC&IE (32.9%) and SS&SOC (28.3%) during the haze events. The contributions of secondary sources and CC&IE increased rapidly during the growth periods of haze events, while that of CD increased during the dissipation period. Diurnal variations in the contribution of secondary sources were mainly related to the accumulation and transformation of corresponding gaseous precursors. In comparison, contributions of CC&IE and VE varied as a function of the domestic heating load and peak levels occurred during the morning and evening rush hours. High contributions of major sources (CC&IE and SS&SOC) during haze events originated mainly from the north and south, while high contribution of a major source (CD) on clean days was from the northwest.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Online monitoring; PM(2.5); Positive matrix factorisation; Principal component analysis; Source apportionment

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Year:  2020        PMID: 32311623     DOI: 10.1016/j.envpol.2020.114532

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  3 in total

1.  Variation of pollution sources and health effects on air pollution before and during COVID-19 pandemic in Linfen, Fenwei Plain.

Authors:  Weijie Liu; Yao Mao; Tianpeng Hu; Mingming Shi; Jiaquan Zhang; Yuan Zhang; Shaofei Kong; Shihua Qi; Xinli Xing
Journal:  Environ Res       Date:  2022-06-24       Impact factor: 8.431

2.  Air pollutant variations in Suzhou during the 2019 novel coronavirus (COVID-19) lockdown of 2020: High time-resolution measurements of aerosol chemical compositions and source apportionment.

Authors:  Honglei Wang; Qing Miao; Lijuan Shen; Qian Yang; Yezheng Wu; Heng Wei
Journal:  Environ Pollut       Date:  2020-12-21       Impact factor: 8.071

3.  Source Identification and Superposition Effect of Heavy Metals (HMs) in Agricultural Soils at a High Geological Background Area of Karst: A Case Study in a Typical Watershed.

Authors:  Qiuye Zhang; Hongyan Liu; Fang Liu; Xianhang Ju; Faustino Dinis; Enjiang Yu; Zhi Yu
Journal:  Int J Environ Res Public Health       Date:  2022-09-09       Impact factor: 4.614

  3 in total

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