Literature DB >> 29454989

One year study of PM2.5 in Xinxiang city, North China: Seasonal characteristics, climate impact and source.

Jinglan Feng1, Hao Yu2, Kai Mi3, Xianfa Su4, Yi Li5, Qilu Li1, Jianhui Sun6.   

Abstract

This study was conducted in order to explore the seasonal characteristics, climate impact and source of PM2.5 in Xinxiang, China. Daily PM2.5 samples were collected at urban site from January to December in 2015. Average PM2.5 concentration was 100.6 ± 65.8 μg m-3 in Xinxiang, which was several times higher than China Ambient Air Quality Standards (GB3095-2012). Secondary inorganic aerosols (SIA) constituted 70% of the total ionic concentrations. The average concentration of SO42- was 6.4 ± 12.0 μg m-3, which ranked the highest among the water-soluble ions analyzed. Seasonal variations of PM2.5 and its major chemical components were significant, most of them with high values in winter and the lowest values in summer, especially with heavier PM2.5 events (more than 200 μg/m3) in December. SIA and OC on polluted days were 2.1-2.3 times higher than those of on clean days. It was estimated that Fe, Li, Na, Mg, Al, K, Ca and Sr were emitted from crustal sources and Pb, Cr, Ni, Cu, Zn, As, Cd and V were emitted from anthropogenic emissions using the EF values. Analysis using the tracer and PCA/MLR revealed that vehicle exhausts were the most important source of PM2.5, which contributed 26.9% of PM2.5 over the whole study period. This study provides detailed composition data and first comprehensive analysis of PM2.5 in Xinxiang during a whole year.
Copyright © 2018. Published by Elsevier Inc.

Entities:  

Keywords:  Major chemical; PM(2.5); Seasonal variations; Source identification

Mesh:

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Year:  2018        PMID: 29454989     DOI: 10.1016/j.ecoenv.2018.01.048

Source DB:  PubMed          Journal:  Ecotoxicol Environ Saf        ISSN: 0147-6513            Impact factor:   6.291


  2 in total

1.  Chemical Composition and Source Apportionment of PM2.5 in Urban Areas of Xiangtan, Central South China.

Authors:  Xiaoyao Ma; Zhenghui Xiao; Lizhi He; Zongbo Shi; Yunjiang Cao; Zhe Tian; Tuan Vu; Jisong Liu
Journal:  Int J Environ Res Public Health       Date:  2019-02-13       Impact factor: 3.390

2.  Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis.

Authors:  Mei-Juan Chen; Pei-Hsuan Yang; Mi-Tren Hsieh; Chia-Hung Yeh; Chih-Hsiang Huang; Chieh-Ming Yang; Gen-Min Lin
Journal:  World J Clin Cases       Date:  2018-08-16       Impact factor: 1.337

  2 in total

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