The oxidative potential of fine ambient particulate matter in Xinxiang, North China: Pollution characteristics, source identification and regional transport

被引:0
|
作者
Liu, Huanjia [1 ,2 ,3 ]
Xu, Mengyuan [1 ]
Yang, Ying [1 ]
Cheng, Ke [1 ]
Liu, Yongli [1 ]
Fan, Yujuan [1 ]
Yao, Dan [1 ]
Tian, Di [1 ]
Li, Lanqing [1 ]
Zhao, Xingzi [1 ]
Zhang, Ruiqin [2 ]
Xu, Yadi [1 ]
机构
[1] Henan Normal Univ, Sch Environm, Key Lab Yellow River & Huai River Water Environm &, Minist Educ,Henan Key Lab Environm Pollut Control, Xinxiang 453007, Peoples R China
[2] Zhengzhou Univ, Sch Ecol & Environm, Zhengzhou 450001, Peoples R China
[3] Zhengzhou Univ, Coll Chem, Zhengzhou 450001, Peoples R China
基金
中国国家自然科学基金;
关键词
PM2.5; Oxidative potentia; DTT assay; Source identification; TIANJIN-HEBEI REGION; WATER-SOLUBLE PM2.5; SOURCE APPORTIONMENT; CHEMICAL-COMPOSITION; AIR-POLLUTION; HEAVY-METALS; LOS-ANGELES; AEROSOL; SITES; ASSOCIATIONS;
D O I
10.1016/j.envpol.2024.124615
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Atmospheric fine particulate matter (PM2.5) can trigger the production of cytotoxic reactive oxygen species (ROS), which can trigger or exacerbate oxidative stress and pulmonary inflammation. We collected 111 daily (similar to 24 h) ambient PM2.5 samples within an urban region of North China during four seasons of 2019-2020. PM2.5 samples were examined for carbonaceous components, water-soluble ions, and elements, together with their oxidative potential (represent ROS-producing ability) by DTT assay. The seasonal peak DTTv was recorded in winter (2.86 +/- 1.26 nmol min(-1) m(-3)), whereas the DTTm was the highest in summer (40.6 +/- 8.7 pmol min(-1) mu g(-1)). WSOC displayed the highest correlation with DTT activity (r = 0.84, p < 0.0001), but the influence of WSOC on the elevation of DTTv was extremely negligible. Combustion source exhibited the most significant and robust correlation with the elevation of DTTv according to the linear mixed-effects model result. Source identification investigation using positive matrix factorization displayed that combustion source (36.2%), traffic source (30.7%), secondary aerosol (15.7%), and dust (14.1%) were driving the DTTv, which were similar to the results from the multiple linear regression (MLR) analysis. Backward trajectory analysis revealed that the major air masses originate from local and regional transportation, but PM2.5 OP was more susceptible to the influence of short-distance transport clusters. Discerning the influence of chemicals on health-pertinent attributes of PM2.5, such as OP, could facilitate a deep understanding of the cause-and-effect relationship between PM2.5 and impacts.
引用
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页数:13
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