Literature DB >> 29284089

Natural Silicon Isotopic Signatures Reveal the Sources of Airborne Fine Particulate Matter.

Dawei Lu1,2, Qian Liu1,2,3, Miao Yu1, Xuezhi Yang1,2, Qiang Fu4, Xiaoshan Zhang1, Yujing Mu1, Guibin Jiang1,2.   

Abstract

Airborne particulate pollution is a critical environmental problem affecting human health and sustainable development. Understanding of the sources of aerosol particles is of extreme importance for regional air pollution control. Here we show that natural Si isotopic signature can be used as a new tool to elucidate the sources of fine particulate matter (PM2.5). Through the analysis of Si isotopic composition (δ30Si) of PM2.5 and its primary sources collected in a typical pollution region - Beijing, we recognized the direct source tracing ability of Si isotopes for PM2.5. The different primary sources of PM2.5 had different Si isotopic signatures. The δ30Si value of PM2.5 ranged from -1.99‰ to -0.01‰ and showed a distinct seasonal trend (isotopically lighter in spring/winter and heavier in summer/autumn). The variations in δ30Si of PM2.5 revealed that Si-isotopically light sources were important sources for Beijing's severe haze pollution and that coal burning was a major cause for the aggregating haze weather in spring/winter in Beijing. We also analyzed several typical haze events by using Si isotopic signatures. As the first study on the natural Si isotopes in the atmospheric environment, this study may reveal an important tool to advance the particulate pollution research and control.

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Year:  2018        PMID: 29284089     DOI: 10.1021/acs.est.7b06317

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

1.  [Recent advances in method development and application of multi-collector inductively coupled plasma mass spectrometry].

Authors:  Luyao Zhang; Zigu Chen; Xuezhi Yang; Dawei Lu; Qian Liu; Guibin Jiang
Journal:  Se Pu       Date:  2021-01

2.  Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning.

Authors:  Xuezhi Yang; Xian Liu; Aiqian Zhang; Dawei Lu; Gang Li; Qinghua Zhang; Qian Liu; Guibin Jiang
Journal:  Nat Commun       Date:  2019-04-08       Impact factor: 14.919

  2 in total

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