These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Ecological geochemical assessment and source identification of trace elements in atmospheric deposition of an emerging industrial area: Beibu Gulf economic zone.
    Author: Zhong C, Yang Z, Jiang W, Hu B, Hou Q, Yu T, Li J.
    Journal: Sci Total Environ; 2016 Dec 15; 573():1519-1526. PubMed ID: 27528485.
    Abstract:
    Industrialization and urbanization have led to a deterioration in air quality and provoked some serious environmental concerns. Fifty-four samples of atmospheric deposition were collected from an emerging industrial area and analyzed to determine the concentrations of 11 trace elements (As, Cd, Cu, Fe, Hg, Mn, Mo, Pb, Se, S and Zn). Multivariate geostatistical analyses were conducted to determine the spatial distribution, possible sources and enrichment degrees of trace elements in atmospheric deposition. Results indicate that As, Fe and Mo mainly originated from soil, their natural parent materials, while the remaining trace elements were strongly influenced by anthropogenic or natural activities, such as coal combustion in coal-fired power plants (Pb, Se and S), manganese ore (Mn, Cd and Hg) and metal smelting (Cu and Zn). The results of ecological geochemical assessment indicate that Cd, Pb and Zn are the elements of priority concern, followed by Mn and Cu, and other heavy metals, which represent little threat to local environment. It was determine that the resuspension of soil particles impacted the behavior of heavy metals by 55.3%; the impact of the coal-fired power plants was 18.9%; and the contribution of the local manganese industry was 9.6%. The comparison of consequences from various statistical methods (principal component analysis (PCA), cluster analysis (CA), enrichment factor (EF) and absolute principle component score (APCS)-multiple linear regression (MLR)) confirmed the credibility of this research.
    [Abstract] [Full Text] [Related] [New Search]