4.0 Article

Chemical compositions and source apportionment of atmospheric PM10 in suburban area of Changsha, China

期刊

出版社

JOURNAL OF CENTRAL SOUTH UNIV
DOI: 10.1007/s11771-010-0515-3

关键词

particulate matters; PM10; chemical composition; receptor modeling; principal component analysis; suburban

资金

  1. Foundation for the Author of National Excellent Doctoral Dissertation of China [FANEDD 200545]
  2. National Natural Science Foundation of China [50408019]
  3. National Key Project of Scientific and Technical Supporting Programs of China [2008BAJ12B03]

向作者/读者索取更多资源

Source apportionment of particulate matters with aerodynamic diameter less than 10 mu m (PM10) was conducted in the suburban area of Changsha, China. PM10 samples for 24 h collected with TEOM 1400a and ACCU system in July and October 2008 were chemically analyzed by the wavelength dispersive X-ray fluorescence (WD-XRF). Source appointment was implemented by the principal component analysis/absolute principal component analysis (PCA/APCA) to identify the possible sources and to quantify the contributions of the sources to PM10. Results show that as the PK concentration is increased from (85.6 +/- 43.7) mu g/m(3) in July 2008 to (107.6 +/- 35.7) mu g/m(3) in October 2008, the concentrations of the anthropogenic elements (P, S, Cl, K, Mn, Ni, Cu, Zn, and Pb) are basically increased but concentrations of the natural elements (Na, Mg, Al, Si, Ca, Ti, and Fe) are essentially decreased. Six main sources of PM10 are identified in the suburban of Changsha, China: soil dust, secondary aerosols, domestic oil combustion, waste incineration, traffic emission, and industrial emission contribute 57.7%, 24.0%, 9.8%, 5.0%, 2.0%, and 1.5%, respectively. Soil dust and secondary aerosols are the two major sources of particulate air pollution in suburban area of Changsha, China, so effective measures should be taken to control these two particulate pollutants.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.0
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据