The fitting component selection model of PM2.5 based on fuzzy clustering
XU Heng-peng1,2, LI Yue1,2, SHI Guo-liang3, WANG Wei1,2, XUAN Shu-yan4
1. College of Computer and Control Engineering, NanKai University, Tianjin 300071, China;
2. College of Software, NanKai University, Tianjin 300071, China;
3. State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, NanKai University, Tianjin 300071, China;
4. Yutian Environmental Protection Agency, Tangshan 064199, China
In current research, there is a lack of uniform standards for components selection in PM2.5 source profile apportionment. Researchers tend to choose the component manually and empirically, leading to a subsequent poor fitting result, or even failures. Concerning on this problem, this paper has proposed an innovative component selection model of PM2.5 source profiles apportionment. On the basis of the physical representative of each component, the proposed model calculates the accuracy of fuzzy clustering as the standard score for selection. The experiments prove that our model outperforms the traditional empirical models. The successful rate for fitting, measured by the fitting errors in 0 to 0.05, grows to 83% by implementing our model, in contrast to rate of 40% from the traditional selection model.
徐恒鹏, 李岳, 史国良, 王玮, 轩淑艳. 基于模糊聚类的PM2.5拟合组分选择模型的研究[J]. 中国环境科学, 2016, 36(1): 12-17.
XU Heng-peng, LI Yue, SHI Guo-liang, WANG Wei, XUAN Shu-yan. The fitting component selection model of PM2.5 based on fuzzy clustering. CHINA ENVIRONMENTAL SCIENCECE, 2016, 36(1): 12-17.
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