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主成分分析在大气质量监测优化布点中的应用
黄玉平,张庆国,汪水兵,古今今
0
(安徽农业大学生命科学学院,合肥230036;安徽农业大学理学院,合肥 230036;;安徽省环境科学研究院,合肥 230061;北京师范大学环境学院,北京 100875)
摘要:
主成分分析法(PCA)以少数的综合变量取代原有的多维变量,在原始数据信息丢失最小的情况下,使数据结构得以简化。作者分析了合肥市新站区大气环境监测数据,采用主成分分析法建立空气质量污染特征因子与污染物之间的数学模型,再用该模型计算出各点位相对污染程度,并对监测布点进行分类。以此选出最佳监测点位,可为大气质量监测优化布点提供方法,为合肥市新站区环境质量的分区和分级治理提供理论依据。
关键词:  主成分分析  合肥市新站区  大气质量  监测数据
DOI:CNKI:34-1162/S.20111025.1029.023
基金项目:国家自然科学基金项目(70271062)和安徽高校省级自然科学研究重点项目(KJ2010A121)资助。
Application of principal components analysis in optimizing sites selection of air quality monitoring
HUANG Yu-ping,ZHANG Qing-guo,WANG Shui-bing,GU Jin-jin
(School of Life Science, Anhui Agricultural University, Hefei 230036;School of Science, Anhui Agricultural University, Hefei 230036;Research Institute of Environmental Science of Anhui , Hefei 230061;School of Environment , Beijing Normal University, Beijing , 100875)
Abstract:
The principal component analysis could substitute for original multi-dimensional variable by a small number comprehensive variable?and simplify data structure under the condition of minimizing loss of original data information. The paper was aimed to analyze the air environment monitoring data of Xinzhan area in Hefei city, using the principal component analysis to establish a mathematical model about the characteristic factors of air quality and pollutants. The model?was made to calculate each monitoring sites, relative pollutional degree and classifying monitoring sites, thus to select the best sites to monitor the air quality. The result provided an optimized selection of air quality monitoring sites, a theoretical basis for classifying air quality of Xinzhan area of Hefei and governing by different levels.
Key words:  principal component analysis (PCA)  Xinzhan area of Hefei  air quality  monitoring data

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