The Application of Principal Component Regression Models in Air Quality Analysis Based on SuperMap GIS

Author

Ma Mintao ; Sun Lei ; Zhu Ruijuan

Author_Institution

Coll. of Environ. & Energy Eng., Beijing Univ. of Technol., Beijing, China

fYear

2011

fDate

12-14 Aug. 2011

Firstpage

1

Lastpage

4

Abstract

In order to make better use of existing environmental data resources and enhance regional environmental management and improve the environment, this paper uses the work method which is effectively integrated by GIS and multivariate statistical analysis technology, through the regional air pollution relevant data of the combination of urban and rural areas in western Beijing, using the principal component analysis and regression analysis based on SuperMap GIS Platform of this paper prophase development method. The corresponding regional environmental problems in the region are targeted extracted, classified, concluded, and analyzed, and rapid exploration and summary of regional environmental pollution rules. This work has some reference value to the improvement of the environmental quality of urban areas.

Keywords

air pollution; atmospheric techniques; environmental monitoring (geophysics); geographic information systems; principal component analysis; regression analysis; China; SuperMap GIS; air quality analysis; environmental data resources; environmental quality; multivariate statistical analysis technology; principal component analysis; principal component regression models; regional environmental management; regional environmental problems; regression analysis; rural areas; urban areas; western Beijing; Air pollution; Atmospheric modeling; Environmentally friendly manufacturing techniques; Geographic Information Systems; Principal component analysis; Regression analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Management and Service Science (MASS), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6579-8

Type

conf

DOI

10.1109/ICMSS.2011.5998623

Filename

5998623