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Cluster analysis of Delhi's ambient air quality data

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Cluster analysis of Delhi's ambient air quality data

Content Provider	Royal Society of Chemistry (RSC)
Author	Saksena, S. Joshi, V. Patil, R. S.
Copyright Year	2003
Abstract	The purpose of this study was to study the spatial patterns of ambient air quality in Delhi in the absence of extensive datasets needed for space-time modeling. A spatial classification was attempted on the basis of ambient air quality data of nine years (1998 is latest year for which published data were available) for three criteria pollutants – nitrogen dioxide, sulfur dioxide, and suspended particulate matter. Monitoring stations take 24-hour samples twice a week. Published monthly average concentration data were used in this study. A hierarchical agglomerative algorithm using the average linkage between groups method and the Euclidean distance metric was used. Cluster analysis indicated that till 1998, by and large, two distinct classes existed. The results of cluster analysis prompted an investigation of systematic biases in the monitored data. No statistically significant differences in the mean concentration of all pollutants were observed between stations belonging to different land-use types (residential and industrial). This fact would be useful, if and when the authorities consider modifying the network or expanding it in Delhi. The results also support the recommendation that Delhi have a uniform standard across all areas. This study has provided a methodology for Indian researchers and practitioners to do an exploratory study of spatial patterns of air pollution and data quality issues in Indian cities using the National Ambient Air Quality Monitoring System data.
Starting Page	491
Ending Page	499
Page Count	9
File Format	HTM / HTML PDF
ISSN	14640325
Volume Number	5
Issue Number	3
Journal	Journal of Environmental Monitoring
DOI	10.1039/b210172f
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Delhi Nitrogen dioxide Sulfur dioxide Particulates Algorithm Euclidean distance Cluster analysis Air pollution
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Management, Monitoring, Policy and Law

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