Saksena S, Joshi V, Patil R S
East West Center, Honolulu, 1601 East West Road, Honolulu, USA.
J Environ Monit. 2003 Jun;5(3):491-9. doi: 10.1039/b210172f.
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.
本研究的目的是在缺乏时空建模所需的大量数据集的情况下,研究德里市环境空气质量的空间格局。基于九年(1998年是可获取已发表数据的最新年份)的三种标准污染物(二氧化氮、二氧化硫和悬浮颗粒物)的环境空气质量数据,尝试进行空间分类。监测站每周两次采集24小时样本。本研究使用已发表的月平均浓度数据。采用了一种层次凝聚算法,该算法使用组间平均连锁法和欧几里得距离度量。聚类分析表明,到1998年,总体上存在两个不同的类别。聚类分析结果促使对监测数据中的系统偏差进行调查。在属于不同土地利用类型(住宅和工业)的监测站之间,未观察到所有污染物平均浓度的统计学显著差异。如果当局考虑在德里修改监测网络或扩大监测网络,这一事实将很有用。研究结果还支持德里所有地区采用统一标准的建议。本研究为印度研究人员和从业人员提供了一种方法,可利用国家环境空气质量监测系统数据,对印度城市的空气污染空间格局和数据质量问题进行探索性研究。