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A Cluster Analysis of Constant Ambient Air Monitoring Data from the Kanto Region of Japan

机译：日本关东地区恒定环境空气监测数据的聚类分析

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This study demonstrates an application of cluster analysis to constant ambient air monitoring data of four pollutants in the Kanto region: NOx, photochemical oxidant (Ox), suspended particulate matter, and non-methane hydrocarbons. Constant ambient air monitoring can provide important information about the surrounding atmospheric pollution. However, at the same time, ambient air monitoring can place a significant financial burden on some autonomous communities. Thus, it has been necessary to reduce both the number of monitoring stations and the number of chemicals monitored. To achieve this, it is necessary to identify those monitoring stations and pollutants that are least significant, while minimizing the loss of data quality and mitigating the effects on the determination of any spatial and temporal trends of the pollutants. Through employing cluster analysis, it was established that the ambient monitoring stations in the Kanto region could be clustered topologically for NOx and Ox into eight groups. From the results of this analysis, it was possible to identify the similarities in site characteristics and pollutant behaviors.
机译：这项研究证明了聚类分析在关东地区四种污染物的持续环境空气监测数据中的应用：NOx，光化学氧化剂（Ox），悬浮颗粒物和非甲烷碳氢化合物。持续的环境空气监测可以提供有关周围大气污染的重要信息。但是，与此同时，环境空气监测会给一些自治社区带来巨大的财务负担。因此，有必要减少监测站的数量和被监测化学品的数量。为此，有必要确定那些监测站和污染物最不重要，同时最大程度地降低数据质量损失并减轻对确定污染物时空趋势的影响。通过聚类分析，可以确定关东地区的环境监测站可以按拓扑将NOx和Ox分为八类。根据分析结果，可以确定站点特征和污染物行为的相似性。

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