Research Center for Sustainable Science and Engineering, Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan.
Department of Environmental Systems, Institute of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8563, Japan.
Int J Environ Res Public Health. 2014 Jul 3;11(7):6844-55. doi: 10.3390/ijerph110706844.
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.
本研究展示了聚类分析在关东地区四种污染物（NOx、光化学氧化剂（Ox）、悬浮颗粒物和非甲烷碳氢化合物）的恒大气监测数据中的应用。恒大气监测可以提供有关周围大气污染的重要信息。然而，与此同时，大气监测也会给一些自治社区带来巨大的经济负担。因此，有必要减少监测站的数量和监测的化学物质的数量。为了实现这一目标，有必要确定那些监测站和污染物是最不重要的，同时最大限度地减少数据质量的损失，并减轻对污染物任何时空趋势的确定的影响。通过使用聚类分析，可以将关东地区的大气监测站在拓扑上对 NOx 和 Ox 进行聚类，分为八组。通过该分析的结果，可以识别出站点特征和污染物行为的相似性。
