Optimized arrangement of constant ambient air monitoring stations in the Kanto region of Japan.

Continuous ambient air monitoring systems have been introduced worldwide. However, such monitoring forces autonomous communities to bear a significant financial burden. Thus, it is important to identify pollutant-monitoring stations that are less efficient, while minimizing loss of data quality and mitigating effects on the determination of spatiotemporal trends of pollutants. This study describes a procedure for optimizing a constant ambient air monitoring system in the Kanto region of Japan. Constant ambient air monitoring stations in the area were topologically classified into four groups by cluster analysis and principle component analysis. Then, air pollution characteristics in each area were reviewed using concentration contour maps and average pollution concentrations. We then introduced three simple criteria to reduce the number of monitoring stations: (1) retain the monitoring station if there were similarities between its data and average data of the group to which it belongs; (2) retain the station if its data showed higher concentrations; and (3) retain the station if the monitored concentration levels had an increasing trend. With this procedure, the total number of air monitoring stations in suburban and urban areas was reduced by 36.5%. The introduction of three new types of monitoring stations is proposed, namely, mobile, for local non-methane hydrocarbon pollution, and Ox-prioritized.

A cluster analysis of constant ambient air monitoring data from the Kanto region of Japan.

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.