Yukio Yanagisawa

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 O_x-prioritized.

This study demonstrates an application of cluster analysis to constant ambient air monitoring data of four pollutants in the Kanto region: NO_x, photochemical oxidant (O_x), 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 NO_x and O_x into eight groups. From the results of this analysis, it was possible to identify the similarities in site characteristics and pollutant behaviors.

Simple and economical measurement of air change rates can be achieved with a passive-type tracer gas doser and sampler. However, this is made more complex by the fact many buildings are not a single fully mixed zone. This means many measurements are required to obtain information on ventilation conditions. In this study, we evaluated the uncertainty of tracer gas measurement of air change rate in n completely mixed zones. A single measurement with one tracer gas could be used to simply estimate the air change rate when n = 2. Accurate air change rates could not be obtained for n ≥ 2 due to a lack of information. However, the proposed method can be used to estimate an air change rate with an accuracy of

While various volatile organic compounds (VOCs) are known to show neurotoxic effects, the detailed mechanisms of the action of VOCs on the autonomic nervous system are not fully understood, partially because objective and quantitative measures to indicate neural abnormalities are still under development. Nevertheless, heart rate variability (HRV) has been recently proposed as an indicative measure of the autonomic effects. In this study, we used HRV as an indicative measure of the autonomic effrects to relate their values to the personal concentrations of VOCs measured by a real-time VOC monitor. The measurements were conducted for 24 hours on seven healthy subjects under usual daily life conditions. The results showed HF powers were significantly decreased for six subjects when the changes of total volatile organic compound (TVOC) concentrations were large, indicating a suppression of parasympathetic nervous activity induced by the exposure to VOCs. The present study indicated these real-time monitoring was useful to characterize the trends of VOC exposures and their effects on autonomic nervous system.

A combined integration analysis and real time monitoring (Peak Capture System) system was developed for volatile organic compounds (VOCs). Individual integration analysis and real time monitoring can be used to qualitatively and quantitatively analyze VOCs in the atmosphere and in indoor environments and determine the variation in total VOC (TVOC) concentration with time, respectively. In the Peak Capture System, real time monitoring was used to predict future elevations in the TVOC concentration (peak), and this was used an indicator of when to collect (capture) ambient air samples for integration analysis. This enabled qualitative and quantitative analysis of VOCs when the TVOC concentration was high. We developed an algorithm to predict variation in the TVOC concentration, and constructed an automatic system to initiate air sampling for integration analysis. With the system, auto-sampling and analysis of VOCs in a conventional house were conducted. In comparison with background concentrations, the results of peak analysis enabled identification of compounds whose concentration rose. This also enabled an evaluation of possible VOC emission sources.