Journal of Japan Society for Atmospheric Environment / Taiki Kankyo Gakkaishi
Online ISSN : 2185-4335
Print ISSN : 1341-4178
ISSN-L : 1341-4178
Current issue
Displaying 1-7 of 7 articles from this issue
Original Paper
  • Hiroshi Takimoto, Hiroshi Kimura, Ayumu Sato, Hiroshi Kanno, Maho Okad ...
    2022 Volume 57 Issue 3 Pages 90-100
    Published: March 30, 2022
    Released on J-STAGE: March 30, 2022

    In geothermal power plants in cold regions, water vapor emitted from cooling towers may cause icing effects on the surrounding trees. Therefore, developing a method for quantitatively predicting the range of the icing influence is important for the environmental impact assessments. In this study, we developed an icing prediction model based on the observation data of icing phenomena at a geothermal power plant. The prediction model is a Gaussian plume model; for the precise simulation, it is important to appropriately set the dispersion parameters due to the significant influences of the surrounding topography. Considering the time span of the actual ice growth, we proposed a method for predicting the 24-hour average icing growth rate. Our prediction model shows a good reproducibility of the time variations in the ice growth rate. The distributions of the 24-hour averaged icing growth rate on a horizontal plane is less sensitive to the dispersion parameters or model settings, which lead to a practical way of predicting possible icing areas in the environmental impact assessments.

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Technical Report
  • Satoshi Toyonaga, Shoei Furusawa, Daisuke Kohara, Shin Araki, Yusuke Y ...
    2022 Volume 57 Issue 3 Pages 77-89
    Published: March 18, 2022
    Released on J-STAGE: March 18, 2022

    In this study, a method for the optimal reduction of the local-scale PM2.5 monitoring network was developed using the network in the Kumamoto prefecture, Japan. The basic concept of the method is to evaluate how well subsets of the PM2.5 networks (i.e., subnetworks) can represent the full network. There are three steps in the method. In the first step, concentration maps were predicted by Regression Kriging as described in the previous study. The maps with subnetworks and the map with full network were statistically compared. The subnetworks with no significant difference from the full network were extracted and passed the first step. In the second step, the passed subnetworks were evaluated by calculating the index value based on the spatial area affected by exclusion of the stations. The subnetwork with the smallest index value was determined as the optimal subnetwork. In the third step, the validation of the optimal subnetwork was carried out by comparison with externally observed values. As a result of these steps, the optimal subnetwork with only a negligible difference to the full networks could be identified. This method will probably be useful for the optimal reduction of the local-scale PM2.5 networks, those governed by local government.

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