Journal of Japan Society for Atmospheric Environment / Taiki Kankyo Gakkaishi
Online ISSN : 2185-4335
Print ISSN : 1341-4178
ISSN-L : 1341-4178
Volume 57, Issue 1
Displaying 1-6 of 6 articles from this issue
  • Tsuyoshi Ohizumi
    2022 Volume 57 Issue 1 Pages 15-23
    Published: January 10, 2022
    Released on J-STAGE: January 10, 2022

    A large emission of air pollutants from the Asian continent has caused transboundary air pollution, especially in Northeastern Asia. This paper evaluates the sulfate deposition in a leeward area of the Asian continent, i.e., the Nagaoka observation station located along the Sea of Japan. We have monitored the atmospheric sulfate deposition and its sulfur isotopic ratio (δ34S) for 28 years at this station as well as the sulfur isotopic ratio of coal and oil used in the source and local areas. The sulfur isotopic ratios of non-sea-salt sulfate (δ34Snss) in Nagaoka ranged from 0.0 to +6.2‰. The isotopic ratios of the local emission and Chinese coal sulfur showed negative and positive values, respectively. Several statistically significant trends were detected regarding the deposition of non-sea-salt sulfate (nss-SO42−) during the study period. The amount of nss-SO42− deposition fluctuated in the mid-1980s, late 1990s to late 2000s, and mid-2000s with fluctuations in the δ34Snss value. Mass balance calculations suggested that sulfur released by coal combustion in China during the 1990s contributed to about 40% of the annual total sulfur deposition in Nagaoka, and its contribution increased to over 60% in the middle of the 2000s. The contribution began to decrease after that peak, which was in agreement with the temporal change in emissions from China.

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  • Fumikazu Ikemori
    2022 Volume 57 Issue 1 Pages 24-33
    Published: January 10, 2022
    Released on J-STAGE: January 10, 2022

    Carbonaceous aerosols account for a large proportion of atmospheric aerosols, including fine particulate matter (PM2.5). Countermeasures against their sources are considered effective in reducing PM2.5. However, because of its complex sources and atmospheric processing, the identification of organic aerosol sources and generation mechanisms are challenging topics. In this review, previous studies regarding the development of a highly time-resolved measurement method of organic tracer compounds and evaluating new tracer compounds for anthropogenic secondary organic aerosols are outlined. Furthermore, studies related to the field observations of radiocarbon and organic tracer compounds and their application to the evaluate the origins and sources of carbonaceous aerosols are presented. Primarily aerosols emitted from biomass burning and secondary organic aerosols are discussed. Furthermore, a summary of issues and perspectives regarding tracer compounds is discussed.

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Original Paper
  • Masayuki Tsujimoto, Kouhei Yamamoto, Takayuki Kameda
    2022 Volume 57 Issue 1 Pages 1-14
    Published: 2022
    Released on J-STAGE: December 18, 2021

    A Land Use Regression (LUR) model was used to assess the health effects of air pollutants. In this study, as the concentration of the air pollutants is significantly affected by meteorological fields, we developed LUR models along with the meteorological model to estimate the monthly average distributions of PM2.5 and NO2. Spatial distributions of the meteorological fields were obtained by using the meteorological model WRF, and used them as candidates for the explanatory variables of the LUR models. We adopted two methods, i.e., the Regression Kriging method and Support-Vector-Regression (SVR) method to develop the regression models. Regarding the completed LUR models, the explanatory variables estimated by the WRF were selected with a high importance in all months, and the estimated distributions showed a relatively high accuracy with R2 values of about 0.7 for both the PM2.5 and NO2. The effect of the introduction of the SVR method to the prediction accuracy was remarkable for NO2, however, that for PM2.5 was not clear. We think that the advantages of introducing the machine learning methods, such as the SVR method, in developing a LUR model becomes clearer by improvement of the WRF settings and the addition of new explanatory variables.

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