Development of the high-resolution disribution model for the exposure assessment of air pollutants and its application to epidemiological study

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K12370
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 64010:Environmental load and risk assessment-related
• Research Institution: Kyoto University
• Principal Investigator: Kouhei Yamamoto 京都大学, エネルギー科学研究科, 助教 (10263154)
• Co-Investigator(Kenkyū-buntansha): 荒木 真 大阪大学, 工学研究科, 招へい研究員 (20794027) ; 島 正之 兵庫医科大学, 医学部, 教授 (40226197)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 大気汚染物質 / 曝露量推定モデル / 気象モデル / 大気化学輸送モデル / 機械学習 / 健康影響評価

Outline of Final Research Achievements

We have developed and improved a statistical model called the Land Use Regression (LUR) model for improving the accuracy of air pollutant exposure estimation. In order to consider the effects of local meteorological factors and long-range transport of air pollutants, affecting the air pollution concentration in Japan. We constructed a hybrid model that incorporates the outputs of meteorological models and air quality models. We also introduced machine learning techniques in constructing the LUR models and assessed the effects of introducing the techniques. Moreover, in order to cooperate with the the epidemiological surveys that have already been conducted, we estimated the exposure concentration over 30 years from the time when PM2.5 monitoring was not carried out, and estimated PM2.5 components concentrations over 10 years in Japan.

Free Research Field

大気環境モデリング

Academic Significance and Societal Importance of the Research Achievements

本研究の成果は，都市域を中心として未解決の問題である大気汚染物質の曝露に伴う健康影響の評価における基礎情報としての曝露量推定手法の発展に貢献すると考えられる．今回開発したモデルを既に実施された疫学調査と連携させることにより，大気汚染と健康の影響の関連についてより高精度の分析が可能になる．