Optimization of the PM<sub>2.5</sub> Monitoring Network using Hybrid Genetic Algorithm

DOI HANDLE Web Site Open Access
  • Araki Shin
    Otsu City Public Health Center Graduate School of Engineering, Osaka University
  • Iwahashi Koki
    Graduate School of Engineering, Osaka University
  • Shimadera Hikari
    Center for Environmental Innovation Design for Sustainability, Osaka University
  • Yamamoto Kouhei
    Graduate School of Energy Science, Kyoto University
  • Kondo Akira
    Graduate School of Engineering, Osaka University

Bibliographic Information

Other Title
  • ハイブリッド遺伝的アルゴリズムによるPM<sub>2.5</sub>モニタリングネットワークの最適化
  • ハイブリッド遺伝的アルゴリズムによるPM₂.₅モニタリングネットワークの最適化
  • ハイブリッド イデンテキ アルゴリズム ニ ヨル PM ₂.₅ モニタリングネットワーク ノ サイテキ カ

Search this article

Abstract

Many studies have discussed the air monitoring network optimization methods using the observations from the networks to be optimized where the spatial representativeness of the network is assumed. Therefore, these methods are difficult to apply to the networks under development. In this study, the hybrid genetic algorithm that combines the standard GA and simulated annealing is applied to the simulated values from an air quality model instead of observations for the optimization of the developing PM2.5 monitoring network in the Kinki region of Japan. The current network is evaluated by comparison to the optimized network. The optimized network is uniformly distributed in general and reproduces the spatial distribution of the simulated concentrations. Although the current network describes the spatial distribution in the high concentration areas, the representativeness of the concentrations of the entire area could be improved by the redistribution of some monitors to the stations monitoring other pollutants. The guideline for the placement of monitors at existing stations is proved to be generally appropriate. The optimization with the weighing factor, based on population, results in a network where more stations are distributed in the higher concentration areas.

Journal

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top