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タイトル: | Optimization of air monitoring networks using chemical transport model and search algorithm |
その他のタイトル: | 化学輸送モデルと探索アルゴリズムを用いた大気モニタリングの最適化 |
著者: | Araki, Shin Iwahashi, Koki Shimadera, Hikari Yamamoto, Kouhei Kondo, Akira |
著者名の別形: | 山本, 浩平 |
キーワード: | PM₂.₅ NO₂ O₃ Genetic algorithm Simulated annealing Japan |
発行日: | Dec-2015 |
出版者: | Elsevier BV |
誌名: | Atmospheric Environment |
巻: | 122 |
開始ページ: | 22 |
終了ページ: | 30 |
抄録: | Air monitoring network design is a critical issue because monitoring stations should be allocated properly so that they adequately represent the concentrations in the domain of interest. Although the optimization methods using observations from existing monitoring networks are often applied to a network with a considerable number of stations, they are difficult to be applied to a sparse network or a network under development: there are too few observations to define an optimization criterion and the high number of potential monitor location combinations cannot be tested exhaustively. This paper develops a hybrid of genetic algorithm and simulated annealing to combine their power to search a big space and to find local optima. The hybrid algorithm as well as the two single algorithms are applied to optimize an air monitoring network of PM2.5, NO2 and O3 respectively, by minimization of the mean kriging variance derived from simulated values of a chemical transport model instead of observations. The hybrid algorithm performs best among the algorithms: kriging variance is on average about 4% better than for GA and variability between trials is less than 30% compared to SA. The optimized networks for the three pollutants are similar and maps interpolated from the simulated values at these locations are close to the original simulations (RMSE below 9% relative to the range of the field). This also holds for hourly and daily values although the networks are optimized for annual values. It is demonstrated that the method using the hybrid algorithm and the model simulated values for the calculation of the mean kriging variance is of benefit to the optimization of air monitoring networks. |
著作権等: | © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ The full-text file will be made open to the public on 1 December 2017 in accordance with publisher's 'Terms and Conditions for Self-Archiving' この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 This is not the published version. Please cite only the published version. |
URI: | http://hdl.handle.net/2433/242226 |
DOI(出版社版): | 10.1016/j.atmosenv.2015.09.030 |
出現コレクション: | 学術雑誌掲載論文等 |

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