DOI: 10.1016/j.atmosenv.2015.09.030
Scopus记录号: 2-s2.0-84941960801
论文题名: Optimization of air monitoring networks using chemical transport model and search algorithm
作者: Araki S ; , Iwahashi K ; , Shimadera H ; , Yamamoto K ; , Kondo A
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 122 起始页码: 22
结束页码: 30
语种: 英语
英文关键词: Genetic algorithm
; Japan
; NO2
; O3
; PM2.5
; Simulated annealing
Scopus关键词: Air pollution
; Genetic algorithms
; Interpolation
; Monitoring
; Simulated annealing
; Air monitoring networks
; Chemical transport models
; Japan
; Monitoring network
; Monitoring stations
; Optimization criteria
; Optimization method
; Search Algorithms
; Algorithms
; nitrogen dioxide
; ozone
; atmospheric pollution
; concentration (composition)
; environmental monitoring
; genetic algorithm
; kriging
; network design
; nitrous oxide
; optimization
; ozone
; particulate matter
; pollutant transport
; simulated annealing
; air monitoring
; air pollutant
; algorithm
; Article
; genetic algorithm
; histogram
; Japan
; kriging
; particulate matter
; priority journal
; simulation
; urban area
; Japan
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: 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. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81334
Appears in Collections: 气候变化事实与影响
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作者单位: Graduate School of Engineering, Osaka University, Japan; Otsu Public Health Center, Shiga, Japan; Graduate School of Energy Science, Kyoto University, Japan
Recommended Citation:
Araki S,, Iwahashi K,, Shimadera H,et al. Optimization of air monitoring networks using chemical transport model and search algorithm[J]. Atmospheric Environment,2015-01-01,122