DOI: 10.1016/j.atmosenv.2018.01.011
Scopus记录号: 2-s2.0-85041482423
论文题名: Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm
作者: Hao Y ; , Xie S
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 177 起始页码: 222
结束页码: 233
语种: 英语
英文关键词: Air quality monitoring network design
; NO2
; Optimization
; SO2
; Urban growth
; WRF/CALPUFF
Scopus关键词: Air quality
; Atmospheric movements
; Genetic algorithms
; Monitoring
; Nitrogen oxides
; Optimization
; Pollution
; Pollution detection
; Urban planning
; Weather forecasting
; Air quality monitoring networks
; Air quality networks
; Atmospheric dispersion modeling
; Non- dominated sorting genetic algorithms
; Spatial representations
; Spatiotemporal patterns
; Weather research and forecasting
; WRF/CALPUFF
; Urban growth
; nitrogen dioxide
; sulfur dioxide
; air monitoring
; air pollutant
; air quality
; air quality standard
; Article
; atmospheric dispersion
; China
; concentration (parameters)
; controlled study
; correlation coefficient
; evaluation study
; genetic algorithm
; mutation rate
; population size
; priority journal
; sensitivity analysis
; simulation
; social problem
; spatiotemporal analysis
; urban area
; violation
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 quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting–California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/83014
Appears in Collections: 气候变化事实与影响
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作者单位: College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing, China
Recommended Citation:
Hao Y,, Xie S. Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm[J]. Atmospheric Environment,2018-01-01,177