A heuristic optimization approach for Air Quality Monitoring Network design with the simultaneous consideration of multiple pollutants.
An interactive optimization methodology for allocating the number and configuration of an Air Quality Monitoring Network (AQMN) in a vast area to identify the impact of multiple pollutants is described. A mathematical model based on the multiple cell approach (MCA) was used to create monthly spatial...
| Publicado en: | Journal of Environmental Management Vol. 88; no. 3; pp. 507 - 517 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
August 2008
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=506822006&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 506822006 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: August 2008 vid: 88 iid: 3 pid: 735 pub: Academic Press Inc. artinfo: ui: 506822006 10.1016/j.jenvman.2007.03.029 ppf: 507 ppct: 10 formats: tig: atl: A heuristic optimization approach for Air Quality Monitoring Network design with the simultaneous consideration of multiple pollutants. aug: au: Elkamel, A. Fatehifar, E. Taheri, M. su: Mathematical models of air quality Mathematical models Air pollution sug: subj: Mathematical models of air quality Mathematical models Air pollution ab: An interactive optimization methodology for allocating the number and configuration of an Air Quality Monitoring Network (AQMN) in a vast area to identify the impact of multiple pollutants is described. A mathematical model based on the multiple cell approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. These spatial temporal patterns were subject to a heuristic optimization algorithm to identify the optimal configuration of a monitoring network. The objective of the optimization is to provide maximum information about multi-pollutants (i.e., CO, NOx and SO2) emitted from each source within a given area. The model was applied to a network of existing refinery stacks and the results indicate that three stations can provide a total coverage of more than 70%. In addition, the effect of the spatial correlation coefficient (RC) on total area coverage was analyzed. The modeling results show that as the cutoff correlation coefficient RC is increased from 0.75 to 0.95, the number of monitoring stations required for total coverage is increased. A high RC based network may not necessarily cover the entire region, but the covered region will be well represented. A low RC based network, on the other hand, would offer more coverage of the region, but the covered region may not be satisfactorily represented. Copyright (c) 2008 Elsevier Ltd. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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