Risk-based prioritization of air pollution monitoring using fuzzy synthetic evaluation technique

Download
  1. (PDF, 731 KB)
  2. Get@NRC: Risk-based prioritization of air pollution monitoring using fuzzy synthetic evaluation technique (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1007/s10661-005-3852-1
AuthorSearch for: ; Search for:
TypeArticle
Journal titleEnvironmental Monitoring and Assessment
Volume105
IssueJune
Pages261283; # of pages: 23
SubjectAHP, air pollution monitoring, fuzzy sets, fuzzy synthetic evaluation
AbstractAir pollution monitoring programs aim to monitor pollutants and their probable adverse effects at various locations over concerned area. Either sensitivity of receptors/location or concentration of pollutants is used for prioritising the monitoring locations. The exposure-based approach prioritises the monitoring locations based on population density and/or location sensitivity. The hazard-based approach prioritises the monitoring locations using intensity (concentrations) of air pollutants at various locations. Exposure and hazard-based approaches focus on frequency (probability of occurrence) and potential hazard (consequence of damage), independently. Adverse effects should be measured only if receptors are exposed to these air pollutants. The existing methods of monitoring location prioritization do not consider both factors (hazard and exposure) at a time. Towards this, a risk-based approach has been proposed which combines both factors: exposure frequency (probability of occurrence/exposure) and potential hazard (consequence). This paper discusses the use of fuzzy synthetic evaluation technique in risk computation and prioritization of air pollution monitoring locations. To demonstrate the application, common air pollutants like CO, NOx, PM10 and SOx are used as hazard parameters. Fuzzy evaluation matrices for hazard parameters are established for different locations in the area. Similarly, fuzzy evaluation matrices for exposure parameters: population density, location and population sensitivity are also developed. Subsequently, fuzzy risk is determined at these locations using fuzzy compositional rules. Finally, these locations are prioritised based on defuzzified risk (crisp value of risk, defined as risk score) and the five most important monitoring locations are identified (out of 35 potential locations). These locations differ from the existing monitoring locations.
Publication date
LanguageEnglish
AffiliationNRC Institute for Research in Construction; National Research Council Canada
Peer reviewedYes
NRC number47731
17024
NPARC number20377451
Export citationExport as RIS
Report a correctionReport a correction
Record identifier3f3dd3c6-668b-4071-aab4-148df1d5673f
Record created2012-07-24
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)