Risk-Based Prioritization of Air Pollution Monitoring Using Fuzzy Synthetic Evaluation Technique

Abstract

/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1007/s10661-005-3852-1Environmental Monitoring and Assessment, 105, 1-3, pp. 261-283, 2005-06-01 Risk-based prioritization of air pollution monitoring using fuzzy synthetic evaluation technique Khan, F. I.; Sadiq, R. 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, NO x , PM 10 and SO x 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.

A Fuzzy Expression Way for Air Quality Index with More Comprehensive Information

“…Clearly, it is fairly unreasonable to assign the same weight to each air pollutant subjectively; since different air pollutants have varying health impacts and hence the corresponding weights of air pollutants are different in the determination of the overall AQI. For the sake of determining the weights of various pollutants, the AHP was applied into subsequent research by Mandal et al [22], Khan and Sadiq [30] and Upadhyay et al [31]. Meanwhile, other substitutable methods, such as expert scoring, fuzzy synthetic evaluation, and so on, can also be used to ascertain the weights of different pollutants.…”

Decision making framework for decreasing the redundancy of air quality monitoring stations in Vojvodina Province (Serbia)

“…Many applications of AHP in the air pollution problems include AHP as part of the solving methodology [7,9,19,20,21]. Authors believe that AHP should be used for the whole AQMS evaluation procedure, because it (1) enables better insight of the decision makers into the problem by decomposing the complex one into the smaller, easier to comprehend, problems; (2) enables analysis of cardinal values of importance criteria and their influence on final decision; (3) includes both qualitative and quantitative decision criteria in the analysis; (4) provides both weights of the decision alternatives and ranks; and (5) supports group decision making.…”

Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors

“…For example, in a study performed by Onkal-Engin et al (2004), an air quality index was developed based on fuzzy inference system, which produced significant results. In another study carried out by Khan and Sadiq (2005), the sensitivity of receptors was also included in an air quality index, which produced better estimation of air quality impact on human health. In a study conducted by Hájek and Olej (2009), criteria air pollutants as well as meteorological parameters were included in the air quality index, using fuzzy inference system.…”