A model-based fuzzy set-OWA approach for integrated air pollution risk assessment

This paper presents a fuzzy set—ordered weighted averaging (FSOWA) approach for the integrated health risk assessment associated with multiple air pollution factors and evaluation criteria. A number of different methods and tools are integrated within the same platform, including Geographic Information System, air quality modeling, fuzzy set, multi-criteria analysis, ordered weighted averaging, and health risk assessment. A degree of fuzziness is incorporated into the air quality criteria by using the fuzzy sets and therefore the absolute criteria is avoided. The health risk and relative importance of various pollution factors are aggregated by two models (Max–min and Max-product composition) with the consideration of uncertainties. The main advantage of FSOWA is capable of revealing the potential interactions among various pollution factors and quantifying the inherent uncertainties and complexities in air pollution integrated health risk assessment. The developed approach is illustrated in a case study of the state of California based on four criteria pollutants (PM2.5, NO2, SO2 and CO). The results demonstrate that the developed approach offers a flexible exploitation for assessing air pollution health risk. This approach may also be applied to much broader environmental problems for multi-criteria decision making to achieve more sustainable environment.