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An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

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Abstract

High concentrations of air pollutants in the ambient environment can result in breathing problems with human communities. Effective assessment of health-impact risk from air pollution is important for supporting decisions of the related detection, prevention, and correction efforts. However, the quality of information available for environmental/health risk assessment is often not satisfactory enough to be presented as deterministic numbers. Stochastic method is one of the methods for tackling those uncertainties, by which uncertain information can be presented as probability distributions. However, if the uncertainties can not be presented as probabilities, they can then be handled through fuzzy membership functions. In this study, an integrated fuzzy-stochastic modeling (IFSM) approach is developed for assessing air pollution impacts towards asthma susceptibility. This development is based on Monte Carlo simulation for the fate of SO2 in the ambient environment, examination of SO2 concentrations based on the simulation results, quantification of evaluation criteria using fuzzy membership functions, and risk assessment based on the combined fuzzy-stochastic information. The IFSM entails (a) simulation for the fate of pollutants in ambient environment, with the consideration of source/medium uncertainties, (b) formulation of fuzzy air quality management criteria under uncertain human-exposure pathways, exposure dynamics, and SPG-response variations, and (c) integrated risk assessment under complexities of the combined fuzzy/stochastic inputs of contamination level and health effect (i.e., asthma susceptibility). The developed IFSM is applied to a study of regional air quality management. Reasonable results have been generated, which are useful for evaluating health risks from air pollution. They also provide support for regional environmental management and urban planning.

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Abbreviations

σ y and σ z :

point source dispersion parameters, denoting standard deviations of lateral and vertical concentration distributions (m)

BMR D :

the basal metabolic rate of SPGs-D (MJ/day)

BMR N :

the basal metabolic rate of SPG-N (MJ/day)

C D :

the most suitable SO2 standard level for SPG-D (μg/m3)

C N :

the most suitable SO2 standard level for SPG-N (N is group number, μg/m3)

D :

decay term of ISC short term model

f LC :

associated probability density function

h e :

plume (or effective stack) height (m)

h s :

physical stack height (m), and Δh is plume rise (m)

K :

a scaling coefficient to convert calculated concentrations to desired units (default value of 1 × 106 for Q in g/s and concentration in μg/m3)

P F :

risk level quantified as probability of system failure

Q :

pollutant emission rate (mass per unit time)

V :

vertical term of ISC short term model

x :

downwind distance (m)

Z i :

mixing height (m)

Z r :

receptor height above ground (flagpole) (m)

μs :

wind speed (m/s) at the release height

Ψ:

the decay coefficient (s−1) (a value of zero means decay is not considered)

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Acknowledgments

The authors are grateful to the editor and the anonymous reviewers for their insightful comments and suggestions. The authors would like to thank for their technical assistance. Thanks are also due to Mr. S. Podwin of SaskPower, Saskatchewan, Canada and Drs. Z. Chen and J.B. Li of the Environmental Informatics Laboratory, University of Regina for their technical assistances. This research was supported by the Major State Basic Research Development Program (2005CB724200 and 2006CB403307).

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Authors and Affiliations

  1. Environmental Systems Engineering Program, University of Regina, Regina, SK, S4S 0A2, Canada

    Heng L. Li

  2. Chinese Research Academy of Environmental Science, North China Electric Power University, Beijing, 100012-102206, China

    Guo H. Huang

  3. Center for studies in Energy and Environment, University of Regina, Regina, SK, S4S 0A2, Canada

    Yun Zou

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  1. Heng L. Li
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  2. Guo H. Huang
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Correspondence to Guo H. Huang.

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Li, H.L., Huang, G.H. & Zou, Y. An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution. Stoch Environ Res Risk Assess 22, 789–803 (2008). https://doi.org/10.1007/s00477-007-0187-1

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