Integrated risk assessment of ambient air quality by stochastic and fuzzy approaches

Ping, Jing (2008) Integrated risk assessment of ambient air quality by stochastic and fuzzy approaches. Masters thesis, Memorial University of Newfoundland.

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The risk associated with a power generation system often refers to the contaminants emission from combustion facilities, which could violate environmental standards and affect human health through various exposure pathways. In this research, an integrated risk assessment by stochastic and fuzzy approaches was applied to systematically examine both probabilistic and possibility uncertainties existing in environmental conditions and evaluation criteria within an ambient air quality management system. The contaminant concentrations in ambient air predicted from a numerical simulation model usually contain probabilistic uncertainties due to the variations in modeling input parameters; while the temporal and spatial variations of environment make the consequences of contaminant concentrations violating relevant guidelines and health evaluation criteria to be linked with possibilistic uncertainties due to the vagueness of expert's judgments. This leads to difficulties in direct implementation of the deterministic environmental guidelines because of the existence of uncertain factors. To help resolve the problem, this study aims at developing a integrated risk assessment system for the management of ambient air quality by stochastic and fuzzy approaches. The objective entails the following tasks: (a) Monte Carlo simulation of sulfur dioxide (SO₂) dispersion in the ambient air through a regulatory steady-state plume numerical modeling system AERMOD, to generate cumulative distribution functions for stochastic uncertainties; (b) fuzzy environment and health risk assessment based on stochastic simulation: quantification of environmental guidelines and health criteria using fuzzy membership functions acquired from a questionnaire survey; determination of risk levels by developing a fuzzy rule-based assessment system. The contaminant of interest in this study is SO₂. The environmental quality guideline was divided into three categories: loose, medium and strict. The environmental-guideline-based risk (ER) and health risk (HR) due to SO₂ inhalation were evaluated to obtain the general risk levels through a fuzzy rule base. The ER and HR levels were divided into five categories of low, low-to-medium, medium, medium-to-high and high, respectively. The general risk levels included six categories ranging from low to high. The fuzzy membership functions and the fuzzy rule base were established through a questionnaire survey. Thus the developed approach was able to integrate fuzzy logic, expert experience, and stochastic simulation within a general framework. The robustness of the evaluation results can be enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. In order to test the feasibility and effectiveness, the developed model was applied to a thermal power station in Atlantic Canada. The results were analyzed under three scenarios with different environmental quality guidelines, leading to the variations of risk levels (based on different degrees of guideline strictness acquired from questionnaire survey). It is indicated that, the integrated risk assessment can more effectively elucidate the relevant environmental and health risks resulting from SO₂ emission. The developed approach can offer a unique tool for quantifying uncertainties in air quality modeling and risk assessment, and also provide realistic support for related decision-making processes.

Item Type: Thesis (Masters)
Item ID: 8835
Additional Information: Includes bibliographical references (leaves 188-196).
Department(s): Engineering and Applied Science, Faculty of
Date: 2008
Date Type: Submission
Library of Congress Subject Heading: Air quality management--Mathematical models; Electric power-plants--Environmental aspects; Environmental risk assessment--Mathematical models

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