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

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 SO 2 in the ambient environment, examination of SO 2 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 humanexposure 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. List of symbolsr y and r 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 SO 2 standard level for SPG-D (lg/m 3 ) C N the most suitable SO 2 standard level for SPG-N (N is group number, lg/m 3 ) 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 Dh is plume rise (m) K a scaling coefficient to convert calculated concentrations to desired units (default value of 1 · 10 6 for Q in g/s and concentration in lg/m 3 ) 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) l s wind speed (m/s) at the release height W the decay coefficient (s -1 ) (a value of zero means decay is not considered)

Uncertain spatial reasoning of environmental risks in GIS using genetic learning algorithms

“…Because it is difficult for experts to examine all of the input and output data from a complex system to find proper rules (Lin and Xu 2006), researchers have proposed hybrid methods, such as mixing fuzzy-set concepts with other methods (Kentel and Aral 2005;Li et al 2008) to solve this problem. One of the most popular mixing approaches is the use of genetic algorithms (GAs) that lead to fuzzy systems.…”

Multi-Perspective Analysis and Spatiotemporal Mapping of Air Pollution Monitoring Data

“…There are two main products when using SOM: (i) a low-dimensional model of the high-dimensional input space, the SOM itself; and (ii) a low-dimensional representation of high-dimensional vectors after they are mapped onto the trained SOM (17, pp. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The former is often useful in explaining patterns observed in the latter, once both are given visual form, as demonstrated in section 4.2.…”

“…SO 4 mostly has the effect of increasing the air acidity that can lead to acid rain, whereas the role of NO 2 is more complicated as it contributes both to tropospheric ozone pollution and may have adverse effects on the cardio-respiratory system (1,2). A variety of statistical techniques can be used to study separately the spatial variation and temporal evolution of air pollutants and the associated risk assessment (e.g., [3][4][5][6][7]. The present work demonstrates a novel approach to study important characteristics of the SO 4 and NO 2 distributions in a composite space-time continuum.…”

Development of a Fuzzy Pattern Recognition Model for Air Quality Assessment of Howrah City

“…Fisher (2003) illustrated that the use of fuzzy sets formalizes the underlying uncertainty and therefore leads to better decision making. Li et al (2008) proposed an integrated fuzzy-stochastic modelling approach for quantifying uncertainties associated with both source/ medium conditions and evaluation criteria and thus assessing air pollution risks. Hajek and Olej (2009) presented a design of AQIs based on tree/cascade hierarchical fuzzy inference systems.…”