A quantitative risk assessment method based on population and exposure distributions using Australian air quality data

Authors
Citation
T. Beer et Pf. Ricci, A quantitative risk assessment method based on population and exposure distributions using Australian air quality data, ENVIRON INT, 25(6-7), 1999, pp. 887-898
Citations number
43
Categorie Soggetti
Environment/Ecology
Journal title
ENVIRONMENT INTERNATIONAL
ISSN journal
01604120 → ACNP
Volume
25
Issue
6-7
Year of publication
1999
Pages
887 - 898
Database
ISI
SICI code
0160-4120(199909/10)25:6-7<887:AQRAMB>2.0.ZU;2-I
Abstract
This paper develops a practical probabilistic method for assessing aggregat e population health risks from different types of population exposures. The method consists of calculating the product of two functions: a population- weighted distribution of concentrations and a concentration-response distri bution. This operation yields the corresponding aggregated health-risk dist ribution function. The method can use alternative exposure-response distrib utions and populations-specific exposure patterns, depending on the context of the assessment. A deterministic sensitivity analysis is included in the methodological aspects of this research. The distributions of concentratio ns are generated by combining area-specific population densities with atmos pheric concentrations for each of the areas where exposure to air pollutant s occurs. The exposure-response functions are developed from the literature . The method is exemplified using alternative exposure probabilities to car bon monoxide, nitrogen dioxide, particulate matter (PM10), and exposure-res ponse models developed specifically for these pollutants for assessing heal th risks, and applied to data from a number of Australian cities. The resul ts, which hold when the functions are monotonic, show single maximum per po llutant, regardless of the choice of exposure and exposure-response distrib ution. Although those maxima are often below the Australian Air Pollution S tandards, there are instances when this is not the case. (C) 1999 Elsevier Science Ltd.