Damages and costs of air pollution: An analysis of uncertainties

Citation
A. Rabl et Jv. Spadaro, Damages and costs of air pollution: An analysis of uncertainties, ENVIRON INT, 25(1), 1999, pp. 29-46
Citations number
42
Categorie Soggetti
Environment/Ecology
Journal title
ENVIRONMENT INTERNATIONAL
ISSN journal
01604120 → ACNP
Volume
25
Issue
1
Year of publication
1999
Pages
29 - 46
Database
ISI
SICI code
0160-4120(199901)25:1<29:DACOAP>2.0.ZU;2-Z
Abstract
This paper evaluates the uncertainties of an impact pathway analysis which traces the fate of each pollutant or other burden, from the source to the r eceptors, using dose-response functions to evaluate the damage. The express ion for the total damage is shown to be largely multiplicative, even though it involves a sum over receptors at different sites. This follows from con servation of matter which implies that overprediction of the dispersion mod el at one site is compensated by underprediction at another; the net error of the total damage arises mostly from uncertainties in the rate at which t he pollutant disappears from the environment. Since the central limit theor em implies that the error distribution for multiplicative processes is like ly to be approximately lognormal, one may be able to bypass the need for a detailed and tedious Monte Carlo calculation. Typical error distributions a re discussed for the factors in the expression for the total damage, in par ticular those of two key parameters: the deposition velocity of atmospheric dispersion models, and the value of statistical life; they are close to lo gnormal. A lognormal distribution for the total damage appears plausible wh enever the dose-response function is positive everywhere. As an illustratio n, results for several types of air pollution damage are shown (health dama ge due to particles and carcinogens, damage to buildings due to SO2, and cr op losses due to O-3): the geometric standard deviation is in the range of 3 to 5. To the extent that the distribution of the result is lognormal, the geometric mean equals the median and the geometric standard deviation has a simple interpretation in terms of multiplicative confidence intervals aro und the median. (C) 1999 Elsevier Science Ltd.