Impact pathway analysis: A tool for improving environmental decision processes

ELSEVIER

FEATURE

IMPACT PATHWAY ANALYSIS: A TOOL FOR I M P R O V I N G E N V I R O N M E N T A L DECISION PROCESSES

A. Rabl and B. Peuportier Ecoles des Mines, Paris, France

This paper proposes that, f o r installations with major health risks, it may be practical and desirable to demand that an environmental impact study demonstrate not only that the emissions kespect all applicable regulations (as is current practice), but that it actually evaluate the impacts, using the impact pathway methodology (i.e., analyze the dispersion o f pollutants and apply dose-response functions to quantify impacts on health, vegetation, etc.). A s a case study we apply the impact pathway analysis to the emissions data f o r an incinerator o f toxic chemical waste, and we obtain several interesting results that could resolve some o f the issues raised during the authorization process. We argue that the uncertainties, even though large, do not negate the value o f the information. The results o f such an impact analysis could also be used to communicate the risks posed by a proposed installation, i f a generally accepted set o f reference risks is developed.

1. Introduction The fear of health hazards due to air and water pollution is one of the main reasons why the authorization of installations such as chemical plants and waste incinerators has frequently been highly contested. In principle such fears should be allayed by the environmental impact statement. However, in current practice most impact studies limit themselves, more or less, to saying " . . . will satisfy all applicable regulations" as far as pollutant emissions are Address requests for reprints to."Ari Rabl, Centre d'Energetique, Ecole des Mines, 60 Blvd St Michel, 75272 Paris, France ENVIRON IMPACT ASSESS REV 1995;15:421-442 © 1995 Elsevier Science Inc. 655 Avenue of the Americas, New York, NY 10010

0195-9255/95/$9.50 SSDI 0195-9255(95)00044-F

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concerned; despite the name "impact study," the actual health impacts are rarely quantified [Wathern 1992, Petts and Eduljee 1994]. Even during a recent European Commission (EC) project concerning toxicwaste incinerators in Belgium, France and the UK, we found that none of the impact statements tried to quantify the impacts or risks - despite the fact that few installations evoke greater fear than incinerators of toxic waste. Most of the impact studies merely showed that the pollutant emissions would respect all regulations. Only one study [ICI 1993] went a step further, by running an atmospheric dispersion model to show that the resulting ambient concentrations of the pollutants would not exceed regulatory exposure limits. The most comprehensive impact analysis we have encountered is for an aluminum smelter in Dunkerque, France, a project o f such magnitude that a large effort could be justified for the impact study [Lallemant 1990]. Not only was an atmospheric dispersion model used but dose-response functions were invoked to show that the major pollutant, hydrogen fluoride, would cause no appreciable damage. Such a thorough analysis helped convince the population that there would be no serious risk, and the installation was approved. Of course, there is a good reason for this state of affairs: until now the quantification of impacts has been extremely difficult and time consuming, to say nothing of difficulties due to scientific uncertainties. Regulatory limits provide an essential simplification: if the limits are sufficiently strict, the population is indeed protected. However, there are pervasive doubts that the limits may not have been chosen correctly, and frequently population or experts do not feel reassured. The NIMBY (not in my backyard) problem follows naturally.

2. Impact Pathway Analysis The logically correct way to analyze environmental impacts is the impact pathway methodology in which the principal steps are the following: • characterization of the relevant technologies and the environmental burdens they impose (e.g., tons of particulates per time emitted by the plant); • calculation of increased pollutant concentration in all affected regions (e.g., ilg/m 3 of particulates, using models of atmospheric dispersion and chemistry); • calculation o f physical impacts (e.g., number of cases of asthma due to these particulates, using dose-response functions); • in some cases a fourth step may be desirable: the economic valuation of these impacts (e.g., multiplication by the cost of an incident of asthma). The numbers are summed over all receptors that are affected. Formally the procedure can be represented as an equation for the incremental cost due to an incremental quantity AQ of a pollutant emitted by the plant Acost = ~

fcost,r(fimpact,r(fdisp~r(AQ))),

(2.1)

r

where the f(...) are functions corresponding to the steps of the pathway analy-

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sis, as labeled by their subscripts, and r represents the receptors (population, crops, buildings, etc.) that may be affected by this burden. The notation allows the possibility that impact and cost may vary by receptor. This equation expresses the damage cost in functional form, hence this methodology is also known under the name damage function. O f course, although this methodology is logically correct, the practical implementation may not always be feasible for lack of appropriate data or models. An incinerator emits a variety of pollutants and each can contribute to a variety of impacts. An exhaustive evaluation of all impacts would not be possible. In practice one has to concentrate on the m a j o r impacts, also called priority pathways, while the others are so small that they can be neglected or treated approximately in lumped fashion. The process is iterative, in response to scientific progress and the concerns o f the local population. Pollutants can be emitted to air, water, or soil. For combustion equipment such as incinerators, the general consensus is that the dominant impacts, other than global warming, are the public health impacts of pollutants that are first emitted into the air, even if they later pass into the water or the soil [ORNL 1994, EC 1995]. Therefore, in the present paper we concentrate on those pollutants.

3. Dose-Response Functions

3.1. The Form o f the Dose-Response Function A central element in the analysis is the dose-response function Y = fimpact(X);

(3.1)

it relates the quantity X o f a pollutant that affects a receptor (e.g. population) to the physical impact Y on this receptor (e.g., incremental number of deaths). In the narrow sense of the term, X should he the dose actually absorbed by a receptor. However, the term dose-response function is also used in a wider sense where X represents the concentration of a pollutant in the ambient air, even though the term exposure-response function would be more correct; in that c a s e fimpact(X) accounts implicitly for the absorption o f the pollutant from the air into the body. Dose-response functions for the classical air pollutants (NOx, SOx, 03, and particulates) are typically of that kind. By definition a dose-response function starts at the origin, and in most cases it increases monotonically with X, as sketched schematically in Fig. 3.1. Doseresponse functions are determined from epidemiological laboratory studies. Since the latter are limited to animals, the extrapolation to humans introduces large uncertainties. Another m a j o r difficulty is that one needs relatively high doses to obtain observable nonzero responses in a sample of realistic size; such doses are usually far in excess of the levels one is concerned with in environmental impact studies. Thus, there is a serious problem of how to extrapolate from the observed data towards low doses. Fig. 3.1 indicates several possibili-

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ties. The simplest is the linear model, i.e., a straight line from the origin through the observed data point(s). Another possibility is a straight line down to some threshold, and zero effect below that threshold. Thresholds occur when an organism has a natural repair mechanism that can prevent or counteract damage up to a certain limit. If nothing is known about a threshold, the dose-response function could be anywhere between zero and the straight line through the origin, for instance the curved line shown in Fig. 3.1. A priori there is no general rule about the extrapolation to low doses, other than there being no known cases of a doseresponse function above the straight line. There is even a case where the same substance causes different cancers according to different dose-response functions, one with and one without threshold. This was established in an experiment (sometimes referred to as the Megamouse experiment) in which some 24,000 mice were exposed to the carcinogen 2-acetyl-amino-fluorene at several different dose levels [Maugh 1978; Frith, Littlefield, and Umholtz 1981]. The response for liver t u m o r is linear whereas the one for bladder t u m o r has a threshold. Being charged with protecting the population from health hazards, government agencies frequently invoke the assumption of linearity because that leads response

P

nonlinear function

linear function

\ function with threshold v

dose FIGURE 3.1. Possible behavior of dose-response functions at low doses: the three functions shown have the same value at P.

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to the most stringent limits on emissions. Alternatively they may set regulatory limits so that no m e m b e r of the public receives a dose that exceeds the lowest value at which an effect has been observed somewhere. For example, the Environmental Protection Agency (EPA) o f the United States assumes linearity for the dose-response functions for cancer, but for toxicity other than cancer it imposes limits according to a threshold below which there is essentially no risk [EPA 1989].

3.2. Implications o f the Threshold f o r the Analysis The form of the dose-response function, Fig. 3.1, has implications for the way an impact analysis is to be carried out. Two extreme cases must be distinguished. One extreme occurs when the dose-response function is a straight line through the origin (no threshold). In that case any incremental pollution causes an impact, and the range of the analysis needs to be extended over hundreds or thousands o f km if most o f the impact is to be included. This situation also pertains in the presence of a threshold, if the background concentration is everywhere above this threshold. For some air pollutants, e.g., particulates, the background in most industrialized countries is above the level where effects are known to occur. Thus, the question of the precise form of the dose-response function at extremely low doses is irrelevant for these pollutants: whatever the threshold, if there is one, it is below the background concentrations of interest. The other extreme occurs if the dose-response function has a threshold that is above the background concentration of the pollutant and if the pollution added by the source does not push the concentration above the threshold. In that case there is no impact. The analysis is simple: it suffices to verify that the resulting concentrations remain below the threshold. A short range (< 50 km) dispersion model is adequate for this purpose because the peak concentration increase certainly occurs within that region. O f course, in view of the uncertainties surrounding dose-response functions, one prefers this condition to be satisfied with a generous margin. A more complicated analysis becomes necessary for intermediate cases, i.e., if there is a threshold that is close to ambient background concentrations. However, for the pollutants considered in this paper these two extreme situations appear to be relevant.

3.3. Acute and Chronic Effects The fact that m a n y toxicity data are determined from relatively high shortterm exposures also poses the question of the relation between acute and chronic effects. Generally an organism suffers more damage from a given quantity of a harmful substance if this quantity is administered in a single dose rather than gradually over a long time. For example, 50 sleeping pills taken all at once maybe enough to kill. In the following analysis we evaluate the toxic effects as if the entire dose were ingested at one time, which seems a conservative assumption.

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3.4. Uncertainty The uncertainty o f dose-response functions varies widely from case to case. The dose-response function for health impacts from the classical air pollutants (particulates, SOx, NOx, and O3) are relatively well established; they typically have been reported with uncertainties that correspond to geometric standard deviations in the range of 1.5 to 2. However, these uncertainty estimates are obtained with a regression of data in a particular study. Effects not taken into account may make the real uncertainty much larger. This problem can be highlighted by writing the dose-response function in the form damage = f(dose, other ?) were "other ?" indicates all the variables that have not been taken into account. Applying a dose response function to a situation different from the one for which it has been derived is problematic if there is a difference in a variable which has not been taken into account. For example, the dose-response functions for particulate matter give little or no indication o f the composition or size distribution of the particles. If the particulates from the source under study are different from the ones on which the function has been based, the results may have significant errors.

4. A Case Study: Impact Pathway Analysis for an Incinerator

4.1. Emissions We will now apply this methodology to quantify the impacts in the vicinity of the incinerator proposed by ICI Chemicals & Polymers in Runcorn, near Liverpool, UK. The analysis begins by listing the emissions from the incinerator, as provided in the environmental impact statement [ICI 1993] and reproduced here in Table 4.1. The list is not very detailed, and no data are given for the composition of the categories total free halogens, volatile organics, particulates, metals, and dioxins. In fact, the list is essentially the list of emissions that are regulated by the government of the UK. Even for the category of chlorinated hydrocarbons (CHCs) the breakdown in part c) of the table is not sufficient for a rigorous pathway analysis because different isomers have different toxicities, to say nothing of the likelihood that the real emissions may be different from what is claimed in the environmental impact statement (the composition o f the input to the incinerator can vary with time and so can the emissions). Furthermore, we do not have sufficiently reliable health impact data for all of the listed emissions. However, in general the regulations and the health studies target those pollutants for which there is some reason to suspect danger. Thus it is likely that the items for which we have data represent a significant part of the dangerous emissions.

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TABLE 4.1.

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Emissions From the Incinerator for Runcorn Plant of ICI

a) Flow rate o f exhaust gases: 15,000 to 28,000 Nm3/h, stack height = 60 m. b) Regulations o f UK Government for release limits (prior to addition o f dilution air), to be satisfied under normal operation. Regulatory limits, mg/Nm 3 Hcl CO NO2 SO2 Total free halogens CHCs Volatile organics Particulates Metals Dioxins

10 50 350 50 5 5 20 20 5 0.000001

c) Average composition o f the CHCs. °7o by weight Ethylene dichloride Trichlorethylene Methylchloride Dichloromethane Chloroform Carbon tetrachloride Perchlorethylene Dichlorethylene, Trichlorethanes, and others

19 37 2 9 2 3 9 19

4.2. Dispersion The dispersion of the gaseous emissions has been calculated by ICI [1993] for its environmental impact study, using a gaussian plume model, considered appropriate for short distances, up to tens o f km [Seinfeld 1986, Zannetti 1990]. Since the removal or chemical transformation of the pollutants becomes important only at large distances, the dispersion of dilute air pollutants near the source is essentially the same regardless of pollutant species. The concentration near the plant is always proportional to the emissions, with the same proportionality constant for all pollutants; therefore, it suffices to calculate a single set o f dilution factors which yields the ground level concentration when multiplied by the concentration in the stack. In the present report we will study only the effects due to continuous exposure; hence we will need only the dilution factors for annual averages. In the impact study the dilution factors have been presented as contour plots. The interpretation of the labels A, B, C, and D of the contours can be found

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TABLE 4.2. The Interpretation of the Labels A, B, C, and D for the Countours o f the Annual Average Dilution Factors in the ICI Impact Study Contour

Dilution factors

A B C D

1,000,000 2,000,000 5,000,000 10,000,000

in Table 4.2; the contour C extends a few km from the plant. CHCs, chlorinated hydrocarbon. The present report focuses on risk comparison, therefore, an approximate estimate will be sufficient. We will consider only the C contour; it encloses a population of approximately 60,000 and the concentrations to which this population is exposed correspond to a mean dilution factor of 2,000,000. This estimate is based on the maps in the ICI study and on the following facts: the town where the plant is located has 120,000 inhabitants, the county has a population density of 1,680 per km 2, and 4,000 people live within 1 mile (1.6 km) of the plant. In the same spirit, the assumption of a gaussian dispersion model is not critical, even though the local topography is sufficiently hilly that such a simple model is arguably not correct in detail. Even if the uncertainty of this estimate is a factor of two above or below the true value, it is less than the uncertainties of most dose-response functions. Having been diffused in the atmosphere, the pollutants can then pass into water or soil, and be absorbed by plants or animals. They can be transmitted to humans by inhalation, ingestion (water or food) or dermal contacts. In the present study we consider only the inhalation pathway. That is certainly appropriate for particulates, because they cause damage only by passing through the lungs. For CHCs, and in particular for dioxins, the other pathways can be important. For instance, for dioxins the dietary intake is estimated to be more than 90o7o of the total, with food from animal origins being the predominant pathway [EPA 1994b, vol. III, p. 9-15]. However, that statement refers to total intake from the environment rather than the incremental intake from a local point source. The pathway through the food chain involves dispersion over large agricultural areas or bodies of water, whereas here we are concerned with local impacts in an urban area. The other pathways can be important for the total regional impacts, but in the local area the inhalation pathway dominateS.

4.3. Specific Health Effects 4.3.1. PARTICt~LATES,SOx, AND NOx. Among the classical air pollutants, particulates, SOx, and NOx, we consider only particulates here because the evi-

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dence for direct health impacts of SOx and NOx is not clear [EC 1995a and b]. NOx is certainly implicated indirectly as a precursor of tropospheric ozone for which health damages are firmly established, but ozone is not a m a j o r problem in this location, far north (53.5°N) and cloudy. The range and diversity of positive studies linking SO2 with acute health effects is substantially greater than for NOx, and h u m a n experimental studies are more suggestive of a real link. The position nevertheless is ambiguous. Arguably, SO2 in itself is not the cause. In several studies, apparent SO2 effects disappear when particulates are measured appropriately. Particulates from a combustion plant burning ash-free material consist in general of a carbon nucleus on which hydrocarbons may be adsorbed. Some of these elements could have carcinogenic impacts, but this issue is not yet settled and we do not consider it here. Concerning toxicity other than carcinogenicity, the main consequences of particulates concern the respiratory system. There are now numerous well-conducted studies linking particulate air pollution with a wide range of acute health effects with no convincing evidence o f a threshold level. Especially damaging are fine particulates, smaller than 3 lxm aerodynamic diameter, because they penetrate deeply into the lungs. To indicate the size in a measurement of particulate concentration, the notation PM is used with a subscript equal to the upper limit of aerodynamic diameters [in Ixm] of the included particulates. The dose-response function for acute mortality is based on short-term correlations (time scales on the order of a day) of mortality data and ambient air pollution concentrations. The certainty is relatively high (confidence intervals around +_ 50°70). but only part o f the effect is captured. The true mortality can be significantly higher due to chronic effects that do not show up in shortterm correlations. Dose-response functions for chronic effects are notoriously difficult to establish with confidence. Recently two important studies have been published on chronic mortality from air pollution [Dockery et al. 1993 and Pope et al. 1995]. These two cohort studies find clear relationships between mortality and fine particulates (PM2.5). Risk estimates from Pope et al. [1995] have been used as best estimate for the chronic dose-response function in Table 4.3a, after converting from PM2.5 to the more commonly measured PM10 [EC 1995]. In Table 4.3b we also show the dose-response functions for morbidity, as an indication o f the type o f damage that can occur. However, in the present paper our main concern is mortality and we do not quantify morbidity impacts from particulates. Also, complete evaluation has shown that the damage costs due to morbidity are secondary compared to those from mortality if one uses the most plausible numbers for the economic valuation [EC 1995a and b, Curtiss et al. 1995]. 4.3.2. CHLORINATEDHYDROCARBONS. For chlorinated hydrocarbons we use the health impact assessment tables (HEAST) provided by EPA, supplemented

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TABLE 4.3. E x p o s u r e - R e s p o n s e F u n c t i o n s for Particulates, in the F o r m Effect = Coefficient * C h a n g e o f A n n u a l A v e r a g e PMIo C o n c e n t r a t i o n [in lxg/m3], as a d a p t e d b y E C [1995b] f r o m the cited references. a) For Mortality Effect Percent Change in Acute Mortality Percent Change in Chronic Mortality

Coefficient 0.104 0.386

Reference Schwartz [1993] Pope et al. [1995]

b) for Acute Morbidity Effect Change in hospital admissions for respiratory infections, per 100,000 persons (all ages), per year Change in hospital admissions for COPD per 100,000 persons (all ages), per year Change in ERVs for COPD per 100,000 persons (all ages), per year Change in ERVs for asthma, per 100,000 persons (all ages), per year Change in hospital visits for childhood croup per 100,000 persons (all ages), per year Change in RADs per 1,000 adults per year Change in "shortness of breath" days per asthmaticper year Change in symptom days, per 1,000 persons (all ages), per year

Coefficient 0.187 0.227 0.72 0.64 2.91 49.9 0.14 465.0

Reference Schwartz[1994] and Burnett et al. [1994] Schwartz[1994] and Burnett et al. [1994] Sunyer et al. [1993] Schwartz et al. [1993] and Bates et al. [1990] Schwartz et al. [1991] Ostro [1987] Ostro et al. [1991] Krupnick et al. [1990]

COPD, chronic obstructive pulmonary disease; ERV, emergency room visit; RAD, restricted activity days. by special data for dioxins. A s u m m a r y o f the E P A data applicable to the I C I emissions is shown in Table 4.4. Some gaps r e m a i n because the E P A d a t a do n o t cover all o f the emissions listed in the I C I i m p a c t study, a n d for certain c o m p o n e n t s the i m p a c t s t u d y does n o t indicate which isomer o f a substance is emitted. T h e U F factors in this table are u n c e r t a i n t y factors; they are used to a d j u s t for m u l t i p l e sources o f u n c e r t a i n t y e n c o u n t e r e d in using e x p e r i m e n t a l data for predicting effects o n h u m a n s , such as interspecies variation, synergism, a n d different route o f exposure (oral versus i n h a l a t i o n ) . T h e values are summ a r i z e d in Table 4.5. 4.3.3. DIOXar~S. D i o x i n is a n a m e for a family o f 75 c h l o r i n a t e d tricyclic aromatic c o m p o u n d s , to which o n e m i g h t a d d 135 closely related c o m p o u n d s , the p o l y c h l o r i n a t e d d i b e n z o f u r a n s . Several o f these are highly toxic, the most toxic being the c o m p o u n d 2,3,7,8-tetrachlorodibenzo-p-dioxin, u s u a l l y abbreviated T C D D ; they are also believed to be carcinogenic. C e r t a i n polychlorin a t e d biphenyls (PCBs) m a y also have dioxin-like toxicity. A toxic equivalence ( T E Q ) factor is used to express the different toxicities o f the different

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T A B L E 4.4. H e a l t h Effect D a t a f o r C h l o r i n a t e d H y d r o c a r b o n s in

T a b l e 4.1 B a s e d o n E P A H e a l t h Effect A s s e s s m e n t S u m m a r y T a b l e ( H E A S T ) [ E P A 1992]. R e f e r e n c e f o r T C D D is T s c h i r l e y [1986] for R f D a n d E P A [1994] f o r slope f a c t o r . Toxicity Other Than Carcinogenicity Substance (Ethylene dichloride) i, 1-Dichloroethane

Subchronic RfD

Chronic RfD

1.0

1 E-1 (UF1,000)

(UF100)

9.1 E-2 6.3 E-3

1,2-Dichloroethane

(Methylchloride) Chloromethane Chloroform Carbon tetrachloride 1,1-Dichlorethylene 1,2-C-dichlorethylene 1,2-T-dichlorethylene 1,1,1-Trichlorethane I, 1,2-Trichlorethane

TCDD (dioxin)

data inadequate for quantitative risk assessment 1.0 E-2 (UF1,000) 7.0 E-3 (UFI00) 9.0 E-3 (UF1,000) 1.0 E-1 (UF300) 2.0 E-1 (UF100) 9.0 E-I (UFI00) 4.0 E-2 (UFI00)

Carcinogenicity Slope Factors Inhalation

8.1 E-2 5.3 E-2 1.2

9 E-2 (UF1,000)

5.7 E-2 2.4 E-10

1.0E + 5

Chronic, life long exposure; RfD, reference dose (mg/kg body weight/day) = threshold below which health risk is considered negligible; slope factor, the probability of cancer occurrence per dose (mg/kg body weight/day); subchronic, occasional exposure, between 2 weeks and 7 years; UF, uncertainty factor, see Table 4.5 below. d i o x i n - l i k e substances. O n l y 7 o f the 75 dioxins, 10 o f the 135 f u r a n s a n d 13 o f the 209 P C B s are t h o u g h t to have d i o x i n - l i k e toxicity, a n d for m o s t o f these t h e toxic e q u i v a l e n c e ( T E Q ) f a c t o r is m u c h less t h a n for T C D D . E P A [1994a, Vol. 3, pp. 3-20] cites m e a s u r e d d a t a for the emissions weighted T E Q f r o m a wide v a r i e t y o f c o m b u s t i o n sources such as waste incinerators; they are all in the range o f 0.001 to 0.2. T h e r e g u l a t o r y limits in Table 4.1 are stated in t e r m s o f TEQ. D i o x i n s a n d f u r a n s can be p r o d u c e d in trace q u a n t i t i e s d u r i n g the c o m b u s t i o n o f c h l o r i n a t e d o r g a n i c c o m p o u n d s . T h e y are d e s t r o y e d by e x p o s u r e to light w i t h i n hours, b u t in the soil they m a y persist for over 10 years [Tschirley 1986]. I m p l i c a t e d in the Seveso a c c i d e n t a n d in t h e a f t e r effects o f the d e f o l i a t i o n with A g e n t O r a n g e in V i e t n a m , d i o x i n s have a c q u i r e d a f r i g h t f u l r e p u t a tion. B a c k g r o u n d e x p o s u r e to d i o x i n s is m o s t l y ( > 9 0 % ) f r o m food. I n l a b o r a t o r y e x p e r i m e n t s with a n i m a l s T C D D has been f o u n d to be o n e

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TABLE 4.5. Uncertainty Factor (UF) [EPA 1992]

Factor Used to Extrapolate From 10 Valid human evidence 100 valid long-term animal studies 1000 animal studies of less than chronic exposure 1 to 10 additional factor used to extrapolate from a LOAEL instead of a NOAEL LOAEL, lowest observed adverse effect level; NOAEL, no observed adverse effect level. o f the most potent toxins known. The lethal dose LDs0 for a variety of animals ranges from 0.6 Ixg per kg body weight for male guinea pigs to 3,000 Ixg per kg for hamsters [Tschirley 1986]. Such wide range of values, more than three orders of magnitude, suggests that extrapolation from one animal species to another is most uncertain. However, Tschirley [1986] cites other evidence, directly relevant to humans. In particular, there is an experiment on prisoners, performed in the "good old days" when such experiments were not yet considered immoral. In one such experiment 60 prisoners were exposed, twice within 2 weeks, to a TCDD dose o f 3 to 114 ng per kg body weight. No symptoms were observed--although we do not know whether any cancer may have been induced over the long term. This fact will allow us to put an upper bound on the noncancer risk from dioxin from the incinerator by making the following very conservative assumptions: • the incinerator emits at the upper limit permitted by the regulations, • the maximum safe dose for continuous exposure corresponds to the lowest dose in the above experiment (2 × 3 ng = 6 ng per kg body weight) spread uniformly over 70 years. The result will be shown in Table 4.6 and indicates that even with these extreme assumptions the population is at least a factor 600 below the threshold for noncancer toxic effects from dioxin. An interesting additional data point, also cited by Tschirley [1986], comes from another experiment with 10 volunteer prisoners who were exposed to a much larger dose of 107 Ixg per kg of body weight. Eight of them developed chloracne, but no other symptoms were noted. Comparison with the above LDs0 values for animals indicates that man is certainly not among the most sensitive species as far as acute dioxin toxicity is concerned. For the carcinogenicity of dioxin EPA [1994, vol. III, p. 9-85] cites a riskspecific dose estimate (1 x 10 -6 risk or one additional cancer in one million exposed) of approximately 0.01 pg T E Q / k g body weight per day. The corresponding slope factor is 1.0 E + 5 as shown in Table 4.4. This value is cited as a plausible upper bound on the risk; true risks may be smaller, or even zero for some members of the population,.

4.4. Relation between concentration a n d dose The relationship between concentration and dose involves all types of intakes: inhalation, ingestion of water, chemicals in soil or food (e.g., fish, vegetables),

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TABLE 4.6. Assumptions for the Calculation o f the Dose [EPA 1989] Man Body weight (kg) Respiration Resting rate Light activity rate Volume of air breathed = IRa x ET Fluid consumption

Woman

Child

70

60

20

7.5 l / m i n 20 l/rain 23 m3/day 2 l/day

6 l/min 19 l / m i n 21 m~/day 1.4 l / d a y

4.8 l / m i n 13 l / m i n 15 m3/day 1.4 1/day

ET, exposure time (hours/day); IRa, inhalation rate (m3/hour).

and dermal contact with chemicals in water or soil. The intake I (mg/kg body weight/day) is related to the concentration C (subscript w for water, a for air, s for soil, f for food) by the following formula. For the reasons given above, we consider only the inhalation route because it appears dominant in this case. The inhalation dose I (in m g / k g body weight/day) is calculated with the formula I =

Ca x IRa x ET x EF x ED

(4.3)

BW x AT where Ca --- concentration (mg/m3); IRa = inhalation rate (m3/hour); ET = exposure time (hours/day); EF = exposure frequency (days/year); ED = exposure duration (years); BW = body weight (kg); AT = averaging time (period over which exposure is averaged, days). The impact on the population is different for men, women, and children; we assume a distribution of 39%, 49%, and 22%, respectively. Also, an assumption is to be made concerning the mobility of the population, because the effect of pollutants depends on the exposure duration. We assume that 60% o f the population will remain more than 7 years in the concerned region, and should thus be counted as chronic exposure (the definition of "chronic" corresponds to the H E A S T data base of EPA: exposures of 2 weeks to 7 years are considered as "subchronic"). Typically, IRa x ET = 20 m3/day for an adult, BW = 70 kg (see Table 4.6). We use the light activity respiration rate, assuming it represents a mean value. We will assume a continuous exposure over 70 years, a conservative assumption because few people will stay put in the same location over their entire life. Thus the factor EF x ED/AT is unity and Eq. 4.3 becomes I =

Ca x (IRa x ET)

(4.4)

BW Combining all of the above assumptions we have, for example, the following calculation for dioxin: mean concentration for dioxins at the stack = 10 -6 m g / m 3 from Table 4.1; dilution factor = 2,000,000 assumed mean within C contour (see Table 4.2);

434

A. RABL AND B. PEUPORTIER

m e a n c o n c e n t r a t i o n w i t h i n t h e C c o n t o u r = 5 × 10-13 m g / m 3 ; d o s e f o r c h i l d r e n f r o m E q . 4.4 w i t h I R a × E T = 15 m 3 / d a y a n d B W = 20 k g with the result 5 × I

10 -13 m g / m 3 × (15 m 3 / d a y )

=

20 Kg = 3.75 ×

10 -13 m g / k g

body weight/day.

T h i s r e s u l t is s h o w n i n t h e s e c o n d c o l u m n o f T a b l e 4.7 b e l o w . F o r p a r t i c u l a t e s we f i n d a m e a n c o n c e n t r a t i o n w i t h i n t h e C c o n t o u r o f 10 - 2 k t g / m 3, a n d a p p l y i n g t h e a c u t e m o r t a l i t y o f T a b l e 4 . 3 a we f i n d t h e i n c r e a s e i n t h e d e a t h r a t e t o b e 0.104°70 × 10 - 2 = 10 -5. M u l t i p l y i n g b y t h e g e n e r a l m o r t a l i t y rate, a p p r o x i m a t e l y 0.011 d e a t h s p e r year, a n d b y t h e p o p u l a t i o n o f 6 0 , 0 0 0 we f i n d t h a t t h e i n c r e m e n t a l n u m b e r o f d e a t h s d u e t o p a r t i c u l a t e s is 0 . 0 0 6 6 d e a t h s p e r year. F o r c a n c e r s o n e m u l t i p l i e s t h e dose, i n m g / k g b o d y w e i g h t / d a y , b y t h e s l o p e f a c t o r o f T a b l e 4.4 w h o s e u n i t s a r e p r o b a b i l i t y o f c a n c e r o c c u r r e n c e p e r dose. T h i s is t h e l i f e t i m e r i s k p e r p e r s o n . To c o n v e r t t o a n a n n u a l r i s k we d i v i d e T A B L E 4.7. R e s u l t s f o r I n c i n e r a t o r a t R u n c o r n ,

UK

Morbidity Other Than Cancer

Substance 1,1-Dichloroethane 1,2-Dichloroethane Methylchloride Chloroform Carbon tetrachloride 1,1 -Dichlorethylene 1,2-C-Dichlorethylene 1,2-T-Dichlorethylene 1,1,1 -Trichlorethane 1,1,2-Trichlorethane Dioxins

RfD (mg/kg body/day)

Safety Factor (RfD/dose) (for children)

Cancer or Deaths

Dose for Children (mg/kg body/day) 3.56E-7 3.56E-7 3.75E-8 3.75E-8 5.63E-8 2.81E -7 2.81E-7 2.81E-7 2.81E -7 2.81E-7 <3.75E-13

1 ? ? 0.01 0.007 0.009 0.1 0.2 0.9 0.04 2.4E-10

2.81E + 06

? 1.6E-5 1.0E-7 1.5E-6 1.5E-6 1.7E -4 ? ? ? 7.9E-6 <1.8E-5

2.67E + 5 1.24E + 5 3.20E + 4 3.56E + 5 7.11E + 5 3.20E + 6 1.42E + 5 >6.26E + 2

Cancers/year

Deaths/year Particulates

6.6E-3 (Acute Mortality)

Only effects within C contour, Table 4.2, are considered (C contour extends a few km from the plant and encloses a total population of about 60,000). Format of the morbidity results is totally different from that of cancer or deaths. For noncancer morbidity the dose-response function is assumed to have a threshold, and the table shows the mean dose to children, the RfD ( = maximum safe dose) and the safety factor ( = ratio). For cancers and particulates the dose-response function is assumed not to have a threshold (or to be above the threshold), and the last column shows the incremental annual cancers or deaths. RfD is based on subchronic data in Table 4.4, except for dioxin; ? = no data available.

I M P A C T P A T H W A Y ANALYSIS FOR D E C I S I O N - M A K I N G

435

by 70 according to the EPA procedure [Cohrssen and Covello 1989, p. 97]. The total increase for the 60,000 inhabitant in the C contour is obtained after carrying out this calculation separately for men, women, and children, and multiplying by the respective numbers o f individuals. For example, for chloroform the concentration is 0.1 m g / m 3 at the stack, and the resulting dose is found as above. The population weighted dose is 2.15 x 10 -8 m g / k g body weight/day. Multiplying by the slope factor from Table 4.4 and by the population of 60,000 (because the dose is already weighted) and dividing by 70 years we obtain the annual number of cancers due to chloroform as: 2.15 x 10 -8 m g / k g / d a y x 8.1 x 10 -2 c a n c e r / ( m g / k g / d a y ) x 60,000 70 years = 1.5 x 10 - 6 cancers/year.

4.5. Results Table 4.7 shows the results calculated according to the above formulas and data, for the 60,000 inhabitants living within the C contour. For noncancer toxicity the table shows the mean dose to children, because they are the most sensitive part of the population. This is to be compared with RfD, defined as m a x i m u m safe dose at which there is essentially no risk of illness. To facilitate the comparison we also show the ratio of RfD and actual dose; this is the safety factor. The fact that this ratio is so large indicates that the morbidity effects are not significant for these emissions, even if the RfD data were off by an order of magnitude or two. For cancers the last column shows the incremental annual deaths within the C contour. With the assumption of a linear dose-response function for cancer, there is no safe lower limit. Strictly speaking this implies that one should calculate the incremental impacts over as large an area as the pollutants are transported. This area can be regional or even global because atmospheric dispersion o f small particulate matter is significant over thousands o f km. This issue of geographical limits does not affect the comparison of mortality rates in a given region, for instance with the C contour. It is interesting to compare the mortality due to carcinogenic emissions with the mortality from particulates. The last column in Table 4.7 shows the incremental deaths per year for the 60,000 inhabitants in the C contour due to the emissions from the incinerator. Particulates are a ubiquitous pollutant, produced in greater or lesser amounts by almost any combustion process, from the engines of our cars to the furnaces of our houses. Perhaps because of this familiarity they have not received much attention from environmental pressure groups. However, if the same dose-response function applies, then the acute mortality rate from particulates is about 40 times larger than that of dichlorethylene, the leading carcinogen in Table 4.7, and several orders of magnitude larger than that of all the other carcinogens in the table. Furthermore, the acute mortality is a lower bound; the true mortality from particulates may be about four times larger as suggested by the (less reliable) chronic mortality number in Table 4.3a.

436

A. R A B L A N D B. P E U P O R T I E R

After all the fears that had been aroused by the dioxin issue, of particular interest are the results for dioxin. Even if the dioxin emissions were as high as the regulatory limits, the actual dose in the C countour would be at least a factor of 600 below the dose at which no symptoms have been observed in an experiment on 60 prisoners [Tschirley 1986]. The number for cancers due to dioxin is an upper bound and the slope factor in Table 4.4 is an upper bound. In any case, the number of dioxin cancers is small compared to the number of deaths from particulates. This does not mean that dioxins are harmless; rather, the emission limit is so low that the risks are small. It is also of interest to compare the incremental dioxin dose due to the incinerator with typical background doses in industrial countries for which EPA [1994] cites values in the range of 3 to 6 × 10 - 9 mg T E Q / k g b o d y weight per day: the increment from the incinerator is four orders of magnitude smaller than the background dose. Note furthermore that the European Union has proposed a new regulation for dioxins, o f 0.1 n g / m 3 TEQ, even a factor of 10 lower than the limit in Table 4.1. To put the mortality risks into perspective, one can note that the rate of deaths from m o t o r vehicle traffic accidents in England and Wales is 8.7 per year per 100,000 [USDOC 1992]. Assuming this number to be representative of the 60,000 inhabitants of contour C, the expected number of traffic deaths is 5.2 per year, three orders o f magnitude higher than the acute mortality from particulates emitted by the ICI incinerator. 5. Uncertainty of the Results There is something paradoxical about the relation between scientific information and public decision-making. Although science tends to be perceived as precise and decision-making as fuzzy, the reality is the other way around: decisions are hard (yes or no) while scientific information is uncertain and incomplete [Funtowicz and Ravetz 1990]. It may be comforting to policy makers to be able to justify their decisions on the basis of scientific evidence, as if it were firm. But if the softness of this basis is not recognized, the conclusions may be quite misleading. An examination of the uncertainties is crucial. There are three major sources of uncertainty in our results, corresponding to the three steps of the impact pathway analysis: 1. the emissions may be different from the regulatory limits, especially if the controls are not sufficiently strict and frequent: 2. for errors in the atmospheric dispersion model one sometimes hears multiplicative confidence intervals on the order of two to five, but for annual averages the accuracy of dispersion models is relatively good and the error should not be larger than about a factor of two if the calculation was carried out right; 3. the largest uncertainty probably comes from the dose-response functions.

I M P A C T P A T H W A Y ANALYSIS FOR D E C I S I O N - M A K I N G

437

For dose-response functions, effects not taken into account may render the true error much larger than the statistical error bounds obtained in a particular study. An example is the difference between the acute and the chronic doseresponse function for mortality from particulates. The uncertainty of the doseresponse functions for some of the carcinogens may well be an order of magnitude or more. Thus, the overall uncertainty is likely to be dominated by that of the dose-response function. In view of such uncertainties, it is advisable to ask what would happen if the true impacts were different by perhaps an order of magnitude. Note that our key results are quite firm: • absolute insignificance o f noncancer effects of dioxins, • absolute insignificance o f noncancer health effects of other CHCs, and • insignificance of the C H C s relative to particulates. First of all these results are for relative risk and thus independent of the density of population or of the dispersion models. Secondly, the safety factors for the noncancer health effects o f other C H C s are so large that even an error of several orders of magnitude would not change the conclusion. Thirdly, the comparison of C H C cancers relative to particulates is independent of the dispersion model. The dose-response function for particulates is quite well established, and, therefore, even an order of magnitude error in the C H C s would not change the conclusion. 6. Communication of Risks

A m a j o r problem with impact studies concerns the communication of the results. Such a study is necessarily full of difficult technical details, far beyond the grasp of even a well-educated layman. What can be done to improve the presentation of the results, to make the essential impacts more comprehensible? One possibility for improving the communication with the public is the comparison of emissions and risks from the proposed project with emissions and risks encountered in everyday life. The use o f risk ladders may be a promising approach for communicating some o f the conclusions of an environmental impact study. To a c c o m m o d a t e the wide range of risks encountered in daily life, a logarithmic scale is customary. To make the use of risk ladders practical for impact statements, one will need to develop a generally accepted set o f reference risks. The proposer of a project could then calculate the risk associated with the project and display it in relation with the reference risks. The list of reference risks could comprise the following examples: • • • •

smoking pesticides on fruits and vegetables health impacts from automotive exhaust radon in homes

438

A. R A B L A N D B. P E U P O R T I E R

• medical x-rays • increased exposure to cosmic radiation during air travel • toxic vapors from various consumer products (flea collars, moth balls, dry-cleaned clothes, insecticides for home and garden, household solvents, and cleaning products, etc.) • mercury in dental fillings • carcinogens in barbecued food • skin cancer from sun bathing • traffic accidents Such a presentation might also be educational in encouraging the public to reflect on the impossibility of eliminating all risks, in particular to put in perspective the "zero emissions" demand of certain environmental pressure groups. One advantage o f relative risk comparisons is to avoid the need for monetization o f incommensurable goods, such as cancer and unemployment. By the same token, the applicability of relative risk comparisons is limited, and questions such as the appropriate absolute level of pollution control remain unresolved. 7. Conclusions

We have focused on environmental impact studies as a key ingredient in the approval process for chemical plants, and, using as case study a proposed toxicwaste incinerator, we have carried out an impact pathway analysis to quantify the expected damage to the health of the local population. Our analysis allows us to draw several interesting conclusions: 1) If this incinerator meets the regulatory emission limits, the incremental effects due to its emissions are entirely negligible compared to risks of everyday life, in particular traffic deaths. As a statement o f relative risk this conclusion depends only weakly on the details of the dispersion and should be typical of installations o f similar technical performance. 2) With current regulatory limits the risks from CHCs, including dioxins, appear small compared to health effects from the familiar particulate emissions. 3) For major and controversial projects, such as chemical plants, it may be advisable to enlarge the scope of environmental impact studies by analyzing the dispersion of emitted pollutants and estimating the impacts o f the project (although in the present case study our results had no effect on the outcome because when they became available, the local government had already decided to approve the installation). 4) Progress in computer technology and information management renders such an analysis increasingly feasible [Cur.tiss and Rabl 1994]. 5) The range o f possible impacts is so large that it would be difficult to pro-

IMPACT PATHWAY ANALYSIS FOR DECISION-MAKING

439

vide a general and exhaustive list o f items that should be addressed by each impact study. Rather, to keep the cost of information within reasonable limits, one should approach each situation iteratively (as is in effect the current practice). In other words, begin by analyzing what appear to be the dominant impacts based on past experience. Then subject the study to one or several reviews by the public and by experts, to identify issues that need improved analysis. 6) In many cases one can be fairly certain about a relative impact, while an absolute impact may be extremely uncertain. Thus, it is possible to compare certain impacts or risks with those from other sources to which we may be exposed, such as exhaust from cars or fireplaces. 7) One could improve the communication of the results of an impact study by writing a s u m m a r y that can be understood by a lay person. It may be helpful for this purpose to develop a generally accepted set of reference risks. In this paper we have evaluated only the impacts in the immediate vicinity o f the plant. However, a c o m m e n t is called for about the geographic range over which the analysis needs to be extended to capture most of the impacts. A look at the results o f long-range atmospheric dispersion models, for instance E M E P [Sandnes 1993], shows that air pollutants are transported over very long distances. For a linear dose-response function without threshold the range of the analysis must be at least 1,000 km to include 80°70 of the total damage. Thus, the dominant impacts of combustion processes are global (CO2 induced climate change) or regional (health impacts from particulates), rather than local. The custom of restricting environmental impact analyses to the local range (less than about 50 km) implies that only a small fraction of the impacts o f the classical air pollutants and a negligible fraction of global warming damage are taken into account. This practice of impact studies is reflected in current decision-making: the decision is made locally without taking regional or global externalities into account. This work has been supported in part by a grant from the European Commission, DG XII, under contract EVSV-CT-92-0156 of the Environment Program and contract Jou2-CT92-0236 of the ExternE program o f JOULE. We are grateful for stimulating discussions with Roland Clift, Brigitte Desaigues, M o n a Dreicer a n d Ren6 Jottrand.

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