Monetary Valuation of Trace Pollutants

Monetary Valuation of Trace Pollutants A Rabl and JV Spadaro, CEP, Ecole des Mines de Paris, France TM Bachmann, European Institute for Energy Research, Karlsruhe, Germany & 2011 Elsevier B.V. All rights reserved.

Abbreviations COI CPI CV DALY DRF EPA ERF EU GDP HALY IPA IQ LCA LE LOAEL NOAEL PCB PTO QALY UWM VOLY VPF VSL WTP YLD YOLL

cost of illness consumer price index contingent valuation disability-adjusted life year dose–response function Environmental Protection Agency exposure–response function European Union gross domestic product health-adjusted life years impact pathway analysis intelligence quotient life cycle assessment life expectancy lowest observed adverse effects level no observed adverse effects level polychlorinated biphenyl person trade-off quality-adjusted life year uniform world model value of a life year value of prevented fatality value of statistical life willingness-to-pay years lost due to disability years of life lost

Introduction In addition to the classical air pollutants (PM, NOx, SO2, O3, and volatile organic compounds), there are numerous additional pollutants emitted in small quantities, some of which are highly toxic. For simplicity these pollutants would be referred to as ‘trace pollutants,’ and they are the subject of this article. By far the most toxic metals emitted to the environment in significant amounts are As, Cd, Cr, Hg, Ni, and Pb. They cause a wide variety of health impacts, cancers and neurotoxicity being of particular concern: As, Cd, Cr, and Ni are carcinogenic, and Hg and Pb neurotoxic. These metals are emitted by certain industrial processes, by waste incineration, and by power plants. Among organic trace pollutants, the most troubling are the polychlorinated dibenzo-p-dioxins (in short,

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dioxins) and the closely related polychlorinated dibenzofurans and polychlorinated biphenyls (PCBs). Dioxins are created in minute quantities as by-products of certain processes where chlorine and organic matter can combine, for example, the incineration of chlorinated plastics. Other sources of dioxins include the steel industry and the production of pesticides. Thanks to stringent environmental regulations, the emission of dioxins in Europe has been drastically reduced. PCBs had been used in the past as transformer oil, but that has been outlawed and there are almost no new sources. However, because of their hydrophobic nature and resistance toward metabolism, these chemicals persist in the environment for many years. Furthermore, they bioaccumulate in fatty tissues of animals and humans. Most of the dose comes from ingestion. The purpose of the monetary valuation of health impacts is to help the government formulate policies for reducing environmental pollution to achieve the greatest benefit. To evaluate the impact and damage cost of a pollutant, one needs to carry out an impact pathway analysis (IPA), tracing the passage of the pollutant from where it is emitted to the affected receptors (population, crops, forests, buildings, etc.). The exposure is calculated by modeling the pathways of the pollutant through the environment to the receptors. To determine the resulting impacts, one needs the respective exposure–response functions (ERFs). Of course, impacts can be quantified only to the extent that the slopes of the ERFs in the relevant dose range are known. Unfortunately, there is a dearth of information. For most substances the only available data indicate a NOAEL (no observed adverse effect level) or LOAEL (lowest observed adverse effect level), usually from animal tests. Such information is not sufficient for impact quantification because it says nothing about the slope of the ERF. In 2002 Pennington et al. proposed a promising method of using LOAEL or NOAEL data for estimating ERFs, but the uncertainties are very large (see the section ‘ERFs for neurotoxic pollutants’). The ERFs should correspond to end points that can be evaluated in monetary terms. That is often a problem in practice because many, if not most, of the end points for which information is available are difficult or impossible to value in monetary terms, at least at the present time, for instance, lung function reduction. Another consideration is the link to exposure: policy makers need to know the benefit of reducing the emission of a

Monetary Valuation of Trace Pollutants

pollutant or the exposure to it. Again, this can pose a problem because some ERFs are available as a function not of exposure or dose, but of bioindices, such as the concentration of Pb in blood. Such information can be used in the present context only if the relation between the bioindex and the exposure or dose is also known. In the present review, end points that do not meet all these criteria will not be examined in detail. The following section presents a very brief overview of methods for analyzing the dispersion of pollutants in the environment and the calculation of the resulting exposure of the population, followed by a section on exposure–response functions, and one by monetary valuation. Some results for selected case studies (waste treatment and coal-fired power plants) are presented in the section ‘‘Case Studies’’. The uncertainties are discussed in the last section.

out that the dose due to ingestion for the trace pollutants is much larger than that due to inhalation, roughly by one to two orders of magnitude. A large variety of models are available for analyzing the pathways. To obtain damage costs, the models should calculate the expectation value of the collective doses for the entire population affected. For linear ERFs without threshold one needs the total dose, and for ERFs with a no-effect threshold the dose above the threshold. The requirement of determining the dose for the entire population rather than just the most highly exposed individuals near the source rules out the numerous models that evaluate only local impacts. The models should evaluate both local and regional impacts (and in the case of Hg even global impacts). The many models that have been developed include the following: The models of MSC–East (Meteorological Syn• thesizing Centre–East) (http://www.msceast.org/); Multimedia models that have been used • for life cyclecompartment assessment (LCA) such as CalTox, IM-

Dispersion Models There are various ways to model the fate of contaminants in the environment. The principal pathways for dispersion in the environment are shown in Figure 1. It turns

•

PACT 2002, and USES-LCA, as well as for external cost assessments such as WATSON; RiskPoll (http://www.arirabl.org).

Emission

Air

Deposition (wet and dry)

Soil Agricultural vegetation

Fresh water Salt water

Seafood

Freshwater fish

Milk

Ingestion dose

Figure 1

857

The principal pathways for the dispersion of pollutants in the environment.

Meat

Inhalation dose

858

Monetary Valuation of Trace Pollutants

Because the EcoSense software developed by the ExternE project series addressed only inhalation exposures toward classical air pollutants, WATSON has been developed to extend the human health impact assessment to ingestion exposures. RiskPoll for trace pollutants is a multimedia version of the ‘uniform world model’ (UWM) of Spadaro and Rabl; it is a simple and transparent model based on the transfer factors published by EPA in 1998. It is part of the RiskPoll package and can be downloaded from www.arirabl.org. Detailed models such as CalTox and WATSON require a great deal of very detailed data. Of course, the developers of the models have these data for their region, but for other regions the data are usually not available, at least not in the required format. Even with the best input data the uncertainties are large because of modeling uncertainties. Therefore, it is sometimes interesting to use a different approach, directly based on data of emissions and doses. If the emissions have been sufficiently stable for a sufficiently long time (compared to the time constants of the environmental dispersion), the ratio of the dose and emission data can provide a good estimate of a transfer factor that can then be used in other situations. Even in the age of massive data and powerful personal computers, such approaches can be valuable; their accuracy can be comparable to detailed models, given the uncertainties of the latter. This idea was applied by Spadaro and Rabl in 2008 for Hg. This article presents the argument for dioxins. First note that most micropollutants are emitted from stacks of sufficient height (usually 40 m or more) such that the impact on the immediate vicinity (usually with very low population density) is only a small percentage of the total impact. Local detail is important only for local impact assessments but not for the calculation of total damage (which is significant over hundreds to thousands of kilometers). Furthermore, most estimates of total damage are needed for applications where the precise site of an installation is either unknown (future installations) or not relevant (regulations that make no site-specific distinctions). Thus, estimates for typical sites are more relevant than precise values for specific sites. For impacts due to inhalation, Spadaro and Rabl showed in 2002 that a very simple formula, called UWM, reproduces the Table 1

results of detailed site-specific calculations within a factor of approximately two in most cases. The UWM estimate for the collective inhalation dose rate D˙ inhal ðmÞ ˙ (kg year1 or g s1) of a primary pollutant is D˙ inhal ðmÞ ˙ ¼ V˙inhal r m=v ˙ dep

where m˙ is the rate at which the pollutant is emitted into the air (kg year1), V˙inhal the population-averaged inhalation rate for which the value 13 m3 (person day)1 is taken, r the average population density (persons km2) within 1000 km of source, and vdep is the deposition velocity of pollutant (dry þ wet) (m s1) (of course, all quantities have to be converted to consistent units). The inhalation rate is based on Chapter 5 of EPA 1997. For the ratio of total dose to inhalation dose for dioxins, Figure II-5, p. 37, of the report ‘Estimating exposure to dioxin-like compounds’ in volume I of the EPA 1994 Executive Summary indicates a typical value of Dtot 119 ¼ 54:1 ¼ Dinhal 2:2

Customary units

V˙ inhal r ˙ m vdep ˙ D˙ inhal ðmÞ Dtot/Dinhal ˙ D˙ inhal ðmÞ

13 1.00E þ 02 1.0 1 4.12E-06 54.1 2.23E-01

Exposure–Response Functions General Considerations It is convenient to use the term ‘effect’ in a qualitative sense (a health end point), whereas the term ‘impact’ is quantitative. For instance, Hg has a variety of neurotoxic effects, and its impact on intellectual performance can be quantified in terms of IQ points. The ERF, also known as dose–response function (DRF), relates the quantity of a pollutant that affects a receptor (e.g., population) to the physical impact on this receptor (e.g., incremental number of hospitalizations). The ERF is a central ingredient in the IPA and merits special attention. A damage can be quantified only if the corresponding ERF is known. Such functions are available for the impacts on human health, building materials,

Consistent units m3 (person day)1 persons km2 kg year1 cm s1 g day1 mg day1

½2

The calculation of the total dose is illustrated in Table 1, for an emission rate of 1 kg year1. The left part of the table shows the quantities in customary units, the right half in consistent MKS units as used for the calculation.

Calculation of the total dose due to atmospheric emission of dioxin at the rate 1 kg year1

Quantity

½1

0.000 150 1.00E-04 3.17E-05 0.01 4.77E-11 54.1 2.58E-09

m3 (person s)1 persons m2 g s1 m s1 g s1 g s1

Monetary Valuation of Trace Pollutants

and crops, caused by a range of pollutants such as primary and secondary (i.e., nitrate and sulfate) particles, ozone, CO, SO2, NOx, benzene, dioxins, As, Cd, Cr, Ni, and Pb. The most comprehensive reference for health impacts is the Integrated Risk Information System (IRIS) database of US EPA (http://www.epa.gov/ncea/iris/ index.html). For application in an IPA, that information often has to be expressed in somewhat different form, accounting for additional factors such as the incidence rate. The principal methods for determining an ERF are epidemiological studies (comparing populations with different exposures) and toxicology (usually studies of mice or rats, or of cell tissue – with large uncertainties from the extrapolation to human populations). Unfortunately, for many pollutants and many impacts the ERFs are very uncertain or not even known at all. For most substances the only available information on noncancer impacts comes from laboratory studies of animals and is reported as thresholds, typically the NOAEL or LOAEL. Knowing thresholds is not sufficient for quantifying impacts; it only provides an answer to the question whether or not there is a risk. The principal exceptions are carcinogens and the classical air pollutants, for which explicit ERFs are known (often on the assumption of linearity without threshold). A method for obtaining a rough estimate of ERFs from NOAEL or LOAEL data is described in the section ‘ERFs from NOAEL or LOAEL information.’ By definition, an ERF starts at the origin, and in most cases, it increases monotonically with dose, as sketched schematically in Figure 2 for typical cases. At very high doses the function may level off in S-shaped fashion due

Response

P Nonlinear

Linear

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to saturation, but here only the low-dose regime is of interest. A major difficulty lies in the fact that one needs relatively high doses in order to obtain observable nonzero responses unless the sample is very large; such doses are usually far in excess of typical ambient concentrations in the EU or in North America. Thus, there is a serious problem of how to extrapolate from the observed data toward low doses. Figure 2 indicates several possibilities for the case where the point P corresponds to the lowest dose at which a response has been measured. The simplest is the linear model, that is, a straight line from the origin through the observed data point(s). The available evidence suggests that a DRF is unlikely to go above this straight line in the low-dose limit. But the straight line model without threshold does appear to be appropriate in many cases, for example, the neurotoxic impacts of Pb. It is also commonly assumed for carcinogens and justified on theoretical grounds for substances that initiate the development of a cancer, although it may not always be correct for substances that merely promote rather than initiate a cancer. Another possibility is the ‘hockey stick’: 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. There is even the possibility of a ‘fertilizer effect’ at low doses, as indicated by the dashed line in Figure 2. This can be observed, for example, in the DRFs for the impact of NOx and SO2 on crops: a low dose of these pollutants can increase the crop yield; in other words, the impact is negative, resulting in benefit. Generally, a fertilizer effect can occur with pollutants that provide trace elements needed by an organism. Another point to keep in mind is that the impact of a pollutant can vary significantly with the chemical form in which it reaches the body and with the route of exposure (inhalation, ingestion, or dermal contact). For example, Cr has been found to be carcinogenic only in the VI oxidation state, and the ingestion of organic As is far less toxic than of inorganic As; this way, the highest exposure to As (i.e., through marine fish and shellfish) does not pose the most relevant human health risk due to its chemical species being of the organic form. ERFs for Carcinogens

With threshold

Dose With fertilizer effect

Figure 2 Possible behavior of dose–response functions at low doses. If P is the lowest dose where a nonzero impact has been observed, the extrapolation to lower doses is uncertain but values higher than linear are unlikely.

ERFs for most carcinogens can be found at the IRIS Web site of US EPA (http://www.epa.gov/ncea/iris/ index.html). For dioxins the most extensive information is contained in two large studies by US EPA in 1994 and 2000. Specific ERFs are listed in Table 2. The air unit risk factor indicates the cancer risk due to inhalation of a substance during an entire lifetime; it is stated in terms of the concentration in the ambient air and has units of

860 Table 2

Monetary Valuation of Trace Pollutants ERFs for selected carcinogens

PCB Dioxins TEQ Benzene Formaldehyde As (inorganic) Cd Cr(VI) Ni (refinery dust)

Air unit risk (mg1 m3)1

Drinking water unit risk (mg1 l1)1

Oral slope factor (mg1 (kgbody day)1)1

1  104a

1  105a

2.2 to 7.8  106 1.3  105 4.3  103d 1.8  103d 1.2  102d 2.4  104d

1.6 to 4.4  106

4  102 to 2.0c 1  106b 1.5 to 5.5  102

5  105

1.5

a

Liver hepatocellular adenomas, carcinomas, cholangiomas, or cholangiocarcinomas. This is based on EPA (2000) Exposure and human health reassessment of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and related compounds: Part III: Integrated summary and risk characterization for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and related compounds. Report EPA/600/P-00/001Bg. Washington, DC: United States Environmental Protection Agency, and is probably an overestimate, and Searl A (2005) Exposure Response Functions for HM Impacts on Human Health. Deliverable 5a of the ESPREME Project, Institute for Occupational Medicine (IOM), Edinburgh, suggests that it may be five times smaller; mortality rate for such cancers may be approximately 50%. Total mass of dioxins has to be multiplied by the toxic equivalence factor TEQ. c Lung cancer (approximately 90% fatal). d Lung cancer, internal organ cancers, and skin cancer. Source: From http://cfpub.epa.gov/ncea/iris/, except for dioxins (accessed June 2010). b

cancers mg1 m3. Likewise the drinking water unit risk factor is based on the concentration of the substance in drinking water and has units of cancers mg1 l1; it assumes a consumption of 2 l day1 during the entire life. The slope factor indicates the upper confidence interval of the 95th percentile estimate of the cancer risk due to ingestion of a substance in units of cancers mg1(kgbody day)1, again at a constant rate during the entire life. To illustrate the use of these ERFs, suppose a person is exposed to a constant concentration of 1 ng m3 of Cd in the air for 70 years. The probability that such a person gets cancer due to this exposure is 1.8  106, and in a population of 1 million such persons, this implies 1.8 additional cancers. Of course in real life, exposures vary over time and space, but, because of the assumed linearity of the ERF for cancers, only the average exposure matters. Another example is the total dioxin dose rate calculated in the last line of Table 1, 2.23E-01 mg day1. Since the ERF is given as slope factor in units of mg per day per kg bodyweight, the dose is divided by the average bodyweight (men, women, and children) of 64 kg based on EPA 1997, to obtain the number of cancers during a 70-year exposure at this dose rate as ½ð2:23E-01 mg day 1 Þ=64 kgbody 1:00Eþ06 ðmg=kgbody dayÞ1 ¼3:48Eþ03 cancers in 70 years

To allocate this to the 1 kg that is emitted in 1 year, divide by 70 to find that the impact per kilogram of dioxin is 3.48Eþ03/70¼50 cancers per kg (taken as toxic equivalent) emitted into air.

ERFs for Neurotoxic Pollutants Neurotoxic pollutants such as Hg and Pb can have a variety of impacts, for instance, ataxia, memory loss, behavioral problems, and learning difficulties. Even if one could develop standardized methods for measuring such impacts, monetary valuation would not be possible for lack of the required detailed valuation studies. There is only one fairly universal indicator of neurotoxic damage that is relatively easy to measure and to value in monetary terms, namely, loss of IQ points. It is of course only a poor proxy for the real damage, but as usual in the field of external costs, an imperfect measure is better than none at all (which would imply zero damage cost). For IQ loss due to Pb, the NEEDS phase of the ExternE 2007 project series assumes a linear no-threshold ERF with slope sER in terms of a 1-year dose as sER ¼ f 1 year  1:14 E  04 IQpoints=ðmg year1 Þ; to be applied to total population

where f1 year is the fraction of the population between 0 and 1 year of age. For IQ loss due to methyl-Hg ingestion, Spadaro and Rabl in 2008 took an ERF slope of sER ¼ 0:036 IQpoints=ðmg day1 Þ

based on the meta-analysis of Axelrad et al. in 2007. To illustrate the use of sER, note that the ERF of Axelrad et al. is based on correlations between the maternal hair concentration and the IQ of the children, and thus it implicitly includes also the effect of diet during early infancy before the IQ of the children was measured. One can assume that the diet of the infants is strongly correlated with that of the mothers. Thus, the ERF slope describes the total lifetime impact on children whose

Monetary Valuation of Trace Pollutants

mothers are exposed to a specified steady state ingestion dose, and the detailed time distribution of the sensitivity to Hg does not matter for the calculation of impacts. If there is no threshold, one obtains the lifetime impact by multiplying sER by the dose of the mothers. For the worldwide average methyl-Hg dose, 2.4 mg day1, this implies an average IQ loss of 0.087 IQ points relative to a world without Hg. Assuming a worldwide average cost of $3890 per IQ point as estimated by Spadaro and Rabl in 2008 (see the section ‘Cancers’), the average cost person is 0.087  $3890 ¼ $340. ERFs from NOAEL or LOAEL Information To estimate ERFs for noncancer health effects when only NOAEL or LOAEL information is available, Pennington et al. in 2002 developed an interesting approach. By correlating NOAEL or LOAEL data with dose–response data for 12 chemicals for which both types of data are available, they found the following relations for linearized ERF slopes in the low-dose regime: sER ¼ 0:062 CFa- h CFsub- chr =NOAELa;sub

and sER ¼ 0:33 CFa- h CFsub- chr =LOAELa;sub

where CFsub-chr and CFa-h are, respectively, the conversion factors from subchronic to chronic exposure and from the animals used in the underlying toxicological studies to humans. These conversion factors are appropriate because the NOAEL and LOAEL data are typically based on subchronic exposures for laboratory animals, as indicated by the subscripts. For CFsub-chr they recommend a value of 3.3, and for the animal-to-human oral conversion factor CFa-h they adopt the body surface area scaling factor (human-to-species bodyweight ratio)1/3 (which implies animal-to-human conversion factors of 1.6 for dogs, 6 for rats, and 13 for mice). Of course the uncertainties are large, yet an estimate based on this approach is likely to give a better estimate of the expectation value of the impact, on average, than simply omitting the substance or impact from an evaluation. It is reassuring that Searl in 2005 found broad agreement (usually within a factor of 2) for health end points of several metals for which independent ERF information was available, but more research is needed. A possible approach is to reexamine the studies used for establishing NOAELs and LOAELs and to estimate the ERF slopes directly from the data in these studies. Since ERFs can address a wide variety of health end points, it would be desirable to present them on a scale with a common impact unit. Monetary valuation can provide the most convenient scale, but only if the monetary values are available for the respective end

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points – which is often not the case. Furthermore, there are analysts, especially in the field of LCA, who refuse the principle of monetary valuation. As an alternative impact scale, one can use DALYs (disability-adjusted life years), QALYs (quality-adjusted life years), or YOLL (years of life lost)-equivalents, as discussed in the section ‘Valuation of DALYs, QALYs, or YOLL-equivalents.’

Monetary Valuation Valuation Methods The goal of the monetary valuation of impacts is to assess the total economic value, including the value of marketed and nonmarketed goods and services. The total economic value of health impact is expressed in monetary units (or costs). It consists of resource costs (such as medical treatment), opportunity costs (lost income), and disutility costs (such as pain and suffering, and reduced life quality). For example, the value of avoiding an asthma attack includes not only the cost of the medical treatment but also the willingness-to-pay (WTP) to avoid the residual suffering. If the WTP for a nonmarket good has been determined correctly, it is like a price, consistent with prices paid for market goods. Economists have developed several tools for determining nonmarket costs; of these tools, contingent valuation (CV) has enjoyed increasing popularity. The basic idea of a CV is to ask people to state the maximum amount they would be willing to pay for a certain good if they could buy it. The results of well-conducted CV studies are considered sufficiently reliable. The ground rule for valuation studies is that they should be based on the preferences of the individuals concerned by the good in question. There is, however, a problem: few individuals have sufficient knowledge and experience to offer a meaningful valuation of the countless possible health impacts, each with a wide range of possible severity levels. People do not have a mental catalog of values for the innumerable goods among which they might have to choose; rather, values tend to be constructed while thinking about the goods. The challenge for CV studies is to provide sufficient information to give the individuals an adequate appreciation of the impact in question. But how can a verbal description (in a paragraph, short enough to be read during the interview) ever convey the suffering of a terminal cancer to someone who has never been close to a patient with such a condition? Furthermore, CV studies are expensive and cannot evaluate more than a few end points at a time. Health-care professionals, however, do have broad knowledge and experience with a wide range of impacts, and generally they also have the necessary empathy to give an informed valuation. And in fact they have developed the DALY and QALY indices, to assess the

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Monetary Valuation of Trace Pollutants

severity of a wide range of health conditions. These indices, described in the section ‘Valuation of DALYs, QALYs, or YOLL-equivalents,’ or related concepts offer an alternative valuation in cases where sufficiently reliable values from CV studies are not available. Valuation of Mortality The damage costs of trace pollutants are dominated by nonmarket goods, especially the valuation of mortality. Usually mortality costs have been evaluated in terms of the so-called value of statistical life (VSL). This term often evokes hostile reactions from people who think that economists try to measure the value of life. The value of life is limitless (to save an individual in danger, no means are spared). Really, VSL is the ‘‘willingness to pay for avoiding a small risk of an anonymous premature death,’’ and the term ‘value of prevented fatality’ (VPF) is more appropriate and less likely to evoke negative reactions. In ExternE 1998, a European-wide value of 3.4 million h was chosen for VPF, close to similar studies in the USA; this value was chosen as the average of the VPF studies that had been carried out in Europe. The uncertainty is large and one could argue for other values in the 1–5 million h range. Currently, ExternE uses 1 million h, based on a CV study by the ExternE team; that is also approximately the value recommended for air pollution deaths by the DG Environment of the European Commission. However, for mortality due to pollution, VPF is not directly relevant. VPF numbers are derived from the valuation of accidental deaths, very different from deaths due to pollution. Since air pollution deaths tend to involve far less YOLL per death than accidents, consideration of the loss of life expectancy (LE) is appropriate. Thus, one needs to know the value of a life year (VOLY). Cancer deaths are also very different from deaths due to accidents, in terms of suffering and LE loss. ExternE had recognized in 1998 that mortality due to the classical air pollutants had to be evaluated in terms of LE loss rather than in terms of the number of premature deaths. ExternE 1998 and 2000 calculated VOLY on theoretical grounds by considering VPF as the net present value of a series of discounted annual values. The ratio of VPF and the value of a YOLL thus obtained depends on the discount rate; it is typically in the 20–30 range. The NewExt phase (2001–03) of ExternE used the questionnaire of Krupnick et al. developed in 2002 for a CV study in France, Italy, and the UK to determine VOLY, with the result of 50 000 h for a year of life lost due to air pollution. More recently, the NEEDS phase of ExternE has carried out a new CV study to measure VOLY, this time with a questionnaire specifically designed to elicit the WTP to avoid the loss of LE due to air pollution. This questionnaire was applied in nine

countries – France, Spain, the UK, Denmark, Germany, Switzerland, Czech Republic, Hungary, and Poland – with a total sample size of 1463. As a result a VOLY of 40 000 h2006 has been recommended, and it is henceforth applied in the ExternE project series. In the UK, Mason et al. in 2008 provided estimates of VOLY (from d20 000 to d58 000) and QALY (from d24 000 to d71 000), based on the VPF (d1.42 million in 2005 prices) that has been adopted by several departments of the UK government; the ranges correspond to different choices of models and discount rates, and the values of VOLY and QALY are somewhat different because of different accounting for quality of life. Valuation of DALYs, QALYs, or YOLL-Equivalents Many morbidity end points, for instance kidney disease, are not fatal but impair the quality of life. For most morbidity end points there are few direct estimates of monetary values or none at all. However, in the field of health evaluation two indicators have been developed for measuring both the quality and the quantity of life lived, as a means of quantifying the benefit of medical or public health interventions. One is the QALYs, the other the DALYs, which can be summarized under the topic ‘health-adjusted life years’ (HALYs). For QALY, each year is assigned a value between 1 for perfect health and 0 for death, whereas for DALY, death is assigned 1 and perfect health 0. DALYs for a disease are the sum of the equivalent years lost due to disability (YLD) and, in the case of premature mortality, of the YOLL due to premature mortality. The DALY, originally developed by the World Health Organization (http://www.who.int), takes into account two aspects that affect the contribution of an individual to the economy: age and discounting. A loss in the future is discounted relative to one now, and the loss of a young or an old person counts less than a loss at midlife. QALY, by contrast, does not discount and treats all ages equally. Probably the most complete listing of QALY weights is the ‘Catalog of Preference Scores,’ formerly at the Center for Risk Analysis of Harvard University, then at the CostEffectiveness Analysis (CEA) Registry of Tufts-New England Medical Center (http://www.tufts-nemc.org/ cearegistry/index.html) but now apparently unavailable. There have been several attempts to make use of information used in the definition of DALYs in the context of life cycle impact assessments as well as in the context of external cost assessments. In contrast to the original DALY concept, these attempts did not employ age weighting and time discounting. They merely employed disability weights to convert morbidity times into YOLL-equivalents (even though the authors somewhat misleadingly still refer to the obtained

Monetary Valuation of Trace Pollutants Table 3 point

863

International Life Sciences Institute classification scheme for human health impact categories and YOLL-equivalents per end

Criteria

Category 1 irreversible/lifeshortening effects

Category 2 probably irreversible/life-shortening effects

Category 3 reversible/non-lifeshortening effects

Examples

 Cancer

 Immunotoxicity

 Irritation (eye, skin, mucosal;

 Reproductive effects  Teratogenic effects (birth

 Neurotoxicity  Nephrotoxicity (kidney

 Sensitization (allergy)  Reversible acute organ or

i.e., transient)

defects)

damage)

system effects (gastrointestinal inflammation)

 Acute fatal or acute severe

 Hepatotoxicity (liver damage)

and irreversible effects (e.g., fatal poisoning)  Mutagenicity

 Pulmonary toxicity (lung damage)

 Cardiotoxicity (heart Weight YOLL-equivalents

1 12.8

damage) 0.1 1.28

0.01 0.128

Adapted from Pennington D, Crettaz P, Tauxe A, Rhomberg L, Brand K, and Jolliet O (2002) Assessing human health response in life cycle assessment using ED10s and DALYs: Part 2 – Noncancer effects. Risk Analysis 22(5): 947–963. and the literature cited therein, with corrected values for YOLL-equivalents.

YOLL-equivalents as DALYs). Originally, the disability weights were obtained from person trade-off (PTO) surveys administered to health-care providers. To allow for the economic principle of individualism in the determination of the disability weights, a different target population could be surveyed potentially also through different elicitation approaches such as risk–risk tradeoffs or standard gamble. In particular, Pennington et al. in 2002 suggested the categories, depicted in Table 3, if explicit data for specific end points cannot be found. However, the values produced by Crettaz et al. in 2002 and used by Pennington et al. the same year were not properly adjusted. The correct procedure yields YOLLequivalents that are 1.9 times larger than those of Pennington et al., and the corrected values are shown in Table 3. If there were a generally accepted monetary value of a DALY, a QALY, or a YOLL-equivalent, one could quantify the costs of most morbidity end points. Health professionals have tended to avoid economic considerations in decisions about treatment choices; only in recent years has there been a recognition of the need for guidelines about the value of a HALY or YOLL-equivalent, but there is still no official consensus of an appropriate monetary value and many health experts remain strongly opposed to any monetary valuation. Nonetheless, as an interim solution it seems reasonable to set the value of a YOLL-equivalent equal to a VOLY of approximately 40 000 h2006, and recent European projects are using this option (e.g., NEEDS and CASES).

Table 4 Years of life lost (YOLL) per world average cancer death, for selected cancers Type of cancer

YOLL due to mortality

YOLL-equivalents due to morbidity

Skin cancer Lung cancer Average cancer

6.09 15.95 12.5

0.19 0.26 0.3

Source: Bachmann TM (2006) Hazardous Substances and Human Health: Exposure, Impact and External Cost Assessment at the European Scale. Amsterdam: Elsevier, based on Keller S-P (2005) Assessing Human Effect Factors for Cancer in Life Cycle Impact Assessment (LCIA). Diploma thesis. Laboratory of Ecosystem Management. Ecole Polytechnique Fe`de`rale de Lausanne (EPFL), Switzerland, Lausanne, p. 30.

Cancers There are many different cancers, and their severity, in terms of suffering, cost, and LE reduction, can vary enormously from case to case. For example, most cases of skin cancer, if detected early enough, are treated successfully during a doctor’s visit. Lung cancer, by contrast, is fatal in approximately 90% of patients (with the customary definition of classifying a cancer as nonfatal if the patient survives at least 5 years after detection). Of course, valuation for applications to environmental policy is based on typical or average cases. For a very rough indication of YOLL-equivalents due to cancers, see Table 4. For nonfatal cancers the monetary value is the sum of the cost of illness (COI), the loss of income during the

864

Monetary Valuation of Trace Pollutants

illness, and the WTP to avoid the suffering. However, the available information is not clear. ExternE 1998 uses a value of 450 000 h for the cost of a nonfatal cancer, based on a survey of American data. This includes COI and lost earnings and an adjustment for the WTP to avoid the suffering (by assuming a ratio WTP/COI of 1.5 for nonfatal cancers). European data indicate a much lower COI than in the USA. For example, Borella et al. in 2002 stated that the total cost of cancers in France is 10 billion h per year for treatment and 15 billion h per year including lost productivity. Since the incidence of cancers is 240 000 new cases per year in France, this implies a cost per case of approximately 42 000 h per cancer for treatment and 63 000 h per cancer including lost productivity. For the WTP to avoid fatal cancers there are two different approaches. One is a lump sum, essentially VPF, possibly increased by a premium beyond an accidentbased VPF because cancers are feared as an especially dreadful form of death. Typical estimates of the cancer premium are on the order of 50% of VPF. However, there is much uncertainty because many CV studies did not detect a significant cancer premium. ExternE 2004 assumed a total cost of 2 million h per cancer death. The other approach is to multiply the YOLL due to the cancer by VOLY (including a correction for discounting, although such a correction is negligible compared to the uncertainties of this approach). The loss of life per cancer death is approximately in the 6–15 years range (depending on the type of cancer), intermediate between accidents and air pollution; for more detailed data, see Table 4. However, even without discounting, the resulting value for a fatal cancer would be very low, 500 000 h for a loss of 12.5 years with VOLY ¼ 40 000 h, whereas VPF is at least twice as large. Such an approach is problematic because it applies to cancer deaths a VOLY obtained by means of a questionnaire based on the very short loss of life due to air pollution (where losses of 3 and 6 months were proposed), and it does not account for the loss of quality of life during the illness. For these reasons a valuation based on VPF plus cancer premium seems more appropriate than one based on YOLL and VOLY. Whatever the valuation of a cancer, one should account for discounting because of the lag between emission and exposure and that between exposure and development of cancer, as discussed in the following section. Loss of IQ Points There is a wide variety of possible neurotoxic impacts, for instance, loss of memory or reduced sensitivity of limbs or senses, but for most of them no monetary values are available nor are any likely to become available. However, loss of IQ points is a good general indicator of

neurotoxic damage, and fairly reliable estimates for the associated costs can be found in the literature, mostly based on lost earnings and remedial education. The basic approach was developed by Schwartz in 1994, who estimated that an incremental 1 IQ point in cognitive ability would raise annual earnings by approximately 1.8%. Subsequently, Salkever in 1995 calculated that a 1-point IQ difference is associated with a roughly 2.4% difference in earnings, and this estimate has been used in recent regulatory analyses in the USA. There are of course uncertainties in these percentages, and the resulting cost of an IQ point depends on additional assumptions, in particular the discount rate and the future growth of earnings. Furthermore, people like to be smart and value a high IQ for more than just the earnings potential; however, such additional benefits are difficult to quantify. In practice most studies use loss of IQ points and earnings potential and remedial education as proxy for the real cost of neurotoxic impacts, even though it is far from ideal. For numerical results the following estimates are cited: and Zegarac in 2001: $ 14 700 per IQ point; • Muir et al. in 2002: $ 14 500 per IQ point; • Grosse and Hammitt in 2005: $ 16 500 per IQ point; • Rice et al. in 2005: $ 22 200 per IQ point; and • Trasande • Griffiths et al. in 2007: $ 11 245 per IQ point. 1999

2000

2000

2000

2000

Adjusting these figures to $2005 by means of the CPI (consumer price index), one obtains a mean of approximately $200518 000 per IQ point. To apply this cost in different countries, one can modify the cost in proportion to the GDPPPP per capita, the per capita GDP adjusted for purchase power parity (http://www.cia.gov/cia/publications/factbook/). Thus, Spadaro and Rabl in 2008 found a worldwide average cost of $3890 per IQ point. Discounting and the Valuation for Long-Term Impacts Most of the impacts of the trace pollutants occur a long time after the emission, for two reasons: (1) the lag between emission and exposure from ingestion and (2) the lag between exposure and the development of a cancer. The lag between emission and ingestion can range from months to several years or even centuries, depending on the pollutant and the environmental pathways. The time for the development of a cancer after exposure is on the order of 10–20 years. For lags shorter than approximately 30 years, the conventional social discount rate is appropriate. Furthermore, toxic metals do not decay and could thus exert harmful impacts for the indefinite future. In practice, the time horizon tends to be reduced, perhaps to timescales on the order of decades or centuries, by

Monetary Valuation of Trace Pollutants

chemical transformations that immobilize the metals in the soil or in ocean sediment (the ultimate resting place for most of the emitted metals). Some organic pollutants, in particular dioxins and PCBs, also persist for fairly long in the environment, but less than metals, the persistence time being on the order of a decade or several decades. Even if the environmental pathways could be analyzed in a satisfactory manner, there is no universally accepted solution for the choice of time horizon and discount rate for the monetary valuation. However, in recent years more and more economists have come to accept the idea that intergenerational damage costs should be discounted at a much lower rate than the conventional social discount rate. Now many studies assume a discount rate that is equal to the social discount rate for the near term but that decreases with time, the pure time preference component approaching zero. However, even if there were agreements on the appropriate discount rate, another rate is just as important: the rate at which the damage costs change over time (before discounting a future cost, one must predict what that cost will be). For example, if the progress of medicine renders cancers as harmless as common cold, it would be absurd to estimate the future damage according to today’s assumptions. Note that the goal of damage cost quantification is to determine the expectation value (together with its uncertainty), not an upper bound. The current practice of most LCA studies to count all cancers equally for the indefinite future is in fact a choice of extreme and unrealistic pessimism (even if humankind were to relapse into another Dark Age, such a choice would be unrealistic because few would survive long enough to develop a cancer). Thus, the quantification involves necessarily subjective judgments about the progress of science and medicine.

Case Studies It may be interesting to illustrate the methodology with the results of two case studies. They were carried out by different authors, with different assumptions, and no attempt was made at bringing them to a single consistent basis, as a way of emphasizing that there are still many unresolved issues where different analysts can make different choices. Waste Incineration Waste incineration is one of the activities where the emission of trace pollutants has been of great concern. For the wastes that remain after source reduction and recycling, the principal disposal methods are landfill and incineration. Here results are cited from a recent study by Rabl, Spadaro, and Zoughaib, who have evaluated the damage costs of these two treatment methods for typical

865

situations in France. They have combined an IPA with a LCA to account for the impacts not only of the treatment facility but also of the other stages, especially the possible recovery of energy and materials. Of course, the impacts and damage costs other than those due to greenhouse gases can vary quite strongly with the site of the installation, in particular with the local and regional population density. The results shown here give an indication of the order of magnitude that can be expected in much of Central Europe. Only the impacts listed in the section ‘ERFs for carcinogens’ have been quantified, and the monetary values are different from the case study on power plants; in particular, cancer deaths are valued at 2 million h. The resulting costs per kilogram of emitted pollutant are listed in Table 5. For Cd, Cr, and Ni, only halation impacts (lung cancer) are included and they have been calculated by EcoSense for LCA applications in Europe. The cost of Hg is based on Spadaro and Rabl’s 2008 calculation, although with somewhat different assumptions. The calculation for dioxins has been illustrated in the section ‘Dispersion models’ for the dose and in ‘ERFs for carcinogens’ for the resulting number of cases, 90 cancers kg1, for a region with average population density of 100 persons km2. For Table 5 a unit cost of 2 million h per cancer has been assumed. Note that the costs in this table may be different from numbers in other publications of ExternE or even what is implied by the sections ‘Dispersion models’ and ‘Exposure–response functions’ of this article because different authors, at different times, may choose somewhat different values of the uncertain input parameters or different assumptions about emission sites. Figure 3 shows the contribution of each stage and of the major pollutants (dioxins and toxic metals are shown as ‘Trace’) for two energy recovery scenarios. The emissions data are assumed equal to the limit values of the respective directives of the EU. The benefits of materials recovery make a small contribution to the total damage cost. The damage costs of waste transport, Table 5 The costs per kilogram of pollutant emitted by incinerators in France Pollutant

d kg1

As Cd Cr(VI) Hg Ni Pb Dioxins (2,3,7,8-TCDD equivalent)

80 39 200 8000 3.8 600 1.85E08

As estimated by Rabl A, Spadaro JV, and Zoughaib A (2008) Environmental impacts and costs of municipal solid waste: A comparison of landfill and incineration. Waste Management & Research 26: 147–162.

866

Monetary Valuation of Trace Pollutants €/t waste

Incineration, partload heat (H = o) Total Direct emissions Energy recovery

PM NOX SO2

Materials recovery Transport −25

−20

−15

−10

−5

0

5

10

CO2+CH4 Trace

15

20

25

€/t waste

Incineration, partload electricity (E = c and o) Total Direct emissions Energy recovery

PM NOX SO2

Materials recovery Transport −25

−20

−15

−10

−5

0

5

10

15

CO2 Trace

20

25

Figure 3 Damage costs of waste treatment, by stage and pollutant. ‘Trace’ ¼ dioxins and toxic metals. H ¼ o: heat recovery displaces oil; E ¼ c&o: electricity displaces oil and coal (50% each). From Rabl A, Spadaro JV, and Zoughaib A (2008) Environmental impacts and costs of municipal solid waste: A comparison of landfill and incineration. Waste Management & Research 26: 147–162.

illustrated with an arbitrary choice of 100 km roundtrip by a 16-tonne truck, are negligible. The only significant contributions come from direct emissions (of the landfill or incinerator) and energy recovery. The benefit of avoided emissions, thanks to energy recovery, is extremely dependent on the specific conditions of the treatment facility. The benefit is greatest if the incinerator heat can be used year round to avoid the burning of a dirty fuel. Producing electricity has less value because the conversion efficiency from incinerator heat is relatively low compared to conventional power plants (which have higher combustion temperature and efficiencies of scale). If the electricity displaces nuclear power, the benefits are negligible. To put Figure 3 in perspective, note that the private costs are on the order of 50 h per tonne of waste for landfill and 100 h per tonne of waste for incineration. Of course, costs are not the only relevant consideration. Electricity Generation from Coal As a second case study, some results are shown for toxic metal emissions, which was initially evaluated by Bachmann in 2006, from external costs of electricity generation from coal-fired power plants at four different sites in Belgium, France, Germany, and the UK. Since no plant-specific information on toxic metal emissions is available, default values are used. The results shown here correspond to the lower limit of metal concentration in coals as quoted by the Joint Research Centre in 2006; with the upper limit they would be an order of magnitude larger. Default emission scenarios for each plant and each metal were run with the help of EcoSense by assuming an emission rate of 1000 kg year1. The resulting deposition rates onto the surface serve as the upper

boundary condition to the WATSON model. ERFs were derived with the help of the relationships published in Crettaz et al. in 2002 and Pennington et al. in 2002. Monetary valuation was performed according to the YOLL-equivalent approach (see the section ‘Valuation of DALYs, QALYs, or YOLL-equivalents’) by employing a VOLY of 50 000 and 75 000 h2000 and discounting at 3% and 0%, respectively. The YOLL-equivalent per average cancer case is 12.8 years. Tables 6(a)–(c) show the results only for the plants with the highest (Belgium) and the lowest (the UK) damage costs, to give an idea of variability with site; the numbers for Germany and France are intermediate. For Cd the costs due to ingestion are much larger than those due to inhalation. That is not the case for Cr because the impacts from ingestion are less severe than from inhalation, despite the larger dose. The costs due to ingestion are substantially smaller when discounted at 3%, owing to the long-lived nature of these pollutants and associated slow dynamics, whereas those due to inhalation exposure reduced only by approximately half. The variation of ingestion impacts between sites is comparable to the inhalation impacts despite the homogenizing effect of trade on food concentrations. In absolute magnitude, the cost per kWhe is extremely small, orders of magnitude smaller than the external costs of the classical air pollutants (NOx, PM, and SO2).

Uncertainties An assessment of the uncertainties of damage costs involves a detailed examination of the uncertainties of each step of the IPA, in particular dispersion, DRF, and monetary valuation. There are several approaches to

Monetary Valuation of Trace Pollutants Table 6(a)

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Characteristics of the power plants

Characteristic

Belgium

France

Germany

UK

Location Latitude (1) Longitude (1) Stack height (m)

Genk-Langerlo 50.97 5.50 140

Cordemais, near Nantes 47.18  1.48 220

Lauffen 49.08 9.18 240

West Burton 53.38  1.50 230

Table 6(b) Heavy metal Cd Cr Pb

Table 6(c) Heavy metal Cd Cr Pb

External costs in d2000 kg1 Exposure route

Belgium

Discount rate Inhalation Ingestion Inhalation Ingestion Inhalation Ingestion

0% 24 5642 16 2.2 n/a 5887

UK 3% 13 337 8.5 0.009 n/a 133

0% 12 3488 8.4 1.7 n/a 3642

3% 6.7 222 4.5 0.013 n/a 74

External costs in 109 d2000 kWh1 e Emission (g kWh1 e )

Exposure route

Belgium

Discount rate Inhalation Ingestion Inhalation Ingestion Inhalation Ingestion

0% 0.28 1582 34 0.61 n/a 1651

uncertainty assessment, including qualitative as well as quantitative assessments. In any case the analysis begins with a careful examination of all the assumptions and all the input data. Ideally one should estimate standard deviation and shape of the probability distribution of the uncertainties for each input. The component uncertainties are then combined to obtain uncertainty of the damage cost. The uncertainties of environmental damages are far too large for the usual error analysis (using only the first term in a Taylor expansion) of physics and engineering. Rigorous systematic assessment of the uncertainties is difficult and few studies have attempted it. Most merely indicate an upper and a lower value, but based on the range of just one input parameter or by simply combining the upper and lower bounds of each input, without taking into account the combination of uncertainties from several different inputs (e.g., atmospheric dispersion, DRF, and monetary valuation). Many damage assessments involve so many different inputs that an analytical solution is not feasible, and of the rare uncertainty analyses that have been done, almost all use Monte Carlo techniques and numerical calculations. Such an approach is powerful, capable of treating any problem, albeit with heavy calculations. Above all, it is

UK 3% 0.15 95 18 0.003 n/a 37

0% 0.15 952 17 0.46 n/a 995

3% 0.08 61 9.3 0.004 n/a 20

opaque: since it yields only numerical results, it is difficult to see how the numbers would change if some inputs are changed. As an alternative the method of Spadaro and Rabl developed in 2008 is applied, which is appropriate in this case because the impact and cost are essentially a product of factors. This approach has the advantage of being simple and transparent, and its accuracy is sufficient because in any case an assessment of uncertainties involves subjective judgments and is very approximate. The uncertainty of each factor is characterized in terms of its geometric standard deviation. Then the geometric standard deviation sg of the product can readily be calculated from the geometric standard deviations sgj of the factors by the following equation: ½lnðsg Þ2 ¼ ½lnðsg1 Þ2 þ ½lnðsg2 Þ2 þ ? þ ½lnðsgn Þ2

½3

assuming that the distributions are statistically independent. The central limit theorem implies that the distribution of the product is approximately lognormal in the limit where the number of factors becomes very large. In practice the distribution is close to lognormal even for a small number of factors, unless the distribution(s) with

868

Monetary Valuation of Trace Pollutants

the largest width is/are far from lognormal. For a lognormal distribution the geometric standard deviation indicates multiplicative confidence intervals, analogous to the additive confidence intervals of the Gaussian distribution. One can show that for the lognormal distribution, the geometric mean mg is equal to the median, and the ratio of mean/geometric mean is given by m ¼ expð0:5 ln 2 ðsg ÞÞ mg

½4

If a quantity with a lognormal distribution has been found to have a geometric mean mg and a geometric standard deviation sg, the probability is approximately 68% for the true value to be in the interval [mg/sg, mgsg] and 95% for it to be in the interval [mg/s2g, mgsg2]. This approach has been used to evaluate the uncertainty of damage cost estimates for dioxins and toxic metals. In view of the great uncertainties of any uncertainty assessment, it suffices to state approximate values for sg. For the toxic metals As, Cd, Cr(VI), Hg, Ni, and Pb, sg is found to be approximately 4 and for dioxins it is approximately 5. Assessing uncertainties in this field is difficult because of a pervasive lack of information; subjective judgment is unavoidable in interpreting whatever information is found. In particular for toxic metals there is the issue of speciation, which affects their entire impact pathway but which is not adequately taken into account in most current assessments. Often one wants to know the resulting distribution when the damages costs for several different impacts are added, for instance, the impacts of all the toxic metals emitted by an incinerator. This point is examined in the section ‘Monetary valuation,’ where a simple approximate method is derived for estimating the geometric mean and geometric standard deviation of the distribution of the sum. It turns out that for typical cases the distribution of a sum of lognormally distributed damage costs is also approximately lognormal. See also: Composite Measures of the Environmental Burden of Disease at the Global Level, Exposure Science: Ingestion, Exposure Science: Inhalation, General Introduction to Valuation of Human Health Risks, Models of Human Exposure to Environmental Contaminants, Optimal Pollution: The Welfare Economic Approach to Correct Market Failures.

Further Reading Axelrad DA, Bellinger DC, Ryan LM, and Woodruff TJ (2007) Doseresponse relationship of prenatal mercury exposure and IQ: An integrative analysis of epidemiologic data. Environmental Health Perspectives 115(4): 609--615.

Bachmann TM (2006) Hazardous Substances and Human Health: Exposure, Impact and External Cost Assessment at the European Scale. Amsterdam: Elsevier. Borella L, Finkel S, Crapeau N, et al. (2002) Volume et couˆt de la prise en charge hospitalie`re du cancer en France en 1999. Bulletin du Cancer 89(9): 809--821. Crettaz P (2000) From Toxic Emissions to Human Health Impact: A Generic Model for Life Cycle Impact Assessment. Dissertation. Swiss Federal Institute of Technology, EPFL, Lausanne. Crettaz P, Pennington D, Rhomberg L, Brand K, and Jolliet O (2002) Assessing human health in life cycle assessment using ED10s and DALYs: Part 1 – Cancer effects. Risk Analysis 22(5): 931--946. Desaigues B, Ami D, Hutchison M, et al. (2007) Final Report on the Monetary Valuation of Mortality and Morbidity Risks from Air Pollution, Framework VI Research Programme (Project no: 502687 ‘New Energy Externalities Developments for Sustainability’ (NEEDS)). EC (2000) Directive 2000/76/EC of the European Parliament and of the Council of 4 December 2000 on the incineration of waste. EPA (1994) Estimating exposure to dioxin-like compounds. Report EPA/ 600/6-88/005Ca, b and c. Washington, DC: United States Environmental Protection Agency. EPA (1997) Exposure Factors Handbook. http://cfpub.epa.gov/ncea/ cfm/recordisplay.cfm?deid ¼ 20563 (accessed June 2010). EPA (1998) Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, Support Materials. US EPA Region 6. US EPA Multimedia Planning and Permitting Division Office of Solid Waste, Center for Combustion Science and Engineering. http:// www.epa.gov/region6/6pd/rcra_c/protocol/protocol.htm (accessed June 2010). EPA (2000) Exposure and human health reassessment of 2,3,7,8tetrachlorodibenzo-p-dioxin (TCDD) and related compounds: Part III: Integrated summary and risk characterization for 2,3,7,8tetrachlorodibenzo-p-dioxin (TCDD) and related compounds. Report EPA/600/P-00/001Bg. Washington, DC: United States Environmental Protection Agency. ExternE (1998) ExternE: Externalities of Energy. vol. 7: Methodology 1998 Update (EUR 19083); vol. 8: Global Warming (EUR 18836); vol. 9: Fuel Cycles for Emerging and End-Use Technologies, Transport and Waste (EUR 18887); vol. 10: National Implementation (EUR 18528). Luxembourg: European Commission, Science Research and Development, Office for Official Publications of the European Communities. ExternE (2004) New Results of ExternE, after the NewExt Project. http:// www.externe.info (accessed June 2010). ExternE (2007) NEEDS-New Energy Externalities Developments for Sustainability. Deliverable 3.7-RS1b/WP3. http://www.needsproject.org/ (accessed July 2010). Griffiths C, McGartland A, and Miller M (2007) A comparison of the monetized impact of IQ decrements from mercury emissions. Environmental Health Perspectives 115(6): 841--847. Groom B, Hepburn C, Koundouri P, and Pearce D (2005) Declining discount rates: The long and the short of it. Environmental and Resource Economics 32(4): 445--493. Grosse SD, Matte TD, Schwartz J, and Jackson R (2002) Economic gains resulting from the reduction in children’s exposure to lead in the United States. Environmental Health Perspectives 110(6): 563--569. Humbert S, Margni M, and Jolliet O (2005) IMPACT 2002 þ : User Guide-Draft for Version 2.1. p. 36. Lausanne, Switzerland: Industrial Ecology & Life Cycle Systems Group, GECOS, Swiss Federal Institute of Technology Lausanne (EPFL). Joint Research Centre (JRC) of the European Commission (2006) Integrated Pollution Prevention and Control: Reference Document on Best Available Techniques for Large Combustion Plants. p. 580. Seville, Spain: Institute for Prospective Technological Studies, Joint Research Centre of the European Commission. Jolliet O, Margni M, Charles R, et al. (2003) IMPACT 2002 þ : A new life cycle impact assessment methodology. International Journal of Life Cycle Assessment 8(6): 324--330. Keller S-P (2005) Assessing Human Effect Factors for Cancer in Life Cycle Impact Assessment (LCIA). Diploma thesis. Laboratory of Ecosystem Management. Ecole Polytechnique Fe´de´rale de Lausanne (EPFL), Switzerland, Lausanne, p. 30.

Monetary Valuation of Trace Pollutants

Krupnick A, Alberini A, Cropper M, et al. (2002) Age, health, and the willingness to pay for mortality risk reductions: A contingent valuation survey of Ontario residents. Journal of Risk and Uncertainty 24(2): 161--186. Markandya A, Hunt A, Ortiz RA, et al. (2004) Mortality Risk Valuation Final Report. NewExt Research Project. European Commission, DG Research. Mason H, Jones-Lee M, and Donaldson C (2008) Modelling the monetary value of a QALY: A new approach based on UK data. Health Economics 18(8): 933--950. McKone TE and Enoch KG (2002) ‘‘CalTOXTM, a multimedia total exposure model.’’ Report LBNL – 47399. Lawrence Berkeley National Laboratory, Berkeley, CA. http://eetd.lbl.gov/ied/ERA (accessed June 2010). Mitchell RC and Carson RT (1989) Using Surveys to Value Public Goods: The Contingent Valuation Method. Washington, DC: Resources for the Future. Muir T and Zegarac M (2001) Societal costs of exposure to toxic substances: Economic and health costs of four case studies that are candidates for environmental causation. Environmental Health Perspectives 109(supplement 6): 885--903. Pennington D, Crettaz P, Tauxe A, Rhomberg L, Brand K, and Jolliet O (2002) Assessing human health response in life cycle assessment using ED10s and DALYs: Part 2 – Noncancer effects. Risk Analysis 22(5): 947--963. Rabl A, Spadaro JV, and Zoughaib A (2008) Environmental impacts and costs of municipal solid waste: A comparison of landfill and incineration. Waste Management & Research 26: 147--162. Rice G and Hammitt JK (2005) Economic Valuation of Human Health Benefits of Controlling Mercury Emissions from US Coal-Fired Power Plants. Boston, MA: Northeast States for Coordinated Air Use Management (NESCAUM).

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