Appendix 2. Objective 2: Uncertainties in assessing risks to human health from contaminated land

Transcription

1 Appendix 2 Objective 2: Uncertainties in assessing risks to human health from contaminated land 1 Introduction The uncertainty table methodology General sources of uncertainty Uncertainties when using models of exposure Case study 1a: Benzene in a residential setting Parameters for the quantitative assessment Results and sensitivity analysis Case study 1b: Benzene at a commercial premises Parameters for the quantitative assessment Results and sensitivity analysis Case study 2: Benzo[a]pyrene in allotments Parameters for the quantitative assessment Results Case study 3: Arsenic at a public open space Parameters for the quantitative assessment Results Case study 4: Cadmium in a residential setting Parameters for the quantitative assessment Results Some conclusions from the case studies Uncertainty in the toxicological endpoint General aspects Specific toxicological endpoints Uncertainty in risk assessments of contaminated land References Introduction Whilst information is available on concentrations of chemicals in soil from contaminated land, it is extremely difficult to accurately estimate human exposure to these concentrations. Routes of exposure are known (ingestion of soil, ingestion of contaminated food, inhalation of vapours etc), but they are poorly characterised. Even where exposure can be quantified, we cannot be confident that we are converting exposure to dose accurately. Thus, any estimate of health impacts arising from contaminated land is highly uncertain. Nevertheless, the methods used to generate risk estimates are often deterministic. Exposure models generally take single values for input parameters related to routes of exposure, concentrations of contaminants in different compartments and human behaviours to yield an estimate of exposure. This is then compared with deterministic values for some appropriate exposure thresholds to calculate a risk quotient. These thresholds either take the form of index doses (IDs) or tolerable daily intakes (TDIs); the derivation of these values is discussed in EA (2009e). The deterministic approach is relatively easy to undertake and communicate and, coupled with legislative safety factors, allows a simple decision on the acceptability of a particular risk. However, the degree of protection associated with such an approach is unknown because both the inputs and, subsequently, the outputs of the risk assessment can be highly uncertain. Page 1 of 1

2 In Hart et al. (in prep.), an uncertainty table approach is introduced that can be used to assess total uncertainty in this type of assessment in a qualitative and structured way. In Section 2, we briefly review the uncertainty table methodology of Hart et al. (in prep.) and describe how our assessments were made. We consider the many types of uncertainty that impact a risk assessment on contaminated land when we use a process model to estimate exposure in Section 3. In Section 4, we consider the uncertainties that specifically surround inputs into an exposure model. In Sections 5 to 10, we take the uncertainty table approach a stage further by using a Monte Carlo approach to quantify uncertainty in exposure estimates for four case studies; these include heavy metals (arsenic and cadmium) and organics (benzene and benzo[a]pyrene), which are relatively well characterised in the literature in terms of pathways of exposure and potential health impacts. The exposure scenarios in the case studies cover residential, commercial and allotment sites. A probabilistic exposure model has been developed using the CLEA model coupled to the Monte Carlo analysis software, Poptools (Hood, 2009). The Contaminated Land Exposure Assessment model (CLEA) has been produced by the Environment Agency to support contaminated land risk assessment. Its use is widespread but not compulsory in contaminated land decision-making. The Monte Carlo model produced within this study generates a distribution of exposure estimates for the case study contaminant allowing uncertainty to be quantified for parameters that are probabilistically modelled. We present the distributions means and standard deviations alongside probability density functions to give an appreciation of the uncertainty. The quantitative uncertainty analyses carried out in this report are for illustration only because we have not modelled the impacts of all sources of uncertainty; therefore, they should not be used as quantitative estimates of the total uncertainty in exposure modelling. The output of an exposure model depends heavily on one equation that is used to calculate the average daily human exposure (ADE) to a chemical from soil; the equation is: where IR is the chemical intake rate, AE is the average proportion of the day that the individual is exposed, and BW is the body weight of the individual. Although IR will be the main driver of uncertainty in the exposure estimate, BW and AE have the potential to have a large impact on the ADE value. The users of CLEA can specify AE and BW directly, and there are several hundred parameters that underpin the value of IR. To improve our understanding of the important parameters, we performed an advanced sensitivity analysis for two of the case studies where we calculated the proportion of variance in the output due to a subset of uncertain parameters and the expected effect each of those inputs has on the output. Finally, in Section 11, we consider the impact that our uncertainty about the ID and TDI for a chemical has on the risk assessment results. Although we have focused on CLEA in this report, a lot of the things identified will apply to other process-based models of exposure to contaminated land. The CLEA model was chosen for consideration as it is freely available and widely used by risk assessors in England and Wales. 2 The uncertainty table methodology An uncertainty table encapsulates a structured approach for qualitative evaluation and expression of uncertainties affecting exposure and risk assessments. As part of this process, the risk assessor uses + and - symbols to represent their beliefs about the influence of the assumptions in the risk assessment and associated uncertainties. The +/- symbols indicate Page 2 of 2

3 whether each source of uncertainty has the potential to increase (+) or decrease (-) the assessment outcome. The number of symbols provides a subjective relative evaluation of the magnitude of the effect (e.g. +++ indicates an uncertainty that could make the reported risk much higher). A is used to represent an unquantified uncertainty that is thought to have no appreciable effect on the estimated risk. If the effect is uncertain, or could vary over a range, lower and upper evaluations are given (e.g. - / ++ or / ++). Finally, the combined impact of all the uncertainties is evaluated subjectively by considering all of the row-wise judgements. Table 1 shows the structure used in this method. If we have many sources of uncertainty it is useful to put them into broad categories and further subdivide the table to reflect this structure. For further details and other examples of the methodology in use, see Hart et al. (in prep.). Table 1. The basic structure of an uncertainty table. Source of uncertainty Concise description of source of uncertainty (e.g. Contribution to exposure from measured contaminant concentrations below the limit of quantification) Insert one row for each source of uncertainty affecting the assessment Overall evaluation of uncertainty affecting the assessment outcome Add narrative text here, describing the assessor s subjective evaluation of the overall degree of uncertainty affecting the assessment outcome, taking account of all the uncertainties identified above. Magnitude and direction of influence on assessment outcome Symbols to show evaluation of influence (e.g / +) Evaluation of overall uncertainty (e.g / +) 3 General sources of uncertainty In Table 2, we list the sources of uncertainty that are associated with assessing the exposure to chemicals from contaminated land. The assessments and comments that are made in Table 2 refer to a situation where a site-specific assessment is being made. In such situations, a risk assessor might use a mathematical model of exposure along with measurements made at the site in question. Page 3 of 3

4 Table 2. Uncertainty table detailing the sources of uncertainty in a risk assessment. Source of uncertainty The ADE is calculated using a model of exposure, and it is not known how far that model deviates from reality. (More detail in Section 4.) The true parameter values for the exposure model are unknown. Often, both average and conservative estimates for input parameters are used. (See Section 4.) Chemical measurements made at an assessment site do not give the complete picture, and the assessments are often set at the maximum concentration found. No measurement error is accounted for in the procedure. Usually, only a range of concentrations found is reported. The laboratory properties of a chemical are assumed to be the same at the contaminated sites despite differing temperatures and pressures. Magnitude and direction of influence on ADE /++ /++ /+ There is often an assumption that a chemical will not degrade over time. / Chemical transport processes are driven by diffusion along with advection in a single direction. A number of inputs into a quantitative risk assessment are averages over space and/or time. For example, temperature and wind speed are only considered as yearly averages. This leads to a lack of appreciation of variation and possible extreme events. The assessment procedure does not allow for made ground to be taken into account. The properties of made ground are not known. Properties of the building are estimated using measured footprints and heights. We could do more measurements or account for the uncertainty in the estimates. It is assumed that young children spend everyday at the affected property. We could allow for a more realistic model of occupation. Overall assessment of uncertainty affecting the ADE By accounting for the uncertainty in the parameters of a quantitative risk assessment, we could get ADE values that are much greater or smaller. As the risk assessment tools err on the conservative side due to their role as screening devices, the current procedures are likely to overestimate the exposure. Also, it is clear that there are a number of modelling issues, and these have the potential to have a major effect on the resulting risk assessment. /+ /+ / /+ /+ /+ / /++ 4 Uncertainties when using models of exposure Table 2 included the uncertainty surrounding the use of a quantitative risk assessment tool used to estimate exposure. In this section, we have focused on the CLEA model to assess this aspect in more detail. The CLEA model has been developed primarily for the derivation of national Soil Guideline Values that apply to a broad range of site conditions and circumstances. The CLEA model can also be used as a starting point for further investigation to decide whether or not a specific site is contaminated to an extent that it is posing a significant risk to health. We have focused on the CLEA model because it is freely available and widely used in England and Wales; it is not the only tool available for this type of assessment. Page 4 of 4

5 CLEA is a mathematical representation of a set of complicated physical processes, and, like all models, it does not equate to reality. In Table 3, we outline the major sources of uncertainty in CLEA s modelling assumptions and their potential effects. The sources of uncertainty that are listed should not be interpreted as detracting from the progress in quantitative risk assessments that CLEA has stimulated, but as a contribution to increased understanding of the uncertainties in current approaches. Some of the information in the table was derived from EA (2009f). Table 3. Uncertainty table detailing the sources of uncertainty in the modelling assumptions used within the CLEA model. Source of uncertainty Magnitude and direction of influence on ADE The equations in the model are not a perfect representation of reality. /++ Average values are often used in site-specific assessments. This could lead to variability in the actual values being underplayed. A lifetime of 75 years is broken into 18 receptor age classes. It is not clear if a young adult would have the same characteristics in middle age. This also leads to a stepwise aging process. Accumulating exposure over a number of years might not be protective for toddlers. For instance, you will get different assessments if you consider a 2 year old alone rather than a 2-4 year old. /+ /++ The chemical concentration in the soil does not degrade over time. / When an assessment includes homegrown food, there is a linear assumption that says body weight is proportional to food consumed. The consumption of homegrown food is based on average consumption rates for adults and children that were calculated using Food Standards Agency data. There is a great deal of variability in the types of food consumed and the estimates of consumption are relatively high. Chemical transport processes are driven by diffusion along with advection in a single direction. Overall assessment of uncertainty affecting the ADE in terms of CLEA modelling assumptions CLEA takes a logical approach to modelling exposure from contaminated land, and the Environment Agency have attempted to justify the choices they have made throughout. There are a number of choices that could have a significant impact on the ADE estimate. /+ / / /++ In CLEA, there are over 450 parameters that an advanced user could specify in a risk assessment for one chemical if they were considering all exposure pathways. Some of those parameters are known because they are chemical properties or accurate measurements; however, there is uncertainty about the majority. In practice, a user will choose the default values for almost all of the parameters of the model. In Table 4, we focus on some of the most important parameters and make judgements about the associated uncertainty. Page 5 of 5

6 Table 4. Uncertainty table detailing the uncertainty in the model parameters. Basic Settings Source of uncertainty The risk assessor must choose which receptor and pathways the model will focus on. In many cases, there will be clear reasons for making these choices. However, these choices impact on the results. Of course, this decision is critical, but we would expect a competent risk assessor to choose the appropriate scenario for their site specific assessment. The soil organic matter content of the soil at the site needs to be specified: these are variable across spatial locations. This parameter occurs in many of the equations that underpin the model. The site measured media concentrations of the chemical are given as single values. This again assumes homogeneity where there could be great variability. One response to this is to give the highest value that has been measured. Advanced chemical data There are many chemical properties that are specified in the model; for example, diffusion coefficients and water solubility. Although many of these have been discovered empirically, enough experimentation on many of the chemicals of interest has been done to reduce the uncertainty about these values. There are eight parameters that characterise the behaviour of a chemical in relation to produce grown on contaminated land. There is much less evidence about the true values for these parameters, and they have the potential to have a large impact on the results if the consumption of homegrown produce is a considered pathway. However, this part of the model is used only where chemical specific uptake data is not available. Advanced soil and building data Eight parameters are used to characterise the soil at the site. Again, these are relatively variable. The user will usually default to a broad class of soils like "sandy loam". There is a set of parameters that focus on climatic variables at the site (including soil temperature and wind speed). These parameters govern the transition of the chemical from the soil into the air as vapour and dust. There is obviously great temporal variability in these parameters. Nine parameters cover the properties of the building and site. Some of these are measurements of the building's dimensions and some, like soil gas ingress rate, are extremely expensive to measure. Of course, not all assessments will include a building, but these parameters have a great impact on the amount of the chemical that is inhaled. Advanced homegrown produce data For each age class, there are number of parameters that govern the amount of homegrown food that is consumed. The modelling of food consumption is very difficult so CLEA's default is to give relatively high values for these parameters to be conservative. This will only make a difference when the homegrown pathway is included. Advanced land use and receptor data Magnitude and direction of influence on ADE /+ /+ /++ /+ /+ /++ /+ / Page 6 of 6

7 Source of uncertainty Body weight and height need to be specified for each age class. Body weight has an impact on the results as the total intake amount is divided by body weight. These are set at mean values reported in Jeffries (2009), but there is obviously great variation in the population. The average time spent at the contaminated site and inside affected buildings needs to be specified by the user. These parameters have a direct influence. Typically, conservative estimates are used in the setting of these parameters. In this part of the model, there are a number of parameters that also directly influence the estimate of intake through the different pathways (e.g. direct ingestion rates and soil-to-skin adherence factors). Relevant scientific studies report many different values. As they are directly linked with the exposure pathways, they could have a big effect on the assessment results. Overall assessment of uncertainty affecting the ADE in terms of CLEA's parameters There is significant uncertainty in the specification of parameters for CLEA. Some of the uncertainty is due to variability in receptors and site conditions, some is due to uncertainty about physical processes and true average values, and a smaller proportion is due to ambiguities in the parameter definitions. Of course, not all of the uncertainties listed here will apply to every exposure assessment as many are pathway specific. Magnitude and direction of influence on ADE /+ /+ /++ 5 Case study 1a: Benzene in a residential setting A risk assessment was carried out on a number of residential properties thought to be at risk from land contamination due to their proximity to a commercial premises. In this case study, we investigate the risks from benzene coming from land contamination beneath a residential property. A report by the consultants engaged by the local authority describes the background to the investigation and the details of the risk assessment that was used to decide that remediation work was necessary. In those details, there are a number of pages dedicated to the parameter choices the risk assessors made when using the exposure model and the actual chemical measurements from the site. The scenario we are considering in this case study is made up of the following: Property: a residential house, Receptor: female aged between 0 and 6 years, Exposure pathways: all except through homegrown produce. 5.1 Parameters for the quantitative assessment In this case study, the focus was on the effect of being uncertain about body weights, inhalation rates and length of time exposed. As the amount of chemical present in the soil drives the whole exposure assessment, some uncertainty about that parameter was included. Also, for each chemical, a value must be given for the soil to dust transport factor. In EA (2009f), there is no specific guidance on how to select this, and, for the example chemicals provided with CLEA, this is simply set at 0.5. There is much uncertainty about this parameter, and some of this has been included in the case study. In all, 21 parameters are considered in this uncertainty analysis. An advanced user of the CLEA software can adjust over 450 parameters. Although we are only considering the uncertainty in a small subset of the parameters, the parameters under consideration should include the most important due Page 7 of 7

8 to the basic structure of an exposure estimate. For the rest of the parameters, values were set using the measurements and estimates of the site investigation report and (where unavailable) the default settings for a comparable scenario in CLEA. For simplicity, normal distributions have been used to represent our uncertainty about the parameters. To specify a normal distribution, a value for the mean and standard deviation of the distribution must be set. For some of CLEA s parameters under consideration, we were able to find suitable reports that covered these. However, for exposure frequencies and occupancies, we made an approximate judgement about length of time it might be reasonable for a child to spend indoor and outdoor at the property. Table 1 shows the mean and standard deviations for each of the parameters. Of course, we could have used any appropriate probability distribution (and have included between-parameter correlations to model the dependence between inhalation rate and body weight for example) and the methodology we have employed would still be valid. In a more thorough analysis, we could model these uncertainties using other distributions that capture the discrete or skewed nature of the beliefs about the parameters. Also, we are not suggesting that the numbers that are given in Table 1 should be used as scientific facts, and more appropriate representations could be used in real risk assessments. Table 5. The normal distributions used for each parameter in the uncertainty analysis (case study 1a). CLEA parameters Parameters of normal distribution Source of information Mean Std dev. Benzene concentration (mg kg -1 dw) Site investigation report Soil-to-dust transport factor (g g -1 dw) See text in section 5.1 Age group Age group Allan and Richardson (1998) Age group and EA (2009f) Age group Age groups 5 and Inhalation rate (m 3 day -1 ) Body weight (kg) Exposure freq. (day year -1 ) Occ. Period (hours day -1 ) Age group Age group Age group Age group Age group Age group All contact (Age group 1) All contact (Age groups 2 and 3) All contact (Age groups 4, 5 and 6) Indoor (Age groups 1, 2, 3 and 4) 18 1 Outdoor (Age groups 1, 2, 3 and 4) Indoor (Age group 5 and 6) 18 1 Outdoor (Age group 5 and 6) Jeffries (2009) See text in section Results and sensitivity analysis Typically, risk assessments on contaminated land are undertaken in a deterministic way where the most likely or conservative values are used as input parameters to the exposure Page 8 of 8

9 model. Using the mean values of Table 5, we are able to calculate average daily intake estimates using CLEA; this is called a plug-in approach. Table 6 sets out the results from the uncertainty analysis for this case study alongside results from a plug-in approach. In the uncertainty analysis, we propagate the uncertainty in the chosen set of parameters (as shown in Table 5) using Monte Carlo methods to arrive at a distribution of possible model outputs. In Table 6, uncertainty about the model s outputs arising from uncertainty about the parameters is shown as standard deviations. Figure 1 gives a clearer appreciation of the uncertainty in the average daily exposure from inhalation. Table 6. Uncertainty analysis results for average daily exposures in case study 1a (all estimates are benzene μg kg -1 BW day -1 and given to two significant figures). Exposure route CLEA default Plug-in Uncertainty analysis estimate estimate Mean Std dev. Direct ingestion Dermal routes Inhalation Total The Environment Agency report (2009b) gives estimates of mean daily intake (MDI) of benzene from sources other than contaminated land. For a 20 kg child, the MDI through inhalation is estimated at 7.4 μg kg -1 BW day -1 ; this is much higher than the extreme percentiles of the probability distribution given in Figure 1. In the Environment Agency report (2009b), there is also an index dose (ID) given to help derive minimal risk levels of benzene in the soil. This is set at 1.4 μg kg -1 BW day -1, and this is also in the extreme tail of the distribution from our uncertainty analysis. From this analysis, it seems extremely unlikely that a young child living at one of the residential properties will exceed the ID for benzene. Also, in Figure 1, the estimated exposure a risk assessor would have got by using CLEA s default settings for this scenario is shown; it is clear to see that this estimate would be conservative in the light of the distribution shown. The EA (2009b) examine in detail a variety of expert opinions and authoritative reviews of the health effects of benzene. Benzene is one of the most studied of occupational and environmental toxicants, and, although banned from most industrial uses as a solvent, it still finds its way into the general environment as a combustion product (e.g. cigarette smoke) and as a component of petrol (around 1%). It is estimated that 50% of an inhaled dose will be absorbed and from ingestion 100% absorption is assumed. Benzene is an accepted genotoxic carcinogen in humans associated with an increased risk of acute myeloid leukaemia (AML) (and perhaps a number of other leukaemias with less weight of evidence) and multi-site tumours in animals. It is also considered to be a germ-cell mutagen. Most risk assessments are based upon an increase in AML and nearly all rely upon a study based on the Pliofilm cohort that was based upon three factories in the USA where benzene was used as a solvent and a number of cases of AML arose. There have been a large number of risk estimates in Europe and the USA for benzene and cancer, both for occupational and environmental purposes. There is uncertainty as to all the tumour types that it can cause, and there is still uncertainty as to its mechanism. The real difficulty is that, even with modern epidemiological techniques, a true carcinogenic effect may not be distinguishable above background levels of these cancer types. A further understanding of the mechanism of cancer induction will be useful, but may not improve our risk assessment ability unless it can be shown that the mechanism of action is non-genotoxic and thus the threshold level can be determined for some initial triggering event. Page 9 of 9

10 Figure 1. Graphical representation of uncertainty about the intake of benzene (case study 1a). There is further information about the potential toxicological effects of this exposure: the WHO Drinking Water Guidelines (WHO, 1993) propose that 1, 10 or 100 µg/l are equivalent to 1 x 10-6, 10-5 and 10-4 excess cancers in the population respectively. However, in the UK EPAQS (1994) estimated that there was no detectable risk at 0.5 ppm that is equivalent to 1,600 µg m -3 for a working lifetime. In converting this to an environmental standard and using 24 hours and 70 years, this equated to 3.2 µg m -3. The WHO Air Quality Guideline (2000) estimated that at 1 µg m -3, a lifetime exposure would give 4.4 x 10-6 to 7.5 x 10-6 excess cases of cancer. The WHO thus proposes that 1.7 µg m -3 will give an excess cancer risk of 1 in 100,000 and this estimate has been endorsed in the EU by EU INDEX (2005). As an extension to this uncertainty analysis, we also considered the sensitivity of CLEA s output to changes in the input parameters listed in Table 5. The methodology we used is described in Oakley and O Hagan (2004). Using these techniques we are able to divide up the variance between the various input parameters. The results are shown in Figure 2, and it is clear that uncertainty about the concentration of benzene at the site is the main driver of uncertainty in our analysis of ADE. In addition to the variance decomposition, we can also plot the main effects of the inputs on CLEA s outputs; that is, the expected change in the output if we alter one of the inputs (this is called a main effect as it ignores the contribution that a parameter may be making from interactions with other parameters). Figure 3 is the main effect plot for the concentration of benzene in the soil. The graph shows that there is an almost linear trend in the ADE as we increase the chemical concentration; this is to be expected. Figure 4 is the main effect plot for the body weight of a child in age group 1. As this is a divisor in the ADE calculations, we get a curved response in the model output. Finally, Figure 5 shows the main effect plot for the soil-to-dust transport factor, which we chose to use in the uncertainty analysis as little is known about it. In comparison to the two other plots, we can see that this parameter is having negligible effect on CLEA s output. This is not surprising since the inhalation ADE for benzene should be dominated by indoor vapour intrusion. Page 10 of 10

11 Figure 2. Contributions to the output variance from the uncertain CLEA parameters (case study 1a). The All other parameters label refers to the parameters of Table 5 that are not explicitly mentioned. Figure 3 The average main effect of benzene concentration on ADE (case study 1a). Page 11 of 11

12 Figure 4. The average main effect of the body weight of age group 1 on ADE (case study 1a). Figure 5. The average main effect of the soil-to-dust transport factor on ADE (case study 1a). We only considered a limited set of parameters in this case study; a more complete sensitivity analyses could help us to update the uncertainty tables we have created for this type of assessment. By knowing what effect an input parameter has on CLEA s outputs, we can make a more informed decision about the potential magnitude of change that including other parameters in the analysis might have. Page 12 of 12

13 6 Case study 1b: Benzene at a commercial premises In addition to the risk assessment on the residential properties potentially affected by the commercial premises, a risk assessment was produced for the commercial premises itself. In this case study, we look at the risks from benzene to a worker at those commercial premises. The scenario we are considering in this case study is made up of the following: Property: commercial premises, Receptor: male adult worker aged 16 to 60, Exposure pathways: all except through homegrown produce. 6.1 Parameters for the quantitative assessment In this case study, we focus on 11 parameters. Again, the chemical concentration, the receptor body weight and the time exposed should be important. For the number of days on site, we set a distribution that is reasonable for a commercial worker that is centred around 220 working days. In addition to these parameters, we look at some climate-related parameters: ambient soil temperature and pressure difference between indoor and outdoor. The distributions for these parameters are estimated using the CLEA default values and a reasonable range to cover some of the uncertainty. The final two parameters that we consider are properties of the building. The distributions for these were estimated using the values given in the site investigation report and giving a large enough standard deviation to cover realistic values. Table 7 details the means and standard deviations we chose for each input parameter. Again, all the other parameters in the CLEA model were set at default values or at measured values from the site investigation report. Table 7. The normal distributions used for each parameter in the uncertainty analysis (case study 1b). CLEA parameters Parameters of normal distribution Source of information Mean Std dev. Benzene concentration (mg kg -1 dw) Site investigation report Soil-to-dust transport factor (g g -1 dw) Exposure frequency indoor (days year -1 ) See text in section 6.1 Exposure frequency outdoor (days year -1 ) Body weight (kg) Body height (m) Jeffries (2009) Inhalation rate (m 3 day -1 ) EA (2009f) Ambient soil temperature (K) Pressure difference (Pa) Living space air exchange (day -1 ) See text in section 6.1 Foundation thickness (m) Results and sensitivity analysis Table 8 sets out the results from the uncertainty analysis for this case study alongside results from a plug-in approach. Figure 6 gives a better appreciation of the uncertainty in the average daily exposure for the worker from all sources. The assessment carried out above Page 13 of 13

14 only considers the amount of benzene coming from the contaminated land and not from other sources that the worker will be exposed to. Table 8. Uncertainty analysis results for average daily exposures in case study 1b (all estimates are benzene μg kg -1 BW day -1 and given to two significant figures). Exposure route CLEA default Plug-in Uncertainty analysis estimate estimate Mean Std dev. Direct ingestion Dermal routes Inhalation Total EA (2009b) gives estimates of mean daily intake (MDI) of benzene from sources other than contaminated land. For a 70 kg adult, the MDI through inhalation is estimated at 2.9 μg kg -1 BW day -1. The index dose is the same as before: 1.4 μg kg -1 BW day -1, and this is also in the far extreme tail of the distribution from our uncertainty analysis. Once again, based on this analysis, it seems extremely unlikely that a site worker will exceed the ID for benzene from contaminated land alone. Nevertheless, as the estimated MDI is greater than the ID, the impact of the additional exposure from contaminated land may need to be considered. In Figure 6, we can also see that an estimate based on CLEA s defaults for a generic commercial land-use scenario would again be conservative yet still below the ID. Figure 6. Graphical representation of uncertainty about the intake of benzene (case study 1b). Page 14 of 14

15 Figure 7. Contributions to the output variance from the uncertain CLEA parameters (case study 1b). Figure 8. The average main effect of the body weight of age group 17 on ADE (case study 1b). Page 15 of 15

16 Figure 9. The average main effect of living space air exchange on ADE (case study 1b). For this case study, we get different results from the variance decomposition: this time it is not dominated by the benzene concentration (although that is still relatively important within the set of parameters being considered). We find that body weight and living space air exchange also have a great effect on CLEA s outputs (Figure 7). The main effect plots for body weight and living space air exchange (in Figures 8 and 9 respectively) show that a relatively small change in those parameters can have a big effect on model output. As in the previous case study, we find that the soil-to-dust transport factor has little impact on the results, which is as we would expect. 7 Case study 2: Benzo[a]pyrene in allotments There is good epidemiological evidence that benzo[a]pyrene causes lung and skin cancer in various groups of workers, and there is experimental evidence that it can cause forestomach tumours when added to the diet in mice even at doses as low as 5 ppm. It is also considered to be a transplacental carcinogen. The most important aspect of risk assessment to note is that all authoritative bodies seem to emphasise the uncertainties in predicting cancer risks from the available data. WHO (1991) stated, The considerable uncertainties in risk estimates require that efforts should be made to minimise human exposure to benzo[a]pyrene as far as is practicable. Most of these bodies have attempted to use quantitative risk assessment but all seem to have used different pivotal studies as the basis for their models. As an example, the WHO (1993) have used a mouse dietary forestomach tumour study (Neal and Rigdon, 1967) to derive a drinking water standard of 0.7 µg/l which they estimate is equivalent to an excess risk of 10-5 cancers. Other bodies have used lung cancer in worker studies as the basis for their estimates. This latter approach seems reasonable for setting air quality standards or guidelines as it avoids interspecies comparison and the use of associated uncertainty factors and the target organ of the lung is the one of concern. The negative side is that benzo[a]pyrene is only one of a range of PAHs to which the workers were exposed and that lung cancer is subject to other confounders. Risk estimates produced by other authoritative bodies use different occupational studies as their pivotal study. Some have tried to use both animal and human studies to derive risk Page 16 of 16

17 estimates of excess lung cancer risk and look for concordance (WHO, Air Quality Guidelines for Europe, 2000). A risk assessment was carried out on an allotment where a strong fuel odour had been reported. Case study 2 focuses on one fuel-related chemical, benzo[a]pyrene, that is classified as a carcinogen. The scenario we are considering in this case study is made up of the following: Property: allotments with no building, Receptor: young female child aged 1 to 6 years, Exposure pathways: all outdoor exposure pathways and homegrown food. We are making the assumption here that because the household uses an allotment that they will be high-end consumers of produce from the allotment. This assumption leads to a conservative risk assessment. 7.1 Parameters for the quantitative assessment We focus on the chemical concentration in the soil, the child s body weight and inhalation rate, and the exposure frequency. As this case study focuses solely on outdoor exposure, we consider the effect of parameters associated with intake through dermal pathways. Parameters that characterised the consumption of homegrown food and the intake of chemicals through that pathway are also considered. For these parameters, values were selected that centred on the CLEA defaults for an allotment land-use scenario with reasonable ranges to cover the uncertainty. The distributions used are set out in Table 9. Table 9. The normal distributions used for each parameter in the uncertainty analysis (case study 2). CLEA parameters Normal distribution Mean Std dev. Source of information Benzo[a]pyrene concentration (mg kg -1 dw) 20 3 Site investigation report Age group Age group Age group EA (2009f) Age group Age group Inhalation rate (m 3 day -1 ) Age group Age group Age group Age group Age group Soil and dust ingestion (g day -1 ) Body weight (kg) Jeffries (2009) Max exposed skin fraction Exposure frequency [all] (days year -1 ) 45 5 Occupancy period [all] (hours day -1 ) See text in section 7.1 Home-grown fraction green Home-grown fraction root Soil loading factor [all] (g g -1 dw) Soil organic matter (%) Site investigation report 7.2 Results Table 10 sets out the results from the uncertainty analysis for this case study alongside results from a plug-in approach. The part of the exposure estimate from the consumption of homegrown fruit and vegetables is the biggest contributor to total exposure. A more refined Page 17 of 17

18 analysis would have to focus on the assumptions that were made in the consumption part of CLEA. The differences between the plug-in estimate and the CLEA default estimate for the other routes are due to the reduction of exposure time in both the direct soil ingestion and the consumption of produce pathways Table 10. Uncertainty analysis results for average daily exposures in case study 2 (all estimates are benzo[a]pyrene μg kg -1 BW day -1 and given to two significant figures). Exposure route CLEA default Plug-in Uncertainty analysis estimate estimate Mean Std dev. Direct ingestion Home-grown Dermal routes Inhalation Total Benzo[a]pyrene is a product of incomplete combustion of fossil fuels. As such, it occurs widely within the environment and has been identified in ambient air, drinking water and foodstuffs. Primary sources of non-occupational exposure to benzo[a]pyrene are outdoor air, indoor air, contaminated food, drinking water and smoking. Estimates for exposure via each of these routes are discussed below. In Figure 10, we plot an approximate density function for the ADE through ingestion pathways. The ID is low for this chemical as no safe limit for intake has been determined for this chemical. Based on an evaluation of all the estimates available to the authors of the Environment Agency report (2002) and the fact that benzo[a]pyrene is a genotoxic carcinogen with no threshold, the report proposed an oral ID of 0.02 μg kg -1 BW day -1 and an inhalation ID of 7x10-5 μg kg -1 BW day -1. This would seem sensible as they used the WHO Guideline for drinking water which had used 700 ng L -1, assuming human consumption of 2 L day -1 would give an intake a day of 1.4 µg day -1, which is equivalent to 20 ng kg -1 BW day - 1. Figure 10 shows that we are almost certain that the ID will be exceeded in this scenario. Also, once again, the CLEA default estimate is on the conservative side. Clearly, the sources of potential error for estimating the true risk for benzo[a]pyrene at low levels from soil are substantial. From the occupational human studies, benzo[a]pyrene is one of many PAHs that may be contributing to the excess lung cancer risk and one can never be sure that one has fully accounted for all the confounders such as smoking which is still the major cause of lung cancer. Also, the human studies are based on inhalation in occupationally-exposed groups such as coke oven workers which are questionable as to their relevance for exposure from soil. Using animal data, where the route of exposure was dietary seems more relevant as the route is relevant and the dose of benzo[a]pyrene was known (and no other PAHs or confounders were present), makes interpretation seem easier. However, we are still left with the animal to human extrapolation for both tumour site specificity and dose-response curves, both of which will have uncertainties. More recently in human studies, researchers have been using biomarkers of exposure such as 1- hydroxypyrene for benzo[a]pyrene and other PAHs and this will lead to better estimates of exposure. This will, however, not be able to address the continued use of existing human studies of exposure to benzo[a]pyrene and other PAHs by risk assessors. It is likely that whether using animal data with quantitative risk estimation and the use of uncertainty factors, or human epidemiological studies using quantitative risk assessment and upperband values for cancer risk, most authoritative bodies have probably over-estimated the true risk at low levels of exposure. This is to be expected as most of the models are intended to be precautionary. The difficulty is in predicting how much over-estimation there is in the current guidelines and this will reflect on any ID that is derived. Page 18 of 18

19 Figure 10. Graphical representation of uncertainty about the intake of benzo[a]pyrene (case study 2). 8 Case study 3: Arsenic in a public open space Arsenic has been generally known for its ability to cause lung cancer via inhalation in workers in a number of occupations where airborne arsenic is encountered. In more recent years, there has been major concern about drinking water contaminated with arsenic at high concentrations and where skin, lung and bladder cancer excesses have occurred. Arsenic is considered to be a genotoxic carcinogen, but the mechanism of causation is unclear. Most of the risk estimates are based upon the induction of cancer and there are a number of authoritative expert groups that have attempted to derive guidance values for air and water. A risk assessment was carried out at a public open space where relatively high concentrations of arsenic had been found. In the past, the site had been used as a dumping ground for local chemical producers and as a domestic waste site. There were a number of other chemicals present at the site that stem from the chemical dumping, and these chemicals are covered in the site investigation report. Here, we focus solely on the arsenic that was present at the site. The scenario we are considering in this case study comprises the following: Property: public open space with no building, Receptor: male adult recreational user aged 16 to 75. Exposure pathways: all outdoor pathways with no homegrown produce. This scenario is different to the other case studies as there are no predefined settings for this type of application within CLEA. We used the commercial land use setting with no building to get comparable output from CLEA. Page 19 of 19

20 8.1 Parameters for the quantitative assessment We focus on the chemical concentration in the soil; the body weight, height and inhalation rate of the two adult age groups; and the exposure frequency. Also, we consider the soil and dust ingestion rate for an adult. For all of these additional parameters, values were selected that centred on the CLEA defaults with reasonable ranges to cover the uncertainty. The distributions used are set out in Table 11. Table 11. The normal distributions used for each parameter in the uncertainty analysis (case study 3). CLEA parameters Parameters of normal distribution Source of information Mean Std dev. Arsenic concentration (mg kg -1 dw) Site investigation report Outdoor exposure frequency (days year -1 ) Outdoor occupancy period (hours day -1 ) See text in section 8.1 Body weight [age group 17] (kg) Body weight [age group 18] (kg) Body height [age group 17] (m) Jeffries (2009) Body height [age group 18] (m) Inhalation rate [age group 17] (m 3 day -1 ) Inhalation rate [age group 18] (m 3 day -1 ) EA (2009f) Soil and dust ingestion rate (g day -1 ) Mean annual wind speed at 10m (m 3 s -1 ) See text in section 8.1 Ambient soil temperature (K) Results For this inorganic chemical, we have found that direct ingestion is the main contributor to total exposure (see Table 12). In fact, inhalation rates and receptor heights have hardly any impact on the exposure assessment. Figure 11 shows the probability density for the ADE through direct ingestion. Table 12. Uncertainty analysis results for average daily exposures in case study 3 (all estimates are arsenic μg kg -1 BW day -1 and given to two significant figures). Exposure route CLEA default Plug-in Uncertainty analysis estimate estimate Mean Std dev. Direct ingestion Dermal routes Inhalation Total EA (2009d) reports an MDI for arsenic of 0.07 μg kg -1 BW day -1 for a 70 kg adult through oral routes. This corresponds to the 98 th percentile of the distribution shown in Figure 11. For healthy humans who are not occupationally exposed, the most significant pathway of exposure to arsenic is through the oral intake of food and beverages. In Figure 11, we have plotted the result of using the default values from CLEA and the average values for exposure frequencies from Table 11. For comparison, in the case of air, the WHO has produced an inhalation ID of μg kg -1 BW day -1 and this is estimated to be equivalent to 1 in 100,000 excess cases of lung cancer. An oral ID has been calculated to give the same level of excess cancer risk and this is Page 20 of 20

21 calculated to be μg kg -1 BW day -1. However, this does create some problems when comparing with the UK drinking water standard of 0.3 μg kg -1 BW day -1. Therefore, the EA (2009d) have recommended that the higher UK drinking water value should be used as an ID. We find that the ID is at the very extreme of the distributed reported above, and our study has shown that the risk is probably acceptable for an adult. Of course, a different receptor or a greater exposure frequency could greatly change these results. Figure 11. Graphical representation of uncertainty about the intake of arsenic (case study 3). The ID from the Environment Agency report (2009d) is thought to be equivalent to 60 excess cases of cancer per 100,000. These risk estimates are based on lung cancer risk and to some extent this could be argued to be reasonable as both inhalation and oral routes seem to carry a risk. In their report, the Environment Agency do note that the ID is likely to be very small and point to some of the weaknesses in the risk assessment studies particularly those where the background controls are poorly understood. The toxicological uncertainties here are likely to be great. One particular source is that low-dose extrapolation is based upon arsenic being a genotoxic carcinogen and thus it is considered not to have a threshold. This may not be the case, and very low levels may carry less risk than is estimated. 9 Case study 4: Cadmium in a residential setting Cadmium is a well-known industrial toxicant and also a ubiquitous environment pollutant commonly found as a soil contaminant, particularly at sites that have had industrial uses. The uptake from the diet is around 5% for males and 10% for females, and the target tissues are the kidney and bone (tubular damage and osteoporosis respectively). In occupational settings where inhalation is the primary route of exposure, it is also considered to be a lung carcinogen. Cadmium is retained in the body (particularly in the kidney and liver) and this is due to binding to a specific binding protein called metallothionein. It has a long half-life in the body that is typically quoted as 14 years. EA (2009c) considers a number of risk estimates from expert groups and authoritative bodies and notes that most of these address early changes to the kidney tubules as measured by increased loss of low molecular weight proteins in the urine. Page 21 of 21