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Quantitative-spatial assessment of the risks associated with high Pb loads in soils around Lavrio, Greece

A. Korrea *, S. Durucanb, A. Koutroumani c

Environmental and Mining Engineering Research Group, Department of Environmental Science and Technology, Imperial College of Science, Technology and Medicine, Royal School of Mines, Prince Consort Road, London SW7 2BP, UK

Abstract

The advantages of quantitative environmental risk assessment techniques over the more

commonly used qualitative approach is widely accepted. Yet, correct implementation of

quantitative risk assessment is a difficult task, given the present state of understanding of

the environmental processes. One important parameter related to the level of risk is the

extent and geographic spread of pollutants. Geographic Information Systems (GIS) provide

a very powerful and highly flexible tool that increases the sophistication of the risk

assessment methodology. Through spatial representation, the estimated risk becomes more

comprehensive, thus facilitating the decision making process. In addition, valuable

qualitative information can be incorporated into the risk assessment procedure with the help

of GIS. This paper illustrates a methodology which incorporates a probabilistic risk

assessment model within a GIS. The case study utilised to illustrate the methodology is a

large industrial area around a number of decommissioned minerals production and

processing sites with known high heavy metal loads at Lavrio, Greece. The spatial

distribution of Pb concentration in soils was derived from 425 soil samples collected over a

total area of 120 km2.

A risk assessment model was constructed to simulate and assess the risk associated with

high Pb loads in soils in the study area. The methodology consists of a typical exposure

assessment model, constructed for adult and child populations. The Pb exposure for both

populations is compared with relevant Reference Dose levels providing hazard quotients.

The results of the quantitative risk assessment study are analysed and presented in the form

of GIS maps covering the study area.

a Fax: 44(0)20 7594 47354. E-mail: a.korre@ic.ac.uk * Corresponding Author b Fax: 44(0)20 7594 47354. E-mail: s.durucan@ic.ac.uk c Fax: 44(0)20 7594 47354. E-mail: akoutr@yahoo.com

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1. Introduction

An increasing number of environmental concerns stem from the problem of land

contamination, the most important being its effects on human health. Effective interaction

between science, policy and public demand accelerates the development in this field. Risk

assessment defines the balance between these forces.

While risk may be defined as a combination of the consequence of a negative effect and the

probability of that effect to occur (Vegter and Ferguson, 1998), risk assessment is the

systematic process for identifying, describing, analysing and quantifying the risk associated

with hazardous substances, processes, activities or events. In the field of contaminated land

research, risk assessment has so far been used mainly for comparative and priority setting

purposes. Within the context of comparative risk analysis, risk is used as an indicator, not as

an absolute quantitative measure describing the environmental or human health impact of

soil and groundwater contamination. It is often argued that, in contaminated soils, the

measurement of the adverse effects can easily be performed. However, due to the difficulties

of performing experiments and because of the need for the prediction of future exposure,

this is not always easily achieved (Ferguson et al., 1998). In order to generalise the risk

assessment procedure, Covello and Merkhofer (1993) have proposed an integrated

methodology which consists of 4 distinct steps as follows:

release assessment,

exposure assessment,

consequence assessment, and

risk estimation.

This methodology, as used by many others before (NCR, 1983; 1994; USEPA, 1995; Petts et

al., 1997), was adopted as the basis for risk assessment model development in the study

reported in this paper.

Characterisation of uncertainties in risk assessment is a well-researched area, particularly

through the use of probabilistic uncertainty analysis for the release and exposure factors.

However, uncertainty analysis in consequence modelling has not equally advanced yet

(Suter, 1993; Finley and Paustenbach, 1994; USEPA, 1995; 1997; Ferguson et al., 1998). The

need to incorporate the uncertainty in the risk estimation process has given the incentive for

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many researchers to use a more complex probabilistic risk assessment methodology, as

opposed to the simpler deterministic risk assessment approach (Moore and Elliot, 1996;

Richardson, 1996; Covello and Merkhofer, 1993).

The work presented in this paper illustrates how a spatial release assessment model

(geostatistical analysis) coupled with a probabilistic exposure assessment model (Monte-

Carlo approach) can be used to quantify risk from Pb exposure in soils within a geographic

database environment (GIS).

2. Risk assessment methodology

In risk assessment for contaminated land, the release assessment step involves the

identification and monitoring of the source, and the use of statistical analysis and modelling

techniques to quantify the sources of risk. In their previous research, the authors have

developed a methodology incorporating statistical, geostatistical and spatial analysis tools to

identify and quantify the sources of soil contamination (Korre, 1997; Korre and Durucan

1999).

The exposure assessment process entails the description of the exposure’s characteristics,

identification of the exposure routes, and description of the exposed population and the

analysis of all the critical variables of the exposure scenario. The exposure assessment model

described below was constructed so as to fulfil these requirements. In the model, the

pathways of the contaminants to humans were restricted to exposure to total Pb contained

in the soil. The default values of exposure frequency, exposure duration and human body

characteristics, as suggested by Petts et al. (1997), were used for the pathways under

examination. Currently, the exposure model does not incorporate spatially referenced

population data, therefore, the spatial referencing of the risk estimates were built upon the

spatial distribution of total Pb concentration of soils established at the release assessment

stage.

In general, the exposure routes that are related to soil exposure are direct soil ingestion and

dermal exposure to soil. It is known from literature that dermal absorption is considered

significant in the case of organic substances and organometallic compounds, but is

negligible in the case of heavy metals (Veerkamp, 1994). Therefore, only the ingestion

pathway was investigated. Equation 1 was used to calculate the Chronic Daily Intake (CDI)

of Pb deriving from the pathway of direct ingestion of contaminated soil,

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Chronic Daily Intake

ATBW

EDEFFICFIRCS

(1)

where CS is the Pb concentration in soil (mg/kg), IR is the ingestion rate of soil from all

sources (mg/day), CF is a conversion factor (10-6 kg/mg), FI is the fraction ingested from the

site as a fraction of the total from all sources (in range 0.0 – 1.0), EF is the exposure frequency

(days/yr), ED is the exposure duration (yrs), BW is the body weight (kg) and AT is

averaging time (days). For non-cancer risks AT = ED * 365.

The above equation takes into consideration the bioavailability of the heavy metal. For the

current study, the fraction of Pb absorbed into the blood stream after ingestion was

considered maximum and was set to one. This represents a ‘worst-case’ scenario, which

increases the influence of the pathway. In a previous study of the bioavailability of Pb via

the ingestion pathway Veerkamp (1994) used 0.3 for the bioavailable fraction of Pb,

however, the value commonly used in commercial risk assessment models is one.

Due to the uncertainty inherent in the exposure model parameters (body weight, ingestion

rate, exposure frequency, etc.) used for the target groups, a stochastic approach was

followed for the estimation of exposure in the risk assessment model. The authors have

generated probability distributions for each exposure model parameter and have performed

a Monte Carlo-type random sampling technique to estimate the mean Chronic Daily Intake

for each estimation.

The U.S. EPA generic Reference Dose (RfD) is a commonly used estimate of exposure for the

human population, including sensitive sub-populations, that is likely to be without an

appreciable risk of deleterious effects during lifetime (IRIS, 1988; Petts et al., 1997). Several

RfD values for Pb exist in the literature. At the consequence assessment step, the authors

have utilised both the U.S. EPA RfD value of 0.1 mg kg-1 day-1 (Petts et al., 1997) and the Aid

for Evaluating the Redevelopment of Industrial Sites (AERIS) RfD value of 0.0035 mg kg-1

day-1 (AERIS, 1991) for comparing the estimates of exposure calculated in the exposure

assessment step. The first RfD is a generic reference level (employed for different routes of

exposure) which has the maximum value found in the literature, while the second one is the

smallest oral exposure RfD found in the literature. No specific RfD values are available for

children, therefore, the same values were used to evaluate child exposure.

In this study, the carcinogenic effects of exposure to Pb was not considered due to the lack of

quantified carcinogenic effects and the absence of comparative measures for Pb. The U.S.

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EPA Integrated Risk Information System (IRIS) database (IRIS, 1988) suggests that the

human evidence available thus far is inadequate to refute or demonstrate any potential

carcinogenicity for humans from Pb exposure. According to the same source, quantifying

cancer risk due to Pb involves many uncertainties some of which may be unique to Pb. Age,

health, nutritional state, body burden, and exposure duration affect the rate of absorption,

release, and excretion of Pb. In addition, current knowledge of Pb pharmacokinetics

indicates that an estimate derived by standard procedures would not truly describe the

potential risk.

The results of the release, exposure and consequence assessment steps were integrated to

provide a quantitative estimate of the likelihood of risk. The exposure model output for each

of the estimation points was a probability distribution for the Chronic Daily Intake. For the

risk estimation step, the exposure estimates were compared with the RfD, providing a

number of exceeding counts in the form of a Hazard Quotient. The number of counts of

these exposure distributions exceeding the RfD chosen as reference in the consequence

assessment step was used to provide a quantitative measure of risk. Finally, in order to

address the uncertainty entailed in the approach, the probability distribution for the CDI

was fit to a standard probability distribution and the Confidence Levels around the mean of

the distributions generated were calculated and compared with the selected RfD value.

After the completion of the risk assessment stage, a sensitivity test on the model parameters

was carried out. The single parameter perturbation technique was utilised to examine the

sensitivity of the Chronic Daily Intake exposure model to variations in 7 of the model

parameters. For this purpose, the parameter in question was kept constant at its minimum

value for 10 respective runs of the model and the mean CDI was calculated for the recorded

outcomes. The same was repeated for the maximum value of the same parameter and

equation 2 was used to calculate the Sensitivity Index.

max

min1Indexy SensitivitCDI

CDI (2)

The closer to zero the calculated SI is, the smaller the correlation between the input

parameter under investigation and the resultant Chronic Daily Intake. If the sensitivity

index of a parameter was found to be close to one, the parameter was labelled as sensitive

and its variance was expected to have a significant effect on the resultant CDI.

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The spatial referencing of the Pb distribution obtained at the release assessment stage

provided the basis for the calculation of spatially referenced quantitative risk estimates to

humans from Pb consumption due to direct ingestion of soil. Once the risk estimates are

introduced into a spatial database such as a GIS, the additional quantitative and qualitative

geographic information can be used for further analysis and interpretation of the results.

3. Geological, mining and environmental background to the Lavrio old mine site, Greece

The Lavrio old mine site is situated at the SE of the Attiki peninsula, about 60 km from

Athens. The region is hilly and dry. A fault running along the Legraina valley northwards

divides the area into two sections. The eastern region, where the small dispersed ore bodies

are found, is commonly known as the metalliferous Lavrio.

The silver bearing structures around Lavrio were known and exploited since the 6th century

BC. Many million tons of rich Pb and Ag ore were mined, resulting in the exhaustion of a

large proportion of the rich deposits. By the end of the Peloponnesian War in 389 BC, only

the large reserves of poor and deeper ore deposits were left unexploited. Brief flickerings of

mining activity continued until the first century AD. After that Lavrio lapsed into inactivity

and oblivion, to revive again only during the last century. Enormous heaps of waste from

mining and metallurgical work carried out over many centuries and several million tons of

tailings and slag, some recovered from the beaches of Lavrio, were sufficiently rich in metals

that the latter day miners re-processed them for many years. 1864 marked the beginning of

the revival of the mines, and an era of high profits for the French company Serpieri-Roux de

Fraissinet and its successors (Marinos and Petrascheck, 1956). All mining activities in the

area ceased in the mid-sixties and the ore processing at the plant north of the city of Lavrio

stopped in 1986.

The primary ore comprises of 2 groups, the Fe-Mn ore and the mixed sulphides which

frequently exist together or alternate. The mixed sulphide minerals are pyrite, sphalerite and

Ag-bearing galena. The Fe-Mn formation consists mainly of manganiferous ankerite and

rhodochrosite, with barite, fluorspar and quartz, subsequently oxidised into limonite,

pyrolousite etc. There are also smaller proportions of other related minerals with As, Bi, Cu,

Ni and Co.

The widely dispersed ore bodies and the sporadic mining all over Lavrio district induced

widely spread high heavy metal loads in the area. The port and the city of Lavrio, situated at

the east coast of the peninsula, has been developed around and on the mining and

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processing waste materials (Fig.1). Epidemiological studies in the area have shown a high

blood Pb burden in school age children which was associated with mental retardation,

slower response rate and increased sickliness (Lavrio Health Centre, 1989; Makropoulos et

al., 1991; 1992; Eikmann et al., 1991; Stavrakis et al., 1994; Kafourou et al., 1997).

Fig. 1. Urban development around the old mine tailings dumps in Lavrio

Previous research on soil pollution assessment and remediation in the Lavrio area include

the work carried out by Korre and Durucan (1995, 1999), Demetriades et al. (1997), Korre

(1997, 1999a, b), Durucan and Korre (1997) and the contaminated soil remediation research

by Kontopoulos et al. (1995a, b, 1996).

Recently, a deterministic risk assessment study based on the source-pathway-target

principle aimed to identify targets for rehabilitation and assess environmental risks around

the Pb smelter north of the city of Lavrio (Kontopoulos et al., 1998). After determining the

sources of contamination -i.e. flotation tailings dam, pyritic tailings dump, smelter-off gas

tunnel, contaminated soils, stored chemicals- the contaminants’ concentrations were

estimated and compared to the Canadian standards for industrial areas. Subsequently,

pathways to human targets were indicated. The outcome of this study was an assessment of

the probability and magnitude of the consequences, and hence of the risk to humans using

simpler linguistic descriptors (high, medium, low, negligible). The magnitude of harm from

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the predicted exposure to the target was further assessed using similar linguistic descriptors

(severe, moderate, mild, negligible). Based on the estimation of the probability and

magnitude, the risk was rated from high to near zero. In another study, dealing with hazard

and exposure assessment in the area, the IEUBK and HESP exposure assessment models

were applied to the Lavrio urban area (Tristan et al., 2000).

4. Risk assessment related to high lead levels in soils around the Lavrio old mine site

The risk assessment study presented in this paper covers an area of approximately 120 km2

at the south-eastern corner of Lavreotiki peninsula (Fig. 2). The natural setting and the

current and historical mining activities at Lavrio indicate a complex exposure scenario, with

multiple-sources and multiple pathways. Due to the significance of Pb and its known

negative health effects in the area, the authors have selected the total Pb concentration in soil

for the initial spatial-quantitative risk assessment study presented here.

Some 425 samples of 1kg average weight were collected from the upper 4-5 cm of soil using

a rectangular sampling pattern (400500 m). The total soil metal concentrations were

analysed by ICP – AES after digestion with HNO3 and HClO4 acid at Imperial College.

Chemical analysis yielded the concentration of 24 elements in the soil samples, including Pb

(Korre, 1999a, b).

Statistical and spatial analysis tools were utilised in order to combine the quantitative

information obtained from the chemical analysis of the soil samples with the site-specific

qualitative information. The primary analysis focused on identifying the levels and

correlation between the elements determined. Through geostatistical analysis, the spatial

distribution of each element in the study area was estimated (Korre, 1997). This provided a

new grid of estimated values for each element, along with estimation errors and the co-

ordinates of each data point, in a 250250 m grid. Geographical data (e.g. elevation, roads,

housing, land use) and the geology of the area were entered into a GIS database along with

the heavy metal load estimates. Simultaneous site surveys carried out during the sampling

process provided site-specific information relating to the type of vegetation and human

activities near the sampling points.

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Fig.2. Risk assessment study area around Lavrio.

Further analysis of the original data aimed at understanding the physical processes driving

the pollution in the area. Dominant processes which controlled the redistribution of

elements in the area were explored using principal component and factor analysis. In order

to distinguish and quantify the multiple coexisting sources of pollution in the area, a

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methodology utilising canonical correlation analysis and geostatistical analysis was

developed (Korre, 1999a; b; Korre and Durucan, 1999). Canonical correlation statistical

analysis enabled the authors to distinguish the natural background from the human induced

soil contamination. Finally, the coupling of statistical analysis tools with geostatistics and

GIS tools allowed the spatial assessment of both soil contamination and its sources (Korre,

1999b).

The geographic database held in GIS served as the host environment for additional spatial

operations such as spatial referencing between quantitative and qualitative information

(geology, topographic relief, human activities) and for the graphical representation of the

results. The GIS system that was used to form the spatial database for the geographical

interpretation of the heavy metal levels was ARC/INFO (Environmental Systems Research

Institute Inc., 1990).

It is recognised that the natural ore occurrence in the area induces elevated levels of heavy

metals, including Pb. However, the contribution of human activities over and above the

background values is undoubted. A GIS map of Pb distribution in the area (Fig.3) illustrates

that areas with an estimated concentration of 500 ppm and below are very limited. The

measured maximum values were well in excess of normal levels -approximately 70 ppm for

non-polluted soils and non-mineralised parent rocks in the area. The maximum

concentrations were found to correlate well with the spread of mining and processing

activities. It was also possible to identify the bays where waste material had been disposed

in the sea (Korre, 1997).

The ordinary kriging estimates of Pb concentration in the soil were utilised as the basis for

the release assessment step. The Chronic Daily Intake of Pb deriving from the pathway of

direct ingestion of contaminated soil was estimated for two population groups using

equation 1. These were male adults who, due to their profession, experience maximum

exposure to soil (e.g. gardeners, farmers) and children of 1-6 years old. The main exposure

parameters and their mean values used for each target group are presented in Table 1.

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Fig. 3. Estimated Pb levels in the study area.

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Table 1. The mean values and probability distributions used for the main parameters in the

exposure model.

Exposure Assessment Parameter Adult value Child value Distribution

Body Weight (kg) 70 16 Normal

Ingestion Rate (mg/day) 100 200 Normal

Exposure Duration (yrs) 30 10 Normal

Exposure Frequency (days/yr) 300 Normal

Fraction Ingested 0.0 – 1.0 Uniform

Conversion Factor (kg/mg) 10-6 Uniform

The mean values listed in Table 1 were used for each parameter and the probability

distributions obtained from 1,000 trials were generated performing a Monte Carlo-type

random sampling so as to cater for uncertainty inherent in the model parameters. In order to

remain consistent with the choice of RfD in the consequence assessment step, the mean

values utilised for the model parameters during the exposure assessment stage were those

suggested by the U.S. EPA (Petts et al., 1997) except for FI and EF. In the case of fraction

ingested (FI), the whole range of values (0.0 to 1.0) was used. Also, instead of using the U.S.

EPA ‘reasonably maximum’ exposure frequency (EF) of 350 days per year, a normal

distribution with a mean of 300 was judged to be more realistic. The choice of statistical

distribution used to randomly sample each parameter was based on the recommendations of

previous researchers (Finley et al., 1994). The standard deviations utilised for generating the

distributions of all other parameters (apart from the uniform distribution generated for the

Conversion Factor) was 10% of the respective mean. The fit of the resulting 1000 values to

the desired distribution was tested before use in the exposure model.

Random combinations of 1000 trials generated for each parameter were submitted to the

direct ingestion model together with one concentration estimate for each estimation point in

a regular 250250 metres grid. The 1000 values of CDI calculated for each estimation point

were then used to describe statistically the exposure to Pb in the study area for the adult and

child populations. The x-y co-ordinates of the Pb concentration values provided the spatial

reference for the CDI statistical distributions.

At this stage of the exposure assessment procedure, the CDI estimates for each of the target

groups were compared with the RfD values of 0.1 mg kg-1 day-1 and 0.0035 mg kg-1 day-1 by

initially calculating the number of exceeding counts out of the 1000 for each estimation

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point. The outcome for each estimation point was then introduced to the GIS database. The

spatial representation of the results yielded a comprehensive picture of the risk to human

health, from direct ingestion of soil, encountered by the population under investigation.

To provide a measure of the uncertainty entailed in the model, confidence levels around the

mean CDI for each estimation point needed to be established in order to provide a

confidence level for the exceedence of the mean above the selected RfD. For this purpose the

CDI distributions were transformed to match a standard statistical distribution and were

tested for 3 different levels of exposure -high, medium and low. In order to achieve

representativeness, the exposure distributions were regenerated after 10,000 trials. After a

series of transformations (i.e. normal, lognormal, beta) the exposure distributions generated

were found to resemble best the truncated normal distribution. This was consistent with the

fact that the estimated distribution approximated closely the experimental exposure

distribution for high levels of exposure. The 95%, 90% and 80% confidence levels around the

CDI means were calculated for each point and the corresponding study area was classified

in one of 4 classes indicating the significance of the corresponding risk.

Finally, the model parameters were tested by applying the single perturbation sensitivity

analysis technique to evaluate their effect on the model outcome. The model results were

recorded for 10 runs each, using the minimum and maximum parameter values in question

and the relevant sensitivity index calculated using equation 2. Table 2 presents the

minimum, maximum and default mean values of the parameters considered, as well as the

sensitivity indices calculated.

The results have revealed that the most sensitive parameters were the fraction ingested (FI)

and the concentration (CS). The wide ranges considered for the fraction ingested (FI) (0.0-

1.0) and for the Pb concentrations (CS) (125-33,503 ppm) were responsible for the high

sensitivity of these parameters. The ingestion rate (IR), exposure duration (ED), exposure

frequency (EF) and the conversion factor (CF) represented a medium level of sensitivity, and

their increase resulted in an increase in the resultant CDI. On the other hand, body weight

(BW) appeared to be a rather insensitive parameter, increase of which caused a decrease in

the value of the resultant CDI.

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Table 2. Exposure parameters considered in the sensitivity analysis and the sensitivity

indices calculated.

Parameter Lower Limit Default Value (mean) Upper Limit Sensitivity Index

CS (mg/kg) 125 2994.433a 33503.6 0.969

BW (kg) 50.6 70 90 0.166

ED (yrs) 19.5 30 38.6 0.578

EF (days/year) 217 300 384.9 0.588

IR (mg/day) 65 100 128.7 0.496

FI 0.0002 0.0 - 1.0 0.968 0.999

CF (kg/mg) 710-7 110-6 1.2810-6 0.456 a arithmetic mean as calculated from the measured concentrations for all estimation points

5. Results and discussion

The exposure to Pb calculated in the exposure assessment step for both adult and child

target groups is depicted in Fig. 4 and Fig. 5 respectively. Topographical and other general

geographic characteristics of the area, valuable in appreciating the significance of the risk on

the exposed population are also provided in these maps. Further spatial data included in the

maps show the nature of mining activity in the study area.

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Fig. 4. Mean exposure to Pb for the adult population in Lavrio.

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Fig. 5. Mean exposure to Pb for the child population in Lavrio.

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The characteristic differences in the parameters of the exposure pathways for the two target

groups (i.e. body weight, ingestion rate) control the differences in the resultant exposures.

Indeed, child exposure is one to two orders of magnitude higher than adult exposure for all

the estimation points. The patterns guiding the high exposure areas are uniformly followed

for both populations, being dictated by the metal concentration patterns shown in Fig. 3. The

highest exposure means are observed around the city of Lavrio, at a wide area south of the

village Agios Konstantinos (Kamariza) and in the NW direction, following the occurrence of

ore deposits and mining activities.

The actual risk estimates for the target groups studied were calculated in the form of counts

and exceedence rates above the selected RfD values. For the adult population, the

probability of exposure to levels higher than the U.S. EPA RfD of 0.1 mg kg-1 day-1 was

found to be zero for all estimated points. Fig. 6 illustrates the spatial distribution of

exceedence counts with respect to the 0.0035 mg kg-1 day-1 AERIS RfD. The highlighted areas

of high exceedence coincide with the areas of high exposure means, as can be seen by

comparing Fig. 4 with Fig. 6.

For the child population on the other hand, there are areas of high exceedence counts above

both the 0.1 mg kg-1 day-1 and the 0.0035 mg kg-1 day-1 as shown in Fig. 7 and Fig. 8. With

respect to the AERIS RfD, alarmingly high exceedence levels are observed over a very wide

section of the peninsula. Yet, when compared with the U.S. EPA RfD value of 0.1 mg kg-1

day-1, the area of high exceedence levels is limited to the region around the city of Lavrio,

Agios Konstantinos and two relatively smaller regions to the NW and south of the

peninsula.

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Fig. 6. Exceedence counts for Pb exposure above the 0.0035 mg kg-1 day-1 RfD for the adult

population.

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Fig. 7. Exceedence counts for Pb exposure above the 0.1 mg kg-1 day-1 RfD for the child

population.

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Fig. 8. Exceedence counts for Pb exposure above the 0.0035 mg kg-1 day-1 RfD for the child

population.

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The uncertainty inherent in the risk assessment methodology selected was assessed by

determining the confidence around the mean of the exposure distributions established for

each estimation grid point. This confidence was expressed with respect to the selected RfD.

Risk ratings were attributed to different cases of the relationship between the mean and the

Reference Dose such that,

areas where the mean exposure exceeds the Reference Dose were rated 4, posing

significant risk which stems from high exposure levels;

areas where the mean is smaller than the Reference Dose but is within the 80%

confidence level from the mean were rated 3;

areas where the mean is smaller than the Reference Dose but is within the 90%

confidence level from the mean were rated 2;

areas where the mean is smaller than the Reference Dose but is within the 95%

confidence level from the mean were rated 1;

all other areas where the Reference Dose is higher than the mean and lie outside the

confidence levels were rated 0, corresponding to low risk levels.

Fig. 9 and Fig. 10 illustrate examples of these ratings for the adult and child populations in

relation to the 0.0035 mg kg-1 day-1 AERIS RfD and 0.1 mg kg-1 day-1 U.S. EPA RfD

respectively.

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Fig. 9 Exceedence rates for Pb exposure above the 0.0035 mg kg-1 day-1 RfD for the adult

population.

23

Fig. 10 Exceedence rates for Pb exposure above the 0.1 mg kg-1 day-1 RfD for the child

population.

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The confidence levels around the mean did not have a significant effect on the final outcome

for any of the adult or child rate calculations. This can be seen by comparing Fig. 6 with Fig.

9 and Fig. 7 with Fig. 10. Indeed, for the majority of the estimated points, the selected RfD

was either much lower than the mean of the distribution, or was significantly higher. The

areas where the Reference Dose was found to be within the 80% Confidence Level from the

mean were very limited.

Apart from giving a comprehensive picture of the harmful exposure’s spatial distribution in

the area, GIS maps provided the means to consider qualitative spatial information in the

assessment of the results. Indeed, the comparison of the risk maps with the road network

and the dwellings’ map of Fig. 2 shows that wide areas of high estimated RfD exceedence

are not easily accessed or are situated far from villages and are mostly covered by forest

land. Yet, concern for the human health is raised for the areas in close proximity to villages

such as Agios Konstantinos (Kamariza) and for the town of Lavrio, as well as for any

households randomly located around the mining and smelting areas.

It is clear that high exceedence and consequently the high risk probability concurs with high

heavy metal concentrations. The pattern of exceedence was the same for all 3 cases

examined. Yet, the effect of the selection of the Reference Dose was significant. When a low

RfD was chosen, the probability of high exposure was significant for both target

populations. The resulting child exposure picture, in particular, appeared to be exaggerated.

On the other hand, when a high RfD was chosen, the probabilities of high exposure were

smaller. In this case, the adult population was not found to be in danger of experiencing

exceeding levels of exposure, while child exposure exceeded the U.S. EPA RfD only in

confined areas. The variability in the risk estimates, based on the selected RfD, highlights the

need for a widely proven and accepted Reference Dose for Pb.

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