1-s2.0-s0883292703002257-main

Hazard assessment on arsenic and lead in soils of Castromilgold mining area, Portugal

Eduardo Ferreira da Silvaa,*, Chaosheng Zhangb, Luıs Serrano Pintoc,Carla Patinhaa, Paula Reisa

aDepartamento de Geociencias, Universidade de Aveiro, Campus de Santiago 3810-193 Aveiro, PortugalbDepartment of Geography and Environmental Change Institute, National University of Ireland, Galway, Ireland

cConnary Minerals, Lugar de Castromil, 4585 Paredes, Portugal

Received 31 March 2003; accepted 27 October 2003

Editorial handling by C. Reimann

Abstract

Castromil is one of the Au mining areas in Portugal that has been abandoned since 1940. Due to the lack of

regulations and environmental education, Castromil is now a residential area suffering from the considerable con-sequences of poorly regulated mining activities; tailings, shafts and adits are present. Geochemical data related toenvironmental studies in old mining areas frequently show extremely high values and very skewed distributions that

need to be properly addressed. Agricultural soils from this region have high concentrations of As and Pb. In this study,the Box–Cox transformation and geostatistics were applied to study heavy element (As and Pb) concentrations in soilsin order to characterize the hazard posed by them in the area. This provides a decision support tool to define the areas

where remedial action is needed in light of the risks to humans and ecosystems and for contaminant migration. Theresults discussed here take into account the hazard-based standards for soils as target and intervention values.# 2003 Elsevier Ltd. All rights reserved.

1. Introduction

Mining is the primary source of heavy metals and canbe responsible for significant impacts on the surround-

ing environments. Mining and milling operations, i.e.grinding, ore concentration and disposal of tailings,provide obvious sources of contamination (Adriano,

1986). In general, the presence of abandoned minesnegatively influences public perceptions of the industry.Abandoned mines are mining sites where mine produc-

tion ceased without any rehabilitation being completedor implemented (van Zyl et al., 2002). It was only duringthe second half of the 20th century that many countries

with a long mining history, especially developed ones,realised the importance of the issue of abandonedmines. The magnitude of the impact of past mining isoften considerable because there are many thousands of

mining sites that continue to pose a real or potentialthreat to human safety and health and/or environmentaldamage.Elements associated with Au–Ag mines, including As,

Cd, Cu, Pb and Zn can be dispersed due to erosion andchemical weathering of tailings. The extent and degreeof trace element contamination around the mines varies,

depending upon the chemical characteristics of, and theminerals occurring in, the tailings. For example, tailingscontaining large quantities of sulphide minerals could

influence neighbouring residential/agricultural landsand watercourses. In contrast, Au mineralizationoccurring in a quartz vein, without sulphides produces

relatively little trace metal contamination (Chon et al.,1995).The rehabilitation of abandoned mining sites is a cri-

tical environmental issue. Soil contamination by trace

elements is a potential problem where residential andagricultural areas are established. The first step in therehabilitation process is to gather existing information

0883-2927/$ - see front matter # 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/j.apgeochem.2003.10.010

Applied Geochemistry 19 (2004) 887–898

www.elsevier.com/locate/apgeochem

* Corresponding author. Fax: +351-234-370605.

E-mail address: [email protected] (E. Ferreira da Silva).

http://www.sciencedirect.com



mailto:[email protected]

about the waste material and the area. This character-ization of waste plays a significant role in determiningwhether or not a site assessment is necessary. A siteassessment must be performed when the characteriza-

tion of wastes indicates that they contain hazardoussubstances which may contaminate the soil, ground-water and surface water, or where they may pose a risk

to human health through direct contact, ingestion orinhalation.Numerous studies have been undertaken on trace

element contamination of soils, plants, waters and sedi-ments as a result of industrial and mining activities, invarious countries (Thornton 1980; Fuge et al., 1989;

Merrington and Alloway, 1994; Pestana et al., 1997).Portugal has a long mining tradition and since Romantimes Au has been one of the raw materials intensivelyexploited in northern Portugal. Castromil mine was one

such Au–Ag mining area in Portugal. However, miningceased in the 1940s and the site was abandoned. Themining waste material, including bare tailings, has been

left without full environmental treatment, and someadits and shafts remain unfenced. Today, the commu-nity of Castromil is sustained by agriculture and light

industry, timber and furniture making are the mainsources of employment. The area, located in the RiverSousa valley, is agriculturally productive, the main pro-

ducts being maize, rye, vegetables and fruits, vinyardsare also important. Maize production utilises more than80% of the area (Pinto, 2001). The old mine area, whichhas potential for redevelopment can be designated a

brownfield area. Previous geochemical surveys carriedout in the area found high contents of As and Pb in thesoils indicating the risk of soil contamination by heavy

elements (Pinto et al., 2002).There are many questions regarding this possible sce-

nario. Generally, one seeks to find out when the con-

centration of a specific contaminant exceeds establishedstandards, where the boundary between contaminatedand uncontaminated areas lies, what the level ofconfidence regarding those boundaries is, how much

contaminant (total mass) is present and what needs tobe removed.Legal regulation of heavy metal content is an impor-

tant issue in many European countries. Despite severalcountries having laws establishing the permitted con-centration of heavy metals in soils and a Soil Remedia-

tion Guideline they still do not exit in Portugal. Due tothis lack of information the standards proposed bySwartjes (1999) were used for this work.

The aim of this study is to start with an orientationinvestigation, in which the first step is an extendedsurvey of the site and surrounding area, possibly leadingto a hypothesis on the spatial variability of the

contaminants.In this study geostatistical methods for spatial data

interpolation are used to assess the contamination in

unsampled areas by creating exposure maps showing theprobability of exceeding the risk-based standards (targetand intervention values) for As and Pb proposed bySwartjes (1999). This probability mapping can provide

important information for decision-making regardingthe evaluation of contaminated locations such as urbanand agricultural areas.

2. Geology and mineralization

The Au–Ag Castromil mining area is located in NWPortugal, about 23 km east of the city of Porto in

Sobreira (Paredes municipality). The Sobreira parish isdensely populated, with the number of inhabitantsincreasing from 17,662 to 73,100 between 1964 and 1991(Fig. 1).

The mineralization of Castromil, situated in the Cen-tro-Iberian zone, is associated with a Hercynian granitecomplex that intrudes meta-sedimentary turbiditic

rocks—shale, siltstone, greywacke and schists (Medeiroset al., 1980). Fig. 1 shows a simplified geological map ofthe area. The geology of the mining area is mainly

composed of coarse-grained two-mica porphyritic gran-ite (essentially biotitic), an aplite vein, schist, quartzveins and alluvium.

The mineralization of the Au–Ag Castromil mine ispolyphasic, polymorphic and was focussed by the con-junction of several structural, hydrothermal and mag-matic phenomena. The most abundant sulphides are

pyrite and arsenopyrite. The Au is associated with asilicification process and the presence of sulphides,occurring in microscopic grains on the surface, within

micro-fractures existing in pyrite grains, or enclosed insecondary oxides.Gold and Ag are very fine grained and essentially occur

in pyrite and, very rarely, in scorodite. According toCouto (1993), 3 mineralization stages have been identified:

I) Pyrite is dominant, followed by arsenopyrite (I) and

native bismuth. Gold could be in the structure ofarsenopyrite and/or pyrite.II) Zinciferous stage: sphalerite+chalcopyrite (spha-

lerite is rare and occurs included in pyrite; chalco-pyrite occurs included in pyrite and is sometimesassociated with pyrrhotite.)

III) Remobilisation stage: arsenopyrite (II)+ galena+gold

Arsenopyrite II is finely crystallised, filling the micro-fractures of arsenopyrite I. Galena is later and occurs inthe micro-fractures of pyrite and arsenopyrite. Goldoccurs associated with galena at the contact between

arsenopyrite and pyrite. Covelite, goethite, scoroditeand carbonates and sulphates are present due to recentsupergene processes.

888 E. Ferreira da Silva et al. / Applied Geochemistry 19 (2004) 887–898

The soil development of the area is, according to the

FAO/UNESCO (1974) classification, characterized bythe presence of a cambisol (Cardoso et al., 1971), whichvaries in depth from 10 to 60 cm. A temperate climateprevails in the area, with an average annual precipita-

tion of 1600 mm (Anuario dos Servicos Hidraulicos,1981).

3. Methods

3.1. Sampling and sample preparation

A total of 106 soil samples (0–15 cm depth) were col-

lected in the area using a square 100�100 m grid (Fig. 2).The lines have a N40�E orientation, perpendicular tothe NW–SE trend of the mineralization. The total areasampled was 1.4 km2 corresponding to a sampling

density of 83 samples/km2, which covered not only thearea affected by the old mining activities, but also thesurrounding agricultural and urban areas.

Soil samples were oven-dried at a constant temper-

ature of 40 �C. After disaggregation, the samples weresieved to pass through a<177 mm screen, and the finefractions were preserved for later chemical analysis.The use of the <177 mm fraction in soil environ-

mental studies can cause some discussion about theselected criteria. It is true that most soil scientists andenvironmental researchers often prefer to analyse a

coarse fraction (<2 mm) which includes more feldspar,quartz and rock fragments (chemical signature of theunderlying bedrock). However, metals in soils are

mainly associated with the fine size fraction, bound withgreater or less strength to clay minerals, organic matterand Fe /Mn hydroxide/oxide.

In fact, the important questions are what minerals orsize fractions in bulk samples concentrate toxic elementsand how bioavailable are they to an ecosystem.Siegel (2002) considers that the true threat to an eco-

system may be obscured by chemical analysis of the totalsoil samples and, that the chemical analysis of the fine-size soil matter, undiluted by coarser sizes, can better

Fig. 1. Study area and simplified geology map.

E. Ferreira da Silva et al. / Applied Geochemistry 19 (2004) 887–898 889

predict the potential of metals to enter an ecosystem and

pollute a food web.Despite this, it is necessary to take into consideration

that using the <177 mm fraction can overestimate whatis actually available to the plant and is non quantitative.However, a study carried out on Portuguese soils

indicates that the correlation between the metal con-

centration in the fine fraction (<177 mm) and coarse(<2 mm) fraction is very good. The spatial distributionof elements shows similar patterns in the two fractionsstudied. The concentrations are generally higher in the

fine fraction, but the difference is not very pronouncedexcept for Arenosols and Podzols (Inacio et al., 2002).Other researchers working in different environments

find similar results (Tarvainen, 1995; Navas andMachın, 2002).

3.2. Chemical analysis

The soil samples were submitted for multi-element

analysis in an accredited Canadian laboratory (ACMEAnal. ISO 9002 Accredited Lab—Canada). An amountof 0.5 g soil was leached in hot (95 �C) aqua regia (HCl–HNO3–H2O) for 1 h. After dilution to 10 ml with dis-

tilled water, the solutions were analysed by InductivelyCoupled Plasma-Emission Spectrometry (ICP-ES) for35 chemical elements including Ag, Al, As, Au, Ba, Bi,

Ca, Co, Cr, Cu, Fe, Ga, K, La, Mg, Mn, Mo, Na, Ni, P,

Pb, S, Sb, Sc, Sr, Th, Ti, U, V and Zn.A rigorous quality control program was implemented

including reagent blanks, duplicate samples, and certi-

fied international reference materials. Precision andaccuracy of the chemical analysis were better than 10%for all the analysed elements.

The bulk sample geochemistry may not reflect the realrisk to an ecosystem from potentially toxic elements.Selective extraction is necessary to clarify bioavailabilityfrom a source and such chemical procedures will be

developed in future work.

3.3. Methodology

3.3.1. Risk assessment standardsAccording to Swartjes (1999) risk based standards are

generic standards (target and intervention values) usedto assess soil quality. These standards allow soil to beclassified as clean, slightly contaminated or seriously

contaminated. Both standards are based on potentialrisks, i.e. the risk that would occur under ‘‘standardized’’conditions, and are employed independent of soil use.These are multifunctional contaminant specific stan-

dards (generic criteria). The actual risks on the site willhave to be determined as a function of soil character-istics, soil use, building characteristics and infrastructure

Fig. 2. Sampling locations superimposed on an aerial photograph.


and, ultimately, human behaviour. The ‘target values’,based on potential risks to ecosystems, are protectivelevels and intended to achieve the desired soil quality.The ‘intervention values’, based on potential risks to

humans and ecosystems, are indicative of seriously con-taminated sites that require immediate remediation(Swartjes, 1999).

The results obtained will assess soil quality as: Con-centration <Target value: clean soil (no restrictions);Concentration > Target value and <Intermediatevalue: slightly contaminated soil (no further investig-ation; minor restrictions can be imposed on soil use);Concentration > Intermediate value: (start further

investigation; if this still results in soil quality<Intervention value, restrictions can be imposed on soiluse). The intermediate value is the average of target andintervention values. The intervention value for soil is the

lowest value of human toxicological and ecotox-icological intervention values. The human toxicologicalintervention value is defined as the concentration of a

contaminant in the soil which would result in an expo-sure equal to the Maximum Permitted Risk Level forintake (MPRhuman) under standardized conditions

(potential exposure).The ecotoxicological intervention value has been

defined as the HC50 (Hazardous Concentration 50, i.e.

50% of the ecosystem threatened) (Swartjes, 1999).These risk assessment standards were used as thresholdvalues to calculate probability maps of soil contamina-tion with heavy metals and As.

3.3.2. Data treatmentThe raw data were subjected to statistical and geo-

statistical analyses. The sets of data for As and Pbwere found to be highly skewed due to the presence ofmultiple populations, possibly a natural background,

a natural ‘‘ore’’ related population and a mine activity/waste population. The Box–Cox transformation wasemployed to normalise the data, as well as to weakenthe negative effects of potential outliers.

The Gaussian assumption is often inappropriate toanalyse geochemical data. The standard geostatisticalmodel assumes data to be stationary, which requires the

mean and variance to be finite and constant in the areaunder investigation. However, in practice this assump-tion is often violated. In such circumstances one solu-

tion is to use de-trending and transformation techniques(Krivoruchko and Gribov, 2002) to condition the data.

These techniques have been used successfully by severalauthors in different scientific domains (Luis and Sousa,2000; Batista et al., 2002; Patinha, 2002; Reis et al.,2002; Patinha et al., 2003; McGrath et al., 2003). In this

paper the Box–Cox transformation is applied in orderto more closely meet the Gaussian assumption.Having explored and determined that the As and Pb

data are non-stationary, and concluded that kriginginterpolation techniques produce maps that are sub-optimal, particularly when producing prediction stan-

dard error maps the Box–Cox transformation (Box andCox, 1962; Howarth and Earle, 1979; Jobson, 1991;Zhang and Zhang, 1996; Zhang et al., 1998) has been

applied:

y ¼

xl � 1

ll 6¼ 0

ln xð Þ l ¼ 0

8><>:

ð1Þ

where y is the transformed value, and x is the value tobe transformed. For a given data set (x1, x2, . . ., xn), theparameter l is estimated based on the assumption thatthe transformed values (y1, y2, . . ., yn) are normally dis-tributed. When l=0, y becomes the logarithm of x.Geostatistics has long focused on surface modelling

of point information and assessment of error prob-ability of resulting surfaces. The implementation of geo-statistical methodologies allows the evaluation oferror in prediction surface models, to carry out statis-

tical estimation, and, most importantly, the creation ofoptimal surfaces which permit the identification of areaswhere permissible levels are exceeded.

In order to identify the extent of contamination by Asand Pb in the study area ordinary kriging was employedfor exposure mapping.

4. Results and discussion

4.1. Assessment of the soil quality

4.1.1. Summary statistics

According to Pinto et al. (2002), the most importanttrace elements related to contamination in the studyarea are As and Pb. Table 1 provides summary statistics

for these elements. The medians are much lower thanthe arithmetic means, which is consistent with the high

Table 1

The arithmetic means and ranges of As and Pb concentrations (mg/kg) in selected surface soils and pollution index (PI) values

Elements
Mean Min. Q1 Median Q3 Max. Std-Dev. Skewness
As
820 31 140 273 958 6909 1269 2.8
Pb
403 5 88 173 394 6295 776 5.2

skewness, showing there are some very high values(outliers).Fig. 3 displays the normal Q–Q plots for As and Pb

variables. These graphs plot the cumulative distribution

function of the raw data against the empirical cumula-tive distribution frequency of Gaussian data sets. Thenormal Q–Q plots shows that both variables do not

follow a normal distribution, and the distributions arehighly positively skewed and also there are some poten-tial outlying values in both datasets.

4.1.2. Data transformationOutliers and high skewness can endanger the spatial

continuity of the variogram function. In order to pro-vide stable variograms and kriging results a transfor-mation was considered as essential. In this study, theBox–Cox transformation was applied to the Pb and As

data sets. The estimated values for the l parameters are�0.146875 for As and �0.075 for Pb. This means thatPb is close to a lognormal distribution, but As is far

away from it and the Box–Cox transformation is neces-sary for As. Although, the log-transformation could beapplied for Pb, but the Box–Cox method provides a

better result. Fig. 4 shows the Q–Q plots for the trans-formed data sets.From Fig. 4 it is clearly illustrated that the data

transformation was effective in causing the distributionsto approach Gaussian.

4.1.3. Spatial structure analysisIn order to include the spatial structure, directional

variograms of raw and transformed data of variables Asand Pb were computed for the main directions of the

sampling grid N50�W, N5�W, N40�E and N85� E,respectively. A theoretical model was fitted to theexperimental variograms of each variable. Table 2

shows the parameters of the spherical models deducedfrom the experimental variograms. For both raw andtransformed data variables, the ellipse of the spatial

structure has a major axis with an orientation of N50�W(parallel to the mineralized aplite).Figs. 5 and 6 display the experimental variograms

for As and Pb (raw and transformed data), computedfor the directions of N50�W and N40�E, respectively.The spatial model shows two structures of continuity: anugget effect (C0) and an anisotropic spherical structure

(C1) representing the variability of regional events. Themodel parameters were used for the following spatialinterpolation.

4.1.4. Probability maps and uncertaintyA key component of the site evaluation process is the

identification of current and future property use. Inthe Castromil area, the current and the future resource usefor soil (property) is residential, industrial or restricted

commercial, and agricultural. The soil exposure path-ways, by which human and ecological receptors may be

Fig. 3. Normal Q–Q plots: (a) Raw data for As; (b) Raw data for Pb.

Fig. 4. Normal Q–Q plots: (a) As after Box–Cox transformation; (b) Pb after Box–Cox transformation.


at risk, are inhalation, dermal, ingestion and the terres-trial food chain for ecological receptors.Depending on the pattern of contamination (location

and magnitude of hot spots) and present and future site

use, it is necessary to determine if the site is suitable forthe proposed use or requires a more detailed definitionof high exposure areas (location of potential risk for

human or environmental receptors and release mechan-isms of contaminant).The model of spatial variability deduced from the

experimental variograms was used to estimate As and

Pb contents in unsampled areas by ordinary kriging.Maps of spatial distribution were calculated for raw andtransformed data.

Table 2

Parameters of the theoretical model fitted to the experimental variograms for As and Pb of raw data and transformed data

Variable
Model Nugget
effect C0

Sill C1
Long range
A1(m)

Short range

A2 (m)

Anisotropy

(m)

Raw data
As Spherical 47844 2303588 750 241 3.1
Pb
Spherical 151161 503871 400 121 3.3
Box–Cox data
As Spherical 0.03 0.39 1500 480 3.1
Pb
Spherical 0.15 0.50 1000 300 3.3
Anisotropy=Long range/Short range.

Fig. 5. Anisotropic variograms for As: (a) raw data; (b) transformed data.


The Box–Cox transformed data were used for theinterpolation and then the results were back trans-formed, using the reverse process of the Box–Coxtransformation, to produce the geochemical maps for

As and Pb. It should be noted that back transformationto the original data provides approximate estimates(Cressie, 1993). In this study, the direct back transfor-

mation may under-estimate the values on their originalscale, and point kriging was applied in order to keep thenegative effect to a minimum.

The kriging variances were used to evaluate theuncertainty level of spatial prediction and thus calcu-late probabilities of exceeding selected intervention

values. The intervention values selected as cut-off valueswere: (a) Ecological Intervention Value (EIV) of 40 mgkg�1 for As and 290 mg kg�1 for Pb; and (b)Human Toxicological Intervention Value (HTIV) of 678

mg kg�1 for As and 530 mg kg�1 for Pb (Swartjes,1999).

Each estimated point at an unsampled location has anassociated kriging variance. Kriging standard deviationis the square root of the kriging variance. Assuming thekriging value follows the normal distribution, the prob-

ability of exceeding a cut-off value can be calculated. Inthis study, the probability maps were produced usingthe transformed data, with the Box–Cox transformation

applied to the intervention values.Figs. 7 and 8 show, respectively, As and Pb geo-

chemical maps (a—raw data, and b—Box–Cox trans-

formed data respectively) and the associated probabilitymaps of soil contamination (c—EIV cut-off, and d—HTIV cut-off, respectively).

The contour levels used for mapping correspond tothe two intervention values (for raw and transformeddata spatial distribution) and the probability levels 0.05,0.1, 0.25, 0.5, 0.75, 0.90, 0.95, 1.0.

Comparing map 7a with map 7b one can see that thearea with contents above HTIV (678 mg kg�1 As) is

Fig. 6. Anisotropic variograms for Pb: (a) raw data; (b) transformed data.


wider for raw data. Considering the EIV cut-off level, itis clear the whole area has an As content above theselected value. Analysing map 7c almost all the area hasa probability of higher than 0.95 of exceeding 40 mg

kg�1As.The comparison of map 8a with 8b allows one to

note: 1) for the HTIV cut-off level, areas with contents

above 530 mg kg�1 Pb are similar for both mapsalthough slightly narrower for the Box–Cox trans-formed data; 2) EIV cut-off level map 8a (raw data)

shows wider areas above this level. Concerning maps 8cand 8d it is important to note that: (1) areas with theprobability of Pb exceeding 290 mg kg�1 higher than

0.75 are restricted to the location of the ore deposits (oldmine area and potential exploration area); (2) areas withthe probability of Pb exceeding 530 mg kg�1 higher than0.75 occur only in disturbed soils of the old mining area.

The analysis of the maps for the As and Pb allowedthe following conclusions to be reached: (1) raw data

mapping for As and Pb estimates the area above thedefined cut-off values, and the Box–Cox transformationproduced an unbiased dataset; (2) areas of high Ascontents and high probability of exceeding the inter-

vention value for soils are mostly related to the mineraldeposits (old mining area of Castromil and mineraliza-tion of Serra da Quinta). Nevertheless, areas of agri-

cultural soils show high probabilities of exceeding theintervention value for As in soils (40 mg kg�1). Theestimated background for As in local soils is 270mg

kg�1 while the estimated value for Portuguese soil(cambisol type) in granite areas is 17 mg kg�1 (Inacio,pers. comm.). This enrichment is due to natural and

anthropogenic sources elevating levels in the area abovethe intervention value for soil. Soils with this range ofvalues could be assumed to present a potential problemfor the ecosystems; 3) Areas of high Pb content and high

probability of exceeding the intervention value for soilsare mostly related to the tailings deposit (old mining

Fig. 7. (a) Geochemical map of As raw data, (b) geochemical map of As transformed data and (c) and (d) probability maps of As

intervention values exceedence.


area of Castromil), the mineral deposit of Serra daQuinta and soils developed in the vicinity

5. Conclusions

The soils in the vicinity of the old Au–Ag mining area

of Castromil and have been contaminated by miningactivity in the past. High concentrations of elementswere found with maximum values of 6909 mg As kg�1

and 6295 mg Pb kg�1 in soil.The use of the Box–Cox transformation ensured that

the data was stationary as required for ordinary kriging

interpolation and the consequent assumption of normaldistribution allowed the calculation of the probability ofexceeding the threshold.The fitted model to the experimental variograms of

raw and transformed data reveals two structures, anugget effect and an anisotropic spherical structurealong a N50�W direction, which is the main direction of

the aplite formation. The Box–Cox transformation pro-vided a better variogram and improved the modellingprocess.According to Swartjes (1999) classification, the prob-

ability map of exceeding the EIV for As classifies thesoils as requiring ‘further investigation’ and as poten-tially hazardous for the ecosystem. The probability map

of exceeding HTIV for As shows that the area ofhazardous soils for humans is restricted to the locationof the mineral deposits.

Swartjes (1999) classification and probability maps ofexceeding the EIV for Pb allow assessing soil quality asslightly contaminated (no further investigation) with the

exception of soils above the mineral deposits. Theprobability maps of 530 mg kg�1 excedance show thatonly a small and narrow area within the explorationfield of the Castromil mine represents a hazard for

humans.Considering the land use it is clear that high prob-

abilities of exceeding the intervention value for soil

Fig. 8. (a) Geochemical map of Pb raw data, (b) geochemical map of Pb transformed data and (c) and (d) probability maps of Pb

intervention values exceedence.


quality are mainly related to forested areas. Thereforethis does not represent an immediate exposure risk tohumans. However, it should be regarded as a hazardoussite because the dynamic processes of oxidation of sul-

phide minerals are taking place in the abandoned tailingdeposits and releasing metals to the surficial environ-ment.

This work was an orientation survey concerningessentially the identification of the extent and spatialvariability of As and Pb contamination using soil qual-

ity criteria. Whether the As and Pb in these con-taminated soils pose a human health risk depends onseveral factors that can only be determined after a

proper site specific risk assessment is undertaken.

Acknowledgements

The authors are grateful to the Aveiro University andthe ELMAS Research Unit for their financial support

during this study. We thank the Geosciences Depart-ment for assistance in the transport and chemical ana-lyses and the Higher Education Authorities of Ireland

and Ministry of Education of Portugal for providing agrant to support Dr. C. Zhang’s travel to Portugal. Theauthors also wish to express their gratitude to the

anonymous reviewer for his constructive remarks.Finally, the authors extend a special appreciation to Dr.Ron Fuge and Dr. Clemens Reimann for their help ingetting this paper published.

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