Three-dimensional data interpolation for environmental purpose: lead in contaminated soils in southern Brazil Tales Campos Piedade & Vander Freitas Melo & Luiz Cláudio Paula Souza & Jeferson Dieckow Received: 28 June 2013 /Accepted: 6 May 2014 # Springer International Publishing Switzerland 2014 Abstract Monitoring of heavy metal contamination plume in soils can be helpful in establishing strategies to minimize its hazardous impacts to the environment. The objective of this study was to apply a new approach of visualization, based on tridimensional (3D) images, of pseudo-total (extracted with concentrated acids) and exchangeable (extracted with 0.5 mol L -1 Ca(NO 3 ) 2 ) lead (Pb) concentrations in soils of a mining and metal- lurgy area to determine the spatial distribution of this pollutant and to estimate the most contaminated soil volumes. Tridimensional images were obtained after interpolation of Pb concentrations of 171 soil samples (57 points × 3 depths) with regularized spline with tension in a 3D function version. The tridimensional visualization showed great potential of use in environ- mental studies and allowed to determine the spatial 3D distribution of Pb contamination plume in the area and to establish relationships with soil characteristics, land- scape, and pollution sources. The most contaminated soil volumes (10,001 to 52,000 mg Pb kg -1 ) occurred near the metallurgy factory. The main contamination sources were attributed to atmospheric emissions of particulate Pb through chimneys. The large soil volume estimated to be removed to industrial landfills or co- processing evidenced the difficulties related to this prac- tice as a remediation strategy. Keywords Geoprocessing . GRASS GIS . Paraview . Metallurgy waste . Particulate Pb Introduction Industrial activities of heavy metal mining and metal- lurgy produce great amounts of rejects that increase risks of soil and ecosystem contamination (Morgan et al. 2007; Udovic and Lestan 2007). Soil surveys conducted in several countries have been devoted to map and study the spatial distribution of soils contami- nated with heavy metals for the purpose of identifying areas with higher concentrations and to correlate these levels with possible contamination sources. These maps have been created by different interpolation methods, often in a two-dimensional environment, with georeferenced sample points usually arranged in a reg- ular grid. In order to evaluate the spatial dependence, kriging is the most widely used technique to interpolate data from soil sampling points. Imperato et al. (2003) in Italy, Maas et al. (2010) in Algeria, and other authors in China (Zhao et al. 2007; Chen et al. 2011; Guo et al. 2012) studied the spatial distribution of heavy metal levels in soils of urban areas and their peripheries by geostatistic techniques. They concluded that, based on the generated maps, the highest Pb concentrations were located in the inner city as a result of vehicle atmospheric emissions. Rodríguez et al. (2009) in Spain and Wei et al. (2009) in China studied the concentration of heavy metal in soils of mining areas. The analyses of maps, generated Environ Monit Assess DOI 10.1007/s10661-014-3808-4 T. C. Piedade : V. F. Melo(*) : L. C. P. Souza : J. Dieckow Soil Science Department, Federal University of Paraná, Rua dos Funcionários, 1540, Juvevê, 80.035-050 Curitiba, Paraná, Brazil e-mail: [email protected]
Transcript
Three-dimensional data interpolation for environmentalpurpose: lead in contaminated soils in southern Brazil
Tales Campos Piedade & Vander Freitas Melo &
Luiz Cláudio Paula Souza & Jeferson Dieckow
Received: 28 June 2013 /Accepted: 6 May 2014# Springer International Publishing Switzerland 2014
Abstract Monitoring of heavy metal contaminationplume in soils can be helpful in establishing strategiesto minimize its hazardous impacts to the environment.The objective of this study was to apply a new approachof visualization, based on tridimensional (3D) images,of pseudo-total (extracted with concentrated acids) andexchangeable (extracted with 0.5 mol L−1 Ca(NO3)2)lead (Pb) concentrations in soils of a mining and metal-lurgy area to determine the spatial distribution of thispollutant and to estimate the most contaminated soilvolumes. Tridimensional images were obtained afterinterpolation of Pb concentrations of 171 soil samples(57 points × 3 depths) with regularized spline withtension in a 3D function version. The tridimensionalvisualization showed great potential of use in environ-mental studies and allowed to determine the spatial 3Ddistribution of Pb contamination plume in the area andto establish relationships with soil characteristics, land-scape, and pollution sources. The most contaminatedsoil volumes (10,001 to 52,000 mg Pb kg−1) occurrednear the metallurgy factory. The main contaminationsources were attributed to atmospheric emissions ofparticulate Pb through chimneys. The large soil volumeestimated to be removed to industrial landfills or co-processing evidenced the difficulties related to this prac-tice as a remediation strategy.
Keywords Geoprocessing . GRASSGIS . Paraview.
Metallurgywaste . Particulate Pb
Introduction
Industrial activities of heavy metal mining and metal-lurgy produce great amounts of rejects that increaserisks of soil and ecosystem contamination (Morganet al. 2007; Udovic and Lestan 2007). Soil surveysconducted in several countries have been devoted tomap and study the spatial distribution of soils contami-nated with heavy metals for the purpose of identifyingareas with higher concentrations and to correlate theselevels with possible contamination sources. These mapshave been created by different interpolation methods,often in a two-dimensional environment, withgeoreferenced sample points usually arranged in a reg-ular grid. In order to evaluate the spatial dependence,kriging is the most widely used technique to interpolatedata from soil sampling points.
Imperato et al. (2003) in Italy, Maas et al. (2010) inAlgeria, and other authors in China (Zhao et al. 2007;Chen et al. 2011; Guo et al. 2012) studied the spatialdistribution of heavy metal levels in soils of urban areasand their peripheries by geostatistic techniques. Theyconcluded that, based on the generated maps, thehighest Pb concentrations were located in the innercity as a result of vehicle atmospheric emissions.Rodríguez et al. (2009) in Spain and Wei et al. (2009)in China studied the concentration of heavy metal insoils of mining areas. The analyses of maps, generated
Environ Monit AssessDOI 10.1007/s10661-014-3808-4
T. C. Piedade :V. F. Melo (*) : L. C. P. Souza : J. DieckowSoil Science Department, Federal University of Paraná,Rua dos Funcionários, 1540, Juvevê, 80.035-050 Curitiba,Paraná, Brazile-mail: [email protected]
by kriging interpolation, showed that the highest Pbconcentrations were located near the piles of tailingsand near the chimneys of the mining industries. Luet al. (2012) also used geostatistic to study the spatialdistribution of heavymetal (As, Cd, Cu, Hg, Pb, and Zn)in agricultural soils of Shunyi District nearby Beijing.The interpretation of Pb distributions maps showed nopatterns of contamination by a specific source, and thelow levels in soils were attributed to parent materialweathering.
Celine et al. (2006) applied the inverse distanceweight (IDW) interpolation method to evaluate the re-lationship between soils contaminated with heavymetals (Cd, Co, Cr, Cu, Ni, Pb, and Zn) and itssources of contamination in the region of Hong Kong,and the results were similar to the previous cited studiesin urban areas.
In Adrianópolis, Paraná State, southern Brazil, min-ing and metallurgy activities of first Pb fusion had beencarried out for more than 50 years. In 1995, the miningoperation was shut down and left almost 177,000 tons ofprocessing Pb waste exposed to the environment with-out any protection. Researches from Andrade et al.(2009), Barros et al. (2010), Kummer et al. (2011), andDuarte et al. (2012) showed strong diffusion of Pb andZn in soil, water, and sediments. These results empha-sized that such pollutants were already making part ofthe food chain, affecting negatively the development ofplants and soil organisms. To complement and expandthese previous studies in Adrianópolis, which consid-ered only seven sampling points in 48.8 ha, the 3Dinterpolation and visualization might be a useful tech-nique, particularly in tracing Pb contamination plume inthe whole area.
The digital, three-dimensional, and interactive envi-ronment has been rarely used in data processing of soilsurveys (Grunwald and Barak 2001; Delarue et al.2009). The development of such application has beenfacilitated by advances in geoprocessing and in graphiccards with 3D acceleration, by larger storage and pro-cessing capacity of computers, and by the greater avail-ability of mathematical functions to interpolate scattereddata, all these incorporated in the geographic informa-tion systems (GISs).
Three-dimensional evaluation was employed byOuyang et al. (2002) to show that Pb concentrationdecreased with depth in sediments of Cedar River andOrtega River, FL, USA, in a study to investigate thecharacteristics and spatial distribution of heavy metals
(Pb, Cu, Zn, and Cd). Grunwald and Barak (2001)applied Virtual Reality Modeling Language (VRML)to create virtual 3D soil landscape and studyrelationships between soil horizons and terraincharacteristics. Delarue et al. (2009) also used a virtualenvironment to rebuild the soil horizons in order tostudy the spatial distribution in the landscape, and al-though they have not generated volumes, the three-dimensional representation helped with interpretationof pedogenetic evolution of soil horizons.
The aim of this environmental study was todevelop and apply 3D interpolation and visualiza-tion techniques on scattered soil data arranged inirregular grid. The sampled area was contaminatedwith Pb due to mining and metallurgy activities insouthern Brazil, and the volumes representing dif-ferent levels of this heavy metal were used toestablish relationship with pollution sources andsoil characteristics. The volumes of Pb contentswere also used to estimate the need of soil remo-bilization in remediation practices.
Materials and methods
Area description and soil sampling
The study was carried out in an area of a former Pbmining and metallurgy in Adrianópolis (CuritibaMetropolitan Region), Paraná State, Brazil (48°55′11.98″ W, 24°41′55.60″ S; 48°53′49.18″ W, 24°40′23.92″ S) (Fig. 1a). Soil samples from the 0–10-, 10–20-, and 20–40-cm layers were collected in 57 samplingpoints distributed on four transects over a selected spotthat was mostly influenced by mining and metallurgyactivities (Fig. 1b). About 0.2 kg of soil sample wasoven-dried at 40 °C for 24 h to eliminate field moisture,ground to pass through 2-mm mesh, and stored at roomtemperatures for chemical analysis.
Data of a previous study conducted by Kummer et al.(2011) in the same area, but based only on seven sam-pling points (soil profiles) (Fig. 1b), were used to sup-port the current study (Table 1).
Pseudo-total and exchangeable Pb contents (171 soilsamples)
Soil pseudo-total Pb content was determined by micro-wave digestion, according to SW 846-3051A method
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(USEPA 2007). An amount of 0.5-g soil sample wasimmersed in concentrated acid mixture [9 mL nitric acid(65 %) and 3 mL hydrochloride acid (36 %)] andpreheated during 5 min at 1,000 W to reach a tempera-ture of 175±5 °C, which was then kept for 10 min.Afterwards, the sample was left to cool down for30 min inside the microwave equipment. The extractswere analyzed by inductively coupled plasma atomicemission spectroscopy (ICP-OES), Optima 3,300 DV,PERKIN ELMER, axial view, radio frequency power of1,300 W, generator radio frequency of 40 MHz, plasmagas flow rate of 15 L min−1, and auxiliary gas flow0.7 L min−1 with time of 25 s to read two replicates.The analytical curves and the calibration solutions wereprepared from dilutions of stock solutions of
1,000 mg L−1 (Titrisol, Merck, Germany), and the spec-tral lines adopted for Pb was 220.353 nm.
Exchangeable Pb was extracted from 2-g soil sampleand mixed with 20 mL 0.5 mol L−1 Ca(NO3)2·4H2Osolution for 1 h (Miller et al. 1986). The suspension wasfiltered and Pb concentration was measured by ICP-OES.
Interpolation and three-dimensional images of Pb spatialdistribution
Data on Pb content were organized in a spreadsheet withthe following structure (Table 2): code sample point(cod); plane coordinates x, y (UTM), and samplingdepth (z) at the point of collection; pseudo-total Pb
Factory
a) b)
Fig. 1 Brazil map with location of Paraná State (PR) and CuritibaMetropolitan Region (CMR) (a) and georeferenced aerial photo ofthe study area under direct influence of mining and metallurgy ofPb (the abandoned metallurgy factory is located near the Ribeira
river) (b). The outline represents the most contaminated area(49.8 ha), where soil was sampled in four transects (57 points).Previously, soil had been sampled in seven points by Kummeret al. (2011)
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content (pPb); exchangeable Pb content (ePb). The threeexamples given in Table 2 correspond to only onesampling point, varying only in the z coordinate(depth) and the pseudo-total and exchangeable Pblevels. Negative notation for the z parameter was usedfor interpolation. The 0–10-, 10–20-, and 20–40-cmlayers were represented as discrete z values of 0, −10,and −20, respectively.
Data were exported into ASCII format (AmericanStandard Code for Information Interchange) for com-patibility with GIS software. The use of GIS tools was
through command lines written in shell script codes,whose syntaxes are available in the instruction manualof GRASS GIS software (Grass Development Team2012).
Before importing the data into the GIS, ageoreferenced environment was created in order to limitthe processing inside the sampling area or bounding box(Fig. 1b). This environment was referred to SAD 69Datum, zone 51W.Gr., and UTM coordinate system. Tocreate the processing region, the command “g.region”was used through the following command line: “g.re-gion n= 7268801 s= 7267642 e= 711794 w=711085 t=0 b=−0.40 res=2.5 res3=2.5 nsres=2.5ewres=2.5 tbres=0.01 –o”, where n, s, e, and w referto the coordinate pairs that limit the left lower corner andthe upper right corner of the bounding box; t and b arethe values of the surface limit and depth limit; res, res3,nsres, ewres, and tbres are the resolution values of dataprocessing.
After setting the study region, data were imported bythe command “v.in.ascii” through the command line“v.in.ascii −z input=/home/talescp/Documentos/
Table 1 Location, classification, and characteristics of the seven soil sampling sites described by Kummer et al. (2011) and identified inFig. 1
Soil UTM (22 J) Altitude (m)/distance (m)a
Brazilianclassification
US taxonomyb Observation
N–S (m) E–W (m)Latitude Longitude
1 7267313 711502 546/1,560 Lithic Neosol Ustorthent Reference soil under native forest at highestelevation. Parent material: carbonate rocks/granitic complex
3 7268555 711287 165/45 Lithic Neosol Ustorthent Close to the factory. Vegetation cover with legumetrees (Leucaena sp.). Parent material: carbonaterocks/granitic complex
5 7268070 711360 316/455 Mixture of soil +coarse waste
– Greater occurrence of metallurgical wastes on thesurface and part incorporated in soil profile
6 7268671 711572 202/295 Quartzarenic Neosol Quartzi-psamment Close to the factory. Vegetation cover of secondaryforest and fern (Pteridium aquilinum). Parentmaterial: quartzite
7 7268499 711158 194/321 Haplic Inceptsol Haplustept The same of soil 3 except for the greater distancefrom the factory
8 7268701 711331 157/64 Fluvic Neosol Ustfluvent Close to the Ribeira river at the lowest elevation.Little vegetation cover (some Poaceae plants).Parent material: fluvial sand deposits, sediments
a Straight-line distance from the sampling point to the factorybApproximate correlation with US Soil Taxonomy
Table 2 File structure and organization to data exportation (themeaning of the terms in the table is described in the text)
Code x y z pPb ePbm mg kg−1
1 711192 7268600 0 1,572.5 3.7
2 711192 7268600 −0.10 2,363.2 7.4
3 711192 7268600 −0.20 2,045.2 6.0
Continues
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adrianopolis/chumbo.csv output=chtot3d fs=, z=3 –o”,where −z creates a 3D vector, input is the directory withthe file to be imported, output is the vector map resultingfrom import command, fs indicates that the file to beimported has comma separate value columns, and z isthe column number where the z values represent sam-pling depth.
A mask (raster map) covering exclusively the sam-pling site was defined after importing the data so that theprocessing was limited only to the study area (Fig. 1b).This procedure was done using four commands. Thefirst one was “v.type”, responsible for transforming avector line in polygon type, using the following com-mand line: “v.type input=mascara output=mascarartype=line,boundary –o”, where input corresponds to amap with vector representation line, type represents theline to polygon conversion, and output is the resultingmap name of this transformation. The second commandwas “v.centroids” which adds a centroid to the mapgenerated by the previous command. It ensures thatthe polygon is closed. The following command linew a s u s e d : “ v. c e n t ro i d s i n p u t=ma s c a r a routput=mascarav –o”. The third command used was“v.to.rast”, which transforms the vector map (closedpolygon with the centroid) in raster map. The commandl i n e us ed was “ v. t o . r a s t i npu t=mascaravoutput=mascara2d use=cat type=area –o”, where inputrefers to the vector map containing the polygon centroid,output is the resulting raster map, and type means thatthe transformation involves an area representation type.The fourth command used was “r.mask”, which effec-tively enables the use of a mask. The following com-mand line was used: “input = r.mask mascara2d –o”,where input is the raster map name used as mask.
After setting the mask, the pseudo-total and ex-changeable Pb concentrations in soil were interpolatedand represented in continuous form (3D) in the land-scape (volumetric surface) by using the regularizedspline with tension (RST) function interpolation(Mitasova andMitas 1993). The interpolation procedureusing the “v.vol.rst” command (Grass DevelopmentTeam 2012) was carried out considering the 171 samplepoints (57 sampling sites×3 layers), arranged in anirregular grid with 2.5-m spatial resolution. The com-mand line used was “v.vol.rst input=chtot3delev=defori3d wcolumn=pPb ten=30 smo=0.1dmin=0.01 zmult=100 –o”, where input is the vectormap to be interpolated, elev is the resulting volumemap,wcolumn is the column data name to be interpolated
(pPb pseudo-total or ePb exchangeable), ten is the ten-sion value used, smo the smoothness value used, dmin isthe minimum distance between points (to remove iden-tical points), and zmult is the number of times that the zvalue must be multiplied to make it proportional to the xand y horizontal coordinate values.
The tension and smoothness are key parameters thatcontrol the interpolation performance and were definedby the cross-validation procedure (Caruso and Quarta1998; Tomczak 1998; Tabios and Salas 1985). Thisprocedure was performed through the modification ofa code written in shell script (Neteler and Mitasova2008). This code uses the Jack-knife cross-validationmethod, also known as leave-one-out, and assists inchoosing the best combination of tension and smooth-ness through the lowest root mean square error (RMSE)value. This method removes one point of the input dataset, performs interpolation with the remaining points,and estimates a value for the position of the removedpoint, and the process is repeated until all points havehad the same procedure. In this shell script code, thetension used vary from 10 to 90 and smoothness from0.1 to 0.9, so for each value of tension, 09 smoothnessvalues are tested. The result of the cross-validationshows the deviations between observed and estimatedpoints by interpolation process.
To evaluate the performance of the interpolation pro-cess and cross-validation procedure, univariate statisticswas used (mean absolute values, standard deviation,RMSE) to show the result with the lowest predictionerror (Robinson and Metternicht 2006; Hofierka et al.2007). The RMSE values (Pb contents—mg kg−1) weredetermined using the following equation:
where Xobs is the observed values, Xest is the estimatedvalues, and n is the number of observations.
3D visualization and estimate of volumeof contaminated soil
After interpolation, the resulting volume map wasexported to VTK format (The Visualization Toolkit)for scientific visualization with Paraview softwarev.3.6.0 (Kitware Inc 2011). This procedure requiredthe use of a mask for the 3D volume. It ensures thatonly data within the perimeter covering the area can be
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exported. This was done by transforming the maskraster map from 2D to 3D mask through the followingcommand line: “r.to.rast3 -m input=mascara2doutput=mascara3d –o”.
After creating the 3D mask, the volume data wereexported through the following command line:“r3.out.vtk -p -s -m input=defori3d, mascara3d out-put=/home/talescp/Documents/mestrado/correcao/vtk/defori3d.vtk top=elevtopr25 bottom=elevbotr25 –o”,where −p indicates a vector data, −s indicates the useof a raster map representing the top of elevation surfacemodel and the bottom of the elevation model, −m indi-cates the use of a 3D mask, input is the input map nametogether with the mask, output is the volume map withVTK extension output, top is the map representing theelevation of the surface relief, and bottom is the maprepresenting the elevation bottom of the relief.
With paraview v.3.6.0 software, only three displayfilters were used: contour, slice; threshold. Contourfilters data through isovalues determined by the user;slice is a filter that allows cuts into the volume, allowingthe visualization inside the volume as cutting profiles;threshold is a filter that extracts scalar values defined bylower and upper limits specified by the user.
The soil volume for remobilization in a possibleremediation work was calculated for each of the fiveconcentration ranges of pseudo-total lead (mg kg−1):2,501 to 5,000, 5,001 to 10,000, 10,001 to 15,000,15,001 to 25,000, and 25,000 to a maximum value of52,000.
Results and discussion
Interpolation procedures
The program default value of tension is 40 and ofsmoothness is 0.1, but their combination showed largeprediction errors, as shown by high RMSE (Table 3).After cross-validation for the 171 points, the lowestRMSE (Pb pseudo-total content of 3,593.1 mg kg−1)was obtained with tension of 30 and smoothness of 0.1.For this combination, the mean absolute value was1,534.6 mg kg−1, and the minimum and maximumvalues of the deviations between observed and estimat-ed values were −27,813.1 and 15,060.0 mg kg−1, re-spectively. These extreme deviations occurred mainlydue to the difficulty of the interpolator in adjusting thesurface at the points located in areas of high
contamination because the transition with areas of lowcontamination is quite abrupt (Hofierka et al. 2007).
To investigate the relationship between observed andestimated values (Pb contents), a scatter plot was made(n=171, 57 sites×3 layers), whose trend line showed acoefficient of determination R2 of 0.97, thus showing avery satisfactory result for the available data.
Three-dimensional distribution of the pseudo-total Pbcontents
The original contents of exchangeable and pseudo-totalPb in soil samples are presented in Table 4. Soils locatedat the highest elevations and far from the factory showedthe smallest Pb contamination (<2,500 mg kg−1) (greenand yellow; Fig. 2b), probably because Pb particlesemitted from the chimneys did not easily reach suchremote terrains. Accordingly, during the field work, lessmetallurgy residue was visually observed in this partthan in the rest of the area.
Kummer et al. (2011) took soil samples in a nativeforest upstream of this study area (site 1; Fig. 1 andTable 1), as a supposedly reference of natural Pb levelsin soil, and found pseudo-total contents 87 and426 mg kg−1 in 0–10- and 10–20-cm layers, respective-ly. Even with careful selection of sampling points, Pblevels were higher than those found in other naturalBrazilian soils using the same extraction method(Campos et al. 2003). The high natural content of Pb(anomalous values) in the soils around the study area,even without anthropic influence, is due to lithogenicorigin, which is associated with occurrence of veins ofgalena (PbS) in the region (Barros et al. 2010).
The spatial distribution of Pb contents in the thematicmaps generated by Lu et al. (2012) (2D interpolation)showed that high concentrations are not necessarilyattributed to human activity, but again the lithogenicorigin was identified as responsible for heavy metallevels above the reference values.
There is an extensive area in yellow (sandy soils withlower Pb contents—181 to 2,500 mg kg−1) in the leftbank of Ribeira River nearby the factory (Fig. 3a).According to Kummer et al. (2011), that restricted area(soil 6—Table 1) showed quartzite as parent material,sandy soils (clay content between 64 and 124 g kg−1),and low organic carbon content (less than 10 g kg−1).These conditions determined low values of CEC atpH 7.0 (less than 6 cmolc kg
−1; Kummer et al. 2011)and favored the leaching of Pb. The predominance of
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Table 3 Combinations of tension and smoothness with respective root mean square error (RMSE)
carbonate rocks/granitic complex (Table 1) results in theformation of more clayey soils in other sites of the studyarea.
Considering the limits of soil quality established bythe Brazilian Environmental Agency (Conama 2009),even the yellow-colored areas represent environmentalrisks. This is because the superior limit of Pb contents(2,500 mg kg−1) surpasses that which requires interven-tions (soil remediation) if the purpose is agricultural(maximum allowed of 180 mg kg−1), residential(300 mg kg−1), and industrial (900 mg kg−1) uses.Thus, without remediation practices, this area cannotbe used for any of these purposes.
The soils with levels between 2,501 to 5,000 mg kg−1
(orange), located in the middle portion of the area(Fig. 2a, b), were more contaminated than the yellowareas because of the great occurrence of piles of metal-lurgy waste on ground surface. The area with the orangecolor contains site 2, which was the one described indetail by Kummer et al. (2011) (Fig. 1 and Table 1). Onthe whole area on the right bank side of Ribeira River,which includes the perimeter of the study area (Fig. 1),was disposed about 177,000 ton of metallurgy Pb waste,with dark color and coarser size (0.5 to 2 mm), directlyon the soil surface (Barros et al. 2010). Rodríguez et al.(2009) also found high levels of total Pb in soils nearwaste piles in mining areas, with contents ranging be-tween 1,243 and 93,900 mg kg−1. Small occurrences of
yellow spots (81 to 2,500 mg kg−1) within the area atlevels between 2,501 and 5,000 mg kg−1 (Fig. 2a, b)show that the distribution of waste in the area was nothomogeneous.
Andrade et al. (2009) conducted experiment onheavy metal phytoextraction using soils of the same areaand concluded that Helianthus annuus (sunflower)showed greater potential to remediate contaminatedsoils at an intermediate level, with Pb contents less than5,000 mg kg−1, represented by yellow and orange areas.
The volumes of soil with Pb contents ranging from5,001 to 10,000 mg kg−1 (brown color) were divided intwo portions in the study area (Fig. 2a–c). In the regionof the smaller soil volume at the higher landscape posi-tion was disposed the largest quantity of waste on theground in relation to the whole study area. Due toerosion and sediment transport, favored by the terrainsteepness (Fig. 1), there was intense mixing betweenwaste and soil horizons, which prevented, for example,the classification of profile 5 (Table 1).
The main source of soil contamination in the brownarea surrounding the factory was possibly the same asthe areas in red (10,001 to 15,000 mg kg−1), purple(15,001 to 25,000 mg kg−1), and black (25,001 to52,000 mg kg−1) colors, representing the maximum Pbcontents (Fig. 2). The proximity of these areas with thetwo factory chimneys (Fig. 1) favored the ground accu-mulation of particulate material rich in Pb in the soil
0–10 cm 10–20 cm 20–40 cm 0–10 cm 10–20 cm 20–40 cm(mg kg−1) (mg kg−1) (mg kg−1) (mg kg−1) (mg kg−1) (mg kg−1)
44 0.7 7.9 0.2 396.2 1,787.1 1,527.6
45 0.4 0.0 0.0 102.0 49.4 59.5
46 24.7 0.0 0.0 1,441.8 399.7 15.0
47 0.8 0.4 1.5 1,171.7 709.1 155.7
48 0.2 0.9 1.6 73.3 25.6 6.1
49 2,188.5 1,029.8 438.4 3,155.7 1,159.3 507.1
50 150.6 87.3 32.7 209.3 208.3 127.2
51 109.9 25.5 16.3 218.9 96.1 54.7
52 69.2 71.9 52.0 58.8 88.5 70.4
53 7.4 1.8 1.7 94.6 42.3 29.6
54 12.4 9.5 19.3 23.3 293.9 310.6
55 241.4 41.3 11.1 384.2 151.4 56.1
56 1,721.4 483.6 181.4 1,552.8 560.0 239.6
57 1,366.4 2,875.1 776.0 2,568.7 3,349.3 656.4
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a) b)
c) d)
e) f)
Area with greater
deposition of solid
waste
Areas close to the
factory chimneys.
Sandy
soils
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surface (Barros et al. 2010; Duarte et al. 2012). Ataround the factory, we did not observe the occurrenceof residues of metallurgy. These results demonstrate thatemissions of particulate Pb were more aggressive to theenvironment than the waste spread on the ground in thehigher areas (yellow, orange, and brown areas in higherelevation). Burgess (1995) discussed that the miningand processing of sulphide ores of Pb produce furtherthan the tailings and slag furnace, gaseous emissionscontaining CO and SO2, and dust particles with 20 to65 % of Pb.
The analysis of the longitudinal slice enabled thevisualization of the intense soil contamination near thefactory (Fig. 3a). In the study conducted by Wei et al.(2009) in China, the highest soil Pb concentrations inmetallurgy areas were also around the chimneys of thefactory.
The highest Pb contents (black) occurred as a coreinside the volume represented by purple color (Fig. 2e),indicating a maximum accumulation in the subsurfacearound the factory. It is important to note that thiscontaminated core was confined in subsurface and did
not emerge at the ground surface. However, the purplecolor, even shrouded by red, can be seen in surface area.This radial decrease of Pb in the volume near the factoryindicates dispersal of heavy metal in areas with greatcontamination in the following direction: black (core)→ purple → red. An additional evidence of such con-tamination was that filters used in chimneys were foundburied in those central soil cores.
Besides the form of contamination, the distribution ofPb also varied according to soil characteristics. Studiesof Pb release in this area showed that the highest levelsof contamination in the 0–10-cm depth, compared to adepth of 20–40 cm, are related to the preferential accu-mulation of this metal on soil surface (Kummer et al.2011). This situation can be attributed to the low solu-bility and strong adsorption of Pb on organic soil col-loids (Welch and Lund 1989; Zhang et al. 2005). InFig. 3b, it is possible to see the higher concentrationsof Pb in the upper layers of soil.
Three-dimensional distribution of the exchangeable Pbcontents
The exchangeable Pb fractions were also higher near thefactory (181 to 4,300 mg kg−1; Fig. 4). The particulateform was responsible for greater soil contamination(higher pseudo-total Pb contents) and promoted highersolubility of Pb than the deposition of this metal viasolid waste in the higher parts of the area. The nonspe-cific binding (outer sphere complex) of exchangeablePb on negative charges of soil colloids (CEC) facilitatesion exchange and its release to the soil solution.
There was an extensive area where the exchangeablePb content, extracted with Ca(NO3)2 solution, was over
Fig. 2 3D distribution of Pb pseudo-total contents (mg kg−1): ayellow—181 to 2,500; orange—2,501 to 5,000; brown—5,001 to10,000; red—10,001 to 15,000; purple—15,001 to 25,000; bdetail for contents below 180 (green) observed after 20 % of theyellow transparency; c contents of 181 to 2,500 (yellow) and above5,001 (orange color omitted); d contents of 181 to 2,500 (yellow)and above 10,001 (orange and brown color omitted, with 20 % ofred transparency); e contents of 181 to 2,500 (yellow) and above15,001 (orange, brown, and red colors omitted, with 20 % ofpurple transparency to allow black color visualization); f contentsof 181 to 2,500 (yellow) and contents between 25,001 and maxi-mum of 52,000 (black), omitting the other colors. The scale andlocation of the area are presented in Fig. 1
�
Fig. 3 Slicing representation of the pseudo-total Pb contents: a longitudinal cut emphasizing the highest levels near the factory; b circularslicing emphasizing the highest levels on the soil surface. The scale and location of the area are presented in Fig. 1
Environ Monit Assess
180 mg kg−1 (yellow, orange, red, and purple). Thissurpasses the 180-mg kg−1 limit for agricultural useestablished by the Brazilian Environmental Agency(Conama 2009), which adopts a much stronger extrac-tion procedure based on nitric–hydrocloric acid treat-ment. Thus, it is considered that the whole extent of soilnear to the factory should be given more concern aboutthe spread of Pb and environmental contamination,mainly because they are closer to the Ribeira River(Fig. 1).
Estimated volume of Pb soil contamination
Andrade et al. (2009), in a study of phytoextraction insoils around the factory, showed that this remediationtechnique was not efficient as evidenced by severe plantphytotoxicity by Pb. An alternative to remediation of theareas near the factory (maximum atmospheric Pb con-tamination: pseudo-total contents between 5,001 and52,000 mg kg−1 and exchangeable contents between181 and 4,300 mg kg−1; Figs. 2 and 4) could be thetechnique of remobilization of contaminated soils toindustry landfills or to co-processing in cementindustries.
Considering pseudo-total Pb contents to estimate thecontaminated soil volume (Fig. 2), it would be necessaryto remove about 59,930 m3 of soil (9.1 % of the total
volume of soil in the study area indicated by the outlinein Fig. 1), which exceeds the level of 5,001 mg kg−1
(brown, red, purple, and black colors) up to 40 cm indepth (Table 5). That in practice would be unfeasible.Successively by Pb concentration range, removing in-creasingly smaller portions of contaminated soils repre-sents the following volumes of earth (Table 5): above10,001 mg kg−1 (2.27 %)—red, purple, and black;above 15,001 mg kg−1 (0.75 %)—purple and black;above 25,001 mg kg−1 (0.18 %)—black. Even the re-moval of soils with Pb 25,001-52,000 mg kg−1 wouldrepresent a large volume of soil—1,172 m3 or 117 loadsof trucks, each one with 10 m3 (Table 5). These dataindicate the practical difficulties to remediation of thearea, especially around the factory.
Conclusions
The use of 3D images enabled the visualization of thespatial distribution of the Pb contamination plume in thearea and the identification of internal volumes (cores) ofhigh metal concentration confined inside the soil matrix.
The environmental contamination by Pbwas affectedby the source of pollution, landscape position (distancefrom the factory), and soil characteristics. The soil vol-umes with the greatest Pb contamination (10,001 to
a)
Areas close to the
factory chimneys
b)Fig. 4 3D distribution ofexchangeable Pb content(mg kg−1): a dark green—1 to 72;light green—73 to 180;yellow—181 to 300; orange—301to 900; red—901 to 1,500;purple—1,501 to maximum 4,300;b orange color omitted and 20 %red color transparency. The scaleand location of the area arepresented in Fig. 1
Table 5 Volume of soil accord-ing to the ranges of Pb pseudo-total contents in the area
Concentration range(mg kg−1)
Soil volume (m3) Proportion in thetotal area (%)
Representationin Fig. 2
Color in Fig. 2
2,501 5,000 126,137 19.19 a, b Orange
5,001 10,000 44,989 6.84 a, b, c Brown
10,001 15,000 10,011 1.52 a, b, c, d Red
15,001 25,000 3,758 0.57 a, b, c, d, e Purple
25,001 52,000 1,172 0.18 e, f Black
Environ Monit Assess
52,000 mg kg−1) occurred closer to the metallurgy fac-tory, except the area with predominance of sandy soils.The main source of contamination of these soils wasattributed to atmospheric emissions (particulate Pb) byfactory chimneys.
The large volume of soil that would eventually betransferred to industry landfills or processed in cementindustries highlighted the difficulty of these alternativesas remediation of the area: the soil volume with Pbconcentration between 15,001 and 25,000 mg kg−1
was estimated to be 3,758 m3, while that between24,001 and 52,000 mg kg−1 was 1,172 m3.
The 3D interpolation and visualization techniquesshowed great potential for application in studies of soilcontamination by organic and inorganic pollutants. Thegreat advantage of this 3D technique is the possibility oftracking the volume of the plume of pollutants in soils,which makes the studies of diagnoses more comprehen-sive and accurate and assists in decision making andprocedures for environmental remediation.
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