Three-dimensional data interpolation for ...

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 Environmental Monitoring and Assessment An International Journal Devoted to Progress in the Use of Monitoring Data in Assessing Environmental Risks to Man and the Environment ISSN 0167-6369 Volume 186 Number 9 Environ Monit Assess (2014) 186:5625-5638 DOI 10.1007/s10661-014-3808-4

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Author's personal copy Environ Monit Assess (2014) 186:5625–5638 DOI 10.1007/s10661-014-3808-4

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 / Published online: 28 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(NO3)2) lead (Pb) concentrations in soils of a mining and metallurgy 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 environmental studies and allowed to determine the spatial 3D distribution of Pb contamination plume in the area and to establish relationships with soil characteristics, landscape, 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 coprocessing evidenced the difficulties related to this practice as a remediation strategy.

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]

Keywords Geoprocessing . GRASS GIS . Paraview . Metallurgy waste . Particulate Pb

Introduction Industrial activities of heavy metal mining and metallurgy 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 contaminated 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 regular 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

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by kriging interpolation, showed that the highest Pb concentrations were located near the piles of tailings and near the chimneys of the mining industries. Lu et al. (2012) also used geostatistic to study the spatial distribution of heavy metal (As, Cd, Cu, Hg, Pb, and Zn) in agricultural soils of Shunyi District nearby Beijing. The interpretation of Pb distributions maps showed no patterns of contamination by a specific source, and the low levels in soils were attributed to parent material weathering. Celine et al. (2006) applied the inverse distance weight (IDW) interpolation method to evaluate the relationship between soils contaminated with heavy metals (Cd, Co, Cr, Cu, Ni, Pb, and Zn) and its sources of contamination in the region of Hong Kong, and the results were similar to the previous cited studies in urban areas. In Adrianópolis, Paraná State, southern Brazil, mining and metallurgy activities of first Pb fusion had been carried out for more than 50 years. In 1995, the mining operation was shut down and left almost 177,000 tons of processing Pb waste exposed to the environment without any protection. Researches from Andrade et al. (2009), Barros et al. (2010), Kummer et al. (2011), and Duarte et al. (2012) showed strong diffusion of Pb and Zn in soil, water, and sediments. These results emphasized that such pollutants were already making part of the food chain, affecting negatively the development of plants and soil organisms. To complement and expand these previous studies in Adrianópolis, which considered only seven sampling points in 48.8 ha, the 3D interpolation and visualization might be a useful technique, particularly in tracing Pb contamination plume in the whole area. The digital, three-dimensional, and interactive environment has been rarely used in data processing of soil surveys (Grunwald and Barak 2001; Delarue et al. 2009). The development of such application has been facilitated by advances in geoprocessing and in graphic cards with 3D acceleration, by larger storage and processing capacity of computers, and by the greater availability of mathematical functions to interpolate scattered data, all these incorporated in the geographic information systems (GISs). Three-dimensional evaluation was employed by Ouyang et al. (2002) to show that Pb concentration decreased with depth in sediments of Cedar River and Ortega River, FL, USA, in a study to investigate the characteristics and spatial distribution of heavy metals

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(Pb, Cu, Zn, and Cd). Grunwald and Barak (2001) applied Virtual Reality Modeling Language (VRML) to create virtual 3D soil landscape and study relationships between soil horizons and terrain characteristics. Delarue et al. (2009) also used a virtual environment to rebuild the soil horizons in order to study the spatial distribution in the landscape, and although they have not generated volumes, the threedimensional representation helped with interpretation of pedogenetic evolution of soil horizons. The aim of this environmental study was to develop and apply 3D interpolation and visualization techniques on scattered soil data arranged in irregular grid. The sampled area was contaminated with Pb due to mining and metallurgy activities in southern Brazil, and the volumes representing different levels of this heavy metal were used to establish relationship with pollution sources and soil characteristics. The volumes of Pb contents were also used to estimate the need of soil remobilization in remediation practices.

Materials and methods Area description and soil sampling The study was carried out in an area of a former Pb mining and metallurgy in Adrianópolis (Curitiba Metropolitan 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 sampling points distributed on four transects over a selected spot that was mostly influenced by mining and metallurgy activities (Fig. 1b). About 0.2 kg of soil sample was oven-dried at 40 °C for 24 h to eliminate field moisture, ground to pass through 2-mm mesh, and stored at room temperatures for chemical analysis. Data of a previous study conducted by Kummer et al. (2011) in the same area, but based only on seven sampling points (soil profiles) (Fig. 1b), were used to support the current study (Table 1). Pseudo-total and exchangeable Pb contents (171 soil samples) Soil pseudo-total Pb content was determined by microwave digestion, according to SW 846-3051A method

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a)

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b)

Factory

Fig. 1 Brazil map with location of Paraná State (PR) and Curitiba Metropolitan Region (CMR) (a) and georeferenced aerial photo of the study area under direct influence of mining and metallurgy of Pb (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 Kummer et al. (2011)

(USEPA 2007). An amount of 0.5-g soil sample was immersed in concentrated acid mixture [9 mL nitric acid (65 %) and 3 mL hydrochloride acid (36 %)] and preheated during 5 min at 1,000 W to reach a temperature of 175±5 °C, which was then kept for 10 min. Afterwards, the sample was left to cool down for 30 min inside the microwave equipment. The extracts were analyzed by inductively coupled plasma atomic emission spectroscopy (ICP-OES), Optima 3,300 DV, PERKIN ELMER, axial view, radio frequency power of 1,300 W, generator radio frequency of 40 MHz, plasma gas flow rate of 15 L min−1, and auxiliary gas flow 0.7 L min−1 with time of 25 s to read two replicates. The analytical curves and the calibration solutions were prepared from dilutions of stock solutions of

1,000 mg L−1 (Titrisol, Merck, Germany), and the spectral lines adopted for Pb was 220.353 nm. Exchangeable Pb was extracted from 2-g soil sample and mixed with 20 mL 0.5 mol L−1 Ca(NO3)2·4H2O solution for 1 h (Miller et al. 1986). The suspension was filtered and Pb concentration was measured by ICPOES. Interpolation and three-dimensional images of Pb spatial distribution Data on Pb content were organized in a spreadsheet with the following structure (Table 2): code sample point (cod); plane coordinates x, y (UTM), and sampling depth (z) at the point of collection; pseudo-total Pb

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Table 1 Location, classification, and characteristics of the seven soil sampling sites described by Kummer et al. (2011) and identified in Fig. 1 Soil UTM (22 J)

Altitude (m)/ Brazilian distance (m)a classification

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 highest elevation. Parent material: carbonate rocks/ granitic complex

2

7268164 711513

326/563

Haplic Cambisol

Ustrochept

Intermediate elevation area. Vegetation cover of dallis grass (Paspalum notatum). Parent material: carbonate rocks/granitic complex

3

7268555 711287

165/45

Lithic Neosol

Ustorthent

Close to the factory. Vegetation cover with legume trees (Leucaena sp.). Parent material: carbonate rocks/granitic complex

5

7268070 711360

316/455

Mixture of soil + coarse waste

–

Greater occurrence of metallurgical wastes on the surface and part incorporated in soil profile

6

7268671 711572

202/295

Quartzarenic Neosol Quartzi-psamment Close to the factory. Vegetation cover of secondary forest and fern (Pteridium aquilinum). Parent material: quartzite

7

7268499 711158

194/321

Haplic Inceptsol

Haplustept

The same of soil 3 except for the greater distance from 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 factory

b

Approximate correlation with US Soil Taxonomy

content (pPb); exchangeable Pb content (ePb). The three examples given in Table 2 correspond to only one sampling point, varying only in the z coordinate (depth) and the pseudo-total and exchangeable Pb levels. Negative notation for the z parameter was used for interpolation. The 0–10-, 10–20-, and 20–40-cm layers were represented as discrete z values of 0, −10, and −20, respectively. Data were exported into ASCII format (American Standard Code for Information Interchange) for compatibility with GIS software. The use of GIS tools was Table 2 File structure and organization to data exportation (the meaning of the terms in the table is described in the text) Code

x m

y

z

ePb

1,572.5

3.7

1

711192

7268600

2

711192

7268600

−0.10

2,363.2

7.4

3

711192

7268600

−0.20

2,045.2

6.0

Continues

0

pPb mg kg−1

through command lines written in shell script codes, whose syntaxes are available in the instruction manual of GRASS GIS software (Grass Development Team 2012). Before importing the data into the GIS, a georeferenced environment was created in order to limit the processing inside the sampling area or bounding box (Fig. 1b). This environment was referred to SAD 69 Datum, zone 51 W.Gr., and UTM coordinate system. To create the processing region, the command “g.region” was used through the following command line: “g.region n = 7268801 s = 7267642 e = 711794 w = 711085 t=0 b=−0.40 res=2.5 res3=2.5 nsres=2.5 ewres=2.5 tbres=0.01 –o”, where n, s, e, and w refer to the coordinate pairs that limit the left lower corner and the upper right corner of the bounding box; t and b are the values of the surface limit and depth limit; res, res3, nsres, ewres, and tbres are the resolution values of data processing. After setting the study region, data were imported by the command “v.in.ascii” through the command line “v.in.ascii −z input=/home/talescp/Documentos/

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adrianopolis/chumbo.csv output=chtot3d fs=, z=3 –o”, where −z creates a 3D vector, input is the directory with the file to be imported, output is the vector map resulting from import command, fs indicates that the file to be imported has comma separate value columns, and z is the column number where the z values represent sampling depth. A mask (raster map) covering exclusively the sampling site was defined after importing the data so that the processing was limited only to the study area (Fig. 1b). This procedure was done using four commands. The first one was “v.type”, responsible for transforming a vector line in polygon type, using the following command line: “v.type input=mascara output=mascarar type=line,boundary –o”, where input corresponds to a map with vector representation line, type represents the line to polygon conversion, and output is the resulting map name of this transformation. The second command was “v.centroids” which adds a centroid to the map generated by the previous command. It ensures that the polygon is closed. The following command line w a s u s e d : “ v. c e n t r o i d s i n p u t = m a s c a r a r output=mascarav –o”. The third command used was “v.to.rast”, which transforms the vector map (closed polygon with the centroid) in raster map. The command l i n e u s e d w a s “ v. t o . r a s t i n p u t = m a s c a r a v output=mascara2d use=cat type=area –o”, where input refers to the vector map containing the polygon centroid, output is the resulting raster map, and type means that the transformation involves an area representation type. The fourth command used was “r.mask”, which effectively enables the use of a mask. The following command 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 exchangeable Pb concentrations in soil were interpolated and represented in continuous form (3D) in the landscape (volumetric surface) by using the regularized spline with tension (RST) function interpolation (Mitasova and Mitas 1993). The interpolation procedure using the “v.vol.rst” command (Grass Development Team 2012) was carried out considering the 171 sample points (57 sampling sites×3 layers), arranged in an irregular grid with 2.5-m spatial resolution. The command line used was “v.vol.rst input=chtot3d elev=defori3d wcolumn=pPb ten = 30 smo = 0.1 dmin=0.01 zmult=100 –o”, where input is the vector map to be interpolated, elev is the resulting volume map, wcolumn is the column data name to be interpolated

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(pPb pseudo-total or ePb exchangeable), ten is the tension value used, smo the smoothness value used, dmin is the minimum distance between points (to remove identical points), and zmult is the number of times that the z value must be multiplied to make it proportional to the x and y horizontal coordinate values. The tension and smoothness are key parameters that control the interpolation performance and were defined by the cross-validation procedure (Caruso and Quarta 1998; Tomczak 1998; Tabios and Salas 1985). This procedure was performed through the modification of a code written in shell script (Neteler and Mitasova 2008). This code uses the Jack-knife cross-validation method, also known as leave-one-out, and assists in choosing the best combination of tension and smoothness through the lowest root mean square error (RMSE) value. This method removes one point of the input data set, performs interpolation with the remaining points, and estimates a value for the position of the removed point, and the process is repeated until all points have had the same procedure. In this shell script code, the tension used vary from 10 to 90 and smoothness from 0.1 to 0.9, so for each value of tension, 09 smoothness values are tested. The result of the cross-validation shows the deviations between observed and estimated points by interpolation process. To evaluate the performance of the interpolation process and cross-validation procedure, univariate statistics was used (mean absolute values, standard deviation, RMSE) to show the result with the lowest prediction error (Robinson and Metternicht 2006; Hofierka et al. 2007). The RMSE values (Pb contents—mg kg−1) were determined using the following equation: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xn  2ffi X −X obs;i est;i i¼1 RMSE ¼ n where Xobs is the observed values, Xest is the estimated values, and n is the number of observations. 3D visualization and estimate of volume of contaminated soil After interpolation, the resulting volume map was exported to VTK format (The Visualization Toolkit) for scientific visualization with Paraview software v.3.6.0 (Kitware Inc 2011). This procedure required the use of a mask for the 3D volume. It ensures that only data within the perimeter covering the area can be

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exported. This was done by transforming the mask raster map from 2D to 3D mask through the following command line: “r.to.rast3 -m input=mascara2d output=mascara3d –o”. After creating the 3D mask, the volume data were exported through the following command line: “r3.out.vtk -p -s -m input=defori3d, mascara3d output=/home/talescp/Documents/mestrado/correcao/vtk/ defori3d.vtk top=elevtopr25 bottom=elevbotr25 –o”, where −p indicates a vector data, −s indicates the use of a raster map representing the top of elevation surface model and the bottom of the elevation model, −m indicates the use of a 3D mask, input is the input map name together with the mask, output is the volume map with VTK extension output, top is the map representing the elevation of the surface relief, and bottom is the map representing the elevation bottom of the relief. With paraview v.3.6.0 software, only three display filters were used: contour, slice; threshold. Contour filters data through isovalues determined by the user; slice is a filter that allows cuts into the volume, allowing the visualization inside the volume as cutting profiles; threshold is a filter that extracts scalar values defined by lower and upper limits specified by the user. The soil volume for remobilization in a possible remediation work was calculated for each of the five concentration 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 of 52,000.

Results and discussion Interpolation procedures The program default value of tension is 40 and of smoothness is 0.1, but their combination showed large prediction errors, as shown by high RMSE (Table 3). After cross-validation for the 171 points, the lowest RMSE (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 was 1,534.6 mg kg−1, and the minimum and maximum values of the deviations between observed and estimated values were −27,813.1 and 15,060.0 mg kg−1, respectively. These extreme deviations occurred mainly due to the difficulty of the interpolator in adjusting the surface at the points located in areas of high

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contamination because the transition with areas of low contamination is quite abrupt (Hofierka et al. 2007). To investigate the relationship between observed and estimated values (Pb contents), a scatter plot was made (n=171, 57 sites×3 layers), whose trend line showed a coefficient of determination R2 of 0.97, thus showing a very satisfactory result for the available data. Three-dimensional distribution of the pseudo-total Pb contents The original contents of exchangeable and pseudo-total Pb in soil samples are presented in Table 4. Soils located at the highest elevations and far from the factory showed the smallest Pb contamination (