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Journal of Geochemical Exploration

journal homepage: www.elsevier.com/locate/gexplo

Proximal sensor analysis of mine tailings in South Africa: An exploratorystudy

Jaco Kocha, Somsubhra Chakrabortyb, Bin Lic, Jennifer Moore Kucerad, Piet Van Deventera,Angelique Daniella, Cindy Faula, Titus Mane, Delaina Pearsond, Bogdan Dudad,Camille A. Weindorff, David C. Weindorfd,⁎

a Department of Geology and Soil Science, Northwest University, Potchefstroom, South Africab Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Indiac Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA, USAd Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, USAe Department of Geography, Babeș-Bolyai University, Cluj-Napoca, Romaniaf Coronado High School, Lubbock, TX, USA

A R T I C L E I N F O

Keywords:Proximal sensorsSpectroscopyMine tailingsSouth Africa

A B S T R A C T

Proximal sensors have rapidly developed in-situ analysis of soil and sediment. Two systems in particular, por-table X-ray fluorescence (PXRF) spectrometry and visible near infrared diffuse reflectance spectroscopy (VisNIRDRS), have gained widespread acceptance for agronomic, pedological, and environmental quality assessment,but have scarcely been utilized to investigate mine tailings in South Africa. This study investigated 419 samplesacross four mines in South Africa using both PXRF and VisNIR DRS. Specifically, the PXRF was used to provideelemental data for comparison to X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDAX)coupled with scanning electron microscopy (SEM) for confirmation of tailings mineralogy. Next, PXRF data wasused to model first derivative (1D) VisNIR reflectance spectra. Results revealed many satisfactory calibrations(R2 > 0.70) relative to PXRF analysis with VisNIR DRS predictive models showing fair (RPD 1.4–2.0) to stable,accurate (RPD > 2.0) prediction of multiple elements. Use of better data partitioning methods and consideringthe target variability is likely to improve model accuracy further. Summarily, this study has established theeffectiveness of using VisNIR DRS for the evaluation of mine tailings, justifying further application developmentwith mine-specific models.

1. Introduction

Mining and mineral extraction are common in many parts of theworld. So-called “strip mining” for coal, salt, or metal ore results inlarge scale disturbance of the original solum, resulting in new soilswhich are considered human transported or human disturbed.Invariably, ore extraction results in processing whereby rocks are cru-shed and subjected to processes such as incineration (smelting) ortreatment with caustic chemicals (e.g., sodium cyanide) (Mpinga et al.,2014). The concentration of the target element in the ore can varywidely and is differentially economically feasible for extraction basedon current market prices. If ore deposits are rife with the element ofinterest, mines operate for prolonged periods of time, byproducts fromthe refinement process quickly accumulate, and can result in a massiveamount of sediment which must be placed somewhere, yet in a mannerconsistent with environmental standards. Often, such byproducts (mine

tailings) are simply piled in large repositories where they can reachkilometers in length and hundreds of meters high. These tailings oftenhave unique properties which vary from mine to mine based upon theoriginal geologic deposit mined and the processing applied to extractthe elements of interest. While extreme in some of their properties,these tailings do meet the definition of Anthorosols, soils which have“been modified profoundly through human activities” (UN-FAO, 2006).

Tailings are quite diverse in their properties both from mine tomine, but also even across tailings deposited at a singular mining site.Vegetative reestablishment on the tailings is particularly importantyet also very difficult to accomplish as extreme soil properties maypresent themselves in the form of high or low soil reaction (pH), sali-nity, compaction, surface crusting, high residual metal content, steepslopes, and limited soil moisture (Tordoff et al., 2000). Nonetheless,vegetation is considered a critical protection measure for securing thetailings in place such that they are less prone to wind or water erosion

http://dx.doi.org/10.1016/j.gexplo.2017.06.020Received 18 July 2016; Received in revised form 29 April 2017; Accepted 28 June 2017

⁎ Corresponding author.E-mail address: david.weindorf@TTU.edu (D.C. Weindorf).

Journal of Geochemical Exploration 181 (2017) 45–57

Available online 30 June 20170375-6742/ © 2017 Elsevier B.V. All rights reserved.

MARK

and movement off site. Tailings can fall into disrepair and pose a threatto surface and groundwater quality, and eolian transport/depositioninto nearby urban areas.

While mine companies frequently employ geologists for evaluationof the ore/minerals of interest, tailing management, environmentalmonitoring, and mitigation strategies remain challenging. Recently,portable x-ray fluorescence (PXRF) spectroscopy (Weindorf et al., 2014)and visible near infrared diffuse reflectance spectrometry (VisNIR DRS)(Horta et al., 2015) have been effectively utilized in a number of studiesto rapidly assess soil properties; many in conjunction with pollutants ordeleterious elements (Chakraborty et al., 2017, 2015; Clark andKnudsen, 2013). For example, Weindorf et al. (2013) studied soils im-pacted by smelting in Zlatna, Romania via PXRF and found that> 95%of the town exceeded the action limit for Zn, Cu, As, and Pb. UsingPXRF in Copşa Mică, Romania, Paulette et al. (2015) found Pb levelsexceeding governmental action limits across 100% of the area. Instudying Ireland soils, Radu and Diamond (2009) found strong corre-lations between PXRF determined metals in soils versus those de-termined by aqua regia digest with atomic absorption spectro-photometry (R2 between 0.843 and 0.996); these determinations weremade on elements typically associated with minerals digestible by aquaregia. Similarly, Jang (2010) compared PXRF to aqua regia digestionand found strong relationships between the datasets for soils in SouthKorea, therefore recommending it as a highly applicable technique forboth in-situ and ex-situ analysis. By comparison, Bray et al. (2009) usedboth mid-infrared and VisNIR DRS to evaluate Cd, Cu, Pb, and Zn inurban soils, concluding that Zn and Cu had the best prediction ac-curacies. Contrariwise, Xie et al. (2012) found only moderate predictivemodel accuracy for Cu, Pb, and Cd in soils near a large Cu smelter inChina; poor model performance was found for Zn. While both PXRF andVisNIR DRS have been shown effective for quantifying metal contentindependently, new research has shown that combining both spectraldata from VisNIR with elemental data from PXRF strengthens predictivemodels considerably (Aldabaa et al., 2015; Wang et al., 2015;Chakraborty et al., 2015). Applied to mine tailings, VisNIR DRS is moreapplicable to tailings/soil/ore mineralogy (Hunt, 1977), while PXRFprovides total elemental data for each sample analyzed. Furthermore,compared with PXRF, VisNIR DRS is a more widely utilized technique,especially with regard to proximal sensor analysis of soil. For example,

the Africa Soil Information Service has an extensive VisNIR spectrallibrary available for soils, with much less PXRF data available.

While PXRF and VisNIR DRS are increasingly used techniques(Bourke and Ross, 2015; Vaillant et al., 2014; Hauff, 2008) in Europe,North America, Asia, and Australia, they have been applied much moresparsely to mining operations in Africa. Specifically, mining companiesare keen to see how proximally sensed data supports their more tradi-tional investigative methods such as electron microscopy and X-raydiffraction analysis both for the evaluation of tailings on-site as well asenvironmental quality assessment of soils in surrounding areas off-sitein South Africa. As such, the objective of this study was to scan SouthAfrican mine tailings using both PXRF and VisNIR DRS, comparingproximal sensor data to traditionally derived physiochemical/miner-alogical data. Using PXRF as elemental baseline data with deference towell established methods, we hypothesize that VisNIR DRS will besignificantly correlated with various elements linked to soil mineralogyand clay content; thus quickly providing discernment of importantdifferences in tailings across mine sites of South Africa. While somegeochemical compositions will elude proximal sensor approaches dueto inherent limitations (e.g., non-differentiation of oxidation state,moisture effects, etc.) they do provide a stream of easily acquiredquantitative data that can be useful for mine tailing assessment. In es-sence, such data can augment and quickly confirm or refute visual in-spection of mine tailings.

2. Materials and methods

2.1. General occurrence and features

In this study, a total of 419 mine tailing samples were collected fromfour mining sites in Northern South Africa (Fig. 1). A brief descriptionof each mineralogical setting follows.

2.1.1. SITE 1The base of the Black Reef Quartzite Formation is characterized as

an unconformity while the upper limit represents a transition into thedolomites of the Oaktree Formation of the Malmani Subgroup. Highconcentrations of pyrite, chlorite, and carbonaceous material are foundin the Black Reef. The lower portion consists of siliceous quartzite with

Fig. 1. Examples of mine tailings analyzed in South Africa.Remnant deposits at sites A) 2, B) 3, C) 3, and D) 1.

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layers of conglomerates. On top of this lower portion, carbonaceousshale and alternating layers of quartzite and shale are found. The basalconglomerate consists of vein-quartz pebbles, chert, quartzite, quartzporphyry, shale, lava, agate, and pyrite and is known as one of theprincipal ore bodies. A layer of well-mineralized conglomerate, rich ingold, is found above the basal conglomerate. This layer also containsaccessory pyrite and carbon. The conglomerate layer is overlain byquartzites and carbonaceous shale. Ore minerals found in the BlackReef include: gold, pyrite, carbon, sphalerite, chalcopyrite, chromite,pyrrhotite, and ilmenite (Aucamp, 2000; King et al., 2007) (Fig. 2).

2.1.2. SITE 2Copper deposits are situated in the Koperberg Suite, an assemblage of

predominantly basic intrusive rocks, forming part of the Nama Group,which in return form part of the Namaqualand Metamorphic Complex. TheKoperberg Suite generally forms narrow dyke-like bodies, associated witholder fold structures and small bodies of megabreccia (Anhaeusser andMaske, 1986). Copper mineralization is typically associated with basic rocks(hypersthenite, norite or mica diorite) that are found in the form of plugs ordykes, folded metamorphic rocks or younger granite. Even though thedistribution of these basic rocks as well as their copper grade does not followa typical pattern, their emplacement was influenced by various shear folds.Chalcopyrite, bornite, chalcocite, and pyrrhotite are the most commonsulfide minerals in the Koperberg Suite, while ilmenite, spinel, and hematiteare less abundant. Pyrite and galena are found in lower concentrations. Themost common ore minerals are magnetite and ilmenite (Beale, 1985). Mi-nerals found in trace amounts include: valleriite, millerite, niccolite, mo-lybdenite, linnaeite, melonite, sylvanite, hessite, coloradoite, and tetra-dymite (Anhaeusser and Maske, 1986) (Fig. 2).

2.1.3. SITE 3Site 3 is situated in the Northern Cape province of South Africa. This

deposit forms part of the Precambrian metavolcanic-metasedimentaryBushmanland Group, which also forms part of the NamaqualandMetamorphic Complex. The sulfide bodies that form part of this depositare hosted in banded iron formation. These iron formation horizons aretypically separated by schist layers which are overlain by quartzite. TheUpper Ore Body consists of magnetite quartzite, magnetite-amphiboliteas well as barite-magnetite (and is enriched in copper), while the LowerOre Body is comprised of a baritic to quartzitic schist which grades intomagnetite-amphibolite. This area frequently contains high concentra-tions of lead and copper with zinc and silver (Bailie et al., 2007)(Fig. 2).

2.1.4. SITE 4In the area of site 4, Kimberlite pipes were emplaced approximately

85 Ma ago into the Turffontein Subgroup and Johannesburg Subgroupof the Upper Central Rand Group which forms part of theWitwatersrand Supergroup (Johnson et al., 2006). These kimberlitesare mainly olivine dominant but maybe accompanied by ilmenite, py-rope, diopside, phlogopite, enstatite, and chromite (Winter, 2010). Thematrix of this group typically contains second generation olivine withone or more primary minerals namely ilmenite, monticellite, phlogo-pite, perovskite, spinel, and apatite (Cairncross, 2004). Carbonate andserpentine constitute a late-stage groundmass. The replacement of pri-mary minerals by serpentine and calcite may occur during the coolingof the kimberlite. Beside the common mineralogy of kimberlites, a widevariety of clay minerals such as montmorillonite, nontronite, halloysite,kaolinite, sepiolite, stevensonite, vermiculite, talc, and saponite

2

1

3

4

Fig. 2. General location of sampling sites in South Africa.

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(Mitchell, 1986) have also been reported within kimberlites, due toweathering (Fig. 2).

2.2. Field collection and lab analysis

Sampling was conducted in May 2015, including samples from foursites; specifically, a Cu mine, a Cu-Zn-Pb mine, an Au-U mine, and akimberlite (diamond) mine. Using the edges of tailings piles asboundaries, samples were collected across the tailings at each site viageneralized transecting with localized randomization such that thenumber of samples collected on each tailings site was ~100. A limitednumber of samples were collected in nearby towns (often towns inwhich mine employees live). Reference soils of the same general area(e.g., 2–6 km away from the mine site) were collected to assess possibleaeolian transport of tailings off-site. At each site, the sampling locationswere geolocated using Garmin E-trex global positioning system re-ceivers (Olathe, KS, USA). Samples were collected with a standard fieldtrowel at depths of 0–5 cm. In most tailings, we observed no substantivedifferences between material at the surface (0–5 cm) and those found atgreater depth. In instances where vegetation, detritus, or crusts werepresent, these were scraped aside such that the underlying un-consolidated soil material could be collected. Samples were placed inplastic bags, sealed and transported to the laboratory for analysis.VisNIR DRS and PXRF scanning were conducted at NorthwestUniversity within 24–48 h after sample collection; all samples werecompletely air dry at the time of scanning. Collected samples were thendisaggregated to pass a 2 mm sieve in preparation for further labcharacterization.

Particle size analysis was achieved through laser diffraction(International Organization for Standardization, 1999) using a Mas-tersizer 2000 (Malvern Instruments Ltd., Worcestershire, UK) instru-ment. A percentage of grain sizes passing 56 different size classes wasreported and used to assign the relative percentages of sand, silt, andclay sized grains.

2.3. X-ray diffraction (XRD) and scanning electron microscopy (SEM)analyses

To confirm the soil mineralogy, powder XRD data of three selectedsoil samples from each mine were collected on a Rigaku Ultima IIIpowder diffractometer (Rigaku Corporation, Tokyo, Japan) equippedwith Cu Kα radiation (λ = 1.54187 Å) and a scintillation detector. The

data were collected in parallel beam geometry utilizing continuousmode from 3 to 80° 2ϴ, step width of 0.02°, and collection time of 3.75 sper step. Data analysis was performed using MDI Jade v9.1.1 software.SEM images of the samples were acquired using an environmentalscanning electron microscope (TM-1000, Hitachi, Japan) with an ac-celerating voltage of 15 KeV. Film samples were placed onto a carbondisk attached to the SEM sample stage, and observed under the SEMwith no special preparation. An S-4700 (Hitachi, Japan) SEM with anEDAX energy dispersive X-Ray (EDX) spectrometer was utilized forelemental analysis, featuring a 30° take-off angle for quantitative ana-lysis, digital imaging, and X-ray mapping. The EDAX SAPPHIRE de-tector has a 20,000:1 peak-to-background ratio, and 128 eV resolutionmaintained at high throughputs. EDAX Phoenix microanalysis systemcontains the EDAM III data acquisition module, which allows for flex-ibility and enhanced performance through digital signal processing forspectral and image acquisition and data reduction.

2.4. Portable x-ray fluorescence spectrometry

An Olympus (Waltham, MA, USA) DP-6000 Delta Premium portableX-ray fluorescence spectrometer was used for soil and tailings analysisper US-EPA (2007) and Weindorf and Chakraborty (2016). The in-strument features a Rh X-ray tube operated at 10–40 KeV. Elementalquantification is accomplished via integrated ultra-high resolution(165 eV) silicon drift detector. The PXRF was operated in GeochemMode, which consists of two beams that scan the soil sequentially. Eachbeam was set to scan for 30 s., such that one complete scan of a samplewas completed in 60 s. Geochem Mode was used to determine elementssuch as: Mg, Al, Si, P, S, K, Ca, Ti, Mg, Fe, Ni, Cu, and Zn. Soils/tailingswere analyzed in the sampling bag as received from the field but withthe instrument placed in direct contact with the soil. As the sampleswere all dry, any dust left on the Prolene thin film covering the apertureof the instrument was cleaned with compressed air and kim wipes be-tween sample scans to prevent any cross contamination. Prior toscanning, the PXRF was calibrated with a ‘316’ alloy calibration clip,tightly fitted over the instrument aperture. Assessment of instrumentperformance was accomplished via scanning of four NIST certified re-ference soils (2710a, 2711a, 2701, and 2782); PXRF reported/NISTcertified [recovery] values were tabulated to calculate appropriatecorrection factors (Table 1).

2.5. Visible near infrared diffuse reflectance spectroscopy

Spectral reflectance of air dried soil samples was measured proxi-mally over the VisNIR region (350–2500 nm) by a portable PSR-3500®VisNIR spectroradiometer (Spectral Evolution, USA). The re-flectance data were resampled to 1 nm output values. Scanning wasdone with a contact probe containing a 5 W halogen lamp, minimizingerrors associated with stray light during measurements. Each samplewas uniformly tiled in a metal moisture box and scanned four times,physically repositioning the probe prior to each scan. The mean of 10internal scans over 1.5 s produced one individual scan. The spectro-radiometer was standardized after every two samples using an NISTcertified white reference. The average spectral curve was calculated andfurther used for spectral preprocessing and subsequent heavy metalpredictive modeling. Necessary treatments of raw average reflectancespectra were accomplished in R version 2.11.0 (R Development CoreTeam, 2014) following the spline fitting methods outlined inChakraborty et al. (2014). The preprocessing method selected was Sa-vitzky–Golay first derivative (1D) for its computational simplicity (Luoet al., 2005). The 1D transformation of the reflectance spectra wasexecuted in the Unscrambler®× 10.3 software (CAMO Software Inc.,Woodbridge, NJ).

Shi et al. (2014) concluded that VisNIR DRS has potential for thesimultaneous estimation of the various heavy metal concentrations insoil. Therefore, for each site, partial least squares regression (PLSR)

Table 1Elemental mean recovery by portable X-ray fluorescence spectrometry and correctionfactors applied based upon NIST certified reference values.

Element NIST2710a

NIST2711a

NIST2701

NIST2782

Meanrecovery

Correctionfactor

Al (%) 0.98 0.99 0.78 0.72 0.87 1.15As (mg kg−1) 0.94 0.69 0.68 0.77 1.30Ca (mg kg−1) 0.66 1.18 1.23 1.12 1.05 0.95Cu (mg kg−1) 1.12 1.24 1.07 1.14 0.88Fe (%) 1.23 1.27 1.18 1.15 1.21 0.83Pb (mg kg−1) 1.04 1.11 1.14 1.10 0.91Mn (mg kg−1) 1.01 1.02 1.311 1.11 0.90P (mg kg−1) 1.46 1.64 1.55 0.65K (%) 0.97 0.90 0.93 1.07Si (%) 0.83 0.79 0.74 0.79 1.27Ti (mg kg−1) 0.94 0.97 0.81 0.90 1.11Zn (mg kg−1) 1.00 1.04 1.02 0.98U (mg kg−1) 1.00 1.00 1.00Th (mg kg−1) 1.39 1.60 1.49 0.67Cd (mg kg−1) 1.22 1.22 0.82Ni (mg kg−1) 0.91 0.91 1.10V (mg kg−1) 3.30 0.8 2.05 0.49Cr (mg kg−1) 0.82 0.82 1.22Mg (mg kg−1) 0.72 0.72 1.39S (mg kg−1) 1.01 1.01 0.99

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models targeting PXRF reported metal contents were built with 1Dspectra. The whole dataset was split a priori into a 70% calibration and30% validation set based on the Kennard-Stone algorithm (Kennard andStone, 1969), which is an adaptive procedure to select the most re-presentative samples based on Euclidean distance. Calibration sampleswere used to establish a prediction model, whereas validation sampleswere used to assess the model's predictive ability (Chen et al., 2015).The regression coefficient (R2), root mean squared error (RMSE) and

residual prediction deviation (RPD) (Aldabaa et al., 2015) were used asobjective criteria.

3. Results and discussion

3.1. Mine tailings characterization

Table 2 exhibits the summary statistics of mine tailing elements

Table 2Summary statistics of elements identified by PXRF in mine tailings at four different mine sites in South Africa.

Statistic Minimum Maximum Range 1st quartile Median 3rd quartile Mean Standard deviation Skewness Kurtosis

mg kg−1

Site 1 (n = 106)Si 225,876.95 510,908.53 285,031.57 345,832.42 409,733.06 439,106.09 393,230.45 65,125.27 −0.50 −0.47S 441.48 31,653.29 31,211.81 3596.95 5674.00 10,082.14 7655.32 6141.82 1.84 3.88K 493.95 32,438.44 31,944.49 4610.82 6894.90 10,953.22 8561.34 6359.29 1.25 1.50Ti 902.21 5730.25 4828.03 1331.46 1711.19 2402.71 1990.76 936.95 1.48 2.13Cr 88.69 450.81 362.12 143.55 185.42 317.32 227.36 108.72 0.62 −0.98Mn 27.63 5312.08 5284.45 42.06 64.79 140.66 328.26 770.88 4.23 20.89Fe 7414.72 65,089.15 57,674.43 13,790.35 19,702.36 27,207.05 22,122.94 10,554.47 1.34 2.10Cu 8.20 48.29 40.09 12.23 16.03 26.70 21.03 12.04 1.01 −0.30Zn 7.49 364.22 356.72 17.78 26.93 51.09 45.75 54.32 3.33 13.11As 3.30 258.45 255.15 47.36 59.32 84.05 70.72 38.84 1.78 4.75Rb 16.11 152.58 136.47 32.06 39.10 51.16 48.08 27.08 1.73 3.11Sr 7.95 35.18 27.23 11.75 14.56 17.01 15.00 4.76 1.62 4.34Y 4.57 36.98 32.41 8.89 11.66 17.47 14.25 7.35 1.30 1.23Zr 76.32 444.45 368.13 134.77 163.00 208.55 187.03 82.51 1.32 1.13Pb 4.522 185.12 180.60 21.99 32.49 54.76 40.60 29.17 2.07 6.22

Site 2 (n = 105)Al 27,730.08 105,717.63 77,987.54 71,445.97 80,516.53 88,665.64 78,388.72 14,694.63 −0.84 0.59Si 96,691.29 372,741.56 276,050.26 185,146.24 274,545.25 311,207.43 251,607.65 74,456.64 −0.32 −1.08P 246.21 5009.15 4762.93 1681.40 2136.33 2765.52 2190.63 850.06 0.32 0.65S 917.00 100,214.64 99,297.63 2414.70 6699.72 28,862.78 17,904.58 19,328.10 1.40 2.19K 2677.12 31,049.40 28,372.27 5858.97 11,274.53 15,373.94 11,286.95 6087.65 0.64 0.05Ti 2583.28 17,013.36 14,430.08 4948.09 7015.73 9702.80 7405.18 2869.21 0.48 −0.41V 73.65 322.36 248.70 159.75 208.81 236.12 201.56 51.82 −0.14 −0.49Cr 41.39 1240.76 1199.37 188.71 597.75 765.51 493.38 315.01 0.08 −1.42Fe 18,742.02 126,952.04 108,210.01 71,416.21 79,404.31 90,299.58 79,927.57 19,871.36 −0.53 1.67Rb 35.87 302.84 266.97 62.71 128.02 142.44 114.48 54.34 0.68 0.78Sr 39.39 659.56 620.17 233.82 265.04 343.71 287.34 97.19 0.66 1.43Y 19.94 120.77 100.83 38.29 61.14 86.98 63.99 28.30 0.22 −1.23Zr 54.46 1108.27 1053.81 210.06 393.27 554.76 389.90 220.32 0.52 −0.16Th 7.73 55.21 47.48 22.26 28.60 35.07 28.80 10.45 0.11 0.10

Site 3 (n = 105)Al 7803.60 93,349.61 85,546.00 23,055.13 26,056.94 29,372.27 28,281.13 13,875.30 3.47 12.51Si 25,512.09 394,013.30 368,501.21 149,578.35 174,452.95 212,069.95 190,284.75 65,411.97 1.26 2.18S 519.64 64,697.07 64,177.43 17,055.51 29,173.69 43,996.65 30,543.80 16,893.03 0.14 −0.95Ti 543.74 3516.49 2972.75 1139.37 1635.19 2027.37 1648.82 642.70 0.55 0.06Fe 9209.66 526,967.07 517,757.40 266,863.45 321,649.05 378,785.59 315,149.72 97,653.50 −1.15 2.35Zn 24.16 28,652.64 28,628.48 4733.65 7649.75 10,510.09 8571.49 5509.55 1.25 2.05Rb 27.62 283.91 256.29 54.35 65.86 81.99 75.33 43.59 3.14 10.47Sr 24.89 96.45 71.56 34.44 40.35 48.94 42.99 12.21 1.51 3.40Zr 7.55 216.46 208.91 26.06 43.60 62.94 50.68 36.68 1.95 4.88Pb 30.23 9825.66 9795.42 2210.24 3072.76 4968.65 3497.36 1781.04 0.46 0.30Th 7.07 59.49 52.41 25.85 32.78 40.55 33.52 11.15 0.06 −0.22U 10.42 45.76 35.34 16.10 22.69 30.35 23.51 8.99 0.57 −0.41

Site 4 (n = 103)Mg 10,089.81 140,960.33 130,870.52 57,328.74 83,179.67 104,311.74 76,758.75 34,431.62 −0.49 −0.71Al 20,641.54 77,057.41 56,415.87 28,066.89 35,714.15 57,026.89 41,996.40 16,417.19 0.54 −1.16Si 111,566.71 300,321.97 188,755.26 150,628.86 164,763.74 215,129.28 182,180.13 43,462.11 0.69 −0.46P 367.46 4342.56 3975.10 1995.03 2412.20 2755.10 2229.51 857.37 −0.45 −0.15K 1642.37 24,696.93 23,054.56 4666.23 6060.36 9141.21 7613.44 4513.09 1.51 1.84Ca 846.89 107,180.38 106,333.49 21,963.19 39,450.92 50,069.17 36,646.22 20,652.12 0.28 0.60Cr 49.86 779.96 730.10 113.93 250.67 372.09 264.22 153.29 0.61 0.24Mn 225.58 2126.96 1901.38 582.01 945.36 1199.60 905.59 364.68 0.01 −0.20Fe 18,625.16 163,863.40 145,238.24 49,121.15 78,554.63 100,848.09 76,571.38 29,747.17 −0.19 −0.69Ni 15.71 916.34 900.63 159.11 471.76 613.82 424.31 253.12 −0.29 −1.16Cu 11.6 211.19 199.59 54.47 89.24 109.33 82.75 38.08 0.11 0.36Zn 22.5 191.57 169.07 74.30 81.34 89.09 79.29 22.68 0.67 6.28Rb 22.63 196.64 174.01 46.06 55.03 79.95 66.53 30.90 1.54 2.37Y 8.91 37.15 28.24 14.12 15.89 20.03 17.34 5.34 1.09 1.13

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Table 3Correlation between PXRF sensed elements (mg kg−1) and clay (%) in mine tailings at four different mine sites in South Africa. Values in bold are different from 0 with a significance levelα = 0.05.

Site 1

Variable Si S K Ti Cr Mn Fe Cu Zn As Rb Sr Y Zr Pb Clay (%)Si 1.00S −0.63 1.00K −0.67 0.31 1.00Ti −0.69 0.33 0.94 1.00Cr −0.71 0.35 0.88 0.92 1.00Mn −0.20 0.05 0.02 0.13 0.00 1.00Fe −0.85 0.48 0.80 0.77 0.80 0.13 1.00Cu −0.36 −0.13 0.28 0.14 0.20 0.03 0.47 1.00Zn −0.40 −0.06 0.45 0.39 0.35 0.15 0.38 0.67 1.00As −0.08 0.01 −0.34 −0.39 −0.16 −0.08 0.11 0.38 −0.03 1.00Rb −0.65 0.17 0.97 0.94 0.87 0.06 0.79 0.33 0.54 −0.31 1.00Sr 0.07 −0.41 −0.21 −0.21 −0.21 0.45 0.03 0.32 0.09 0.46 −0.08 1.00Y −0.67 0.23 0.94 0.96 0.91 0.03 0.78 0.29 0.53 −0.33 0.96 −0.19 1.00Zr −0.60 0.42 0.83 0.91 0.91 −0.03 0.66 −0.05 0.19 −0.35 0.78 −0.37 0.86 1.00Pb −0.28 0.02 −0.02 −0.08 −0.11 0.63 0.28 0.44 0.22 0.35 0.02 0.65 −0.08 −0.27 1.00Clay (%) −0.56 −0.02 0.63 0.57 0.55 −0.02 0.60 0.55 0.71 −0.02 0.73 0.11 0.69 0.34 0.20 1.00

Site 2

Variables Al Si P S K Ti V Cr Fe Rb Sr Y Zr Th Clay (%)Al 1.00Si 0.80 1.00P 0.32 0.36 1.00S −0.60 −0.85 −0.32 1.00K 0.63 0.85 0.10 −0.74 1.00Ti 0.47 0.70 0.52 −0.73 0.59 1.00V 0.55 0.64 0.65 −0.68 0.46 0.82 1.00Cr 0.57 0.80 0.49 −0.77 0.62 0.81 0.77 1.00Fe −0.50 −0.51 0.29 0.43 −0.56 −0.16 −0.13 −0.21 1.00Rb 0.58 0.86 0.12 −0.83 0.91 0.70 0.57 0.74 −0.59 1.00Sr −0.13 −0.56 −0.16 0.52 −0.64 −0.54 −0.29 −0.50 0.22 −0.71 1.00Y 0.54 0.67 0.67 −0.64 0.47 0.81 0.77 0.79 −0.07 0.57 −0.50 1.00Zr 0.52 0.73 0.56 −0.71 0.58 0.82 0.79 0.85 −0.11 0.67 −0.51 0.81 1.00Th 0.42 0.53 0.35 −0.57 0.45 0.65 0.56 0.64 −0.03 0.52 −0.31 0.68 0.74 1.00Clay (%) 0.00 0.14 0.19 −0.23 0.13 0.22 0.19 0.23 0.06 0.14 −0.37 0.33 0.20 0.19 1.00

Site 3

Variables Al Si S Ti Fe Zn Rb Sr Zr Pb Th U Clay (%)Al 1.00Si 0.49 1.00S −0.30 −0.50 1.00Ti 0.20 0.12 0.18 1.00Fe 0.29 0.02 −0.45 −0.44 1.00Zn 0.10 −0.20 0.10 −0.40 0.36 1.00Rb 0.26 0.03 0.19 −0.02 −0.06 0.08 1.00Sr 0.17 0.14 0.20 0.59 −0.28 −0.30 −0.03 1.00Zr 0.23 0.00 0.32 0.24 −0.21 0.06 0.00 0.23 1.00Pb −0.23 −0.30 0.78 0.33 −0.48 −0.16 0.28 0.37 0.29 1.00Th 0.06 −0.18 0.05 −0.40 0.59 0.56 0.04 −0.28 0.07 −0.03 1.00U 0.23 0.08 −0.48 −0.37 0.73 0.34 0.00 −0.23 −0.19 −0.50 0.36 1.00Clay (%) −0.01 −0.13 −0.24 0.13 0.09 0.06 −0.21 −0.05 −0.13 −0.25 −0.19 0.18 1.00

Site 4

Variables Mg Al Si P K Ca Cr Mn Fe Ni Cu Zn Rb Y Clay (%)Mg 1.00Al −0.87 1.00Si −0.79 0.93 1.00P 0.87 −0.79 −0.76 1.00K −0.70 0.87 0.78 −0.61 1.00Ca 0.58 −0.76 −0.75 0.62 −0.68 1.00Cr 0.73 −0.65 −0.63 0.62 −0.52 0.44 1.00Mn 0.65 −0.54 −0.51 0.58 −0.36 0.42 0.67 1.00Fe 0.85 −0.79 −0.74 0.76 −0.60 0.52 0.65 0.60 1.00Ni 0.95 −0.88 −0.83 0.89 −0.71 0.60 0.72 0.64 0.82 1.00Cu 0.84 −0.81 −0.79 0.84 −0.63 0.56 0.60 0.58 0.77 0.88 1.00Zn −0.11 0.25 0.11 0.00 0.46 −0.22 −0.10 −0.02 −0.01 −0.07 0.05 1.00

(continued on next page)

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identified by PXRF for all four sites. For all sites, most analytes ex-hibited high variation, implying the inherent geochemical diversity andvariation in degree of mineralization of the analyzed tailing samples(Johnson et al., 2000). As expected, intra-site variability of elements

analyzed by PXRF exhibited wide ranges. For instance, at sites 1 and 3Pb concentrations ranged from 4.52 to 185.12 and 30.23 to9825.66 mg kg−1, respectively.

Except for Si and Cr, all other elements in site 1 were highly

Table 3 (continued)

Site 4

Rb −0.67 0.86 0.74 −0.58 0.96 −0.69 −0.51 −0.34 −0.61 −0.69 −0.60 0.46 1.00Y −0.60 0.75 0.65 −0.52 0.87 −0.58 −0.42 −0.19 −0.51 −0.60 −0.57 0.44 0.85 1.00Clay (%) −0.32 0.41 0.34 −0.25 0.32 −0.43 −0.30 −0.24 −0.33 −0.26 −0.34 0.17 0.33 0.34 1.00

Fig. 3. Plots showing a) mineralogy as determined by x-ray diffraction, b) elemental composition as determined by EDAX, and c) scanning electron microscopy image of a randomlyselected mine tailing sample from site 1 in South Africa.

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positively skewed along with high kurtosis, signifying several sampleswith low analyte concentrations and a small number of samples withhigh elemental concentrations (Table 2). Similar trends were observedfor site 2 (S), site 3 (Al, Si, Rb, Zn, and Sr), and site 4 (K, Rb, and Y).Except for S, Mn, As, Sr, and Pb, all elements analyzed by PXRF fromsite 1 were significantly correlated with the clay-sized particle contentof mine tailings (Table 3). Following the similar trend, except Zn allelements analyzed by PXRF of site 4 were significantly correlated withclay contents. Conversely, only five (S, Ti, Cr, Sr, and Y) and two (S andPb) elements exhibited significant correlation with clay contents at sites2 and site 3, respectively.

Samples from site 1 exhibited almost 1.5–2.15 folds higher Si con-tent than the other three sites, with a mean concentration of 39.32%signifying marked enrichment of silicate minerals. This justification wassubstantiated by XRD and SEM/EDAX analyses (Fig. 3). The XRD basedmineralogical characterization of a randomly selected sample (asidentified from the powder diffraction file) from site 1 exhibited thatthe most common mineral types were quartz, muscovite and hydro-niumjarosite (Fig. 3a). These were confirmed by the EDAX character-ization which identified the prevalence of Si along with minute quan-tities S, K, Ti, and Fe (Fig. 3b), also implying the possible presence ofpyrite. Although it was anticipated that the occurrence of Pb and Ascould be linked with sulfides (Navarro et al., 2008), the presence ofgalena (PbS) was not resolved by XRD. These results either implied theeffect of post-treatments of mine tailings which might have recoveredslight quantities of PbS from the tailings or the occurrence of heavymetals in non-crystalline forms (Rodriguez et al., 2009). Hence, it wasapparent that the whole chemical composition of mine tailings couldnot be resolved by only XRD and EDAX, thus requiring additional

sensors such as PXRF for total elemental analysis.Further, for site 2 the PXRF-reported higher concentrations of Si, Al,

K, and Fe were validated by the XRD pattern of another randomly se-lected sample from site 2, indicating the prevalence of silicon oxidealpha, coquimbite, hydroniumjarosite and primary mineralizationproducts like pyrite (Supplementary material SM, Fig. SM-1a).TheEDAX pattern also showed the presence of Si, Al, K, and Fe, as identifiedby PXRF (Fig. SM-1b). The whole XRD pattern fitting and Rietveld re-finement of a randomly selected sample from site 3 showed the pre-sence of magnetite (33% wt) and Cu (8.7% wt), as expected (Fig. SM-2a) and was further supported by the EDAX pattern (Fig. SM-2b).Although the results of Cu analyzed by PXRF were not presented inTable 2 due to its absence in some analyzed samples, it ranged from 10to 2693 mg kg−1 in 90 samples. In site 4, the higher values of Ca, Mg,Si, and Fe analyzed by PXRF were validated by the XRD and EDAXanalyses of a randomly selected sample which showed the occurrencesof calcite, fluorohectorite and traces of pigeonite (Fig. SM-3a) alongwith the EDAX analyzed peaks of Ca, Mg, Si, and Fe (Fig. SM-3b). Alongwith a number of mineralogical phases, the incidence of Fe and Mn atsite 4 can be attributed to the occurrence of some non-crystallineoxyhydroxides which were not recognized by XRD (Navarro et al.,2008).

3.2. Average reflectance spectra

The average VisNIR reflectance spectrum along with a swath in-dicating two standard deviation, computed from all samples of a givensite are shown in Fig. 4 and clearly demonstrate the diversity of thesamples. Strong absorption over the visible (350–700 nm) range for all

Site 1 Site 2

Site 3 Site 4

Fig. 4. The average VisNIR reflectance spectrum along with a swath representing two standard deviation from four mining sites in South Africa. The solid line represents the averagespectrum while the dotted lines represent two standard deviations.

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four sites indicated the concavity characteristic of the Fe oxides in-cidence (Vitorello and Galvão, 1996). Wu et al. (2005) reported that inthe visible region, Fe2O3 imparts spectral signatures owing to its ca-pacity to bond with other metal cations and O-H groups. Moreover, theabundance of several clay minerals left their spectral imprints arisingfrom hydroxyl, water, and carbonate overtones and combination bandsin the 1100–2500 nm range, as previously identified by Clark (1999).Except for site 3, average spectra from the other three sites exhibited awater absorption feature at 1900 nm. Although concentrations oftransition metals like Cu were below 4000 mg kg−1 in the analyzedsamples, the presence of an unfilled d shell might be responsible for itsspectral signature in sites 1, 2, and 3 near ~830 nm (Wu et al., 2007).Notably, the abundance of Ca (3.66%) at site 4 was reaffirmed by ex-amining the average reflectance spectra which clearly exhibited the

dominant band at ~2300 nm (3υ3), due to the presence of calcite. Thepresence of a vibrational band near ~1450 nm due to OH at site 1 gavethe indication of muscovite occurrence (Clark, 1999), as identified bythe XRD. Moreover, the presence of a weak absorption band at~2250 nm indicated the presence of hydroniumjarosite at site 1 (Krennet al., 2001).

3.3. PLSR calibrations and validations

The ability of VisNIR DRS to predict elemental concentrations inmine tailing was a major focus of this study. Given the accuracy ofPXRF for making elemental analyses (Rouillon and Taylor, 2016;Gazley and Fisher, 2014; Piercey and Devine, 2014; Vanhoof et al.,2013), PLSR models in combination with 1D spectral pretreatment were

Table 4Summary of model calibration and validation statistics using VisNIR DRS spectra and PXRF elements in mine tailings.

Site Analyte Calibration R2 Calibration RMSE (mg kg−1) Validation R2 Validation RMSE (mg kg−1) RPDa Slope

Site 1 Si 0.79 29,880.14 0.53 42,451.44 1.48 0.63S 0.95 1268.82 0.50 4969.41 1.43 0.74K 0.77 3167.55 0.54 3733.38 1.50 0.88Ti 0.60 657.48 n.s. 776.04 0.78 0.43Cr 0.63 64.53 0.11 103.46 1.09 0.36Fe 0.66 6450.95 0.18 8075.38 1.12 0.57Cu 0.58 8.45 n.s. 10.92 0.41 0.99Zn 0.46 39.31 0.45 40.56 1.37 0.43As 0.51 28.15 0.12 32.33 1.08 0.39Rb 0.71 15.13 0.31 19.73 1.22 0.69Sr 0.23 4.36 0.10 3.85 1.07 0.19Y 0.65 4.44 0.30 5.46 1.22 0.64Zr 0.74 41.46 0.26 67.69 1.18 0.67Pb 0.54 21.36 n.s. 24.94 0.87 0.20Mn 0.53 569.28 0.33 483.81 1.25 0.79

Site 2 Al 0.81 6177.42 0.80 6763.2 2.27 0.91Si 0.89 24,352.63 0.82 32,317.14 2.43 0.84P 0.28 719.51 0.17 724.15 1.12 0.27S 0.99 1213.97 0.68 13,583.23 1.79 0.66K 0.84 2243.02 0.75 3529.92 2.03 0.71Ti 0.54 1976.23 0.72 1411.28 1.94 0.69V 0.62 32.19 0.55 33.57 1.51 0.65Cr 0.81 135.39 0.75 157.24 2.05 0.75Fe 0.80 8645.99 0.61 12,979.61 1.62 0.70Rb 0.90 16.32 0.87 19.85 2.88 0.90Sr 0.68 56.25 0.52 61.41 1.47 0.56Y 0.63 17.13 0.73 14.41 1.95 0.71Zr 0.57 145.62 0.49 152.32 1.42 0.57Th 0.37 8.75 0.23 7.55 1.16 0.32

Site 3 Al 0.89 4556.11 0.83 5871.73 2.46 1.05Si 0.54 43,474.71 0.52 46,916.03 1.46 0.50S 0.72 8404.27 0.70 10,207.06 1.86 0.58Ti 0.21 614.45 n.s. 555.73 0.91 0.16Fe 0.71 51,217.48 0.72 53,188.46 1.92 0.69Zn 0.16 4957.73 0.20 5017.10 1.18 0.20Rb 0.77 18.64 0.84 20.81 2.54 0.73Sr 0.13 10.84 n.s. 13.09 1.00 0.08Zr 0.63 43.44 0.58 20.27 1.56 0.56Pb 0.85 601.90 0.62 1283.96 1.65 0.50Th 0.27 8.90 0.06 11.96 1.05 0.05U 0.36 7.27 0.31 7.08 1.22 0.33

Site 4 Mg 0.84 13,001.73 0.73 19,524.51 1.95 0.72Al 0.87 5801.76 0.70 8968.47 1.86 0.81Si 0.94 10,633.15 0.86 15,657.35 2.70 0.90P 0.83 343.84 0.74 450.97 1.99 0.96K 0.98 569.57 0.81 2447.95 2.34 0.76Ca 0.98 2424.35 0.64 13,486.33 1.71 0.89Cr 0.62 97.44 0.07 118.76 1.05 0.75Mn 0.79 149.26 0.34 347.15 1.25 0.38Fe 0.87 10,800.04 0.86 10,152.94 2.70 1.01Ni 0.92 65.59 0.79 126.492.20 2.20 0.95Cu 0.86 14.66 0.67 19.02 1.77 0.86Rb 0.97 4.47 0.75 18.15 2.02 0.75Zn 0.73 10.95 0.28 21.62 1.20 0.22Y 0.74 2.50 0.50 4.25 1.44 0.42

a RPD, residual prediction deviation.

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implemented to predict analytes for each site with independent vali-dation, producing a total of 15, 14, 12, and 14 models for sites 1, 2, 3,and 4, respectively (Table 4). In this research we used only 1D spectrasince it is one of the filters which can smooth out the reflectance signalwithout much destruction of its original properties; it is a commonlyused approach in VisNIR DRS. Aldabaa et al. (2015) reported thathigher spectral pretreatment like 1D can also eliminate the negativeinfluence of water signals (1900 nm). Moreover, Gholizadeh et al.(2015) reported better soil metal prediction accuracy while using 1D

spectra. Moros et al. (2009) proposed that accuracy of calibrationsmodels using un-processed reflectance spectra is influenced by its op-erating and surrounding environment. Despite researchers having oc-casionally reported reasonably good metal predictions with un-pro-cessed reflectance spectra (Kooistra et al., 2001), the use ofpreprocessed spectra is still recommended.

Since RPD is the ratio of standard deviation and RMSE, modelpredictability decreases when validation set standard deviation iscomparatively larger than estimation error (RMSE). Chang et al. (2001)

0 10000 20000 30000

010000

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Measured K (mg Kg−1

)

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ted

K (

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)

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Measured Si (mg Kg−1

)P

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Si (m

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Measured S (mg Kg−1

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Measured Mn (mg Kg−1

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Mn

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Measured Y (mg Kg−1

)

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Y (

mg

Kg

−1)

0 50 100 150

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Measured Rb (mg Kg−1

)P

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Rb

(m

g K

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)

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10

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Measured Zr (mg Kg−1

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Zr (

mg

Kg

−1)

Fig. 5. Plots showing PXRF-measured vs. PLSR calibration (blue circles) and validation (red dots) model-predicted elements of mine tailings at site 1 in South Africa using first derivativereflectance spectra. The solid line denotes the regression line, while the dashed line represents the 1:1 line. (For interpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

50 100 150 200 250 300

50

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Measured Rb (mg Kg−1

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(m

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Measured Si (mg Kg−1

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4e+04 8e+04

4e

+0

48

e+

04

Measured Al (mg Kg−1

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g K

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Measured Cr (mg Kg−1

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Measured K (mg Kg−1

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20 40 60 80 100 120

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Measured Y (mg Kg−1

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5000 10000 15000

50

00

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00

Measured Ti (mg Kg−1

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Ti (m

g K

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−20000 20000 60000 100000

−2

00

00

20

00

06

00

00

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00

00

Measured S (mg Kg−1

)

Predic

ted

S (

mg K

g−1

)

Fig. 6. Plots showing PXRF-measured vs. PLSR calibration (blue circles) and validation (red dots) model-predicted elements of mine tailings at site 2 in South Africa using first derivativereflectance spectra. The solid line denotes the regression line, while the dashed line represents the 1:1 line. (For interpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

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categorized the accuracy and stability of their spectroscopy modelsbased on the RPD values of the validation set. RPDs > 2.0 were con-sidered stable and accurate predictive models; RPD values between 1.4and 2.0 indicated fair models that could be improved by more accuratepredictive techniques; RPD values< 1.4 indicated poor predictive ca-pacity.

Model validation RPD values indicated that for site 1, fair modelswere obtained only for three elements (Si, S, and K) while other ele-ments showed poor model results (Table 4). Elements for site 3

produced slightly better results where Al (RPD = 2.46) and Rb(RPD = 2.54) produced stable models while five elements (Si, S, Fe, Zr,Pb, and U) exhibited fair model validation performance. On the con-trary, Si, K, Fe and Ni produced accurate models (RPD values> 2) forsite 4 while five elements (Mg, Al, P, Ca and Y) produced fair modelswith RPD values ranging between 1.44 and 1.99. The best correlationbetween VisNIR spectra and PXRF measured elements were obtainedfrom site 2 where five elements (Al, Si, K, Cr, and Rb) produced stablemodels with RPD values> 2 and seven elements (S, Ti, V, Fe, Sr, Y and

50 100 150 200 250

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Measured Rb (mg Kg−1

)

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)

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Measured Al (mg Kg−1

)P

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Measured S (mg Kg−1

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Measured Zr (mg Kg−1

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1e+05 2e+05 3e+05 4e+05

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Measured Si (mg Kg−1

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Measured Mn (mg Kg−1

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Mn (

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)

Fig. 7. Plots showing PXRF-measured vs. PLSR calibration (blue circles) and validation (red dots) model-predicted elements of mine tailings at site 3 in South Africa using first derivativereflectance spectra. The solid line denotes the regression line, while the dashed line represents the 1:1 line. (For interpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

150000 250000

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Measured Si (mg Kg−1

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Measured Fe (mg Kg−1

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Measured Rb (mg Kg−1

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Measured P (mg Kg−1

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(m

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Measured Al (mg Kg−1

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Fig. 8. Plots showing PXRF-measured vs. PLSR calibration (blue circles) and validation (red dots) model-predicted elements of mine tailings at site 4 in South Africa using first derivativereflectance spectra. The solid line denotes the regression line, while the dashed line represents the 1:1 line. (For interpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

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Zr) produced fair models. Figs. 5–8 show the predicted results versusactual results for the best eight elements for sites 1, 2, 3, and 4, re-spectively, using both calibration and validation datasets. In general, allmodels showed overestimation at lower analyte values and under-estimation at higher analyte values. Some of these underestimationscould be due to the relative scarcity of observations at the higher endsof the property scales, which was expected since several analytes werepositively skewed (Table 2).

Upon examining Table 3, it was clear that for some analytes poorvalidation R2 was obtained despite exhibiting reasonable calibrationperformance (For example: Cr in site 4). Notwithstanding the com-parative benefit of using the Kennard-Stone algorithm over the randomsampling, concentrations-wise alternative splitting and other data par-titioning techniques, a limitation of the former is that it does not con-sider the variability of the target variable (Galvão et al., 2005). Thus, itcan be concluded that the incorporation of target variability in the datapartitioning process might improve the distribution of calibration andvalidation samples, ultimately enhancing the robustness and predict-ability of the multivariate models, especially for the heterogeneousgeochemical nature of mine tailings.

Of late, an extensive review of literature made by Horta et al. (2015)advocated that the predictability of non-spectrally active heavy metalsis generally attributed to their adsorption to spectrally active SOM, Fe/Al oxides, and clay minerals. As evidenced by the XRD and EDAXanalyses, mine tailing samples consists of a host of clay minerals and Fe-oxides, which are responsible for VisNIR DRS based predictions ofmetals in this study. Despite some metals in mine tailings present intraces, their covariation with spectrally active chromophores are re-sponsible for acceptable PLSR model generalization. For example, insite 1 tailings, the high calibration R2 of the PLSR-K model (0.77) can beattributed to the significant correlation between spectrally featureless Kand clay content (ρ= 0.63) (Table 3). Notably, Wang et al. (2013)observed that Fe and Rb were of particular significance to predict claycontent via NIR spectroscopy. Our results converged with these findingssince both Rb and Fe were reasonably predicted by VisNIR DRS (Site 1),probably due to their significant correlation with clay (Tables 3 and 4).

3.4. Practical implications

Summarily, our study has confirmed the potential of using PXRF forquick elemental characterization of mine tailings, supplementing con-ventional analyses like XRD and EDAX. Further it was established thatVisNIR DRS can reasonably predict the elemental suite of many minetailings; thus quickly providing judgment on significant differences intailings across different mine sites. Again, VisNIR DRS is proximalsensing approach more commonly used than PXRF for soils analysis inAfrica. Together, PXRF and VisNIR DRS may represent new and prac-tical approaches for economic, quick and non-invasive mine tailingcharacterization. One challenge for this study was the heterogeneousgeochemical nature of mine tailings from variable types of mines and itis expected that elemental predictability can be increased with in-creasing geochemical homogeneity. Moreover, one should considerinterferences between elements in XRF spectra while interpreting thePXRF results (Gazley and Fisher, 2014). Further research is warrantedto decide on optimum sample size when these proximal soil sensors areutilized for rapid characterization of mine tailings along with miti-gating other challenges like moisture variability.

4. Conclusions

Proximal sensor analysis of mine tailings was investigated at fourdifferent mining sites across South Africa. Data obtained from PXRFspectrometry strongly supported the mineralogy obtained through XRDand EDAX analysis, and was used to model VisNIR spectra. In manyinstances, VisNIR spectra effectively modeled the elemental data. Manycorrelation coefficients were> 0.70 and RPDs supported fair to strong

model predictability. However, stronger predictive models could likelybe produced if models are centered on mine sites with relativelyhomogeneous mineralogy and with the adoption of better data parti-tioning methods. The spectral reflectance patterns observed differedsubstantively from other studies of soil, owing to their unique physi-cochemical properties inherited either genetically from parent materialrock (ore deposits) and/or physical and chemical processing and ex-traction methods. The alacrity and accuracy of these portable, proximalsensors provide important tools for use in the mining industry andshould be further studied for site specific applications.

Acknowledgements

The authors are grateful for financial support from the BL AllenEndowment in Pedology at Texas Tech University which made thisresearch possible.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.gexplo.2017.06.020.

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