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Spectroscopic-based assessment of the content and geochemical behaviour of arsenic in a highly heterogeneous sulphide-rich mine waste dump

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Abstract

Soil pollution by arsenic is a serious environmental problem in many mining areas. Quick identification of the amount and extent of the pollution is an important basis for developing appropriate remediation strategies. In a case study, 55 soil samples were collected from a highly heterogeneous waste dump around the Sarcheshmeh copper mine, south east Iran. Samples’ visible and near-infrared (VNIR) reflectance spectra were measured, transformed to absorbance and then pre-processed using Savitzky–Golay first-derivative (FD) and Savitzky–Golay second-derivative (SD) transformation methods. The obtained spectra were then subjected to three regression models including principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) for predicting arsenic concentration. The best prediction accuracies were obtained by SVR and PLSR methods applied on first-derivative pre-processed spectra with R 2 values of 0.81 and 0.69, respectively. It was found that VNIR spectroscopy is a successful method for predicting As concentration in contaminated soils of the dumpsites. Study of the prediction mechanism showed that the intercorrelation between arsenic and spectral features of soil including iron oxy/hydroxides and clay minerals was the major mechanism enabling the prediction of arsenic concentration. However, higher values of correlation coefficients at ~460, ~560 and ~590 nm suggested the internal association between arsenic and iron minerals as the more important mechanism for prediction. This conclusion supported previous speciation studies conducted in the same waste dump using improved correlation analysis and chemical sequential extraction method.

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References

  • Adeline KRM, Gomez C, Gorretta N, Roger JM (2017) Predictive ability of soil properties to spectral degradation from laboratory Vis-NIR spectroscopy data. Geoderma 288:143–153. doi:10.1016/j.geoderma.2016.11.010

    Article  Google Scholar 

  • Alloway B (2013) Heavy metals in soil: trace metals and metalloids in soil and their bioavailability. Springer, Reading

    Book  Google Scholar 

  • Bao N, Wu L, Ye B, Yang K, Zhou W (2017) Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory. Geoderma 288:47–55. doi:10.1016/j.geoderma.2016.10.033

    Article  Google Scholar 

  • Bech J, Poschenrieder C, Llugany M, Barceló J, Tume P, Tobias F, Barranzuela J, Vásquez E (1997) Arsenic and heavy metal contamination of soil and vegetation around a copper mine in Northern Peru. Sci Total Environ 203:83–91

    Article  Google Scholar 

  • Ben-Dor E, Banin A (1990) Near-infrared reflectance analysis of carbonate concentration in soils. Appl Spectrosc 44:1064–1069

    Article  Google Scholar 

  • Ben-Dor E, Chabrillat S, Demattê J, Taylor G, Hill J, Whiting M, Sommer S (2009) Using imaging spectroscopy to study soil properties. Remote Sens Environ 113:S38–S55

    Article  Google Scholar 

  • Cambou A, Cardinael R, Kouakoua E, Villeneuve M, Durand C, Barthès BG (2016) Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field. Geoderma 261:151–159. doi:10.1016/j.geoderma.2015.07.007

    Article  Google Scholar 

  • Cattle JA, McBratney A, Minasny B (2002) Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination. J Environ Qual 31:1576–1588

    Article  Google Scholar 

  • Centeno JA, Tseng C-H, Van der Voet GB, Finkelman RB (2007) Global impacts of geogenic arsenic: a medical geology research case. AMBIO J Hum Environ 36:78–81

    Article  Google Scholar 

  • Chakraborty S, Weindorf DC, Paul S, Ghosh B, Li B, Ali MN, Ghosh RK, Ray D, Majumdar K (2015) Diffuse reflectance spectroscopy for monitoring lead in landfill agricultural soils of India. Geoderma Reg 5:77–85

    Article  Google Scholar 

  • Chakraborty S, Weindorf DC, Deb S, Li B, Paul S, Choudhury A, Ray DP (2017) Rapid assessment of regional soil arsenic pollution risk via diffuse reflectance spectroscopy. Geoderma 289:72–81

    Article  Google Scholar 

  • Chang C-W, Laird DA, Hurburgh CR Jr (2005) Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties. Soil Sci 170:244–255

    Article  Google Scholar 

  • Chen T, Chang Q, Clevers J, Kooistra L (2015) Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy. Environ Pollut 206:217–226

    Article  Google Scholar 

  • Cheng Y-Y, Huang N-C, Chang Y-T, Sung J-M, Shen K-H, Tsai C-C, Guo H-R (2017) Associations between arsenic in drinking water and the progression of chronic kidney disease: a nationwide study in Taiwan. J Hazard Mater 321:432–439. doi:10.1016/j.jhazmat.2016.09.032

    Article  Google Scholar 

  • Choe E, van der Meer F, van Ruitenbeek F, van der Werff H, de Smeth B, Kim K-W (2008) Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: a case study of the Rodalquilar mining area, SE Spain. Remote Sens Environ 112:3222–3233. doi:10.1016/j.rse.2008.03.017

    Article  Google Scholar 

  • Choe E, Kim K-W, Bang S, Yoon I-H, Lee K-Y (2009) Qualitative analysis and mapping of heavy metals in an abandoned Au–Ag mine area using NIR spectroscopy. Environ Geol 58:477–482

    Article  Google Scholar 

  • Clark RN, King TV, Klejwa M, Swayze GA, Vergo N (1990) High spectral resolution reflectance spectroscopy of minerals. J Geophys Res Solid Earth 95:12653–12680

    Article  Google Scholar 

  • Collell C, Gou P, Arnau J, Comaposada J (2011) Non-destructive estimation of moisture, water activity and NaCl at ham surface during resting and drying using NIR spectroscopy. Food Chem 129:601–607

    Article  Google Scholar 

  • da Silva Chagas C, de Carvalho Junior W, Bhering SB, Calderano Filho B (2016) Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. Catena 139:232–240

    Article  Google Scholar 

  • Farifteh J, Van der Meer F, Atzberger C, Carranza E (2007) Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN). Remote Sens Environ 110:59–78

    Article  Google Scholar 

  • Fordham A, Norrish K (1983) The nature of soil particles particularly those reacting with arsenate in a series of chemically treated samples. Soil Res 21:455–477

    Article  Google Scholar 

  • Gannouni S, Rebai N, Abdeljaoued S (2012) A spectroscopic approach to assess heavy metals contents of the mine waste of Jalta and Bougrine in the north of Tunisia. J Geogr Inf Syst 4:242

    Google Scholar 

  • Genú AM, Demattê JAM (2011) Prediction of soil chemical attributes using optical remote sensing. Acta Sci Agron 33:723–727

    Article  Google Scholar 

  • Gholizadeh A, Boruvka L, Saberioon M (2015a) Spectroscopic approach to assess potentially toxic elements of reclaimed dumpsites in the Czech Republic. Int J Environ Sci Dev 6:571

    Article  Google Scholar 

  • Gholizadeh A, Borůvka L, Saberioon MM, Kozak J, Vašát R, Němeček K (2015b) Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Res 10:218–227

    Article  Google Scholar 

  • Gholizadeh A, Borůvka L, Vašát R, Saberioon M, Klement A, Kratina J, Tejnecký V, Drábek O (2015c) Estimation of potentially toxic elements contamination in anthropogenic soils on a brown coal mining dumpsite by reflectance spectroscopy: a case study. PLoS ONE 10:e0117457

    Article  Google Scholar 

  • Gomez C, Lagacherie P, Coulouma G (2012) Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data. Geoderma 189:176–185

    Article  Google Scholar 

  • Hearst MA, Dumais ST, Osman E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13:18–28

    Article  Google Scholar 

  • Hong-Yan R, Zhuang D-F, Singh A, Jian-Jun P, Dong-Sheng Q, Run-He S (2009) Estimation of As and Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: a case study. Pedosphere 19:719–726

    Article  Google Scholar 

  • Jannesar Malakooti S, Shafaei Tonkaboni SZ, Noaparast M, Doulati Ardejani F, Naseh R (2014) Characterisation of the Sarcheshmeh copper mine tailings, Kerman province, southeast of Iran. Environ Earth Sci 71:2267–2291

    Article  Google Scholar 

  • Jung MC, Thornton I, Chon H-T (2002) Arsenic, Sb and Bi contamination of soils, plants, waters and sediments in the vicinity of the Dalsung Cu–W mine in Korea. Sci Total Environ 295:81–89

    Article  Google Scholar 

  • Kemper T, Sommer S (2002) Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ Sci Technol 36:2742–2747

    Article  Google Scholar 

  • Khorasanipour M, Aftabi A (2011) Environmental geochemistry of toxic heavy metals in soils around Sarcheshmeh porphyry copper mine smelter plant, Rafsanjan, Kerman, Iran. Environ Earth Sci 62:449–465

    Article  Google Scholar 

  • Knox N, Grunwald S, McDowell M, Bruland G, Myers D, Harris W (2015) Modelling soil carbon fractions with visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy. Geoderma 239:229–239

    Article  Google Scholar 

  • Kooistra L, Wehrens R, Leuven R, Buydens L (2001) Possibilities of visible–near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Anal Chim Acta 446:97–105

    Article  Google Scholar 

  • Kooistra L, Wanders J, Epema G, Leuven R, Wehrens R, Buydens L (2003) The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal Chim Acta 484:189–200

    Article  Google Scholar 

  • Li M (2006) Ecological restoration of mineland with particular reference to the metalliferous mine wasteland in China: a review of research and practice. Sci Total Environ 357:38–53

    Article  Google Scholar 

  • Madejova J, Komadel P (2001) Baseline studies of the clay minerals society source clays: infrared methods. Clays Clay Miner 49:410–432

    Article  Google Scholar 

  • Martens H, Naes T (1989) Multivariate calibration. Wiley, Chichester

    Google Scholar 

  • McCarty G, Reeves J, Reeves V, Follett R, Kimble J (2002) Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci Soc Am J 66:640–646

    Article  Google Scholar 

  • Mori M, K-i Tsunoda, Aizawa S, Saito Y, Koike Y, Gonda T, Abe S, Suzuki K, Yuasa Y, Kuge T (2017) Fractionation of radiocesium in soil, sediments, and aquatic organisms in Lake Onuma of Mt. Akagi, Gunma Prefecture using sequential extraction. Sci Total Environ 575:1247–1254

    Article  Google Scholar 

  • Mouazen AM, Kuang B, De Baerdemaeker J, Ramon H (2010) Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 158:23–31. doi:10.1016/j.geoderma.2010.03.001

    Article  Google Scholar 

  • Nachman KE, Ginsberg GL, Miller MD, Murray CJ, Nigra AE, Pendergrast CB (2017) Mitigating dietary arsenic exposure: current status in the United States and recommendations for an improved path forward. Sci Total Environ 581:221–236

    Article  Google Scholar 

  • Nawar S, Buddenbaum H, Hill J, Kozak J, Mouazen AM (2016) Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil Tillage Res 155:510–522. doi:10.1016/j.still.2015.07.021

    Article  Google Scholar 

  • Nayak PS, Singh B (2007) Instrumental characterization of clay by XRF, XRD and FTIR. Bull Mater Sci 30:235–238

    Article  Google Scholar 

  • Niazi NK, Singh B, Van Zwieten L, Kachenko AG (2011) Phytoremediation potential of Pityrogramma calomelanos var. austroamericana and Pteris vittata L. grown at a highly variable arsenic contaminated site. Int J Phytoremediation 13:912–932

    Article  Google Scholar 

  • Niazi NK, Singh B, Minasny B (2015) Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site. Int J Environ Sci Technol 12:1965–1974

    Article  Google Scholar 

  • Nocita M, Stevens A, Noon C, van Wesemael B (2013) Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma 199:37–42

    Article  Google Scholar 

  • Nocita M, Stevens A, Toth G, Panagos P, van Wesemael B, Montanarella L (2014) Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biol Biochem 68:337–347

    Article  Google Scholar 

  • Rodionov A, Pätzold S, Welp G, Pude R, Amelung W (2016) Proximal field Vis-NIR spectroscopy of soil organic carbon: a solution to clear obstacles related to vegetation and straw cover. Soil Tillage Res 163:89–98

    Article  Google Scholar 

  • Rossel RAV, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158:46–54

    Article  Google Scholar 

  • Saeys W, Mouazen AM, Ramon H (2005) Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosyst Eng 91:393–402

    Article  Google Scholar 

  • Santra P, Singh R, Sarathjith M, Panwar N, Varghese P, Das B (2015) Reflectance spectroscopic approach for estimation of soil properties in hot arid western Rajasthan, India. Environ Earth Sci 74:4233–4245

    Article  Google Scholar 

  • Schneider IL, Teixeira EC, Rolim SB, Hallouche B (2015) Study of reflectance spectroscopy in river sediments. Int J Adv Remote Sens GIS 4:1271–1285

    Article  Google Scholar 

  • Shamsoddini A, Raval S, Taplin R (2014) Spectroscopic analysis of soil metal contamination around a derelict mine site in the Blue Mountains, Australia. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2:75

    Article  Google Scholar 

  • Shi T, Cui L, Wang J, Fei T, Chen Y, Wu G (2013) Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil 366:363–375. doi:10.1007/s11104-012-1436-8

    Article  Google Scholar 

  • Shi T, Chen Y, Liu Y, Wu G (2014) Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals. J Hazard Mater 265:166–176

    Article  Google Scholar 

  • Siebielec G, McCarty GW, Stuczynski TI, Reeves JB (2004) Near-and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content. J Environ Qual 33:2056–2069

    Article  Google Scholar 

  • Song Y, Li F, Yang Z, Ayoko GA, Frost RL, Ji J (2012) Diffuse reflectance spectroscopy for monitoring potentially toxic elements in the agricultural soils of Changjiang River Delta, China. Appl Clay Sci 64:75–83

    Article  Google Scholar 

  • Sun W, Zhang X (2017) Estimating soil zinc concentrations using reflectance spectroscopy. Int J Appl Earth Obs Geoinf 58:126–133

    Article  Google Scholar 

  • Thomas R (2013) Practical guide to ICP-MS: a tutorial for beginners. CRC Press, Boca Raton

    Google Scholar 

  • Vasques G, Grunwald S, Sickman J (2008) Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146:14–25. doi:10.1016/j.geoderma.2008.04.007

    Article  Google Scholar 

  • Vicente LE, de Souza Filho CR (2011) Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Remote Sens Environ 115:1824–1836

    Article  Google Scholar 

  • Wang Q, Li P, Chen X (2012) Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application: a laboratory experiment. Geoderma 170:103–111

    Article  Google Scholar 

  • Waterman GC, Hamilton R (1975) The Sar Cheshmeh porphyry copper deposit. Econ Geol 70:568–576

    Article  Google Scholar 

  • White WB (1971) Infrared characterization of water and hydroxyl ion in the basic magnesium carbonate minerals. Am Mineral 56:46–53

    Google Scholar 

  • Wijewardane NK, Ge Y, Morgan CL (2016) Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization. Geoderma 267:92–101

    Article  Google Scholar 

  • Wold S, Martens H, Wold H (1983) The multivariate calibration problem in chemistry solved by the PLS method. In: Kagstrom B, Ruhe A (eds) Matrix pencils. Lecture notes in mathmatics. Springer, Berlin, pp 286–293

    Chapter  Google Scholar 

  • Wu Y, Chen J, Wu X, Tian Q, Ji J, Qin Z (2005) Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils. Appl Geochem 20:1051–1059

    Article  Google Scholar 

  • Wu Y, Chen J, Ji J, Gong P, Liao Q, Tian Q, Ma H (2007) A mechanism study of reflectance spectroscopy for investigating heavy metals in soils. Soil Sci Soc Am J 71:918–926

    Article  Google Scholar 

  • Xie XL, Pan XZ, Sun B (2012) Visible and near-infrared diffuse reflectance spectroscopy for prediction of soil properties near a copper smelter. Pedosphere 22:351–366

    Article  Google Scholar 

  • Xuemei L, Jianshe L (2014) Using short wave visible-near infrared reflectance spectroscopy to predict soil properties and content. Spectrosc Lett 47:729–739. doi:10.1080/00387010.2013.840315

    Article  Google Scholar 

  • Yousefi S, Doulati Ardejani F, Ziaii M, Esmaeil Zadeh E, Abedi A, Karamoozian M (2013) Identification of the origin and behaviour of arsenic in mine waste dumps using correlation analysis: a case study Sarcheshmeh copper mine. Int J Min Geo-Eng 47:139–149

    Google Scholar 

  • Yousefi S, Doulati Ardejani F, Ziaii M, Abedi A, Zadeh EE (2015a) Investigating the origin and geochemical behaviour of toxic elements within the waste dumps using statistical analyses: a case study at waste dumps of Sarcheshmeh copper mine, SE of Iran. Environ Earth Sci 73:1555–1572

    Article  Google Scholar 

  • Yousefi S, Doulati Ardejani F, Ziaii M, Karamoozian M (2015b) The speciation of cobalt and nickel at mine waste dump using improved correlation analysis: a case study of Sarcheshmeh copper mine. Environ Dev Sustain 17:1065–1084

    Article  Google Scholar 

  • Yu X, Liu Q, Wang Y, Liu X, Liu X (2016) Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. Catena 137:340–349

    Article  Google Scholar 

  • Zheng GH (2011) Prediction of As in soil with reflectance spectroscopy. Spectrosc Spectr Anal 31:173–176

    Google Scholar 

  • Zhuang DF (2009) Analysis of visible and near-infrared spectra of As-contaminated soil in croplands beside mines. Spectrosc Spectr Anal 29:114–118

    Google Scholar 

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Acknowledgements

The authors wish to express their gratitude to the Research and Development Division of the Sarcheshmeh Copper Complex for their cooperation to access the sampling and analysis facilities.

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Authors and Affiliations

  1. Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran

    Vahid Khosravi

  2. School of Mining, College of Engineering, University of Tehran, Tehran, Iran

    Faramarz Doulati Ardejani

  3. Nehbandan Mining College, University of Birjand, Nehbandan, Iran

    Saeed Yousefi

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  1. Vahid Khosravi
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  2. Faramarz Doulati Ardejani
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  3. Saeed Yousefi
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Khosravi, V., Doulati Ardejani, F. & Yousefi, S. Spectroscopic-based assessment of the content and geochemical behaviour of arsenic in a highly heterogeneous sulphide-rich mine waste dump. Environ Earth Sci 76, 459 (2017). https://doi.org/10.1007/s12665-017-6793-4

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