Skip to main content
SpringerLink
Log in

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box–Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov–Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Albuquerque MTD, Antunes IMHR, Seco MFM, Roque NM, Sanz G (2014) Sequential Gaussian simulation of uranium spatial distribution—a transboundary watershed case study. Procedia Earth Pl Sci 8:2–6

    Article  CAS  Google Scholar 

  • Bargaoui ZK, Chebbi A (2009) Comparison of two kriging interpolation methods applied to spatiotemporal rainfall. J Hazard Mater 365(1-2):56–73

    Google Scholar 

  • Blake WH, Walsh RPD, Reed JM, Barnsley MJ, Smith J (2007) Impacts of landscape remediation on the heavy metal pollution dynamics of a lake surrounded by non-ferrous smelter waste. Environ Pollut 148(1):268–280

    Article  CAS  Google Scholar 

  • Box GEP, Cox DR (1964) An analysis of transformations. J R Stat Soc B (Methodological) 26:211–252

    Google Scholar 

  • Campbell JE, Moen JC, Ney RA, Schnoor JL (2008) Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks. Environ Pollut 152(2):267–273

    Article  CAS  Google Scholar 

  • Cattle JA, McBratney AB, Minasny B (2002) Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination. J Environ Qual 31(5):1576–1588

    Article  CAS  Google Scholar 

  • Clemente R, Dickinson NM, Lepp NW (2008) Mobility of metals and metalloids in a multi-element contaminated soil 20 years after cessation of the pollution source activity. Environ Pollut 155(2):254–261

    Article  CAS  Google Scholar 

  • Dai L, Wei H, Wang L (2007) Spatial distribution and risk assessment of radionuclides in soils around a coal-fired power plant: a case study from the city of Baoji, China. Environ Res 104(2):201–208

    Article  CAS  Google Scholar 

  • Dragović R, Gajić B, Dragović S, Đorđević M, Đorđević M, Mihailović N, Onjia A (2014) Assessment of the impact of geographical factors on the spatial distribution of heavy metals in soils around the steel production facility in Smederevo (Serbia). J Clean Prod 84:550–562

    Article  Google Scholar 

  • Franco C, Soares A, Delgado J (2006) Geostatistical modeling of heavy metal contamination in the top soil of Guadiamar river margins using a stochastic simulation technique. Geoderma 136(3-4):852–864

    Article  CAS  Google Scholar 

  • Franssen HJWMH, Eijnsbergen ACV, Stein A (1997) Use of spatial prediction techniques and fuzzy classification for mapping soil pollutants. Geoderma 77(2-4):243–262

    Article  CAS  Google Scholar 

  • Fu W, Tunney H, Zhang C (2010) Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application. Soil Till Res 106(2):185–193

    Article  Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, NY, p 483

    Google Scholar 

  • Goovaerts P, Trinh HT, Demond A, Franzblau A, Garabrant D, Gillespie B (2008) Geostatistical modeling of the spatial distribution of soil dioxins in the vicinity of an incinerator: theory and application to Midland, Michigan. Environ Sci Technol 42(10):3648–3654

    Article  CAS  Google Scholar 

  • Grimalt JO, van Drooge BL, Ribes A, Fernández P, Appleby P (2004) Polycyclic aromatic hydrocarbon composition in soils and sediments of high altitude lakes. Environ Pollut 131:13–24

    Article  CAS  Google Scholar 

  • Heie LS, Meland S, Ljønes M, Salbu B, Strømseng AE (2010) Short-term temporal variations in speciation of Pb, Cu, Zn and Sb in a shooting range runoff stream. Sci Total Environ 408(11):2409–2417

    Article  Google Scholar 

  • Hill ID, Hill R, Holder RL (1976) Algorithm as 99: fitting Johnson curves by moments. Appl Stat 25(2):180–189

    Article  Google Scholar 

  • Jobson J (1991) Applied multivariate data analysis: regression and experimental design categorical and multivariate methods. Springer, New York

    Book  Google Scholar 

  • Juang KW, Lee DY, Ellsworth TR (2001) Using rank order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site. J Environ Qual 30(3):894–903

    Article  CAS  Google Scholar 

  • Komnitsas K, Modisb K (2009) Geostatistical risk estimation at waste disposal sites in the presence of hot spots. J Hazard Mater 164(2-3):1185–1190

    Article  CAS  Google Scholar 

  • Krige DG, Magri EJ (1982) Studies of the effects of outliers and data transformation on variogram estimates for a base metal and a gold ore body. J Int Assoc Math Geol 14(6):557–564

    Article  CAS  Google Scholar 

  • Liu G, Bi RT, Wang SJ, Li FS, Guo GL (2013) The use of spatial autocorrelation analysis to identify PAHs pollution hotspots at an industrially contaminated site. Environ Monit Assess 185(11):9549–9558

    Article  CAS  Google Scholar 

  • McGrath D, Zhang C, Carton OT (2004) Geostatistical analyses and hazard assessment on soil lead in Silvermines area, Ireland. Environ Pollut 127(2):239–248

    Article  CAS  Google Scholar 

  • Meirvenne MV, Goovaerts P (2001) Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold. Geoderma 102(1-2):75–100

    Article  Google Scholar 

  • Mostert MMR, Ayoko GA, Kokot S (2012) Multi-criteria ranking and source identification of metals in public playgrounds in Queensland, Australia. Geoderma 173–174:173–183

    Article  Google Scholar 

  • Mueller TG, Pusuluri NB, Mathias KK, Cornelius PL, Barnhisel RI, Shearer SA (2004) Map quality for ordinary kriging and inverse distance weighted interpolation. Soil Sci Soc Am J 68(6):2042–2047

    Article  CAS  Google Scholar 

  • Orton TG, Rawlins BG, Lark RM (2009) Using measurements close to a detection limit in a geostatistical case study to predict selenium concentration in topsoil. Geoderma 152(3-4):269–282

    Article  CAS  Google Scholar 

  • Park JJ, Shin KI, Cho K (2004) Evaluation of data transformations and validation of a spatial model for spatial dependency of Trialeurodes vaporariorum populations in a cherry tomato greenhouse. J Asia-Pacific Entomol 7(3):289–295

    Article  Google Scholar 

  • Saby N, Arrouays D, Boulonne L, Jolivet C, Pochot A (2006) Geostatistical assessment of Pb in soil around Paris, France. Sci Total Environ 367(1):212–221

    Article  CAS  Google Scholar 

  • Sante MD, Mazzieri F, Pasqualini E (2009) Assessment of the sanitary and environmental risks posed by a contaminated industrial site. J Hazard Mater 171(1-3):524–534

    Article  Google Scholar 

  • Senesil GSG, Baldassarre N, Radina SB (1999) Trace element inputs into soils by anthropogenic activities and implications for human health. Chemosphere 39(2):343–377

    Article  Google Scholar 

  • Slifker JF, Shapiro SS (1980) The Johnson system: selection and parameter estimation. Technometrics 22(2):239–246

    Article  Google Scholar 

  • USEPA. (2007a). SW-846 method 8270D. Semivolatile organic compounds by gas chromatography/mass spectrometry (GC/MS). http://www.epa.gov/osw/hazard/testmethods/sw846/pdfs/8270d.pdf. Accessed February 2007

  • USEPA. (2007b) SW-846 method 8272. Parent and alky polycyclic aromatic in sediment pore water by solid-phase microextraction and gas chromatography/mass spectrometry in selected ion monitoring mode. http://www.epa.gov/osw/hazard/testmethods/pdfs/8272.pdf. Accessed December 2007.

  • Verstraete S, Meirvenne MV (2008) A multi-stage sampling strategy for the delineation of soil pollution in a contaminated brownfield. Environ Pollut 154(2):184–191

    Article  CAS  Google Scholar 

  • Webster R, Oliver MA (2001) Geostatistics for environmental scientists. Wiley, New York, NY, p 271

    Google Scholar 

  • Wu J, Norvell WA, Welch RM (2006) Kriging on highly skewed data for DTPA-extractable soil Zn with auxiliary information for pH and organic carbon. Geoderma 134(1-2):187–199

    Article  CAS  Google Scholar 

  • Wu C, Wu J, Luo Y, Zhang H, Teng Y, DeGloria SD (2011) Spatial interpolation of severely skewed data with several peak values by the approach integrating kriging and triangular irregular network interpolation. Environ Earth Sci 63(5):1093–1103

    Article  Google Scholar 

  • Xie Y-F, Chen T-B, Lei M, Zheng G-D, Song B, Li X-Y (2010) Impact of spatial interpolation methods on the estimation of regional soil Cd. Acta Scientiae Circumstantiae 130:847–854 (In Chinese)

    Google Scholar 

  • Yan W, Mahmood Q, Peng D, Fu W, Chen T, Wang Y, Li S, Chen J, Liu D (2015) The spatial distribution pattern of heavy metals and risk assessment of moso bamboo forest soil around lead–zinc mine in Southeastern China. Soil Till Res 153:120–130

    Article  Google Scholar 

  • Yuan F, Li XH, Bai XY, Jowitt SM, Zhang MM, Jia C, Zhou TF (2010) Comparison of normalisation methods for non-normal distributed soil geochemical data: a case study from the Tongling metallogenic district, Yangtze belt, Anhui Province, China. Trans Inst Min Metall B 119(4):227–235

    CAS  Google Scholar 

Download references

Acknowledgments

This research was supported by the Young Scientists Fund of National Natural Science Foundation of China (Grant No. 41401236; 40901249), the Youth Science and Technology Research Foundation of Shanxi, China (Grant No. 2015021166), and the National High Technology Research and Development Program of China (863 Program: 2013AA06A206).

Author information

Authors and Affiliations

  1. Research Center for Scientific Development in Fenhe River Valley, Taiyuan Normal University, Taiyuan, 030012, China

    Geng Liu & Junjie Niu

  2. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Anwai Dayangfang 8, Beijing, 100012, China

    Chao Zhang & Guanlin Guo

Authors
  1. Geng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junjie Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guanlin Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guanlin Guo.

Additional information

Responsible editor: Marcus Schulz

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, G., Niu, J., Zhang, C. et al. Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies. Environ Sci Pollut Res 22, 20121–20130 (2015). https://doi.org/10.1007/s11356-015-5122-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-015-5122-2

Keywords

Search

Navigation