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Risk analysis for remediation of contaminated sites: the geostatistical approach

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Abstract

The assessment of the risks associated with contamination by elevated levels of pollutants is a major issue in most parts of the world. The risk arises from the presence of a pollutant and from the uncertainty associated with estimating its concentration, extent and trajectory. The uncertainty in the assessment comes from the difficulty of measuring the pollutant concentration values accurately at any given location and the impossibility of measuring it at all locations within a study zone. Estimations tend to give smoothed versions of reality, with the smoothing effect being inversely proportional to the amount of data. If risk is a measure of the probability of pollutant concentrations exceeding specified thresholds, then the variability is the key feature in risk assessment and risk analysis. For this reason, geostatistical simulations provide an appropriate way of quantifying risk by simulating possible “realities” and determining how many of these realities exceed the contamination thresholds, and, finally, provides a means of visualizing risk and the geological causes of risk. This study concerns multivariate simulations of organic and inorganic pollutants measured in terrain samples to assess the uncertainty for the risk analysis of a contaminated site, an industrial site in northern Italy that has to be remediated. The main geostatistical tools are used to model the local uncertainty of pollutant concentrations, which prevail at any unsampled site, in particular by means of stochastic simulation. These models of uncertainty have been used in the decision-making processes to identify the areas targeted for remediation.

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References

  • Andrews GE, Askey R, Roy R (1999) Hermite polynomials. Cambridge University, Cambridge

    Google Scholar 

  • APAT (2006), ‘Nota inerente il calcolo della concentrazione rappresentativa della sorgente’, Technical report, APAT Agenzia per la Protezione dell’Ambiente e per i Servizi Tecnici-Servizio Tecnologie del Sito e Siti Contaminati, Via Vitaliano Brancati 48, 00144, Roma

  • Armstrong M (1998) Basic linear geostatistics. Springer, Berlin

    Book  Google Scholar 

  • Armstrong M, Dowd P (1994) Geostatistical simulations. In: Proceedings of the Geostatistical Simulation Workshop, Fontainebleau, France, 27–28 May 1993, Kluwer Academic Publishers, Dordrecht

  • Bleines CJ, Deraisme F, Geffory N, Jeannee S, Perseval F, Rambert D, Renard O, Torres, Touffait Y (2004) ‘Isatis Software Manual’, Geovariances & Ecole Des Mines De Paris, Fontainebleu

  • Burgess T, Webster R (1980a) ‘Optimal interpolation and isarithmic mapping of soil properties, I. The variogram and punctual kriging’. J Soil Sci 31:315–331

    Article  Google Scholar 

  • Burgess T, Webster R (1980b) ‘Optimal interpolation and isarithmic mapping of soil properties, II. Block kriging’. J Soil Sci 31:333–341

    Article  Google Scholar 

  • Burgess T, Webster R (1980c) ‘Optimal interpolation and isarithmic mapping of soil properties, III. Changing drift and universal kriging’. J Soil Sci 31:505–524

    Article  Google Scholar 

  • Buttafuoco G, Tallarico A, Falcone G, Guagliardi I (2010) A geostatistical approach for mapping and uncertainty assessment of geogenic radon gas in soil in an area of southern Italy. Environmental Earth Sciences 61(3):491–505. doi:10.1007/s12665-009-0360-6

    Article  Google Scholar 

  • Candeias C, Ferreira da Silva E, Salgueiro A, Pereira H, Reis A, Patinha C, Matos J, Avila P (2011) The use of multivariate statistical analysis of geochemical data for assessing the spatial distribution of soil contamination by potentially toxic elements in the Aljustrel mining area (Iberian Pyrite Belt, Portugal). Environ Earth Sci 62(7):1461–1479. doi:10.1007/s12665-010-0631-2

    Article  Google Scholar 

  • Chiles JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Chichester, Wiley

    Google Scholar 

  • Clark I, Harper WV (2000) Practical Geostatistics 2000, vol 1. Ecosse North America, LLC, Columbus

    Google Scholar 

  • Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74:829–836

    Article  Google Scholar 

  • Cressie N (1993) Statistics for spatial data. Wiley & Sons Inc, New York

    Google Scholar 

  • Deutsch CV, Journel AG (1998) GSLIB, geostatistical software library and user’s guide. Oxford University, New York

    Google Scholar 

  • Dowd P (1982) Lognormal kriging—the general case. Math Geol 14(5):475–499

    Article  Google Scholar 

  • Dowd PA (1983) Conditional simulation of inter-related beds in an oil deposit. In: Verly G (ed) Geostatistics for natural resources characterization. D. Reidel Publishing, Netherlands, pp 1031–1043

    Google Scholar 

  • Dowd PA (1997) ‘Risk in mineral projects: analysis, perception and management’. Trans Inst Min Metall (Sect A Min Ind) 106:A9–A18

    Google Scholar 

  • EEC (1991) ‘91/414/EEC—Council Directive of 15 July 1991 concerning the placing of plant protection products on the market’(91/414/EEC), Technical report, European Communities—Office for Official Publications of the European Communities 194

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University, Oxford

    Google Scholar 

  • Goovaerts P (1999) Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89(1–2):1–45. doi:10.1016/S0016-7061(98)00078-0

    Google Scholar 

  • Goovaerts P (2001) Geostatistical modelling of uncertainty in soil science. Geoderma 103(1–2):3–26

    Article  Google Scholar 

  • Goovaerts P, Avruskin G, Meliker J, Slotnick M, Jacquez G, Nriagu J (2004) Modeling uncertainty about pollutant concentration and human exposure using geostatistics and a space-time information system: application to arsenic in groundwater of southeast Michigan. In: ‘6th international symposium on spatial accuracy assessment in natural resources and environmental sciences, 28 June–1 July

  • Heuvelink G (1998) Error propagation in environmental modeling with GIS. Taylor and Francis, London

    Google Scholar 

  • Isaaks EH, Srivastava RM (1989) Applied Geostatistics. Oxford University, Oxford

    Google Scholar 

  • Johnson RA, Bhattacharyya GK (2001) Statistics, principles and methods. John Wiley and Sons, New York

    Google Scholar 

  • Journel A, Huijbregts C (1978) Mining Geostatistics. Academic Press, London

    Google Scholar 

  • Lantuejoul C (2002) Geostatistical simulation: models and algorithms. Springer, Berlin

    Google Scholar 

  • Lark RM, Ferguson RB (2004) Mapping risk of soil nutrient deficiency or excess by disjunctive and indicator kriging. Geoderma 118(1–2):39–53. doi:10.1016/S0016-7061(03)00168-X

    Google Scholar 

  • Liu X, Wu J, Xu J (2006) Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS. Environ Pollut 141(2):257–264. doi:10.1016/j.envpol.2005.08.048

    Google Scholar 

  • Matheron G (1963) Principles of geostatistics. Econ Geol 58(8):1246–1266

    Article  Google Scholar 

  • Matheron G (1965) Les variables régionalisées et leur estimation: une application de la théorie des fonctions aléatoires aux sciences de la nature. Masson, Paris

    Google Scholar 

  • MATT (1999) ‘Decreto ministeriale 25/10/1999, N. 471 Regolamento recante criteri, procedure e modalità per la messa in sicurezza, la bonifica e il ripristino ambientale dei siti inquinati, ai sensi dell’articolo 17 del decreto legislativo 5 febbraio 1997, n. 22, e successive modificazioni e integrazioni. Gazz. Uff. Suppl. Ordin. n. 293 del 15/12/1999’, Technical report, Ministero dell’Ambiente e della Tutela del Territorio, Republic of Italy, Pubblicato sul Supplemento Ordinario 218/L alla Gazzetta Ufficiale n. 293 del 15 dicembre 1999, 51 pp

  • Ravenscroft PJ (1994) Conditional simulation for mining: practical implementation in an industrial environment. In: Armstrong M, Dowd PA (eds) Geostatistical simulations. Kluwer Academic Publishers, Dordrecht, pp 79–87

    Google Scholar 

  • Rossi RE, Borth PW, Tollefson JJ (1993) Stochastic simulation for characterizing ecological spatial patterns and appraising risk. Ecol Appl 3(4):719–735. http://www.jstor.org/stable/1942103

    Google Scholar 

  • Shifteh Some’e B, Hassanpour F, Ezani A, Miremadi S, Tabari H (2011) Investigation of spatial variability and pattern analysis of soil properties in the northwest of Iran. Environ Earth Sci 1–16. doi:10.1007/s12665-011-0993-0

  • Singh A, Singh AK, Maichle RW (2004) ‘ProUCL Version 3.0 User Guide, EPA/600/R04/079’, USEPA. Las Vegas, Nevada

    Google Scholar 

  • Tercan EA, Akcan E (2004) Assessment of uncertainty associated with grade–tonnage curves using geostatistical simulation. Min Technol IMM Trans Sect A 113(2):129–136(128)

    Google Scholar 

  • USEPA (2002) ‘Calculating upper confidence limits for exposure point concentrations at hazardous waste sites’ (OSWER Directive 9285.6–10), Technical report, United States Environmental Protection Agency (USEPA), Office of Emergency and Remedial Response (OSWER), Washington, DC

  • Verly G (1993) Sequential Gaussian Cosimulation: a simulation method integrating several types of information. In: Soares Amilcar (ed) Geostatistics-troia. Kluwer, Dordrecht, pp 543–545

    Chapter  Google Scholar 

  • Wackernagel H (2003) Multivariate geostatistics: an introduction with applications. Springer, Berlin

    Google Scholar 

  • Webster R, Oliver MA (2001) Geostatistics for environmental scientists (statistics in practice). John Wiley & Sons, New York

    Google Scholar 

Download references

Acknowledgments

This project was made possible by the courtesy of Dr. Paolo Mazzoni, Eng. Cristina Ruggeri, Eng. Giovanna Monti and all members of the “Studio Geotecnico Italiano” (Milan, Italy), which provided the data sets used together with a lot of explanations about the data and the geology of study areas. The authors are indebted to Prof. Luigi Carmignani and the staff of the CGT Center for GeoTechnologies (University of Siena, Italy), who helped them in several parts of this work. Finally, Enrico Guastaldi do not know how to thank Professor Peter A. Dowd and Dr. Chaoshui Xu, now at University of Adelaide, for lectures and advices during the MSc course at the Department of Mining and Mineral Engineering, University of Leeds, during the past few years. The Authors would like to thank the anonymous reviewers of the Environmental Earth Sciences Journal for their valuable comments, which highly improved the manuscript.

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Authors and Affiliations

  1. CGT-Centro di GeoTecnologie, Università degli Studi di Siena, Via Vetri Vecchi, 34, 52027, San Giovanni Valdarno (AR), Italy

    Enrico Guastaldi

  2. Studio Geotecnico Italiano S.r.l., Via Ripamonti 89, 20141, Milan, Italy

    Andrea Alessandro Del Frate

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  1. Enrico Guastaldi
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  2. Andrea Alessandro Del Frate
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Correspondence to Enrico Guastaldi.

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Guastaldi, E., Del Frate, A.A. Risk analysis for remediation of contaminated sites: the geostatistical approach. Environ Earth Sci 65, 897–916 (2012). https://doi.org/10.1007/s12665-011-1133-6

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