- -

Contaminant Source Identification in Aquifers: A Critical View

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Contaminant Source Identification in Aquifers: A Critical View

Mostrar el registro completo del ítem

Gómez-Hernández, JJ.; Xu, T. (2022). Contaminant Source Identification in Aquifers: A Critical View. Mathematical Geosciences. 54(2):437-458. https://doi.org/10.1007/s11004-021-09976-4

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/189073

Ficheros en el ítem

Thumbnail
Nombre: Gomez-HernandezXu ...
Tamaño: 1.039Mb
Formato: PDF
Descripción: Versión editorial
Abrir/Preview

Metadatos del ítem

Título: Contaminant Source Identification in Aquifers: A Critical View
Autor: Gómez-Hernández, J. Jaime Xu, Teng
Entidad UPV: Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports
Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Universitat Politècnica de València. Instituto Universitario de Ingeniería del Agua y del Medio Ambiente - Institut Universitari d'Enginyeria de l'Aigua i Medi Ambient
Fecha difusión:
Resumen:
[EN] Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. ...[+]
Palabras clave: Simulation-optimization , Backward tracking , Bayesian approach , Machine learning , Surrogate models , Heuristic approaches
Derechos de uso: Reconocimiento (by)
Fuente:
Mathematical Geosciences. (issn: 1874-8961 )
DOI: 10.1007/s11004-021-09976-4
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s11004-021-09976-4
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109131RB-I00/ES/APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSES/
info:eu-repo/grantAgreement/Fundamental Research Funds for the Central Universities//B200201015/
info:eu-repo/grantAgreement/JPDE//B19052/
Agradecimientos:
The first author wishes to acknowledge the financial contribution of the Spanish Ministry of Science and Innovation through Project No. PID2019-109131RB-I00, and the second author acknowledges the financial support from ...[+]
Tipo: Artículo

References

Aanonsen S, Nævdal G, Oliver D, Reynolds A, Vallès B (2009) The ensemble Kalman filter in reservoir engineering—a review. SPE J 14(3):393–412

Aghasi A, Mendoza-Sanchez I, Miller EL, Ramsburg CA, Abriola LM (2013) A geometric approach to joint inversion with applications to contaminant source zone characterization. Inverse Prob 29(11):115014. https://doi.org/10.1088/0266-5611/29/11/115014

Ala NK, Domenico PA (1992) Inverse analytical techniques applied to coincident contaminant distributions at Otis Air Force Base, Massachusetts. Groundwater 30(2):212–218. https://doi.org/10.1111/j.1745-6584.1992.tb01793.x [+]
Aanonsen S, Nævdal G, Oliver D, Reynolds A, Vallès B (2009) The ensemble Kalman filter in reservoir engineering—a review. SPE J 14(3):393–412

Aghasi A, Mendoza-Sanchez I, Miller EL, Ramsburg CA, Abriola LM (2013) A geometric approach to joint inversion with applications to contaminant source zone characterization. Inverse Prob 29(11):115014. https://doi.org/10.1088/0266-5611/29/11/115014

Ala NK, Domenico PA (1992) Inverse analytical techniques applied to coincident contaminant distributions at Otis Air Force Base, Massachusetts. Groundwater 30(2):212–218. https://doi.org/10.1111/j.1745-6584.1992.tb01793.x

Amirabdollahian M, Datta B (2013) Identification of contaminant source characteristics and monitoring network design in groundwater aquifers: an overview. J Environ Protect. https://doi.org/10.4236/jep.2013.45a004

Aral MM, Guan J (1996) Genetic algorithms in search of groundwater pollution sources. In: Advances in groundwater pollution control and remediation. Springer, Netherlands, Dordrecht, pp 347–369. https://doi.org/10.1007/978-94-009-0205-3_17

Ayvaz MT (2016) A hybrid simulation–optimization approach for solving the areal groundwater pollution source identification problems. J Hydrol 538:161–176. https://doi.org/10.1016/j.jhydrol.2016.04.008

Bagtzoglou AC, Tompson AFB, Dougherty DE (1991) Probabilistic simulation for reliable solute source identification in heterogeneous porous media. In: Water resources engineering risk assessment. Springer, Berlin, pp 189–201. https://doi.org/10.1007/978-3-642-76971-9_12

Bagtzoglou AC, Dougherty DE, Tompson AFB (1992) Application of particle methods to reliable identification of groundwater pollution sources. Water Resour Manag 6(1):15–23. https://doi.org/10.1007/BF00872184

Cao T, Zeng X, Wu J, Wang D, Sun Y, Zhu X, Lin J, Long Y (2019) Groundwater contaminant source identification via Bayesian model selection and uncertainty quantification. Hydrogeol J 27(8):2907–2918. https://doi.org/10.1007/s10040-019-02055-3

Capilla JE, Rodrigo J, Gómez-Hernández JJ (1999) Simulation of non-Gaussian transmissivity fields honoring piezometric data and integrating soft and secondary information. Math Geosci 31(7):907–927

Carrera J (1984) Estimation of aquifer parameters under transient and steady-state conditions. PhD thesis, University of Arizona, Department of Hydrology and Water Resources

Carrera J, Neuman SP (1986) Estimation of aquifer parameters under transient and steady state conditions. 1. Maximum likelihood method incorporating prior information. Water Resour Res 22(2):199–210

Chen Y, Zhang D (2006) Data assimilation for transient flow in geologic formations via ensemble Kalman filter. Adv Water Resour 29(8):1107–1122

Dagan G (1982) Stochastic modeling of groundwater flow by unconditional and conditional probabilities: 2. The solute transport. Water Resour Res 18(4):835–848

Datta B, Beegle J, Kavvas M, Orlob G (1989) Development of an expert system embedding pattern recognition techniques for pollution source identification. University of California-Davis, Technical report

Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367

Gómez-Hernández J, Wen XH (1994) Probabilistic assessment of travel times in groundwater modeling. Stoch Hydrol Hydraul 8(1):19–55

Gorelick SM (1981) Numerical management models of groundwater pollution. Ph.D., Stanford University

Gorelick SM, Evans B, Remson I (1983) Identifying sources of groundwater pollution: an optimization approach. Water Resour Res 19(3):779–790. https://doi.org/10.1029/WR019i003p00779

Haario H, Laine M, Mira A, Saksman E (2006) Dram: efficient adaptive McMC. Statist Comput 16(4):339–354

Hosseini AH, Deutsch CV, Mendoza CA, Biggar KW (2011) Inverse modeling for characterization of uncertainty in transport parameters under uncertainty of source geometry in heterogeneous aquifers. J Hydrol 405(3–4):402–416. https://doi.org/10.1016/j.jhydrol.2011.05.039

Hwang JC, Koerner RM (1983) Groundwater pollution source identification from limited monitoring data. Part 1—theory and feasibility. J Hazard Mater 8:105–119

Jha MK, Datta B (2014) Linked simulation–optimization based dedicated monitoring network design for unknown pollutant source identification using dynamic time warping distance. Water Resour Manag 28(12):4161–4182. https://doi.org/10.1007/s11269-014-0737-5

Jin X, Ranjithan RS, Mahinthakumar GK (2014) A monitoring network design procedure for three-dimensional (3D) groundwater contaminant source identification. Environ Forensics 15(1):78–96. https://doi.org/10.1080/15275922.2013.873095

Li L, Zhou H, Franssen H, Gómez-Hernández J (2011) Groundwater flow inverse modeling in non-multigaussian media: performance assessment of the normal-score ensemble kalman filter. Hydrol Earth Syst Sci Discuss 8(4):6749–6788

Li L, Zhou H, Gómez-Hernández JJ (2011) A comparative study of three-dimensional hydraulic conductivity upscaling at the macro-dispersion experiment (made) site, Columbus Air Force Base, Mississippi (USA). J Hydrol 404(3–4):278–293

Li L, Zhou H, Gómez-Hernández J, Hendricks Franssen H (2012) Jointly mapping hydraulic conductivity and porosity by assimilating concentration data via ensemble Kalman filter. J Hydrol 428:152

Mahinthakumar GK, Sayeed M (2005) Hybrid genetic algorithm-local search methods for solving groundwater source identification inverse problems. J Water Resourc Plan Manag 131(1):45–57. https://doi.org/10.1061/(asce)0733-9496(2005)131:1(45)

Mahinthakumar GK, Sayeed M (2006) Reconstructing groundwater source release histories using hybrid optimization approaches. Environ Forensics 7(1):45–54. https://doi.org/10.1080/15275920500506774

Mirghani BY, Mahinthakumar KG, Tryby ME, Ranjithan RS, Zechman EM (2009) A parallel evolutionary strategy based simulation–optimization approach for solving groundwater source identification problems. Adv Water Resour 32(9):1373–1385. https://doi.org/10.1016/j.advwatres.2009.06.001

Singh RM, Datta B (2004) Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network. Environ Forensics 5(3):143–153. https://doi.org/10.1080/15275920490495873

Singh RM, Datta B, Jain A (2004) Identification of unknown groundwater pollution sources using artificial neural networks. J Water Resour Plan Manag 130(6):506–514. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:6(506)

Skaggs TH, Kabala ZJ (1994) Recovering the release history of a groundwater contaminant. Water Resour Res 30(1):71–79. https://doi.org/10.1029/93WR02656

Snodgrass MF, Kitanidis PK (1997) A geostatistical approach to contaminant source identification. Water Resour Res 33(4):537–546. https://doi.org/10.1029/96WR03753

Tarantola A (2005) Inverse problem theory and methods for model parameter estimation. SIAM, Philadelphia

Todaro, D’Oria M, Tanda MG, Gómez-Hernández JJ (2021) Ensemble smoother with multiple data assimilation to simultaneously estimate the source location and the release history of a contaminant spill in an aquifer. J Hydrol. https://doi.org/10.1016/j.jhydrol.2021.126215

Wagner BJ (1992) Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling. J Hydrol 135(1–4):275–303. https://doi.org/10.1016/0022-1694(92)90092-A

Wasilkowski GW, Wozniakowski H (1995) Explicit cost bounds of algorithms for multivariate tensor product problems. J Complex 11(1):1–56

Woodbury A, Sudicky E, Ulrych TJ, Ludwig R (1998) Three-dimensional plume source reconstruction using minimum relative entropy inversion. J Contam Hydrol 32(1–2):131–158. https://doi.org/10.1016/S0169-7722(97)00088-0

Woodbury AD, Ulrych TJ (1996) Minimum relative entropy inversion: theory and application to recovering the release history of a groundwater contaminant. Water Resour Res 32(9):2671–2681. https://doi.org/10.1029/95WR03818

Xu T, Gómez-Hernández JJ (2016) Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering. Water Resour Res 52(8):6587–6595. https://doi.org/10.1002/2016WR019111

Xu T, Gómez-Hernández JJ (2018) Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter. Adv Water Resour 112:106–123. https://doi.org/10.1016/j.advwatres.2017.12.011

Xu T, Gómez-Hernández JJ, Zhou H, Li L (2013) The power of transient piezometric head data in inverse modeling: an application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. Adv Water Resour 54:100–118. https://doi.org/10.1016/j.advwatres.2013.01.006

Xu T, Jaime Gómez-Hernández J, Li L, Zhou H (2013) Parallelized ensemble Kalman filter for hydraulic conductivity characterization. Comput Geosci 52:42–49

Yeh HD, Lin CC, Chen CF (2016) Reconstructing the release history of a groundwater contaminant based on AT123D. J Hydro-Environ Res 13:89–102. https://doi.org/10.1016/j.jher.2015.06.001

Zeng L, Shi L, Zhang D, Wu L (2012) A sparse grid based Bayesian method for contaminant source identification. Adv Water Resour 37:1–9. https://doi.org/10.1016/j.advwatres.2011.09.011

Zhou H, Gómez-Hernández JJ, Li L (2014) Inverse methods in hydrogeology: evolution and recent trends. Adv Water Resour 63:22–37

Zhou Z, Tartakovsky DM (2021) Markov chain Monte Carlo with neural network surrogates: application to contaminant source identification. Stoch Environ Res Risk Assess 35(3):639–651

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem