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Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering
[EN] When a contaminant is detected in a drinking well, source location, initial contaminant release time, and initial contaminant concentration are, in many cases, unknown; the responsible party may have disappeared and the identification of when and where the contamination happened may become difficult. Although contaminant source identification has been studied extensively in the last decades, we proposeto our knowledge, for the first timethe use of the ensemble Kalman filter (EnKF), which has proven to be a powerful algorithm for inverse modeling. The EnKF is tested in a two-dimensional synthetic deterministic aquifer, identifying, satisfactorily, the source location, the release time, and the release concentration, together with an assessment of the uncertainty associated with this identification.Financial support to carry out this work was received from the Spanish Ministry of Economy and Competitiveness through project CGL2014-59841-P. All data used in this analysis are available from the authors.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 Resources Research. 52(8):6587-6595. https://doi.org/10.1002/2016WR019111S65876595528Aral, M. M., Guan, J., & Maslia, M. L. (2001). Identification of Contaminant Source Location and Release History in Aquifers. Journal of Hydrologic Engineering, 6(3), 225-234. doi:10.1061/(asce)1084-0699(2001)6:3(225)Butera, I., Tanda, M. G., & Zanini, A. (2012). Simultaneous identification of the pollutant release history and the source location in groundwater by means of a geostatistical approach. Stochastic Environmental Research and Risk Assessment, 27(5), 1269-1280. doi:10.1007/s00477-012-0662-1Chen, Y., Oliver, D. S., & Zhang, D. (2009). Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization. Journal of Petroleum Science and Engineering, 66(1-2), 1-14. doi:10.1016/j.petrol.2008.12.002Cupola, F., Tanda, M. G., & Zanini, A. (2014). Laboratory sandbox validation of pollutant source location methods. Stochastic Environmental Research and Risk Assessment, 29(1), 169-182. doi:10.1007/s00477-014-0869-4Evensen, G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343-367. doi:10.1007/s10236-003-0036-9Gorelick, S. M., Evans, B., & Remson, I. (1983). Identifying sources of groundwater pollution: An optimization approach. Water Resources Research, 19(3), 779-790. doi:10.1029/wr019i003p00779Gzyl, G., Zanini, A., Frączek, R., & Kura, K. (2014). Contaminant source and release history identification in groundwater: A multi-step approach. Journal of Contaminant Hydrology, 157, 59-72. doi:10.1016/j.jconhyd.2013.11.006Franssen, H. J. H., & Kinzelbach, W. (2009). Ensemble Kalman filtering versus sequential self-calibration for inverse modelling of dynamic groundwater flow systems. Journal of Hydrology, 365(3-4), 261-274. doi:10.1016/j.jhydrol.2008.11.033Ma, R., Zheng, C., Zachara, J. M., & Tonkin, M. (2012). Utility of bromide and heat tracers for aquifer characterization affected by highly transient flow conditions. Water Resources Research, 48(8). doi:10.1029/2011wr011281Mahar, P. S. (2000). Water Resources Management, 14(3), 209-227. doi:10.1023/a:1026527901213McDonald , M. A. Harbaugh 1988Michalak, A. M., & Kitanidis, P. K. (2003). A method for enforcing parameter nonnegativity in Bayesian inverse problems with an application to contaminant source identification. Water Resources Research, 39(2). doi:10.1029/2002wr001480Michalak, A. M., & Kitanidis, P. K. (2004). Application of geostatistical inverse modeling to contaminant source identification at Dover AFB, Delaware. Journal of Hydraulic Research, 42(sup1), 9-18. doi:10.1080/00221680409500042Neupauer, R. M., & Lin, R. (2006). Identifying sources of a conservative groundwater contaminant using backward probabilities conditioned on measured concentrations. Water Resources Research, 42(3). doi:10.1029/2005wr004115Neupauer, R. M., & Wilson, J. L. (1999). Adjoint method for obtaining backward-in-time location and travel time probabilities of a conservative groundwater contaminant. Water Resources Research, 35(11), 3389-3398. doi:10.1029/1999wr900190Woodbury, A., Sudicky, E., Ulrych, T. J., & Ludwig, R. (1998). Three-dimensional plume source reconstruction using minimum relative entropy inversion. Journal of Contaminant Hydrology, 32(1-2), 131-158. doi:10.1016/s0169-7722(97)00088-0Woodbury, A. D., & Ulrych, T. J. (1996). Minimum Relative Entropy Inversion: Theory and Application to Recovering the Release History of a Groundwater Contaminant. Water Resources Research, 32(9), 2671-2681. doi:10.1029/95wr03818Xu, T., & Gómez-Hernández, J. J. (2015). Probability fields revisited in the context of ensemble Kalman filtering. Journal of Hydrology, 531, 40-52. doi:10.1016/j.jhydrol.2015.06.062Xu, T., Jaime Gómez-Hernández, J., 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. Advances in Water Resources, 54, 100-118. doi:10.1016/j.advwatres.2013.01.006Yeh, H.-D., Chang, T.-H., & Lin, Y.-C. (2007). Groundwater contaminant source identification by a hybrid heuristic approach. Water Resources Research, 43(9). doi:10.1029/2005wr004731Zheng , C. 2010 MT3DMS v5. 3 Supplemental User's Guide Technical Report to the US Army Engineer Research and Development CenterZhou, H., Gómez-Hernández, J. J., Hendricks Franssen, H.-J., & Li, L. (2011). An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering. Advances in Water Resources, 34(7), 844-864. doi:10.1016/j.advwatres.2011.04.01
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Contaminant Source Identification in Building Hvac Systems Using Adjoint Probability Method
Although high efficiency filter is one critical component in the Air Handler Unit (AHU), HVAC system is potential contaminant emission source. Released contaminants can be transported through HVAC system and impacts the indoor air quality (IAQ). Effective control and improvement measures are required to remove the contaminant source located in HVAC systems in order to eliminate its influence on the IAQ. Accurate and fast identification of contaminant sources in HVAC systems makes it. This thesis studies the application of adjoint backward probability model in identification of contaminant source in Building HVAC system. The adjoint backward probability model was mostly applied to identify contaminant source information in groundwater and inside building. According to the similar properties between water and air, and same contaminant transport fate in water and air, the adjoint probability model is applied to study the contaminant source identification in HVAC systems. Sensors are used to detect contaminant concentration change in certain sampling locations of HVAC ductwork. Using sensor detection information, we can trace back and find the source information. In this research CONTAM is used to provide a steady state airflow field. A simple building model with three zones and detailed duct work is built. This model is applied into later research in identification of contaminant source in HVAC system. Four cases are analyzed in the research to study the application of adjoint backward probability method. The first case is identifying an instantaneous contaminant source location with known source release time and source release mass. The second case is identifying the location of a dynamic contaminant source with known release time and known release mass. The third case is identifying source release time and release location simultaneously for a decaying contaminant source with known source release mass. The fourth case is identifying the location of a dynamic contaminant source in a two-floor building with known release time and known release mass. The conclusions come to that a sensor network with two sensors reading historical concentrations can identify source information accurately. Further, in future research, contaminant source information will be recovered without knowing any source information in advance
Rapid Assessment of Intertidal Wetland Sediments
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Site investigation techniques for DNAPL source and plume zone characterisation
Establishing the location of the Source Area BioREmediation (SABRE)
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Modelling of radionuclide migration through the geosphere with radial basis function method and geostatistics
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Contamination source inference in water distribution networks
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The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks
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