Zhu, Yinying
(2022)
*Development of Inverse Modeling Method for Emission Source Identification in River Pollution Incidents.*
PhD thesis, Concordia University.

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## Abstract

Accidental spills and illegal chemical discharges have occurred worldwide, causing adverse effects on human health and the ecosystem. Inverse models play an essential role in source identification based on limited concentration data observed at monitoring sites. However, most studies have been conducted on the inverse inference of pollution sources in atmospheric and groundwater environments. Few studies have focused on identifying pollution sources in rivers. In addition, multiple source identification, uncertainty quantification and sensitivity analysis of inverse models are rarely considered. An integrated inverse modeling system is developed in this thesis for source identification in river pollution incidents, including three independent inverse modeling approaches for an instantaneous point source (IPS), a continuous point source (CPS), and multi-point sources (MPS), respectively. The inverse modeling approach estimates source parameters with uncertainty concerns by combining a water quality model and a probabilistic inverse method based on observed pollutant concentrations.

First, the IPS inverse modeling approach is developed based on the Metropolis-Hastings (MH) method. Then the developed MH-based IPS approach is tested and compared with a genetic algorithm (GA)-based IPS approach for a hypothetical case and a real case study. Results confirm that the MH-based IPS approach performs better than the GA-based IPS approach in terms of accuracy and stability for IPS source identification. According to the sensitivity analysis, the emission mass of the pollution source positively correlates with the dispersion coefficient and the river cross-sectional area, whereas the flow velocity significantly affects the identified values of release location and release time. Second, the CPS inverse modeling approach employed the up-to-date DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to estimate source parameters. The DREAM-based CPS inverse modeling approach accurately performs in a hypothetical case and a field tracer case. Moreover, compared to the MH-based and GA-based CPS approaches for CPS identification, the DREAM-based CPS approach has an advantage in accuracy, computation time, and reconstructing the time series of pollutant concentrations. The accuracy of the approach can be improved by decreasing observation errors, increasing the monitoring number, and deciding monitoring locations closer to the spill site. Third, the MPS inverse modeling approach is developed based on the DREAM algorithm. The one-point, two-point, and three-point source problems are considered in case studies to validate the feasibility and accuracy of the DREAM-based MPS approach. Among the three identified source parameters, the identification error of the release time tends to rise obviously in response to the increase in pollution sources. Subsequently, an integrated inverse modeling system with a graphical user interface is established based on the three developed inverse modeling approaches, and its efficiency is tested through case studies. In conclusion, the integrated system can serve as a helpful tool for source identification, model validation, and pollution prediction in the assessment and management of emergency pollution incidents.

Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
---|---|

Item Type: | Thesis (PhD) |

Authors: | Zhu, Yinying |

Institution: | Concordia University |

Degree Name: | Ph. D. |

Program: | Civil Engineering |

Date: | 5 August 2022 |

Thesis Supervisor(s): | Chen, Zhi |

Keywords: | Source identification; Inverse model; Genetic algorithm;Metropolis-Hastings algorithm; DiffeRential Evolution Adaptive Metropolis algorithm |

ID Code: | 991203 |

Deposited By: | Yinying Zhu |

Deposited On: | 27 Oct 2022 14:14 |

Last Modified: | 27 Oct 2022 14:14 |

## References:

Addepalli, B., Sikorski, K., Pardyjak, E. R., & Zhdanov, M. S. (2011). Source characterization of atmospheric releases using stochastic search and regularized gradient optimization. Inverse Problems in Science and Engineering, 19(8), 1097-1124.Alapati, S., & Kabala, Z. J. (2000). Recovering the release history of a groundwater contaminant using a non‐linear least‐squares method. Hydrological processes, 14(6), 1003-1016.

Allen, C. T., Young, G. S., & Haupt, S. E. (2007). Improving pollutant source characterization by better estimating wind direction with a genetic algorithm. Atmospheric Environment, 41(11), 2283-2289.

Atmadja, J., & Bagtzoglou, A. C. (2001). State of the art report on mathematical methods for groundwater pollution source identification. Environmental forensics, 2(3), 205-214.

Ayvaz, M. T. (2016). A hybrid simulation–optimization approach for solving the areal groundwater pollution source identification problems. Journal of Hydrology, 538, 161-176.

Bagtzoglou, A. C., & Atmadja, J. (2005). Mathematical methods for hydrologic inversion: The case of pollution source identification. Water pollution, 65-96.

Bieringer, P. E., Young, G. S., Rodriguez, L. M., Annunzio, A. J., Vandenberghe, F., & Haupt, S. E. (2017). Paradigms and commonalities in atmospheric source term estimation methods. Atmospheric Environment, 156, 102-112.

Bilotta, G. S., Burnside, N. G., Cheek, L., Dunbar, M. J., Grove, M. K., Harrison, C., & Davy-Bowker, J. (2012). Developing environment-specific water quality guidelines for suspended particulate matter. Water Research, 46(7), 2324-2332.

Cantelli, A., D'orta, F., Cattini, A., Sebastianelli, F., & Cedola, L. (2015). Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources. Atmospheric Environment, 115, 36-46.

Cauchemez, S., Carrat, F., Viboud, C., Valleron, A. J., & Boelle, P. Y. (2004). A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data. Statistics in medicine, 23(22), 3469-3487.

Chapra, S. C. (2008). Surface water-quality modeling. Waveland press Inc., Long Grove.

Cheng, W. P., & Jia, Y. (2010). Identification of contaminant point source in surface waters based on backward location probability density function method. Advances in Water Resources, 33(4), 397-410.

Dai, Y. H., & Yuan, Y. (2000). Nonlinear conjugate gradient methods. Shanghai Science and Technology Publisher, Shanghai.

Datta, B., Chakrabarty, D., & Dhar, A. (2011). Identification of unknown groundwater pollution sources using classical optimization with linked simulation. Journal of Hydro-Environment Research, 5(1), 25-36.

Fan, Y., Yu, L., & Shi, X. (2021). Uncertainty quantification and partition for multivariate risk inferences through a factorial multimodel Bayesian copula (FMBC) system. Journal of Hydrology, 598, 126406.

Fischer, M. J., & Ladner, R. E. (1979). Propositional dynamic logic of regular programs. Journal of computer and system sciences, 18(2), 194-211.

Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457-472.

Ghane, A., Mazaheri, M., & Samani, J. M. V. (2016). Location and release time identification of pollution point source in river networks based on the Backward Probability Method. Journal of environmental management, 180, 164-171.

Gill, J. (2014). Bayesian methods: A social and behavioral sciences approach (Vol. 20). CRC Press, Boca Raton, Florida.

Gorelick, S. M., Evans, B., & Remson, I. (1983). Identifying sources of groundwater pollution: An optimization approach. Water Resources Research, 19(3), 779-790.

Guo, G., & Cheng, G. (2019). Mathematical modelling and application for simulation of water pollution accidents. Process Safety and Environmental Protection, 127, 189-196.

Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of hydrologic engineering, 4(2), 135-143.

Haario, H., Laine, M., Mira, A., & Saksman, E. (2006). DRAM: efficient adaptive MCMC. Statistics and computing, 16(4), 339-354.

Haario, H., Saksman, E., & Tamminen, J. (1999). Adaptive proposal distribution for random walk Metropolis algorithm. Computational statistics, 14(3), 375-395.

Haario, H., Saksman, E., & Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli, 223-242.

Hadamard, J., 1923. Lectures on Cauchy’s Problem in Linear Partial Differential Equations. Yale University Press, New Haven.

Han, F., & Zheng, Y. (2018). Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach. Advances in Water Resources, 116, 77-94.

Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109.

Haupt, S. E., Pasini, A., & Marzban, C.. (2008). Artificial Intelligence Methods in the Environmental Sciences. Springer, Berlin, p. 424.

Hazart, A., Giovannelli, J. F., Dubost, S., & Chatellier, L. (2014). Inverse transport problem of estimating point-like source using a Bayesian parametric method with MCMC. Signal Processing, 96, 346-361.

Helton, J. C., Davis, F. J., & Johnson, J. D. (2005). A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling. Reliability Engineering & System Safety, 89(3), 305-330.

Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, Cambridge.

Hutchinson, M., Oh, H., & Chen, W. H. (2017). A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Information Fusion, 36, 130-148.

Isakov, V., & Kindermann, S. (2000). Identification of the diffusion coefficient in a one-dimensional parabolic equation. Inverse problems, 16(3), 665.

Jackson, P. R., & Lageman, J. D. (2014). Real-time piscicide tracking using Rhodamine WT dye for support of application, transport, and deactivation strategies in riverine environments. US Geological Survey Scientific Investigations Report, 2013–5211. Retrieved from http://dx.doi.org/10.3133/sir20135211

Jia, H., Xu, T., Liang, S., Zhao, P., & Xu, C. (2018). Bayesian framework of parameter sensitivity, uncertainty, and identifiability analysis in complex water quality models. Environmental Modelling & Software, 104, 13-26.

Jiang, J., Han, F., Zheng, Y., Wang, N., & Yuan, Y. (2018). Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis. Frontiers of Environmental Science & Engineering, 12(5), 1-16.

Jiang, J., Chen, Y., & Wang, B. (2019). Pollution Source Identification for River Chemical Spills by Modular-Bayesian Approach: A Retrospective Study on the ‘Landmark’Spill Incident in China. Hydrology, 6(3), 74.

Jing, L., Chen, R., Bai, X., Meng, F., Yao, Z., Teng, Y., & Chen, H. (2018). Utilization of a Bayesian probabilistic inferential framework for contamination source identification in river environment. In MATEC Web of Conferences. EDP Sciences, Les Ulis, France, Vol. 246, p. 02035.

Jing, P., Yang, Z., Zhou, W., Huai, W., & Lu, X. (2019). Inversion of multiple parameters for river pollution accidents using emergency monitoring data. Water Environment Research, 91(8), 731-738.

Jing, P., Yang, Z., Zhou, W., Huai, W., & Lu, X. (2020). Inverse estimation of finite-duration source release mass in river pollution accidents based on adjoint equation method. Environmental Science and Pollution Research, 27(13), 14679-14689.

Khlaifi, A., Ionescu, A., & Candau, Y. (2009). Pollution source identification using a coupled diffusion model with a genetic algorithm. Mathematics and computers in simulation, 79(12), 3500-3510.

Laloy, E., & Vrugt, J. A. (2012). High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM (ZS) and high‐performance computing. Water Resources Research, 48(1).

Li, Z., Mao, X. Z., Li, T. S., & Zhang, S. (2016). Estimation of river pollution source using the space-time radial basis collocation method. Advances in Water Resources, 88, 68-79.

Liang, S., Jia, H., Xu, C., Xu, T., & Melching, C. (2016). A Bayesian approach for evaluation of the effect of water quality model parameter uncertainty on TMDLs: A case study of Miyun Reservoir. Science of the Total Environment, 560, 44-54.

Liu, J., & Wilson, J. L. (1995). Modeling travel time and source location probabilities in two-dimensional heterogeneous aquifer. In Proc. 5th WERC Technology Development Conference, 59-76.

Liu, Y., Yang, P., Hu, C., & Guo, H. (2008). Water quality modeling for load reduction under uncertainty: a Bayesian approach. Water Research, 42(13), 3305-3314.

Long, K. J., Haupt, S. E., & Young, G. S. (2010). Assessing sensitivity of source term estimation. Atmospheric environment, 44(12), 1558-1567.

Ma, D., Deng, J., & Zhang, Z. (2013). Comparison and improvements of optimization methods for gas emission source identification. Atmospheric Environment, 81, 188-198.

Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., & Reed, P. M. (2014). Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environmental Modelling & Software, 62, 271-299.

Malve, O., & Qian, S. S. (2006). Estimating nutrients and chlorophyll a relationships in Finnish lakes. Environmental science & technology, 40(24), 7848-7853.

Mazaheri, M., Mohammad Vali Samani, J., & Samani, H. M. V. (2015). Mathematical model for pollution source identification in rivers. Environmental Forensics, 16(4), 310-321.

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.

Michalak, A. M., & Kitanidis, P. K. (2004). Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling. Water Resources Research, 40(8).

Ministry of Ecology and Environment of the People’s Republic of China. Environmental quality standard for surface water GB 3838-2002. Retrieved from http://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/shjbh/shjzlbz/200206/t20020601_66497.shtml

Moghaddam, M. B., Mazaheri, M., & Samani, J. M. V. (2021). Inverse modeling of contaminant transport for pollution source identification in surface and groundwaters: a review. Groundwater for Sustainable Development, 15, 100651.

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900.

Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of hydrology, 10(3), 282-290.

Neupauer, R. M., & Wilson, J. L. (2003). Backward location and travel time probabilities for a decaying contaminant in an aquifer. Journal of contaminant hydrology, 66(1-2), 39-58.

O'Loughlin, E. M., & Bowmer, K. H. (1975). Dilution and decay of aquatic herbicides in flowing channels. Journal of Hydrology, 26(3-4), 217-235.

Paliwal, R., Sharma, P., & Kansal, A. (2007). Water quality modelling of the river Yamuna (India) using QUAL2E-UNCAS. Journal of environmental management, 83(2), 131-144.

Rad, P. R., & Fazlali, A. (2020). Optimization of permeable reactive barrier dimensions and location in groundwater remediation contaminated by landfill pollution. Journal of Water Process Engineering, 35, 101196.

Rudolph, G. (1994). Convergence analysis of canonical genetic algorithms. IEEE Trans. Neural Networks, 5(1), 96-101.

Runkel, R. L., Bencala, K. E., Broshears, R. E., & Chapra, S. C. (1996). Reactive solute transport in streams: 1. Development of an equilibrium‐based model. Water Resources Research, 32(2), 409-418.

Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: a guide to assessing scientific models (Vol. 1). New York: Wiley.

Sharma, A. (2018). Guided stochastic gradient descent algorithm for inconsistent datasets. Applied Soft Computing, 73, 1068-1080.

Shen, J., & Zhao, Y. (2010). Combined Bayesian statistics and load duration curve method for bacteria nonpoint source loading estimation. Water Research, 44(1), 77-84.

Sheng, M., Liu, J., Zhu, A. X., Rossiter, D. G., Liu, H., Liu, Z., & Zhu, L. (2019). Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model. Agricultural and Forest Meteorology, 278, 107659.

Singh, J., Knapp, H. V., Arnold, J. G., & Demissie, M. (2005). Hydrological modeling of the Iroquois river watershed using HSPF and SWAT 1. JAWRA Journal of the American Water Resources Association, 41(2), 343-360.

Singh, R. M., & Datta, B. (2006). Identification of groundwater pollution sources using GA-based linked simulation optimization model. Journal of hydrologic engineering, 11(2), 101-109.

Singh, S. K., & Rani, R. (2014). A least-squares inversion technique for identification of a point release: Application to Fusion Field Trials 2007. Atmospheric environment, 92, 104-117.

Skaggs, T. H., & Kabala, Z. J. (1994). Recovering the release history of a groundwater contaminant. Water Resources Research, 30(1), 71-79.

Skaggs, T. H., & Kabala, Z. J. (1998). Limitations in recovering the history of a groundwater contaminant plume. Journal of Contaminant Hydrology, 33(3-4), 347-359.

Spiegelhalter, D., Thomas, A., Best, N., & Lunn, D. (2003). WinBUGS user manual version 1.4. Retrieved from. https://www.mrc-bsu.cam.ac.uk/wp-content/uploads/manual14.pdf (accessed 10 January 2020).

Tang, H. W., Xin, X. K., Dai, W. H., & Xiao, Y. (2010). Parameter identification for modeling river network using a genetic algorithm. Journal of Hydrodynamics, 22(2), 246-253.

Tasdighi, A., Arabi, M., Harmel, D., & Line, D. (2018). A Bayesian total uncertainty analysis framework for assessment of management practices using watershed models. Environmental Modelling & Software, 108, 240-252.

Ter Braak, C. J. (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and Computing, 16(3), 239-249.

Verlicchi, P., Al Aukidy, M., & Zambello, E. (2012). Occurrence of pharmaceutical compounds in urban wastewater: removal, mass load and environmental risk after a secondary treatment—a review. Science of the total environment, 429, 123-155.

Vrugt, J. A. (2016). Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation. Environmental Modelling & Software, 75, 273-316.

Vrugt, J. A., Gupta, H. V., Bouten, W., & Sorooshian, S. (2003). A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water resources research, 39(8), 1201.

Vrugt, J. A., Ter Braak, C. J., Clark, M. P., Hyman, J. M., & Robinson, B. A. (2008). Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resources Research, 44(12), W00B09.

Vrugt, J. A., Ter Braak, C. J. F., Diks, C. G. H., Robinson, B. A., Hyman, J. M., & Higdon, D. (2009). Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International journal of nonlinear sciences and numerical simulation, 10(3), 273-290.

Wang, H., & Jin, X. (2013). Characterization of groundwater contaminant source using Bayesian method. Stochastic environmental research and risk assessment, 27(4), 867-876.

Wang, J., & Zabaras, N. (2006). A Markov random field model of contamination source identification in porous media flow. International Journal of Heat and Mass Transfer, 49(5-6), 939-950.

Wang, J., Zhao, J., Lei, X., & Wang, H. (2018). New approach for point pollution source identification in rivers based on the backward probability method. Environmental pollution, 241, 759-774.

Wang, J., Zhao, J., Lei, X., & Wang, H. (2019). An effective method for point pollution source identification in rivers with performance-improved ensemble Kalman filter. Journal of Hydrology, 577, 123991.

Wang, P. C., & Shoup, T. E. (2011). Parameter sensitivity study of the Nelder–Mead simplex method. Advances in Engineering Software, 42(7), 529-533.

Wang, S. D., & Shen, Y.M. (2005). Three high-order splitting schemes for 3D transport equation. Applied Mathematics and Mechanics, 26(8), 1007-1016.

Węglarczyk, S. (2018). Kernel density estimation and its application. In MATEC Web of Conferences. EDP Sciences, Les Ulis, France, Vol. 23, p. 00037.

Wei, G. Z., Zhang, C., Li, Y., Liu, H.X., & Zhou, H. (2016). Source identification of sudden contamination based on the parameter uncertainty analysis. Journal of Hydroinformatics, 18(6), 919-927.

Wu, W., Ren, J., Zhou, X., Wang, J., & Guo, M. (2020). Identification of source information for sudden water pollution incidents in rivers and lakes based on variable-fidelity surrogate-DREAM optimization. Environmental Modelling & Software, 133, 104811.

Yang, H., Shao, D., Liu, B., Huang, J., & Ye, X. (2016). Multi-point source identification of sudden water pollution accidents in surface waters based on differential evolution and Metropolis–Hastings–Markov Chain Monte Carlo. Stochastic Environmental Research and Risk Assessment, 30(2), 507-522.

Yao, H., Qian, X., Yin, H., Gao, H., & Wang, Y. (2015). Regional risk assessment for point source pollution based on a water quality model of the Taipu River, China. Risk Analysis, 35(2), 265-277.

Yu, S., He, L., & Lu, H. (2016). An environmental fairness based optimisation model for the decision-support of joint control over the water quantity and quality of a river basin. Journal of Hydrology, 535, 366-376.

Yustres, Á., Asensio, L., Alonso, J., & Navarro, V. (2012). A review of Markov Chain Monte Carlo and information theory tools for inverse problems in subsurface flow. Computational Geosciences, 16(1), 1-20.

Zhang, G. L., Liu, X. X., & Zhang, T. (2009). The impact of population size on the performance of GA. In 2009 International Conference on Machine Learning and Cybernetics, Vol. 4,1866-1870. IEEE.

Zhang, J., Man, J., Lin, G., Wu, L., & Zeng, L. (2018). Inverse modeling of hydrologic systems with adaptive multifidelity Markov chain Monte Carlo simulations. Water Resources Research, 54(7), 4867-4886.

Zhang, J., Zeng, L., Chen, C., Chen, D., & Wu, L. (2015). Efficient Bayesian experimental design for contaminant source identification. Water Resources Research, 51(1), 576-598.

Zhang, S. P., & Xin, X. K. (2017). Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm. Applied Water Science, 7(4), 1955-1963.

Zhang, Y., Arabi, M., & Paustian, K. (2020). Analysis of parameter uncertainty in model simulations of irrigated and rainfed agroecosystems. Environmental Modelling & Software, 126, 104642.

Zeunert, S., & Meon, G. (2020). Influence of the spatial and temporal monitoring design on the identification of an instantaneous pollutant release in a river. Advances in Water Resources, 146, 103788.

Zheng, X., & Chen, Z. (2011). Inverse calculation approaches for source determination in hazardous chemical releases. Journal of Loss Prevention in the Process Industries, 24(4), 293-301.

Zhu, Z., Motta, D., Jackson, P. R., & Garcia, M. H. (2017). Numerical modeling of simultaneous tracer release and piscicide treatment for invasive species control in the Chicago Sanitary and Ship Canal, Chicago, Illinois. Environmental Fluid Mechanics, 17(2), 211-229.

Zufferey, N. (2012). Metaheuristics: some principles for an efficient design. Computer Technology and Application, 3(6), 446-462. Retrieved from https://archive-ouverte.unige.ch/unige:26156

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