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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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Front. Environ. Sci. Eng.    2023, Vol. 17 Issue (7) : 85    https://doi.org/10.1007/s11783-023-1685-1
RESEARCH ARTICLE
Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm
Hailong Yin1,2(), Yiyuan Lin1,3, Huijin Zhang1,2, Ruibin Wu1,2, Zuxin Xu1,2()
1. Key Laboratory of Yangtze River Water Environment of Ministry of Education, Tongji University, Shanghai 200092, China
2. Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
3. Fujian Provincial Key Laboratory of Green Building Technology, Fujian Academy of Building Research Co. Ltd., Fuzhou 350108, China
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Abstract

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking.

● Model is not stuck in local optimal solutions for high-dimensional problem.

● Model can estimate source parameters accurately with known river water levels.

● Both sudden spill incident and normal sewage inputs into the river can be tracked.

● Model is superior to the traditional approaches based on the test cases.

Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model to accomplish this identification is essential. In this paper, an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge. The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method (MCMC). The methodological framework is tested using a designed case of a sudden wastewater spill incident (i.e., source location, flow rate, and starting and ending times of the discharge) and a real case of multiple sewage inputs into a river (i.e., locations and daily flows of sewage sources). The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites. It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space. In comparison, the inverse models based on the popular random walk Metropolis (RWM) algorithm and microbial genetic algorithm (MGA) do not produce reliable estimates for the two scenarios even after multiple simulation runs, and they fall into locally optimal solutions. Since much more water level data are available than water quality data, the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data, especially for rivers receiving significant sewage discharges.
Keywords Identification of pollution sources      Water quality restoration      Bayesian inference      Hydrodynamic model      Inverse problem     
Corresponding Author(s): Hailong Yin,Zuxin Xu   
Issue Date: 06 February 2023
 Cite this article:   
Hailong Yin,Yiyuan Lin,Huijin Zhang, et al. Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm[J]. Front. Environ. Sci. Eng., 2023, 17(7): 85.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1685-1
https://academic.hep.com.cn/fese/EN/Y2023/V17/I7/85
Fig.1  Schematic diagram of the inverse modeling framework developed to identify the emission source parameters.
Fig.2  Architecture of the coupling of the improved Bayesian-MCMC algorithm and the hydrodynamic diffusion wave model.
Fig.3  Observed time-series of the water levels at two monitoring sites for Case I.
Fig.4  Study area for Case II.
Fig.5  Posterior distribution and maximum a posterior estimation of the source parameters using the improved Bayesian-MCMC algorithm: (a) source discharge; (b) source location; (c) starting discharge time; and (d) ending discharge time.
Fig.6  Comparison of the estimated source results obtained using the MGA, RWM, and improved Bayesian-MCMC: (a) source discharge, (b) source location, (c) starting discharge time, and (d) ending discharge time.
Fig.7  Comparison of estimated source results obtained using the improved Bayesian-MCMC with a spatial grid of 500 m for (a) one monitoring site, (b) two monitoring sites, and (c) four monitoring sites.
Fig.8  Comparison of the estimated flows obtained using the MGA, RWM, and improved Bayesian-MCMC for the four sewage inputs from (a) an urban village, (b) Sili River, (c) Banqiao River, (d) a municipal outlet.
Fig.9  Posterior probability distribution and maximum a posterior estimation for sewage inputs from (a) an urban village, (b) Sili River, (c) Banqiao River, (d) a municipal outlet.
1 B Addepalli , K Sikorski , E R Pardyjak , M S Zhdanov . (2011). Source characterization of atmospheric releases using stochastic search and regularized gradient optimization. Inverse Problems in Science and Engineering, 19(8): 1097–1124
https://doi.org/10.1080/17415977.2011.589901
2 J Atmadja , A C Bagtzoglou . (2001). State of the art report on mathematical methods for groundwater pollution source identification. Environmental Forensics, 2(3): 205–214
https://doi.org/10.1006/enfo.2001.0055
3 M T Ayvaz . (2016). A hybrid simulation-optimization approach for solving the areal groundwater pollution source identification problems. Journal of Hydrology (Amsterdam), 538: 161–176
https://doi.org/10.1016/j.jhydrol.2016.04.008
4 E G Bekele, J W Nicklow (2007). Multi-objective automatic calibration of SWAT using NSGA-II. Journal of Hydrology (Amsterdam), 341(3–4): 165–176
https://doi.org/10.1016/j.jhydrol.2007.05.014
5 Q Bu , D Wang , Y Zheng , Z Wang , J Gu . (2014). Identification and ranking of the risky organic contaminants in the source water of the Danjiangkou reservoir. Frontiers of Environmental Science & Engineering, 8(1): 42–53
https://doi.org/10.1007/s11783-013-0499-y
6 D Chen , R A Dahlgren , J Lu . (2013). A modified load apportionment model for identifying point and diffuse source nutrient inputs to rivers from stream monitoring data. Journal of Hydrology (Amsterdam), 501: 25–34
https://doi.org/10.1016/j.jhydrol.2013.07.034
7 W P Cheng , Y Jia . (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
https://doi.org/10.1016/j.advwatres.2010.01.004
8 S Das , S S Mullick , P N Suganthan . (2016). Recent advances in differential evolution: an updated survey. Swarm and Evolutionary Computation, 27(4): 1–30
https://doi.org/10.1016/j.swevo.2016.01.004
9 B Datta , D Chakrabarty , A Dhar . (2011). Identification of unknown groundwater pollution sources using classical optimization with linked simulation. Journal of Hydro-environment Research, 5(1): 25–36
https://doi.org/10.1016/j.jher.2010.08.004
10 B R Faulkner . (2008). Bayesian modeling of the assimilative capacity component of nutrient total maximum daily loads. Water Resources Research, 44(8): 218–227
https://doi.org/10.1029/2007WR006638
11 G Freni, G Mannina (2010). Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution. Journal of Hydrology (Amsterdam), 392(1–2): 31–39
https://doi.org/10.1016/j.jhydrol.2010.07.043
12 L García , J Barreiro-Gomez , E Escobar , D Tellez , N Quijano , C Ocampo-Martinez . (2015). Modeling and real-time control of urban drainage systems: A review. Advances in Water Resources, 85: 120–132
https://doi.org/10.1016/j.advwatres.2015.08.007
13 A Gelman , D B Rubin . (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4): 457–472
https://doi.org/10.1214/ss/1177011136
14 A Ghane , M Mazaheri , J Mohammad Vali Samani . (2016). Location and release time identification of pollution point source in river networks based on the Backward Probability. Journal of Environmental Management, 180: 164–171
https://doi.org/10.1016/j.jenvman.2016.05.015
15 J Gill (2014). Bayesian Methods: A Social and Behavioral Sciences Approach. Boca Raton: CRC Press
16 I Harvey . (2011). The microbial genetic algorithm. In: Kampis G, Karsai I, Szathmáry E, eds. Advances in Artificial Life: Darwin Meets von Neumann (Part II). Budapest: Springer-Verlag Berlin Heidelberg, 126–133
17 J Huang , H Yin , S C Chapra , Q Zhou . (2017). Modelling dissolved oxygen depression in an urban river in China. Water (Basel), 9(7): 520–538
https://doi.org/10.3390/w9070520
18 J Huang , H Yin , S Jomma , M Rode , Q Zhou . (2016). Identification of Pollutant Sources in a Rapidly Developing Urban River Catchment in China. Vienna: EGU General Assembly, EPSC2016–13299
19 H Jia , T Xu , S Liang , P Zhao , C Xu . (2018). Bayesian framework of parameter sensitivity, uncertainty, and identifiability analysis in complex water quality models. Environmental Modelling & Software, 104: 13–26
https://doi.org/10.1016/j.envsoft.2018.03.001
20 J P Jiang , F Han , Y Zheng , N N Wang , Y X Yuan . (2018). Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis. Frontiers of Environmental Science & Engineering, 12(5): 6
https://doi.org/10.1007/s11783-018-1081-4
21 P Jing , Z Yang , W Zhou , W Huai , X Lu . (2019). Inversion of multiple parameters for river pollution accidents using emergency monitoring data. Water Environment Research, 91(8): 731–738
https://doi.org/10.1002/wer.1099
22 E Laloy , J A Vrugt . (2012). High-dimensional posterior exploration of hydrologic models using multiple-try DREAMZS and high-performance computing. Water Resources Research, 48(1): W01526
https://doi.org/10.1029/2011WR010608
23 A Massoudieh , M Kayhanian . (2013). Bayesian chemical mass balance method for surface water contaminant source apportionment. Journal of Environmental Engineering, 139(2): 250–260
https://doi.org/10.1061/(ASCE)EE.1943-7870.0000645
24 M Mazaheri , J Mohammad Vali Samani , H M V Samani . (2015). Mathematical model for pollution source identification in rivers. Environmental Forensics, 16(4): 310–321
https://doi.org/10.1080/15275922.2015.1059391
25 B Y Mirghani , K G Mahinthakumar , M E Tryby , R S Ranjithan , E M Zechman . (2009). A parallel evolutionary strategy based simulation-optimization approach for solving groundwater source identification problems. Advances in Water Resources, 32(9): 1373–1385
https://doi.org/10.1016/j.advwatres.2009.06.001
26 I A Mirza , D Vieru . (2017). Fundamental solutions to advection–diffusion equation with time-fractional Caputo–Fabrizio derivative. Computers & Mathematics with Applications (Oxford, England), 73(1): 1–10
https://doi.org/10.1016/j.camwa.2016.09.026
27 R Nag , B K Markey , P Whyte , V O’flaherty , D Bolton , O Fenton , K G Richards , E Cummins . (2021). A Bayesian inference approach to quantify average pathogen loads in farmyard manure and slurry using open-source Irish datasets. Science of the Total Environment, 786: 147474
https://doi.org/10.1016/j.scitotenv.2021.147474
28 S P Neuman , L Xue , M Ye , D Lu . (2012). Bayesian analysis of data-worth considering model and parameter uncertainties. Advances in Water Resources, 36: 75–85
https://doi.org/10.1016/j.advwatres.2011.02.007
29 K Opara , J Arabas . (2018). Comparison of mutation strategies in Differential Evolution–A probabilistic perspective. Swarm and Evolutionary Computation, 39(4): 53–69
https://doi.org/10.1016/j.swevo.2017.12.007
30 H Oubanas , I Gejadze , P O Malaterre , F Mercier . (2018). River discharge estimation from synthetic SWOT-type observations using variational data assimilation and the full Saint-Venant hydraulic model. Journal of Hydrology (Amsterdam), 559: 638–647
https://doi.org/10.1016/j.jhydrol.2018.02.004
31 S Sharifi , M M Haghshenas , T Deksissa , P Green , W Hare , A Massoudieh . (2014). Storm water pollution source identification in Washington, DC, using Bayesian chemical mass balance modeling. Journal of Environmental Engineering, 140(3): 04013015
https://doi.org/10.1061/(ASCE)EE.1943-7870.0000809
32 B Shi , J Jiang , B Sivakumar , Y Zheng , P Wang . (2018). Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory. Water Research, 134: 140–152
https://doi.org/10.1016/j.watres.2018.01.057
33 S K Singh , R Rani . (2014). A least-squares inversion technique for identification of a point release: application to Fusion Field Trials 2007. Atmospheric Environment, 92: 104–117
https://doi.org/10.1016/j.atmosenv.2014.04.012
34 N R Siriwardene, B J C Perera (2006). Selection of genetic algorithm operators for urban drainage model parameter optimisation. Mathematical and Computer Modelling, 44(5–6): 415–429
https://doi.org/10.1016/j.mcm.2006.01.002
35 R V Thomann, J A Mueller (1987). Principles of Surface Water Quality Modeling and Control. New York: Harper & Row Publishers
36 S Umer . (2015). Investigation into mutation operators for microbial genetic algorithm. In: 2015 7th International Joint Conference on Computational Intelligence (IJCCI). Lisbon: IEEE, 299–305
37 J A Vrugt . (2016). Markov chain monte carlo simulation using the DREAM software package: theory, concepts, and MATLAB implementation. Environmental Modelling & Software, 75: 273–316
https://doi.org/10.1016/j.envsoft.2015.08.013
38 O Wani , A Scheidegger , J P Carbajal , J Rieckermann , F Blumensaat . (2017). Parameter estimation of hydrologic models using a likelihood function for censored and binary observations. Water Research, 121(9): 290–301
https://doi.org/10.1016/j.watres.2017.05.038
39 Z Xu , Y Qu , S Wang , W Chu . (2021). Diagnosis of pipe illicit connections and damaged points in urban stormwater system using an inversed optimization model. Journal of Cleaner Production, 292: 126011
https://doi.org/10.1016/j.jclepro.2021.126011
40 Z Xu , L Wang , H Yin , H Li , B R Schwegler . (2016). Source apportionment of non-storm water entries into storm drains using marker species: modeling approach and verification. Ecological Indicators, 61: 546–557
https://doi.org/10.1016/j.ecolind.2015.10.006
41 H Yang , D Shao , B Liu , J Huang , X Ye . (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
https://doi.org/10.1007/s00477-015-1191-5
42 G Zhang , X Liu , S Wu , Z Hua , L Zhao , H Xue , P Wang . (2021). Identification of pollution sources in river based on particle swarm optimization. Journal of Hydrodynamics, 33(6): 1303–1315
https://doi.org/10.1007/s42241-021-0101-1
43 Y Zhao , A Sharma , B Sivakumar , L Marshall , P Wang , J Jiang . (2014). A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments. Environmental Modelling & Software, 57: 216–226
https://doi.org/10.1016/j.envsoft.2014.03.005
44 Y Zhao , B Zheng , H Jia , Z Chen . (2019). Determination sources of nitrates into the Three Gorges Reservoir using nitrogen and oxygen isotopes. Science of the Total Environment, 687: 128–136
https://doi.org/10.1016/j.scitotenv.2019.06.073
45 Z Zhao , H Yin , Z Xu , J Peng , Z Yu . (2020). Pin-pointing groundwater infiltration into urban sewers using chemical tracer in conjunction with physically based optimization model. Water Research, 175: 115689
https://doi.org/10.1016/j.watres.2020.115689
46 W Zhi , L Li , W Dong , W Brown , J Kaye , C Steefel , K H Williams . (2019). Distinct source water chemistry shapes contrasting concentration-discharge patterns. Water Resources Research, 55(5): 4233–4251
https://doi.org/10.1029/2018WR024257
47 Q Zhu , A Gu , D Li , T Zhang , L Xiang , M He . (2021a). Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm. Frontiers of Environmental Science & Engineering, 15(6): 136
48 Y Zhu , Z Chen , Z Asif . (2021b). Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: inverse modeling, sensitivity, and uncertainty analysis. Environmental Pollution, 285: 117497
https://doi.org/10.1016/j.envpol.2021.117497
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