Water quality in an urbanized river basin impacted by multi-pollution sources: from comprehensive surveys to modelling

Luu, Minh Thi Nguyet; Dinh, Truong; Trinh, Anh Duc; Doc, Nga T.

doi:10.2306/scienceasia1513-1874.2021.014

Cited by 6 publications

(2 citation statements)

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“…Both the RIVE model and its parameters were coupled in two river water quality models: RIVERSTRAHLER (Billen et al, 1994) and ProSe (Even et al, 1998). These two models are calibrated and validated on real case applications in different river basins over the world, such as in the Danube River (Romania and Bulgaria) (Garnier et al, 2002), in the Day-Nhue River (Vietnam) (Luu et al, 2021), in the Grand Morin river (France) (Flipo et al, 2004(Flipo et al, , 2007, in the Lule and Kalix rivers (Sweden) (Sferratore et al, 2008), in the Mosel River (Germany) (Garnier et al, 1999), in the Red River system (Vietnam and China) (Quynh et al, 2014), in the Scheldt River (Belgium and Netherlands) (Billen et al, 2005;Thieu et al, 2009), in the Seine River (France) (Even et al, 2004(Even et al, , 2007Raimonet et al, 2015;Vilmin et al, 2015Vilmin et al, , 2016Garnier et al, 2020), in the Somme river (France) (Thieu et al, 2009(Thieu et al, , 2010, and in the Zenne River (Belgium) (Garnier et al, 2013). In the C-RIVE model, DO in the water column depends on physical, bacterial, and phytoplanktonic processes (Fig.…”

Section: C-rive Biogeochemical Modelmentioning

confidence: 99%

How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?

et al. 2023

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Abstract. The development of accurate water quality modeling tools is necessary for integrated water quality management of river systems. Even though some water quality models can simulate dissolved oxygen (DO) concentrations accurately during high-flow periods and phytoplankton blooms in rivers, significant discrepancies remain during low-flow periods, when the dilution capacity of the rivers is reduced. We use the C-RIVE biogeochemical model to evaluate the influence of controlling parameters on DO simulations at low flow. Based on a coarse model pre-analysis, three sensitivity analyses (SAs) are carried out using the Sobol method. The parameters studied are related to bacterial community (e.g., bacterial growth rate), organic matter (OM; partitioning and degradation of OM into constituent fractions), and physical factors (e.g., reoxygenation of the river due to navigation and wind). Bacterial growth and mortality rates are found to be by far the two most influential parameters, followed by bacterial growth yield. More refined SA results indicate that the biodegradable fraction of dissolved organic matter (BDOM) and the bacterial growth yield are the most influential parameters under conditions of a high net bacterial growth rate (= growth rate − mortality rate), while bacterial growth yield is independently dominant in low net growth situations. Based on the results of this study, proposals are made for in situ measurement of BDOM under an urban area water quality monitoring network that provides high-frequency data. The results also indicate the need for bacterial community monitoring in order to detect potential bacterial community shifts after transient events such as combined sewer overflows and modifications in internal processes of treatment plants. Furthermore, we discuss the inclusion of BDOM in statistical water quality modeling software for improvement in the estimation of organic matter inflow from boundary conditions.

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Section: C-rive Biogeochemical Modelmentioning

confidence: 99%

How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?

et al. 2023

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“…These applications (Tab. A1) were carried out for different networks and scales as well as various degrees of anthropogenic impacts in a wide climatic gradient using either Riverstrahler (possibly with its Seneque or pyNuts environments) or PROSE/PROSE-PA, such as the Seine River (France) (Billen et al, 1994;Garnier et al, 1995;Even et al, 1998Even et al, , 2004Even et al, , 2007Billen et al, 2007;Servais et al, 2007;Thieu et al, 2009Thieu et al, , 2010Vilmin et al, 2015b, a;Aissa-Grouz et al, 2016;Vilmin et al, 2016;Desmit et al, 2018;Vilmin et al, 2018;Romero et al, 2019;Marescaux et al, 2020;Wang et al, 2022), the Danube river (Romania and Bulgaria) (Garnier et al, 2002), the Red River (China and Vietnam) (Le et al, 2010;Phuong Quynh et al, 2014;Le et al, 2015;Nguyen et al, 2016) and its distributary Day-Nhue River (Luu et al, 2021), the Lule and Kalix rivers (Sweden) (Sferratore et al, 2008), the Scheldt river (Belgium and Netherlands) (Billen et al, 2005;Thieu et al, 2009), the Zenne River (Belgium) (Garnier et al, 2013), the Mosel River (Germany) (Garnier et al, 1999a), the Somme River (France) (Thieu et al, 2009(Thieu et al, , 2010, the Loire River (France) (Garnier et al, 2018a), the Lot River (France) (Garnier et al, 2018b) and the Orgeval watershed (France) (Flipo et al, 2004(Flipo et al, , 2007Garnier et al, 2014). Moreover, the RIVE model has been appli...…”

Section: Introductionmentioning

confidence: 99%

The community-centered aquatic biogeochemistry model unified RIVE v1.0: a unified version for water column

Wang¹,

Thieu²,

Billen³

et al. 2023

Preprint

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Abstract. Research on mechanisms of organic matter degradation, bacterial activities, phytoplankton dynamics, and other processes has led to the development of numerous sophisticated water quality models since one of the first in 1925, based on first order kinetics for organic matter degradation. The community-centered aquatic biogeochemistry model RIVE was initially developed in 1994 and has since been integrated into several software programs such as Seneque-Riverstrahler, pyNuts-Riverstrahler, ProSe/ProSe-PA and Barman. After 30 years of research, the use of different programming languages including Qbasic, Visual Basic, Fortran, ANSI C and Python, as well as parallel evolution and the addition of new formalisms, raise questions about their comparability. This paper presents a unified version of the RIVE model for the water column, including formalisms for bacterial communities (heterotrophic and nitrifying), primary producers, zooplankton, nutrients, inorganic carbon, and dissolved oxygen cycles. The unified RIVE model is open source and implemented in Python 3 to create pyRIVE 1.0, and in ANSI C to create C-RIVE 0.32. The organic matter degradation module is validated by simulating batch experiments. The comparability of the pyRIVE 1.0 and C-RIVE 0.32 softwares is verified by modeling a river stretch case study, which considers the full biogeochemical cycles (microorganisms, nutrients, carbon, and oxygen) in the water column, as well as the effects of light and water temperature. The results show that the simulated concentrations of all state variables, including microorganisms and chemical species, are very similar for pyRIVE 1.0 and C-RIVE 0.32. This open-source project highly encourages contributions from the aquatic biogeochemistry community to further advance the project and achieve common objectives.

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Anthropogenic impacts on the water chemistry of a transboundary river system in Southeast Asia

Trinh,

Do,

Panizzo

et al. 2024

Journal of Asian Earth Sciences: X

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Water quality in an urbanized river basin impacted by multi-pollution sources: from comprehensive surveys to modelling

Cited by 6 publications

References 0 publications

How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?

How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?

The community-centered aquatic biogeochemistry model unified RIVE v1.0: a unified version for water column

Anthropogenic impacts on the water chemistry of a transboundary river system in Southeast Asia

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