The role of organic matter and bacterial physiology on river metabolism at low flow

2021 ◽  
Author(s):  
Masihullah Hasanyar ◽  
Nicolas Flipo ◽  
Thomas Romary ◽  
Shuaitao Wang
Keyword(s):  
Water Quality ◽  
Organic Matter ◽  
Bacterial Growth ◽  
Water Quality Management ◽  
Sensitivity Analyses ◽  
Low Flow ◽  
Biogeochemical Model ◽  
Bacterial Physiology ◽  
Bacterial Yield ◽  
Influential Parameters

<p>Development of accurate water quality modelling tools is necessary for integrated water quality management of river systems. The existing water quality models can simulate dissolved oxygen (DO) concentration quite well in rivers, however, there are discrepancies during summer low flow season which are assumed to be due to the heterotrophic bacterial decomposition of organic matter (OM) (Wang, 2019). Therefore, we used the C-RIVE biogeochemical model in order to evaluate the influence of controlling parameters on the DO simulations at low flow.</p><p>Four Sobol’ sensitivity analyses (SA) were carried out based on an evolving strategy of reduction in the number of parameters and hiding the inter-parameter interactions. The studied parameters are bacterial (such as growth rate of bacteria), OM-related (repartition and degradation of OM into constituent fractions) and physical (for instance reaeration of river due to navigation and wind) whose variation ranges are selected based on a detailed literature review.</p><p>Bacterial growth and mortality rates are by far the two most influential parameters followed by bacterial yield and the share of biodegradable dissolved organic matter (BDOM). More refined SA results indicate that depending on the net bacterial growth (=growth – mortality) being low or high, the bacterial yield and BDOM concentration are the most influential parameters, respectively. Reaeration constant due to navigation and the bacterial uptake of substrate are the other two influential parameters identified in this work. </p><p>The results of this study highlight the importance of accurate in-situ sampling and measurement of these influential parameters in order to reduce modelling uncertainties, as well as the necessity for a suitable sampling frequency in order to characterize potential bacterial community switch during transient events such as combined sewer overflows. </p><p>References:</p><p>Wang, S. (2019). Simulation Du Métabolisme de La Seine Par Assimilation de Données En Continu. These de doctorat, Paris Sciences et Lettres</p>

scholarly journals Fuzzy Optimization Model for Waste Load Allocation in a River with Total Maximum Daily Load (TMDL) Planning

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2618
Author(s):  
Jae Heon Cho ◽  
Jong Ho Lee
Keyword(s):  
Water Quality ◽  
Wastewater Treatment ◽  
Water Quality Management ◽  
Fuzzy Optimization ◽  
Fuzzy Model ◽  
Low Flow ◽  
Satisfaction Level ◽  
Waste Load Allocation ◽  
Pollutant Load ◽  
Waste Load

In traditional waste load allocation (WLA) decision making, water quality-related constraints must be satisfied. Fuzzy models, however, can be useful for policy makers to make the most reasonable decisions in an ambiguous environment, considering various surrounding environments. We developed a fuzzy WLA model that optimizes the satisfaction level by using fuzzy membership functions and minimizes the water quality management cost for policy decision makers considering given environmental and socioeconomic conditions. The fuzzy optimization problem was formulated using a max–min operator. The fuzzy WLA model was applied to the Yeongsan River basin, which is located in the southwestern part of the Korean Peninsula and Korean TMDLs were applied. The results of the fuzzy model show that the pollutant load reduction should be increased in the Gwangju 1 and Gwangju 2 wastewater treatment plants (WWTPs) and in subcatchments with high pollutant load. In particular, it is necessary to perform advanced wastewater treatment to decrease the load of 932 kg ultimate biochemical oxygen demand (BODu)/day in the large-capacity Gwangju 1 WWTP and reduce the BODu emission concentration from 4.3 to 2.7 mg/L during the low-flow season. The satisfaction level of the fuzzy model is a relatively high at 0.81.

scholarly journals Progress, challenges, and opportunities in enhancing NOM flocculation using chemically modified chitosan: a review towards future development

2020 ◽  
Vol 6 (1) ◽  
pp. 45-61 ◽  
Author(s):  
Paripurnanda Loganathan ◽  
Michael Gradzielski ◽  
Heriberto Bustamante ◽  
Saravanamuthu Vigneswaran
Keyword(s):  
Water Quality ◽  
Organic Matter ◽  
Natural Organic Matter ◽  
Bacterial Growth ◽  
Future Development ◽  
Raw Water ◽  
Modified Chitosan ◽  
Chemically Modified ◽  
Challenges And Opportunities ◽  
Flocculation Process

Natural organic matter (NOM) occurs ubiquitously in water bodies and this can greatly affect feed or raw water quality (taste, colour, odour, bacterial growth). Chemically modified chitosan can effectively remove NOM by the flocculation process.

scholarly journals Water-quality modelling of the Upper Mersey river system using an object-oriented framework

2001 ◽  
Vol 3 (3) ◽  
pp. 173-194 ◽  
Author(s):  
Maria Spanou ◽  
Daoyi Chen
Keyword(s):  
Water Quality ◽  
Water Quality Management ◽  
Object Oriented ◽  
River System ◽  
Low Flow ◽  
Quality Model ◽  
Water Quality Simulation ◽  
The United Kingdom ◽  
Frequency Curves

The aim of this paper is to present the recent advances in the development of an object-oriented software system for water-quality management, and discuss the results from its application to the study of the Upper Mersey river system in the United Kingdom. The software has been extended and includes tools for the construction of flow duration and low-flow frequency curves using different methods, the sensitivity analysis and parameter estimation of the water-quality model, and the stochastic simulation of the mass balance at the discharge points of point-source effluents. The application of object-orientation has facilitated the extension of the software, and supported the integration of different models in it. The results of the case study are in general agreement with published values. They also include low flow estimates at the ungauged river sites based on actual data for the artificial sources, and water-quality simulation results, which have not been presented earlier in the literature for the Upper Mersey system.

scholarly journals Nitrate and biochemical oxygen demand change in a typical Midwest headwater stream in the past two decades

2020 ◽  
Vol 3 (1) ◽  
pp. 519-537
Author(s):  
M. D. Shahin Alam ◽  
Bangshuai Han ◽  
Amy Gregg ◽  
John Pichtel
Keyword(s):  
Water Quality ◽  
Biochemical Oxygen Demand ◽  
Water Quality Management ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Agricultural Runoff ◽  
Low Flow ◽  
Organic Contamination ◽  
Novel Approach ◽  
Rich Data

Abstract Nitrate and organic contamination from Midwest rivers, including the White River at Muncie, IN, has been an on-going concern and contributes to the hypoxic zone in the Gulf. Despite rich data, recent water quality changes have rarely been investigated. This study employed 16 years of continuous monitoring data, including biochemical oxygen demand (BOD), dissolved oxygen (DO), and nitrate–nitrite as nitrogen (NN) from five sites near Muncie, and analyzed the water quality trend and pollution sources. A novel approach, Weighted Regression on Time, Discharge and Seasons that allows for the representation of long-term water quality patterns by considering seasonal variance and discharge-related effects over time, is adopted. Flow-normalized BOD and NN concentration and flux both increased, and DO concentration and flux decreased. However, the changes vary among sites. Muncie wastewater treatment plant and combined sewage outflows (CSOs) contribute remarkably to NN pollution during low-flow seasons. Urban and agricultural runoff, and CSOs impact BOD levels. Agricultural runoff contribution to BOD is increasing in recent years. Seasonal patterns of nitrate and BOD in the river are also analyzed. The results are helpful for watershed managers to re-think conservation practices and have indications to water quality management beyond the study area.

The application of the activated sludge model No. 1 to a river environment

1997 ◽  
Vol 36 (5) ◽  
pp. 201-208 ◽  
Author(s):  
F. Maryns ◽  
W. Bauwens
Keyword(s):  
Water Quality ◽  
Activated Sludge ◽  
River Water ◽  
Water Quality Management ◽  
River Water Quality ◽  
Sensitivity Analyses ◽  
Quality Models ◽  
Activated Sludge Model ◽  
Water Quality Models ◽  
Modelling Approach

The current tendency towards an integrated approach for water quality management gives rise to a demand for consistent methods for linking dynamic wastewater treatment models with river water quality models. Linking such models is difficult because of the mutual structural differences with regard to variable and parameter definitions as well as process descriptions. This paper proposes to use the same modelling approach for the simulation of activated sludge treatment and natural self-purification in rivers. Since the standard Activated Sludge Model No. 1 (ASM1) is found to be far more conceptual and consistent than traditional river water quality models, the suitability of the ASM1 modelling approach has been assessed. The traditional ASM1 matrix has been adapted and extended to a river environment and has subsequently served as the basis of an ASM1-type water quality model for the River Dender in Flanders. Sensitivity analyses on this model showed that the most sensitive parameters in the ASM1 formulation of biological decay are the ones determining hydrolysis. The model efficiently calculates BOD concentrations but the predicted DO concentrations are not very accurate, mainly because of the remaining uncertainty about the many ASM1-parameters in river conditions. This indicates the need for determining new typical value ranges for these parameters in a river environment.

Sign in / Sign up

Export Citation Format

Share Document