Literature DB >> 31466210

Optimum positioning of wastewater treatment plants in a river network: A model-based approach to minimize microbial pollution.

Sulagna Mishra1, David Kneis2, Thomas U Berendonk2, Antoine Aubeneau3.   

Abstract

Microbial pollution in river networks is widespread, threatening human health and activities. Wastewater treatment plants are a major source of microbial pollution that affects downstream communities. We propose a simple modeling approach to identify possible hot-spots of microbial pollution in river networks receiving treated wastewater. We consider every reach in a river network as a potential site for the disposal of treated wastewater and we identify the corresponding section of the downstream river where the concentration of indicator bacteria exceeds a prescribed threshold value. In this paper, we introduce the methodology and demonstrate its application to a small river basin (Lockwitzbach, Germany). We computed the lengths of the polluted river sections for different scenarios in order to separately identify the impacts of hydrological boundary conditions and bacterial retention processes. Effective parameters describing bacterial retention were inferred from field samples. The proposed modeling approach can be used to generate dynamic maps of safe and vulnerable zones in a river network. Our approach helps disentangle the effects of network structure, hydrological variability and in-stream processes on the location and length of unsafe river sections. Our model can be used to identify optimal sites for the discharge of treated wastewater. For example, in the Lockwitzbach basin, we show that relocating the existing effluent discharge could reduce the stream length affected by severe microbial pollution by almost 30%.
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  E. coli; Hydrological model; In-stream parameter estimation; Stochastic climate generator; Water quality; Watershed management tool

Mesh:

Year:  2019        PMID: 31466210     DOI: 10.1016/j.scitotenv.2019.07.035

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  2 in total

Review 1.  The ecology of plasmid-coded antibiotic resistance: a basic framework for experimental research and modeling.

Authors:  Martin Zwanzig
Journal:  Comput Struct Biotechnol J       Date:  2020-12-29       Impact factor: 7.271

2.  Effect of Urbanization on the River Network Structure in Zhengzhou City, China.

Authors:  Hongxiang Wang; Lintong Huang; Jianwen Hu; Huan Yang; Wenxian Guo
Journal:  Int J Environ Res Public Health       Date:  2022-02-21       Impact factor: 3.390

  2 in total

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