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. 2013 Feb 15:445-446:79-93.
doi: 10.1016/j.scitotenv.2012.12.022. Epub 2013 Jan 11.

Monitoring and modelling to support wastewater system management in developing mega-cities

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Monitoring and modelling to support wastewater system management in developing mega-cities

Juan Pablo Rodríguez et al. Sci Total Environ. .

Abstract

Urban drainage system models can be useful to assess and manage system performance and to plan its development. However, due to data and computational costs, sophisticated, high-resolution contemporary models of the sewer system may not be applicable. This constraint is particularly marked in developing country mega-cities where catchments can be large, data tend to be scarce, and there are many unknowns, for example regarding sources, losses and wrong connections. This paper presents work undertaken over the last 7 years to develop a suitable monitoring and modelling framework to support operation and development of the wastewater system of Bogotá (Colombia). Components of the framework covered here are: (a) the flow and water quality database, (b) a wastewater pollution load generator, and (c) a semi-distributed sewer network model, which aims at a complexity that matches the information available from the previous two components. Results from a catchment within Bogotá, area 150 km(2) and with 2.5 million inhabitants, show that the model outputs capture the scale and dynamics of the observed concentrations and loads at various points on the sewer system. However uncertainty is high because much of variability of observed dry weather flow profiles is apparently random. Against this variability, the effects of in-sewer processes were not identifiable except where backwaters caused particularly high retention times. Hence the work has resulted in an operational model with a scientifically justified, yet useful, level of complexity for Bogotá. More generally, the work demonstrates the value of monitoring and modelling programmes, including having modellers actively involved in monitoring specification and operations; and the insights into suitable level of model complexity that may be gained by uncertainty and sensitivity analysis.

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