An emission time series generator for pollutant release modelling in urban areas

Keyser, Webbey De; Gevaert, Veerle; Verdonck, Frederik; Baets, Bernard De; Benedetti, Lorenzo

doi:10.1016/j.envsoft.2009.09.009

Cited by 37 publications

(13 citation statements)

References 12 publications

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“…The first method [10] uses pollutant release patterns derived from literature to generate dynamic influent data aggregating the punctual emissions from the database. The second method [11] interpolates available influent data at e.g., a daily timescale to e.g., hourly dynamics.…”

Section: Introductionmentioning

confidence: 99%

Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling

Langeveld

Daal

Schilperoort³

et al. 2017

Water

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Wastewater treatment plants (WWTP) typically have a service life of several decades. During this service life, external factors, such as changes in the effluent standards or the loading of the WWTP may change, requiring WWTP performance to be optimized. WWTP modelling is widely accepted as a means to assess and optimize WWTP performance. One of the challenges for WWTP modelling remains the prediction of water quality at the inlet of a WWTP. Recent applications of water quality sensors have resulted in long time series of WWTP influent quality, containing valuable information on the response of influent quality to e.g., storm events. This allows the development of empirical models to predict influent quality. This paper proposes a new approach for water quality modelling, which uses the measured hydraulic dynamics of the WWTP influent to derive the influent water quality. The model can also be based on simulated influent hydraulics as input. Possible applications of the model are filling gaps in time series used as input for WWTP models or to assess the impact of measures such as real time control (RTC) on the performance of wastewater systems.

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Section: Introductionmentioning

confidence: 99%

Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling

Langeveld

Daal

Schilperoort³

et al. 2017

Water

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“…Other influent generators could be analysed as well (e.g. Muschalla et al 2006;Langergraber et al 2008;Alex et al 2009;de Keyser et al 2010), obtaining valuable information about the parameters with the strongest influence related to shortterm and long-term dynamics. In the case of using a different influent generator, the same flow rate descriptors could be calculated, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…During recent years, several DIDSG have been developed with multiple applications (see for example Muschalla et al 2006;Langergraber et al 2008;Alex et al 2009). de Keyser et al (2010) developed a model that creates time series of traditional and micro-pollutants from their emission sources in the urban catchment. Similarly, Ort et al (2005) developed a stochastic model describing shortterm variations of benzotriazole concentrations (a chemical in dishwasher detergents).…”

mentioning

confidence: 99%

Global sensitivity analysis of the BSM2 dynamic influent disturbance scenario generator

Flores-Alsina

Gernaey

Jeppsson

2012

Water Science and Technology

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This paper presents the results of a global sensitivity analysis (GSA) of a phenomenological model that generates dynamic wastewater treatment plant (WWTP) influent disturbance scenarios. This influent model is part of the Benchmark Simulation Model (BSM) family and creates realistic dry/wet weather files describing diurnal, weekend and seasonal variations through the combination of different generic model blocks, i.e. households, industry, rainfall and infiltration. The GSA is carried out by combining Monte Carlo simulations and standardized regression coefficients (SRC). Cluster analysis is then applied, classifying the influence of the model parameters into strong, medium and weak. The results show that the method is able to decompose the variance of the model predictions (R(2)> 0.9) satisfactorily, thus identifying the model parameters with strongest impact on several flow rate descriptors calculated at different time resolutions. Catchment size (PE) and the production of wastewater per person equivalent (QperPE) are two parameters that strongly influence the yearly average dry weather flow rate and its variability. Wet weather conditions are mainly affected by three parameters: (1) the probability of occurrence of a rain event (Llrain); (2) the catchment size, incorporated in the model as a parameter representing the conversion from mm rain · day(-1) to m(3) · day(-1) (Qpermm); and, (3) the quantity of rain falling on permeable areas (aH). The case study also shows that in both dry and wet weather conditions the SRC ranking changes when the time scale of the analysis is modified, thus demonstrating the potential to identify the effect of the model parameters on the fast/medium/slow dynamics of the flow rate. The paper ends with a discussion on the interpretation of GSA results and of the advantages of using synthetic dynamic flow rate data for WWTP influent scenario generation. This section also includes general suggestions on how to use the proposed methodology to any influent generator to adapt the created time series to a modeller's demands.

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“…This element is not included within the IUWS borders, but it is a sine qua non of the IUWS model. In fact, pollutant and water fluxes are estimated by a specifically developed application (De Keyser et al, 2008a, 2009a and the resulting emission (or release) time series for the different sub-catchments are used as input to the IUWS model. • Sewer network: water flows from combined and separate systems are collected and routed across the catchment.…”

Section: Methodology the Integrated Urban Wastewater Systemmentioning

confidence: 99%

“…This tool is applicable to MPs, traditional pollutants (COD, etc.) and wastewater flow rates, and its inputs can be provided by the emission strings database or by manually entering the information (De Keyser et al, 2008a, 2009a.…”

Section: Generating Input For the Iuws Modelmentioning

confidence: 99%

Dynamic Transport and Fate Models for Micro-pollutants in Integrated Urban Wastewater Systems

Benedetti¹,

Vezzaro²,

Gevaert³

et al. 2009

proc water environ fed

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The modelling of urban water quality (i.e. wastewater, stormwater) is a growing issue in water management. New regulations stress the importance of estimating the loads and the fate of micro-pollutants (MPs) in the urban water cycle. The models available to simulate the transport and removal of "traditional" pollutants such as overall organic pollution, nutrients and suspended solids in the different components of the urban water system (i.e. sewer network, stormwater treatment units, wastewater treatment plant, receiving waters) were extended with processes affecting the fate of MPs (i.e. physical, chemical and biological) depending on the compound's inherent properties. This paper describes the modelling approach and some possible applications of these unit process models including their combination to simulate urban water systems by means of three examples, developed within the EU project SCOREPP.

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An emission time series generator for pollutant release modelling in urban areas

Cited by 37 publications

References 12 publications

Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling

Empirical Sewer Water Quality Model for Generating Influent Data for WWTP Modelling

Global sensitivity analysis of the BSM2 dynamic influent disturbance scenario generator

Dynamic Transport and Fate Models for Micro-pollutants in Integrated Urban Wastewater Systems

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