Jean-Luc Bertrand-Krajewski

The use of long-term on-line turbidity measurements for the calculation of urban stormwater pollutant concentrations, loads, pollutographs and intra-event fluxes

This paper presents one of the largest databases on the quality of urban wet weather discharges measured since the development of continuous in-sewer water quality sensors in the late 1990s. Five years of continuous turbidity measurements enabled the validation of 263 and 239 rainfall events, respectively on two experimental catchments in Lyon (France), Chassieu (185 ha separate sewer) and Ecully (245 ha combined sewer). Except for high rainfall events of summer and second half of winter, analysis of database representativeness showed that all seasons were relatively well represented. As a first analysis of the database, traditional tools used in the urban drainage field were applied to assess: i) statistics and analysis of distributions of TSS and COD events loads and event mean concentrations (EMCs) and ii) the correlations between these statistics and events characteristics and iii) M(V) curves describing the intra-event mass distribution. Results showed that: i) EMCs and loads were approximately log-normally distributed, with a clear impact from wastewater contribution in Ecully, ii) EMCs are not correlated with storm event characteristics, whereas loads have shown significant correlation with key storm event variables such as total event volume, rainfall depth, maximum rainfall intensity and discharge and iii) M(V) curves dynamic could be classified in three categories, however with no clear correlation with storm event characteristics. The visual analysis of continuous time series of TSS and COD pollutographs, derived from turbidity time series showed that event pollutographs were highly variable, due to complex interacting processes during and between events, and suggests that further progress in knowledge and modelling of urban wet weather pollutant loads and pollutographs should be based on more detailed analyses of continuous time series rather, than on the traditional single event approach.

Standardized uncertainty analysis for hydrometry: a review of relevant approaches and implementation examples

Abstract The water-centric community has continuously made efforts to identify, assess and implement rigorous uncertainty analyses for routine hydrological measurements. This paper reviews some of the most relevant efforts and subsequently demonstrates that the Guide to the expression of uncertainty in measurement (GUM) is a good candidate for estimation of uncertainty intervals for hydrometry. The demonstration is made by implementing the GUM to typical hydrometric applications and comparing the analysis results with those obtained using the Monte Carlo method. The results show that hydrological measurements would benefit from the adoption of the GUM as the working standard, because of its soundness, the availability of software for practical implementation and potential for extending the GUM to hydrological/hydraulic numerical simulations. Editor D. Koutsoyiannis Citation Muste, M., Lee, K. and Bertrand-Krajewski, J.-L., 2012. Standardized uncertainty analysis for hydrometry: a review of relevant approaches and implementation examples. Hydrological Sciences Journal, 57 (4), 643–667.

From mess to mass: a methodology for calculating storm event pollutant loads with their uncertainties, from continuous raw data time series

With the increasing implementation of continuous monitoring of both discharge and water quality in sewer systems, large data bases are now available. In order to manage large amounts of data and calculate various variables and indicators of interest it is necessary to apply automated methods for data processing. This paper deals with the processing of short time step turbidity time series to estimate TSS (Total Suspended Solids) and COD (Chemical Oxygen Demand) event loads in sewer systems during storm events and their associated uncertainties. The following steps are described: (i) sensor calibration, (ii) estimation of data uncertainties, (iii) correction of raw data, (iv) data pre-validation tests, (v) final validation, and (vi) calculation of TSS and COD event loads and estimation of their uncertainties. These steps have been implemented in an integrated software tool. Examples of results are given for a set of 33 storm events monitored in a stormwater separate sewer system.

Computational fluid dynamics modelling of flow and particulate contaminants sedimentation in an urban stormwater detention and settling basin

Sedimentation is a common but complex phenomenon in the urban drainage system. The settling mechanisms involved in detention basins are still not well understood. The lack of knowledge on sediment transport and settling processes in actual detention basins is still an obstacle to the optimization of the design and the management of the stormwater detention basins. In order to well understand the sedimentation processes, in this paper, a new boundary condition as an attempt to represent the sedimentation processes based on particle tracking approach is presented. The proposed boundary condition is based on the assumption that the flow turbulent kinetic energy near the bottom plays an important role on the sedimentation processes. The simulated results show that the proposed boundary condition appears as a potential capability to identify the preferential sediment zones and to predict the trapping efficiency of the basin during storm events.

Numerical simulation of flushing effect on sewer sediments and comparison of four sediment transport formulas

Results of numerical modeling of sediment flushing in a combined sewer reach are herein presented. Simulations were performed by using a numerical model based on the solution of the De SaintVenant–Exner equations by the TVD MacCormack scheme. The model, previously validated by means of data derived from laboratory experiments, was applied to the Lacassagne trunk sewer in Lyon, France. A Hydrass flushing gate was put into operation at this site in 2003 to remove sediments accumulated during 3 years. A 5-month long experimental campaign was carried out to measure the sediment profile evolution during flushing operations. Four sediment transport formulas were used for the numerical simulations and compared. The model proved to be able to reproduce correctly the global evolution of the sediment profiles as a function of the number of flushing operations.

Calibration of UV/Vis spectrophotometers: A review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers.

UV/Vis spectrophotometers have been used for one decade to monitor water quality in various locations: sewers, rivers, wastewater treatment plants (WWTPs), tap water networks, etc. Resulting equivalent concentrations of interest can be estimated by three ways: i) by manufacturer global calibration; ii) by local calibration based on the provided global calibration and grab sampling; iii) by advanced calibration looking for relations between UV/Vis spectra and corresponding concentrations from grab sampling. However, no study has compared the applied methods so far. This collaborative work presents a comparison between five different methods. A Linear Regression (LR), Support Vector Machine (SVM), EVOlutionary algorithm method (EVO) and Partial Least Squares (PLS) have been applied on various data sets (sewers, rivers, WWTPs under dry, wet and all weather conditions) and for three water quality parameters: TSS, COD total and dissolved. Two criteria (r(2) and Root Mean Square Error RMSE) have been calculated - on calibration and verification data subsets - to evaluate accuracy and robustness of the applied methods. Values of criteria have then been statistically analysed for all and separated data sets. Non-consistent outcomes come through this study. According to the Kruskal-Wallis test and RMSEs, PLS and SVM seem to be the best methods. According to uncertainties in laboratory analysis and ranking of methods, LR and EVO appear more robust and sustainable for concentration estimations. Conclusions are mostly independent of water matrices, weather conditions or concentrations investigated.

Source characterisation and loads of metals and pesticides in urban wet weather discharges

Contaminants in urban wet weather discharges originate from a number of sources such as materials from wet and dry atmospheric deposition, wastewaters, urban surface erosion, traffic-related activities, in-sewer deposits, etc. In the current study, four contributions (rainwater, dry atmospheric deposition, dry weather discharge and catchment surface + possible erosion of in-sewer deposits) to the total concentrations of priority substances have been assessed at the outlet of two urban catchments (one residential catchment with a combined system and one industrial area with a separate stormwater system) for 12 storm events (six for each catchment). Mass balances were calculated for seven metals and four pesticides, as well as for total suspended solids and chemical oxygen demand. The respective contributions of dry and wet atmospheric deposition, wastewater and catchment surface differ for each pollutant type, corresponding to different land use, activities, environments and sewer systems. For most of the pollutants, the catchment surface appears to be the main contribution, with significant storm event variability, excepted for atrazine in one catchment.

On calibration data selection: The case of stormwater quality regression models

A stormwater quality model should be calibrated and verified against available data before it can be confidently used. This paper mainly examines two questions: how do the size and selection of calibration data sets affect model performances and how should the calibration data sets be selected. Regression models are used to simulate stormwater quality (TSS and COD) with variables characterizing rainfall and flow characteristics. Based on large databases of three catchments in France, several models are calibrated and verified with different data subsets. It is confirmed that the selection of calibration data sets leads to significant uncertainty in model performance. The information content in the calibration data sets is also important in addition to their size. Generally model performances can be improved by using a large size of calibration data sets and by selecting calibration data that are representative of all data. Three methods endeavoring to improve model performance by selecting calibration data either according to model outputs or model inputs are developed based on the principle of choosing calibration data that are representative of the whole data set. The effectiveness of the three selection methods is demonstrated by their application on databases of the three catchments. Model performances can be generally improved by?selection methods. The selection methods based on model inputs that consider multi-dimension information perform better than the method with one-dimension information consideration. Highlights? Size and selection of calibration data lead to uncertainty in model performance. ? Selection methods are developed to choose data that are representative of all data. ? Selection methods improve model performance compared to random selection. ? Selection methods using multi-dimension information are recommended.

Influence of Local Calibration on the Quality of Online Wet Weather Discharge Monitoring: Feedback From Five International Case Studies

This paper reports about experiences gathered from five online monitoring campaigns in the sewer systems of Berlin (Germany), Graz (Austria), Lyon (France) and Bogota (Colombia) using ultraviolet-visible (UV-VIS) spectrometers and turbidimeters. Online probes are useful for the measurement of highly dynamic processes, e.g. combined sewer overflows (CSO), storm events, and river impacts. The influence of local calibration on the quality of online chemical oxygen demand (COD) measurements of wet weather discharges has been assessed. Results underline the need to establish local calibration functions for both UV-VIS spectrometers and turbidimeters. It is suggested that practitioners calibrate locally their probes using at least 15-20 samples. However, these samples should be collected over several events and cover most of the natural variability of the measured concentration. For this reason, the use of automatic peristaltic samplers in parallel to online monitoring is recommended with short representative sampling campaigns during wet weather discharges. Using reliable calibration functions, COD loads of CSO and storm events can be estimated with a relative uncertainty of approximately 20%. If no local calibration is established, concentrations and loads are estimated with a high error rate, questioning the reliability and meaning of the online measurement. Similar results have been obtained for total suspended solids measurements.

Calibration of stormwater quality regression models: a random process?

Regression models are among the most frequently used models to estimate pollutants event mean concentrations (EMC) in wet weather discharges in urban catchments. Two main questions dealing with the calibration of EMC regression models are investigated: i) the sensitivity of models to the size and the content of data sets used for their calibration, ii) the change of modelling results when models are re-calibrated when data sets grow and change with time when new experimental data are collected. Based on an experimental data set of 64 rain events monitored in a densely urbanised catchment, four TSS EMC regression models (two log-linear and two linear models) with two or three explanatory variables have been derived and analysed. Model calibration with the iterative re-weighted least squares method is less sensitive and leads to more robust results than the ordinary least squares method. Three calibration options have been investigated: two options accounting for the chronological order of the observations, one option using random samples of events from the whole available data set. Results obtained with the best performing non linear model clearly indicate that the model is highly sensitive to the size and the content of the data set used for its calibration.