Wiley wastewater quality monitoring and treatment_8 doc

JWBK117-2.2 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 Comparison of Flow Measurement Techniques 139 Sydney, and in a 1 m wide rectangular flume at the River Hydraulics and Hydrology Section of the Civil Engineering Research Institute of the Hokkaido Development Bureau in Japan. Many of these tests were done over long periods involving a range of flows, and for all these tests the average error between the ADFM flow measurement and those of the laboratory rating or dye was less than 2 %. The ADFM has been found to be highly accurate in a wide range of tests without at-site calibration. The disadvantage of the ADFM is moderately high cost (three to four times that of Doppler AVFMs). When choosing between AVFMsand the ADFM the potential cost of inaccurate measurements should be weighed against the extra cost of the ADFM. One case where high accuracy was sought was the Thames Tideway Study of the large combined sewers from the London, UK, area that drain into the Thames River for which 18 ADFMs were deployed and have provided high accuracy at sites with very complex hydraulics (Curling et al., 2003). 2.2.7 COMPARISON OF FLOW MEASUREMENT TECHNIQUES In 1995, the US Geological Survey (USGS) in cooperation with the Federal Highway Administration outfitted a 61 m length of straight, 137 cm diameter, 0.2 % slope, concrete storm-sewer pipe in Madison (WI, USA) with multiple instruments for the purpose of comparing these instruments. The details and results of this study are briefly summarized in Church et al. (1999). However, additional details of this field test were obtained as a written communication from D.W. Owens of the USGS Wisconsin Water Science Center (D.W. Owens, personal communication, 1998). Because this field test involved three of the previously discussed flow measurement techniques it is presented as a separate section. Owens (D.W. Owens, personal communication, 1998) reported that the test site had the following characteristics that are typical of storm sewer locations where discharge monitoring may be desired: (1) The concrete pipe sections had settled different depths creating pipe joints that acted as minor controls during lower flow conditions. As the water level increased, the smaller controls were drowned out. (2) The flow conditions at the site change rapidly because of the small drainage area (77.7 ha), high amount of impervious surface, and intense summer rainstorms. (3) Access to the pipe is limited creating a hazardous condition when the pipe is flowing. (4) Standard discharge measurements are nearly impossible to collect because of the access and rapidly changing flow. He also noted that the site was subject to relatively minor sediment loads. JWBK117-2.2 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 140 Sewer Flow Measurement The standard for judging the accuracy of the flows obtained from the various measurement techniques was a MPB flume (Kilpatrick and Kaehrle, 1986) that had been rated using 243 dye-dilution flow measurements over a flow range of 0.057 to 2.32 m 3 /s (hereafter referred to as ‘the measurement standard’). The average percent- age error in the dye dilution discharge calculations was estimated as 4 % with a range from 1 to 14 % (D.W. Owens, personal communication, 1998). Fifty runoff events were monitored during a 6-month period and the resulting hydrographs and total storm runoff volumes obtained with the flow measurement techniques were com- pared with those obtained with the measurement standard. The flow measurement techniques evaluated included: (1) Critical-flow flumes in the form of the theoretical rating for the MPB flume. (2) Manning’s equation applied at three locations in the study pipe. (3) AVFMs–Automated Data Systems (ADS), ISCO 4250, and American Sigma 950 Doppler AVFMs and Marsh–McBirney Flow-Tote electromagnetic AVFM. Data were collected at 1 min intervals for all meters except the ADS meter for which a 2.5 min interval was used. The meters were placed in series in the pipe. The comparisons of the measured hydrographs revealed the following (D.W. Owens, personal communication, 1998): (1) The hydrographs obtained from the AVFMs are noisier than hydrographs ob- tained with the measurement standard. Inspection of the data showed that this resulted from erratic velocity measurements. (2) The AVFMs had periodic velocity dropouts wherein the velocity measurement dropped down to a value that was much lower than the previous and following measurements. (3) At higher flows (>0.4 m 3 /s), the Doppler AVFMs tended to underestimate the flow. At lower stages, the Doppler signal tended to work better. These results are indicative of the range bias for deeper flows that is common for the Doppler AVFMs. (4) The electromagnetic AVFM tended to be the closest to the measurement stan- dard. Furthermore, the electromagnetic velocity measurements displayed less noise than the Doppler measurements. (5) The theoretical discharge for the MPB flume closely matched the measurement standard. (6) The Manning equation technique produced mixed results based on monitoring location in the pipe. Box plots were made of the percentage differences between the results of the various techniques/equipment and the measurement standard for the total storm JWBK117-2.2 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 Conclusions and Perspectives 141 runoff volume (Church et al., 1999). Table 2.2.1 was prepared using the same data used to prepare Figure 7 in Church et al. (1999), which was provided by D.W. Owens of the USGS Wisconsin Water Science Center. The comparison of the total storm volumes in Table 2.2.1 yielded the following results: (1) The electromagnetic AVFM yielded the best overall results with a median error of 0.4 % and an interquartile range of −9.4 to 4.4 %. (2) The theoretical rating of theMPB flume also yielded good results with a median error of +10.8 % and an interquartile range of 2.7 to 17.9 %. (3) All uncorrected Doppler AVFMs underestimated total storm volumes with me- dian errors ranging from −6.6 to −28.8 % and mean errors ranging from −10.1 to −30.5 %. Again an indicator of the range bias for deeper flows. (4) One of the Manning’s equation sites was affected by backwater resulting in a median error of nearly 100 %. Another Manning’s equation site was affected by drawdown resulting in 25 % of the storms having underestimates greater than 30 %. The final Manning’s equation site was not affected by either backwater or drawdown and had a median error of 24.4 % and an interquartile range of −0.4 to 36.8 %. It is difficult to derive general results from measurement comparisons at one site, but Church et al. (1999) raised two important conclusions from this study. The data clearly indicate the need to calibrate the flow measurement device using measure- ments obtained with an independent method. Further, although flow measurement techniques can be adjusted using verification data to minimize bias, the very large uncertainty in flow measurements exhibited by some of the flow measurement tech- niques is likely to remain after the adjustments. 2.2.8 CONCLUSIONS AND PERSPECTIVES Many methods are available for measurement of flow in sewerage systems. Flumes have been available since the 1930s, and electromagnetic and acoustic methods for velocitymeasurementhavebeenused sincethe 1970sand1980s, respectively. During these long periods of use, manufacturers and users have fine-tuned the equipment so that reliable measurements may be obtained in real-time by telephone line or radio transmission. If real-time data are desired, users must pay special attention to the accessibility of the site to power and phone lines or radio transmission to a central station. All the flow measurement equipment is capable of yielding accurate discharges for the appropriate hydraulic conditions (although the range of appropriate conditions for Manning’s equation is quite limited). Flumes, electromagnetic flow meters and ADFMs have been found to yield high accuracy (within ±5 %) for a wide range of flow conditions. However, flumes and electromagnetic meters may be difficult JWBK117-2.2 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 Table 2.2.1 Summary of storm volume errors in per cent relative to the rated modified Palmer–Bowlus flume for various flow measurement techniques/ equipment applied in a 137 cm diameter in Madison (WI, USA). (Data provided by D.W. Owens, USGS Wisconsin Water Science Center) Technique/Equipment Mean Median Flow weight (mean) Min. 25th Percentile 75th Percentile Max. No. of storms Modified Palmer–Bowlus flume theoretical rating 10.1 10.8 10.2 −20.5 2.7 17.9 23.9 50 Electomagnetic AVFM/Marsh–McBirney Flow-Tote −2.2 0.4 0.2 −30.1 −9.4 4.4 24.0 43 Doppler AVFM/American Sigma 950 −30.5 −28.8 −28.0 −58.6 −36.9 −26.0 −8.6 42 Doppler AVFM/ISCO 4250 −10.1 −6.6 −12.4 −27.8 −19.9 −4.4 11.8 33 Doppler AVFM/Automated Data Systems −18.5 −19.0 −22.1 −38.8 −27.7 −14.0 −13.8 29 Manning’s equation/Location 1 −11.6 −0.6 −18.5 −69.8 −30.5 8.0 16.8 50 Manning’s equation/Location 2 18.8 24.4 8.4 −30.1 −0.4 36.8 64.8 48 Manning’s equation/Location 3 99.5 99.3 86.5 34.0 73.0 121.7 155.2 48 142 JWBK117-2.2 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 References 143 to install at some locations. ADFMs are easier to install, but are more costly than acoustic Doppler area–velocity flow meters. Thus, users must consider site condi- tions, cost, use of the data, and desired accuracy when selecting the appropriate flow meter for the project at hand. Probes for measuring dissolved oxygen concentration, conductivity and temper- ature in real-time are commonly used in treatment plants and stream systems. Their use in sewerage systems has been limited due to the possibility of damage by debris in the confined space of the sewer pipe and the difficulty to keep the probes clean in the harsh sewer environment. Other probes for measuring nutrients and other chem- ical constituents in real-time are in development. As these probes are improved, development of ways to use them in sewerage systems could be very valuable and is encouraged as a topic of future research and development. REFERENCES Alley, W.M. (1977) Guide for Collection, Analysis, and Use of Urban Stormwater Data: A Con- ference Report, Easton, MD, 28 Novembar–3 December 1976. ASCE. Anon. (1996) World Water Environ. Eng., April, 36. Baughen, A.J. and Eadon, A.R. (1983) J. Inst. Water Poll. Cont., 82(1), 77–86. B¨orzs¨onyi, A. (1982) Advances in Hydrometry, IAHS Publ. No. 134, 19–23. Church, P.E., Granato, G.E. and Owens, D.W. (1999) Basic Requirements for Collecting, Documenting, and Reporting Precipitation and Stormwater-Flow Measurements. US Geo- logical Survey Open-File Report 99–255. Curling, T., Leafe, M., and Metcalfe, M. (2003) Avoiding the Pitfalls of Dynamic Hydraulic Con- ditions with Real-Time Data. Available online at http://www.mgdinc.com/pdfs/Thames%20 Tideway%20Hydraulic%20Results.pdf. Day, T.J. (1996) Water Eng. Manage., 143(4), 22–24. Diskin, M. (1977) J. Irrig. Drain. Div., ASCE, 102(IR3), 383–387. Doney, B. (1999a) Water Eng. Manage., 146(11), 32–34. Doney, B. (1999b) Water Eng. Manage., 146(11), 11–12. Drake, T. (1994) Water Eng. Manage., 141(12), 34. Hager, W.H. (1989) J. Irrig. Drain., ASCE, 114(3), 520–534. Hughes, A.W., Longair, I.M., Ashley, R.M. and Kirby, K. (1996) Water Sci. Technol., 33(1), 1–12. Hunter, R.M., Hunt, W.A., and Cunningham, A.B. (1991) Water Environ. Technol., 3(2), 47–51. Huth, S. (1998) Water Technol., 21, 78–80. Johnson, E.H. (1995) Water SA, 21(2), 131–138. Kilpatrick, F.A. and Kaehrle, W.R. (1986) Trans. Res. Rec., 1073, 1–9. Lanfear, K.J. and Coll, J.J. (1978) Water Sewage Works, 125(3), 68–69. Ludwig, R.G. and Parkhurst, J.D. (1974) J. WPCF, 46(12), 2764–2769. Marsalek, J. (1973) Instrumentation for Field Studies of Urban Runoff. Research Program for the Abatement of Municipal Pollution Under the Provisions of the Canada-Ontario Agreement on Great Lakes Water Quality, Ontario Ministry of the Environment, Project 73-3-12. Melching, C. S. and Yen, B.C. (1986) Slope influence on storm sewer risk. In Stochastic and Risk Analysis in Hydraulic Engineering, B. C. Yen, ed. Water Resources Publications, Littleton, CO, pp. 79–89. JWBK117-2.2 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 144 Sewer Flow Measurement Metcalf, M.A. and Edelh¨auser, M. (1997) Development of a velocity profiling Doppler flow meter for use in the wastewater collection and treatment industry. Paper Presented at WEFTEC ’97, available on-line at: http://www.mgdinc.com/pdfs/WEFTEC%20’97%20Velocity%20 Profiling%20for%20 Wastewater%20Collection%20and%20Treatment.pdf. Newman, J.D. (1982) Proc. Int. Symp. Hydrometeorology, 15–26. Palmer, H.K. and Bowlus, F.D. (1936) Adaptation of Venturi flumes to flow measurements in conduit. Trans. ASCE, 101, 1195–1216. Parr, A.D., Judkins, J.F., and Jones, T.E. (1981) J. WPCF, 53(1), 113–118. Soroko, O. (1973) Water and waste flow measurement. TAPPI (Atlanta, GA) Engineering Confer- ence, Boston, MA, 9 October 1973 (Preprinted Proceedings), pp. 187–203. Valentin, F. (1981) Water Sci. Technol., 13(8), 81–87. Waite, A.M., Hornewer, N. and Johnson, G.P. (2002) Monitoring and Analysis of Combined Sewer Overflows, Riverside and Evanston, Illinois, 1997–99. US Geological Survey Water-Resources Investigations Report 01-4121. Watt, I.A. and Jefferies, C. (1996) Water Sci. Technol., 33(1), 127–137. Wells, E.A. and Gotaas, H.D. (1958) Design of Venturi flumes in circular conduits. Trans. ASCE, 123, 749–771. Wenzel Jr., H.G. (1975) J. Hydr. Div., ASCE, 101(HY1), 115–133. Weyand, M. (1996) Water Sci. Technol., 33(1), 257–265. Wright, J.D. (1991) Water Environ. Technol., 3(9), 78–87. JWBK117-2.3 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 2.3 Monitoring in Rural Areas Ann van Griensven and V´eronique Vandenberghe 2.3.1 Introduction 2.3.1.1 Monitoring for the European Union Water Framework Directive 2.3.1.2 Characterisation of Rural Areas and Pollution 2.3.1.3 Joint Use of Modelling and Monitoring 2.3.2 A Case Study 2.3.2.1 The Dender River in Flanders, Belgium 2.3.2.2 The Model Using ESWAT 2.3.2.3 Sensitivity Analysis 2.3.2.4 Uncertainty Analysis 2.3.2.5 Discussion 2.3.3 Automated Monitoring 2.3.3.1 Automated Monitoring Stations 2.3.3.2 The Control of the Station – GSM Communication 2.3.3.3 The Control of the Station – Internet Communication 2.3.3.4 Maintenance and Calibration 2.3.3.5 Discussion 2.3.4 Conclusions and Perspectives References Wastewater Quality Monitoring and Treatment Edited by P. Quevauviller, O. Thomas and A. van der Beken C  2006 John Wiley & Sons, Ltd. ISBN: 0-471-49929-3 JWBK117-2.3 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 146 Monitoring in Rural Areas 2.3.1 INTRODUCTION 2.3.1.1 Monitoring for the European Union Water Framework Directive Recently the European Union has approved the European Union Water Framework Directive (EU WFD). This directive claims that by the end of 2015 a ‘good status of surface water’ and a ‘good status of groundwater’ should be achieved (European Union, 2000). To make sure that the new water policy will succeed, a profound analysis of the actual and future state of the water is necessary. In this context, the evaluation of emissions into river water will be important. To that end, the EU WFD provides several guidelines for monitoring the water bodies, leaving the practical implementation to the local governments. Since urban pollution has been strongly reduced in many western countries by collection and treatment of the urban wastewater, the remaining water quality problems require advanced management and optimisation techniques in an integrated manner. ‘Inte- grated’ is a term with many interpretations, but also a dangerous term to be used. Whereas ‘integrated water management’ at first referred to a holistic approach that linked the sewer–wastewater treatment plant–river systems, it soon became apparent that goals of good water quality were not reached, causing awareness that some other sources of pollution were involved. Indeed, after large investments to reduce urban pollution, managers were confronted with pollution from rural areas (Figure 2.3.1). Figure 2.3.1 Sources of pollution in a river basin JWBK117-2.3 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 Introduction 147 2.3.1.2 Characterisation of Rural Areas and Pollution Rural areas should not necessarily be considered as pollutant areas. Not-intensive grazing for instance has beneficial effects on erosion reduction and does not cause excessive nutrient loads to the receiving systems. In Europe, evolution towards more intensive practices took place during the past decades and has caused an increase of nutrient release into theenvironment(Poirot,1999).Underthe Common Agricultural Policy of the EU, the Gross Value Added (GVA) of the agricultural sector has raised sharply over the last 25 years. This was mainly due to increased investments giving in increase in the volume of production (Barthelemy and Vidal, 1999). The measures have generally led to a reduction of permanent grassland in favour of wheat, maize, the appearance of oilseed and protein crops and annual crops as fodder. Livestock production has also followed a trend to intensification, where small extensive hold- ings are replaced by modern and specialised ones. These ‘nonland-bound’ farms resulted in a considerable growth in the livestock sector (Boschma et al., 1999). In particular, pig husbandry constitutes the most intensive type. The intensification in livestock production and crop culture has led to a high application of nutrients to agricultural land. Livestock manure is the second most important source in the EU. The Netherlands and Belgium had the highest input of nitrogen from manure per hectare coming mostly from pig production (Pau Val and Vidal, 1999). Within European soils, 115 million hectares suffer from water erosion and 42 million hectares from wind erosion (Montarella, 1999). Most agricultural activities are considered to be nonpoint sources. This is not the case for the large ‘nonland-bound’ farms that are agricultural enterprises where a large number of animals are kept and raised in confined areas. The feed is generally brought to the animals, rather than the animals grazing or otherwise seeking feed in pastures, fields or rangeland. Such activities are treated in a similar manner to other industrial sources of pollution. Whereas point-source pollution can be measured by monitoring the discharge and the water quality, diffuse pollution sources are very difficult to monitor because the sources are distributed along the river. 2.3.1.3 Joint Use of Modelling and Monitoring An integrated approach with regard to the nonpoint and diffuse pollution creates new challenges for monitoring and modelling, but it also promotes the interaction between these two. The water bodies are highly complex systems as they hold many unknowns and uncertainties due to the incomplete understanding of the processes, to scaling aspects and to the high variability of the variables in time and space. Consequently, it is not possible to develop one perfect model or to design an optimal monitoring network with present information and knowledge. An adaptive approach is therefore needed: besides linking available data and thereby improving the concep- tual understanding of the water system, models may indicate errors and inadequacies in the monitoring network. Conversely, the model is revised and updated as new data JWBK117-2.3 JWBK117-Quevauviller October 10, 2006 20:18 Char Count= 0 148 Monitoring in Rural Areas become available. The effects of a pollution load into the river can be evaluated using models, especially for diffuse pollution, coming from rural areas, because complete monitoring of diffuse pollution input is impossible. Due to the characteristics of such a pollution that comes from land use practices, fertiliser and pesticide use, these are subject to different processes like runoff, leakage to groundwater, uptake by plants, conversion in the soil and absorption by soil particles. All these can be modelled, however, for several reasons those model outputs are uncertain (Beck, 1987). Model outcome uncertainties can become very large due to: r input uncertainty; r model uncertainty; r uncertainty in the estimated model parameter values; r mathematical uncertainty. Therefore, estimating and calculating the diffuse pollution to a river can be subject to large input uncertainties, so in this chapter we will focus on monitoring with a view to making the input uncertainties of a model that calculates diffuse pollution towards a river smaller. To optimally allocate the efforts necessary to reduce those input uncertainties, it is useful to evaluate the sensitivity of the outputs, the water quality, to the different inputs needed for calculation of diffuse pollution. In this study we focus on the diffuse pollution of nitrate in the water due tofertiliser use. With the use of an efficient Monte Carlo method based on Latin Hypercube sampling (McKay, 1988), the contribution to the uncertainty by each of the inputs is calculated. The methodology is applied to the Dender basin in Flanders, Belgium. The following sections give a description of the river basin for the case study, the model environment and the applied methodology, which consists of a sensitivity and uncertainty analysis based on the studies performed by Vandenberghe et al. (Vandenberghe et al., 2005). 2.3.2 A CASE STUDY 2.3.2.1 The Dender River in Flanders, Belgium The catchment of the river Dender has a total area of 1384 km 2 and has an average discharge of 10 m 3 /s at its mouth. Figure 2.3.2 shows how the Dender basin is situated in Flanders. As about 90 % of the flow results from storm runoff and the point sources make very little contribution, the flow of the river is very irregular with high peak discharges during intensive rain events and very low flows during dry periods (Bervoets et al., 1989). The river Dender is heavily polluted. Part comes from point-pollution (e.g. industry) but also from diffuse sources of pollution originating mainly from agricultural activity. Although there is an unmistakable relation between intensive agricultural activity and the occurrence of high nutrient concentrations in [...]... areas of landcover, soil and management within the subbasins The main soil classes are sand, loamsand, silty loam and impervious areas For landuse, five classes are important: impervious areas, forests, pasture, corn (maize and corn) and land for common agricultural use (crop culture, not corn) About 30 % of the landuse is pasture, while crop farming represents ca 50 % of the landuse To build the model,... for an integral modelling of the water quantity and quality processes in river basins The diffuse pollution sources are assessed by considering crop and soil processes The crop simulations include growth and growth limitations, uptake of water and nutrients and several land management practices The in-stream water quality model is based on QUAL2E (Brown and Barnwell, 1987) The spatial variability of... model SWIM (soil water infiltration and move)] from arable land in large river basins (Krysanova and Haberlandt, 2001) showed that the relative importance of natural and anthropogenic factors affecting nitrogen leaching in the Saale river basin was as follows: (1) soil; (2) climate; (3) fertilisation rate; and (4) crop rotation Reducing the uncertainty on inputs for soil and climate depends on better equipment... also very sparse and conversions had to be made to make the data useful for the model (Smets, 1999) Data on fertiliser and manure use were provided by The Flemish Institute for Land Use (Vlaamse Landmaatschappij, VLM) They provided data on the nutrient use and production for each municipality in Flanders In SWAT, one has to specify for each subbasin the total amount of fertiliser and the detailed composition... the filters and the sensors and, if necessary, calibrate the sensors A LabView program guides the calibration process After the calibration, a log file with the new calibration parameters is logged and the measuring mode is started again 2.3.3.5 Discussion AMSs are useful tools for monitoring in rural areas under the conditions that the monitoring system coincides with good maintenance and quality control... pasture in subbasins 16 and 12, on farming land in subbasins 4 and on corn in subbasins 5, 11 and 15 The dates of planting and harvesting the crops appear not to be important To reduce the output uncertainty it is best to focus on data about amounts of fertiliser applied on crops In the uncertainty analysis, the effects of uncertain input of fertiliser use and land management on nitrate concentrations... evaluate and draw conclusions from model predictions 2.3.3 AUTOMATED MONITORING The Dender model clearly illustrates the high variability in water quantity and quality in rural areas It is therefore hard to plan monitoring for certain preferred conditions (dry period versus rain event) In practice, it may not be feasible to catch these short events through manual sampling 2.3.3.1 Automated Monitoring. .. type and land use a number of Hydrological Response Units (HRUs) can be defined For each HRU, the ESWAT model simulates the processes involved in the land phase of the hydrological cycle, and computes runoff, sediment and chemical loading Based on the areas of each HRU, the results are then summed for each subbasin Input information for each subbasin is grouped into categories for unique areas of landcover,... evaluation and quantification of the impacts of land management practices on nitrogen wash-off to surface water is therefore very important In this study we focus on fertilisation rate and time of fertiliser application on the most important crops for the Dender river basin For the application of fertiliser for the different land uses three application dates were assumed; 1 March, 1 April and 1 May Also,... inputs and the analysis of the outputs, a program was written to couple UNCSAM, the program used for the SA (Janssen et al., 1992) with the management input files of ESWAT We used the standardised regression coefficient (SRC) as an indication of the relative importance of the different inputs: SRCi = y/Sy xi /Sxi with y/ xi being the change in output due to a change in an input factor and Sy , Sxi the standard . −6.6 −12.4 −27 .8 −19.9 −4.4 11 .8 33 Doppler AVFM/Automated Data Systems − 18. 5 −19.0 −22.1 − 38. 8 −27.7 −14.0 −13 .8 29 Manning’s equation/Location 1 −11.6 −0.6 − 18. 5 −69 .8 −30.5 8. 0 16 .8 50 Manning’s. equation/Location 2 18. 8 24.4 8. 4 −30.1 −0.4 36 .8 64 .8 48 Manning’s equation/Location 3 99.5 99.3 86 .5 34.0 73.0 121.7 155.2 48 142 JWBK117-2.2 JWBK117-Quevauviller October 10, 2006 20: 18 Char Count=. Proceedings), pp. 187 –203. Valentin, F. (1 981 ) Water Sci. Technol., 13 (8) , 81 87 . Waite, A.M., Hornewer, N. and Johnson, G.P. (2002) Monitoring and Analysis of Combined Sewer Overflows, Riverside and Evanston,