Environ Sci Pollut Res (2014) 21:5311–5317 DOI 10.1007/s11356-013-2395-1 CHEMICAL, MICROBIOLOGICAL, SPATIAL CHARACTERISTICS AND IMPACTS OF CONTAMINANTS FROM URBAN CATCHMENTS: CABRRES PROJECT

Assessment of the contribution of sewer deposits to suspended solids loads in combined sewer systems during rain events A. Hannouche & G. Chebbo & C. Joannis

Received: 19 July 2013 / Accepted: 25 November 2013 / Published online: 5 December 2013 # Springer-Verlag Berlin Heidelberg 2013

Abstract Within the French observatories network SOERE “URBIS,” databases of continuous turbidity measurements accumulating hundreds of events and many dry weather days are available for two sites with different features (Clichy in Paris and Ecully in Lyon). These measurements, converted into total suspended solids (TSS) concentration using TSS– turbidity relationships and combined with a model of runoff event mean concentration, enable the assessment of the contribution of sewer deposits to wet weather TSS loads observed at the outlet of the two watersheds. Results show that the contribution of sewer deposits to wet weather suspended solid’s discharges is important but variable (between 20 and 80 % of the mass at the outlet depending on the event), including a site allegedly free of (coarse) sewer deposits. The uncertainties associated to these results are assessed too.

Keywords Sewer deposits . Combined sewer . Wastewater . Runoff . Turbidity . TSS . Uncertainties

Responsible editor: Philippe Garrigues A. Hannouche (*) : G. Chebbo LEESU (UMR-MA-102), UPEC, UPEMLV, ENPC, Agro ParisTech, Université Paris-Est, 6 et 8 avenue Blaise Pascal, Cité Descartes, 77455 Champs-sur-Marne Cedex 2, France e-mail: [email protected] G. Chebbo e-mail: [email protected] C. Joannis Faculty of Engineering III, Lebanese University, Hadath, Lebanon e-mail: [email protected] G. Chebbo Division Eau & Environnement, IFSTTAR, Route de Bouaye, BP 4129, 44341 Bouguenais Cedex, France

Introduction The control of pollution linked to urban wet weather discharges (UWWD) has become a major concern in the sanitation problems, supported by an increasingly strict regulatory framework including the obligation to achieve the good ecological status of recipient water bodies by 2015 imposed by the EU Water Framework Directive in 2000 (2000/60/EC). During recent decades, many studies on the sources and fluxes of pollutants in combined sewer system were conducted. The results highlighted the importance of UWWD and the negative impact of this pollution on the recipient water bodies (Saget et al. 1995; Ellis and Hvitved-Jacobsen 1996; Passerat et al. 2011). They confirmed the importance of total suspended solids (TSS) as major vectors of certain contaminants transported during wet weather (Chebbo et al. 1995; Ashley et al. 2005; Gasperi et al. 2011) and highlighted the key role played by the processes of sedimentation and erosion of sewer deposits (Ahyerre et al. 2001; Celestini et al. 2007; Gasperi et al. 2010). Contaminants in UWWD at the outlet of a combined sewer system come from three sources: wastewater (WW), runoff (RS), and in-sewer deposits (SD). Several studies have evaluated the contribution of these three sources using a mass balance where the SD term is assessed as a difference between the values observed at the outlet and assessed or measured for WW and RS terms (Krejci et al. 1987; Chebbo 1992; Gromaire et al. 2001; Gasperi et al. 2010). The results highlight the important or even major role of deposits as a significant source of TSS, during rain events. Moreover, the accurate quantification of WW and RS and their variability has often been approached in a simplified manner. Indeed, these studies have been conducted on a limited number of rainfall events by comparing them to few days of dry weather on the basis of samples taken in situ. The limited number of data does not cover the wide TSS variability observed in combined sewer network (Lacour et al. 2009;

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Metadier and Bertrand-Krajewski 2012). For these reasons, it is difficult to assess the uncertainties in the results. In this context, the French observatories in urban hydrology SOERE URBIS (A long-term Observation System for research and Experimentation on urban environment) are composed of OPUR-Paris (Observatory of Urban Pollutants in Île-deFrance/Paris region), OTHU-Lyon (Field Observatory for Urban Water Management in Lyon-France) and ONEVUNantes (Observatory of urban environments of NantesFrance) have coordinated their efforts to improve the reliability and reproducibility of continuous measurements of turbidity (an indirect measure of the TSS concentration) in combined sewer system (Joannis et al. 2008; Joannis et al. 2010). This has allowed acquiring rich databases, especially at the outlet of two catchments with different characteristics: Clichy in Paris and Ecully in Lyon. The systematic exploitation of these databases must enable an assessment, with good representativeness and acceptable accuracy, of the different contributions of the three sources to the masses transported during a wide sample of rain events at the outlet of both catchments. This paper aims at (1) assessing the contribution of each of the three sources, mentioned above, to the mass of a rain event and (2) estimating the uncertainties that affect these assessments. Firstly, we present the sites and databases used. Then, we detail the methodology developed to evaluate the contributions of the three sources. Finally, the results obtained are presented and discussed.

Methodology Experimental data Site descriptions This study benefits from data obtained on two experimental sites served by a combined sewer system with different characteristics within the French observatories in urban hydrology: Clichy in Paris and Ecully in Lyon. Table 1 summarizes the main characteristics and the used databases on both sites.

The two sewerage networks of both sites have different characteristics. The sewerage network of Clichy has a low slope (0.1 %), oversized for dry weather wastewater and has known deposit areas, while the sewerage network of Ecully is very steep (2.7 %) and has no known areas of deposits listed by agents of the Grand Lyon (Metadier and BertrandKrajewski 2012).

Databases used in this study Continuous measurements of flow and turbidity acquired on the sites of Clichy and Ecully were supplemented by other databases to achieve the objective of this study. Table 1 summarizes the data sources used. The data of continuous measurement of flow and turbidity (expressed in formazin attenuation unit (FAU)) in 2006 at the outlet of the catchment of Clichy cover 88 rainfall events and 221 dry weather days with a time step of 1 min. This database has been validated by (Lacour 2009) as part of her thesis. To convert the turbidity into TSS concentration, we used the TSS– turbidity calibration data obtained at two sites in Nantes, France, in the framework of the OneVu Observatory: Cordon Bleu and Saint-Mihiel (turbidity in FAU, the same measurement practices as those in Clichy). This database consists of about 60 rain events and 10 dry weather days (between 5 and 20 samples collected instantly per event). Indeed, the few TSS–turbidity (FAU) data available in Paris region were collected by the Water and Sewage Services Department of Seine-Saint-Denis (DEA93). The exploitation of these data shows that the average TSS–turbidity relationship in Seine-Saint-Denis is close to that of Cordon Bleu and Saint-Mihiel. The data of Cordon Bleu and Saint-Mihiel are much more comprehensive than those of DEA93. It is very rich in dry and wet weather conditions and can cover the variability in the TSS–turbidity relationship for different hydrological contexts (Hannouche et al. 2011). Thus, we used the data of Nantes to convert the turbidity into TSS concentration at Clichy (Paris) in the rest of this work. Flow and turbidity (expressed in formazin nephelometric unit (FNU); turbidity measurement method is different from that in Clichy) measurements were performed continuously with a time step of 2 min at the outlet of the catchment of

Table 1 Sites and databases available to this study Place

Site

Land use

Paris OPUR Clichy Dense urban

RC

Mean slope (%) Surface (ha) Used data

0.68 0.10

Lyon OTHU Ecully Urban (residential) 0.15 2.7

942 245

Flow and turbidity (FAU) at a time step of 1 min collected in 2006 at the outlet of Clichy site (88 rain events and 210 dry weather days) Flow and turbidity (FNU) at a time step of 2 min collected between 2004 and 2008 at the outlet of Ecully site (239 rainfall events and 180 dry weather days) + TSS–turbidity calibration data in dry and wet weather

RC runoff coefficient, FAU formazin attenuation unit, FNU formazin nephelometric unit

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Ecully. This database has been validated over the period 2004– 2008 by (Metadier and Bertrand-Krajewski 2011b) and divided into 239 rainfall events (2004–2008) and 180 dry weather days (2007–2008). TSS–turbidity (FNU) relationship at Ecully was built from samples collected instantly during both dry and wet weather conditions between 2004 and 2008. Note that the two different methods of measurements on both sites do not cause any problems for the evaluation of TSS fluxes, but the measured turbidity values are different (Joannis et al. 2010). As far as runoff is concerned, we do not have any turbidity or TSS concentration data specific for these two sites. So, we chose a model of distribution of runoff event mean concentration to cover all possible values of these concentrations. For Clichy, we transposed data available for the site of Marais in Paris. This database is comprised of measurement of TSS event mean concentration for runoff by type of surface (roofs, roads, and streams) for 31 rainfall events sampled between 1996 and 1997 (Gromaire-Mertz et al. 1999). Actually, Clichy site presents surface types and urbanization similar to those of the Marais site. In addition, the roads on both catchment areas of Clichy and Marais are cleaned daily by the city of Paris. For Ecully, we reported also a distribution from the literature for comparable residential sites (EPA 2005). Mass balance and simulations To assess the contribution of the three sources (WW, RS, and SD) to the TSS load transported during a rainfall event, a mass balance approach between the inlet and outlet of the network of each catchment area has been established for each rain event for which data were available. The mass balance equation was used to calculate the mass M SD provided by the mobilization of existing deposits in the network: M SD ¼ M Outlet −M WW −M RS

where M Outlet M WW M RS

TSS load measured at the outlet of the catchment area during a rainfall event Wastewater mass transported to the outlet during the rainfall event Mass contributed by runoff during the rainfall event.

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Calibrated continuous turbidity time series were used to calculate TSS concentrations and the mass (M Outlet ). However, at both sites, turbidity measurements are not the same (turbidity is expressed in FAU Clichy and FNU Ecully). So, TSS–turbidity relationships are different. Therefore, the method to evaluate the TSS load at the outlet (M Outlet) is described below for each site. For Clichy From the database presented above for Clichy, Hannouche et al. (2011) found a strong TSS–turbidity linear relationship at the rain event scale. Nevertheless, it varies between rainfall events in an unpredictable manner compared to the general characteristics of rainfall events (precipitation, rainfall intensity, previous dry weather period…). Thus, we developed a method to account for this variability in the assessment of M Outlet. This method takes into account the fact that conversion errors are not independent of each other for a given rainfall event. The shape of TSS–turbidity relationship used is linear TSS=a ×T (Hannouche et al. 2011), and its distribution defined by the slope a was adjusted following a log-normal distribution from the sample of 60 rainfall events. So, random draws implied by the Monte Carlo method were carried out in several stages. Firstly, we draw a conversion relationship (it is constant in dry weather conditions); then, for each measured value of turbidity, we draw a prediction error entailed by this relationship. Finally, we draw errors on turbidity and flow rates measurement, and we calculate a value for the mass of the event M Outlet, taking into account this particular combination of errors. The whole process is repeated many times to obtain a distribution of possible values of mass for this event M Outlet. The method of assessment of errors associated to this mass is detailed in Hannouche (2012). For Ecully TSS–turbidity relationship used at Ecully is a sitespecific second-degree polynomial relation, determined by Williamson regression from simultaneous measurements of turbidity and TSS concentrations in various samples collected during both dry and wet weather conditions (BertrandKrajewski et al. 2008; Metadier and Bertrand-Krajewski 2011b). Using the prediction error of the TSS–turbidity relationship and the uncertainty of measurement of turbidity and flow rates, M Outlet distribution is determined. A detailed description of this method is reported in Metadier and Bertrand-Krajewski (2011b).

Volume and mass at the outlet (V Outlet and M Outlet) Volume and mass of wastewater (V WW and M WW) At both sites, the volume of water transported to the outlet (V Outlet) was calculated by the integration of the flow measured at the outlet on the duration of the rainfall event, and uncertainty calculations were then conducted assuming that the successive errors of flow measurement are random.

Assuming that the fluxes contributed by wastewater during rainfall event did not differ from fluxes observed in the same conditions (hour, day, and season) during dry weather, the volume V WW and the mass M WW of wastewater are estimated

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from measurements obtained during dry weather conditions at the outlet of both sites. Two methods are used. For Clichy The volume V WW and the mass M WW were evaluated using a distribution model developed in the framework of the thesis of Hannouche (2012). This model, developed from data acquired by a continuous measurement of flow and turbidity in dry weather in Clichy, simulates the wastewater hydrograph and pollutograph transited during the rainfall event by combining two components: – –

A component representing the variability of daily volumes and masses explained by a seasonal factor. A second component representing the relative distribution of hourly volumes and masses within a day and the interday variability of this distribution. This component was modeled by means of multiple profiles identified using automatic classification and interpreted later as working days and holidays.

For one (or more) given day(s), this model provides a set of profiles of flows and turbidity fluxes of wastewater. From these profiles, we determined the distribution of volume V WW and mass M WW on the duration of an event observed during the day (using the Monte Carlo method). The variability of these volume and mass is due to the variability of wastewater profiles that may have contributed to the fluxes of this rainfall event. In dry weather conditions, Hannouche et al. (2011) did not find a significant difference between TSS and turbidity relationships at the day scale. A linear relationship TSS=a ×T is then sufficient. The prediction error of the TSS–turbidity relationship and the uncertainty of measurement of turbidity and flow rates have also been taken into account in the simulations. For Ecully We used the method presented in Metadier and Bertrand-Krajewski (2011a) to evaluate the masses and volumes of wastewater and the associated uncertainties during dry weather. This method is based on the research of dry weather days as similar as possible to the flow and turbidity signals just before and after the rainfall event. The modeling error due to the variability of these signals and uncertainty of measurement were also taken into account. Note that both methods gave similar mean values, but they model the error due to the variability of dry weather flow and turbidity profiles in a different way (Hannouche 2012). Volume and mass of runoff (V RS and M RS) The volume of runoff V RS is calculated by the difference between the measured volume at the outlet V Outlet and the estimated volume of wastewater V WW: V RS ¼ V Outlet −V WW :

For each rain event, we calculated a distribution of mass M RS. This distribution is obtained by the product of the distribution of volume V RS and that of TSS event mean concentration of runoff C RS. So, M RS =C RS ×V RS. For Clichy, the distribution of C RS was calibrated by a lognormal distribution (mean=62 mg l−1, standard deviation= 36 mg l−1) from a sample of about 30 rainfall events. This distribution cannot be applied to the catchment area of Ecully which is residential. In addition, the land and local practices are significantly different from those of Paris. We therefore used a log-normal distribution (mean=100 mg l−1, standard deviation=176 mg l−1) selected from the literature (EPA 2005) for residential catchment area. With these parameters, we can achieve C RS values higher than 500 mg/l with a probability of 4 %. In addition, these values are rarely seen on residential sites (Rossi 1998; Saget 1994). Simulations For a rainfall event, we simulate a distribution of 5,000 values for each of the three masses: M Outlet, M WW, and M RS. The distribution of the mass M SD contributing to the mass of a rainfall event was evaluated by using the mass balance equation. We calculate the percentage of contribution ðM RS =M Outlet ; M WW = M Outlet ; and M SD =M Outlet Þ of each source to the mass M Outlet transited at the outlet during the rainfall event. For each source, we calculate the average contribution and its dispersion shown as a 95 % confidence interval.

Results and discussion Contribution of different sources to the volume of water Figure 1a, b shows the distributions of the volume V Outlet, V WW, and V RS for all rainfall events at both sites. Wastewater generates a significant fraction of the total volume of rainfall event V Outlet (1st and 3rd quartiles (q 25–q 75), 50–80 % in Clichy and 20–60 % in Ecully). Cumulated over all events, wastewater accounted for 49±3 % of the total volume at the outlet of Clichy catchment area and 29±0.5 % of the total volume at the outlet of Ecully catchment area. Contribution of different sources to TSS loads Average contributions Figure 2 shows the average contribution of each source for all rainfall events in the form of Tukey box plots at both sites. This graphical method allows the study of the distribution of a data set using its mean (cross mark), median (q 50), lower (q 25)

Environ Sci Pollut Res (2014) 21:5311–5317

(b)

Average contribution (%)

100

%WW

2

12

12 0

76

51

0%

Event index RS

and upper (q 75) quartiles, and the extremes. Both the lower and upper whiskers define the so-called “adjacent” values, which are determined from the interquartile deviation Iqr= q 75 −q 25, and are greater or equal to q 75 +1.5×Iqr and less or equal to q 75 +1.5×Iqr. At Clichy, wastewater proves to be a major source. It generates between 32 and 48 % (q 25–q 75) of the TSS load at rainfall event scale. Runoff is characterized by low contributions in TSS load compared to the other two sources of TSS— between 7 and 13 % (q 25–q 75). Mobilization of the stock is an important source in TSS. It generates between 42 and 57 % (q 25–q 75) of the event mass. Values outside boxes or extreme values vary between 5 and 75 % for wastewater, 1 and 25 % for runoff, and between 25 and 80 % for the stock of deposits. These results corroborate the results obtained by the program OPUR (phase 2) on the same site (Gasperi et al. 2010). On an average of 15 events, 50 % of TSS came from the stock of deposits and also those from other sites (Marais, Sebastopol, Quais, Coteaux, Clichy aval) and those obtained by Gromaire et al. (2001) on the Marais catchment area. At Ecully, average contributions attributed to wastewater vary between 11 and 43 % (q 25–q 75), those caused by runoff

Fig. 2 Distributions of average contributions of each source to the total mass of all rainfall events in Clichy and Ecully (rectangles correspond to 25 % around the median, and the “whiskers” to + 49.65 %

73

79

56

86

82

75

64

0%

25%

31

25%

50%

54

50%

75%

36

Water volume (%)

75%

WW

Ecully

100%

34

Clichy

100%

93 10 2

(a) Water volume (%)

Fig. 1 Contribution of wastewater and runoff to the total volume (sorted in ascending order). Error bars represent the 95 % empirical confidence intervals

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Event index WW RS

vary between 11 and 32 %, and those attributed to stock of deposits vary between 30 and 62 %. Note that these values depend on the distribution of runoff selected. However, if we make the mass balance in reverse under the assumption M SD = 0 (absence of stock of deposits), the runoff (“non-wastewater”) reaches concentrations of 3,000 mg/l (mean=500 mg/l). Such values are not observed for such type of catchment areas (Rossi 1998). These results show that the contribution of the stock is not limited to specific networks such as Paris (old networks, low slope, oversized sewer for dry weather flows and heavily fouled) but is also evident for sloping network having no known areas of accumulation of stock. In other words, the problems of silting and contribution from deposits to wet weather pollution are not necessarily related.

Variability and uncertainty on the various contributions Figure 3 shows the percentage contribution of WW, RS, and SD to the TSS event load in Clichy and Ecully and their 95 % empirical confidence intervals (95 % CI).

%RS

%SD

%WW

%RS

%SD

75

50

25

0

Clichy

Ecully

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75 50 25

100 80 60 40 20 0

0 82

15

65

14

85

23

%WW

75 50 25 0

4

55

100

%SD

75 50 25

9 16 36

5

0 18

15

1

22

1

0

14

Event index

Event index

13 9 85 19 3 19 14 5 35 20 9 14 0

25

100

19

Event index

Contribution (%)

Contribution (%)

50

80 18 2 23 7 20 0 20

Contribution (%)

%RS

75

14 3 21 5

20

Event index

Event index

100

40

0 44

84

60

1

66

%SD

80

15

67

33 22 1 63 10 3 14 8

23

Ecully

100

%WW

Contribution (%)

%RS

Contribution (%)

Contribution (%)

Clichy

100

Event index

Fig. 3 Percentage contribution of each source listed in ascending order of contribution for all events in Clichy and Ecully. The bars represent the 95 % empirical confidence interval

RS contributions at Ecully are larger and more variable than those at Clichy. This is due to the choice of distribution of runoff event mean concentration C RS. For a rainfall event, the distribution of WW contribution at Clichy has more variability than those at Ecully. This is due to the difference in methods used to assess the variability of wastewater profiles. However, this does not affect the average value of this contribution (Hannouche 2012). At Clichy, we can observe some rainfall events for which the contribution of stock may be negligible (six events of ∼7 % of events in Clichy of which 95 % CI contains the zero). In contrast for 80 % of the events, the contribution of stock is between 20 and 70 % with a confidence level of 95 %. At Ecully, there are also some rainfall events whose stock of deposits contribution can be insignificant (17 % of events). However, this contribution depends strongly on the choice of parameters of the distribution of C RS. According to the selected distribution C RS, for 55 % of the events, the contribution of stock of deposits is more than 20 % with a confidence level of 95 %.

Effect of variation of wastewater fluxes In the previous calculation, we assumed that the wastewater during wet weather conditions does not differ from those during dry weather conditions. Still, if they do not settle down during wet weather, the hypothesis of the previous calculation constitutes an approximation of the stock of deposits contribution (low hypothesis).

If we consider that there is no sedimentation during wet weather conditions, this increases the contribution of wastewater and therefore reduces that of sewer deposits (high hypothesis). To assess the impact of non-sedimentation of this wastewater during wet weather on the contribution of sewer deposits, we considered that the eroded mass is equal to the deposited mass at the annual scale. Thus, we distributed the eroded mass of the year 2006 on the dry weather periods at Clichy site. Then, we recalculated the wastewater fluxes during the rainfall event assuming that the deposition is linear. Results show that the sedimentation rate in dry weather is about 10 % of daily wastewater fluxes. This decreases the contribution of sewer deposits between 5 and 10 % of TSS events load, but the source of sewer deposits remains major as 65 % of the events have a contribution between 20 and 70 %.

Conclusion The assessments made on the representative databases consolidate the results suggested by other similar studies: the TSS loads transited at the outlet of a catchment area during a rainfall event does not match the sum of the masses from the wastewater and runoff that is minor. The contribution of deposits in the system is between 20 and 80 % of the TSS load observed at the outlet during rain event. The robustness of this estimate has been established by a study of sensitivity to uncertainties and assumptions that affect the balance sheet.

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These results confirm and refine the estimates obtained previously on the Parisian sewer system. The results also show that this contribution is not specific to the Parisian sewer network which has a low slope and known deposit areas in its combined sewer system. However, the contribution of sewer deposits is important in a sewer like Ecully, a site allegedly free of (coarse) sewer deposits, which has a slope of 2.7 % unlike the Clichy network, a site heavily fouled, that has a slope of 0.14 %.

Acknowledgments This study has been performed within the framework of the French observatory network SOERE “URBIS”. The authors gratefully acknowledge the partners of OPUR, OTHU, and OneVu and the SEPIA Conseils firm for their combined financial and technical support.

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Assessment of the contribution of sewer deposits to suspended solids loads in combined sewer systems during rain events.

Within the French observatories network SOERE "URBIS," databases of continuous turbidity measurements accumulating hundreds of events and many dry wea...
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