- Email: [email protected]
The first flush in sewer systems
~
Pergamon
Waf. Sci. rech. Vol. 33. No.9. pp. 101-IOS. 1996. CopyrighllrJ 1996 IAWQ. Published by Elsevier Science Ltd Prinled in Great Britatn. All rights reserved. 0273-1223/96 S15'00 +0000
PH: S0273-1223(96)OO375-7
THE FIRST FLUSH IN SEWER SYSTEMS Agnes Saget*, Ghassan Chebbo* and Jean-Luc Bertrand-Krajewski** • CERGRENE (ENPC-ENGREF) la Courtine. Noisy Ie Grand Cidex, 93/67. France •• CIRSEE, 38 rue du President Wilson, 78230 I.e Pecq. France
ABSTRACT The firsl flush phenomenon of urban wei weather discharges is presently a controversial subject. Scienlists do nOl agree with its reality. nor with its influences on the size of treatment works. Those disagreements mainly result from the unclear definition of the phenomenon. The objecti ve of this article is first to provide a simple and clear definition of the first flush and lhen to apply il 10 real data and 10 obtain results about ils appearance frequency. The data originate from the French database based on the quality of urban wet weather discharges. We use 80 events from 7 separately sewered basins. and 117 events from 7 combined sewered basins. The main result is thaI the first flush phenomenon is very scarce. anyway too scarce to be used to elaborate a treatment strategy against pollution generated by urban wet weather discharges. Copyright@ 1996IAWQ. Published by Elsevier Science LId.
KEYWORDS Biological oxygen demand; chemical oxygen demand; combined sewers; first flush; load distribution; regression; storm sewers; suspended solids.
INTRODUCTION Urban wet weather discharges are recognized as significant sources of pollution to receiving waters. The treatment works do not have sufficient capacity, and probably will not have, to treat the whole effluent. As such, the important questions are: which part of the effluent is the most polluted, and therefore which part must be treated above all? These uncertainties lead us to the question: is the first flush phenomenon frequent? When we say first flush, we suggest that the pollution load is in the first part of the flow, bUI we are unable to quantify the pollution load and the corresponding volume. Nevertheless. the answer is very important for the size of the treatment works. and especially for storage decantation tanks. If the first flush is frequent, then the structures do not need large capacities, they can merely intercept the first part of the event to intercept most of the pollution load. and to protect receiving waters. In this article our objectives are: - to give a clear definition of the first flush; - to analyse the shape of the pollution load distribution with the discharged volume; - to estimate the appearance frequency of the first flush. in order to know if it is a reliable phenomenon for sizing treatment works; 101
A. SAGET el al.
102
METHODS The variation of the pollutant concentration during an event is described by the pollutograph. To compare different events, an adimensional curve is more pertinent. It is the reason why to study the pollution load distribution, we use for each event and each pollutant, the cumulative load divided by the total pollution load versus the cumulative volume divided by the total volume of the event. For this kind of representation, the diagonal means that the concentration is assumed to be constant during the event. This kind of curve is named L(V). Each curve L(V) can be fitted by a function like: Y=Xa Y is the fraction of the discharged pollution load and X is the corresponding fraction of volume.
If X = 0, Y = 0 and if X = 1, Y = 1. The parameter a is calculated by linear regression because: Y=
xa <=> logY = a 10gX
The value of the parameter characterizes the deviation of the curve from the diagonal. In order to sort out the curves, we define 6 areas (Fig. 1. and Table 1). Moreover, we define the first flush when at least 80% of the pollution load is transferred in the first 30% ofthe volume, that also means that a < 0.185 and the L(V) curve is in the area I. This is an arbitrary and abrupt definition, but it is easy to apply. Table 1. Definition of the L(V) area according to the value of "a"
- - - - - - - - - - - - - - - - -Area
Values of the par~e~~
Deviatio~ ~,!m ~:~~~
positive
_
1
0 < a s 0.185
2
0.185 < a s 0.862
moderate deviation above the diagonal
3
0.862 < a s 1.000
little deviation above the diagonal
4
1.000 < a s 1.159
5 ~
negative
little deviation below the diagonal
_
strong deviation below the diagonal
1.159 < as 5.395 ~395 ~
strong deviation above the diagonal
moderate deviation below the diagonal
a < ~___________
_
-----_.- -
-~
"C
~0,8 .~
{0'6
00,4
.~ ~0,2
II-
0,2
0,4 0,6 Fraction of volume
0,8
Figure I. Definition of the L(V) curve areas.
We estimate the parameter for each available event for suspended solids (55), for chemical oxygen demand (COD) and for the 5 day biological oxygen demand (BODS). We give the range of values for the parameter and try to link them to some particular characteristics, that have been chosen among the basin characteristic~ or among the rainfall event characteristics.
First flush in sewer systems
103
Then. the percentages of events in each area are calculated for events from storm sewers. then from combined sewers. That makes possible to compare the basins and the pollutants. and to identify the events that have a first flush phenomenon (curve in the area I). DATA The data come from a French database based on the quality of urban wet weather discharges (Table 2). We extract the pollutographs of suspended solids. chemical oxygen demand and 5 day biological oxygen demand. Among the selected basins. there are 7 storm sewers and 7 combined sewers. We keep the events with at least 5 concentration measures per pollutograph. The maximum number of concentration for a pollutograph is 28. For small events. with a low number of concentrations. we are aware that the L(V) curve could be non representative. But for only 2 events among 198. the first measurement point represents more than 30% of the volume (exactly 32 and 33%). We have the following number of events: for storm sewers. 80 for SS, 80 for COD, 77 for BODS; for combined sewers. 117 for SS, 117 for COD. 91 for BOD5. Table 2. Characteristics of the drainage basins - ---------------
Basin
Duration (month)
Stonn sewers AixZup Aix Nord Maurepas V~lizy
Centre Urbain Ulis Sud Malnoue Combined sewers Mantes Entzheim La Briche 0011 La Briche 011 La Briche PHI La Briche Enghien La Briche PLB .-
16 16 16 12 13 12 13 11 17 14 14 14 14 14
-----_._-------- ---- - - - ----------- --------Date Active Time of Population Imper Average area concentr. viousness Density slope
.___ 1~)___<"~L._~t.
01/10/80 01110/80 01109/80 0I109n4 11104/81 01l01n8 16/03/81
12 12 15 20 24 27 29
._--------~~.
26107n8 03/05/87 01104/83 01104/83 01104/83 01104/83 01104/83
8 10 467 657 694 1380 4600
- -------~---
(inhab~~__
20 45 30 20 40 35 40
78 35 60 54 24 41 35
100 40 100 94
15
39 39
67 41
58
('100)_
29 65 5
57 7
32
- - - - - - - - - - - - - - - - - _ . -------
RANGE OF THE PARAMETER "A" The L(V) curves are replaced by an equation such as Y = XI. The parameter "a" is calculated by regression. Then, one of the results is the correlation coefficient. The coefficients range from 0.87 to 1.00, the average value is 0.98 and the standard deviation is 0.02. This means that the relationships between load and volume is always strong. and we can reasonably rely on the results. The figures 2 and 3 show the cumulative distribution of the events according to the value of the parameter. Figure 2 represents storm sewers, and figure 3 combined sewers. From the curves. we extract synthetic infonnation gathered in the table 3. Those results show that the variations among the values are important. That means that there are very big differences from one event to another. This proves that the curves from a basin cannot be replaced by a single average curve without losing a large amount of information.
104
area ~ C
~
Q)
A. SAGET er al.
2
3
4
5
100%11----1T:::::l=~;;;:;;;......-----~
80%
'0 60% '#. Q)
> 40%
~ "5
E 20%
::J
o
0% +-~=t-_+_-+__t_+-4-_t_+_'_t~_+_-+-+__t_+-+-_t_+_I~~ o 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 parameter a
-55
-COD"
BOD5
Figure 2. Cumulative distribution of the parameter for the storm sewers.
-
area 1 100%
2
3
4
5
(J)
c: Q)
> Q)
-
80%
0
60%
Q)
40%
::J! 0
> :p
as
"5 E 20% ::J 0
0% +-t--4---~+=-+--+--+-!-+--'~-4--+-+---+-+-+-+-<--+-+-+-+-+....j o 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 parameter a
-55
-COD - - BOD5
Figure 3. Cumulative distribution of the parameter for the combined sewers.
When we distinguish between the two types of sewer, storm sewer or combined sewers, we cannot find a range devoted to one of them: whatever the value, we can find a parameter from a storm sewer as well as from a combined sewer. In other words, simply by the shape of a L(V) curve, it is impossible to recognize a storm sewer from a combined sewer. When we compare the pollutants, it is impossible to define some ranges where there are only parameters for a single pollutant. Moreover, during an event, the L(V) curves for the pollutants do not have the same order during all the events. But, for most of the events in the first 40% of the volume, the SS's curve is below the COD or BOD5's curve: the COD and BOD5 are discharged first. In other words, a given fraction of volume contains a load of COD or BODS higher than that of SS. This implies that with treatment in mind. if the structure sizes are estimated to reach a given efficiency for SS, this efficiency would also be reached for the other pollutants.
First nush in sewer systems
lOS
Table 3. Range of values of the parameter Pollutant
~ _
Sewer
SS
storm combined storm combined storm combined
COD BOD5
Minimum
..
_--
Maximum
Average
.._ - - - - - - - - -
0.152 0.274 0.282 0.265 0.271 0.449
-------0.769 0.926 0.681 0.852 0.669 0.832
-_ ... _----- -2.023 1.506 1.375 1.233 1.379 1.203
-~--
--------
Standard deviation -- _. ---0.307 0.186 0.215 0.171 0.238 0.170
----
In attempt to highlight some of the possible influences of the characteristics on the parameter, we draw the values of the parameter versus a characteristic. Among the basin characteristics, we have chosen the active area, the time of concentration, and the average slope. The active area is available for every basin. but some values are missing for the time of concentration and slope. Figure 4 shows the values of the parameter for SS versus the active area. It is striking that, from this figure, we cannot extract any tendancy: the range of values are the same for small as for large basins. With the time of concentration and average slope, the variations look like those with active area. There appears to be no relationships between the basin characteristics and the pollution load distribution.
2,5 , - - - - - - - - - - - - - - - - - - - - ,
...
2
. . . . . . . . . . . . .0
~
~ 1,5 ~
[
..••••..•
.
:~~ '0'
~
'1' . '1' .. --- .
•• _••_••:~; ••• _ -••• -.i_ _ -. _ ••• -~_ •• ~oo
1
Cll
0,5
o
O+---+-..........-....>++----+-......................-+4-----4--+--+-+-t-++++--+-+~_..........j 1
10
o
100 Active area, ha
Storm sewers
x
1000
10000
Combined sewers
Figure 4. Parameter "a" versus active area of the basins for suspended solids.
After the basin characteristics that are structural and independant of the events, we investigate the rainfall event characteristics. Among them, we have chosen the rainfall depth to represent the size of the event, the maximum rainfall intensity over 5 minutes to represent the erosive capacity of the rainfall, and the antecedent dry weather period to represent the antecedent weather conditions. Figure 5 shows the values of the parameter for SS versus the antecedent dry weather period. On this figure, it is not possible to see any tendancy, just as for the other characteristics: the ranges of values are the same for small rainfall events (less than 10 mm of rainfall depth or less than 20 mmIh of intensity) as well as for big events (respectively more than 20 mm or more than 40 mmIh). Moreover, the antecedent dry weather period does not explain the value of the parameter. In particular, we cannot explain the smallest values (the closest values to the first flush phenomenon) by high values of antecedent dry weather periods.
A. SAGET et al.
106
2,5
...
2
Q)
~ 1,5
... ell ell
a.
- - 0°-
ell
o
0,5
%."
"
- 8- _..
~
0
o
0 200
0
'0' - - --
400
600
800
1000
Antecedent dry weather period, h o Storm sewers
" Combined sewers
Figure 5. Parameter a versus antecedent dry weather period of the events for suspended solids.
With our data, there appears to be no relationships between the pollution load distribution and the chosen characteristics. An L(V) curve is the result of several complex phenomena that we cannot merely replace by a single characteristic. In particular, a long antecedent dry weather period or an important slope over the basin are not conditions sufficient enough to entail a first flush. FIRST FLUSH FREQUENCY Figures 6 and 7 display the percentage of events in the 6 different areas defined above. Figure 6 represents the events from storm sewers, and figure 7 shows that of combined sewers. For storm sewers, most of the events (more than 65%) are in the area 2, where the L(V) curve has a moderate deviation above the diagonal. For combined sewers, the shape of the distribution is almost the same for COD and BOD5 (50 and 60% of the events in area 2) but for SS the events appear to be equally distributed between areas 2, 3 and 4. For areas 3 and 4, the curves are not very different from the diagonal. From these figures, we can then say that there is a little difference between the sewers. 100% , - - - - - - - - - - - - - - - - - - - - ,
90% 80% ~ ell 70% ~ 60% ~ 50% ~ 40% '0 30%
10%
0% +-O---+-.......-----.-.......---,............-'-+---'-''-''''----j 2 3 4 5 6 Area number
S5
0 COD •
BODS
Figure 6. Percentage of events per L{V) area for storm sewers.
107
First flush in sewer systems
100% r - - - - - - - - - - - - - - - - - - - - - - - , 90% nl 80% ~ nl 70% CD
a. 60%
-E
50% ~ 40% '0 30% eft. 20% 10% 0% -l----j-L'"'-'---j-L'"'-'__.,-<...L..J__+-1...:L..J__+ - - _ - - - j 2 3 4 5 6 Area number Q)
DSS
DeOD .BOD5
Figure 7. Percentage of events per L(V) curve area for combined sewers.
The figures also show that there is little differences between the pollutants. To put it more precisely, the percentage of events in area 2 for SS is lower than the percentage for COD or BOD5, the difference can reach 20%. This result highlights a previous remark, that the COD and BOD5 are discharged first. As far as the first flush is concerned, only I event (I among 197) for SS is in the area I (our definition of the flfSt flush). This event comes from a storm sewer. There is no such event for combined sewers. Then we can already conclude, that the first flush phenomenon is very scarce. Moreover the figures 2 and 3 show that it is possible to change the threshold defining the first flush without significantly changing the results. Indeed, for example, 20% of the events have a parameter less than 0.6. This is not yet the majority of the events, nor the first flush since with 0.6, 30% of the volume contains only 50% of the pollution load. The definition of first flush is arbitrarily chosen, it is then more interesting to analyse the distribution of the parameter independently of the areas. From figures 2 and 3, we extract for each kind of sewer, the a values corresponding of the following different characteristic curves: L(V)max: the upper curve, with the minimum value of a L(V)90: 90% of the curves lie below L(V)50: as many curves below as above M(V)min: the lower curve, with the maximum value of a Thus, it is possible to change the threshold defining the first flush without significantly changing the results. Indeed, for example, 10% of the events have a parameter less than 0.4. This is not yet the majority of the events, nor the first flush since with 0.4, 80% of the pollution load is contained in no less than 59% of the volume. Table 4. Some characteristic values of "a" and the corresponding volumes containing 80% of the SS load L(V)Curve
..
- ._---- --
a value
L(V)max L(V)9O L(V)50 L(V)IO ._ ..~V)min .
0.152 0.414 0.726 1.114
.__ ~.0~3
Storm sewers - ---------. % of volume containing of SS --.load -" .__ ._- 80% -_. ---- _._ .. . . 23 59 73 82 90
a value
_
0.274 0.702 0.935 1.131 1.506
Combined sewers------- -----.. _-% of volume containing 80% of SS load ------44 73 79 82 86
A. SAGET et al.
108
CONCLUSIONS We have extracted from a French database the available pollutographs, and we have studied the frequency of the first flush phenomenon. For 197 events from 14 basins, we studied the cumulative load versus cumulative volume curve. We have fitted a model to each curve, in order to estimate the parameter of the equation Y = Xa , where Y is the fraction of the pollution load and X the fraction of the volume. We define the first flush by a parameter less than 0.185. The study of the parameter values shows that they can be very different from one event to another. Gathering the events of storm sewers, then those of combined sewers, we cannot find any difference between the two groups, likewise between the curves of SS, COD or BOD5. Then, at first sight, none of the groups may be replaced by a single characteristic curve. In order to explain the variability of the parameter, we were looking for the relationships between the parameter and some basin characteristics (active area, time concentration and average slope) or rainfall event characteristics (rainfall depth, maximal intensity over 5mn and antecedent dry weather period). But we failed to come up with any plausible explanation for why the parameters vary. The range of values are the same for small as for large basins, and for intense as for small rainfall events. For storm sewers, most of the parameters (more than 65%) are between 0.185 and 0.862, meaning that the curves have a moderate deviation above the diagonal. For combined sewers, the parameters are eqUally distributed between 0.185 and 1.159. Then, for a significative fraction of the events, the curves show no significant deviation from the diagonal. For all of the events of our records, the first flush is observed only once. Moreover, changing the first flUsh definition does not change our main conclusion which is that the first flush phenomenon is very scarce, and cannot be used to elaborate a treatment strategy. The results further suggest that treatment works need big capacities to be efficient enough and to intercept the most polluted fraction of an effluent. REFERENCES Bertrand-Krajewski, J.·L., Chebbo. G. and Saget, A. (1995). The first flush phenomenon (or how to deflate the myth). Repon (in french) for the working group Pluvial AGHTM. Geiger. W. F. (1987). Flushing effects in combined sewer systems. In: Proceedings of the 4th International Conference on Urban Storm Drainage, Lausanne, pp 40-46. Saget. A. (1994). Database about the quality of the urban discharges during wet weather: Distribution of the discharged pollution sizes of treatment structures. Ph 0 Thesis of Ecole Nationale des Ponts el Chauss~es. 217p +annexes. '
Pergamon
Waf. Sci. rech. Vol. 33. No.9. pp. 101-IOS. 1996. CopyrighllrJ 1996 IAWQ. Published by Elsevier Science Ltd Prinled in Great Britatn. All rights reserved. 0273-1223/96 S15'00 +0000
PH: S0273-1223(96)OO375-7
THE FIRST FLUSH IN SEWER SYSTEMS Agnes Saget*, Ghassan Chebbo* and Jean-Luc Bertrand-Krajewski** • CERGRENE (ENPC-ENGREF) la Courtine. Noisy Ie Grand Cidex, 93/67. France •• CIRSEE, 38 rue du President Wilson, 78230 I.e Pecq. France
ABSTRACT The firsl flush phenomenon of urban wei weather discharges is presently a controversial subject. Scienlists do nOl agree with its reality. nor with its influences on the size of treatment works. Those disagreements mainly result from the unclear definition of the phenomenon. The objecti ve of this article is first to provide a simple and clear definition of the first flush and lhen to apply il 10 real data and 10 obtain results about ils appearance frequency. The data originate from the French database based on the quality of urban wet weather discharges. We use 80 events from 7 separately sewered basins. and 117 events from 7 combined sewered basins. The main result is thaI the first flush phenomenon is very scarce. anyway too scarce to be used to elaborate a treatment strategy against pollution generated by urban wet weather discharges. Copyright@ 1996IAWQ. Published by Elsevier Science LId.
KEYWORDS Biological oxygen demand; chemical oxygen demand; combined sewers; first flush; load distribution; regression; storm sewers; suspended solids.
INTRODUCTION Urban wet weather discharges are recognized as significant sources of pollution to receiving waters. The treatment works do not have sufficient capacity, and probably will not have, to treat the whole effluent. As such, the important questions are: which part of the effluent is the most polluted, and therefore which part must be treated above all? These uncertainties lead us to the question: is the first flush phenomenon frequent? When we say first flush, we suggest that the pollution load is in the first part of the flow, bUI we are unable to quantify the pollution load and the corresponding volume. Nevertheless. the answer is very important for the size of the treatment works. and especially for storage decantation tanks. If the first flush is frequent, then the structures do not need large capacities, they can merely intercept the first part of the event to intercept most of the pollution load. and to protect receiving waters. In this article our objectives are: - to give a clear definition of the first flush; - to analyse the shape of the pollution load distribution with the discharged volume; - to estimate the appearance frequency of the first flush. in order to know if it is a reliable phenomenon for sizing treatment works; 101
A. SAGET el al.
102
METHODS The variation of the pollutant concentration during an event is described by the pollutograph. To compare different events, an adimensional curve is more pertinent. It is the reason why to study the pollution load distribution, we use for each event and each pollutant, the cumulative load divided by the total pollution load versus the cumulative volume divided by the total volume of the event. For this kind of representation, the diagonal means that the concentration is assumed to be constant during the event. This kind of curve is named L(V). Each curve L(V) can be fitted by a function like: Y=Xa Y is the fraction of the discharged pollution load and X is the corresponding fraction of volume.
If X = 0, Y = 0 and if X = 1, Y = 1. The parameter a is calculated by linear regression because: Y=
xa <=> logY = a 10gX
The value of the parameter characterizes the deviation of the curve from the diagonal. In order to sort out the curves, we define 6 areas (Fig. 1. and Table 1). Moreover, we define the first flush when at least 80% of the pollution load is transferred in the first 30% ofthe volume, that also means that a < 0.185 and the L(V) curve is in the area I. This is an arbitrary and abrupt definition, but it is easy to apply. Table 1. Definition of the L(V) area according to the value of "a"
- - - - - - - - - - - - - - - - -Area
Values of the par~e~~
Deviatio~ ~,!m ~:~~~
positive
_
1
0 < a s 0.185
2
0.185 < a s 0.862
moderate deviation above the diagonal
3
0.862 < a s 1.000
little deviation above the diagonal
4
1.000 < a s 1.159
5 ~
negative
little deviation below the diagonal
_
strong deviation below the diagonal
1.159 < as 5.395 ~395 ~
strong deviation above the diagonal
moderate deviation below the diagonal
a < ~___________
_
-----_.- -
-~
"C
~0,8 .~
{0'6
00,4
.~ ~0,2
II-
0,2
0,4 0,6 Fraction of volume
0,8
Figure I. Definition of the L(V) curve areas.
We estimate the parameter for each available event for suspended solids (55), for chemical oxygen demand (COD) and for the 5 day biological oxygen demand (BODS). We give the range of values for the parameter and try to link them to some particular characteristics, that have been chosen among the basin characteristic~ or among the rainfall event characteristics.
First flush in sewer systems
103
Then. the percentages of events in each area are calculated for events from storm sewers. then from combined sewers. That makes possible to compare the basins and the pollutants. and to identify the events that have a first flush phenomenon (curve in the area I). DATA The data come from a French database based on the quality of urban wet weather discharges (Table 2). We extract the pollutographs of suspended solids. chemical oxygen demand and 5 day biological oxygen demand. Among the selected basins. there are 7 storm sewers and 7 combined sewers. We keep the events with at least 5 concentration measures per pollutograph. The maximum number of concentration for a pollutograph is 28. For small events. with a low number of concentrations. we are aware that the L(V) curve could be non representative. But for only 2 events among 198. the first measurement point represents more than 30% of the volume (exactly 32 and 33%). We have the following number of events: for storm sewers. 80 for SS, 80 for COD, 77 for BODS; for combined sewers. 117 for SS, 117 for COD. 91 for BOD5. Table 2. Characteristics of the drainage basins - ---------------
Basin
Duration (month)
Stonn sewers AixZup Aix Nord Maurepas V~lizy
Centre Urbain Ulis Sud Malnoue Combined sewers Mantes Entzheim La Briche 0011 La Briche 011 La Briche PHI La Briche Enghien La Briche PLB .-
16 16 16 12 13 12 13 11 17 14 14 14 14 14
-----_._-------- ---- - - - ----------- --------Date Active Time of Population Imper Average area concentr. viousness Density slope
.___ 1~)___<"~L._~t.
01/10/80 01110/80 01109/80 0I109n4 11104/81 01l01n8 16/03/81
12 12 15 20 24 27 29
._--------~~.
26107n8 03/05/87 01104/83 01104/83 01104/83 01104/83 01104/83
8 10 467 657 694 1380 4600
- -------~---
(inhab~~__
20 45 30 20 40 35 40
78 35 60 54 24 41 35
100 40 100 94
15
39 39
67 41
58
('100)_
29 65 5
57 7
32
- - - - - - - - - - - - - - - - - _ . -------
RANGE OF THE PARAMETER "A" The L(V) curves are replaced by an equation such as Y = XI. The parameter "a" is calculated by regression. Then, one of the results is the correlation coefficient. The coefficients range from 0.87 to 1.00, the average value is 0.98 and the standard deviation is 0.02. This means that the relationships between load and volume is always strong. and we can reasonably rely on the results. The figures 2 and 3 show the cumulative distribution of the events according to the value of the parameter. Figure 2 represents storm sewers, and figure 3 combined sewers. From the curves. we extract synthetic infonnation gathered in the table 3. Those results show that the variations among the values are important. That means that there are very big differences from one event to another. This proves that the curves from a basin cannot be replaced by a single average curve without losing a large amount of information.
104
area ~ C
~
Q)
A. SAGET er al.
2
3
4
5
100%11----1T:::::l=~;;;:;;;......-----~
80%
'0 60% '#. Q)
> 40%
~ "5
E 20%
::J
o
0% +-~=t-_+_-+__t_+-4-_t_+_'_t~_+_-+-+__t_+-+-_t_+_I~~ o 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 parameter a
-55
-COD"
BOD5
Figure 2. Cumulative distribution of the parameter for the storm sewers.
-
area 1 100%
2
3
4
5
(J)
c: Q)
> Q)
-
80%
0
60%
Q)
40%
::J! 0
> :p
as
"5 E 20% ::J 0
0% +-t--4---~+=-+--+--+-!-+--'~-4--+-+---+-+-+-+-<--+-+-+-+-+....j o 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 parameter a
-55
-COD - - BOD5
Figure 3. Cumulative distribution of the parameter for the combined sewers.
When we distinguish between the two types of sewer, storm sewer or combined sewers, we cannot find a range devoted to one of them: whatever the value, we can find a parameter from a storm sewer as well as from a combined sewer. In other words, simply by the shape of a L(V) curve, it is impossible to recognize a storm sewer from a combined sewer. When we compare the pollutants, it is impossible to define some ranges where there are only parameters for a single pollutant. Moreover, during an event, the L(V) curves for the pollutants do not have the same order during all the events. But, for most of the events in the first 40% of the volume, the SS's curve is below the COD or BOD5's curve: the COD and BOD5 are discharged first. In other words, a given fraction of volume contains a load of COD or BODS higher than that of SS. This implies that with treatment in mind. if the structure sizes are estimated to reach a given efficiency for SS, this efficiency would also be reached for the other pollutants.
First nush in sewer systems
lOS
Table 3. Range of values of the parameter Pollutant
~ _
Sewer
SS
storm combined storm combined storm combined
COD BOD5
Minimum
..
_--
Maximum
Average
.._ - - - - - - - - -
0.152 0.274 0.282 0.265 0.271 0.449
-------0.769 0.926 0.681 0.852 0.669 0.832
-_ ... _----- -2.023 1.506 1.375 1.233 1.379 1.203
-~--
--------
Standard deviation -- _. ---0.307 0.186 0.215 0.171 0.238 0.170
----
In attempt to highlight some of the possible influences of the characteristics on the parameter, we draw the values of the parameter versus a characteristic. Among the basin characteristics, we have chosen the active area, the time of concentration, and the average slope. The active area is available for every basin. but some values are missing for the time of concentration and slope. Figure 4 shows the values of the parameter for SS versus the active area. It is striking that, from this figure, we cannot extract any tendancy: the range of values are the same for small as for large basins. With the time of concentration and average slope, the variations look like those with active area. There appears to be no relationships between the basin characteristics and the pollution load distribution.
2,5 , - - - - - - - - - - - - - - - - - - - - ,
...
2
. . . . . . . . . . . . .0
~
~ 1,5 ~
[
..••••..•
.
:~~ '0'
~
'1' . '1' .. --- .
•• _••_••:~; ••• _ -••• -.i_ _ -. _ ••• -~_ •• ~oo
1
Cll
0,5
o
O+---+-..........-....>++----+-......................-+4-----4--+--+-+-t-++++--+-+~_..........j 1
10
o
100 Active area, ha
Storm sewers
x
1000
10000
Combined sewers
Figure 4. Parameter "a" versus active area of the basins for suspended solids.
After the basin characteristics that are structural and independant of the events, we investigate the rainfall event characteristics. Among them, we have chosen the rainfall depth to represent the size of the event, the maximum rainfall intensity over 5 minutes to represent the erosive capacity of the rainfall, and the antecedent dry weather period to represent the antecedent weather conditions. Figure 5 shows the values of the parameter for SS versus the antecedent dry weather period. On this figure, it is not possible to see any tendancy, just as for the other characteristics: the ranges of values are the same for small rainfall events (less than 10 mm of rainfall depth or less than 20 mmIh of intensity) as well as for big events (respectively more than 20 mm or more than 40 mmIh). Moreover, the antecedent dry weather period does not explain the value of the parameter. In particular, we cannot explain the smallest values (the closest values to the first flush phenomenon) by high values of antecedent dry weather periods.
A. SAGET et al.
106
2,5
...
2
Q)
~ 1,5
... ell ell
a.
- - 0°-
ell
o
0,5
%."
"
- 8- _..
~
0
o
0 200
0
'0' - - --
400
600
800
1000
Antecedent dry weather period, h o Storm sewers
" Combined sewers
Figure 5. Parameter a versus antecedent dry weather period of the events for suspended solids.
With our data, there appears to be no relationships between the pollution load distribution and the chosen characteristics. An L(V) curve is the result of several complex phenomena that we cannot merely replace by a single characteristic. In particular, a long antecedent dry weather period or an important slope over the basin are not conditions sufficient enough to entail a first flush. FIRST FLUSH FREQUENCY Figures 6 and 7 display the percentage of events in the 6 different areas defined above. Figure 6 represents the events from storm sewers, and figure 7 shows that of combined sewers. For storm sewers, most of the events (more than 65%) are in the area 2, where the L(V) curve has a moderate deviation above the diagonal. For combined sewers, the shape of the distribution is almost the same for COD and BOD5 (50 and 60% of the events in area 2) but for SS the events appear to be equally distributed between areas 2, 3 and 4. For areas 3 and 4, the curves are not very different from the diagonal. From these figures, we can then say that there is a little difference between the sewers. 100% , - - - - - - - - - - - - - - - - - - - - ,
90% 80% ~ ell 70% ~ 60% ~ 50% ~ 40% '0 30%
10%
0% +-O---+-.......-----.-.......---,............-'-+---'-''-''''----j 2 3 4 5 6 Area number
S5
0 COD •
BODS
Figure 6. Percentage of events per L{V) area for storm sewers.
107
First flush in sewer systems
100% r - - - - - - - - - - - - - - - - - - - - - - - , 90% nl 80% ~ nl 70% CD
a. 60%
-E
50% ~ 40% '0 30% eft. 20% 10% 0% -l----j-L'"'-'---j-L'"'-'__.,-<...L..J__+-1...:L..J__+ - - _ - - - j 2 3 4 5 6 Area number Q)
DSS
DeOD .BOD5
Figure 7. Percentage of events per L(V) curve area for combined sewers.
The figures also show that there is little differences between the pollutants. To put it more precisely, the percentage of events in area 2 for SS is lower than the percentage for COD or BOD5, the difference can reach 20%. This result highlights a previous remark, that the COD and BOD5 are discharged first. As far as the first flush is concerned, only I event (I among 197) for SS is in the area I (our definition of the flfSt flush). This event comes from a storm sewer. There is no such event for combined sewers. Then we can already conclude, that the first flush phenomenon is very scarce. Moreover the figures 2 and 3 show that it is possible to change the threshold defining the first flush without significantly changing the results. Indeed, for example, 20% of the events have a parameter less than 0.6. This is not yet the majority of the events, nor the first flush since with 0.6, 30% of the volume contains only 50% of the pollution load. The definition of first flush is arbitrarily chosen, it is then more interesting to analyse the distribution of the parameter independently of the areas. From figures 2 and 3, we extract for each kind of sewer, the a values corresponding of the following different characteristic curves: L(V)max: the upper curve, with the minimum value of a L(V)90: 90% of the curves lie below L(V)50: as many curves below as above M(V)min: the lower curve, with the maximum value of a Thus, it is possible to change the threshold defining the first flush without significantly changing the results. Indeed, for example, 10% of the events have a parameter less than 0.4. This is not yet the majority of the events, nor the first flush since with 0.4, 80% of the pollution load is contained in no less than 59% of the volume. Table 4. Some characteristic values of "a" and the corresponding volumes containing 80% of the SS load L(V)Curve
..
- ._---- --
a value
L(V)max L(V)9O L(V)50 L(V)IO ._ ..~V)min .
0.152 0.414 0.726 1.114
.__ ~.0~3
Storm sewers - ---------. % of volume containing of SS --.load -" .__ ._- 80% -_. ---- _._ .. . . 23 59 73 82 90
a value
_
0.274 0.702 0.935 1.131 1.506
Combined sewers------- -----.. _-% of volume containing 80% of SS load ------44 73 79 82 86
A. SAGET et al.
108
CONCLUSIONS We have extracted from a French database the available pollutographs, and we have studied the frequency of the first flush phenomenon. For 197 events from 14 basins, we studied the cumulative load versus cumulative volume curve. We have fitted a model to each curve, in order to estimate the parameter of the equation Y = Xa , where Y is the fraction of the pollution load and X the fraction of the volume. We define the first flush by a parameter less than 0.185. The study of the parameter values shows that they can be very different from one event to another. Gathering the events of storm sewers, then those of combined sewers, we cannot find any difference between the two groups, likewise between the curves of SS, COD or BOD5. Then, at first sight, none of the groups may be replaced by a single characteristic curve. In order to explain the variability of the parameter, we were looking for the relationships between the parameter and some basin characteristics (active area, time concentration and average slope) or rainfall event characteristics (rainfall depth, maximal intensity over 5mn and antecedent dry weather period). But we failed to come up with any plausible explanation for why the parameters vary. The range of values are the same for small as for large basins, and for intense as for small rainfall events. For storm sewers, most of the parameters (more than 65%) are between 0.185 and 0.862, meaning that the curves have a moderate deviation above the diagonal. For combined sewers, the parameters are eqUally distributed between 0.185 and 1.159. Then, for a significative fraction of the events, the curves show no significant deviation from the diagonal. For all of the events of our records, the first flush is observed only once. Moreover, changing the first flUsh definition does not change our main conclusion which is that the first flush phenomenon is very scarce, and cannot be used to elaborate a treatment strategy. The results further suggest that treatment works need big capacities to be efficient enough and to intercept the most polluted fraction of an effluent. REFERENCES Bertrand-Krajewski, J.·L., Chebbo. G. and Saget, A. (1995). The first flush phenomenon (or how to deflate the myth). Repon (in french) for the working group Pluvial AGHTM. Geiger. W. F. (1987). Flushing effects in combined sewer systems. In: Proceedings of the 4th International Conference on Urban Storm Drainage, Lausanne, pp 40-46. Saget. A. (1994). Database about the quality of the urban discharges during wet weather: Distribution of the discharged pollution sizes of treatment structures. Ph 0 Thesis of Ecole Nationale des Ponts el Chauss~es. 217p +annexes. '