# Urban Runoff Characteristics in Combined Sewer Overflows (CSOs): Analysis of Storm Events in Southeastern Spain

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{MAXSS}) by means of probability distributions. With regard to rainy events, several researchers [7,18,19,20] performed regression analysis between TSS and hydrological and hydraulic variables (Table 1). These statistical models gave rise to adjusted pollution parameters, which can be used for forecasting transported pollution characteristics along storm events, providing useful information to establish effective policies of combined sewer overflow control. As these studies are usually catchment specific, an important aim would be to define standard coefficients for a wide range of catchment conditions [18].

_{MAXtb}), the time to the peak of the pollutograph (TPP), and the time to the descent of pollutograph (TDP).

## 2. Experimental Methods

#### 2.1. Description of the Experimental Catchment Areas

_{maxdw}= 10.000 m

^{3}/h. At the outlet pipe of each catchment, monitoring equipments were placed in Quality Control Stations (QCS) (Figure 1).

#### 2.2. Monitoring Equipment

#### 2.3. Hydraulic Model

_{0}, is around 20 mm/h, while the minimum infiltration, f

_{c}, is 1 mm/h. The decay coefficient, k, has been estimated at 4.14 h

^{−1}. The Manning’s roughness coefficients for pervious and impervious areas were used as calibration parameters of the model. Initial values were selected from the literature [21,22]: 0.02–0.045 for pervious areas and 0.01–0.015 for impervious areas. The Manning roughness coefficient for conduits varies from 0.011 to 0.013 depending on the pipe material.

_{c}, is obtained for each catchment. The concentration time, T

_{c}, may be decomposed into two terms: overland runoff time, T

_{E}, and in-sewer run time, T

_{R}[3]:

_{c}in non-urban catchments is presented by Temez [24]:

_{c}is the concentration time modified for urban catchments that takes into account the in-sewer run time, and μ is the fraction of the area that is impervious. The time of concentration of the catchment was obtained introducing a uniform precipitation in each sub-catchment with a duration higher enough to assure that all the surface contributes to the outfall.

_{c}, calculated by the hydraulic model and by Equation (3) are presented in the following table:

## 3. Results and Discussion

#### 3.1. Variables Definition from Wet Weather Measured Events

_{TOTAL}, the maximum rainfall intensity during ten minutes I

_{max10}, the mean intensity I

_{mean}, and the number of antecedent dry days DWP. There are also others parameters such as the maximum runoff flow rate Q

_{max}

_{,}the mean runoff flow rate Q

_{mean}, the time to hydrograph peak TPH, the total runoff volume EV, the part of the runoff volume that would correspond to dry weather EV

_{dw}, the difference between both EV

_{ww}, the maximum turbidity registered during the episode C

_{MAXtb}, the mean event turbidity value EMC

_{tb}, and the time to the peak of pollutograph TPP.

_{MAXtb}, and the time to the peak of pollutograph TPP.

#### 3.2. Predictor Variables Correlations

_{max}, TPH, P

_{TOTAL}, I

_{max10}, C

_{MAXtb}, and TPP. The correlation matrix is presented in Table 7 and Table 8 for S1 and San Felix catchments, respectively. The Pearson’s correlation coefficient has been applied to the sample of each pair of variables. From the matrices, correlation between the time to pollutograph peak, TPP, and the time to hydrograph peak, TPH, is observed with values of R

^{2}= 0.79 and 0.94 for S1 and San Felix, respectively. The dry period preceding the episode, DWP, and the maximum concentration of turbidity, C

_{MAXtb}, show good relation, especially in the S1 Catchment with R

^{2}= 0.68. There is also certain relationship between the maximum concentration of turbidity, C

_{MAXtb}, with the total precipitation and the maximum flow in the San Felix Catchment.

#### 3.3. Pollution Prediction Indexes. Statistical Model

_{MAXtb}, and, on the other hand, the time elapsed from the beginning of the event until the maximum peak of the pollutograph, TPP. To achieve this, two indexes of prediction are adjusted: the time to peak of the pollutograph index, I

_{TPP}, and the maximum concentration index, I

_{CMAX}.

#### 3.3.1. Time to the Peak of Pollutograph Index, I_{TPP}

_{TOTAL_ANNUAL}is the total precipitation expected in a year, adopting 350 mm for this region. In Equation (4), the time to the peak of the hydrograph, TPH, is considered. Its significant relation with TPP is shown in the correlation matrices (Table 7 and Table 8).

_{c}, reflects the time of the catchment to respond as a function of: (i) how the rain is distributed proportionally to TPH; and (ii) the shape of the catchment inversely proportional to T

_{c}. The second term, P

_{TOTAL}/P

_{TOTAL_ANNUAL}, magnitude of the rainfall, is also proportional to the time to the peak of the pollutograph. In this Equation, the term P

_{TOTAL}/P

_{TOTAL_ANNUAL}has lower influence on the index than TPH/T

_{c}. However, its inclusion improves the adjustment from R

^{2}= 0.87 to R

^{2}= 0.96. Figure 6 shows the linear relationship between the pollutograph time to the peak index, defined in previous equation, and the time to the peak of pollutograph.

#### 3.3.2. Maximum Concentration Index, I_{CMAX}

_{CMAX}arises with the objective of estimating the maximum concentration of turbidity, C

_{MAXtb}, from predictor variables quantified in rainfall events. Together with the I

_{TPP}, they allow estimating both the magnitude of the pollutograph and the location in time of the higher turbidity. The equation for I

_{CMAX}is defined as

^{2}) and L is the catchment flow length (km).

_{CMAX}, calculated by Equation (6) for each rainfall event, and the maximum of measured turbidity in each event, C

_{MAXtb}. Most values fit around a line except four cases:

- (i)
- S1_4: It is an episode with very low precipitation (3 mm). Thus, although the previous dry period is very extensive (24 days), the prediction index yields lower values of the maximum concentration.
- (ii)
- S1_9 and S1_10: The measured maximum concentration values are below the trend followed by the other data. The possible reason of this phenomenon seems to be related with the energy of both events. The rains on these two days are characterized by an average intensity of low precipitation (less than 1 mm/h), so that the catchment and the network were slowly washed.
- (iii)
- SF_8: It is similar to episode S1_4. The precipitation is very low (2.3 mm).

_{MAXtb}and I

_{CMAX}fit to a line with a correlation coefficient of R

^{2}= 0.91.

_{CMAX}, and the maximum concentration of turbidity, C

_{MAXtb}, is given by a linear regression

_{MAXtb}. Moreover, proposed methodology also allows estimating TPP from hydraulic and hydrological parameters.

#### 3.4. Development of Pollutographs from Prediction Indexes

_{a}is the number of intervals of 5 min in which the ascent time is divided; n indicates the interval (0 ≤ n ≤ N

_{a}) in units of five minutes; and C

_{a}and C

_{0}are the turbidity values at each interval n and at the beginning of the pollutograph, respectively (measured in NTU). The value of C

_{0}is assumed as the dry weather value at the beginning of the episode.

_{MAXtb}and TPP have been calculated from prediction indexes, the time to the descent of pollutograph, TDP, can be defined as the time interval between the instant of the maximum concentration and the time when the concentration reaches dry weather values. The time to the descent of pollutograph value has been adjusted for each of the catchments showing a linear relationship with pollutograph time to the peak. The adjustment of TDP for each catchment is given by

_{d}is the number of intervals of five minutes in which the descent time is divided; n indicates the interval (N

_{a}≤ n ≤ N

_{d}) in units of five minutes; and C

_{d}and C

_{TStb}are the turbidity values at each n interval and at the end of the event, corresponding to TDP time, respectively (measured in NTU). Equation (11), when n = N

_{a}, ln(n − N

_{a}+ 1) = 0, and C

_{a}= C

_{d}= C

_{MAXtb}, assures continuity.

## 4. Conclusions

_{TPP}, presented in Equation (4), and the maximum concentration index, I

_{CMAX}, shown in Equation (6).

_{MAXtb}, as seen in Equation (8).

_{TOTAL}, and other variables such as the dry weather period, DWP, and the time to peak of the hydrograph, pollutographs can be simulated for multiple prognosis scenarios. Thus, the methodology provides information of the pollution during wet weather in combined sewer systems.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Notation

A | Catchment area (ha) |

C_{MAXSS} | Maximum concentration for suspended solids (mg/L) |

C_{MAXtb} | Maximum concentration for turbidity (NTU) |

C_{SMSS} | Specific mobilized volume of suspended solids (kg/ha) |

C_{a}, C_{d}, C_{0}, C_{TStb} | Turbidity at the ascent, descent, beginning and end part of pollutographs, respectively (NTU) |

D_{rain} | Rainfall duration (h) |

D_{runoff} (h) | Runoff duration (h) |

DWP | Dry weather period (days) |

DWR | Proportion of consecutive dry weather days previous to the event in the last month |

EMC | Mean event concentration (mg/L) |

EMC_{tb} | Mean event concentration of turbidity (NTU) |

EV | Event runoff volume (m^{3}) |

I_{CMAX} | Pollution prediction index associated to the maximum concentration of turbidity (-) |

I_{max} | Maximum rainfall intensity (mm/h) |

I_{max10} | Maximum 10 minutes rainfall intensity (mm/h) |

I_{mean} | Mean rainfall intensity (mm/h) |

I_{TTP} | Pollution prediction index associated to the time to the peak of pollutograph (-) |

L | Catchment flow length (km) |

N_{a}, N_{d} | Number of intervals of five minutes in which the pollutograph is divided, applied to ascent and descent part respectively (-) |

P_{TOTAL} | Total event rainfall (mm) |

P_{TOTAL_ANNUAL} | Total precipitation expected in a year, adopting 350 mm for this region |

Q_{max} | Maximum event inflow (m^{3}/s) |

Q_{mean} | Mean event inflow (m^{3}/s) |

Q_{maxdw} | Maximum dry weather inflow (m^{3}/s) |

QCS | Quality Control Station, place where monitoring instrumentation is located |

RD | Rain depth (mm) |

S | Mean slope of the catchment (m/m) |

T_{c} | Time of concentration of urban catchment (h) |

T_{c_rural} | Time of concentration of rural catchment (h) |

TDP | Descent time of the pollutograph (h) |

TPH | Peak time of hydrograph (h) |

TPP | Time to the peak of pollutograph (h) |

TSS | Total suspended solids (mg/L) |

TSS_{TE} | Cumulative suspended solids per event (kg/event) |

TSS_{ff} | Total pollutant load in the first flush (kg/event) |

M(V) | Cumulative load and runoff ratios |

Μ | Ratio of impervious area with total area (-) |

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**Figure 4.**Flow depth measured and calculated by the hydraulic model during wet weather at Quality Control Stations S1 and San Felix.

**Figure 5.**Rainfall, hydrographs and turbidity-pollutograph of events: S1_1; S1_3; S1_5; S1_6; S1_8; San Felix_1; San_Felix_3; San Felix_5; San Felix_6; and San Felix_7.

**Figure 9.**Comparison between measured pollutographs and calculated ones through the suggested methodology.

Variables and Adjusted Parameters | Gupta and Saul [18] | LeBoutillier et al. [19] | Gromaire et al. [20] | Del Rio [7] | ||
---|---|---|---|---|---|---|

Variables | Hydrology | I_{max} (mm/h) | X | X | X | |

D_{rain} (h) | X | X | X | |||

DWP (day) | X | X | X | |||

RD (mm) | X | X | X | |||

I_{max5} (mm/h) | X | |||||

T_{c} (h) | X | |||||

A (ha) | X | |||||

Hydraulic | Q_{max} (m^{3}/s) | X | X | |||

Q_{mean} (m^{3}/s) | X | |||||

Q_{maxdw} (m^{3}/s) | X | |||||

TPH (h) | X | |||||

TPP (h) | X | |||||

Adjusted parameters | TSS_{TE} (kg/event) | X | ||||

TSS_{ff} (kg/event) | X | X | ||||

C_{MAXSS} (mg/L) | X | |||||

C_{SMSS} (kg/ha) | X |

_{max}= maximum rainfall intensity; D

_{rain}= rainfall duration; DWP = dry weather period; RD = rain depth; I

_{max5}= maximum five-minute rainfall intensity; T

_{c}= time of concentration of the catchment; A = catchment area; Q

_{max}= maximum event inflow; Q

_{mean}= mean event inflow; Q

_{maxdw}= maximum dry weather inflow; TPH = time to the peak of the hydrograph; TPP = time to the peak of the pollutograph; TSS

_{TE}= cumulative suspended solids per event; TSS

_{ff}= total pollutant load in the first flush; C

_{MAXSS}= maximum suspended solids concentration; and C

_{SMSS}= specific mobilized volume of suspended solids.

Catchment | San Félix | S1 |
---|---|---|

Area of catchment (km^{2}), A | 14.89 | 47.53 |

Population density (inh/km^{2}) | 14,250 | 2685 |

Ratio of imperviousness (m^{2}/m^{2}) | 0.47 | 0.21 |

Mean slope (m/m), S | 0.0043 | 0.0013 |

Catchment flow length (km), L | 10.75 | 17.00 |

Length combined sewerage (km) | 513.15 | 616.84 |

Description | Element | Number |
---|---|---|

Hydrology | Pluviographs | 39 |

Subcatchments | 4553 | |

Hydraulics | Nodes | 6073 |

Outfalls | 58 | |

Tanks | 70 | |

Links | 6304 | |

Pump stations | 100 |

Catchment | Concentration Time, T_{c} (min) | |
---|---|---|

Hydraulic Model | Equation (3) | |

S1 | 193.75 | 200 |

San Felix | 87.14 | 80 |

San Felix Catchment | |||||||||
---|---|---|---|---|---|---|---|---|---|

Event code | SF_1 | SF_2 | SF_3 | SF_4 | SF_5 | SF_6 | SF_7 | SF_8 | SF_9 |

Year | 2014 | 2014 | 2015 | 2015 | 2015 | 2015 | 2015 | 2016 | 2016 |

Date (dd-m) | 17 June | 14 December | 22 March | 11 June | 5 September | 27 September | 15 January | 9 May | 4 June |

P_{TOTAL} (mm) | 7.34 | 30.71 | 2.47 | 10.13 | 56.47 | 7.53 | 11.57 | 6.80 | 2.27 |

I_{mean} (mm/h) | 4.63 | 1.23 | 0.99 | 1.40 | 2.26 | 2.44 | 2.28 | 1.01 | 2.27 |

I_{max10} (mm/h) | 19.67 | 8.86 | 2.76 | 5.32 | 18.76 | 5.28 | 4.71 | 4.84 | 6.04 |

DWP (days) | 15 | 10 | 1 | 22 | 19 | 17 | 17 | 18 | 22 |

Q_{max} (L/s) | 2710 | 2930 | 1580 | 2530 | 3170 | 2590 | 2650 | 2260 | 1680 |

Q_{mean} (L/s) | 945.00 | 1396.53 | 746.67 | 1217.95 | 1726.96 | 1073.25 | 1173.92 | 989.42 | 680.19 |

Q_{max}/Q_{meandw} | 10.04 | 10.85 | 5.85 | 9.37 | 11.74 | 9.59 | 9.81 | 8.37 | 6.22 |

TPH (min) | 35 | 60 | 105 | 80 | 95 | 130 | 205 | 180 | 90 |

EV (m^{3}) | 32,493 | 155,853 | 25,536 | 64,308 | 195,837 | 38,637 | 60,222 | 46,305 | 22,038 |

EV_{dw} (m^{3}) | 10,467 | 31,638 | 11,028 | 17,046 | 30,993 | 9846 | 15,729 | 13,149 | 10,335 |

EV_{ww} (m^{3}) | 22,026 | 12,4215 | 14,508 | 47,262 | 164,844 | 28,791 | 44,493 | 33,156 | 11,703 |

E_{ww}/EV_{dw} | 0.68 | 0.80 | 0.57 | 0.73 | 0.84 | 0.75 | 0.74 | 0.72 | 0.53 |

C_{MAXtb} (NTU) | 626 | 851 | 481 | 770 | 994 | 765 | 830 | 498 | 593 |

EMC_{tb} (NTU) | 251 | 279 | 286 | 319 | 270 | 308 | 440 | 301 | 318 |

TPP (min) | 64.81 | 99.88 | 111.89 | 110.18 | 132.82 | 128.67 | 165.61 | 156.51 | 64.81 |

S1 Catchment | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|

Event code | S1_1 | S1_2 | S1_3 | S1_4 | S1_5 | S1_6 | S1_7 | S1_8 | S1_9 | S1_10 |

Year | 2014 | 2014 | 2014 | 2015 | 2015 | 2015 | 2015 | 2016 | 2016 | 2016 |

Date (dd-m) | 24 June | 22 September | 14 December | 20 May | 11 June | 27 September | 15 January | 30 January | 21 March | 4 April |

P_{TOTAL} (mm) | 12.06 | 3.29 | 30.37 | 3.03 | 8.69 | 8.18 | 11.16 | 14.19 | 27.48 | 13.46 |

I_{mean} (mm/h) | 1.90 | 0.67 | 1.22 | 1.35 | 1.16 | 2.80 | 2.09 | 3.70 | 0.99 | 0.71 |

I_{max10} (mm/h) | 4.24 | 2.55 | 7.62 | 3.86 | 4.24 | 5.87 | 5.33 | 8.25 | 4.39 | 7.05 |

DWP (days) | 7 | 5 | 10 | 24 | 22 | 17 | 17 | 15 | 13 | 12 |

Q_{max} (L/s) | 2360 | 1520 | 2480 | 1560 | 2350 | 2430 | 2440 | 1970 | 2150 | 2580 |

Q_{mean} (L/s) | 1567.34 | 960.00 | 1617.77 | 790.00 | 1393.44 | 1580.00 | 1690.00 | 1190.00 | 1700.87 | 1390.00 |

Q_{max}/Q_{meandw} | 5.06 | 3.10 | 5.22 | 2.55 | 4.49 | 5.10 | 5.45 | 3.84 | 7.81 | 4.48 |

TPH (min) | 130.0 | 175.2 | 110.0 | 150.0 | 150.0 | 109.8 | 205.2 | 145.2 | 155.0 | 135.0 |

EV (m^{3}) | 74,292 | 32,349 | 184,911 | 32,628 | 75,246 | 50,091 | 65,745 | 63,048 | 210,663 | 99,084 |

EV_{dw} (m^{3}) | 12,975 | 10,242 | 36,918 | 14,592 | 18,495 | 10,884 | 12,891 | 14,661 | 39,162 | 21,297 |

EV_{ww} (m^{3}) | 61,317 | 22,107 | 147,993 | 18,036 | 56,751 | 39,207 | 52,854 | 48,387 | 171,501 | 77,787 |

E_{ww}/EV_{dw} | 0.83 | 0.68 | 0.80 | 0.55 | 0.75 | 0.78 | 0.80 | 0.77 | 0.81 | 0.79 |

C_{MAX} (mg/L) | 588 | 522 | 788 | 822 | 767 | 652 | 698 | 728 | 564 | 496 |

EMC_{tb} (NTU/L) | 174 | 257 | 171 | 299 | 226 | 229 | 292 | 239 | 147 | 237 |

TPP (min) | 96.03 | 107.12 | 100.37 | 103.06 | 105.33 | 90.19 | 120.65 | 110.65 | 119.82 | 104.76 |

DWP | Q_{max} | TPH | P_{TOTAL} | I_{max10} | C_{MAXtb} | TPP | |
---|---|---|---|---|---|---|---|

DWP | 1.00 | −0.01 | 0.09 | −0.29 | 0.02 | 0.68 | 0.07 |

Q_{max} | 1.00 | −0.35 | 0.24 | 0.55 | 0.02 | −0.31 | |

TPH | 1.00 | −0.32 | −0.43 | −0.10 | 0.79 | ||

P_{TOTAL} | 1.00 | 0.50 | 0.01 | 0.24 | |||

I_{max10} | Symmetrical | 1.00 | 0.21 | −0.06 | |||

C_{MAXtb} | 1.00 | −0.08 | |||||

TPP | 1.00 |

DWP | Q_{max} | TPH | P_{TOTAL} | I_{max10} | C_{MAXtb} | TPP | |
---|---|---|---|---|---|---|---|

DWP | 1.00 | 0.28 | 0.12 | 0.10 | 0.13 | 0.32 | 0.19 |

Q_{max} | 1.00 | −0.12 | 0.74 | 0.61 | 0.84 | 0.06 | |

TPH | 1.00 | −0.18 | −0.61 | −0.08 | 0.94 | ||

P_{TOTAL} | 1.00 | 0.59 | 0.80 | 0.11 | |||

I_{max10} | Symmetrical | 1.00 | 0.48 | −0.52 | |||

C_{MAXtb} | 1.00 | 0.16 | |||||

TPP | 1.00 |

Catchment | Slope (m/m) | Catchment Flow Length, L (km) | S (km^{2}) | F_{shape} |
---|---|---|---|---|

S1 | 0.0013 | 17.00 | 47.53 | 1.645 |

San Félix | 0.0043 | 10.75 | 14.89 | 1.288 |

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## Share and Cite

**MDPI and ACS Style**

García, J.T.; Espín-Leal, P.; Vigueras-Rodriguez, A.; Castillo, L.G.; Carrillo, J.M.; Martínez-Solano, P.D.; Nevado-Santos, S.
Urban Runoff Characteristics in Combined Sewer Overflows (CSOs): Analysis of Storm Events in Southeastern Spain. *Water* **2017**, *9*, 303.
https://doi.org/10.3390/w9050303

**AMA Style**

García JT, Espín-Leal P, Vigueras-Rodriguez A, Castillo LG, Carrillo JM, Martínez-Solano PD, Nevado-Santos S.
Urban Runoff Characteristics in Combined Sewer Overflows (CSOs): Analysis of Storm Events in Southeastern Spain. *Water*. 2017; 9(5):303.
https://doi.org/10.3390/w9050303

**Chicago/Turabian Style**

García, Juan Tomás, Pablo Espín-Leal, Antonio Vigueras-Rodriguez, Luis G. Castillo, José M. Carrillo, Pedro D. Martínez-Solano, and Simón Nevado-Santos.
2017. "Urban Runoff Characteristics in Combined Sewer Overflows (CSOs): Analysis of Storm Events in Southeastern Spain" *Water* 9, no. 5: 303.
https://doi.org/10.3390/w9050303