# Quantitative Impact Assessment of Sewer Condition on Health Risk

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Research Catchments

#### 2.2. Description of In-Sewer Defects

#### 2.3. Model Set-Up

#### 2.4. Model Validation

#### 2.5. Monte Carlo Simulations

#### 2.6. Quantification Flooding Frequencies

_{floodable}) is the total area available for the storage of floodwater at a specific node. As a result, it is the sum of the contributing areas draining to this node.

^{2}. In order to assess the sensitivity of the results for the flooded area, next to this value, threshold values for the flooded area of 50 and 75 m

^{2}are applied as well. For each threshold area, a corresponding threshold water level is calculated given the shape of the flood cone (Figure 6). The maximum threshold level for the flood depth is limited to 0.15 m in order to avoid levels above the sidewalks and to exclude the part of the flood cone representing flat and inclining roofs. This limitation is only necessary at locations with very small contributing areas.

#### 2.7. Quantification of Health Risks

_{event}) for the different pathogens Campylobacter, Cryptosporidium, Giardia, norovirus, and enterovirus. It is assumed that, except for enteroviruses, all waterborne pathogens are infectious. This can be justified by the fact that the different pathogens lead to similar complaints concerning public health and that one pathogen was likely to prevail to cause a gastrointestinal infection. As a result, the overall probability of infection per exposure event (P

_{infection/event}) is quantified by summation of the values for each pathogen.

## 3. Results

#### 3.1. Results ‘Tuindorp’ Catchment

#### 3.1.1. Frequency of Flooding

#### 3.1.2. Duration of Flooding

#### 3.1.3. Probability of Infection

^{2}) using a t-test. The test statistic for the t-test is calculated as follows. The difference between the reference value and the median of distribution function is divided by the standard error of the distribution function. This standard error equals the standard deviation of the distribution function divided by the square root of the sample size (i.e., the number of Monte Carlo runs). The corresponding p-value of the test statistic is calculated using the cumulative distribution function of the Students’ t-distribution with 1 degree of freedom for a one-tailed p-value. The calculated p-value is compared with a significance level of 0.5%. Since the distribution functions are not Gaussian and, therefore, violate t-test assumptions, the data are transformed prior to significance testing. For ‘Tuindorp’ the transformation equals 1/x

^{3}.

^{2}is illustrated in Figure 11. This figure shows that the impact of sedimentation on infection probability varies within the catchment.

#### 3.2. Results ‘Loenen’ Catchment

#### 3.2.1. Frequency of Flooding

#### 3.2.2. Probability of Infection

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 3.**Comparison of reported and simulated flood areas in the ‘Tuindorp’ catchment during the rainfall event on 4 November 2013 in order to validate the InfoWorks model.

**Figure 4.**Long-term rainfall series of De Bilt (The Netherlands). Observed rainfall volumes (by the Royal Dutch Meteorological Institute) during the period 1955–1964. (

**a**) 15-min time serie of rainfall; (

**b**) 1-day time series of rainfall.

**Figure 5.**Average (top) and standard deviation (bottom) of relevant model output parameter (i.e., average infection probability per year). (

**a**) Average value of model output parameter; (

**b**) Standard deviation of model output parameter.

**Figure 6.**Flood cone on top of a manhole in the hydrodynamic model storing water above street level. (

**a**) The relationship between flood volume and water level above the street; (

**b**) Three threshold areas 50, 75, and 100 m

^{2}, including the corresponding threshold water levels h

_{50}, h

_{75}, and h

_{100}for assessing the sensitivity of the results.

**Figure 7.**Average number of flooding events per manhole based on the Monte Carlo simulations for the ‘Tuindorp’ catchment.

**Figure 8.**Manholes in the ‘Tuindorp’ catchment with an increasing maximum flood duration: (

**a**) Larger than 30 min; (

**b**) Larger than one hour; (

**c**) Larger than two hours; (

**d**) Larger than four hours.

**Figure 10.**Spatial distribution of children in the ‘Tuindorp’ catchment: black dots (21% of population) and white dots (12% of population) [34].

**Figure 11.**Example of spatial distribution of infection probability for children per year per manhole for a specific system state (i.e., Monte Carlo run) of the ‘Tuindorp’ catchment.

**Figure 12.**Average number of flooding events per manhole based on the Monte Carlo simulations for the ‘Loenen’ catchment.

Characteristics | Tuindorp Catchment | Loenen Catchment |
---|---|---|

Area use | Residential | Residential |

Catchment area | Flat | Mildly sloping |

System type | Combined | Combined |

System structure | Looped | Partly branched |

Ground level/surface level (m above average sea level) | 0.75–2.25 | 17.8–28.6 |

Average surface slope (mm/m) | 3.0 | 8.8 |

Average pipe slope (mm/m) | 2.8 | 3.8 |

Contributing area (ha) | 56.9 | 23.4 |

Number of CSO structures (-) | 5 | 2 |

Storage volume (m^{3}) | 4669 (=8.2 mm) | 900 (=3.85 mm) |

Volume storage settling tank (m^{3}) | 822 (=1.4 mm) | 0 |

Number of pumping stations (-) | 1 | 1 |

Pumping capacity (m^{3}/h) | 800 ^{a} | 209 |

Number of inhabitants (-) | 10,656 | 2100 |

Dry weather flow (m^{3}/h) (including leakage water) | 157 | 78 |

^{a}Based on flow measurements as described in Van Bijnen et al. [20]. According to the municipal administration, the pumping capacity is 540 m

^{3}/h.

**Table 2.**Summary statistics of duration (in minutes) of flooding events in the ‘Tuindorp’ catchment.

Flooded Area | ||||
---|---|---|---|---|

50 m^{2} | 75 m^{2} | 100 m^{2} | ||

Average event duration (min) | Mean | 19.60 | 19.32 | 19.05 |

95%-interval | 2–64.08 | 1.67–63.98 | 1–63.62 | |

Min. event duration (min) | Mean | 4.23 | 4.17 | 4.02 |

95%-interval | 1–36 | 1–33 | 1–31 | |

Max. event duration (min) | Mean | 37 | 36.77 | 36.49 |

95%-interval | 3–127 | 3–127 | 1–127 |

**Table 3.**Summary statistics of probability of infection per year for adults in the ‘Tuindorp’ catchment.

Flooded Area | Reference (×10^{−3})(No Sedimentation) | Median (×10^{−3})(MC Simulation) | Shift^{a} (×10^{−3}) | 95% (×10^{−3}) |
---|---|---|---|---|

50 m^{2} | 5.3 | 8.1 | 2.8 | 1.4 |

75 m^{2} | 5.0 | 7.6 | 2.7 | 1.5 |

100m^{2} | 4.7 | 7.2 | 2.5 | 1.5 |

^{a}= median − reference.

**Table 4.**Summary statistics of probability of infection per year for children in the ‘Tuindorp’ catchment.

Flooded Area | Reference (×10^{−2})(No Sedimentation) | Median (×10^{−2})(MC Simulation) | Shift^{a} (×10^{−2}) | 95% (×10^{−2}) |
---|---|---|---|---|

50 m^{2} | 5.1 | 7.5 | 2.5 | 1.2 |

75 m^{2} | 4.8 | 7.1 | 2.4 | 1.3 |

100 m^{2} | 4.5 | 6.8 | 2.2 | 1.3 |

^{a}= median − reference.

**Table 5.**Summary statistics of probability of infection per year for adults in the ‘Loenen’ catchment.

Flooded Area | Reference (×10^{−3})(No Sedimentation) | Median (×10^{−3})(MC Simulation) | Shift^{a} (×10^{−3}) | 95% (×10^{−3}) |
---|---|---|---|---|

50 m^{2} | 0.64 | 2.5 | 1.8 | 3.6 |

75 m^{2} | 0.62 | 2.3 | 1.7 | 3.5 |

100 m^{2} | 0.58 | 2.1 | 1.6 | 3.4 |

^{a}= median − reference.

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**MDPI and ACS Style**

Van Bijnen, M.; Korving, H.; Langeveld, J.; Clemens, F.
Quantitative Impact Assessment of Sewer Condition on Health Risk. *Water* **2018**, *10*, 245.
https://doi.org/10.3390/w10030245

**AMA Style**

Van Bijnen M, Korving H, Langeveld J, Clemens F.
Quantitative Impact Assessment of Sewer Condition on Health Risk. *Water*. 2018; 10(3):245.
https://doi.org/10.3390/w10030245

**Chicago/Turabian Style**

Van Bijnen, Marco, Hans Korving, Jeroen Langeveld, and François Clemens.
2018. "Quantitative Impact Assessment of Sewer Condition on Health Risk" *Water* 10, no. 3: 245.
https://doi.org/10.3390/w10030245