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INTRODUCTION
Nowadays, the city of Tunja is experiencing a phenomenon of occupation or alteration of the natural channels of rainwater or runoff that lead these flows to the receiving bodies, thus causing an impact on the drainage since the hydrology of the natural basins is not respected, which affects their drainage capacity. In addition, there is considerable and accelerated urban development in the city’s northeastern sector, which has caused concern due to this and the continuous substitution of drainage surfaces that allow infiltration by hard and impermeable areas, causing overflows and floods that affect the citizens (Amaya-Tequia, 2019).
In a city whose aqueduct and sewerage networks are growing, it is necessary to know the operating behavior of the system. Knowing the hydraulic operation of the networks in real time is a vital tool to identify areas of conflict, vulnerabilities, and risks. Thus, it is also possible to propose prevention measures, dictate alternative solutions, optimize the functioning of the urban drainage system, prevent damage and problems in the structures, prioritize resources, meet contractual targets, and improve the citizens’ quality of life (Gerencia de Planeación y Construcciones, 2017).
The over-dimensioning of the elements of a unitary urban sewerage system, even though it allows the transport of flows collected from rainfall runoff, provides a significant potential for sedimentation of wastewater solids during dry periods. This is because runoff speeds are, in many cases, inadequate to keep them in suspension. As a result, during periods without rainfall, the ducts act as reservoirs for said sediments, thus affecting water quality (Seco & Gómez-Valentín, 2011). Earlier, there was a tendency to not consider water quality aspects in the design and operation of drainage systems. This trend remains in the development of most projects (Rodríguez, 2005)
The management of pollution associated with urban runoff is a difficult problem to solve, especially considering the stochastic nature of rainfall and the hydrological regimes of some rivers (Obermann et al., 2009). The natural channels of some regions have flows whose seasonal differences are significant, which makes them more sensitive to discharges from unit systems at times when the water flow provided by the spillways may be of the same order or greater than the flow rate. The assessment of urban rainwater quality is of great importance for the current approach to integrated urban drainage management. The characteristics of wastewater can be determined in different ways depending on the specific purpose. A sampling software for water characterization and quality control involves careful analysis of the samples type, the number of samples, and the parameters to be studied (Romero-Rojas, 2004).
It is important to know where and how the flows are incorporated into the network in order to be able to analyze their hydraulic behavior. For this reason, together with the Veolia Aguas de Tunja S.A. E.S.P. company, a measurement and modeling of the contaminants present in the water was carried regarding BOD (biological oxygen demand), COD (chemical oxygen demand), lead, TSS (total suspended solids), and nitrogen, with the purpose of improving rainfall drainage to avoid economic, environmental, and public health consequences as a result of climate change and its impact on the design flow of urban drainage systems (Veolia Tunja, 2017)
In addition to the above, an analysis of the possible generation of sulfides is also carried out, since one of the main causes for the deterioration of drainage systems is microbiological corrosion. An example of this is Thiobacillus Thiooxidans, which is also known as Acidthiobacillus thiooxidans, a sulfur-oxidizing organism that has been reported in sewerage systems in several countries including Mexico, the United States, Japan, Belgium, and China. The restoration costs of the concrete elements affected by microbiological corrosion represent a great investment in some countries and cities such as Germany, where it accounts for 40% of the US$100 billion invested in wastewater infrastructure; Belgium, where it accounts for 10% of the total expenditure; and Los Angeles, USA, where approximately US$400 million are invested. It is also estimated that the United States spends around US$25 billion annually on the maintenance of sewerage systems (Cortés & Vera, 2019). The corrosion of concrete structures is very common in the world; it is a silent phenomenon that must be addressed because of the large amount of money that is lost in performing maintenance works (Cortés & Vera, 2019).
METHODOLOGY
1. Area characterization and sampling
Topological base drainage network of the Santa Inés district (Amaya-Tequia, 2019)
Once the activities involving inspection, network cadastre, verification of the connectivity of the system, and identification of initial wells were performed, the delimitation of the Santa Inés district, as indicated in Figure 1, was carried out.
The points where the wastewater sampling was conducted are also indicated in Figure 1. These points were chosen given their location, as they are at the beginning, the middle (where a significant part of the system’s water is collected), and at the end of the network.
2. EPA modeling - SWMM (Gironás et al., 2009)
SWMM’s rainwater management model is a dynamic rain runoff simulation model that calculates quantity and quality mainly in urban areas. It operates in a collection of sub-catchment areas that receive precipitation and generate runoff and polluting loads. Routing transports the runoff through a pipe system, tracking its quality within each sub-uptake and the quality of the water in each pipe during a simulation period composed of multiple time steps.
This software shows how to simulate the accumulation and washing of pollutants in an urban basin. The influence of different land uses on pollutant accumulation is considered, and the average concentrations of events such as exponential functions are used to represent the washing process. The quality of surface runoff is an extremely important but very complex problem in the study of wet climate flows and their environmental impact. SWMM provides a flexible set of mathematical functions that can be calibrated to estimate the accumulation of pollutants on the Earth’s surface during dry weather periods, as well as their release from runoff during storm events.
3. Hydrogen sulfide (US Environmental Protection Agency, 1974)
Hydraulic characterization
Hydraulic characterization of sewage is necessary to determine the correlation between existing and predicted sulfur concentrations. It requires measuring the speed, flow depth, and slope of the sewer, by means of hydraulic relations. The flow rate and depth are obtained at the time of sampling of sulfides.
Speed can be measured through several methods (e.g., flotation speed). The slope of the culvert can be measured on the ground or taken in the form of constructed plans. The actual hydraulic roughness coefficient (n) can be calculated using the Manning Equation (1):
where:
Wastewater characterization
Parameters such as biochemical oxygen demand, pH, temperature, dissolved oxygen, and sulfate need to be controlled; their concentrations can be measured by individual or composite laboratory tests.
The daily mean sulfide concentration and pH values can be calculated by applying a correction factor (which is derived from the diurnal sulfide variation graph) to the sulfide concentrations measured in one day. Since speed is one of the determining factors in the generation of sulfides, it is feasible that all three conditions of possible sulfide generation exist in a pipe at some point within a period of 24 h. Therefore, for definition purposes, the three categories that define the sulfide generation characteristics of a pipeline are based on the average speed for the maximum flow period of 6 h.
Predictive equations
The quantitative Pomeroy and Parkhurst method for the prediction of sulfides has proven its effectiveness in studies conducted in California, Louisiana, and Texas. It was developed and is applicable only for partially filled trunk sewage culverts when conditions are favorable for sulfide accumulation. Since misleading results may be obtained under other conditions, indicators are evaluated to determine whether there is a possibility of sulfide generation before using this method.
Sulfur generation indicators
The recommended method of analysis is to evaluate the formula Z and A/B curves. If any of the indicators shows that conditions are favorable for the generation of sulfides, the Pomeroy and Parkhurst method is used to determine if there is a real problem. If both indicators show no potential for sulfide generation, no further analysis is required.
Z formula
The first equation to express the necessary conditions for sulfide generation in gravity nets was developed in 1946. This formula did not deal with sulfide levels, but merely with whether a build-up of sulfide could occur. In 1950, Davy presented a more complete formula that related the Reynolds number, the BOD5 effectiveness, the flow cross-sectional area, and the surface width. This work was later modified by Pomeroy to develop what is known as the Z formula:
Where:
Z: defined function
eBOD: effective BOD5, (mg/L)
Q: flow rate (ft3/s)
S: slope
P: wet perimeter (ft)
b: width (ft)
RESULTS
1. Characterization
The sewerage system of the neighborhood is of combined nature since the pipe system collects and transports both waste and rainwater. The pipes are built from concrete (96%) and PVC (4%). The outflow well provides the right conditions for the installation of a flow meter, with a depth to level of 1,58 m. The main collector is built from concrete pipe with a diameter of 30 inches. A simultaneous monitoring of rainfall and flow during 4 months was carried out, and the representativeness of these events was executed by cross-referencing the data recorded in the measuring equipment, verifying the magnitude and time of occurrence between hydrograms and hietograms (Amaya-Tequia, 2019). Within the monitoring period (year 2018), six representative rainfall events were captured, the model of this research was implemented on July 16th because sampling was conducted on that day, with the results shown in Table 32 and obtaining a maximum flow of 161,13 l/s.
Table 2 Rainfall events, precipitated volume, and associated return period
| Event | Total rain (mm) | Rainfall volume (m3) | Duration (min) | Intensity (mm/h) | Return period |
|---|---|---|---|---|---|
| July 16th | 3,75 | 598,50 | 35 | 6,43 | < 2 years |
Source: Authors
Sampling was carried out for 24 h in dry and rainy periods at the established points of the network. During the sampling, in situ parameters such as pH, temperature, and percentage of dissolved oxygen were measured, and the corresponding samples were taken to the laboratory in order to analyze the other parameters. Tables 3, 4, and 5 present the results at each of the sampling points in the study area.
Table 3 Results for point 3955
| Parameter | Method | Units | Dry period | Rainy period |
|---|---|---|---|---|
| BOD-5 | SM5210 B | mg O2/L | 315,6 | 96 |
| COD | SM 5220 B | mg O2/L | 522,16 | 481 |
| Chlorides | SM 4500-CL-D | mg Cl-/L | 60,69 | 52,0 |
| pH | SM 4500-H-B | 6,94 | 7,96 | |
| TSS | SM 2540-D | mg/L | 132,5 | 86,0 |
| Sulfates | SM 4500-SO4-E | mg SO4-/L | 61,90 | 68,9 |
| Sulfides | SM 4500 S2-F | mg S-/L | <4 | 350 |
Source: Authors
Table 4 Results for point 3910
| Parameter | Method | Units | Dry period | Rainy period |
|---|---|---|---|---|
| BOD-5 | SM5210 B | mg O2/L | 372,15 | 267 |
| COD | SM 5220 B | mg O2/L | 541,86 | 481 |
| Chlorides | SM 4500-CL-D | mg Cl-/L | 53,55 | 37 |
| pH | SM 4500-H-B | 6,89 | 8,10 | |
| TSS | SM 2540-D | mg/L | 115 | 85 |
| Sulfates | SM 4500-SO4-E | mg SO4-/L | 44,90 | 63,7 |
| Sulfides | SM 4500 S2-F | mg S-/L | <4 | 294 |
Source: Authors
Table 5 Results for point 4091
| Parameter | Method | Units | Dry period | Rainy period |
|---|---|---|---|---|
| BOD-5 | SM5210 B | mg O2/L | 365,165 | 321 |
| COD | SM 5220 B | mg O2/L | 522,16 | 518 |
| Chlorides | SM 4500-CL-D | mg Cl-/L | 60,69 | 41 |
| pH | SM 4500-H-B | 7,04 | 7,04 | |
| TSS | SM 2540-D | mg/L | 117,5 | 30 |
| Sulphates | SM 4500-SO4-E | mg SO4-/L | 74,05 | 86,9 |
| Sulphides | SM 4500 S2-F | mg S-/L | <4 | 385 |
Source: Authors
2. SWMM model
To this date, there are several studies on modeling sewerage systems. It has been deduced that the spatial simplification scale influences the results of the SWMM simulation (Marcor & Pedraza, 2012). The total concentration time of the urban watershed of the study area was estimated via the Carter method at 21,60 minutes, considering the weighted average slope of sub-basins and the main length of the flow (Amaya-Tequia, 2019).
In 2012, a study was conducted with the purpose of determining specific quality patterns of the behavior of the sewerage system of a sector in Catalonia, where the unitary network was calibrated and validated with data of rainfall episodes. For ease of access, the SWMM software and the variables adopted in the simulation were tailored when performing the calibration and validation of the proposed model (Seco & Gomez-Valentín, 2011). This model treats each basin as a non-linear reservoir obtained by the continuity and the Manning equation for each sub-basin (Hogue, et al., 1988). Figure 2 presents the continuity of the simulated runoff in the model.
Figure 3 shows the runoff quality in the continuity balance throughout the study area. Input loads are expressed as "Initial Buildup" prior to the start of the simulation, "Surface Buildup" during the dry period, and "Wet Deposition" pollutants in the rain. The output loads include "Infiltration Loss", generated by direct rain; "Surface Runoff", the pollutant load which includes a portion of accumulation; and the continuity report, indicated by "Remaining Buildup".
Figure 4 shows the quality routing. Only runoff loads through the transport system are shown. Dry weather is not shown; it is only supplied by the user through external inputs. Therefore, the three variables represented are "Wet Weather Inflow", "External Outflow", and "Final Stored Mass". The entry for wet weather is the same.
Figure 5 compares the results of the measured flow with respect to the different pollutants established in the model at the point at the outlet of the basin. For all pollutants, the concentrations exceeded the maximum flow, thus demonstrating the initial analysis in which the importance of the accumulation, washing, and transport processes was mentioned, given that it is the remains of the pollutants generated that affect the flow.
Figure 6 shows the behavior of all the pollutants studied in a rainy period. It is evident that BOD5 has a lower concentration than the others and COD is the one with the highest concentration.
2. Hydrogen sulfide prediction
Table 6 shows the results of all sections of the study area. Green indicates that sulfide is rarely produced; yellow indicates a probability that it will occur; and red shows hydrogen sulfide accumulation.
Table 6 Hydrogen sulfide production probability
| Tranche | eBOD5 (mg/l) | S | Flow rate (ft3/s)) | P (ft) | b (ft) | Z |
|---|---|---|---|---|---|---|
| PATLAL4741 | 2,73 | 0,0028 | 0,0074 | 0,85 | 0,772 | 286,948 |
| PATLAL4742 | 785,70 | 0,0072 | 0,3517 | 1,15 | 1,008 | 14.961,449 |
| PATLAL4743 | 699,03 | 0,0047 | 0,3510 | 1,74 | 1,274 | 19.732,557 |
| PATLAL4744 | 61,98 | 0,0054 | 0,3687 | 2,19 | 1,306 | 1.959,816 |
| PATLAL4745 | 452,33 | 0,0013 | 0,1928 | 1,22 | 1,050 | 25.010,071 |
| PATLAL4746 | 377,38 | 0,0026 | 0,0773 | 1,08 | 0,962 | 19.325,388 |
| PATLAL4747 | 0,00 | 0,0070 | 0,0000 | 0,33 | 0,320 | 0,000 |
| PATLAL4748 | 0,00 | 0,0060 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL4749 | 484,43 | 0,0004 | 0,1056 | 1,18 | 1,030 | 55.131,607 |
| PATLAL4750 | 641,96 | 0,0029 | 0,2525 | 0,15 | 1,179 | 2.428,179 |
| PATLAL4751 | 80,70 | 0,0136 | 0,3524 | 1,74 | 1,274 | 1.334,034 |
| PATLAL4752 | 717,51 | 0,0018 | 3,0452 | 3,21 | 2,394 | 15.509,457 |
| PATLAL4754 | 853,94 | 0,0012 | 2,7259 | 3,16 | 2,379 | 23.519,386 |
| PATLAL4757 | 0,00 | 0,0050 | 0,0000 | 0,37 | 0,366 | 0,000 |
| PATLAL4758 | 0,00 | 0,0083 | 0,0064 | 1,04 | 0,937 | 0,000 |
| PATLAL4759 | 32,86 | 0,0029 | 0,0675 | 1,55 | 1,214 | 1.898,148 |
| PATLAL4760 | 48,38 | 0,0046 | 0,2892 | 2,27 | 1,296 | 1.878,098 |
| PATLAL4761 | 921,83 | 0,0017 | 3,9376 | 3,41 | 2,443 | 20.049,830 |
| PATLAL4762 | 887,79 | 0,0006 | 4,0877 | 3,31 | 2,421 | 30.969,852 |
| PATLAL4763 | 913,54 | 0,0000 | 4,3991 | 3,16 | 2,379 | 0,000 |
| PATLAL4764 | 5,54 | 0,0035 | 0,0650 | 1,77 | 1,280 | 320,329 |
| PATLAL4765 | 874,58 | 0,0032 | 4,5093 | 3,36 | 2,432 | 13.064,168 |
| PATLAL4766 | 48,20 | 0,0021 | 0,2140 | 2,86 | 2,176 | 2.319,844 |
| PATLAL4768 | 590,94 | 0,0042 | 0,1900 | 1,41 | 1,203 | 18.467,157 |
| PATLAL4769 | 0,00 | 0,0039 | 0,0000 | 0,65 | 0,623 | 0,000 |
| PATLAL4771 | 862,19 | 0,0009 | 4,6746 | 3,67 | 2,481 | 25.334,140 |
| PATLAL4772 | 60,10 | 0,0028 | 0,3945 | 2,33 | 1,286 | 2.816,970 |
| PATLAL4773 | 463,27 | 0,0020 | 0,2214 | 1,25 | 1,069 | 19.934,308 |
| PATLAL4774 | 0,00 | 0,0009 | 0,0000 | 0,61 | 0,573 | 0,000 |
| PATLAL4775 | 974,49 | 0,0066 | 0,2472 | 1,15 | 0,967 | 22.679,642 |
| PATLAL4776 | 0,00 | 0,0053 | 0,0000 | 0,66 | 0,623 | 0,000 |
| PATLAL4777 | 0,00 | 0,0205 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL4778 | 0,00 | 0,0025 | 0,0000 | 0,00 | 0,030 | 0,000 |
| PATLAL4779 | 828,45 | 0,0031 | 0,2338 | 1,15 | 0,967 | 28.491,066 |
| PATLAL4780 | 402,73 | 0,0029 | 0,2137 | 1,15 | 1,008 | 14.134,925 |
| PATLAL4781 | 40,38 | 0,0034 | 0,3803 | 2,01 | 1,311 | 1.452,415 |
| PATLAL4782 | 873,11 | 0,0018 | 4,9479 | 3,67 | 2,481 | 17.733,161 |
| PATLAL4783 | 863,97 | 0,0006 | 5,2470 | 3,62 | 2,476 | 29.716,490 |
| PATLAL4784 | 0,00 | 0,0037 | 0,0000 | 0,78 | 1,134 | 0,000 |
| PATLAL4785 | 36,42 | 0,0037 | 0,3489 | 1,90 | 1,303 | 1.236,264 |
| PATLAL4787 | 870,89 | 0,0027 | 5,5373 | 3,62 | 2,476 | 13.859,458 |
| PATLAL4788 | 879,92 | 0,0015 | 5,7527 | 3,62 | 2,476 | 18.495,793 |
| PATLAL4789 | 875,06 | 0,0018 | 5,7255 | 3,52 | 2,461 | 16.470,123 |
| PATLAL4790 | 27,54 | 0,0073 | 0,2080 | 2,15 | 1,142 | 1.018,695 |
| PATLAL4791 | 307,30 | 0,0012 | 0,2211 | 1,18 | 0,926 | 18.608,470 |
| PATLAL4792 | 1.030,76 | 0,0020 | 0,2483 | 1,87 | 1,146 | 59.200,972 |
| PATLAL4794 | 1.267,01 | 0,0040 | 0,2444 | 1,33 | 1,053 | 40.473,978 |
| PATLAL4795 | 0,00 | 0,0035 | 0,0000 | 0,70 | 0,657 | 0,000 |
| PATLAL4796 | 31,83 | 0,0053 | 0,2352 | 1,69 | 1,143 | 1.037,811 |
| PATLAL4797 | 0,00 | 0,0029 | 0,0000 | 0,74 | 0,689 | 0,000 |
| PATLAL4798 | 0,00 | 0,0037 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL4799 | 0,00 | 0,0025 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL4800 | 0,00 | 0,0057 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL4801 | 0,00 | 0,0047 | 0,0000 | 0,00 | 0,031 | 0,000 |
| PATLAL4802 | 0,00 | 0,0035 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL4803 | 0,00 | 0,0018 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL4804 | 0,00 | 0,0019 | 0,0000 | 0,70 | 0,657 | 0,000 |
| PATLAL4805 | 641,97 | 0,0044 | 0,2066 | 1,12 | 0,951 | 19.181,969 |
| PATLAL4806 | 836,67 | 0,0027 | 0,3704 | 1,55 | 1,120 | 31.057,307 |
| PATLAL4807 | 0,00 | 0,0053 | 0,0000 | 0,40 | 0,392 | 0,000 |
| PATLAL4809 | 22,86 | 0,0040 | 0,0346 | 1,16 | 0,917 | 1.379,208 |
| PATLAL4810 | 57,18 | 0,0086 | 0,3931 | 2,04 | 1,125 | 1.518,157 |
| PATLAL4811 | 925,85 | 0,0042 | 6,1080 | 3,26 | 2,408 | 10.606,031 |
| PATLAL4994 | 70,92 | 0,0108 | 0,2617 | 2,01 | 1,311 | 1.627,460 |
| PATLAL4995 | 501,12 | 0,0020 | 0,1208 | 1,31 | 1,105 | 27.077,951 |
| PATLAL4996 | 105,79 | 0,0100 | 0,2850 | 1,98 | 1,310 | 2.424,133 |
| PATLAL4997 | 654,80 | 0,0014 | 0,1649 | 1,49 | 1,191 | 39.640,400 |
| PATLAL4998 | 1.025,13 | 0,0035 | 0,1896 | 1,12 | 0,986 | 33.712,539 |
| PATLAL4999 | 0,00 | 0,0046 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL5000 | 0,00 | 0,0034 | 0,0000 | 0,00 | 0,035 | 0,000 |
| PATLAL5001 | 0,00 | 0,0032 | 0,0000 | 0,75 | 0,712 | 0,000 |
| PATLAL7594 | 40,84 | 0,0007 | 0,0046 | 0,66 | 0,623 | 9.346,264 |
| PATLAL7595 | 0,00 | 0,0022 | 0,0000 | 0,46 | 0,450 | 0,000 |
| PATLAL7596 | 540,94 | 0,0062 | 0,0791 | 0,81 | 0,746 | 17.278,768 |
| PATLAL7597 | 0,00 | 0,0047 | 0,0000 | 0,61 | 0,586 | 0,000 |
| PATLAL7598 | 1.077,87 | 0,0062 | 0,1190 | 0,85 | 0,772 | 30.387,260 |
| PATLAL7599 | 364,71 | 0,0010 | 0,1151 | 1,18 | 1,030 | 27.500,090 |
| PATLAL7600 | 1.070,14 | 0,0038 | 0,3535 | 1,34 | 1,121 | 29.416,646 |
| PATLAL7601 | 434,09 | 0,0101 | 0,3496 | 1,28 | 1,087 | 7.212,652 |
| PATLAL7604 | 0,00 | 0,0054 | 0,0000 | 0,81 | 0,746 | 0,000 |
| PATLAL7606 | 0,00 | 0,0044 | 0,0000 | 0,57 | 0,545 | 0,000 |
| PATLAL7607 | 1.038,02 | 0,0018 | 0,0646 | 0,85 | 0,772 | 66.811,409 |
| PATLAL7608 | 245,85 | 0,0024 | 0,0600 | 0,91 | 0,820 | 14.086,141 |
| PATLAL7609 | 709,01 | 0,0051 | 0,2211 | 1,01 | 0,882 | 18.706,940 |
| PATLAL7610 | 1.001,47 | 0,0145 | 0,3563 | 1,18 | 1,030 | 13.436,455 |
| PATLAL7612 | 680,12 | 0,0024 | 0,4248 | 1,84 | 1,677 | 20.397,905 |
| PATLAL7613 | 671,07 | 0,0039 | 0,7674 | 2,12 | 1,873 | 13.211,050 |
| PATLAL7615 | 908,96 | 0,0014 | 1,3003 | 2,37 | 2,031 | 26.256,416 |
| PATLAL7616 | 572,65 | 0,0016 | 1,4592 | 2,61 | 2,159 | 15.333,006 |
| PATLAL7619 | 872,21 | 0,0028 | 2,5246 | 2,89 | 2,285 | 15.472,388 |
| PATLAL7620 | 0,00 | 0,0038 | 0,0000 | 0,52 | 0,501 | 0,000 |
| PATLAL7621 | 311,75 | 0,0014 | 0,0650 | 0,95 | 0,842 | 23.382,543 |
| PATLAL7622 | 854,89 | 0,0026 | 0,2260 | 1,18 | 1,030 | 31.519,325 |
| PATLAL7623 | 353,90 | 0,0019 | 0,2468 | 1,34 | 1,121 | 15.548,603 |
| PATLAL7624 | 851,85 | 0,0040 | 0,7999 | 1,55 | 1,214 | 18.593,306 |
| PATLAL7625 | 263,12 | 0,0081 | 0,8013 | 1,82 | 1,291 | 4.449,585 |
| PATLAL7626 | 71,53 | 0,0042 | 0,0321 | 0,81 | 0,746 | 3.718,041 |
| PATLAL7627 | 84,73 | 0,0033 | 0,0300 | 0,89 | 0,821 | 5.086,538 |
| PATLAL7628 | 384,87 | 0,0016 | 0,1010 | 1,08 | 0,962 | 23.324,631 |
| PATLAL7629 | 0,00 | 0,0073 | 0,0000 | 0,23 | 0,227 | 0,000 |
| PATLAL7630 | 0,00 | 0,0067 | 0,0000 | 0,65 | 0,623 | 0,000 |
| PATLAL7631 | 0,00 | 0,0087 | 0,0000 | 0,40 | 0,392 | 0,000 |
| PATLAL7632 | 5,24 | 0,0020 | 0,0028 | 0,78 | 0,719 | 877,806 |
| PATLAL7633 | 318,60 | 0,0018 | 0,1596 | 1,23 | 1,007 | 16.701,222 |
| PATLAL7634 | 277,06 | 0,0015 | 0,3348 | 1,43 | 1,088 | 13.416,357 |
| PATLAL7635 | 922,40 | 0,0006 | 0,2458 | 1,22 | 1,050 | 70.174,637 |
| PATLAL7636 | 158,64 | 0,0140 | 0,2437 | 1,15 | 1,008 | 2.435,488 |
| PATLAL9265 | 229,37 | 0,0039 | 0,0406 | 0,75 | 0,712 | 11.200,975 |
| PATP4812 | 0,00 | 0,0080 | 0,0000 | 0,00 | 0,076 | 0,000 |
Fuente: Authors
Oxygen influence on sulphur generation (Roca-Hernández, 2012)
In a sewer system that circulates by gravity, water is aerated. This process can be slow in large collectors due to the gentle slope and flow depth. The rate increases in smaller pipes, and this does not happen in pressure systems, so the oxygen is consumed in a shorter time, thus yielding a higher concentration of sulfides. Oxygen is consumed by microorganisms present in the body of water (in the biofilm of the pipes), and the rate of consumption and oxygen can vary depending on the distance that the wastewater has to travel due to the diffusion in the biologically active film of the pipe wall.
The structure of the biofilm is formed by several layers, with an aerobic and an anaerobic zone. If the former prevails, the conditions will be given for sulfate reduction to occur. The relationship between these zones is delimited by the concentration of organic matter. If the oxygen concentration in the current is close to zero, then not all the sulfide can be oxidized and passed into the current.
CONCLUSIONS
The behavior of the physicochemical and microbiological characteristics measured along the sewerage network at the three established points evidences that there is spatial uniformity in the analyzed parameters and that there is little variability in their behavior when the first wash occurs at each point. All this, considering the results indicated in Tables 4, 5, and 6.
The design of the hydrodynamic model of urban drainage quality of the northeastern sector of Tunja, Santa Inés neighborhood, was performed, calibrated, and validated via the SWMM 5.1 software based on data obtained in the field.
The implementation of the model allowed concluding that the sewerage of the city of Tunja, despite being combined, and considering the current dumping regulations in Colombia, namely Resolution 0631 of 2015 (Ministerio de Ambiente y Desarrollo Sostenible, 2015) which indicates the maximum allowable values for any type of dumping, requires prior treatment to avoid contamination in the dry period. As indicated by the rule, the maximum allowed value for suspended solids is 90 mg/L. This limit was exceeded in all three points. During the rainy period, although the value was not exceeded at the two points near the exit, it was very close to the limit: 85 and 86 mg/L, respectively.
It is recommended that treatment alternatives be considered in the activated sludge process, based on the relationship between BOD and COD, since waste can be degraded through a biological process (Ramos-Velandia, 2017).
Given that the Z formula has generally been successful in predicting the occurrence of sulfide problems in gravity sewers, for our network, specifically with 96% of the pipelines, approximately 48% show no likelihood of sulfur generation. For pipelines that are likely to generate hydrogen sulfide, it is recommended to ensure proper aeration in order to avoid gas accumulation.
