Modeling Sulfides, pH and Hydrogen Sulfide Gas in the Sewers of San Francisco Jes Vollertsen1*, Nohemy Revilla2, Thorkild Hvitved-Jacobsen1, Asbjørn Haaning Nielsen1

ABSTRACT: An extensive measuring campaign targeted on sewer odor problems was undertaken in San Francisco. It was assessed whether a conceptual sewer process model could reproduce the measured concentrations of total sulfide in the wastewater and H2S gas in the sewer atmosphere, and to which degree such simulations have potential for further improving odor and sulfide management. The campaign covered measurement of wastewater sulfide by grab sampling and diurnal sampling, and H2S gas in the sewer atmosphere was logged. The tested model was based on the Wastewater Aerobic/Anaerobic Transformations in Sewers (WATS) sewer process concept, which never had been calibrated to such an extensive dataset. The study showed that the model was capable of reproducing the general levels of wastewater sulfide, wastewater pH, and sewer H2S gas. It could also reproduce the general variability of these parameters, albeit with some uncertainty. It was concluded that the model could be applied for the purpose in mind. Water Environ. Res., 87, 1980 (2015). KEYWORDS: measurements.

sewer odor, modeling, hydrogen sulfide, field

doi:10.2175/106143015X14362865226752

Introduction Most wastewaters are not particularly odorous at the time they are discharged to the conveyance system; whether or not they obtain an obnoxious smell is mainly governed by microbial processes in the network. The wastewater redox conditions are especially important in this context. The presence of oxygen or other highly oxidized compounds, such as nitrate, largely inhibit the formation of malodorous compounds, while absence of oxidized compounds allow processes to proceed and form a wide range of malodorous compounds. Hydrogen sulfide is one of these compounds. When managing odors from conveyance systems, hydrogen sulfide typically is a priority compound as it is also corrosive and hazardous to health. There are, however, a number of other malodorous substances which also contribute to the odor profile of wastewater (Stuetz and Frechen, 2001; Mu˜noz et al., 2010). Compared to sulfide in the water phase and hydrogen sulfide gas in the sewer atmosphere, other malodorous substances are much more challenging to measure and knowledge of their formation and fate in conveyance systems is limited. Hydrogen sulfide is therefore typically used as a model compound, even though this may lead to an underestimation of odor risks as hydrogen sulfide is more readily oxidized in both 1

* Aalborg University, Department of Civil Engineering, Sofiendalsvej 11, 9200 Aalborg SV, Denmark; e-mail: [email protected]

2

San Francisco Public Utilities Commission (SFPUC), San Francisco, CA 94124 1980

the wastewater and the sewer atmosphere than other malodorous substances (Hvitved-Jacobsen et al., 2013; Rudelle et al., 2013). Wastewater conveyance networks are often large and complex systems. At the same time microbial, chemical and physical processes proceed in wastewater, biofilms, sediments, sewer atmosphere and moist sewer surfaces, compounds continuously exchange between these phases. To gain an overview of obnoxious compounds in a complex conveyance system, and to assess management strategies, a detailed analysis of such a network is hence called for (Vollertsen et al., 2011). Like many large cities in warmer climates, San Francisco occasionally experiences odor nuisances from its conveyance system and odor complaints are not uncommon. It is hence a priority of San Francisco’s Wastewater Enterprise (WWE) to plan, operate and maintain the system to a degree where odor nuisances are kept at a minimum. The majority of San Francisco is served by combined sewers, many of which date to the 1920s or earlier. In many of these catch basins odor seals are ineffective, leading to odorous air being vented at street level, which then causes nuisance in the city. Most often, odor is perceived at the eastern coast along the San Francisco Bay, where the system was extended with interceptors and storage volume to reduce coastal pollution in the 1980s. The latter was created as inline storage with large-diameter circular pipes and boxes that also convey the wastewater flow during dry weather. While the intercepting sewers and storage structures have mitigated coastal pollution, they have also contributed to malodors. A range of odor control measures have therefore been implemented, reducing but not eliminating problems. In the cause of the ongoing work to fight malodors in the city, WWE decided to analyze the formation and occurrence of malodors in depth. The approach chosen was to apply a sewer process model to the eastern part of the city, the bayside drainage area. The first attempt to formulate a sewer process model was made by Pomeroy and Bowlus (1946) as a relationship between wastewater BOD, temperature and flow velocity below which sulfide buildup in a sewer network would be prevented. During the second half of the century this basic concept was significantly developed by, amongst others, Pomeroy (1970), Thistlethwayte (1972), Boon and Lister (1975), Pomeroy and Parkhurst (1977), Hvitved-Jacobsen et al. (1988), Matos (1992) and Nielsen et al. (1998). These models were and are still widely in use and have been included in many institutional manuals, for example Thistlethwayte (1972), USEPA (1974), USEPA (1985), ASCE (1989) and Melbourne and Metropolitan Board of Works (1989). Based on earlier sewer process models, the first Water Environment Research, Volume 87, Number 11

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numerical sewer process models that could simulate complex networks were developed and applied, namely the INTERCEPTOR model (Witherspoon et al., 2004) and its later uses, such as Ward et al. (2012). In the later years a new type of sewer process model has evolved, based on a conceptually more detailed understanding of biological and chemical processes in conveyance systems. The latter models are still research tools on an experimental stage and not mature and commercially available like, for example, hydrodynamic sewer network models or models for simulating wastewater treatment process (Vollertsen et al., 2011; Sharma et al., 2013). It was hence an objective of the WWE to assess the extent to which a conceptual sewer process model could be implemented and calibrated to a complex network and then used for detailed mapping of odor problems, for choosing and optimizing management strategies, and for planning network designs that minimize odor. A model based on the WATS concept (Wastewater Aerobic/anaerobic Transformations in Sewers) was chosen for this purpose. This concept has been developed at Aalborg University, Denmark, over the last three decades (Hvitved-Jacobsen et al., 2013), and a mathematical model based on this concept has lately been assessed for catchment-scale simulations (Vollertsen et al., 2011; 2013). This concept also constitutes the starting point of two other sewer process models at present being developed for studies of insewer process (Sharma et al., 2008; Donckels et al., 2013). The purpose of the present work was to evaluate a sewer process model based on a conceptual sound understanding of in-sewer processes. One of the objectives was to identify the extent to which such a model can reproduce levels of wastewater sulfide and sewer atmosphere hydrogen sulfide of an extended and complex drainage area. The present study distinguishes itself from previous studies by an extensive monitoring program for calibrating and assessing the model performance. Methods In the following, the non-systematic term ‘sulfide’ is used to denominate the sum of hydrogen sulfide, hydrosulfide ions and sulfide ions in the water phase (H2S þ HS- þ S2-). The term ‘hydrogen sulfide’ is used to denominate the gaseous compound H2S, which can be in the water phase or the gas phase. When the hydrogen sulfide content of the gas phase is specifically addressed, the term ‘H2S gas’ is used. Finally, the term ‘total sulfides’ is applied to denominate the sum of sulfide and sulfide precipitates. The Drainage Area. The San Francisco Bayside drainage area (Figure 1) covers roughly 65 km2 and comprises 1056 km of sewer lines, of which 9 km are force mains. The larger force mains are located along the San Francisco Bay where the wastewater is pumped to the South East Treatment plant (Figure 1). The network consists of more than 22 000 individual pipes of varying diameter and slope. The storage boxes are of concrete while a variety of materials are used for pipes. The more recent large size pipes are typically of concrete while large but older pipes often are of brick. In total, the length of pipes vulnerable to H2S gas attack—that is pipes made of concrete or other materials containing cement—is 159 km. The total length of brick sewers, where the mortar between bricks can be attacked by H2S gas, is 91 km. Sulfide is managed in the drainage area and at the inlet to the South East Treatment Plant (Figure 1). At the latter, sodium November 2015

hypochlorite is applied to manage sulfide entering the treatment works. In the drainage area, ferrous chloride is injected into 2 intercepting mains upstream of North Shore Pump Station, namely at the intersection of The Embarcadero and Green Street and at the intersection of North Point Street and Columbus Avenue. Ferrous chloride is furthermore dosed at the Griffith Pump Station. Hydrogen peroxide is injected to the box in Brannan Street at the intersection with 4th Street. Upstream of Channel Pump Station, sodium hypochlorite is added to the box in Berry Street at the intersection with 4th Street and optionally at the intersection with 6th Street. Field Measurements. A field monitoring campaign was conducted in the period from June to October 2013 where 12 locations were selected for monitoring. At 10 of these locations wastewater grab samples were taken for determination of alkalinity (n¼276) (n is the number of samples), ammonia (n¼276), COD (n¼275), dissolved oxygen (n¼264), pH (n¼273), sulfate (n¼274), total sulfides (n¼123), temperature (n¼273), BOD (n¼44), and TKN (n¼14). Dissolved oxygen, pH, and temperature were measured on site with portable meters (dissolved oxygen: HACH DOmeter HQ40d multi (LDO101) and Hach SensION; pH and temperature: Thermo Scientific Orion 3-Star). Other parameters were analyzed at the WWE laboratory applying standard methods (APHA, 2005) for analysis of alkalinity (2320 B), ammonia (4500 C), COD (5220 D), BOD (5210 B), TKN (4500 B), and total sulfides (4500 D). Sulfate was analyzed according to US EPA 375.4 (US EPA, 1979). The laboratory was certified for all analytes but sulfate and total sulfides. Initially, zinc acetate and sodium hydroxide were both used for total sulfide sample conservation in the field. However, the precipitants formed due to the sodium hydroxide caused analytical problems and those results had to be rejected. Instead, samples were conserved with zinc acetate only. The latter method has been the method of choice for numerous studies on sulfide related processes in sewers and been proven robust and consistent (e.g. Nielsen et al., 2008). Time proportional samples were collected at eight locations using ISCO 3700 auto samplers (Figure 1). This sampler was chosen as it uses a peristaltic pump for sampling and not a vacuum chamber, as the latter would cause stripping of H2S gas and compromise sample results. Prior to sampling, zinc acetate was added to each of the samplers 24 flasks so that sampled wastewater was conserved immediately. A total of 388 subsamples were collected, preserved with zinc acetate, and analyzed for total sulfides. At 11 locations H2S gas concentrations were measured in the sewer atmosphere using Odalog Type L2 gas loggers (0 to 200 ppm and 0 to 50 ppm with an absolute accuracy of 2 ppm), accumulating to a total 1043 days of validated measurements. During the measuring campaign the addition of ferrous chloride in the northeast part of the drainage area, upstream of North Shore Pump Station, was 637 kg Fe/day, an estimated one-third of which was added at North Point Street / Columbus Avenue while an estimated two-thirds was added at Embarcadero / Green Street. The addition in the southeast part of the drainage area, at Griffith Pump Station, was 129 kg Fe/day. North of Chanel Pump Station, 363 kg H2O2/day of hydrogen peroxide was added at Brannan Street while 535 kg OCl-/day of hypochlorite was added at Berry Street / 4th Street. The addition to the inflow of the South East Treatment Plant was 933 kg OCl-/day. The latter addition occurred downstream of the 1981

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Figure 1—The bayside drainage area, sampling points, and locations of sulfide management (color versions of figures appear online). sampling location and did not affect the sampling results. All sampling points but a, b and c were located downstream of ferrous iron dosing stations. Sampling points h and j were furthermore located downstream of hydrogen peroxide and hypochlorite dosing stations. Rather little precipitation was measured at the Southeast Treatment Plant throughout the measuring campaign. The first precipitation came in the period from June 23 to June 26 with 0.5, 0.8, 4.1, and 0.5 mm of daily precipitation, respectively. The next precipitation events occurred on August 8 with 2.0 mm and on September 21 with 14.0 mm of precipitation. Data collected on these rainy days were all excluded from the study. In-Sewer Process Model. Sulfide in the wastewater, H2S gas in the sewer atmosphere as well as other quality parameters were modeled applying a numerical formulation of the WATS concept for simulation of in-sewer processes. The numerical formulation is termed the ‘‘WATS model’’. It is however important to note that the model applied in this study is just one of many possible formulations of the WATS concept, similar to many numerical formulations of activated sludge models being developed (and marketed) based on the activated sludge model concept presented in e.g. Henze et al. (2000). The WATS concept lays out an approach for simulating microbial, chemical, and physical processes in liquid and gas phases of sewer networks. The 1982

approach is deterministic and akin but not identical to approaches for simulating activated sludge treatment processes or anaerobic digester processes (Henze et al., 2000; Batstone et al., 2002). It is based on extensive experimental studies on sewer processes that have been presented in numerous publications throughout the previous decades. Hvitved-Jacobsen et al. (2013) gives a detailed overview of the concept as well as process kinetics and stoichiometry. The WATS model applied in the present study is programmed in Delphi Pascal and includes processes related to the cycles of carbon and sulfur under varying redox conditions. Table 1 gives an overview of these processes while Table 2 gives an overview of the sulfide management technologies included in the numerical model. Hydraulics of the WATS Model. The WATS model can hold a large number of pipes and nodes. It simulates the dry weather flow in gravity mains and force mains applying stationary, nonuniform hydraulics. In other words, it simulates phenomena like backwater but not dynamic waves. Diurnal flow variations are modeled as time series of stationary flows, i.e. assuming that variations in flow rates over the day are slow. A stationary flow approach was in the current case preferred over a non-stationary flow approach, as the large catchment size in combination with the complexity of the in-sewer processes and the need for Water Environment Research, Volume 87, Number 11

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Table 1—Overview of the most important processes and management technologies included in the current WATS model.

Table 2—Overview of sulfide management technologies included in the current WATS model and their mode of operation.

Transport processes Wastewater flow in the sewer network Gas flow in the sewer network Ventilation of sewer gas into the urban atmosphere and intake of atmospheric air into the sewer Liquid-gas and gas-liquid mass transfer of gaseous compounds such as O2, H2S, CO2 and mercaptanes between wastewater and sewer atmosphere. Mass transfer is simulated in gravity pipes, as well as at drop structures and at other points of high turbulence Diffusion of substrates into biofilms and products out of biofilms Release of biomass from biofilms Biological transformation processes in bulk water, biofilms and sediments Growth and maintenance of heterotrophic biomass on readily degradable organic matter with oxygen, nitrate, nitrite and sulfate as electron acceptors Decay of heterotrophic biomass Hydrolysis of organic matter into readily degradable organic matter (fast, medium and slowly hydrolysable fractions) with rates depending on redox conditions (aerobic, anoxic, anaerobic) Fermentation of fermentable organic matter into volatile fatty acids Formation of malodorous compounds by fermentation Formation of sulfide and the corresponding oxidation of readily degradable organic matter as well as reduction of sulfate Reduction of oxygen, nitrate, nitrite, and sulfate (sulfate reduction only in biofilms and sediments) Sulfide oxidation by oxygen, nitrate, and nitrite CO2 formation and consumption due to biological processes Processes in the sewer gas compartment Sorption of H2S gas on sewer walls and subsequent chemical and biological oxidation thereof Concrete corrosion caused by biological oxidation of H2S gas Chemical processes Sulfide oxidation by oxygen in bulk water and biofilms Sulfide oxidation by strong oxidizing agents such as hydrogen peroxide or hypochlorite Precipitation of sulfide by ferrous and ferric iron Acid-base reactions and related pH changes for the buffer systems of carbonate, ammonia, amine groups, sulfides, phosphate, carboxyl groups Acid-base reactions and related pH changes due to added compounds, such as iron salts, acids, bases Acid-base reactions and related pH changes due precipitation of sulfide and mass transfer of H2S and CO2 between wastewater and sewer atmosphere

Sulfide management technology

stochastic modeling would have made simulation times prohibitive. Furthermore, as the model only addresses dry weather flow and the change of flow with time is slow, the error made by choosing a stationary flow approach is deemed small. The model is a two-phase flow model where both liquid and gas flow is simulated. Taking liquid and gas travel times into account, it routes wastewater and sewer gas from the points of origin to the points of discharge, e.g. a treatment works. The gas flow is driven by the drag which the flowing water imparts on the sewer gas. The resulting gas flow velocity is found assuming stationary and uniform gas flow conditions in a pipe and hence balancing the drag imparted by the flowing water and the friction at the pipe wall. The concept is based on work done by Ward et al. (2011) and Wang et al. (2012), and the detailed approach for determining drag and friction is given by Bentzen November 2015

Addition of oxygen

Addition of nitrate

Addition of ferrous and ferric iron Addition of strong oxidizers such as hydrogen peroxide, hypochlorite or permanganate Addition of alkaline compounds

Applying sacrificial concrete pipes

Mechanism Suppression of sulfide formation Biological and chemical oxidation of sulfide present in the wastewater Suppression of sulfide formation Biological oxidation of sulfide present in the wastewater Precipitation of sulfide present in the wastewater Chemical oxidation of sulfide present in the wastewater Increased pH causes reduced H2S gas release. It furthermore causes an increase of the rate by which sulfide is chemically oxidized by oxygen Corroding concrete takes up H2S gas and thereby reduces the H2S gas concentrations in the sewer atmosphere

et al. (2014). The network is simulated open to the urban atmosphere at nodes. Gas is discharged at a node if the simulated gas flow capacity of the upstream pipe(s) is higher than that of the downstream pipe(s), while gas is drawn into the sewer where the opposite is the case. In the latter case the sewer headspace air is diluted by the ambient air on a volume-tovolume basis with respect to both H2S gas and CO2 gas. Stochastic Modeling. Processes in sewers are highly variable in time and space and it is hence appropriate to apply a stochastic approach for modeling them (Vollertsen et al., 2005). In the present work this was done applying a Monte Carlo approach where the model was run 1000 times with model parameters randomly drawn from parameter distributions. The parameters and their distributions were partly obtained from previous studies such as Tanaka et al. (2000), Abdul-Talib et al. (2005) and Vollertsen et al. (1999; 2005) and partly from model calibration. The present model holds some 110 model parameters, of which not all are equally sensitive and hence held constant during the simulations. More sensitive parameters were assigned linear, normal, or log-normal distributions, depending on previously obtained knowledge on their statistical distribution. In general, parameters related to temperature dependency, biological inhibition, chemical sulfide oxidation, sulfide precipitation, anaerobic fermentation, and physical processes and conditions were held constant while other parameters were assigned distributions. The most sensitive parameters were the biofilm sulfide formation rate, the concentrations of the COD fractions at the wastewater source, the pH at the wastewater source, the initial CO2 content of the sewer gas and the concrete corrosion rate. Of these parameters, the COD fractions were treated as log-normal distributed while the others were treated as normal distributed. The distribution of the values of the COD-fractions, temperature, pH, sulfate, and ammonia at the wastewater source, were adjusted to obtain the best calibration 1983

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Figure 2—Diurnal pattern of flow and COD entering the sewer network (color versions of figures appear online). against measured values of total COD, temperature, pH, sulfate, ammonia and alkalinity in the network. Pipe Geometries and Flows. The geometry of the conveyance system, that is data such as length, shape, dimensions, material, roughness, and position in 3-dimensional space, was exported from an InfoWorks model of the San Francisco conveyance system and then imported into the WATS model. The dry weather flow, including diurnal patterns for weekdays and weekend days, was exported from the same model. Each wastewater source had its own dry weather flow pattern and Figure 2 shows the average pattern of the flow entering the sewer network at the wastewater sources. The wastewater inflow was furthermore assigned a diurnal pattern of COD concentration (Figure 2) as wastewater strength is known to vary over the day (Gudjonsson et al., 2002). Wastewater from food-processing traders was addressed separately as it could exhibit rather high COD levels. A total of 22 industries were identified, and each was assigned a COD concentration based on measurements of

trader effluent quality. Trader’s wastewater contributed with 0.4% of the diurnal flow of the drainage area. This flow was distributed over the day as a constant flow from 7 a.m. to 7 p.m. and zero flow for the rest of the day. Results and Discussion Model Calibration. The model was calibrated against measurements obtained at the locations shown in Figure 1. The distributions of alkalinity, pH, ammonia, sulfate and COD in the wastewater grab samples are shown in Figure 3. The measured alkalinity, ammonia, COD, sulfate, BOD and temperature were used to determine wastewater composition at the points where wastewater enters the network as these parameters undergo comparably little change in the conveyance system. For each parameter, the median value of the whole data set (all sampling locations) was used for this purpose. Some of the data sets obtained at a sampling location contained samples with very high values and some of the data sets were not normally

Figure 3—Wastewater alkalinity, pH, ammonia, sulfate and COD of grab samples (color versions of figures appear online). 1984

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Figure 4—Measured and simulated distributions of total sulfides, sewer atmosphere H2S gas, and wastewater pH. Each pair of simulated and measured concentration refers to one sampling location (Figure 1) (color versions of figures appear online). distributed. The more extreme case in this respect was Berry Street / 4th Street where especially COD was very high. This high level of COD seemed obviously wrong as it was not reflected in similar high COD levels at downstream locations. While the cause for the high COD measurements is not known for sure, it seems likely that it was due to sampling problems where a grab sample might not only have contained wastewater but also sewer sediments. The model was calibrated to the wastewater total sulfides content, the wastewater pH, and the H2S gas concentrations in the sewer atmosphere. For each site, all data for each parameter was lumped and the model calibrated to the median of the measurements applying an automated algorithm minimizing the overall simulation error. The model was then run stochastically and the variability (e.g. standard variation) of key parameters adjusted to reflect the overall variability of the measurements. The importance of model parameters varied depending on which parameter to calibrate against. When the target parameter was the total sulfides content, key parameters were the biofilm sulfide formation rate and the concentrations of the COD fractions at the wastewater source. When the target was wastewater pH, key parameters were wastewater source pH and sewer gas initial CO2 content, while all these parameters in combination with the concrete corrosion rate were key November 2015

parameters when calibrating against the H2S gas concentrations in the sewer atmosphere. The result of the model calibration is shown in Figure 4. The agreement between measured and simulated values for the target parameters is deemed good, taking into account the variability of measurements obtained in the drainage area. The model could in general reproduce both the level and the variation of the data measured at the various sampling locations. Comparing the measured and simulated distributions, it is evident that there was a somewhat larger variability for measured data compared to simulated data (Figure 4). The median of the span covered by the 25th to 75th percentiles was 26% larger for measured sulfide compared to simulated sulfide. For H2S gas this difference was 51%, while it was 54% for pH. When applying the Mann-Whitney Rank Sum Test to test whether or not there was a statistically significant difference between simulated and measured concentrations at a sampling location, the comparison became poorer. Only in 4 out of the 10 pairs of simulated and measured wastewater total sulfide concentrations was there no statistically significant difference between the two data populations at a sampling location. For pH the picture was similar, that is for 4 out of the 10 pairs of simulated and measured wastewater pH values there was no statistically significant difference. For H2S gas analysis in the 1985

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sewer atmosphere, all of the 11 sampling stations showed a statistically significant difference between the two data populations. On the other hand, when comparing how many of the 25th to 75th percentile intervals of measured and simulated distributions overlapped, the comparison was good. Of the 10 sampling locations for wastewater total sulfide, 9 had distributions that overlapped in the 25th to 75th percentile intervals. For the wastewater pH it was 10 out of 10 sampling locations, while for H2S gas in the sewer atmosphere, there were 7 out of 11 sampling locations that overlapped in the 25th to 75 th percentile intervals. All parameters and all sampling locations overlapped in the 10th to 90th percentile intervals. It is noteworthy in this context that even though medians are rather close, the Mann-Whitney Rank Sum Test can state that they are statistically different as the test compares the whole population, i.e. even though the mean might be the same, the test will fail if the variability around this mean differs significantly between data sets. For example, the difference in sewer atmosphere H2S gas medians at SEP010 (inflow to the South East Treatment plant) (Figure 4) was only 8% and well within the bounds of the respective 25th to 75th percentile intervals, but the measured variation of the H2S gas concentration was quite larger than the simulated variation, leading to the conclusion that the two datasets originate from different data populations. Part of the mismatch between measured and simulated distributions did probably originate from uncertainties related to sampling and analyses. The sampling uncertainty covers how well the sampled liquid or gas represents the flows during the period for which the sample is sought to be representative. Sampling uncertainty can be random but centered around an otherwise correct mean or systematically biased. Both types of errors must have affected the sampling data. Systematic errors probably occurred for H2S gas sampling as H2S is not homogenously distributed in a sewer headspace. Hence the placement of H2S gas loggers will systematically have affected the measured values. For the liquid samples, precipitated sulfides could lead to a systematic bias as solids are not equally distributed in a wastewater flow. The analytical uncertainty should be random around an otherwise representative median value. However, for the H2S gas data, many measurements were close to zero and a slight offset of the instrument zero point would hence have led to large relative differences between measurement and simulation. For sulfide and pH such systematic error were not expected and taking many samples should equal out the error on the median value. It must, however, have increased the variation of the measured data distributions. On top of this comes model uncertainty. All in all, it is difficult to assess how much of the discrepancy between the measured and simulated data distributions shown in Figure 4 originate from which type of uncertainty. Figure 5 shows the measured and simulated diurnal variations of total sulfides in the wastewater for 4 of the sampling sites. Similar to the shown sites, no clear diurnal variability was detected in the measurements at any of the 10 sites. There were, however, days with high levels of total sulfides and days with low levels of total sulfides as well as days with much variation from hour to hour. All days were dry days, as were the days before sampling, and the differences can hence not be attributed to 1986

effects of storm runoff. Furthermore, the dosing of chemicals for sulfide management was stable during the measuring campaign and the differences therefore not likely to be caused by changes in amounts of dosed chemicals. The reason for the variability between days, as well as the short-term variability from hour to hour, is not well understood. Such variation is not uncommon for sewer systems as indicated by field measurements reported by e.g. Boon et al. (1998), Nielsen et al. (2008) and Oviedo et al. (2012). A factor contributing to the variability could be that wastewater shows significant short-term variations in terms of organic matter composition as reported by Gudjonsson et al. (2002) for an intercepting sewer. Another contributing factor could be shortterm erosion of sewer sediments occurring even though the hydraulic regime is fairly constant such as reported by Lange and Wichern (2013). Sewer sediments contain sulfide-producing biomass, and this would hence lead to sporadic increases in bulk water concentration. Both these phenomena would lead to increased sulfide formation in plugs of wastewater and could hence cause such peaks as were observed in the measuring campaign. At sampling locations downstream of stations where ferrous iron had been added to manage sulfide, there might also have been a sporadic occurrence of high concentrations of iron sulfides in the collected wastewater. Such iron sulfides would be included in the analysis result for total sulfides as the analytical method yields the sum of all sulfides in the wastewater. In this study it was assumed that the precipitated sulfides either accumulated in the sewer sediments, which are flushed out during storm events, or adsorbed to larger particles, which are conveyed as sediment bed load at the sewer invert. In theory, such material should hence not be contained in a samples collected from the sewer liquid phase. However, such material might sporadically become entrained in the liquid from which the samples were collected and hence give rise to occasional peaks of total sulfides as those seen in Figure 5. The model did not reproduce the high variability in total sulfides seen in the measured data sets, probably because the above-mentioned phenomena were not included in the sewer process concept and hence not reproduced by the model. On the other hand, the model reproduced the general level of sulfide in the conveyance system reasonably well and was also in agreement with the measurements about wastewater total sulfide concentration not having a clear diurnal trend. It must furthermore be noted that the total sulfides content at location e (Embarcadero near Bay Street) was strongly affected by dosing of ferrous iron at Embarcadero/Green Street. According to the model the total sulfide level would have been some 1 to 2 mg/L higher if no ferrous iron had been added upstream of the sampling location. While the sum of ferrous iron added at Embarcadero/Green Street and North Point Street/Columbus Avenue was well known, the exact distribution between the two stations was only known approximately, causing a similar uncertainty in the model predictions. Figure 6 shows the daily variations in measured sewer atmosphere concentration of H2S gas for 4 of the sites. For all sites, including those not shown, there was a tendency towards diurnal variation when looking at the average behavior over many days. In most cases the H2S gas levels tended to be lowest between roughly 4 a.m. and 9 a.m. while levels tended to be Water Environment Research, Volume 87, Number 11

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Figure 5—Measured diurnal pattern of wastewater total sulfides content at 4 of the sampling sites and the simulated median diurnal pattern (color versions of figures appear online). highest in the late afternoon and evening. The simulations of the diurnal variations show that the general level of H2S gas could be reproduced reasonably well by the model. For 3 of the 4 shown locations (a, e, j) the diurnal variation in H2S gas concentration could be reproduced to some extent, albeit the measured variation over the day was mostly larger than the simulated variation. For location k, the measurements showed the highest H2S gas concentrations between midnight and 9 a.m. while the model indicated that this period should experience the lowest H2S gas levels. The reason for this discrepancy is not known. For the 7 sites not shown, the general level of H2S gas was also simulated well and the diurnal variations were reproduced to a quality similar to the 4 sites shown. The statistical trend towards a diurnal variation in H2S gas levels is most likely due to a similar trend in sulfide in the wastewater. Other explanations such as a variation in pH not captured by the model are unlikely to be large enough to account for such variations as were seen (Figure 6). In general, the agreement between model and measurements is deemed good, especially taking into account the complexity of the drainage area, the large variability in measured parameters, measurement uncertainties, and the simplifying assumptions, which had to be made when November 2015

simulating the system. Regarding the latter, it was for example assumed that all wastewater (except for a small fraction coming from traders) had identical composition. This is obviously not correct, but in practice it is not possible to determine wastewater composition and many other boundary conditions. It is an inherent problem of modeling processes in sewers that many parameters and also physical aspects of the conveyance system (e.g. sediment deposits) are largely indeterminable. This study does, however, show that when a conceptually sound sewer process model is calibrated to a detailed monitoring dataset, general concentration levels as well as variability can be reproduced to a degree where the model can be applied as a tool to support sewer management and planning. The model can, for example, be applied to assess alternative sulfide management strategies or to identify critical areas where monitoring of sewer concrete corrosion should be intensified. Conclusions The measuring campaign revealed that wastewater total sulfide in the Bayside drainage area of San Francisco underwent rapid short-term concentration changes and that no diurnal patterns could be identified. However, when analyzing long time 1987

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Figure 6—Measured and simulated diurnal variation of sewer atmosphere H2S gas content at 4 of the sampling sites. The sites a, e, j, and k covered 130, 116, 156, and 84 days of measurement, respectively (color versions of figures appear online). series of measured sewer H2S gas, a clear tendency towards diurnal variation was revealed. This seemed to indicate that even though the wastewater total sulfide measurements did not show diurnal tendencies, measured diurnal variations of sewer atmosphere H2S gas might be due to a masking by a combined effect of large natural variability and the number of gas measurements being many times larger than the number of wastewater measurements. A numerical sewer process model based on the WATS concept was able to reproduce the general levels of measured wastewater sulfide concentrations, sewer atmosphere H2S gas concentrations as well as wastewater pH values throughout the Bayside drainage area of San Francisco. Also, the general trend in measured parameter variability could be reproduced by the model. However, when it came to parameter variability at individual monitoring sites the accuracy was limited. Probable reasons for this discrepancy are the occurrence of sewer processes not included in the concept, an inherent indeterminability of a number of boundary conditions, model uncertainties, as well as uncertainties in sampling and chemical analysis. The relative importance of the latter is deemed to play a major role in 1988

the present study as concentrations of sulfide and H2S gas were low, causing comparative high relative uncertainties. Submitted for publication September 28, 2014; revised manuscript submitted December 17, 2014; accepted for publication January 15, 2015. References Abdul-Talib, S.; Ujang, Z.; Vollertsen, J.; Hvitved-Jacobsen, T. (2005) Model Concept for Nitrate and Nitrite Utilization During Anoxic Transformations in the Bulk Water Phase of Municipal Wastewater Under Sewer Conditions. Water Sci. Technol., 52(3), 181-189 American Society of Civil Engineers (ASCE) (1989). Sulfide in Wastewater Collection and Treatment Systems; ASCE Manuals and Reports on Engineering Practice No. 69; ASCE: New York, p. 324. Batstone, D. J.; Keller, J.; Angelidaki, I.; Kalyuzhnyi, S. V.; Pavlostathis, S. G.; Rozzi, A.; Sanders, W. T. M.; Siegrist, H.; Vavilin, V. A. (2002) Anaerobic Digestion Model No.1 (ADM1). London: IWA publishing. Bentzen, T. R.; Østertoft, K. K.; Vollertsen, J.; Fuglsang, E. D.; Nielsen, A. H. (2014) Air Flow in Gravity Sewers – Determination of Wastewater Drag Coefficient. Proceedings of the Conference Odors and Air Pollutants, Miami, Florida, May 31-June 3, 2014. Water Environment Research, Volume 87, Number 11

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