Hydrogen Sulfide Generation and Control in Sewer Systems with Drop Structures and Pump Stations
Bibliographic record
Abstract
The build-up of hydrogen sulfide in sewer systems can lead to odor nuisances and corrosion of concrete sewers. The City of Edmonton, Alberta, Canada, like many other cities worldwide, is facing sewer odor nuisance and pipe corrosion issues. Field studies were carried out in the sanitary sewer trunk with drops and pump station in Steinhauer area and in the sewer trunk with drops in Bonnie Doon area to identify causes of H2S in both areas and the effect of drops on H2S release so as to develop proper odor control strategies. In addition, four bioreactors imitating force mains of pump stations were operated to evaluate sulfide generation rate. Ferric and nitrate were added into two of the four bioreactors to investigate their effects on sulfide control. Sulfide formation rates in the force mains were also evaluated in the field and continuous nitrate dosing was applied in the wet well of the Big Lake pump station to investigate its effectiveness on sulfide control. In Steinhauer area, relatively high concentrations of H2S were detected at the beginning and the end of the trunk with odor complaints. At the beginning of the trunk, sulfide emission was mainly caused by the increased stripping effect of the drop structures. The pump operation at the end of the trunk led to the long hydraulic retention time (HRT) of the sewage and the subsequent sulfide generation in the trunk and wet well. The calibrated sulfide generation models by using field measurements in the trunk were applied to assess the proposed mitigation strategy, pump operation optimization, and this strategy was found to be able to eliminate sulfide generation in the study trunk sewer. In Bonnie Doon area, the liquid sulfide concentration in the upstream trunk was low (less than 1.0 mg/L), and no H2S gas was detected in the head space. However, high H2S gas concentration was detected in the middle reach of the trunk due to the stripping effect of the three drops (2.7 m, 5.2 m, and 2.0 m) along the trunk. The released H2S at drops was transported to downstream trunk and tributaries in the sewer system and caused odor concerns at these locations. These drops played an important role on H2S release, and the overall H2S mass transfer coefficient at drops was much higher than that in normal gravity sewers. The overall oxygen and H2S mass transfer coefficient were estimated to be around 200 h-1 and 300 h-1 at first two drops, respectively. Sulfide controlled by ferric in the lab-scale bioreactor was only via chemical oxidation and precipitation and total sulfate reducing bacteria (SRB) population did not change. The heterotrophic nitrate reducing bacteria (hNRB), Thauera, outcompeted SRB for organic matter as electron donor as the dominant bacteria when nitrate was added at the beginning of the pump cycle. The sulfide generation was controlled by hNRB activities. A cost-effective nitrate dosing strategy is proposed to add the nitrate at the end of the pump cycle instead of at the beginning of the pump cycle which can save up to 75% nitrate dosage. The co-existence of sulfide and nitrate stimulated the development of nitrate-reducing, sulfide-oxidizing bacteria (NR-SOB), Sulfurovum, which was responsible for sulfide removal in the presence of nitrate. Both ferric and nitrate did not have long-lasting inhibitory/toxic effect on sulfate reduction. The sulfide production rates at the force mains of four pump stations were estimated to be 0.08~0.15 g/m2h under 20 ℃. The 1/2-order biofilm kinetics of DCOD was used to model the sulfide generation rate in the force main and the modelled rate constant for field trial was around 0.006. In the discharge manhole of the Big Lake pump station, sulfide concentration was measured at around 20 mg/L while the H2S gas concentration reached up to 400~500 ppm. A continuous dosing of 85 mg/L nitrate in the pump wet well could completely suppress the sulfide generation in the force main. An optimized dosing strategy is to add the nitrate at a location close to the point of sulfide control rather than in the wet well and the dosing rate should be proportional to the HRT of the wastewater in the between the point of nitrate addition and the end of the force main.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".