Experimental and modeling investigations on the unexpected hydrogen sulfide rebound in a sewer receiving nitrate addition: Mechanism and solution

Augmented machine learning for sewage quality assessment with limited data

Physical, chemical, and biological processes within sewers significantly alter sewage composition during conveyance. This leads to the formation of sulfide and methane-compounds that contribute to sewer corrosion and greenhouse gas emissions. Reliable modeling of these compounds is essential for effective sewer management, but the development of machine learning (ML) models is hindered by differences in data accessibility and sampling frequencies of water quality variables. Here we present a mechanistically enhanced hybrid (ME-Hybrid) model that combines mechanistic modeling with data-driven approaches. This model harmonizes datasets with varying sampling frequencies and generates synthetic samples for ML training, thereby enhancing the monitoring of methane and sulfide in sewers. The optimal ME-Hybrid model integrates the backpropagation neural network with mechanistic frequency harmonization. We demonstrate that the ME-Hybrid model outperforms pure ML and linear interpolation in capturing fluctuating trends and extremes of sulfide concentrations, achieving a coefficient of determination (R2) of 0.94. Synthetic samples generated through mechanistic augmentation closely approximate real samples in modeling performance, statistical distribution, and data structure. This enables the model to maintain high predictive accuracy (R2 > 0.76) for sulfide even when trained on only 50 % of the dataset. Additionally, the ME-Hybrid model successfully assesses sewer methane concentrations with an R2 of 0.94, validating its applicability and generalization ability. Our results provide a reliable methodological framework for modeling and prediction under data scarcity. By facilitating better monitoring and management of sewer systems, the ME-Hybrid model aids in the development of strategies that minimize environmental impacts, enhance urban resilience, and ultimately lead to sustainable urban water systems.

Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems

The frequent occurrence of safety incidents in sewer systems due to the emergency toxicity of hydrogen sulfide (H2S) necessitate timely and efficient prediction, early warning and real-time control. However, various factors influencing H2S generation and emission leads to a substantial computational burden for the existing dynamic sewer process models and fails to timely control the H2S exposure risk. The present study proposed a swift prediction model (SPM) that combined the validated dynamic sewer process model (the biofilm-initiated sewer process model, BISM) with a high-speed machine learning algorithm (MLA), achieving accurately and swiftly predict the dissolved sulfide (DS) concentration and H2S concentration in a specific sewer network. Based on Gradient Boosting Decision Tree-based SPM, the simulated concentrations of DS and H2S are 1.95 mg S/L and 214 ppm, respectively, which are closely to the field-measured values of 1.82 mg S/L and 219 ppm. Notably, SPM achieved a computation time of less than 0.3 s, and a significant improvement over BISM (> 5000 s) for the same task. Moreover, the real-time and dynamic dosing scheme facilitated by SPM outperformed the conventional constant dosing scheme provided by dynamic sewer process model, which significantly improved the H2S control completion rate from 69 % to 100 %, and achieved a significant reduction in chemical dosage. In conclusion, the integration of dynamic sewer process models with MLA addresses the inadequacy of monitoring data for MLA training, and thus pursues swift prediction of H2S generation and emission, and achieving real-time, effective, and economic control of H2S in complex sewer networks.

Enhanced mechanistic insights and performance optimization: Controlling methane and sulfide in sewers using nitrate dosing strategies

Nitrate has been used for nearly 80 years as a chemical to control problematic gases in the sewer system. However, few studies have explored simultaneous control in biofilm and sediment using different strategies. Here, we introduced a nitrate dosing method involving an initial high shock followed by low level dosing, tested at two distinct frequencies in a lab-scale sewer reactor <110 days. Long-term investigation revealed that the more frequent 20 min interval dosing slightly surpassed the 1 h interval method when applying the same hourly dose of 30 mg N/L (sulfide control: 98.3 ± 1.7 % vs 97.9 ± 1.5 %; methane control: 89.8 ± 4.5 % vs 83.4 ± 6.7 %). 16 s rRNA gene amplicon sequencing revealed biofilm detachment and sediment stratification, which can be attributed to the differing effects of nitrate dosing on biofilm and sedimentary microbial interactions. Dominant bacteria such as Thauera and Thiobacillus performed autotrophic denitrification and nitrate-reducing sulfide-oxidation in conjunction with methane oxidizers. These microbes collaboratively control sulfide and methane emissions from sediment. Our findings suggest that nitrate supports the diversity and versatility of their metabolism in the sewer system.

Multifaceted benefits of magnesium hydroxide dosing in sewer systems: Impacts on downstream wastewater treatment processes

Magnesium hydroxide [Mg(OH)2] is a non-hazardous chemical widely applied in sewer systems for managing odour and corrosion. Despite its proven effectiveness in mitigating these issues, the impacts of dosing Mg(OH)2 in sewers on downstream wastewater treatment plants have not been comprehensively investigated. Through a one-year operation of laboratory-scale urban wastewater systems, including sewer reactors, sequencing batch reactors, and anaerobic sludge digesters, the findings indicated that Mg(OH)2 dosing in sewer systems had multifaceted benefits on downstream treatment processes. Compared to the control, the Mg(OH)2-dosed experimental system displayed elevated sewage pH (8.8±0.1vs 7.1±0.1), reduced sulfide concentration by 35.1%±4.9% (6.7±0.9mgSL-1), and lower methane concentration by 58.0%±4.9% (19.1±3.6mgCODL-1). Additionally, it increased alkalinity by 16.3%±2.2% (51.9±5.4mgCaCO3L-1), and volatile fatty acids concentration by 207.4%±22.2% (56.6±9.0mgCODL-1) in sewer effluent. While these changes offered limited advantages for downstream nitrogen removal in systems with sufficient alkalinity and carbon sources, significant improvements in ammonium oxidation rate and NOx reduction rate were observed in cases with limited alkalinity and carbon sources availability. Moreover, Mg(OH)2 dosing in upstream did not have any detrimental effects on anaerobic sludge digesters. Magnesium-phosphate precipitation led to a 31.7%±4.1% reduction in phosphate concertation in anaerobic digester sludge supernatant (56.1±10.4mgPL-1). The retention of magnesium in sludge increased settleability by 13.9%±1.6% and improved digested sludge dewaterability by 10.7%±5.3%. Consequently, the use of Mg(OH)2 dosing in sewers could potentially reduce downstream chemical demand and costs for carbon sources (e.g., acetate), pH adjustment and sludge dewatering.

A critical review of chemical uses in urban sewer systems

Chemical dosing is the most used strategy for sulfide and methane abatement in urban sewer systems. Although conventional physicochemical methods, such as sulfide oxidation (e.g., oxygen/nitrate), precipitation (e.g., iron salts), and pH elevation (e.g., magnesium hydroxide/sodium hydroxide) have been used since the last century, the high chemical cost, large environmental footprint, and side-effects on downstream treatment processes demand a sustainable and cost-effective alternative to these approaches. In this paper, we aimed to review the currently used chemicals and significant progress made in sustainable sulfide and methane abatement technology, including 1) the use of bio-inhibitors, 2) in situ chemical production, and 3) an effective dosing strategy. To enhance the cost-effectiveness of chemical applications in urban sewer systems, two research directions have emerged: 1) online control and optimization of chemical dosing strategies and 2) integrated use of chemicals in urban sewer and wastewater treatment systems. The integration of these approaches offers considerable system-wide benefits; however, further development and comprehensive studies are required.

Hydrogen sulfide control in sewer systems: A critical review of recent progress

In sewer systems where anaerobic conditions are present, sulfate-reducing bacteria reduce sulfate to hydrogen sulfide (H₂S), leading to sewer corrosion and odor emission. Various sulfide/corrosion control strategies have been proposed, demonstrated, and optimized in the past decades. These included (1) chemical addition to sewage to reduce sulfide formation, to remove dissolved sulfide after its formation, or to reduce H₂S emission from sewage to sewer air, (2) ventilation to reduce the H₂S and humidity levels in sewer air, and (3) amendments of pipe materials/surfaces to retard corrosion. This work aims to comprehensively review both the commonly used sulfide control measures and the emerging technologies, and to shed light on their underlying mechanisms. The optimal use of the above-stated strategies is also analyzed and discussed in depth. The key knowledge gaps and major challenges associated with these control strategies are identified and strategies dealing with these gaps and challenges are recommended. Finally, we emphasize a holistic approach to sulfide control by managing sewer networks as an integral part of an urban water system.