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1. Real time control schemes for improving water quality from bioretention cells

   https://doi.org/10.2166/bgs.2019.924  

   Persaud, P. P.; Akin, A. A.; Kerkez, B.; McCarthy, D. T.; Hathaway, J. M. (August 2019, Blue-Green Systems)

   Abstract Extreme weather and the proliferation of impervious areas in urban watersheds increases the frequency of flood events and deepens water quality concerns. Bioretention is a type of green infrastructure practice developed to mitigate these impacts by reducing peak flows, runoff volume, and nutrient loads in stormwater. However, studies have shown inconsistency in the ability of bioretention to manage some pollutants, particularly some forms of nitrogen. Innovative sensor and control technologies are being tested to actively manage urban stormwater, primarily in open water stormwater systems such as wet ponds. Through these cyber-physical controls, it may be possible to optimize storage time and/or soil moisture dynamics within bioretention cells to create more favorable conditions for water quality improvements. A column study testing the influence of active control on bioretention system performance was conducted over a nine-week period. Active control columns were regulated based on either maintaining a specific water level or soil moisture content and were compared to free draining (FD) and internal water storage standards. Actively controlled bioretention columns performed similarly, with the soil moisture-based control showing the best performance with over 86% removal of metals and TSS while also exhibiting the highest ammonium removal (43%) and second highest nitrate removal (74%). While all column types showed mostly similar TSS and metal removal trends (median 94 and 98%, respectively), traditionally FD and internal water storage configurations promoted aerobic and anaerobic processes, respectively, which suggests that actively controlled systems have greater potential for targeting both processes. The results suggest that active controls can improve upon standard bioretention designs, but further optimization is required to balance the water quality benefits gained by retention time against storage needs for impending storms. 

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2. Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

   https://doi.org/10.3390/w12113222  

   Saliba, Sami M.; Bowes, Benjamin D.; Adams, Stephen; Beling, Peter A.; Goodall, Jonathan L. (November 2020, Water)

   null (Ed.)

   Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC. 

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3. Integrating the Spatial Configurations of Green and Gray Infrastructure in Urban Stormwater Networks

   https://doi.org/10.1029/2023WR034796  

   Chen, Xiating; Davitt‐Liu, Indigo; Erickson, Andrew J.; Feng, Xue (December 2023, Water Resources Research)

   Abstract Green infrastructure (GI) practices improve stormwater quality and reduce urban flooding, but as urban hydrology is highly controlled by its associated gray infrastructure (e.g., stormwater pipe network), GI's watershed‐scale performance depends on its siting within its associated watershed. Although many stormwater practitioners have begun considering GI's spatial configuration within a larger watershed, few approaches allow for flexible scenario exploration, which can untangle GI's interaction with gray infrastructure network and assess its effects on watershed hydrology. To address the gap in integrated gray‐green infrastructure planning, we used an exploratory model to examine gray‐green infrastructure performance using synthetic stormwater networks with varying degrees of flow path meandering, informed by analysis on stormwater networks from the Minneapolis‐St. Paul Metropolitan Area, MN, USA. Superimposed with different coverage and placements of GI (e.g., bioretention cells), these gray‐green stormwater networks are then subjected to different rainfall intensities within Environmental Protection Agency's Storm Water Management Model to simulate their hydrological benefits (e.g., peak flow reduction, flood reduction). Although only limited choices of green and gray infrastructure were explored, the results show that the gray infrastructure's spatial configuration can introduce tradeoffs between increased peak flow and increased flooding, and further interacts with GI coverage and placement to reduce peak flow and flooding at low rainfall intensity. However, as rainfall intensifies, GI ceases to reduce peak flow. For integrated gray‐green infrastructure planning, our results suggest that physical constraints of the stormwater networks and the range of rainfall intensities must be considered when implementing GI. 

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4. Changes in long‐term water quality of Baltimore streams are associated with both gray and green infrastructure

   https://doi.org/10.1002/lno.10947  

   Reisinger, Alexander J.; Woytowitz, Ellen; Majcher, Emily; Rosi, Emma J.; Belt, Kenneth T.; Duncan, Jonathan M.; Kaushal, Sujay S.; Groffman, Peter M. (July 2018, Limnology and Oceanography)

   <italic>Abstract</italic> The steadily rising global urban population has placed substantial strain on urban water quality, and this strain is projected to increase for the foreseeable future. Considerable attention has been given to the hydrological and physico‐chemical effects of urbanization on stream ecosystems. However, due to the relative infancy of the field of urban ecology, long‐term water quality analyses in urban streams are sparse. Using a 15‐yr stream chemistry monitoring record from Baltimore, Maryland, we quantified long‐term trends in nitrate, phosphate, total nitrogen, total phosphorus, chloride, and sulfate export at several sites along a rural–urban gradient. We found no significant change in solute export at most sites, although we did find specific patterns of interest for certain solutes. For example, nitrogen export declined at the most headwater urban site, while phosphorus export declined at the most downstream urban site. Coupling long‐term monitoring with data on gray and green infrastructure management throughout the landscape, we established relationships between solute export at the most downstream urban monitoring site and sanitary sewer overflows (SSOs), best management practice (BMP) implementation, and road salt application rates. Phosphorus export was correlated with BMP implementation in the watershed, whereas nitrogen export was related to SSOs. Despite highly urbanized watersheds, water quality does not appear to be declining at most of these sites, suggesting that current management may have limited further impairment. Results of our study suggest that both gray and green infrastructure are key for maintaining and improving water quality in this highly urbanized watershed. 

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5. Real-Time Control of Exogenous Carbon Dosing in a Denitrifying Woodchip Bioreactor Treating Agricultural Drainage

   https://doi.org/10.1021/acsestengg.4c00020  

   Zhang, Zihao; Echavarria, Sofia; Sang, Yi; Weidman, Gianna R; Napp, Nils; Reid, Matthew C (June 2024, ACS ES&T Engineering)

   NA (Ed.)

   Nature-based treatment technologies such as denitrifying woodchip bioreactors (WBRs) are employed to manage nitrogen (N) pollution from agricultural nonpoint sources. Due to variability in environmental conditions like temperature and discharge, it is challenging to achieve consistent treatment effectiveness with these passive systems. To improve nitrate (NO3–) load reductions in a field-scale WBR in New York State during cool spring weather, we designed a system for controlled exogenous carbon (C) dosing, allowing rates of C dosing to respond in real time to changing discharge and NO3– concentrations. Treatment efficiencies for NO3–, acetate mass balances, and other bioreactor properties were monitored from April 5 to June 10, 2023. Biostimulation with 7.5 mg C/L acetate (assuming complete mixing of injected acetate with bioreactor pore water) increased NO3– removal rates up to 5-fold compared to a model-based scenario of baseline bioreactor performance, and were as high as 0.4 mg NO3––N L–1 h–1 while water temperatures were <12 °C. Increasing acetate concentrations beyond 7.5 mg C/L did not confer a clear improvement in NO3– removal rates. Cumulative N load reductions increased from 11.3% under the baseline scenario without C dosing to 24.1% with C dosing. The mass ratio of metabolized C to additional N removal was 2.5:1, although the total dosed C/N mass ratio was 5.1:1 due to incomplete acetate utilization in the reactor. We found evidence that C dosing could enhance the future release of dissolved organic N (DON) and dissolved organic C related to biofilm sloughing. The expense of acetate, with a cost efficiency of 86 USD/kg N, was the main cost driver of the real-time control approach. Our results demonstrate the potential of real-time control of C dosing to meaningfully improve nonpoint source N removal during cool spring conditions but also highlight opportunities for methods to improve acetate utilization efficiencies in order to improve the overall cost-effectiveness of the approach. 

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