Geospatial Variability of Fluorescent Dissolved Organic Matter in Urban Watersheds: Relationships with Land Cover and Wastewater Infrastructure.

We investigated the fluorescent dissolved organic matter (FDOM) composition in two watersheds with variable land cover and wastewater infrastructure, including sanitary sewers and septic systems. A four-component parallel factor analysis model was constructed from 295 excitation-emission matrices recorded for stream samples to examine relationships between FDOM and geospatial parameters. The contributions of humic acid- and fulvic acid-like fluorescence components (e.g., C1, C2, C3) were fairly consistent across a 12 month period for the 27 sampling sites. In contrast, the protein-like fluorescence component (C4) and a related ratiometric wastewater indicator (C4/C3) exhibited high variability in urban tributaries, suggesting that some sites were impacted by leaking sewer infrastructure. Principal component analysis indicated that urban areas clustered with impervious surfaces and sanitary sewer density, and cross-covariance analysis identified strong positive correlations between C4, impervious surfaces, and sanitary sewer density at short lag distances. The presence of wastewater was confirmed by detection of sucralose (up to 1,660 ng L-1) and caffeine (up to 1,740 ng L-1). Our findings not only highlight the potential for C4 to serve as an indicator of nearby, compromised sanitary sewer infrastructure, but also suggest that geospatial data can be used to predict areas vulnerable to wastewater contamination.

Quantifying Methane Influx from Sewer into Wastewater Treatment Processes.

Wastewater treatment contributes substantially to methane (CH4) emissions, yet monitoring and tracing face challenges because the treatment processes are often treated as a "black box". Particularly, despite growing interest, the amount of CH4 carryover and influx from the sewer and its impacts on overall emissions remain unclear. This study quantified CH4 emissions from six wastewater treatment plants (WWTPs) across China, utilizing existing multizonal odor control systems, with a focus on Beijing and Guiyang WWTPs. In the Beijing WWTP, almost 90% of CH4 emissions from the wastewater treatment process were conveyed through sewer pipes, affecting emissions even in the aerobic zone of biological treatment. In the Guiyang WWTP, where most CH4 from the sewer was released at the inlet well, a 24 h online monitoring revealed CH4 fluctuations linked to neighborhood water consumption and a strong correlation to influent COD inputs. CH4 emission factors monitored in six WWTPs range from 1.5 to 13.4 gCH4/kgCODrem, higher than those observed in previous studies using A2O technology. This underscores the importance of considering CH4 influx from sewer systems to avoid underestimation. The odor control system in WWTPs demonstrates its potential as a cost-effective approach for tracing, monitoring, and mitigating CH4.

Methanogenic Potential of Sewer Microbiomes and Its Implications for Methane Emission.

The sewer system, despite being a significant source of methane emissions, has often been overlooked in current greenhouse gas inventories due to the limited availability of quantitative data. Direct monitoring in sewers can be expensive or biased due to access limitations and internal heterogeneity of sewer networks. Fortunately, since methane is almost exclusively biogenic in sewers, we demonstrate in this study that the methanogenic potential can be estimated using known sewer microbiome data. By combining data mining techniques and bioinformatics databases, we developed the first data-driven method to analyze methanogenic potentials using a data set containing 633 observations of 53 variables obtained from literature mining. The methanogenic potential in the sewer sediment was around 250-870% higher than that in the wet biofilm on the pipe and sewage water. Additionally, k-means clustering and principal component analysis linked higher methane emission rates (9.72 ± 51.3 kgCO2 eq m-3 d-1) with smaller pipe size, higher water level, and higher potentials of sulfate reduction in the wetted pipe biofilm. These findings exhibit the possibility of connecting microbiome data with biogenic greenhouse gases, further offering insights into new approaches for understanding greenhouse gas emissions from understudied sources.

Target and Nontarget Screening to Support Capacity Scaling for Substance Use Assessment through a Statewide Wastewater Surveillance Network in New York.

Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

Enhancement of sewer sediment control and disruption of adhesive gelatinous sediment structure using low-dose calcium peroxide.

Large quantities of sediments in urban sewer systems pose significant risk of pipe clogging and corrosion. Owing to their gel-like structure, sewer sediments have strong resistance to hydraulic shear stress. This study proposed a novel approach to weaken the erosion resistance of sewer sediments by destroying viscous gel-like biopolymers in sediments with low doses of calcium peroxide (CaO2). After treatment with 10-50 mg g-1 TS of CaO2, the critical erosion shear stress was significantly reduced by 25.7%-59.9%. The sediment aggregates gradually disintegrated into small diameter particles with increasing CaO2 dosage. Further analysis showed that the strong oxidizing and alkaline environment induced by CaO2 treatment led to cell lysis and changes in the composition and property of extracellular polymeric substances (EPS). After CaO2 treatment, aromatic proteins and humic acid-like substances associated with adhesion translocated from the inner EPS layers to outer layers while being disintegrated into small organic molecules. Concomitantly, CaO2 treatment disrupted the main functional groups (-OH, COO-, C-N, CO, and CN) in inner EPS layers, thus weakening EPS adhesion. Analysis of protein secondary structure and zeta potential reflected the reduced aggregation capacity of sediment microorganisms and loosening of sediment structure after CaO2 treatment. Thus, CaO2 treatment facilitated fragmentation and disaggregation of the gelatinous structure of sewer sediments. Such green strategy decreased the cost of sewer sediment disposal by 42.10-68.95% when compared to water flushing, and it would improve the self-cleaning capacity of sewer system and efficiency of dredging equipment.

Effects of urban catchment characteristics on combined sewer overflows.

Pollution from Combined Sewer Overflows (CSOs) cause diffuse environmental problems, which are still not satisfactorily addressed by current management practices. In this study, a sensitivity analysis was conducted on several CSO environmental impact indicators, with respect to parameters that characterise climate, urban catchment and the CSO structure activation threshold. The sensitivity analysis was conducted by running 10000 simulations with the Storm Water Management Model, using a simplified modelling approach. The indicators were calculated at yearly scale to evaluate overall potential effects on water bodies. The results could be used to estimate pollution load ranges, known the values of the input parameters, and to investigate suitable strategies to reduce pollution of the receiving water bodies. The percentage of impervious surface of the catchment was found the most influent parameter on all the indicators, and its reduction can contain the discharged pollutant mass. The activation threshold, instead, resulted the second least influent parameter on all the indicators, suggesting that its regulation alone would not be a suitable strategy to reduce CSO pollution. However, along with the reduction of the imperviousness, its increase could effectively decrease the concentration of pollutant in the overflow. The results also indicate that neither adopting sustainable urban drainage practices, nor interventions on the CSO device, significantly affect the frequency of the overflows. Therefore, restricting this latter was found to be ineffective for the reduction of both the discharged pollutant mass and the concentration of pollutant in the overflow.

Fluorescence fingerprint as an indicator to identify urban non-point sources in urban river during rainfall period.

Nowadays, the urban non-point source (NPS) pollution gradually evolved as the main contributor to urban water contamination since the point source pollution was effectively controlled. It was imperative to perform urban NPS identification in urban river to meet the requirements of precise source governance. In this study, the real-time detection about water quality parameters and fluorescence fingerprints (FFs) was performed for BX River and its outlets during rainfall period. EEM-PARAFAC and component similarity analyses discovered that the pollution encountered by BX River mainly came from road runoff and untreated municipal wastewater (UMWW) overflow. The C1 (tryptophan-like) and C3 (terrestrial humic-like) components located at Ex/Em = âˆ¼230(280)/340 and ∼275/430 nm were both detected in these two kinds of urban NPS. The C2 components of road runoff and UMWW overflow displayed remarkable differences, which located at Ex/Em = 250/385 and 245/365 nm, respectively, thus could be served as indicators for distinguishing them. During rainfall period, the outflow from rainwater outlets (RWOs) constantly showed similar FF features to road runoff, while the FFs of outflow from combined sewer outlets (CSOs) alternated between those of road runoff and UMWW overflow. The FF features of sections in BX River changed in response to the dynamic variations in FFs of the outlets, which revealed real-time pollution causes of BX River. This work not only realized the identification and differentiation of urban NPS, but also elucidated the dynamic variations of pollution characteristics throughout the entire process of "urban NPS-outlets-urban river", and demonstrated the feasibility of FF technique in quickly diagnosing the pollution causes of urban river during rainfall period, which provided important guidance for urban NPS governance.

A machine learning approach for predicting and localizing the failure and damage point in sewer networks due to pipe properties.

As a basic infrastructure, sewers play an important role in the innards of every city and town to remove unsanitary water from all kinds of livable and functional spaces. Sewer pipe failures (SPFs) are unwanted and unsafe in many ways, as the disturbance that they cause is undeniable. Sewer pipes meet manholes frequently, unlike water distribution systems, as in sewers, water movement is due to gravity and manholes are needed in every intersection as well as through pipe length. Many studies have been focused on sewer pipe failures and so on, but few investigations have been done to show the effect of manhole proximity on pipe failure. Predicting and localizing the sewer pipe failures is affected by different parameters of sewer pipe properties, such as material, age, slope, and depth of the sewer pipes. This study investigates the applicability of a support vector machine (SVM), a supervised machine learning (ML) algorithm, for the development of a prediction model to predict sewer pipe failures and the effects of manhole proximity. The results show that SVM with an accuracy of 84% can properly approximate the manhole effects on sewer pipe failures.

Towards the outbreak tail, what is the public opinion about wastewater surveillance in the United States?

National opinions on a wide variety of public health topics can change over time and have highly contextual nuances. This study is a follow-up to prior inquiries into the knowledge of wastewater-based epidemiology, privacy concerns surrounding sample collection, and the use of data acquired, along with privacy awareness from an online survey conducted in the metropolitan United States during the winter of 2023. Mentions of wastewater-surveillance-related terms in the media remained common. Towards the outbreak tail in 2023, public support for surveillance of toxins (91%), diseases (91%), terrorist threats (87%), illicit drugs (70%), prescription medications (69%), and gun residue (60%) remained high. There was less support for surveillance of alcohol consumption (49%), mental illness (46%), healthy eating (37%), and lifestyle behaviors (35%). In terms of geographic scale, most respondents supported citywide surveillance (85%) with markedly lower levels of support for smaller (less anonymous) geographic scales covered by specific locations. Wastewater surveillance does not receive the public pushback that other COVID-19-related health system actors have witnessed. Instead, the public supports the expansion of wastewater surveillance as a standard to complement public health tools in other areas of health protection.

Implementation of two-phase modeling of hydrogen sulfide in fresh market's combined sewers in Rat Burana, Bangkok.

Hydrogen sulfide (H2S) is one of the sewer gases commonly found in wastewater collection systems. This anaerobic degradation product causes issues, ranging from odor nuisances and health hazards to pipe corrosion. Several studies have provided an understanding of H2S formation mechanism, including simulations of H2S emissions in sewers, especially in pressurized systems. However, the present models necessitate a large amount of data due to the complexity of the H2S processes and common routine-monitoring water quality parameters may not fit the requirements. This study aims to simulate the fate and transport of H2S in both air and water phases in combined sewers, with a realization of practicableness of the application. The study case is centered around a fresh market in Bangkok, where the sewers are commonly plagued with garbage-related issues. These challenges pose difficulties for site monitoring across various aspects, necessitating the application of unconventional methods. On-site hydrodynamics, wastewater quality, and H2S gas concentration data were monitored on hourly and daily bases. It was found that the sulfides in the combined sewerage were correlated with sewage quality, e.g., COD, sulfate (SO42-), and pH concentrations in particular. The model results were in an acceptable range of accuracy (R2 = 0.63; NSE = 0.52; RMSE = 1.18) after being calibrated with the measured hydrogen sulfide gas concentration. The results lead to the conclusion that the simplified model is practical and remains effective even in sewers with untraditional conditions. This could hold promise as a fundamental tool in shaping effective H2S mitigation strategies.

Biofiltration of gaseous mixtures of dimethyl sulfide, dimethyl disulfide and dimethyl trisulfide: Effect of operational conditions and microbial analysis.

The efficient removal of volatile sulfur compounds (VSCs), such as dimethyl sulfide (DMS), dimethyl disulfide (DMDS) and dimethyl trisulfide (DMTS), is crucial due to their foul odor and corrosive potential in sewer systems. Biofilters (BFs) offer promise for VSCs removal, but face challenges related to pH control and changing conditions at full scale. Two BFs, operated under acidophilic conditions for 78 days, were evaluated for their performance at varying inlet concentrations and empty bed residence times (EBRTs). BF1, incorporating 4-6 mm marble limestone for pH control, outperformed BF2, which used NaHCO3 in the nutrient solution. BF1 displayed better resilience, maintained a stable pH of 4.6 ± 0.6, and achieved higher maximum elimination capacities (ECmax, 41 mg DMS m-3 h-1 (RE 38.3%), 146 mg DMDS m-3 h-1 (RE 83.1%), 47 mg DMTS m-3 h-1 (RE 93.1%)) at an EBRT of 56 s compared to BF2 (9 mg DMS m-3 h-1 (RE 7.1%), 9 mg DMDS m-3 h-1 (RE 4.8%) and 11 mg DMTS m-3 h-1 (RE 26.6%)). BF2 exhibited pH stratification and decreased performance after feeding interruptions. The biodegradability of VSCs followed the order DMTS > DMDS > DMS, and several microorganisms were identified contributing to VSCs degradation in BF1, including Bacillus (14%), Mycobacterium (11%), Acidiphilium (7%), and Acidobacterium (3%).

Deep learning, geometric characterization and hydrodynamic modeling for assessing sewer defect impacts on urban flooding: A case study in Guangzhou, China.

Deep learning techniques have offered innovative and efficient tools for accurate and automated detection of sewer defects by leveraging large-scale sewer data and advanced feature learning algorithms. However, there has been a lack of thorough characterization of the geometric properties of segmented defects, let alone systematically calculate the severity level of sewer defects and quantitatively evaluate their impacts on flood conditions in hydrodynamic models. This study proposed a comprehensive framework and related metrics to accurately and automatically detect, segment, characterize, and evaluate the impacts of sewer defects on flooded nodes and volumes by integrating a DeepLabv3+-based segmentation technique, an automated geometric characterization and severity quantification module, and a GIS and SWMM-based hydrodynamic modeling. The results clearly showed in details where and how much the urban flooding was affected by the different defect types. The segmentation model achieved satisfactory detection performance, with mean pixel accuracy (MPA), mean intersection over union (MIoU), and frequency weighted intersection over union (FWIoU) of 0.99, 0.74 and 0.95, respectively. In terms of severity level quantification, there were 98%, 90%, 90% and 83% of predictions consistent with real conditions for falling off, obstacle, disjoint and leakage. It was shown that the number of surcharging manholes and total flood volume (TFV) were greatly affected by sewer defects, with over 16% increase in TFVs under all investigated rainfall events. The results addressed the impacts of sewer defects on urban flooding and demonstrated the powerful tools provided by the proposed framework for decision-making on sewer defect detection and management.

Connecting blue-green infrastructure elements to reduce combined sewer overflows.

By infiltrating and retaining stormwater, Blue-Green Infrastructure (BGI) can help to reduce Combined Sewer Overflows (CSOs), one of the main causes of urban water pollution. Several studies have evaluated the ability of individual BGI types to reduce CSOs; however, the effect of combining these elements, likely to occur in reality, has not yet been thoroughly evaluated. Moreover, the CSO volume reduction potential of relevant components of the urban drainage system, such as detention ponds, has not been quantified using hydrological models. This study presents a systematic way to assess the potential of BGI combinations to mitigate CSO discharge in a catchment near Zurich (Switzerland). Sixty BGI combinations, including four BGI elements (bioretention cells, permeable pavement, green roofs, and detention ponds) and four different implementation rates (25%, 50%, 75%, and 100% of the available sewer catchment area) are evaluated for four runoff routing schemes. Results reveal that BGI combinations can provide substantial CSO volume reductions; however, combinations including detention ponds can potentially increase CSO frequency, due to runoff prolongation. When runoff from upstream areas is routed to the BGI, the CSO discharge reductions from combinations of BGI elements differ from the cumulative CSO discharge reductions achieved by individual BGI types, indicating that the sum of effects from individual BGI types cannot accurately predict CSO discharge in combined BGI scenarios. Moreover, larger BGI implementation areas are not consistently more cost-effective than small implementation areas, since the additional CSO volume reduction does not outweigh the additional costs. The best-performing BGI combination depends on the desired objective, being CSO volume reduction, CSO frequency reduction or cost-effectiveness. This study emphasizes the importance of BGI combinations and detention ponds in CSO mitigation plans, highlighting their critical factors-BGI types, implementation area, and runoff routing- and offering a novel and systematic approach to develop tailored BGI strategies for urban catchments facing CSO challenges.

The ecology of the sewer systems: Microbial composition, function, assembly, and network in different spatial locations.

Microbial induced concrete corrosion (MICC) is the primary deterioration affecting global sewers. Disentangling ecological mechanisms in the sewer system is meaningful for implementing policies to protect sewer pipes using trenchless technology. It is necessary to understand microbial compositions, interaction networks, functions, alongside assembly processes in sewer microbial communities. In this study, sewer wastewater samples and microbial samples from the upper part (UP), middle part (MP) and bottom part (BP) of different pipes were collected for 16S rRNA gene amplicon analysis. It was found that BP harbored distinct microbial communities and the largest proportion of unique species (1141) compared to UP and MP. The community in BP tended to be more clustered. Furthermore, significant differences in microbial functions existed in different spatial locations, including the carbon cycle, nitrogen cycle and sulfur cycle. Active microbial sulfur cycling indicated the corrosion risk of MICC. Among the environmental factors, the oxidation‒reduction potential drove changes in BP, while sulfate managed changes in UP and BP. Stochasticity dominated community assembly in the sewer system. Additionally, the sewer microbial community exhibited numerous positive links. BP possessed a more complex, modular network with higher modularity. These deep insights into microbial ecology in the sewer system may guide engineering safety and disaster prevention in sewer infrastructure.

Real-time control of urban drainage systems using neuro-evolution.

With climate change and urbanization, existing urban drainage systems are being stressed beyond their design capacity in many parts of the world. Real-time control (RTC) can improve the performance of these systems and reduce the need for system upgrades. However, developing optimal control policies for RTC is a challenging research area due to computational demands, high uncertainties and system dynamics. This study presents a new RTC method using neuro-evolution for controlling combined sewer overflow (CSO) in urban drainage systems. Neuro-evolution is an approach to neural network research by evolutionary algorithms. Neuro-evolution realizes RTC by training the control policy in advance, thus avoiding the online optimization process in the application period. The simulation results of the benchmark Astlingen network indicate that the trained control policy outperforms the equal filling degree strategy in terms of CSO volume reduction and robustness in the face of tank level uncertainty. The performance analysis of the typical CSO events shows that the control policy mainly makes positive contributions during 'small' CSO events rather than 'large' ones. In particular, the effectiveness of the control policy in 'small' CSO events is more prominent in the initial phase of the events compared with the final phase. This work stands to support a foundation for future studies in the control of urban water systems based on neuro-evolution.

Flow dynamics and turbulent coherent structures around sediment reduction plates of a sewer system.

In the management of urban drainage networks, great interest has been generated in the removal of sediments from sewer systems. The unsteady three-dimensional (3D) flow and turbulent coherent structures surrounding sediment reduction plates in a sewer system are investigated by means of the detached-eddy simulation (DES). Particular emphasis is given to detailing the instantaneous velocity and vorticity fields within the grooves, along with an examination of the three-dimensional, long-term, average flow structure at a Reynolds number of approximately 105. Velocity vectors demonstrate continuous flapping of the flow on the groove wall, periodically interacting with ejections of positive and negative vorticity originating from the grooves. The interaction between the three-dimensional groove flow and the shear flow leads to the downstream transport of patches of positive and negative vorticity, which significantly influence sediment transport. The high-velocity shear flows and strong vortices generated in undulating topography, as identified by the Q-criteria, are the key factors contributing to the efficient sediment reduction capabilities of the sediment reduction plates. The sediment reduction plates with partially enclosed structures exhibit low sedimentation rates in grooves on the plate, a broader acceleration region, and a lesser impact on the flow capacity. The results improve the understanding of the hydrodynamics and turbulent coherent structures surrounding the sediment reduction plates while elucidating the driving factors behind the enhancement of sediment scouring and suspension capacities. These results indicate that the redesign of the plates as partially enclosed structures contributes to further improving their sediment reduction performance.

Adjusting for dilution in wastewater using biomarkers: A practical approach.

We developed a biomarker-based approach to quantify in-sewer dilution by measuring wastewater quality parameters (ammoniacal-N, orthophosphate, crAssphage). This approach can enhance the environmental management of wastewater treatment works (WWTW) by optimising their operation and providing cost-effective information on the health and behaviour of populations and their interactions with the environment through wastewater-based epidemiology (WBE). Our method relies on site specific baselines calculated for each biomarker. These baselines reflect the sewer conditions without the influence of rainfall-derived inflow and infiltration (RDII). Ammoniacal-N was the best candidate to use as proxy for dilution. We demonstrated that the dilution calculated using biomarkers correlates well with the dilution indicated by measured flow. In some instances, the biomarkers showed much higher dilution than measured flows. These differences were attributed to the loss of flow volume at wastewater treatment works due to the activation of combined sewer overflows (CSOs) and/or storm tanks. Using flow measured directly at the WWTW could therefore result in underestimation of target analyte loads.

The effect of green infrastructure on resilience performance in combined sewer systems under climate change.

Climate change is currently reshaping precipitation patterns, intensifying extremes, and altering runoff dynamics. Particularly susceptible to these impacts are combined sewer systems (CSS), which convey both stormwater and wastewater and can lead to combined sewer overflow (CSO) discharges during heavy rainfall. Green infrastructure (GI) can help mitigate these discharges and enhance system resilience under historical conditions; however, the quantification of its effect on resilience in a future climate remains unknown in the literature. This study employs a modified Global Resilience Analysis (GRA) framework for continuous simulation to quantify the impact of climate change on CSS resilience, particularly CSOs. The study assesses the efficacy of GI interventions (green roofs, permeable pavements, and bioretention cells) under diverse future rainfall scenarios based on EURO-CORDEX regional climate models (2085-2099) and three Representative Concentration Pathways (2.6, 4.5, 8.5 W/m2). The findings underscore a general decline in resilience indices across the future rainfall scenarios considered. Notably, the total yearly CSO discharge volume increases by a range of 145 % to 256 % in response to different rainfall scenarios. While GI proves effective in increasing resilience, it falls short of offsetting the impacts of climate change. Among the GI options assessed, green roofs routed to pervious areas exhibit the highest adaptive capacity, ranging from 9 % to 22 % at a system level, followed by permeable pavements with an adaptation capacity between 7 and 13 %. By linking the effects of future rainfall scenarios on CSO performance, this study contributes to understanding GI's potential as a strategic tool for enhancing urban resilience.

Exploring optimal deep tunnel sewer systems to enhance urban pluvial flood resilience in the gangnam region, South Korea.

Urban pluvial flooding is becoming a global concern, exacerbated by urbanization and climate change, especially in rapidly developing areas where existing sewer systems lag behind growth. In order to minimize a system's functional failures during extreme rainfalls, localized engineering solutions are required for urban areas chronically suffering from pluvial floods. This study critically evaluates the Deep Tunnel Sewer System (DTSS) as a robust grey infrastructure solution for enhancing urban flood resilience, with a case study in the Gangnam region of Seoul, South Korea. To do so, we integrated a one-dimensional sewer model with a rapid flood spreading model to identify optimal routes and conduit diameters for the DTSS, focusing on four flood-related metrics: the total flood volume, the flood duration, the peak flooding rate, and the number of flooded nodes. Results indicate that, had the DTSS been in place, it could have reduced historical flood volumes over the last decade by 50.1-99.3%, depending on the DTSS route. Regarding the conduit diameter, an 8 m diameter was found to be optimal for minimizing all flood-related metrics. Our research also developed the Intensity-Duration-Frequency (IDF) surfaces in three dimensions, providing a correlation between simulated flood-related metrics and design rainfall characteristics to distinguish the effect of DTSS on flood risk reduction. Our findings demonstrate how highly engineered solutions can enhance urban flood resilience, but they may still face challenges during extreme heavy rainfalls with a 80-year frequency or above. This study contributes to rational decision-making and emergency management in the face of increasing urban pluvial flood risks.

Phosphorus removal in surface flow treatment wetlands for domestic wastewater treatment: Global experiences, opportunities, and challenges.

Treatment Wetlands (TWs) are widely used for the treatment of domestic wastewater, with an increasing emphasis on provision of multiple co-benefits. However, concerns remain regarding achieving stringent phosphorus (P) discharge limits, system robustness and resilience, and associated guidance on system design and operation. Typically, where P removal is intended with a passive TW, surface flow (SF) systems are the chosen design type. This study analysed long-term monitoring datasets (2-30 years) from 85 full-scale SF TWs (25 m2 to 487 ha) treating domestic sewage with the influent load ranging from 2.17 to 54,779 m3/d, including secondary treatment, tertiary treatment, and combined sewer overflows treatment. The results showed median percentage removals of total P (TP) and orthophosphate (Ortho P) of 28% and 31%, respectively. Additionally, median areal mass removal rates were 5.13 and 2.87 gP/m2/yr, respectively. For tertiary SF TWs without targeted upstream P removal, 80% of the 44 systems achieved ≤3 mg/L annual average effluent total P. Tertiary SF TWs with targeted upstream P removal demonstrated high robustness, delivering stable effluent TP < 0.35 mg/L. Seasonality in removal achieved was absent from 85% of sites, with 95% of all systems demonstrating stable annual average effluent TP concentrations for up to a 30-year period. Only two out of 32 systems showed a significant increase in effluent TP concentration after the initial year and remained stable thereafter. The impact of different liner types on water infiltration, cost, and carbon footprint were analysed to quantify the impact of these commonly cited barriers to implementation of SF TW for P removal. The use of PVC enclosed between geotextile gave the lowest additional cost and carbon footprint associated with lining SF TWs. Whilst the P-k-C* model is considered the best practice for sizing SF TWs to achieve design pollutant reductions, it should be used with caution with further studies needed to more comprehensively understand the key design parameters and relationships that determine P removal performance in order to reliably predict effluent quality.