Current Stormwater Harvesting Guidelines Are Inadequate for Mitigating Risk from Campylobacter During Nonpotable Reuse Activities

Occurrence, Sources and Virulence Potential of Arcobacter butzleri in Urban Municipal Stormwater Systems

A. butzleri is an underappreciated emerging global pathogen, despite growing evidence that it is a major contributor of diarrheal illness. Few studies have investigated the occurrence and public health risks that this organism possesses from waterborne exposure routes including through stormwater use. In this study, we assessed the prevalence, virulence potential, and primary sources of stormwater-isolated A. butzleri in fecally contaminated urban stormwater systems. Based on qPCR, A. butzleri was the most common enteric bacterial pathogen [25%] found in stormwater among a panel of pathogens surveyed, including Shiga-toxin producing Escherichia coli (STEC) [6%], Campylobacter spp. [4%], and Salmonella spp. [<1%]. Concentrations of the bacteria, based on qPCR amplification of the single copy gene hsp60, were as high as 6.2 log10 copies/100 mL, suggesting significant loading of this pathogen in some stormwater systems. Importantly, out of 73 unique stormwater culture isolates, 90% were positive for the putative virulence genes cadF, ciaB, tlyA, cjl349, pldA, and mviN, while 50-75% of isolates also possessed the virulence genes irgA, hecA, and hecB. Occurrence of A. butzleri was most often associated with the human fecal pollution marker HF183 in stormwater samples. These results suggest that A. butzleri may be an important bacterial pathogen in stormwater, warranting further study on the risks it represents to public health during stormwater use.

Stormwater runoff treatment through electrocoagulation: antibiotic resistant bacteria removal and its transmission risks

Recently, increasing attention has been paid to antibiotic resistant bacteria (ARB) in stormwater runoff. However, there were little data on ARB removal through electrocoagulation (EC) treatment. In this study, batch experiments were conducted to investigate key designs for ARB removal, role of SS, effects of water matrix, and potential risks after EC treatment under the pre-determined conditions. EC treatment with 5 mA/cm2 of current density and 4 cm of inter-electrode distance was optimal with the highest ARB removal (3.04 log reduction for 30 min). The presence of SS significantly improved ARB removal during EC treatment, where ARB removal increased with the increase of SS levels when SS less than 300 mg/L. Large ARB removal was found under particles with size lower than 150 μm with low contribution (less than 10%) of the settlement without EC treatment, implying that the enhancement of ARB adsorption onto small particles could be one of the reasonable approaches for ARB removal through EC treatment. ARB removal increased firstly and then decreased with the increase of pH, while had proportional relationship with conductivity. After the optimal condition, there were weak conjugation transfer but high transformation frequency (5.5 × 10-2 for blaTEM) for target antibiotic resistance genes (ARGs), indicating that there could be still a risk of antibiotic resistance transformation after EC treatment. These suggested that the combination of EC and other technologies (like electrochemical disinfection) should be potential ways to control antibiotic resistance transmission through stormwater runoff.

BioRTC model enables exploration of real time control strategies for stormwater biofilters

Biofilters with real time control (RTC) have great potential to remove microbes from stormwater to protect human health for uses such as swimming and harvesting. However, RTC strategies need to be further explored and optimised for each specific location or end-use. This paper demonstrates that the newly developed BioRTC model can fulfil this requirement and allow effective and efficient exploration of the potential of RTC applications. We describe the development of BioRTC as the first RTC model for stormwater biofilters, including: selection of a 'base' model for microbial removal prediction, its modification to include RTC capabilities, as well as calibration and validation. BioRTC adequately predicted the performance of two previously developed RTC strategies, with Nash Sutcliffe Efficiency (Ec) ranging from 0.65 to 0.80. In addition, high parameter transferability was demonstrated during model validation, where we employed the parameter sets calibrated for another biofilter study without RTC to predict the performance of RTC biofilters. We then employed the BioRTC model to explore RTC applications on a hypothetical biofilter system located at the outlet of an existing catchment. With different scenarios, we tested the impact of input parameters such as RTC set-points and design characteristics, and evaluated the influence of operational conditions on the microbial removal performance of the hypothetical biofilter with RTC. The results showed that strategy rules, set-point values, and biofilter design all govern the performance of RTC biofilters, and that operational conditions could impact the suitability of different RTC strategies. Particularly, the presence of Pareto fronts established that muti-objective optimisation is necessary to balance competing needs. These results underscore the importance of RTC, which allows for local experimentation, climate change adaptation, and adjustment to changing demands for the harvested water. Furthermore, they illustrate the practical use of the newly developed BioRTC model, enabling researchers and practitioners to explore and assess potential RTC strategies and scenarios quickly and cost-effectively.

Performance of a gross pollutant trap-biofilter and sand filter treatment train for the removal of organic micropollutants from highway stormwater (field study)

This field study assessed the occurrence, event mean concentrations (EMCs), and removal of selected organic micro-pollutants (OMPs), namely, polycyclic aromatic hydrocarbons (PAHs), petroleum hydrocarbons (PHCs), nonylphenol (NP), 4-t-octylphenol (OP), and bisphenol A (BPA), in a gross pollutant trap (GPT)-biofilter/sand filter stormwater treatment train in Sundsvall, Sweden. The effects of design features of each treatment unit, including pre-sedimentation (GPT), sand filter medium, vegetation, and chalk amendment, were investigated by comparing the units' removal performances. Overall, the treatment train removed most OMPs from highway runoff effectively. The results showed that although the sand filter provided moderate (<50 % for phenolic substances) to high (50-80 % for PAHs and PHCs) removal of OMPs, adding a vegetated soil layer on top of the sand filter considerably improved the removal performance (by at least 30 %), especially for BPA, OP, and suspended solids. Moreover, GTP did not contribute to the treatment significantly. Uncertainties in the removal efficiencies of PAHs and PHCs by the filter cells increased substantially when the ratio of the influent concentration to the limit of quantification decreased. Thus, accounting for such uncertainties due to the low OMP concentrations should be considered when evaluating the removal performance of biofilters.

New insights into the pollutant composition of stormwater treating wetlands

With intensified climate change and urbanisation, constructed wetland (CW) serves as an alternative to conventional wastewater treatment plants. In Australia, the primary function of CW is to reduce sediments, nutrients from runoffs and attenuate floods. However, water quality analysis after construction is limited, hence, pollutant composition in established CWs and target pollutants in many guidelines remain outdated. To refresh the understanding of pollutants in urban discharges, this study reviewed two CWs in industrialised regions of Victoria, Australia. A total number of 26 pollutants were analysed in the collected water and sediment samples from both CW. The findings highlighted excessive concentrations of Zinc, Aluminium, Iron and Copper in one wetland and less commonly found pollutants like Barium, Titanium and Strontium are also detected. While Arsenic, Zinc, Copper, Nickel and hydrocarbons' accumulations are particularly significant in the other wetland. This study also reviews the pollutants discovered in 136 stormwater wetlands and covers the sources and impacts of various metal pollutants in stormwater runoffs. Overall, it is found that the concentrations of Zinc, Aluminium and Iron are particularly high in the CWs reviewed. This study brings attention to the pollutants profile of established CWs and the impact of heavy metals on the aquatic environment. The findings from this research revealed that the existing design and management guidelines for constructed wetlands in urban catchments are lacking in reduction targets for metal pollutants, thus improvements are essential to safeguard the water quality and performance of CWs.

BoSL FAL pump: A small, low-cost, easily constructed, 3D-printed peristaltic pump for sampling of waters

Water sampling is an essential undertaking for water utilities and agencies to protect and enhance our natural resources. The high variability in water quality, however, often necessitates a spatially distributed sampling program which is impeded by high-cost and large sampling devices. This paper presents the BoSL FAL Pump - a low-cost, easily constructed, 3D-printed peristaltic pump which can be made from commonly available components and is sized to suit even the most space constrained installations. The pump is 38 mm in height and 28 mm in diameter, its components cost $19 AUD and the construction time is just 12 min (excluding 3D printing times). The pump is driven by a direct current motor which is commonly available, cheap and allows for flexibility in the energy supply (5-12 V). Optionally, the pump has a Hall effect sensor and magnet to detect rotation rates and pumping volumes to improve the accuracy of pumping rates/volumes. The pump can be easily controlled by commonly available microcontrollers, as demonstrated by this paper which implements the ATmega328P on the Arduino Uno R3. This paper validates the pump for long-term deployments at flow rates of up to 13 mL per minute in 0.14 mL volume increments at accuracy levels of greater than 99%. The pump itself is scalable, allowing for a wider range of pumping rates when, for example, large volume samples are required for pathogen and micropollutant detection.

Machine learning approaches for predicting the performance of stormwater biofilters in heavy metal removal and risk mitigation

The increasing amount of data on biofilter treatment performance over the past decade has made it possible to use data-driven approaches to explore the relationships between biofilter performance and a range of input variables. The knowledge gap lies in lack of models to predict the biofilter performance considering both design and operational variables, especially for heavy metals. In this study, we tested three machine learning (ML) approaches, namely multilinear regression (MLR), artificial neural network (NN), and random forest (RF), to predict biofilter outflow concentrations of heavy metals (Cd, Cr, Cu, Fe, Ni, Pb and Zn) using a range of design and operational factors as input variables. The results show that RF performed relatively better than other two models, with median Nash-Sutcliffe Efficiency (NSE) values of 0.995, 0.317, 0.762, 0.636, 0.726, 0.896 and 0.656 for Cd, Cr, Cu, Fe, Ni, Pb and Zn, respectively during model training. However, all the models were less accurate during model validation, with the better performance found for Cd (average NSE=0.964), Zn (0.530) and Ni (0.393) and poorer performance observed for Cu (0.219), Pb (0.058), Fe (-0.054) and Cr (-0.062). Infiltration rate (IR) and inflow concentration (C_in) were sensitive to all pollutants' removal in biofilters. The ratio of system size to catchment size was also found to be important for Zn, Ni and Cd, while ponding depth was an important variable for Cd. Based on thousands of hypothetical design and operational scenarios (generated using raw data), the best ML models were used to predict the biofilter outflow concentrations and estimate the risk quotient (RQ) values with regards to reuse of treated stormwater for various purposes. Results suggest that biofilters were able to reduce health risks associated with heavy metals in stormwater and therefore produce reliable water fit for reuses such as irrigation, swimming, and toilet flushing. Modelling results showed that biofiltration did not meet the requirements for drinking when Cd contamination exists. Explorative analysis also demonstrated how the key operational and design variables can be optimised to further reduce the health risks that can be fit for drinking purposes (i.e., RQ value <1).

Community Perceptions and Knowledge of Modern Stormwater Treatment Assets

Modern stormwater treatment assets are a form of water sensitive urban design (WSUD) features that aim to reduce the volumes of sediment, nutrients and gross pollutants discharged into receiving waterways. Local governments and developers in urban areas are installing and maintaining a large number of stormwater treatment assets, with the aim of improving urban runoff water quality. Many of these assets take up significant urban space and are highly visible and as a result, community acceptance is essential for effective WSUD design and implementation. However, community perceptions and knowledge about these assets have not been widely studied. This study used a survey to investigate community perceptions and knowledge about stormwater treatment assets in Brisbane, Australia. The results suggest that there is limited community knowledge of these assets, but that communities notice them and value their natural features when well-maintained. This study suggests that local governments may be able to better inform residents about the importance of these assets, and that designing for multiple purposes may improve community acceptance and support for the use of Council funds to maintain them.

Real time control of biofilters delivers stormwater suitable for harvesting and reuse

Stormwater biofilters have great potential to treat stormwater for harvesting and reuse, but their variable performance in pathogen removal requires further optimisation prior to widespread uptake. This paper provides the first evidence that real time control (RTC) of stormwater biofilters can mitigate the impact of operational characteristics that result in poor microbial removal. We developed two RTC strategies and validated them using long-term laboratory experiments, utilising biofilters with a raised outlet pipe that creates a submerged zone. The first RTC strategy focuses on delivering the best water quality for harvesting and reuse or for recreational waterways. It has two components which ensure adequate treatment (microbial die-off): (1) it retains water in the biofilter for at least two days before allowing any further inputs into the system, and (2) the input volume is restricted to the submerged zone's pore volume. This strategy was effective and significantly improved water quality in the biofilter effluent. However, since the system favours bypassing influent to ensure good quality effluent, only 28.4% of the stormwater was treated. This still resulted in a 62.3% reduction in the influent E. coli load because the system was effective at removing E. coli under controlled conditions. The second RTC strategy builds upon the first strategy, and focuses on delivering a balance between good water quality for harvesting and protecting the environment (i.e., lower bypass). Three hours before the next rainfall event begins, the water that has remained in the biofilter's submerged zone for at least two days is drained and collected for harvesting through a bottom pipe. When stormwater inflow begins, the bottom pipe is closed and the biofilter operates without control, with water leaving the biofilter to the environment via the raised outlet pipe. The harvested effluent of this RTC strategy met the Australian stormwater harvesting guideline requirements for dual reticulation with indoor and outdoor use and irrigation of commercial food crops. Although only 5.4% of stormwater was collected for harvesting in this strategy, the environment was better protected because of a significantly reduced bypass volume. Our experiments also showed that the nutrient and sediment removal was high for both RTC strategies. This study presents the first stepping stone toward RTC of stormwater biofilters, demonstrating that these systems can deliver safe stormwater for harvesting and reuse, and for active recreational uses.

Creating a hierarchy of hazard control for urban stormwater management

Urban stormwater reuse is becoming increasingly prevalent to overcome the serious urban water scarcity being experienced around the world. Therefore, the adoption of reliable approaches to minimise the human health risk posed by pollutants commonly present in urban stormwater such as heavy metals and polycyclic aromatic hydrocarbons (PAHs) is critical for safe stormwater reuse. This study collected a total of 40 pollutant build-up samples and analysed the concentrations of nine heavy metals and 15 PAH species. Based on pollutant build-up data, pollutant concentrations in stormwater were estimated through modelling. Risk assessment was conducted using an existing model developed by previous studies. The study outcomes confirmed that simply evaluating the individual pollutant concentrations based on guideline threshold values cannot comprehensively estimate the overall human health risk posed by these pollutants. Accordingly, it is recommended that the assessment of the overall human health risk should be based on the pollutant mix present as provided by the models discussed in this paper. The study also demonstrated the practical application of a robust risk assessment model to derive the hierarchy of hazard control to provide a reliable underpinning to urban stormwater risk management. The outcomes suggest that decentralised hazard control methods such as the provision of custom designed Water Sensitive Urban Design (WSUD) measures can be implemented in priority areas with high risk from stormwater pollution based on the risk assessment undertaken. Distributed hazard control methods can be applied to reduce the generation of primary toxic pollutants, especially chromium (Cr) and heavy PAHs, through elimination and substitution measures. The percentage reduction in traffic volume required to mitigate the human health risk can be quantified through the risk models presented. The study outcomes will contribute to the development of efficient, targeted and reliable stormwater management strategies and to identify viable opportunities for stormwater reuse.

A review on microbial contaminants in stormwater runoff and outfalls: Potential health risks and mitigation strategies

Demands on global water supplies are increasing in response to the need to provide more food, water, and energy for a rapidly growing population. These water stressors are exacerbated by climate change, as well as the growth and urbanisation of industry and commerce. Consequently, urban water authorities around the globe are exploring alternative water sources to meet ever-increasing demands. These alternative sources are primarily treated sewage, stormwater, and groundwater. Stormwater including roof-harvested rainwater has been considered as an alternative water source for both potable and non-potable uses. One of the most significant issues concerning alternative water reuse is the public health risk associated with chemical and microbial contaminants. Several studies to date have quantified fecal indicators and pathogens in stormwater. Microbial source tracking (MST) approaches have also been used to determine the sources of fecal contamination in stormwater and receiving waters. This review paper summarizes occurrence and concentrations of fecal indicators, pathogens, and MST marker genes in urban stormwater. A section of the review highlights the removal of fecal indicators and pathogens through water sensitive urban design (WSUD) or Best Management Practices (BMPs). We also discuss approaches for assessing and mitigating health risks associated with stormwater, including a summary of existing quantitative microbial risk assessment (QMRA) models for potable and non-potable reuse of stormwater. Finally, the most critical research gaps are identified for formulating risk management strategies.

Biotreatment technologies for stormwater harvesting: critical perspectives

Biotreatment technologies offer many advantages for passive stormwater treatment before harvesting, but performance can be variable and sensitive to system design, construction, operation and maintenance. While there is substantial research underpinning pollutant removal, hydraulic function, internal processes and optimal design, specific focus upon stormwater harvesting is relatively limited. Recent advances in system design include testing media amendments for targeted pollutant removal, enhanced pathogen removal using antimicrobial plants, and broadening technology application. However, the production of reliable fit-for-purpose water requires the development of robust validation methodologies to meet public safety expectations. While foundation studies exist, more needs to be done to extend the validation framework, monitor and control system performance and operation in real-time, and apply standards and regulatory checks.

A Semi-distributed Model for Predicting Faecal Coliform in Urban Stormwater by Integrating SWMM and MOPUS

The microbial contamination of urban stormwater has an important impact on human health and stormwater reuse. This study develops an exploratory semi-distributed model, MOPUS_S, which can simulate faecal coliform levels in separate sewer systems in urban catchments. The MOPUS_S was built by coupling the SWMM model and the microbial MOPUS model. The parameters associated with the deposition and wash-off of microorganisms were more influential than those related to microorganism survival processes. Compared to other existing bacterial models, MOPUS_S showed comparable performance in predicting faecal coliform concentrations. The performance varied largely between rainfall events, with Nash-Sutcliffe efficiency (NSE) values ranging from −5.03 to 0.39 and R² ranging from −0.02 to 0.83, respectively. The model simulation results for low and medium concentrations were better than those for the peak concentrations. Poor simulation results of peak concentrations obviously affect the overall model performance. In general, MOPUS_S could be capable of predicting the faecal coliform load in urban catchments and be a useful tool for urban stormwater management planning.

Reliable, Resilient and Sustainable Urban Drainage Systems: An Analysis of Robustness under Deep Uncertainty

Reliability, resilience and sustainability are key goals of any urban drainage system. However, only a few studies have recently focused on measuring, operationalizing and comparing such concepts in a world of deep uncertainty. In this study, these key concepts are defined and quantified for a number of gray, green and hybrid strategies, aimed at improving the capacity issues of an existing integrated urban wastewater system. These interventions are investigated by means of a regret-based approach, which evaluates the robustness (that is the ability to perform well under deep uncertainty conditions) of each strategy in terms of the three qualities through integration of multiple objectives (i.e., sewer flooding, river water quality, combined sewer overflows, river flooding, greenhouse gas emissions, cost and acceptability) across four different future scenarios. The results indicate that strategies found to be robust in terms of sustainability were typically also robust for resilience and reliability across future scenarios. However, strategies found to be robust in terms of their resilience and, in particular, for reliability did not guarantee robustness for sustainability. Conventional gray infrastructure strategies were found to lack robustness in terms of sustainability due to their unbalanced economic, environmental and social performance. Such limitations were overcome, however, by implementing hybrid solutions that combine green retrofits and gray rehabilitation solutions.

Precipitation influences pathogenic bacteria and antibiotic resistance gene abundance in storm drain outfalls in coastal sub-tropical waters

Stormwater contamination can threaten the health of aquatic ecosystems and human exposed to runoff via nutrient and pathogen influxes. In this study, the concentrations of 11 bacterial pathogens and 47 antibiotic resistance genes (ARGs) were determined by using high-throughput microfluidic qPCR (MFQPCR) in several storm drain outfalls (SDOs) during dry and wet weather in Tampa Bay, Florida, USA. Data generated in this study were also compared with the levels of fecal indicator bacteria (FIB) and sewage-associated molecular markers (i.e., Bacteroides HF183 and crAssphage markers) in same SDOs collected in a recent study (Ahmed et al., 2018). Concentration of FIB, sewage-associated markers, bacterial pathogens and many ARGs in water samples were relatively high and SDOs may be potentially hotspots for microbial contamination in Tampa Bay. Mean concentrations of culturable E. coli and Enterococcus spp. were tenfold higher in wet compared to dry weather. The majority of microbiological contaminants followed this trend. E. coli eaeA, encoding the virulence factor intimin, was correlated with levels of 20 ARGs, and was more frequently detected in wet weather than dry weather samples. The bla_KPC gene associated with carbapenem resistant Enterobacteriaceae and the beta-lactam resistant gene (bla_NPS) were only detected in wet weather samples. Frequency of integron genes Intl2 and Intl3 detection increased by 42% in wet weather samples. Culturable E. coli and Enterococcus spp. significantly correlated with 19 of 47 (40%) ARG tested. Sewage-associated markers crAssphage and HF183 significantly correlated (p < 0.05) with the following ARGs: intl1, sul1, tet(M), ampC, mexB, and tet(W). The presence of sewage-associated marker genes along with ARGs associated with sewage suggested that aging sewage infrastructure contributed to contaminant loading in the Bay. Further research should focus on collecting spatial and temporal data on the microbiological contaminants especially viruses in SDOs.