Review of trace organic chemicals in urban stormwater: Concentrations, distributions, risks, and drivers.

Urban stormwater, increasingly seen as a potential water resource for cities and towns, contains various trace organic chemicals (TrOCs). This study, conducted through a comprehensive literature review of 116 publications, provides a detailed report on the occurrence, concentration distribution, health, and ecological risks of TrOCs, as well as the impact of land use and rainfall characteristics on their concentrations. The review uncovers a total of 629 TrOCs detected at least once in urban stormwater, including 228 pesticides, 132 pharmaceutical and personal care products (PPCPs), 29 polycyclic aromatic hydrocarbons (PAHs), 30 per- and polyfluorinated substances (PFAS), 28 flame retardants, 24 plasticizers, 22 polychlorinated biphenyls (PCBs), nine corrosion inhibitors, and 127 other industrial chemicals/intermediates/solvents. Concentration distributions were explored, with the best fit being log-normal distribution. Risk assessment highlighted 82 TrOCs with high ecological risk quotients (ERQ > 1.0) and three with potential health risk quotients (HQ > 1.0). Notably, 14 TrOCs (including six PAHs, five pesticides, three flame-retardants, and one plasticizer) out of 68 analyzed were significantly influenced by land-use type. Relatively weak relationships were observed between rainfall characteristics and pollutant concentrations, warranting further investigation. This study provides essential information about the occurrence and risks of TrOCs in urban stormwater, offering valuable insights for managing these emerging chemicals of concern.

Low-cost monitoring systems for urban water management: Lessons from the field.

Sound urban water management relies on extensive and reliable monitoring of water infrastructure. As low-cost sensors and networks have become increasingly available for environmental monitoring, urban water researchers and practitioners must consider the benefits and disadvantages of such technologies. In this perspective paper, we highlight six technical and socio-technological considerations for low-cost monitoring technology to reach its full potential in the field of urban water management, including: technical barriers to implementation, complementarity with traditional sensing technologies, low-cost sensor reliability, added value of produced information, opportunities to democratize data collection, and economic and environmental costs of the technology. For each consideration, we present recent experiences from our own work and broader literature and identify future research needs to address current challenges. Our experience supports the strong potential of low-cost monitoring technology, in particular that it promotes extensive and innovative monitoring of urban water infrastructure. Future efforts should focus on more systematic documenting of experiences to lower barriers to designing, implementing, and testing of low-cost sensor networks, and on assessing the economic, social, and environmental costs and benefits of low-cost sensor deployments.

A Low-Cost Radar-Based IoT Sensor for Noncontact Measurements of Water Surface Velocity and Depth.

We designed an out-of-water radar water velocity and depth sensor, which is unique due to its low cost and low power consumption. The sensor is a first at a cost of less than USD 50, which is well suited to previously cost-prohibited high-resolution monitoring schemes. This use case is further supported by its out-of-water operation, which provides low-effort installations and longer maintenance-free intervals when compared with in-water sensors. The inclusion of both velocity and depth measurement capabilities allows the sensor to also be used as an all-in-one solution for flowrate measurement. We discuss the design of the sensor, which has been made freely available under open-hardware and open-source licenses. The design uses commonly available electronic components, and a 3D-printed casing makes the design easy to replicate and modify. Not before seen on a hydrology sensor, we include a 3D-printed radar lens in the casing, which boosts radar sensitivity by 21 dB. The velocity and depth-sensing performance were characterised in laboratory and in-field tests. The depth is accurate to within ±6% and ±7 mm and the uncertainty in the velocity measurements ranges from less than 30% to 36% in both laboratory and field conditions. Our sensor is demonstrated to be a feasible low-cost design which nears the uncertainty of current, yet more expensive, velocity sensors, especially when field performance is considered.

A Low-Cost, Low-Power Water Velocity Sensor Utilizing Acoustic Doppler Measurement.

Current commercial sensors to monitor water flow velocities are expensive, bulky, and require significant effort to install. Low-cost sensors open the possibility of monitoring storm and waste water systems at a much greater spatial and temporal resolution without prohibitive costs and resource investment. To aid in this, this work developed a low-cost, low-power velocity sensor based on acoustic Doppler velocimetry. The sensor, costing less than 50 USD is open-source, open-hardware, compact, and easily interfaceable to a wide range of data-logging systems. A freely available sensor design at this price point does not currently exist, and its novelty is in enabling high-resolution real-time monitoring schemes. The design is capable of measuring water velocities up to 1200 mm/s. The sensor is characterised and then verified in an in-field long-term test. Finally, the data from this test are then used to evaluate the performance of the sensor in a real-world scenario. The analysis concludes that the sensor is capable of effectively measuring water velocity.

Passive Sampling of SARS-CoV-2 for Wastewater Surveillance.

The shedding of pathogens by infected humans enables the use of sewage monitoring to conduct wastewater-based epidemiology (WBE). Although most WBE studies use data from large sewage treatment plants, timely data from smaller catchments are needed for targeted public health action. Traditional sampling methods, like autosamplers or grab sampling, are not conducive to quick ad hoc deployments and high-resolution monitoring at these smaller scales. This study develops and validates a cheap and easily deployable passive sampler unit, made from readily available consumables, with relevance to the COVID-19 pandemic but with broader use for WBE. We provide the first evidence that passive samplers can be used to detect SARS-CoV-2 in wastewater from populations with low prevalence of active COVID-19 infections (0.034 to 0.34 per 10,000), demonstrating their ability for early detection of infections at three different scales (lot, suburb, and city). A side by side evaluation of passive samplers (n = 245) and traditionally collected wastewater samples (n = 183) verified that the passive samplers were sensitive at detecting SARS-CoV-2 in wastewater. On all 33 days where we directly compared traditional and passive sampling techniques, at least one passive sampler was positive when the average SARS-CoV-2 concentration in the wastewater equaled or exceeded the quantification limit of 1.8 gene copies per mL (n = 7). Moreover, on 13 occasions where wastewater SARS-CoV-2 concentrations were less than 1.8 gene copies per mL, one or more passive samplers were positive. Finally, there was a statistically significant (p < 0.001) positive relationship between the concentrations of SARS-CoV-2 in wastewater and the levels found on the passive samplers, indicating that with further evaluation, these devices could yield semi-quantitative results in the future. Passive samplers have the potential for wide use in WBE with attractive feasibility attributes of cost, ease of deployment at small-scale locations, and continuous sampling of the wastewater. Further research will focus on the optimization of laboratory methods including elution and extraction and continued parallel deployment and evaluations in a variety of settings to inform optimal use in wastewater surveillance.

A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks.

High-resolution data collection of the urban stormwater network is crucial for future asset management and illicit discharge detection, but often too expensive as sensors and ongoing frequent maintenance works are not affordable. We developed an integrated water depth, electrical conductivity (EC), and temperature sensor that is inexpensive (USD 25), low power, and easily implemented in urban drainage networks. Our low-cost sensor reliably measures the rate-of-change of water level without any re-calibration by comparing with industry-standard instruments such as HACH and HORIBA's probes. To overcome the observed drift of level sensors, we developed an automated re-calibration approach, which significantly improved its accuracy. For applications like monitoring stormwater drains, such an approach will make higher-resolution sensing feasible from the budget control considerations, since the regular sensor re-calibration will no longer be required. For other applications like monitoring wetlands or wastewater networks, a manual re-calibration every two weeks is required to limit the sensor's inaccuracies to ±10 mm. Apart from only being used as a calibrator for the level sensor, the conductivity sensor in this study adequately monitored EC between 0 and 10 mS/cm with a 17% relative uncertainty, which is sufficient for stormwater monitoring, especially for real-time detection of poor stormwater quality inputs. Overall, our proposed sensor can be rapidly and densely deployed in the urban drainage network for revolutionised high-density monitoring that cannot be achieved before with high-end loggers and sensors.

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.

Understanding spatiotemporal variability of in-stream water quality in urban environments - A case study of Melbourne, Australia.

To support sustainable urban planning and the design of water pollution mitigation strategies, the spatial and temporal trends of water quality in urban streams needs to be further understood. This study analyses over ten years of surface water quality data from 53 upstream catchments (20 of them predominated by a single type of land use) and two lowland sites across Greater Melbourne, Australia. We evaluated the impact of various catchment characteristics, especially urban land uses, on spatial and temporal urban water quality trends. Here, we focused on common urban pollutants: total suspended solids (TSS), total phosphorous (TP), total nitrogen (TN), zinc (Zn), copper (Cu) and nickel (Ni). Site median nutrient and heavy metal concentrations were negatively correlated with the catchment's elevation and its average annual rainfall. Further analysis shows that such trends were driven by the geographical pattern of Melbourne - i.e. low-laying sites tend to have less rainfall and be more urbanised. Only median concentrations of heavy metals (Zn and Cu) were correlated to catchment imperviousness. Further characterising of the urban environment was done into specific land uses (residential, industrial and commercial), yet median concentrations of all pollutants were not significantly correlated with land uses. This is because simple metrics, such as land use proportions, do not adequately reflect the significant variability in pollution sources that can exist even within the same land use type. Indeed, our temporal analysis found that the water quality difference between catchments with similar land uses is likely caused by their site-specific pollutant sources (construction and illegal discharge) and environmental management actions (wastewater management actions) regardless of similarities in land use. A 3-stage urbanisation cycle (development, operation and renewal) is suggested to further explain the urban water quality variance, but more data from small areas of an urban catchment is required to directly understand the unique impact of each urbanisation stage on water quality.

Testing of new stormwater pollution build-up algorithms informed by a genetic programming approach.

Pollution build-up and wash-off processes are often included in urban stormwater quality models. However, these models are often unreliable and have poor performance at large scales and in complicated catchments. This study tried to improve stormwater quality models by adopting the genetic programming (GP) approach to generate new build-up algorithms for three different pollutants (total suspend solids - TSS, total phosphorus - TP and total nitrogen - TN). This was followed by testing of the new models (also traditional build-up and wash-off models as benchmark) using data collected from different catchments in Australia and the USA. The GP approach informed new sets of build-up algorithms with the inclusion of not just the typical antecedent dry weather period (ADWP), but also other less 'traditional' variables - previous rainfall depth for TSS and maximum air temperatures for TP and TN simulation. The traditional models had relatively poor performance (Nash-Sutcliffe coefficient, E < 0.0), except for TP at Gilby Road (GR) (E = 0.21 in calibration and 0.43 in validation). Improved performance was observed using the models with new build-up algorithms informed by GP. Taking TP at GR for example, the best performing model had E of 0.46 in calibration and 0.54 in validation. The best performing models for TSS, TP, and TN are often different, suggesting that specific models shall be used for different pollutants. Insights into further improvements possible for stormwater quality models were given. It is recommended that in addition to the typical build-up and wash-off process, new generations of stormwater quality models should be able to account for the non-conventional pollutant sources (e.g. cross-connections, septic tank leakage, illegal discharges) through stochastic approaches. Emission inventories with information like intensity-frequency-duration (IFD) of pollutant loads from each type of non-conventional source are suggested to be built for stochastic modelling.