Calibration and sensitivity analysis of a novel water flow and pollution model for future city planning: Future Urban Stormwater Simulation (FUSS).

Planning for future urban development and water infrastructure is uncertain due to changing human activities and climate. To quantify these changes, we need adaptable and fast models that can reliably explore scenarios without requiring extensive data and inputs. While such models have been recently considered for urban development, they are lacking for stormwater pollution assessment. This work proposes a novel Future Urban Stormwater Simulation (FUSS) model, utilizing a previously developed urban planning algorithm (UrbanBEATS) to dynamically assess pollution changes in urban catchments. By using minimal input data and adding stochastic point-source pollution to the build-up/wash-off approach, this study highlights calibration and sensitivity analysis of flow and pollution modules, across the range of common stormwater pollutants. The results highlight excellent fit to measured values in a continuous rainfall simulation for the flow model, with one significant calibration parameter. The pollution model was more variable, with TSS, TP and Pb showing high model efficiency, while TN was predicted well only across event-based assessment. The work further explores the framework for the model application in future pollution assessment, and points to the future work aiming to developing land-use dependent model parameter sets, to achieve flexibility for model application across varied urban catchments.

Illicit discharge detection in stormwater drains using an Arduino-based low-cost sensor network.

Illicit discharges in urban stormwater drains are a major environmental concern that deteriorate downstream waterway health. Conventional detection methods such as stormwater drain visual inspection and dye testing have fundamental drawbacks and limitations which can prevent easy location and elimination of illegal discharges in a catchment. We deployed 22 novel low-cost level, temperature and conductivity sensors across an urban catchment in Melbourne for a year to monitor the distributed drainage network, thereby detecting likely illicit discharges ranging from a transitory flow with less than 10 minutes to persistent flows lasting longer than 20 hours. We discuss rapid deployment methods, real-time data collection and online processing. The ensemble analysis of all dry weather flow data across all sites indicates that: (i) large uncertainties are associated with discharge frequency, duration, and variation in water quality within industrial and residential land uses; (ii) most dry weather discharges are intermittent and transient flows which are difficult to detect and not simply due to cross-connections with the sewerage network; (iii) detectable diurnal discharge patterns can support mitigation efforts, including policies and regulatory measures (e.g., enforcement or education) to protect receiving waterways; and, (iv) that it is possible to cost effectively isolate sources of dry weather pollution using a distributed sensor network.

Towards water sensitive cities in Asia: an interdisciplinary journey.

Rapid urbanisation, population growth and the effects of climate change drive the need for sustainable urban water management (SUWM) in Asian cities. The complexity of this challenge calls for the integration of knowledge from different disciplines and collaborative approaches. This paper identifies key issues and sets the stage for interdisciplinary research on SUWM in Asia. It reports on the initial stages of a SUWM research programme being undertaken at Monash University, Australia, and proposes a framework to guide the process of interdisciplinary research in urban water management. Three key themes are identified: (1) Technology and Innovation, (2) Urban Planning and Design, and (3) Governance and Society. Within these themes 12 research projects are being undertaken across Indonesia, China, India and Bangladesh. This outward-looking, interdisciplinary approach guides our research in an effort to transgress single-discipline solutions and contribute on-ground impact to SUWM practices in Asia.

Evaluating Escherichia coli removal performance in stormwater biofilters: a preliminary modelling approach.

Stormwater biofilters are not currently optimised for pathogen removal since the behaviour of these pollutants within the stormwater biofilters is poorly understood. Modelling is a common way of optimising these systems, which also provides a better understanding of the major processes that govern the pathogen removal. This paper provides an overview of a laboratory-scale study that investigated how different design and operational conditions impact pathogen removal in the stormwater biofilters. These data were then used to develop a modelling tool that can be used to optimise the design and operation of the stormwater biofilters. The model uses continuous simulations where adsorption and desorption were dominant during wet weather periods and first order die-off kinetics were significant in dry periods between the wet weather events. Relatively high Nash Sutcliffe Efficiencies (>0.5) indicate that the calibrated model is in good agreement with observed data and the optimised model parameters were comparable with values reported in the literature. The model's sensitivity is highest towards the adsorption process parameter followed by the die-off and desorption rate parameters, which implies that adsorption is the governing process of the model. Vegetation is found to have an impact on the wet weather processes since the adsorption and desorption parameters vary significantly with the different plant configurations. The model is yet to be tested against field data and needs to be improved to represent the effect of some other biofilter design configurations, such as the inclusion of the submerged zone.

Uncertainty analysis in urban drainage modelling: should we break our back for normally distributed residuals?

This study presents results on the assessment of the application of a Bayesian approach to evaluate the sensitivity and uncertainty associated with urban rainfall-runoff models. The software MICA was adopted, in which the prior information about the parameters is updated to generate the parameter posterior distribution. The likelihood function adopted in MICA assumes that the residuals between the measured and modelled values have a normal distribution. This is a trait of many uncertainty/sensitivity procedures. This study compares the results from three different scenarios: (i) when normality of the residuals was checked but if they were not normal then nothing was done (unverified); (ii) normality assumption was checked, verified (using data transformations) and a weighting strategy was used that gives more importance to high flows; and (iii) normality assumption was checked and verified, but no weights were applied. The modelling implications of such scenarios were analysed in terms of model efficiency, sensitivity and uncertainty assessment. The overall results indicated that verifying the normality assumption required the models to fit a wider portion of the hydrograph, allowing a more detailed inspection of parameters and processes simulated in both models. Such an outcome provided important information about the advantages and limitations of the models' structure.

Removal of hydrophilic, hydrophobic, and charged xenobiotic organic compounds from greywater using green wall media.

Green walls offer a novel on-site approach for greywater treatment and reuse in densely build urban environments. However, they need to be engineered for effective removal of a wide range of emerging contaminants such as xenobiotic organic compounds (XOCs), which may be present in greywater due to extensive use of personal care products and household chemicals. This study used laboratory column design and batch experiments to investigate the performance of three lightweight green wall media (coco coir, zeolite, and perlite) and their mixture in three different combinations for the removal of twelve XOCs, covering wide range of hydrophilic, hydrophobic, and charged pollutants in greywater. The experiments were designed to assess the removal of targeted XOCs under different operational condition (i.e., hydraulic loading, infiltration rate, drying) and uncover the dominant mechanisms of their removal. Results showed excellent removal (>90%) of all XOCs in coco coir and media mix columns at the start of the experiment (i.e., fresh media and initial 2 pore volume (PV) of greywater dosing). The removal of highly hydrophobic and positively charged XOCs remained high (>90%) under all operational conditions, while hydrophilic and negatively charged XOCs exhibited significant reduction in removal after 25 PV and 50 PV, possibly due to their low adsorption affinity and electrostatic repulsion from negatively charged media. The effect of infiltration rate on the removal of XOCs was not significant; however, higher removal was achieved after 2-weeks of drying in coco coir and media mix columns. The dominant removal mechanism for most XOCs was found to be adsorption, however, a few hydrophilic XOCs (i.e., acetaminophen and atrazine) exhibited both adsorption and biodegradation removal processes. While findings showed promising prospects of unvegetated media for removing XOCs from greywater, long term studies on vegetated green wall systems are needed to understand any synergetic contribution of plants and media in removing these XOCs.

Performance of enviss™ stormwater filters: results of a laboratory trial.

An experimental study was undertaken by Monash University to develop and test enviss™ stormwater treatment and harvesting technologies - non-vegetated filtration systems with an extremely low footprint. This paper focuses on the water quality and hydraulic performance of two systems tested over a 'year' of operation in a Melbourne climate: (1) REUSE enviss™ filters, designed for stormwater harvesting systems for non-potable supply substitution, and (2) WSUD enviss™ filters, developed to treat urban stormwater prior to discharge to downstream systems. The presence of chlorine as a disinfection agent proved to be very efficient for the removal of microorganisms in REUSE enviss™ filters. WSUD enviss™ filters had the benefit of providing an elevated nutrient treatment performance, due to an extended depth of filter media. However, nutrient outflow concentrations (total nitrogen (TN) in particular) were found to increase during the testing period. Also, extended dry weather periods were found to have a detrimental effect on the treatment performance of almost all pollutants for both filters (nutrients, Escherichia coli and heavy metals). Although hydraulic conductivity results indicated two or three sediment trap replacements per year are required to maintain filtration rates, it is expected that the compressed loading rate schedule overestimated this maintenance frequency.

Zinc-sulphate-heptahydrate coated activated carbon for microbe removal from stormwater.

There is a need to develop effective stormwater filters for passive (without any addition of chemicals or energy) and effective removal of pathogens in order to mainstream stormwater harvesting. This study focuses on the development of coated granular activated carbon (GAC) filtration material in order to develop filters for effective removal of pathogens from urban stormwater. Several laboratory trials were performed to gauge the effectiveness of the filters, which use a mixture of the zinc-sulphate-heptahydrate coated GAC and sand, on the removal of Escherichia coli (E. coli) from semi-natural stormwater. On average, a 98% removal of the inflow concentration of E. coli was achieved. Furthermore, there was also an improvement of approximately 25% in the removal of phosphorous. However, it was found that the treated material was leaching zinc. It was important to determine whether the observed removal of E. coli was indirectly caused by the sampling methodology. The results showed that the inactivation of the E. coli in the collected sample was small compared with the inactivation which actually occurred within the filter. This provides much promise to the filter, but the presence of zinc in the outflow demonstrates the need for further investigation into the stabilisation of the coating process.

Evaluating Escherichia coli removal performance in stormwater biofilters: a laboratory-scale study.

Biofilters are common, low energy technologies used for the treatment of urban stormwater. While they have shown promising results for the removal of stormwater microorganisms, certain factors affect their performance. Hence, this study investigated the effects of particle-microbial interaction, inflow concentration, antecedent microbial levels and plant species on microbial removal capacity. A biofilter column study was set up to evaluate removal performance and a sequential filtration procedure was used to estimate microbial partitioning. The columns were dosed with different concentrations of free phase Escherichia coli only and E. coli mixed with stormwater sediment. Results indicate that the microbial removal is significantly affected by inflow concentration and antecedent microbial levels. Leaching was only observed when a relatively low inflow concentration event occurred within a short period after a very high inflow concentration event. Finally, Lomandra longifolia showed better removal compared with Carex appressa.

The comparative performance of lightweight green wall media for the removal of xenobiotic organic compounds from domestic greywater.

Green walls can provide an aesthetic approach to treat domestic greywater in urban landscapes. However, the widespread adoption of green walls for greywater treatment depends on its performance to remove the emerging contaminants from greywater such as xenobiotic organic compounds (XOCs). In this study, the performance of five lightweight green wall media types (zeolite, perlite, date seeds, coffee grinds, and coco coir) was evaluated for the removal of six XOCs representing a range of hydrophilic to hydrophobic organic micropollutants in domestic greywater (acetaminophen, diethyltoluamide, bisphenol A, oxybenzone, triclosan, nonylphenol). The adsorption affinity of targeted XOCs on different green wall media types, the role of contact time on XOCs removal, and the impact of background pollutants in greywater matrix on the adsorption of XOCs were analysed. Results indicate that removal of XOCs was higher using carbonaceous waste materials (date seeds, coffee grinds, and coco coir) as compared to natural minerals (zeolite and perlite). Moreover, the adsorption of XOCs increased with the increase in pollutant hydrophobicity. All XOCs showed highest removal using coco coir with fast adsorption kinetics, achieving 90% of the removal in 30 min. The only exception was acetaminophen that showed best removal using zeolite but exhibited slow adsorption kinetics with 90% of the removal attained in 24 h. The initial adsorption kinetics (<30 min) of XOCs in greywater were adversely affected by the presence of background pollutants, indicating the need of higher residence time of greywater in green wall system for better removal of XOCs. Based on the findings of this batch study, it is recommended to design a green wall system with more than 30 min of greywater residence time using a mixture of coco coir and zeolite for effective removal of XOCs from domestic greywater.

Retention of heavy metals by stormwater filtration systems: breakthrough analysis.

Biofiltration systems are widely used to mitigate the impacts of stormwater on receiving waters, however their long-term capacity to retain heavy metals has not previously been assessed. Accelerated-dosing laboratory experiments were used to assess the likelihood of breakthrough occurring for three different types of soil-based filter media that are commonly used in stormwater biofilters. In all cases, breakthrough of zinc (Zn) was observed, but not of cadmium (Cd), copper (Cu) and lead (Pb). If biofiltration systems are sized so that they are large relative to their catchment (at least 2-3% of its area) or have a deep filter layer (at least 0.5 m deep), then breakthrough will not occur for at least ten years and probably longer. However, after the equivalent of 12-15 years of operation, Cd, Cu and Zn had accumulated in the filter media to levels that exceeded human health and/or ecological guidelines. Further, depending on the design, it is possible that spent filter media may be classified as contaminated soil and thus require special disposal.

Hydraulic and treatment performance of pervious pavements under variable drying and wetting regimes.

Pervious pavements are an effective stormwater treatment technology. However, their performance under variable drying and wetting conditions have yet to be tested, particularly under a continuous time scale. This paper reports on the clogging behaviour and pollutant removal efficiency of three pervious pavement types over 26 accelerated years. These pavements were monolithic porous asphalt (PA), Permapave (PP) and modular Hydrapave (HP). Over a cycle of 13 days, the period of which was equivalent to the average annual Brisbane, Australia rainfall (1,200 mm), the pavements were randomly dosed with four different flows. Drying events of 3 h duration were simulated during each flow. Inflow and outflow samples were collected and analysed for Total Suspended Solids (TSS), Total Phosphorus (TP) and Total Nitrogen (TN). To evaluate the rate of clogging, a 1 in 5 year Brisbane storm event was simulated in the 6th, 8th, 12th, 16th, 20th and 24th week. Under normal dosing conditions, none of the pavements showed signs of clogging even after 15 years. However, under storm conditions, both PA and HP started to clog after 12 years, while PP showed no signs of clogging after 26 years. The drying and various flow events showed no effects in TSS removal, with all systems achieving a removal of approximately 100%. The average TP removal was 20% for all flows except for low flow, which had a significant amount of leaching over time. Leaching from TN was also observed during all flows except high flow. The TSS, TP and TN results observed during storm events were similar to that of high flow.

The development of a novel approach for assessment of the first flush in urban stormwater discharges.

The management of stormwater pollution has placed particular emphasis on the first flush phenomenon. However, definition and current methods of analyses of the phenomena contain serious limitations, the most important being their inability to capture a possible impact of the event size (total event volume) on the first flush. This paper presents the development of a novel approach in defining and assessing the first flush that should overcome these problems. The phenomenon is present in a catchment if the decrease in pollution concentration with the absolute cumulative volume of runoff from the catchment is statistically significant. Using data from seven diverse catchments around Melbourne, Australia, changes in pollutant concentrations for Total Suspended Solids (TSS) and Total Nitrogen (TN) were calculated over the absolute cumulative runoff and aggregated from a collection of different storm events. Due to the discrete nature of the water quality data, each concentration was calculated as a flow-weighted average at 2 mm runoff volume increments. The aggregated concentrations recorded in each increment (termed as a 'slice' of runoff) were statistically compared to each other across the absolute cumulative runoff volume. A first flush is then defined as the volume at which concentrations reach the 'background concentration' (i.e. the statistically significant minimum). Initial results clearly highlight first flush and background concentrations in all but one catchment supporting the validity of this new approach. Future work will need to address factors, which will help assess the first flush's magnitude and volume. Sensitivity testing and correlation with catchment characteristics should also be undertaken.

Sensitivity analysis of an urban stormwater microorganism model.

This paper presents the sensitivity analysis of a newly developed model which predicts microorganism concentrations in urban stormwater (MOPUS--MicroOrganism Prediction in Urban Stormwater). The analysis used Escherichia coli data collected from four urban catchments in Melbourne, Australia. The MICA program (Model Independent Markov Chain Monte Carlo Analysis), used to conduct this analysis, applies a carefully constructed Markov Chain Monte Carlo procedure, based on the Metropolis-Hastings algorithm, to explore the model's posterior parameter distribution. It was determined that the majority of parameters in the MOPUS model were well defined, with the data from the MCMC procedure indicating that the parameters were largely independent. However, a sporadic correlation found between two parameters indicates that some improvements may be possible in the MOPUS model. This paper identifies the parameters which are the most important during model calibration; it was shown, for example, that parameters associated with the deposition of microorganisms in the catchment were more influential than those related to microorganism survival processes. These findings will help users calibrate the MOPUS model, and will help the model developer to improve the model, with efforts currently being made to reduce the number of model parameters, whilst also reducing the slight interaction identified.

Stormwater quality models: performance and sensitivity analysis.

The complex nature of pollutant accumulation and washoff, along with high temporal and spatial variations, pose challenges for the development and establishment of accurate and reliable models of the pollution generation process in urban environments. Therefore, the search for reliable stormwater quality models remains an important area of research. Model calibration and sensitivity analysis of such models are essential in order to evaluate model performance; it is very unlikely that non-calibrated models will lead to reasonable results. This paper reports on the testing of three models which aim to represent pollutant generation from urban catchments. Assessment of the models was undertaken using a simplified Monte Carlo Markov Chain (MCMC) method. Results are presented in terms of performance, sensitivity to the parameters and correlation between these parameters. In general, it was suggested that the tested models poorly represent reality and result in a high level of uncertainty. The conclusions provide useful information for the improvement of existing models and insights for the development of new model formulations.

Impact of input data uncertainties on urban stormwater model parameters.

The use of urban drainage models requires careful calibration, where model parameters are selected in order to minimize the difference between measured and simulated results. It has been recognized that often more than one set of calibration parameters can achieve similar model accuracy. A probability distribution of model parameters should therefore be constructed to examine the model's sensitivity to its parameters. With increasing complexity of models, it also becomes important to analyze the model parameter sensitivity while taking into account uncertainties in input and calibration data. In this study a Bayesian approach was used to develop a framework for quantification of impacts of uncertainties in the model inputs on the parameters of a simple integrated stormwater model for calculating runoff, total suspended solids and total nitrogen loads. The framework was applied to two catchments in Australia. It was found that only systematic rainfall errors have a significant impact on flow model parameters. The most sensitive flow parameter was the effective impervious area, which can be calibrated to completely compensate for the input data uncertainties. The pollution model parameters were influenced by both systematic and random rainfall errors. Additionally an impact of circumstances (e.g. catchment type, data availability) has been recognized.

Pollutant removal performance of field-scale stormwater biofiltration systems.

The pollutant removal performance of three separate stormwater biofiltration systems in two different climates was assessed. At one of the sites, rain events were simulated, while actual runoff events were monitored at the other two sites. In all cases, concentrations of total suspended solids (TSS), copper, lead and zinc were effectively and reliably reduced, despite variations in inflow concentrations. Two biofiltration systems also effectively reduced phosphorus concentrations, however the third system discharged elevated phosphorus concentrations relative to inflow; this is attributed to poor specification of filter media properties. Effluent nitrogen concentrations were more variable at all sites and ranged from being substantially lower to considerably higher than inflow concentrations. Flow was also measured at two sites, where it was determined that volumetric reductions in runoff further improved pollutant removal. TSS and heavy metals will be reliably removed by a wide range of soil-based filter media, as will phopshorus, as long as the phosphorus content of the filter media is low. However, nitrogen removal remains a challenge because it is easily transformed to soluble forms and is influenced by wetting and drying. These results are essentially consistent with related laboratory studies.

Analysis of parameter uncertainty of a flow and quality stormwater model.

Uncertainty is intrinsic to all monitoring programs and all models. It cannot realistically be eliminated, but it is necessary to understand the sources of uncertainty, and their consequences on models and decisions. The aim of this paper is to evaluate uncertainty in a flow and water quality stormwater model, due to the model parameters and the availability of data for calibration and validation of the flow model. The MUSIC model, widely used in Australian stormwater practice, has been investigated. Frequentist and Bayesian methods were used for calibration and sensitivity analysis, respectively. It was found that out of 13 calibration parameters of the rainfall/runoff model, only two matter (the model results were not sensitive to the other 11). This suggests that the model can be simplified without losing its accuracy. The evaluation of the water quality models proved to be much more difficult. For the specific catchment and model tested, we argue that for rainfall/runoff, 6 months of data for calibration and 6 months of data for validation are required to produce reliable predictions. Further work is needed to make similar recommendations for modelling water quality.

New prebiotic chemistry inspired filter media for stormwater/greywater disinfection.

Greywater and stormwater have received significant attention due to increasing water scarcity. Passive filtration such as biofiltration has been a popular treatment method with its low energy input and environmental friendliness. However, pathogen removal capacity needs improvement to achieve safe water quality. In this study, a prebiotic chemistry inspired copolymer based on aminomalononitrile and 3,4,5-trihydroxybenzaldehyde (AMNT30) was introduced to develop antimicrobial media for passive filtration. The AMNT30 polymer provided an adhesive coating on zeolite substrates following a spontaneous polymerisation process at room temperature. AMNT30 coated media were investigated for metal loading capacity, surface morphology, E. coli removal and metal leaching after filtration of different water sources (i.e. stormwater, greywater, and deionised water) at low/high conductivity. The coating enhanced metal ion loading on the surface and demonstrated that >8 log reduction of E. coli can be achieved for silver loaded materials compared to a 1 log reduction for copper loaded materials. The coating also increased the stability of the metals on the media irrespective of inflow characteristics. This study provided the first example using AMNT30 to create antimicrobial water purification media. It is expected that this technology will find applications in the water treatment industry.

Nutrient and sediment removal by stormwater biofilters: a large-scale design optimisation study.

A large-scale column study was conducted in Melbourne, Australia, to test the performance of stormwater biofilters for the removal of sediment, nitrogen and phosphorus. The aim of the study was to provide guidance on the optimal design for reliable treatment performance. A variety of factors were tested, using 125 large columns: plant species, filter media, filter depth, filter area and pollutant inflow concentration. The results demonstrate that vegetation selection is critical to performance for nitrogen removal (e.g. Carex appressa and Melaleuca ericifolia performed significantly better than other tested species). Whilst phosphorus removal was consistently very high (typically around 85%), biofilter soil media with added organic matter reduced the phosphorus treatment effectiveness. Biofilters built according to observed 'optimal specifications' can reliably remove both nutrients (up to 70% for nitrogen and 85% for phosphorus) and suspended solids (consistently over 95%). The optimally designed biofilter is at least 2% of its catchment area and possesses a sandy loam filter media, planted with C. appressa or M. ericifolia. Further trials will be required to test a wider range of vegetation, and to examine performance over the longer term. Future work will also examine biofilter effectiveness for treatment of heavy metals and pathogens.