Abstract Per- and polyfluoroalkyl substances (PFAS) and industrial persistent, mobile, and toxic (iPMT) compounds pose growing risks to water quality. However, quantitative tools to prioritize compounds for remediation and evaluate exposure under varying environmental scenarios remain limited. This paper presents the first application of a Bayesian probabilistic early warning exposure assessment (EWEX) model to evaluate PFAS and iPMTs in indirect potable water reuse (IPR) systems. By integrating literature-derived and site-specific data, the model explicitly accounts for uncertainty and provides a flexible framework for scenario analysis. The model’s performance was demonstrated for PFOS, trifluoromethanesulfonic acid (TFMSA), and carbamazepine (CBZ), revealing sensitivity to data quality and emphasizing the importance of robust monitoring and transferable data sets. In addition to removal via treatment processes, dilution in the environment substantially reduces contaminant concentrations, highlighting the influence of the hydrological setting and the associated climate-related environmental factors on exposure outcomes. The EWEX framework serves as a decision-support tool for utilities and regulators, facilitating risk-informed management of emerging contaminants in water reuse systems. Future work should expand the data set for poorly studied compounds, refine treatment process representations, especially for short-chain PFAS, and validate model outputs through real-world monitoring.
Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based metamodels grant improved fidelity and speed; however, they are only applicable to the water network they were trained on. To address this issue, we investigate graph neural networks (GNNs) as metamodels for WDSs. GNNs leverage the networked structure of WDS by learning shared coefficients and thus offering the potential of transferability. This work evaluates the suitability of GNNs as metamodels for estimating nodal pressures in steady-state EPANET simulations. We first compare the effectiveness of GNN metamodels against multi-layer perceptrons (MLPs) on several benchmark WDSs. Then, we explore the transferability of GNNs by training them concurrently on multiple WDSs. For each configuration, we calculate model accuracy and speedups with respect to the original numerical model. GNNs perform similarly to MLPs in terms of accuracy but take longer to execute but may still provide substantial speedup. Our preliminary results indicate that GNNs can learn shared representations across different networks, although assessing the feasibility of truly general metamodels requires further work.
An innovative tool for modeling the specific flood volume was presented that can be applied to assess the need for stormwater network modernization as well as for advanced flood risk assessment. Field measurements for a catchment area in Kielce, Poland, were used to apply the model and demonstrate its usefulness. This model extends the capability of recently developed statistical and machine learning hydrodynamic models developed from multiple runs of the US Environmental Protection Agency (EPA) Storm Water Management Model (SWMM). The extensions enable the inclusion of (1) the characteristics of the catchment and its stormwater network, calibrated model parameters expressing catchment retention, and the capacity of the sewer system; (2) extended sensitivity analysis; and (3) risk analysis. Sensitivity coefficients of calibrated model parameters include correction coefficients for percentage area, flow path, depth of storage, and impervious area; Manning roughness coefficients for impervious areas; and Manning roughness coefficients for sewer channels. Sensitivity coefficients were determined with respect to rainfall intensity and characteristics of the catchment and stormwater network. Extended sensitivity analysis enabled an evaluation of the variability in the specific flood volume and sensitivity coefficients within a catchment, in order to identify the most vulnerable areas threatened by flooding. Thus, the model can be used to identify areas particularly susceptible to stormwater network failure and the sections of the network where corrective action should be taken to reduce the probability of system failure. The simulator developed to determine the specific flood volume represents an alternative approach to the SWMM that, unlike current approaches, can be calibrated with limited topological data availability; therefore, the aforementioned simulator incurs a lower cost due to the lower number and lower specificity of data required.
A large variety of substances are used in building materials to improve their properties. In recent years, attention to organic additives used, for example, in renders, façade paints or roof sealing sheets has increased as these compounds have been detected in urban stormwater runoff and surface waters. In this paper, we show the extent of emissions induced by rain events in two study sites in Berlin. For this purpose, stormwater runoff from roofs, façades, and in storm sewers was sampled and analysed over a period of 1.5 years in two residential catchments. Results show that, in particular, the biocides diuron and terbutryn from façades, the root protection agents mecoprop and MCPA in bituminous sheeting, and zinc from roofs and façades reach concentrations in the stormwater sewer that exceed limit values for surface waters. Additionally, transformation products of the biocides were also detected. However, many other analysed substances were below the quantification limit or inconspicuous in their concentration levels. The emissions, modelled with the software COMLEAM, demonstrate that in urban areas the limit values in smaller surface waters are exceeded during wet weather. Furthermore, the orientation of the buildings to wind-driven rain is essential for the emitted load from façades. The calculated mass balances of both catchments show that a major portion of all substances remains on-site and infiltrates diffusely or in swales, while the remaining portion is discharged to stormwater sewers. For example, in one of the two study sites, <5% of diuron emissions are discharged to surface waters. Infiltration, in particular, is therefore a crucial pathway of pollution for soil and groundwater. Measures for source control are proposed to mitigate the leaching of environmentally relevant substances from construction materials.
Combined sewer overflows (CSOs) are of major environmental concern for impacted surface waterbodies. In the last decades, major storm events have become increasingly regular in some areas, and meteorological scenarios predict a further rise in their frequency. Consequently, control and treatment of CSOs with respect to best practice examples, innovative treatment solutions, and management of sewer systems are an inevitable necessity. As a result, the number of publications concerning quality, quantity, and type of treatments has recently increased. This review therefore aims to provide a critical overview on the effects, control, and treatment of CSOs in terms of impact on the environment and public health, strict measures addressed by regulations, and the various treatment alternatives including natural and compact treatments. Drawing together the previous studies, an innovative treatment and control guideline are also proposed for the better management practices.
