Urban water, sewer, and road infrastructures are simultaneously facing renewal demands, necessitating more cost-efficient rehabilitation strategies. Coordinating interventions across co-located assets can lower long-term expenditures, yet strategic-level modelling approaches remain limited. This study evaluates two such methods: joint cohort survival analysis (JCSA), which models either separate or systematic coordination, and the multi-utility rehabilitation modeller (MURM), which introduces a coordination window to represent intermediate policies. Both methods were applied to residential street cohorts constructed in Luleå, Sweden, during 1965–1975. The results indicate that JCSA and MURM produce consistent outcomes under no-coordination and systematic coordination scenarios. In contrast, under moderate coordination, MURM predicts substantially lower accumulated capital costs (€50.5 m) than JCSA (€58.8 m), corresponding to a 12–17% reduction. These findings demonstrate the analytical advantages of MURM for strategic asset management, offering decision-makers a more realistic representation of coordination policies and their long-term financial implications.

Stormwater control measures (SCMs) are effective measures to reduce the risks of urban flooding and pollutants being transferred to receiving water bodies. However, the selection of appropriate SCMs and maintenance is essential to ensure reliable performance in both the short and long term. To improve insights into these factors, a residential catchment was modelled using the EPA Storm Water Management Model (SWMM) with various SCM alternatives covering different spatial distributions and degrees of naturalness. Changes over time were also considered to assess performance decline after clogging due to insufficient maintenance. The results confirmed the effectiveness of SCM alternatives for flood mitigation by demonstrating reduced runoff, peak flows and flood volumes compared with a baseline model with a conventional drainage system. Stormwater quality modelling showed decreased pollutant concentrations and loads for the SCM alternatives. Clogging of the media was found to have a substantial impact on efficiency. For complex constructed SCMs, the proportion of overflow increased greatly, resulting in higher pollutant loads and concentrations and a greater risk of flooding. Conversely, simpler constructed SCMs showed substantially decreased runoff and peak flows due to decreased infiltration of stormwater. The results highlight the importance of careful planning, design and maintenance of SCMs.

Blue-Green Infrastructure (BGI) in urban areas is primarily designed for stormwater management, but it also offers multifunctional benefits that support environmental, social, and economic sustainability. This study evaluates the benefits of seven BGI design alternatives in three types of urban areas in Sweden, representing typical cases in many parts of the world: Inner City catchment, Residential Suburbs, and New Urban Housing development. Long-term benefits (normalized per capita) were assessed and monetized using the Benefits Estimation Tool (CIRIA - UK Construction Industry Research and Information Association). Common benefits in all area types included Amenity, Education, Flooding, Health, Water quality, and Water quantity. The greatest benefits were realised in Residential Suburb and Inner City areas, particularly with BGI schemes based on open dry detention or bioretention with swales. Benefits were less pronounced in the New Urban Housing area. Sedimentation ponds and bioretention facilities performed best in the Residential Suburb. Net Present Value (NPV) was positive in the Inner City and Residential Suburb areas, but negative in the New Urban Housing area, indicating lower cost-effectiveness. Benefit-Cost Ratio calculations indicated that the per capita return on investment for constructing BGI is highest in Residential Suburbs even though the NPV was greatest in Inner City areas. The methodological framework and results are discussed in the light of making the findings transferable to other European cities and international contexts with similar environmental and urban characteristics.

Urban runoff carries a variety of compounds many of which are largely unknown organic pollutants occurring in low concentrations. This study combines analyses targeting known contaminants (metals, phthalates and nonylphenols, octylphenols and their ethoxylates) with effect-based methods that indicate the mixture effects of known and unknown substances. The potential chemical hazard of roof runoff was investigated during a rain event sampled in June in Luleå, northern Sweden. The investigation included measures of oxidative stress response, aryl hydrocarbon (AhR) activation, and endocrine disruption (androgenic, antiandrogenic and estrogenic effects) of the dissolved and the particulate phases in the water. In the runoff, significant metal release from copper (5320 μg Cu/L), zinc (8690 μg Zn/L) and galvanized roofs (8050 μg Zn/L) was observed, whereas for organic compounds only DINP (280 μg/L) and DEHP (1.8 μg/L) were detected from one of the PVC roofs. Results on biological effects showed cytotoxic interference for most samples and genotoxicity for all samples. For each assay, specific effects were detected in ≥50 % of the samples except for androgenic activity. Most commonly, oxidative stress and AhR activity were observed. For oxidative stress, the range of BEQ concentrations was <14–131 μg/L tBHQeq (dissolved phase) and 9.04–50.8 μg/L tBHQeq (particulate phase). For AhR activity, the range was <0.427–5.8 ng/L TCDDeq (dissolved phase) and 0.383–23.5 ng/L TCDDeq (particulate phase). Particles accounted for a high share of activity in runoff for the AhR assay (>70 % for majority of samples), whereas for the other assays the share was more variable. Results of this study suggest that chemicals present in the roof runoff may cause biological effects that are higher compared to drinking water samples and in similar ranges to those previously reported for stormwater from larger catchments for oxidative stress and AhR activity.

Adoption of sensor-based on-site monitoring in urban-water engineering enables the collection of high-resolution data, but malfunctions and environmental disturbances can pose a serious challenge in uptake and use, as they result in data gaps and loss of information. One solution to handle these data gaps is the use of machine-learning (ML) algorithms. This study compared widely used deterministic interpolation methods and data-driven methods – particularly neural networks – for reconstructing extended data gaps in urban runoff quality time series. The analysis is based on runoff water quality data from a small and relatively homogeneous section of an urban road. While simpler interpolation methods achieved high overall accuracy, ML models outperformed the simpler methods during highly dynamic periods – those that are typically more challenging to capture and often of particular interest in stormwater management contexts. The findings highlight both the limitations and the potential of each approach, emphasizing the particular importance of understanding context and data conditions when selecting the most suitable method for the task.

The database comprises 15 datasets collected at 15 stormwater and snowmelt runoff monitoring sites across Sweden, covering 182 events from 2000 to 2023, including 117 stormwater and 65 snowmelt events, as well as 40 dry-weather sewer baseflow samples. The database comprises event mean concentration of 20 metal(loid)s (total, dissolved, and bioavailable), key physicochemical parameters including total suspended solids (TSS), total and dissolved organic carbon (TOC and DOC), turbidity (Turb), pH, electrical conductivity (EC) and temperature, as well as rainfall characteristics and runoff flow data.

Stormwater bioretention systems are primarily designed to manage stormwater quality as well as restore more natural hydrology during less intense rainfall events. Thus, design modifications are required to manage intense rain events resulting from climate change. This study examined 54 bioretention system design combinations by varying four design factors: filter media fraction of the total soil depth (i.e. filter media + drainage gravel layer), hydraulic conductivity of the filter media, ponding depth, and storage connection diverting surface water to the storage layer through a pipe. Using a calibrated field-scale Storm Water Management Model (SWMM), the performance of these designs was assessed for volume reduction during common rain events, peak flow reduction during a 10-year return interval rain event (180 mm, 1 h), and the number of overflows in a year. Using a storage connection consistently improved performance, enhancing volume reduction by up to 32% and reducing overflow events (up to 11 of 29), as expected since the storage layer provides additional high-porosity detention. Higher filter media fractions (85%) improved volume reduction during common rain events by approximately 50%. Interestingly, lower fractions (15%) were more beneficial for reducing overflow events (in about 17 out of 29 events annually), suggesting that higher porosity and drainage can sometimes prevent excessive surface ponding. These findings underscore the potential of bioretention system designs to enhance climate resilience and emphasize the need for a balanced approach that improves volume reduction during common rain events while also reducing overflow events during high-intensity rainfall.

Urbanization increases impervious surfaces, generating higher stormwater volumes and pollutant loads that challenge conventional drainage systems. While nature-based solutions (NBS) are widely used for stormwater management, compact stormwater treatment systems (CSTS) provide a decentralized alternative suited to space-limited urban areas. However, their evaluation is hindered by inconsistent international testing protocols. This study compares three major CSTS performance standards, the British Water Code of Practice (BW CoP), the New Jersey Corporation for Advanced Technology (NJCAT) program, and the Deutsches Institut für Bautechnik (DIBt) protocol, to identify methodological differences and testing implications. Based on this comparison, a modified DIBt protocol is developed, expanded to include nutrient (phosphorus and nitrogen) tests, was applied to a filter chamber under controlled laboratory conditions. Results showed particle removal ranging from 25% at high flow to 97% at low flow conditions, hydrocarbon removal remaining consistently high (98–99%), and metal removal efficiencies varying with flow, copper increasing from 19 to 95% and zinc from slightly negative (release) to 88%. Nutrient tests indicated phosphorus removal from 32 to 84% and negative nitrogen removal rates. These findings highlight the need for harmonized testing frameworks reflecting regional conditions to improve CSTS evaluation and implementation.

This meta-analysis integrates data from 182 runoff events across 15 urban catchments representing diverse land uses and seasonal conditions to prioritize metals of concern in urban discharges. Using a data-driven risk assessment, concentrations and risks of 20 metal (loid) s were compared against international databases and regulatory thresholds to consider both acute and chronic effects and bioavailability for seven metals. Event-and site-level analyses revealed large temporal and spatial variability driven by specific emission sources, land use, and seasonal hydrology. Spatially,“Parking/Road”,“Main Road/Highway”, and “Mixed Urban” catchments contributed most to overall metal loads and risk levels. Seasonally, snowmelt showed higher total concentrations and acute/chronic risks (due to elevated TSS and pH), while stormwater contained relatively higher dissolved and bioavailable fractions; only Co and Al showed notable chronic risk on the bioavailable fraction in snowmelt, whereas Cu and Pb did so in stormwater. Overall, Zn, Cu, Al, Fe, and Co ranked as the highest-risk metals, followed by Mn, V, Ba, and Ni. Results highlight the need for dynamic, chemistry-dependent management that integrates targeted source reduction, solids management, and treatment, while considering both metal speciation and runoff chemistry.