Rapid urbanization, industrialization, and agricultural intensification have made runoff pollution an increasingly prominent environmental issue. This study used meta-analysis and geographically and temporally weighted regression model (GTWR) to analyze runoff pollution in 50 Chinese cities, quantifying the impact of socio-economic factors on runoff pollution. The results indicated a significant spatial differentiation in runoff pollution. The Event Mean Concentration (EMC) of TN, TP, and COD in all regions exceeded Class V limits by 1.54 to 8.70 times, suggesting rainfall could cause severe shock impacts on receiving water bodies. In the northern region, fertilizer consumption (FC) and first industry output (FI) were the main drivers of traditional pollutants (TSS, NH3-N, TN, TP, COD), with contribution of 13.04%-14.79%. In southern economically developed regions, secondary industry output (SI) and transportation activities drove heavy metal pollution (Zn, Cu, Pb), with contributions ranging from 14.38% to 15.23%. Moreover, the heavy metals were highly homologous (correlation coefficients>0.4). Taking major cities as an example, COD and NH3-N concentrations can be predicted by key factors like annual rainfall (AR), SI, and FC: a 5% increase in FC raises COD and NH3-N concentrations by 4.90% and 4.88%, respectively. The study recommended integrating the TMDL (Total Maximum Daily Load) system with economic incentives (e.g., a $77.50 monthly fee per 1000 m(2) of impervious surface) and implementing area-specific control strategies. This research provides a scientific basis for identifying runoff pollution drivers and optimizing urban stormwater management.

This study identifies rainfall events in the Yangtze River Basin between 1968 and 2018 from the perspective of spatial-temporal connectivity, analyses their geometric morphology and internal structure, and reveals the disaster-causing patterns of rainfall of varying intensities. The results indicate that the frequency of heavy rainfall events exhibits a ‘fragmented’ increase, with heavy rain events accounting for 84.9% of the total. The centre of gravity of general heavy rainfall shows a trend of shifting northwards and eastwards, consistent with the movement of the monsoon belt; meanwhile, the rate of eastward movement of extreme rainfall reaches 2.60 km/year, with a southward movement of 0.06 km/year, and spatial variability has increased significantly. As intensity increases, circularity and solidity decrease: heavy rainfall events exhibit regular morphology and dense structure; extreme rainfall events have loose morphology and a more concentrated circularity index. Furthermore, the distribution of the ratio of peak to mean values for extreme rainfall events is flatter, the spatial attenuation coefficient shifts towards negative values, and the main peaks predominantly occur in the middle to late stages of the events. The conclusions indicate that traditional methods tend to incorrectly classify loosely structured events as independent occurrences, whereas in reality, the same weather system can trigger the superposition of upstream and downstream flood peaks through spatial-temporal connectivity. Heavy rainfall events are prone to causing localized cumulative urban flooding, whilst extreme rainfall events are highly likely to trigger sudden, large-scale floods.

During public health emergencies, sodium hypochlorite disinfectants and their primary residual, chloride ions, may enter bioretention systems through surface runoff. The impact of these substances on the function and structure of such systems remains unknown. This study evaluates the negative effects of disinfectants on the structural components (plants and substrates) within bioretention systems, as well as the efficacy of bioretention systems in removing chlorinated disinfectants using Iris and Hylotelephium erythrostictum. Results showed that: (1) The higher the direct application dosage of disinfectant, the greater the decrease in leaf chlorophyll SPAD value, leading to increased enzymatic activities of various enzymes within the substrate, inhibiting plant growth. (2) Composite pollution from disinfectants and chloride ions increased substrate electrical conductivity. Enzyme activities increased with escalating composite pollution concentrations below 10 mg/L sodium hypochlorite but declined gradually with sustained high pollution levels. (3) Bioretention facilities removed chlorinated disinfectants efficiently (up to 95 % removal efficiency). NaCl and NaClO concentrations in runoff affected Cl- accumulation in substrates, with higher concentrations leading to greater accumulation. In conclusion, during public health emergencies, the large-scale spraying of sodium hypochlorite and its main residual chloride ions can enter the bioretention facility along with surface runoff, which may have adverse effects on the function or structure of the facility.

Urban separate sewer systems face significant challenges from rainfall-derived infiltration and inflow (RDII) during the wet season. To achieve the integrated optimization of operational safety, energy consumption, and carbon emissions, this study proposes a dynamic optimal control method. A real-time regulation framework was developed by coupling a Storm Water Management Model (SWMM) hydraulic model with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm within a Model Predictive Control (MPC) structure. Based on real-time water level risks, the framework adaptively adjusts the priority among three objectives: overflow reduction, pumping station energy consumption, and methane emission potential. Using a real separate sewer network in CZ city as a case study, the method was evaluated under light, moderate, and heavy rainfall scenarios. Results show that, compared with traditional rule-based control (RBC) and fixed-weight static model predictive control (SMPC), the proposed dynamic model predictive control (DMPC) strategy reduces overflow by 37.2% during heavy rain, and achieves 16.5% energy savings and a 15.8% reduction in methane emission potential during light rain. The strategy also balances network storage utilization, mitigates local overload, and demonstrates enhanced robustness to rainfall forecast errors, providing an effective technical solution for safe, energy-efficient, and low-carbon urban drainage operation.

Urban water bodies—encompassing rivers, lakes, surface waters, and groundwater—play critical roles in maintaining urban hydrological balance. However, stormwater runoff increasingly transports emerging contaminants (ECs), including heavy metals (HMs), microplastics (MPs), and per- and polyfluoroalkyl substances (PFAS), which pose significant threats to water quality and aquatic ecosystems. Although EC removal in urban waters is widely studied, the mediating role of stormwater remains poorly quantified. We conducted the first bibliometric analysis (1994–2024) of EC fate in stormwater-mediated urban waters, analyzing 1344 publications from Web of Science. Our analysis identified three key trends: (1) sustained global research focus on polycyclic aromatic hydrocarbons (PAHs) and heavy metals, led by major contributing nations; (2) a marked post-2020 shift toward microplastics and PFAS as priority contaminants;(iii) Persistent gaps in real-time monitoring, cross-media contaminant flux quantification, and synergistic removal mechanisms. Extreme rainfall (intensified by climate change) and contaminant interactions synergistically elevate ecological risks. Future research must prioritize real-time sensors, ecological risk assessment frameworks, and policy-driven green infrastructure deployment to enhance urban water resilience.

Urbanization and climate change are intensifying runoff pollution, jeopardizing aquatic ecosystems and human health through degraded water quality. Traditional monitoring methods have proven inadequate for conducting timely impact assessments. This study investigates the impacts of runoff pollution on the water quality of receiving water bodies in the economic development zone of Beijing, China. By considering environmental factors such as rainfall and land cover types, the InfoWorks ICM model was applied to an economic development zone in Beijing. The study revealed significant relationships between rainfall characteristics, grassland proportions, and pollution levels at stormwater drainage outfalls and receiving water bodies. Specifically, shorter rainfall return periods could lead to pollution concentrations at outfalls increasing by as much as 3.75 times. In receiving water bodies, when the rainfall return period was less than 10 years, the dilution effect of rainfall outweighed the pollution effect. Additionally, the receiving water bodies exhibited significant spatial and temporal variations in water quality. Reducing pollution in receiving waters can be achieved by controlling stormwater outfall pollution within the first 15-18 min of outflow. Furthermore, managing pollution from light rainfall events (1.2-1.5 mm/h) and implementing segmented river control measures significantly reduce overall COD and NH3-N in rivers by 12.5 % and 44 %, respectively. This study provides essential insights for managing stormwater pollution and protecting receiving waters.

Rainwater is an important non-traditional water resource, and the economic benefits of rainwater harvesting and utilization vary considerably across different climatic conditions. This paper constructs typical rainwater harvesting (RWH) models based on road watering and green space watering reuse pathways, as well as typical flat water year and cistern emptying time in three distinct climate zones of China: Urumqi (located in a semi-arid area), Baoding (located in a semi-humid area), and Shantou (located in a humid area). Using the Infoworks ICM model, long-term simulations (30 years) were conducted to evaluate the economic advantages of rainwater utilization across these climate zones. The Particle Swarm Optimization (PSO) was employed to optimize the cistern volume by integrating Net Present Value (NPV) and Benefit Cost Ratio (BCR). Additionally, multiple nonlinear regression analyses were employed to quantify the relationship between various catchment subsurfaces and the optimal reservoir volume. Sensitivity analyses, including Sobol sensitivity analysis, were performed for parameters such as subsurface characteristics, water price, discount rate, and number of days for emptying the impoundment. The study results indicated that the change curves of NPV and BCR in all three cities exhibit an increasing and then decreasing trend with the increase of reservoir volume under different reservoir setup scenarios. The optimal volume of the reservoir could be articulated through the fitting equation that relates the green space ratio to the road ratio. The discount rate and water price significantly impacted the optimal volume of storage tanks, with the sensitivity of the parameter of the number of days of emptying of the storage tanks to the optimal volume being greater in Shantou, a city in a high humid area. Conversely, in Urumqi, a semi-arid area, the variation of the parameter of greenland rate had a greater sensitivity to the volume of storage tanks. These findings provide valuable insights for the design and implementation of rainwater harvesting and utilization systems, offering practical guidance for sustainable urban development.

The combined effects of rapid urbanization and climate change are increasingly exacerbating the risk of urban flooding. This study develops a data-efficient framework for estimating a city’s Urban Stormwater Carrying Capacity (USCC)—the maximum stormwater volume that can be safely infiltrated, stored, and conveyed. The framework couples three rainfall scenarios—frequent, heavy, and extreme—with nine widely adopted drainage and storage measures, ranging from green spaces and permeable pavements to pipes and underground emergency reservoirs, and expresses USCC through a streamlined water-balance equation. Applied to the 24 km2 Zhangmian River district in Weifang, China, the framework yields capacities of 4.84, 5.86, and 9.80 × 106 m3 for the three scenarios, respectively; underground reservoirs supply ≈ 40% of the extreme-event capacity. Sensitivity analysis shows that increasing the imperviousness coefficient from 0.65 to 0.85 raises peak drainage demand by 30.8%, whereas halving reservoir depth lowers total capacity by 27.8%. Because the method requires only rainfall depth, land-cover data, and basic facility dimensions, it enables rapid, transparent scenario testing and helps planners prioritize cost-effective upgrades. The approach is transferable to other cities and can be extended to incorporate water quality or digital-twin modules in future research.

At present, China attaches great importance to the development of new quality productive forces. As the spatial projection carriers of these forces, strategic emerging industrial spaces exhibit high-output efficiency that calls for theoretical explanation. Existing research has largely focused on the internal efficiency of industries, with insufficient attention paid to the mechanisms of spatial attributes—particularly the systematic analysis of spatial economic factors such as intensification, functional mixing, and location–bid rent relations. To address this gap, this study draws upon 78 samples of strategic emerging industrial spaces from China and abroad. By employing quantitative analyses of the density–scale relationship, calculating functional mixing through the information entropy model, and assessing spatial bid rent effects, the study uncovers the underlying causes of the high efficiency observed in new industrial spaces. The results demonstrate that high development intensity and high functional mixing are distinctive characteristics of strategic emerging industrial spaces. Their bid rent capacity in core urban areas exceeds traditional land rent gradients, presenting an empirical challenge to the Alonso model. This high-density, high-mixing spatial pattern fosters reverse industrial clustering in urban cores through mechanisms such as knowledge spillovers, industrial chain collaboration, and innovation network agglomeration, thereby reshaping the theoretical framework of spatial economics. The findings provide a partial explanation for the high-output performance of new industrial spaces and offer a theoretical foundation for optimizing industrial space policies and planning supply strategies.

Urban expansion has led to changes in land use patterns, with the increased urban surface area replacing natural infiltration channels for stormwater. This can contribute to higher water temperatures of recipient water bodies, thus reducing the quality of aquatic ecosystems. Current research primarily focuses on the impact of thermal pollution on aquatic organisms and the effectiveness of low-impact development facilities in controlling thermal pollution. However, there is a lack of research addressing the capacity of water bodies to handle heat loads and control thermal pollution. This paper monitored the current thermal pollution of the underlying surface in the study area, and the characteristics of thermal pollution change at different periods of 6 h were analyzed. Additionally, a mathematical model was developed to incorporate the new parameter of lake thermal load carrying ratio (LTR) into the design of the Sponge City Renovation. The results showed that although the highest event runoff mean temperature was observed for rainfall events during the afternoon period, the total runoff thermal load was highest during the early morning period. The maximum LTR reached 145 % for the lake. Based on the LTR parameter to calculate the scale of plot modification, 16 % of the concrete underlayment in the Daxing campus can be modified into green space with the addition of 14,180 m2 of bioretention facilities, which can effectively reduce the LTR value by up to 48 %, at which time the water body will no longer be exposed to the risk of thermal pollution. The LTR index can be utilized in low-impact development and renovation projects to effectively assess the thermal pollution risk of stormwater runoff generated by plots. This assessment holds high practical value for evaluating the environmental impact of such developments.

In response to the increasing frequency of urban rainstorms, this study focuses on investigating the friction coefficient related to pedestrian instability under urban road flooding conditions. The objective is to conduct an in-depth analysis of the friction coefficient between pedestrians and the ground in actual flood scenarios and its variations, providing practical data to support future pedestrian safety assessments under flood conditions. Wet friction coefficient experiments were conducted under waterlogged conditions, with real human subjects tested across various operational scenarios. A buoyancy calculation formula was introduced to explore the impact of pressure changes caused by buoyancy on the human body in water, influencing the friction coefficient. An exponential relationship between pressure and the friction coefficient was established. Furthermore, by considering factors such as outsole hardness, ground type, and pressure variations with water depth, a dynamic method for selecting the friction coefficient was proposed, offering a scientific basis for determining friction coefficient thresholds associated with pedestrian instability risks. Experimental results indicate that, in the combination of hydrophilic materials with experimental asphalt and cement pavements, the friction coefficient under waterlogged conditions is generally higher than under dry conditions. However, as pressure increases, the friction coefficient of rubber materials decreases. This study concludes that the selection of the friction coefficient in pedestrian instability analysis should be treated as a dynamic process, and relying on a fixed friction coefficient for force analysis of pedestrian instability may lead to significant inaccuracies.

Urbanization and climate change amplify urban flooding risks, demanding efficient, data-minimal tools to strengthen flood resilience. This study presents a pioneering multi-dimensional framework that quantifies the contributions of source reduction, stormwater pipes, and drainage/flood control systems, circumventing the need for intricate hydrological models. Leveraging rainfall depth (mm), runoff volume (m3), and peak flow rate (m3/h) provides a comprehensive evaluation of stormwater management efficacy. Applied to a hypothetical city, City A, under 30- and 50-year rainfall scenarios, the framework reveals efficiencies of 91.0% for rainfall depth and runoff volume, and 90.8% for peak flow in the 30-year case (9% shortfall), declining to 75.7% peak flow efficiency with a 24.3% deficit in the 50-year scenario, underscoring constraints in extreme-event response. Contributions analysis shows stormwater pipes (42.8–47.6%, mean: 46.0%) and drainage/flood control (40.8–43.2%, mean: 41.6%) predominate, while source reduction adds 11.6–14.0% (mean: 12.4%). A primary contribution lies in reducing data demands by approximately 70% compared to traditional approaches, rendering this framework a practical, scalable solution for flood management and sponge city design in data-limited settings. These findings elucidate system vulnerabilities and offer actionable strategies, advancing urban flood resilience both theoretically and practically.

In recent years, the frequency of extreme rainfall events has increased due to climate change, resulting in a higher risk of flooding in urban underground spaces, particularly subway systems. Although effective equipment and technologies have emerged to mitigate flood risks in subway stations, there is still a lack of comprehensive flood risk management strategies. Therefore, it is crucial to assess the flood risk management level of subway stations as a first step toward devising effective flood prevention and control plans. This study takes the Beijing subway network as an example and uses complex network theory to establish a weighted undirected subway network model to determine the relative importance of each subway station. Passenger flow statistics are then conducted for each route, and the passenger exposure of each route is analyzed under flooding circumstances. Ultimately, the flood risk index for different subway stations is determined based on the flood risk map. The technique for order preference by similarity to ideal solution (TOPSIS), network weighting method, and analytic hierarchy process (AHP) are employed to analyze the flood risk management level of subway stations. The findings indicate that the flood risk management level of Pinganli, Xizhimen, and Hujialou subway stations of Beijing is relatively high, making these subway stations particularly vulnerable to comprehensive disasters, including operational disruptions and passenger exposure, in the event of flooding. This study has significant academic value in the field of flood risk management in subway systems by applying complex network theory to assess the importance and passenger exposure of subway stations in urban operation, effectively capturing the flood risk associated with subway stations, and providing a comprehensive understanding of the functionality of subways as a public transportation mode.

Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts’ waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing’s historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of “1 h rainfall intensity water logging index” and “the waterlogging risk index in historical and cultural districts” were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts.

When using runoff infiltration devices to remove nitrogen and phosphorus pollutants from urban runoff, the quality of the effluent is affected by the length of dry spells between rain events. This study presents a novel analysis of how these dry periods impact the device’s effectiveness in removing pollutants and the resulting biological succession within the filter. Our analysis examines nitrogen and phosphorus removal in a rainwater filtration context, providing new insights into how dry period duration influences infiltration system performance. The results indicate that biological processes have a significant impact on reducing total nitrogen (TN) and total phosphorus (TP) contents under different drying periods. A 3-day drying period is most effective for reducing TN through biological processes, while a 7-day period is best for TP reduction. This suggests that moderately extending the drying period improves TP removal efficiency but does not enhance TN removal. The dominant bacterial phylum responsible for denitrification and phosphorus removal is Proteobacteria, with Pseudomonas and Acinetobacter as the leading genera. As the drying period lengthens, the dominant genera shift from Pseudomonas to Massilia. At a 3-day drying period, denitrification primarily occurs through Pseudomonas on the surfaces of maifanite and zeolite. At a 7-day dry-out period, Acinetobacter is mainly responsible for phosphate removal on maifanite surfaces. However, after a 14-day dry-out period, both biomass and bioactivity of Pseudomonas and Acinetobacter decrease, leading to reduced efficiency in removing nitrogen and phosphorus pollutants from runoff infiltration devices. These results aid in developing runoff infiltration devices for specific scenarios and offer crucial guidance for regulating runoff pollution control technologies.

Human safety is paramount in flood disasters. Current research indicates that the majority of fatalities in such disasters are due to people moving in water. Existing studies on human stability in floods have primarily focused on the static resistance of a standing posture against water flow, neglecting the realistic scenario where people need to move and attempt self-rescue in the aftermath of destabilization. This paper introduces an analysis of the stability during the self-rescue process following a fall in floodwaters, providing insights into the baseline risks of human impact in floods. The self-rescue process is defined as the recovery to a standing position after a fall, segmented into four postures: sitting, kneeling, squatting, and standing. Additionally, considering the significant variability of the current method (D×v water depth multiplied by flow velocity) used to assess human stability in floods, this research thoroughly investigates previously undefined parameters, including submerged volume, frontal area, wet friction coefficient, and flow resistance coefficient. This leads to the development of a physically meaningful self-rescue risk assessment formula, which is validated against previous studies for accuracy, with the aim of contributing new insights to flood risk management and public education.

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In order to address the issue of combined sewer overflows (CSOs), W city has constructed a large-scale storage tank with a volume of 220,000 m3. The storage tank is planned for CSO control in the near term and stormwater runoff pollution control in the long term. However, the actual operation of the storage tank is unsatisfactory. This paper elucidates the design scheme and operation mode of the tank and analyzes the challenges encountered during its design and operation. A storm water management model (SWMM) model was constructed to simulate the effect of the storage tank working in a combined sewer system (CSS), a separate sewer system (SSS) and a decentralized storage situation. This study determined that during the 2022 rainy season, the actual reduction in pollutants by the storage tank was only about 60% of the designed value. As a result, the inadequate treatment capacity of the downstream wastewater treatment plant (WWTP) resulted in the water being retained in the tank for a long time, leading to unsatisfactory operation outcomes. If the storage tank works in SSS and the problem of water retention can be solved, it could reduce the total runoff volume by 30% and the total amount of pollutants by 40% during the same rainy season. At the same time, under the premise of constant total storage volume, if decentralized storage tanks were used to control runoff pollution, the reduction effect can be increased by up to 11.6% compared with that of the centralized storage.

Urban surface temperatures are high in summertime, and thermal pollution caused by heat transfer from pavement to stormwater runoff is harmful to aquatic ecosystems. However, there is a lack of studies investigating the temperature change pattern during rainstorms and evaluating the effects of bioretention on dynamic characteristics of thermal pollution. Therefore, this study selected a 1.05 ha parking lot retrofitted with five individual bioretention cells in Beijing as the object to compare the temperature and volume of stormwater runoff before and after bioretention treatment. In the LID parking lot, the average EMT and EMXT (event maximum temperature) of runoff decreased by 2.28 °C and 4.18 °C, respectively, and the median percent thermal load reduction was 90.6%. Data analysis from 15 summer rainfall events showed that the sequence of factors affecting runoff EMT (event mean temperature) was average air temperature, max air temperature, max solar radiation, and rainfall peak 5-min intensity. Bioretention profoundly changed the thermal dynamic characteristics of stormwater runoff. Surface runoff temperatures generally showed a decreasing trend over time. The temperature change pattern of LID parking lot outflow was synchronized with that of the inflow and varied with different grades of precipitation. The probability of the peak temperature ahead of peak flow decreased from 80% to 53%, suggesting that 27% of the thermal first-flush effect of thermal pollution from the urban surface was alleviated by site-scale bioretention implementation. The site-scale bioretention combination had a lower effluent temperature and a higher thermal load reduction rate than single-scale solutions. These results fill the gap in research on the thermal pollution reduction process of bioretention. Furthermore, they can guide the optimization of bioretention design methods and strategies to protect urban water bodies from the stormwater runoff thermal pollution.