Evaluating the response and adaptation of urban stormwater systems to changed rainfall with the CMIP6 projections.

Climate change is altering urban rainfall characteristics, leading to extreme urban stormwater and, particularly, more frequent flooding. Due to the uncertainty of climate change, the responses of urban drainage systems to climate change are becoming more complicated. This complexity makes it difficult for decision makers to assess whether urban infrastructure is sufficiently resilient to cope with flood risks. In this study, the Xiao Zhai area, a high-density urban area of China, was used as an example. A quantitative method for assessing these risks and the resilience of urban drainage systems to future urban stormwater was developed. First, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), the variation and uncertainty of future rainfall in the study area were analysed. A high-fidelity hydro-hydraulic model was developed to analyse the influence of climate change on future urban stormwater. Finally, the relationship between urban flood risk and the resilience of urban drainage systems was evaluated. The results show that the temporal distribution of future rainfall from 2023 to 2100 is relatively uniform. However, the number of heavy rainfall events increases significantly during this period. The flood risk caused by future rainfall was one level higher than the historical flood risk. For example, the flood risk caused by future 5a rainfall is equal to the flood risk from historical 10a rainfall. The correlations between the spatial distributions of flood risk and resilience are 0.49-0.63. Urban drainage systems urgently need to be improved and refined in areas with flood risk and low resilience to become more resilient to climate change. Rational planning of grey-green rainwater facilities in flood risk and low resilience areas can improve the rainwater system's resilience to 0.67-0.95 for climate change.

Temporal and spatial variability of water quality in an urban wetland and the effects of season and rainfall: a case study in the Daguan Wetland, China.

Urban wetlands provide multiple functions including water treatment, recreation, and education, but they are also highly vulnerable, so it is important to monitor wetland water quality to ensure wetland health. In this study, water quality parameters of an urban wetland and rainfall were monitored at 6 sites for 1 year. The correlation analysis of water quality parameters and spatial-temporal variability analysis of water quality were carried out. Besides, the effects of season and rainfall on the wetland water quality were evaluated by the comprehensive water quality identification index (CWQII). These results have shown that there is a significant correlation between nutrient pollutants and Chl-a. Wetland water quality changed with the seasons, but it also varied due to changes in rainfall and location. The water quality of the shallow areas both had high susceptibility and response to seasonal changes and rainfall, but the water quality of the deepwater area was relatively stable. The CWQIIs in different seasons were ranked: Winter (5.98) > spring (4.67) > autumn (4.66) > summer (4.26), and the CWQIIs of different rainfall intensities were ranked: torrential rain (5.09) > heavy rain (4.88) > light rain (4.50) > no rain (4.39) > moderate rain (3.95). The results of this study distinctly explained the effects of season and rainfall on water quality in an urban wetland in a subtropical monsoon climate zone and would be helpful to the policymakers and concerned authorities in developing better water quality management strategies for these wetlands.

Facile one-step fabrication of superhydrophobic melamine sponges by poly(phenol-amine) modification method for effective oil-water separation.

Although polydopamine (PDA)-related modification is widely studied in the fabrication of superhydrophobic sponges, the high cost of dopamine limits its widespread application. To imitate PDA modification, a low-cost and facile one-step poly(phenol-amine) modification was performed on melamine sponges in this study. Low-cost catechol and diethylenetriamine (DETA) were used as the monomers, and n-dodecanethiol was used as an additive in the one-step modification. The results confirmed that the poly(phenol-amine) aggregations were successfully anchored on the sponge skeleton surface and that the aggregations were formed via the Schiff base reaction and the Michael addition reaction. Furthermore, the as-prepared sponges still showed excellent mechanical properties after modification. Additionally, the optimally modified sponge (MS-0.5) exhibited superhydrophobic properties with a contact angle value above 150° under various environments, high oil-absorption capacity for various oils and organic solvents, high continuous oil-water separation performance with efficiency greater than 98.8% in 30 cycles, outstanding demulsification performance with 99.52% toward oil-in-water emulsion, and excellent recoverability and long-term stability. Thus, this work provides a feasible facile one-step modification method that can be used in place of PDA-related modification.

Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models.

Integrating powerful machine learning models with flood risk assessment and determining the potential mechanism between risk and the driving factors are crucial for improving flood management. In this study, six machine learning models were utilized for flood risk assessment of the Pearl River Delta, in which the Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) models were firstly applied in this field. Twelve indices were chosen and 2000 sample points were created for model training and testing. Hyperparameter optimization of the models was conducted to ensure fair comparisons. Due to uncertainty in the sample dataset, recorded inundation hot-spots were utilized to validate the rationality of the flood risk zoning maps. After determining the optimal model, the driving factors of different flood risk levels were investigated. Urban and rural areas and coastal and inland areas were also compared to determine the flood risk mechanism in different highest-risk areas. The results showed that the GBDT performed best and provided the most reasonable flood risk result among the six models. A comparison of the driving factors at different risk levels indicated that the disaster-inducing factor, disaster-breeding environment, and disaster-bearing body were not definitely becoming more serious as the flood risk increased. In the highest-risk areas, rural areas were featured by worse disaster-breeding environment than urban areas, and the disaster-inducing factors of coastal areas were more serious than those of inland areas. Moreover, the Digital Elevation Model (DEM), maximum 1-day precipitation (M1DP), and road density (RD) were the top three significant driving factors and contributed 52% to flood risk. This study not only expands the application of machine learning and deep learning methods for flood risk assessment, but also deepens our understanding of the potential mechanism of flood risk and provides insights into better flood risk management.

Hydrology and rainfall runoff pollutant removal performance of biochar-amended bioretention facilities based on field-scale experiments in lateritic red soil regions.

Bioretention has been found to lower the effluent loads of various pollutants from rainfall runoff. However, it is still a challenge to effectively use bioretention for rainfall runoff control in lateritic red soil regions where have high rainfall intensity and low soil infiltration capacity. Hence, in this study, the hydrologic performance and rainfall runoff pollutant removal capacity of field-scale biochar-amended bioretention facilities were tested with four rainfall recurrence periods under different biochar distributions, internal water storage (IWS) zone heights, and exfiltration conditions. The results confirmed that incorporation of biochar into planting soil would improve its water content raising capacity (WCRC), especially when the biochar was uniformly mixed with the lateritic red soils. Besides, more infiltrating from the planting soil layer and higher IWS zone heights effectively enhanced WCRC of the stone chip packing layer. For runoff volume control, adding biochar and increasing the IWS zone height could effectively improve runoff volume control capacity. Besides, the unlined bioretention had a higher runoff volume control capacity than lined bioretention. Considering runoff pollutant removal performance, biochar could contribute to significantly improving the runoff pollutant event mean concentration removal rate (Rc) of nutrient pollutants (TN, NO3-N, NH3-N, and TP). The average runoff pollutant load removal rate (Rl) of different biochar distributions decreased as follows: biochar was uniformly mixed with the lateritic red soils > biochar was stratified with the lateritic red soils > biochar was excluded in the planting soil layer. The average Rc and average Rl of all pollutants except COD under different IWS zone heights decreased as follows: 40 cm > 20 cm > 0 cm. Meanwhile, the average Rl of the lined bioretention with an IWS zone height of 0 cm was lower than that of the unlined bioretention. Overall, higher rainfall recurrence periods would reduce the treatment capacity of bioretention facilities.

Urbanization and climate change impacts on future flood risk in the Pearl River Delta under shared socioeconomic pathways.

Climate change and urbanization are converging to challenge the flood control in the Pearl River Delta (PRD) due to their adverse impacts on precipitation extremes and the urban areas environment. Previous studies have investigated temporal changes in flood risk with various single factor, few have considered the joint effects of climate change, urbanization and socio-economic development. Here, based on the representative concentration pathway (RCP) scenarios, we conducted a comprehensive assessment of future (2030-2050) flood risk over the PRD combined with a thorough investigation of climate change, urbanization and socio-economic development. Precipitation extremes were projected using the regional climate model RegCM4.6, and urbanization growth was projected based on the CA-Markov model. The economic and population development was estimated by the shared socio-economic pathways (SSPs). Flood risk mapping with different RCPs-urbanization-SSPs scenarios was developed for the PRD based on the set pair analyze theory. The results show that climate change and urbanization are expected to exacerbate flood risk in most parts of the PRD during the next few decades, concurrently with more intense extreme precipitation events. The high flood risk areas are projected mainly in the urban regions with unfavorable terrain and dense population. The highest flood risk areas are expected to increase by 8.72% and 19.80% under RCP4.5 and RCP8.5 scenarios, respectively. Reducing greenhouse gas emissions may effectively mitigate the flood risk over the PRD. This study highlight the links between flood risk and changing environment, suggesting that flood risk management and preventative actions should be included in regional adaptation strategies.

First flush of non-point source pollution and hydrological effects of LID in a Guangzhou community.

To study the first flush effect of nonpoint source pollution in the Guangzhou community unit, runoff from roads, roofs, and green spaces during three rainfall events was collected and analyzed for pollutants. Nine runoff pollution indices were considered. The dimensionless cumulative curve of pollutant mass vs. volume, the first flush coefficient (b) and the mass first flush ratio (MFFn) were used to assess the first flush effect of different underlying surfaces. The assessment results pointed out that the roof was most prone to first flush effect. And ammonia nitrogen and phosphorus were the main pollutants in the first flush in the study area. For a quantitative analysis of the first flush, the Storm Water Management Model (SWMM) was used to simulate the hydrological effect of low impact development (LID) implementation in the community. The results showed that the first flush strength was reduced after setting LID. And LID measures, such as green roofs and sunken green spaces, contribute to flood control and rainwater purification. This research can be relevant regarding for constructing sponge cities and reducing the pollution caused by the first flush.

Synergistic effect of PCPE1 and sFRP2 on the processing of procollagens via BMP1.

This article corrects: Synergistic effect of PCPE1 and sFRP2 on the processing of procollagens via BMP1, Volume 593, Issue 1, 119-127. Article first published 09 November 2018. https://doi.org/10.1002/1873-3468.13291 Dr Daniel S. Greenspan was inadvertently omitted from the list of authors in the original publication. The correct list of authors is as shown above. Dr Daniel S. Greenspan affiliation and contact details are as follows: Department of Cell and Regenerative Biology, University of Wisconsin, Room 4503, WIMRII, 1111 Highland Ave, Madison, WI 53705, dsgreens@wisc.edu.

Synergistic effect of PCPE1 and sFRP2 on the processing of procollagens via BMP1.

Procollagen processing is essential for organ development and tissue functions. Both procollagen C-proteinase enhancer 1 (PCPE1) and secreted frizzled-related protein 2 (sFRP2) play vital roles in collagen formation via regulating the procollagen C-proteinase activity of bone morphogenetic protein 1 (BMP1). However, whether the two proteins exert a synergistic effect on BMP1 activity remains unclear. Here, simultaneous knockdown of sFRP2 and PCPE1 led to less collagen formation in mouse embryonic fibroblasts and dorsalized phenotypes in zebrafish embryos. Further studies revealed a direct interaction between the Frizzled domain of sFRP2 and the complement/Uegf/BMP-1 domain of PCPE1, which enhances the cleavage activity of BMP1 on procollagen. These results suggest that double silencing of sFRP2 and PCPE1 may provide a strategy for treating fibrosis diseases caused by collagen deposition.

Temperature Dependence of Hourly, Daily, and Event-based Precipitation Extremes Over China.

Theoretically, precipitation extremes will increase at a rate of 6-7% with temperature increasing, namely the Clausius-Clapeyron relationship. However, many gauge observations suggest a peak structure of the relationship between precipitation extremes and atmospheric temperature, deviating from the Clausius-Clapeyron relationship. In this study, a comprehensive investigation about the temperature dependence of precipitation extremes (hourly, daily, and event-based) across China is implemented. The results confirm the widespread existence of the peak structure for daily and hourly precipitation extremes and show that (1) there is a generally positive spatial correlation between the precipitation extremes at the peak and temperature at the peak, and this scaling rate is close to the C-C rate; (2) the scaling of event-based extremes for precipitation amount with temperature follows a similar pattern to the daily precipitation extremes while the event-based precipitation intensity does not show a peak structure; (3) the decrease of rain duration is the main cause for the peak structure of the rain amount scaling.

Urban stormwater inundation simulation based on SWMM and diffusive overland-flow model.

With rapid urbanization, inundation-induced property losses have become more and more severe. Urban inundation modeling is an effective way to reduce these losses. This paper introduces a simplified urban stormwater inundation simulation model based on the United States Environmental Protection Agency Storm Water Management Model (SWMM) and a geographic information system (GIS)-based diffusive overland-flow model. SWMM is applied for computation of flows in storm sewer systems and flooding flows at junctions, while the GIS-based diffusive overland-flow model simulates surface runoff and inundation. One observed rainfall scenario on Haidian Island, Hainan Province, China was chosen to calibrate the model and the other two were used for validation. Comparisons of the model results with field-surveyed data and InfoWorks ICM (Integrated Catchment Modeling) modeled results indicated the inundation model in this paper can provide inundation extents and reasonable inundation depths even in a large study area.

Application of the stormwater management model to a piedmont city: a case study of Jinan City, China.

The stormwater management model (SWMM) was adapted and calibrated to Jinan, a typical piedmont city in China, to verify the large-scale applicability of the model to piedmont cities. Fourteen storms were used for model calibration and validation. The calibrated model predicted the measured data with satisfactory accuracy and reliability. A sensitivity analysis was then conducted to evaluate the impact of the model parameters; it showed that: (1) the model outputs were most sensitive to imperviousness and conduit roughness; and (2) infiltration parameters and depression storage play an important role in total runoff and peak flow. The urban drainage system of Jinan was assessed using urban design storms with the calibrated model, and the effects of engineered flood control measures were evaluated. The overall results demonstrate that SWMM is applicable on a large scale to piedmont cities.

Ecological risks assessment and pollution source identification of trace elements in contaminated sediments from the Pearl River Delta, China.

Sediments from 14 stations in the Foshan Waterway, a river crossing the industrial district of Guangdong Province, South China, were sampled and subsequently analyzed. The 14 stations were selected for the pollution discharging features of the river, such as the hydrology, the distribution of pollution sources, and the locations of wastewater outlets. The ecological risks were assessed, and the pollution sources were identified to provide valuable information for environmental impact assessment and pollution control. The spatial variability was high and the range were (in milligrams per kilogram dry weight): Pb, 46.0~382.8; Cu, 33.7~ 482.3; Zn, 62.2~1,568.7; Ni, 28.5~130.7; Cr, 34.7~1,656.1; Cd, 0.50~8.53; Hg, 0.02~8.27; and As, 5.77~66.09. The evaluation results of enrichment factor and potential ecological risk index indicate that the metal pollution in the surface and bottom sediments were severely polluted and could pose serious threat to the ecosystem in most stations. Although the hazard levels of the trace element differed among the stations, Hg was the most serious pollutant in all stations. The results of principal component analysis (PCA) show that the discharge of industrial wastewater is the most important polluting factor whereas domestic sewage, which contains a large amount of organic substances, accelerates metal deposition. And potential pollution sources were identified by the way of integrating the analysis results of PCA and data gained from the local government. Therefore, the conclusion is drawn that Foshan Waterway is seriously polluted with trace elements, both in the surface sediment (0 to 20 cm) and the bottom sediments (21 to 50 cm) are contaminated.