Microplastics (MPs) are an emerging pollutant and a worldwide issue. A wide variety of MPs and tyre wear particles (TWPs) are entering and spreading in the environment. TWPs can reach waterbodies through runoff, where main contributing particulate matter comes from impervious areas. In this paper, TWPs and other types of MPs that were transported with the runoff of a high populated-impervious urban area were characterised. Briefly, MPs were sampled from sediments in a stormwater detention reservoir (SDR) used for flood control of a catchment area of ∼36 km², of which 73% was impervious. The sampled SDR is located in São Paulo, the most populated city in South America. TWPs were the most common type of MPs in this SDR, accounting for 53% of the total MPs; followed by fragments (30%), fibres (9%), films (4%) and pellets (4%). In particular, MPs in the size range 0.1 mm–0.5 mm were mostly TWPs. Such a profile of MPs in the SDR is unlike what is reported in environmental compartments elsewhere. TWPs were found at levels of 2160 units/(kg sediment·km² of impervious area) and 87.8 units/(kg sediment·km street length); MP and TWP loadings are introduced here for the first time. The annual flux of MPs and TWPs were 7.8 × 10¹¹ and 4.1 × 10¹¹ units/(km²·year), respectively, and TWP emissions varied from 43.3 to 205.5 kg/day. SDRs can be sites to intercept MP pollution in urban areas. This study suggests that future research on MP monitoring in urban areas and design should consider both imperviousness and street length as important factors to normalize TWP contribution to urban pollution.

Reservoirs typically have elevated fish mercury (Hg) levels compared to natural lakes and rivers. A unique feature of reservoirs is water-level management which can result in sediment exposure to the air. The objective of this study is to identify how reservoir water-level fluctuations impact Hg cycling, particularly the formation of the more toxic and bioaccumulative methylmercury (MeHg). Total-Hg (THg), MeHg, stable isotope methylation rates and several ancillary parameters were measured in reservoir sediments (including some in porewater and overlying water) that are seasonally and permanently inundated. The results showed that sediment and porewater MeHg concentrations were over 3-times higher in areas experiencing water-level fluctuations compared to permanently inundated sediments. Analysis of the data suggest that the enhanced breakdown of organic matter in sediments experiencing water-level fluctuations has a two-fold effect on stimulating Hg methylation: 1) it increases the partitioning of inorganic Hg from the solid phase into the porewater phase (lower log Kd values) where it is more bioavailable for methylation; and 2) it increases dissolved organic carbon (DOC) in the porewater which can stimulate the microbial community that can methylate Hg. Sulfate concentrations and cycling were enhanced in the seasonally inundated sediments and may have also contributed to increased MeHg production. Overall, our results suggest that reservoir management actions can have an impact on the sediment-porewater characteristics that affect MeHg production. Such findings are also relevant to natural water systems that experience wetting and drying cycles, such as floodplains and ombrotrophic wetlands.

During the 20th century, the local economy of the Upper Loire Basin (ULB) was essentially based on industrial coal mining extraction. One of the major French coal districts with associated urban/industrial activities and numerous coking/gas plants were developed in the Ondaine-Furan subbasins, two tributaries of the upper Loire main stream. To determine the compositional assemblage, the level and the potential sources of contamination, the historical sedimentary chronicle of the 16 US EPA priority polycyclic aromatic hydrocarbons (PAHs) has been investigated. PAH concentrations were determined using gas chromatography/mass spectrometry (GC/MS) in a dated core, sampled in the Villerest flood-control reservoir located downstream of the Ondaine-Furan corridor (OFC). The most contaminated sediments were deposited prior to 1983 (Σ16PAHs ca. 4429–13,348 ng/g) and during flood events (Σ16PAHs ca. 6380 ng/g – 1996 flood; 5360 ng/g – 2003 flood; 6075 ng/g – 2008 flood), especially in medium and high molecular weight PAHs. Among them, typical pyrogenic PAHs such as FLT, PYR, BbF and BaP were prevalent in most of the core samples. In addition, some PAHs last decade data is available from the Loire Bretagne Water Agency and were analyzed using high-performance liquid chromatography with postcolumn fluorescence derivatization (HPLC/FLD). These results confirm that the most highly contaminated sediments were found downstream of OFC (Σ16PAHs ca. 2264–7460 ng/g). According to the observed molecular distribution, PAHs are originated largely from high-temperature pyrolytic processes. Major sources of pyrogenic PAHs have been emphasized by calculation of specific ratios and by comparison to reported data. Atmospheric deposition of urban and industrial areas, wood combustion and degraded coal tar derived from former factories of coking/gas plants seem to be the major pyrogenic sources. Specifically, particular solid transport conditions that can occur during major flood events lead us to emphasize weathering of former contamination sources, such as more preserved coal tar.

PURPOSE OF THE REVIEW: Freshwater wetlands are found in various climatic zones ranging from tropics to tundra, and their roles from groundwater recharge and flood control to water quality management and biodiversity protection are well recognized. Phosphorus (P) is a limiting nutrient for algal growth in freshwater systems, including wetlands. Various physico-chemical and biological characteristics of wetlands regulate cycles of nutrients such as P. Thus, estimating internal loading of P in wetlands would be crucial in the formulation of effective P management strategies in the wetland systems. This review and limnological data presented may offer needed knowledge/evidence for the effective control of P inputs in wetlands and provide insights on possible ways for interventions in controlling eutrophication and saving the ecosystem from collapse. RECENT FINDINGS: Various ways of P losses such as agriculture, urbanization, etc., to the water bodies have severely impacted water quality of wetlands by altering physical and chemical nature of the P compounds and release bound P to the water columns. Studies indicate that P sorption–desorption dynamic, mineralization, and enzymatic hydrolysis of P in freshwater wetlands’ soils/sediments are crucial in causing internal loading or sink of P in wetland systems. Thus, extensive studies on abovementioned arenas are crucial to restore natural freshwater wetlands or to increase the efficiency of constructed wetlands in retaining P. In general, researchers have elucidated significant amounts of limnological data to understand eutrophication processes in freshwater wetlands; however, studies on the interactions of P stability and hydro-climatic changes are not well understood. Such changes could significantly influence localized limnology/microenvironments and exacerbate internal P loading in freshwater wetlands; thus, studies in such direction deserve the attention of scientific communities.

Stormwater ponds can provide flood protection and efficiently treat stormwater using sedimentation. As the ponds also host aquatic biota and attract wildlife, there is a growing concern that the sediment bound pollutants negatively affect aquatic organisms and the surrounding ecosystem. In this study, we used three methods to assess the accumulation and the potential ecological risk of 13 different heavy metals and metalloids (e.g. trace elements) including both elements that are frequently monitored and some which are rarely monitored in sediment from 5 stormwater ponds located within catchments with predominately industrial activities. Ecological risk for organisms in the older ponds was observed for both commonly (e.g. Cd, Cu, Zn) and seldom (e.g. Ag, Sb) monitored trace elements. The 3 methods ranked the degree of contamination similarly. We show that methods usually used for sediment quality assessment in aquatic ecosystems can also be used for screening the potential risk of other trace elements in stormwater ponds and may consequently be useful in stormwater monitoring and management. Our study also highlights the importance of establishing background conditions when conducting ecological risk assessment of sediment in stormwater ponds.

Twenty antibiotics were investigated to evaluate the degree of antibiotic pollution, the temporal and spatial antibiotic distribution and the ecological risks in the Xiaoqing River basin (main stream). The total antibiotic concentrations in surface water and sediment were 0.99 to 832.4 ng L⁻¹ and 9.71 to 7841.61 ng g⁻¹, respectively, and that ofloxacin was the dominant antibiotic. However, ofloxacin, erythromycin, clarithromycin and sulfamethoxazole posed high risks to algae, among which clarithromycin presented the highest risk quotients (23.8). In addition, there were spatial and temporal differences in the antibiotic concentration distribution. Temporally, the following trend was detected: dry season > normal season > wet season; spatially, the following trend was detected: Jinan > Dongying > Binzhou > Zibo > Weifang. Meanwhile, we used the PCA-MLR model to quantify the contribution rate of the four sewage treatment plants A, B, C and D. Factor 1 (co-sources A, B, C, D) contributed 64.1% of the total antibiotic concentration in the Xiaoqing River. According to the estimated flux into the sea, approximately 972.31 kg of antibiotics were discharged into Bohai Bay in 2017, posing a potential threat to the marine ecosystem. As a comprehensive river channel used for flood control, waterlogging, irrigation and shipping, its water quality safety is of great significance to the surrounding residents and ecological safety. Therefore, further investigations of antibiotic pollution and source contribution are necessary.

Flood prediction has gained prominence world over due to the calamitous socio-economic impacts this hazard has and the anticipated increase of its incidence in the near future. Artificial intelligence (AI) models have contributed significantly over the last few decades by providing improved accuracy and economical solutions to simulate physical flood processes. This study explores the potential of the AI computing paradigm to model the stream flow. Artificial neural network (ANN), fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS) algorithms are used to develop nine different flood prediction models using all the available training algorithms. The performance of the developed models is evaluated using multiple statistical performance evaluators. The predictability and robustness of the models are tested through the simulation of a major flood event in the study area. A total of 12 inputs were used in the development of the models. Five training algorithms were used to develop the ANN models (Bayesian regularization, Levenberg Marquardt, conjugate gradient, scaled conjugate gradient, and resilient backpropagation), two fuzzy inference systems to develop fuzzy models (Mamdani and Sugeno), and two training algorithms to develop the ANFIS models (hybrid and backpropagation). The ANFIS model developed using hybrid training algorithm gave the best performance metrics with Nash-Sutcliffe Model Efficiency (NSE) of 0.968, coefficient of correlation (R²) of 97.066%, mean square error (MSE) of 0.00034, root mean square error (RMSE) of 0.018, mean absolute error (MAE) of 0.0073, and combined accuracy (CA) of 0.018, implying the potential of using the developed models for flood forecasting. The significance of this research lies in the fact that a combination of multiple inputs and AI algorithms has been used to develop the flood models. In summary, this research revealed the potential of AI algorithm-based models in predicting floods and also developed some useful techniques that can be used by the Flood Control Departments of various states/regions/countries for flood prognosis.

Climate change may result in increased variability in rainfall intensity in the future, leading to more frequent flooding and a substantial loss of lives and properties. To mitigate the impact from flooding events, flood control facilities need to be designed and operated more efficiently, which requires a better understanding of the relationship between climate change and flood events. This study proposed a framework combining the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models to assess the impact of climate change on flood events. HEC-HMS is one of the most commonly used hydrologic models in the USA, and CMIP5 provides the latest climate data for potential future climate scenarios. The proposed approach is applied to the Nippersink Creek watershed, which shows that 10-, 25-, 50-, and 100-year precipitations for the low, medium, and high emission scenarios are all greater than the historic observations. The corresponding 10-, 25-, 50-, and 100-year floods are remarkably higher than in the historic observations for the three climate scenarios. The high emission scenario results in dramatically increased flood risks in the future. The case study demonstrates that the framework combining HEC-HMS and CMIP5 is easy to use and efficient for assessing climate change impacts on flood events. It is a valuable tool when complicated and distributed hydrologic modeling is not an option because of time or monetary constraints.

Deji Reservoir is situated in the middle of Taiwan on the upstream catchment of the Dajia stream with an area of over 60,160 ha. The embankment stands 180 m high and is the tallest concrete arch dam in Taiwan. This dam stores 1.7 billion cubic meters of valid volume water. It provides over 370 million kilowatt-hours of electricity annually. It is also an important facility for operating flood control, hydroelectricity, irrigation, public water supply, etc. Seventy-two percent of the terrain is at 2,000- to 3,000-m altitude, and 5.2% is above 3,200-m altitude. More than 59% of the area is covered with steep topography of 55% slope. Only 7.9% of the area is shown with gentle slopes of less than 30% slope, which are located among the two banks of main streams with the altitudes ranging from 1,500 to 2,500 m. Most of the gentle slopes are used for temperate zone fruit, vegetable, and tea plantations. This land-use immediately adjoins a reservoir catchment region, resulting in an enormous impact on the mountainous environment. This study reviewed human-developed land-use area to properly address and evaluate norms for mitigating the impacts on the reservoir. The initial investigation brought up the parameters of gradient, slope movement types and processes, distance to the reservoir, location of developed area and distance with the farm road, etc. Local investigation and global information system technology were conducted in this research. We focused on segregating the terrain types of indisposed land-use. A different land-use management strategy is also analyzed.

Runoff forecasting is essential for the reasonable use of regional water resources, flood prevention, and mitigation, as well as the development of ecological civilization. Runoff is influenced by the intersection of many factors, and the change process is extremely complex, showing significant stochasticity, nonlinearity, and heterogeneity, making traditional prediction models less adaptable. The Hodrick–Prescott filter (HP filter) is a well-established signal separation method. The traditional GM(1,1) model is capable of portraying the growth trend of the time series but cannot capture the periodic characteristics of the time series. Therefore, a novel coupled prediction model-HPF-GM(1,1) model is proposed in this study and applied to the runoff prediction of the Zhuzhou section of Xiangjiang River in Hunan Province. This model enables to separate seasonal factors from non-seasonal factors in the runoff time series, and introduce seasonal factors based on the traditional GM(1,1) model, which solves the challenge that the traditional GM(1,1) model is unable to predict seasonal time series. The results show that the HPF-GM(1,1) model has a mean relative error of 4.82%, a root mean square error of 7.44, and a Nash efficiency coefficient of 0.93, which is better than the traditional GM(1,1) model, the DGGM(1,1) model and the SGM(1,1) model of prediction accuracy. Obviously, the HP filter provides a new approach to data pre-processing of runoff series and the proposed HPF-GM(1,1)-coupled model extends new ideas for nonlinear runoff prediction.