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.

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.

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.

Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naïve Bayes (NïB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NïB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications.

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.

Floods are among the most devastating environmental hazards that directly and indirectly affect people’s lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

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.