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.

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.

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.

In most developed countries, such as the USA, the E.U., and East Asia, the importance of public infrastructure asset management has been stressed for a long time. Among the various types of public infrastructure, sewerage systems are one of the most cost-intensive facilities to manage. Sewerage systems are considered highly difficult to manage due to the undetermined level of service needed, different standards of user satisfaction, and the large gap of service understanding between experts and users. To address these issues, this study aims to define the appropriate target level of service improvement by combining consumers’ expected level of service and complaint data. In this study, the case of the inland flood management project in South Korea is investigated because of the global trend of increasing flood damage. The complaint data represent the frequency of flood damage in the area. Using the contingent valuation method, we found that people want to use 25% of their current monthly sewage bill on the management project. In addition, the results of this study demonstrate that people prefer to deal with the problems caused by old service infrastructure when it can be handled at a lower cost during early stages.

In recent years, flooding has not only disrupted social growth but has also hampered economic development. In many nations, this global epidemic has affected lives, property, and financial damage. Pakistan has experienced many floods in the past several years. Due to economic, social, and climate change, Pakistan is at risk of flooding. In order to overcome this problem, the institutions of the country have taken various measures. However, these measures are not sufficient enough to ensure the safety of communities and areas that are prone to disasters with a rapid onset. Hence, it is imperative to forecast future flood-related risks and take necessary measures to mitigate the adverse impacts and losses caused by floods. This article is aimed at exploring floods in Pakistan, analyze the adverse effects of floods on humans and the environment, and propose possible sustainable options for the future. The aqueduct flood analyzer software was used to examine the impact of floods on gross domestic product (GDP), urban damage, and people livelihood, with several years of flood protection plans. To adequately assess the future changes, various flood protection levels and three scenarios for each level of protection were employed, which represent the socio-economic and climate change. The findings revealed that if there is no flood protection, a 2-year flood has a 50% probability of flood occurrence in any given area and may cause no significant impact on GDP, population, and urban damage. Similarly, the probability of a flood occurrence in a five-year flood is 20%, which may cause the country’s GDP about $20.4 billion, with 8.4 million population at risk and $1.4 billion urban damage. Furthermore, a 10-year flood has a 10% probability of flood occurrence and may affect the national GDP by $28.9 billion, with 11.9 million affected population and $2.4 billion urban damage in Pakistan. The government of Pakistan should devise appropriate climate change policies, improve disaster preparedness, build new dams, and update relevant departments to mitigate the adverse effects of flooding.