Downstream Stations Research Articles

A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models

Abstract. In ocean or Earth system model applications, the riverine freshwater inflow is an important flux affecting salinity and marine stratification in coastal areas. However, in climate change studies, the river runoff based on climate model output often has large biases on local, regional, or even basin-wide scales. If these biases are too large, the ocean model forced by the runoff will drift into a different climate state compared to the observed state, which is particularly relevant for semi-enclosed seas such as the Baltic Sea. To achieve low biases in riverine freshwater inflow in large-scale climate applications, a bias correction is required that can be applied in periods where runoff observations are not available and that allows spatial transferability of its correction factors. In order to meet these requirements, we have developed a three-quantile bias correction that includes different correction factors for low-, medium-, and high-percentile ranges of river runoff over Europe. Here, we present an experimental setup using the Hydrological Discharge (HD) model and its high-resolution (1/12°) grid. First, bias correction factors are derived at the locations of the downstream stations with available daily discharge observations for many European rivers. These factors are then transferred to the respective river mouths and mapped to neighbouring grid boxes belonging to ungauged catchments. The results show that the bias correction generally leads to an improved representation of river runoff. Especially over northern Europe, where many rivers are regulated, the three-quantile bias correction provides an advantage compared to a bias correction that only corrects the mean bias of the river runoff. Evaluating two NEMO (Nucleus for European Modelling of the Ocean) model simulations in the German Bight indicated that the use of the bias-corrected discharges as forcing leads to an improved simulation of sea surface salinity in coastal areas. Although the bias correction is tailored to the high-resolution HD model grid over Europe in the present study, the methodology is suitable for any high-resolution model region with a sufficiently high coverage of river runoff observations. It is also noted that the methodology is applicable to river runoff based on climate hindcasts, as well as on historical climate simulations where the sequence of weather events does not match the actual observed history. Therefore, it may also be applied in climate change simulations.

Research on sedimentation characteristics of the end bend water extraction project of cascade reservoir backwater

Abstract The operation mode of upstream and downstream cascade power stations significantly impacts the river bed in the water intake section, leading to complex sediment deposition characteristics in the curved river section at the reservoir’s end. In this research, a standard physical model for bed load and suspended load at a scale of λl=λh=100 was conducted, combined with the operation mode of upstream and downstream power stations, to investigate the sediment deposition patterns in the intake section and to study the sediment deposition characteristics in the intake area. The experimental results reveal that bed load sediment predominantly accumulates on the right bank (convex bank) of the river, with no bed load sediment accumulation at the mushroom head layout of the water intake project on the left bank (concave bank). When the operation of upstream and downstream power stations is uncoordinated, suspended sediment settles in the water intake section, with no sediment accumulation in the local area of the mushroom head of the intake due to the disturbance effect. Safety sensitivity analysis indicates that the safety of the water intake project can be ensured.

Real-time predictive control assessment of low-water head hydropower station considering power generation and flood discharge

In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.

Scalar mixing and entrainment in an axisymmetric jet subjected to external turbulence

The present study aims to understand the process of turbulent entrainment into a jet, as affected by background turbulence, using scalar statistics. Planar laser-induced fluorescence was employed to capture the orthogonal cross sections of the jet at a fixed downstream station with varying background turbulence intensities and length scales. The conditional scalar profiles revealed that the thickness of the scalar turbulent/turbulent interface is greater than that of the traditional turbulent/non-turbulent interface, and the interfacial thickness is an increasing function of the background turbulence intensity. Although nibbling remains the primary entrainment mechanism in the far field, increased occurrence of concentration “holes” within the interfacial layer in the presence of ambient turbulence suggests a more significant role of large-scale engulfment in the turbulent/turbulent entrainment process (although still below 1% of the total mass flux). Enhanced contribution of the area of detached jet patches (i.e., “islands”) to that of the main jet is hypothesized to be evidence of intense detrainment events in the background turbulence. This can potentially contribute to a reduced net entrainment into the jet, which manifests as less negative values of scalar skewness within the jet core.

Utilizing a optimized method for evaluating vapor recovery equipment control efficiency and estimating evaporative VOC emissions from urban oil depots via an extensive survey

As an important intermediary between upstream refineries and downstream urban gas stations, volatile organic compound (VOC) emissions from urban oil depots were often disregarded, underestimating their environmental and health implications. An extensive investigation of urban depots' fuel composition and operational dynamics was conducted nationwide. We developed a novel approach that integrates theoretical models with easily measurable operational data from the depots to evaluate the efficiency of post-treatment devices in actual situations. Even in well-managed oil depots, the actual control efficiency of vapor recovery units fluctuates between 63 % and 85 %, depending on the concentration of hydrocarbon vapors in the intake of the device. The national emission factors for gasoline, diesel, and aviation kerosene at a national level were 6.64 ± 1.16, 2.07 ± 0.42, and 6.17 ± 1.05 tons per 10,000 tons, respectively. In 2019, China's urban oil depots emitted 165 thousand tons of VOC. Enhancing control strategies by optimizing the physical and chemical parameters of refined oil, improving storage capacity and turnover efficiency, and upgrading storage tanks had the potential to reduce emissions by more than 60 %. However, a 30 % failure rate in these systems could negate the benefits of these improved strategies.

Unseen riverine risk: Spatio-temporal shifts of microplastic pollution and its bioavailability in freshwater fish within the Ikopa River urban system.

Growing concern over microplastic pollution, driven by their widespread accumulation in the environment, stresses the need for comprehensive assessments. This study investigates the spatial and temporal distribution of microplastics in the Ikopa River (Antananarivo - Madagascar), which flows through a densely populated area, and examines their correlation with contamination levels in local fish species. By analyzing upstream and downstream stations across wet and dry seasons, only a notable increase in microplastic concentration downstream during the wet season was observed, ranging from 138.6 ± 9.0 to 222.0 ± 24.5 particles m-3, with polyethylene-co-vinyl acetate being the predominant polymer at 62.3 ± 5.13% of the total sampled polymers. This distribution underlines the impact of urban activities on pollution levels. Fish species, gambusia and Nile tilapia, were assessed for microplastic occurrence in gills and gastrointestinal tracts. Higher contamination rates were found in gambusia, enlightening the influence of feeding behaviour and fish habitat on microplastics contamination. Ingestion of microplastics directly from the water column was evident in both species, with the detection of high-density plastics such as polytetrafluoroethylene and polyvinyl chloride suggesting likely sediment contamination. This research highlights the widespread contamination of aquatic environments and its direct impact on local wildlife, pointing to a clear requirement for effective pollution management strategies.

Study on the regularities of flow‐sediment transport and sedimentation in the lower Yellow River

AbstractThe training effectiveness of the lower Yellow River (LYR) depends on the understanding of the regularity of flow‐sediment transport and riverbed sedimentation. The measured data of daily flow discharge and sediment transport rate at the five hydrological stations (Xiaolangdi [Xld], Huayuankou [Hyk], Gaocun [Gc], Aishan [As], and Lijin [Lj]) in the LYR during the period from 1960 to 2017 are used to investigate the regularity of flow‐sediment transport and sedimentation in the LYR. The Xld station is used as the inlet control station, and the LYR is divided into four segments using four other stations, and the whole year is divided into three periods, namely, the dry season, the flood period, and the nonflood period of the wet season. On this basis, the relationships between the sediment transport rates at the four stations (Hyk, Gc, As, and Lj) and the rates at their respective closest upstream stations are analyzed in each of the three periods. According to the incoming sediment coefficient of the Xld station, the flow and sediment processes in the three periods are classified, and the refined equations for the relationship between the sediment transport rates at the downstream station and its upstream station are established. The results show that the calculated amount and process of erosion and deposition in each period and each segment of the LYR using the equations are in good agreement with the measured values. The relationship equations established in this study can conveniently predict the amount of erosion and deposition in different periods and different segments of the LYR in the future, which is of great significance to the rapid decision of the impact of the construction and operation of hydraulic projects in the upper and middle reaches of the Yellow River on the sedimentation in the LYR.

Influence of SMAP soil moisture retrieval assimilation on runoff estimation across South Asia

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (>70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system’s representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia.

River dust-induced air pollution in a changing climate: A study of Taiwan's Choshui and Kaoping Rivers

This study investigates river dust episodes along the Choshui and Kaoping Rivers in Taiwan, focusing on their spatiotemporal distribution and correlation with hydrometeorological factors (temperature, precipitation, relative humidity, and wind speed). Using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm and time-dependent intrinsic correlation (TDIC) analysis, we identified significant annual and diurnal correlations between PM10 concentrations and these factors. The analysis revealed that wind speed at Lunbei station had a positive annual correlation with PM10, while other factors exhibited significant negative correlations. Seasonal variations in PM10 correlations with temperature, relative humidity, and wind speed were observed, aligning with the prevailing seasons of river dust episodes. Wind motion analysis highlighted diurnal associations with land-sea breezes and annual correlations with the winter monsoon. Specifically, the Choshui River's dust events coincided with the northeast monsoon, whereas the Kaoping River's events occurred during the northwest and southwest monsoons. The study also uncovered that downstream stations (Lunbei and Daliao) were more prone to severe dust events than upstream stations (Douliu and Pingtung). These findings enhance our understanding of the dynamics and environmental impacts of river dust episodes, providing valuable insights for air quality management and health risk mitigation.

Collapse of a giant iceberg in a dynamic Southern Ocean marine ecosystem: In situ observations of A-68A at South Georgia

Large icebergs (>20 km long) are responsible for most of the freshwater discharged into the Southern Ocean. We report on in situ and satellite observations made during the break-up phase around South Georgia of the giant tabular iceberg A-68A. The in situ measurements were obtained during a 4-day visit by a research vessel in February 2021, where physical, chemical and biological measurements were made at a range of distances away from the main and subsidiary icebergs. These results were compared to a far-field station 133 km away. Upstream of the iceberg field, water column structure was similar to ambient water although there was evidence of iceberg-associated phytoplankton as a likely remnant of the passage of the icebergs. Nevertheless, enhancement of primary productivity along the path of the icebergs was not resolved in either in situ or monthly mean satellite observations. There was a considerable brash-ice field moving ahead of the icebergs which limited the number of downstream sampling stations. One downstream station within 2 km of iceberg A-68P showed several ice-melt influenced features that distinguished it from most other stations. Firstly, there was a strong stratified meltwater influenced layer that reached to around 120 m. This had the effect of deepening underlying water masses, with the core of the temperature minimum layer around 50 m deeper than elsewhere. Secondly, there was evidence of rapid downward displacement of both particulate material and certain phytoplankton taxa that may be a further result of this water mass deepening. Thirdly, macronutrient profiles were altered, with concentrations of nitrate, silicic acid and phosphate characteristic of deeper layers being found closer to the surface and a dilution of the ambient nutrient pool just above the iceberg draft that we ascribe to meltwater released from basal melting. Meanwhile, nutrient recycling processes associated with organic matter remineralisation were also modified by the physical restructuring of the water column and biotic components. Finally, the ice-associated phytoplankton taxa Pseudo-nitszchia/Nitszchia, found in both upstream and downstream locations, were abundant at this < 2 km-distant station through melting out from the iceberg and subsequent rapid growth. Overall, we resolved alterations to water column structure, nutrient profiles and phytoplankton community composition at fine to medium scales around the iceberg field. Nevertheless, although there may have been longer term and larger scale impacts, the dynamic oceanographic environment, including the presence of a strong oceanographic front and shelf-edge processes, dominated during the collapse of A-68A.

Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network

The prediction of water plant flow should establish relationships between upstream and downstream hydrological stations, which is crucial for the early detection of flow anomalies. Long Short-Term Memory Networks (LSTMs) have been widely applied in hydrological time series forecasting. However, due to the highly nonlinear and dynamic nature of hydrological time series, as well as the intertwined coupling of data between multiple hydrological stations, the original LSTM models fail to simultaneously consider the spatiotemporal correlations among input sequences for flow prediction. To address this issue, we propose a novel flow prediction method based on the Spatiotemporal Attention LSTM (STA-LSTM) model. This model, based on an encoder–decoder architecture, integrates spatial attention mechanisms in the encoder to adaptively capture hydrological variables relevant to prediction. The decoder combines temporal attention mechanisms to better propagate gradient information and dynamically discover key encoder hidden states from all time steps within a window. Additionally, we construct an extended dataset, which preprocesses meteorological data with forward filling and rainfall encoding, and combines hydrological data from multiple neighboring pumping stations with external meteorological data to enhance the modeling capability of spatiotemporal relationships. In this paper, the actual production data of pumping stations and water plants along the East-to-West Water Diversion Project are taken as examples to verify the effectiveness of the model. Experimental results demonstrate that our STA-LSTM model can better capture spatiotemporal relationships, yielding improved prediction performance with a mean absolute error (MAE) of 3.57, a root mean square error (RMSE) of 4.61, and a mean absolute percentage error (MAPE) of 0.001. Additionally, our model achieved a 3.96% increase in R2 compared to the baseline model.

Spatial and Temporal Variation of C, N, and S Stable Isotopes and Seagrass Coverage Related to Eutrophication Stress in Zostera marina

Zostera marina is an ecologically valuable species that has been declining due to anthropogenic environmental stressors. In this study, spatial and temporal indicators of eelgrass stress, such as coverage and biomass, were compared with the isotopic composition of C, N, and S to understand the mechanism(s) of plant stress. Eelgrass samples were collected in June, July, and August of 2020 at five stations along an estuary spatial gradient in the southern Gulf of St. Lawrence to measure above- and below-ground biomass and tissue isotopes in eelgrass leaves and roots/rhizomes. Eelgrass biomass was lowest at the innermost sampling station, which coincided with eutrophication-induced hypoxia relative to outer sampling stations. δ13C levels at the upstream station were depleted compared to downstream stations. Comparatively, δ15N and δ34S findings were not correlated with plant biomass. Thus, sulfide intrusion was not a major stressor for eelgrass in this estuary. Between the years 2014 and 2020, eelgrass coverage was found to have increased, which coincided with high and low recorded external nutrient loads from the Wheatley River, respectively. Ultimately, these findings indicate that isotopic composition and biomass can be useful in assessing the health of eelgrass in temperate estuaries.

Utilizing sequential modeling in collaborative method for flood forecasting

Flood forecasting has been a major challenge in hydrology for decades. A variety of approaches have been developed, including numerical models and data-driven models, such as Deep Learning (DL), for disaster early-warning and monitoring systems. This study proposed two combination models, exploring different feature separation techniques to improve the 12-hour multi-step discharge prediction. Specifically, two different approaches were implemented, namely the separation of wet and dry periods (Combination Model No. 1; CM-1) and the separation of flood and non-flood events (Combination Model No. 2; CM-2) for comparison with the baseline model trained on an entire dataset. Multivariate time series discharge data were used, measured at upstream and downstream stations within the same river basins. Experiments were conducted in three case study areas in Thailand: the Lower Loei River Basin, the Upper Nan River Basin, and the Upper Chi River Basin.In the proposed model combination approach, three deep-learning models were investigated: vanilla recurrent neural networks (RNNs) and their variants, long short-term memory networks (LSTMs), and gated recurrent units (GRUs). In general, CM-2 exhibited superior performance compared to the other models in most of the experimented neural networks, while the CM-1 approach outperformed the baseline model. This emphasized the advantages of our proposed feature separation framework, particularly in improving model performance, with respect to flood events. However, some variations were observed in the Upper Chi River Basin because the more complex models, such as LSTMs and GRUs, possibly overfitted the training set with less fluctuating data. Our study highlighted the significant contribution of combining two models through feature separation, tailored to specific periods, thereby enhancing the accuracy of flood predictions.

PreK-12 school and citywide wastewater monitoring of the enteric viruses astrovirus, rotavirus, and sapovirus

Wastewater monitoring is an efficient and effective way to surveil for various pathogens in communities. This is especially beneficial in areas of high transmission, such as preK-12 schools, where infections may otherwise go unreported. In this work, we apply wastewater disease surveillance using school and community wastewater from across Houston, Texas to monitor three major enteric viruses: astrovirus, sapovirus genogroup GI, and group A rotavirus. We present the results of a 10-week study that included the analysis of 164 wastewater samples for astrovirus, rotavirus, and sapovirus in 10 preK-12 schools, 6 wastewater treatment plants, and 2 lift stations using newly designed RT-ddPCR assays. We show that the RT-ddPCR assays were able to detect astrovirus, rotavirus, and sapovirus in school, lift station, and wastewater treatment plant (WWTP) wastewater, and that a positive detection of a virus in a school sample was paired with a positive detection of the same virus at a downstream lift station or wastewater treatment plant over 97 % of the time. Additionally, we show how wastewater detections of rotavirus in schools and WWTPs were significantly associated with citywide viral intestinal infections. School wastewater can play a role in the monitoring of enteric viruses and in the detection of outbreaks, potentially allowing public health officials to quickly implement mitigation strategies to prevent viral spread into surrounding communities.

Forecasting train arrival delays on the Ankara – Eskişehir high-speed line in Turkey

Railway operations may experience delays due to technical issues or weather conditions. Accurate prediction of such delays can enhance the quality of rail transport services and the effectiveness of railway operations. The study has developed the arrival delay prediction model using random forest regression based on the train operation data from the Ankara - Eskişehir high-speed train line in Turkey. The model can simultaneously predict arrival delays at all downstream stations on this line and continuously update these predictions as new information about train movements becomes available. The accuracy rates of the model vary from 76% to 99% under a 1-min prediction error. The results show that incorporating variables related to weather conditions and technical problems related to train control systems into the model improves prediction performance. The contribution of these variables to the model performance increases as the prediction horizon widens. The model results suggest that the model predictions may assist network managers in making better decisions about train operations. In order to evaluate the model's performance from the passengers' point of view, the study has proposed two methods: the proportion of late predictions and the stability of forecasts. The findings indicate that most trains (between 96.7% and 99%) have stable arrival delay predictions at target stations. The proportion of 2-min (or greater) late predictions, which means that the predicted delay exceeds the actual delay by 2 min or more, fluctuates from 14% to 0.5%, depending on the prediction horizon. Although the ratio for the short horizons (one station ahead) becomes relatively low, it is necessary to be cautious when using the model predictions to inform passengers because a prediction of more than 1 min late for short horizons might have negative consequences (e.g., misleading passengers to leave stations).

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis and prediction is conducted for Johor River Basin incorporating the upstream to downstream water quality monitoring station data of the river. In this research, the numerical method is first used to calculate the WQI and identify the classes for validating the prediction results. Then, two ensemble and optimized machine learning models including gradient boosting regression (GB) and random forest regression (RF) are employed to predict the WQI. The study area selected is the Johor River basin located in Johor, Peninsular Malaysia. The initial phase of this study involves analyzing all available data on parameters concerning the river, aiming to gain a comprehensive understanding of the overall water quality within the river basin. Through temporal analysis, it was determined that Mg, E. coli, SS, and DS emerge as critical factors affecting water quality in this river basin. Then, in terms of WQI calculation, feature importance method is used to identify the most important parameters that can be used to predict the WQI. Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. Two ensemble ML approaches are chosen to predict the WQI in the study area and achieved a R2 of 0.86 for RF-based regression and 0.85 for GB-based ML technique. Finally, this research proves that using only the biochemical oxygen demand (BOD), the chemical oxygen demand (COD) and percentage of dissolved oxygen (DO%), the WQI can be predicted accurately and almost 96 times out of 100 sample, the water class can be predicted using GB ensembled ML algorithm. Moving forward, stakeholders may opt to integrate this research into their analyses, potentially yielding economic reliability and time savings.

Linking direct rainfall hydrodynamic and fuzzy loss models for generating flood damage map

ABSTRACT This research work proposes a combined method for mapping flood loss in catchment scale in which direct rainfall modelling and fuzzy approach are linked. The direct rainfall modelling was carried out using HEC-RAS 2D in which rainfall event hyetograph was defined as the boundary condition, and infiltration layer and roughness layer were other main inputs of the model. The fuzzy loss model was developed to assess direct-tangible damages of the flood in which expert opinions were applied to generate verbal fuzzy rules of flood loss. In this model, depth and velocity are inputs and normalized flood loss (between 0 and 1) is output. The results of the direct rainfall model and the fuzzy loss model were combined to generate loss map using python scripting in geographical information system. The output of direct rainfall model was verified based on recorded depths at downstream hydrometric station in which the Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE) were applied as the evaluation indices. Due to acceptability of indices (NSE = 0.75, RMSE = 0.83 m), the direct rainfall model was reliable. Maximum flood loss was 0.91 in the case study. Using the proposed approach is recommendable for to improve flood damage assessment in the catchments.

A comprehensive multivariate investigation of the water quality of Kallada River in Kerala, India

The Kallada River is a remarkable water resource in Kerala and reviving several villages through its glorifying journey. An investigation was directed to evaluate the spatial and temporal status of water quality in Kallada River, Kerala using multivariate statistical tools from February 2019 to January 2021. The status of fifteen environmental variables was detected by using standard methodology. The increased levels of conductivity, total dissolved solids, biochemical oxygen demand, hardness, alkalinity, calcium, and magnesium were detected during the post-monsoon (PoM) season and those parameters were tend to increase towards the downstream stations. Some other variables such as turbidity, dissolved oxygen, NO3−, SiO2, and PO43− were higher during the monsoon (MoN) season. Temperature, pH, and chemical oxygen demand were higher during the pre-monsoon (PrM) season. Furthermore, multivariate statistical tools like principal component analysis (PCA), agglomerative cluster analysis (AHC), and pearsons correlation were used to assess spatio-temporal changes in water quality to reveal pollution. Contrarily, the midstream stations such as K6, K10, and the entire downstream area (K11 to K15) of the Kallada River is under the threats of many anthropogenic activities such as sewage effluents, sand mining and agricultural runoff when compared to the entire upstream stations (K1 to K5), and three midstream stations (K7 to K9). The presence of various organic and inorganic pollutants begins from K6 and drastically rises at K11. Hence, efforts are required to prevent the contamination in the Kallada River.

Time-varying microplastic contributions of a large urban and industrial area to river sediments

The quantification of microplastic (MP) pollution in rivers is often constrained by a lack of historical data on a multi-decadal scale, which hinders the evaluation of public policies. In this study, MP contents and trends were analyzed in dated sediment cores sampled upstream and downstream of a large metropolis, in environmental deposits that exhibited consistent sedimentation patterns from the 1980s to 2021. After a thorough sedimentological analysis, MPs were quantified in samples by micro Fourier Transform InfraRed spectroscopy (μFTIR imaging) and a density separation and organic matter digestion procedure. Microplastics recorded in the upstream core are relatively ubiquitous all along the dated sequence. The results also confirmed a sever increase of microplastics levels in the downstream core, by one order of magnitude, and an increase of polymer types. Polypropylene, polyethylene, and polystyrene represent ubiquitous contamination and were predominant at the two stations, whereas polyvinyl chloride and polytetrafluoroethylene were suspected to be abundant at the downstream station, but were not detected at the upstream station. Their presence could be linked to local contamination from specific industrial sources that manufactured and utilized these polymers. Surprisingly, in the downstream station sediment has recorded a relative improvement in polymers associated with industrial sources since the 2000s and, to a lesser extent, for ubiquitous ones since the 2010s. This trend of mitigation diverges from that of global assessments, that assume uncontrolled MP pollution, and suggest that European Union wastewater policy and regulation on industrial discharges have positively influenced water quality, and certainly also on MPs. However, the accumulation of microplastics remains high in recent deposits and raises the emerging concern of the long-term management of these reservoirs.

Responses of the macroinvertebrate community to urban wastewater pollution in the upper Ouémé Basin in Benin

In Benin, most of urban wastewaters are discharged into rivers without any prior treatment. The objective of this study was to assess the effects of urban wastewater on the macroinvertebrate communities of the upper Ouémé River in Benin. To address this question, 30 stations located on five rivers were monitored in the dry and the wet seasons. For each station and each season, 12 samples of macroinvertebrates following standardized French multi-habitat sampling protocol were collected and physico-chemical parameters were recorded. Three types of stations were chosen on each river: two control stations located upstream of the wastewater discharge points, two stations impacted in the urban area and receiving urban wastewaters, and two stations downstream of the wastewater discharge points to measure the resilience of a set of river characteristics. Urban wastewater impacted the water quality by mainly increasing electrical conductivity and the nutrient concentrations. Wastewaters also deeply impacted the diversity and the composition of the invertebrate community. The Indval index highlighted three indicator taxa for the control stations (Caenidae, Baetidae and Ephemerellidae), one for the impacted stations (Chironomidae), and two for the downstream stations (Libellulidae and Lestidae). We also observed ecosystem resilience a few hundred meters downstream of the discharge points. These results challenge managers on the degradation of river water quality in the upper Ouémé River, but also reveal good self-purification capacities of the watercourses likely to promote the resilience of these ecosystems.