Stream Network Research Articles

Stream restoration can reduce nitrate levels in agricultural landscapes

The EU Water Framework Directive (WFD) has emphasized that altered stream/river morphology and diffuse pollution are the two major pressures faced by European water bodies at catchment scales. Increasing efforts have been directed toward restoration to meet WFD standards for ecological health, but this work has achieved limited success. One challenge is that little is known about how morphological changes (i.e., re-meandering) may affect nitrate retention within whole stream networks. We investigated this issue in the well-monitored Bode catchment (3200 km2) in central Germany. First, we implemented a fully distributed process-based mHM-Nitrate model, exploring its performance over the period from 2015 to 2018. Second, we simulated the effects of restoring more natural stream morphology (i.e., increasing sinuosity) on nitrate retention. The mHM-Nitrate model performed well in replicating daily discharge and nitrate concentrations (median Kling-Gupta values of 0.78 and 0.74, respectively). Within the stream network, mean and standard deviation (SD) of gross nitrate retention efficiency was 5.1 ± 0.61 % and 74.7 ± 23.2 % in the winter and summer, respectively; this measure took into account both denitrification and assimilatory uptake. In the summer, the denitrification rate was about twice as high in a lowland sub-catchment dominated by agricultural lands as in a mountainous sub-catchment dominated by forested areas (median ± SD of 204 ± 22.6 and 102 ± 22.1 mg N m−2 d−1, respectively). Similarly, in the same season, the assimilatory uptake rate was approximately five times higher in streams surrounded by lowland agricultural areas than in streams in higher-elevation, forested areas (median ± SD of 200 ± 27.1 and 39.1 ± 8.7 mg N m−2 d−1, respectively). This suggests that restoration strategies targeted at lowland agricultural areas may have a greater potential for increasing nitrate retention. In our simulation, restoring stream sinuosity was found to increase net nitrate retention efficiency by up to 25.4 ± 5.3 %; greater effects were seen in small streams. Taken together, our results indicate that restoration efforts should consider augmenting stream sinuosity to increase nitrate retention and decrease nitrate concentrations at the catchment scale.

Intra-annual patterns in biofilm communities and cellulose decomposition in a headwater stream network with spatially variable groundwater inputs

Intra-annual patterns in biofilm communities and cellulose decomposition in a headwater stream network with spatially variable groundwater inputs

Dam Sites Identification using Multi-Criteria Analysis and Spatial Weighted Overlay. The Case of Kapseret Sub-County, Kenya

Water shortage is a common phenomenon in many parts of Kenya in the dry season, including the Kapseret Sub-County. However, water harvesting has seldom been practised, despite its high potential to alleviate water shortages in the dry season. This is largely influenced by a lack of access to dams and pans. The objective of this study was to identify potential dam sites for water harvesting in Kapseret Sub County, Uasin Gishu County, Kenya. Multiple criteria analysis and weighted overlay were performed on ArcGIS to map suitable sites for the location of dams. The multiple criteria considered in site suitability analysis were land use and land cover (LULC), slope, and proximity to streams, institutions, roads, and airports. Digital Elevation Model (DEM) of 30 m resolution was downloaded from the USGS website and used to process stream network, slope, and contours. Landsat 8 satellite imagery taken on January 2022 were downloaded from the USGS website and used to generate LULC data. It was established that the Kapseret basin has moderate to highly suitable zones for dam siting, covering 74.66% of the area with only 25.34% of the land being unsuitable. Further analysis using contours identified four potential dam sites with a combined capacity of 3,436,500 m3. The study concluded that the potential for water harvesting is high in the area as significant portions of the land are generally suitable for dam siting. It was recommended that action be taken by the county and other stakeholders to develop dams in suitable zones so as to increase access to water, particularly in the dry season when there are shortages

Downstream impacts of peatland drainage on headwater stream biodiversity and ecosystem functioning

Intensive peatland drainage alters the physico-chemical status of the recipient streams, potentially leading to the loss of biodiversity and impaired ecosystem functioning. However, the extent of these changes, and particularly their impacts on downstream ecosystems, remain poorly understood. We studied the downstream effects of peatland drainage on stream biodiversity (aquatic bryophytes and macroinvertebrates) and key ecosystem processes (primary productivity, organic matter (OM) decomposition and OM standing stock). Our survey design comprised upstream (directly below drainage network) and downstream (∼300 m downstream) locations in 18 boreal headwater streams encompassing an extensive gradient of peatland drainage intensity (0–48 %). Drainage modified environmental conditions, with nutrient (TP, TN) and dissolved organic carbon (DOC) concentrations and inorganic sediment cover increasing with intensifying drainage, in both upstream and downstream sites. Species richness of both benthic macroinvertebrates and bryophytes was strongly reduced with increasing drainage intensity. Upstream and downstream sites responded in a similar manner, suggesting wide-ranging impacts of drainage on stream biodiversity. Regardless of site location, ecosystem processes were unrelated to drainage intensity. Our results confirm that intense peatland drainage not only modifies environmental conditions and biodiversity in adjoining streams, but these impacts propagate further downstream in the stream network. To prevent further degradation of drainage-impacted freshwater ecosystems, large-scale peatland restoration is needed, with prioritization of sites with the greatest potential (least drainage-induced damage) for biodiversity recovery.

Towards a continental-scale riverine bathymetry dataset using readily-available data and simple hydraulic models

Channel bathymetry data is important to both advanced hydraulic models and mobility analyses. Yet, obtaining bathymetry data at large scales is difficult. Here, we propose a method to create a continuous topobathymetric dataset that can be applied at large-scales with high-resolution using simple hydraulic models, gridded digital elevation model (DEM) data, a stream network dataset, and an average flow rate for every stream being evaluated (the latter two are often available from continental-scale streamflow models). The approach “burns-in” one of three bathymetric profiles (trapezoidal, triangular, or parabolic) into autogenerated cross-sections sampled from the DEM. Using spatial averaging, the cross-sections are then used to create a continuous topobathymetric dataset, which provides a realistic initial elevation surface for use in advanced hydraulic models. The ability of the bathymetric profiles to replicate maximum depth and hydraulic geometry was tested in the Mississippi River Basin at 11 sites with observed bathymetry data. For the majority of test sites, the observed maximum depth fell between the minimum and maximum thalweg depths calculated using the three bathymetric profile shapes, with the trapezoidal profile being the most accurate in capturing maximum depth (relative depth error of −4.2%). Based on hydraulic geometry, the triangular bathymetric profile performed best in the smaller streams while the trapezoid performed best in larger rivers. A test case along the Missouri River showed the topobathymetric dataset generated is easily implemented into advanced hydraulic models.

Comparison of machine learning algorithms to predict dissolved oxygen in an urban stream.

Water quality monitoring for urban watersheds is critical to identify the negative urbanization impacts. This study sought to identify a successful predictive machine learning model with minimal parameters from easy-to-deploy, low-cost sensors to create a monitoring system for the urban stream network, Hunnicutt Creek, in Clemson, SC, USA. A multiple linear regression model was compared to machine learning algorithms k-nearest neighbor, decision tree, random forest, and gradient boosting. These algorithms were evaluated to understand which best predicted dissolved oxygen (DO) from water temperature, conductivity, turbidity, and water level change at four locations along the urban stream. The random forest algorithm had the highest performance in predicting DO for all four sites, with Nash-Sutcliffe model efficiency coefficient (NSE) scores > 0.9 at three sites and > 0.598 at the fourth site. The random forest model was further examined using explainable artificial intelligence (XAI) and found that temperature influenced the DO predictions for three of the four sites, but there were different water quality interactions depending on site location. Calculating the land cover type in each site's sub-watershed revealed that different amounts of impervious surface and vegetation influenced water quality and the resulting DO predictions. Overall, machine learning combined with land cover data helps decision-makers better understand the nuances of urban watersheds and the relationships between urban land cover and water quality.

Predicting the QOE of Video Streaming in Communication Networks

The Video streaming QoE (Quality of Experience) index consists of a series of qualitative factors that are difficult to measure. On the other hand, other Video streaming indices, such as the KPIs (Key Performance Indicators) are easily and physically measurable. 
 
 This paper introduces a method to predict QoE based on KPIs measures in (wired or wireless) communication networks. Besides the possible academic interest, the method may practically be of interest to the network operator. Indeed, to ensure compliance with the SLA (Service Level Agreement) he would like to predict how the QoE can change as a consequence of a new network management alternative. 
 
 To perform prediction, the problem is that the network operator first needs to know how the KPIs would change due to the alternative, and then find a way to derive (say mathematically) the QoE from the new KPIs. 
 
 The contribution of this paper is a simulation/mathematical approach to solving the problem. First,  a simulation method is introduced to know how the KPIs would change as a consequence of the new alternative, and then a valid KPI/QoE mathematical relationship is introduced to derive the new QoE from the new KPIs.
 
 The paper is organized as follows: in Section 1 an introduction is given to the definition of QoE in Video streaming. In Section 2  the status of the art from the literature on the KPI/QoE mathematical models is dealt with, and a valid model is identified that derives the Video streaming QoE from the network KPIs. In Section 3, a simulated network is introduced to know how the KPIs would change as a consequence of a new network management alternative.  Finally, Section 4 uses the identified mathematical relationship to predict the Video streaming QoE from the measured KPIs. 
 
 The considered application is an LTE (Long Term Evolution) network, but the approach can be extended to any communication network, wired or wireless from 3G onwards.

GEOMORPHOLOGY, TERRACES OF VARIOUS DOMINE OF ALAKNANDA ITS TRIBUTARIES AND NEOTECTONISAM, GARHWAL HIMALAYA PARTS OF CHAMOLI TEHRI UTTAKASHI & PAURI UTTAR PRADESH (UTTRARKHAND) INDIA

The Geological and Geomorphological study in Upper Ganga basin has been attempted in parts of de Uttarkashi,Chamoli, Pauri and Tehri districts, an area of 10000 Sq.kms in parts of QA sheet 53J and 53 N on 1:50000 scale of Garhwal Himalaya U.P has been covered presently known as Uttrakhand State of Union of India. Out of total area of study, an area about 3500 sq.Kms (1:50000 scale) has been selected for detailed Geological, Geomorphological and Sedimentological study. The area of Upper Ganga basin consisting of Alaknanda, Bhagirathi, Bhilangna, Nandakini, Mandakini, Pindar, Dhauli- Ganga Bal- Ganga, Madhmeshwar Ganga and Berhi Ganga. The Alaknanda is chracterised by six terraces followed by Bhagirathi with five terraces, Bhilangna Nandakini four terraces, Mandakini /Pindar/Dhauli-Ganga /Balganga three terraces, Madhmshwar Ganga two terraces and Berhi Ganga one terrace, amidst these Alaknanda is trunk stream and others are tributaries. Geomorphologically, the area is divided in to seven geomorphic units viz High relief formerly glaciated area, Structural hills, Denudational hills, Area of mass wasting, High level dissected fans River Terraces and Present day flood plain of Alaknanda and its tributaries. These units area developed in response to lithology to erosional land depositional activities and tectonic in which they are embedded. Each unit is characterized by distinct drainage, diagnostic geomorphic landform elements and features, photo charcters and morphogenetic expression. The other geomorphic features and elements identified in the area are point bar, channel bar, alluvial fans ,talus cone , rock cut terraces fan cut terraces , abandoned and fossil channel courses,epigenetic gorges, strant lines landslides, rock fall scree and scree slope, retreating scarp, abandoned cirque, cirque morines, arêtes, threshold, horn peak and glacial lake. The area genetically comprised of terraces of three domains, viz. glacial, fluvio-glacia and fluvial which represent distinct environment of sedimentation of Pleistocene, late Pleistocene and Holocene time during Quaternary period. The glacial terraces are identified at an average elevation of 1150 m above m.s.l. the fluvio-glacial terraces at an average elevation of 975 m above m.s.l. and fluvial terraces at an average elevation between 650 to 900 m above m.s.l. The Badrinath temple is situated in glaciated trough valley of Alaknanda, which comprise of, four levels of the glacial terraces (lateral moraine), cirque moraine on mountain flanks and the terminal moraine at the base of valley. whereas Kedarnath in the upstream of Mandakini, where number of well developed cirques were identified on high mountain, they are mostly arm chair shaped hollows with a steep to vertical head walls, concave floor and a threshold, these cirques are noticed at an average height of about 4500 m. The Kedarnath temple is situated in glaciated trough valley on quaternary platform formed by coalescing of cirque, lateral moraine and terminal moraine. The-Himalayan thrust like Central thrust Srinagar thrust, Alaknanda fault, Tons thrust show flattening dip suggesting that they lie at comparatively at shallow depth at short distance, down dip from their out crops, recent movements along the trace of these thrust/ faults are not of high and longer magnitude but have considerably affected Quaternary terraces of glacial, fluvio-glacial and fluvial domain of Alaknanda and its tributaries. Beside drainage, river bed profile, landscape architect and over all morphogenetic expression of the area, this bears the adverse impact of tectonic activity. The imprints of neotectonisam associated in the area indicate that these thrust /faults are active and have, signatures of sinking of landscape, mass wasting activities and posed neosiemic hazards and tectano-ecological problems, bears signatures of sinking of the area, mass wasting activities and neosiemic hazards. The geomophological study of the area, overall morphogenetic illustration, imprints of neotectonisam, disposition of terrces of different domains, mass wasting activities, drainage net, channel morphology, landform elements of glacial, fluvioglacial and fluvial domain of Alaknanda and its tributaries indicate that tectonically the area is active and slow movements are taking plae along thrust, faults and lineament. In the area North of Wazri in Jamuna valley, North of Uttarkashi around Sainj in Bhagirathi valley, around Tugnath and Chamoli and South of Joshimuth in Alaknanda valley revealed that there is sharp persistent curvilinear break in topography, which has geothermal manifestation at Wazri Gagnani anda Tugnath in the form of emitation of hot springs. This curvilinear line has further reconfigurated the neo stream net works system and affected morphogenetic expression it appears to be due to horizontal movement of sub - tectonic plate towards south, which, is sole collective and cumulative causative factor in dislocation of tectonic ecology of the area and has resulted micro earthquake shocksand and segmental sinkiking of area. The overall study revealed that the the area is sensitive active and is vulnerable to any techtono- seismic event it is matter of serious concerned and needs further attention.

Spatio-Temporal Model of a Product–Sum Simulation on Stream Network Based on Hydrologic Distance

The modeling of spatio-temporal processes is crucial in many fields such as environmental science, hydrology, and water storage engineering. A basic concept of the spatio-temporal model is an auto-covariance function. The stream network model based on the Euclidean distance as the auto-covariance function for investigating river pollution will potentially face a serious accuracy problem, due to the material or water pollutant movement on the water only occurring within the river network and the distance travelled not following the path of the river network. The use of the hydrologic distance may overcome the problem because, statistically, the Euclidean distance-based auto-covariance function is not valid if it is a negative-definite. Therefore, this study develops a spatio-temporal model of the generalized product–sum based on the hydrologic distance with a positive-definite property. The proposed model is built and, subsequently, it is then verified mathematically to be positive-definite. Furthermore, the relationships between the hydrologic distance, the Euclidean distance, and spatial configurations are also investigated. The simulation study result shows the proposed spatio-temporal model is positive-definite for some semivariogram models. The relationships between spatial configurations in the stream network follow an exponential model.

On the impact of spatial heterogeneity and drift rate in a three-patch two-species Lotka–Volterra competition model over a stream

In this paper, we study a three-patch two-species Lotka–Volterra competition patch model over a stream network. The individuals are subject to both random and directed movements, and the two species are assumed to be identical except for the movement rates. The environment is heterogeneous, and the carrying capacity is lager in upstream locations. We treat one species as a resident species and investigate whether the other species can invade or not. Our results show that the spatial heterogeneity of environment and the magnitude of the drift rates have a large impact on the competition outcomes of the stream species.

Changes in the surface water – Groundwater interactions of the Murray-Darling basin (Australia) over the past half a century

The Murray-Darling Basin is Australia’s most stressed water resource and most contentious in the sharing of those water resources. Our hydrological models used for water management have no information on the surface water – groundwater interactions within a reach and so implicitly assume that their relationship is stationary. This study seeks to test this assumption by looking at the direction of surface water – groundwater exchanges and how they have changed over the 49-year period from 1970 to 2019 at the whole of basin scale (492 river reaches). This was achieved by comparing the groundwater level at 59,340 bores to the elevation of the surface water at the closest point on the stream network. Each point evaluated on the stream network was classified as either gaining (water from groundwater) or losing (water to groundwater) at an annual scale. These point scale observations were aggregated to the reach scale (as used in river systems models) for analysis of trends though time. The proportion of bores predicting losing conditions was falling through the 1970’s and 1980’s before reaching a minimum of 48% in 1995/96. From this period the proportion of bores predicting losing conditions increased to a maximum at the end of the analysis period of 78% in 2018/19. This finding at the point scale is replicated at the reach scale. The reaches with greater than 80% of bores predicting losing conditions had a minimum in 1993/94 (at 40%) and increased to 66% at the end of the analysis period. These observations are consistent with the management challenges of the time. In the 1980’s and 1990’s gaining conditions leading to increasing salt loads in the streams caused by groundwater discharge were a major problem for downstream water users. Since the turn of the century drought and water scarcity have been the major challenges with falling groundwater levels leading to increases in losing conditions. This study has shown that the surface water – groundwater interactions are not stationary and have been changing through time with many reaches having changed direction from gaining to losing throughout the analysis period. This is highlighting a deficiency in our hydrological models that cannot replicate this change in flux direction and therefore leading to errors in predicting low flows.

Analysis and countermeasures of the “7.20” flood in Zhengzhou

ABSTRACT The “7.20” extreme rainstorm in Zhengzhou caused a serious urban flood. The reasons are as follows: In addition to being affected by global climate change, the rapid urbanization of Zhengzhou in the last 40 years has caused tremendous changes in the city’s water system, underlying surface, urban topography, vertical system, urban underground facilities, all of which will directly or indirectly become factors of urban floods. Then, in response to the increasing frequency of urban floods, it is proposed to develop an overall flood resilience strategy led by the urban stream network; to establish a “large sponge city system of watersheds” and a resilient city’s disaster prevention concept; and to improve the urban flood emergency management system. This is done to adjust measures to local conditions and collaborate with various departments to improve the disaster response capacity of urban floods.

Spatial scale impacts microbial community composition and distribution within and across stream ecosystems in North and Central America.

A mechanistic understanding of factors that structure spatiotemporal community composition is a major challenge in microbial ecology. Our study of microbial communities in the headwaters of three freshwater stream networks showed significant community changes at the small spatial scale of benthic habitats when compared to changes at mid- and large-spatial scales associated with stream order and catchment. Catchment (which included temperate and tropical catchments) had the strongest influence on community composition followed by habitat type (epipsammon or epilithon) and stream orders. Alpha diversity of benthic microbiomes resulted from interactions between catchment, habitat, and canopy. Epilithon contained relatively more Cyanobacteria and algae while Acidobacteria and Actinobacteria proportions were higher in epipsammic habitats. Turnover from replacement created ~60%-95% of beta diversity differences among habitats, stream orders, and catchments. Turnover within a habitat type generally decreased downstream indicating longitudinal linkages in stream networks while between habitat turnover also shaped benthic microbial community assembly. Our study suggests that factors influencing microbial community composition shift in dominance across spatial scales, with habitat dominating locally and catchment dominating globally.

Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)

Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)

Counting piglet suckling events using deep learning-based action density estimation

Analysis of piglet suckling behaviour is important for the evaluation of piglet nutrient ingestion, health, welfare, and affinity with the sow. In this study, an action density estimation network (ADEN) was proposed for counting the events of piglet suckling followed by automated analysis of suckling behaviour. ADEN is a two-stream network primarily composed of 1) a network stream that processes video images with a higher frame rate (faster stream) and 2) a network stream that processes video images with a lower frame rate (slower stream). Each stream consists of a ResNet-50 with five convolutional stages. A multi-stage attention connection (MSAC), composed of four Spatial-Temporal-Channel (STC) multi-attention structures, is proposed to bridge Faster Stream and Slower Stream and capture discriminative features. The output attention features from each faster stream stage are laterally fused into the corresponding slower stream stage in a concatenating manner. Following this, the features from the last convolutional stages in the Slow stream and Fast stream are fused using concatenation and are then decoded by using three convolutional layers. The last convolutional layer outputs a heatmap on the action density of piglet suckling behaviour. Finally, the number of suckling events is predicted by integrating all the pixel values in the heatmap. Experimental and comparative tests were conducted to validate the effectiveness of the proposed ADEN with a training dataset and a test dataset from 14 pig pens. The 507 video clips (126,750 images for 7 h) from the 1-9th pens were selected as training datasets. The 143 video clips (35,750 images for 2 h) from the 10-13th pens were selected as short-term test datasets. One untrimmed video (162,000 images for 9 h) from the 14th pen was used to ultimately evaluate the action density estimation performance of the ADEN. ADEN was compared with seven approaches and its superiority was demonstrated with an r = 0.938, an RMSE = 1.080, and a MAE = 0.967 in short video clips and r = 0.982, MAE = 0.161, and RMSE = 0.563 in the untrimmed long video. The ADEN proved it feasible to predict the number of suckling events by using action density estimation.

A novel parallel merge neural network with streams of spiking neural network and artificial neural network

Some neuroscientific studies have demonstrated that the human brain is a complex integrated spatiotemporal system. The human brain supports a wide range of models, algorithms and coding schemes. Inspired by this point, we propose a novel neural network model, the parallel merge neural network (PMNN), which processes spatial and temporal information in parallel with two streams: a spiking neural network (SNN) and an artificial neural network (ANN). The information learned through these two streams is finally merged and flows to the final output layer. Unlike existing wide networks that process a single type of information, the PMNN can learn from the rich spatial, temporal and spatiotemporal information underlying training samples in a manner that is similar to that of the human brain. The experimental results with five UCI datasets and the MNIST dataset demonstrate that the PMNN is very efficient in terms of convergence and generalizable in terms of accuracy, generation, macro-recall, macro-precision and macro-F1.

Extending Height Above Nearest Drainage to Model Multiple Fluvial Sources in Flood Inundation Mapping Applications for the U.S. National Water Model

AbstractHeight Above Nearest Drainage (HAND), a drainage normalizing terrain index, is a means able of producing flood inundation maps (FIMs) from the National Water Model (NWM) at large scales and high resolutions using reach‐averaged synthetic rating curves. We highlight here that HAND is limited to producing inundation only when sourced from its nearest flowpath, thus lacks the ability to source inundation from multiple fluvial sources. A version of HAND, known as Generalized Mainstems (GMS), is proposed that discretizes a target stream network into segments of unit Horton‐Strahler stream order known as level paths (LPs). The FIMs associated with each independent LP are then mosaiced together, effectively turning the stream network into discrete groups of homogeneous unit stream order by removing the influence of neighboring tributaries. Improvement in mapping skill is observed by significantly reducing false negatives at river junctions when the inundation extents are compared to FIMs from that of benchmarks. A more marginal reduction in the false alarm rate is also observed due to a shift introduced in the stage‐discharge relationship by increasing the size of the catchments. We observe that the improvement of this method applied at 4%–5% of the entire stream network to 100% of the network is about the same magnitude improvement as going from no drainage order reduction to 4%–5% of the network. This novel contribution is framed in a new open‐source implementation that utilizes the latest combination of hydro‐conditioning techniques to enforce drainage and counter limitations in the input data.

From market to environment – consumption-normalised pharmaceutical emissions in the Rhine catchment

Direct and indirect threats by organic micropollutants can only be reliably assessed and prevented if the exposure to these chemicals is known, which in turn requires a confident estimate of their emitted amounts into the environment. APIs (Active Pharmaceutical Ingredients) enter surface waters mostly through the sewer system and wastewater treatment plants (WWTPs). However, their effluent fluxes are highly variable and influenced by several different factors that challenge robust emission estimates. Here, we defined a dimensionless, theoretically consumption-independent ‘escape factor’ (kesc) for estimating the amount of APIs (expected to be) present in WWTP effluents. The factor is determined as the proportion of marketed and actually emitted amounts of APIs. A large collection of German and Swiss monitoring datasets were analyzed to calculate stochastic kesc values for 31 APIs, reflecting both the magnitude and uncertainty of consumption-normalised emissions. Escape factors provide an easy-to-use tool for the estimation of average API emissions and expected variability from numerous WWTPs given that consumption data are provided, thereby supporting simulation modeling of the fate of APIs in stream networks or exposure assessments.

Synthesis and optimization of a selective treatment network of wastewater streams contaminated with paracetamol

The synthesis and optimization of a selective treatment system for contaminated wastewater using Fenton and sono-Fenton oxidation processes is addressed in this work. The problem investigated can be useful for the selective treatment of hospital effluents contaminated with traces of some drug, and the removal of paracetamol is used as a study case. A nonlinear programming model (NLP) that uses a single treatment unit with a defined volume is proposed for the removal of paracetamol by using reported degradation kinetics for each advanced oxidation process evaluated. The cost of the treatment system is considered to be directly proportional to the treated flow. The use of the proposed model is illustrated with the solution of a case study that shows its versatility to achieve optimal treatment systems. Results show that, for the same set of effluents to be treated, the sono-Fenton process exhibits a better paracetamol remotion efficiency, i.e., requires less flow through the treatment unit. As higher concentrations of paracetamol are demanded in the discharge, the lesser is the flow of contaminant effluent that is sent to the treatment unit as well. Whilst, as lower concentrations of paracetamol are demanded in the discharge, an opposing effect is observed. Finally, a discussion of the removal ratio of paracetamol in the overall process and in the treatment unit as a function of the discharged limit of paracetamol in the effluent is addressed.

A screening study of the spatial distribution and cumulative toxicity of agricultural pesticides in the European Union’s waters

Pesticides can be an important stressor to aquatic ecosystems, and their use is strictly regulated in the European Union (EU). However, data on the use of pesticides are rather limited and poorly available, and monitoring is often insufficient to characterize their actual exposure and impact. The aim of the work presented here is to harness the limited data available and assess, for the first time, the distribution of concentrations and toxicity of 148 pesticide active substances (AS) for the whole EU. Starting from available estimates of pesticide use in agriculture and a simple screening-level model of their fate and transport, we quantify pesticide concentrations in soil and water. A comparison with monitoring data shows that predicted water concentrations are in plausible orders of magnitude, hence the model can be regarded as a first-approximation representation of the distribution of pesticides in the environment. The toxicity of individual pesticide active substances (AS) is characterized by their concentrations divided by the respective no observed effect concentrations (NOEC) for aquatic organisms, which represents the “toxic units” (TU) of each AS. The cumulative toxicity of pesticides in soils and streams of the EU is obtained by summing the TU of individual AS. We estimate that the toxicity of individual AS is generally well below 0.1 TU, indicating relatively safe environmental exposure. However, the cumulative toxicity of a mixture of AS can exceed 0.1 toxic units (TU) for more than 27% of the length of the EU’s stream network, and 1 TU for more than 4%. The cumulative toxicity at a given location is driven by only a handful of AS, but these differ from site to site reflecting the variability of pesticide use. Still, we estimate that only about 20 AS out of 148 appear among the top contributors to cumulative toxicity in most cases. While our assessment suggests a relatively widespread risk due to pesticide pollution, it also points to the important limitations concerning knowledge of pesticide use and monitoring of pesticide occurrence in the environment. These limitations need to be addressed in order to evaluate more accurately the effectiveness of EU pesticide policies. The assessment represents a proof-of-concept of a method that can be applied in support of the monitoring of pesticide policies implementation in the EU and elsewhere, once pesticide use can be estimated.