In addition to common pollutants such as organic matter and nutrients, an ever-growing range of chemicals increasingly threatens the quality of rivers and lakes, as well as the health of aquatic ecosystems. Understanding the main sources and transport pathways of these substances therefore represents a key scientific and management challenge.Within the framework of the Tethys project, a hazardous substance emission model was developed for the Danube River Basin (DRB) through close cooperation among nine Danube countries. The modelling work was based on the systematic collection of concentration and emission data for multiple transport pathways, resulting in a substance-specific inventory that served as the foundation for emission modelling.A common Danube-wide modelling tool was implemented using the MoRE (Modelling of Regional Emissions) framework. The model represents between four and eleven emission pathways for three substance groups: potentially toxic elements (PTEs), including six heavy metals and arsenic; industrial chemicals represented by the two most widespread per- and polyfluoroalkyl substances (PFOS and PFOA); and human pharmaceuticals represented by a widely used analgesic (diclofenac) and a psychoactive compound (carbamazepine). In addition to major point sources, the model accounts for numerous diffuse pathways, including groundwater, surface runoff, tile drainage, erosion, atmospheric deposition, and various legacy pollution sources such as landfills, aerodromes, and industrial disposal sites.The modelling framework includes a newly developed retention approach that explicitly accounts for riverine retention for each substance group, as well as an uncertainty assessment module designed to quantify parameter uncertainty. This module is implemented within an R-based computational engine of the MoRE model.Model validation was performed using long-term river monitoring data from existing operational monitoring networks, complemented by additional datasets collected by partner institutions during project implementation. Discharge data were provided by the participating countries.The modelling results indicate that erosion, groundwater, municipal and industrial wastewater systems are the dominant emission pathways for PTEs, with pronounced spatial variability along the DRB. Point source contributions dominate in the Upper Danube, whereas agricultural-related diffuse pollution becomes increasingly important in the Lower Danube. For PFASs and pharmaceuticals, municipal wastewater represents the main emission pathway in most sub-catchments. In the case of PFASs, soils also act as relevant reservoirs, and associated pathways such as surface runoff, erosion, groundwater flow, and tile drainage may contribute substantially to riverine loads.

Despite the tremendous efforts to improve river water quality, chemical contamination remains a significant issue. Besides well-known contaminants, in recent years, pollutants of industrial origin received increasing attention because of the huge knowledge gap regarding their occurrence, fate and environmental risks. Moreover, such pollutants often exhibit high concentration fluctuations over time, which makes them less predictable and measurable with classical short-time campaigns.This study provides insights into the different sources of chemical contamination of the Rhine River based on temporal high-frequency LC-HRMS monitoring data from a single location. A newly developed prioritization strategy selected nearly 3000 substances as potentially major contaminants. A novel classification analysis based on temporal behavior identified 53% of these compounds (accounting for 62% of the time-integrated intensity recorded in the dataset) as originating from irregular emission sources. Irregular emissions can originate from industrial production cycles. After delimiting other potential irregular sources, we have strong evidence indicating that a considerable share of the irregular emissions likely comes from industrial activities. This finding is supported by the structural elucidation of sixteen irregularly emitted substances, for which the industrial origin was successfully confirmed. Those compounds include 3-chloro-5-(trifluoromethyl)pyridine-2-carboxylic acid and 4-(dimethylamino)-2,2-diphenylpentanenitrile. In addition, 40 other compounds exhibited temporal emission patterns similar to the sixteen industrial compounds, which strongly suggests a common contamination source. Finally, 100 top-ranking compounds were selected for further structural elucidation and emission reduction measures. The computational approach outlined within this study can be effectively applied in other large river catchments to identify unknown contaminants stemming from industrial sources.

The large, shallow Lake Balaton (Hungary) has experienced rapid salinization since the 1970s. This study investigated the causes of salinization and aimed at predicting the effects of climate change. Monthly mass balance models for chloride and sodium were calibrated using water balance and water quality monitoring data (1976–2022) to analyze the effects of climate change (2022−2100) through ensemble modeling under the IPCC RCP 4.5 scenario. Current (2016–2020) emission inventories were developed for both chloride and sodium. The long-term (1921–2022) emission inventory of chloride was used to build a simplified chloride balance model for the catchment.Historical salinization occurred with almost constant external loading, in parallel with the increasing water residence times. According to the mass balance model, 18–28 % of the cumulative chloride and sodium loads has been accumulated in lake sediments, potentially slowing recovery of the lake from salinization. Climate change was predicted to aggravate salinization by further reducing the water balance surplus. Even the extremely high chloride concentrations of the future will remain well below the drinking water limit, but they may adversely affect the aquatic ecosystem. Both agriculture and road deicing contributed about one-third of current chloride emissions. Wastewater accounted for <20 % due to significant wastewater diversion to adjacent catchments. The rapid intensification of Hungarian agriculture from the mid-1960s, followed by a sudden economic collapse in 1990, resulted in a large emission peak of chloride in the 1970–80s, providing a unique opportunity to estimate the long-term retention of chloride in the catchment. We estimated that 30 % of the chloride emitted since 1921 may still be present in groundwater/soils.

Land-use change may significantly influence streamflow. The semi-empirical model PhosFate was used to analyze the impact of land use and climate change on streamflow by choosing the Guishui watershed as a pilot site and then expanding, applying it to all of North China. The Guishui watershed (North Beijing, China) has experienced a dramatic decline in its streamflow in recent decades. Parallel to this, significant land-use change has happened in this area; afforestation programs have increased forest cover from 41% (1980) to 59% (2013) and a similar increase in forest cover can also be observed in North China. Managing flow decline requires separating climatic and direct human-influenced effects. The results showed the following: (1) Afforestation is a major factor that decreased total flow in the Guishui watershed from 1996 to 2014; total flow increased by around 24% more than the actual dataset in the constant scenario (no afforestation) and decreased by 5% more than the actual dataset in the forest scenario (all agriculture land use transferred to forests). (2) When forest coverage increases, the Qinghai–Tibet Plateau and the Loess Plateau are the most sensitive areas regarding total flow in North China; the total flow change rate increased by up to 25% in these two areas when land use shifted from sparse vegetation to mixed forests. After analyzing the contributions of these two factors, we formulated recommendations on future afforestation practices for North China. In the central–north and northwest districts, the annual precipitation is under 520 mm and 790 mm, respectively, and the practice of afforestation should be more carefully planned to prevent severe damage to streams. This research also proved that the PhosFate model can be used in North China, which would be a practical tool for watershed management.

Identifying a chemical's potential for biotransformation in the aquatic environment is crucial to predict its fate and manage its potential hazards. Due to the complexity of natural water bodies, especially river networks, biotransformation is often studied in laboratory experiments, assuming that study outcomes can be extrapolated to compound behavior in the field. Here, we investigated to what extent outcomes of laboratory simulation studies indeed reflect biotransformation kinetics observed in riverine systems. To determine in-field biotransformation, we measured loads of 27 wastewater treatment plant effluent-borne compounds along the Rhine and its major tributaries during two seasons. Up to 21 compounds were detected at each sampling location. Measured compound loads were used in an inverse model framework of the Rhine river basin to derive k'bio,field values - a compound-specific parameter describing the compounds' average biotransformation potential during the field studies. To support model calibration, we performed phototransformation and sorption experiments with all the study compounds, identifying 5 compounds that are susceptible towards direct phototransformation and determining Koc values covering four orders of magnitude. On the laboratory side, we used a similar inverse model framework to derive k'bio,lab values from water-sediment experiments run according to a modified OECD 308-type protocol. The comparison of k'bio,lab and k'bio,field revealed that their absolute values differed, pointing towards faster transformation in the Rhine river basin. Yet, we could demonstrate that relative rankings of biotransformation potential and groups of compounds with low, moderate and high persistence agree reasonably well between laboratory and field outcomes. Overall, our results provide evidence that laboratory-based biotransformation studies using the modified OECD 308 protocol and k'bio values derived thereof bear considerable potential to reflect biotransformation of micropollutants in one of the largest European river basins.

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.

Assessing the persistence of organic micropollutants from field data has been notoriously laborious, requiring extensive data including emissions and chemical properties, and the application of detailed mass-balance models, which often contain parameters that are impossible to measure. To overcome some of these obstacles, we developed the concept of persistence benchmarking for large rivers that receive numerous emissions and provide enough residence time to observe the dissipation of compounds. We estimated the dissipation rate constants of 41 compounds (mostly active pharmaceutical ingredients) from five measurement campaigns in the Rhine and Danube rivers using concentration rate profiles with respect to carbamazepine. Dissipation rates clearly distinguished between known fast- and slow-degrading compounds, and campaign-specific boundary conditions had an influence on a minor subset of compounds only. Benchmarking provided reasonable estimates on summer total system half-lives in the Rhine compared to previous laboratory experiments and a mass-balance modeling study. Consequently, benchmarking can be a straightforward persistence assessment method of continuously emitted organic micropollutants in large river systems, especially when it is supported by field monitoring campaigns of proper analytical quality and spatial resolution.

After 25 years of apparently successful eutrophication management, a record-breaking mixed bloom of Ceratium furcoides and Aphanizomenon flos-aquae developed unexpectedly in 2019 in the large, polymictic Lake Balaton. The peak concentration of chlorophyll exceeded 300 mg/m(3), 1.5 times higher than the pre-management maxima. The external load was insufficient to provide the phosphorus (P) required to support this bloom, and its taxonomic composition was radically different from blooms in the 1980s. We hypothesised: (1) that unusually long periods of intermittent stratification led to anoxic P release from the sediments, providing the required P; and (2) that the surprising bloom composition indicated a potential regime shift. To test hypothesis 1, we analysed decade-long high-frequency data on drivers of lake metabolism recorded at a shallow near-shore observatory. To explore likely dissolved oxygen (DO) conditions in deep water in 2019, we performed a sensitivity analysis of a one-dimensional DO model driven by the General Ocean Turbulence Model, the latter being calibrated against high-frequency hydrometeorological data measured in the open water. Hypothesis 2 was examined by a sequential t-test analysis applied to multidecadal (1976-2020) data on phytoplankton biomass and composition, nutrient loads and summer mean air temperature. We found that DO depletion (<1 g/m(3)) was the immediate precursor of the large 2019 bloom. The synergistic impact of a climatic regime shift and multiple coincident management actions provoked a shift in phytoplankton composition in 2013 and forced the ecosystem into the state of internal eutrophication. In the present ecosystem state, similarly large blooms could develop in any summer depending on the sequence of weather events. Within the present, warmer climate regime identified here, the effectiveness of external nutrient control in eutrophication management may be limited by changes in the thermal structure. Therefore, it is vital to supplement external nutrient control with internal nutrient control strategies, such as water level regulation, to manage eutrophication and prevent future blooms. Large, continuous polymictic lakes are susceptible to small changes in water level, because the likelihood of stratification (and thus the likeliness of a eutrophication event) increases steeply and non-linearly with increasing depth. With a climate that continues to warm, and the associated changes expected in internal lake processes, it is possible that we will see regime shifts (such as that identified in Lake Balaton) across more lakes globally. Managing these conditions will require case-specific modifications of lake management plans.

During the past decades, on-line monitoring of freshwater lakes has developed rapidly. To use high frequency time-series in lake management, novel models are needed that are simple and provide insight into the complexity of phytoplankton dynamics. Chlorophyll a (Chl), a proxy for phytoplankton biomass and environmental drivers were monitored on-line in large, shallow Lake Balaton during the vegetation periods between 2001 and 2018. Growth and non-growth (G and non-G) states of algae were deduced from daily change in Chl. Random forests (RF) were used to find stochastic response rules of phytoplankton to growth-supporting environmental habitat templates. The stochastic G/non-G state was translated into long-term daily biomass dynamics by a deterministic biomass model to assess uncertainty and to distinguish between inevitable and unpredictable blooms. A biomass peak was qualified as inevitable or unpredictable if the lower 95% confidence limit of simulations exceeded or remained at the baseline Chl level, respectively. Compared to a stochastic null model based on monthly Markovian transition probabilities, RF-based models captured wax and wane of biomass realistically. Timing of peaks could be better simulated than their magnitude, likely because habitat templates were primarily determined by light whereas peak sizes might depend on unmeasured processes, such as phosphorus availability. In general, algal growth was favored by wind-induced sediment resuspension that decreased light availability but simultaneously enhanced the P supply. Seasonal temperature and an integral of departures from the "normal" seasonal temperature over 2 to 3 generations were important drivers of phytoplankton growth, whereas short-term (diel and day to day) changes in water temperature appeared to be irrelevant. Four types of years could be distinguished during the study period with respect to algal growth conditions. The present modeling approach can reasonably be used even in highly variable aquatic environments when 3 to 4 years of daily data are available.

Studying aquatic biotransformation of chemicals in laboratory experiments, i.e., OECD 308 and OECD 309 studies, is required by international regulatory frameworks to prevent the release of persistent chemicals into natural water bodies. Here, we aimed to address several previously described shortcomings of OECD 308/309 studies regarding their variable outcomes and questionable environmental relevance by broadly testing and characterizing a modified biotransformation test system in which an aerated water column covers a thin sediment layer. Compared to standard OECD 308/309 studies, the modified system showed little inter-replicate variability, improved observability of biotransformation, and consistency with first-order biotransformation kinetics for the majority of 43 test compounds, including pharmaceuticals, pesticides, and artificial sweeteners. To elucidate the factors underlying the decreased inter-replicate variability compared to OECD 309 outcomes, we used multidimensional flow cytometry data and a machine learning-based cell type assignment pipeline to study cell densities and cell type diversities in the sediment and water compartments. Our here presented data on cell type composition in both water and sediment allows, for the first time, to study the behavior of microbial test communities throughout different biotransformation simulation studies. We found that sediment-associated microbial communities were generally more stable throughout the experiments and exhibited higher cell type diversity than the water column-associated communities. Consistently, our data indicate that aquatic biotransformation of chemicals can be most robustly studied in test systems providing a sufficient amount of sediment-borne biomass. While these findings favor OECD 308-type systems over OECD 309-type systems to study biotransformation at the water-sediment interface, our results suggest that the former should be modified toward lower sediment-water ratios to improve observability and interpretability of biotransformation.

Chemicals’ half-lives derived from biotransformation simulation studies are central metrics for persistence assessment in international regulatory frameworks. To determine the persistence of chemic...

This paper presents the result of suspended and deposited sediment measurements on the floodplain of Long Xuyen Quadrangle-An Giang (LXQ-AG) along the Cambodia - Vietnam border during a flood event in 2018. GIS and Remote sensing techniques were used to build maps for the flooded areas. Water level and discharge were also measured on main channels and on the floodplain. The two correlation equations between deposition sediment, Suspended Sediment concentration (SSC) and discharge were build. The results of Landsat interpretting showed that inundation area of the floodplain was 35,765 ha. SSC and deposited sediment decreased with distance from the main channels. The results showed thattotal deposited sediment mass brought to the floodplain during a single flood event in 2018 was 5.023 million tons (14.04 kg/m2).

Environmental management decisions should be made based on solid scientific evidence that relies on monitoring and modeling. In practice, changing economic, societal, and political boundary conditions often interfere with management during large, long, and complex projects. The result may be a sub-optimal development path that may finally diverge from the original intentions and be economically or technically ineffective. Nevertheless, unforeseen benefits may be created in the end. The Kis-Balaton wetland system is a typical illustration of such a case. Despite tremendous investments and huge efforts put in monitoring and modeling, the sequence of decisions during implementation can hardly be considered optimal. We use a catchment model and a basic water quality model to coherently review the impacts of management decisions during the 30-year history. Due to the complexity of the system, science mostly excelled in finding explanations for observed changes after the event instead of predicting the impacts of management measures a priori. In parallel, the political setting and sectoral authorities experienced rearrangements during system implementation. Despite being expensive as a water quality management investment originally targeting nutrient removal, the Kis-Balaton wetland system created a huge ecological asset, and thereby became worth the price.

The study describes a Bayesian statistical model of selected water quality variables in 102 catchments. The model successfully described both the spatial and temporal variability of certain variables, and performed quite well at describing the site-specific means for all variables. Based on the results, the model can serve as a valuable prediction tool in the calibration region (and potentially adapted elsewhere too).

The study describes a Bayesian statistical model of selected water quality variables in 102 catchments.The model successfully described both the spatial and temporal variability of certain variables, and performed quite well at describing the site-specific means for all variables.Based on the results, the model can serve as a valuable prediction tool in the calibration region (and potentially adapted elsewhere too).The main issue with the manuscript is that the otherwise valuable work is presented in an unsuitable (and constantly evolving) context.The title appropriately focuses on the main element of the study, the model and emphasised predictions as the primary C1

Inverse modeling is a common practice to decompose observed processes into constituents that are unobservable or difficult to measure. To achieve this goal, a mechanistic model is calibrated to fit the observations and thereby the model produces a coherent set of constituent estimates. A disadvantage of this procedure is that any disagreement between the model assumptions and reality potentially introduces bias and other statistical artifacts into the constituents and their relations. Lake metabolism is recently most often followed by high‐frequency measurements of dissolved oxygen, and inverse modeling with simple conceptual models is used to couple oxygen dynamics to ecosystem‐wide aggregated metabolic rates, such as net ecosystem production (NEP). These models rely on estimates of gas exchange and community respiration. Using a model of a simple ecosystem and field data, we demonstrate that typical relations between modeled metabolic rates frequently do not follow patterns expected from synthetic ecosystems and that estimation errors strongly influence calculations by producing strong, spurious correlations. Correlation artifacts can be expected during inverse modeling, whenever observed time series are decomposed into poorly known or unmeasured processes that can compensate for the effect of each other.

Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which require large sampling and analytical efforts to obtain representative data on the actual water quality.Models can also be used to predict to what degree streams are affected by these pollutants. However, spatially distributed modelling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas where predictions are needed.To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge. For larger catchments, routing was explicitly considered by coupling the iWaQa to the AQUASIM model.The model was calibrated with datasets from three different small catchments (0.5–24.6 km2) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was validated for herbicides and biocides in Switzerland for different years on 12 catchments of much larger size (31–35 899 km2) and for herbicides for the entire Rhine basin upstream of the Dutch–German border (160 000 km2) without any modification. For most compound–catchment combinations, the model predictions revealed a satisfactory correlation (median r2: 0.5) with the observations. The peak concentrations were mostly predicted within a factor of 2 to 4 (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated; the predictions of the actual timing of peak concentrations, however, was generally poor.Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinize model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for example seen in background concentrations that are systematically overestimated in certain regions, which is most probably linked to the modelled coupling of background concentrations to land use intensity.Despite these limitations the findings indicate that key drivers and processes are reasonably well approximated by the model and that such a simple model that includes land use as a proxy for compound use, weather data for the timing of herbicide applications and discharge or precipitation as drivers for transport is sufficient to predict the timing and level of peak concentrations within a factor of 2 to 3 in a spatially distributed manner at the scale of large river basins.