Surface Water Research Articles

The Fungal Side of the Story: Saprotrophic- vs. Symbiotrophic-Predicted Ecological Roles of Fungal Communities in Two Meromictic Hypersaline Lakes from Romania

Over three-quarters of Earth’s surface exhibits extreme environments where life thrives under harsh physicochemical conditions. While prokaryotes have often been investigated in these environments, only recent studies have revealed the remarkable adaptability of eukaryotes, in particular fungi. This study explored the mycobiota of two meromictic hypersaline lakes, Ursu and Fără Fund, in Transylvania (Romania). The intrinsic and extrinsic fungal diversity was assessed using amplicon sequencing of environmental DNA samples from sediments, water columns, surrounding soils, and an associated rivulet. The fungal communities, illustrated by the 18S rRNA gene and ITS2 region, exhibited contrasting patterns between the lakes. The ITS2 region assessed better than the 18S rRNA gene the fungal diversity. The ITS2 data showed that Ascomycota was the most abundant fungal group identified in both lakes, followed by Aphelidiomycota, Chytridiomycota, and Basidiomycota. Despite similar α-diversity levels, significant differences in fungal community structure were observed between the lakes, correlated with salinity, total organic carbon, total nitrogen, and ammonium. Taxonomic profiling revealed depth-specific variations, with Saccharomycetes prevalent in Ursu Lake’s deeper layers and Lecanoromycetes prevalent in the Fără Fund Lake. The functional annotation using FungalTraits revealed diverse ecological roles within the fungal communities. Lichenized fungi were dominant in Fără Fund Lake, while saprotrophs were abundant in Ursu Lake. Additionally, wood and soil saprotrophs, along with plant pathogens, were more prevalent in the surrounding soils, rivulet, and surface water layers. A global overview of the trophic relations in each studied niche was impossible to establish due to the unconnected graphs corresponding to the trophic interactions of the analyzed fungi. Plotting the unweighted connected subgraphs at the genus level suggests that salinity made the studied niches similar for the identified taxa. This study shed light on the understudied fungal diversity, distribution, and ecological functions in hypersaline environments.

Performance of Machine Learning, Artificial Neural Network (ANN), and stacked ensemble models in predicting Water Quality Index (WQI) from surface water quality parameters, climatic and land use data

Assessing water quality is essential for managing freshwater resources, safeguarding ecosystems, and guaranteeing public health. Traditional water quality assessment methods suffer from seasonal sampling, multi-parameter requirements, and labor-intensive sampling processes, which are major constraints for the frequent monitoring of vast river basins. To overcome this issue, the study modeled the remote sensing-based climatic and land use parameters with Principal Component Analysis (PCA) to leverage Artificial Neural Networks (ANN) and machine learning (ML) algorithms to predict the Water Quality Index (WQI). The Weighted Arithmetic Water Quality Index (WAWQI) method was used to calculate the WQI of the Godavari River Basin for the available 19 stream water quality parameters (SWQPs). Further, PCA was applied to reduce the dimensionality of the parameters from 19 to 6. These results led to the development of two modeling methods to predict the WQI. In the first method, the correlation-based model was developed to predict WQI by evaluating six SWQPs. The second method, the causal-effect model, uses land use and meteorological factors to determine WQI using causality. Using advanced AutoML techniques, the initial pool of 40 ML models was meticulously evaluated and refined, culminating in the selection of the top three exemplary models such as Extreme Gradient Boosting (XGB), Extra Trees (ET), and Random Forest (RF). In both methods, XGB models show better prediction, with the coefficient of determination (R2) value of 0.95 during training and 0.83 during testing in method one. Whereas in the second method, R2 of 0.93 in training and 0.80 in testing are obtained. Further, XGB, ET, and ANN outputs were stacked with each model to enhance these results in both methods. Among these three stacked models, the stacked ANN_ML model performed better compared to stacked XGB_ML and stacked ET_ML. In the first method, the stacked ANN_ML model predicts R2 values of 0.95 and 0.91 for training and testing. In the second method, 0.95 and 0.90 for training and testing are obtained using stacked ANN_ML model. These findings emphasize the stacked model prediction ability to capture nonlinear relationships in the parameters and the novel approach of land use and climate parameters based WQI prediction, which replace the laborious, time-consuming SWQP measurements.

Assessment of Emergency Water Sources Using Electrical Resistivity Tomography: A Case Study in the Longmen Shan Fault Zone

The Longmen Shan fold and thrust belt, situated on the eastern margin of the Qinghai–Tibet Plateau, is prone to disasters like earthquakes and debris flows. Thus, applying rapid assessment methods for emergency water sources in disaster-affected areas is crucial for local populations and ensuring an effective response to disasters. In this study, we employed electrical resistivity tomography (ERT) to investigate groundwater resources in post-disaster regions. By integrating the results from ERT profiles with geological and borehole information, we determined the lithology and depth of aquifers. Additionally, we analyzed groundwater recharge and discharge patterns relative to surface water during various precipitation periods and generated a hydrogeological profile for the region. Borehole information confirmed our inferred lithology and aquifer depth, thereby ensuring a reliable water supply during emergencies. This study demonstrates the feasibility of ERT for rapidly identifying water resources in geologically complex environments, providing a scientific foundation for water resource management in disaster-prone regions.

Fouling of Reverse Osmosis (RO) and Nanofiltration (NF) Membranes by Low Molecular Weight Organic Compounds (LMWOCs), Part 1: Fundamentals and Mechanism.

Reverse osmosis (RO) and nanofiltration (NF) are ubiquitous technologies in modern water treatment, finding applications across various sectors. However, the availability of high-quality water suitable for RO/NF feed is diminishing due to droughts caused by global warming, increasing demand, and water pollution. As concerns grow over the depletion of precious freshwater resources, a global movement is gaining momentum to utilize previously overlooked or challenging water sources, collectively known as "marginal water". Fouling is a serious concern when treating marginal water. In RO/NF, biofouling, organic and colloidal fouling, and scaling are particularly problematic. Of these, organic fouling, along with biofouling, has been considered difficult to manage. The major organic foulants studied are natural organic matter (NOM) for surface water and groundwater and effluent organic matter (EfOM) for municipal wastewater reuse. Polymeric substances such as sodium alginate, humic acid, and proteins have been used as model substances of EfOM. Fouling by low molecular weight organic compounds (LMWOCs) such as surfactants, phenolics, and plasticizers is known, but there have been few comprehensive reports. This review aims to shed light on fouling behavior by LMWOCs and its mechanism. LMWOC foulants reported so far are summarized, and the role of LMWOCs is also outlined for other polymeric membranes, e.g., UF, gas separation membranes, etc. Regarding the mechanism of fouling, it is explained that the fouling is caused by the strong interaction between LMWOC and the membrane, which causes the water permeation to be hindered by LMWOCs adsorbed on the membrane surface (surface fouling) and sorbed inside the membrane pores (internal fouling). Adsorption amounts and flow loss caused by the LMWOC fouling were well correlated with the octanol-water partition coefficient (log P). In part 2, countermeasures to solve this problem and applications using the LMWOCs will be outlined.

Recurrent Flooding and Household Food Access in Central Java, Indonesia.

It is unknown how recurring flooding impacts household diet in Central Java. We aimed to assess how recurrent flooding influenced household food access over 22 years in Central Java by linking the Global Surface Water dataset (GSW) to the Indonesian Family Life Survey. We examined linear and nonlinear relationships and joint effects with indicators of adaptive capacity. We measured recurrent flooding as the fraction of district raster cells with episodic flooding from 1984-2015 using GSW. Food access outcomes were household food expenditure share (FES) and dietary diversity score (DDS). We fit generalized linear mixed models and random forest regression models. We detected joint effects with flooding and adaptive capacity. Wealth and access to credit were associated with improved FES and DDS. The effect of wealth on FES was stronger in households in more flood-affected districts, while access to credit was associated with reduced odds of DDS in more flood-affected districts. Flooding had more predictive importance for FES than for DDS. Access to credit, a factor that ordinarily improves food access, may not be effective in flood-prone areas. Wealthier households may be better able to adapt in terms of food access. Future research should incorporate land use data to understand how different locales are affected and further understand the complexity of these relationships.

Hydrological processes in multi-layered aquifers of a karst watershed with coal mining activity: Insights from hydrochemistry and isotopes

Study regionThe Laochang Karst watershed (LCKW) is located in eastern Yunnan Province, southwestern China. It is the representative karst area affected by coal-mining activities in southwestern China. Study focusIdentifying hydrological processes of multi-layered aquifers in karst watersheds is challenging due to complex natural and anthropogenic processes. This study attempts to clarify the hydrological conceptual model of the LCKW using hydrochemistry and D, O, Sr, S, and C isotopes. New hydrological insights for the regionSurface water and multi-layered groundwater have the hydrochemical types of SO4-Ca·Mg, HCO3·SO4-Ca, and HCO3-Ca. Meteoric water and condensate were the major recharge sources. The main processes dominating hydrochemical compositions consist of sulfide oxidative dissolution, carbonate dissolution, positive cation exchange, and agricultural activities. Elevated SO42− concentration in the mine water, river water and shallow coalbed water mainly originated from the oxidation of pyrite in the coal-bearing strata of the Longtan Formation. whereas the deeper layers and groundwater away from the mines were hardly contaminated by SO42− due to the presence of aquiclude. HCO3− concentrations of surface water and multi-layered groundwater were mainly derived from carbonate dissolution and soil CO2, and mine water was also influenced by atmospheric CO2. Positive cation exchange contributed to increasing Na+ concentration. Agricultural activities contributed NO3−, Cl−, and K+ ions in aquifers, especially near large karst fallout caves. A hydrological model of multi-layered aquifers in the LCKW was built based on the above results. These findings will provide valuable guidance for understanding the hydrological processes of complex karst watersheds worldwide.

Multi-objective simulation–optimization for water resources management and uses in multi-dam systems in low-water regions

One of the challenging issues in surface water resources management is the optimal operation of multi-dam systems. In addition to the applicability in determining the optimal pattern of operation of existing dams, this approach can be implemented in specifying the optimal capacity of under design dams. In this research, the simulation–optimization technique is used along with the reservoir zoning method in order to specify the optimal role curve of each of the dams in a multi-dam system (i.e., the Marun and Jarreh dams) in monthly intervals. The objective of this study is to develop a multi-objective algorithm on the basis of the zoning of reservoirs for the optimal operation of multi-dam systems. In this technique, instead of increasing the reliability of supplying demands in the whole period regardless the dry months, some of the inflow of rainy periods is stored in dam reservoirs to be consumed in dry months in order to mitigate the severity of deficiency. To examine the efficiency of the proposed model, the results obtained from the system operation in the current conditions are compared under two scenarios including optimal and reference. In the reference scenario, during 30-years, in some dry and low-water years, especially in the last six years of planning, the percentage of the demand supply is zero or less than five percent in several consecutive months. After optimizing the system, the minimum supply percentage in critical and dry months reaches 30 to 60 percent. Also, in the optimization scenario, the percentage of downstream ecological demands is improved. This research indicates that using the solution of this research leads to the better management of reservoirs in multi-dam systems and reduces the severity of deficiency in supplying various uses in low-water months.

Exploring spatial and seasonal water quality variations in Kelani River, Sri Lanka: a latent variable approach.

Water quality degradation poses a significant challenge globally, especially in developing nations like Sri Lanka. Extensive monitoring programs designed to address escalating river pollution collect multiple water quality parameters over extended periods and varied locations. However, the sheer volume of data can be overwhelming, making it difficult to process effectively and interpret accurately using conventional methods. In this study, latent variable (LV) and unsupervised machine learning techniques were used to investigate spatial and seasonal variations of surface water quality for 17 parameters across 17 locations along the Kelani River, Sri Lanka, using monthly water quality parameters from 2016 to 2020. Pearson's correlation matrix identified 10 parameters significantly affecting water quality variations and factor analysis (FA) generated five LVs, accounting for 77% of the total variance in the dataset. The identified LVs showed multiple methods of river pollution.Hierarchical clustering analysis and self-organizing mapping methods clustered stations in a closely analogous manner. Stations near industrial zones and the river mouth showed higher water quality variance, often exceeding national guidelines. Correlation testing revealed strong relationships between water quality and catchment hydrometeorological variations during monsoonal seasons. Spatial analyses showed increased LV variance in the Lower Kelani River Basin, indicating higher pollutant levels in different seasons. Industrial effluents (LV-2 and LV-4) and domestic and municipal sewage (LV-3 and LV-5) exhibit greater seasonal fluctuations. The results showed that the proposed LV approach has the potential to assist authorities in addressing water pollution amidst the complexity of multiple water quality parameters.

Assessing suitability of surface water from Oued El Gourzi for irrigation east of Alger

The study was conducted to determine the suitability of surface water from Oued El Gourzi for irrigation in the Fesdis area, Algeria, during irrigation season of July 2022. The suitability was assessed by analysing eight water samples collected from various sites along the Oued. A range of physicochemical parameters were examined, including EC, pH, Mg2+, Na+, Ca2+, K+, HCO3– and Cl–, alongside other indices such as sodium absorption ratio (SAR), soluble sodium percentage (SSP), residual sodium carbonate (RSC), permeability index (PI) and magnesium adsorption ratio (MAR) using standard procedures. The Richards classification shows that these waters are characterised by high salinity and low alkalinity hazard (C3–S1). According to the Wilcox classification, the majority of these waters are of doubtful quality, with only 25% exhibiting good quality for irrigation. Based on the RSC and MAR, all water samples are deemed safe and suitable for irrigation. However, PI values suggest that all sampling sites are of marginal quality for irrigation (class II). In terms of sodium and chloride concentration, all water samples were deemed unsuitable for irrigation. Based on these results, the waters pose risks for irrigation, particularly due to salinity, necessitating the implementation of special management practices and the selection of salt-tolerant crops.

Source apportionment and ecological risk assessment of antibiotics in Dafeng River Basin using PMF and Monte-Carlo simulation.

Antibiotics, prevalent in aquatic ecosystems, pose a grave threat to human health and the ecological well-being. This paper performed a case study on Dafeng River Basin in southern China. Specifically, techniques including positive matrix factorization (PFM) and Monte-Carlo simulation were employed to comprehensively investigate the spatial variations, possible sources, and ecological risks of antibiotics in four groups: sulfonamides (SAs), macrolides (MLs), quinolones (QNs), and tetracyclines (TCs). The major findings were as follows: first, 43 and 39 antibiotics were detected in the surface water and sediments of the basin, respectively, where the respective total content were ND-490.08ng/L and ND-144.34μg/kg, and the QNs and TCs were the two dominating groups. Second, the highest antibiotic content in surface water (441.43ng/L) was observed in the midstream area, whereas the highest concentration in sediments (68.41μg/kg) was found in the upstream region. Third, the investigation identified five sources of antibiotics discharged to surface water: domestic sewage, agricultural drainage, livestock discharge, sewage treatment plants, and aquaculture; three sources were detected for antibiotics in sediments: aquaculture, sewage treatment plants, and livestock discharge. Fourth, QNs had a significantly higher ecological risk than the other three groups of antibiotics, and livestock discharge (31.4% contribution) and aquaculture (23.4% contribution) were the main sources of risks of antibiotic contamination in Dafeng River Basin. This study is expected to provide some reference for control and risk management of antibiotic pollution in Dafeng River Basin.

Hydrochemical variation characteristics and driving factors of surface water in arid Areas—a case study of Beichuan River in Northwest China

Examining the chemical properties of river water and the controlling factors is crucial for devising efficacious strategies in water resources management and ecological conservation. This study investigates the hydrochemical characteristics and driving factors of the Beichuan River in the arid region of Northwest China. Surface water samples were collected during wet and dry seasons, and analyzed using hydrochemical diagrams, mathematical statistics, and principal component analysis (PCA). The results show that the pH value of Beichuan River is generally weakly alkaline, the main hydrochemical types are HCO3-Ca, and the average TDS are 224 mg/L and 236 mg/L respectively, which are higher than the world average level (115 mg/L). The seasonal variation of hydrochemical components is mainly controlled by rainfall, showing that the concentrations of Na+, Cl− and NO3− in the wet season are higher than those in the dry season, while the concentrations of other chemical components show an opposite trend, while the spatial variation is mainly controlled by human activities, and the concentrations of hydrochemical components show a gradual increasing trend from upstream to downstream, especially Na+, Mg2+, Cl− and NO3−. Rock weathering is the key natural factor controlling the Hydrochemical Composition of Beichuan River. Na+ and Cl− are mainly from the dissolution of silicate, Ca2+ and Mg2+ are mainly from the weathering of carbonate rocks and silicate, and SO42- is mainly from the dissolution of evaporite. It is noted that human activities, especially domestic sewage and agricultural runoff, contribute significantly to NO3− in the water body. PCA identified rock weathering and agricultural runoff as major wet-season factors, while domestic sewage predominantly affects the dry season. This study can provide a scientific basis for the rational development of water resources and ecological environment protection in arid areas.

Geoenvironmental Effects of the Hydric Relationship Between the Del Sauce Wetland and the Laguna Verde Detritic Coastal Aquifer, Central Chile

In the central region of Chile, the Mega-Drought together with the demographic increase near the coast threatens groundwater availability and the hydrogeological functioning of coastal wetlands. To understand the hydric relationship between an aquifer and a wetland in a semi-arid coastal region of Central Chile (Valparaíso, Chile), as well as its geoenvironmental effects, four data collection campaigns were conducted in the wetland–estuary hydric system and surroundings, between 2021 and 2022, including physical, hydrochemical, and isotopic analyses in groundwater (n = 16 sites) and surface water (n = 8 sites). The results generated a conceptual model that indicates a hydraulic connection between the wetland and the aquifer, where the water use in one affects the availability in the other. With an average precipitation of 400 mm per year, the main recharge for both systems is rainwater. Three specific sources of pollution were identified from anthropic discharges that affect the water quality of the wetland and the estuary (flow from sanitary landfill, agricultural and livestock industry, and septic tank discharges in populated areas), exacerbated by the infiltration of seawater laterally and superficially through sandy sediments and the estuary, increasing salinity and electrical conductivity in the coastal zone (i.e., 3694 µS/cm). The Del Sauce subbasin faces strong hydric stress triggered by the poor conservation state of the riparian–coastal wetland and groundwater in the same area. This study provides a detailed understanding of hydrological interactions and serves as a model for understanding the possible effects on similar ecosystems, highlighting the need for integrated and appropriate environmental management.

Study and scaling-up of multi-tip pulsed-corona air discharges for degradation of paracetamol

Abstract This study investigates the paracetamol removal efficiency by multi-tip pulsed corona discharges, highlighting the conditions avoiding mutual effects between two consecutive streamer branching discharges generated by each tip. The results show that the production of reactive oxygen and nitrogen species (RONS) in the liquid phase and the efficiency of paracetamol removal are influenced by the distance between two contiguous tips due to the mutual effects for small inter-tip distances between 4 mm and 8 mm in the case of a fixed inter-electrode distance of 5 mm (i.e. distance between the tip summit and the water surface). Beyond an inter-tip distance of 12 mm (over twice the gap distance), the mutual effects of branching discharges were no longer observed since the field lines did not overlap, making the production and absorption of liquid-phase species more efficient. Furthermore, RONS production was almost linear when moving from one to four tip electrodes: [H2O2]1tip: 0.52 mg l–1 versus [H2O2]4tips: 1.99 mg l–1, a ratio of 3.82. Optimum values were reached for an inter-tip distance of 16 mm. In this case, the enhancement factor in this reactor configuration is the surface integration between the plasma and the liquid surface to be treated. Furthermore, the results show a quasi-exponential increase in the percentage of paracetamol degradation as a function of the number of electrodes, from 4% with a mono-tip configuration to over 78% for a treatment with a four tip one.

An elastic piezoelectric nanomembrane with double noise reduction for high-quality bandpass acoustics

Polymer piezoelectrics with high electromechanical energy conversion (HEEC) are very promising for flexible acoustoelectric devices. However, reducing thickness and improving ordered polarization and ferroelectricity while maintaining high mechanical strength pose enormous fabrication challenges for polymer piezoelectric membranes—additionally, noise management in the acoustoelectric conversion remains an open issue. Here, we present a hydro-levitation superspreading approach for fabricating polymer nanomembranes with ordered crystalline phases and sub-nanostructures on the water surface. The elastic piezoelectric nanomembrane (EPN) is only 335 nanometers thick and consists of a conductance-stable piezoelectric layer sandwiched between two elastic damping layers. Such an all-in-one EPN can reduce background noise with low autocorrelation in the environment, suppress spurious noise caused by poor circuit contact, and achieve bandpass filtering of acoustic signals at human voice frequencies. This nanomembrane holds promise in repairing the auditory system of patients with tympanic membrane perforation and in a wide range of other acoustoelectric conversion fields.

Decision Support Framework for Water Quality Management in Reservoirs Integrating Artificial Intelligence and Statistical Approaches

Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding.

The Safe Drinking Water Act at 50: Another Identity Crisis

Key TakeawaysThe Safe Drinking Water Act (SDWA) of 1974 (amended in 1986, 1996, and 2018) shows the tension between purity and safety, cost and benefits, and balancing risks, down to today's policy debates.Following the 1996 amendments, regulatory successes include arsenic, radionuclides, filter backwash recycling, total coliform, disinfectants and disinfection byproducts, and enhanced surface water treatment.With recent drinking water standards, such as the 2024 per‐ and polyfluoroalkyl substances rule, based on very low detection levels, the SDWA continues to generate intense public dialogue.

Unveiling interactions mediated by B vitamins between diatoms and their associated bacteria from cocultures.

Unveiling the interactions among phytoplankton and bacteria at the level of species requires axenic isolates to experimentally demonstrate their mutual effects. In this study, we describe the interactions among the diatoms Pseudo-nitzschia granii and Chaetoceros tenuissimus and their associated bacterial species, isolated from surface water of a coastal upwelling system using coculture experiments. Microalgae growth was assessed in axenic monocultures or in coculture with each of their co-isolated bacteria in the presence or absence of B vitamins. Pseudo-nitzschia granii growth was limited by B-vitamin supply, except when cultured with the bacteria Jannaschia cystaugens, which seemed to provide adequate levels of B vitamins to the diatom. Chaetoceros tenuissimus growth was reduced in the absence of B vitamins. Moreover, the growth of C. tenuissimus was stimulated by Alteromonas sp. and Celeribacter baekdonensis during the exponential growth. These results show a diversity of specific interactions between the diatoms and co-isolated bacteria, ranging from allelopathy to commensalism. Understanding how interactions between phytoplankton and bacteria modulate the structure and function of marine microbial plankton communities will contribute to a greater knowledge of plankton ecology and improve our ability to predict nutrient fluxes in marine ecosystems or the formation of blooms in a context of global change.

Genome editing in ubiquitous freshwater Actinobacteria.

To advance bioproduction or bioremediation in large, unsupervised environmental systems such as ponds, wastewater lagoons, or groundwater systems, it will be necessary to develop diverse genetically amenable microbial model organisms. Although we already genetically modify a few key species, tools for engineering more microbial taxa, with different natural phenotypes, will enable us to genetically engineer multispecies consortia or even complex communities. Developing genetic tools for modifying freshwater bacteria is particularly important, as wastewater, production ponds or raceways, and contaminated surface water are all freshwater systems where microbial communities are already deployed to do work, and the outputs could potentially be enhanced by genetic modifications. Here, we demonstrate that common tools for genome editing can be used to inactivate specific genes in two representatives of a very widespread, environmentally relevant group of Actinobacteria. These Actinobacteria are found in almost all tested surface freshwater environments, where they co-occur with primary producers, and genome-editing tools in these species are thus a step on the way to engineering microbial consortia in freshwater environments.

Predicting Accumulation and Potential Edge-of-Field Loss of Phosphorus to Surface Water from Diverse Ecosystems

Phosphorus (P) is an important nutrient essential for agricultural production, but it is highly reactive, leading to its soil accumulation and making it susceptible to environmental impact footprints. The goal of our study was to determine the critical threshold values of both soluble reactive P (SRP) and oxalate-extracted P (Ox-P) to predict soil P accumulation and its susceptibility to edge-of-field loss. Composite soils were collected from geo-referenced ecosystems within the Lake Erie drainage basin under agriculture in northwestern Ohio, USA. Soils were analyzed for SRP, Ox-P, Fe, and Al concentrations to calculate P sorption capacity, P saturation ratio, degree of P saturation (DPS), and P storage capacity (SPSC). A threshold P saturation ratio of 0.12 (~ 24% DPS), corresponding to 2.4 mg SRP/kg (equivalent Ox-P), was determined to calculate SPSC for predicting the risk of SRP accumulation. A significant relationship between the SPSC and SRP suggested that soils under all the agroecosystems had accumulated SRP compared to the forest. Surface soils (0–10 cm depth) under tilled, chemically fertilized, and organically managed corn (Zea mays)-soybean (Glycine max (L) Merr.) rotations, including those treated with chicken and dairy manure, exhibited excessive SRP accumulation, making them susceptible to edge-of-field losses. While the soils at 10–20 cm depth were acting as transitional, the deeper soils (20–30 cm depth) still acted as a net sink. When accounting for bulk density to calculate SPSC stocks, it showed that surface soils across the agroecosystems were saturated with 148 to 240 kg SRP/ha and were susceptible to edge-of-field loss to the water systems. In conclusion, we suggest that SPSC could be used as an early indicator to predict the risk of SRP accumulation and its potential edge-of-field loss to Lake Erie from agroecosystems.

Geochemistry of urban waters and their evolution within the urban landscape

Urban populations and the sprawl of urban environments are increasing in the United States as well as globally. The local hydrologic cycle is directly impacted by urban development through greater generation of surface runoff and export of water through subterranean pipes networks to surface water bodies. These pipe networks carry waters that have potentially dramatic effects on the chemistry of groundwater and surface water bodies. In this work, we sampled waters from the Olentangy River and two subterranean outfalls that flow into the river in Columbus, Ohio United States. We measured the major ion, nutrient, and dissolved silica concentrations of each water source to identify how the urban landscape impacts the chemistry of a river that travels from an agricultural landscape to an urban environment. The outfalls had elevated concentrations of all major ions (Na+, K+, Mg2+, Ca2+, Cl−, SO42-) and H4SiO4. However, the Olentangy river typically had greater NO3− and soluble reactive phosphorus (SRP) concentrations. Sources of elevated ion export include road salts and combined storm runoff (Na+, Cl−), municipal water treatment practices (K+, Na+, SO42-), and concrete pipe weathering (Ca2+, Mg2+, K+, H4SiO4, SO42-). Utilizing stable isotopes of water, δ18O and δ2H, we identified that the water in the pipe networks is typically a mix of multiple precipitation events, but there is evidence of flushing following high-volume precipitation events. The contribution of high TDS waters from subterranean urban outfalls modified the ion abundance in the Olentangy river and produces a tendency towards freshwater salinization syndrome. This is particularly apparent when comparing the chemistry of the urban Olentangy to the agricultural corridor of the river and its other source waters. This research details the transformation of a river as it flows from an agricultural to urban landscape and provides data on the chemistry of source waters that facilitate the river’s chemical changes.