Real-time monitoring of nutrients and dissolved organic matter in rivers: Capturing event dynamics, technological opportunities and future directions

Nutrient loading and stream order shape benthic and pelagic spring algal biomass in a large, temperate river basin (Elbe River)

Eutrophication persists in many freshwater systems despite extensive efforts to control nutrient emissions from point and diffuse sources. While intensely studied at local or regional scales, the joint response of benthic and pelagic algae to nutrient loading across entire river networks remains poorly understood. Here, we assessed spatial patterns of pelagic and benthic algal biomass in response to point source and diffuse phosphorus loading in the Elbe River Basin, a temperate, transboundary river network, based on extensive monitoring data and with the parsimonious hydro-ecological model CnANDY (Coupled Complex Algal-Nutrient Dynamics). We referenced our simulations to median river discharge data and phosphorus inputs from point (1,900 wastewater treatment plants) and diffuse sources, determined with the MoRE model and CORINE land cover analysis. We found distinct spatial eutrophication patterns across the river network and complex responses to local and cumulative anthropogenic nutrient emissions. Lower stream orders, particularly those in urban and agricultural areas, showed the highest dissolved phosphorus concentration and benthic algae density. Conversely, pelagic algae dominated higher stream orders, influenced by nutrient transport from lower-order streams to downstream reaches. The validated CnANDY model effectively identified eutrophication hotspots, enabling prioritized nutrient and eutrophication management. Although extensive monitoring data were available, systematic gaps in established monitoring schemes limited the model calibration and validation. Therefore, we advocate for a revision and propose model-aided eutrophication monitoring at the river basin scale with representative coverage of all stream orders from up to downstream and the algal biomass in the benthic and pelagic compartments.

Improving monitoring of dissolved organic matter from the wastewater treatment plant to the receiving environment: A new high-frequency in situ fluorescence sensor capable of analyzing 29 pairs of Ex/Em wavelengths

A high-frequency, in situ fluorescence probe, called Fluocopée®, has been developed in order to better monitor variations in both the quality and quantity of dissolved organic matter within various aquatic environments (e.g. wastewater, receiving environments) thanks to a wide choice of 29 measured Excitation/Emission wavelength pairs. This advance pave the way to new measurement possibilities in comparison with existing probes, which are usually only able to measure 1-4 fluorophores. The qualification tests of the Fluocopée® probe indicate a high level of accuracy for the measurements of tyrosine, tryptophan and humic acids solutions. Good repeatability and reproducibility are also observed. For the first time, this tool has been deployed in an urban watershed (Bougival, Seine River, downstream of Paris) and in the settled effluent from a wastewater treatment plant (Seine aval, Achères, France). This new high-frequency in situ probe offers great application potential, including organic matter quality and quantity monitoring at drinking and wastewater treatment plants (treatment optimization) and in continental and marine waters (the fate of organic matter in biogeochemical cycles).

A novel mathematical template for developing fDOM probe fluorescence signal correction models for freshwaters

The reliable application of field deployable fluorescent dissolved organic matter (fDOM) probes is hindered by several influencing factors which need to be compensated. This manuscript describes the corrections of temperature, pH, turbidity and inner filter effect on fluorescence signal of a commercial fDOM probe (fDOMs). For this, Australian waters with wide ranging qualities were selected, e.g. dissolved organic carbon (DOC) ranging from ∼1 to ∼30 mg/L, specific UV absorbance at 254 nm from ∼1 to ∼6 L/m/mg and turbidity from ∼1 to ∼ 350 FNU. Laboratory-based model calibration experiments (MCEs) were performed. A model template was developed and used for the development of the correction models. For each factor, data generated through MCEs were used to determine model coefficient (α) values by fitting the generated model to the experimental data. Four discrete factor models were generated by determination of a factor-specific α value. The α values derived for each water of the MCEs subset were consistent for each factor model. This indicated generic nature of the four α values across wide-ranging water qualities. High correlation between fDOMs and DOC were achieved after applying the four-factor compensation models to new data (r, 0.96, p < 0.05). Also, average biases (and %) between DOC predicted through fDOMs and actual DOC were decreased by applying the four-factor compensation model (from 3.54 (60.9%) to 1.28 (16.7%) mg/L DOC). These correction models were incorporated into a Microsoft EXCEL-based software termed EXOf-Correct for ready-to-use applications.

Interpretation of river water quality data is strongly controlled by measurement time and frequency

Water quality monitoring at high temporal frequency provides a detailed picture of environmental stressors and ecosystem response, which is essential to protect and restore lake and river health. An effective monitoring network requires knowledge on optimal monitoring frequency and data variability. Here, high-frequency hydrochemical datasets (dissolved oxygen, pH, electrical conductivity, turbidity, water temperature, total reactive phosphorus, total phosphorus and nitrate) from six UK catchments were analysed to 1) understand the lowest measurement frequency needed to fully capture the variation in the datasets; and 2) investigate bias caused by sampling at different times of the day. The study found that reducing the measurement frequency increasingly changed the interpretation of the data by altering the calculated median and data range. From 45 individual parameter-catchment combinations (six to eight parameters in six catchments), four-hourly data captured most of the hourly range (>90 %) for 37 combinations, whilst 41 had limited impact on the median (<0.5 % change). Twelve-hourly and daily data captured >90 % of the range with limited impact on the median in approximately half of the combinations, whereas weekly and monthly data captured this in <6 combinations. Generally, reducing sampling frequency had most impact on the median for parameters showing strong diurnal cycles, whilst parameters showing rapid responses to extreme flow conditions had most impact on the range. Diurnal cycles resulted in year-round intra-daily variation in most of the parameters, apart from nutrient concentrations, where daily variation depended on both seasonal flow patterns and anthropogenic influences. To design an optimised monitoring programme, key catchment characteristics and required data resolution for the monitoring purpose should be considered. Ideally a pilot study with high-frequency monitoring, at least four-hourly, should be used to determine the minimum frequency regime needed to capture temporal behaviours in the intended focus water quality parameters by revealing their biogeochemical response patterns.

Snapshot Sampling May Not Be Enough to Obtain Robust Estimates for Riverine Microplastic Loads

Wastewater treatment plants (WWTPs) have been described as key contributors of microplastics (MPs) to aquatic systems, yet temporal fluctuations in MP concentrations and loads downstream are underexplored. This study investigated how different sampling frequencies (hourly, weekly, and monthly) affect MP estimates in a stream linked to a single WWTP. Utilizing fluorescence microscopy and Raman spectroscopy, considerable hourly variations in MP concentrations were discovered, while the polymer composition remained consistent. This temporal variability in MP loads was influenced by MP concentration, discharge rates, or a mix of both. These results show a high uncertainty, as relying on sparse snapshot samples combined with annual discharge data led to significant uncertainties in MP load estimates (over- and/or underestimation of emissions by 3.8 billion MPs annually at this site). Our findings stress the necessity of higher-frequency sampling for better comprehending the hydrodynamic factors influencing MP transport. This improved understanding enables a more accurate quantification of MP dynamics, crucial for downstream impact assessments. Therefore, preliminary reconnaissance campaigns are essential for designing extended, representative site-monitoring programs and ensuring more precise trend predictions on a larger scale.

Development of a basin-scale total nitrogen prediction model by integrating clustering and regression methods

Nutrient runoff into rivers caused by human activity has led to global eutrophication issues. The Nakdong River in South Korea is currently facing significant challenges related to eutrophication and harmful algal blooms, underscoring the critical importance of managing total nitrogen (T-N) levels. However, traditional methods of indoor analysis, which depend on sampling, are labor-intensive and face limitations in collecting high-frequency data. Despite advancements in sensor allowing for the measurement of various parameters, sensors still cannot directly measure T-N, necessitating surrogate regression methods. Therefore, we conducted T-N predictions using a water quality dataset collected from 2018 to 2022 at 157 observatories within the Nakdong River basin. To account for the water quality characteristics of each location, we employed a clustering technique to divide the basin and compared a Gaussian mixture model with K-means clustering. Moreover, optimal regressor for each cluster was selected by comparing multiple linear regression (MLR), random forest, and XGBoost. The results showed that forming four clusters via K-means clustering was the most suitable approach and MLR was reasonably accurate for all clusters. Subsequently, recursive feature elimination cross-validation was used to identify suitable parameters for T-N prediction, thus leading to the construction of high-accuracy T-N prediction models. Clustering was useful not only for improving the regressors but also for spatially analyzing the water quality characteristics of the Nakdong River. The MLR model can reveal causal relationships and thus is useful for decision-making. The results of this study revealed that the combination of a simple linear regression model and clustering method can be applied to a wide watershed. The clustering-based regression model showed potential for accurately predicting T-N at the basin level and is expected to contribute to nationwide water quality management through future applications in various fields.

A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation

Accurate predictions of river water quality are vital for sustainable water management. However, even the powerful deep learning model, i.e., long short-term memory (LSTM), has difficulty in accurately predicting water quality dynamics owing to the high non-stationarity and data limitation in a changing environment. To wiggle out of quagmires, wavelet analysis (WA) and transfer learning (TL) techniques were introduced in this study to assist LSTM modeling, termed WA-LSTM-TL. Total phosphorus, total nitrogen, ammonia nitrogen, and permanganate index were predicted in a 4 h step within 49 water quality monitoring sites in a coastal province of China. We selected suitable source domains for each target domain using an innovatively proposed regionalization approach that included 20 attributes to improve the prediction efficiency of WA-LSTM-TL. The coupled WA-LSTM facilitated capturing non-stationary patterns of water quality dynamics and improved the performance by 53 % during testing phase compared to conventional LSTM. The WA-LSTM-TL, aided by the knowledge of source domain, obtained a 17 % higher performance compared to locally trained WA-LSTM, and such improvement was more impressive when local data was limited (+66 %). The benefit of TL-based modeling diminished as data quantity increased; however, it outperformed locally direct modeling regardless of whether target domain data was limited or sufficient. This study demonstrates the reasoning for coupling WA and TL techniques with LSTM models and provides a newly coupled modeling approach for improving short-term prediction of river water quality from the perspectives of non-stationarity and data limitation.

Low-cost monitoring systems for urban water management: Lessons from the field

Sound urban water management relies on extensive and reliable monitoring of water infrastructure. As low-cost sensors and networks have become increasingly available for environmental monitoring, urban water researchers and practitioners must consider the benefits and disadvantages of such technologies. In this perspective paper, we highlight six technical and socio-technological considerations for low-cost monitoring technology to reach its full potential in the field of urban water management, including: technical barriers to implementation, complementarity with traditional sensing technologies, low-cost sensor reliability, added value of produced information, opportunities to democratize data collection, and economic and environmental costs of the technology. For each consideration, we present recent experiences from our own work and broader literature and identify future research needs to address current challenges. Our experience supports the strong potential of low-cost monitoring technology, in particular that it promotes extensive and innovative monitoring of urban water infrastructure. Future efforts should focus on more systematic documenting of experiences to lower barriers to designing, implementing, and testing of low-cost sensor networks, and on assessing the economic, social, and environmental costs and benefits of low-cost sensor deployments.

HUM: A review of hydrochemical analysis using ultraviolet-visible absorption spectroscopy and machine learning

There is a need to develop improved methods for water quality analysis. Traditionally, water quality analysis is performed in a laboratory on discrete samples or in the field with simple sensors, but these methods have inherent limitations. Ultraviolet-visible absorption spectroscopy (UVAS) is a commonly used laboratory technique for water quality analysis and is being applied more broadly in combination with machine learning (ML) to allow for the detection of multiple analytes without sample pretreatments. This methodology (referred to here as Hydrochemical analysis using Ultraviolet-visible absorption spectroscopy and Machine learning; 'HUM') can be applied in the laboratory or in situ while requiring less time, labor, and materials compared to traditional laboratory analysis. HUM has been used for the quantification of a variety of chemicals in a variety of settings, but information is lacking related to instrumental setup, sample requirements, and data analysis procedures. For instance, there is a need to investigate the influence of spectral parameters (e.g., sensitivity, signal-to-noise ratio, and spectral resolution) on measurement error. There is also a lack of research aimed at developing ML algorithms specifically for HUM. Finally, there are emerging concepts such as sensor fusion and model-sensor fusion which have been applied to similar fields but are not common in studies involving HUM. This review suggests the need for further studies to better understand the factors that influence HUM measurement accuracy along with the need for hardware and software developments so that the methodology can ultimately become more robust and standardized. This, in turn, could increase its adoption in both academic and non-academic settings. Once the HUM methodology has matured, it could help to reduce the environmental impacts of society by improving our understanding and management of environmental systems through high-frequency data collection and automated control of water quality in environmentally relevant systems.

What do macroinvertebrate indices measure? Stressor-specific stream macroinvertebrate indices can be confounded by other stressors

Monitoring programmes worldwide use biota to assess the "health" of water bodies. Indices based on biota are used to describe the change in status of sites over time, to identify progress against management targets and to diagnose the causes of biological degradation. A variety of numerical stressor-specific biotic indices have been developed based on the response of biota to differences in stressors among sites. Yet, it is not clear how variation in pressures within sites, over what time period, and in what combination has the greatest impact on different biotic groups. An understanding of how temporal variation in pressures influences biological assessment indices would assist in setting achievable targets and help focus catchment-scale mitigation strategies to ensure that they deliver the desired improvements in biological condition.Hydrochemical data provided by a network of high-frequency (15 or 30 min) automated monitoring stations over 3 years were matched to replicated biological data to understand the influence of spatio-temporal variation in pollution pressures on biological indices. Hydrochemical data were summarised in various ways to reflect central tendency, peaks, troughs and variation over 1-90 days before the collection of each biological sample. An objective model selection procedure was used to determine which hydrochemical determinand, and over what time period, best explained variation in the biological indices.Stressor-specific indices derived from macroinvertebrates which purportedly assess stress from low flows, excess fine sediment, nutrient enrichment, pesticides and organic pollution were significantly inter-correlated and reflected periods of low oxygen concentration, even though only one index (ASPTWHPT, average score per taxon) was designed for this purpose. Changes in community composition resulting from one stressor frequently lead to confounding effects on stressor-specific indices.Variation in ASPTWHPT was best described by dissolved oxygen calculated as Q5 over 10 days, suggesting that low oxygen events had most influence over this period. Longer-term effects were apparent, but were masked by recovery. Macroinvertebrate abundance was best described by Q95 of stream velocity over 60 days, suggesting a slower recovery in numbers than in the community trait reflected by ASPTWHPT.Although use of ASPTWHPT was supported, we recommend that additional independent evidence should be used to corroborate any conclusions regarding the causes of degradation drawn from the other stressor-specific indices. The use of such stressor-specific indices alone risks the mistargeting of management strategies if the putative stressor-index approach is taken to be more reliable than the results herein suggest.

Evaluating the impacts of biochemical processes on nitrogen dynamics in a tide gate-controlled river flowing into the South China Sea

This study aimed to evaluate the nitrogen (N) dynamics in Lijiang River, a tide gate-controlled river flowing into South China Sea, and to quantify the biochemical processes affecting nitrate fate and transport during the closed-tide gate period. The continuous on-line water monitoring indicates a chemostatic NH₄⁺-N pattern with respect to variable discharges in the upstream section. The survey via daily grab water sampling from July to December 2020 at four equidistant locations in the lower stretch showed that a gradual increase in NO₃^--N and decrease in NH₄⁺-N concentrations occurred along the river from upstream to downstream sections and with the time from September to December (the closed-tide gate period). The mean difference between nitrification and denitrification rate peaked at 0.43 mg L^-1 d^-1 in October in the upper section and gradually reduced to -0.26 mg L^-1 d^-1 in December in the middle section, indicating the increased advantage of denitrification over nitrification with time. A gradual increase in the mean NO₃^--N assimilatory uptake rate with time and a decrease from upstream to downstream were also observed. These results show that the closed-tide gate promoted N biotransformation in Laingian River and significant N removal was achieved through coupled nitrification-denitrification.

Data Augmentation for a Virtual-Sensor-Based Nitrogen and Phosphorus Monitoring

To better control eutrophication, reliable and accurate information on phosphorus and nitrogen loading is desired. However, the high-frequency monitoring of these variables is economically impractical. This necessitates using virtual sensing to predict them by utilizing easily measurable variables as inputs. While the predictive performance of these data-driven, virtual-sensor models depends on the use of adequate training samples (in quality and quantity), the procurement and operational cost of nitrogen and phosphorus sensors make it impractical to acquire sufficient samples. For this reason, the variational autoencoder, which is one of the most prominent methods in generative models, was utilized in the present work for generating synthetic data. The generation capacity of the model was verified using water-quality data from two tributaries of the River Thames in the United Kingdom. Compared to the current state of the art, our novel data augmentation-including proper experimental settings or hyperparameter optimization-improved the root mean squared errors by 23-63%, with the most significant improvements observed when up to three predictors were used. In comparing the predictive algorithms' performances (in terms of the predictive accuracy and computational cost), k-nearest neighbors and extremely randomized trees were the best-performing algorithms on average.

A case study: The deployment of a novel in situ fluorimeter for monitoring biological contamination within the urban surface waters of Kolkata, India

The quality and health of many of our vital freshwater systems are poor. To tackle this with ever increasing pressures from anthropogenic and climatic changes, we must improve water quality monitoring and devise and implement more appropriate water quality parameters. Recent research has highlighted the potential for Peak T fluorescence (tryptophan-like fluorescence, TLF) to monitor microbial activity in aquatic systems. The VLux TPro (Chelsea Technologies Ltd., UK), an in situ real-time fluorimeter, was deployed in different urban freshwater bodies within Kolkata (West Bengal, India) during March 2019. This study is the first to apply this technology in surface waters within a densely populated urban area. Spot-sampling was also undertaken at 13 sampling locations enabling physicochemical analysis, bacterial enumeration and determination of nutrient (nitrate and phosphate) concentrations. This case study has demonstrated the ability of an in situ fluorimeter, VLux TPro, to successfully identify both biological contamination events and potential elevated microbial activity, related to nutrient loading, in complex surface freshwaters, without the need for expensive and time-consuming laboratory analysis.

Improvement of On-Site Sensor for Simultaneous Determination of Phosphate, Silicic Acid, Nitrate plus Nitrite in Seawater

Accurate, on-site determinations of macronutrients (phosphate (PO43-), nitrate (NO3-), and silicic acid (H4SiO4)) in seawater in real time are essential to obtain information on their distribution, flux, and role in marine biogeochemical cycles. The development of robust sensors for long-term on-site analysis of macronutrients in seawater is a great challenge. Here, we present improvements of a commercial automated sensor for nutrients (including PO43-, H4SiO4, and NO2- plus NO3-), suitable for a variety of aquatic environments. The sensor uses the phosphomolybdate blue method for PO43-, the silicomolybdate blue method for H4SiO4 and the Griess reagent method for NO2-, modified with vanadium chloride as reducing agent for the determination of NO3-. Here, we report the optimization of analytical conditions, including reaction time for PO43- analysis, complexation time for H4SiO4 analysis, and analyte to reagent ratio for NO3- analysis. The instrument showed wide linear ranges, from 0.2 to 100 μM PO43-, between 0.2 and 100 μM H4SiO4, from 0.5 to 100 μM NO3-, and between 0.4 and 100 μM NO2-, with detection limits of 0.18 μM, 0.15 μM, 0.45 μM, and 0.35 μM for PO43-, H4SiO4, NO3-, and NO2-, respectively. The analyzer showed good precision with a relative standard deviation of 8.9% for PO43-, 4.8% for H4SiO4, and 7.4% for NO2- plus NO3- during routine analysis of certified reference materials (KANSO, Japan). The analyzer performed well in the field during a 46-day deployment on a pontoon in the Kiel Fjord (located in the southwestern Baltic Sea), with a water supply from a depth of 1 m. The system successfully collected 443, 440, and 409 on-site data points for PO43-, Σ(NO3- + NO2-), and H4SiO4, respectively. Time series data agreed well with data obtained from the analysis of discretely collected samples using standard reference laboratory procedures and showed clear correlations with key hydrographic parameters throughout the deployment period.

Making waves: Time for chemical surface water quality monitoring to catch up with its technical potential

A comprehensive real-time evaluation of the chemical status of surface water bodies is still utopian, but in our opinion, it is time to use the momentum delivered by recent advanced technical, infrastructural, and societal developments to get significantly closer. Procedures like inline and online analysis (in situ or in a bypass) with close to real-time analysis and data provision are already available in several industrial sectors. In contrast, atline and offline analysis involving manual sampling and time-decoupled analysis in the laboratory is still common practice in aqueous environmental monitoring. Automated tools for data analysis, verification, and evaluation are changing significantly, becoming more powerful with increasing degrees of automation and the introduction of self-learning systems. In addition, the amount of available data will most likely in near future be increased by societal awareness for water quality and by citizen science. In this analysis, we highlight the significant potential of surface water monitoring techniques, showcase "lighthouse" projects from different sectors, and pin-point gaps we must overcome to strike a path to the future of chemical monitoring of inland surface waters.

Characterization and biogeochemical implications of dissolved organic matter in aquatic environments

Dissolved organic matter (DOM) is viewed as one of the most chemically active organic substances on earth. It plays vital roles in the fate, bioavailability and toxicity of aquatic exogenous chemical species (e.g., heavy metals, organic pollutants, and nanomaterials). The characteristics of DOM such low concentrations, salt interference and complexity in aquatic environments and limitations of pretreatment for sample preparation and application of characterization techniques severely limit understanding of its nature and environmental roles. This review provides a characterization continuum of aquatic DOM, and demonstrate its biogeochemical implications, enabling in-depth insight into its nature and environmental roles. A synthesis of the effective DOM pretreatment strategies, comprising extraction and fractionation methods, and characterization techniques is presented. Additionally, the biogeochemical dynamics of aquatic DOM and its environmental implications are discussed. The findings indicate the collection of representative DOM samples from water as the first and critical step for characterizing its properties, dynamics, and environmental implications. However, various pretreatment procedures may alter DOM composition and structure, producing highly variable recoveries and even influencing its subsequent characterization. Therefore, complimentary use of various characterization techniques is highly recommended to obtain as much information on DOM as possible, as each characterization technique exhibits various advantages and limitations. Moreover, DOM could markedly change the physical and chemical properties of exogenous chemical species, influencing their transformation and mobility, and finally altering their potential bioavailability and toxicity. Several research gaps to be addressed include the impact of pretreatment on the composition and structure of aquatic DOM, molecular-level structural elucidation for DOM, and assessment of the effects of DOM dynamics on the fate, bioavailability and toxicity of exogenous chemical species.

Event-scale hysteresis metrics to reveal processes and mechanisms controlling constituent export from watersheds: A review✰

Due to the increased availability of high-frequency measurements of stream chemistry provided by in situ sensors, researchers have gained more access to relationships between stream discharge and constituent concentrations (C-Q relationships) at event-scales. Existing studies reveal that event-scale C-Q relationships are mostly non-linear and exhibit temporal lags between peaks (or troughs) of hydrographs and chemographs, resulting in apparent hysteresis effects. In this paper, we summarize and introduce tools and methods in hysteresis analysis, especially the history and progresses of metrics to quantify hysteresis patterns. In addition, this paper provides a typical workflow to conduct event-scale hysteresis analysis, such as how to obtain the access to high-frequency measurements, existing methods to delineate storm events, approaches to classify and quantify hysteresis patterns, possible features/properties controlling hysteresis patterns, statistical methods to identify features at play, and strategies to deliver the inferences from hysteresis analysis. Lastly, we discuss some potential limitations that arise in the workflow and possible future work to address the challenges, including the development of advanced quantitative hysteresis metrics, generalized and standardized tools to delineate events and the integration of hysteresis analysis with numerical modeling. This paper aims to provide a critical overview of technical approaches for hysteresis analysis for researchers and hopefully foster their interests to advance our understanding of complex mechanisms in event-scale hydro-biogeochemical processes.

Adaptive monitoring approach to assess dissolved organic matter dynamics during rainfall events

Rainfall events induce water quality transformation in river systems influenced by the watershed land use and hydrology dynamics. In this context, an adaptive monitoring approach (AMA) is used to assess non-point sources (NPS) of pollution events, through dissolved organic matter (DOM) contribution. The case study is a monitoring site in a semi-urban watershed characterized by NPS contribution. An integrated quali-quantitative method for DOM based on dissolved organic carbon (DOC) content, spectroscopic techniques of excitation-emission fluorescence (EEF), and UV-visible absorbance is proposed. The results indicate a mix of allochthonous and autochthonous DOM characteristics from NPS sources associated to vegetation area influence (A₂₈₅/DOC of 15.43 L (g cm)^-1 and SUVA₂₅₄ of 2.11 L (mg m)^-1). The EEF signals showed more humic-like than protein-like characteristics with peaks A and C (approximately 5.72 r.u.) more intense than peaks B, T₁, and T₂ (approximately 4.33 r.u.), indicating NPS from the soil leachate. The absorbance ratio values indicate a mix of organic compounds with greater proportion of refractory characteristics with high aromaticity and molecular weight (approximately A₃₀₀/A₄₀₀ of 4.15 and A₂₅₀/A₃₆₅ of 4.48), associated with the surface wash-off of accumulated residual and subsurface soil erosion, which contribute to complex organic matter structures. The fluorescence indexes, overall, indicated allochthonous sources with intermediate humic characteristics (FI ≈ 1.43, BIX ≈ 0.65, and HIX ≈ 7.98). The proposed integrated optical property strategy represents an opportunity for better understanding of DOM dynamic assessment for identifying potential mitigation techniques for organic pollution control and improving water quality conditions.

A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers

It’s vital to explore critical indicators when identifying potential pollution sources of urban rivers. However, the variations of urban river water qualities following temporal and spatial disturbances were highly local-dependent, further complicating the understanding of pollution emission laws. In order to understand the successional trajectory of water qualities of urban rivers and the underlying mechanisms controlling these dynamics at local scale, we collected daily monitoring data for 17 physical and chemical parameters from seven on-line monitoring stations in Nanfeihe River, Anhui, China, during the year 2018. The water quality at tributaries were similar, while that at main river was much different. A seasonal ‘’turning-back” pattern was observed in the water quality, which changed significantly from spring to summer but finally changed back in winter. This result was possibly regulated by seasonally-changed dissolved oxygen and water temperature. Linear mixed models showed that the site 2, with the highest loads of pollution, contributed the highest (β = 0.316, P < 0.001) to the main river City Water Quality Index (CWQI) index, but site 5, the geographically nearest site to main river monitoring station, did not show significant effect. In contrast, site 5 but not site 2 contributed the highest (β = 0.379, P < 0.001) to the main river water quality. Therefore, CWQI index was a better index than water quality to identify potential pollution sources with heavy loads of pollutants, despite temporal and spatial disturbances at local scales. These results highlight the role of aeration in water quality controlling of urban rivers, and emphasized the necessity to select proper index to accurately trace the latent pollution sources.

A comprehensive review of catchment water quality monitoring using a tiered framework of integrated sensing technologies

Due to the growing threat of climate change, new advances in water quality monitoring strategies are needed now more than ever. Reliable and robust monitoring practices can be used to improve and better understand catchment processes affecting the water quality. In recent years the deployment of long term in-situ sensors has increased the temporal and spatial data being obtained. Furthermore, the development and research into remote sensing using satellite and aerial imagery has been incrementally integrated into catchments for monitoring areas that previously might have been impossible to monitor, producing high-resolution data that has become imperative to catchment monitoring. The use of modelling in catchments has become relevant as it enables the prediction of events before they occur so that strategic plans can be put in place to deal with or prevent certain threats. This review highlights the monitoring approaches employed in catchments currently and examines the potential for integration of these methods. A framework might incorporate all monitoring strategies to obtain more information about a catchment and its water quality. The future of monitoring will involve satellite, in-situ and air borne devices with data analytics playing a key role in providing decision support tools. The review provides examples of successful use of individual technologies, some combined approaches and identifies gaps that should be filled to achieve an ideal catchment observation system.

Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

The overarching hypothesis of this study was that temporal microbial potentiometric sensor (MPS) signal patterns could be used to predict changes in commonly monitored water quality parameters by using artificial intelligence/machine learning tools. To test this hypothesis, the study first examines a proof of concept by correlating between MPS's signals and high algae concentrations in an algal cultivation pond. Then, the study expanded upon these findings and examined if multiple water quality parameters could be predicted in real surface waters, like irrigation canals. Signals generated between the MPS sensors and other water quality sensors maintained by an Arizona utility company, including algae and chlorophyll, were collected in real time at time intervals of 30 min over a period of 9 months. Data from the MPS system and data collected by the utility company were used to train the ML/AI algorithms and compare the predicted with actual water quality parameters and algae concentrations. Based on the composite signal obtained from the MPS, the ML/AI was used to predict the canal surface water's turbidity, conductivity, chlorophyll, and blue-green algae (BGA), dissolved oxygen (DO), and pH, and predicted values were compared to the measured values. Initial testing in the algal cultivation pond revealed a strong linear correlation (R² = 0.87) between mixed liquor suspended solids (MLSS) and the MPSs' composite signals. The Normalized Root Mean Square Error (NRMSE) between the predicted values and measured values were <6.5%, except for the DO, which was 10.45%. The results demonstrate the usefulness of MPSs to predict key surface water quality parameters through a single composite signal, when the ML/AI tools are used conjunctively to disaggregate these signal components. The maintenance-free MPS offers a novel and cost-effective approach to monitor numerous water quality parameters at once with relatively high accuracy.

Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression

Stream nutrient concentrations exhibit marked temporal variation due to hydrology and other factors such as the seasonality of biological processes. Many water quality monitoring programs sample too infrequently (i.e., weekly or monthly) to fully characterize lotic nutrient conditions and to accurately estimate nutrient loadings. A popular solution to this problem is the surrogate-regression approach, a method by which nutrient concentrations are estimated from related parameters (e.g., conductivity or turbidity) that can easily be measured in situ at high frequency using sensors. However, stream water quality data often exhibit skewed distributions, nonlinear relationships, and multicollinearity, all of which can be problematic for linear-regression models. Here, we use a flexible and robust machine learning technique, Random Forests Regression (RFR), to estimate stream nitrogen (N) and phosphorus (P) concentrations from sensor data within a forested, mountainous drainage area in upstate New York. When compared to actual nutrient data from samples tested in the laboratory, this approach explained much of the variation in nitrate (89%), total N (85%), particulate P (76%), and total P (74%). The models were less accurate for total soluble P (47%) and soluble reactive P (32%), though concentrations of these latter parameters were in a relatively low range. Although soil moisture and fluorescent dissolved organic matter are not commonly used as surrogates in nutrient-regression models, they were important predictors in this study. We conclude that RFR shows great promise as a tool for modeling instantaneous stream nutrient concentrations from high-frequency sensor data, and encourage others to evaluate this approach for supplementing traditional (laboratory-determined) nutrient datasets.

Combining in-situ fluorometry and distributed rainfall data provides new insights into natural organic matter transport dynamics in an urban river

Urbanization alters the quality and quantity of Dissolved Organic Matter (DOM) fluxes to rivers potentially leading to water quality problems and impaired ecosystem function. Traditional synoptic and point sampling approaches are generally inadequate for monitoring DOM source dynamics. To identify links between spatial heterogeneity in precipitation and DOM dynamics, we used a unique approach combining high spatial and temporal resolution precipitation datasets featuring point, catchment, and land-cover weighted precipitation to characterise catchment transport dynamics. These datasets were linked to fluorescence records from an urban stream (Bourn Brook, Birmingham, UK). Humic-like fluorescence (HLF: Ex. 365 nm, Em. 490 nm) and Tryptophan-like fluorescence (TLF: Ex. 285 nm, Em. 340 nm) were measured, (plus river flow and turbidity) at 5 min intervals for 10 weeks during Autumn 2017. The relationship between discharge (Q) and concentration (C) for TLF and HLF were strongly chemodynamic at low Q (<Q50) but TLF was chemostatic when Q exceeded this threshold. Figure of eight hysteresis was the most common response type for both HLF and TLF, indicating that DOM sources shift within and between events. Key drivers of DOM dynamics were identified using regression analysis and model outputs using point, catchment-averaged, and land-use weighted precipitation were compared. Antecedent rainfall was identified as the most important predictor (negative relationship) of TLF and HLF change suggesting DOM source exhaustion. Precipitation weighted by land cover showed that urbanization metrics were linked to increased TLF:HLF ratios and changes in hysteresis index. This study presents a novel approach of using land-cover weighted rainfall to enhance mechanistic understanding of DOM controls and sources. In contrast, catchment-average rainfall data have the potential to yield stronger understanding of TLF dynamics. This technique could be integrated with existing high resolution in-situ datasets to enhance our understanding of DOM dynamics in urban rivers.

A Co-Nanoparticles Modified Electrode for On-Site and Rapid Phosphate Detection in Hydroponic Solutions

Conventional strategies for determining phosphate concentration is limited in efficiency due to the cost, time, and labor that is required in laboratory analysis. Therefore, an on-site and rapid detection sensor for phosphate is urgently needed to characterize phosphate variability in a hydroponic system. Cobalt (Co) is a highly sensitive metal that has shown a selectivity towards phosphate to a certain extent. A disposable phosphate sensor based on the screen-printed electrode (SPE) was developed to exploit the advantages of Co-nanoparticles. A support vector machine regression model was established to predict the concentration of phosphate in the hydroponic solutions. The results showed that Co-nanoparticles improve the detection limit of the sensor in the initial state. Meanwhile, the corrosion of Co-nanoparticles leads to a serious time-drift and instability of the electrodes. On the other hand, the coefficient of variation of the disposable phosphate detection chip is 0.4992%, the sensitivity is 33 mV/decade, and the linear range is 10-1-10-4.56 mol/L. The R2 and mean square error of the buffer-free sensor in the hydroponic solution are 0.9792 and 0.4936, respectively. In summary, the SPE modified by the Co-nanoparticles is a promising low-cost sensor for on-site and rapid measurement of the phosphate concentration in hydroponic solutions.

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Comparing the performance of three methods to assess DOM dynamics within two distinct glacierized watersheds of the tropical Andes

Dissolved organic matter (DOM) is recognized as a good indicator of water quality as its concentration is influenced by land use, rainwater, windborne material and anthropogenic activities. Recent technological advances make it possible to characterize fluorescent dissolved organic matter (FDOM), the fraction of DOM that fluoresces. Among these advances, portable fluorometers and benchtop fluorescence excitation and emission spectroscopy coupled with a parallel factor analysis (EEM-PARAFAC) have shown to be reliable. Despite their rising popularity, there is still a need to evaluate the extent to which these techniques can assess DOM dynamics at the watershed scale. We compare the performance of in-situ measurements of FDOM with laboratory measurements of fluorescence spectroscopy within the context of two distinct glacierized watersheds in Peru. Glacierized watersheds represent unique testing environments with contrasting DOM conditions, flowing from pristine, vegetation-free headwaters through locations with obvious anthropogenic influences. We used an in-situ fluorometer and a portable multimeter to take 38 measurements of FDOM, pH and turbidity throughout the two catchments. Additionally, samples were analyzed in the laboratory using the EEM-PARAFAC method. Results were compared to dissolved organic carbon (DOC) measurements using standard high-temperature catalytic oxidation. Our results show that the three techniques together were able to capture the DOM dynamics for both studied watersheds. Taken individually, all three methods allowed detection of the watershed DOM main points of sources but in a more limited way. Due to the narrow bandwidth of the portable fluorometer used in the study, FDOM measurements were almost non-detectable to protein-like substances. Indeed, the more demanding EEM-PARAFAC was able to both differentiate between potential sources of DOM and provide an estimate of relative concentrations of different organic components. Finally, similar to FDOM but to a lesser extent, the DOC measurements showed some limits where protein-like substances make up most of the DOM composition.

Monitoring Approaches for Faecal Indicator Bacteria in Water: Visioning a Remote Real-Time Sensor for E. coli and Enterococci

A comprehensive review was conducted to assess the current state of monitoring approaches for primary faecal indicator bacteria (FIB) E. coli and enterococci. Approaches were identified and examined in relation to their accuracy, ability to provide continuous data and instantaneous detection results, cost, environmental awareness regarding necessary reagent release or other pollution sources, in situ monitoring capability, and portability. Findings showed that several methods are precise and sophisticated but cannot be performed in real-time or remotely. This is mainly due to their laboratory testing requirements, such as lengthy sample preparations, the requirement for expensive reagents, and fluorescent tags. This study determined that portable fluorescence sensing, combined with advanced modelling methods to compensate readings for environmental interferences and false positives, can lay the foundations for a hybrid FIB sensing approach, allowing remote field deployment of a fleet of networked FIB sensors that can collect high-frequency data in near real-time. Such sensors will support proactive responses to sudden harmful faecal contamination events. A method is proposed to enable the development of the visioned FIB monitoring tool.

A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records

To provide more precise understanding of water quality changes, continuous sampling is being used more in surface water quality monitoring networks. However, it remains unclear how much improvement continuous monitoring provides over spot sampling, in identifying water quality changes over time. This study aims (1) to assess our ability to detect trends using water quality data of both high and low frequencies and (2) to assess the value of using high-frequency data as a surrogate to help detect trends in other constituents. Statistical regression models were used to identify temporal trends and then to assess the trend detection power of high-frequency (15 min) and low-frequency (monthly) data for turbidity and electrical conductivity (EC) data collected across Victoria, Australia. In addition, we developed surrogate models to simulate five sediment and nutrients constituents from runoff, turbidity and EC. A simulation-based statistical approach was then used to the compare the power to detect trends between the low- and high-frequency water quality records. Results show that high-frequency sampling shows clear benefits in trend detection power for turbidity, EC, as well as simulated sediment and nutrients, especially over short data periods. For detecting a 1% annual trend with 5 years of data, up to 97% and 94% improvements on the trend detection probability are offered by high-frequency data compared with monthly data, for turbidity and EC, respectively. Our results highlight the benefits of upgrading monitoring networks with wider application of high-frequency sampling.

Moving beyond the Technology: A Socio-technical Roadmap for Low-Cost Water Sensor Network Applications

In this paper, we critically review the current state-of-the-art for sensor network applications and approaches that have developed in response to the recent rise of low-cost technologies. We specifically focus on water-related low-cost sensor networks, and conceptualize them as socio-technical systems that can address resource management challenges and opportunities at three scales of resolution: (1) technologies, (2) users and scenarios, and (3) society and communities. Building this argument, first we identify a general structure for building low-cost sensor networks by assembling technical components across configuration levels. Second, we identify four application categories, namely operational monitoring, scientific research, system optimization, and community development, each of which has different technical and nontechnical configurations that determine how, where, by whom, and for what purpose low-cost sensor networks are used. Third, we discuss the governance factors (e.g., stakeholders and users, networks sustainability and maintenance, application scenarios, and integrated design) and emerging technical opportunities that we argue need to be considered to maximize the added value and long-term societal impact of the next generation of sensor network applications. We conclude that consideration of the full range of socio-technical issues is essential to realize the full potential of sensor network technologies for society and the environment.

Temporal hydrochemical dynamics of the River Wensum, UK: Observations from long-term high-resolution monitoring (2011-2018)

In 2010, the UK government established the Demonstration Test Catchment (DTC) initiative to evaluate the extent to which on-farm mitigation measures can cost-effectively reduce the impacts of agricultural water pollution on river ecology whilst maintaining food production capacity. A central component of the DTC platform was the establishment of a comprehensive network of automated, web-based sensor technologies to generate high-temporal resolution (30 min) empirical datasets of surface water, groundwater and meteorological parameters over a long period (2011-2018). Utilising 8.9 million water quality measurements generated for the River Wensum, this paper demonstrates how long-term, high-resolution monitoring of hydrochemistry can improve our understanding of the complex temporal dynamics of riverine processes from 30 min to annual timescales. This paper explores the impact of groundwater-surface water interactions on instream pollutant concentrations (principally nitrogen, phosphorus and turbidity) and reveals how varying hydrochemical associations under contrasting flow regimes can elicit important information on the dominant pollution pathways. Furthermore, this paper examines the relationships between agricultural pollutants and precipitation events of varying magnitude, whilst demonstrating how high-resolution data can be utilised to develop conceptual models of hydrochemical processes for contrasting winter and summer seasons. Finally, this paper considers how high-resolution hydrochemical data can be used to increase land manager awareness of environmentally damaging farming operations and encourage the adoption of more water sensitive land management practices.

High frequency fluorescence monitoring reveals new insights into organic matter dynamics of an urban river, Birmingham, UK

Natural organic matter (NOM) is fundamental to many biogeochemical processes in river ecosystems. Currently, however, we have limited knowledge of NOM dynamics across the spectrum of flow conditions as previous studies have focused largely on storm events. Field deployable fluorescence technology offers new opportunities to explore both stochastic and predictable diel NOM dynamics at finer time-steps and for longer periods than was hitherto possible, thus yielding new insight into NOM sources, processing, and pathways. Hourly fluorescence data (humic-like fluorescence [Peak C] and tryptophan-like fluorescence [Peak T]) and a suite of hydro-climatological variables were collected from an urban river (Birmingham, UK). We explored monthly concentration-discharge (C-Q) patterns using segmented regression and assessed hysteretic and flushing behaviour for Peak C, T and turbidity to infer source zone activation. Diel patterns were assessed during low flow periods. Wavelet analysis identified strong diurnal variations in Peak C with early morning peaks while no diel dynamics were apparent for Peak T. Using generalised linear modelling relationships between Peak C periodicity and both solar radiation and time since previous storm/scouring event were identified. Breakpoints and positive slopes for C-Q relationship highlighted chemodynamic behaviour for NOM over most of the monitoring period, with Peak T mobilised more relative to Peak C during high Q. Hysteresis patterns were highly variable but flushing behaviour of Peak T and C suggested exhaustion of humic compounds during long duration events and following successive rainfall events. Peak T flushing was correlated with Q magnitude highlighting the potential for combined sewer overflows to act as important NOM sources despite significant dilution potential. This research highlights the potential of real-time, field deployable fluorescence spectroscopy as a viable method for providing insight into diel and transport driven NOM dynamics.

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

PURPOSE

This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science.

METHODS

Web of Science and Google Scholar were used to review published papers spanning the period 2013-2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018-2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017.

SCOPE

Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing.

CONCLUSIONS

The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

Research questions to facilitate the future development of European long-term ecosystem research infrastructures: A horizon scanning exercise

Distributed environmental research infrastructures are important to support assessments of the effects of global change on landscapes, ecosystems and society. These infrastructures need to provide continuity to address long-term change, yet be flexible enough to respond to rapid societal and technological developments that modify research priorities. We used a horizon scanning exercise to identify and prioritize emerging research questions for the future development of ecosystem and socio-ecological research infrastructures in Europe. Twenty research questions covered topics related to (i) ecosystem structures and processes, (ii) the impacts of anthropogenic drivers on ecosystems, (iii) ecosystem services and socio-ecological systems and (iv), methods and research infrastructures. Several key priorities for the development of research infrastructures emerged. Addressing complex environmental issues requires the adoption of a whole-system approach, achieved through integration of biotic, abiotic and socio-economic measurements. Interoperability among different research infrastructures needs to be improved by developing standard measurements, harmonizing methods, and establishing capacities and tools for data integration, processing, storage and analysis. Future research infrastructures should support a range of methodological approaches including observation, experiments and modelling. They should also have flexibility to respond to new requirements, for example by adjusting the spatio-temporal design of measurements. When new methods are introduced, compatibility with important long-term data series must be ensured. Finally, indicators, tools, and transdisciplinary approaches to identify, quantify and value ecosystem services across spatial scales and domains need to be advanced.

Investigation of watershed nutrient export affected by extreme events and the corresponding sampling frequency

Although the real-time monitoring technique has been widely applied due to the improvement of sensors, development of traditional sampling methods is still worth of being discussed due to the economically feasibility. Currently, extreme events (e.g. extreme rainfall caused by climate change) play a relatively important role in nutrient export. However, impacts of extreme events on the optimization of sampling strategy is still not well addressed despite the uncertainty of different frequency sampling programs has been sufficiently discussed in previous studies. Therefore, the corresponding impact of extreme events impact on the optimization of sampling strategy was investigated by examining temporal (i.e., inter-annual and seasonal) variations of available data. Uncertainty of nutrient flux estimates under different sampling frequencies was explored by subsampling daily monitoring data. Results showed that uncertainty in flux estimates differed between nitrogen and phosphorus. The relative error (RE) in annual TN flux estimates ranged from -4.2% to 2.4% (once per three-day) to -21.4-31.1% (monthly sampling), while the RE in annual TP flux estimates varied from -14.1% to 8.2% (once per three-day) to -65.9%-163.4% (monthly sampling). Biweekly and weekly sampling routines are considered the optimal sampling program for total nitrogen (TN) and for total phosphorus (TP) when the extreme events impact were not been considered. The uncertainty of flux estimates with different sampling frequencies increased with the increasing extreme events. High level of uncertainty occurred in years with the most extreme events in 2012 (RE: 21.4-69.0% for TN, 33.3-96.6% for TP), while the lowest can be found in 2011 (RE: 0-20.7% for TN, 0-48.3% for TP) (with the fewest extreme events). In addition, uncertainty in TN and TP flux estimates was generally greater during summer season than during other seasons. These results highlighted the important role of extreme events in nutrient export. Approximately half of the annual TN and TP flux occurred in some extreme days that only accounted for less than 20% in the same year. The onset of these extremes of nutrient export was likely due to the stormflow with addition of external fertilizer and the direct discharge of surface ponding water from paddy fields during special periods of time. These results would be helpful for the optimization of sampling strategy.

Water quality dynamic during rainfall episodes: integrated approach to assess diffuse pollution using automatic sampling

Diffuse pollution caused by rainfall events potentially affects water quality in rivers and, therefore, must be investigated in order to improve water quality planning and management recovery strategies. For these, a quali-quantitative approach was used to monitor the water quality parameters in a river located in a semi-urban watershed area based upon automatic sampling. Thirteen water quality parameters were measured during five rainfall events. Events ranged from 2.3 to 56.8 mm and water peak flows from 3.3 to 4.5 m³/s. The pollutographs measured showed a standard pattern for total suspended solids (TSS). However, for the other chemical parameters, as total phosphorous (TP) and dissolved organic carbon (DOC), the dilution effects were more evident. It was possible to observe the rainfall influence mainly for physical parameters indicating a mass transport pattern for diffuse pollutants, which increased, for example, the amount of TSS in the river. Furthermore, hydrological characteristics were relevant considering the pollutant behavior. Antecedent dry periods, ranging from 1.3 days to 21.4 days, and critical time, from 2.0 to 10.4 h, are determinants to evaluate non-traditional water quality impacts in the river. In general, each rainfall episode has its own characteristics, which produces distinct mass contribution and temporal behavior, being challenging in making generalization. Therefore, the results indicate that diffuse pollution has to be considered to establish future decision-making strategies to water resources management.

Using dissolved organic matter fluorescence to identify the provenance of nutrients in a lowland catchment; the River Thames, England

Catchment based solutions are being sought to mitigate water quality pressures and achieve multiple benefits but their success depends on a sound understanding of catchment functioning. Novel approaches to monitoring and data analysis are urgently needed. In this paper we explore the potential of river water fluorescence at the catchment scale in understanding nutrient concentrations, sources and pathways. Data were collected from across the River Thames basin from January 2012 to March 2015. Analysing emission excitation matrices (EEMs) using both PARAFAC and optimal area averaging produced consistent results for humic-like component 1 and tryptophan-like component 4 in the absence of a subset of samples that exhibited an unusual peak; illustrating the importance of inspecting the entire EEM before using peak averaging methods. Strong relationships between fluorescence components and dissolved organic carbon (DOC), soluble reactive phosphorus (SRP), and ammonium clearly demonstrated its potential, in this study basin, as a field based surrogate for nutrients. Analysing relationships between fluorescence, catchment characteristics and boron from across the basin enabled new insights into the provenance of nutrients. These include evidence for diffuse sources of DOC from near surface hydrological pathways (i.e. soil horizons); point source inputs of nutrients from sewage effluent discharges; and diffuse contributions of nutrients from agriculture and/or sewage (e.g. septic tanks). The information gained by broad scale catchment wide monitoring of fluorescence could support catchment managers in (a) prioritising subcatchments for nutrient mitigation; (b) providing information on relative nutrient source contributions; and (c) providing evidence of the effectiveness of investment in pollution mitigation measures. The collection of high resolution fluorescence data at the catchment scale and, in particular, over shorter event timescales would complement broad scale assessments by enhancing our hydro-biogeochemical process understanding.

Hysteresis analysis of nitrate dynamics in the Neuse River, NC

Anthropogenic activities have caused N saturation in many terrestrial ecosystems. The transfer of nutrients and sediments to freshwater environments has resulted in water quality impairments including eutrophication, increased turbidity, ecosystem acidification, and loss of biodiversity. Storm events account for the transport of a large proportion of nutrients and sediments found in watersheds on an annual basis. To implement effective water-quality management strategies, the importance of surface and subsurface flow paths during storm events and low flow conditions need to be quantified. The increased availability of optical in-situ sensors makes high-frequency monitoring of catchment fluxes practical. In this study, we present a high-resolution nitrate monitoring record over a 10-year period in the Neuse River Basin near Clayton, North Carolina. The relationship between discharge and nitrate concentration for 365 storm events are categorized into hysteresis classes that indicate different transport mechanisms into the river. Storm events over the entire period of this study are divided between clockwise, counter-clockwise, and complex hysteresis patterns, indicating multiple nitrate flow paths during different seasons and years. Logistic regression of a suite of environmental variables demonstrates that antecedent soil moisture is a significant factor in determining the storm hysteresis class, with the odds of counter-clockwise hysteresis increasing by 10.3% for every 1 percentage point increase in the soil moisture. There is also an overlying seasonal effect, which indicates that dry soil conditions and frequent small storms during summer leads to greater nitrate transport on the rising limb, in contrast to slower, groundwater-driven inputs during the rest of the year.

Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers

The quality standards for surface waters increase steadily bearing new challenges for water policy. Precise knowledge of the sources and transport pathway of various impacts in a catchment area is of particular importance for any management activities. Online measurements with high temporal resolution are particularly suited for this purpose especially in small and middle scale catchments. In this paper we present an approach applying mobile measuring stations in which commercial available sensors and wet chemical analysers are combined in a new set to enable real-time monitoring of various parameters. The resulting data and the interpretation of their relationships allow the identification of diverse pollution situations in a river. In this paper some examples of impacts from diffuse and point sources are given to illustrate the high information density obtained through the use of this system.

Development of a nickel oxide/oxyhydroxide-modified printed carbon electrode as an all solid-state sensor for potentiometric phosphate detection

This work describes the preparation, characterization and use of a nickel oxide/oxyhydroxide-printed carbon electrode as an efficient potentiometric phosphate sensor.

Microbial Processing and Production of Aquatic Fluorescent Organic Matter in a Model Freshwater System

Organic matter (OM) has an essential biogeochemical influence along the hydrological continuum and within aquatic ecosystems. Organic matter derived via microbial processes was investigated within a range of model freshwater samples over a 10-day period. For this, excitation-emission matrix (EEM) fluorescence spectroscopy in combination with parallel factor (PARAFAC) analysis was employed. This research shows the origin and processing of both protein-like and humic-like fluorescence within environmental and synthetic samples over the sampling period. The microbial origin of Peak T fluorescence is demonstrated within both synthetic samples and in environmental samples. Using a range of incubation temperatures provides evidence for the microbial metabolic origin of Peak T fluorescence. From temporally resolved experiments, evidence is provided that Peak T fluorescence is an indication of metabolic activity at the microbial community level and not a proxy for bacterial enumeration. This data also reveals that humic-like fluorescence can be microbially derived in situ and is not solely of terrestrial origin, likely to result from the upregulation of cellular processes prior to cell multiplication. This work provides evidence that freshwater microbes can engineer fluorescent OM, demonstrating that microbial communities not only process, but also transform, fluorescent organic matter.

Development of a Cost-Effective Sensing Platform for Monitoring Phosphate in Natural Waters

A sensing platform for the in situ, real-time analysis of phosphate in natural waters has been realised using a combination of microfluidics, colorimetric reagent chemistries, low-cost LED-based optical detection and wireless communications. Prior to field deployment, the platform was tested over a period of 55 days in the laboratory during which a total of 2682 autonomous measurements were performed (854 each of sample, high standard and baseline, and 40 × 3 spiked solution measurements). The platform was subsequently field-deployed in a freshwater stream at Lough Rea, Co., Galway, Ireland, to track changes in phosphate over a five day period. During this deployment, 165 autonomous measurements (55 each of sample, high standard, and baseline) were performed and transmitted via general packet radio service (GPRS) to a web interface for remote access. Increases in phosphate levels at the sampling location coincident with rainfall events (min 1.45 µM to max 10.24 µM) were detected during the deployment. The response was found to be linear up to 50 µM PO43−, with a lower limit of detection (LOD) of 0.09 µM. Laboratory and field data suggest that despite the complexity of reagent-based analysers, they are reasonably reliable in remote operation, and offer the best opportunity to provide enhanced in situ chemical sensing capabilities. Modifications that could further improve the reliability and scalability of these platforms while simultaneously reducing the unit cost are discussed.

Impacts of land-use and land-cover change on stream hydrochemistry in the Cerrado and Amazon biomes

Studies on the impacts of land-use and land-cover change on stream hydrochemistry in active deforestation zones of the Amazon agricultural frontier are limited and have often used low-temporal-resolution datasets. Moreover, these impacts are not concurrently assessed in well-established agricultural areas and new deforestations hotspots. We aimed to identify these impacts using an experimental setup to collect high-temporal-resolution hydrological and hydrochemical data in two pairs of low-order streams in catchments under contrasting land use and land cover (native vegetation vs. pasture) in the Amazon and Cerrado biomes. Our results indicate that the conversion of natural landscapes to pastures increases carbon and nutrient fluxes via streamflow in both biomes. These changes were the greatest in total inorganic carbon in the Amazon and in potassium in the Cerrado, representing a 5.0- and 5.5-fold increase in the fluxes of each biome, respectively. We found that stormflow, which is often neglected in studies on stream hydrochemistry in the tropics, plays a substantial role in the carbon and nutrient fluxes, especially in the Amazon biome, as its contributions to hydrochemical fluxes are mostly greater than the volumetric contribution to the total streamflow. These findings demonstrate that assessments of the impacts of deforestation in the Amazon and Cerrado biomes should also take into account rapid hydrological pathways; however, this can only be achieved through collection of high-temporal-resolution data.

A novel high-frequency groundwater quality monitoring system

High-frequency, long-term monitoring of water quality has revolutionized the study of surface waters in recent years. However, application of these techniques to groundwater has been limited by the ability to remotely pump and analyze groundwater. This paper describes a novel autonomous groundwater quality monitoring system which samples multiple wells to evaluate temporal changes and identify trends in groundwater chemistry. The system, deployed near Fresno, California, USA, collects and transmits high-frequency data, including water temperature, specific conductance, pH, dissolved oxygen, and nitrate, from supply and monitoring wells, in real-time. The system consists of a water quality sonde and optical nitrate sensor, manifold, submersible three-phase pump, variable frequency drive, data collection platform, solar panels, and rechargeable battery bank. The manifold directs water from three wells to a single set of sensors, thereby reducing setup and operation costs associated with multi-sensor networks. Sampling multiple wells at high frequency for several years provided a means of monitoring the vertical distribution and transport of solutes in the aquifer. Initial results show short period variability of nitrate, specific conductivity, and dissolved oxygen in the shallow aquifer, while the deeper portion of the aquifer remains unchanged-observations that may be missed with traditional discrete sampling approaches. In this aquifer system, nitrate and specific conductance are increasing in the shallow aquifer, while invariant changes in deep groundwater chemistry likely reflect relatively slow groundwater flow. In contrast, systems with high groundwater velocity, such as karst aquifers, have been shown to exhibit higher-frequency groundwater chemistry changes. The stability of the deeper aquifer over the monitoring period was leveraged to develop estimates of measurement system uncertainty, which were typically lower than the manufacturer's stated specifications, enabling the identification of subtle variability in water chemistry that may have otherwise been missed.

Municipal wastewater effluent characterization and variability analysis in view of an ozone dose control strategy during tertiary treatment: The status in Belgium

Ozonation is known for removing trace organic contaminants (TrOCs) from secondary wastewater effluent. However, its implementation and overall efficiency on a broad scale depends on effluent characteristics, which can differ both in time as well as between different treatment plants (nowadays referred to as water resource recovery facilities (WRRFs)). Therefore, water quality was assessed over time at 15 different Belgian sampling locations to increase the understanding of effluent variability in view of online control of the tertiary ozonation step. Conventional and surrogate parameters as well as those specifically related to tertiary ozonation (e.g. instantaneous ozone demand) were assessed. Little differences between the different locations were found for spectral measurements (e.g. UVA₂₅₄ or fluorescence). The small amount of observed outliers was clearly site or event dependent. A lower variability (for spectral measurements) is advantageous in simplifying the development and application of a generic control framework based on these spectral measurements. In addition, also variations in TrOC concentration levels seemed to be small, as the concentration of most individual compounds resided within one order of magnitude over multiple sampling events at two different WRRFs. The combination of this low variability in TrOC levels in the effluent before ozonation with a control strategy using a TrOC removal efficiency set-point, allows to indicatively assess absolute TrOC levels after ozonation. In contrast, significant variations between different plants (especially smaller sized plants) were observed and could be related to the conventional water quality parameters alkalinity (correlated with the electrical conductivity) and pH which are both known to have an influence on the ozonation process. This confirms that a differential dosing control strategy (i.e. accounting for the matrix reactivity) should be applied instead of one solely based on the (organic) effluent load before ozonation.

Distributed electrochemical sensors: recent advances and barriers to market adoption

Despite predictions of their widespread application in healthcare and environmental monitoring, electrochemical sensors are yet to be distributed at scale, instead remaining largely confined to R&D labs. This contrasts sharply with the situation for physical sensors, which are now ubiquitous and seamlessly embedded in the mature ecosystem provided by electronics and connectivity protocols. Although chemical sensors could be integrated into the same ecosystem, there are fundamental issues with these sensors in the three key areas of analytical performance, usability, and affordability. Nevertheless, advances are being made in each of these fields, leading to hope that the deployment of automated and user-friendly low-cost electrochemical sensors is on the horizon. Here, we present a brief survey of key challenges and advances in the development of distributed electrochemical sensors for liquid samples, geared towards applications in healthcare and wellbeing, environmental monitoring, and homeland security. As will be seen, in many cases the analytical performance of the sensor is acceptable; it is usability that is the major barrier to commercial viability at this moment. Were this to be overcome, the issue of affordability could be addressed. Graphical Abstract ᅟ.

Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication

Chromophoric dissolved organic matter (CDOM) is an important optically active substance in aquatic environments and plays a key role in light attenuation and in the carbon, nitrogen and phosphorus biogeochemical cycles. Although the optical properties, abundance, sources, cycles, compositions and remote sensing estimations of CDOM have been widely reported in different aquatic environments, little is known about the optical properties and composition changes in CDOM along trophic gradients. Therefore, we collected 821 samples from 22 lakes along a trophic gradient (oligotrophic to eutrophic) in China from 2004 to 2015 and determined the CDOM spectral absorption and nutrient concentrations. The total nitrogen (TN), total phosphorus (TP), and chlorophyll a (Chla) concentrations and the Secchi disk depth (SDD) ranged from 0.02 to 24.75 mg/L, 0.002-3.471 mg/L, 0.03-882.66 μg/L, and 0.05-17.30 m, respectively. The trophic state index (TSI) ranged from 1.55 to 98.91 and covered different trophic states, from oligotrophic to hyper-eutrophic. The CDOM absorption coefficient at 254 nm (a(254)) ranged from 1.68 to 92.65 m^-1. Additionally, the CDOM sources and composition parameters, including the spectral slope and relative molecular size value, exhibited a substantial variability from the oligotrophic level to other trophic levels. The natural logarithm value of the CDOM absorption, lna(254), is highly linearly correlated with the TSI (r² = 0.92, p < .001, n = 821). Oligotrophic lakes are distinguished by a(254)<4 m^-1, and mesotrophic and eutrophic lakes are classified as 4 ≤ a(254)≤10 and a(254)>10 m^-1, respectively. The results suggested that the CDOM absorption coefficient a(254) might be a more sensitive single indicator of the trophic state than TN, TP, Chla and SDD. Therefore, we proposed a CDOM absorption coefficient and determined the threshold for defining the trophic state of a lake. Several advantages of measuring and estimating CDOM, including rapid experimental measurements, potential in situ optical sensor measurements and large-spatial-scale remote sensing estimations, make it superior to traditional TSI techniques for the rapid monitoring and assessment of lake trophic states.

Handling the phosphorus paradox in agriculture and natural ecosystems: Scarcity, necessity, and burden of P

This special issue of Ambio compiles a series of contributions made at the 8th International Phosphorus Workshop (IPW8), held in September 2016 in Rostock, Germany. The introducing overview article summarizes major published scientific findings in the time period from IPW7 (2015) until recently, including presentations from IPW8. The P issue was subdivided into four themes along the logical sequence of P utilization in production, environmental, and societal systems: (1) Sufficiency and efficiency of P utilization, especially in animal husbandry and crop production; (2) P recycling: technologies and product applications; (3) P fluxes and cycling in the environment; and (4) P governance. The latter two themes had separate sessions for the first time in the International Phosphorus Workshops series; thus, this overview presents a scene-setting rather than an overview of the latest research for these themes. In summary, this paper details new findings in agricultural and environmental P research, which indicate reduced P inputs, improved management options, and provide translations into governance options for a more sustainable P use.