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High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function., Environmental Science & Technology, 57 (12), ISSN:0013-936X, ISSN:1520-5851

We investigated the utility of using synchronous measurements to create nitrogen (N) emission and retention maps of agricultural areas. Total N (TN) emissions from agricultural areas in three different Danish pilot catchments (1800–3737 ha) and within sub-catchments (100–1200 ha) were determined by a source apportionment approach. Intensive daily (main gauging stations) and fortnightly (synchronous stations) monitoring of discharge, TN, and nitrate-N (NO3-N) concentrations was conducted for two years. The groundwater N retention was calculated as the difference between a model-calculated NO3-N leaching from agricultural fields and the calculated agricultural N emission. The average annual N leaching and N emission in the three catchments amounted to 68, 48, and 58 kg N/ha and 6, 30, and 40 kg N/ha, respectively. The N retention in groundwater in the three catchments, calculated based on either TN or NO3-N emissions, amounted to 26 and 44%, 44 and 57%, and 93 and 97%, respectively, with large variations within two of the main catchments. From this study, we conclude that synchronous measurements in streams provide a good opportunity for developing local N emission and N retention maps. However, NO3-N should be used when dealing with N retention calculation at the finer resolution scale of 100–300 ha catchments.

Climate change in combination with land use alterations may lead to significant changes in soil erosion and sediment fluxes in streams. Optical turbidity sensors can monitor with high frequency and can be used as a proxy for suspended sediment concentration (SSC) provided there is an acceptable calibration curve for turbidity measured by sensors and SSC from water samples. This study used such calibration data from 31 streams in 11 different research projects or monitoring programmes in six Northern European countries. The aim was to find patterns in the turbidity-SSC correlations based on stream characteristics such as mean and maximum turbidity and SSC, catchment area, land use, hydrology, soil type, topography, and the number and representativeness of the data that are used for the calibration. There were large variations, but the best correlations between turbidity and SSC were found in streams with a mean and maximum SSC of >30–200 mg/l, and a mean and maximum turbidity above 60–200 NTU/FNU, respectively. Streams draining agricultural areas with fine-grained soils had better correlations than forested streams draining more coarse-grained soils. However, the study also revealed considerable differences in methodological approaches, including analytical methods to determine SSC, water sampling strategies, quality control procedures, and the use of sensors based on different measuring principles. Relatively few national monitoring programmes in the six countries involved in the study included optical turbidity sensors, which may partly explain this lack of methodological harmonisation. Given the risk of future changes in soil erosion and sediment fluxes, increased harmonisation is highly recommended, so that turbidity data from optical sensors can be better evaluated and intercalibrated across streams in comparable geographical regions.

Electromagnetic induction (EMI) is used widely for hydrological and other environmental studies. The apparent electrical conductivity (ECa), which can be mapped efficiently with EMI, correlates with a variety of important soil attributes. EMI instruments exist with several configurations of coil spacing, orientation, and height. There are general, rule-of-thumb guides to choose an optimal instrument configuration for a specific survey. The goal of this study was to provide a robust and efficient way to design this optimization task. In this investigation, we used machine learning (ML) as an efficient tool for interpolating among the results of many forward model runs. Specifically, we generated an ensemble of 100 000 EMI forward models representing the responses of many EMI configurations to a range of three-layer subsurface models. We split the results into training and testing subsets and trained a decision tree (DT) with gradient boosting (GB) to predict the subsurface properties (layer thicknesses and EC values). We further examined the value of prior knowledge that could limit the ranges of some of the soil model parameters. We made use of the intrinsic feature importance measures of machine learning algorithms to identify optimal EMI designs for specific subsurface parameters. The optimal designs identified using this approach agreed with those that are generally recognized as optimal by informed experts for standard survey goals, giving confidence in the ML-based approach. The approach also offered insight that would be difficult, if not impossible, to offer based on rule-of-thumb optimization. We contend that such ML-informed design approaches could be applied broadly to other survey design challenges.

I april startede jeg en erhvervs ph.d. hos firmaet EnviDan og Aarhus Universitet, der omhandler brugen af sensorer med høj tidsopløsning i overvågningen af danske vandløb. I projektet vil vi undersøge nye aspekter i, og viden om, denfremtidige anvendelse af BIG data indsamlet med høj tidsopløsning ved brug af både vand-, nitrat,- og turbiditetssensorer. Til at forstå denne enormemængde data anvender vi bl.a. machine learning.

Electromagnetic induction (EMI) is used widely for hydrological and other environmental studies. The apparent electrical conductivity (ECa), which can be mapped efficiently with EMI, correlates with a variety of important soil attributes. EMI instruments exist with several configurations of coil spacing, orientation, and height. There are general, rule-of-thumb guides to choose an optimal instrument configuration for a specific survey. The goal of this study was to use machine learning (ML) to improve this design optimization task. In this investigation, we used machine learning as an efficient tool for interpolating among the results of many forward model runs. Specifically, we generated an ensemble of 100,000 EMI forward models representing the responses of many EMI configurations to a range of three-layer subsurface models. We split the results into training and testing subsets and trained a decision tree (DT) with gradient boosting (GB) to predict the subsurface properties (layer thicknesses and EC values). We further examined the value of prior knowledge that could limit the ranges of some of the soil model parameters. We made use of the intrinsic feature importance measures of machine learning algorithms to identify optimal EMI designs for specific subsurface parameters. The optimal designs identified using this approach agreed with those that are generally recognized as optimal by informed experts for standard survey goals, giving confidence in the ML-based approach. The approach also offered insight that would be difficult if not impossible to offer based on rule-of-thumb optimization. We contend that such ML-informed design approaches could be applied broadly to other survey design challenges

N-loads from subsurface, drains, and groundwater-fed surface (bypass) flows via two riparian zones (crop field and wetland) to a second order stream were investigated by sampling of shallow and deep groundwater on both sides and monthly measurements of bypass flows from springs, drains, and stream including water quality (nitrate). A push-pull test in the crop field gave a first-order denitrification rate (0.23 day–1). Reactive transport modeling evaluated observations of water chemistry and denitrification processes in the groundwater below the crop field showing that nitrate was completely removed by denitrification with pyrite in the aquifer (model rates of 0.6–2.5 mmol NO3 L−1 yr−1). A drain in the crop field routed ∼10% (bypass) of the regional groundwater inflow to the stream. Buffer efficiency was high at 90%. The wetland on the other side of the stream hosts several locations of nitrate-rich bypass flow from springs, predominantly through a non-maintained drainage system of drainpipes and ditches with bypass flow accounting for 59% of the regional flow input. Nitrate was completely removed in groundwater by denitrification with dissolved organic matter in shallow groundwater. The regional inflow and N load to the wetland is amongst the highest recorded and data shows that the N load to the stream is very high. The buffer efficiency ranged from 45% to 83% depending on if all bypass flows contributed to the stream or only the two with visible outflow. A conceptual model for nitrate removal efficiency as a function of Damköhler number and percent bypass flow is proposed.

Integrated buffer zones (IBZ) are novel mitigation measures designed to decrease the loading of nitrogen (N) transported by subsurface drainage systems from agricultural fields to streams. In IBZ, drainage water flows into a pond with free water surface followed by an inundated, vegetated filterbed. This design provides an environment favorable for denitrification and thus a decrease in nitrate concentration is expected as water flow through the IBZ. However, due to the establishment of anaerobic conditions, there is a risk for increasing emissions of the greenhouse gases nitrous oxide (N2O) and methane (CH4). In this year-long study, we evaluated the N removal efficiency along with the risk of N2O and CH4 emissions from two pilot-scale IBZs (IBZ1 and 2). The two IBZs had very different yearly removal efficiencies, amounting to 29% and 71% of the total N load at IBZ1 and 2, respectively. This was probably due to differences in infiltration rates to the filterbed, which was 22% and 81% of the incoming water at IBZ1 and 2, respectively. The site (IBZ2) with the highest removal efficiency was a net N2O sink, while 0.9% of the removed nitrate was emitted as N2O at IBZ1. Both IBZs were net sources of CH4 but with different pathways of emission. In IBZ1 CH4 was mainly lost directly to the atmosphere, while waterborne losses dominated in IBZ2. In conclusion, the IBZs were effective in removing N three years after establishment, and although the IBZs acted as greenhouse gas sources, especially due to CH4, the emissions were comparable to those of natural wetlands and other drainage transport mitigation measures.

N-loads from subsurface, drains, and groundwater-fed surface (bypass) flows via two riparian zones (crop field and wetland) to a second order stream were investigated by sampling of shallow and dee...

A direct relation between antibiotic use and resistance has been shown at country level. We aim to investigate the association between antibiotic prescribing for patients from individual Dutch primary care practices and antibiotic resistance of bacterial isolates from routinely submitted urine samples from their patient populations. Practices&rsquo, antibiotic prescribing data were obtained from the Julius Network and related to numbers of registered patients. Practices were classified as low-, middle- or high-prescribers and from each group size-matching practices were chosen. Culture and susceptibility data from submitted urine samples were obtained from the microbiology laboratory. Percentages of resistant isolates, and resistant isolates per 1000 registered patients per year (population resistance) were calculated and compared between the groups. The percentages of resistant Escherichia coli varied considerably between individual practices, but the three prescribing groups&rsquo, means were very similar. However, as the higher-prescribing practices requested more urine cultures per 1000 registered patients, population resistance was markedly higher in the higher-prescribing groups. This study showed that the highly variable resistance percentages for individual practices were unrelated to antibiotic prescribing levels. However, population resistance (resistant strains per practice population) was related to antibiotic prescribing levels, which was shown to coincide with numbers of urine culture requests. Whether more urine culture requests in the higher-prescribing groups were related to treatment failures, more complex patient populations, or to general practitioners&rsquo, testing behaviour needs further investigation.

Linket i højre side henviser til DCA rapporten. Dokumentet over linket er selve leveringen til ministeriet med følgebrev.I DCA-rapporten er der foretaget mindre sproglige og redaktionelle rettelser i forhold til versionen, som blev leveriet til ministeriet den 8. juli 2020. Derudover er der i kapitlet Afgasning af husdyrgødning kombineret med højere udnyttelseskrav for afgasset gødning sket en justering af beregningsgrundlaget af hensyn til overensstemmelse med seneste rådgivningsnotat på området. Ændringen påvirker ikke den kvalitative vurdering af klimaeffekten i kataloget. I Bilag 3, Klimagasser, er der desuden tilføjet et forklarende afsnit vedr. den indirekte N2O-emissionsfaktor relateret til nitratudvaskning.

Rapporten er et katalog over virkemidler, som kan målrettes til de områder, hvorbåde risikoen for fosfortab er stor, og hvor vandmiljøet er fosforfølsomt. Virkemidlernekan anvendes i en målrettet fosforindsats i de kommende vandområdeplaner 2021-2027. Kataloget indeholder samtidig forslag til afværgeforanstaltninger modfosfortab i den nationale vådområdeindsats.

A managed riparian lowland in a glacial landscape (Holtum catchment, Denmark) was studied to quantify the relative importance of subsurface and surface flow to the recipient stream. The hydrogeological characterization combined geoelectrical methods, lithological logs, and piezometric heads with monthly flow measurements of springs, a ditch, and a drain, to determine seasonality and thereby infer flow paths. In addition, groundwater discharge through the streambed was estimated using temperature and water-stable isotopes as tracers. The lowland received large groundwater inputs with minimal seasonal variations from adjacent upland aquifers. This resulted in significant amounts of groundwater-fed surface flow to the stream, via man-made preferential flow paths comprising ditches, drainage systems, and a pond, and via two natural springs. Roughly, two thirds of the stream gain was due to surface flow to the stream, mainly via anthropogenic alterations. In contrast, direct groundwater discharge through the streambed accounted for only 4% of the stream flow gain, although bank seepage (not measured) to the straightened and deepened stream potentially accounted for an additional 17%. Comparison to analogous natural flow systems in the catchment substantiate the impact of anthropogenic alterations of riparian lowlands for the subsurface and surface flow components to their streams.