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The in-situ high-frequency monitoring of total nitrogen (TN) and total phosphorus (TP) in rivers is a challenge and key to instant water quality judgment and early warning. Based on the physical and chemical association between TN/TP and sensor-measurable predictors, we proposed a novel "indirect" measurement method for TN and TP in rivers. This method combines the timeliness of multi-sensor and the accuracy of intelligent algorithms, utilizing 188,629 data sets from 131 water monitoring stations across China. Under 5 algorithms and 4 predictor group scenarios, the results showed that: (1) extra tree regression (ETR) with 6 predictors exhibited the best precision, and the mean determination coefficient (R2) of TN and TP inversion across 131 stations reached 0.78 ± 0.25 and 0.79 ± 0.22 respectively; (2) among 6 potential predictors, the importance degrees of temperature, electrical conductivity, NH4-N, and turbidity were greater than that of pH and DO, and >80 % of stations exhibited acceptable prediction accuracy (R2 > 0.6) when the number of predictors (P) ranged from 4 to 6, which showed good tolerability to predictor variations; (3) the accurate classification rates of water quality standard (ACRws) of all stations based on TN and TP reached 90.41 ± 6.96 % and 92.33 ± 6.41 %; (4) in 9 regions/basins of China, this method showed universal application potential with no significant prediction difference. Compared with laboratory test, water quality automatic monitoring station, and remote sensing inversion, the proposed method offers high-frequency, high-precision, regional adaptability, low cost, and stable operation under rainy, cloudy, and nighttime conditions. The new method may provide important technological support for timely pollutant tracing, pre-warning, and emergency control for river pollution.

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Access to safe, reliable, and equitable water services in urban settings of low- and middle-income countries remains a critical challenge toward achieving Sustainable Development Goal 6.1, but progress has either slowed or stagnated in recent years. A pilot water kiosk network funded by the United States Millennium Challenge Corporation was implemented by the Sierra Leone Millennium Challenge Coordinating Unit into the intermittent piped water distribution network of Freetown, Sierra Leone, as a private-public partnership to improve water service provision for households without reliable piped water connections and to reduce non-revenue water. This study employs the use of high-frequency instrumentation to monitor, model, and assess the functionality of this water kiosk network over 2,947 kiosk-days. Functionality was defined via functionality levels on a daily basis through monitored stored water levels and modeled water withdrawals. The functionality levels across the kiosk network were found to be 34% operational, 30% offline, and 35% empty. Statistically significant (p<0.001) determinants of functionality were found for several predictors across the defined thresholds. Finally, modeling of water supply, water demand and withdrawal capacity, and water storage was conducted to further explain findings and provide additionally externally relevant support for kiosk operations.

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High-frequency nitrate-N (NO3--N) data are increasingly available, while accurate assessments of in-stream NO3--N retention in large streams and rivers require a better capture of complex river hydrodynamic conditions. This study demonstrates a fusion framework between high-frequency water quality data and hydrological transport models, that (1) captures river hydraulics and their impacts on solute signal propagation through river hydrodynamic modeling, and (2) infers in-stream retention as the differences between conservatively traced and reactively observed NO3--N signals. Using this framework, continuous 15-min estimates of NO3--N retention were derived in a 6th-order reach of the lower Bode River (27.4 km, central Germany), using long-term sensor monitoring data during a period of normal flow from 2015 to 2017 and a period of drought from 2018 to 2020. The unique NO3--N retention estimates, together with metabolic characteristics, revealed insightful seasonal patterns (from high net autotrophic removal in late-spring to lower rates, to net heterotrophic release during autumn) and drought-induced variations of those patterns (reduced levels of net removal and autotrophic nitrate removal largely buffered by heterotrophic release processes, including organic matter mineralization). Four clusters of diel removal patterns were identified, potentially representing changes in dominant NO3--N retention processes according to seasonal and hydrological conditions. For example, dominance of autotrophic NO3--N retention extended more widely across seasons during the drought years. Such cross-scale patterns and changes under droughts are likely co-determined by catchment and river environments (e.g., river primary production, dissolved organic carbon availability and its quality), which resulted in more complex responses to the sequential droughts. Inferences derived from this novel data-model fusion provide new insights into NO3- dynamics and ecosystem function of large streams, as well as their responses to climate variability. Moreover, this framework can be flexibly transferred across sites and scales, thereby complementing high-frequency monitoring to identify in-stream retention processes and to inform river management.

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High phosphorus (P) concentrations are commonly observed in lakes during algal blooms despite massive efforts on external nutrient reduction. However, the knowledge about the relative contribution of internal P loading linked with algal blooms on lake phosphorus (P) dynamics remains limited. To quantify the effect of internal loading on P dynamics, we conducted extensive spatial and multi-frequency nutrient monitoring from 2016 to 2021 in Lake Taihu, a large shallow eutrophic lake in China, and its tributaries (2017-2021). The in-lake P stores (ILSP) and external loading were estimated and then internal P loading was quantified from the mass balance equation. The results showed that the in-lake total P stores (ILSTP) ranged from 398.5 to 1530.2 tons (t), and exhibited a dramatic intra- and inter-annual variability. The annual internal TP loading released from sediment ranged from 1054.3 to 1508.4 t, which was equivalent to 115.6% (TP loading) of the external inputs on average, and responsible for the fluctuations of ILSTP on a weekly scale. High-frequency observations exemplified that ILSTP increased by 136.4% during algal blooms in 2017, while by only 47.2% as a result of external loading after heavy precipitation in 2020. Our study demonstrated that both bloom-induced internal loading and storm-induced external loading are likely to run counter significantly to watershed nutrient reduction efforts in large shallow lakes. More importantly, bloom-induced internal loading is higher than storm-induced external loading over the short term. Given the positive feedback loop between internal P loadings and algal bloom in eutrophic lakes, which explains the significant fluctuation of P concentration while nitrogen concentration decreased. It is emphasized that internal loading and ecosystem restoration are unignorable in shallow lakes, particularly in the algal-dominated region.

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Aquatic communities are frequently exposed to pesticides at sublethal concentrations, known to affect fitness parameters such as feeding, reproduction and population growth. Beside adverse effects, beneficial responses to toxicants at low concentrations may also occur. Positive effects, however, are thought to involve trade-offs. To identify such trade-offs, we quantified the population level effects on Daphnia magna during population carrying capacity in laboratory nanocosms after exposure to a single pulse of the pyrethroid insecticide esfenvalerate, including ultra-low concentrations ≤1/30 EC50. Population abundance and biomass were monitored 3 times per week for 3 months using a non-invasive imaging technique. High concentrations ≥1/10 EC50 resulted in reduced fitness endpoints. In contrast, ultra-low concentrations in the range of 0.01 µg/L significantly increased the population abundance of small (+160 %), medium (+130 %) and large organisms (+340 %) and their combined biomass (+200 %) during the 2 months after exposure. During the first five days after exposure to 0.01 µg/L and 0.03 µg/L esfenvalerate, population biomass increased by 0.1 mg/day while staying constant in the controls. While high control mortality makes firm conclusions about population responses of D. magna to esfenvalerate difficult, we hypothesize that population increases at ultra-low concentrations may be due to a hormetic response, where reduced intraspecific competitiveness is the trade-off that enables this response.

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Woodchip bioreactors have gained popularity in many countries as a conservation practice for reducing nitrate load to freshwater. However, current methods for assessing their performance may be inadequate when nitrate removal rates (RR) are determined from low-frequency (e.g., weekly) concurrent sampling at the inlet and outlet. We hypothesised that high-frequency monitoring data at multiple locations can help improve the accuracy of quantifying nitrate removal performance, enhance the understanding of processes occurring within a bioreactor, and therefore improve the design practice for bioreactors. Accordingly, the objectives of this study were to compare RRs calculated using high- and low-frequency sampling and assess the spatiotemporal variability of the nitrate removal within a bioreactor to unravel the processes occurring within a bioreactor. For two drainage seasons, we monitored nitrate concentrations at 21 locations on an hourly or two-hourly basis within a pilot-scale woodchip bioreactor in Tatuanui, New Zealand. A novel method was developed to account for the variable lag time between entry and exit of a parcel of sampled drainage water. Our results showed that this method not only enabled lag time to be accounted for but also helped quantify volumetric inefficiencies (e.g., dead zone) within the bioreactor. The average RR calculated using this method was significantly higher than the average RR calculated using conventional low-frequency methods. The average RRs of each of the quarter sections within the bioreactor were found to be different. 1-D transport modelling confirmed the effect of nitrate loading on the removal process as nitrate reduction followed Michaelis-Menten (MM) kinetics. These results demonstrate that high-frequency temporal and spatial monitoring of nitrate concentrations in the field allows improved description of bioreactor performance and better understanding of processes occurring within woodchip bioreactors. Thus, insights gained from this study can be used to optimise the design of future field bioreactors.

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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.

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With ever greater frequency, wetlands and shallow lakes that had been diverted for agriculture are being re-established to reduce nutrient loss and greenhouse gas emission, as well as to increase biodiversity. Here, we investigate drivers of water column light attenuation (Kd) at multiple time scales and locations in Lake Fil, Denmark, during the first five years after its re-establishment in 2012. We found that Kd was generally high (overall mean: 3.4 m-1), with resuspended sediment particles and colored dissolved organic matter being the main contributors. Using daily time series of light attenuation recorded at four stations, we used a generalized additive model to analyze the influence of wind speed and direction on Kd. This model explained a high proportion of the variation (R2 = 0.62, RMSE = 0.74 m-1, and MAE = 0.55 m-1) and showed that higher wind speed increased Kd on the same day and, with smaller influence, on the next day. Furthermore, we found a significant influence of wind direction and an interaction between wind speed and wind direction, a combination that suggests that short-term variations in light climate depends on the interplay between wind direction and sources of particles. Wind from non-prevailing directions thus influence Kd more, as it can activate previously deposited particles. The maximum colonization depths of submerged vegetation occurred at ~2-6% of sub-surface light from 2014 to 2016 and peaked at 1.2 m in 2016. The fast, day-to-day variation of Kd in Lake Fil reveals the importance of wind on light climate and in turn biological elements such as phytoplankton and submerged macrophyte development in shallow lakes. The implications are essential for the prior planning and management of future lake re-establishment.

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Synthetic Plant Protection Products (PPPs) are a key element for a large part of today's global food systems. However, the transport of PPPs and their transformation products (TPs) to water bodies has serious negative effects on aquatic ecosystems. Small streams in agricultural catchments may experience pronounced concentration peaks given the proximity to fields and poor dilution capacity. Traditional sampling approaches often prevent a comprehensive understanding of PPPs and TPs concentration patterns being limited by trade-offs between temporal resolution and duration of the observation period. These limitations result in a knowledge gap for accurate ecotoxicological risk assessment and the achievement of optimal monitoring strategies for risk mitigation. We present here high-frequency PPPs and TPs concentration time-series measured with the autonomous MS2Field platform that combines continuous sampling and on-site measurements with a high-resolution mass spectrometer, which allows for overcoming temporal trade-offs. In a small agricultural catchment, we continuously measured 60 compounds at 20 minutes resolution for 41 days during the growing season. This observation period included 8 large and 15 small rain events and provided 2560 concentration values per compound. To identify similarities and differences among the compound-specific concentration time-series, we analysed the entire dataset with positive matrix factorisation. Six factors sufficiently captured the overall complexity in concentration dynamics. While one factor reflected dilution during rainfall, five factors identified PPPs groups that seemed to share a common history of recent applications. The investigation per event of the concentration time-series revealed a surprising complexity of dynamic patterns; physico-chemical properties of the compounds did not influence the (dis)similarity of chemographs. Some PPPs concentration peaks led while others lagged by several hours the water level peaks during large events. During small events, water level peaks always preceded concentration peaks, which were generally only observed when the water levels had almost receded to pre-event levels. Thus, monitoring schemes relying on rainfall or water level as proxies for triggering sampling may lead to systematic biases. The high temporal resolution revealed that the Swiss national monitoring integrating over 3.5 days underestimated critical concentration peaks by a factor of eight to more than 32, captured 3 out of 11 exceedances of legal acute quality standards (the relevant values in the Swiss Water Protection Law) and recorded 1 out of 9 exceedances of regulatory acceptable concentrations (the relevant values for the PPPs registration process). MS2Field allowed for observing unexpected and overlooked pesticide dynamics with consequences for further research but also for monitoring. The large variability in timing of concentration peaks relative to water level calls for more in-depth analyses regarding the respective transport mechanisms. To perform these analyses, spatially distributed sampling and time-series of geo-referenced PPPs application data are needed.

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The frequency of heatwave events in Europe is increasing as a result of climate change. This can have implications for the water quality and ecological functioning of aquatic systems. We deployed three spectroradiometer WISPstations at three sites in Europe (Italy, Estonia, and Lithuania/Russia) to measure chlorophyll-a at high frequency. A heatwave in July 2019 occurred with record daily maximum temperatures over 40 °C in parts of Europe. The effects of the resulting storm that ended the heatwave were more discernable than the heatwave itself. Following the storm, chlorophyll-a concentrations increased markedly in two of the lakes and remained high for the duration of the summer while at one site concentrations increased linearly. Heatwaves and subsequent storms appeared to play an important role in structuring the phenology of the primary producers, with wider implications for lake functioning. Chlorophyll-a peaked in early September, after which a wind event dissipated concentrations until calmer conditions returned. Synoptic coordinated high frequency monitoring needs to be advanced in Europe as part of water management policy and to improve knowledge on the implications of climate change. Lakes, as dynamic ecosystems with fast moving species-succession, provide a prism to observe the scale of future change.

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Freshwater lakes are dynamic ecosystems and provide multiple ecosystem services to humans. Sudden changes in lake environmental conditions such as cyanobacterial blooms can negatively impact lake usage. Automated high-frequency monitoring (AHFM) systems allow the detection of short-lived extreme and unpredictable events and enable lake managers to take mitigation actions earlier than if basing decisions on conventional monitoring programmes. In this study a cost-benefit approach was used to compare the costs of implementing and running an AHFM system with its potential benefits for three case study lakes. It was shown that AHFM can help avoid human health impacts, lost recreation opportunities, and revenue losses for livestock, aquaculture and agriculture as well as reputational damages for drinking water treatment. Our results showed that the largest benefits of AHFM can be expected in prevention of human health impacts and reputational damages. The potential benefits of AHFM, however, do not always outweigh installation and operation costs. While for Lake Kinneret (Israel) over a 10-year period, the depreciated total benefits are higher than the depreciated total costs, this is not the case for Lough Gara (Ireland). For Lake Mälaren in Sweden it would depend on the configuration of the AHFM system, as well as on how the benefits are calculated. In general, the higher the frequency and severity of changes in lake environmental conditions associated with detrimental consequences for humans and the higher the number of lake users, the more likely it is that the application of an AHFM system is financially viable.

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In temperate lakes, it is generally assumed that light rather than temperature constrains phytoplankton growth in winter. Rapid winter warming and increasing observations of winter blooms warrant more investigation of these controls. We investigated the mechanisms regulating a massive winter diatom bloom in a temperate lake. High frequency data and process-based lake modeling demonstrated that phytoplankton growth in winter was dually controlled by light and temperature, rather than by light alone. Water temperature played a further indirect role in initiating the bloom through ice-thaw, which increased light exposure. The bloom was ultimately terminated by silicon limitation and sedimentation. These mechanisms differ from those typically responsible for spring diatom blooms and contributed to the high peak biomass. Our findings show that phytoplankton growth in winter is more sensitive to temperature, and consequently to climate change, than previously assumed. This has implications for nutrient cycling and seasonal succession of lake phytoplankton communities. The present study exemplifies the strength in integrating data analysis with different temporal resolutions and lake modeling. The new lake ecological model serves as an effective tool in analyzing and predicting winter phytoplankton dynamics for temperate lakes.

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The tail of the reservoir is the unstable zone regarding water quality and phytoplankton community. Therefore, it is the crucial zone in aquatic ecosystem transitions. To understand the transition characteristics and driving mechanisms of water environment dynamics, high-frequency monitoring of the water environment and phytoplankton community in the tail of a deep and large reservoir, the Xin'anjiang Reservoir in southeast of China, was conducted using a water quality monitoring buoy and three-day interval water sampling during 18 months. Results show clear seasonal thermal and oxygen stratification in the river mouth of the reservoir. The nutrient and chlorophyll-a concentrations also show stratifying phenomena during the thermal stratification period. Heavy rain and inflow quickly consume the stratification. Nutrient concentrations were highly dynamic in the river mouth. The total phosphorus ranges from 0.011 mg·L-1 to 0.188 mg·L-1, and total nitrogen ranges from 0.75 mg·L-1 to 2.76 mg·L-1. Dissolved phosphorus comprised 56% of total phosphorus, and dissolved nitrogen occupied 88% of total nitrogen, respectively. Nutrient concentrations were influenced strongly by rainfall intensity and inflow rate. Total phosphorus and nitrogen concentrations were significantly related to the three-day accumulated rainfall. Nutrient concentrations in the flood season (March to June) were significantly higher than in the non-flood season (P<0.001). Seasonal phytoplankton proliferation also significantly influenced by total phosphorus concentration. The phytoplankton community changes significantly with seasons and flood events. Bacillariophytea was generally dominant throughout the year, with the predominant genus of Fragilaria spp., Cyclotella spp., Synedra spp., and Melosira spp. Cyanophyta biomass peaked in July, August, and September, with the dominant genus of Aphanizomenon spp., Microcystis spp., and Oscillatoria spp. Apart from the high temperature, storm inflow events also triggered Cyanophyta proliferation. The proliferation of Chlorophyta was similar to Cyanophyta, with the predominant genus of Pediastrum spp. and Closterium spp.. While the Cryptophyta biomass peaked during March to May, with the predominant genus of Cryptomonas spp.. Redundancy analysis shows that the influence factors of phytoplankton community dynamics include the inflow rate, temperature, water level, water transparency, total nitrogen, total phosphorus, and nitrogen to phosphorus ratio. The meteorological and hydrological factors were major factors for phytoplankton dynamics during later autumn and winter, while the nutrient will be the co-driving factors of phytoplankton community dynamics during summer and early autumn. The research confirmed the huge influence of the intensity rainfall event on the water environment in reservoirs and described the key environmental conditions for phytoplankton community dynamics. The research is useful for the design of the monitoring and forecasting system for water safety in drinking water source reservoirs.

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Urban surface runoff from storms impacts the water quality dynamics of downstream ecosystems. While these effects are well-documented in mesic regions, they are not well constrained for arid watersheds, which sustain longer dry periods, receive intense but short-lived storms, and where stormwater drainage networks are generally isolated from sewage systems. We used a network of high-frequency in situ water quality sensors located along the Middle Rio Grande to determine surface runoff origins during storms and track rapid changes in physical, chemical, and biological components of water quality. Specific conductivity (SpCond) patterns were a reliable indicator of source, distinguishing between runoff events originating primarily in urban (SpCond sags) or non-urban (SpCond spikes) catchments. Urban events were characterized by high fluorescent dissolved organic matter (fDOM), low dissolved oxygen (including short-lived hypoxia <2 mg/L), smaller increases in turbidity and varied pH response. In contrast, non-urban events showed large turbidity spikes, smaller dissolved oxygen sags, and consistent pH sags. Principal component analysis distinguished urban and non-urban events by dividing physical and biogeochemical water quality parameters, and modeling of DO along the same reach demonstrated consistently higher oxygen demand for an urban event compared to a non-urban event. Based on our analysis, urban runoff poses more potential ecological harm, while non-urban runoff poses a larger problem for drinking water treatment. The comparison of our results to other reports of urban stormwater quality suggest that water quality responses to storm events in urban landscapes are consistent across a range of regional climates.

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River water quality monitoring at limited temporal resolution can lead to imprecise and inaccurate classification of physicochemical status due to sampling error. Bayesian inference allows for the quantification of this uncertainty, which can assist decision-making. However, implicit assumptions of Bayesian methods can cause further uncertainty in the uncertainty quantification, so-called second-order uncertainty. In this study, and for the first time, we rigorously assessed this second-order uncertainty for inference of common water quality statistics (mean and 95th percentile) based on sub-sampling high-frequency (hourly) total reactive phosphorus (TRP) concentration data from three watersheds. The statistics were inferred with the low-resolution sub-samples using the Bayesian lognormal distribution and bootstrap, frequentist t test, and face-value approach and were compared with those of the high-frequency data as benchmarks. The t test exhibited a high risk of bias in estimating the water quality statistics of interest and corresponding physicochemical status (up to 99% of sub-samples). The Bayesian lognormal model provided a good fit to the high-frequency TRP concentration data and the least biased classification of physicochemical status (< 5% of sub-samples). Our results suggest wide applicability of Bayesian inference for water quality status classification, a new approach for regulatory practice that provides uncertainty information about water quality monitoring and regulatory classification with reduced bias compared to frequentist approaches. Furthermore, the study elucidates sizeable second-order uncertainty due to the choice of statistical model, which could be quantified based on the high-frequency data.

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Hydrochemical behavior and dissolved carbon dynamics are highly-sensitive to hydrological variations in the monsoon-influenced karstic critical zone which has high chemical weathering rates and experiences strong anthropogenic impact. Continuous high-frequency monitoring in the spring outlet of a karstic catchment in Southwestern China revealed that most hydrochemical variables changed distinctively in response to hydrologic variations, influenced by mixing of different sources and miscellaneous biogeochemical processes. Na+, K+ and SO42- varied significantly with hydrology, showing weak chemostatic behavior controlled by dilution. The flushing effect and random behavior of NO3- and Cl- likely reflect agricultural inputs from high throughflow. Soil CO2 in infiltrated water supports carbonate weathering, enabling DIC (dissolved inorganic carbon) and weathering products (e.g., Ca2+ and Mg2+) to maintain chemostatic behavior. Biogenic DIC exhibited a stronger chemostatic response than carbonate sources and was the foremost control in DIC behavior. Carbon exchange between DIC and DOC (dissolved organic carbon) did not significantly influence DIC concentration and δ13C due to very low DOC concentration. More DOC was exported by flushing from increasing discharge. Hysteretic analysis indicated that the transport processes were controlled by proximal sources mixing and diverse mobilization in various periods responding to rainstorms. NO3- and Cl- presented different hysteresis behavior as sourced from agricultural activities. DOC increased on the hydrograph rising limb and was controlled by a transport-limited regime. However, the hysteresis behavior of most weathering products and DIC were regulated by a process-limited regime in the karstic critical zone. Overall, biogeochemical processes, hydrogeological properties, storm intensity/magnitude and the timing of storms (antecedent conditions) are main factors influencing the response of hydrochemical variables and dissolved carbon to storm events.

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This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time-displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time-displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber-physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation.

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Freshwater ecosystems including lakes and reservoirs are hot spots for retention of excess nitrogen (N) from anthropogenic sources, providing valuable ecological services for downstream and coastal ecosystems. Despite previous investigations, current quantitative understanding on the influential factors and underlying mechanisms of N retention in lentic freshwater systems is insufficient due to data paucity and limitation of modeling techniques. Our ability to reliably predict N retention for these systems therefore remains uncertain. Emerging high frequency monitoring techniques and well-developed ecosystem modeling shed light on this issue. In the present study, we explored the retention of NO3-N during a five-year period (2013-2017) in both annual and weekly scales in a highly flushed reservoir in Germany. We found that annual-averaged NO3-N retention efficiency could be up to 17% with an overall retention efficiency of ∼4% in such a system characterized by a water residence time (WRT) of ∼4 days. On the weekly scale, the reservoir displayed negative retention in winter (i.e. a source of NO3-N) and high positive retention in summer (i.e. a sink for NO3-N). We further identified the critical role of Chl-a concentration together with the well-recognized effects from WRT in dictating NO3-N retention efficiency, implying the significance of biological processes including phytoplankton dynamics in driving NO3-N retention. Furthermore, our modeling approach showed that an established process-based ecosystem model (PCLake) accounted for 58.0% of the variance in NO3-N retention efficiency, whereas statistical models obtained a lower value (40.5%). This finding exemplified the superior predictive power of process-based models over statistical models whenever ecological processes were at play. Overall, our study highlights the importance of high frequency data in providing new insights into evaluating and modeling N retention in reservoirs.

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High-frequency sensor measurements enable calculation of continuous autotrophic nitrate uptake rate based on its intrinsic relationship with gross primary production (GPP). The spatiotemporally available data offer prospects to advance process understandings across scales. We used continuous 15-min data (2011-2015) from a forest upstream reach and an agricultural downstream reach of the Selke River, Germany. Based on the high-frequency data, we developed a parsimonious approach for regionalizing the autotrophic uptake rate, considering effects of global radiation and riparian shading. For networked modeling, we integrated this approach into the fully distributed mesoscale hydrological nitrate model (mHM-Nitrate). Daily GPP-based uptake rate calculations showed distinct seasonal patterns and ranges in the agricultural and forest streams (mean values were 80.9 and 15.5 mgNm-2d-1, respectively). Validation in the two streams showed acceptable performance (R2 = 0.47 and 0.45, respectively) and spatial transferability of the regionalization approach, given its parsimony. Networked modeling results showed high spatiotemporal variability in nitrate transport and uptake throughout the river network. The magnitude of gross uptake increased, whereas uptake efficiency decreased significantly along stream order. Longitudinal analysis in the main stem of the Selke River revealed that riparian shading and inter-annual hydrochemical variations strongly influenced daily dynamics of the uptake efficiency. This study provides a parsimonious and transferable procedure for regionalizing in-stream autotrophic nitrate uptake based on high-frequency data at reach scale. Integrating this approach in the mHM-Nitrate model allows detailed nitrate transport and in-stream uptake processes to be investigated throughout river networks.

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The aim of this paper is to better understand the functioning of the River Selle (northern France) during dry weather and storm events, to assess the impact of a town on the surface water quality and to suggest qualitative assumptions on the vulnerability of water quality to weather conditions. Two high-frequency monitoring stations covering the Cateau-Cambrésis town were deployed during 4 months in 2016. River flow, water temperature, pH, dissolved oxygen, turbidity, conductivity, total organic carbon, nitrates and phosphates were monitored every 10 min. The water supply of the River Selle is mainly dominated by groundwater as shown by the behaviour of the river flow, the water temperature and the nitrate concentrations in both surface water and groundwater. The reference station located at the upstream of the river (Saint Souplet) exhibits low anthropogenic pressure during dry weather but is significantly impacted during storm events. At the downstream of the Cateau-Cambrésis town, the water quality is severely impacted by phosphates during dry weather mainly due to wastewater inputs into the river. An additional load of pollution is highlighted during storm events. According to our results, the water quality of the River Selle would degrade if actions to reduce dry-weather and storm events pollution sources are not undertaken rapidly. Moreover, nutrients, particularly phosphates, are clearly in excess in this system. Efforts to combat soil leaching and the revision of sewage systems and urban wastewater treatment in the catchment are two key points to tackle. Finally, this study shows the importance of understanding the current behaviour of a given river towards dry weather and storm events before suggesting local scenarios of the impact of climate change on surface water quality.