Reduced Sampling Frequency Research Articles

Impact of reduced sampling frequency of illicit drug wastewater monitoring in the Netherlands

Drug consumption estimates are traditionally based on surveys or information from police seizures. Alternatively, residues of illicit drugs in untreated wastewater (influent) can be used to calculate mass loads and subsequently estimate drug consumption in the community throughout the week. For this purpose, wastewater is commonly sampled for seven consecutive days within the Sewage analysis CORe group Europe (SCORE), while other sampling schemes may be implemented in long-term studies outside this consortium. The current study demonstrates how sampling frequency of illicit drug residues in the influent of wastewater treatment plants (WWTPs) affects the derived weekly average. Thirty WWTPs were sampled over the course of 12 years and influents were analyzed for five drugs (metabolites): 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, amphetamine, benzoylecgonine (a metabolite of cocaine), and 11-nor-9-Carboxy tetrahydrocannabinol (THC-COOH). Subsequently, small and large WWTPs were grouped with a threshold of 100,000 inhabitants. After data curation, standardized loads were calculated (mg/d per 1000 inhabitants). Weekly averages of loads of the drug residues were calculated based on six scenarios (sampling one to six weekdays) and compared to the weekly average in the control situation (sampling seven weekdays) in a Monte Carlo simulation. Results indicate that drug residues with more dynamic loads over a week require more frequent sampling. The analysis illustrates that a decreased sampling frequency (4 or 5 days per week) still leads to a representative weekly average for all drugs tested when a deviation up to a factor of 1.25 is deemed acceptable. However, knowledge on typical levels is necessary to define outliers. We therefore recommend to study dynamics in drug residue loads for WWTPs before reducing sampling frequency in long term monitoring programs.

Combining wastewater surveillance and case data in estimating the time-varying effective reproduction number

Wastewater surveillance has been increasingly acknowledged as a useful tool for monitoring transmission dynamics of infections of public health concern, including the coronavirus disease (COVID-19). While a range of models have been proposed to estimate the time-varying effective reproduction number (Rt) utilizing clinical data, few have harnessed the viral concentration in wastewater samples to do so, leaving uncertainties about the potential precision gains with its use. In this study, we developed a Bayesian hierarchical model which simultaneously reconstructed the latent infection trajectory and estimated Rt. Focusing on the 2022 and early 2023 COVID-19 transmission trends in Singapore, where mass community wastewater surveillance has become routine, we performed estimations using a spectrum of data sources, including reported case counts, hospital admissions, deaths, and wastewater viral loads. We further explored the performance of our wastewater model across various scenarios with different sampling strategies. The results showed consistent estimates derived from models employing diverse data streams, while models incorporating more wastewater samples exhibited greater uncertainty and variation in the inferred Rts. Additionally, our analysis revealed prominent day-of-the-week effect in reported case counts and substantial temporal variations in ascertainment rates. In response to these findings, we advocate for a hybrid approach leveraging both clinical and wastewater surveillance data to account for changes in case-ascertainment rates. Furthermore, our study demonstrates the possibility of reducing sampling frequency or sample size without compromising estimation accuracy for Rt, highlighting the potential for optimizing resource allocation in surveillance efforts while maintaining robust insights into the transmission dynamics of infectious diseases.

Sampling frequency, load estimation and the disproportionate effect of storms on solute mass flux in rivers

Riverine discharge (Q) and dissolved concentrations (C) dictate solute mass export from watersheds. Commonly Q is tracked at a much higher frequency than C for most major solutes, leading to the necessity of load estimation algorithms which are often based on sparse data. The result is that the disproportionate effects of short-duration events (e.g., storms) on solute mass fluxes are poorly known. Here we use novel lab-in-the-field instrumentation to compare high temporal-resolution (∼30 min to 7 h) datasets of major ion chemistry collected over a year of continuous monitoring in three watersheds ranging over four orders of magnitude in drainage area. In these diverse settings, we quantify the errors associated with common load estimation algorithms and reduced sampling frequencies. When sample frequencies are coarsened, the mass flux of solutes which are diluted by storm events (i.e., Ca2+, Mg2+, Na+, Cl− and SO42−) are systematically overestimated, while nutrients which become mobilized by these events (K+ and NO3−) are underestimated. This is most pronounced in the largest river, and strongly tied to the increasing likelihood that storm events are missed as sampling frequencies decrease. Coarsening our high-resolution data to monthly sampling frequency yields an average overestimate of 8 % for Na+ and an average underestimate of 32.5 % for K+ across the three watersheds, illustrating clear implications for estuary and coastal water eutrophication, chemical weathering budgets, and agricultural land management practices. SynopsisA new ‘lab-in-the-field’ technology produces continuous high-frequency records of the full suite of major ions in rivers. These data highlight the disproportionate effect of large storms on catchment solute exports and the error associated with temporally coarse monitoring.

Dissolved organic carbon export in a small, disturbed peat catchment: Insights from long‐term, high‐resolution, sensor‐based monitoring

AbstractUnderstanding dissolved organic carbon (DOC) export dynamics from carbon‐rich environments is critical. Peatlands act as terrestrial carbon stores, and consequently supply substantial amounts of DOC to drainage. This DOC flux is temporally heterogeneous and subject to long‐ and short‐term variability. Ultrahigh temporal resolution sampling (< hourly) is still in‐frequent in peatland catchments. We used a field‐deployable —ultraviolet–visible light spectrometer (Spectro::lyser™) and monitored DOC flux from a temperate peatland over 31 months to examine seasonal and event dynamics. DOC concentration varied from 6.8 to 63.5 mg L−1, in the higher reported range for peatlands and showed clear seasonal (high‐summer, low‐winter) variability coinciding with elevated biological productivity in the peatland. Discharge was an unreliable predictor of instantaneous DOC concentration overall, with antecedent water temperatures proving the most reliable predictor overall. Discharge drove total DOC export in the catchment, where the top 10% of flow events, accounted for 41.3% of all DOC exported—increasing to 84.6% in the top 50% of flow events. Total estimated catchment DOC flux was sensitive to measurement frequency: increasing from every 30 min to daily altered export estimates by < 1%, increasing to > 10% at 1‐week intervals. The variation in estimated flux increased approximately linearly with reduced sampling frequency, reaching > 40% at monthly intervals. High‐resolution data reveal the large amount of within‐site complexity of DOC export dynamics in a temperate peatland and provide evidence on the subsequently recommended sampling frequency for the future elucidation of detailed DOC budgets in these environments.

Precise Voltage Regulation of Unbalanced Bipolar DC-Grid-Based Charging Stations: A New Multiobjective Predictive Control Approach

This paper proposes a new multi-objective predictive control approach and an optimized switching-scheme to precisely regulate the dc-voltages in bipolar dc-grid based charging stations working under critical conditions. An angular region is pre-selected to reduce the size of control sets to seven candidates within minimized angle error of 30-degree. Then, two subregions are located to further simplify finite sets without losing options for multi-objective optimization. Last, a hybrid switching scheme is proposed to improve current regulation working under a reduced sampling frequency. Delayed-signal-cancellation (DSC) technique and instantaneous power theory are also employed to ensure a resilient operation under grid-voltage distortions. Consequently, the proposed control strategy can precisely balance dc-voltages, track voltage references, and regulate grid-injected currents against unbalanced dc-loads and ac-grid distortions. Matlab/Simulink and an experimental prototype are utilized to investigate the comparative performances between the proposed approach and the recent predictive control methods. The simulation and experiments show significant improvements on dc-link voltages including reference tracking, dc-voltage balance, fast dynamic response, and harmonic reduction of ac-currents under a reduced sampling frequency.

Developing a classification system to assign activity states to two species of freshwater turtles.

Research in ecology often requires robust assessment of animal behaviour, but classifying behavioural patterns in free-ranging animals and in natural environments can be especially challenging. New miniaturised bio-logging devices such as accelerometers are increasingly available to record animal behaviour remotely, and thereby address the gap in knowledge related to behaviour of free-ranging animals. However, validation of these data is rarely conducted and classification model transferability across closely-related species is often not tested. Here, we validated accelerometer and water sensor data to classify activity states in two free-ranging freshwater turtle species (Blanding's turtle, Emydoidea blandingii, and Painted turtle, Chrysemys picta). First, using only accelerometer data, we developed a decision tree to separate motion from motionless states, and second, we included water sensor data to classify the animal as being motionless or in-motion on land or in water. We found that accelerometers separated in-motion from motionless behaviour with > 83% accuracy, whereas models also including water sensor data predicted states in terrestrial and aquatic locations with > 77% accuracy. Despite differences in values separating activity states between the two species, we found high model transferability allowing cross-species application of classification models. Note that reducing sampling frequency did not affect predictive accuracy of our models up to a sampling frequency of 0.0625 Hz. We conclude that the use of accelerometers in animal research is promising, but requires prior data validation and development of robust classification models, and whenever possible cross-species assessment should be conducted to establish model generalisability.

Effect of pressure insole sampling frequency on insole-measured peak force accuracy during running

Pressure sensing insoles enable us to estimate forces under the feet during activities such as running, which can provide valuable insight into human movement. Pressure insoles also afford the opportunity to collect more data in more representative environments than can be achieved in laboratory studies. One key challenge with real-world use of pressure insoles is limited battery life which restricts the amount of data that can be collected on a single charge. Reducing sampling frequency is one way to prolong battery life, at the cost of decreased measurement accuracy, but this trade-off has not been quantified, which hinders decision-making by researchers and developers. Therefore, we characterized the effect of decreasing sampling frequency on peak force estimates from pressure insoles (Novel Pedar, 100 Hz) across a range of running speeds and slopes. Data were downsampled to 50, 33, 25, 20, 16 and 10 Hz. Force peaks were extracted due to their importance in biomechanical algorithms trained to estimate musculoskeletal forces and were compared with the reference sampling frequency of 100 Hz to compute relative errors. Peak force errors increased exponentially from 0.7% (50 Hz) to 9% (10 Hz). However, peak force errors were < 3% for all sampling frequencies down to 20 Hz. For some pressure insoles, sampling rate is inversely proportional to battery life. Therefore, these findings suggest that battery life could be increased up to 5x at the expense of 3% errors. These results are encouraging for researchers aiming to deploy pressure insoles for remote monitoring or in longitudinal studies.

Motion Capture Sensing Technologies and Techniques: A Sensor Agnostic Approach to Address Wearability Challenges

Body area sensing systems specifically designed for motion capture need to consider the wearer’s comfort and wearability criteria. In this paper, after studying body models and their approximation by link-segment models, the kinematics and inverse kinematics problems for determining motion are explored. Different sensor technologies and related motion capture systems are then discussed within the context of wearability and portability challenges of the systems. For such systems, the weight and size of the system need to be kept small and the system should not interfere with the user’s movements. The requirements will be considered in terms of portability: portable motion capture systems should be less sensitive in accurate positioning of sensors and have more battery lifetime or less power consumption for their wider adoption as an assisted rehabilitation platform. Therefore, a proposed signal processing technique is validated in a controlled setting to address the challenges. By reducing sampling frequency, the power consumption will be reduced but there would be more variability in data whereas by utilising an adaptive filtering approach the variation can be compensated for. It is shown how by using the technique it is possible to reduce the energy consumption; therefore, the potential to decrease the battery size leading to a less bulky on-body sensing system with more comfort to the wearer.

SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater settled solids correlate well with coronavirus disease 2019 (COVID-19) incidence rates (IRs). Here, we develop distributed lag models to estimate IRs using concentrations of SARS-CoV-2 RNA from wastewater solids and investigate the impact of sampling frequency on model performance. SARS-CoV-2 N gene and pepper mild mottle virus (PMMoV) RNA concentrations were measured daily at four wastewater treatment plants in California. Artificially reduced data sets were produced for each plant with sampling frequencies of once every 2, 3, 4, and 7 days. Sewershed-specific models that related daily N/PMMoV to IR were fit for each sampling frequency with data from mid-November 2020 through mid-July 2021, which included the period of time during which Delta emerged. Models were used to predict IRs during a subsequent out-of-sample time period. When sampling occurred at least once every 4 days, the in- and out-of-sample root-mean-square error changed by <7 cases/100 000 compared to daily sampling across sewersheds. This work illustrates that real-time, daily predictions of IR are possible with small errors, despite changes in circulating variants, when sampling frequency is once every 4 days or more. However, reduced sampling frequency may not serve other important wastewater surveillance use cases.

Improving prediction accuracy of soil water storage through reducing sampling frequency

Knowledge of spatiotemporal dynamics of soil water storage (SWS) is essential for hydrological modeling and vegetation restoration in semi-arid areas. However, characterizing the temporal stability of SWS at a regional-scale requires time-consuming and labor-intensive manual sampling. Moreover, the influence of soil depth on temporal stability of SWS is not systematic. This study aimed to investigate the influences of sampling frequency and soil depth on SWS and SWS temporal stability. We measured soil moisture at 20-cm intervals in the soil profiles to a depth of 3 m using a neutron probe at 135 locations along a 1340-m long transect on 14 dates from 2012 to 2013. Results showed that sampling frequency did not influence the mean SWS (P < 0.05), while sampling frequency significantly affected temporal stability characteristics including Spearman’s rank correlation coefficient (rs), standard deviation of mean relative difference (SDRD), the number of locations with SDRD < 5%, and the representative locations. Temporal stability of SWS increased with the increasing soil thickness and depth, which increases the possibility of the number of representative locations in deep soil. Although the mean SWSs of all soil depths can be predicted accurately at each sampling frequency, the prediction accuracy improved when sampling frequency was reducing. The values of R2 ranged from 0.769 to 0.978 at 15-day sampling frequency, and from 0.987 to 0.998 at 45-day sampling frequency. Soil moisture stability may be more important than the soil water regime during prediction of soil moisture. These findings can provide guidelines for optimizing soil moisture sampling strategies and benefit management of water resources in semi-arid watershed.

Water quality monitoring with purpose: Using a novel framework and leveraging long-term data

Water quality monitoring programs are developed to meet goals including attaining regulatory compliance, evaluating long-term environmental changes, or quantifying the impact of an emergency event. Methods for developing these programs often fail to address multiple aspects of development (hazard identification, parameter selection, monitoring locations/frequency) simultaneously. We develop a framework for monitoring program development that is both versatile and systematic, the Hazard Based Water Quality Monitoring Planning framework, and apply it to the Quabbin watershed in Massachusetts, USA. We use a novel application of dataset deconstruction of long-term water quality datasets and the Seasonal Kendall test for trends to evaluate the effects of sampling frequency on long-term trend detection at several watershed sites. Results showed that when sampling frequency is decreased, ability to detect statistically significant trends often decreases. Absolute error in trend slopes between biweekly (twice monthly) and reduced sampling frequencies was relatively small for specific conductance and turbidity but was high for total coliform, likely due to interannual variation in rainfall and temperature We found that no one sampling reduction method resulted in a consistently lower absolute error compared to the “truth” (biweekly sampling), highlighting the importance of evaluating conditions that may affect water quality at sites in different parts of a watershed. We demonstrate the framework's usefulness, particularly for parameter and sampling frequency selection, using methods that can be readily applied to other watershed systems.

Simplifying residual nitrogen (Nmin) sampling strategies and crop response

Globally, important nitrogen fertilizer decisions are often made at the early stages of plant development, when plants are not yet sufficiently indicative of their needs. Optimizing nitrogen fertilization of crops requires assessing the available soil mineral nitrogen content (Nmin) at the beginning of the season and was assessed exemplarily for wheat and maize. Since 2020 a mandatory soil Nmin analysis for each crop on a representative field is required in Germany in zones characterized by increased nitrate concentrations in the groundwater, encompassing approximately 28 % of the arable land. However, soil analysis is time-consuming, costly, and labor-intensive and requires further optimization beyond the current practice.In this study, wheat fields in 2018 and 2019 and maize fields in 2018 were sampled in a grid pattern in spring, and the soil nitrate-N content was determined in 30 cm layers down to 60 cm soil depth in 11 fields and further down to 90 cm soil depth in two of the fields. For each single and the combined soil depths, all fields could be sampled with a deviation of less than 10 kg nitrate-N ha−1 with only two soil samples. Overall, the reduced field-specific soil sampling strategy proved advantageous compared to crop-specific, regionally representative Nmin values offered by the official advisory authorities based on multiple averaged field investigations with an increased sampling intensity of 16 samples per field. Further, the reduced field-specific soil sampling strategy delivered more precise Nmin values by 11.2 kg nitrate-N ha−1 for wheat fields and slightly less precise values by 4.8 kg nitrate-N ha−1 for maize fields. The reduced field-specific soil sampling has great potential to reduce analysis and soil sampling costs. Nitrogen fertilization experiments supported the usefulness of the new simplified Nmin strategy. Multispectral in-season satellite imagery from Sentinel-2 could not adequately capture the spatial nitrate-N level differences and confirms soil samplings needs early in the season.Because most farmers base their fertilization strategy on regionally crop-specific aggregated Nmin values delivered by the official advisory system, we consider a simplified strategy to be more indicative of the field-specific soil Nmin status, which is eased by a markedly reduced sampling frequency and further simplifies the analysis by aggregating the separately analyzed soil depths to only one single depth. Further, currently available simplified on-farm analyses of the soil nitrate content allow for conducting more intensive field-specific soil Nmin analysis, thus contributing to improved nitrogen demand management and decreasing adverse environmental effects.

Uncertainty in phytoplankton-based lake ecological status classification: Implications of sampling frequency and metric simplification

We examined the influence of sampling frequency and the elimination of selected metrics from the Phytoplankton Multimetric for Polish Lakes (PMPL index) on lake ecological status classification. The effect of sampling frequency was analysed by calculating the official PMPL index using data from four sampling periods (848 lake-years; 478 Polish lakes), and compared the Pmpl-based classification to that assessed from PMPL variants calculated using one, two and three sampling periods. The effect of metric elimination was analysed by comparing the official three-metric PMPL classification with two variants, a one-metric PMPL including only the concentration of chlorophyll-a and a two-metric variant including total phytoplankton biomass and summer cyanobacterial biomass. Correlation strength between the official PMPL and simplified variants weakened by reducing sampling frequency but varied by period combinations. Uncertainty of ecological status classification increased in both simplification types but it was greatest when using the one-metric (chlorophyll-a based) variant. Sampling reduction from four to three periods resulted in 0.5–6.5% of lakes being over- or under-classified (total lake-year misclassification: 3.7–6.7%). Highest uncertainty of classification to at least good status occurred when the spring sampling period was excluded (6.5%). The risk of misclassification increased to 4.2–14.9% when two sampling periods were used. When classification was based only on a one-period (late summer) PMPL, 12.1% of lake-years were misclassified to at least good and 9.3% to below-good ecological status. The risk of misclassification to at least good ecological status was 2% when the two-metric (biomass-based) PMPL index was used and 28.9% when the one-metric (chlorophyll-a based) was used. Lowest misclassification error (0.5%–5.2%) occurred when a three-period (spring-August-October), three-metric PMPL index was used, but as this 5.2% would result in substantial status under-classification, we recommend further research to possibly optimize a three-period PMPL variant to reduce the costs of lake ecological monitoring in Poland.

Ammonia emission modelling and reduced sampling strategies in cage-based laying hen facilities

Ammonia (NH 3 ) is the major source of N loss related to laying hen facilities. Previous studies are often based on continuous monitoring of NH 3 concentration and ventilation rate in the facilities during long periods. The objectives of this study were (i) to relate climate and production management parameters to model NH 3 emission, (ii) to check the accuracy of the proposed model and (iii) to evaluate the accuracy of reduced sampling strategies in estimating NH 3 emission. Four datasets of laying hen facilities located in areas representing different climates in Spain were used for model development and reduced sampling strategies evaluation. Indoor and outdoor temperatures, relative moisture, ventilation rate, NH 3 concentration, hen live weight and feed N consumption were introduced in the statistical analysis. Outdoor temperature was the main factor related to NH 3 emission. Tests showed that the variation between the model estimates and on-farm calculated NH 3 losses was, on average, ±15%. Four sampling frequencies were tested to evaluate reduced sampling strategies. The reduction of the sampling frequency resulted in an increase of estimation error ranging from <±10% for weekly to ±20% for bi-monthly sampling. It was concluded that (i) NH 3 emission estimation by using simple explanatory variables like outdoor temperature, and (ii) applying sampling reduction strategies can be feasible means to reduce the effort and labour demand of continuous monitoring by obtaining NH 3 emission estimates with an assumable associated error. • Ammonia emission model was established from empirical laying hen facilities datasets. • The model explained that 46% of NH 3 emission was based on the outdoor temperature. • Variations between estimated and measured emission were below 15%. • Four reduced sampling frequency strategies were tested. • Errors associated to the reduction of the sampling frequency varied from 8% to ±20%.

Design of a Linear Time-Varying Model Predictive Control Energy Regulator for Grid-Tied VSCs

This article presents an energy regulator based on a Model Predictive Control (MPC) algorithm for a Voltage Source Converter (VSC). The MPC is formulated to optimise the converter performance according to the weights defined in an objective function that trades off additional features, such as current harmonic distortion, reactive power tracking and DC bus voltage oscillation. Differently from most approaches found in the research literature, the MPC proposed here considers the coupling dynamics between the AC and DC sides of the VSC. This study is focused on the example case of a single-phase VSC, which presents a nonlinear relationship between its AC and DC sides and a sustained double-line frequency power disturbance in its DC bus. To reduce the burden of the MPC, the controller is formulated to benefit from the slow energy dynamics of the system. Thus, the cascaded structure typically used in the control of VSCs is kept and the MPC is set as an energy regulator at a reduced sampling frequency while the current control relies on a fast inner controller. The computational burden of the algorithm is further reduced by using a linear time-varying approximation. The controller is presented in detail and experimental validation showing the performance of the algorithm is provided.

A LoRa sensor network for monitoring pastured livestock location and activity.

Precision technologies for confinement animal agricultural systems have increased rapidly over the past decade, though precision technology solutions for pastured livestock remain limited. There are a number of reasons for this limited expansion of technologies for pastured animals, including networking availability and reliability, power requirements, and expense, among others. The objective of this work was to demonstrate a rapidly deployable long-range radio (LoRa) based, low-cost sensor suite that can be used to track location and activity of pastured livestock. The sensor is comprised of an inexpensive Arduino-compatible microprocessor, a generic MPU-9250 motion sensor which contains a 3-axis accelerometer, 3-axis magnetometer, and a 3-axis gyroscope, a generic GPS receiver, and a RFM95W generic LoRa radio. The microprocessor can be programmed flexibly using the open source Arduino IDE software to adjust the frequency of sampling, the data packet to send, and what conditions are needed to operate. The LoRa radio transmits to a Dragino LoRa gateway which can also be flexibly programmed through the Arduino IDE software to send data to local storage or, in cases where a web or cellular connection is available, to cloud storage. The sensor was powered using a USB cord connected to a 3,350 mAh lithium-ion battery pack. The Dragino gateway was programmed to upload data to the ThingSpeak IoT application programming interface for data storage, handling, and visualization. Evaluations showed minimal benefit associated with reducing sampling frequency as a strategy to preserve battery life. Packet loss ranged from 40% to 60%. In a 3 d evaluation on pastured sheep, the sensor suite was able to report GPS locations, inertial sensor readings, and temperature. Preliminary demonstrations of our system are satisfactory to detect animal location based on GPS data in real-time. This system has clear utility as a lower-cost strategy to deploy flexible, useful precision technologies for pasture-based livestock species.

On the requirements on spatial accuracy and sampling rate for transport mode detection in view of a shift to passive signalling data

GPS based campaigns have been hailed as an alternative to transportation surveys that promise relatively high accuracy at a relatively low burden on the participants and fewer forgotten trips. However they still necessitate the recruitment of participants and are thus potentially biased and certainly not encompassing significant parts of the population. Given the high penetration of mobile phones, passive tracking by telephone providers would alleviate those two shortcomings at the cost of reduced sampling frequency and positional accuracy. The trade-off in quality has not yet been quantified and therefore recommendations on sensible thresholds are not yet available. In this study therefore, instead of presenting yet another method for mode of transport classification, we therefore compare the performance of existing mode detection schemes under deteriorating sampling rates and positional accuracies. As a possibility to compensate for the deteriorating signal we also calculate features from users’ positional histories that could be beneficial if their behaviour is repetitive. The evaluation is not only based on pointwise accuracy, but includes quality measures that pertain to trips as a whole. We find that the necessary accuracy and sampling rate for applications will depend on whether the information of whole trajectories can be used, or whether only the current information is available. The former being relevant to ex-post analyses while the latter situation appears more frequently in near-time analyses. For segmentwise classification, there is no major impact on the quality of the classification by the tested levels of spatial accuracies as long as the sampling intervals can be kept at or below a minute, whereas for point based classification the sampling interval should be between 30 s and a minute and increasing spatial accuracy always improves the classification.

Traffic data reconstruction based on compressive sensing with neighbor regularization

AbstractThe production and collection of the mass traffic data in the vehicle network will result in wasted bandwidth and data transmission delay. The mobile edge computing (MEC) technology is able to reduce the transmission cost and provide fast interactive response. However, the amount of data between roadside units (RSUs) and MEC servers has not decreased. Compressive sensing (CS) technology can reduce the sampling frequency and reconstruct the signal with even fewer samples than the sampling theorem requires. Therefore, in this article, we use CS technology to reduce the data in the “last mile” of traffic edge computing network. We propose two traffic data reconstruction models, which use different neighbor regularization methods in the objective function of CS reconstruction model. Different from the previous studies, we leverage the low‐rank nature and the neighbor relationship between detectors to improve the reconstruction accuracy of different missing scenes. To validate the improved reconstruction models, we have carried out extensive experiments based on the actual traffic flow data of the freeway system under different loss rates and loss patterns. The missing data are caused by reduced sampling frequency and accidental transmission loss so that it is closer to the real fact. The experimental results show that even if the traffic data from the RSUs to MEC servers are decreased by 50%, the complete data can be reconstructed accurately.

The importance of observer effort on the accuracy of breeding success estimates in the Common Guillemot Uria aalge

ABSTRACT Capsule: A data-thinning approach was used to assess the effects of reducing the frequency of nest-checks on estimates of breeding success of Common Guillemots Uria aalge. Inter-year and inter-colony differences in fledging age and their implications for setting a minimum age after which a chick could be assumed to have fledged were evaluated. Aims: To assess the consequences of reducing sampling frequency on the estimation of breeding success, and on the robustness of the assumption that breeding has been successful if a chick survives to 15 days old. Methods: Breeding success, ages at fledging and loss of chicks were estimated from daily checks at two Scottish colonies over a six-year period. Data-thinning was used to assess the consequences of reducing checks from daily to every two or three days. Breeding success was recalculated assuming that all chicks surviving to 15 days fledged. Results: Reducing the frequency of checks from daily to every second or third day resulted in a small but statistically significant increase in the estimate of success. Between 20% and 25% of chick losses occurred when chicks were ≥15 days old. Assuming that these chicks had fledged resulted in significant increases in breeding success. Conclusion: Assumptions about whether or not a chick fledged had a greater impact on the estimate of breeding success than reducing the frequency of nest-checks from daily to every second or third day. There was no threshold age after which a chick could be assumed to have fledged. These findings are relevant to other monitoring schemes where there is pressure to reduce input. Sampling methods used in monitoring schemes need to be clearly stated and changes in protocols documented so that sampling effects can be incorporated into future analyses.

The concentration-discharge slope as a tool for water quality management

Recent technological breakthroughs of optical sensors and analysers have enabled matching the water quality measurement interval to the time scales of stream flow changes and led to an improved understanding of spatially and temporally heterogeneous sources and delivery pathways for many solutes and particulates. This new ability to match the chemograph with the hydrograph has promoted renewed interest in the concentration-discharge (c-q) relationship and its value in characterizing catchment storage, time lags and legacy effects for both weathering products and anthropogenic pollutants. In this paper we evaluated the stream c-q relationships for a number of water quality determinands (phosphorus, suspended sediments, nitrogen) in intensively managed agricultural catchments based on both high-frequency (sub-hourly) and long-term low-frequency (fortnightly-monthly) routine monitoring data. We used resampled high-frequency data to test the uncertainty in water quality parameters (e.g. mean, 95th percentile and load) derived from low-frequency sub-datasets. We showed that the uncertainty in water quality parameters increases with reduced sampling frequency as a function of the c-q slope. We also showed that different sources and delivery pathways control c-q relationship for different solutes and particulates. Secondly, we evaluated the variation in c-q slopes derived from the long-term low-frequency data for different determinands and catchments and showed strong chemostatic behaviour for phosphorus and nitrogen due to saturation and agricultural legacy effects. The c-q slope analysis can provide an effective tool to evaluate the current monitoring networks and the effectiveness of water management interventions. This research highlights how improved understanding of solute and particulate dynamics obtained with optical sensors and analysers can be used to understand patterns in long-term water quality time series, reduce the uncertainty in the monitoring data and to manage eutrophication in agricultural catchments.