High Monitoring Frequency Research Articles

Wastewater Surveillance Provides Spatiotemporal SARS-CoV-2 Infection Dynamics

Wastewater surveillance (WWS) can leverage its wide coverage, population-based sampling, and high monitoring frequency to capture citywide pandemic trends independent of clinical surveillance. Here we conducted a nine months daily WWS for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 12 wastewater treatment plants (WWTPs), covering approximately 80% of the population, to monitor infection dynamics in Hong Kong, China. We found that the SARS-CoV-2 virus concentration in wastewater was correlated with the daily number of reported cases and reached two pandemic peaks three days earlier during the study period. In addition, two different methods were established to estimate the prevalence/incidence rates from wastewater measurements. The estimated results from wastewater were consistent with findings from two independent citywide clinical surveillance programmes (rapid antigen test (RAT) surveillance and serology surveillance), but higher than the cases number reported by the Centre for Health Protection (CHP) of Hong Kong, China. Moreover, the effective reproductive number (Rt) was estimated from wastewater measurements to reflect both citywide and regional transmission dynamics. Our findings demonstrate that large-scale intensive WWS from WWTPs provides cost-effective and timely public health information, especially when the clinical surveillance is inadequate and costly. This approach also provides insights into pandemic dynamics at higher spatiotemporal resolutions, facilitating the formulation of effective control policies and targeted resource allocation.

Multi-UAV Cooperative Path Planning with Monitoring Privacy Preservation

UAVs have shown great potential application in persistent monitoring, but still have problems such as difficulty in ensuring monitoring frequency and easy leakage of monitoring path information. Therefore, under the premise of covering all monitoring targets by UAVs, it is necessary to improve the monitoring frequency of the target and the privacy protection of the monitoring intention as much as possible. In response to the above problems, this research proposes monitoring overdue time to evaluate the monitoring frequency and monitoring period entropy in order to evaluate the ability to ensure monitoring privacy protection. It then establishes a multi-UAV cooperative persistent monitoring path planning model. In addition, the multi-group ant colony optimization algorithm, called overdue-aware multiple ant colony optimization (OMACO), is improved based on the monitoring overdue time. Finally, an optimal flight path for multi-UAV monitoring with high monitoring frequency and strong privacy preservation of monitoring intention is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node and the privacy preservation of the UAV monitoring path and has great significance for enhancing security monitoring and preventing intrusion.

Real-time measurement of total nitrogen for agricultural runoff based on multiparameter sensors and intelligent algorithms

Real-time monitoring of non-point source (NPS) pollution is challenging owing to the minute-scale change in runoff flow and concentration under rainfall condition. In this study, we proposed a real-time measurement method for total nitrogen (TN) by combining the timeliness of sensor detection and the accuracy of intelligent algorithms, based on the physical and chemical relationships between TN and sensor-measured indexes. Extra tree regression was selected as the TN inversion algorithm, which has high precision, high computational efficiency, and better ability in over-fitting control. The results show that: (1) the real-time inversion algorithm of TN can achieve the monitoring frequency at the minute scale (<5 min); (2) the method performs well (R2>0.9) when the training and testing datasets are from similar environmental backgrounds (fields or ditches); (3) in the case of partial variable missing, this method can still realize TN inversion, and the prediction accuracy is acceptable (R2>0.7) under the number of missing variables (n) ≤ 2, which makes up for the flaws of missing or abnormal data caused by sensor malfunctions. Overall, the proposed real-time measurement method of TN has stable data acquisition, high precision, and high monitoring frequency. In addition, the method is not limited by cloudy, rainy, or nighttime conditions. Compared with methods such as laboratory test, remote sensing inversion, and water quality automatic monitoring station, our method has obvious advantages in runoff monitoring of NPS pollution, which mainly occurs in small and micro water bodies. The new real-time measurement of TN for runoff may provide important technological support for pre-warning and emergency control of NPS pollution.

Experimental study on three simple tracers for the assessment of extraneous water into sewer systems.

In recent years, three simple tracers (conductivity, turbidity and temperature) have shown their advantages to many other tracers for tracing and assessment of extraneous water (or inflow and infiltration, I/I) into sewer systems due to low detection cost and high monitoring frequency. A better understanding of the error and uncertainty of the three simple tracers on the quantification of I/I will help to improve the reliability and reduce the cost of actual projects. A large-scale experimental model simulating a 36 m long sewer was constructed for conducting extraneous water flow tests including groundwater infiltration, wastewater inflow and hot water inflow under different I/I flow rates and concentrations. The accuracy and uncertainty of the three tracers were estimated, and their correlation with tracer concentration difference before and after extraneous inflow was also analyzed. Experimental results provide guidance for the practical applicability of the three tracers under different I/I conditions.

Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site.

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.

Boosting the monitoring of phytoplankton in optically complex coastal waters by combining pigment-based chemotaxonomy and in situ radiometry

Research about the occurrence and extent of the cyanobacterial blooms in the Baltic Sea is critical due to their increased magnitude and frequency. Monitoring of the blooms is complicated due to their spatially and temporally heterogeneous nature. For adequate assessment of the water quality, phytoplankton dynamics needs to be tracked in large areas with high monitoring frequency. The main objectives of this study were (1) to describe phytoplankton community composition by pigment-based chemotaxonomy and validate the results with microscopy; (2) to improve the retrieval of information about phytoplankton community by combining remote sensing with laboratory based approaches (3) to develop a region-specific algorithm to calculate cyanobacteria biomass from reflectance spectra; (4) to detect and quantify potentially toxic bloom-forming cyanobacteria with molecular methods. In our study the reflectance-based chlorophyll a (Chl a) values overestimated the High-performance Liquid Chromatography (HPLC) values although the correlations with HPLC Chl a measurements were very strong (rp ∼ 0.8, p < 0.001). We found that 709 nm/620 nm reflectance ratio correlated strongly (rp = 0.75, p < 0.01) to cyanobacteria wet biomass in CDOM-rich Väinameri even at low cyanobacterial biomass levels. Correlations between pigment-based chemotaxonomy and microscopy were significant in case of cyanobacteria (rp = 0.73, p < 0.01), cryptophytes (rp = 0.71, p < 0.05) and dinoflagellates (rp = 0.64, p < 0.05).

High Frequency Data Acquisition System for Modelling the Impact of Visitors on the Thermo-Hygrometric Conditions of Archaeological Sites: A Casa di Diana (Ostia Antica, Italy) Case Study.

The characterization of the microclimatic conditions is fundamental for the preventive conservation of archaeological sites. In this context, the identification of the factors that influence the thermo-hygrometric equilibrium is key to determine the causes of cultural heritage deterioration. In this work, a characterization of the thermo-hygrometric conditions of Casa di Diana (Ostia Antica, Italy) is carried out analyzing the data of temperature and relative humidity recorded by a system of sensors with high monitoring frequency. Sensors are installed in parallel, calibrated and synchronized with a microcontroller. A data set of 793,620 data, arranged in a matrix with 66,135 rows and 12 columns, was used. Furthermore, the influence of human impact (visitors) is evaluated through a multiple linear regression model and a logistic regression model. The visitors do not affect the environmental humidity as it is very high and constant all the year. The results show a significant influence of the visitors in the upset of the thermal balance. When a tourist guide takes place, the probability that the hourly temperature variation reaches values higher than its monthly average is 10.64 times higher than it remains equal or less to its monthly average. The analysis of the regression residuals shows the influence of outdoor climatic variables in the thermal balance, such as solar radiation or ventilation.

Interactive monitoring in reservoirs using NUSwan – preliminary field results

Abstract Water quality monitoring of large freshwater bodies is usually slow and laborious, resulting in very sparse samples. This paper presents the design and preliminary field results from a network of low-cost mobile robots called NUSwan, a system aimed at addressing this issue. NUSwan robots can autonomously traverse a reservoir to provide real-time water quality data over the Internet, and allow users to modify the sampling missions interactively based on their interpretation of the data. The quality of measurements generated is comparable to that obtained from standard manual sampling. Moreover, the system incurs little logistics overhead while allowing higher monitoring frequency and spatial coverage. We present an example to show NUSwan's capability in detecting the emergence of water quality hotspots. Lastly, we demonstrate the potential of using data collected from a short mission carried out by NUSwan to rapidly assess the relationship between water quality indicators.

TESTING MICROVARIABILITY IN QUASAR DIFFERENTIAL LIGHT CURVES USING SEVERAL FIELD STARS

Microvariability consists in small time scale variations of low amplitude in the photometric light curves of quasars, and represents an important tool to investigate their inner core. Detection of quasar microvariations is challenging for their non-periodicity, as well as the need for high monitoring frequency and high signal-to-noise ratio. Statistical tests developed for the analysis of quasar differential light curves usually show either low power or low reliability, or both. In this paper we compare two statistical procedures that include several stars to perform tests with enhanced power and high reliability. We perform light curve simulations of variable quasars and non-variable stars, and analyze them with statistical procedures developed from the F-test and the analysis of variance. The results show a large improvement in the power of both statistical probes, and a larger reliability, when several stars are included in the analysis. The results from the simulations agree with those obtained from observations of real quasars. The high power and high reliability of the tests discussed in this paper improve the results that can be obtained from short and long time scale variability studies. These techniques are not limited to quasar variability; on the contrary, they can be easily implemented to other sources such as variable stars. Their applications to future research and to the analysis of large field photometric monitoring archives can reveal new variable sources.

Molecular identification of small cetacean samples from Peruvian fish markets

In the last 60 years, incidental entanglement in fishing gears (so called by-catch) became the main cause of mortality worldwide for small cetaceans and is pushing several populations and species to the verge of extinction. Thus, monitoring and quantifying by-catches is an important step towards proper and sustainable management of cetacean populations. Continuous studies indicated that by-catches and directed takes of small cetaceans in Peru greatly increased since 1985. Legal measures banning cetacean takes, enforced in 1994 and 1996, ironically made monitoring highly problematic as fishers continue catching these animals but utilize or dispose of carcasses clandestinely. Hence, in locations where cetaceans are landed covertly or already butchered, molecular genetic methods can provide the only means of identification of the species, sex, and sometimes the population of each sample. Here, we generate and analyse a fragment of the mitochondrial DNA cytochrome b gene and 5 nuclear microsatellite markers from 182 meat and skin samples of unidentified small cetaceans collected at three Peruvian markets between July 2006 and April 2007. Our results, compared to past surveys, indicate that Lagenorhynchus obscurus, Phocoena spinipinnis, Tursiops truncatus, Delphinus capensis, and D. delphis continue to be caught and marketed, but that the relative incidence of P. spinipinnis is highly reduced, possibly because of population depletion. The small number of possible sampling duplicates demonstrates that a high monitoring frequency is required for a thorough evaluation of incidental catches in the area. A wide public debate on by-catch mitigation measures is greatly warranted in Peru.

Microlensing Sensitivity to Earth‐Mass Planets in the Habitable Zone

Microlensing is one of the most powerful methods that can detect extrasolar planets and a future space-based survey with a high monitoring frequency is proposed to detect a large sample of Earth-mass planets. In this paper, we examine the sensitivity of the future microlensing survey to Earth-mass planets located in the habitable zone. For this, we estimate the fraction of Earth-mass planets that will be located in the habitable zone of their parent stars by carrying out detailed simulation of microlensing events based on standard models of the physical and dynamic distributions and the mass function of Galactic matter. From this investigation, we find that among the total detectable Earth-mass planets from the survey, those located in the habitable zone would comprise less than 1% even under a less-conservative definition of the habitable zone. We find the main reason for the low sensitivity is that the projected star-planet separation at which the microlensing planet detection efficiency becomes maximum (lensing zone) is in most cases substantially larger than the median value of the habitable zone. We find that the ratio of the median radius of the habitable zone to the mean radius of the lensing zone is roughly expressed as $d_{\rm HZ}/r_{\rm E}\sim 0.2(m/0.5 M_\odot)^{1/2}$.