Local Monitoring Research Articles

A New Open Channel Flow Correction Method Based on Different Boundary Combinations in Hydraulic Modeling

In open-channel water transfer projects, hydrodynamic modeling has been one of the tools commonly used by the scheduling staff to perceive the changes in the water condition of the channel. The reliability of the flow monitoring data has not been guaranteed due to restrictions in the flow monitoring method. Flow monitoring errors directly reduce the accuracy of hydrodynamic modeling calculations, making it impossible to accurately grasp the real-time changes in the internal water conditions of the channel. In this paper, by utilizing the characteristics of the hydrodynamic model itself, which can be solved discretely with multiple boundary conditions, the method of correcting the boundary anomaly data is proposed by changing the combination of hydrodynamic boundaries. This solves the problem of the inaccuracy of hydrodynamic model calculations due to errors in the monitoring data and improves the accuracy of the hydrodynamic model calculations. In the actual operation process, it is found that the method proposed in this paper solves the problem of hydrodynamic model misalignment caused by flow observation errors by correcting the flow during the flow adjustment period, which greatly improves the calculation accuracy of the hydrodynamic model. Secondly, for a stable flow period, the method proposed in this paper can quickly capture and correct the local monitoring of abnormal data and improve the overall hydrodynamic simulation accuracy.

Groundwater Quality Assessment in the Area of the Zinc Smelter in Miasteczko Śląskie (Poland) Using Selected Metal Indices

Groundwater quality assessment in the region of potential pollution sources can be performed through the use of index-based methods. The aim of this paper was to present the results of the groundwater quality assessment in the region of the zinc smelter in Miasteczko Śląskie (southern Poland) for the Quaternary aquifer based on monitoring data from 2000, 2011, and 2022. The following parameters were used in the analysis: Pb, Cd, Ni, Cu, Fe, Mn, and Zn. Three indices were calculated, i.e., the landfill water pollution index (LWPI), heavy metal pollution index (HPI), and metal index (MI). The values of the indicators suggest that in the described study area, there are waters that are under the strong influence of the zinc smelter infrastructure and landfills. The highest values obtained are about 17 for LWPI, about 66 for HPI, and about 133 for MI. These values indicate significant anthropogenic pollution of the groundwater of the Quaternary aquifer in the study area. Taking into account the values obtained for all three indices, it can be concluded that the HPI indicator is the least reliable due to the need to select two limit values (background and ideal content in waters for each of the parameters), which would suggest that in some cases, waters are not very contaminated. The other two indices show similarities (mainly in their spatial distribution), and only the choice of parameters determines which of the indices should be used. A comparison of the results of local groundwater quality monitoring with the results of the calculated indices indicate that the values of the calculated MI indices are closest to the actual water quality status.

An IoT System for Air Pollution Monitoring with Safe Data Transmission.

Air pollution has become a global issue due to rapid urbanization and industrialization. Bad air quality is Europe's most significant environmental health risk, causing serious health problems. External air pollution is not the only issue; internal air pollution is just as severe and can also lead to adverse health outcomes. IoT is a practical approach for monitoring and publishing real-time air quality information. Numerous IoT-based air quality monitoring systems have been proposed using micro-sensors for data collection. These systems are designed for outdoor air quality monitoring. They use sensors to measure air quality parameters such as CO2, CO, PM10, NO2, temperature, and humidity. The data are acquired with a set of sensors placed on an electric car. They are then sent to the server. Users can subscribe to the list and receive information about local pollution. This system allows real-time localized air quality monitoring and sending data to customers. The work additionally presents a secure data transmission protocol ensuring system security. This protocol provides system-wide attack resiliency and interception, which is what existing solutions do not offer.

Near-Infrared Off-Axis integrated cavity output spectroscopic sensor system for ammonia leakage measurement in cold store using embedded Multi-Core processor

A near-infrared off-axis integrated cavity output spectroscopic (OA-ICOS) sensor system for ammonia (NH3) leakage detection was developed. A distributed feedback (DFB) laser was used to target the absorption line of NH3 at 6612.167 cm−1. Experiments were conducted to evaluate the performance of the sensor system. When the averaging time is 0.8 s, the detection limit (1σ) of NH3 is ∼ 1.21 parts-per-million by volume (ppmv) using wavelength modulation spectroscopy (WMS). The 10–90 % response time of the NH3 sensor system is ∼ 7.2 s. Based on the developed local monitoring and control platform (LMCP), cloud server, remote monitoring and control end (RMCE), the unattended NH3 leakage detection experiments were carried out in a cold store located in Jilin City. The experimental results confirmed the rapid detection capability of the reported sensor system for NH3 leakage as well as the capability of remote monitoring and control.

The Royal College of Ophthalmologists' National Ophthalmology Database Study of Cataract Surgery: Report 13, monitoring post-cataract surgery endophthalmitis rates-the rule of X.

Cataract surgical safety has improved over recent decades, with endophthalmitis rates before 2006 typically 0.13-0.15% compared with the most recent UK national estimate of 0.02%. There remains, however, substantial variation in reported rates from different centres. Due to the low event rate, this disparity may not be noticed and opportunities to improve therefore be missed. We propose a method of monitoring post-cataract endophthalmitis rates that would help centres with higher rates identify this. A statistical tool, available to download or use online, permits comparison of local endophthalmitis rate with the estimated UK rate of 0.02%. Centres are encouraged to maintain a register of endophthalmitis cases, and when the number reaches a threshold (X cases), either in a certain time period or in a fixed number of procedures, then the centre can consider itself as an outlier and trigger local investigations to improve infection control. Example outputs are offered, such as for a unit doing 5000 cataracts annually, a value of X is suggested such that the third case of endophthalmitis (X = 3) in a 12-month period would give 85% confidence, the fourth case 90% confidence and the fifth case 95% confidence that the true endophthalmitis rate for that unit was higher than the national average. This statistical tool provides a basis for units to set a threshold number of cases of endophthalmitis within a given period that would trigger local processes, thus helping inform local monitoring processes for this rare but potentially catastrophic complication of cataract surgery.

Trends in the Utilization of Surgical Modalities for the Treatment of Drug-Resistant Epilepsy: A Comprehensive 10-Year Analysis Using the National Inpatient Sample.

Epilepsy is considered one of the most prevalent and severe chronic neurological disorders worldwide. Our study aims to analyze the national trends in different treatment modalities for individuals with drug-resistant epilepsy and investigate the outcomes associated with these procedural trends in the United States. Using the National Inpatient Sample database from 2010 to 2020, patients with drug-resistant focal epilepsy who underwent laser interstitial thermal therapy (LITT), open surgical resection, vagus nerve stimulation (VNS), or responsive neurostimulation (RNS) were identified. Trend analysis was performed using piecewise joinpoint regression. Propensity score matching was used to compare outcomes between 10 years prepandemic before 2020 and the first peak of the COVID-19 pandemic. This study analyzed a total of 33 969 patients with a diagnosis of drug-resistant epilepsy, with 3343 patients receiving surgical resection (78%), VNS (8.21%), RNS (8%), and LITT (6%). Between 2010 and 2020, there was an increase in the use of invasive electroencephalography monitoring for seizure zone localization (P = .003). There was an increase in the use of LITT and RNS (P < .001), while the use of surgical resection and VNS decreased over time (P < .001). Most of these patients (89%) were treated during the pre-COVID pandemic era (2010-2019), while a minority (11%) underwent treatment during the COVID pandemic (2020). After propensity score matching, the rate of pulmonary complications, postprocedural hematoma formation, and mortality were slightly higher during the pandemic compared with the prepandemic period (P = .045, P = .033, and P = .026, respectively). This study indicates a relative decrease in the use of surgical resections, as a treatment for drug-resistant focal epilepsy. By contrast, newer, minimally invasive surgical approaches including LITT and RNS showed gradual increases in usage.

Plasmonic immunosensors based on spoon-shaped waveguides for fast and on-site ultra-low detection of ochratoxin A in coffee samples

The high toxicity and occurrence of ochratoxin A (OTA) in grains and foods has been a growing concern due to the impacts on health and the economy in many countries. In this sense, simplified devices with high sensitivity and specificity for local monitoring are enthusiastically pursued. In this work, we report for the first time the detection of ochratoxin A in coffee samples using a spoon-shaped waveguide immunosensor. The biosensor was built with the surface of the spoon-shaped waveguide covered by a 60 nm layer of gold to enable the SPR phenomenon. The measurements indicated a linear relationship between the change in the SPR phenomenon values and the OTA concentration in the range from 0.2 ppt to 5 ppt. When analyzed in coffee samples, the biosensor was highly selective and did not suffer matrix interference. The developed biosensor represents a promising analytical device for coffee quality analyses, as it is portable, simple, and suitable for onsite detection of target analytes.

Closing the Loop: Enhancing Local Monitoring of Child Poverty to Leave No Child Behind.

Research on the Leave No One Behind principle of the Sustainable Development Goals (SDGs) within the context of the Agenda 2030 is currently prevalent; however, research on monitoring child poverty at the sub-national (local) level is still limited. This paper addresses this gap by examining indicators developed for monitoring the phenomenon at different territorial levels (global, European, and national) and assessing their territorial transposition locally, using the city of Cadiz, Spain, as a case study. Interviews with local stakeholders reveal that despite the availability and access to related indicators and data, relevant actors must enhance their efforts to utilize such indicators effectively. Based on desktop research and qualitative analysis, the paper delivers recommendations for improving local monitoring of child poverty in Europe and inducing policy changes. This knowledge can inform targeted interventions, policy formulation, and resource allocation to tackle child poverty and promote equitable and inclusive societies.

Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydney’s suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.

Surface deposits and intracellular concentration of atmospheric particles in foliage of urban forests in the Valley of Mexico

Foliage of urban vegetation can be used as a bioindicator of air pollution, including heavy metals. We measured surface and intracellular concentration of copper, zinc, lead, nickel, chromium, cobalt and cadmium in Fraxinus uhdei leaves from three urban forests of the Metropolitan Area of the Valley of Mexico (ZMVM) during the four seasons of the year and assessed its correlation with distance to nearest roads; we also compared obtained concentrations with the local atmospheric monitoring network. We performed a two-factor experimental design: factor forest with three levels (Naucalli Park, Forests and Tlalpan) and factor season with four levels, to analyze spatio-temporal differences in heavy metal concentration. Fifteen Fraxinus uhdei trees were selected per site and a foliage sample was taken. A t-test was applied for comparisons between deposits, while an analysis of variance and Tukey's test was used to compare between seasons and forest. Relationship between surface concentrations and distance to the streets surrounding the forests was obtained using the Stepwise method. The obtained results indicate that there are higher concentrations superficially than intracellular in all metals analyzed, except for nickel. Copper was highest in Parque Naucalli, lead, nickel, chromium and cadmium in Bosques Chapultepec and zinc in Bosques Tlalpan. Highest concentrations occurred in summer for copper, zinc, nickel and chromium and in winter for lead and cadmium. We found that the surface concentration of copper, zinc, lead and cadmium in the foliage is higher when trees are closer to avenues. Our research indicates that trees can be valuable bioindicators for air pollution, as they not only mirror the diverse pollution sources but also provide insights into pollutant exposure based on proximity to those sources.

Sick building syndrome: do outdoor pollutants and pollen affect it?

Sick building syndrome (SBS) refers to non-specific complaints, including upper-respiratory irritative symptoms, headaches, fatigue, and rash, which are usually associated with a particular building by their temporal pattern of occurrence and clustering among inhabitants or colleagues. The aim of the study was to determine the association between the clinical manifestations of sick building syndrome with outdoor pollutants and airborne pollen. It was a descriptive and prospective observational study conducted from November 2021 to April 2022. It included subjects over 18 years old who completed an online survey on sick building syndrome (general symptoms, nasal, ocular, oropharyngeal, and skin symptoms) presented at home, housing information and personal history. The APS-330 from Pollen Sense ® was used to obtain data on pollen in the air and the local pollution monitoring system (SIMA) to obtain information regarding pollutants. For statistical analysis, SPSS version 16 was used. A total of 402 surveys were included; 91% of the subjects reported having at least 1 symptom. Females presented more general symptoms (fatigue and headache) than males. Subjects with a personal history of atopy showed a higher prevalence of practically all symptoms. Airborne pollen exposure was positively associated with mucosal symptoms in eyes and nose. Outdoor fungi spore exposure was positively associated with oculo-nasal and cutaneous symptoms in the scalp. This study found significant associations with female gender and a history of atopy, which suggests a higher risk for these subjects. Despite the limitations of the study, we can conclude that there is an association between the clinical manifestations of sick building syndrome with indoor and outdoor pollution.

Assessment of particulate matter (PM) in ambient air of different settings and its associated health risk in Haripur city, Pakistan.

Particulate matter (PM) is a major air pollutant causing serious health problems. The aim of the present study was to find out concentration of PM in ambient air and its associated health risk in Haripur city, Pakistan. Twenty-three samples were taken at various educational institutes, hospitals, recreational areas and industries in Haripur city. Concentration of PM2.5 (µg/m3) and PM10 (µg/m3) was measured with Youngteng YT-HPC 3000A portable PM counter. The results revealed that values of both PM2.5 and PM10 were above the permissible limits (35 µg/m3 for PM2.5 and 150 µg/m3 for PM10) set by Environmental Protection Agency Pakistan (Pak-EPA) in all the educational institutes, hospitals, recreational areas and industries investigated. Furthermore, significant (p<0.05) variation was found in the concentration of both PM2.5 and PM10 in all the educational institutes, hospitals, recreational areas, and industries studied. The concentration of PM2.5 was positively correlated with the concentration of PM10 in all the sampling sites. Therefore, from 1-14 scale standard of health index, the values of PM2.5 and PM10 exhibited that the ambient air quality of Haripur city Pakistan is under high risk. If the regulatory authorities such as Environmental Protection Agency, Health Department and Local Government monitor PM pollution in different settings of Haripur city, then a decrease can be possible in the pollution level. The remedies that can be taken to overcome the problem of ambient air pollution such as PM are plantation of trees at the sites where there are higher levels of air pollutants and use of masks on personal protection basis along with implementation of pollution control system in industries of Hattar Industrial Estate Haripur city, Pakistan.

Online Circular Contrast Perimetry via a Web-Application: Establishing a Normative Database for Central 10-Degree Perimetry.

To establish a normative database using a central 10-degree grid pattern for the online circular contrast perimetry (OCCP) application. Fifty participants with mean age 65 ± 13 years were selected for this study. One eye from each participant that met inclusion criteria was randomly included in the cohort. The web-application delivered online 52-loci perimetry in a central 10-degree pattern using circular flickering targets. These targets consist of concentric sinusoidal alternating contrast rings. Users were guided by the application to the correct viewing distance and head position using in-built blind spot localization and webcam monitoring. A spinning golden star was used as the fixation target and patients performed the test in a darkened room following standard automated perimetry (SAP). The reliability rates and global indices for OCCP were similar to SAP. OCCP mean sensitivity reduced with age at a similar rate to SAP. Mean sensitivity per loci of 10-degree OCCP was greater than SAP by 1.24 log units (95% CI 1.23 to 1.26) and obeyed a physiological hill of vision. Small differences existed in mean sensitivities between OCCP and SAP which increased with increasing spot eccentricity. Mean deviation (MD) displayed good agreement between the two tests. Central 10-degree online circular contrast perimetry via a computer-based application has comparable perimetric results to standard automated perimetry in a normal cohort.

Auditable and Verifiable Federated Learning Based on Blockchain-Enabled Decentralization.

Auditability and verifiability are critical elements in establishing trustworthiness in federated learning (FL). These principles promote transparency, accountability, and independent validation of FL processes. Incorporating auditability and verifiability is imperative for building trust and ensuring the robustness of FL methodologies. Typical FL architectures rely on a trustworthy central authority to manage the FL process. However, reliance on a central authority could become a single point of failure, making it an attractive target for cyber-attacks and insider frauds. Moreover, the central entity lacks auditability and verifiability, which undermines the privacy and security that FL aims to ensure. This article proposes an auditable and verifiable decentralized FL (DFL) framework. We first develop a smart-contract-based monitoring system for DFL participants. This monitoring system is then deployed to each DFL participant and executed when the local model training is initiated. The monitoring system records necessary information during the local training process for auditing purposes. Afterward, each DFL participant sends the local model and monitoring system to the respective blockchain node. The blockchain nodes representing each DFL participant exchange the local models and use the monitoring system to validate each local model. To ensure an auditable and verifiable decentralized aggregation procedure, we record the aggregation steps taken by each blockchain node in the aggregation contract. Following the aggregation phase, each blockchain node applies a multisignature scheme to the aggregated model, producing a globally verifiable model. Based on the signed global model and the aggregation contract, each blockchain node implements a consensus protocol to store the validated global model in tamper-proof storage. To evaluate the performance of our proposed model, we conducted a series of experiments with different machine learning architectures and datasets, including CIFAR-10, F-MNIST, and MedMNIST. The experimental results indicate a slight increase in time consumption compared with the state-of-the-art, serving as a tradeoff to ensure auditability and verifiability. The proposed blockchain-enabled DFL also saves up to 95% communication costs for the participant side.

Case Report of Physical Therapy Performed Under Local Cerebral Oxygen Saturation Monitoring for Two Patients with Acute Subarachnoid Hemorrhage

Case Report of Physical Therapy Performed Under Local Cerebral Oxygen Saturation Monitoring for Two Patients with Acute Subarachnoid Hemorrhage

Building a Flexible and Resource-Light Monitoring Platform for a WLCG-Tier2

Software development projects at Edinburgh identified a desire to build and manage our own monitoring platform. This better allows us to support the developing and varied physics and computing interests of our Experimental Particle Physics group. This production platform enables oversight of international experimental data management, local software development projects and active monitoring of lab facilities within our research group. Larger sites such as CERN have access to many resources to support generalpurpose centralised monitoring solutions such as MONIT. At a WLCG Tier2 we only have access to a fraction of these resources and manpower. Recycling nodes from grid storage and borrowed capacity from our Tier2 Hypervisors has enabled us to build a reliable monitoring infrastructure. This also contributes back to our Tier2 management improving our operational and security monitoring. Shared experiences from larger sites gave us a head-start in building our own service monitoring (FluentD) and multi-protocol (AMQP/STOMP/UDP datagram) messaging frameworks atop both our Elasticsearch and OpenSearch clusters. This has been built with minimal hardware and software complexity, maximising maintainability, and reducing manpower costs. A secondary design goal has also been the ability to migrate and upgrade individual components with minimal service interruption. To achieve this, we made heavy use of different layers of containerisation (Podman/Docker), virtualization and NGINX web proxies. This presentation details our experiences in developing this platform from scratch with a focus on minimal resource use. This includes lessons learnt in deploying and comparing both an Elasticsearch and OpenSearch clusters, as well as designing various levels of automation and resiliency for our monitoring framework. This has culminated in us effectively indexing, parsing and storing &gt;200GB of logging and monitoring data per day.

Мониторинг состояния растительности в районе разработки Тасеевского месторождения (Забайкальский край)

The article presents the results of an environmental survey conducted in the development area of the Taseyev gold deposit of the Baleysky district of the Trans-Baikal Territory. Environmental monitoring was carried out on the most vulnerable component of the natural environment in order to ensure the environmental safety of the facility during the implementation of the construction of a heap leaching complex on the basis of the technogenic waste deposit “Baleyskoy ZIF‑1” of the Trans-Baikal Territory. The current state of the vegetation cover was studied. An analysis of the flora of the technogenically disturbed territory of the Taseevskoye field was carried out. As part of the local monitoring of the gold deposit and its environs, 4 types of plant complexes were identified – forest, meadow-steppe, near-river and synanthropic. According to the results of the study, a satisfactory state of plant communities was revealed. A quantitative indicator of diversity has been established, which is more than 80 species within the boundaries of the allotment. Species listed in the Red Book of the Trans-Baikal Territory, growing in the background areas of this field, are noted. A change in the natural vegetation cover on the area of the technogenically disturbed landscape was recorded as a result of industrial work – the movement of soil masses, dumping and excavation. The territory is covered by a chaotic network of country roads that disturb the soil and soil cover. Along the roads gully erosion is developed. There is a negative impact and a decrease in the recreational ecosystem services of the Baleisky district due to the production activities of gold mining enterprises (insufficient formation of a comfortable environment for the population). The presence of synanthropic communities in the anthropogenically disturbed area with traces of household waste dumps, sewage disposal, changes in the microrelief during industrial development on the river is traced Unda. In general, despite the adverse factors affecting the vegetation, plants adapt to stressful situations, all disturbed areas are capable of self-healing.

Thin-Film Transistor-Based Sensor Interface Circuits Enabling Distributed Local In-Module Solar Cell Temperature Monitoring

Accurate efficiency prediction, improved reliability, and extended lifetime of solar modules are the key driving factors for investment and research in photovoltaic (PV) systems. Enhanced performance and more refined models are realized by incorporating data derived from localized monitoring of solar modules. This work combines interface circuits designed in flexible thin-film transistors (TFTs) with distributed temperature sensors integrated into solar cell lamination enabling in-module within-cell PV monitoring. A TFT multiplexer selects between sensors and is commanded through a serial peripheral interface (SPI) to reduce the IO connections, while the low offset unity-gain TFT buffer enables precise signal transfer. The in-cell sensors together with TFT read-out circuitry demonstrate accurate outdoor temperature monitoring, comparable to silicon integrated circuits.

Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios.

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 μg m-3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources.

Research on multi-UAV air pollution source localization algorithm based on emotional quantity

With the advancement of industrialization, the problem of atmospheric environmental pollution is becoming more and more prominent. To solve this problem, an unmanned aerial vehicle (UAV) as an airborne platform was used to design an air pollution source localization method based on an anxiety-auction algorithm and verify the feasibility of the algorithm through simulation analysis and indoor source localization experiments. The algorithm innovatively introduces the concept of anxiety in psychology into the traditional auction algorithm. By enabling each drone to make a “rational” auction time decision based on its emotional state, team resources can be conserved, and overall source localization efficiency can be enhanced. Based on different environmental factors and conditions, the number of drones and other multi-perspective comparison analyses with the traditional auction algorithm, the analysis results show that the anxiety-auction algorithm performs better in terms of success rate and distance ratio. This paper also built a set of atmospheric pollutant source localization platforms, consisting of an ultra-wideband (UWB) indoor positioning device, UAV platform, source localization monitoring and control module, and the indoor source localization experiment of atmospheric pollutants based on multiple UAVs was successfully designed and carried out.