Monitoring Methodology Research Articles

Testing of Shoreline Erosion Monitoring Methodologies for Heritage at Risk Sites: Pockoy Island, South Carolina, USA

Abstract We review shoreline monitoring methodologies used by members of the South Carolina Department of Natural Resources (SCDNR) Archaeology, Geology, and Wildlife Biology teams from February 2021 to December 2022 on Pockoy Island in Charleston County, South Carolina, USA. Our project objectives were to better understand the driving forces behind the landward movement of the shoreline (transgression), to apply new understanding to the rate of shoreline erosion of the island that directly impacts the Pockoy Island Shell Ring Complex (38CH2533), and to establish best practice for future community science monitoring efforts. Each member of our team used a different shoreline monitoring methodology (a nested methodology approach). Multiple unoccupied aerial vehicle (UAV)-derived orthoimagery datasets, on-the-ground transect measurements, and Arrow Gold real-time kinematic (RTK) unit measurements have been collected monthly following significant storms or king (perigean) tide events. Moving forward, the erosion transect approach tested within this project will serve as the foundation for community science monitoring at heritage at-risk sites in South Carolina. In this article, we introduce initial efforts in establishing a community science monitoring program in South Carolina that will influence future research, land management, and policy, and we propose how our research might be adapted for other sites at risk.

Evaluation of sampling methods of Diaphorina citri Kuwayama (Hemiptera: Liviidae) in two citrus growing areas

Efficient, easy to implement and low-cost monitoring methodologies are necessary to obtain information on arthropod pest populations and to implement the most convenient and timely phytosanitary control practices. To optimize the sampling and monitoring of the Asian citrus psyllid, Diaphorina citri (Hemiptera: Liviidae), an insect associated with the transmission of Candidatus Liberibacter asiaticus and Ca. L. americanus, which cause Huanglongbing (HLB), a disease with a great impact on citrus orchards, three methods of adult sampling (yellow sticky traps, sweep net, and stem tap) were evaluated. For immature sampling, vegetative shoots were checked. The results demonstrate that the population density at the time of sampling affects the effectiveness and sensitivity of the sampling methods. Yellow sticky traps capture more adults and are the only effective method at low psyllid densities. Stem tap and sweep net are less expensive methods; however, they do not detect adults nor correlate with the number of nymphs and eggs in vegetative shoots when adult density is low. For adults, an optimal sample size was determined for each method. For yellow sticky traps, 3 to 5 traps for a 2-hectare plot with weekly frequency are recommended. For immatures, it is recommended to estimate the percentage of infestation by inspecting 45 to 55 vegetative shoots well-distributed within a 2-hectare plot, as a practical measure for farmers and extensionists to monitor D. citri.

Inhomogeneity detection within a head-sized phantom using tracking of charged nuclear fragments in ion beam therapy

Objective.The highly conformal carbon-ion radiotherapy is associated with an increased sensitivity of the dose distributions to internal changes in the patient during the treatment course. Hence, monitoring methodologies capable of detecting such changes are of vital importance. We established experimental setup conditions to address the sensitivity of a monitoring approach based on secondary-fragment tracking for detecting clinically motivated air cavity dimensions in a homogeneous head-sized PMMA phantom in 40 mm depth.Approach.The air cavities were positioned within the entrance channel of a treatment field of 50 mm diameter at three lateral positions. The measured secondary-fragment emission profiles were compared to a reference measurement without cavities. The experiments were conducted at the Heidelberg Ion-Beam Therapy Center in Germany at typical doses and dose rates.Main results.Significances above a detectability threshold of 2σfor the larger cavities (20 mm diameter and 4 mm thickness, and 20 mm diameter and 2 mm thickness) across the entire treatment field. The smallest cavity of 10 mm diameter and 2 mm thickness, which is on the lower limit of clinical interest, could not be detected at any position. We also demonstrated that it is feasible to reconstruct the lateral position of the cavity on average within 2.8 mm, once the cavity is detected. This is sufficient for the clinicians to estimate medical effects of such a cavity and to decide about the need for a control imaging CT.Significance.This investigation defines well-controlled reference conditions for the evaluation of the performance of any kind of treatment monitoring method and its capability to detect internal changes within head-sized objects. Four air cavities with volumes between 0.31 cm3and 1.26 cm3were narrowed down around the detectability threshold of this secondary-fragment-based monitoring method.

Intelligent temperature measuring thermal spray multilayer thermal barrier coatings based on embedded thin film thermocouples

Thermal barrier coatings (TBCs) have garnered significant attention as crucial protective components for turbine blades. However, the current use of TBCs is limited by their singular functionality and the inability to accurately obtain the temperature gradient distribution within the coatings. Addressing the aforementioned issues, this paper proposes an intelligent thermal barrier coating embedded with thin-film thermocouples. This method not only provides effective thermal protection but also facilitates the precise measurement of the internal temperature gradient within the coating. To mitigate the thermal mismatch in TBCs under high-temperature environments, which can compromise their lifespan, this study employs multi-objective optimization of structural parameters to design an optimal coating thickness. This strategy ensures both superior thermal protection and extended service life. The intelligent temperature-sensing TBCs were fabricated using atmospheric plasma spraying and magnetron sputtering, followed by comprehensive characterization. To validate the performance of the intelligent temperature-sensing TBCs, static tests were conducted in a muffle furnace. The results demonstrated that the sensors exhibit excellent repeatability and high-temperature durability. Furthermore, a test platform replicating the thermal shock conditions of an engine environment was developed. This platform confirmed that the intelligent temperature-sensing TBCs are capable of accurately measuring the internal temperature gradient within the coating under engine-like conditions, offering a novel methodology for engine monitoring and diagnostics.

Deformation monitoring and data denoising without clean data for sandbars protective flexible mattress in the Yangtze River

The stability of the shoreline, such as sandbars, is vital for navigation and flood control in the Yangtze River, and the flexible mattresses are widely used to protect shorelines and submerged structures from erosion. To determine the long-term stability of the sandbars, it can be achieved by analyzing the monitoring data of the protective flexible mattress deformation. These mattresses are deployed in challenging environments, where traditional monitoring methods fall short in capturing their subtle, short-term deformations. To address this gap, this study introduces a novel deformation monitoring method based on Distributed Fiber Optic Sensing (DFOS) tailored for flexible mattresses and proposes a BiLSTM-CNN model for denoising the measured data, which circumvents the need for clean reference data. Key findings include: (1) For the monitoring of flexible mattresses, armored optical sensing cables should be installed from the perimeter inward, arranged in a U-shape post-submersion on the surface of the flexible mattress, and installed simultaneously with construction. The strain is measured by the single-end BOTDR. (2) The proposed BiLSTM-CNN model which is established without clean data, outperformed traditional denoising techniques like wavelet transform and Kalman filtering, effectively preserving essential strain responses while suppressing noise, enabling accurate depiction of the deformation of the flexible mattress, and demonstrating its applicability in complex monitoring scenarios. (3) The flexible mattress monitoring method was successfully deployed in the Yudaizhou shoreline protection engineering project. The BiLSTM-CNN denoising model, combined with a post-adjust method to process the different periods’ data, resulted in analyzable strain data. Comprehensive analysis of long-term strain monitoring data indicates that the flexible mattress in the monitoring area remains stable. This study’s significant contribution lies in advancing the methodology for monitoring and denoising strain data from underwater flexible mattresses, thereby enhancing the understanding and management of erosion protection systems in challenging environments.

Deep learning-enhanced remote sensing-integrated crop modeling for rice yield prediction

This study introduces a novel crop modeling approach based on cutting-edge computational tools to advance crop production monitoring methodologies, and, thereby, tackle global food security issues. Our approach pioneers integrating deep learning and remote sensing with process-based crop models to enhance rice yield predictions while leveraging the strengths and weaknesses of each model. We developed and evaluated four models based on distinct deep neural network architectures: feed-forward neural network, long short-term memory (LSTM), gated recurrent units, and bidirectional LSTM. All the models demonstrated high predictive accuracies, with percent biases of 0.74–2.62 and Nash–Sutcliffe model efficiencies of 0.954–0.996; however, the LSTM performed best among the four models. Notably, the models' performances varied when applied to regional datasets that were not included in the training phase; this highlighted the critical need for diverse training data to enhance model robustness. This research marks a significant advancement in agricultural modeling by combining state-of-the-art computational techniques with established methodologies, setting a new standard for crop yield prediction.

Environmental legislation analysis improvement approach of global marine plastic pollution from the perspective of holistic system view

Marine plastic pollution (MPP) has posed an unavoidable challenge to the conservation of marine ecosystems, escalating at an unprecedented rate. It extends beyond visible pollution, infiltrating the food chain and microcirculation, ultimately affecting the life and health of marine organisms. Of even greater concern is the fact that MPP has been found to penetrate human bloodstreams. The international community increasingly focuses on MPP, and has formulated a series of laws and regulations. This article analyses marine pollution prevention legislation within the context of international environmental resolutions and conventions, including those established by the United Nations, the European Union law and the domestic legislation of sovereign states. It is evident that the current legislation has played a pivotal role in the preventing MPP. However, global legislation on preventing MPP remains fragmented. The problems existing in the current legislation should be reviewed from the holistic systems perspective, and the integrity and systematicness of new plastics convention should be demonstrated. The proposed Marine Plastics Convention should emphasize environmental justice, protect the rights of vulnerable populations, lower the threshold for risk prevention, and focus on addressing residual risks. It must include clear provisions for regulating hydrosphere plastic pollution (HPP) to mitigate land-based pollution and scientifically define fundamental legal concepts to foster coordinated action among States. Moreover, the convention should establish standardized monitoring methodologies and assessment criteria to ensure accurate evaluation of the pollution status.

2.B. Scientific session: Learning from intercountry comparisons in digital healthcare transition monitoring

Abstract In many countries, healthcare is facing several challenges, including increasing healthcare demand, healthcare workforce shortages, rising healthcare costs, and keeping healthcare accessible. It is thought that the use of digital health might help to tackle these challenges. However, digital health programs and interventions are often not (systematically) monitored or evaluated. Monitoring of the digital healthcare transition in countries over time helps policymakers and healthcare professionals to investigate which digital services work well, and which do not, to (partly) solve the experienced challenges. In Europe, several countries monitor the digital healthcare transition within the primary and secondary care in their country. Comparing and sharing the monitoring methodologies between countries and describing the current state of the digital healthcare transition in primary and secondary care, will help to learn from other countries about how digital health could help to tackle the frequently experienced healthcare challenges. During this workshop, three countries throughout Europe (the Netherlands, Italy and Denmark) will briefly share their methodologies used for monitoring the digital healthcare transition in their country and will provide an up-to-date overview of the current state of the digital healthcare transition in primary and secondary care in their country. Challenges, achievements and future plans for the monitoring of this transition will also be shortly discussed in 10-minute presentations. Further, there will be 25 minutes for audience interaction which will be moderated by the chairperson. During this discussion, there is time to hear experiences from other countries and explore best practices in monitoring the digital healthcare transition. Key messages • The digital healthcare transition can be monitored in different ways. Experiences from three European countries may inspire others to organize national monitoring programs. • The speed and nature of the digital healthcare transition differs between European countries. Experiences from three European countries provide insight into state of the art of national transitions.

Entire loosening stage monitoring of bolted joints via nonlinear electro-mechanical impedance spectroscopy

This article proposes a nonlinear electro-mechanical impedance spectroscopy (NEMIS) methodology for the entire loosening stage monitoring of bolted joints. Unlike the conventional electro-mechanical impedance spectroscopy (EMIS) implemented in the frequency domain, NEMIS utilizes a temporal interrogating signal to procure the developed nonlinear impedance spectrum. It can harness the structural resonance information akin to the conventional EMIS, while concurrently capturing the contact acoustic nonlinearity (CAN) features with only one solitary Piezoelectric Wafer Active Sensor (PWAS). To elucidate the underlying principles of NEMIS, a comparative analysis juxtaposing the conventional EMIS, nonlinear ultrasonic techniques, and the proposed NEMIS was conducted. Subsequently, a reduced-order analytical model was established to simulate the rough contact interfaces of the bolted joints under various looseness states. Parametric studies were carried out to demonstrate the resonance deviation and nonlinear features resulting from the bolt looseness. Ultimately, the experimental implementation of NEMIS for the entire loosening stage monitoring of the bolted joint was performed compared with the conventional EMIS method. It was proved that NEMIS could detect both the incipient bolt loosening at an embryo stage and severe loosening of the bolted joint at the terminal period, integrating the continuous monitoring capability of conventional EMIS and the sensitivity of nonlinear ultrasonic methodology. The quantification on the severity of bolt loosening was accomplished via both linear and nonlinear damage indices, exhibiting a consistent trend with the gradations of bolt looseness. The article culminates in a summary, concluding remarks, and suggestions for future work.

The Integral Assessment of Playing Tactics in National Football Teams

Objectives. The objective of the study was to determine the features of competitive activities among national football teams based on the integral assessment of playing tactics. Materials and methods. The study was conducted during the 2024 European Football Championship. The competitive activities of national football teams from European countries (Germany, Spain, England, Switzerland, France, Portugal, Netherlands, Turkey) were analyzed, focusing on the playing tactics in 50 halves. In order to carry out the study, the following methods were used: theoretical analysis of literature sources; pedagogical observation of competitive activities; analysis of video materials of competitive activities; methods of mathematical statistics. The statistical analysis of the research results was conducted using descriptive mathematical statistics. The sample was characterized by determining the arithmetic mean, standard deviation (S), and coefficient of variation (V). The significance of observed differences in the indicators was assessed using the parametric Student’s t-test for independent samples. Prior to this, the data’s conformity to a normal distribution was verified using the Shapiro-Wilk W-test. Results. A methodology for the integral assessment of the tactics undertaken by national football teams was developed. The integral assessment of team tactics consisted of 10 specific coefficients that reflect the main components of football players’ competitive activities in the phases of ball possession and recovery. The competitive activities in 50 halves of the 2024 European Football Championship were analyzed. The average value of the integral assessment of the tactics in national football teams was determined to be 5.62 ± 0.43 points. The findings revealed significant tactical variations between the teams, with Spain’s use of high pressing and creative play contributing to their championship victory. A ten-point scale for evaluating the tactics of national football teams was developed, on the basis of which the level of competitive activities is interpreted. Conclusions. The developed methodology for monitoring and analyzing the tactics of football teams allows for targeted management influences on the competitive activities of football players at the operational, current, and stage levels.

Detection of Volcanic Ash Using Goes-16 Satellite Imagery: Case Study of Popocatépetl Volcano, Mexico

Objective: The objective of this study is to investigate the accuracy of volcanic ash detection using satellite imagery, to improve existing monitoring tools to adapt them to the Popocatépetl volcano in Mexico. Theoretical Framework: Volcanic ash detection via remote sensing employs Pavolonis' algorithms that compare radiances in the thermal and mid-infrared spectrum. This approach differentiates ash from clouds and water vapor by leveraging its differential absorption. Satellites such as GOES-16, which capture multispectral data, optimize the continuous detection and monitoring of ash dispersion, even in areas of low concentration. Method: Three volcanic ash detection algorithms were compared using images from the Popocatépetl eruption event on May 21, 2023: two developed by Pavolonis, which combine thermal and mid-infrared bands, and a third, a spectral adaptation designed to enhance precision at the edges of the ash cloud, tailored to the study area. Results were validated with an RGB ash image provided by NOAA. Results and Discussion: Pavolonis' algorithms provided a solid foundation for general detection, but the third algorithm, specifically designed for this study, significantly improved edge detection where classification conditions are more complex. This improvement was reflected in a higher agreement with the RGB ash image used as a reference. However, limitations related to atmospheric interference were identified, which require further adjustments in low ash concentration scenarios. Research Implications: The findings of this research have significant practical and theoretical implications for volcanic risk management and environmental protection. Enhanced accuracy in volcanic ash detection can optimize monitoring and early warning strategies, reducing risks to public health and infrastructure. Originality/Value: This study contributes to the literature by introducing a specific spectral adaptation to improve edge detection of volcanic ash, a novel approach not previously addressed. The research's relevance lies in its ability to refine monitoring methodology, providing more effective tools for volcanic risk management and offering a model applicable to other areas facing similar remote sensing challenges.

UAV-MSSH: A novel UAV photogrammetry-based framework for mining surface three-dimensional movement basin monitoring

Monitoring the surface three-dimensional movement caused by underground coal mining is a critical and challenging task. Despite many methods have been proposed in previous works, they focus more on the subsidence, with insufficient discussions on the horizontal displacement and the underlying coupled mechanisms between them. To address these issues, we introduce a novel UAV photogrammetry-based framework for mining surface three-dimensional movement basin monitoring, named UAV-MSSH. This framework consists of two branches: vertical subsidence and horizontal displacements. For the subsidence branch, we propose an improved cloth simulation filtering algorithm based on slope zone indexing (CSFS) to model high-precision subsidence basins. In the horizontal displacement branch, we develop a probability integral prediction prior-guided COSI-Corr (PIM-CC) method for displacement detection by bridging the gaps between the horizontal displacement and subsidence. Employing this framework, we monitored the surface three-dimensional movement using the UAV oblique photogrammetry method at a working face in western China. The accuracy evaluation results with net station GNSS data show that the RMSE of the subsidence basin is 161 mm, the relative error is 3.1 %, and the RMSEs of the east–west and north–south components are 99 mm and 85 mm, respectively, which demonstrates the effectiveness and practicality of our proposed UAV-MSSH framework in mining surface monitoring tasks. This study establishes an innovative and proven methodology for UAV monitoring in the mining field, which is valuable for safe production, disaster warning, and ecological restoration.

Satellite-derived bathymetry using Sentinel-2 in mesotidal coasts

Coastal zones are strategic environments of high socioeconomic, political, and ecological value, with over half of the world’s population residing within 200 km of the coast. This proximity highlights their vulnerability to extreme events, which are exacerbated by global changes, leading to significant coastal impacts such as erosion, flooding, and ecosystem services deterioration. Consequently, efficient and operational methodologies for continuous monitoring are urgently needed to face these challenges. Bathymetric data are essential for understanding coastal dynamics, yet traditional data collection methods are often constrained by logistical challenges and high costs. Spaceborne remote sensing techniques offer significant advantages over traditional ground-based methods, particularly in terms of cost-effectiveness and operational efficiency. Over the last half-century, different Satellite-derived bathymetry (SDB) methodologies have been developed; however, challenges still persist. In this research, we applied a robust SDB methodology to three different study sites: Cíes Islands, Baiona Bay, and Vao beach within the Ría de Vigo, Galicia (NW Spain). These areas offer diverse and complex mesotidal environments to test for the very first time the methodology's efficacy. SDB was retrieved with a median absolute error (MedAE) ranging from 0.35 m to 1.55 m for depths up to 14 m. Results with different data source were evaluated, obtaining MedAE for nautical charts ranging from 0.46 m to 1.55 m. The precision between the data sources were quite close. In addition, multi-image composite was generated using images coinciding with both low tide (LT) and high tide (HT) conditions across the three zones. The lowest MedAE values were consistently obtained in images classified as LT (0.46 m) corresponding to Vao area. The results highlight the potential of nautical charts as a reliable source of calibration data for SDB, confirm the effectiveness of multi-image and switching models to correct artifacts and turbidity, considering tidal effects, improving single image approaches, and leverage visible bands for precise depth retrieval under varying conditions.

Towards Environmental Sustainability: Integrating RS and GIS for Ecology Assessment

The environment provides numerous benefits for human well-being, such as ecosystem services. These services directly and indirectly depend on the physical state of the ecosystem. However, the over-exploitation of natural resources along with socio-economic factors, i.e. economic growth, poverty, agriculture expansion, population growth and weak environmental governance and regulation for fulfilling the human needs for food, fuel, and shelter, is leading to the degradation of the ecological health of an area. Therefore, monitoring and assessment of spatial and temporal changes in the ecology of a region in terms of vital ecological services is very critical and would help the decision-makers to develop and plan appropriate adaptation and mitigation measures for the conservation of natural ecosystems at various scales to ensure environmental sustainability. The present study aimed to assess the ecological status of the Mahi Bajaj Sagar catchment area in Rajasthan (India) from the year 2000 to 2020, using remote sensing-based indices. Indicators such as greenness, dryness, and heat index have been selected as per the pressure-state-response (PSR) framework. Multi-spectral remote sensing data and image processing methods have been used to estimate these indicators, and a remote sensing-based Ecological Status Index (RSBI) has been generated by their integration using the principal component analysis (PCA) to assess the ecological status of the Mahi Bajaj Sagar catchment. Findings from the study indicate a consistent decline in the overall ecological status of the Mahi Bajaj catchment, where decreased forest areas have a pronounced effect on ecological health. Interestingly, approximately 43.6% of the area displayed resilience to changes in ecological status; however, 36.4% of the area exhibited signs of ecological degradation. Our study underscores the efficacy of a remote sensing-based approach in quantifying and detecting ecological changes, offering a promising methodology for monitoring and assessing the ecological health of large areas that can help promote environmental sustainability. Keywords: Environmental Sustainability, Ecological Status, RSBI, Remote Sensing, PSR Framework

Refined semi-empirical models for soil moisture retrieval in spaceborne GNSS-Reflectometry: Evaluation across diverse land cover types

The satellite-based Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a promising technique for detecting surface soil moisture (SSM), which plays a pivotal role in various applications. Different approaches have been developed for SSM retrieval, including parametric semi-empirical models and non-parametric machine learning methods. This study, however, specifically focuses on constructing and evaluating semi-empirical fit models. To this end, the study compares the effectiveness of our enhanced pixel-by-pixel model, the land cover-based linear model, and the Reflectivity-Vegetation-Roughness (R-V-R) model across different land surface types, aiming to both evaluate their efficacy and identify potential limitations. In the assessment, various factors were taken into consideration, such as the correction for vegetation and roughness attenuation, fitting functions employed, and the utilization of a lookup table (LUT). The results of the evaluation showed variations in the performance of the retrieval models across different land cover types, highlighting the impact of the choice of fitting functions and attenuation correction strategies on the accuracy of soil moisture retrieval. The pixel-by-pixel model demonstrated the highest prediction accuracy, with an unbiased root mean square difference (ubRMSD) of 0.056 cm3/cm3 and a correlation coefficient of 0.896. By showcasing these outcomes, this research underscores the significance of accounting for surface conditions and integrating relevant data to enhance the accuracy of GNSS-R SSM retrieval, thereby contributing to the advancement of SSM monitoring methodologies.

Multidisciplinary assessment of seasonal ground displacements at the Hatfield Moors gas storage site in a peat bog landscape

The study aims to analyse ground displacement conditions observed over an Underground Gas Storage (UGS) site located at Hatfield Moors (United Kingdom), with a focus on understanding its implications for decarbonization efforts. The location serves as an active onshore storage site and was used as an analogy to assess ground motion implications around Carbon Capture and Storage (CCS) by the British Geological Survey (BGS) as part of the SENSE (Assuring integrity of CO2 storage sites through ground surface monitoring) project. Given the value of continuous and real-time monitoring of ground movements induced by gas storage activities, the study leverages satellite Interferometric Synthetic Aperture Radar (InSAR) data to assess the environmental impact of UGS operations. Using free and open-source Sentinel-1 satellite data, ground motion patterns over Hatfield Moors are analysed, highlighting displacements ranging from − 5.0 to -10.0 mm/year within the peat bog. In addition, the Time Series (TS) of ground displacement from January 2018 to December 2022 reveals a seasonality in ground motion, with uplift observed in late winter and subsidence in late summer, showing a periodicity of approximately 1 year and a magnitude of +/-10.0 mm. Through in-depth analysis, the study highlights the need to understand the underlying causes of ground fluctuations at gas storage sites. This paper shows that InSAR has the versatility to integrate seamlessly with different monitoring tools and methodologies, opening avenues for comprehensive and holistic analyses. Cross-correlation analyses further elucidate temporal relationships between different datasets by evaluating InSAR time series, UGS injection/withdrawal data and piezometric data. This involves decomposing the TS into distinct components, including trend, seasonality and residuals. The case of Hatfield Moors shows a significant discrepancy between the UGS data and the InSAR TS, while also demonstrating a clear correlation between the groundwater data and the InSAR TS. By integrating insights from geology, hydrology and remote sensing technologies, the study navigates the complexities inherent in areas of overlapping phenomena. Accurate interpretation is essential for informed decision making, particularly at sites such as Hatfield Moors, where the convergence of natural peat motion and storage operations highlights the need for interdisciplinary analysis to understand the underlying causes of ground fluctuation.

Rapid green analytical methodology for simultaneous monitoring of nitrosamines and semi-volatile organic compounds in water and human urine samples.

Nitrosamines and semi-volatile organic compounds (SVOCs) are carcinogenic contaminants in water and biological matrices. Conventional analytical methods often struggle to detect trace concentrations due to poor extraction efficacies. This study presents a novel, low-cost, in-syringe-assisted fast extraction cum cleanup technique coupled with GC-FID for monitoring four nitrosamines and two SVOCs in drinking water and human urine samples to measure the contamination and exposure levels. This extraction protocol combines a novel green in-syringe liquid-liquid extraction step using dimethyl carbonate as the green extraction solvent, coupled with a semi-automated solid-phase extraction cleanup process. Then, the final extractant is analyzed using gas chromatography-flame ionization detection (GC-FID) for monitoring. The method demonstrated excellent linearity (R2 > 0.998) between 1.5 and 500ngmL⁻1 for all six target compounds. Detection limits ranged from 1.0 to 2.0ngmL⁻1. Extraction recoveries were between 87 and 105% for both urine samples and water samples. Intra-day and inter-day precision were below 9% RSD. The blue applicability grade index evaluation scored 70.0, indicating good practical applicability. The developed analytical protocol offers a sensitive, accurate, low-cost, rapid, and environmentally friendly method for simultaneously quantifying multiple nitrosamines and SVOCs in environmental and human samples. Its performance characteristics and sustainability metrics suggest the potential for broad application in monitoring and exposure studies.

The application of continuous audit and monitoring methodology: A government medication procurement case

In the government procurement process, waste and abuses are hard to avoid, and there are usually latencies between the event occurrence and detection works. The continuous audit methodology has been adopted by many public and private firms to actively monitor transactions and detect possible anomalies. Compared to the adoptions in the private sector, implementations of continuous audit and monitoring of government procurement analyses are limited. This study applies the continuous monitoring and audit methodology to the medication procurement data of a Brazilian city government, and it aims to help internal auditors identify exceptions and anomalies in procurements for further investigation. By introducing the comparison of exogenous data, this study further provides cost management opportunities.

Fine-scale monitoring of catkins reveals an association between catkin concentration and plant community characteristics and microclimate

Catkins, as a significant source of plant-caused pollution, disrupts daily human activities and industrial processes. Despite their impact, catkins have not been included in official environmental quality monitoring indicators, leading to a deficiency in scientifically rigorous collection and monitoring methodologies, as well as a lack of ecological prevention and management strategies. In this study, we introduced a fine-scale monitoring approach for catkins. Qualitative and quantitative relationships between catkin concentrations, plant community characteristics and microclimate factors were elucidated by analyzing on-site catkin concentration data from 33 representative plant communities in Beijing. Furthermore, we summarized the ecological strategies for the prevention and management of these catkins. The results indicated that (1) TS (three-dimensional green volume of trees in the catkin source layer), SB (three-dimensional green volume of shrubs in the catkin barrier layer), GB (three-dimensional green volume of ground cover plants in the catkin barrier layer), T (three-dimensional green volume of trees in the whole plant community), W (three-dimensional green volume of the whole plant community), species diversity, and relative air humidity were key plant community characteristics and microclimate factors influencing catkin concentration. Among these factors, TS, T, W, and relative air humidity showed a significant positive correlation with catkin concentration, while SB, GB, and species diversity exhibited a significant negative correlation with catkin concentration. (2) All seven key factors exhibited nonlinear relationships with catkin concentration. (3) TS served as the primary deciding factor for catkin concentration within the plant community. When TS > 744.0755 m3, the secondary decision factor for catkin concentration was GB. Otherwise, the determinants were SB and species diversity. The results showed that enhancing tree species diversity, enhancing the three-dimensional green volume of shrubs and ground cover plants, and increasing air humidity were practical means to facilitate the sedimentation of catkins. The measures used to obstruct catkins vary depending on the TS. When catkin source plants are abundant within a plant community, it is advisable to prioritize increasing ground cover plants. Conversely, when fewer sources of such plants exist, emphasis can be placed on augmenting mid-layer shrubs and diversifying plant species. These findings provide a scientific foundation for the planting design and stock optimization of communities containing catkin source plants.

Development of a Methodology for the Monitoring of Socio-Economic Indicators of Private Forest Owners towards Sustainable Forest Management: The Case of Lithuania

The development of a monitoring system for the socio-economic indicators of private forest owners includes the comprehensive collection of various socio-economic data. These data encompass general information about private forests owners, the need for monitoring, periodicity, and other relevant factors. This holistic approach allows for a detailed assessment of the social and economic conditions of private forest owners, as well as the economic efficiency of their operations, ultimately aiming to achieve sustainable forest management. This research builds upon previous studies by the authors that investigated the need for monitoring of the socio-economic indicators of private forests and involved interviews with forest experts. Based on the data obtained from these earlier efforts, this study aims to present a refined methodology for monitoring of the socio-economic indicators of Lithuanian private forest owners. Based on the findings of this research, it is highly recommended that monitoring be implemented at the state level across the entire territory of the Republic of Lithuania. The results of this research show that the monitoring of the economic and social indicators of private forest owners benefits not only the owners but also the state and society as a whole. Residence size, property size, and association membership emerged as the most significant factors influencing the need for forest monitoring, while knowledge in forestry, distance to the forest, and gender showed weaker but still notable effects. The research results suggest that monitoring can help to ensure the sustainable management of forests and the maintenance of ecosystem services, in addition to contributing to the region’s economic development. It is recommended that this monitoring be conducted every five years.