Area Monitoring Research Articles

The surge for local innovative institutions for transformation: community-based monitoring in the Andes

Abstract Non-technical summary There is abundant research about the impacts that large-scale mining produces on territories to the detriment of their social and environmental sustainability. However, during our research in Chile and Peru, we also identified local transformative initiatives that pursue sustainable development by proposing alternatives to how the socio-ecological impacts of natural resource extraction are produced and distributed throughout society. Specifically, we ask the question: How do local communities in Chile and Peru that are affected by mining activities engage in community-based environmental monitoring (CBEM)? Technical summary By examining how local communities in Chile and Peru engage in community environmental monitoring, this paper argues that local political organisation and institutional innovations are crucial for the emergence of transformations towards sustainability. Local political organisation and mobilisation can create a window of opportunity for discussion about extractive activities and their impacts, as well as possible proposals for alternatives. Institutional innovations triggered by local political work can lead to the implementation of such initiatives. Our findings are based on qualitative case studies of CBEM in Chile and Peru, in areas with high levels of environmental degradation due to mining. In Chile we analysed a case of community air monitoring in a copper processing area, and in Peru a case of community water monitoring in a mining area. Drawing on debates on social transformation and political ecology theory, this study aims to show CBEM promotes changes towards a more democratic and preventive environmental governance, and encourages the recognition of environmental injustices. Social media summary This paper analyses how local communities in Chile and Peru engage in community environmental monitoring in areas affected by the presence of extractive industries. We identified local transformative initiatives that pursue sustainable development by proposing alternatives to how the socio-environmental impacts of natural resource extraction are produced and distributed in society. Our findings are based on qualitative case studies of community-based air and water monitoring in extractive areas in Chile and Peru.

Оценка экологического риска и интегрального показателя загрязнения приземного слоя атмосферы в зоне наблюдения ФГУП «ПО «Маяк»

The purpose of the work is to estimate the level of contamination of the atmospheric surface layer by technogenic radionuclides in the monitoring area of the Production Association "Mayak" (“Mayak”). For these purposes the integrated pollution index and the summarized radiation risk index to the impact on biota based on radiation monitoring data were used. The environmental risk was estimated with the account of radiation exposure intensity, the monitoring area size and temporary pollution dynamics. The correlation of plutonium isotopes emissions and the radionuclide volumetric activity in the atmospheric surface layer was studied. Before burying radioactive waste and elimination of the lake area the “Karachai” emissions of plutonium isotopes were not the core factor determining the content of radionuclides in the atmospheric surface layer in the "Mayak" monitoring area. After the lake bury with the Karachaevsky rocky soil in 2015, the relationship between plutonium emissions and its volumetric activity in the atmospheric air over Ozersk and the village of Metlino has been monitored in the following years. From results of radioactivity monitoring in the “Mayak” monitoring area it follows that the integrated index of atmospheric pollution is much lower than 1, it indicates that the atmospheric pollution by plutonium radionuclides in the “Mayak” area is environmentally safe. The data, presented in ICRP publications and the Roshydromet recommendations are not exceeded. The main contributors to the integrated index of the atmospheric air pollution are plutonium isotopes. The estimate of the summarized risk index of atmospheric air pollution in the monitoring area, calculated in accordance with the recommendations of Roshydromet, is more than 10, but less than 20, it corresponds to the weak radiation impact on the "Mayak" Production Association. In this case, regular estimation of the impact of technogenic factors on radiation situation in the territory. Taking into account the potential radiation hazard of the enterprise, it is recommended to continue monitoring atmospheric air pollution with plutonium and other technogenic radionuclides.

Bibliometric analysis of predictive monitoring: Trends and prospects

Predictive monitoring is a multi-disciplinary research topic applied in various sectors. Despite considerable advances in some areas, others have enjoyed different success. This bibliometric analysis aims to provide an overview of research into predictive monitoring. Thus, we will analyze a corpus of articles dealing with predictive monitoring using bibliometric methods and tools. In this way, our results are intended to guide future research into predictive monitoring in areas requiring further attention from researchers, academic institutions, or industry.

Research on the Development of New Sensors and Sensor Detection Technology in the Context of Big Data

Abstract In the context of big data, traditional detection algorithms can no longer meet today’s needs. For traditional sensors, there are shortcomings such as long working times, high complexity, and high false detection rates. This paper proposes a new type of sensor and an algorithm for detecting anomalies in sensors based on Flink. The sensor network senses the monitoring targets in the monitoring area in real time and transmits them to the end users through wireless communication. Then, the fusion rule for target detection in sensor networks is studied. Then, the detection algorithm is computed using Flink and predicted using a sliding window and ARIMA model on the Flink platform. Then, the confidence intervals of the prediction results are calculated, and the outliers are evaluated. Finally, the K-Means++ algorithm is used for clustering and probability assessment of previously obtained outliers. Then, compared with traditional sensors, comparative analysis shows that the proposed method in this paper has some advantages in terms of both sensitivity (S) and quality factor (Q). The value of S is more than 320 than the 2D PhC sensor array. The value of Q is more than 5295 than the 2D PhC sensor array. This paper’s new sensor detection method is more diverse than the traditional method. The detection time is reduced. The detection accuracy is also improved compared to the traditional method. It can be summarized by comparison that the new sensor in this paper is more excellent than the traditional method of detection.

A Two-Layer Control Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Network

A Two-Layer Control Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Network

Assessing early language and communication development: An e-health approach using online applications

The early assessment of language and communication abilities is critical to meet the needs and challenges of inclusive societies. We developed a novel e-health approach whereby well-known assessment tools are available through online applications that allow a quick (computer, tablet, smartphone) on-screen assessment, making health monitoring and intervention in this field highly flexible and possible in a rich variety of contexts and users, including practitioners, educators, researchers, and caregivers. The app versions of the MacArthur-Bates Communicative Development Inventory Short forms (CDI) and the Communication and Symbolic Behavior Scales Developmental Profile (CSBS-DPTM) Infant-Toddler Checklist offer automatic scoring and percentile profiles for the individual infant and toddler. The easy, quick, decentralized access to health monitoring in the sensitive areas of cognitive and language development fosters the early identification of risk for language and communication impairments, early intervention and diagnosis, with individual, social and economic benefits.

Automated Monitoring and Emergency Response System for Sensitive Areas Along High-Speed Railway Lines

The safety of high-speed railroads is an important indicator of travelers’ trust, and it is also an important factor in determining whether or not the operation volume can reach the expected goal. Therefore, high-speed railroad companies in Europe, the United States, and Japan have invested heavily in establishing safety monitoring systems in order to avoid the occurrence of disasters and to strengthen the mechanism of crisis response and emergency treatment. High-speed railroad operation may occur in a variety of accidents, different disaster warning, emergency response and rescue, need to have different equipment, contingency measures, and well-trained personnel to deal with. Therefore, the safety standards for high-speed rail systems in advanced countries around the world are much higher than those for traditional train systems. The theories and technologies of disaster prevention and relief have matured both at home and abroad. This study combines the theories and technologies of disaster prevention with GIS, such as monitoring instruments, satellite positioning, remote sensing detection, radio transmission, the Internet, database management, and information management, to provide effective support for decision-making and analysis of commanders, to rapidly assist in crisis management, and to strengthen the mechanism of emergency response.

A new two-step road extraction method in high resolution remote sensing images.

High-resolution remote sensing technology is an efficient and low-cost space-to-earth observation strategy, which can carry out simultaneous monitoring of large-scale areas. It has incomparable advantages over ground monitoring solutions. Traditional road extraction methods are mainly based on image processing techniques. These methods usually only use one or a few features of images, which is difficult to fully deal with the real situation of roads. This work proposes a two-steps network for the road extraction. First, we optimize a pix2pix model for image translation to obtain the required map style image. Images output by the optimized model is full of road features and can relief the occlusion issues. It can intuitively reflect information such as the position, shape and size of the road. After that, we propose a new FusionLinkNet model, which has a strong stability in the road information by fusing the DenseNet, ResNet and LinkNet. Experiments show that our accuracy and learning rate have been improved. The MIOU (Mean Intersection Over Union) value of the proposed model in road extraction is over 80% in both DeepGlobe and Massachusetts road dataset. The figures are available from https://github.com/jsit-luwei/training-dataset.

Геоэкологическое картирование застройки на паводкоопасных территориях городов Шилки и Нерчинска Забайкальского края с использованием спектрального индекса NDBI и нейронной сети

At all times, the problem of floods has been relevant due to their destructiveness, frequency and poor predictability. The situation is complicated by the fact that floodplains are used for economic, recreational and other activities, thereby exposing the risk of destruction and degradation of any objects and human waste products during floods. Therefore, mapping and spatiotemporal monitoring of flood-prone areas in order to analyze and predict the likely material and economic damage to facilities of economic activity from the threat of floods is an urgent scientific problem that can be solved by geographic information systems using satellite data processed by spectral indices and convolutional neural networks. The object of the study is the flood-prone territories of Shilka and Nerchinsk (Transbaikal Region). The purpose is to assess the accuracy of economically developed territories decryption in flood-prone areas of Shilka and Nerchinsk in order to determine the effective threshold for separation of the studied facilities. Research objectives are as follows: collection and formation of an archive of Earth remote sensing data of the Landsat-8 OLI program; inference of satellite images on a neural network and their use as a reference image; calculation of NDBI spectral indices based on Landsat-8 data; determination of the areas of buil­­t-up and economically developed territories in the images; assessment of the accuracy and comparative analysis of the results of the development facilities allocation and economic activity; identification of advantages, disadvantages, applicability limits of the methods used to decrypt buildings and objects of economic activity in flood-prone areas. The accuracy and reliability of the results of the decrypting buildings and objects of economic activity by calculating the NDBI spectral index, the inference of a convolutional neural network according to Landsat 8 OLI data in flood-prone areas of Shilka and Nerchinsk have been evaluated. The most effective methods, parameters and thresholds for decrypting buildings and objects of economic activity have been determined in order to monitor the development of flood-prone areas and control the flood-prone situation.

Assessing forest changes in Yok Don National Park and surrounding areas, Dak Lak province, Vietnam

Forests serve as vital ecosystems, playing a pivotal role in preserving biodiversity, maintaining air and water quality, and regulating the climate, all while delivering many benefits to humanity and serving as a critical resource for diverse industries. This study investigated changes in forest cover within Yok Don National Park and surrounding areas in Dak Lak province, Vietnam, over the period from 2001 to 2021. The primary objective was to identify decadal shifts in forest cover through the utilization of freely accessible satellite remote sensing data and geographic information systems (GIS) tools. The maximum likelihood classification technique was employed to categorize Landsat images captured in 2001, 2011, and 2021. Furthermore, we evaluated the efficacy of the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) based classifications in detecting forest cover changes when compared to supervised classification. Initial estimates placed forest cover at 2796.91 km2, constituting 88.44% of the total land area in 2001, which subsequently declined to 2609.97 km2 (82.53%) in 2011, and further reduced to 1843.57 km2 (58.29%) in 2021. Severely reduced forest cover is often associated with the expansion of agriculture on the forest edge; other contributing factors include logging and illegal production land. Notably, the NDVI outperformed the SAVI in detecting forest cover changes. The findings of this research are anticipated to contribute to ongoing studies and enhance the effective monitoring of this protected forest area, thus providing invaluable insights for decision-makers.

In field use of water samples for genomic surveillance of infectious spleen and kidney necrosis virus (ISKNV) infecting tilapia fish in Lake Volta, Ghana.

Viral outbreaks are a constant threat to aquaculture, limiting production for better global food security. A lack of diagnostic testing and monitoring in resource-limited areas hinders the capacity to respond rapidly to disease outbreaks and to prevent viral pathogens becoming endemic in fisheries productive waters. Recent developments in diagnostic testing for emerging viruses, however, offers a solution for rapid in situ monitoring of viral outbreaks. Genomic epidemiology has furthermore proven highly effective in detecting viral mutations involved in pathogenesis and assisting in resolving chains of transmission. Here, we demonstrate the application of an in-field epidemiological tool kit to track viral outbreaks in aquaculture on farms with reduced access to diagnostic labs, and with non-destructive sampling. Inspired by the "lab in a suitcase" approach used for genomic surveillance of human viral pathogens and wastewater monitoring of COVID19, we evaluated the feasibility of real-time genome sequencing surveillance of the fish pathogen, Infectious spleen and kidney necrosis virus (ISKNV) in Lake Volta. Viral fractions from water samples collected from cages holding Nile tilapia (Oreochromis niloticus) with suspected ongoing ISKNV infections were concentrated and used as a template for whole genome sequencing, using a previously developed tiled PCR method for ISKNV. Mutations in ISKNV in samples collected from the water surrounding the cages matched those collected from infected caged fish, illustrating that water samples can be used for detecting predominant ISKNV variants in an ongoing outbreak. This approach allows for the detection of ISKNV and tracking of the dynamics of variant frequencies, and may thus assist in guiding control measures for the rapid isolation and quarantine of infected farms and facilities.

Application of unmanned aircraft vehicles and techniques ground laser scanning for measurement of dimensional and quality indicators of tree stand

Currently, the use of measuring methods and laser scanning tools is one of the most relevant and priority areas for solving the problems of monitoring and evaluation of forest plantations. Such measurement can be performed independently or in combination with aerial video shooting, and can also be carried out by ground-based studies on test areas. According to a number of indicators, laser scanning surpasses others known today, such as aerial photography, earth sounding, etc., remote methods for assessing the qualitative and quantitative characteristics of the forest fund. The use of aerial and ground-based monitoring of forest areas with the use of lasers makes it possible to achieve high accuracy in determining the main forest taxational parameters. It is advisable to use the formed methodological principles and techniques when developing specific methods for determining indicators as separate factors determining the appropriate types of forest accessibility.

The impact of ground control points for the 3D study of grapevines in steep slope vineyards

Data acquisition through unmanned aerial vehicles (UAVs) has become integral to the study of agricultural crops, especially for multitemporal analyses spanning the entire growing season. Ensuring accurate data alignment is essential not only to maintain data quality but also to leverage the continuous monitoring of the same area over time. Ground control points (GCPs) play a critical role in geolocating UAV data. Their absence can lead to planimetric and altimetric discrepancies, which are particularly impactful in 3D plant-level studies. This study is centered on the examination of misalignment effects in a challenging steep slope vineyard environment and their impacts on 3D alignment accuracy. For this purpose, a UAV equipped with an RGB camera to capture imagery at two distinct flight heights. Various scenarios, each involving a different number of GCPs, were assessed to evaluate their impact on alignment precision. The methodology employed holds potential for assessing geolocation accuracy in complex 3D environments, providing value insights for vineyard monitoring.

El impacto combinado de la sequía y los cambios en el uso del suelo sobre los recursos hídricos en el Tabular Atlas Medio, Marruecos

The quantitative degradation of water resources in the Tabular Middle Atlas is linked to climate and changes in agricultural practices. The relationship between these variables is identified through the analysis of hydroclimatic data, fieldwork, and the processing of Landsat satellite images in two representative sectors of the Middle Atlas. Interviews with local populations and piezometric measurements performed in the Guigou depression show that the groundwater table has dropped from a few meters deep to over 45 meters in several locations. Additionally, monitoring of the surface water area of Aoua lake between September 1984 and September 2022 from satellite images indicates remarkable variations, with periods of prolonged drying. The critical situation of water resources in this sector of Morocco is explained by the recurrence of drought periods of varying severity, increasing temperatures, and the progressive extension of irrigated areas across the depressions. In three decades, irrigated areas have multiplied by more than five in the Guigou depression and by more than three in the Imouzzer-Aoua depressions. Changes in climatic parameters associated with modifications in agricultural practices have strongly influenced water resources in the Tabular Middle Atlas.

Analyzing morphologic dynamics in poor urban areas through earth observation: The case of the Purulia, West Bengal, India

The slum is the shadow of urbanization, representing a lack of essential services, substandard housing, a congested, unhygienic environment, and poverty. Slum morphology depicts the structural arrangement of a slum area, including the road network, building arrangement, cultural artefacts, etc. Slums in megacities like Mumbai, Kolkata, Delhi, etc, have developed naturally or as a result of urbanization. However, apart from megacities, only some areas or few towns, particularly those serving as headquarters of the districts, have a considerable impact on their hinterlands. Purulia town, as the district headquarters, acts as a pull factor, which stimulates the growth of the slum area of its hinterland. Hence, Purulia was selected as a study area. The focus of this study is the morphological dynamicity of slums in Purulia, therefore the temporal analysis is necessary. Additionally, it aims to apprehend the trend and pattern of slum expansion over time in Purulia municipality. This study uses satellite images and secondary data sources (DPR for BLC under Housing for All in slums, Purulia Municipality, 2015–2016) to analyze the dynamicity of slum morphology. Structural appurtenance of the area was measured using spatial variables including, the number of buildings, and the size of the building, building orientation, building density, and heterogeneity. This spatiotemporal analysis depicts inter and intra-slum variation and detects distinct but predominantly high morphological transformation throughout the eight slums. A compact building structure with a particular heterogeneous pattern is shown in many slums. However, some slums remain almost static, experiencing minimal but haphazard morphological transformation. Street patterns, including lane-to-lane, road-to-lane, house-to-house connections, water bodies' presence, and urban planning regulations such as zoning restrictions on high-rise construction etc., influence the slum growth and transform the morphological dynamicity. Apart from structural morphology, it also reflects solid social bonding and cohesion among slum communities. Overall, this research emphasizes the intricate interplay of factors driving morphological dynamicity within the slum area, encompassing socio-economic conditions, population dynamics, neighborhood characteristics, and governmental policies. It emphasizes the need for extensive policy planning for slum upgrading and creating strategies for dwellers to secure land ownership. It also implements a systematic approach to constant monitoring of poor slum areas to capture morphological dynamicity using very high-resolution Earth observation data. This may provide a better understanding of morphological transformations over time and guide policy-making decisions.

Remote Sensing for Environmental Monitoring and Remediation of Areas Degraded by Mining Activities in the Democratic Republic of Congo: Call for Action

Remote Sensing for Environmental Monitoring and Remediation of Areas Degraded by Mining Activities in the Democratic Republic of Congo: Call for Action

Project monitoring model in central governmental administration

Purpose: The aim of this article is to present the project monitoring model in the central government administration as a set of interrelated, complementary elements that influence the implementation of strategic projects on a nationwide scale and affect tens of millions of Polish citizens. Design/methodology/approach: Based on a literature review and analysis of the existing system, prospective directions for the monitoring of strategic projects were selected. As a result of this work, a model was created that was assessed for significance and replicability. The most experienced group of experts and practitioners in this field took part in the CAWI survey. Findings: The high level of significance of all areas of the model has been proven and its universal nature has been confirmed, thanks to the possibility of being used in other administrative units. Research limitations/implications: Due to limitations resulting from security procedures for research conducted in central government administration units, only closed questions were used in the research. In the future, the study can be carried out on another, larger population, having previously prepared it appropriately. Practical implications: The practical use of the model in administration units can contribute to increasing transparency, efficiency and automation of planning and implementation of projects. Social implications: Increasing the quality of implemented projects in accordance with the presented model may have a large, positive impact on the final beneficiaries - society, in terms of effective use of public funds, shortened project implementation time, and obtaining project results consistent with expectations. Originality/value: The article presents a new model of project monitoring in central government administration, which may be useful for scientists conducting research in the area of project monitoring and management in other countries. The article falls into the area of projectification of the public sector. Keywords: project management, central government administration, Project Management Office (PMO), significance assessment, feasibility assessment. Category of the paper: Research paper.

Molecularly imprinted polymer-based electrochemical sensors for monitoring the persistent organic pollutants chlorophenols.

Because of the serious risks they pose to the environment and public health, chlorophenols (CPs), a typical class of the most persistent organic pollutants, have drawn increasing attention. Monitoring CPs effectively has become a pressing and difficult problem. The rapidly increasing need for onsite and real-time CP detection has led to the consideration of electrochemical sensing as a workable solution. Molecularly imprinted polymer (MIP)-based electrochemical sensing has emerged as a promising area for environmental monitoring in response to this analytical problem. MIPs, in conjunction with miniature electrochemical transducers, provide the opportunity to detect target analytes in situ. These devices have the advantages of great chemical and physical stability, cheap production costs, good selectivity, and quick response times. Most studies suggest that these sensors use nanoparticles to improve their analytical properties, especially sensitivity. Furthermore, these sensors have successfully used real water samples without the need for time-consuming pretreatment procedures. This article provides an overview of electrochemical MIP-based sensors reported to detect CPs in water samples. To obtain the highest sensitivity, special consideration is given to the fabrication of the sensors, which includes the use of various functional monomers, sensing platforms, and materials. Several other parameters are also discussed, including the linear concentration range, limit of detection, and the types of water samples that were examined.

Bridging the Gap between Technology and Medicine through the Revolutionary Impact of the Healthcare Internet of Things on Remote Patient Monitoring

Healthcare Internet of Things (IoT) initiatives that aim to integrate technology and medicine are shaking the sector to its foundations. The revolutionary potential of the proposed strategy is shown here as we investigate the far-reaching consequences of the Healthcare IoT on remote patient monitoring. The beginning sets the stage by underlining the significance of bridging the gap between technology and medicine. Our multi-pronged approach comprises Internet of Things (IoT) remote monitoring, cloud-based analysis, artificial intelligence (AI) integrated diagnostics, real-time alerts, and predictive analytics. Our study's results demonstrate that the proposed approach is superior to the status quo. The area of remote patient monitoring has profited considerably from the employment of traditional approaches, such as the fusion of data from wearable sensors, analysis in the cloud, diagnostics that utilize artificial intelligence, real-time monitoring, predictive modeling, and smart alarm systems. The suggested strategy, however, performs very well across all of the most important measures of assessment. Comparatively, the accuracy rate of the conventional wearable sensor fusion approach was only 76%, whereas our suggested method reached 89%. Our strategy was also more accurate than the standard approach (88% vs. 73%). When compared to the recall rate of 68% produced by conventional methods, our suggested strategy significantly outperformed the competition. It's a great option for hospitals and clinics since it improves diagnostic precision and speed without breaking the bank.

Cooperative Sentinel Surveillance of Malaria in Laiza and Nearby Areas of Myanmar and Importation Threat Monitoring - China, 2019-2023.

Laiza and nearby areas (LNA) in Myanmar are identified as the primary malaria hotspots in the bordering regions of Yunnan Province, China. Six sentinel surveillance sites were established at the China-Myanmar border in LNA to monitor malaria. Data from 2019 was used as a baseline to analyze malaria incidence and trends in LNA and Myanmar, as well as the importation of malaria cases into China from 2019 to 2023. Plasmodium vivax was the predominant species, representing 99.95% (14,060/14,066) of confirmed malaria cases in LNA. A total of 8,356 malaria cases were identified in 2023, with an annual parasite incidence (API) of 19.78 per 100 person-years. Compared to 2019, the incidence rate ratio was 21.47 (95% confidence interval: 18.84, 24.48), indicating that the API in 2023 was 21.47 times higher than that in 2019. In Yunnan, out of 1,016 reported cases, 545 imported cases (53.64%) originated from LNA and spread to 18 (13.95%) out of 129 counties. Ten provinces in China, including Yunnan, reported imported malaria cases from LNA in Myanmar. The increase in population, particularly among internally displaced persons, along with inadequate healthcare services, has led to a notable resurgence of malaria in LNA. This resurgence poses a risk to preventing the re-emergence of malaria transmission in China. There is an urgent need for novel collaborative policies, as well as financial and technical assistance, to enhance malaria control efforts in LNA, Myanmar.