An embedded environmental monitoring data collecting system

Spatial and temporal processes visualization for marine environmental data using particle system

At present, the environmental monitoring of the substation suffers from complex wiring, low intelligence, high staff work intensity, and a lack of information-based overall management. Moreover, the distribution of substations is relatively scattered, making it complex and costly to set up a dedicated communication network. In response to these problems, this paper combines modern communication technology, sensor technology, and mobile intelligent terminal development technology to study an overall information-based substation environmental monitoring system. The monitoring system proposed in this paper includes a main controller, a data collection terminal, and a mobile communication terminal. The environmental data collected by the main controller is sent to the server through the communication method of subscription and substation using wireless communication technology, and the interface of the mobile communication terminal can display indoor environmental monitoring data. When the environmental data of the Substation exceeds the set threshold value to issue an early warning alert, the user can control it remotely through the mobile communication terminal to improve the indoor environment of the Substation. The system has a simple operation interface, low power consumption, and strong expansion compatibility, which effectively realizes remote monitoring and control of the substation environment and has good engineering application value.

Environmental monitoring refers to the tools and techniques designed to observe an environment, characterize its quality, and establish environmental parameters, to accurately quantify the impact an activity has on an environment. A warmer climate may result in lower thermal efficiency and reduced load-including shutdowns in thermal power plants. It is found in research that a rise in temperature of 1°C reduces the supply of nuclear power by about 0.5% through its effect on thermal efficiency. A drastic change in air pressure also indicates there could be a significant climate change. In the event of a radiological release accident, environmental data is required to reduce radiation exposure to humans. That’s why environmental monitoring is very important for a nuclear power plant. It can be a crucial matter for the industries also because environmental monitoring helps industries operate responsibly, minimize negative impacts on the planet, and contribute to a more sustainable future. An IoT-based system can do environmental monitoring. Anyone using an IoT-based system can get environmental data like temperature, pressure, humidity, etc. Here the projected system delivers sensor data which are got from the environment to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real-time data using graphical information of the environment.

Here we synthesise the results of three participatory workshops to explore sustainable development priorities in eastern Africa, and discuss these in the context of Earth and environmental science. The planet is a core pillar of sustainable development, and the engagement of Earth and environmental scientists is vital to achieving the UN Sustainable Development Goals (SDGs). In 2017, the British Geological Survey gathered 76 delegates from 48 organisations at three workshops in Nairobi (Kenya), Lusaka (Zambia), and Dar es Salaam (Tanzania). Using the SDGs as a reference tool, participants (i) identified development priorities at regional (eastern Africa) and national scales, (ii) explored the Earth and environmental science research and data needs to help address these, and (iii) co-designed relevant science-for-development projects. Participants identified sustainable development priorities to be basic (or immediate) needs, including zero hunger (SDG 2), education (SDG 4), ending poverty (SDG 1), and water and sanitation (SDG 6). Participants also described examples of Earth and environmental science research, training, technologies, monitoring and management to support sustainable development. Emerging themes included environmental data (collection, management, integration, access), policy and regulations (integrating environmental science, and policy coherence), resource management (degradation, pollution and environmental protection), and scientific education and understanding (training, knowledge exchange, public understanding of science). A comparative synthesis of existing regional and national development strategies indicates that current narratives of development interventions do not fully capture the opportunities from environmental data integration and policy coherence. Greater engagement with and by the Earth and environmental science community could help to advance these themes to support sustainable development in eastern Africa. This would support efforts to reduce environmental degradation, improve natural resource management, and inform the utilisation of natural resources to improve economic growth and social wellbeing.

ObjectiveTo increase the availability and accessibility of standardized environmental health data for public health surveillance and decision-making.IntroductionIn 2002, the United States (US) Centers for Disease Control and Prevention (CDC) launched the National Environmental Public Health Tracking Program (Tracking Program) to address the challenges in environmental health surveillance described by the Pew Environmental Commission (1). The report cited gaps in our understanding of how the environment affects our health and attributed these gaps to a dearth of surveillance data for environmental hazards, human exposures, and health effects. The Tracking Program’s mission is to provide information from a nationwide network of integrated health and environmental data that drives actions to improve the health of communities. Accomplishing this mission requires a range of expertise from environmental health scientists to programmers to communicators employing the best practices and latest technical advances of their disciplines. Critical to this mission, the Tracking Program must identify and prioritize what data are needed, address any gaps found, and integrate the data into the network for ongoing surveillance.MethodsThe Tracking Program identifies important environmental health topics with data challenges based on the recommendations in the Pew Commission report as well as input from federal, state, territorial, tribal, and local partners. For each topic, the first step is to formulate the key surveillance question, which includes identifying the decision-maker or end user. Next, available data are evaluated to determine if the data can answer the question and, if not, what enhancements or new data are needed. Standards are developed to establish data requirements and to ensure consistency and comparability. Standardized data are then integrated into the network at national, state, and local levels. Standardized measures are calculated to translate the data into the information needed. These measures are then publically disseminated via national, state, and local web-based portals. Data are updated annually or as they are available and new data are added regularly. All data undergo a multi-step validation process that is semi-automated, routinized, and reproducible.ResultsThe first set of nationally consistent data and measures (NCDM) was released in 2008 and covered 8 environmental health topics. Since then the NCDM have grown to cover 14 topics. Additional standardized data and measures are integrated into the national network resulting in 23 topics with standardized 450 measures (Figure). On the national network, measures can be queried via the Data Explorer, viewed in the info-by-location application, or connected to via the network’s Application Program Interface (API). On average, 15,000 and 3300 queries are run every month on the Data Explorer and the API respectfully. Additional locally relevant data are available on state and local tracking networks.Gaps in data have been addressed through standards for new data collections, models to extend available data, new methodologies for using existing data, and expansion of the utility of non-traditional public health data. For example, the program has collaborated with the Environmental Protection Agency to develop daily estimates of fine particulate matter and ozone for every county in the conterminous US and to develop the first national database of standardized radon testing data. The program also collaborated with the National Aeronautics and Space Administration and its academic partners to transform satellite data into data products for public health.The Tracking Program has analyzed the data to address important gaps in our understanding of the relationship between negative health outcomes and environmental hazards. Data have been used in epidemiologic studies to better quantify the association between fine particulate matter, ozone, wildfire smoke, and extreme heat on emergency department visits and hospitalizations. Results are translated into measures of health burden for public dissemination and can be used to inform regulatory standards and public health interventions.ConclusionsThe scope of the Tracking Program’s mission and the volume of data within the network requires the program to merge traditional public health expertise and practices with current technical and scientific advances. Data integrated into the network can be used to (1) describe temporal and spatial trends in health outcomes and potential environmental exposures, (2) identify populations most affected, (3) generate hypotheses about associations between health and environmental exposures, and (4) develop, guide, and assess the environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors. The program continues to expand the data within the network and the applications deployed for others to access the data. Current data challenges include the need for more temporally and spatially resolved data to better understand the complex relationships between environmental hazards, health outcomes, and risk factors at a local level. National standards are in development for systematically generating, analyzing, and disseminating small area data and real-time data that will allow for comparisons between different datasets over geography and time.

Beehive monitoring systems are very important for understanding behavior of bees and to have a better overview of environmental parameters impact on their activities. Such information is very useful for agriculture, but in saving bees as well. Although there are various systems for monitoring beehives, there is still a growing need for low-cost monitoring solutions. In this paper we proposed an integrated system for video-based honey bee counting and a multi-sensors platform for environmental parameters monitoring. The honey bees counting system aims to evaluate foraging activity of bees through the number of honey bees exiting and entering the beehive, while the multi-sensors platform is used for remote monitoring of environmental parameters near the beehive. The proposed electronic system for remote monitoring of environmental parameters in a bee yard has specific features such as battery-power supply, portability, and a unit for transmitting data to the remote server. On a relatively small data set, that includes measured and acquisitioned environmental parameters as well as counted honey bees, we have shown how effort of honey bees is coupled to the coincident environmental parameters near the hive.

Marine environmental monitoring and data platform technology plays a pivotal role in advancing marine scientific research, sustainable resource development, ecological conservation, and the effective utilization of ocean resources. Despite its growing importance in addressing global environmental and economic challenges, a comprehensive and systematic review of recent advancements in this field remains lacking. To address this gap, this paper synthesizes and analyzes academic literature published between 2021 and 2025, sourced from reputable databases including Scopus and Web of Science, while adhering to the PRISMA systematic review standards. It delineates core technologies employed in marine environmental monitoring, such as advanced sensor systems, robust data acquisition and transmission methods, and innovative data processing and analysis techniques. Furthermore, the study examines the architectural functionalities, data sharing mechanisms, and interoperability standards that underpin modern marine data platforms. The paper also addresses critical technical challenges encountered in deep-water monitoring operations, including equipment durability under extreme conditions, significant economic constraints, data management complexities, and emerging privacy and security concerns. Finally, future development trajectories are outlined, emphasizing the transformative potential of novel materials and artificial intelligence (AI) in enhancing deep-water monitoring capabilities, alongside the urgent need for strengthened global collaboration to improve data sharing protocols and management frameworks. Collectively, the continuous evolution of marine monitoring technologies promises to provide increasingly intelligent, integrated, and systematic support for global marine protection efforts and sustainable resource stewardship.

The challenge of collecting large scale environmental data lies in reducing the cost of allocating data sensors while improving the data quality. Compared with traditional centralized data collecting methods, obtaining environmental data by distributed "crowds" can largely reduce the cost. However, designing such crowdsourcing systems requires an appropriate incentive mechanism to encourage providing accurate and rare environmental data. In this paper, we propose to utilize blockchain-based incentive mechanisms to address the problem. We design and evaluate a blockchain-based system for large scale environmental data acquisition, which consists of a sensor layer to collect distributed environmental data, a valuation layer to evaluate the quality of the data collected, a consensus layer to incentive and motivate high quality data collection, and a ledger layer to record the incentive transactions and the qualified environmental data. The incentive mechanism in the consensus layer is achieved with a Proof-of-Data-Value (PODV) protocol adapted from the Practical Byzantine Fault Tolerance (PBFT) [11] consensus algorithm. We carry out experiments to compare the PODV with contemporary blockchain census protocols including Proof-of-Work (POW) [16] and Proof-of-Stake (POS)[18]. The experimental results show that the proposed system outperforms in encouraging crowds to provide accurate and rare environmental data, and imply that the PODV are superior in terms of throughput and environmental friendliness.

Smartphone-based colorimetric sensing with reference calibration and ensemble machine learning for enhanced detection of nitrite and ammonium ions.

A novel electrochemiluminescence (ECL) sensor for detection of brilliant blue FCF (BB) has been developed using Ru@Zn-MOF/nafion modified GCE (glass carbon electrode) in this research. Different from conventional method for usage of Ru(bpy)32+ in solution-phase, Ru(bpy)32+ here was immobilized on a zinc-metal-organic-framework (Zn-MOF). After adding BB, a significant quenching phenomenon of ECL intensity was observed. The behavior of BB on the quenching effect of Ru(bpy)32+/Zn-MOF in different conditions was investigated thoroughly and the detection limit was achieved to 2.5 × 10-8 M in an optimized condition. Furthermore, the interference of some conventional ions and amino acids to the detection of BB was also investigated. Additionally, the composite showed a good effect on the detection of BB in commercial samples. The proposed sensor provided a promising platform for food safety analysis, environmental monitoring and clinical testing.

In this paper, we present the design and proof of concept of a low-cost, self-sustained mobile surface vehicle for environmental monitoring. The vehicle is equipped with water quality sensors and cameras for image acquisition and two thrusters allowing it to maneuver. The device is part of a citizen science system wherein volunteers help in the environmental recovery of polluted bodies of water by performing online research and classification tasks. While aiding in environmental recovery, the users become more proficient in wildlife classification and water quality assessment. Telemetry collected by the vehicle is wirelessly uploaded to a web-based interface allowing volunteers to access images, positioning information, and water quality sensor data. The citizen science system allows for the study of social networking and human-computer interaction based on user activity. Specifically, the platform can be utilized to experiment with different interfaces and social interactions in hypothesis-driven research on human-computer interactions.

A Semi-Supervised Machine Learning Model for Risk Analysis of Urinary Tract Infections in People with Dementia

HighlightsWhat are the main findings?A modular, sensor-enhanced ROUV platform was successfully designed, integrated, and validated under real aquaculture conditions.High-resolution, depth-resolved measurements of temperature, salinity, pH, and dissolved oxygen were obtained using a mobile underwater platform.What are the implications of the main findings?Mobile underwater robotic platforms can effectively complement fixed monitoring stations in aquaculture environments.The proposed ROUV enables adaptive, localized, and depth-resolved environmental assessment near fish cages.Remotely operated underwater vehicles (ROUVs) have been attracting more attention lately as they are considered to be operationally versatile, capable of real-time communication, and can be fitted with various sensor payloads for environmental monitoring purposes. This study presents the design, development, and field validation of a sensor-enhanced ROUV platform tailored for environmental monitoring and aquaculture applications. The vehicle is equipped with a modular set of sensors for temperature, pH, dissolved oxygen (DO), and electrical conductivity (EC) along with separate signal-conditioning circuits for each sensor and real-time data acquisition from tethered architecture. The general system concept is modularity, reproducibility, and robustness in a marine environment. In situ measurements were performed at an active aquaculture site in the North Aegean Sea throughout several seasons during 2025. Using this system, depth-resolved measurements were obtained with sensor accuracies of ±0.1 °C (temperature), ±0.05 pH units, ±0.05 mg/L (dissolved oxygen), and ±2% (electrical conductivity). The following sections describe the development and aquaculture testing of the platform, which yielded stable and repeatable operation in real conditions.

BackgroundSystematic data collection on species and their exploited environments is of key importance for conservation studies. Within the less-known environments, the subterranean ones are neither easy to be studied, nor to be explored. Subterranean environments house a wide number of specialised organisms, many of which show high sensitivity to habitat alteration. Despite the undeniable importance to monitor the status of the subterranean biodiversity, standardised methodologies to record biotic and abiotic data in these environments are still not fully adopted, impeding therefore the creation of comparable datasets useful for monitoring the ecological condition in the subterranean environments and for conservation assessment of related species.New informationIn this work we describe a methodology allowing the collection of standardised abiotic and biotic data in subterranean environments. To show this, we created a large dataset including information on environmental features (morphology and microclimate) and abundance of five predators (one salamander, three spiders and one snail) occurring in seven caves of the Monte Albo (Sardinia, Italy), an important biodiversity hotspot. We performed 77 surveys on 5,748 m2 of subterranean environments througout a year, recording 1,695 observations of the five cave predators. The fine-scale data collection adopted in our methodology allowed us to record detailed information related to both morphology and microclimate of the cave inner environment. Furthermore, this method allows us to account for species-imperfect detection when recording presence/abundance data.

Background: Aisyiyah Bantul's Leading Elementary School faces challenges in maintaining an optimal learning environment, particularly regarding temperature, humidity, and lighting. This community service program introduces Learnviro, an IoT-based solution for real-time environmental monitoring, enabling schools to make data-driven decisions to enhance learning conditions. Contribution: Learnviro empowers teachers by providing real-time data, allowing them to make informed adjustments that support differentiated instruction. The system also improves school management by optimizing classroom conditions and infrastructure maintenance. Method: The program consists of four stages: (1) collaboration with partner schools to assess needs, (2) installation of IoT devices in classrooms, (3) teacher training on interpreting and applying environmental data, and (4) continuous technical support. Data collection involved observations, interviews, and questionnaires, analyzed descriptively to evaluate the program’s effectiveness. Results: Learnviro effectively provided real-time environmental data, improving teachers’ proficiency in utilizing IoT-based insights for classroom management. Students reported increased comfort and focus, while schools experienced better infrastructure maintenance through data-driven decision-making. Conclusion: Learnviro has proven to be an effective tool for optimizing classroom conditions and empowering the school community. By integrating IoT technology into education, this initiative demonstrates the practical application of intelligent environmental monitoring in addressing real-world.