Environmental Parameters Research Articles

A 360-degree imagery-multisensor system for visualizing environmental parameters in architecture and urban spaces.

This research has designed a 360-degree imagery-multisensor system aiming to capture and visualize environmental parameters in architecture and urban spaces. Unlike existing tools, this system enables simultaneous recording of both imagery and non-imagery environmental data, including lighting, thermal, air quality, sound, and physical space parameters, within a 360-degree field of view. Lighting conditions are captured using panoramic high dynamic range imagery, complemented by a 360-degree array of sensors measuring illuminance levels and spectral power distribution. Thermal and air quality conditions are recorded using 360-degree thermal imagery, combined with hygrometers and air particle meters. Sound levels are also monitored across the full 360-degree field. The system is built using 3D printing technologies and Raspberry Pi computers, equipped with various sensor modules. Custom Python scripts enable real-time data capture, processing, and visualization. This cost-effective, easy-to-manufacture, programmable, and customizable innovation is aimed at students and educators in design and architecture, as well as building engineers. Furthermore, integrating imagery and sensor data supports the development of immersive virtual and augmented reality applications, offering new opportunities for education and the exploration of effective design solutions.

First contours of autecology of freshwater heterotrophic euglenoids - results of two-year research on 18 freshwater bodies.

First contours of autecology of freshwater heterotrophic euglenoids - results of two-year research on 18 freshwater bodies.

Comparative proteomic analysis reveals the response mechanism of freshwater leech (Whitmania pigra) under heat-stress challenge.

Comparative proteomic analysis reveals the response mechanism of freshwater leech (Whitmania pigra) under heat-stress challenge.

Microbial diversity, enzyme activity, metal contamination, and their responses to environmental drivers in an Indo-Burmese freshwater wetland.

Microbial diversity, enzyme activity, metal contamination, and their responses to environmental drivers in an Indo-Burmese freshwater wetland.

Dataset of benthic foraminiferal community structure from sediment eDNA of Sundarbans mangrove ecosystem.

Dataset of benthic foraminiferal community structure from sediment eDNA of Sundarbans mangrove ecosystem.

Potential contribution of foreign-flagged recreational and craft vessels to the introduction and spread of non-indigenous species: A preliminary assessment for Brazil.

Potential contribution of foreign-flagged recreational and craft vessels to the introduction and spread of non-indigenous species: A preliminary assessment for Brazil.

The sources of bioavailable toxic metals in sediments regulated their aggregated form, environmental responses and health risk-a case study in Liujiang River Basin, China.

The sources of bioavailable toxic metals in sediments regulated their aggregated form, environmental responses and health risk-a case study in Liujiang River Basin, China.

Comparative Analysis of Environmental Parameters and Growth Performance of Orange-spotted Grouper (Epinephelus coioides) in RAS, Earthen Ponds, and Natural Habitats: Integrating Water Quality, Soil Chemistry, and Heavy Metal Dynamics

Comparative Analysis of Environmental Parameters and Growth Performance of Orange-spotted Grouper (Epinephelus coioides) in RAS, Earthen Ponds, and Natural Habitats: Integrating Water Quality, Soil Chemistry, and Heavy Metal Dynamics

A new index based on Gini coefficient for evaluating the distribution uniformity of environmental parameters in buildings

A new index based on Gini coefficient for evaluating the distribution uniformity of environmental parameters in buildings

Nuanced effects of relative body size on social motivation in a highly gregarious catfish.

Nuanced effects of relative body size on social motivation in a highly gregarious catfish.

Health risk assessment of indoor formaldehyde exposure across Chinese residences: Effects of building material grades.

Health risk assessment of indoor formaldehyde exposure across Chinese residences: Effects of building material grades.

РОЗРОБЛЕННЯ СИСТЕМИ АВТОМАТИЗОВАНОГО ДОГЛЯДУ ЗА

The article is devoted to the development of an automated care system for indoor plants based on the use of an Arduino microcontroller and IoT technologies. The system contains soil moisture, temperature, and light sensors that monitor the main environmental parameters for effective plant care. The structural and schematic diagrams and algorithm of the system were developed. A prototype of the system was implemented. The prototype of the system was tested in real conditions, which confirmed the correctness of the decisions made, as well as the efficiency and usability of the system. It was found that the system provides automatic plant care, monitoring their condition and minimizing user intervention. It has been shown that automatic irrigation provides an optimal level of humidity without the risk of waterlogging, and adjustable lighting contributes to the uniform development of plants regardless of external changes in illumination. The developed system saves up to 30% of water and 25% electricity due to the adaptive irrigation and lighting algorithm. Key words: Automation system, care, indoor plants, IoT, Arduino, sensors, monitoring

Global warming impacts on marine diversity and key indicator species in East Asia

Marine ecosystems in the northwest Pacific are known for their high biodiversity, with many marine species, from tiny plankton to huge macroalgae. Marine biodiversity is affected by irreversible global warming, mainly the increase of greenhouse gas emissions from anthropogenic activities. For the past several decades, marine ecosystems in East Asia have been changing due to the increase in sea surface temperature (SST), which has changed some important environmental factors, including rainfall patterns, extreme weather, and ocean circulation. Such modifications in the environmental parameters have altered the physiology, phenology, and distribution pattern of marine organisms. The SST increase has also altered population and community structure, and the functioning of the ecosystem. Certain subtropical and tropical fauna and flora are now extending their range of distribution from the warm southern area to the temperate regions, disrupting or modifying the biotic interactions in the temperate ecosystem. Collaborative conservation projects and responsible policies are crucial to safeguard the environmental value of East Asia's marine ecosystems for future generations and mitigate the negative effects of global warming on marine biodiversity. Conservation and management of East Asia's marine environments hold significant implications for global biodiversity and ecological balance, making them a pivotal focus in addressing climate change challenges in marine ecosystems.

Sustainable Smart Homes in Remote Areas Using IoT and AI Technologies

This research paper presents study on the design and implementation of smart homes in remote or off-grid areas using the Internet of Things (IoT) and Machine Learning (ML). In recent years, the growing demand for sustainable living solutions has emphasized the need for intelligent systems capable of addressing the unique challenges faced by geographically isolated or infrastructure-deficient communities. The proposed approach focuses on integrating real-time data acquisition and intelligent decision-making into the daily operations of homes powered by renewable energy sources. The system employs IoTbased sensors to monitor various environmental and operational parameters such as temperature, humidity, energy consumption, and appliance activity. These data streams are then analyzed using lightweight ML algorithms that enable predictive load forecasting, early fault detection, and user behavior modeling. This dual-layered approach allows for proactive energy management, improving the reliability and longevity of off-grid energy systems while enhancing user comfort. A case study involving a solar-powered prototype deployed in a rural setting demonstrates the feasibility and benefits of the proposed system. The results indicate a significant increase in energy efficiency and reduction in manual intervention, with the system dynamically adjusting operations based on predicted energy availability and user patterns. Furthermore, the implementation addresses several core challenges such as limited power, unreliable connectivity, and restricted technical support through robust, low-power designs and adaptive communication strategies. By merging IoT and ML technologies, the proposed smart home model contributes to building sustainable and intelligent living environments in remote locations. Future research will explore edge AI integration, scalable community-level networks, and the role of federated learning to enhance security and personalization.

A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care

The IoT Smart Cradle for Baby Monitoring System & Infant Care is introduced as an innovative solution to address critical gaps in contemporary infant care. This system integrates Internet of Things (IoT) technology, machine learning, and smart automation to offer a safer, more responsive, and comfortable environment for babies. A significant challenge in current infant care is the limitations of traditional monitoring systems. These systems often fail to provide comprehensive, real-time monitoring of essential environmental parameters and lack automated responses to an infant’s immediate needs, potentially increasing parental anxiety and compromising infant safety and well-being. This smart cradle is designed to overcome these limitations by employing a comprehensive network of sensors—including temperature, humidity, gas, noise sensors, and a cry-detection microphone—to monitor the baby’s needs and environmental conditions in real time. Microcontrollers like Raspberry Pi and NodeMCU use intelligent machine-learning algorithms to process the collected data and trigger adaptive responses. These responses include regulating temperature and humidity, filtering harmful gases, and activating a motorized rocking mechanism to soothe the infant. A dedicated mobile application offers parents secure, real-time monitoring and control over the cradle’s functions. The system demonstrates high accuracy in sensor readings, with temperature and humidity measurements reaching approximately 99.6% accuracy, and cry detection achieving approximately 93.2% accuracy. User feedback indicates that 95% of parents found the interface easy to use, and 87% reported a positive impact on their parenting experience. In contrast to traditional solutions that often require manual intervention or provide limited automation, this smart cradle uses predictive analytics to proactively address potential discomforts and hazards, thus presenting a more reliable, intelligent, and user-friendly solution for modern parenting.

Simulation Analysis of the Leakage and Diffusion Risk of a Hydrogen Storage System in Hydrogen Aircraft

Hydrogen is an alternative energy source for the aviation industry due to its renewability and cleanliness, although this novel application needs to be reassessed for the potential leakage risk. For this reason, we take a small hydrogen-powered aircraft as the research object and investigate hydrogen diffusion behavior in the cabin after 35 MPa onboard hydrogen storage system leakage. Firstly, the effectiveness of the numerical simulation model is verified. Secondly, the numerical simulation model is utilized to simulate the changes in hydrogen mole fraction in the cabin under various scenario conditions (different leakage diameters, directions, and environment parameters). Finally, we investigate the impact of ventilation. Forced ventilation could significantly reduce the hydrogen mole fraction in the cabin in a short time. However, forced ventilation also promotes the diffusion of residual hydrogen in the cabin, resulting in a large proportion of the volume having a hydrogen mole fraction greater than 0.04, but it can significantly reduce the proportion of high hydrogen mole fraction (>0.1 or >0.2) regions.

Multiple Environmental Factors Shaping Hopanoid-Producing Microbes Across Different Ecosystems

Hopanoids are a series of important lipid biomarkers in the bacterial cellular membranes that are found ubiquitously in different spatial and temporal environments. Squalene-hopane cyclase, a key and prerequisite molecular component of the hopanoid biosynthesis pathway, is encoded by the sqhC gene. To investigate the composition, niche, and distribution of microbial sqhC-containing communities, we analyzed hopanoid producer data and environmental parameters across different ecosystems on the basis of sequencing reads of peat samples from increasing gradient depths across peatland profile C in the Dajiuhu Peatland, as well as data collected from available published papers. The results indicated that the acidic Dajiuhu Peatland harbored mainly Acidobacteria (59.16%) among its sqhC-containing groups. The main composition of hopanoid producers in the peatland was different from that in other ecosystems, with Alphaproteobacteria found in soil (37.78%), cave (48.21%), hypersaline lagoon (34.04%), and marine (32.59%) ecosystems; Betaproteobacteria, Gammaproteobacteria, and Deltaproteobacteria found in reef (100%), acid mine drainage (55.00%), and estuary, mangrove, and harbor (39.66%) ecosystems; and an unknown cluster found in freshwater (29.43%) and hot spring (89.58%) ecosystems. Compared with other phyla or sub-phyla, Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria were the most widespread, occurring in eight ecosystems. Peatland was significantly separated from the other nine ecosystem modules in the occurrence network, and the marine ecosystem had the greatest impact on the eco-network of sqhC microbes. An RDA indicated that pH, DO, salinity, and TOC had significant impacts on sqhC-containing microbial communities across the different ecosystems. Our results will be helpful to understanding the diversity, composition, and distribution of the sqhC community and its response to multiple environmental factors across different ecosystems.

Analysis and Calculation of the Effects of Key Parameters on Beam Quality During Laser Transmission in Atmosphere

Compared to the effects of cavity mirror thermal deformation, cavity mirror misalignment, and the non-uniform distribution of the gain medium on laser beam quality, atmospheric disturbances have a more disruptive influence on laser beam quality. In order to investigate this effect, the variation of laser beam quality during the atmospheric transmission process was simulated and calculated under different parameters in the GLAD software environment. The calculation parameters primarily include the output laser power density, laser wavelength, atmospheric coherence length, altitude of the laser emission point, and focal length of the focusing mirror. The simulation results showed that: (1) As the output laser power density increases, the atmospheric perturbation effects such as atmospheric turbulence, thermal blooming, and wind speed become more pronounced, the laser beam diffuses at the irradiated target location, the spot appears fragmented, and the laser beam quality gradually deteriorates, the actual focus position of the laser gradually shifts forward, and when the output laser power density increases from 100[Formula: see text]W/cm2 to 2000[Formula: see text]W/cm2, the corresponding focus moves from 3.0[Formula: see text]km to 1.6[Formula: see text]km. (2) The longer the laser wavelength, the better the laser beam quality, and the Strehl ratio corresponding to the 1.315[Formula: see text][Formula: see text]m wavelength is approximately twice that of the 0.85[Formula: see text][Formula: see text]m wavelength. (3) As atmospheric disturbance increases, the laser power density distribution at the irradiated target location begins to exhibit discrete peaks, and the laser beam quality gradually decreases. It is found that when the atmospheric coherence length exceeds 20[Formula: see text]cm, the laser intensity distribution gradually approaches an ideal state. In addition, with the increase in atmospheric coherence length, the actual laser focus position gradually shifts back when the atmospheric coherence length increased from 5[Formula: see text]cm to 10[Formula: see text]cm, the corresponding focus position moved from 2.0[Formula: see text]km to 3.0[Formula: see text]km, closer to the geometric focus position.(4) As the altitude of the laser emission point increases, the focusing effect of the laser spot improves, and the laser intensity distribution becomes closer to the ideal far-field distribution. The actual focus position of the laser gradually shifts back, when the altitude increases from 0[Formula: see text]km to 7[Formula: see text]km, the focus moves from 2.0[Formula: see text]km to 2.9[Formula: see text]km, which is closer to the geometric focus position, and the efficiency of the laser also increases. In addition, when the altitude increases from 3[Formula: see text]km to 7[Formula: see text]km, the corresponding laser power density and beam quality increase by 1.7[Formula: see text]times and 3.5[Formula: see text]times, respectively. (5) As the focal length of the focusing mirror increases, the laser beam quality improves gradually, and the atmospheric transmission quality of the laser also enhances progressively. These findings provide guidance for pre-correction of adaptive optics systems in lasers and for assessing long-distance laser transmission efficiency.

Information System for Operational Monitoring of the City's Water Environment Based on IoT Technologies

This work deals with the issue of operational environmental monitoring of the urban water environment. The specified general provisions regarding the functioning of monitoring methods and the monitoring programme of water bodies. Maximum permissible concentrations of various water quality indicators are set. The main sources of pollution and the main requirements for water quality control systems are defined. On the basis of the above, an industrial parameter control system based on the principles of the Internet of Things technology for remote monitoring and operator control was developed. In the process of performing the work, structural and functional schemes of the operational monitoring system were created and local and remote level work algorithms were compiled. The analysis of measurements of a large number of parameters of the water environment was carried out. The proposed approach proved the possibility of ensuring high accuracy and reliability of measurement results obtained using the developed computer monitoring system.

A Smart IoT-Based Real-Time System for Monitoring Storage Conditions of Maize Grains

Maize is a cereal with many uses across the world. In developing countries, it is valued for its dried cereal, which has been used as a stable source of food. This cereal may be boiled and eaten as it is or may be dried and ground and made into porridge or Ugali, a popular carbohydrate source. The storage of maize grains is critical to ensuring food security and preventing post-harvest losses due to spoilage, pest infestation, and unfavourable environmental conditions. Traditional storage monitoring methods entailed using support staff to visit storage areas and measure conditions using manual methods which are inefficient and labour-intensive, leading to delays in the detection of adverse storage conditions and spoilages. In this paper, an Internet of Things (IoT)-based real-time storage conditions monitoring system that leverages Long Range Wide Area Network (LoRaWAN) technology to enhance communication efficiency and data transmission over long distances with low power consumption is presented. The system consists of smart sensors deployed in maize storage facilities to collect environmental parameters such as temperature, humidity, and carbon dioxide (CO2) levels which are critical for maize storage. The collected data is transmitted via LoRaWAN to a cloud-based platform for real-time visualization, analysis and proactive detection of changing situations using Machine Learning (ML) techniques. This approach provides an efficient, scalable, and cost-effective solution for improving maize storage, especially in resource-constrained environments since it limits manual human intervention. In addition, the technology prevents losses due to fungal infections, resulting in adequate high-quality maize supply to curb hunger and improve livelihoods which is the second United Nations’ Sustainable Development Goals of alleviating hunger and achieving food security