Environmental Monitoring System Research Articles

Real-time air quality monitoring based on locally developed unmanned aerial vehicle and low-cost smart electronic device

This article deals with the development of real-time air quality monitoring based on Unmanned Aerial Vehicles (UAVs) and low-cost Internet of Things (IoT) devices. This investigation aims to design and develop an UAV-based platform that can monitor a large number of air pollutants in real-time with high spatial and temporal resolution. The proposed environmental monitoring system consists of five main elements, namely the UAV, sensors, data storage module, programmable card, and IoT communication module. Estimated pollutants such as particulate matter (PM2.5) and toxic gases (carbon monoxide CO, nitrogen dioxide NO2, and carbon dioxide CO2) are detected by low-cost sensors. The ZigBee wireless protocol is used for communication between the PC and UAV. This work is carried out to assess the air quality in urban areas, given the heavy road traffic and the emissions of some companies. The data analyzed were collected from December 2, 2022 to January 3, 2023, in two major cities of Cameroon, Douala and Kribi. The periodic average values of the detected pollutants are 222 ± 22 μg/m3 and 85.7 ± 8.6 μg/m3 for PM2.5, 560.8 ± 1.0 ppm and 555.6 ± 1.0 ppm for CO2, 4.2 ± 0.2 ppm and 0.7 ± 0.1 ppm for NO2, and 27.6 ± 2.8 ppm and 4.5 ± 0.5 ppm for CO in Douala and Kribi respectively. This made it possible to have an air quality index (AQI) of 444.2 for Douala City and 171.3 for Kribi City. These high values indicate poor air quality during the measurement period.

CuO nanoparticles passivated 2D MXene-based voltammetric sensor for detecting environmental hazardous pollutant

This research explores a novel method for electrochemical sensing of the organic pollutant diclofenac sodium. Glassy carbon electrodes (GCEs) were modified with ultrasonically prepared MXene/CuO nanoparticle composites, demonstrating a wide linear detection range of 0.34 µM–31.67 µM for the contaminant. The study involved synthesizing 2D MXene via HF etching and DMSO delamination, and CuO nanoparticles through a hydrothermal process. Powder XRD, FE-SEM, EDAX, HR-TEM, and SAED techniques characterized the synthesized materials and composites. Cyclic voltammetry (CV), differential pulse voltammetry (DPV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS) were employed to analyze the electrochemical behaviour of the nanocomposites. Furthermore, the binding interaction between the MXene/CuO composite and diclofenac sodium was investigated using molecular modelling and semi-empirical approaches. The results revealed that CuO nanoparticle incorporation significantly enhances the sensitivity of MXene towards diclofenac. This novel sensor exhibited satisfactory performance, suggesting its potential application as a robust platform for environmental monitoring systems.

Atmospheric scattering model and dark channel prior constraint network for environmental monitoring under hazy conditions

Environmental monitoring systems based on remote sensing technology have a wider monitoring range and longer timeliness, which makes them widely used in the detection and management of pollution sources. However, haze weather conditions degrade image quality and reduce the precision of environmental monitoring systems. To address this problem, this research proposes a remote sensing image dehazing method based on the atmospheric scattering model and a dark channel prior constrained network. The method consists of a dehazing network, a dark channel information injection network (DCIIN), and a transmission map network. Within the dehazing network, the branch fusion module optimizes feature weights to enhance the dehazing effect. By leveraging dark channel information, the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the output of the deep learning model aligns with physical laws, we reconstruct the haze image using the prediction results from the three networks. Subsequently, we apply the traditional loss function and dark channel loss function between the reconstructed haze image and the original haze image. This approach enhances interpretability and reliability while maintaining adherence to physical principles. Furthermore, the network is trained on a synthesized non-homogeneous haze remote sensing dataset using dark channel information from cloud maps. The experimental results show that the proposed network can achieve better image dehazing on both synthetic and real remote sensing images with non-homogeneous haze distribution. This research provides a new idea for solving the problem of decreased accuracy of environmental monitoring systems under haze weather conditions and has strong practicability.

Smart Infant Incubator

We propose a solution called "Smart Infant Incubator" which aims at enhancing the maintenance of new born in various hospitals to make it much better and safer. The main idea of the project is to implement an ESP32-based intelligent environment monitoring system in the incubator from which it measures the humidity and temperature data on the spot where the incubator is placed. The system uses sensors like DHT11 for live temperature and humidity readings, an intelligent alert system and it is integrated with the Rainmaker platform for remote monitoring. The power system is given special attention which includes a Li-ion cell and a charging module to charge the cell. Safety features such as overcharge protection for the battery are also added. The report gives a clear picture of the steps that were performed in the project to accomplish the goals and also the results and analysis of the project. The prototype can be very useful in improving the nursery wards where we will consider this technology to be integrated with the incubators which will be very useful in maintaining the newborns safely.

Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster’s hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system’s ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures.

Environmental Monitoring and Control System of Green House with Microcontroller and GSM Using IoT Devices

This paper presents a comprehensive solution for monitoring and controlling greenhouse environments using microcontroller technology and GSM connectivity through IoT devices. The system integrates various sensors to collect real-time data on environmental parameters such as temperature, humidity, soil moisture, and light intensity within the greenhouse. A microcontroller unit processes the sensor data and executes control actions to optimize environmental conditions for plant growth. Through GSM connectivity, the system enables remote monitoring and control, allowing users to access greenhouse data and adjust settings from anywhere using a mobile device or computer. The proposed system offers efficient management of greenhouse conditions, leading to improved crop yield, reduced resource consumption, and enhanced sustainability in agriculture. Continuing from the abstract, the system architecture employs a microcontroller, such as Arduino or Raspberry Pi, as the central processing unit. The microcontroller interfaces with a variety of sensors including temperature sensors, humidity sensors, soil moisture sensors, and light sensors to continuously monitor the greenhouse environment. Data collected from these sensors are processed in real-time by the microcontroller to make informed decisions regarding environmental control parameters such as irrigation, ventilation, and lighting. Control actions are executed autonomously based on predefined thresholds and algorithms to maintain optimal growing conditions for the plants. The inclusion of GSM connectivity enables the system to communicate data and receive commands remotely. Users can access the greenhouse monitoring system through a web or mobile application, providing real-time updates on environmental conditions and allowing for remote control of the greenhouse parameters. Furthermore, the integration of IoT devices allows for scalability and flexibility in the system architecture, facilitating the addition of new sensors or control mechanisms as needed. Additionally, data analytics and machine learning techniques can be implemented to further optimize the system's performance and predictive capabilities. Overall, the proposed monitoring and control system offers a cost-effective, efficient, and sustainable solution for greenhouse management, with potential applications in commercial agriculture as well as small-scale or hobbyist setups

A Compact-Sized Fully Self-Powered Wireless Flowmeter Based on Triboelectric Discharge.

Flow sensing exhibits significant potential for monitoring, controlling, and optimizing processes in industries, resource management, and environmental protection. However, achieving wireless real-time and omnidirectional sensing of gas/liquid flow on a simple, self-contained device without external power support has remained a formidable challenge. In this study, a compact-sized, fully self-powered wireless sensing flowmeter (CSWF) is introduced with a small size diameter of down to less than 50mm, which can transmit real-time and omnidirectional wireless signals, as driven by a rotating triboelectric nanogenerator (R-TENG). The R-TENG triggers the breakdown discharge of a gas discharge tube (GDT), which enables flow rate wireless sensing through emitted electromagnetic waves. Importantly, the performance of the CSWF is not affected by the R-TENG's varied output, while the transmission distance is greater than 10m. Real-time wireless remote monitoring of wind speed and water flow rate is successfully demonstrated. This research introduces an approach to achieve a wireless, self-powered environmental monitoring system with a diverse range of potential applications, including prolonged meteorological observations, marine environment monitoring, early warning systems for natural disasters, and remote ecosystem monitoring.

Participatory Geomonitoring for Future Mining—Resilience Management in the Cavern Storage Epe (Germany)

Integrated geo- and environmental monitoring in mining represents a high-dimensional challenge (location, altitude/depth, time and sensors). This is challenging for experts but poses great problems for a multitude of participants and stakeholders in building up a complete process understanding. The Epe research cooperation aims to elucidate the ground movement at the Epe cavern storage facility with a public participation process. The research cooperation was founded by the city of Gronau, the citizens’ initiative cavern field Epe, the company EFTAS, Münster, and the Research Center of Post-Mining at the Technische Hochschule Georg Agricola, Bochum. This research cooperation is the first in Germany to involve direct collaboration between science and the public. In the cavern field, which has been in operation since the 1970s, brine is extracted, and at the same time natural gas, crude oil and helium, as well as hydrogen in the future, are stored in the subsurface. The technical focus of this work was the development of a high-resolution spatiotemporal analysis of ground movements. The area is monitored annually by the mining company’s mine surveyor. The complexity of the monitoring issue lies in the fact that the western part is a bog area and a former bog area. Furthermore, the soils in the eastern part are very humus-rich and show strong fluctuations in the groundwater and therefore complex hydraulic conditions. At the same time, there are few fixed scatterers or prominent points in the area that allow high-resolution spatiotemporal monitoring using simple radar interferometry methods. Therefore, the SBAS method (Small Baseline Subset), which is based on an aerial method, was used to analyze the radar interferometric datasets. Using an SBAS analysis, it was possible to evaluate a time series of 760 scenes over the period from 2015 to 2023. The results were integrated with the mine survey maps on the ground movement and other open geodata on the surface, the soil layers and the overburden. The results show complex forms of ground movement. The main influence is that of mining. Nevertheless, the influence of organic soils with drying out due to drought years and uplift in wet years is great. Thus, in dry years, ground subsidence accelerates, and in wet years, ground subsidence not only slows down but in some cases also causes uplift. This complexity of ground movements and the necessary understanding of the processes involved has been communicated to the interested public at several public information events as part of the research cooperation. In this way, an understanding of the mining process was built up, and transparency was created in the subsurface use, also as a part of the energy transition. In technical terms, the research cooperation also provides a workflow for developing the annual mine survey maps into an integrated geo- and environmental monitoring system with the development of a transparent participatory geomonitoring process to provide resilience management to a mining location.

Water Pollution Detection Using Video Surveillance and Deep Learning

Aiming at the problems that the traditional water quality prediction model is generally not high in prediction accuracy and robustness, a water pollution prediction using deep learning in water environment monitoring big data is proposed. Objective. To optimize and improve the prediction accuracy of the water quality prediction model. Firstly, in the water environment monitoring system, the Internet of Things big data technology is used to accurately sense and monitor the real-time data of sewage treatment equipment and sewage quality. Then, the deep belief network (DBN) is used to build the water pollution prediction model, and the collected sewage treatment data is analyzed to predict the water quality status. Finally, particle swarm optimization algorithm is used to dynamically optimize the number of hidden layer neural units and learning rate in the DBN prediction model, which makes the prediction results more scientific and accurate. Based on the sampling data of Shanghai Jinze Reservoir, the proposed model is experimentally analyzed. The results show that the probability of accurate location of the pollution source is not less than70%. And under the two indicators of chemical oxygen demand and biological oxygen demand, the root mean square error and correlation coefficient are 3.073, 0.9892 and 1.958, 0.9565, respectively, which are better than other comparison models.

Self-powered wireless environmental monitoring system for in-service bridges by galloping piezoelectric-triboelectric hybridized energy harvester

Self-powered wireless environmental monitoring system for in-service bridges by galloping piezoelectric-triboelectric hybridized energy harvester

Strategic Planning and Ecological Safety Evaluation of University Campuses on Green Marketing Principles

Strategic planning of university campuses (UC) aims to harmonize nature, humans, and architectural complexes, enhancing the positive university image through specialized green and recreation zones. Highly urbanized UC areas, particularly with nearby roads, encounter increased technogenic load and pose additional health hazards. The purpose of the paper is the evaluation of the environmental safety level of the UC recreational territories on the example of campuses of Kyiv universities, located nearby highly loaded roads, using a complex of express methods of analysis, mathematical modeling and criterion-based approach, considering the main green economy principles. The scientific novelty of the paper and the authors’ contribution is in the assessment of the environmental safety level of city-type UC recreational territories being under the significant technogenic load from vehicle emissions. Such express analysis methods as densimetry, viscometry, stalagmometry, conductometry, potentiometry and optical methods, and in particular, refractometric studies have been used to assess the impact of vehicle emissions. The mathematical modeling and computational experiment have been carried out for dispersion fields of the main vehicle emissions pollutants. Ecologically acceptable distances from the road considering the specifics of recreational territory have been determined using the integral index of technogenic load. Propositions for minimizing human health hazards and improving the system of environmental monitoring of such territories have been provided and substantiated. The study also confirms the positive effects of environmental management and green marketing principles synergy, benefiting both nature and humans as well as the university image. Obtained results can be used for the design and reconstruction of UC and their recreational areas, as well as for planning roads located nearby the campuses, improving traffic on them. It is also convenient to foresee the enhancement of the environmental activities in the student research work and create student communities for monitoring the UC environmental state and the development of eco-startup projects.

Smart Irrigation and Pesticides System for Enhancing Harvesting in Agriculture Using IoT

Agriculture plays a vital role in the economy of many countries, serving as a primary source of food. However, traditional farming practices often face challenges such as inefficient water usage, over-reliance on pesticides, and inconsistent monitoring of crop conditions. In recent years, the Internet of Things (IoT) has emerged as a promising technology to address these challenges by providing real-time monitoring and control of agricultural processes. This leads in excessive water use, which has an impact on agricultural output. This integrated system means that farmers only need to perform little manual monitoring. The opening and closing of the reservoir and field gates, as well as the verification of the plant's age and the matching optimal water level necessary for that time of plant age, are all automated. Farmers may use their mobile phones to check the water level in the field since they are continually receiving updates on the field work. The most labour-intensive and time consuming activity in agriculture is water management. In our project, the water level in the agriculture harvest sting is measured, and if it is lower than the required level, the paddy field is automatically irrigated for the water source or tank using a water pump. It can comprehensively monitor air temperature and humidity, light intensity, ultraviolet light and other environmental elements. In specific use, the agricultural production environment monitoring system starts from the actual needs of the plantation zone and is assembled according to the environmental elements it needs to monitor.

Интегрированная геоинформационная система для экологического мониторинга Обь-Иртышского речного бассейна

Показаны результаты разработки геоинформационной веб-системы, предназначенной для формирования единой научно обоснованной системы высокотехнологического комплексного экологического мониторинга (гидрологического, гидрохимического, гидробиологического) Обь-Иртышского речного бассейна и позволяющей оценивать экологическое состояние водных объектов с учетом изменяющегося климата и увеличения антропогенной нагрузки. Представлены архитектура геоинформационной системы, основные модули и компоненты, функциональные возможности и интерфейс пользователя. Рассмотрена структура проблемно-ориентированной базы данных, используемой для хранения и обработки информации в составе геоинформационной веб-системы. The results of the development of a geographic information web system (GIS) are presented, designed to form a uniҥed scientiҥcally based system of high-tech integrated environmental monitoring (hydrological, hydrochemical, hydrobiological) of the Ob-Irtysh river basin. It allows assessing the ecological state of water bodies and accounting for the climate change and the increasing anthropogenic load. The architecture of a geoinformation system based on a service approach is presented. When implementing a GIS, the API-Gateway programming pattern was used. The system provides the main modules and components of the GIS, its functionality and user interface designed to display expeditionary data. The structure of a problem-oriented database used for storing and processing information as part of a geoinformation web system is considered. The database was built using the CUASHI HIS data model and a standardized relational database structure proposed by the Consortium of Universities for the Development of Hydrology to store and describe point observations. This data model is quite universal and can be used to organize the storage, search and retrieval of information collected by many organizations to solve the problems of integrated environmental monitoring. The layout of such database with a single expandable system of directories allows collecting and combining the most important data on the subject area of research and use the data for subsequent analysis, calculations, and display of results. The obtained results are applicable for organization of integrated monitoring of the ecological state of Subarctic water bodies, the implementation of environmental policy, educational and educational activities and solving important socio-economic and environmental problems, arising within the framework of the Strategy for the Development of the Arctic Zone of the Russian Federation and ensuring national security until 2035. The developed GIS provides diverse information about the state of water bodies. Software interfaces allows receiving data from various measuring instruments and complexes, as well as downloading the results of expeditionary work with the help of web interface

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

ENVIRONMENTAL MONITORING IN KAZAKHSTANI CONTEXT – STATE-OF-THE-ART AND CHALLENGES FOR INDUSTRIAL REGION

Environmental issues remain a pressing concern for Kazakhstani industrial regions. While legislative requirements for emissions into the environment permanently become increasingly stringent, the environmental monitoring system lacks several structural factors. This article delves into the fundamental requirements for environmental monitoring systems and overviews methods and models for monitoring emissions into the environment based on sources and impact factors. The structure of classical monitoring is outlined in detail, examining methods based on international experience with existing management and control systems. The reasons and perspectives for an environmental monitoring system for industrial facilities in the Pavlodar region are discussed. The energy sector should gain the special attention due to its high density and significant environmental impact in the region. It can be concluded that imperfections in legislation and outdated infrastructure conditions have led to the threatening ambient air pollution. While the observations based on the retrospective time-series data currently show state-of-the-art, the urgent call for implementation of industrial intelligent information-analytical system may play a pivotal role in decision-making in air quality management. Keywords: environmental monitoring, greenhouse gases emissions, environmental pollution, Industry 4.0, air quality.

Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)

Wireless sensor networks those based on the Internet of Things (IoT) have evolved fast in numerous areas in recent years. Devices and sensors may interact with each other without human involvement in the IoT network. An IoT-based system where people and things are connected by wired and wireless technology including Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE, and Bluetooth is now one of the most popular technical ideas. The potential of the Internet of Things (IoT) has received enormous attention from the corporate and scientific communities during the past 10 years. The IoT can provide a number of significant benefits to application spaces. Researchers may be able to show crucial facts about IoT-based smart environmental monitoring systems. IoT has faced a variety of challenges, including authentication, differentiating evidence, accessibility and security and protection. Sociotechnical trust systems have also been examined in the past in various studies (STTS). Existing smart settings are now dealing with serious IoT security, protection, and STTS challenges. Trust-the-board will be highlighted and characterized, as well as how it should be utilized in the Internet of Things. As a result, the potential benefits of data aggregation solutions that use less energy while increasing the organization's lifespan are enormous. IoT-WSN data aggregation methods and computations have been reimagined. Specifically, this article examines the writing with reference to the views of sensor systems administrators in order to preserve energy and gather data.

Organization and Construction of Scientific Research Management of the Environmental Monitoring Department

Taking the scientific research management of the environmental monitoring Center as an example, this paper analyzes the nature of scientific research and the main mode of scientific research management of the environmental monitoring department under existing condi- tions, the existing drawbacks and future development tries to find the research direction and management mode of environmental monitoring scientific research through the interpretation of the implementation plan of environmental monitoring network construction and regional plan- ning, and closely combines the characteristics of scientific research management of environmental monitoring system. Put forward the scien- tific research management plan idea and the concrete steps that can be realized.

Low voltage driven P3HT/PS phototransistor for ultra-high power efficiency UV sensing

This study explores the physical properties and electrical characteristics of a regioregular poly 3-hexylthiophene (rr-P3HT) semiconductor blended with the polymer insulator polystyrene. Given the high operating voltage required by conventional P3HT transistors, we employed a hybrid dielectric layer, combining plasma-reacted metal oxide with a polymer pre-layer (AlOx/PMMA and HfOx/PMMA), to reduce the operating voltage of P3HT transistors. As a result, an operating voltage lower than −5 V for the blended sample was achieved. The structural, morphological, and electrical properties of the prepared blended sample were investigated using XRD, AFM, XPS, UV–Visible spectroscopy, Raman spectroscopy, and I–V characteristics. The blended sample exhibits improved crystalline ordering and an extended effective conjugation length, accompanied by an increased formation of dendritic P3HT fibrils that enhance UV absorption. PS captures generated electrons, effectively delaying their recombination with generated holes in P3HT. This leads to heightened UV responsivity in P3HT/PS transistors. The key discovery of this study is the ultra-low power consumption UV sensing device. The blended sample, illuminated with a UV intensity of 550 μW/cm2, exhibited the highest sensitivity of 194.5, nearly 60 times higher than that of pristine P3HT. Consequently, our investigated device shows promise for applications in visible-blind sunlight or environmental monitoring systems, given its ultra-high-power efficiency.

Design of Environmental Monitoring and Alarm System Based on Microcontroller

With the development of society, the improvement of science and technology, and the continuous optimization of production and life, people's living standards are also constantly improving. Therefore, people are starting to pay more and more attention to the comfort level of indoor environment. Housing is not only a place for family reunion and living, but also an important material guarantee for people's lives. However, with the rampant use of decoration materials and the increasing popularity of daily necessities, the hidden dangers of the living environment have greatly increased. Indoor environmental pollution has become one of the serious killers affecting modern human health, seriously affecting people's production and life. Therefore, it is particularly important to monitor the indoor environment, which not only needs to sensitively detect the concentration of various harmful gases, but also has an alarm function to constantly remind of dangers. The main functions of this design include: Connecting the microcontroller with temperature, humidity, and gas sensors to achieve real-time collection and reading of indoor temperature and humidity values, as well as monitoring gas concentration, and achieving the expected results. The display circuit was designed using LCD. The buzzer alarm function was used to automatically alarm when the gas concentration and temperature/humidity values exceeded the set standard values. When the temperature and humidity exceed the limit, the LCD display can immediately prompt and combine with LED alarm. When the gas concentration exceeds the limit, LED alarm is used.

Automated system for intertidal mollusk keeping with tidal cycle imitation

The article describes a working prototype of an automated laboratory setup for intertidal mollusk keeping with tidal cycle imitation. The setup currently consists of two groups of five aquariums. At high tide, water is gravity fed into the aquariums from a common water tank; at low tide, water is gravity drained into a common collection tank. Water is transferred between the tanks using a special pump. The control unit consists of a networked Arduino Mega board and two Arduino Nano boards. The Arduino Mega board is responsible for the environmental monitoring systems, data logging, and automatic seawater change, as well as a software-based selfchecking and decision-making system. One of the Arduino Nano boards is responsible for realizing the tidal cycle; the other board is responsible for the remote monitoring and control of the setup via SMS. The results of pilot experiments indicate a significant reduction in labor costs in mollusk keeping. The developed setup can be used to keep a wide range of intertidal animals for studying their biology, assessing the impact of environmental factors, and biotesting, as well as for educational purposes.