Search Results (75)

The article describes an Arduino-based, portable, remotely usable weather monitoring station capable of measuring temperature, relative humidity, pressure, and carbon monoxide (CO) concentration and transmitting the collected data to the Cloud through the mobile telephony network. The main modules of the station are as follows: a DHT11 sensor for temperature and relative humidity sensing, a BMP180 sensor for pressure monitoring (with temperature compensation), a MQ7 sensor for the monitoring of the CO concentration, an Arduino Uno board, a GSM SIM900 module, and a buzzer, which is activated when the temperature exceeds 35 °C. The station operates as follows: the Arduino Uno board gathers the data collected by the sensors and, by means of the GSM SIM900 module, it transmits the data to the Cloud by using the mobile telephony network as well as the ThingSpeak software which is an open-code IoT application that, among others, enables saving and recovering of sensing and monitoring data. The main novelty of this work is the combined use of the GSM network and the Cloud which enhances the portability and usability of the proposed system and enables remote collection of data in a straightforward way. Additional merits of the system are the easiness and the low cost of its development (owing to the easily available, low-cost hardware combined with an open-code software) as well as its modularity and scalability which allows its customization depending on specific application it is intended for. The system could be used for real-time, remote monitoring of essential environmental parameters in spaces such as farms, warehouses, rooms etc. Full article

This paper presents a new model for low-cost solar-powered Automatic Weather Stations based on the ESP-32 microcontroller, modern sensors, and intelligent forecasts for Brasilia. The proposed system relies on compact, multifunctional sensors and features an open-source firmware project and open-circuit board design. It includes a BME688, AS7331, VEML7700, AS3935 for thermo-hygro-barometry (plus air quality), ultraviolet irradiance, luximetry, and fulminology, besides having a rainfall gauge and an anemometer. Powered by photovoltaic panels and batteries, it operates uninterruptedly under variable weather conditions, with data collected being sent via WiFi to a Web API that adapts the MZDN-HF (Meteorological Zone Delimited Neural Network–Hourly Forecaster) model compilation for Brasilia to produce accurate 24 h multivariate forecasts, which were evaluated through MAE, RMSE, and R² metrics. Installed at the University of Brasilia, it demonstrates robust hardware performance and strong correlation with INMET’s A001 data, suitable for climate monitoring, precision agriculture, and environmental research. Full article

The increasing frequency and intensity of heat waves, combined with urban heat islands (UHIs), pose significant public health challenges. Implementing low-cost, real-time monitoring networks with distributed stations within the smart city framework faces obstacles in transforming urban spaces. Accurate data are essential for assessing these effects. This paper compares different network types in a medium-sized city in western Spain and their implications for UHI identification quality. The study first presents a purpose-built monitoring network using Open-Source platforms, IoT technology, and LoRaWAN communications, adhering to World Meteorological Organization guidelines. Additionally, it evaluates two citizen weather observer networks (CWONs): one from a commercial smart device company and another from a global community connecting environmental sensor data. The findings highlight several advantages of bespoke monitoring networks over CWON, including enhanced data accessibility and greater flexibility to meet specific requirements, facilitating adaptability and scalability for future upgrades. However, specialization is crucial for effective deployment and maintenance. Conversely, CWONs face limitations in network uniformity, data shadow zones, and insufficient knowledge of real sensor situations or component characteristics. Furthermore, CWONs exhibit some data inconsistencies in probability distribution and scatter plots during extreme heat periods, as well as improbable UHI temperature values. Full article

Smart cities are widely regarded as a promising solution to urbanization challenges; however, environmental aspects such as outdoor thermal comfort and urban heat island are often less addressed than social and economic dimensions of sustainability. To address this gap, we developed and evaluated an affordable, scalable, and cost-effective weather station platform, consisting of a centralized server and portable edge devices to facilitate urban heat island and outdoor thermal comfort studies. This edge device is designed in accordance with the ISO 7726 (1998) standards and further enhanced with a positioning system. The device can regularly log parameters such as air temperature, relative humidity, globe temperature, wind speed, and geographical coordinates. Strategic selection of components allowed for a low-cost device that can perform data manipulation, pre-processing, store the data, and exchange data with a centralized server via the internet. The centralized server facilitates scalability, processing, storage, and live monitoring of data acquisition processes. The edge devices’ electrical and shielding design was evaluated against a commercial weather station, showing Mean Absolute Error and Root Mean Square Error values of 0.1 and 0.33, respectively, for air temperature. Further, empirical test campaigns were conducted under two scenarios: “stop-and-go” and “on-the-move”. These tests provided an insight into transition and response times required for urban heat island and thermal comfort studies, and evaluated the platform’s overall performance, validating it for nuanced human-scale thermal comfort, urban heat island, and bio-meteorological studies. Full article

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article

The Senise red pepper, known as peperone crusco, is a protected geographical indication (PGI) product from Basilicata, Italy, traditionally consumed dried. Producers use semi-open greenhouses to meet PGI standards, but significant losses are caused by rot from microorganisms thriving in high moisture, temperature, and humidity, which also encourage pest infestations. To minimize losses, a low-cost alert system was developed. The study, conducted in summer 2022 and 2023, used external parameters from the ALSIA Senise weather station and internal sensors monitoring the air temperature and humidity inside the greenhouse. Since rot is complex and difficult to model, an artificial intelligence (AI)-based approach was adopted. A feed forward neural network (FFNN) estimated greenhouse climate conditions as if it were empty, comparing them with actual values when peppers were present. This revealed the most critical period was the first 3–4 days after introduction and identified a critical air relative humidity threshold. The system could also predict microclimatic parameters inside the greenhouse with red peppers, issuing warnings one hour before risk conditions arose. In 2023, it was tested by comparing predicted values with previously identified thresholds. When critical levels were exceeded, greenhouse operators were alerted to adjust conditions. In 2023, pepper rot decreased. Full article

The Global Navigation Satellite System (GNSS) has made important progress in Earth observation and applications. With the successful design of the BeiDou Navigation Satellite System (BDS), four global navigation satellite systems are available worldwide, together with Galileo, GLONASS, and GPS. These systems have been widely employed in positioning, navigation, and timing (PNT). Furthermore, GNSS refraction, reflection, and scattering signals can remotely sense the Earth’s surface and atmosphere with powerful implications for environmental remote sensing. In this paper, the recent developments and new application progress of multi-GNSS in Earth observation are presented and reviewed, including the methods of BDS/GNSS for Earth observations, GNSS navigation and positioning performance (e.g., GNSS-PPP and GNSS-NRTK), GNSS ionospheric modelling and space weather monitoring, GNSS meteorology, and GNSS-reflectometry and its applications. For instance, the static Precise Point Positioning (PPP) precision of most MGEX stations was improved by 35.1%, 18.7%, and 8.7% in the east, north, and upward directions, respectively, with PPP ambiguity resolution (AR) based on factor graph optimization. A two-layer ionospheric model was constructed using IGS station data through three-dimensional ionospheric model constraints and TEC accuracy was increased by about 20–27% with the GIM model. Ten-minute water level change with centimeter-level accuracy was estimated with ground-based multiple GNSS-R data based on a weighted iterative least-squares method. Furthermore, a cyclone and its positions were detected by utilizing the GNSS-reflectometry from the space-borne Cyclone GNSS (CYGNSS) mission. Over the years, GNSS has become a dominant technology among Earth observation with powerful applications, not only for conventional positioning, navigation and timing techniques, but also for integrated remote sensing solutions, such as monitoring typhoons, river water level changes, geological geohazard warnings, low-altitude UAV navigation, etc., due to its high performance, low cost, all time and all weather. Full article

Weather and soil water dictate farm operations such as irrigation scheduling. Low-cost and open-source agricultural monitoring stations are an emerging alternative to commercially available monitoring stations because they are often built from components using open-source, do-it-yourself (DIY) platforms and technologies. For irrigation management in an experimental vineyard located in Quiroga (Lugo, Spain), we faced the challenge of installing a low-cost environmental and soil parameter monitoring station composed of several nodes measuring air temperature and relative humidity, soil temperature, soil matric potential, and soil water content. Commercial solutions were either too expensive or did not meet our needs. This challenge led us to design the low-cost sensor system that fulfilled our requirements. This node is based on the ESP32 chip, and communication between the nodes and the gateway is carried out by LoRa technology. The gateway is also based on the ESP32 chip. The gateway uploads the data to an FTP server using a Wi-Fi connection with a 4G router while simultaneously storing the data on a memory card. The programming of the code for the nodes and the gateway is performed using the Arduino IDE. The equipment developed is proven to be effective and for managing vineyard irrigation based on the built-in sensors, with replicable results. It is, however, essential to calibrate the capacitive sensors for measuring soil water content in each soil type in order to enhance their ability to produce reliable results. In addition, the limits marking the beginning and end of irrigation tasks must be adjusted to local conditions and according to the producer’s specific vineyard objectives. Full article

The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM_2.5 and PM₁₀ fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results obtained from two systems equipped with the same sensor model were compared. Excellent linear correlation was observed between the results, with differences in measurements falling within instrumental accuracy. Two instruments equipped with different sensors, models Novasense SDS011 and Plantower PMSA003, were placed at the same site. These were complemented by a weather station to measure meteorological parameters. Upon comparing the atmospheric particulate matter concentrations measured by the two instruments, it was observed that there was a good linear correlation for PM_2.5 and a poor linear correlation for PM₁₀. Additionally, the PMSA003 sensor appeared to consistently record higher concentrations than the SDS011 sensor. During periods influenced by natural sources and/or anthropogenic activities at the regional and/or local scale, i.e., the dispersal of Saharan sands, forest fires, and local events using fireworks, abnormal concentrations of atmospheric particulate matter were detected. Despite the inherent limitations in precision and accuracy, both low-cost instruments were able to identify periods with abnormal concentrations of atmospheric particulate matter, regardless of their source or type. Full article

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments. Full article

In both developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need for extensive sampling. Furthermore, these particles are often enriched with potentially toxic trace elements and organic pollutants. This study evaluates both the composition of atmospheric dust accumulated during a certain timespan on Hedera helix and Senecio cineraria leaves and the potential for their use as bio-monitors. The test plants were positioned near automatic air quality monitoring stations at four different sites with respectively high, moderate and low traffic intensity. The gravimetric deposition of PM10 and PM2.5 on leaves was compared with data recorded by the monitoring stations and related to the weather conditions reported by Argentina’s National Meteorological Service. To determine the presence of trace elements enriching the PM deposited on leaves, two analytical techniques were applied: XRF (not destructive) and ICP (destructive). The results indicated that only in the unpaved street location (site 2) did PM10 and PM2.5 concentrations (90 µg m⁻³ and 9 µg m⁻³) in the air exceed more than five times WHO guidelines (15 µg m⁻³ and 5 µg m⁻³). However, several trace elements were found to be enriching PM deposited on leaves from all sites. Predominantly, increased concentrations of Cd, Cu, Ti, Mn, Zn and Fe were found, which were associated with construction, traffic and unpaved street sources. Furthermore, based on its capability to sequester above 2800 µg cm⁻² of PM10, 2450 µg cm⁻² of PM2.5 and trace elements, Senecio cineraria can be taken into consideration for adoption as a bio-monitor or even for PM mitigation. Full article

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM₁, PM_2.5, and PM₁₀ particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data. Full article

This study presents a production and development process of an IoT-based weather monitoring and emergency notification system for confined-space environments. The system comprises four working hardware stations cooperating through a data transfer command, an open-source data management system, and a cloud database. The system was preliminarily tested in a relevant confined-space area known as the Muang-On Cave, located in Chiang Mai, Thailand. The system was used to monitor weather conditions and detect emergency signals at all stations for seventeen days during the wet to dry transitional season. The data, including temperature, relative humidity, carbon dioxide, total volatile organic compounds, and emergency codes, were displayed on the web server every 80 to 110 s. However, the extremely humid conditions in the cave actively affected the erroneous readings of gas-detecting sensors that should be accounted for further improvement. Since the system was devised from low-cost electrical and non-electrical materials and open-source software, the total capital cost of the system production indicates a relatively low cost estimated at nearly USD 200. Testing the studied system in other natural caves elsewhere is highly recommended for system stability assessment. Full article

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality. Full article

Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to explore the impact of meteorological variables on thermal simulations, particularly focusing on ships. Using TRNSYS (TRaNsient System Simulation) software (v17), renowned for its capability to model complex energy systems within buildings, the significance of incorporating meteorological data into thermal simulations was analyzed. The investigation centered on a patrol vessel stationed in a port in Galicia, northwest Spain. To ensure accuracy, we not only utilized the vessel’s dimensions but also conducted in situ temperature measurements onboard. Furthermore, a dedicated weather station was installed to capture real-time meteorological data. Data from multiple sources, including Meteonorm and MeteoGalicia, were collected for comparative analysis. By juxtaposing simulations based on meteorological variables against those relying solely on in situ measurements, we sought to discern the relative merits of each approach in enhancing the fidelity of thermal simulations. Full article