Combining a traditional weather station with radiation monitors draws the public's attention to the magnitude of background radiation and its typical variation while providing early indications of unplanned radiological releases, such as nuclear power plant accidents or terrorist acts. Several networks of combined weather and radiation monitoring sensors exist, but these fail to be affordable for broad distribution. This work involves creating an affordable system to accumulate data from multiple locations into a single open-source database. The data collected should thus serve as a friendly database for high school students. The system is designed around an inexpensive sensor package featuring a cup anemometer, wind direction vane, and tip bucket rain gauge. A Raspberry Pi 4 microcomputer interfaces through RJ11 and RJ45 connectors to these and other sensors. Custom-designed circuits were implemented on printed circuit boards supporting sensor chips for temperature, pressure, humidity, and air electrical resistance. The outdoor board communicates with ultraviolet light, soil moisture, and temperature sensors, relaying data using wired connections indoors where a Raspberry Pi 4 and indoor circuit board are located. The indoor board employs wireless internet protocol to communicate with a homemade Geiger-Mueller counter and a consumer-grade temporal radon monitor. The system employs an internet connection to transfer data to a cloud-based storage system. This enables a website with continuously updated pages dedicated to each established system to display collected data. Weatherproofed fused filament fabricated indoor and outdoor cases were designed. Sensor functions were tested for functionality and accuracy.
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http://dx.doi.org/10.1097/HP.0000000000001855 | DOI Listing |
Sci Rep
December 2024
Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.
Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO), Ozone (O), Sulphur Dioxide (SO), Fine Particles Matter (PM), Coarse Particles Matter (PM), and Ammonia (NH).
View Article and Find Full Text PDFActa Trop
December 2024
Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan, S7N 5C8, Canada; Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada. Electronic address:
Environ Monit Assess
December 2024
Department of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 602105, Tamil Nadu, India.
Knowledge of soil temperature (ST) is important for analysing environmental conditions and climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning (ML) paradigms including random forest (RF), radial basis neural network (RBNN), multi-layer perceptron neural network (MLPNN), and co-active neuro-fuzzy inference system (CANFIS) were used for estimation of daily ST at different soil depths (i.
View Article and Find Full Text PDFFront Public Health
December 2024
Department of Public Health, School of Public Health in Bytom Medical University of Silesia in Katowice, Piekarska, Poland.
In 2019, ozone was responsible for about 365,000 premature deaths worldwide (6.21 million healthy life years lost) and acute ozone exposure led to 16,800 premature deaths in the European Union. The aim of the study was to estimate the influence of NO, NO, wind direction (WD) wind speed (WS), air temperature (TA), and total radiation (GLR) on ozone concentration levels.
View Article and Find Full Text PDFInt J Biometeorol
December 2024
Department of Ecosystem Science and Management, University of Wyoming, Laramie, WY, 82071, USA.
For non-hibernating species within temperate climates, survival during severe winter weather often depends on individuals' behavioral response and available refugia. Identifying refugia habitat that sustains populations during adverse winter conditions can be difficult and complex. This study provides an example of how modeled, biologically relevant snow and weather information can help identify important relationships between habitat selection and dynamic winter landscapes using greater sage-grouse (Centrocercus urophasianus, hereafter "sage-grouse") as a model species.
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