Search Results (405)

As population growth and climate change intensify pressures on agriculture, innovative strategies are vital for ensuring food security, optimizing resources, and protecting the environment. This study introduces a novel approach to predictive agriculture by utilizing the unique properties of terpenes, specifically S(-)-limonene, emitted by plants under stress. Advanced sensors capable of detecting subtle limonene variations offer the potential for early stress diagnosis and precise crop interventions. This research marks a significant leap in sensor technology, introducing an innovative active sensing material that combines molecularly imprinted polymer (MIP) technology with electrospinning. S(-)-limonene-selective MIP nanoparticles, engineered using methacrylic acid (MAA) and ethylene glycol dimethacrylate (EGDMA), were synthesized with an average diameter of ~160 nm and integrated into polyvinylpyrrolidone (PVP) nanofibers reinforced with multiwall carbon nanotubes (MWCNTs). This design produced a conductive and highly responsive sensing layer. The sensor exhibited rapid stabilization (200 s), a detection limit (LOD) of 190 ppb, and a selectivity index of 73% against similar monoterpenes. Optimal performance was achieved at 55% relative humidity, highlighting environmental conditions’ importance. This pioneering use of polymeric MIP membranes in chemiresistive sensors for limonene detection opens new possibilities for monitoring VOCs, with applications in agricultural stress biomarkers, contaminant detection, and air quality monitoring, advancing precision agriculture and environmental protection. Full article

Chinese Assembled Solar Greenhouses (CASGs) in the Gobi Desert region face significant diurnal temperature variations, with excessively high temperatures during the day and low temperatures at night, which adversely affect crop growth. Traditional temperature regulation technologies are hindered by high energy consumption, high costs, and severe pollutants. To address these issues, this study designed a heating system suitable for CASGs in the Gobi Desert region, integrating solar air source heat pump technology with underground pipe systems. The power consumption and performance of the system were assessed by comparing temperature and humidity in an experimental greenhouse (with the system), a control greenhouse (without the system), and outdoor environments under various typical climate conditions. The results indicated that the system exhibited excellent performance in both daytime heat absorption and nighttime heat release. Specifically, during operation, the maximum daytime temperature in the experimental greenhouse was reduced by up to 5 °C, while the minimum nighttime temperature increased by up to 8 °C, effectively preventing crop frost damage. The system achieved heat absorption rates of 14 to 16 KJ s⁻¹ and heat release rates of 36.5 to 37.5 KJ s⁻¹, with average coefficients of performance (COP) of 4.33 and 4.81. Compared to traditional heating methods using coal, gas, and electricity, the system reduced energy consumption by 84.7%, 81.3%, and 79.1%, respectively, and decreased greenhouse gas emissions by 8.24 t, 6.52 t, and 5.67 t, respectively. This system exhibits outstanding thermal efficiency, energy savings, and environmental benefits, while also showing promising economic benefits with a payback period of four years, providing a reliable heating solution for CASGs in the Gobi Desert region. Full article

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%. Full article

Tetracentron sinense is a tall deciduous tree and represents the only remaining species of Tetracentron. Currently, the spatial distribution pattern of T. sinense and its associated influencing factors remain unclear, thus hindering its protection and rational utilization. In this study, we employed the point pattern method to analyze the spatial distribution patterns of four representative populations of T. sinense distributed in Baima Snow Mountain, Dafengding, Leigong Mountain, and Foping in China. The results reveal that the T. sinense populations in Baima Snow Mountain, Dafengding, and Leigong Mountain exhibited an aggregated distribution on small (0–10 m) or specific scales, with their spatial distribution patterns shifting from aggregated to random as the scale increased. In contrast, the population of T. sinense in Foping showed a random distribution at all scales. In relation to the factors influencing the spatial distribution patterns of T. sinense, we found that young trees played a crucial role and had a substantial impact on their distribution. Furthermore, adult trees contributed to the aggregated distribution of T. sinense saplings on smaller scales (0–10 m). Additionally, we identified Acer erianthum, Prunus conradinae, and Rhododendron anthosphaerum as key associated species that influenced the formation of spatial distribution patterns of T. sinense. Finally, air humidity and soil moisture content were found to exert a significant effect on the spatial distribution patterns of T. sinense populations. For the protection of T. sinense in situ, it is necessary to increase the number of young trees, enhance the availability of microhabitat factors for its seedlings, and utilize key companion species to promote heterogeneity, which can optimize resource utilization and foster population rejuvenation. Full article

As a potential consequence of climate change, the intensity and frequency of dust storms are increasing. A dust storm arises when strong winds blow loose dust from a dry surface, transporting soil particles from one place to another. The environmental and human health impacts of dust storms are substantial. Accordingly, studying the monitoring of this phenomenon and predicting its pathways for early decision making and warning are vital. This study employs deep learning methods to predict dust storm pathways. Specifically, hybrid CNN-LSTM and ConvLSTM models have been proposed for the 24 h-ahead prediction of dust storms in the region under study. The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product that includes the dust particles and the meteorological information, such as surface wind speed and direction, relative humidity, surface air temperature, and skin temperature, is used to train the proposed models. These contextual features are selected utilizing the random forest feature importance method. The results indicate an improvement in the performance of both models by considering the contextual information. Moreover, a 0.2 increase in the Kappa coefficient criterion across all forecast hours indicates the CNN-LSTM model outperforms the ConvLSTM model when contextual information is considered. Full article

The dual challenges of global aging and intensifying urban heat demand innovative, evidence-based strategies to foster thermally and psychologically comfortable environments for vulnerable populations, particularly the elderly. Despite the documented benefits of urban greenery, the species-specific impacts of urban trees on thermal comfort and well-being remain underexplored. This study investigates how distinct tree species—Camphora officinarum (camphor), Platanus acerifolia (London plane), and Ginkgo biloba (ginkgo)—regulate urban microclimates and support elderly well-being during hot summer days. Conducted at five sites in Shanghai, including a control site and four vegetated plots, this study engaged 210 elderly participants. Microclimatic variables were measured using the physiological equivalent temperature (PET) alongside air temperature, humidity, and wind speed. Physiological responses, assessed through heart rate variability (HRV), and psychological outcomes, evaluated via validated self-report scales, were analyzed. The results revealed that dense-canopy trees significantly reduced PET, enhanced thermal comfort, and improved ROS and SVS scores, while lower LF/HF ratios indicated reduced physiological stress. Correlation analyses underscored the pivotal role of canopy density (SVF) in fostering psychological and physiological well-being. Camphor and London plane trees consistently provided the greatest benefits, emphasizing the importance of species selection in urban greening strategies. These findings underscore the critical role of species selection in urban forestry to mitigate heat stress and foster age-friendly resilience. Practical implications emphasize integrating dense-canopy species into urban landscapes to enhance microclimate regulation and public health. Full article

Livestock farms are recognized sources of ammonia emissions, impacting nearby regions’ fine dust particle concentrations, though the full extent of this impact remains uncertain. Air dispersion models, commonly employed to estimate particulate matter (PM) levels, are heavily reliant on data quality, resulting in varying levels of accuracy. This study compares the performance of both air dispersion models and spatiotemporal deep learning models in estimating PM concentrations in Republic of Korea’s livestock-farming areas. Hourly PM concentration data, alongside temperature, humidity, and air pressure, were collected from seven monitoring stations across the study area. Using a 200 m × 200 m prediction grid, forecasts were generated for both 1 h and 24 h intervals using the Graz Lagrangian model (GRAL) and a one-dimensional convolutional neural network combined with the long short-term memory algorithm (1DCNN-LSTM). Results highlight the potential of the deep learning model to enhance PM prediction, indicating its promise as an effective alternative or supplement to conventional air dispersion models, particularly in data-scarce areas such as those surrounding livestock farms. Gaining a comprehensive understanding and evaluating the advantages and disadvantages of each approach would offer valuable scientific insights for monitoring atmospheric pollution levels within a specific area. Full article

In this paper, the sensitivity to sub-ppm NO₂ concentration of 50 nm thick Ga-doped ZnO (GZO) films grown by RF magnetron sputtering is studied. The films were annealed under dry air for 4 h at either 500 °C, 600 °C, or 700 °C. The increase in the annealing temperature leads to an improvement of the crystallinity while no significant evolution of the surface grain size is observed. The electrical resistance of the thin films was measured at 250 °C under neutral argon atmosphere, humid air reference atmosphere, and reference atmosphere polluted by 100 ppb of NO₂. An increase in sensitivity to NO₂ is noted for samples annealed at 600 °C, leading to a response R_NO2/R_air of ~10 for 100 ppb of NO₂. Finally, photoluminescence spectra are compared with their electrical resistance at 250 °C under the various atmospheres to understand this phenomenon. It is proposed that the origin of the NO₂ maximum sensitivity for films annealed at 600 °C is the consequence of a specific annihilation of point defects resulting in an increase in the relative concentration of oxygen vacancies, which improves selectivity toward NO₂. Full article

Contrails, or condensation trails, left by aircraft, significantly contribute to global warming by trapping heat in the Earth’s atmosphere. Despite their critical role in climate dynamics, the environmental impact of contrails remains underexplored. This research addresses this gap by focusing on the use of CubeSats for real-time contrail monitoring, specifically over major air routes such as the Europe–North Atlantic Corridor. The study proposes a 3 × 3 CubeSat constellation in highly eccentric orbits, designed to maximize coverage and data acquisition efficiency. Simulation results indicate that this configuration can provide nearly continuous monitoring with optimized satellite handovers, reducing blackout periods and ensuring robust multi-satellite visibility. A machine learning-based system integrating space-based humidity and temperature data to predict contrail formation and inform flight path adjustments is proposed, thereby mitigating environmental impact. The findings emphasize the potential of CubeSat constellations to revolutionize atmospheric monitoring practices, offering a cost-effective solution that aligns with global sustainability efforts, particularly the United Nations Sustainable Development Goal 13 (Climate Action). This research represents a significant step forward in understanding aviation’s non-CO₂ climate impact and demonstrates the feasibility of real-time contrail mitigation through satellite technology. Full article

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity. Full article

The accumulation of anesthetic gas residues in surgery units can pose health risks to healthcare professionals, highlighting the need to establish effective protection measures. This study evaluated waste anesthetic gas levels in a local health unit in northern Portugal to identify high-exposure areas during surgeries using general anesthesia. Measurements of desflurane, sevoflurane, carbon dioxide, air temperature, and relative humidity were taken during 20 surgeries carried out over approximately six months. The results showed that the thermal conditions were not adequately controlled, particularly the relative humidity levels. The detected WAG concentrations fluctuated across different locations, with concerning peaks being detected in specific settings. Desflurane levels reached 8.79 ppm in the general surgery room (GSR) and averaged 3.13 ppm in the recovery room (RR), while the sevoflurane levels averaged 2.06 ppm in the RR. High concentrations exceeding the recommendations of the U.S. National Institute for Occupational Safety and Health (NIOSH) were notably observed after endotracheal tube removal. In short surgeries, anesthetic gas levels exceeded safety limits, while long surgeries caused peaks in sevoflurane levels. Longer surgeries and higher occupancy were significantly linked to increased levels of WAG and carbon dioxide, emphasizing the need to improve ventilation and environmental controls to safeguard healthcare professionals. 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 role of urban vegetation in urban air quality is usually assessed by considering only the pollutant removal capacity of the plants. This study aims to show, for the first time, the effects of vegetation on air pollutant concentrations through its effects on meteorology, separately from its biogenic emissions. It also investigates how air quality changes when only biogenic emissions are altered by using plants with different emission factors, as well as the potential effects of introducing new vegetation into urban areas. These assessments were conducted using atmospheric modelling systems currently employed for air quality forecasting and planning, configured specifically for the cities of Bologna and Milan. Simulations were performed for two representative months, July and January, to capture summer and winter conditions, respectively. The variability in air concentrations of ozone (O₃), nitrogen dioxide (NO₂), and particulate matter (PM10) within the municipal boundaries was assessed monthly. When evaluating the impact of future vegetation, changes in temperature, wind speed, and relative humidity were also considered. The results indicate that vegetation influences air quality more significantly through changes in meteorological conditions than through biogenic emissions. Changes in biogenic emissions result in similar behaviours in O₃ and PM10 concentrations, with the latter being affected by the changes in the concentrations of secondary biogenic aerosols formed in the atmosphere. Changes in NO₂ concentrations are controlled by the changes in O₃ concentrations, increasing where O₃ concentrations decrease, and vice versa, as expected in highly polluted areas. Meteorologically induced vegetation effects also play a predominant role in depositions, accounting for most of the changes; however, the concentrations remain high despite increased deposition rates. Therefore, understanding only the removal characteristics of vegetation is insufficient to quantify its effects on urban air pollution. Full article

The escalating environmental threat of indoor overheating, exacerbated by global climate change, urbanisation, and population growth, poses a severe risk to public health worldwide, specifically to those regions which are exposed to extreme heat events, such as Australia. This study delves into the critical issue of overheating within residential buildings, examining the existing state of knowledge on overheating criteria and reviewing overheating guidelines embedded in (a) international standards and (b) national building codes. Each regulatory document is analysed based on its underlying thermal comfort model, metric, and indices. The advantages and limitations of each document are practically discussed and for each legislative document and standard, and the quantitative measures have been reviewed, analysed, and summarised. The findings illuminate a global reliance on simplistic indices, such as indoor air temperature and operative temperature, in the existing regulatory documents. However, other critical environmental parameters, such as relative humidity, indoor air velocity, and physiological parameters including metabolic heat production and clothing insulation, are often not included. The absence of mandatory regulations for overheating criteria in residential buildings in some countries, such as in Australian homes, prompts the call for a holistic approach based on a thermal index inclusive of relevant environmental and physiological parameters to quantify heat stress exposure based on human thermal regulation. Gaps and limitations within existing guidelines are identified, and recommendations are proposed to strengthen the regulatory framework for overheating risk assessment in residential buildings. The findings hold significance for policymakers, building energy assessors, architects, and public health professionals, providing direction for the improvement of existing, and development of new, guidelines that aim to enhance indoor thermal condition and population health while ensuring energy efficiency and sustainability in the building stock. Full article

Near-surface air temperature (Tair) is critical for addressing urban challenges in China, particularly in the context of rapid urbanization and climate change. While many studies estimate Tair at a national scale, they typically provide only daily data (e.g., maximum and minimum Tair), with few focusing on sub-daily urban Tair at high spatial resolution. In this study, we integrated MODIS-based land surface temperature (LST) data with 18 auxiliary data from 2013 to 2023 to develop a Tair estimation model for major Chinese cities, using random forest algorithms across four diurnal and seasonal conditions: warm daytime, warm nighttime, cold daytime, and cold nighttime. Four model schemes were constructed and compared by combining different auxiliary data (time-related and space-related) with LST. Cross-validation results were found to show that space-related and time-related variables significantly affected the model performance. When all auxiliary data were used, the model performed best, with an average RMSE of 1.6 °C (R² = 0.96). The best performance was observed on warm nights with an RMSE of 1.47 °C (R² = 0.97). The importance assessment indicated that LST was the most important variable across all conditions, followed by specific humidity, and convective available potential energy. Space-related variables were more important under cold conditions (or nighttime) compared with warm conditions (or daytime), while time-related variables exhibited the opposite trend and were key to improving model accuracy in summer. Finally, two samples of Tair patterns in Beijing and the Pearl River Delta region were effectively estimated. Our study offered a novel method for estimating sub-daily Tair patterns using open-source data and revealed the impacts of predictive variables on Tair estimation, which has important implications for urban thermal environment research. Full article