Search Results (322)

Heating, Ventilating, and Air Conditioning (HVAC) systems are important and essential for use in our daily comfort, either in homes, work, or transportation. And it is crucial to study the air movement coming from the inlet diffuser for a better design to enhance thermal comfort and energy consumption. The primary objective of the presented work is to investigate the thermal comfort within a faculty office occupied by two faculty members using the Computational Fluid Dynamics (CFD) methodology. First, an independent mesh study was performed to reduce the uncertainty related to the mesh size. In addition, the presented CFD approach was validated against available experimental data from the literature. Then, the effect of inlet air temperature and velocity on air movement and temperature distribution is investigated using Ansys Fluent. To be as reasonable as possible, the persons who occupy the office, lights, windows, tables, the door, and computers are accounted for in the CFD simulation. After that, the Predicted Mean Vote (PMV) was evaluated at three different locations inside the room, and the approximate total energy consumption was obtained for the presented cases. The CFD results showed that, for the presented cases, the sensation was neutral with the lowest energy consumption when the supply air velocity was 1 m/s and the temperature was 21 °C. Full article

Over recent decades, phase-change materials (PCMs) have gained prominence as latent-heat thermal energy storage systems in building envelopes because of their high energy density. However, only PCMs that complete a full daily charge–discharge cycle can deliver meaningful energy and carbon-emission savings. This simulation study introduces a methodology that simultaneously optimizes PCM integration for storage efficiency, indoor thermal comfort, and energy savings. Two new indicators are proposed: overall storage efficiency (EC_n), which consolidates heating and cooling-efficiency ratios into a single value, and the performance factor (PF), which quantifies the PCM’s effectiveness in maintaining thermal comfort. Using EnergyPlus v8.9 coupled with DesignBuilder, a residential ASHRAE 90.1 mid-rise apartment was modeled in six warm-temperate (Cfb) European cities for the summer period from June 1 to August 31. Four paraffin PCMs (RT-22/25/28/31 HC, 20 mm thickness) were tested under natural and controlled ventilation strategies, with windows opening 50% when outdoor air was at least 2 °C cooler than indoors. Simulation outputs were validated against experimental cubicle data, yielding a mean absolute indoor temperature error ≤ 4.5%, well within the ±5% tolerance commonly accepted for building thermal simulations. The optimum configuration—RT-25 HC with temperature-controlled ventilation—achieved PF = 1.0 (100% comfort compliance) in all six cities and delivered summer cooling-energy savings of up to 3376 kWh in Paris, the highest among the locations studied. Carbon-emission reductions reached 2254 kg CO₂-e year⁻¹, and static payback periods remained below the assumed 50-year building life at a per kg PCM cost of USD 1. The EC_n–PF framework, therefore, provides a transparent basis for selecting cost-effective, energy-efficient, and low-carbon PCM solutions in warm-temperate buildings. Full article

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest $R^2$ (0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article

The goal of this study was to look at changes in mean air temperatures, minimum air temperatures, maximum air temperatures, dew points, and precipitation over each of 1033 lakes in the coterminous United States over the summer months in the years 1981–2024. Near-surface water temperatures in the same lakes were calculated with equations using 8-day mean daily air temperatures, latitude, elevation, and the year of sampling. Over the past 43 years, there have been changes in air temperatures over many lakes of the United States with generally increasing trends for minimum air temperatures and mean air temperatures during the months of June through September. The greatest increases have been in daily minimum air temperatures followed by the mean daily air temperatures. Maximum daily air temperatures did not show a statistically significant increase for the summer season but did show a significant increase for the month of September. Along with the changes in the climate, the near-surface water temperatures of the lakes of the United States on average showed increases of 0.33 °C decade⁻¹ for the four summer months and increases for each of the summer months. Full article

Extreme air temperatures along with the synoptic conditions leading to their appearance are examined for the Mediterranean region for the 85-year period of 1940–2024. The data used are daily (04UTC and 12UTC) grid point (1° × 1°) values of 2 m air temperature, 850 hPa air temperature, and 1000 hPa and 500 hPa geopotential heights, obtained from the ERA5 database. For 12UTC and 04UTC, the 2 m air temperature anomalies are calculated and are used for the definition of Extremely High Temperature Days (EHTDs) and Extremely Low Temperature Days (ELTDs), respectively. Overall, 3787 EHTDs and 4872 ELTDs are defined. It is found that EHTDs are evidently more frequent in recent years (increased by 305% since the 1980s) whereas ELTDs are less frequent (decreased by 41% since the 1980s), providing a clear sign of warming of the Mediterranean climate. A multivariate statistical analysis combining factor analysis and k-means clustering, known as spectral clustering, is applied to the data resulting in the definition of nine EHTD and seven ELTD clusters. EHTDs are mainly associated with intense solar heating, blocking anticyclones and warm air advection. ELTDs are connected to intense radiative cooling of the Earth’s surface, cold air advection and Arctic outbreaks. This is a unique study for the Mediterranean region utilizing the high-resolution ERA5 data collected since the 1940s to define and investigate the variability of both high and low temperature extremes using a validated methodology. Full article

The main objective of this work was to evaluate new variants of passive heating systems used for horticultural crop cycles planted in the cold period in low-cost greenhouses on the Mediterranean Spanish coast (a mild-winter area). The double low cover (DLC) is variant of the conventional fixed plastic screen that reduces the air volume and increases the airtightness around crops. Three identical DLCs were installed inside a typical greenhouse, and the microclimate measured in the three DLCs was similar. The DLCs reduced the solar radiation transmissivity coefficient by around 0.05 but increased the mean daily substrate and air temperatures (up to 1.6 and 3.6 °C, respectively). They also modified the air humidity, although this can be modulated by opening the vertical sheets located on the greenhouse aisles (DLC vents). The black plastic mulch forming an air chamber around the substrate bags (BMC), a new mulch variant used in substrate-grown crops, increased the substrate temperature with respect to the conventional black mulch covering the entire ground surface. The combination of BMC plus DLC increased the mean daily substrate temperature by up to 2.9 °C, especially at night. Low tunnels covered with transparent film and with a spun-bonded fabric sheet were also compared, and both materials were efficient heating systems regarding substrate and air temperatures. Low tunnels combined with the DLC substantially increased air humidity, but this can be partially offset by opening the DLC vents. The combination of low tunnels and DLC does not seem recommendable for greenhouse crops planted in winter, since both systems reduce solar radiation transmissivity. Full article

This study investigates the impact of desert dust on precipitation patterns using multi-model simulations. Dust-based processes of formation/removal of ice nuclei (IN) and cloud condensation nuclei (CCN) are investigated by using both the online access model WRF-CHIMERE and the online integrated model WRF-Chem. Comparisons of model predictions with rainfall measurements (GRISO: Spatial Interpolation Generator from Rainfall Observations) over the Italian peninsula show the models’ ability to reproduce heavy orographic precipitation in alpine regions. To quantify the impact of the mineral dust transport concomitant to the atmospheric river (AR) on cloud formation, a sensitivity study is performed by using the WRF-CHIMERE model (i) by setting dust concentrations to zero and (ii) by modifying the settings of the Thompson Aerosol-Aware microphysics scheme. Statistical comparisons revealed that WRF-CHIMERE outperformed WRF-Chem. It achieved a correlation coefficient of up to 0.77, mean bias (MB) between +3.56 and +5.01 mm/day, and lower RMSE and MAE values (~32 mm and ~22 mm, respectively). Conversely, WRF-Chem displayed a substantial underestimation, with an MB of −25.22 mm/day and higher RMSE and MAE values. Our findings show that, despite general agreement in spatial precipitation patterns, both models significantly underestimated the peak daily rainfall in pre-alpine regions (e.g., 216 mm observed at Malga Valine vs. 130–140 mm simulated, corresponding to a 35–40% underestimation). Although important instantaneous changes in precipitation and temperature were modeled at a local scale, no significant total changes in precipitation or air temperature averaged over the entire domain were observed. These results underline the complexity of aerosol–cloud interactions and the need for improved parameterizations in coupled meteorological models. Full article

Accurate predictions of beach user numbers are important for coastal management, resource allocation, and minimising safety risks, especially when considering surf-zone hazards. The present work applies an XGBoost model to predict beach attendance from automatically video-derived data, incorporating input variables such as weather, waves, tide, and time (e.g., day hour, weekday). This approach is applied to data collected from Biscarrosse Beach during the summer of 2023, where beach attendance varied significantly (from 0 to 2031 individuals). Results indicate that the optimal XGBoost model achieved high predictive accuracy, with a coefficient of determination ($R^2$) of 0.97 and an RMSE of 70.4 users, using daily mean weather data, tide and time as input variables, i.e., disregarding wave data. The model skilfully captures both day-to-day and hourly variability in attendance, with time of day (hour) and daily mean air temperature being the most influential variables. An XGBoost model using only daily mean temperature and hour of the day even shows good predictive accuracy ($R^2$ = 0.90). The study emphasises the importance of daily mean weather data over instantaneous measurements, as beach users tend to plan visits based on forecasts. This model offers reliable, computationally inexpensive, and high-frequency (e.g., every 10 min) beach user predictions which, combined with existing surf-zone hazard forecast models, can be used to anticipate life risk at the beach. Full article

Puerto Rico has experienced recent increases in annual, seasonal and daily temperatures that have been associated with climate change. More recently, the island has been experiencing an increase in the frequency of extremely warm days that are causing significant environmental and socio-economic impacts. This study focuses on examining how annual, seasonal and daily temperatures have changed over recent decades in 12 historical sites spread across the island for the 1970–2024 period and how it relates to climate change. The Mann–Kendall tests for trends were employed for the annual and seasonal series to identify areas of the island where warming has been found to be statistically significant. The 90th, 95th, and 99th percentiles of daily temperature series were also analyzed. This study found that Puerto Rico has experienced significant warming from 1970 to 2024, with the most consistent increases in minimum temperatures, especially during the summer and nighttime hours. The frequency of extreme heat events has increased across nearly all stations in different areas of the island. Stepwise regression models identified surface air temperature (SAT), sea surface temperature (SST), and total precipitable water (TPW) as the most influential regional climate predictors driving mean temperature trends and the occurrence of extreme heat events. Full article

The aim of this study was to examine variations in the hydrological regime of the Krapina River from 1964 to 2023. The river basin spans 1263 km² and is characterized by a temperate, humid continental climate with warm summers. Hydrological data from the Kupljenovo gauging station, which monitors 91.1% of the basin (1150 km²), indicate an average annual discharge of 11.2 m³/s, ranging from 3.25 m³/s to 18.3 m³/s. Over the 60-year study period, the minimum mean daily discharges show a statistically insignificant increasing trend, while the mean annual and maximum annual mean daily discharges exhibit statistically insignificant declines. Annual precipitation averages 1037 mm, varying between 606 mm and 1459 mm, with a non-significant decreasing trend. In contrast, the mean annual air temperatures demonstrate a statistically significant increasing trend, with a pronounced intensification beginning in 1986. The annual runoff coefficients series exhibits a statistically insignificant downward trend, with an average value of 0.293 (range: 0.145–0.399). Application of the New Drought Index (NDI) revealed a marked increase in the frequency of strong and extreme droughts since 2000. Full article

The quality of medicinal plants is closely related to the ecological factors of their growing environment, as their efficacy is reflected in the content of key medicinal components, which in turn indicates the quality of the plants. This study measured the daily variations in major constituents, including lobetyolin, polysaccharides, and total flavonoids, in Codonopsis pilosula (Franch.) Nannf., which in the Changzhi and Jincheng regions of Shanxi Province, China is known as Lu Tangshen. Throughout its growth cycle. Additionally, the study explored the effects of 11 ecological factors (both climatic and soil variables) on the primary medicinal components of C. pilosula. Through block experiments and comparisons between future data predictions and actual measurements, the reliability of the model and the consistency of block experimental data were ultimately confirmed. Principal component analysis (PCA), stepwise multiple linear regression analysis, and nonlinear polynomial modeling were employed to investigate the relationships between ecological factors and quality-related constituents (polysaccharides, total flavonoids, and lobetyolin). The results showed that linear models effectively explained daily temperature (DT) with an adjusted R² exceeding 0.8, but due to the inherently nonlinear nature of the data, it is evident that linear models are fundamentally inadequate for accurately capturing the underlying relationships. Therefore, their fit for total flavonoids and lobetyolin was suboptimal. The introduction of nonlinear polynomial models (second-, fourth-, and fifth-order) significantly improved the model fit, indicating the existence of complex nonlinear relationships between ecological factors and medicinal components. For polysaccharides, the fourth-order model demonstrated the best performance, while fifth-order models were required to adequately describe the relationships for total flavonoids and lobetyolin. Based on the best models, the optimal ranges for key ecological factors were identified: polysaccharides were best influenced by atmospheric pressure (AP) between 9.1 and 9.3 kPa, air relative humidity (ARH) between 30% and 60%, 40 cm soil mean annual temperature (40cmMAT) between 27.5 °C and 28.5 °C, soil pH between 9.68 and 9.72, and soil nitrogen (N) content between 7 and 9 mg/kg. For total flavonoids, narrow optimal ranges were observed for temperature, humidity, and pH (MAT between 10 °C and 15 °C, 40cmMAT between 27.5 °C and 28.5 °C, and pH between 9.68 and 9.72). Lobetyolin showed optimal conditions at AP of 9.1 to 9.3 kPa, 40cmMAT of 28.0 °C to 28.5 °C, ARH of 65% to 75%, pH near 9.70, and days after planting (DAP) between 10 and 50. The adoption of higher-order polynomial models clarified critical nonlinear inflection points and optimal ecological ranges, providing a refined reference for enhancing the content of medicinal components. These findings offer valuable insights for precision cultivation strategies aimed at improving the quality of C. pilosula. Full article

Exposure to high surface ozone (O₃) concentrations, which is a major air pollutant and greenhouse gas, constitutes a significant public health concern, especially considering the potential adverse impact of climate change on future O₃ values. The implementation of increasingly effective methods to assess the factors determining the formation and variability of O₃ is, therefore, of great significance. In this study, a methodological approach combining both supervised and unsupervised machine learning algorithms (MLAs) with the Shapley additive explanations (SHAP) method was used to understand the key factors behind O₃ variability and to explore the nonlinear relationships linking O₃ to these factors. The SHAP analysis carried out at different event scales indicated (i) the dominant role of the meteorological variables in driving O₃ variability, mainly relative humidity, wind speed, and temperature throughout the study period; (ii) an increase in the contribution of temperature, nitrogen oxides, and carbon monoxide to high O₃ concentrations during a selected pollution event; (iii) the predominant effect of wind speed and relative humidity in shaping the O₃ daily patterns clustered using the k-means technique. The results obtained are expected to be useful for the definition of effective measures to prevent and/or mitigate the health damage associated with ozone exposure. Full article

When planning a solar energy conversion system, having sufficiently reliable values of the monthly average daily solar radiation (MADSR) on a horizontal surface is essential. Traditionally, estimates based on other climatological variables for which more information is available have been relied upon to compensate for the lack of direct solar radiation measurements. Solar radiation varies widely, which requires the creation of site-specific forecast models. By using artificial neural network (ANN) models or similar methods using historical datasets, the monthly average daily solar radiation can be easily assessed. To verify the validity of the established ANN model, a series of analyses was performed using the mean squared error, the coefficient of determination (R²), and the mean absolute error. The study used a dataset collected from nine weather stations in Saudi Arabia from 1985 to 2000. The input parameters for the ANN model were the maximum air relative humidity, latitude, the maximum ambient air temperature, longitude, the minimum ambient air temperature, the minimum air relative humidity, sunshine duration, location altitude, and the corresponding month. The R² for the whole test dataset was 0.8449. Furthermore, a sensitivity analysis using the established ANN model showed that site elevation (location altitude) had the most significant effect on MADSR on a horizontal surface, with a contribution value of 14.66%. The analysis results show that the ANN model accurately estimates MADSR on horizontal surfaces regardless of seasonal variations in weather conditions. Furthermore, this work is important not only for its contribution to the shape of information in solar radiation forecasting but also for establishing the practical application of ANNs in renewable energy management. The results of this work will help improve the utilization of solar energy and support sustainable energy efforts. Furthermore, the proposed ANN model is believed to be useful for predicting MADSR on horizontal surfaces in other locations in Saudi Arabia with similar climatic conditions to the study sites. Furthermore, the ANN approach may be functional to the basic strategy of a solar arrangement and is suitable for forecasting other meteorological data. Full article

Climate change increases the importance of maintaining environmental conditions suitable for preventive conservation within museums. The microclimates at the Natural History Museum of Vienna, a large national collection housed within a classical building, were studied using >200 data loggers placed from mid 2021 to provide thermo-hygrometric measurements at 15 min intervals. Daily mean temperatures showed exhibition halls typically had the warmest rooms. This was due to the heating in winter and open windows on summer days. The halls can become even hotter than the outside temperature. In winter, most areas of the museum were very dry, as heating lowered the relative humidity, typically to 25–35% for the coldest season. Opening hours imposed daily and weekly cycles on the internal climate. There was little difference between sunny and shaded parts of the building or adjacent offices, corridors and depots. Similarly, the microclimate at the floor resembled that of the room air some ~2 m above. Mechanically controlled microclimates in cold storage areas maintained 10 °C and relative humidity ~50%, but this had become increasingly difficult in hot summers. While there was little apparent damage to the collection, at times, the museum had an extreme indoor climate: very hot in the summer and dry in the winter. Full article

Forests play a crucial role in carbon cycling, contributing significantly to global carbon cycling and climate change mitigation, but their capture strength is sensitive to the climatic zone in which they operate and its adjoining environmental stressors. This research investigated the carbon dynamics of a typical deciduous forest, the Daniel Boone National Forest (DBNF), in the Mixed-Humid climate of Kentucky, USA, employing the Eddy Covariance technique to quantify temporal CO₂ exchanges from 2016 to 2020 and to assess its controlling biometeorological factors. The study revealed that the DBNF functioned as a carbon sink, sequestering −1515 g C m⁻² in the study period, with a mean annual Net Ecosystem Exchange (NEE) of −303 g C m⁻²yr⁻¹. It exhibited distinct seasonal and daily patterns influenced by ambient sunlight and air temperature. Winter months had the lowest rate of CO₂ uptake (0.0699 g C m⁻² h⁻¹), while summer was the most productive (−0.214 g C m⁻² h⁻¹). Diurnally, carbon uptake peaked past midday and remained a sink overnight, albeit negligibly so. Light and temperature response curves revealed their controlling effect on the DBNF trees’ photosynthesis and respiration. Furthermore, clear seasonality patterns were observed in the control of environmental variables. The DBNF is a carbon sink consistent with other North American deciduous forests. Full article