Search Results (553)

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article

This study proposes a new air treatment system that integrates dehumidification, cooling, and water recovery using a Horizontal Air–Ground Heat Exchanger (HAGHE) combined with Peltier cells. The airflow generated by a fan flows through an HAGHE until it meets a septum on which Peltier cells are placed, and then separates into two distinct streams that lap the two surfaces of the Peltier cells: one stream passes through the cold surfaces, undergoing both sensible and latent cooling with dehumidification; the other stream passes through the hot surfaces, increasing its temperature. The two treated air streams may then pass through a mixing chamber, where they are combined in the appropriate proportions to achieve the desired air supply conditions and ensure thermal comfort in the indoor environment. A Computational Fluid Dynamics (CFD) analysis was carried out to simulate the thermal interaction between the HAGHE and the surrounding soil. The simulation focused on a system installed under the subtropical climate conditions of Nairobi, Africa. The simulation results demonstrate that the HAGHE system is capable of reducing the air temperature by several degrees under typical summer conditions, with enhanced performance observed when the soil is moist. Condensation phenomena were triggered when the relative humidity of the inlet air exceeded 60%, contributing additional cooling through latent heat extraction. The proposed HAGHE–Peltier system can be easily powered by renewable energy sources and configured for stand-alone operation, making it particularly suitable for off-grid applications. Full article

In drought-prone regions without irrigation systems, effective agrotechnologies such as subsoiling are crucial for enhancing soil infiltration and water retention. However, the effects of subsoiling can vary depending on crop type and environmental conditions. Despite previous research, there is limited understanding of the contrasting responses of C3 (sunflower) and C4 (maize) crops to subsoiling under drought stress. This study addresses this knowledge gap by assessing the effectiveness of subsoiling as a drought mitigation practice on Haplic Chernozem in Northern Bulgaria, integrating ground-based and remote sensing data. Soil physical parameters, leaf area index (LAI), canopy temperature, crop water stress index (CWSI), soil moisture, and yield were evaluated under both conventional tillage and subsoiling for the two crops. A variety of optical and radar descriptive remote sensing products derived from Sentinel-1 and Sentinel-2 satellite data were calculated for different crop types. Consequently, the use of machine learning, utilizing all the processed remote sensing products, enabled the reasonable prediction of LAI, achieving a coefficient of determination (R²) after a cross-validation greater than 0.42 and demonstrating good agreement with in situ observations. Results revealed differing responses: subsoiling had a positive effect on sunflower, improving LAI, water status, and slightly increasing yield, while it had no positive effect on maize. These findings highlight the importance of crop-specific responses in evaluating subsoiling practices and demonstrate the added value of integrating unmanned aerial systems (UAS) and satellite-based remote sensing data into agricultural drought monitoring. Full article

Understanding the geographic distribution of mammal species is essential for informed conservation planning, maintaining local ecosystem stability, and addressing research gaps, particularly in data-deficient regions. This study investigated the distribution and richness of 20 mammal species within Nyerere National Park (NNP), a large and understudied protected area in Southern Tanzania. We applied species distribution models (SDMs) using presence data collected through ground surveys between 2022 and 2024, combined with environmental variables derived from remote sensing, including land surface temperature, vegetation indices, soil moisture, elevation, and proximity to water sources and human infrastructure. Models were constructed using the Maximum Entropy (MaxEnt) algorithm, and performance was evaluated using the Area Under the Curve (AUC) metric, yielding high accuracy ranging from 0.81 to 0.97. Temperature (32.3%) and vegetation indices (23.4%) emerged as the most influential predictors of species distributions, followed by elevation (21.7%) and proximity to water (14.5%). Species richness, estimated using a stacked SDM approach, was highest in the northern and riparian zones of the park, identifying potential biodiversity hotspots. This study presents the first fine-scale SDMs for mammal species in Nyerere National Park, offering a valuable ecological baseline to support conservation planning and promote sustainable ecotourism development in Tanzania’s southern protected areas. Full article

Medium-deep borehole heat exchanger (BHE) systems represent an emerging form of ground source heat pump technology. Their heat transfer process is significantly influenced by geothermal gradient and soil stratification, typically simulated using segmented finite line source (SFLS) models. However, this approach involves computationally intensive procedures that hinder practical engineering implementation. Building upon an SFLS model adapted for complex geological conditions, this study proposes a comprehensive simplified algorithm: (1) For soil stratification: A geothermally-weighted thermal conductivity method converts layered heterogeneous media into an equivalent homogeneous medium; (2) For geothermal gradient: A temperature correction method establishes fluid temperatures under geothermal gradient by superimposing correction terms onto uniform-temperature model results (g-function model). Validated through two engineering case studies, this integrated algorithm provides a straightforward technical tool for heat transfer calculations in BHE systems. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article

In various applications, IoT temporal data play a crucial role in accurately predicting future trends. Traditional models, including Rolling Window, SVR-RBF, and ARIMA, suffer from a potential accuracy decrease because they generally use all available data or the most recent data window during training, which can result in the inclusion of noisy data. To address this critical issue, this paper proposes a new forecasting technique called Adaptive Holt–Winters (AHW). The AHW approach utilizes two models grounded in an exponential smoothing methodology. The first model is trained on the most current data window, whereas the second extracts information from a historical data segment exhibiting patterns most analogous to the present. The outputs of the two models are then combined, demonstrating enhanced prediction precision since the focus is on the relevant data patterns. The effectiveness of the AHW model is evaluated against well-known models (Rolling Window, SVR-RBF, ARIMA, LSTM, CNN, RNN, and Holt–Winters), utilizing various metrics, such as RMSE, MAE, p-value, and time performance. A comprehensive evaluation covers various real-world datasets at different granularities (daily and monthly), including temperature from the National Climatic Data Center (NCDC), humidity and soil moisture measurements from the Basel City environmental system, and global intensity and global reactive power from the Individual Household Electric Power Consumption (IHEPC) dataset. The evaluation results demonstrate that AHW constantly attains higher forecasting accuracy across the tested datasets compared to other models. This indicates the efficacy of AHW in leveraging pertinent data patterns for enhanced predictive precision, offering a robust solution for temporal IoT data forecasting. Full article

To address thermal imbalance and ground temperature degradation in shallow geothermal heat pump (GSHP) systems in severely cold climates, this study analyzes a typical logistics building using an hourly dynamic load model. Multiyear simulations were conducted to investigate the coupling between building load variation and soil thermal response. The results indicate that with a cumulative heating load of 14.681 million kWh and cooling load of 6.3948 million kWh, annual heat extraction significantly exceeds heat rejection, causing ground temperature to decline by about 1 °C per year. Over five and ten years, the cumulative drops reached 2.65 °C and 4.71 °C, respectively, leading to a noticeable reduction in borehole heat exchanger performance and system COP. The study quantitatively evaluates ground temperature and heat exchange degradation, highlighting the key role of load imbalance. To mitigate long-term thermal deterioration, strategies such as load optimization, summer heat reinjection, and operational adjustments are proposed. The findings offer guidance for the design and sustainable operation of GSHP systems in cold regions. Full article

The artificial ground freezing method (AGF) is widely used in underground construction to reinforce the ground and ensure construction safety. This study systematically evaluates the implementation of the artificial ground freezing method in the construction of a metro tunnel cross-passage, with a focus on analyzing the soil’s thermo-mechanical behavior and assessing safety performance throughout the construction process. A combined approach integrating field monitoring and refined three-dimensional numerical simulation using FLAC3D is adopted, considering critical factors, such as freezing pipe inclination, thermo-mechanical coupling, and ice–water phase transitions. Both field data and simulation results demonstrate that increasing the density of freezing pipes accelerates temperature reduction and intensifies frost heave-induced displacements near the pipes. After 45 days of active freezing, the freezing curtain reaches a thickness of 3.7 m with an average temperature below −10 °C. Extending the freezing duration beyond this period yields negligible improvement in curtain performance. Frost heave deformation develops rapidly during the initial phase and stabilizes after approximately 25 days, with maximum vertical displacements reaching 12 cm. Significant stress concentrations occur in the soil adjacent to the freezing pipes, with shield tunnel segments experiencing up to 5 MPa of stress. Thaw settlement is primarily concentrated in areas previously affected by frost heave, with a maximum settlement of 3 cm. Even after 45 days of natural thawing, a frozen curtain approximately 3.3 m thick remains intact, maintaining sufficient structural strength. The refined numerical model accurately captures the mechanical response of soil during the freezing and thawing processes under realistic engineering conditions, with field monitoring data validating its effectiveness. This research provides valuable guidance for managing construction risks and ensuring safety in similar cross-passage and cross-river tunnel projects, with broader implications for underground engineering requiring precise control of frost heave and thaw settlement. Full article

This paper presents a numerical study of the thermal behavior of grounding electrodes subjected to fault currents, focusing on Joule heating within both the electrode and the surrounding soil. A one-dimensional transient model is developed, accounting for heat generation due to both internal resistance in the electrode and current leakage into the soil. The model incorporates the temperature dependence of electrical resistivity, particularly emphasizing the nonlinear and material-specific behavior observed in soils, as captured by three different resistivity models. The temperature–resistivity coupling induces a feedback mechanism that dynamically alters the current distribution and the resulting temperature profiles. A numerical procedure was implemented to simulate this process, following a computational flowchart that captures the interaction between thermal and electrical fields over time. The model was applied to synthetic test cases involving different soil types, segmentation strategies, and resistivity behaviors. The results reveal significant differences between resistivity models, affecting both the magnitude and distribution of grounding potential and temperature fields. In particular, elevated temperatures were observed in regions where current density concentrates—such as corners and exposed ends of the electrode—highlighting the need for targeted reinforcement to prevent thermal degradation. The proposed model provides a practical tool for evaluating the thermal performance of grounding systems under extreme conditions, offering insight into design optimization and material selection. Full article

Radon’s radioactive decay is the main natural source of small air ions near the ground. Its exhalation from soil is affected by meteorological factors, while aerosol pollution reduces air ion concentrations through ion-particle attachment. This study aimed to analyze correlations between radon, ions, and air pollution under varying conditions and to assess potential health impacts. Measurements were taken at two sites: in early autumn at a suburban part of Belgrade with relatively clean air, and in late autumn in central Belgrade under polluted conditions, with low temperatures and high humidity. Parameters measured included radon, small air ions, particle size distribution, PM mass concentration, temperature, humidity, and pressure. Results showed lower radon concentrations in late autumn due to high soil moisture and absence of nocturnal inversions. Radon and air ion concentrations exhibited a strong positive correlation for both polarities under suburban conditions, whereas measurements in the urban setting revealed a weak negative correlation, despite radon concentrations in soil gas being approximately equal at both sites. Small ion levels were also reduced, mainly due to suppressed radon exhalation and increased aerosol concentrations, especially ultrafine particles. A strong negative correlation (r < −0.5) was found between small air ion concentrations and particle number concentrations in the 20–300 nm range, while larger particles (300–1000 nm and >1 µm) showed weak or no correlation due to their lower and more stable concentrations. In contrast, early autumn measurements showed a diurnal cycle of radon, characterized by nighttime maxima and daytime minima, unlike the consistently low values observed in late autumn. Full article

Living mulch intercropping systems are considered as nature-based solutions with a low environmental footprint for managing weeds, improving biodiversity and agroecosystem sustainability. In drylands, however, they may increase intra/inter-specific competition for water, reducing crop productivity. We tested conservation tillage (TLG) carob plots with and without irrigation (TLG_irr; TLG_dry) vs. rainfed intercropping systems of carob and (i) thyme (Thymbra capitata; T-System) or (ii) clover (Trifolium squarrosum; C-System), strategically planted on the south (sun)-exposed soil side (SES) of carobs, to reduce soil temperature/evaporation. Carob water relations, productivity and environmental footprints were examined for three years under semi-arid, low weed-competition (Skarinou-SKR) and arid high weed-competition (Vrysoules-VRY) conditions in Cyprus. Carob yield efficiency (kg/m³) in SKR, was >27% higher for the T-System (p < 0.05; SES cover ca. 85%; year-3), matching a higher leaf water content (p < 0.001) compared to TLG_dry. The T-System reached 28% and 56% of TLG_irr yields during very dry and normal rainfall years; TLG_dry yields approached zero. For VRY, no negative impacts on carob leaf water, at 25% SES cover, were found. SKR’s C-System improved leaf water content (p < 0.05) for only one year. The T-System also outperformed TLG_irr and TLG_dry in terms of reducing irrigation needs and energy consumption, breaking new grounds for dryland agroforestry. Full article

This study provides a comprehensive spatiotemporal analysis of sand and dust storms (SDSs) in Uzbekistan using ground-based meteorological data from 2010 to 2023. The results reveal significant spatial heterogeneity in the SDS activity, with the highest frequency of SDS days observed in the southern and western regions, including Surkhandarya, Kashkadarya, Bukhara, Khorezm, and Republic of Karakalpakstan. In the most vulnerable areas, such as Karakalpakstan, Surkhandarya, and Kashkadarya, the annual number of SDS days can exceed 80 in certain years, reflecting a high recurrence of extreme dust events in certain climatic zones. About 53% of the SDS events were regional, affecting several stations, while 47% were localized, indicating a combination of large-scale dust transport and localized emissions. Seasonal patterns showed a peak SDS activity between March and August, coinciding with the dry season characterized by elevated temperatures, reduced soil moisture, and intense agricultural activity, all of which contribute to the surface exposure and increased vulnerability. This study found a significant variation in the event duration across regions, with Karakalpakstan and Surkhandarya experiencing the highest proportion of prolonged events due to its orography and persistent southerly wind patterns. Using ERA5 data and a decision tree regressor, the analysis identified the wind direction and mean wind speed as the most influential meteorological factors, followed by the maximum wind speed and soil temperature, with other variables such as solar radiation and soil moisture playing moderate roles. This study highlights the importance of regional wind patterns and geomorphology in SDS formation, with prevailing wind directions from the northwest, west, and south. The integration of the ERA5 reanalysis and machine learning techniques offers significant potential for improving SDS monitoring and studies. Full article

Extensive wildfires and logging have affected the Russian boreal forests in recent decades. Scots pine (Pinus sylvestris L.) forests are widespread in Russia and are one of the most disturbed tree species in Siberia. However, the effects of disturbance on soil CO₂ efflux in the vast Siberian forests are still poorly understood. We used the LI 8100A infrared gas analyzer to study changes in soil CO₂ efflux into the atmosphere in mature Scots pine forests in the Siberian central taiga five–six years following fires and logging. Measurements of soil CO₂ efflux rates were performed on sites where automatic weather stations have been continuously operational since 2022, which gives us temporal patterns of meteorological fluctuations across forests with different disturbance histories. We found significant differences in soil efflux rates depending on the site and disturbance characteristics. In the undisturbed dry lichen-dominated forest, CO₂ efflux was 4.8 ± 2.1 µmol m⁻² s⁻¹, while in the wet moss-dominated forest it was 2.3 ± 1.3 µmol m⁻² s⁻¹, with soil efflux in Sphagnum sp. being twofold of that in feather moss. Both fire and logging significantly reduced CO₂ efflux, with a smaller reduction in soil CO₂ efflux observed in the moss-dominated plots (5%–40%) compared to the lichen-dominated plots (36%–55%). The soil efflux rate increased exponentially with increasing topsoil temperatures in lichen-dominated Scots pine sites, with disturbed plots showing less dependence compared to undisturbed forest. In the wet moss-dominated Scots pine forest, we found no significant dependence of soil efflux on temperature for all disturbance types. We also found a positive moderate relationship between soil efflux and forest floor depth in both lichen- and moss-dominated Scots pine forests across all the plots studied. Our findings advance the understanding of the effects of fire and logging on the carbon cycle and highlight the importance of accounting for disturbance factors in Earth system models due to changing climate and anthropogenic patterns. Full article

The short-term performance of ground heat exchangers (GHEs) is crucial for the optimal design of ground-source heat pumps (GSHPs), enhancing their contribution to sustainable energy solutions. Local short-time thermal response functions, or short-time g-functions (STGFs) derived from thermal response tests (TRTs), are of great interest for predicting the heat exchange due to their fast and simple applicability. The aim of this work is to perform a sensitivity analysis to assess the impact of thermal parameter variability and TRT operating conditions on the accuracy of the average fluid temperature (T_f) predictions obtained through a local STGF. First, the uncertainties associated with the borehole thermal resistance (R_b), transmitted from the soil volumetric heat capacity (C_S) or some models dependent on GHE characteristics, such as the Zeng model, were found to have a low impact in T_f resulting in long-term deviations of ±0.2 K. Second, several TRTs were carried out on the same borehole, changing input parameters such as the volumetric flow rate and heat injection rate, in order to obtain their corresponding STGF. Validation results showed that each T_f profile consistently aligned well with experimental data when applying intermittent heat rate pulses (being the most unfavorable scenario), implying deviations of ±0.2 K, despite the variabilities in soil conductivity (λ_S), soil volumetric heat capacity (C_S), and borehole thermal resistance (R_b). Full article