Search Results (3,672)

Optimizing aeration, fertilization, and irrigation is vital for improving greenhouse tomato production while mitigating soil greenhouse gas (GHG) emissions. This study investigated the combined effects of three aeration levels (A1: single Venturi, A2: double Venturi, CK: no aeration), two fertilization rates (F1: 180 kg/ha, F2: 240 kg/ha), and two irrigation levels (I1: 0.8 E_pan, I2: 1.0 E_pan) on tomato yield, CO₂, N₂O, and CH₄ emissions, net GHG emissions, net global warming potential (NGWP), and GHG intensity (GHGI) across Spring–Summer and Autumn–Winter seasons. Results showed that aeration and fertilization significantly increased CO₂ and N₂O emissions but reduced CH₄ emissions. Warmer conditions in Spring–Summer elevated all GHG emissions and yield compared to Autumn–Winter seasons. Tomato yield, net GHG emissions, NGWP, and GHGI were 12.05%, 24.3%, 14.46%, and 2.37% higher, respectively, in Spring–Summer. Combining the Maximal Information Coefficient and TOPSIS models, the optimal practice was A1-F1-I1 in Spring–Summer and A2-F1-I1 in Autumn–Winter seasons. These results provide a theoretical basis for selecting climate-smart management strategies that enhance yield and environmental sustainability in greenhouse tomato systems. Full article

Monitoring the development of greenhouse crops is essential for optimising yield and ensuring the efficient use of resources. A system for monitoring hemp (Cannabis sativa L.) cultivation under greenhouse conditions using computer vision has been developed. This system is based on open-source automation software installed on a single-board computer. It integrates various temperature and humidity sensors and surveillance cameras, automating image capture. Hemp seeds of the Tiborszallasi variety were sown. After germination, plants were transplanted into pots. Five specimens were selected for growth monitoring by image analysis. A surveillance camera was placed in front of each plant. Different approaches were applied to analyse growth during the early stages: two traditional computer vision techniques and a deep learning algorithm. An average growth rate of 2.9 cm/day was determined, corresponding to 1.43 mm/°C day. A mean MAE value of 1.36 cm was obtained, and the results of the three approaches were very similar. After the first growth stage, the plants were subjected to water stress. An algorithm successfully identified healthy and stressed plants and also detected different stress levels, with an accuracy of 97%. These results demonstrate the system’s potential to provide objective and quantitative information on plant growth and physiological status. Full article

With increasing wastewater treatment demands and decarbonization goals, synergistic reduction in pollutants and green house gas (GHG) emissions is crucial. High process emissions like N₂O pose significant challenges, yet optimized carbon reduction strategies for conventional plants are lacking. This study developed three mathematical models to quantify the impact of dissolved oxygen (DO), influent salinity, and C/N ratio on direct emissions (CH₄, N₂O) and indirect emissions. Response Surface Methodology (RSM) optimized these factors to minimize GHG emissions under three accounting scenarios: (1) plants with CH₄ reuse systems: salinity = 0.5 g L⁻¹, DO = 3.67 mg L⁻¹, C/N = 12.75; (2) plants focusing solely on direct emissions: salinity = 0.5 g L⁻¹, DO = 3.35 mg L⁻¹, C/N = 3; and (3) plants assessing total emissions: salinity = 0.5 g L⁻¹, DO = 2.5 mg L⁻¹, C/N = 7.18. Key findings indicated that increasing salinity exacerbated greenhouse gas emissions. Elevated DO levels in the aerobic stage reduced N₂O emissions but increased indirect emissions in the A²/O process. Higher C/N ratios promoted anaerobic CH₄ production, but sufficient carbon reduced N₂O by enabling complete heterotrophic denitrification. A 60−day continuous GHG emissions monitoring campaign was conducted at a WWTP to validate the actual emission reductions achievable under the identified optimal control conditions. An analysis and comparison of operational and economic costs were also performed. The findings provide practical insights into sustainable GHG emission management and offer potential solutions to advance the synergistic reduction in GHG emissions and pollutants. Full article

Anthropogenic climate change threatens production of essential natural resources, such as food, fiber, fresh water, and provisioning of ecosystem services such as carbon sequestration, increasing the risk of societal collapse. The Human and Nature Dynamics (HANDY) model simulates the effect of resource overexploitation on societal collapse but lacks representation of feedbacks between climate change and resource regeneration in ecological systems. We extend the HANDY model by integrating models of climate change and ecological function to examine the risk of societal collapse. We conducted a sensitivity analysis of our expanded model by systematically varying key parameters to examine the range of plausible socio-ecological conditions and evaluate model uncertainty. We find that lowered greenhouse gas emissions and resilient ecosystems can delay societal collapse by up to approximately 500 years, but that any scenario with greater than net-zero greenhouse gas emissions ultimately leads to societal collapse driven by climate-induced loss of ecosystem function. Reductions in greenhouse gas emissions are the most effective intervention to delay or prevent societal collapse, followed by the conservation and management of resilient ecological systems to sequester atmospheric carbon. Full article

An advanced gasification module (AGM) for green hydrogen production involves a small-scale biomass gasification process owing to the low energy density of biomass. Therefore, significant heat loss and the endothermic nature of gasification system require additional fossil fuel heat, increasing CO₂ emissions. This study focuses on bioenergy conversion with carbon capture and utilization (BECCU), where carbon-neutral CO₂ from biomass gasification is captured and reused as a gasifying agent to reduce the greenhouse gas intensity of green hydrogen. BECCU is expected to achieve negative emissions and enhance gasification efficiency by promoting conversion of char and tar through CO₂ gasification. To evaluate the effectiveness of BECCU in the AGM, we conducted a sensitivity analysis of the reformer temperature and S/C ratio using process simulation combined with life cycle assessment. In both sensitivity analyses, the GWP for CO₂ capture was lower compared with conventional conditions, considering recovered CO₂ from purification and the additional steam generated through heat recovery. This suggests improved hydrogen yields from enhanced char and tar conversion. Consequently, the GWP was reduced by more than 50%, demonstrating BECCU’s effectiveness in the AGM. This represents a step toward operating biomass gasification systems with lower environmental impact and contributing to sustainable energy production. Full article

The dataset presented in this manuscript consists of three distinct sets of data collected during a laboratory experiment aimed at quantifying the emissions of greenhouse gases (GHGs), specifically methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O). The experiment was conducted in three phases, each initiated at different times. The first phase began on 6 June 2022, using a biocover composed of 60% fine-fraction waste, 20% clay soil, and 20% stabilized compost. The second phase commenced on 26 August 2022, with two biocover variants: one composed of 50% fine-fraction waste and 50% clay soil, and the other consisting of 40% fine-fraction waste, 40% clay soil, and 20% shredded paper. The final phase started on 27 October 2022, introducing two biocovers: one containing 25% dried algae, 25% fine-fraction waste, 25% gravel (0–20 mm), and 25% ash, and the other composed of 40% fine-fraction waste, 40% dried algae, and 20% chernozem. Emission assessments were conducted three weeks after the biocover installation to allow for settling and stabilization, followed by weekly measurements two to three days before irrigation with 250 mL of water to simulate field conditions. GHG emission quantification was carried out using the Cavity Ring-Down Spectroscopy gas measurement device, Picarro G2508. This dataset offers substantial scientific value for advancing biocover technologies aimed at reducing GHG emissions in landfill environments, particularly for mitigating methane emissions. In addition to initial experimental use, the dataset offers a wide range of possibilities for reuse, including modeling landfill gas emissions, validating gas flow measurement methods, developing machine learning models, and performing meta-analyses. Its detailed structure facilitates multi-faceted environmental research and supports optimization of landfill management. Full article

To characterize the spatiotemporal distribution of temperature and airflow in single-span plastic-film greenhouses, we coupled field experiments with three-dimensional computational fluid dynamics (CFD) simulations in a warm–temperate region of China. Model reliability and validity were evaluated against field measurements. The average and maximum relative errors between simulated and measured values were 6% and 9%, respectively. Significant spatial heterogeneity in both temperature and airflow was observed. Vertically, temperature rose with height; horizontally, it declined from the center toward the sidewalls. Under prevailing meteorological conditions, the daily maxima occurred at distinct elevations above the fan-vent outlets. Airflow was most vigorous near the vents, whereas extensive stagnant zones aloft reduced overall ventilation efficiency. These findings provide a quantitative basis for designing single-span plastic film greenhouses in China’s hot–humid regions, informing ventilation improvements, and guiding future optimization efforts. Full article

The article discusses the current challenges facing the energy sector in the context of climate policy, technological transformation, and the urgent need to increase energy efficiency while reducing greenhouse gas emissions. Modern thermal energy conversion technologies are analyzed, including supercritical steam and gas–steam cycles, as well as distributed systems using renewable fuels and microturbines. Particular attention is given to innovative systems with isothermal expansion, which theoretically allow operation close to the efficiency limit defined by the Carnot cycle. The study presents calculation results for conventional systems (steam, gas with regeneration, and Organic Rankine Cycle) and proposes a novel isothermal air turbine cycle. In a combined gas–steam configuration, the proposed cycle achieved an efficiency exceeding 43% at a relatively low heat source temperature of 700 K, clearly outperforming conventional steam and ORC systems under the same thermal conditions. The use of a simple working medium (air), combined with the potential for integration with renewable energy sources, makes this concept a promising and viable alternative to traditional Rankine and Brayton cycles in thermally constrained applications. Full article

Accurate detection of cherry tomato clusters and their ripeness stages is critical for the development of intelligent harvesting systems in modern agriculture. In response to the challenges posed by occlusion, overlapping clusters, and subtle ripeness variations under complex greenhouse environments, an improved YOLO11-based deep convolutional neural network detection model, called AFBF-YOLO, is proposed in this paper. First, a dataset comprising 486 RGB images and over 150,000 annotated instances was constructed and augmented, covering four ripeness stages and fruit clusters. Then, based on YOLO11, the ACmix attention mechanism was incorporated to strengthen feature representation under occluded and cluttered conditions. Additionally, a novel neck structure, FreqFusion-BiFPN, was designed to improve multi-scale feature fusion through frequency-aware filtering. Finally, a refined loss function, Inner-Focaler-IoU, was applied to enhance bounding box localization by emphasizing inner-region overlap and focusing on difficult samples. Experimental results show that AFBF-YOLO achieves a precision of 81.2%, a recall of 81.3%, and an mAP@0.5 of 85.6%, outperforming multiple mainstream YOLO series. High accuracy across ripeness stages and low computational complexity indicate it excels in simultaneous detection of cherry tomato fruit bunches and fruit maturity, supporting automated maturity assessment and robotic harvesting in precision agriculture. Full article

Recent research has demonstrated a presence inside the seeds of several plant species of endophytic bacteria that can directly or indirectly interact with germination and seedling growth. Phytoplasmas are plant-pathogenic bacteria that severely impact the agricultural productivity of several crops, including alfalfa, a crucial forage crop in which seed transmission was reported. Therefore, understanding the transmission pathways of phytoplasmas is essential for developing effective control strategies. This study investigates the seed transmission of phytoplasmas in alfalfa using seeds collected in Oman in 2002 and kept in a dry environment in a laboratory for 20 years. The sterilized seeds were germinated and grown in agar medium under sterile conditions and transplanted in soil under greenhouse-controlled insect-proof conditions. Utilizing polymerase chain reaction (PCR) and nested PCR followed by RFLP and sequencing analyses, the alfalfa seedlings were screened for the phytoplasma presence. The detection of phytoplasmas in 16SrIII, 16SrV, 16SrX, and 16SrXII groups was achieved, confirming the preliminary results obtained in the 2002 testing of the same seed batches. This finding indicates that seed transmission could be a critical pathway for the spread of these pathogens in alfalfa, considering their survival in seeds for more than 20 years. Further investigations into the mechanisms of seed transmission and the development of resistant alfalfa varieties are essential to enhance the sustainability and productivity of alfalfa cultivation, thereby supporting the agricultural sector’s efforts to meet the growing demand for high-quality forages. Full article

Efficient irrigation management is essential for optimizing yield and quality in specialty crops like hot peppers (Capsicum chinense), particularly under controlled greenhouse environments. This study employed a novel sensor-based system integrating soil moisture and sap flux monitoring to evaluate water use dynamics in Capsicum chinense, a species for which such applications have not been widely reported. Three cultivars—Habanero, Helios, and Lantern—were grown under three volumetric soil moisture contents: low (15%), medium (18%), and high (21%). Water uptake was measured at leaf (transpiration, stomatal conductance) and plant levels (sap flux via heat balance sensors). Photosynthesis, fruit yield, and capsaicinoid concentrations were assessed. Compared to high irrigation, medium and low irrigation increased photosynthesis by 16.6% and 22.2%, respectively, whereas high irrigation favored greater sap flux and vegetative growth. Helios exhibited an approximately 8.5% higher sap flux as compared to Habanero and about 10% higher as compared to Lantern. Helios produced over 30% higher fruits than Habanero and Lantern under high irrigation. Habanero recorded the highest pungency, with a capsaicinoid level of 187,292 SHU—exceeding Lantern and Helios by 56% and 76%, respectively. Similarly, nordihydrocapsaicin and dihydrocapsaicin accumulation were more cultivar-dependent than irrigation-dependent. No significant interaction between cultivar and irrigation was observed, indicating genotype-driven water use strategies. Our study contributes to precision horticulture by integrating soil moisture and sap flux sensors to reveal cultivar-specific water use strategies in Capsicum chinense, thereby demonstrating the potential of an integrated sensor-based irrigation system for efficient irrigation management under increasing water scarcity in protected environments. As a preliminary greenhouse study aimed at maintaining consistent irrigation throughout the growing season across three volumetric soil moisture levels, these findings provide a foundation for subsequent validation and exploration under diverse soil moisture conditions including variations in stress duration, stress frequency, and stress application at different phenological stages. Full article

Ascorbic acid is a non-enzymatic antioxidant compound essential for plant defense under salt stress conditions. It can induce salt stress tolerance and enable the use of saline water in hydroponic cultivation with substrates. This study evaluated the effect of foliar application of ascorbic acid on the yield and postharvest quality of ‘Laranja’ cherry tomatoes grown in saline nutrient solutions under a substrate-based hydroponic system. The experiment was conducted in a greenhouse in Campina Grande, Paraíba, Brazil, in a randomized block design in a 5 × 5 factorial arrangement, corresponding to five levels of electrical conductivity of the saline nutrient solution—SNS (2.1—Control, 2.8, 3.5, 4.2, and 4.9 dS m⁻¹) and five concentrations of ascorbic acid—AA (0, 150, 300, 450, and 600 mg L⁻¹), with four replications. Salinity above 2.1 dS m⁻¹ reduced yield components and phenolic compound content. However, the saline nutrient solution of 4.9 dS m⁻¹ combined with 600 mg L⁻¹ foliar application of AA increased fruit firmness, soluble solids, and titratable acidity. Additionally, SNS of 4.9 dS m⁻¹ enhanced the levels of vitamin C, flavonoids, and anthocyanins. While AA improved postharvest quality of cherry tomatoes, it did not increase production under salt stress. Foliar application is thus a promising approach for enhancing fruit quality of cherry tomatoes grown in hydroponic systems using saline water, supporting sustainable production in semiarid regions. Full article

This study evaluated the qualitative and quantitative performance of lettuce (cv. Romana) grown using different cultivation systems under Mediterranean greenhouse conditions equipped with photoluminescent glass panels. Five systems were compared: outdoor soil (PSO), indoor soil (PSI), aeroponic (A), hydroponic with inorganic nutrients (HSN), and hydroponic with organic nutrients (HSO). Morphological, physiological, and quality parameters were measured alongside solar irradiance and extended PAR. The results showed that aeroponics significantly outperformed other systems in fresh weight (52.7 g), photosynthetic pigments, and carotenoids, while HSO showed the lowest yield and quality. Although PSO had the highest antioxidant activity and phenolic content, it exhibited poor yield due to lower water use efficiency and light-induced stress. The PCA analysis highlighted distinct groupings among systems, with A linked to yield and pigment concentration, and PSO associated with antioxidant traits. Despite a 44.8% reduction in solar radiation inside the greenhouse, soilless systems—especially aeroponics—proved effective for maintaining high productivity and quality. These findings support the integration of soilless systems and photoluminescent technologies as sustainable strategies for high-efficiency lettuce production in controlled environments. Full article

This study presents a scenario-based assessment of the future sensitivity of minimal low-water runoff to climate change in Western Kazakhstan. An ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), combined with dynamically downscaled projections for Central Asia, was applied to estimate minimal monthly runoff during the summer–autumn and winter low-water periods for the rivers of the Zhaiyk–Caspian water management basin. The analysis covers three future time horizons: 2040 (2031–2050), 2060 (2051–2070), and 2080 (2071–2090), under two greenhouse gas concentration scenarios: SSP3-7.0 (moderately high emissions) and SSP5-8.5 (high emissions). The results reveal a pronounced seasonal contrast in the projected hydrological response. During the winter low-water period, a steady increase in minimal runoff is projected for all rivers, with the most significant changes observed for the Or, Zhem, Temir, and Shagan rivers. This increase is primarily driven by higher winter precipitation, increased thaw frequency, and enhanced infiltration recharge. Conversely, despite modest increases in summer–autumn precipitation, minimal runoff during the summer–autumn low-water period is projected to decline significantly, particularly in the southern basins, due to elevated evapotranspiration rates and soil moisture deficits associated with rising air temperatures. These findings emphasize the importance of developing seasonally differentiated, climate-resilient water management strategies to mitigate low-flow risks and ensure water security under future climate conditions in arid and semi-arid regions. Full article

The accurate estimation of methane reserves in closed coal mines is crucial for supporting clean energy recovery and reducing greenhouse gas emissions. This study addresses the technical challenges associated with complex geological conditions and limited post-closure data in China’s closed mines. Three mainstream estimation methods—the material balance, resource composition, and decline curve—are systematically reviewed and applied to a case study in the Huoxi Coalfield. Results indicate that the material balance method provides upper-bound estimates but is highly sensitive to incomplete historical data, whereas the resource composition method yields more conservative and geologically realistic values. Although the decline curve method is not applied in this case, it offers potential for forecasting when long-term monitoring data are available. A multi-method integration approach, supported by enhanced data archiving and uncertainty assessments, is recommended to improve the accuracy and reliability of methane reserve evaluations in post-mining environments. Full article