Search Results (99)

Against the backdrop of advancing the “dual carbon” goals (carbon peaking and carbon neutrality), the “fishery–photovoltaic complementary” model—integrating solar power generation with salt pan production—has been widely adopted in Tianjin. However, large-scale photovoltaic (PV) facility construction exerts complex impacts onsalt panns, a wetland ecosystem of unique ecological value, by blocking sunlight, altering local microclimates, and regulating water evaporation. Currently, systematic field studies on the comprehensive effects of PV facilities onsalt pans ecosystems remain scarce, particularly those focusing on impacts on primary producers and key environmental factors. Pond sediments harbor the densest and most diverse aquatic microbial communities. In this study, sediment samples were collected from four typical ponds in Tianjin’salt panan region in April, July, and September 2024. Post sample processing, multiple statistical analyses were conducted, including alpha diversity indexing, species abundance clustering, and beta diversity analysis (non-metric multidimensional scaling, NMDS). The results showed the following: (1) Microbial communities existed in both PV-equipped and non-PV areas, indicating no significant correlation between PV presence and alpha diversity indices. (2) Species and genus compositions aggregated in PV-equipped areas with generally consistent community structures, whereas they displayed high dispersion in non-PV areas. This regulatory effect of PV facilities was relatively stable, with deviations only at a few sampling sites, confirming that PV presence significantly affects community composition patterns at both species and genus levels. (3) Cluster heatmap analysis revealed distinct seasonal variations in clustering relationships between sampling stations and microbial genera. Among dominant genera, only Desulfotignum was unaffected by PV facilities or seasonal changes, while the distribution of other dominant genera was significantly influenced by PV construction. Full article

How to achieve the goal of water–carbon synergistic optimization in greenhouse crop production under water-saving irrigation strategies constitutes a key pathway for the development of protected agriculture. Our study takes muskmelon and tomato with drip irrigation in greenhouses as an example and establishes different irrigation levels based on cumulative surface evaporation (E_p) from a 20 cm pan. Here, four irrigation amounts (0.6 E_p, 0.8 E_p, 1.0 E_p, and 1.2 E_p) were set for muskmelon, and three irrigation amounts (0.5 E_p, 0.7 E_p, and 0.9 E_p) were set for tomato, and then a two-year fixed-site field experiment was conducted. The growth rates of both crops were significantly higher under full-water-supply treatments (M1.0 and M1.2 for muskmelon, T0.9 for tomato) than under water-deficient treatments (M0.8 and M0.6 for muskmelon, T0.5 for tomato) (p < 0.05) at the flowering stage, while the opposite was true at the harvesting stage. More than 85% of root systems were distributed in the soil layer, ranging from 0 to 40 cm, and the average RLD under M1.0 and T0.9 was significantly higher than that under other treatments by 14.3%~27.6% (p < 0.05). Muskmelon yields at 1.0 E_p were 22.9%~45.7% higher than those at 0.6 E_p and 0.8 E_p, while tomato yields peaked at 0.9 E_p and were 17.0%~19.4% higher than those under the other two treatments. Daily average soil CO₂ emission fluxes of muskmelon under M1.2 were 9.2%~32.2% higher than those of other treatments respectively, and that of tomato under T0.9 was more than 20% higher than under T0.7 and T0.5 treatments, respectively. The WHCNS-Veg model demonstrated excellent performance in simulating SWC, LAI, and soil CO₂ emission fluxes. The RMSE for SWC simulation ranged from 0.013 to 0.022 cm³·cm⁻³, for LAI simulation, it varied from 0.103 to 0.210 cm²·cm⁻², and for soil CO₂ emission flux simulation, it changed from 1.057 to 2.188 kg·hm⁻². It should be noted that the performance was higher under high irrigation levels than under water deficit levels. These results can provide a scientific basis for optimizing greenhouse irrigation schedules and regulating water–carbon synergy under different water resource conditions. Full article

To further improve the prediction accuracy for greenhouse crop evapotranspiration (ET) under different irrigation conditions and enhance irrigation water use efficiency, this study proposes three methods to revise the Priestley–Taylor (PT) model coefficient α for calculating ET at different growth stages: (1) considering the leaf senescence coefficient f_S, plant temperature constraint parameter f_t, and soil water stress index f_sw to correct α (MPT model); (2) combining the Penman–Monteith (PM) model to inversely calculate α (PT-M model); (3) using the machine learning XGBoost algorithm to optimize α (PT-M_(XGB) model). Accordingly, this study observed the cumulative evaporation (Ep) of a 20 cm standard evaporation pan and set two different irrigation treatments (K0.9: 0.9Ep and K0.5: 0.5Ep). We conducted field measurements of meteorological data inside the greenhouse, tomato physiological and ecological indices, and ET during 2020 and 2021. The above three methods were then used to dynamically simulate greenhouse tomato ET. Results showed the following: (1) In 2020 and 2021, under K0.9 and K0.5 irrigation treatments, the MPT model mean coefficient α for the entire growth stage was 1.27 and 1.26, respectively, while the PT-M model mean coefficient α was 1.31 and 1.30. For both models, α was significantly lower than 1.26 (conventional value) during the seedling stage and the flowering and fruiting stage, rose rapidly during the fruit enlargement stage, and then gradually declined toward 1.26 during the harvest stage. (2) Predicted ET (ET_e) using the PT-M model underestimated the observed ET (ET_m) by 8.71~16.01% during the seedling stage and the harvest stage, and overestimated by 1.62~6.15% during the flowering and fruiting stage and the fruit enlargement stage; the errors compared to ET_m under both irrigation treatments over two years was 0.1~3.3%, with an R² of 0.92~0.96. (3) The PT-M_(XGB) model achieved higher prediction accuracy, with errors compared to ET_m under both irrigation treatments over two years of 0.35~0.65%, and R² above 0.98. The PT-M_(XGB) model combined with the XGBoost algorithm significantly improved prediction accuracy, providing a reference for the precise calculation of greenhouse tomato ET. Full article

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and climatic controls of PE across seven major climate zones. Multiple decomposition techniques revealed a dominant annual cycle and a pronounced peak in 2018, while a decreasing interannual trend was observed nationwide. Spatial analyses showed a clear north–south contrast, with the strongest declines occurring in northern China. A random forest (RF) model was employed to quantify the contributions of climatic variables, achieving high predictive performance. RF results indicated that the dominant drivers of PE varied substantially across climate zones: sunshine duration (as a proxy for solar radiation) and air temperature mainly controlled PE in humid regions, while wind speed and relative humidity (RH) exerted stronger influences in arid and semi-arid regions. The widespread decline in northern China is consistent with concurrent changes in wind speed and sunshine duration, together with humidity conditions, which modulate evaporative demand at monthly scales. These findings highlight substantial spatial heterogeneity in PE responses to climate forcing and provide insights for drought assessment and water resource management in a warming climate. Full article

The lack of knowledge regarding the water footprint (WF) of bananas in the Guayas province of Ecuador, assessed in local terms, creates an information gap concerning the consumptive and sustainable use of water. Therefore, this study aimed to determine the WF of the cultivation and packaging process of this fruit. The Hoekstra methodology was followed, using the evaporation pan procedure for crop evapotranspiration based on a 43-year historical record (1980–2023) and the USDA method for effective precipitation, selecting nine banana farms within the zone. The grey WF was assessed following two approaches: a simple procedure assuming a 10% leaching rate of agrochemicals was followed during the rainy season, and water losses through percolation were accounted for during the dry season. Nitrogen was considered as the pollutant element, while for the grey WF assessment in packaging, active chlorine in wastewater was measured. The WF was determined to be 351.4 m³ t⁻¹, distributed as 45.0% green WF, 49.0% blue WF, and 6.0% grey WF. The grey WF is distributed as 74.7% in the field and 25.3% in the packaging process. Consequently, a moderate impact on groundwater and surface water resources is inferred; however, the irrigation management applied in the zone contributes to reduced contamination of these sources. Full article

Background/Objectives: As a formulation strategy to produce fixed-dose combinations with amorphous solid dispersions of poorly soluble drugs, active coating of tablets is an under-investigated topic. Methods: In this study, ibuprofen, with a boiling point of 157 °C, was used as a model drug, and an ibuprofen–HPMCAS coating was loaded on the surface of placebo tablets using a widely used perforated laboratory pan-coater with a single two-component nozzle. Acetone, acetonitrile, and DMSO, with different boiling points and evaporation kinetics, were used as the organic solvents. HPMCAS solutions in the mentioned solvents demonstrated different viscosities due to different solvent–polymer interactions, as indicated by different solution turbidity. The concentrations of the organic solvent-containing coatings were selected based on desirable flow rates in comparison with the reference Opadry^® II coating dispersion. Coatings were applied at the same pan rotation speed, but atomising and pattern air pressure, as well as drying conditions, were different. Results: The content of residual solvents in coatings was determined with gas chromatography: low-boiling-point acetone and acetonitrile content was below the LOD, while the content of DMSO, with a boiling point of 189 °C, comprised 1.5 wt.%. A pharmacopoeial approach was utilised to assess uniformity of dosage units via uniformity of content. The accuracy of dosing decreased from acetone- and acetonitrile- to DMSO-based coatings. Because of the high boiling point of DMSO in comparison to ibuprofen, the DMSO-based coating process was the longest, and the amount of ibuprofen loss was the highest. In turn, the precision of dosing via active coating increased from acetone to acetonitrile and to DMSO. The R.S.D. of the uniformity of content decreased along with coating time and fit the power function well (R² = 0.9843). Conclusions: Therefore, to answer the main question of this study, proper drug dosing (in terms of accuracy and precision) using drug loading via tablet coating with this specific equipment is possible. Depending on the dose precision desired, the duration of the coating process can vary. Full article

This study focuses on the Alentejo and Algarve regions of southern Portugal, which is characterized by a typical Mediteranean climate. In the Mediterranean region, evaporation plays a significant role in reservoir water budgets. Therefore, estimating water surface evaporation is essential for efficient reservoir water management. This study aims to (i) assess the reservoir evaporation pattern in southern Portugal from meteorological offshore measures, (ii) benchmark various indirect methods for evaluating reservoir evaporation at a monthly scale, and (iii) provide recommendations on the most suitable indirect method to apply in operational practices. This study presents meteorological data collected from floating weather stations on instrumented platforms across nine reservoirs in Alentejo and Algarve. This is the first time that so many offshore local measurements have been made available in a Mediterranean climate region. The reservoir evaporation was estimated by the Energy Budget (Bowen Ratio) method, having concluded that monthly evaporation rates across the nine reservoirs ranged from 0.8 mm d${\phantom{­}}^{-1}$ in winter to 4.6 mm d${\phantom{­}}^{-1}$ in summer, with an annual average of 2.7 mm d${\phantom{­}}^{-1}$. Annual evaporation values ranged from 750 to 1230 mm, showing a positive gradient from the northern Alentejo region to the southwest Algarve region. To evaluate the performance of five empirical and semi-empirical evaporation indirect methods, a benchmarking analysis was conducted. The indirect methods studied are Mass Transfer (MT), Penman (PEN), Priestley and Taylor (PT), Thornthwaite (THOR), and Pan Evaporation (PE). Regarding the MT method, an N function of a reservoir superficial area is presented for the Mediterranean climate regions. In the Pan Evaporation method, the pan coefficient was considered equal to one. The benchmarking analysis revealed that all studied methods produced estimates that had good correlation with the Energy Budget method’s results across all reservoirs. All the methods showed small biases at the monthly scale, particularly in the dry semester. The estimates’ evaporation variability depended on the reservoir. Overall, the evaluation of evaporation methods concluded that (i) the stakeholders should considerer having an evaporation pan offshore; (ii) to manage the water balance of the studied reservoirs, the manager must apply the method with the best performance, depending on the data available; (iii) to manage other reservoirs located in the Mediterranean climate region, the manager must compare reservoir characteristics and the data available in order to choose the most suitable method to apply. Full article

Coastal ecosystems, located at the interface of terrestrial and marine environments, provide significant ecological functions and resource value. Coastal salt pans, as critical coastal resources with significant implications for coastal ecosystem health and resource management, have attracted extensive research attention. However, current studies on the extraction of spatiotemporal patterns of coastal salt pans remain relatively limited and superficial. This study takes coastal salt pans in Laizhou Bay as a case study, proposing a hierarchical classification method—Salt Pan Feature-Enhanced Fusion Image Random Forest (SPFEFI-RF)—based on multi-index synergy guidance and deep-shallow feature fusion, achieving high-precision extraction of coastal salt pans. First, a Modified Water Index (MWI) and Salt Pan Crystallization Index (SCI) were constructed from image spectral features, specifically targeting the extraction of evaporation ponds. Concurrently, a salt pan sample dataset was developed for the DeepLabv3+ (DL) method to extract deep semantic features and perform multi-scale feature fusion. Subsequently, a three-channel fusion strategy—R(MWI)-G(SCI)-B(DL)—was employed to produce the Salt Pan Feature-Enhanced Fusion Image (SPFEFI), enhancing distinctions between salt pans and background land cover. Finally, the Random Forest (RF) classifier using shallow spectral features was applied to extract salt pan information, further optimized by spatial domain denoising techniques. Results indicate that the SPFEFI-RF approach effectively extracts coastal salt pan features, achieving an overall accuracy of 92.29% and a spatial consistency of 85.14% with ground-truth data. The SPFEFI-RF method provides advanced technical support for high-precision extraction of global coastal salt pan spatiotemporal characteristics, optimizing coastal zone management decisions and promoting the sustainable development of coastal ecosystems and resources. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

To address the scientific challenges of low water–fertilizer use efficiency and the difficulty in achieving the synergistic improvement of the yield and quality in solar greenhouse cucumber production on the North China Plain, this study investigated the effects of varying water and nitrogen supplies on cucumber growth, yields, water–nitrogen use efficiency, and quality. The aim was to establish optimized water and nitrogen management strategies for high-yield, high-quality, and resource-efficient cultivation. A two-factor completely randomized design was implemented, with three irrigation levels (W1: $1.0E_{p-20}$, W2: $0.75E_{p-20}$, and W3: $0.5E_{p-20}$) based on cumulative pan evaporation and four nitrogen application amounts (N1: 432 kg·ha⁻¹, N2: 360 kg·ha⁻¹, N3: 288 kg·ha⁻¹, N4: 216 kg·ha⁻¹). Cucumber growth indicators were observed during the growing season, and the water and nitrogen application rates were scientifically optimized. The results showed that a full water and nitrogen supply enhanced the leaf area index, dry weight accumulation, and yield. Moderate water and nitrogen savings had a minimal impact on plant growth and production while significantly improving the water and fertilizer use efficiency. Using principal component analysis to comprehensively evaluate the cucumber quality, it was found that the irrigation amount had a significant impact on quality, with the quality improving as the irrigation amount decreased. By employing a regression formula and spatial analysis methods, this study optimized the water and nitrogen application rates with the goals of maximizing the cucumber yield, water–nitrogen efficiency, and quality. For spring cucumbers, the recommended combination is an irrigation amount of 225~240 mm and a nitrogen application amount of 350~380 kg·ha⁻¹. For autumn cucumbers, the recommended combination is an irrigation amount of 105~120 mm and a nitrogen application amount of 375~400 kg·ha⁻¹. This research provides theoretical and technical support for high-yield, high-quality, and efficient irrigation and nitrogen management in solar greenhouses in the North China Plain. Full article

An automatic, weight-based, small 20 cm diameter pan was used for real-time calculations of evaporation and precipitation in a semiarid environment. The water evaporated from the evaporimeter (E_P) was found to be a significant predictor of evapotranspiration (ET_O; r² = 0.84), which was calculated with the Penman–Monteith (P-M) equation by retrieving climatic data from a weather station. The results revealed seasonal variations of the pan coefficient (K_P; dimensionless), with a mean value estimated at 0.84 (±0.16). Validation of ET_O measurements using a calibrated regression model (ET_O = 0.831*E_P + 0.025), against the P-M equation indicated a high correlation coefficient (r² = 0.99, slope of the regression line of 0.9). The present paper evaluates and discusses the potential of using a reduced-size pan for real-time monitoring of water evaporation and precipitation, proposing an open-source irrigation decision support system. Full article

Doce de leite is a caramel-like confection, mainly produced in several Latin American countries, with increasing popularity worldwide. This overview outlines nine distinct industrial technologies for the production of doce de leite: (1) total batch manufacturing process; (2) batch manufacturing system with fractionated mix addition; (3) manufacturing with pre-concentration in a vacuum evaporator and finishing in an open pan; (4) manufacturing with pre-concentration in a vacuum evaporator, finishing in an open pan, and lactose micro-crystallization; (5) continuous flow manufacturing with total concentration in a vacuum evaporator and a viscosity control holding tank (hot well); (6) manufacturing with total concentration in a vacuum evaporator and sterilization in an autoclave system; (7) manufacturing with sucrose pre-caramelization and a total batch system; (8) manufacturing in colloidal mill without an evaporation process; and (9) manufacturing based of doce de leite bars with a sucrose crystallization stage. We conducted a literature review to gather data on the discussed processes and their principal characteristics, which may be pertinent to doce de leite manufacturers. The choice of a specific process will depend on the desired doce de leite characteristics, the type of doce de leite to be produced, and the manufacturing company’s requirements. When properly integrated, these technologies contribute to efficient and profitable production, yielding high-quality products with appropriate chemical, microbiological, and sensory characteristics at an industrial scale. Full article

Rice serves as the primary food source for the majority of the world’s population. In terms of irrigation water, the highest volume of irrigation water is utilized in paddy irrigation. Excessive water use causes both waste of limited water resources and various environmental problems. The drip irrigation method with high water use efficiency will reduce both the need for irrigation water and the environmental footprint of paddy production. This study was conducted to investigate the effects of two different irrigation intervals (2 and 4 days) and four irrigation levels (150%, 125%, 100%, and 75% of evaporation from a Class-A pan) on the nutritional traits of three different paddy cultivars (Ronaldo, Baldo, and Osmancık). Increasing irrigation intervals and decreasing irrigation levels reduced the nutritional properties (protein, oil, starch) of the rice grains. In addition, increasing irrigation levels also increased the phytic acid and dietary fiber contents. The highest protein (7.14%) and total starch (87.10%) contents were obtained from the 150% irrigation treatments. The highest amylose content (20.74%) was obtained from the 75% irrigation treatment. In general, it was found that irrigation levels should be applied at 125% and 150% to increase the mineral content of rice grains. Although water deficits decreased the nutritional properties of the paddy cultivars, drip irrigation at an appropriate level did not have any negative effects on nutritional traits. Full article

Despite the continuous development of greenhouse cultivation technology, the influence mechanism of covering conditions on the root distribution of greenhouse crops remains unclear, which is emerging as a significant research topic at present. The interaction between mulching and irrigation plays a key role in the root growth of greenhouse tomatoes, but its specific impact awaits in-depth exploration. To explore the response patterns of greenhouse crop root distribution to the drip irrigation water amount under mulching conditions, the tomato was chosen as the research object. Three experimental treatments were set up: mulched high water (Y0.9), non-mulched high water (N0.9), and mulched low water (Y0.5) (where 0.9 and 0.5 represent the cumulative evaporation from a 20 cm standard evaporation pan). We analyzed the water and thermal zone of tomato roots as well as the root distribution. Based on this, a root distribution model was constructed by introducing a mulching factor (f_m) and a water stress factor (K_s). After carrying out two years of experimental research, the following results were drawn: (1) The average soil water content in the 0–60 cm soil layer was Y0.9 > N0.9 > Y0.5, and the average soil temperature in the 0–30 cm soil layer was Y0.5 > Y0.9 > N0.9. (2) The interaction between mulching and irrigation had a significant impact on the distribution of tomato roots. In the absence of mulch, the root surface area, average root diameter, root volume, and root length density initially increased and then decreased with depth, with the maximum root distribution concentrated around the 20 cm soil layer. Under mulched conditions, roots were predominantly located in the top layer (0–20 cm). Under the film mulching condition, the distribution range of root length density of low water (Y0.5) was wider than that of high water (Y0.9). (3) Root length density exhibited a significant cubic polynomial relationship with both the soil water content and soil temperature. In the N0.9 treatment, root length density had a bivariate cubic polynomial relationship with soil water and temperature, with a coefficient of determination (R²) of 0.97 and a normalized root mean square error (NRMSE) of 20%. (4) When introducing the film mulching factor (f_m) and water stress factor (K_s) into the root distribution model to simulate the root length density distribution of Y0.9 and Y0.5, it was found that the NRMSE was 22% and R² was 0.90 under the Y0.9 treatment, and the NRMSE was 24% and R² was 0.98 under the Y0.5 treatment. This study provides theoretical support for the formulation of scientifically sound irrigation and mulching management plans for greenhouse tomatoes. Full article

The modeling of pan evaporation (Ep) trends in Slovak river sub-basins was conducted using advanced artificial intelligence (AI) techniques algorithms to accurately calculate evaporation rates based on daily climate data from 2010 to 2023 across eight sub-basins in the Slovak Republic. The AI modeling results reveal that the Bodrog, Hornád, Ipeľ, Morava, Slaná, and Váh river basins are experiencing increases in evaporation, while the Dunaj and Hron rivers show declining trends. This divergence may indicate varying ecological factors influencing the evaporation dynamics of each river. A comprehensive set of 28 machine learning (ML) and deep learning (DL) models was employed, including ML techniques such as linear regression, tree-based, support vector machines (both with and without kernels), ensemble, and Gaussian process methods; as well as DL approaches like neural networks (narrow, medium, wide, bilayered, and trilayered). Among these, stepwise linear regression provided the most optimal fit. The minimum redundancy maximum relevance (mRMR) method was utilized for feature selection to balance relevance and redundancy effectively. The results suggest that emphasizing relative humidity (RH) and minimum temperature (t_min) significantly enhances accuracy, highlighting the critical roles of these factors in modeling pan evaporation trends. The results offer precise evaporation analyses to improve water management and lessen scarcity. Full article