Search Results (10,545)

Waste stabilization ponds (WSPs) in humid, subtropical climates rely on stable temperatures and mechanical aeration to promote microbial activity. These critical infrastructures can lack operational resources to ensure efficient treatment, which can impact downstream communities. This study aims to use remote water quality sensor data to establish trends in a yearly dataset and correlate various water quality parameters for simplistic identification of pond health. A facultative WSP was monitored in two stages: the primary settling over a period of 14 months to evaluate partially treated water, and the secondary treatment pond for a period of 11 months to monitor final stage water quality parameters. A statistical analysis was performed on the measured parameters (dissolved oxygen, temperature, conductivity, pH, turbidity, nitrate, and ammonium) to establish a comprehensive yearly, seasonal, and monthly dataset to show fluctuations in water parameter correlations. Standard relationships in dissolved oxygen, conductivity, pH, and temperature were traced during the seasonal fluctuations, which provided insight into nitrogen processing by microbial communities. During this study, the summer period showed the most variability, specifically a deviation in the dissolved oxygen and temperature relationship from a yearly moderate negative correlation (−0.593) to a moderate positive correlation (0.459), indicating a direct relationship. The secondary treatment pond data showed more nitrogen species correlation, which can indicate final cycling during seasonal transitions. Understanding pond dynamics can lead to impactful, proactive operational decisions to address pond imbalance or chemical dosing for final treatment. By establishing parameter correlations, facilities with WSPs can strategically integrate sensor networks for real-time pond health and treatment efficiency monitoring during seasonal fluctuations. Full article

The interaction between groundwater and surface water can be significant in lakes or irrigation channels, as well as in large dam reservoirs or along portions of them. To evaluate this interaction at a sampling location directly controlled by a large dam equipped with reversible pump-turbines, data from Rn-222 and physicochemical parameters at specific depths and times were obtained and studied using Principal Component Analysis and Hierarchical Clustering. Dimension 1 explains 45.3% of the total variability in the original data, which can be interpreted as the result of external factors related to seasonal variability (e.g., temperature, turbulent flow, and precipitation), while Dimension 2 explains up to 31.2% and can be interpreted as the variability related to groundwater inputs. Five hierarchical clusters based on these dimensions were considered and were related to the temporal variability observed in the water column throughout the year, as well as the depth relationships observed between successive surveys. A hypothesis-driven conceptual piston-like effect model is proposed for groundwater–surface water interactions, considering the identified relationships between variables, including higher Rn-222 concentrations in surface water after heavy rain. According to this simplified conceptual model, water infiltrates in a weathered granitic recharging area; during heavy rain, it is forced through the fracture systems of a lesser-weathered granite. Thus, an overall increase in pressure over the hydrological system forces the older radon-enriched water to discharge into the Mondego River. This work highlights the importance of exploratory techniques such as PCA and Hierarchical Clustering, in addition to underlying knowledge of the geological setting, for the proposal of simplified conceptual models that help in the management of important reservoirs. This work also demonstrates the utility of Rn-222 as a simple tracer of groundwater discharge into surface water. Full article

Swimming pools, symbols of luxury in tourism-driven Greece, raise concerns about water consumption in water-scarce regions. This study assesses their hydrological impact in two regions of Southern Greece, West Mani (Peloponnese) and Naxos Island (Cyclades), within the water–energy–food nexus framework, evaluating the resulting trade-offs. Using satellite imagery, we identified 354 pools in West Mani (11,738 m²) and 556 in Naxos (26,825 m²). Two operational scenarios were evaluated: complete seasonal emptying and refilling (Scenario 1) and one-third annual water renewal (Scenario 2). Annual water use ranged from 39,000 to 51,000 m³ in West Mani and 98,000 to 124,000 m³ in Naxos—equivalent to the needs of 625–2769 and 1549–6790 people in West Mani and Naxos, respectively. In Naxos, this volume could alternatively irrigate 27–40 hectares of potatoes, producing food for 700–1500 people. Energy requirements, particularly where desalination is used, further increase the burden, with Naxos pools requiring 384–846 MWh annually. Although swimming pools are highly visible water consumers, their overall contribution to water scarcity is modest compared to household and agricultural uses. Their visibility, however, amplifies public concern. Rainwater harvesting, requiring collection areas 10–24 times larger than pool surface areas, especially in residential and hotel settings, could make pools largely self-sufficient. Integrating such measures into water management and tourism policy can help balance luxury amenities with resource conservation in water-scarce Mediterranean regions. Full article

Snow is an essential meteorological variable and an indicator of the changing climate. Its variations, particularly in snow depth and snow water equivalent, result mainly from changes in winter precipitation and air temperature. Recently, these conditions have been thoroughly investigated worldwide, revealing a general prevailing decline in precipitation and increasing tendencies in air temperatures. However, no systematic or up-to-date studies for Bulgaria exist. The main goal of the current project is to fill this national knowledge gap in the snow conditions in our mountains. For that purpose, we used 31 stations with altitudes ranging from 527 to 2925 m a.s.l. for the period between 1961 and 2020, covering two significant reference climatic periods. We extracted data about snow cover maximums, mean air temperatures, and precipitation amounts for the whole winter season in mountainous regions from October to April; however, we mainly present the results for the three winter months: December, January, and February. Most of the stations do not demonstrate any significant trends for snow depth maximums, except for the three lower stations in central west Bulgaria, which show significant increases. On the opposite end of the scale, two of the highest stations demonstrated notable decreases. The time series for the precipitation amounts are also predominantly indefinite. Significant decreasing trends can be found at the highest three alpine stations. The change in the mean seasonal air temperature is predominantly positive—17 of the stations show positive trends, and for 12, the increases are significant. The altitude of the strongest seasonal temperature rise lies between 1000 and 1700 m. Finally, due to the obvious nonlinearity of some of the time series, we decided to check for change points and a nonlinear approach to fit the data. This analysis demonstrates general changes in the investigated characteristics from the beginning of the 1970s to the middle of the 1980s. Full article

Salvia fruticosa, S. officinalis, S. pomifera ssp. pomifera, S. ringens, and S. tomentosa have multiple potential uses in floriculture and the pharmaceutical industry, serving sustainable horticulture and landscaping. The aim was to develop effective asexual propagation protocols for the exploitation of the above species. Thus, the effect of cutting origin, season of cutting collection, and various indole-3-butyric acid (IBA) treatments on rooting stem cuttings was examined. Shoot-tip cuttings were collected either from greenhouse or wild mother plants, in November, February, May, and August and were treated either with Rhizopon dusting powder 0.5% w/w IBA or immersion for 1 min in 0–6000 mg L⁻¹ IBA solution. The cuttings were then placed for rooting in a 1:1 (v/v) peat–perlite substrate, under mist, for 2 weeks and on the greenhouse bench in semi-shade for another 4 weeks. More efficient rooting was succeeded by cuttings, (i) of S. tomentosa, followed by S. fruticosa and S. pomifera ssp. pomifera, while S. officinalis was the most difficult to root, (ii) from greenhouse plants, (iii) collected in autumn or spring, and (iv) treated with Rhizopon dusting powder or 1500 mg L⁻¹ IBA solution. Higher dry weight values of the rooted cuttings were found in autumn. Conclusively, rooting of Salvia spp. cuttings depended on species, mother plants’ physiological state, time of cutting collection, climatic conditions, and auxin application. Full article

Current temperature prediction methods often focus on time-series information while neglecting the contributions of different meteorological factors and the context of varying time steps. Accordingly, this study developed a Dual-Attention-BiLSTM (a bidirectional long short-term memory network with dual attention mechanisms) network model, which integrates a bidirectional long short-term memory (BiLSTM) network model with random forest-based feature selection and two self-designed attention mechanisms. A sensitivity analysis was conducted to evaluate the influence of the attention mechanisms. This study focuses on Shijiazhuang City, China, which has a temperate continental monsoon climate with significant seasonal and daily variations. The data were sourced from ERA5-Land, comprising hourly near-surface temperature and related meteorological variables for the year of 2022. The results indicate that integrating the two attention mechanisms significantly improves the model’s prediction performance compared to using BiLSTM alone. The mean absolute error between simulation results ranges from 0.80 °C to 1.08 °C, with a reduction of 0.17 °C to 0.39 °C, and the root mean square error ranges from 1.17 °C to 1.37 °C, with a reduction of 0.12 °C to 0.22 °C. Full article

River ice is a common feature in most Canadian rivers and streams during the cold season. River channel hydraulics under ice conditions may cause higher water levels at a relatively lower discharge compared to the open-water flood events. Elevated water levels resulting from river ice processes throughout fall freeze-over, mid-winter, and spring break-up are important hydrologic events with diverse morphological, ecological, and socio-economic impacts. This study analyzes the timing of maximum water levels (occurring during freeze-over, spring break-up, and open-water periods) and the typology of maximum ice-related events (at freeze-over, mid-winter, and spring break-up) using data from the Canadian River Ice Database. The study also compares annual maximum water levels during the river ice and open-water periods at selected hydrometric stations from 1966 to 2015, divided into two 25-year windows: 1966–1990 and 1991–2015. A return period classification method was applied to define ice-influenced, open-water, and mixed-regime conditions. The results indicate that the majority of ice-influenced maximum water levels occurred during spring break-up (~79% in 1966–1990 and ~69% in 1991–2015), followed by fall freeze-up (~13% and ~23%) and mid-winter break-up (~8% and ~7%) for the two periods, respectively. Among 15 stations analyzed for 1966–1990 and 42 stations for 1991–2015, the proportion of annual maximum water levels dominated by open-water conditions increased from 47% to 55%, while ice-dominated events decreased from 13% to 12%, and mixed-regime events dropped from 40% to 33%. However, a focused comparison of eight common stations revealed minimal change in the distribution of water level-generating events between the two periods. The findings offer valuable insights into the spatial distribution of maximum water level-generating mechanisms across Canada. Full article

The Lower Rio Grande Valley (LRGV) of Texas is a biodiversity hotspot due to its high alpha, beta, and gamma diversity and high regional endemism, which are at high risk of degradation. The region has lost 95% of its native thornforest habitat primarily due to agricultural and urban expansion. This study aims to evaluate the current vegetative structure and composition of restored thornforest sites located in the LRGV to identify restoration methods and site characteristics that affect forest restoration outcomes. Twelve restored thornforest sites were selected for this study that varied in time since restoration, patch size, degree of isolation, and method of restoration. Canopy, understory, and ground layer vegetation were evaluated at six survey points per restored site (n = 72), and 17 environmental variables were incorporated into univariate and multivariate analyses to identify factors influencing restored plant communities. Actively restored sites showed higher overall richness, abundance, and diversity than passively restored sites. More isolated patches had higher overall richness, abundance, and diversity, and longer times since restoration began increased richness and diversity. Higher abundances of Urochloa maxima, an invasive grass, altered community composition and reduced diversity in each forest layer and overall and reduced richness in the canopy and ground layers. Important considerations for thornforest restoration in the LRGV should include invasive grass prevalence; proximity to riparian and seasonal wetland habitats; landscape factors that influence water availability; and patch geography, including shape, size, and proximity to other forest patches. Full article

This study aimed to forecast monthly PM_2.5 concentrations in Zabol, one of the world’s most dust-prone regions, using four time series models: SARIMA, SARIMAX enhanced with Fourier terms (selected based on spectral peak analysis), TBATS, and a novel hybrid ensemble. Spectral analysis identified a dominant annual cycle (frequency 0.083), which justified the inclusion of two Fourier harmonics in the SARIMAX model. Results demonstrated that the hybrid model, which optimally combined forecasts from the three individual models (with weights ω₂ = 0.628 for SARIMAX, ω₃ = 0.263 for TBATS, and ω₁ = 0.109 for SARIMA), outperformed all others across all evaluation metrics, achieving the lowest AIC (1835.04), BIC (1842.08), RMSE (9.42 μg/m³), and MAE (7.43 μg/m³). It was also the only model exhibiting no significant residual autocorrelation (Ljung–Box p-value = 0.882). Forecast uncertainty bands were constant across the prediction horizon, with widths of approximately ±11.39 μg/m³ for the 80% confidence interval and ±22.25 μg/m³ for the 95% confidence interval, reflecting fixed absolute uncertainty in the multi-step forecasts. The proposed hybrid framework provides a robust foundation for early warning systems and public health management in dust-affected arid regions. Full article

This observational retrospective study aimed to examine the association between team behaviour during periods of one-goal leads and subsequent match outcomes while accounting for team level and match location. All matches (n = 380) of the English Premier League (EPL) during the season 2023/24 were analyzed. Team behaviour was evaluated by shots every 10 min during a one-goal lead (SP10MDOGL), a time-normalized indicator of offensive activity that reflects a team’s strategic orientation while protecting a narrow lead. Mixed effects multinomial logistic regression was used to establish the association between SP10MDOGL and the match outcome. Results indicated that increased SP10MDOGL was strongly associated with a higher likelihood of both drawing (Odds ratio (OR) = 2.37, 95% confidence interval (CI) = 1.29–4.33; Cohen’s d (d) = 0.47) and winning (OR = 3.38; 95%CI = 1.93–5.92; d = 0.67) compared to losing. This association remained consistent across high-, intermediate-, and low-level teams regardless of whether they played at home or away. These findings suggest that maintaining an offensive approach through an increased number of shots during a one-goal lead is associated with a higher likelihood of securing positive match outcomes within the elite-level football context, such as the EPL. Soccer coaches should consider implementing proactive offensive strategies when protecting a narrow lead, regardless of their team level and match location. Full article

In Central Europe, the fawning season of roe deer (Capreolus capreolus) directly overlaps with meadow and alfalfa harvest, typically from late May to early June. During these operations, tens or more likely hundreds of thousands of fawns are mutilated by agricultural machinery. To mitigate this unethical mortality, wildlife managers often deploy odor repellents to drive roe deer individuals from high-risk fields before mowing. Therefore, we evaluated repellent efficacy in a paired design. The abundance of roe deer was quantified by drones equipped with thermal cameras before and after repellent application and then compared with untreated control meadows. Results showed high adult abundance that did not differ significantly among treatments. The highest median was paradoxically observed on meadows “after application” (8.25 ind./10 ha), followed by “not treated” meadows (7.92 ind./10 ha), and “before application” (5.72 ind./10 ha). For fawns, differences between treated and untreated plots were likewise non-significant. Their numbers increased over time after application, consistent with the peak of parturition in the second half of May. Overall, the study confirms that the tested odor repellent, when applied according to the manufacturer’s protocol, did not reduce roe deer presence on meadows. This underscores the need to consider alternative approaches, such as the use of thermal-imaging drones combined with the subsequent translocation of detected fawns to safe locations. Full article

Objectives: SailGP is an international professional mixed-sex sailing competition, which uses F50 foiling catamarans capable of reaching speeds up to ~100 km/h. This seminal study assesses injuries and illnesses observed by male and female sailors during trainings and competitions in SailGP’s third season. This study aims to assess injury and illness incidence, comparing results with other professional sailing events and high-performance sports. In addition, injury and illness risk factors (sex and position) will be explored with the goal to reduce morbidity for future seasons. Materials and Methods: This retrospective cohort design analysed medical records of male and female sailors during SailGP’s third season (April 2022 to May 2023). Risk factors assessed included sailor sex, sailor position (helm, strategist, grinder, flight controller and wing trimmer), sailing venue, wind speed and mechanism of injury/nature of illness. International Olympic Committee reporting guidelines on injuries and illnesses were followed, including the STROBE-SIIS checklist. Confidence intervals were set at 95%, statistical tests were two-sided and p-values < 0.05 were considered statistically significant. Results: A total of 40 on-water injuries were reported in 32 athletes. Injury incidence was greater during competitions than trainings, with strategists and then grinders being the most frequently injured positions. Competition injury incidence was 32.6 per 1000 h and 6.42 injuries per 365 days. Training injury incidence was 2.62 injuries per 1000 h and 3.82 injuries per 365 days. Knee, ankle, hand and head injuries were most prevalent, with three concussions observed during trainings and competitions (two female and one male). Direct impacts and falls during manoeuvres caused most injuries. Overall injury incidence (IRR = 2.69 [95% CI 1.41–5.16]), risk of training injuries (RR = 3.75 [95% CI 1.59–8.83], p = 0.001), risk of competition injuries (RR = 1.79 [95% CI 0.65–4.90], p = 0.25) and overall concussion risk (RR = 10.04 [95% CI 0.91–110.46], p = 0.02) were greater in females. Ten sailors accounted for 17 illnesses. Females had a 3.33 increase in training and competition illnesses (IRR = 3.33 [95% CI 0.94–11.81]). Conclusions: Competition injury incidence was higher than previous reported sailing studies. Knee injuries were most prevalent and direct impacts caused most injuries. Female sailors reported a higher injury and illness incidence. These results may guide injury prevention efforts and the development of an IOC-equivalent consensus statement. Future studies should examine time loss. Full article

The temperature of the thermal energy storage unit is a critical parameter for the stable operation of seasonal borehole thermal energy storage (BTES) systems. However, existing temperature prediction models predominantly focus on estimating single-point temperatures or borehole wall temperatures, while lacking effective methods for calculating the average temperature of the storage unit. This limitation hinders accurate assessment of the thermal charging and discharging states. Furthermore, some models involve complex computations and exhibit low operational efficiency, failing to meet the practical engineering demands for rapid prediction and response. To address these challenges, this study first develops a thermal response model for the average temperature of the storage unit based on the finite line source theory and further proposes a simplified engineering algorithm for predicting the storage unit temperature. Subsequently, two-dimensional discrete convolution and Fast Fourier Transform (FFT) techniques are introduced to accelerate the solution of the storage unit temperature distribution. Finally, the model’s accuracy is validated against practical engineering cases. The results indicate that the single-point temperature engineering algorithm yields a maximum relative error of only 0.3%, while the average temperature exhibits a maximum relative error of 1.2%. After employing FFT, the computation time of both single-point and average temperature engineering algorithms over a 10-year simulation period is reduced by more than 90%. When using two-dimensional discrete convolution to calculate the temperature distribution of the storage unit, expanding the input layer from 200 × 200 to 400 × 400 and the convolution kernel from 25 × 25 to 51 × 51 reduces the time required for temperature superposition calculations to approximately 0.14–0.82% of the original time. This substantial improvement in computational efficiency is achieved without compromising accuracy. Full article

The aim of this study is to determine the reliability of regular and spatial cross-validation methods in predicting subfield-scale maize yields using phenological measures derived by Sentinel-2. Three maize fields from eastern Croatia were monitored during the 2023 growing season, with high-resolution ground truth yield data collected using combine harvester sensors. Sentinel-2 time series were used to compute two vegetation indices, Enhanced Vegetation Index (EVI) and Wide Dynamic Range Vegetation Index (WDRVI). These features served as inputs for three machine learning models, including Random Forest (RF) and Bayesian Generalized Linear Model (BGLM), which were trained and evaluated using both regular and spatial 10-fold cross-validation. Results showed that spatial cross-validation produced a more realistic and conservative estimate of the performance of the model, while regular cross-validation overestimated predictive accuracy systematically because of spatial dependence among the samples. EVI-based models were more reliable than WDRVI, generating more accurate phenomenological fits and yield predictions across parcels. These results emphasize the importance of spatially explicit validation for subfield yield modeling and suggest that overlooking spatial structure can lead to misleading conclusions about model accuracy and generalizability. Full article

Nitrous oxide (N₂O), a potent greenhouse gas (GHG) and major contributor to climate change, is primarily released through agricultural activities. To better understand and quantify how land management practices, local climate conditions, and soil physicochemical properties affect these agricultural N₂O emissions, we conducted a review of the peer-reviewed literature on N₂O emission from corn [Zea mays L.] and soybean [Glycine max (L.) Merr.] fields. We evaluated the seasonal, cumulative effects of three nitrogen fertilizer rates—no fertilizer (0), low (<188 kg N ha⁻¹), and high (188–400 kg N ha⁻¹)—tillage practices, local climate (precipitation and temperature), soil texture, and soil pH on soil N₂O emissions. This meta-analysis included 77 articles for corn and 22 articles for soybean fields. Average N₂O emissions during the corn rotation were 2.34 and 2.45 kg N₂O-N ha⁻¹ season⁻¹ under low and high N fertilizer rates, respectively, and were both substantially (p < 0.0001) greater than those of non-fertilized corn fields (0.91 kg N₂O-N ha⁻¹ season⁻¹). Non-fertilized soybean fields showed seasonal N₂O emissions of 0.74 kg N₂O-N ha⁻¹, while low fertilizer application triggered a sharp increase (1.87 kg N₂O-N ha⁻¹) in N₂O emissions by roughly 2.5 times (p < 0.028). Increased temperature did not significantly (p > 0.05) affect the emission of N₂O from fertilized or non-fertilized corn fields. Regardless of fertilization and tillage practices, our analysis, including Principal Component Analysis, revealed that in corn fields, precipitation and soil pH are the dominant factors influencing soil N₂O emissions. This study uniquely quantifies the influence of climate–soil factors, such as precipitation and soil pH, alongside agronomic practices, on N₂O emissions, offering new insights beyond previous reviews focused primarily on fertilizer rates or tillage effects. Full article