Search Results (5,954)

Environmental monitoring systems generate large volumes of multivariate time series data from heterogeneous sensors, including those measuring soil, weather, and air quality parameters. However, sensor malfunctions and transmission failures frequently lead to missing values, compromising the performance of downstream analytical and predictive models. To address this challenge, this study presents a comprehensive and systematic evaluation of previously proposed hybrid architecture that interleaves Long Short-Term Memory (LSTM) layers with a Multi-Head Attention mechanism in a “sandwiched” setting (LSTM–Attention–LSTM) for robust multivariate data imputation in environmental IoT datasets. The first LSTM layer captures short-term temporal dependencies, the attention layer emphasises long-range relationships among correlated features, and the second LSTM layer re-integrates these enriched representations into a coherent temporal sequence. The model is evaluated using multiple environmental datasets of soil temperature, meteorological (precipitation, temperature, wind speed, humidity), and air quality data across missingness levels ranging from 10% to 90%. Performance is compared against baseline methods, including K-Nearest Neighbour (KNN) and Bidirectional Recurrent Imputation for Time Series (BRITS). Across all datasets, the Hybrid model consistently outperforms baseline methods, achieving MAE reductions exceeding 50% and reaching over 80% in several scenarios, along with RMSE reductions of up to approximately 85%, particularly under moderate to high missingness conditions. An ablation study further examines the contribution of each layer to overall model performance. Results demonstrate that the proposed Hybrid model achieves superior accuracy and robustness across datasets, confirming its effectiveness for environmental sensor data imputation under varying missing data conditions. Full article

Ammonium nitrogen (NH₄⁺-N) removal and recovery from wastewater have been critical issues worldwide and key to achieving a sustainable nitrogen cycle and circular economy. In this study, we designed and constructed a pilot-scale air stripping system integrated with a nutrient-capture unit and evaluated the effective pH, temperature, and airflow conditions for maximising NH₄⁺-N removal and recovery from dairy processing wastewater (DPW). Our results demonstrated that increasing pH and temperature substantially enhances NH₄⁺-N removal via air stripping, with higher airflow rates further improving performance. Under these conditions (pH 11, 32 °C, and 300 L min⁻¹), NH₄⁺-N removal from synthetic wastewater reached ≈40% after 6 h air stripping. In comparison, real DPW exhibited slightly lower removal efficiency under the same conditions, achieving ≈34%, likely due to its more complex matrix. Additionally, incorporating a chemical precipitation step followed by filtration prior to air stripping removed NH₄⁺-N from DPW, achieving ≈43%. However, extending the stripping duration under identical conditions significantly improved removal performance, increasing NH₄⁺-N removal in DPW to ≈70%. The downstream capturing system, consisting of acid bath and granulated activated carbon (GAC), consistently recovered 70–95% of the released ammonia (NH₃) when even upstream NH₄⁺-N removal via air stripping was moderate. The GAC effectively adsorbed the volatilised NH₃, achieving adsorption capacities of up to ≈18 mg/kg. Overall, this integrated system demonstrates strong potential for simultaneous NH₄⁺-N removal and recovery from industrial wastewater streams, offering notable environmental benefits. Full article

Investigating vegetation dynamics and their drivers in ecologically vulnerable regions is essential for evaluating ecological restoration outcomes. This study examined the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its influencing factors in Fengning county, the Bashang region from 2001 to 2023 using land use transition matrix, trend analysis, and geographical detector methods. Key findings include the following: (1) Land use transition exhibited a clear phased pattern, shifting from cropland-to-grassland conversion (2001–2010) to grassland-to-forest conversion (2010–2023). (2) The annual mean NDVI increased significantly, showing a southeast–northwest spatial gradient consistent with landforms. The long-term trend followed a sequential “degradation–improvement–consolidation” trajectory. (3) Factor detection identified land use type as the primary driver of vegetation spatial heterogeneity (q = 0.297), highlighting the dominant influence of human activities. (4) Interaction detection demonstrated bivariate enhancement for all factor pairs, with the combination of land use type and precipitation yielding the highest explanatory power (q = 0.440). This underscores that vegetation dynamics are predominantly governed by nonlinear interactions between human-driven land use and climate. The research highlights the effectiveness of ecological restoration policies and offers valuable insights for guiding future ecosystem management in ecologically fragile areas under climate change. Full article

Efficient phosphorus recovery is of great significance for sustainable wastewater management and resource recycling. While chemical precipitation is widely used, its effectiveness under complex multi-factor conditions remains challenging to predict and optimize. This study compiled a multidimensional dataset from recent experimental literature, encompassing key operational parameters (reaction time, temperature, pH, stirring speed) and dosages of three metal precipitants (Al³⁺, Ca²⁺, Fe³⁺) to systematically evaluate and benchmark phosphorus recovery performance across these distinct systems, six machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gaussian Process Regression (GPR), Elastic Net, Artificial Neural Network (ANN), and Partial Least Squares Regression (PLSR)—were developed and cross-validated. Among them, the GPR model exhibited superior predictive accuracy and robustness. ($R^2$ = 0.69, $RMSE$ = 0.54). Beyond achieving high-fidelity predictions, this study advances the field by integrating interpretability analysis with Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP). These analyses identified distinct controlling factors across systems: reaction time and pH for aluminum, Ca²⁺ dosage and alkalinity for calcium, and phosphorus loading with stirring speed for iron. The revealed factor-specific mechanisms and synergistic interactions (e.g., among pH, metal dose, and mixing intensity) provide actionable insights that transcend black-box prediction. This work presents an interpretable Machine Learning (ML) framework that offers both theoretical insights and practical guidance for optimizing phosphorus recovery in multi-metal systems and enabling precise control in wastewater treatment operations. Full article

In Long Short-Term Memory (LSTM)-based runoff forecasting models, the selection of input schemes is critically important. This study, using daily rainfall and runoff data from the Jingle Basin (2006–2014), investigated three input schemes to evaluate their forecasting efficacy and employed the Shapley Additive Explanation (SHAP) method to quantitatively analyze variable contributions. The results demonstrate that LSTM model performance deteriorates with increasing lead time, achieving optimal accuracy at a 1-day lead (MAE: 0.90 m³/s, RMSE: 3.09 m³/s, NSE: 0.84). The results, validated by significance testing, are reasonable; incorporating precipitation characteristics significantly enhances model performance compared to baseline schemes, reducing RMSE by 6–34% and improving NSE by 9–14%. SHAP analysis reveals antecedent runoff as the dominant influencing factor, accounting for 65.9–84.7% of total importance. Furthermore, the contributions of trend, seasonal, and residual components progressively increase with extended lead times, demonstrating non-negligible roles in forecast outcomes. These findings, confirmed by significance testing, provide quantitative insights into input variable contributions to target uncertainty and enhance the mechanistic understanding of precipitation-runoff relationships, offering valuable references for optimizing hydrological forecasting systems. Full article

This study examines the high-temperature degradation of Hastelloy C276, a corrosion-resistant nickel-based alloy, during exposure to combustion products generated by methane and 99% cracked ammonia. Using a high-pressure optical combustor (HPOC) at 4 bar and exhaust temperatures of 815–860 °C, standard tensile specimens were exposed for five hours to fully developed post-flame exhaust gases, simulating real industrial turbine or burner conditions. The surfaces and subsurface regions of the samples were analysed using scanning electron microscopy (SEM; Zeiss Sigma HD FEG-SEM, Carl Zeiss, Oberkochen, Germany) and energy-dispersive X-ray spectroscopy (EDX; Oxford Instruments X-MaxN detectors, Oxford Instruments, Abingdon, United Kingdom), while mechanical properties were evaluated by tensile testing, and the gas-phase compositions were tracked in detail for each fuel blend. Results show that exposure to methane causes moderate oxidation and some grain boundary carburisation, with localised carbon enrichment detected by high-resolution EDX mapping. In contrast, 99% cracked ammonia resulted in much more aggressive selective oxidation, as evidenced by extensive surface roughening, significant chromium depletion, and higher oxygen incorporation, correlating with increased NO_x in the exhaust gas. Tensile testing reveals that methane exposure causes severe embrittlement (yield strength +41%, elongation −53%) through grain boundary carbide precipitation, while cracked ammonia exposure results in moderate degradation (yield strength +4%, elongation −24%) with fully preserved ultimate tensile strength (870 MPa), despite more aggressive surface oxidation. These counterintuitive findings demonstrate that grain boundary integrity is more critical than surface condition for mechanical reliability. These findings underscore the importance of evaluating material compatibility in low-carbon and hydrogen/ammonia-fuelled combustion systems and establish critical microstructural benchmarks for the anticipated mechanical testing in future work. Full article

Rubber particles have been proven to have the advantages of improving the energy absorption effect and enhancing the friction between soil particles when used to modify the soil. The rubber-modified soil technology also provides a new solution for the pollution-free disposal of waste rubber. However, when rubber particles are used to modify collapsible loess, they cannot significantly enhance its strength. Previous studies have not systematically clarified whether combining rubber particles with different cementation mechanisms can overcome this limitation, nor compared their shear mechanical effectiveness under identical conditions. In view of this, a dual synergistic strategy is implemented by combining rubber with lime and rubber with enzyme-induced calcium carbonate precipitation (EICP). Direct shear tests and scanning electron microscopy are used to evaluate four modification approaches: rubber alone, lime alone, rubber with EICP, and rubber with lime. Accordingly, shear strength, cohesion, and internal friction angle are quantified. At a vertical normal stress of 100 kPa and above, samples modified with rubber and lime (7–9% lime and 6–8% rubber) achieve peak shear strength values of 200–203 kPa, representing an 86.4% increase compared to rubber alone. Microscopic analysis reveals that calcium silicate hydrate gel effectively anchored rubber particles, forming a composite structure with a rigid skeleton and elastic buffer. In comparison, the rubber and EICP group (10% rubber) shows a substantial increase in internal friction angle (24.25°) but only a modest improvement in cohesion (16.5%), which is due to limited continuity in the calcium carbonate bonding network. It should be noted that the performance of EICP-based modification is constrained by curing efficiency and reaction continuity, which may affect its scalability in conventional engineering applications. Overall, the combination of rubber and lime provided an optimal balance of strength, ductility, and construction efficiency. Meanwhile, the rubber and EICP method demonstrates notable advantages in environmental compatibility and long-term durability, making it suitable for ecologically sensitive applications. The results offer a framework for loess stabilization based on performance adaptation and resource recycling, supporting sustainable use of waste rubber in geotechnical engineering. Full article

Hydrological behavior plays a critical role in seasonally dry forest ecosystems, as it underpins water availability for multiple productive activities, including forestry, agriculture, grazing, and urban supply. This study evaluated the hydrological effects of thinning treatments in a semi-arid oak–pine forest of Chihuahua, Mexico, using a Before–After–Control–Impact (BACI) design. Three Micro-catchments (MC) with initially comparable tree density and canopy cover were monitored during the rainy seasons of 2018 (pre-thinning) and 2019 (post-thinning). Thinning treatments were applied at 20% and 60% canopy cover in two MC, while a third remained unthinned as a 100% control. Precipitation and surface runoff were recorded at the event scale, and data were analyzed using Weibull probability models with a log link to capture the frequency and magnitude of runoff events. Precipitation patterns were broadly comparable across years, although 2018 included an extreme storm event (59 mm). In contrast, runoff volumes in 2019 were lower despite marginally higher seasonal rainfall, reflecting the absence of large storms. Statistical modeling indicated that for each additional millimeter of precipitation, mean runoff increased by approximately 12%, although thinning significantly altered baseline conditions. Relative to 2018, mean runoff ratios were 0.087 in the 100% canopy catchment, 0.296 in the 60% treatment, and 0.348 in the 20% treatment, suggesting that reduced canopy cover retained proportionally more runoff than the control. BACI contrasts confirmed that thinned catchments maintained higher proportions of runoff than the unthinned control, although statistical significance was marginal for the 20% canopy treatment. Overall, the study provides ecohydrological insights relevant to the management of semi-arid forest ecosystems. Full article

Accurate precipitation forecasting is paramount for water security and disaster mitigation, yet it remains formidable due to atmospheric stochasticity and the inherent class imbalance in rainfall datasets. This study proposes an integrated “architecture-feature-augmentation” framework to circumvent these limitations. Through a systematic evaluation of CNN-LSTM and Transformer architectures, we delineate distinct performance profiles: The Transformer model, when coupled with feature engineering and physics-informed augmentation, yields a peak F1-score of 0.1429, marking the optimal configuration for harmonizing precision and recall. Conversely, CNN-LSTM demonstrates superior robustness in extreme event detection, consistently maintaining high recall rates (up to 0.90) across diverse scenarios. We identify feature engineering as a critical performance modulator, substantially bolstering CNN-LSTM’s baseline metrics while enabling the Transformer to realize its maximum predictive capacity. Although synthetic oversampling techniques—such as SMOTE and GAN—effectively extend the detection range for heavy precipitation, physics-informed augmentation provides the most consistent performance gains, particularly in multi-class contexts. We conclude that the Transformer, augmented by physical constraints, is the optimal candidate for high-precision requirements, whereas CNN-LSTM, integrated with synthetic augmentation, offers a more sensitive alternative for early warning systems prioritizing recall. These findings provide empirical guidance for advancing extreme weather preparedness and strategic water resource management. Full article

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R² values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L⁻¹, and RMSE of 0.14 and 0.91 mg L⁻¹ for NO₃⁻-N and NH₄⁺-N during calibration, and R² values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L⁻¹, and RMSE of 0.10 and 0.34 mg L⁻¹ during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO₃⁻-N) and ammonium-nitrogen (NH₄⁺-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO₃⁻-N and NH₄⁺-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO₃⁻-N and NH₄⁺-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions. Full article

High-resolution satellite precipitation products are key inputs for basin-scale rainfall estimation, but they still exhibit substantial biases in complex terrain and during heavy rainfall. Recent multi-source fusion studies have shown that simply stacking multiple same-type microwave satellite products yields only limited additional gains for high-quality precipitation estimates and may even introduce local degradation, suggesting that targeted correction of a single, widely validated high-quality microwave product (such as IMERG) is a more rational strategy. Focusing on the mountainous, gauge-sparse Lüshui River basin with pronounced relief and frequent heavy rainfall, we use GPM IMERG V07 as the primary microwave product and incorporate CHIRPS, ERA5 evaporation, and a digital elevation model as auxiliary inputs to build a daily attention-enhanced CNN–LSTM (A-CNN–LSTM) bias-correction framework. Under a unified IMERG-based setting, we compare three network architectures—LSTM, CNN–LSTM, and A-CNN–LSTM—and test three input configurations (single-source IMERG, single-source CHIRPS, and combined IMERG + CHIRPS) to jointly evaluate impacts on corrected precipitation and SWAT runoff simulations. The IMERG-driven A-CNN–LSTM markedly reduces daily root-mean-square error and improves the intensity and timing of 10–50 mm·d⁻¹ rainfall events; the single-source IMERG configuration also outperforms CHIRPS-including multi-source setups in terms of correlation, RMSE, and performance across rainfall-intensity classes. When the corrected IMERG product is used to force SWAT, daily Nash-Sutcliffe Efficiency increases from about 0.71/0.70 to 0.85/0.79 in the calibration/validation periods, and RMSE decreases from 87.92 to 60.98 m³ s⁻¹, while flood peaks and timing closely match simulations driven by gauge-interpolated precipitation. Overall, the results demonstrate that, in gauge-sparse mountainous basins, correcting a single high-quality, widely validated microwave product with a small set of heterogeneous covariates is more effective for improving precipitation inputs and their hydrological utility than simply aggregating multiple same-type satellite products. Full article

Vascular endothelial growth factor receptor 3 (VEGFR3) assumes a pivotal role in regulating the development and maintaining the structural integrity of the lymphatic system. Decreased activity of VEGFR3 can precipitate aplasia or hypoplasia of lymphatic system components, culminating in primary lymphedema. To date, numerous genetic variants have been identified within the FLT4 gene, which encodes VEGFR3; however, the majority of these remain uncharacterised and are classified as ‘variants of uncertain significance’. In preceding investigations involving FLT4 sequence analysis conducted on individuals presenting with primary lymphedema, we identified several rare genetic variants that possess the potential to modulate the functional activity of VEGFR3, including the heterozygous variant c.3175G>C (p.A1059P). Preliminary assessments encompassing clinical characteristics, family history, and predictive computational algorithms indicated that this variant was likely pathogenic. Consequently, this study presents the results of functional evaluation of the mutant VEGFR3 activity in cell models overexpressing the FLT4 variant c.3175G>C. VEGFC-dependent VEGFR3 phosphorylation and FLT4 expression were reduced in cells with c.3175G>C FLT4 variant compared to wild-type, confirming the pathogenic role of c.3175G>C in primary lymphedema. Full article

The previous studies have suggested that the cap-and-trade carbon emissions trading scheme (ETS) was effective in reducing greenhouse gas emissions and atmospheric pollution. Are there other environmental benefits of this policy? This research question remains unanswered in the literature. Our study reports that China’s carbon ETS significantly improved the land surface ecological quality (LSEQ). The study analyzes the data of 328 Chinese cities during 2005–2020. A difference-in-differences (DID) regression model is used for quantitative policy evaluation. The land surface ecological quality is measured by a synthetic indicator of the remote sensing ecological index (RSEI). There are three main findings. (1) On average, the carbon ETS improved the land surface ecological quality index by 0.0113, which contributed 51% of the ecological quality improvement in ETS-implementing regions in the post-policy period. The positive effect of the policy increased over time. (2) The implementation of the carbon ETS reduced pollution emissions, promoted green innovation, and expanded the share of land with natural vegetation coverage. These phenomena provide explanations for why the policy improved the land surface ecological quality. (3) The policy effect exhibited some heterogeneities contingent on local climatic conditions. The effect was stronger in regions with more precipitation, shorter sunlight duration, and higher temperature. Full article

Over the last decade, bark beetle outbreaks have significantly impacted forests in Central Europe, causing extensive loss of forest cover. We evaluated the impact of partial deforestation in three mountain forest catchments in the Jeseníky Mountains, comparing them with the unaffected Červík catchment (Beskydy Mountains) and the severely affected Pekelský stream catchment (Czech-Moravian Highlands). Atmospheric deposition in the catchments was similar, with total element input driven primarily by precipitation volumes rather than ion concentrations. We did not observe the hypothesized increase in DOC and nitrogen export, although nitrate outflow was slightly higher than atmospheric input in two cases. Significant export of calcium, magnesium, and bicarbonates was driven mainly by the geology of the individual catchments. The limited impact of bark beetle outbreaks on DOC dynamics can be attributed to the relatively low proportion of clear-cut areas and the rapid development of ground vegetation on impacted sites. Full article

Shiqian County, located within a key geothermal fluids belt in Guizhou Province, China, has abundant underground hot water resources. Therefore, elucidating the hydrogeochemical characteristics and formation mechanisms of thermal mineral water in this area is essential for evaluating and sustainably utilizing regional geothermal fluids. This study focuses on the Shiqian Hot Spring Group and employs integrated analytical techniques, including rock geochemistry, hydrogeochemistry, isotope hydrology, digital elevation model (DEM) data analysis, remote sensing interpretation, geological surveys, mineral saturation index calculations, and PHREEQC-based inverse hydrogeochemical modeling, to elucidate its hydrogeochemical characteristics and formation mechanisms. The results show that strontium concentrations range from 0.06 to 7.17 mg/L (average 1.65 mg/L) and metasilicic acid concentrations range from 19.46 to 65.51 mg/L (average 33.64 mg/L). Most samples meet the national standards for natural mineral water and are classified as Sr-metasilicic acid type. Isotope analysis indicates that the geothermal water is recharged by meteoric precipitation at elevations between 911 m and 1833 m, mainly from carbonate outcrops and fracture zones on the southwestern slope of Fanjingshan, and discharges south of Shiqian County. The dominant hydrochemical types are HCO₃·SO₄-Ca·Mg and HCO₃-Ca·Mg. Strontium is primarily derived from carbonate rocks and celestite-bearing evaporites, whereas metasilicic acid mainly originates from quartz dissolution along the upstream groundwater flow path. PHREEQC-based inverse modeling indicates that, during localized thermal mineral water runoff in the middle-lower reaches or discharge areas, calcite dissolves while dolomite and quartz tend to precipitate, reflecting calcite dissolution-dominated water–rock interactions and near-saturation conditions for some minerals at late runoff stages. Full article