Search Results (79)

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R² = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha⁻¹), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R² = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha⁻¹), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R² = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha⁻¹, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha⁻¹); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article

Load forecasting has become a key tool, especially for distribution system operators, to ensure optimal grid management and control. In recent years, attention has shifted toward probabilistic load forecasting (PLF), as it can model forecast uncertainty. Because electricity demand is strongly influenced by time-dependent factors such as seasonal patterns and daily habits, non-parametric PLF methods are particularly suitable because they make no assumptions about the distribution of variables. This study focuses on quantile regression (QR), a widely studied non-parametric PLF technique that models forecast uncertainty by only assuming a linear dependency among variables. It is applied every hour to forecast the daily consumption of three large public buildings—an elderly healthcare center, a biomedical research facility, and a polyclinic—with different demand variability profiles. Forecasts are carried out using real-world consumption data and evaluated considering both univariate and multivariate approaches. The performance of both QR approaches is rigorously evaluated against that of two persistence-based methods through standard evaluation metrics. For the univariate case, two aggregation levels are considered: single buildings and aggregation of buildings. The results confirm the effectiveness of both uQR and mQR, which consistently outperform persistence-based benchmarks. In terms of the pinball loss (PL) function, the QR approaches exhibit values ranging from 1% to 1.8% across all case studies. Both approaches demonstrate reliable and sharp prediction intervals (PIs); for example, for the PI(10–90) using the uQR, the PI coverage probability (PICP) ranges from 0.78 to 0.89 and the PI normalized average width (PINAW) from 0.09 to 0.26. Overall, uQR achieves lower PL, whereas mQR yields slightly better PICP and PINAW results for the building characterized by an irregular and unpredictable consumption profile. Full article

Urban air pollution from road traffic remains a major public health concern, with its spatial variability at neighbourhood scales strongly influenced by urban morphology. This study investigates how urban form affects the dispersion of traffic-related PM_2.5 in four Italian cities (Lecce, Bari, Milan and Rome) representing diverse climatic and morphological contexts. Seasonal simulations were conducted using the ADMS-Roads dispersion model, integrating detailed road geometries, standardized traffic emissions, and city-level meteorological data for 2019–2021. Urban morphology was characterized at 100 m resolution using building plan area fraction (λ_p), street-canyon aspect ratio and mean building height derived from GIS analyses. Statistical analysis combined random forest regression with partial dependence plots and quantile regression to explore both average and distributional effects. Results reveal a generally negative association between λ_p and PM_2.5 in Lecce, Milan, and Rome, particularly at higher concentration quantiles, suggesting that denser urban fabrics may mitigate extreme pollution episodes. Bari exhibits a weaker and more heterogeneous response, highlighting the influence of local wind regimes and traffic distribution. Wind speed and temperature consistently reduce PM_2.5 across all cities, while street geometry effects are non-linear and season-dependent. These findings demonstrate the importance of considering urban morphology alongside traffic and meteorology when designing strategies to reduce exposure. Importantly, the methodological framework presented here, combining high-resolution dispersion modelling with interpretable machine-learning analyses, is transferable to other urban contexts, providing a robust approach to assess morphology–pollution interactions beyond the studied cities. Full article

Introduction: Patients with extremely elevated IL-6 levels remain poorly characterized, and no specific plasma concentration has been established to reliably predict mortality or guide immunomodulatory interventions. We hypothesized that extreme hypercytokinemia is associated with increased mortality in sepsis. The primary objective was to identify, in patients with hyperinflammatory endotype, an IL-6 threshold associated with a significantly elevated risk of death. Methods: We conducted a retrospective, single-center observational study based on a historical cohort of adult patients with consecutive activation of the in-hospital sepsis code, a prospective and standardized institutional care pathway, at Vall d’Hebron University Hospital between July 2018 and December 2024. Patients fulfilling Sepsis-2 diagnostic criteria and criteria for severe sepsis or septic shock were eligible. Plasma interleukin-6 (IL-6) levels were routinely determined in all patients. The analysis included patients with complete clinical and laboratory data available in the study database. To identify the IL-6 threshold associated with critical risk of death, a cumulative conditional relative frequency analysis was performed. A quantile-based analysis was conducted using predefined intervals of 1000 pg/mL and 15,000 pg/mL. A multivariable logistic regression analysis was conducted to identify clinical and laboratory parameters independently associated with IL-6 > 15,000 pg/mL and outcome. Results are presented as odds ratios (ORs). Survival differences were assessed using Kaplan–Meier analysis. Results: Overall mortality was 31% in the 1669 patients analyzed. Median IL-6 concentration was 772 pg/mL (IQR: 164–8750 pg/mL) with significantly higher levels in non-survivors (2137 pg/mL, IQR: 267–34,758). A critical IL-6 cutoff of 14,930 pg/mL was identified, which was rounded to 15,000 pg/mL for clinical applicability. IL-6 > 15,000 pg/mL was associated with increased mortality (OR 2.22, 95% CI: 1.12–5.36). Kaplan–Meier analysis revealed significantly reduced survival in patients above this IL-6 threshold (p < 0.0001). Conclusions: In this cohort of patients with severe sepsis or septic shock, plasma IL-6 levels > 15,000 pg/mL defined a critical threshold beyond which mortality risk exceeded survival probability. Critical hypercytokinemia may serve as a clinically relevant biomarker to identify patients with sepsis and multiorgan dysfunction who could benefit from precision immunomodulatory therapies. Full article

This study presents a multiscale, uncertainty-aware hybrid deep learning approach addressing the short-term wind speed prediction problem, which is critical for the reliable planning and operation of wind energy systems. Wind signals are decomposed using adaptive variational mode decomposition (VMD), and the resulting wind components are processed together with meteorological data through a dual-stream CNN–BiLSTM architecture. Based on this multiscale representation, probabilistic forecasts are generated using quantile regression to capture best- and worst-case scenarios for decision-making purposes. Unlike fixed prediction intervals, the proposed approach produces adaptive prediction bands that expand during unstable wind conditions and contract during calm periods. The developed model is evaluated using four years of meteorological data from the Afyonkarahisar region of Türkiye. While the proposed model achieves competitive point forecasting performance (RMSE = 0.700 m/s and MAE = 0.54 m/s), its main contribution lies in providing reliable probabilistic forecasts through well-calibrated uncertainty quantification, offering decision-relevant information beyond single-point predictions. The proposed method is compared with a classical CNN–LSTM and several structural variants. Furthermore, SHAP-based explainability analysis indicates that seasonal and solar-related variables play a dominant role in the forecasting process. Full article

Chlorophyll-a (Chl-a) concentration is a key indicator of coastal ecosystem health, reflecting both primary productivity and the ecosystem’s response to climate change and human activities. This study quantifies long-term Chl-a trends in the Yellow and Bohai Seas using a multi-source remote sensing reconstruction dataset generated with deep learning algorithms. Quantile regression was applied to assess changes across the 75th, 50th, and 25th percentiles, and environmental drivers—including sea surface temperature, mixed layer depth, wind speed, and sea surface height anomalies—were evaluated in representative regions such as estuaries, aquaculture zones, and offshore waters. From 2005 to 2024, Chl-a concentrations declined across the 75th, 50th, and 25th percentiles, with rates of −4.82 × 10⁻³, −4.50 × 10⁻³, and −4.09 × 10⁻³ mg·m⁻³·a⁻¹, respectively (where “a” denotes year). The decline also showed strong seasonal differences, with summer decreases (−0.0638 mg·m⁻³·a⁻¹) substantially greater than winter (−0.04 mg·m⁻³·a⁻¹). Spatially, the decline was more pronounced in high-concentration nearshore waters, with rates of −0.0283 mg·m⁻³·a⁻¹ in the Qinhuangdao region, compared to −0.0137 mg·m⁻³·a⁻¹ in deeper offshore waters. Mixed-layer depth and wind speed emerged as the primary physical controls, with nearshore declines driven by enhanced vertical mixing and offshore changes dominated by mesoscale oceanic processes. These findings provide new insights for modeling and managing coastal ecosystems under combined climate and anthropogenic pressures. Full article

Due to their wide application, there is a large amount of residual antibiotics in our environment and food, raising concerns about health risks to children. In this study, 302 primary-school students in Hainan Province, China, were recruited to collect urine samples and questionnaires. The internal exposure levels of sixteen antibiotics and three metabolites in urine were determined by high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS), and the contents of DNA oxidative damage markers, 8-hydroxy-2′-deoxyguanosine (8-OHdG) and lipid peroxidation marker malondialdehyde (MDA), were also measured. Antibiotics and their metabolites were frequently detected, with a total concentration of < LOD-4.58 × 10³ ng/mL. Binary logistic regression analysis revealed that the detection frequency of DFs of antibiotics was associated with animal-derived foods, such as red meat with fluoroquinolones (FQs) (OR = 76.4, 95% CI 1.68–3479), poultry with norfloxacin (NFX) (OR = 6.56, 95% CI 1.08–39.9), and aquatic products with ciprofloxacin (CIP) (OR = 3.96, 95% CI 1.32–11.9). Cumulative risk assessment based on microbial effects showed a hazard index of 3.5 for children, mainly driven by azithromycin (45.6%), oxytetracycline (18.1%), and CIP (33.9%). Multiple linear regression indicated that lipid peroxidation was significantly associated with high quantiles of three antibiotic classes, while DNA oxidation was positively correlated with all antibiotic classes except FQs. These findings indicate that children in Hainan are widely exposed to antibiotics. Although the exposure levels are generally low, chronic low-dose antibiotic exposure may contribute to disease development and oxidative stress damage. Full article

Urban river restoration provides significant ecological and social benefits, yet its market valuation remains underexamined in rapidly urbanizing inland cities. This study estimates the economic value of integrated blue–green spaces generated by the Bai River Ecological Restoration Project in Nanyang, China, using a spatially explicit hedonic pricing framework that links geocoded resale transactions with NDVI-based vegetation measures. Properties located within blue–green zones—areas jointly characterized by restored waterways and enhanced riparian greening—command an average price premium of 17.9% (CNY 1509/m²). Visual accessibility further increases housing values, although interaction effects indicate diminishing marginal premiums where multiple amenities co-occur. Quantile regressions show stronger capitalization effects in lower- and middle-priced segments, suggesting that ecological improvements may yield broad-based rather than elite-focused benefits. Spatial dependence diagnostics confirm significant autocorrelation, and Spatial Error Model estimates remain consistent with the baseline results. Overall, the findings provide robust evidence of supra-additive blue–green synergies and demonstrate the utility of combining NDVI with spatial econometric hedonic modeling. The study offers a transferable framework for supporting nature-based urban planning and informing cost–benefit evaluations of integrated ecological restoration initiatives. Full article

This study aims to forecast the combined impacts of drought and flood in the future using an integrated framework. This framework integrates U-Net++, quantile mapping (QM), Copula models, and ISIMIP3b gridded large-scale discharge data (1985–2014, 2021–2050, and 2071–2100). Copula models analyze compound effects in four dimensions to determine return periods for droughts and floods. The standalone U-Net++ and its integration with multiple linear regression, multiple nonlinear regression, M5 model tree, multivariate adaptive regression splines, and QM downscaled ISIMIP3b model river flows. U-Net++QM outperformed other models, with a 58% lower RRMSE. Ensemble GCMs showed less uncertainty than other models in river flow downscaling. For the Ensemble model, the highest drought severity was −300, the maximum duration was 300 months, flood peak flow reached 12,000 m³/s, and intervals lasted up to 22 months. Moreover, the return periods of compound events for this model ranged from 50 to 3000 years. Future river flow projections, using the Ensemble model and emission scenarios (SSP126, SSP370, and SSP585), showed increased vulnerability in 2071 and 2025 versus the observed period. Introducing an integrated framework serves as a management tool for addressing extreme combined phenomena under climate change. Full article

Exposure to environmental pollutants, including toxic metals, volatile organic compounds (VOCs), and per- and polyfluoroalkyl substances (PFAS), has been increasingly linked to impaired kidney function. However, the combined effects of these exposures, along with essential elements, on kidney health remain poorly understood. This study aimed to evaluate the independent and cumulative or mixture effects of toxic metals (cadmium, lead, and mercury), essential elements (iron, manganese, and selenium), PFAS (PFOA and PFOS), and VOCs (m-/p-xylene and o-xylene) on kidney function as measured by estimated glomerular filtration rate (eGFR). Using data from the National Health and Nutrition Examination Survey (NHANES), we applied multiple imputation to address missing data and implemented statistical techniques, including Bayesian Kernel Machine Regression (BKMR), quantile g-computation, and Weighted Quantile Sum Regression (WQSR) to assess complex exposure–response relationships, including non-linear, potential synergistic, and antagonistic effects. The results indicated that several exposures were correlated, particularly o-xylene with m-/p-xylene (r = 0.77), Cd with Pb (r = 0.46), and PFOS with PFOA (r = 0.61). eGFR was negatively associated with Pb, PFOS, PFOA, and Hg. In the BKMR analysis, overall posterior inclusion probabilities (PIPs) highlighted PFOS, Cd, Se, Mn, and Fe as the most influential exposures. Quantile g-computation highlighted Cd and Mn as major contributors, while WQSR modeling confirmed Mn as a key contributor. The findings underscore the importance of considering complex interactions in environmental exposure assessments. While essential elements may offer protective effects, toxic metals, PFAS, and VOCs remain critical contributors to kidney dysfunction. These insights highlight the need for integrative risk assessment approaches and public health strategies aimed at mitigating harmful exposures while promoting optimal nutrient balance. Full article

The aim of this study was to analyze long-term trends and spatial variability of PM10 concentrations in Poland during the period 2019–2024, based on data from the European Air Quality Monitoring System (EAQ). The analysis covered nine locations representing three types of areas: large agglomerations (Warsaw, Kraków, Katowice), medium-sized cities (Wrocław, Poznań), and spa towns (Ciechocinek, Lądek-Zdrój, Świnoujście). An integrated statistical approach was applied, including Welch’s ANOVA, Linear Mixed Models (LMM), Generalized Additive Models (GAM), and Quantile Regression (FDR–BH). Mean PM10 concentrations in the analyzed period ranged from 17.43 µg/m³ in Świnoujście to 31.16 µg/m³ in Kraków, with 30.17 µg/m³ in Katowice and 27.90 µg/m³ in Warsaw. The largest differences between locations were observed during smog episodes —the 90th percentile values reached 56.61 µg/m³ in Kraków, 49.99 µg/m³ in Katowice, and 29.19 µg/m³ in Świnoujście. In most locations, a downward trend in PM10 levels was recorded over time; however, regional differences persist. The GAMs confirmed strong seasonality (winter maximum, summer minimum), while quantile regression indicated that the highest risk of smog episodes occurs in southern Poland. The novelty of this study lies in the integration of three complementary modeling approaches (LMM, GAM, and Quantile Regression) in the analysis of the spatio-temporal variability of PM10, as well as in the innovative comparison-unique in the literature-of agglomerations, medium-sized cities, and spa towns in Poland based on a uniform, reference EAQ dataset. This approach made it possible to reveal persistent environmental disparities of significant relevance to the national anti-smog policy and enables a more realistic assessment of environmental risk within the European research context. Full article

Real-time control of web traffic is a critical issue for network operators and service providers. It helps ensure robust service and avoid service interruptions, which has an important financial impact. However, due to the high speed and volume of actual internet traffic, standard multivariate time series models are inadequate for ensuring efficient real-time traffic management. In this paper we introduce a new model for functional time series analysis, developed by combining a local linear smoothing approach with an $L^1$-robust estimator of the quantile’s derivative. It constitutes an alternative, robust estimator for functional modal regression that is adequate to handle the stochastic volatility of high-frequency of web traffic data. The mathematical support of the new model is established under functional dependent case. The asymptotic analysis emphasizes the functional structure of the data, the functional feature of the model, and the stochastic characteristics of the underlying time-varying process. We evaluate the effectiveness of our proposed model using comprehensive simulations and real-data application. The computational results illustrate the superiority of the nonparametric functional model over the existing conventional methods in web traffic modeling. Full article

Fencing demands a unique blend of endurance, explosive power, and asymmetric neuromuscular control. This study compared physiological profiles of elite (top 25 nationally ranked, n = 16) and non-elite (positions 26–102, n = 33) Polish male fencers using the Fencing Endurance Test (FET), countermovement jump (CMJ), 5-m sprint, body composition, and heart rate (HR) metrics. FET duration, CMJ-derived explosive power (flight time, reactive strength index), and relative lean mass were also assessed in relation to competitive experience. Quantile regression (age & BMI-adjusted), ROC analysis, and Spearman correlations evaluated group differences. Elite fencers demonstrated superior FET duration (median difference: +1.84 min, p < 0.0001), CMJ performance (e.g., 10.4 W/kg higher peak power, p = 0.014), and relative lean mass (+7.7%, p < 0.001), despite comparable 5-m sprint times. Elite athletes also showed more efficient HR recovery (HRR1) and lower pre-FET resting HR (p < 0.05). Competitive experience correlated strongly with FET endurance (rho = 0.62), CMJ power (rho = 0.42), and lean mass (rho = 0.55). ROC analysis identified FET ≥ 14.3 min, CMJ flight time ≥0.581 s, and ≥10 years of experience as optimal discriminators of elite status (AUCs 0.86–0.90). These findings confirm that elite performance is characterized by superior sport-specific endurance and explosive power, independent of age/BMI. The FET and CMJ emerge as practical tools for monitoring training progress, with identified thresholds serving as benchmarks for elite preparation. Training programs should prioritize individualized development of these traits, acknowledging inter-athlete variability in physiological strengths. Future research should explore sport-specific acceleration metrics and extended FET protocols for elite athletes. Full article

Survival analysis is a fundamental tool for modeling time-to-event data in healthcare, engineering, and finance, where censored observations pose significant challenges. While traditional methods like the Beran estimator offer nonparametric solutions, they often struggle with the complex data structures and heavy censoring. This paper introduces three novel survival models, iSurvM (imprecise Survival model based on Mean likelihood functions), iSurvQ (imprecise Survival model based on Quantiles of likelihood functions), and iSurvJ (imprecise Survival model based on Joint learning), that combine imprecise probability theory with attention mechanisms to handle censored data without parametric assumptions. The first idea behind the models is to represent censored observations by interval-valued probability distributions for each instance over time intervals between event moments. The second idea is to employ the kernel-based Nadaraya–Watson regression with trainable attention weights for computing the imprecise probability distribution over time intervals for the entire dataset. The third idea is to consider three decision strategies for training, which correspond to the proposed three models. Experiments on synthetic and real datasets demonstrate that the proposed models, especially iSurvJ, consistently outperform the Beran estimator from accuracy and computational complexity points of view. Codes implementing the proposed models are publicly available. Full article

Accurate prediction of wind speed is critical for wind power generation and bias correction serves as an effective tool to enhance the precision of climate model forecasts. This study evaluates the effectiveness of three bias correction methods—Quantile Regression at the 50th percentile (QR50), Linear Regression (LR), and Optimal Threat Score (OTS)—for improving wind speed predictions at a height of 70 m from the NCEP CFSv2 model in Shanxi Province, China. Using observational data from nine wind towers (2021–2024) and corresponding model hindcasts, we analyze systematic biases across lead times of 1–45 days. Results reveal persistent model errors: overestimation of low wind speeds (<6 m/s) and underestimation of high wind speeds (>6 m/s), with the Root Mean Square Error (RMSE) exceeding 1.5 m/s across all lead times. Among the correction methods, QR50 demonstrates the most robust performance, reducing the mean RMSE by 11% in October 2023 and 10% in February 2024. Correction efficacy improves significantly at longer lead times (>10 days) and under high RMSE conditions. These findings underscore the value of regression-based approaches in complex terrain while emphasizing the need for dynamic adjustments during extreme wind events. Full article