In this paper, we address the problem of parameter identification for a Wiener nonlinear system with an autoregressive (AR) noise model. We propose two novel algorithms, namely the Wiener system generalized gradient iterative (WS-GGI) algorithm and the Wiener system zebra optimization (WS-ZO) algorithm. The WS-GGI algorithm is rooted in the gradient search principle, while the WS-ZO algorithm is a metaheuristic approach characterized by its robust parallel search capability. The core concept of the WS-ZO method is to identify the optimal solution by simulating the random movements of zebras within the search space and evaluating the objective function. The effectiveness of the proposed algorithms is demonstrated through experimental data and further compared with the recursive generalized least squares (RGLS) algorithm and the particle swarm optimization (PSO) algorithm. In conclusion, our findings indicate that these two new algorithms offer significant advantages in terms of accuracy and computational efficiency.

Atmospheric correction is crucial for quantitative remote sensing. However, an insufficient radiative transfer model (RTM) under polluted conditions limits the accuracy of high-resolution satellite data and hinders practical applications. We propose a novel atmospheric correction framework, regional aerosol clustering-assisted neural networks for atmospheric correction (RACNN), which combines an RTM with a multilayer neural network (MLNN). Ground-based aerosol data from three AErosol RObotic NETwork (AERONET) sites in Beijing were used to cluster typical aerosol conditions, including regional aerosol pollution (AP), and the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model-simulated spectral data under diverse atmospheric scenarios. The MLNN was trained to learn the mapping between top-of-atmosphere (TOA) reflectance and surface reflectance, incorporating geometric and spectral information. Model performance was evaluated using both simulated and field-measured data. The results demonstrate that the proposed method achieves high prediction accuracy across various spectral bands, with an overall root-mean-square error (RMSE) of approximately 0.03 sr(-1) and a correlation coefficient (R) exceeding 0.94. Among different seasons, the highest prediction accuracy was observed in summer and fall, while lower accuracy was noted in winter and spring. These seasonal differences may be attributed to the varying aerosol properties and vegetation status. Specifically, the model exhibited robust performance on land-cover types such as forests and croplands, whereas relatively higher prediction errors were found in grasslands. The proposed RACNN method outperforms traditional corrections in both accuracy and efficiency under clear skies and in the presence of AP, demonstrating the potential of physics-informed machine learning (ML) for scalable, high-resolution atmospheric correction.

Tracking ecosystem productivity in fast-evolving estuarine wetlands is often constrained by the trade-off between spatial detail and temporal continuity in satellite observations. To address this, we developed a reproducible fusion–VPM framework that integrates multi-sensor data to map Gross Primary Production (GPP) at a high spatiotemporal resolution. By combining the Flexible Spatiotemporal Data Fusion (FSDAF) method with a Time-Series Linear Fitting Model (TSLFM), we constructed a continuous 30 m, 8-day vegetation index record for China’s Yellow River Delta (YRD) from 2000 to 2021. This record was propagated through the Vegetation Photosynthesis Model (VPM) to simulate GPP and quantify the relative contributions of land-use/land-cover change (LUCC) versus environmental factors. The results show a marginally significant increase in total GPP (9.74 Gg C a−1, p = 0.074) over the last two decades. Deconvolution of driving factors reveals that 87.45% of the GPP increase occurred in stable land-cover areas, where the Enhanced Vegetation Index (EVI) was the dominant driver (explaining 79.97% of the variability). In areas undergoing LUCC, the net effect on GPP primarily reflected the combined influences of artificial saline–alkali wetland expansion and cropland expansion: water-to-vegetation conversions enhanced GPP, whereas vegetation-to-water conversions fully offset these gains. This study demonstrates the efficacy of spatiotemporal data fusion in overcoming observational gaps and provides a transferable analytical framework for diagnosing carbon dynamics in complex, dynamic deltaic ecosystems. This study not only provides a critical, high-resolution assessment of carbon dynamics for the YRD but also delivers a generalizable analytical framework for mapping and attributing GPP trends in complex deltaic ecosystems worldwide.

PM2.5 and O3 double-high pollution is a complex process, depending on precursors’ emissions, atmospheric chemical processes and meteorological factors. In this study, 29 national monitoring sites of air quality in Fujian Province from 2018 to 2022 were selected to explore the spatiotemporal distributions of co-occurring PM2.5 and O3 pollution. We built an XGBoost machine learning model to elucidate the main drivers of PM2.5 and O3 pollution levels. The results showed that the days with double-high pollution (DHP) were primarily occurred in April and September in the coastal areas of Southeast China. The analysis results of SHAP (SHapley Additive exPlanations) values suggested that RH, NO2, PM2.5, U10, T and V10 were the main drivers on high O3 concentration. NO2 was the most significant contributor to PM2.5 levels, followed by O3, T, and RH, suggesting the influence of increased atmospheric oxidation capacity. During the DHP period in 2022, due to abnormal climatic conditions, reduced RH and a significant decrease in PM2.5 and NO2 concentrations contributed to the O3 increase, with model-attributed contributions of 26

BACKGROUND:How long-term ambient benzene exposure affects mental health, and through what biological mechanisms, remains unclear. We aimed to investigate associations between ambient benzene and risks of single and multiple psychiatric disorders (MPD), and explore biological aging as a potential mediator. METHODS:In a historical cohort of 410,207 UK Biobank participants, annual ambient benzene concentrations were estimated at residential addresses using 1 × 1 km spatial grids. Accelerated biological aging was assessed using PhenoAge acceleration. Cox models were used to evaluate associations between benzene exposure and nine psychiatric outcomes identified using ICD-10 codes from linked health records, adjusting for confounders. Bootstrap-based mediation analysis examined the mediating role of accelerated PhenoAge. RESULTS:Over 14.80 years of median follow-up, 64,270 participants developed psychiatric disorders. Each interquartile range increase in benzene was significantly associated with elevated risks of any psychiatric disorder (hazard ratio [HR] 1.040, 95% confidence interval [CI] 1.028-1.053) and MPD (HR 1.062, 1.039-1.086), along with mood disorders, major depressive disorder, anxiety disorders, and sleep disorders, most showing significant linear trends across exposure quartiles. Nonlinear dose-response patterns indicated disproportionately steeper risks at lower concentrations. Age and urbanicity were significant effect modifiers, with stronger associations among younger and rural participants. Accelerated PhenoAge significantly mediated the benzene-psychiatric associations, accounting for 7.85% (95% CI 5.57%-11.12%) for any psychiatric disorder and 17.26% (9.01%-80.54%) for MPD, with similar mediation observed for anxiety, substance use, and sleep disorders. CONCLUSION:Long-term ambient benzene exposure is associated with higher risks of psychiatric disorders, partly through accelerated biological aging. These findings support the refinement of benzene emission standards and monitoring policies.

Aerosols exert a significant influence on Earth’s climate system via radiative forcing, cloud formation, and air quality. Despite regional variability in their optical properties, coastal boundary aerosols remain poorly characterized, which limits the accuracy of climate assessments. In this study, we develop a hybrid classification framework that combines k-means clustering and a multilayer perceptron neural network to classify coastal aerosols. Using observations from 58 global sites in the Aerosol Robotic Network, we identify four representative coastal aerosol regimes: urban and industrial pollution aerosol, mineral dust aerosol, biomass-burning smoke aerosol, and marine aerosol dominated by sea salt. Our findings reveal strong seasonal dominance in coastal aerosol composition, with mineral dust accounting for up to 75% of the total aerosol burden in summer. Multiwavelength optical properties indicate that the wavelength gradient of aerosol optical depth may decrease from 0.3 to 0.12, highlighting regime-dependent spectral variability. Coarse-mode aerosol optical depth also increases substantially in winter, reaching levels approximately three times those observed in other seasons. Distinguishing coastal aerosol regimes across regions and seasons can improve climate-model evaluation and support evidence-based policies to protect vulnerable coastal ecosystems worldwide.

Background Digestive diseases are a major cause of health burden globally. Understanding the relative contribution of various risk factors to this burden is essential for developing effective reduction strategies. However, there is a gap in knowledge regarding the global burden of digestive diseases attributable to risk factors.Objective This study aimed to assess the global burden of digestive diseases attributable to risk factors from 1990 to 2021.Design We analysed data from the Global Burden of Diseases 2021, covering 12 digestive diseases and 10 risk factors across 204 countries and territories. Age-standardised rates of disability-adjusted life-years (DALYs) were estimated. The estimated annual percentage change determined annual percent change.Results In 2021, there were 3.30 million deaths and 116.73 million DALYs from digestive diseases globally, accounting for 43.3% and 45.9% of all digestive disease deaths and DALYs, respectively. Males accounted for 74.42 million digestive disease DALYs attributable to risk factors, representing 49.1% of total DALYs in males, whereas females accounted for 42.31 million DALYs, representing 41.1%. Unsafe water, sanitation, and handwashing was the leading risk factor in both males and females (26.95 million and 17.8% in males; 24.67 million and 24.0% in females). The age-standardised DALY rates of digestive diseases attributable to environmental and occupational risks and behavioural risks decreased, while that attributable to metabolic risks increased.Conclusion The risk factors highly associated with economic and social development caused a substantial digestive disease burden. The highest burdens of environmental and occupational and behavioural risks were concentrated in countries in sub-Saharan Africa. More global cooperation is needed in the field of digestive diseases to promote health equity and achieve the Sustainable Development Goals.

Carbon satellites, as an essential means of obtaining atmospheric XCO2 concentration, play a key role in monitoring the global carbon cycle. However, the differences in observation platforms, resolutions, and inversion algorithms among different satellites lead to apparent inconsistencies among multi-source XCO2 data, which limits the joint application and comprehensive analysis of the data. In this paper, we develop a framework named MCF-XCO2 (Multi-source Consistency Fusion of XCO2) for correcting multi-source satellite XCO2 observations and performing uncertainty-weighted fusion. The method leverages high-precision satellite products as references, while minimizing the need for direct ground-based correction, to enhance the consistency and overall accuracy of multi-source observations. Based on this framework, multi-source satellite data, including GOSAT, GOSAT-2, OCO-2, and OCO-3, were integrated to construct a sparsely gridded global XCO2 fusion dataset at 0.01 degrees x 0.02 degrees nominal spatial resolution and nominal daily sampling, reflecting available observations. The findings indicate that the fused dataset shows improved coverage in grids with available observations compared to individual satellite products, improved accuracy, and better consistency over time. Independent validation against TCCON ground-based observations further confirms the method's effectiveness, with R2 = 0.91, RMSE = 1.09 ppm, bias = 0.07 ppm, and MRE = 0.2 %. The spatial and temporal dynamic analysis of the fused dataset reveals the typical spatial structure and seasonal variation of global carbon concentration, demonstrating the potential application of this dataset in studying the carbon cycle. The MCF-XCO2 framework is also designed to accommodate future satellite missions, supporting timely updates and extended temporal coverage.

Wetland carbon sink is considered to be one of the most important components of the global carbon cycle. Space-borne remote sensing serves as a vital data source for the classification and carbon sink estimation of wetland. However, inadequate spatial resolution often impedes the accurate classification of different vegetation types, substantially affecting the precision of carbon sink assessments. We propose a framework for the accurate classification and dynamic carbon storage monitoring in coastal wetland vegetation, combining its spectral, textural, and phenological features from meter-level Gaofen images during 2019∼2024. We found the optimal classification accuracy and processing time can be achieved with 2-m resolution imagery, with the overall classification accuracy of 92.4% and a Kappa coefficient of 0.89. Results of Tianjin Beidagang Wetland of Bohai Bay in North China indicate an increase in total carbon storage from 2.87 × 106 t C to 3.12 × 106 t C, with an average annual sequestration increase of 0.05 × 106 t C. Phragmites australis exhibited the highest carbon storage per unit area at 4.8 kg C/m2/yr. The invasion of Spartina alterniflora enhanced local carbon storage by about 35%, potentially threatening the stability of the wetland carbon sink. This technical framework would be helpful for reducing the uncertainty of the coastal blue carbon assessment.

Maintaining functional capacity and social participation is essential for healthy ageing, yet the association between socioeconomic status (SES) and these outcomes across diverse global contexts remains inadequately quantified. This cross-sectional analysis used harmonised individual-level data from eight longitudinal ageing studies across 22 countries in adults aged ≥ 60 years (n = 70 189). A composite SES index was derived from education and household wealth. Associations between SES and three ICF-based outcomes (muscle strength, physical performance, and community participation) were assessed using multilevel mixed-effects logistic regression to account for the nested structure of the data. Among 70 189 participants (median age 68 years [IQR 63–74]; 50.9

Space-borne multiangle polarization remote sensing is considered to be one of the most important tools to obtain global aerosol parameters in assessment of climate change. Accurate calibration is a prerequisite for quantitative polarization remote sensing. However, most research focuses on the polarization calibration methods and the monitoring of the calibration coefficient stability. Few studies investigate the polarization calibration verification of the subsequent new satellite sensors. The directional polarimetric camera (DPC) onboard the Chinese Terrestrial Ecosystem Carbon Inventory Satellite (abbreviated as TECIS, with the Chinese name "Gou Mang") is a brand-new polarization sensor. To evaluate the polarization calibration of this sensor, we propose a verification scheme for polarization calibration, which can verify both the degree of polarization (DoP) and polarized reflectance. The accuracy of DoP is verified by using sunglint on the ocean. When detecting the sunglint, constraints such as observational geometry, cloud identification, and wind speed are introduced, and the polarized reflectance is atmospherically corrected according to the marine aerosol model and aerosol optical depth (AOD) from MODIS. The accuracy of polarized reflectance at the top of atmosphere (TOA) is verified based on AERONET inversion products and the bidirectional polarization distribution functions (BPDFs). Experiments show that the accuracies of DoP of the three polarization channels (490, 670, 865 nm) of DPC are 5.57%, 2.07%, and 1.97%, respectively, and the average accuracy of the multiangle polarized reflectance at the TOA of the 865 nm channel is 0.209%.

Photovoltaic (PV) power is regarded as one of the most critical renewable energy sources for mitigating climate change. The generation process of PV power is significantly influenced by meteorological and geographic factors, resulting in intermittent and fluctuating variations. Accurate short-term PV power prediction is essential for optimizing the utilization of PV resources in grid integration. In this paper, we presented a multiscale network with mixed features and extended regional weather forecasts for predicting short-term photovoltaic power. To unravel complex temporal patterns, multiscale modeling is employed to learn temporal patterns from both local and global perspectives, with features mixed in temporal and variant dimensions, respectively. Additionally, the original model is improved with specially designed modules to manage multiple input data sources. Building on this, the effectiveness of incorporating regional meteorological forecasts for PV power prediction is evaluated. Based on the observed PV power data from five PV stations of China, comparative experiments show that the proposed model outperforms all baseline models in most cases, as measured by R2 and RMSE. This model achieves optimal results with an R2 of 0.706 when incorporating the future weather parameters. Furthermore, it shows improvements of at least 0.007, 0.018, 0.027, and 1.491 in MAE, MSE, RMSE, and SAMPE, respectively, compared to other models. The results also indicate that this model achieves the lowest RSME values on sunny and rainy days. This improvement in predicting short-term photovoltaic power has the potential to enhance grid stability and further promote the development of renewable energy.

BACKGROUND:The mental health impacts of long-term ambient benzene exposure remain incompletely understood. We aim to investigate the association between long-term exposure to low-concentration ambient benzene and mental disorders in the general population. METHOD:Estimated annual benzene concentrations from UK-wide air pollution maps were linked to health data from 410,605 eligible UK Biobank participants. A nested case-control analysis was performed to assess the risk of all-cause and ten specific mental disorders, using 1:4 risk-set matching with replacement (matching each case to up to four controls). Conditional logistic regression estimated odds ratios (ORs) and 95 % confidence intervals (CIs), and restricted cubic spline models evaluated exposure-response relationships. Subgroup analyses identified potential vulnerable populations. RESULTS:In fully adjusted models, per interquartile range (IQR) increase in benzene exposure was associated with higher risks of all-cause mental disorders (OR: 1.19, 95 % CI: 1.16-1.23), mood disorders (OR: 1.12, 95 % CI: 1.06-1.19), anxiety disorders (OR: 1.31, 95 % CI: 1.25-1.38), substance use disorders (OR: 1.23, 95 % CI: 1.16-1.31), and sleep disorders (OR: 1.13, 95 % CI: 1.05-1.23). Significant associations were also observed within six subtypes: depressive episodes, panic disorder, phobic anxiety disorder, post-traumatic stress disorder, alcohol use disorder, and tobacco use disorder. Exposure-response curves were predominantly nonlinear, with risks elevated even at low concentrations, suggesting no clear safe threshold. The associations were generally consistent across subgroups, with stronger risks among individuals without hypertension or diabetes. LIMITATIONS:We cannot establish causality. CONCLUSION:Long-term exposure to low-level ambient benzene concentrations is associated with increased risks of all-cause and specific mental disorders. These findings provide evidence to inform air pollutant management policy.

Aerosol acidity (pH) plays an important role in the multiphase chemical processes of atmospheric particles. In this study, we demonstrated the seasonal trends of aerosol pH calculated with the ISORROPIA-II model in a coastal city of southeast China. We performed quantitative analysis on the various influencing factors on aerosol pH, and explored the responses of aerosol pH to different PM2.5 and O-3 pollution levels. The results showed that the average aerosol pH was 2.92 +/- 0.61, following the order of winter > spring > summer > autumn. Sensitivity tests revealed that SO42-, NHx , T and RH triggered the variations of aerosol pH. Quantitative analysis results showed that T (37.9%-51.2%) was the main factors affecting pH variations in four seasons, followed by SO42- (6.1%-23.7%), NHx (7.2%-22.2%) and RH (0-14.2%). Totally, annual mean meteorological factors (52.9%) and chemical compositions (41.3%) commonly contributed the aerosol opH in the coastal city. The concentrations of PM2.5 was positively correlated with aerosol liquid water content ( R-2 = 0.53) and aerosol pH ( R-2 = 0.26), indicating that the increase in pH was related with the elevated NH4 NO3 and decreased SO42-, and also the changes of T and RH. The Ox (O-3 + NO2 ) was moderately correlated with aerosol pH ( R-2 = -0.48), attributable to the fact that the proportion of SO42- increased under high T , low RH conditions. The study strengthened our understand- ing of the contributions of influencing factors to aerosol pH , also provided scientific evidences for chemical processes of atmospheric particles in coastal areas. (c) 2024 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

China faces the dual challenge of population aging and rising disability rates, creating an "aging-disability overlap" that places unprecedented pressure on the nation's healthcare system. This paper analyzes the complex mechanisms underlying the intersection of aging and disability, revealing that chronic and degenerative diseases are primary contributors to disability among older adults, with disability rates significantly increasing with age and comorbidities. Despite China's implementation of long-term care insurance pilot programs and community-based elderly care services, multiple challenges persist: insufficient financing sustainability, inconsistent assessment standards, regional development imbalances, professional talent shortages, and social-cultural prejudices. In response, this paper proposes a comprehensive strategy framework aligned with "Healthy China 2030"'s goals of strengthening disability prevention and early intervention mechanisms; enhancing long-term care services with diversified security systems; fostering professional talent development in geriatric and rehabilitation medicine; promoting research and application of intelligent assistive technologies; and creating barrier-free communities within an inclusive social environment. Through these coordinated approaches, we aim to improve the quality of life for older adults with disabilities and facilitate dignified and high-quality healthy aging.

As the global population ages, multimorbidity has become a critical public health issue. We analyzed 332,012 adults from the UK Biobank (2006-2022) to investigate the association between biological age-measured by the Klemera-Doubal method (KDM-BA) and phenotypic age (PhenoAge)-and a new comorbidity model encompassing physical, psychological, and cognitive disorders, with overall mortality outcomes over a median follow-up of 13.6 years. Logistic regression models examined the association between baseline health status and accelerated aging, while Cox proportional hazards models assessed mortality risk and disorder development. Cross-sectional analysis showed that accelerated aging was linked to higher comorbidity prevalence. Longitudinal follow-up revealed that individuals in the highest quartile (Q4) of aging speed (residual difference between estimated biological age and chronological age) had a 16%-17% higher risk of developing a single disorder, a 41%-44% higher risk of multimorbidity, and a 54% higher overall mortality risk compared with the lowest quartile (Q1). Among those with baseline single disorder, dual comorbidity, and triple morbidity, Q4 mortality risk increased by 89%-116%, 118%-166%, and 119%-156%, respectively. Multistate Markov models confirmed that accelerated aging (residual > 0) increased the risk of transitioning to disorder, comorbidity, and death by 12%-37%. Individuals aged 45 with triple comorbidity lost an average of 5.3 years in life expectancy (LE), further reduced by 5.8 to 7.0 years due to accelerated aging. This study highlights that KDM-BA and PhenoAge robustly predict multimorbidity trajectories, mortality, and shortened LE, supporting their integration into risk stratification frameworks to optimize interventions for high-risk populations.

Elucidating the meteorology and emissions contribution of O3 variation is a crucial issue for implementing effective measures for O3 pollution control. We quantified the impacts of meteorology and emissions on O3 variability during spring and autumn from 2019 to 2022, using multi-year continuous observations. A machine learning (ML)-based de-weathering model revealed that meteorology accounted for a greater proportion of O3 variability (71.9% in spring and 57.4% in autumn) compared to emissions (28.1% and 42.6%, respectively). In spring, relative humidity (RH, 22.8%) and wind speed (WS, 13.7%) were key drivers, contributing to O3 decreases and increases, respectively. During autumn, temperature (T, 10.8%) and surface solar radiation (SSR, 9.45%) were the dominant factors, both contributing to O3 production. We assessed the O3 formation sensitivity based on VOCs emissions sources and evaluated the importance of emission by O3 production rate (P(O3)) calculated from box model and the positive matrix factorization (PMF) model. Vehicle emissions and solvent use were identified as the major contributors to O3 formation from 2019 to 2022 and reducing them would be beneficial for O3 pollution control. This study elucidates the relative roles of meteorological conditions and anthropogenic emissions in O3 variability and key insights for formulating future O3 control policies.

AIMS:The epidemiology and age-specific patterns of lifetime suicide attempts (LSA) in China remain unclear. We aimed to examine age-specific prevalence and predictors of LSA among Chinese adults using machine learning (ML). METHODS:We analyzed 25,047 adults in the 2024 Psychology and Behavior Investigation of Chinese Residents (PBICR-2024), stratified into three age groups (18-24, 25-44, ≥ 45 years). Thirty-seven candidate predictors across six domains-sociodemographic, physical health, mental health, lifestyle, social environment, and self-injury/suicide history-were assessed. Five ML models-random forest, logistic regression, support vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Naive Bayes-were compared. SHapley Additive exPlanations (SHAP) were used to quantify feature importance. RESULTS:The overall prevalence of LSA was 4.57% (1,145/25,047), with significant age differences: 8.10% in young adults (18-24), 4.67% in adults aged 25-44, and 2.67% in older adults (≥45). SVM achieved the best test-set performance across all ages [area under the curve (AUC) 0.88-0.94, sensitivity 0.79-0.87, specificity 0.81-0.88], showing superior calibration and net clinical benefit. SHAP analysis identified both shared and age-specific predictors. Suicidal ideation, adverse childhood experiences, and suicide disclosure were consistent top predictors across all ages. Sleep disturbances and anxiety symptoms stood out in young adults; marital status, living alone, and perceived stress in mid-life; and functional limitations, poor sleep, and depressive symptoms in older adults. CONCLUSIONS:LSA prevalence in Chinese adults is relatively high, with a clear age gradient peaking in young adulthood. Risk profiles revealed both shared and age-specific predictors, reflecting distinct life-stage vulnerabilities. These findings support age-tailored suicide prevention strategies in China.

Photovoltaic (PV) power generation is widely considered as the most important way to reduce energy carbon emissions. Accurate prediction of PV power remains a significant challenge in coastal areas with high population density, primarily due to the limitations in regional weather forecasting. In this study, we present an optimal selection strategy of typical meteorological parameters for PV power prediction in the Yangtze River Delta region of China, one of the highest electricity demand regions in the world. We find that evaporation and relative humidity are the most noteworthy meteorological factors in PV power prediction influencing coastal areas, with correlation coefficients of -0.77 and -0.52, respectively. PV power prediction is improved by similar to 30 % by incorporating weather forecasting with appropriate meteorological parameters, especially under thicker cloud conditions. This improvement of PV prediction by meteorological selection not only aids in optimizing energy distribution but also plays a crucial role in reducing carbon emissions.