Search Results (369)

Megacity clusters are the key battlegrounds for carbon emission reduction in China, and the polycentric spatial structure of these clusters has a profound impact on their carbon emission intensity. This paper focuses on five major megacity clusters: the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), the middle reaches of the Yangtze River (MRYR), and the Chengdu–Chongqing (CY) City Clusters. We construct an inter-period panel dataset spanning from 2002 to 2023 and utilize an index of polycentric spatial structure, which equally considers both morphology and functionality. A fixed-effects model is employed, and the Lind–Mehlum U-shape test is applied to identify the nonlinear relationship. Additionally, a two-step approach is used to examine the mediating effect of industrial agglomeration, while interaction terms help identify the moderating effects of technological innovation and transport infrastructure. The results indicate a significant U-shaped relationship between the polycentric structure of megacity clusters and carbon emission intensity. When the polycentric spatial structure index reaches a specific threshold, carbon emission intensity is minimized, suggesting that a moderate degree of polycentricity is most conducive to carbon reduction. Mechanism analysis reveals that industrial agglomeration functions as a significant mediator, whereas technological innovation and transport infrastructure serve as critical moderators in this relationship. Based on these findings, we propose several policy recommendations: to guide the moderate adjustment of the polycentric structure of city clusters with stage-specific targets, optimize the mechanism of industrial synergy and transfer, differentiate the allocation of innovation resources, and achieve a fine-tuned alignment between the transport system and spatial structure. These measures will support the high-quality, low-carbon transformation of city clusters. Full article

This study investigates the spatial patterns and drivers of carbon emissions across China’s three major urban agglomerations—Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)—from 2011 to 2020. A sequential analytical framework was employed to examine emission inequality, spatial dependence, dynamic transitions, and multi-scale drivers. Specifically, the Gini and Theil indices were used to quantify and decompose regional disparities. Spatial clustering patterns and heterogeneity were then identified through global and local Moran’s I analysis. Following this, spatial Markov chains modeled state transitions and neighborhood spillover effects. Finally, the Spatial Durbin Model (SDM) was applied to distinguish between the direct and indirect effects of key socioeconomic drivers. The findings reveal that disparities in emissions are largely driven by factors within each region. In BTH, heavy industrial lock-in accounts for 47.1% of the within-group inequality. By contrast, the YRD and PRD show noticeable convergence, achieved through industrial synergy and technological restructuring, respectively. The mechanisms of spatial spillover also differ across regions. In the YRD, emissions exhibit strong clustering tied to geographic proximity, with Moran’s I consistently above 0.6. In BTH, policy linkages play a more central role in shaping emission patterns. Meanwhile, in the PRD, widespread technological diffusion weakens the conventional distance-decay effect. The influence of key drivers varies notably among the urban agglomerations. Economic growth has the strongest scale effect in the PRD, reflected by a coefficient of 0.556. Industrial transformation significantly lowers emissions in the YRD, with a coefficient of −0.115. Technology investment reduces emissions in BTH (−0.124) and the PRD (−0.076), but is associated with a slight rebound in the YRD (0.037). Overall, these results highlight the persistent path dependence and distinct spatial interdependencies of carbon emissions in each region. This underscores the need for tailored mitigation strategies that are coordinated across administrative boundaries. Full article

This paper proposes an adaptive layer-dependent threshold function (ALDTF) for denoising electrocardiogram (ECG) and multimode optical fiber-based cardiopulmonary signals. Based on wavelet transform, the method employs a layer-dependent threshold function strategy that utilizes the non-zero periodic peak (NZOPP) of the signal’s normalized autocorrelation function to adaptively determine the optimal threshold for each decomposition layer. The core idea applies soft thresholding at lower layers (high-frequency noise) to suppress pseudo-Gibbs oscillations, and hard thresholding at higher layers (low-frequency noise) to preserve signal amplitude and morphology. The experimental results show that for ECG signals contaminated with baseline wander (BW), electrode motion (EM) artifacts, muscle artifacts (MA), and mixed (MIX) noise, ALDTF outperforms existing methods—including SWT, DTCWT, and hybrid approaches—across multiple metrics. It achieves a ΔSNR improvement of 1.68–10.00 dB, ΔSINAD improvement of 1.68–9.98 dB, RMSE reduction of 0.02–0.56, and PRD reduction of 2.88–183.29%. The method also demonstrates excellent performance on real ECG and optical fiber cardiopulmonary signals, preserving key diagnostic features like QRS complexes and ST segments while effectively suppressing artifacts. ALDTF provides an efficient, versatile solution for physiological signal denoising with strong potential in wearable real-time monitoring systems. Full article

In China’s rapidly urbanizing coastal areas, inclusive green development (IGD) has become an important way to achieve a reduction in economic development disparities, environmental sustainability, and social equity. This study investigates the spatiotemporal dynamics and structural drivers of IGD across 54 coastal cities within three marine economic zones (MEZs) using a hybrid analytical framework that integrates evaluation techniques, inequality decomposition, spatial factor detection, and spatial econometrics. The result shows that a distinctive “four-pillar” spatial structure has emerged, centered on the Shandong Peninsula, Yangtze River Delta (YRD), West Coast of the Taiwan Strait, and Pearl River Delta (PRD). Spatial autocorrelation has intensified since 2020, indicating the cumulative effect of China’s post-2020 regional integration policies and digital infrastructure investments, which accelerated resource flows between cities. Spatial econometric analysis further reveals that economic development and equitable public service provision are the most influential drivers, while public investment in R&D and digital transformation exhibit significant cross-city spillover effects. The findings highlight the importance of regionally adaptive and digitally integrated strategies to promote inclusive and sustainable urban development in coastal economies. Therefore, efforts should be intensified to strengthen the role of core cities as diffusion engines for neighboring areas, with a strategic focus on regional digital transformation and R&D investment, to advance inclusive and sustainable development in coastal economies. Full article

Substantial black carbon (BC) emissions in China have raised serious concerns owing to their significant influence on climate change and health. However, knowledge around the relative contributions of emissions and meteorological conditions to BC dynamics is limited but essential for air pollution management. Therefore, emission-driven (BC_EMI) and meteorology-driven (BC_MET) BC concentrations in China during 2000–2019 were quantified by a machine learning framework, focusing on five regions (NC: North China, YRD: Yangtze River Delta, PRD: Pearl River Delta, SCB: Sichuan Basin, and CC: Central China). Furthermore, driving mechanisms of key meteorological factors were investigated using Shapley Additive Explanation (SHAP). Results show a dominant role of emissions in shaping BC variability, with ratios of regional average BC_EMI changes to total changes ranging from −140.50% to 76.40%. Especially, the most pronounced decrease occurred in NC during 2013–2019, with BC_EMI dropping by 1.56 μg/m³. Even so, the impact of extremely adverse meteorological conditions on BC variations cannot be ignored. The highest annual mean BC_MET in YRD (0.17 μg/m³) and PRD (0.30 μg/m³) was observed in 2004, while positive BC_MET in NC, SCB, and CC peaked in 2013, with values of 0.26, 0.18, and 0.18 μg/m³, respectively. Regarding SHAP values of each feature, meteorological effects in NC, YRD, SCB and CC were dominated by boundary layer height and temperature, whereas those in PRD were mainly regulated by precipitation and wind. These findings provide a new perspective for attributing BC variability and offer valuable insights for optimizing regional BC control strategies and air quality models. Full article

Intangible cultural heritage (ICH) constitutes a vital component of cultural diversity and a defining element of regional identity. Understanding its spatial patterns and determinants is fundamental to informing robust conservation strategies and ensuring its continuity across generations. This research employs kernel density analysis, average nearest neighbor analysis, and Poisson regression to examine the spatial distribution patterns and determinants of 3576 national, provincial, and municipal ICH items across 21 prefecture-level cities in Guangdong Province, China. The research results show the following: (1) All ICH categories in Guangdong province exhibit a significant spatial clustering, with Quyi (Chinese folk performing arts) demonstrating the most pronounced agglomeration, followed by traditional opera and traditional music. (2) Kernel density estimates display pronounced hotspots in the Guangzhou–Foshan core of the Pearl River Delta (PRD) and in Eastern Guangdong’s Chaozhou–Shantou corridor, while each heritage category displays its own geographically distinct footprint. (3) From the perspective of natural factors, ICH items are predominantly located in areas characterized by flat topography, proximity to rivers, and a mild subtropical climate, notably the coastal regions of the PRD, Eastern Guangdong, and Western Guangdong. These areas also possess superior resource endowments and transportation infrastructure. (4) Regarding socioeconomic factors, the analysis results point out distinct socioeconomic influences. Specifically, a larger registered population and higher per capita Gross Domestic Product (GDP) correspond to more ICH items. However, two factors demonstrate negative relationships: the total resident population and the level of dialect diversity. This study systematically elucidates the spatial distribution characteristics of ICH in Guangdong Province and their key influencing factors. The outcomes offer critical empirical evidence, thereby informing the design and implementation of optimized ICH conservation measures, promoting coordinated regional cultural development, and achieving the sustainable utilization of ICH resources. Full article

China’s sinuous coastline is increasingly threatened by land subsidence driven by complex geological conditions and intensive human activity. Using year-round Sentinel-1A acquisitions for 2023 and SBAS-InSAR processing, we generated the first millimetre-resolution subsidence velocity field covering the 50 km coastal buffer of mainland China. We elucidated subsidence patterns and their drivers and quantified the associated socio-economic risks by integrating 1 km GDP and population data. Our analysis shows that ~55.77% of the coastal zone is subsiding, exposing 97.42 million residents and CNY 16.41 billion of GDP. Four hotspots—Laizhou Bay, northern Jiangsu, the Yangtze River Delta (YRD) and the Pearl River Delta (PRD)—exhibit the most pronounced deformation. Over-extraction of groundwater is identified as the primary driver. The 15 m resolution subsidence product provides an up-to-date, high-precision dataset that effectively supports sustainable development research in coastal hazard prevention, territorial spatial planning, and sea-level rise studies. Full article

With the continuous expansion of the digital economy, its share in China’s overall economy has been steadily increasing. Against the backdrop of the national “dual-carbon” goals, an important question arises: how does the digital economy contribute to carbon reduction? This study selects panel data from nine cities in the Pearl River Delta (PRD) urban agglomeration between 2011 and 2023. The development level of the digital economy is measured using the entropy weight method and an index system. A two-way fixed effects model and a mediation effect model are then employed to empirically examine the relationship and mechanisms between the digital economy and urban carbon emissions. The main findings are as follows: (1) the development of the digital economy exerts a significant negative regulatory effect on carbon emissions, which remains robust after a series of tests; (2) heterogeneity analysis reveals that the inhibitory effect of the digital economy on carbon emissions is more evident in economically advanced cities, and the development level of metropolitan areas significantly influences this relationship; (3) mechanism analysis indicates that stronger environmental regulation significantly enhances the carbon reduction effect of the digital economy; and (4) the scale of e-commerce in the PRD plays a “suppression effect”, offsetting the original carbon-increasing effect of the digital economy and emerging as the key factor underlying its net carbon-reducing impact. Based on these results, the paper provides policy recommendations to better leverage the digital economy in supporting regional carbon reduction. Full article

Electrocardiograms (ECGs) are critical for cardiovascular disease diagnosis, but their accuracy is often compromised by electrode motion (EM) artifacts—large, nonstationary distortions caused by patient movement and electrode-skin interface shifts. These artifacts overlap in frequency with genuine cardiac signals, rendering traditional filtering methods ineffective and increasing the risk of false alarms and misdiagnosis, particularly in wearable and ambulatory ECG applications. To address this, we propose the Feature-Shuffle Multi-Head Attention Autoencoder (FMHA-AE), a novel architecture integrating multi-head self-attention (MHSA) and a feature-shuffle mechanism to enhance ECG denoising. MHSA captures long-range temporal and spatial dependencies, while feature shuffling improves representation robustness and generalization. Experimental results show that FMHA-AE achieves an average signal-to-noise ratio (SNR) improvement of 25.34 dB and a percentage root mean square difference (PRD) of 10.29%, outperforming conventional wavelet-based and deep learning baselines. These results confirm the model’s ability to retain critical ECG morphology while effectively removing noise. FMHA-AE demonstrates strong potential for real-time ECG monitoring in mobile and clinical environments. This work contributes an efficient deep learning approach for noise-robust ECG analysis, supporting accurate cardiovascular assessment under motion-prone conditions. Full article

The coupling of partial root-zone drying (PRD) with nitrogen forms exerts an interactive “water-promoted fertilization” effect, which enhances rice (Oryza sativa L.) growth and development, improves water use efficiency (WUE), mediates the expression of aquaporins (AQPs), and alters root water conductivity. In this study, gene cloning and CRISPR-Cas9 technologies were employed to construct overexpression and knockout vectors of the OsPIP2;1 gene, which were then transformed into rice (cv. Meixiangzhan 2). Three water treatments were set: normal irrigation (CK); partial root-zone drying (PRD); and 10% PEG-simulated water stress (PEG), combined with a nitrogen form ratio of ammonium nitrogen (NH₄⁺) to nitrate nitrogen (NO₃⁻) at 50:50 (A50/N50) for the coupled treatment of rice seedlings. The results showed that under the coupled treatment of PRD and the aforementioned nitrogen form, the expression level of the OsPIP2;1 gene in roots was upregulated by 0.62-fold on the seventh day, while its expression level in leaves was downregulated by 1.84-fold. Overexpression of OsPIP2;1 enabled Meixiangzhan 2 to maintain a higher abscisic acid (ABA) level under different water conditions, which helped rice reduce water potential and enhance water absorption. Compared with the CK treatment, overexpression of OsPIP2;1 increased the superoxide dismutase (SOD) activity of rice under PRD by 26.98%, effectively alleviating tissue damage caused by excessive accumulation of O₂⁻. The physiological and biochemical characteristics of OsPIP2;1-overexpressing rice showed correlations under PRD and A50/N50 nitrogen form conditions, with WUE exhibiting a significant positive correlation with transpiration rate, chlorophyll content, nitrogen content, and Rubisco enzyme activity. Overexpression of OsPIP2;1 could promote root growth and increase the total biomass of rice plants. The application of the OsPIP2;1 gene in rice genetic engineering modification holds great potential for improving important agricultural traits of crops. This study provides new insights into the mechanism by which the AQP family regulates water use in rice and has certain significance for exploring the role of AQP genes in rice growth and development as well as in response to water stress. Full article

Currently, ozone (O₃) has become one of the primary air pollutants in China, underscoring the importance of analyzing ozone formation sensitivity (OFS) for effective pollution control. Ozone sensitivity indices serve as effective tools for OFS identification. Among them, the ratio of volatile organic compounds (VOCs) to nitrogen oxides (NO_x)—such as the formaldehyde-to-nitrogen dioxide ratio (FNR, defined as HCHO/NO₂, where HCHO represents VOCs and NO₂ represents NO_x)—is one of the most widely used satellite-based indicators. Recent studies have highlighted glyoxal (CHOCHO) as another critical ozone precursor, prompting the proposal of the glyoxal-to-nitrogen dioxide ratio (GNR, CHOCHO/NO₂) as an alternative metric. This study systematically compares the performance of FNR and GNR across four major urban agglomerations in China: Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Chengdu–Chongqing (CY) region, by integrating satellite remote sensing with ground-based observations. Results reveal that both indices exhibit consistent spatial trends in OFS distribution, transitioning from VOC-limited regimes in urban centers to NO_x-limited regimes in surrounding suburban areas. However, differences emerge in threshold values and classification outcomes. During summer, FNR identifies urban areas as transitional regimes (or VOC-limited in regions such as YRD and PRD), while suburban areas are classified as NO_x-limited. In contrast, GNR, which shows heightened sensitive to anthropogenic VOCs (AVOCs), exhibits a more restricted spatial extent in the transition regimes. By autumn, most urban areas shift toward VOC-limited regimes, while suburban regions remain NO_x-limited. Thresholds for both VOCs and NO_x increase during this period, with GNR demonstrating stronger sensitivity to NO_x. These findings underscore that the choice between FNR and GNR directly influences OFS determination, as their differing responses to biogenic and anthropogenic emissions lead to different conclusions. Future research should focus on integrating the complementary strengths of both indices to develop a more robust OFS identification method, thereby providing a theoretical basis for formulating effective ozone control strategies. Full article

Objective: This study aims to explore the feasibility of a non-invasive and simple method for discriminating between health and periodontitis (PRD), facilitating early and objective diagnosis of PRD before detectable periodontal attachment loss and monitoring treatment outcomes. Methods: Salivary samples were collected from 16 PRD-free patients (G1) and 10 patients with PRD (G2). The analysis included salivary matrix metalloproteinase-8 (MMP-8), major anti-inflammatory interleukins (IL-4 and IL-10), pro-inflammatory cytokines (IL-1β, IL-8, and interferon α [IFN-α]), and the cytokine IL-6. Clinical and salivary assessments were performed at baseline (TP0) for both groups and after periodontal treatment for G2 (TP1). Results: PRD indices were significantly higher in G2-TP0, lower in G1, and intermediate in G2-TP1. Except for IL-6, the biomarkers were significantly correlated with nearly all PRD clinical indices. Logistic regression and receiver operating characteristic (ROC) curve analyses showed statistical significance for MMP-8, IL-1β, IL-4, IL-8, and IL-10 when comparing G1 and G2 at TP0. MMP-8 was also significant when comparing G2-TP0 and G2-TP1, while IL-1β and IL-10 showed borderline significance. IL-8 was significant when comparing G1 and G2-TP1. Conclusions: The molecular network demonstrated great potential for early diagnosis and monitoring of therapy response, providing a promising basis for future research. Among the biomarkers, MMP-8, IL-1β, IL-4, IL-8, and IL-10 showed the strongest statistical correlations with the clinical indices. The inflammation-related biomolecules behaved differently among untreated PRD (G2-TP0), treated (G2-TP1), and healthy individuals (G1). Healthy individuals and those with treated PRD may regulate inflammation significantly differently from those with untreated PRD. Full article

We explored the mass concentrations of water-soluble ions in PM_2.5 and their variations across different time scales and concentration levels. Using the Positive Matrix Factorization (PMF) model and backward trajectory analysis, we focused on identifying the sources of PM_2.5 and its water-soluble ion fractions, with particular emphasis on regional transport. The findings reveal that the average mass concentration of total water-soluble ions in Hainan between 1 August 2021 and 31 July 2022 was 7.0 ± 4.4 µg m⁻³, constituting 73.5% ± 24.4% of PM_2.5. Secondary ions (SO₄²⁻, NO₃⁻, NH₄⁺) were dominant, accounting for 84.0% ± 12.4% of the total water-soluble ions, followed by sea-salt particles. Seasonal variations were pronounced, with the highest concentrations observed in winter and the lowest in summer. The results of the PMF analysis showed that secondary sources, combustion sources, dust sources, and oceanic sources are the main sources of PM_2.5 at the monitoring site. The potential sources and transport pathways of water-soluble ions exhibit distinct seasonal characteristics, with the land-based outflows from the YRD–PRD–Fujian corridor controlling Hainan’s PM_2.5 maxima, while southerly marine air delivers the annual minimum; seasonal alternation between dust/secondary aerosols (winter–spring), combustion (autumn), and oceanic dilution (summer) dictates the island’s air-quality rhythm. Full article

Background: Electrocardiogram (ECG) signals are crucial for cardiovascular diagnosis, but their analysis face challenges from noise contamination, compression difficulties due to their non-stationary nature, and the inherent complexity of its morphological components, particularly for low-amplitude P- and T-waves obscured by noise. Methodology: This study proposes a novel, multi-stage framework for ECG signal denoising, compressing, and component decomposition. The proposed framework leverages the sparsity of ECG signal to denoise and compress these signals using autoencoder-driven dictionary (AE-DD) with matching pursuit. In this work, a data-driven dictionary was developed using a regularized autoencoder. Appropriate trained weights along with matching pursuit were used to compress the denoised ECG segments. This study explored different weight regularization techniques: L1- and L2-regularization. Results: The proposed framework achieves remarkable performance in simultaneous ECG denoising, compression, and morphological decomposition. The L1-DAE model delivers superior noise suppression (SNR improvement up to 18.6 dB at $-3$ dB input SNR) and near-lossless reconstruction ($\mathrm{MSE}<{10}^{-5}$). The L1-AE dictionary enables high-fidelity compression (CR = 28:1 ratio, $\mathrm{MSE}\approx 0.58\times {10}^{-5}$, PRD = 2.1%), outperforming non-regularized models and traditional dictionaries (DCT/wavelets), while its trained weights naturally decompose into interpretable sub-dictionaries for P-wave, QRS complex, and T-wave enabling precise, label-free analysis of ECG components. Moreover, the learned sub-dictionaries naturally decompose into interpretable P-wave, QRS complex, and T-wave components with high accuracy, yielding strong correlation with the original ECG ($r=0.98$, $r=0.99$, and $r=0.95$, respectively) and very low MSE ($1.93\times {10}^{-5}$, $9.26\times {10}^{-4}$, and $3.38\times {10}^{-4}$, respectively). Conclusions: This study introduces a novel autoencoder-driven framework that simultaneously performs ECG denoising, compression, and morphological decomposition. By leveraging L1-regularized autoencoders with matching pursuit, the method effectively enhances signal quality while enabling direct decomposition of ECG signals into clinically relevant components without additional processing. This unified approach offers significant potential for improving automated ECG analysis and facilitating efficient long-term cardiac monitoring. Full article

Aromatics and their key oxidation intermediate such as formaldehyde and dicarbonyl compounds (glyoxal and methyglyoxal) are crucial precursors for ozone (O₃) and secondary organic aerosols (SOA). However, the spatial–temporal variation in aromatics’ contribution to these intermediate species and O₃/SOA over Eastern China during the past decades remains insufficiently quantified. This study combines satellite observations of formaldehyde and glyoxal column densities (2008–2014) with an innovative tracer method implemented in the Community Multiscale Air Quality (CMAQ) modeling system to quantify aromatic-driven dicarbonyl chemistry. Simulations of summer and autumn in 2010, 2012, 2014, and 2016 are conducted to demonstrate the change in aromatics and its impact through the years. Estimated primary and intermediate VOCs show good consistency with measurements at a supersite; and the simulated vertical column density of formaldehyde and glyoxal agree with satellite observations in spatial distributions. The contribution of aromatic hydrocarbons to the columnar concentration of glyoxal has seen a significant increase since 2010, which can, to some extent, explain the interannual trend of glyoxal column concentrations in key regions of Beijing–Tianjin–Heibei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). A cross-comparison reveals a good consistency between the observed glyoxal columnar concentrations to formaldehyde columnar concentration ratio (R_GF) from satellite measurements and the high contribution areas of aromatics to glyoxal: pronounced values are observed in the above three key regions in Eastern China. Additionally, the applicability of R_GF and its indicative nature in Eastern China was discussed, revealing notable seasonal and regional variations in R_GF. Revised R_GF thresholds ([0.015–0.03] for models vs. [0.04–0.06] for satellites) improve summer precursor classification, while a threshold of >0.04 could distinguish the areas with high anthropogenic impacts during autumn. These findings advance understanding of VOC oxidation pathways in polluted regions, providing critical insights for ozone and secondary organic aerosol mitigation strategies. The integrated satellite model approach demonstrates the growing atmospheric influence of aromatics amid changing emission patterns in Eastern China. Full article