Search Results (3,373)

The impact of PM_2.5 pollution on the health inequality of vulnerable groups is a core issue in environmental justice research. However, existing studies in China mostly focus on severely polluted areas in northern China. They lack comparative cases in economically developed eastern regions. They also rarely consider changes in the impact of air pollution on residents’ health amid rapid urbanization. Based on multi-source data, this study employed spatial visualization, spatial autocorrelation analysis and spatial regression models. It investigated the impact of PM_2.5 pollution on the health inequality of vulnerable elderly groups in 92 districts and counties of Jiangsu Province from 2010 to 2020. The results show that: first, the regional pattern of health inequality between PM_2.5 pollution and vulnerable elderly groups in Jiangsu has continuously evolved, with a “lower in the south and higher in the north” pollution pattern and high overlap between high-pollution areas and high elderly health risk areas in northern Jiangsu. Second, the spatial coupling between PM_2.5 and elderly health risks has gradually strengthened, showing significant positive spatial agglomeration in 2020, confirming obvious spatial agglomeration characteristics of air pollution’s health impact. Third, the adverse health impact of PM_2.5 on vulnerable elderly groups became significant in 2020, exhibiting cumulative and lagged characteristics; urbanization and regional coordinated development have played a positive role in alleviating regional health inequality, while a lagging energy structure further exacerbates the health vulnerability of the elderly. This study fills the gap of insufficient research on economically developed eastern regions and provides targeted empirical references for urban refined governance and precise prevention and control of environmental health inequality. Full article

Urban heat islands present a significant obstacle to climate adaptation strategies, yet the interplay between surface and atmospheric thermal elements is not fully understood. This research investigates the spatial relationship between land surface temperature (LST) and near-surface air temperature (TAIR) across Zagreb’s 218 local councils during the summer of 2024, assessing the premise that these constitute separate thermal dimensions with varying land cover correlations. Landsat 8/9-derived LST and CERRA-derived TAIR, temporally aligned to the Landsat overpass slot (09:00 UTC), were examined through spatial autocorrelation (Moran’s I, Getis–Ord Gi*), correlation analysis, and Fisher’s z-tests to compare the effects of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The findings indicated partial coupling (r = 0.537, R² = 0.288), with 71.2% of the variance remaining unexplained, suggesting considerable surface-atmospheric decoupling. Furthermore, hot spot overlap analysis revealed limited convergence (11.9% of neighborhoods), while 44.5% displayed divergent thermal extremes. Land cover showed much stronger connections with LST (NDVI: r = −0.970, R² = 0.941; NDBI: r = +0.973, R² = 0.947) than with TAIR (NDVI: r = −0.478; NDBI: r = +0.496), representing reductions in explained variance of 63–64% (p < 0.001). These findings suggest that surface and atmospheric urban heat are related but distinct thermal aspects. Full article

Ammonia (NH₃) from intensive agriculture is a primary precursor for secondary fine particulate matter (PM_2.5), necessitating mitigation under the EU National Emission Ceilings (NEC) Directive. This study evaluated a novel feed-based intervention assessed under real-scale commercial conditions in weaning and growing pig units. Indoor NH₃ concentrations were monitored at high frequency (2 h resolution), and treatment effects were analyzed using a Circular Block Bootstrap (CBB) approach to account for diurnal cyclicity and temporal autocorrelation. In the weaning unit, where pits were fully emptied before the trial, the mean indoor NH₃ concentration decreased from 7.51 ppm to 1.37 ppm, representing an 81.7% reduction. In the growing unit, which operated under pre-existing slurry and an overflow system, a significant reduction of 20.9% was observed (from 5.45 ppm to 4.31 ppm). These results demonstrate the intervention’s efficacy in preventing NH₃ release from fresh excreta and suggest that its impact in systems managed under slurry overflow can be further optimized by initially activating pre-existing material. This infrastructure-free solution offers a scalable, economically sustainable pathway to align livestock production with zero-pollution targets while supporting multiple Sustainable Development Goals related to human health, worker welfare, and environmental protection. Full article

Amid escalating global crises, tourism sustainability is threatened by heightened industry vulnerability, yet the intrinsic coupling of tourism industry vulnerability (TIV) and resilience (TIR) remains underexplored via systemic theoretical frameworks. This study aimed to define TIV/TIR as industry-specific constructs and develop an integrated analytical model grounded in dissipative structure theory to characterize tourism systems’ crisis responses. We selected Southwest China’s ethnic minority regions (Guizhou, Guangxi, Yunnan) as cases, using 2015–2024 prefecture-level panel data to explores the spatio-temporal differentiation characteristics of TIV/TIR. Results revealed severe COVID-19-induced TIV surges in 2020–2021, followed by rapid TIR rebounds; TIV and TIR exhibited a significant negative correlation with regional heterogeneity. Most cities showed high TIV–low TIR, with Guizhou displaying negative TIV-TIR spatial autocorrelation and Guangxi–Yunnan showing TIR clustering; inter-city TIV disparities widened while TIR levels converged, leading to a low-vulnerability, balanced-resilience tourism system by 2024. This research introduces the novel sensitivity-adaptive capacity-recovery (SACR) framework, advancing understanding of TIV-TIR dynamics and providing targeted empirical insights for tourism resilience building and sustainable development in resource-dependent destinations. Full article

Rapid urbanization in plateau mountain regions exacerbates the tension between rigid resource demands and fragile ecological carrying capacities. Enhancing the resilience of the Water–Energy–Food (W–E–F) nexus is therefore essential for coping with external shocks. This study constructs a multidimensional resilience evaluation framework based on the Pressure-State-Response (PSR) model, taking the Kunming Metropolitan Area—a typical plateau mountain region—as a case study. Integrating the TOPSIS model, Coupling Coordination Degree (CCD) model, and spatial autocorrelation analysis, we systematically assessed both individual subsystem and comprehensive W–E–F nexus resilience from 2005 to 2020. Results show that W–E–F nexus resilience generally improved from 2005 to 2020, but subsystem development remained uneven across space, with water resilience characterized by a peripheral-high/central-low pattern, energy resilience by a core-high/periphery-low structure, and food resilience by the strongest spatial heterogeneity and volatility. By 2020, the mean comprehensive resilience reached 0.67, with 58.3% of counties above the average, exhibiting significant spatial clustering. Based on resilience levels and limiting subsystems of 2020, the metropolitan area was classified into Enhancement, Adjustment, and Maintenance zones, comprising 6, 16, and 2 counties respectively, to support differentiated regional governance. This study provides a spatially explicit regulation paradigm to bolster urban resilience against resource constraints and climate uncertainty. Full article

Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor driver analysis, with insufficient in-depth exploration of the interactions among multiple factors and their associated nonlinear mechanisms. To address this gap, this study focuses on the Wumeng Mountain area, a typical ecologically fragile karst region in Southwest China. By comprehensively employing methods such as Theil–Sen Median trend analysis, land use transfer matrix, standard deviation ellipse, and spatial autocorrelation analysis, this study systematically reveals the spatiotemporal evolution characteristics of forest cover from 1985 to 2024. On this basis, an integrated eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) model is introduced to construct an indicator system comprising 16 driving variables, including elevation, slope, aspect, temperature, precipitation, soil type, soil pH, soil thickness, soil organic matter, soil moisture content, GDP, population, distance from water, distance from railway, distance from grade highway, and distance from government. This model quantifies the influence intensity of each driving factor on forest change. The main findings are as follows: (1) From 1985 to 2024, the forest cover rate in the Wumeng Mountain area significantly increased from 54.7% to 60.2%, exhibiting a “high-low-high” heterogeneous spatial distribution pattern along the northeast-southwest axis; (2) Forest increase primarily originated from the conversion of cropland and grassland, with contribution rates reaching 93.58% and 5.9%, respectively, indicating an overall trend of “increase in low-value areas and decrease in high-value areas”; (3) Forest cover change is driven by both natural and anthropogenic factors, with dominant driving factors exhibiting phased replacement over time. Overall, this is manifested as long-term stable constraints exerted by natural background factors, alongside strong disturbances from anthropogenic factors such as social-economic, and transportation-related activities. Natural factors remain the primary driving force behind changes in forest cover. The core findings of this study elucidate the complex driving factors of forest change in karst mountainous areas, thereby providing scientific support for the precise management of regional forest resources, the planning of ecological restoration projects, and the implementation of sustainable development strategies. Full article

During the COVID-19 pandemic, Health Canada temporarily exempted pharmacists from specific restrictions under the Controlled Drugs and Substances Act (CDSA), allowing them to prescribe opioids. However, it is not yet established whether opioid dispensing patterns differ between urban and rural pharmacists. This study aims to assess the impact of the CDSA subsection 56(1) temporary exemption on opioid prescribing practices among urban and rural pharmacists between 1 February 2018 and 30 April 2022. Descriptive statistics and visualizations assessed differences in opioid prescribing between urban and rural pharmacists under the CDSA exemption. Initial analyses employed linear regression to examine changes, followed by evaluation of temporal dependence using autocorrelation and residual analysis. When appropriate, a suitable time series model was subsequently applied. Following the CDSA exemption, the mean weekly proportion of opioid claims prescribed by urban pharmacists increased from 0.0% to 1.03%. In contrast, rural pharmacists’ prescriptions rose from 0.0% to 0.35%. The estimated mean level change was 0.667% for urban pharmacists (95% CI: 0.520–0.838%, p < 0.0001) and 0.201% for rural pharmacists (95% CI: 0.140–0.291%, p < 0.0001). The study identified distinct differences in opioid prescribing practices between urban and rural pharmacists in Nova Scotia. Furthermore, opioid prescriptions increased steadily across all patient groups, indicating evolving patterns of opioid use within the province. Full article

A probabilistic data-driven framework for voltage quality forecasting in distribution transformers based on Gaussian process regression and high-resolution field measurements is presented. Voltage time series acquired under real operating conditions were modeled using composite covariance functions designed to capture long-term trends and stochastic multi-scale fluctuations. The proposed approach enables simultaneous prediction and uncertainty quantification, allowing direct compliance assessment with voltage quality standards. The additive Gaussian process models achieved coefficients of determination above 0.75 and produced statistically uncorrelated residuals, indicating an adequate representation of the intrinsic temporal structure. However, the predictive intervals exhibit a certain level of undercoverage, indicating that, while uncertainty is effectively quantified, there is still room for improvement in calibration. The selected kernel structures revealed distinct physical regimes in the voltage dynamics, including smooth steady operation, moderately irregular behavior associated with localized disturbances, and multi-scale stochastic variability. For benchmarking purposes, results were compared with those obtained from a stochastic damped harmonic oscillator with restoring force, a naive model, a seasonal naive model and an Autoregressive Integrated Moving Average model. The oscillator model, the naive model, the seasonal naive model, and the Autoregressive Integrated Moving Average model generated strongly autocorrelated residuals, whereas the Gaussian process models yielded consistent white-noise residuals that outperformed all the other models. These findings demonstrate that probabilistic Gaussian process modeling provides an interpretable, scalable, and uncertainty-aware alternative for predictive voltage quality assessment in modern distribution systems. Full article

Urban heat risk is intensifying globally, yet the relative contributions of climate warming and demographic restructuring to spatiotemporal risk change remain poorly understood, particularly in post-socialist cities experiencing simultaneous thermal intensification and population aging. This study develops a Heat Risk Population Index (HRPI) integrating satellite-derived land surface temperature, CERRA reanalysis air temperature, and census-based demographic sensitivity for 218 Zagreb neighborhood councils (2001–2024). A multi-scale analytical framework combining additive decomposition, enhanced partial correlations, and geographically weighted regression (GWR) was applied to disentangle the drivers of heat risk change. HRPI increased significantly across all neighborhood councils (mean ΔHRPI = 0.197, p < 0.001), with strong positive spatial autocorrelation (Moran’s I = 0.416). While air temperature change dominated the city-wide mean increase (72.1%), demographic sensitivity change explained the largest share of spatial variance across neighborhood councils (partial r = 0.677 vs. 0.524 for air temperature), driven by spatially heterogeneous demographic transitions—youth out-migration, aging-in-place in southeastern post-socialist estates, and gentrification in central districts. GWR substantially outperformed global OLS (ΔAICc = 60.1; Adj. R²: 0.649 → 0.816), with local demographic effect sizes varying fivefold across the city. These results demonstrate that heat risk drivers operate at distinct spatial scales: climate dominates city-wide magnitude while demographics determine spatial differentiation. Effective adaptation requires universal thermal interventions combined with spatially targeted demographic strategies in identified hotspot neighborhoods. The multi-scale framework is applicable to other post-socialist cities undergoing concurrent climate and demographic change. Full article

The Beijing–Tianjin–Hebei (BTH) coordinated-development strategy provides a county-level setting for examining how transport-led regional restructuring reshaped the relationship between human activity and land–environment conditions. Using a balanced panel of 200 county-level units from 2010 to 2020, we work with two linked subsystems: the human-activity subsystem (H), which combines transport integration and economic upgrading, and the land–environment subsystem (L), which combines land-use transition and ecological response. Pooled entropy weighting, a coupling-coordination index, spatial autocorrelation analysis, and fixed-effects differential-response models are used to trace temporal change, spatial clustering, and post-2014 heterogeneity within BTH. Mean coupling coordination (D) rose from 0.5430 to 0.6012, but the increase came mainly from the rise of H, while L changed only slightly. Positive spatial autocorrelation persisted throughout the period. Counties in the Beijing–Tianjin ring kept higher absolute coordination levels, yet after 2014, they improved more slowly than non-ring counties because land–environment adjustment lagged behind changes within H. Relative to key ecological function zones, agricultural counties—and to a lesser extent urbanized counties—posted faster gains in D, again mainly through H. The results show that in BTH, regional integration did not move the two subsystems in lockstep: transport reorganization and economic upgrading advanced faster than land–environment adjustment, so durable county coordination still depended on land governance, ecological regulation, and policies matched to territorial functions. Full article

Under the strategic context of building a transportation powerhouse in China, the transportation sector faces the dual challenge of reducing emissions while improving efficiency. This study constructs a two-dimensional regional classification framework based on the “economic-carbon” dimension and systematically investigates the coordinated evolution of the development level (TD) and carbon emission intensity (TCEI) of the transportation systems in 31 provinces of China from 2014 to 2023, using methods such as entropy weight TOPSIS, the coupling coordination degree (CCD) model, kernel density estimation (KDE), spatial autocorrelation analysis, and the XGBoost-SHAP explainable machine learning framework based on transfer learning. The study finds that (1) TD shows a fluctuating upward trend, while TCEI continues to decline, with regional imbalances; (2) in terms of time, CCD shows a general upward trend with an N-shaped evolution; spatially, CCD presents a pattern of stronger coordination in the east and weaker in the west, with sustained regional heterogeneity, forming a development pattern of “Region I leading, Region II breaking through, Region III maintaining, Region IV catching up”; and (3) regarding the driving factors, freight volume, transport industry output value, and passenger turnover are the core driving factors of CCD, with significant regional heterogeneity in their mechanisms. This study provides a systematic analytical framework and differentiated policy tools for promoting coordinated regional development of green transportation. Full article

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning. Full article

Stable leak localization in CCUS injection wellbores remains difficult under strong background noise. This study proposes a leak localization method based on autocorrelation time delay features of leakage acoustic signals. Considering the generation of confined leakage noise and its waveguide propagation in the wellbore, a time delay relationship between the direct acoustic component and the bottom-reflected echo is established, and an integrated workflow is developed for signal preprocessing, normalized autocorrelation analysis, feature extraction, and location inversion. Experimental results show that the dominant autocorrelation time delay feature remains stable under varying leakage conditions, while leak position changes produce distinguishable peak–valley patterns. The proposed method achieves high localization accuracy, with absolute errors of 0.018–0.046 m and relative errors of 0.76–1.93% under the tested conditions. A field application in a CO₂ injection well further demonstrates its practical feasibility. Overall, the method provides a stable and effective approach for leak localization in CCUS injection wellbores and shows potential for engineering applications in noisy environments. Full article

As a key component of ecosystems, the synergistic relationship between wetland carbon storage and habitat quality is vital for maintaining ecological functions, and its evolution is profoundly influence by changes in wetlands. This study focuses on wetlands in western Jilin Province. Based on four sets of land use data from 2010 to 2023 and utilizing the InVEST model, combined with methods such as spatial autocorrelation, the Coupled Coordination Degree Model, and the GeoDetector, the study analyzed the co-variation of carbon storage and habitat quality, as well as their response to landscape patterns. The study found that between 2010 and 2023, the wetland area increased by a net 858.13 km², and landscape fragmentation was generally alleviated, although local connectivity continued to degrade. Regional carbon storage increased by 68.1%, totaling 7.43 × 10⁶ Mg, while the habitat quality index exhibited high spatiotemporal stability, fluctuating marginally between 0.609 and 0.621. Spatially, high-value areas remained primarily concentrated within nature reserves. Results of bivariate spatial autocorrelation analysis revealed a strengthening of spatial positive autocorrelation between carbon storage and habitat quality, with Moran’s I increasing from 0.410 to 0.501. The coupled coordination degree model further confirmed that the level of synergy between the two services exhibited a pattern of higher values in the north and lower values in the south, and that areas of high coordination expanded significantly outward following restoration projects. GeoDetector analysis indicates that the largest patch index is the core factor driving the synergistic development of ecosystem services. The results also suggest that the integrity of core wetland patches and a heterogeneous landscape pattern can promote the synergistic improvement of carbon storage and habitat quality through boundary effects and habitat complementarity. Full article

Using panel data from 11 prefecture-level cities in Zhejiang Province (2014–2023), this study applies the entropy method, spatial autocorrelation analysis, and an obstacle-factor diagnosis model to examine the spatiotemporal evolution, regional disparities, and constraints on urban–rural integration. The results show a steady upward trend in urban–rural integration alongside significant regional disparities. This reveals a complex pattern marked by the coexistence of convergence and divergence. Spatially, a clear “northeast–high, southwest–low” pattern is observed, with local adjustments within a stable framework, reflecting a “stable core and entrenched low-value areas.” Spatial agglomeration is characterized by “dual-core agglomeration with a predominantly non-significant periphery,” dominated by homogeneous “high–high” and “low–low” clusters, with no statistically significant spatial outliers. Obstacle factor diagnosis indicates markedly uneven constraining effects across subsystems, with spatial integration exhibiting the highest degree of obstacles. The composition of primary obstacle factors is highly stable, and obstacle structures differ significantly across city tiers. These findings elucidate the spatiotemporal evolution and core constraints of urban–rural integration in Zhejiang, offering a theoretical and decision-making basis for advancing high-quality urban–rural integration in the region. Full article