Search Results (381)

The urban heat island (UHI) effect represents a critical urban climate phenomenon arising from the combined pressures of rapid urbanization and climate warming. Although its association with carbon emissions has received increasing scholarly attention, the underlying behavior-mediated pathways and cross-regional spillover patterns remain insufficiently understood. Using multi-source geospatial data for the Yangtze River Delta urban agglomeration from 2014 to 2023, this study develops a multi-scale analytical framework integrating 1 km urban agglomeration exploratory analysis and 5 km spatial econometric modeling. Anthropogenic Energy Activity Intensity (AEAI) is constructed as a proxy for energy-related human activities, and a spatial Durbin model, combined with a spatial mediation approach, is employed to examine the spatial associations and statistically mediated pathways within the “heat-energy-carbon” nexus. The results indicate that: (1) carbon emissions exhibit significant positive spatial spillover effects, consistent with thermal diffusion processes and socioeconomic network interactions; (2) AEAI represents a substantial partial statistical mediation pathway in the association between UHI and carbon emissions, accounting for 44.63% of the total association. This suggests that the UHI–carbon emission linkage is partly embedded in spatial patterns of energy-intensive human activities rather than reflecting a purely direct thermal effect. These findings suggest that regional climate governance may need to move beyond single-city interventions and purely physical cooling strategies toward integrated approaches that combine cross-regional coordination with behavioral regulation. Promoting passive cooling-oriented urban planning and demand-side energy transitions may help reduce carbon lock-in risks and support the development of climate-resilient urban agglomerations. Full article

Rapid urbanization and climate change have intensified the interconnected challenges of surface heating, air pollution, and declining ecosystem functions, with important implications for regional sustainability. Taking Henan Province, China, as the study area, this study selected 2013, 2018, and 2023 as representative years and used land surface temperature (LST), fine particulate matter (PM2.5), ozone (O3), and net primary productivity (NPP) to characterize the thermal environment, air pollution, and carbon sequestration capacity. Pearson correlation analysis, multiple linear regression, and XGBoost-SHAP were integrated to examine bivariate associations, independent linear associations, factor importance, nonlinear responses, and potential threshold characteristics associated with natural, ecological, and anthropogenic factors. The results showed marked spatial differences in the four environmental variables. The multiple linear regression models explained 57.4–69.0% of the variation in LST, 23.8–72.0% in O3, 81.0–84.8% in PM2.5, and 57.4–62.5% in NPP. Natural factors generally showed relatively large and temporally stable standardized coefficients. Precipitation and potential evapotranspiration were positively associated with LST, whereas elevation and precipitation were negatively associated with PM2.5 and O3. NDVI showed an environmentally favorable pattern, being negatively associated with LST, PM2.5, and O3 but positively associated with NPP. Anthropogenic variables generally exhibited smaller and less temporally stable coefficients. The XGBoost models demonstrated good predictive performance, particularly for PM2.5, with R² values of 0.945, 0.920, and 0.905 in 2013, 2018, and 2023, respectively. SHAP analysis identified DEM, PRE, PET, and NDVI as the main contributors to model predictions and revealed nonlinear responses and potential threshold characteristics. These findings indicate that coordinated management of vegetation cover, hydrothermal conditions, and urban development can support heat mitigation, air pollution control, ecosystem productivity, and more sustainable, climate-resilient, and low-carbon development in rapidly urbanizing regions. Full article

Air pollution in Almaty, Kazakhstan, poses a critical public health challenge intensified by the city’s basin topography and seasonal thermal inversions that trap anthropogenic emissions. The sparse stationary network (~5 stations for ~2 million inhabitants) lacks the spatial and temporal resolution needed to capture intra-urban variability. We present the design, deployment, and field evaluation of a low-cost distributed Internet of Things (IoT) network of six custom nodes—Winsen ZPHS01B multi-parameter modules with Raspberry Pi Zero 2 W edge units, at an estimated principal-component cost of ~US$100 per node—operated during a winter-spring campaign (February–April 2025) and yielding over 70,000 measurements of PM_2.5, PM₁₀, CO₂, temperature, and relative humidity. The system’s novelty lies in three integrated engineering features: an Active Airflow Stabilization enclosure that decouples sampling from external wind, context-aware adaptive edge filtering that reduces transmitted data volume by ~40%, and a secure Edge-DMZ-Core telemetry pipeline. Node readings were cross-validated against a Qingping Air Monitor Pro with documented traceability to FEM-grade reference analyzers (R² = 0.89–0.95), and city-scale consistency was confirmed against the national monitoring dashboard; the network is therefore characterized as providing internally consistent low-cost observations rather than reference-equivalent concentrations. Daily mean PM_2.5 exceeded the WHO 24 h guideline (15 µg/m³) on 84% of monitored days, with February concentrations (54.4 µg/m³) significantly above March (21.9 µg/m³; p < 0.001). A high PM_2.5/PM₁₀ ratio (~0.96), measured at the physically consistent nodes, together with higher weekend concentrations, points to coal-based residential heating as the most likely dominant source. A coupled WRF-SILAM framework is configured for future model-observation integration. The system offers a reproducible, scalable, and cost-effective template for ambient particulate monitoring in resource-constrained cities with complex terrain. Full article

Net primary productivity (NPP) is a key indicator of the carbon sequestration capacity of terrestrial ecosystems, and its dynamics are jointly influenced by climate change and human activities. However, quantitatively disentangling their respective contributions and clarifying their non-linear interactions remains challenging. In this study, remote sensing, meteorological, and anthropogenic data were integrated to investigate the spatiotemporal dynamics of vegetation NPP in the Yellow River Basin (YRB) from 2000 to 2024. Six scenarios were constructed to quantify the relative contributions of climate change and human activities. Furthermore, an XGBoost-SHAP framework was employed to elucidate the underlying non-linear driving mechanisms. The results indicate that vegetation NPP exhibited a significant increasing trend over the study period, with a rapid recovery phase after 2012 and a peak in 2024 (351.75 gC·m⁻²·a⁻¹), representing a 71.43% increase compared with the baseline period. Spatially, the upper reaches were primarily climate-driven (58.74%), the middle reaches showed a strong synergistic effect between climate and human factors (97.41%), while the lower reaches were dominated by human activities (73.02%). The XGBoost-SHAP analysis identifies land surface temperature (LST) as the primary moderator of carbon sequestration across river basins (mean SHAP > 12.0). The driving mechanisms exhibit a clear longitudinal shift, transitioning from a heat-dominated regime in the upper reaches to a complex interplay of precipitation and intense urbanization in the middle and lower reaches. These non-linear interactions reveal critical feedback loops between natural hydrological constraints and urban expansion pressures. These findings clarify the drivers of regional carbon sequestration, providing a scientific basis for targeted ecological management and carbon neutrality strategies in the YRB. Full article

Myrmecochory is a form of seed dispersal mediated by ants. Although this mechanism of dispersal has received less research attention than other dispersal processes, the wide distribution and high biomass of ants mean that it can strongly influence plant dispersal patterns. In particular, the underlying mechanisms and key agents of myrmecochory remain poorly understood in the context of anthropogenic perturbations; furthermore, such research is especially scarce in East Asia. This review aims to elucidate the ecological mechanisms underlying myrmecochory, to explore how this interaction may be affected by urbanization and climate change, and to determine its potential ecological role in disturbed ecosystems. We first review past research on the three major hypotheses proposed for the emergence of ant-mediated seed dispersal—directed dispersal, distance dispersal, and predator avoidance. We then compile taxonomic information on myrmecochorous plants and ants from global databases and regional literature, expanding the checklist of Korean myrmecochorous plants to 130 species and reclassifying them as endangered, rare, or endemic. Our synthesis suggests that invasive ants could threaten myrmecochory by displacing native myrmecochorous ants, increasing seed predation, and facilitating the dispersal of invasive plants. Moreover, the urban heat island effect and habitat fragmentation could disturb the dispersal, germination, and growth of myrmecochorous plants, threats that may be further intensified by climate-driven phenological mismatches. Consequently, in temperate East Asian countries experiencing anthropogenically generated environmental changes, myrmecochory emerges as a pivotal ecological process that underscores ecosystem vulnerability and resilience. Ultimately, incorporating these plant–ant interactions into biodiversity monitoring is essential for predicting ecosystem shifts and designing robust, proactive conservation strategies in changing environments. Full article

Regionally, compound agroecological stress arising from both natural and anthropogenic emergy inputs may influence maize transcriptomic responses; however, evidence across multiple scales remains limited. We developed a reproducible five-step framework integrating a macro-level compound stress index, molecular response modules, cross-scale coupling, spatial continuity, and independent field validation. Nine variables (emergy indicators ELR, Fn, and NEYR; climate; soil; and terrain) were PCA-weighted into a Composite Abiotic Stress Intensity Index (CASI; first three PCs = 83.7%; and prefecture-level Moran’s I = 0.463). Across 15 public RNA-seq datasets (286 samples), WGCNA identified five separable modules (drought–heat, reproductive stage heat, low nitrogen/phosphorus, osmotic salt, and chronic compound), 270 core genes, and four cross-module hubs (ZmDREB2A, ZmHSFA2, ZmWRKY33, and ZmNRT2.1). With n = 21, the sCCA (r₁ = 0.81, permutation p = 0.003; LOO-CV r = 0.71), random forest, and spatial error model all confirmed coupling between ELR and the drought–heat module (β = 0.51, p = 0.008). PLS-DA four-zone partitioning (Q² = 0.548) and a county-level second-order trend surface (R² = 0.67) verified spatial continuity. GSVA on five independent field RNA-seq datasets yielded 74.4 to 82.8% core gene directional consistency and Cliff’s δ of 0.59 to 0.68 (large effect), avoiding circular reasoning. The framework enables molecular analysis for precision agriculture and climate-resilient breeding. Full article

Urban green infrastructure is increasingly exposed to fine-scale thermal heterogeneity generated by anthropogenic point-source heat emissions, yet the leaf-level responses of adjacent vegetation to such localized stress remain poorly understood. Here, we examined whether air-conditioner (AC) exhaust, a widespread point-source heat emitter, is associated with functional trait shifts in Fraxinus chinensis street trees, and whether easily measurable leaf traits can serve as candidate indicators for ecological monitoring. Using a matched treatment–control field comparison, we compared trees located 2 m from operating AC units with unaffected controls and quantified nine leaf functional traits together with concurrent microclimate variables. AC exhaust created a distinct compound heat–drought–wind micro-environment at the 2 m patch scale, with higher air temperature (+6.3 °C), lower relative humidity (−12.3 percentage points), and higher wind speed (5.2-fold). Exposed trees showed a coordinated shift toward more resource-conservative leaf traits: leaf dry matter content (+14.8%), tissue density (+13.6%), leaf thickness (+6.3%), and stomatal density (+11.7%) increased significantly, whereas specific leaf area (−10.6%), leaf area (−12.5%), chlorophyll content index (−4.6%), and stomatal area (−10.4%) decreased significantly. The observed “small-and-numerous” stomatal configuration suggests altered stomatal regulation, although its implications for transpiration-driven cooling require direct physiological validation. Exploratory structural equation modeling suggested associations among AC-exhaust exposure, leaf economic strategy, and stomatal traits; stomatal regulation showed the highest proportion of model-explained variance (R² = 0.598), but this value should not be interpreted as direct evidence of impairment severity or restoration potential. Leaf dry matter content, specific leaf area, and stomatal density emerged as sensitive and practical candidate indicators of AC-exhaust-associated leaf functional shifts. These findings support precautionary management near AC exhaust outlets, while specific planting-distance thresholds and zoning frameworks require future validation through distance-gradient or manipulative experiments. Full article

Coastal ecosystems are increasingly threatened by human activities, highlighting the need for sensitive tools to assess environmental risk. An active biomonitoring approach, using the Mediterranean mussel (Mytilus galloprovincialis), was employed to evaluate anthropogenic chemical contamination in the North Ionian Sea, a still poorly studied area, by comparing mussel health status before (PrePT) and after (PostPT) the peak tourist season. Bioaccumulation of metal(loid)s was quantified in whole organisms. Oxidative stress was assessed in the gills and digestive gland through catalase (CAT), superoxide dismutase (SOD), lipid peroxidation (LPO), and oxidized carbonyl proteins (OMP). Neurotoxicity was evaluated via acetylcholinesterase (AChE) activity, while gene expression of stress-related biomarkers was analysed for metallothioneins (mt10, mt20), sod, cat, Glutathione S-transferase (gst), and Heat Shock Protein 70 (hsp70). Results suggest a progressive contaminant accumulation likely associated with intensified summer anthropogenic activity. Biomarker responses revealed clear activation of oxidative stress, with tissue-specific patterns. The findings confirm the effectiveness of active biomonitoring and multibiomarker approach in assessing coastal water quality and provide valuable baseline data for the management of marine ecosystems. Full article

Linear heritage corridors are increasingly exposed to spatially heterogeneous pressures from climate change and human activities, yet integrated geospatial frameworks for corridor-scale risk identification remain limited. Taking the Beijing–Hangzhou Grand Canal as a representative linear World Heritage corridor, this study developed a GIS-based spatiotemporal assessment framework to quantify natural risk, anthropogenic pressure, and their coupled patterns during 1995–2024. Approximately 350 canal segments were constructed as comparable assessment units and linked with 49 heritage sites and 18 World Heritage canal sections through a multi-scale spatial framework integrating canal sections, buffer zones, and heritage sites. Natural risk was characterized using extreme temperature, precipitation, and drought indices, while anthropogenic pressure was represented by nighttime lights, population density, impervious surface, and road density. The results reveal a clear north–south gradient in integrated natural risk, with higher values concentrated in the southern canal sections. Among the three natural-risk modules, temperature, precipitation, and drought contributed weights of 0.594, 0.242, and 0.164, respectively, indicating the dominant role of heat-related processes. The first two principal components of anthropogenic pressure explained 80.8% of the total variance. Four dominant coupling types were identified, among which the dual high-pressure type was concentrated mainly in the southern canal and marked the most critical areas of compound risk. This study provides a geospatial approach for hotspot detection and spatial decision support for the conservation of large linear heritage systems. Full article

This study presents a comprehensive assessment of longwave radiation variability in the Arctic based on unique measurements collected at the North Pole drifting station SP-28 in 1987. The primary objective is to compare these historical observations with modern datasets from the Surface Heat Budget of the Arctic Ocean (SHEBA, 1997–1998) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020) to evaluate long-term changes in the Arctic radiation regime. Continuous longwave radiation measurements were obtained using high-precision spectral pyrgeometers to identify Arctic haze. The results show that in 1987, Arctic haze layers enhanced the downward longwave flux by 15–20 W·m⁻² and increased atmospheric emissivity. In contrast, MOSAiC observations reveal emissivity values that closely match aerosol-free model calculations, indicating a substantial decline in Arctic haze and the diminishment of radiatively significant aerosol layers. This shift is in alignment with the long-term reduction of global anthropogenic sulfur dioxide emissions across the Northern Hemisphere. Full article

The oceans have absorbed most of the excess heat generated by anthropogenic climate change, yet the temporal structure of this warming remains insufficiently understood. This study analyses global temperature anomaly records from polar, tropical, and hemispheric regions over the period January 1850–October 2025, using fractionally integrated time-series methods to characterize long-range dependence and persistent warming. The results reveal statistically significant long memory across all regions, with particularly high persistence in the tropical Atlantic and the eastern North Pacific, as well as robust warming trends in polar and hemispheric aggregates series. These findings indicate that ocean warming is a structurally persistent process with implications for environmental governance. The strong climatic inertia observed suggests that policy frameworks with short planning horizons may underestimate long-term risks, underscoring the need to incorporate long-memory processes into climate risk assessments and the design of mitigation and adaptation strategies. Full article

Since the Industrial Revolution, anthropogenic activities, primarily fossil fuel combustion, have driven a sharp increase in ${\mathrm{CO}}_2$ emissions, making them the principal driver of global climate change. Precise monitoring and quantification of ${\mathrm{CO}}_2$ emissions are essential for effective greenhouse gas mitigation. Traditional “bottom-up” inventories often suffer from limited timeliness, low spatial resolution, and significant uncertainties. Satellite remote sensing offers an alternative “top-down” approach for emission estimation. Compared to existing ${\mathrm{CO}}_2$ sensors, ${\mathrm{NO}}_2$ observation satellites provide higher spatiotemporal resolution. Given that ${\mathrm{NO}}_2$ and ${\mathrm{CO}}_2$ are co-emitted during combustion with a stable relationship, ${\mathrm{NO}}_2$ can serve as an effective proxy to indirectly derive ${\mathrm{CO}}_2$ emissions. In this study, an exploratory framework for city-scale ${\mathrm{CO}}_2$ estimation was developed using TROPOMI ${\mathrm{NO}}_2$ column concentrations, MERRA-2 wind fields, EDGAR inventory and the ODIAC inventory. The analysis focused on seven major cities in Shandong Province, China, from April to September 2022. By integrating a wind-rotation technique with a line density model and the differential evolution (DE) algorithm, we derived ${\mathrm{NO}}_2$ emissions and atmospheric lifetimes. The ${\mathrm{NO}}_2{\mathrm{-to-CO}}_2$ relationship was established based on sector-weighted inventory data to quantify fossil-fuel ${\mathrm{CO}}_2$ fluxes. The results identify Qingdao, Jinan, and Linyi as emission hotspots, followed by Rizhao, with lower emissions observed in Yantai, Liaocheng, and Jining. Comparison with the ODIAC inventory illustrates that this framework provides a top-down constraint for identifying localized emission characteristics and potential discrepancies in bottom-up datasets. This study offers a complementary tool for near-real-time urban carbon monitoring during the non-heating season. Full article

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities. Full article

Rapid urbanization combined with global climate change is intensifying the Surface Urban Heat Island (SUHI) effect worldwide, posing significant risks to human health, thermal comfort, and quality of life in cities. Characterized by notably higher temperatures in urban areas compared to their rural surroundings, the SUHI phenomenon is driven by factors such as increased built-up density and reduced vegetation cover. In this context, open-source remote sensing data, particularly from the Landsat satellite series, play a crucial role in studying surface urban heat islands. Available freely, Landsat’s multispectral and thermal imagery provides extensive spatial coverage and consistent temporal frequency, enabling long-term diachronic analyses. This study leverages a 40-year time series (1984–2024) of Landsat thermal data to map surface temperature variations in urban environments between Kenitra and Rabat cities, facilitating the identification of heat-excess zones linked to anthropogenic factors. Based on the results obtained, the LU/LC maps show that the study area is characterized by the notable growth of urbanization over the period 1984–2024, particularly in the dynamic poles of the region such as the city centers of Kénitra, Rabat, and Sale. This dynamic is highlighted by an increase from 1.8% to 3% in the total area of the region, accompanied by a remarkable decrease in agricultural land and bare soils. The evaluation of the Random Forest (RF) model’s performance also indicates that it successfully classified the data and predicted the LU/LC classes effectively, as confirmed by metric indices such as the Receiver Operating Characteristic curve and the Kappa index, which present very high average values exceeding 90%. Furthermore, the exploitation of the thermal bands of Landsat images provided relevant information on surface temperature variation. The SUHI maps show that the Rabat-Sale-Kenitra (RSK) region experienced a progressive increase in temperature over the study period, rising from 27 °C in 1984 to 44 °C in 2024. This value could increase further due to the continuous dynamics of urbanization. Together, these tools provide a robust framework for understanding the spatiotemporal dynamics of surface urban heat islands and support sustainable urban planning. Full article

Most Portuguese aquaculture farms are located in estuaries and coastal lagoons, which are highly productive, nutrient-rich transition zones that are also among the most vulnerable to anthropogenic pressures and climate change. This study assesses Ria Formosa’s suitability for grooved carpet shell (Ruditapes decussatus) aquaculture, accounting for projected climate change and a potential increase in clam farming production. The methodology involved implementing a numerical modeling system to map key physico-chemical variables under historical (1995–2014) and future (2081–2100) conditions. Model outputs were then used to compute a suitability index (SI), which was converted into aquaculture suitability maps for this species. Results indicate that the hydrodynamic and transport components reproduced tidal propagation and the transport of salinity and heat effectively. In contrast, simulations of water quality variables were less accurate, reflecting the greater complexity and uncertainty in representing biochemical processes. Across both time periods, environmental conditions were generally less favorable in winter and more favorable in spring. Water temperature and chlorophyll-a concentration emerged as the dominant drivers of seasonal suitability. Projections suggest that Ria Formosa may become increasingly suitable for grooved carpet shell aquaculture by the end of the century. However, expanding production could compromise ecological balance, reduce resilience, and constrain the system’s long-term sustainable development. Full article