Search Results (3,344)

Children are among the most sensitive groups to air pollution. This study focuses on Chinese children aged 0–16 years, integrating six waves of tracking data from the China Family Panel Studies (CFPS, 2012–2022), the ChinaHighAirPollutants (CHAP) dataset, and MOD11A1 land surface temperature (LST) data, covering 20,241 samples across 25 provinces. Using the eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) framework, we quantified the relative contributions of fine particulate matter (PM_2.5), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and climate factors to children’s respiratory disease risk. The overall area under curve (AUC) was 0.6765, with urban and rural sub-models achieving 0.6576 and 0.6864, respectively. SHAP analysis revealed that the temporal variable ranked first, reflecting population-level improvements from 2012 to 2022; age ranked second, with a 70.1% prevalence in the 0–6 age group. Rural PM_2.5 contribution was approximately 1.68 times that of urban areas; the O₃ effect showed opposite directions between urban (risk) and rural (protective association) settings; solid fuel contribution in rural areas was approximately 2.25 times the urban level. Regional clustering analysis identified differentiated environmental drivers across five geographic types. These findings provide a quantitative basis for differentiated regional prevention strategies. Full article

Soil moisture (SM) is a pivotal state variable of the terrestrial hydrosphere, modulating energy partitioning, agricultural productivity and extreme-event propagation. This study analyzes 43 years (1982–2024) of data to assess soil moisture (SM) dynamics in the Yellow River Basin (YRB). Results indicate a statistically significant basin-wide SM decline across weekly, monthly, and annual scales, with grid-scale slopes ranging from −2.26 × 10⁻⁴ to 8.32 × 10⁻⁵ m³ m⁻³ month⁻¹. Spatially, non-farm areas retain higher SM than cultivated lands, with a distinct upstream-to-downstream variability pattern. While alpine headwaters show moistening, pervasive drying characterizes mid- and lower-catchments. Critically, transitional landscapes are approaching tipping points, risking shifts into persistently wetter or drier stable states where minor perturbations could lock ecosystems into new conditions. This underscores the urgent need for targeted climate-adaptation interventions. Generalized additive modeling identifies surface net solar radiation, soil temperature, and vapor pressure deficit as dominant drivers across multiple temporal scales. Their respective contributions, averaged across the basin, accounted for 29.4%, 25.3%, and 23.0% of the explained variance. Additionally, actual evapotranspiration emerged as a significant driver on the weekly scale, particularly within the center of the basin. These findings enhance process-based understanding of SM variability and provide a scientific foundation for adaptive water-resource management in the YRB. Full article

Utilizing MODIS LST data from 2003 to 2024, in conjunction with multi-source remote sensing data including DEM, land use, NDVI, and nighttime lights, this study conducts a remote sensing quantitative assessment and spatiotemporal characteristic analysis of the urban heat island (UHI) effect in Yinchuan City. An improved urban-rural dichotomy approach was adopted to select rural background areas, and elevation correction of land surface temperature was performed based on the zonal ordinary least squares (OLS) regression to eliminate systematic errors caused by topographic differences. The results show that: (1) From 2003 to 2024, the overall intensity of the UHI in Yinchuan City showed a slight downward trend, while the UHI area continued to expand, presenting the characteristics of “decreasing intensity and expanding scope”; (2) The UHI exhibited concentrated and contiguous distribution in summer, and the cold island phenomenon was significant in winter, reflecting the typical seasonal contrast between summer and winter; (3) The global Moran’s I value increased from 0.39 to 0.82, indicating a significant enhancement in the spatial agglomeration of the UHI; (4) The standard deviation ellipse analysis revealed that the centroid of the UHI migrated toward the westward as a whole, which was consistent with the main axis of urban construction. The research results reveal the long-term evolution law and spatial pattern characteristics of the UHI effect in Yinchuan City, and provide a scientific reference for ecological planning and thermal environment regulation of cities in arid regions. These findings enhance the understanding of long-term urban thermal environment dynamics and provide important scientific support for sustainable urban planning, climate adaptation, and ecological management in arid regions. The study contributes to the quantitative monitoring of urban environmental sustainability and supports sustainable development goals related to climate action and sustainable cities. Full article

Rapid urbanization has intensified urban surface thermal stress, yet how blue–green spaces (BGs) are associated with land surface temperature (LST) under different urban morphological contexts remains insufficiently understood. Using Suzhou, China, as a case study, this study integrates Landsat imagery from five representative years (2000, 2005, 2010, 2016, and 2022) with a 100 m local climate zone (LCZ) dataset to examine BGs–LST relationships over time. Two BGs indicators are considered: BGs proportion and the within-grid local dispersion of BGs, represented by BGs_std. The results show that LST in Suzhou’s built-up area exhibits a “rise–decline–rise” pattern during the study period, whereas BGs proportions evolve differently across LCZ types. Regression slope analysis shows that higher BGs proportion is generally associated with lower LST across most LCZ types and study years. Relatively stable negative associations are observed in LCZ 2, LCZ 3, LCZ 6, LCZ 9, and LCZ 10. Pearson correlation analysis further shows that BGs_std is generally positively associated with LST and that this relationship tends to strengthen over time. Relatively stronger associations are observed in LCZ 1, LCZ 3, LCZ 5, and LCZ 6 in some years. These findings suggest that BGs–LST relationships should be interpreted not only in terms of BGs proportion, but also in relation to urban form and within-unit BGs organization. This study provides an LCZ-based empirical perspective on BGs–LST associations in the context of a rapidly urbanizing city. Full article

Rapid urbanisation and climate change are intensifying urban heat stress, posing significant challenges for climate-responsive urban planning. Digital and data-driven approaches, including GIS, remote sensing, microclimate simulation, and artificial intelligence (AI), have advanced urban climate analysis; however, their capacity to support human-centred planning remains insufficiently synthesised. This review analyses 78 peer-reviewed studies (2015–2025) to evaluate how digital methods address urban microclimate and outdoor thermal comfort. The reviewed studies are classified into four methodological groups: spatial data analytics, simulation-based models, parametric and optimisation workflows, and AI-driven or hybrid approaches. The results show that the majority of studies rely on proxy indicators, such as land surface temperature and sky view factor, while physiologically based comfort indices (e.g., PET and UTCI) are applied in a limited proportion of studies and remain largely confined to microscale simulations. A persistent scale mismatch is identified between large-scale analytics and pedestrian-level thermal experience, alongside geographic and climatic biases, particularly in hot-arid regions. Unlike previous reviews, this study integrates digital methodologies, urban microclimate processes, and human-centred thermal comfort within a unified framework. The findings provide actionable insights for planners and designers by supporting the integration of thermal comfort into multi-scale, climate-responsive decision-making. Full article

The study is based on Land Surface Temperature (LST) and Air Temperature data and Nonparametric Data Envelopment Analysis (DEA) technique to evaluate heat efficiency and detect anomalies in the thermal regime in the Eastern Black Sea Region, particularly in Hopa and Artvin, during the period 2000–2024. The regulating role of the Black Sea has resulted in Hopa having the warmest and most stable temperature patterns, with daytime temperatures 1.8 to 3.7 °C higher than Artvin. Previous DEA analysis of daytime temperatures has shown that the 2018–2020 period had the highest daily temperatures, while the 2001–2010 decade was characterized by the highest nighttime temperatures. A future heat map based on Monte Carlo simulation using six climate change scenarios indicates that in the most optimistic case, assuming a temperature increase of +0.8 °C, efficiency scores could increase as high as 0.995. On the other hand, if global warming leads to a sudden temperature increase above +7.2 °C, there is a 21.7% climate efficiency loss. Sensitivity analysis showed that technological innovation and good governance are the main positive factors affecting climate efficiency. Random Forest (RF) and SHapley Additive Explanations (SHAP) analyses were applied to determine the impact of climate factors on DEA scores and also indicated areas requiring risk assessment. The findings highlight the importance of considering location-specific climate adaptation strategies. Based on the observed thermal contrasts between coastal and inland environments, potential adaptation considerations may include urban heat management and agricultural water stress in coastal areas such as Hopa, and cold-climate resilience and energy-efficient infrastructure in inland locations such as Artvin. Full article

Urban Heat Islands (UHIs) are widely analyzed using Land Surface Temperature (LST), yet most studies remain limited to single cities, rely on a single machine-learning model, analyze LST alone, and use inconsistent Surface Urban Heat Island Intensity (SUHII) definitions, which restrict cross-city comparability and broader generalization. This study introduces an explainable artificial intelligence (XAI) framework implemented in Google Earth Engine (GEE) to analyze census-tract summer surface heat (2018–2024) across eight climatically contrasting U.S. cities. The main novelty is a standardized tract-scale cross-city framework that jointly models LST and SUHII using a consistent SUHII definition, a common physical predictor set, city-held-out nested cross-validation, and SHAP-based interpretation, allowing absolute surface heat to be distinguished from relative within-city heat anomaly; this combination is rarely implemented within a single urban heat study. Multiple machine-learning models were evaluated, with ensemble trees performing best: Extreme Gradient Boosting (XGBoost) best predicted SUHII (R² = 0.879; RMSE = 0.213), while Extra Trees best predicted LST (R² = 0.908; RMSE = 0.745 °C). SHapley Additive exPlanations (SHAP) indicate that SUHII is driven primarily by impervious surface fraction and surface moisture availability, whereas LST is structured by latitude and mean summer air temperature. Overall, the framework provides interpretable multi-city attribution of urban surface heat drivers with demonstrated cross-city generalization. Full article

This study investigates a typical valley city, Lanzhou, China, to reveal the nonlinear relationships and interaction mechanisms between gray–green space morphology and seasonal diurnal land surface temperature (LST) using multi-source remote sensing and land use data. A comprehensive morphological indicator system encompassing scale, complexity, connectivity, and structural integrity was constructed through landscape metric screening and the CRITIC objective weighting method, combined with the XGBoost-SHAP explainable machine learning framework. The findings highlight that: (1) Gray–green space impacts on LST exhibit significant seasonal and diurnal variations—daytime LST is predominantly governed by gray space morphology (e.g., fragmentation degree), while nighttime LST is driven by green space morphology (e.g., coverage intensity). (2) Key indicators demonstrate pronounced nonlinear and threshold characteristics: the cooling effect of green space coverage intensity (GCI) saturates beyond 0.25; gray space morphological structure factor (GRMSF) demonstrates cooling potential when exceeding 0.25, mitigating its warming effect. (3) Significant synergistic interaction effects exist between gray and green spaces. Interaction analysis reveals that “high green coverage with low structural connectivity of gray space” produces optimal synergistic cooling effects, representing the most effective spatial configuration for nighttime LST mitigation. This study deepens theoretical and methodological understanding of the complex relationships between spatial morphology and thermal environments, providing quantified, temporally differentiated spatial optimization guidance for climate-adaptive planning in valley cities. Full article

Nocturnal urban heat presents significant but understudied risks within tropical megacities, where high humidity and heat storage in built-up areas prevent nighttime thermal recovery and intensify chronic heat stress. This study investigates the nocturnal surface urban heat island (SUHI) dynamics in the Bangkok Metropolitan Region (BMR) over two decades (2003–2023) with a daytime SUHI comparative baseline. We examined long-term thermal variations using MODIS land surface temperature data and Landsat urban–rural classification. The results demonstrate an increase in nighttime land surface temperature (LST) of 0.109, with nocturnal SUHI proving more persistent than its daytime counterpart with a temperature difference as high as 2.0 °C between urban and rural areas during the night. While daytime SUHI peaked at 6.3 °C in April 2011, with the strongest effects during April–May, nocturnal SUHI exhibited less seasonal variability but sustained elevated values throughout the year. Heat-retaining nocturnal hotspots have expanded from central Bangkok to newly developed urban areas. Cross-correlation analysis suggests that El Niño–Southern Oscillation (ENSO) strongly modulates SUHI anomalies, with maximum cross-correlations for a time lag of 3 months. These results suggest the need for urban adaptation strategies that specifically address nocturnal heat, as well as design strategies such as improved ventilation, high-emissivity materials, green infrastructure allowing evapotranspiration, and cooling centers for vulnerable populations to enhance thermal resilience across the BMR. Full article

This study examines land surface temperature (LST) variability over Malta, a small island in the central Mediterranean, using satellite observations compared with in situ near-surface air temperature (NSAT) measurements. The analysis focuses on the comparison between satellite-derived LST and local atmospheric thermal conditions for urban and rural land cover types. LST data from Landsat-8, MODIS (Terra and Aqua), and Sentinel-3A and 3B were analysed over a six-month period (September 2024 to February 2025). Monthly morning and evening field campaigns were conducted at 19 monitoring sites distributed across the island, during which NSAT, relative humidity, wind speed, and wind direction were recorded. Morning comparisons showed strong correlations between satellite-derived LST and in situ NSAT, i.e., Pearson’s correlation coefficient, r, in the range of 0.82–0.85. Landsat-8 exhibited a slight positive bias (+1.04 °C), while MODIS and Sentinel-3 Level-2 products showed negative biases (−3.82 °C and −1.89 °C, respectively). Nighttime comparisons revealed larger negative biases for MODIS (−6.91 °C) and Sentinel-3 (−6.89 °C). After empirical-based harmonisation, these discrepancies were reduced to near-zero mean bias, maintaining strong correlations. Spatial analysis indicated a persistent nocturnal urban heat island (UHI) effect, with urban areas retaining more heat than rural zones. Morning patterns showed seasonal modulation: during late summer and early autumn, rural areas exhibited higher surface temperatures due to sparse vegetation and exposed soils, whereas during cooler months the urban signal became more pronounced as vegetation recovery enhanced rural cooling. Overall, the results demonstrate the usefulness of multi-sensor satellite observations, interpreted alongside ground-based measurements for characterising thermal behaviour in small island environments. Full article

Urban morphology influences the distribution and variation of land surface temperature (LST) by altering surface cover type. However, the coupling effects of the daily LST cycle and the multidimensional morphological driving mechanisms remain insufficiently explored in existing studies. This study, based on ECOSTRESS diurnal LST data, focuses on Harbin, a representative city in China’s cold climate regions. By integrating land cover data, urban morphology vector data, and interpretable machine learning models, it investigates the intricate relationship between urban morphology indicators and LST over a 24 h cycle under cold climate conditions. LST prediction was carried out using Gradient Boosting Decision Trees (XGBoost), Random Forests (RF), Support Vector Machines (SVM), and Multiple Linear Regression (MLR), with an evaluation of the prediction accuracy of each model. The findings indicated the following: (1) The influence of 2D and 3D urban morphological indicators on LST exhibits significant seasonal variation, with Building Otherness (BO), Mean Building Height (BH), and the Normalized Difference Vegetation Index (NDVI) exerting notable impacts on LST in both winter and summer. (2) Significant interactions exist between certain urban morphological indicators that can effectively reduce LST, when the Patch Land Area Proportion (PLAND) exceeds 40%, increasing the Largest Patch Index (LPI) contributes to lowering LST in summer. (3) Among the evaluated machine learning algorithms, XGBoost demonstrates the highest prediction accuracy. This study provides scientific insights for urban planning and policy development, aiding in the optimization of urban morphological designs to effectively regulate LST. Full article

Malaria continues to pose a significant public health challenge in Nigeria, yet there has not been much research utilizing machine-learning techniques to forecast malaria risk. This study developed a machine-learning model that predicts malaria risk by leveraging demographic, environmental, and GPS data from the Nigerian Demographic and Health Survey (DHS) covering the years 2000 to 2020. The dataset was pre-processed and split into a training set (with 406 respondents) and a test set (with 102 respondents). Random Forest (RF), Gradient Boosting (GB) and Linear Regression (LR) algorithms were employed to assess their predictive performance. The RF stood out with the best accuracy, achieving the lowest mean squared error (MSE = 0.0053) and the highest coefficient of determination (R² = 0.6364). Thus, RF was recognized as the most effective model for predicting malaria risk. The regression equation with positive coefficients (like population density = 0.0141, travel time = 0.0019, minimum temperature = 0.0082, temperature in January = 0.0265, and dry land surface temp = 0.0368) indicate that higher feature values are associated with increased malaria prevalence, while negative coefficients (such as rainfall = −0.0122, nightlights composite = −0.03, potential evapotranspiration = −0.09 and insecticide treated nets = −0.02) suggest that as the feature increases, the prevalence decreases. This study underscores the potential of the RF approach in improving early predictions of malaria risk and can guide targeted interventions to control malaria in areas at high risk. Full article

Against the backdrop of accelerated terrestrial hydrological cycling and the increasing concurrence of drought-heatwave compound extremes under global warming, regional land-atmosphere coupling has emerged as a central mechanism shaping climate feedbacks and trajectories of ecosystem carbon uptake. However, prior studies spanning climatic regimes, observational scales, and data sources have often yielded contradictory conclusions. Here, we challenge these fragmented perspectives by constructing an integrated LST-SM-ET-GPP chain that jointly represents land surface temperature, soil moisture, evapotranspiration, and gross primary productivity, thereby linking water availability, surface energy balance, and plant physiological processes within a unified framework. We synthesize a conceptual diagnostic roadmap for interpreting land-atmosphere coupling across observations and models. When ecosystems operate in humid, energy-limited environments, radiative and advective controls should be prioritized to diagnose system forcing. By contrast, as the system becomes water-depleted, attribution must shift to a nonlinear regime transition framework governed by a critical soil moisture threshold. This threshold mechanism implies that, once the system enters the moisture-limited regime, even modest declines in soil moisture can trigger a rapid weakening of evaporative cooling, substantially amplifying LST anomalies and strongly suppressing GPP. The competitive regulation of stomatal conductance by atmospheric demand (vapor pressure deficit, VPD) and terrestrial supply (rootzone soil moisture) further explains why the “dominant” controlling factor can dynamically reverse across hydrothermal states, timescales, and stages of extreme-event evolution. Notably, the steady-state coupling assumption may break down under flux “flooring” during extreme drought, or when structural buffering such as deep root water uptake is present, delineating strict applicability bounds for existing diagnostic frameworks. Finally, current assessments remain constrained by multiple uncertainties, particularly the lack of ET partitioning constraints, representativeness biases arising from clear-sky observations and sampling-depth limitations, and systematic errors in Earth system model simulations during the warm season. Full article

Rising urban temperatures have become a critical constraint to urban ecosystem resilience and livability due to rapid urbanization. This study proposes a novel intra-city zoning scheme, named component morphological blocks (CMBs), which classifies built-up areas into six types characterized by multidimensional urban canopy morphologies. The XGBoost-SHAP model, optimized via Bayesian tuning, was employed to examine the relative contributions of 16 potential driving variables to block-scale land surface temperature (LST). The results show that: (1) LST gradually increases with increasing building density in the warm seasons. The average building height (BH) exhibits a positive correlation with shaded area, thereby reducing LST on the block scale; (2) hotspots are mainly concentrated in function-oriented blocks with hotspot distribution indices of 1.85, 1.96, 1.24, and 1.14, respectively. Coldspots are largely observed in blue–green space in the warm seasons; (3) BH dominates the LST across seasons, while the building-related factors make a prominent impact on LST in warm seasons. The contribution of vegetation canopy density is followed by BH during autumn and winter (12.2%, 10.9%); (4) a distinct transition occurs between summer normalized difference built-up index (NDBI) and fractional vegetation cover around an NDBI of 0.1. In winter, the interaction between 2D and 3D vegetation factors indicates a shift in their relative contributions from negative to positive as they increase. This study demonstrates that CMBs serve as an effective choice for characterizing LST patterns at the block scale, providing insights for sustainable urban development aimed at mitigating the urban heat island effect. Full article

Uneven urbanization can lead to significant quantities of vacant and abandoned land while exacerbating urban heat island (UHI) effects and simultaneously adversely affecting socioeconomically disadvantaged communities. This study examines the correlation between land surface temperature (LST) and urban vacant and abandoned land in socially vulnerable neighborhoods in Houston, TX, USA, where extreme heat can present significant environmental and public health challenges. Six critical study locations exhibiting a social vulnerability index (SVI) over 0.7 and average land surface temperature (LST) values surpassing 82 °F (27.8 °C) are analyzed through spatial analytics and drone footage. Findings indicate that vegetated vacant spaces help mitigate urban heat by decreasing land surface temperature, but abandoned structures exacerbate temperatures due to heat retention from non-permeable surfaces. Findings suggest that elevated socioeconomic vulnerability correlates with increased land surface temperature, exacerbating heat-related hazards in at-risk communities. In this six-site sample, the abandonment rate exhibited a positive correlation with the site mean land surface temperature (exploratory linear fit: +2.42 °F [0.74, 4.11]/+1.35 °C [0.41, 2.28] per +1% increase in abandonment; to be interpreted as exploratory and potentially confounded). Results provide critical insights for climate resilience planning and urban heat reduction through high-resolution thermal and geographical analysis, highlighting the impact of vacant and abandoned land on LST. Such findings endorse certain urban cooling techniques, including land reutilization and green infrastructure, to enhance environmental equality and adaptation. Full article