Search Results (4,042)

In the context of global warming, thermal environmental problems in coastal urban ag-glomerations have become increasingly prominent. This study focuses on the urban ag-glomeration around Hangzhou Bay, constructs annual heat island intensity classification maps based on MODIS summer land surface temperature (LST) data from 2000 to 2020, analyzes the spatiotemporal patterns of heat islands, and investigates their driving mechanisms using the Extreme Gradient Boosting and Shapley Additive exPlanations (XGBoost-SHAP) model. The results show that: (1) the high-frequency area of strong heat islands expanded by 62.10% during the study period, extending from early built-up areas to newly developed coastal zones, with the spatial pattern transitioning from point-like distribution to areal agglomeration; (2) significant differences exist between the north and south coasts, where strong heat island center migration on the north coast is consistent with impervious surface expansion, whereas the south coast is significantly influenced by coastal wetland siltation; (3) impermeable surfaces and wind speed are key factors affecting LST, with impermeable surfaces acting as the primary driver of temperature increase, while wind speed plays a significant role in moderating temperatures. This study provides a scientific basis for thermal environment regulation in coastal urban agglomerations. Full article

Wetlands and surface-water features regulate the thermal environment of cities through evaporative cooling, yet in arid metropolitan regions these hydrological buffers are scarce and rarely quantified against urban heat. Here, we link satellite-derived surface-water wetness to land surface temperature (LST) and urban heat island (UHI) intensity in Riyadh, Saudi Arabia, using an explainable Geospatial Artificial Intelligence (GeoAI) framework. We assembled 2000 cloud-masked Landsat 8/9 sample points for July 2014 and 2024 in Google Earth Engine and derived the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and two surface-water indices, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Water Index (NDWI), together with LST, UHI, terrain and population. Surface-water wetness was the strongest cool-side correlate of thermal stress: MNDWI related negatively to LST (r = −0.48) and to UHI intensity (r = −0.53), stronger than either vegetation or built-up density (both p < 0.001). Each 0.1 increase in MNDWI corresponded to a 2.2 °C reduction in LST. Five machine-learning algorithms predicted LST with test R² of 0.71–0.76 and UHI with R² of 0.68–0.72, and SHapley Additive exPlanations (SHAPs) identified MNDWI as the single most important thermal driver, ahead of elevation and vegetation. Point-level LST rose by 1.99 °C between 2014 and 2024 (p < 0.001), while open surface water was absent from all 2000 samples, indicating a hydrological deficit in the city’s thermal regulation. These findings suggest that protecting and expanding blue–green features along corridors such as Wadi Hanifah offers a measurable cooling lever for arid-city climate adaptation. Full article

Extreme temperatures are an emerging public health concern due to their significant impact on humans, yet the evidence remains limited in tropical countries. This study examined the non-linear relationship between ambient temperature and non-accidental and cause-specific mortality in two northern parts of Peninsular Malaysia, from 2011 to 2019. Daily mortality and meteorological data were analyzed using a quasi-Poisson Generalized Linear Model with a Distributed Lag-Non-Linear model to estimate the relationship between temperature and mortality. A U-shaped and J-shaped relationship was observed for the cumulative effects of 21-day lag periods for Kedah and Penang, respectively. The minimum mortality temperature (MMT) at 27.4 °C in Kedah and 28.2 °C in Penang was observed. Extremely high temperatures were associated with an increased non-accidental mortality, with a 16% increase at cumulative lag days 0–3 in Kedah and a 21% increase at cumulative lag days 0–7 in Penang. Vulnerable groups included individuals with respiratory diseases, the elderly, both genders and those residing in both urban and rural areas. These findings highlight the acute impact of heat on mortality in Malaysia and underscore the need for targeted public health interventions. Strengthening heat-health warning systems, improving healthcare preparedness, and prioritizing vulnerable populations are essential to mitigate the health impacts of rising temperatures in tropical regions. Full article

Urbanization is a major driver of land-use change and ecological shifts, especially in semi-arid regions with high environmental sensitivity. This study examined urban land growth and its ecological impacts in Astana, Kazakhstan, from 2000 to 2020 and forecasted trends for 2030. Landsat imagery was classified using a Support Vector Machine (SVM) approach, and ecological conditions were assessed through spectral indices, including Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), a Tasseled Cap Wetness index (Wet), and a Normalized Difference Bare-Soil and Built-up Index (NDBSI). The Future Land Use Simulation (CA–Markov) model simulated land use under Business-as-Usual (BAU) and Ecological Priority (EP) scenarios. The results showed a significant increase in built-up land, mainly at the expense of cropland and grassland, with increased landscape fragmentation and rising LST, indicating intensifying urban heat. Ecological indices showed spatially varied responses, with localized greening in protected areas and overall environmental pressure in expanding zones. Scenario simulations suggest that policy interventions under the EP scenario can mitigate cropland loss, limit fragmentation, and enhance ecological connectivity compared with BAU. Overall, the findings show that integrating remote sensing, machine learning, and scenario modeling offers an effective framework for assessing urban–ecological dynamics and supports evidence-based planning for sustainable urban development in semi-arid cities. 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

This study presents a BIM-integrated life cycle analysis framework (screening-level) for climate-responsive urban energy performance assessment at district scale. The methodology addresses the need for consistent evaluation of operational energy demand under both design interventions and future climate conditions. A mixed-use district in Milan is used as a case study, where parametric BIM massing models (LOD 200–300) are coupled with building energy simulation to analyze three scenarios: a baseline configuration (S0), an envelope optimization scenario (S1), and a future climate scenario based on CMIP6 morphed weather data (S2). The framework enables comparative assessment of energy performance across consistent geometric, operational, and climatic assumptions. Results indicate that envelope optimization reduces energy use intensity by approximately 15–22% across building typologies. Under future climate conditions, cooling demand increases significantly, while reduced heating requirements result in a total district energy use intensity of 33.6 kWh/m²·year (1.60 GWh/year). An indicative carbon assessment based on simulated energy use highlights cooling-driven electricity as the dominant contributor to operational emissions under future conditions. The findings demonstrate that climate change primarily redistributes energy demand between heating and cooling rather than uniformly increasing total consumption, and confirm the value of BIM-integrated, scenario-based workflows for supporting climate-responsive urban design decisions. Full article

While the effects of urbanization are widely studied, the effects of soil sealing, particularly in the case of Hungary, have only received limited attention in recent years. Our study aimed at understanding the underutilized capacity of urban soils at the national level. We have applied a 20 m resolution, spatially explicit daily water balance-based methodology to calculate the potential water dynamics for the top 75 cm of the soils currently covered by urban fabric in Hungary, for the time period of 1971–2024. We aimed to utilize primarily publicly available data and open-source software to support further use and development. Our results indicated that these (currently sealed) soil surfaces could allow between 0.14 and 0.29 km³ of water to infiltrate into the soil, equaling about 7% of the estimated annual water withdrawal in Hungary. The on-site evaporation from these surfaces would produce about 400 PJ of total cooling service annually, corresponding to an average of 145 MJ/m². Our findings highlighted the water storage potential of soils in Hungary, particularly in urban areas, supporting the future application of nature-based solutions and blue-green infrastructure. Full article

Against the backdrop of rapid urbanization and climate warming, the urban heat island (UHI) effect has severely affected ecological security and public health. Existing studies have often focused on single-city analyses or large-sample averages, with insufficient attention to the nonlinear driving mechanisms of UHI under different hydrothermal contexts. This study selects Wuhan and Xi’an as representative cities, constructing an explainable machine learning framework to interpret and compare UHI intensity across feature importance, nonlinear responses, factor interactions, and spatial differentiation. The results show that, in Wuhan, the top five factors contribute 62.4%, reflecting a composite dominance of ecology, spatial morphology, location, and human activities. In Xi’an, the top five factors contribute 72.0%, indicating a more concentrated dominant structure. Nonlinear responses reveal that key factors like NDVI have distinct effect thresholds and mechanisms in the two cities. Spatially, Wuhan displays a continuous gradient pattern characterized by center-promoting and peripheral-suppressing effects, whereas Xi’an presents a block-like mosaic structure composed of multiple juxtaposed districts. These differences suggest that UHI mitigation should move beyond a uniform control model and instead adopt climate-sensitive strategies that account for the dominant factor combinations, response thresholds, and spatial organization of each city. The proposed framework and findings provide scientific support for understanding UHI mechanisms under different hydrothermal contexts and offer targeted implications for thermal environment regulation and spatial planning in cities with similar climatic and environmental characteristics. Full article

Global warming has increased the frequency and intensity of extreme high-temperature events, with evolution patterns differing substantially under various temperature–humidity combinations. This study used observational data from 21 meteorological stations in Zhejiang Province (1980–2019) and applied a mutually exclusive classification framework based on dual thresholds of dry-bulb and wet-bulb temperatures to categorize extreme high-temperature events into dry-type (DHW), humid-type (HHW), and compound-type (CHW). The results show that DHW frequency, duration, and intensity all exhibited significant increasing trends, with frequency rising at 0.32/10a and intensity at 1.92 °C/10a. HHW occurred with low frequency and showed no significant trend across the study period. CHW intensity increased significantly at 2.85 °C/10a, while frequency and duration remained stable. Spatially, DHW concentrated in northern and central inland areas, whereas CHW dominated along the eastern coastal belt, reflecting the contrasting influences of land–sea thermal contrast and moisture availability. Urbanization showed significant positive correlations with all DHW indicators and negative correlations with HHW trends, indicating an amplifying effect on dry heat through surface warming and reduced evapotranspiration, and a suppressive effect on humid heat through reduced surface moisture availability. These findings demonstrate that the intensification of extreme heat in this region is dominated by dry-type events, and that urbanization plays a dual role in amplifying dry heat while suppressing humid heat, providing a scientific basis for differentiated heat risk management and climate-adaptive urban planning. Full article

A methodology for the development of nonlinear model predictive control for thermal management of a 40-ton fuel-cell heavy-duty truck is presented, using the medium-temperature cooling circuit as a case study. The approach integrates control-oriented modeling, parameter estimation, and experimental validation based on drivetrain test bench measurements under controlled high-temperature ambient conditions. A lumped-parameter model of the medium-temperature circuit, including coolant, oil, electric motors, and power-electronics auxiliaries, is derived and implemented in a Simulink environment, with heat-transfer parameters calibrated from test bench data and radiator air-side resistance and fan characteristics derived from CFD simulations and manufacturer specifications, respectively. Model parameters are identified using a systematic estimation procedure and the resulting model is validated against long-duration roller test measurements, achieving coefficients of determination above R² = 0.9 and normalized RMSE values below 10% for all key temperatures. The validated model is then used as the prediction model in a model predictive controller that manipulates radiator fan and coolant-pump speeds, while treating component heat losses, vehicle speed and ambient temperature as measured disturbances. The controller is evaluated in a model-in-the-loop environment for representative long-haul and urban driving cycles and different ambient temperatures, and its performance is benchmarked against conventional rule-based and PI-based control strategies. Depending on the driving cycle and ambient conditions, the proposed NMPC reduces cooling system energy consumption by up to 39.6% compared to a PI controller (VECTO Urban Delivery cycle, 35 °C ambient), with an average reduction of 16.6% across all investigated driving cycles and ambient conditions, without a significant increase in average or maximum coolant temperature. The proposed methodology provides a transferable workflow for developing predictive thermal management control in fuel-cell heavy-duty vehicles and other complex automotive cooling systems. 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

Urban outdoor exercise spaces are important public infrastructures for physical activity, but their users may be exposed to concurrent air pollution and unfavorable thermal environmental conditions. This study developed an activity-weighted framework to assess the compound ozone–heat exposure risk in urban outdoor exercise spaces. Taking the central districts of Beijing as the study area, we integrated the mobile phone signaling-derived visitation frequency, 1 km ground-level O₃ estimates, the 30 m Landsat-derived land surface temperature (LST), the land cover composition, road network indicators, and three-dimensional building morphology variables. An activity-weighted compound ozone–heat exposure risk index (COHER) was constructed by combining the normalized daily visitation frequency, monthly mean O₃, and area of interest (AOI)-level mean LST. The results showed that the visitation frequency, O₃, and LST exhibited mismatched spatial patterns, highlighting the need for compound exposure assessment. COHER values ranged from 0.0000 to 0.1918 and were strongly right-skewed, with 49 outdoor exercise spaces identified as the top 10% high-risk sites. These high-risk spaces had a substantially higher visitation frequency and mean LST than the remaining spaces, whereas O₃ differences were small and not statistically significant. Exploratory XGBoost–SHAP analysis suggested that the built-up intensity, building height variability, and potential airflow obstruction were relatively important environmental correlates of COHER. The proposed framework provides a relative place-based screening tool for identifying priority outdoor exercise spaces for exposure-sensitive planning and risk mitigation. Full article

Urban canopy mitigates urban heat, yet how the spatial configuration of high- and low-canopy layers shapes population heat exposure across a national urban system remains insufficiently understood. Drawing on a panel of 369 Chinese prefecture-level cities for 2000, 2005, 2010, 2015, and 2020, this study constructs a population-weighted thermal-exposure metric—the Human Heat Exposure Index (HEI)—and stratifies urban vegetation into high- and low-canopy classes based on Chinese Land Cover Dataset (CLCD) land-cover types. Multiscale Geographically Weighted Regression (MGWR) and Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP)-based interpretation are combined to identify spatially varying associations and nonlinear marginal effects of stratified canopy patterns on HEI. HEI shows a persistent south–high, north–low spatial structure, with Global Moran’s I stable at approximately 0.85 throughout the study period. High-canopy edge density and cohesion are increasingly associated with reduced heat exposure in densely built regions, while low-canopy mean patch area and edge density retain explanatory power across all years through near-surface evapotranspirative regulation. The marginal cooling effect of vegetation strengthens appreciably only above an Normalized Difference Vegetation Index (NDVI) of approximately 0.6, and the apparent inflection ranges for impervious surface proportion and standardized solar radiation lie near 25% and 0.4, respectively. These findings suggest that in cities with high impervious loads, cooling-network connectivity and within-zone canopy configuration matter more than additional canopy area alone, and that planning targets should be calibrated to climate zone, city type, and existing surface conditions. 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

Lokoja, the capital of Kogi State, Nigeria, situated at the confluence of the Niger and Benue Rivers, has experienced rapid urban expansion alongside heightened environmental risks, including flooding and ecosystem degradation. Using multi-temporal Landsat imagery (2000, 2010, 2020, 2024), Random Forest classification, and landscape metrics, this study analyses spatio-temporal patterns of urban growth and fragmentation in this underrepresented mid-sized African city. Urban land cover expanded from 6668 ha in 2000 to 15,985 ha in 2024 (net ~140% growth), following a non-linear trajectory of rapid expansion (2000–2010), partial consolidation (2010–2020), and renewed growth with intensified fragmentation (2020–2024). This growth caused severe ecological impacts: dense forest declined by 99.7% (from 373 ha to 1 ha), woodland by 73.9%, and core natural land cover by 23% to 13.8% of the landscape, below critical ecological thresholds. Edge density rose by 121%, exacerbating urban heat, runoff, and biodiversity loss, while apparent gains in grassland largely reflect secondary succession rather than recovery. This study recommends enforcing development restrictions below 10 m in elevation, with 100 m riparian buffers; restoring 500 ha of native corridors; mandating 20% urban tree canopy cover; and establishing community-based green space monitoring. The findings provide empirical insights into sustainability challenges facing mid-sized African cities and offer transferable strategies for ecologically sensitive urban planning. Full article