Search Results (1,440)

Due to global warming, extreme temperature events have become increasingly prevalent, posing significant threats to both socioeconomic development and human safety. While previous studies have extensively examined the influence of individual climatic circulation systems on extreme temperature, the combined effects of multiple concurrent circulation patterns remain poorly understood. Using daily temperature data from 29 meteorological stations in Chongqing (1960–2019), this study employs linear trend analysis, correlation analysis, and random forest (RF) models to analyze spatiotemporal variations in the intensity and frequency of extreme temperature. We selected 21 climate indicators from three categories—atmospheric circulation, sea surface temperature (SST), and sea-level pressure (SLP)—to identify the primary drivers of extreme temperatures and quantify their respective contributions. The key findings are as follows: (1) All extreme intensity indices exhibited an increasing trend, with the TXx (annual maximum daily maximum temperature) showing the higher trend (0.03 °C/year). The northeastern region experienced the most pronounced increases. (2) Frequency indices also displayed an upward trend. This was particularly evident for the TD35 (number of days with maximum temperature ≥35 °C), which increased at an average rate of 0.16 days/year, most notably in the northeast. (3) The Western Pacific Subtropical High Ridge Position Index (GX) and Asia Polar Vortex Area Index (APV) were the dominant climate factors driving intensity indices, with cumulative contributions of 26.0% to 33.4%, while the Western Pacific Warm Pool Strength Index (WPWPS), Asia Polar Vortex Area Index (APV), North Atlantic Subtropical High Intensity Index (NASH), and Indian Ocean Warm Pool Strength Index (IOWP) were the dominant climate factors influencing frequency indices, with cumulative contributions of 46.4 to 49.5%. The explanatory power of these indices varies spatially across stations, and the RF model effectively identifies key circulation factors at each station. In the future, more attention should be paid to urban planning adaptations, particularly green infrastructure and land use optimization, along with targeted heat mitigation strategies, such as early warning systems and public health interventions, to strengthen urban resilience against escalating extreme temperatures. Full article

As an important ecological barrier and animal husbandry resource base in arid and semi-arid areas, grassland degradation directly affects regional ecological security and sustainable development. Ili Prefecture is located in the western part of Xinjiang, China, and is a typical grassland resource-rich area. However, in recent years, driven by climate change and human activities, grassland degradation has become increasingly serious. In view of the lack of comprehensive evaluation indicators and the inconsistency of grassland evaluation grade standards in remote sensing monitoring of grassland resource degradation, this study takes the current situation of grassland degradation in Ili Prefecture in the past 20 years as the research object and constructs a comprehensive evaluation index system covering three criteria layers of vegetation characteristics, environmental characteristics, and utilization characteristics. Net primary productivity (NPP), vegetation coverage, temperature, precipitation, soil erosion modulus, and grazing intensity were selected as multi-source indicators. Combined with data sources such as remote sensing inversion, sample survey, meteorological data, and farmer survey, the factor weight coefficient was determined by analytic hierarchy process. The Grassland Degeneration Comprehensive Index (GDCI) model was constructed to carry out remote sensing monitoring and evaluation of grassland degradation in Yili Prefecture. With reference to the classification threshold of the national standard for grassland degradation, the GDCI grassland degradation evaluation grade threshold (GDCI reduction rate) was determined by the method of weighted average of coefficients: non-degradation (0–10%), mild degradation (10–20%), moderate degradation (20–37.66%) and severe degradation (more than 37.66%). According to the results, between 2000 and 2022, non-degraded grasslands in Ili Prefecture covered an area of 27,200 km², representing 90.19% of the total grassland area. Slight, moderate, and severe degradation accounted for 4.34%, 3.33%, and 2.15%, respectively. Moderately and severely degraded areas are primarily distributed in agro-pastoral transition zones and economically developed urban regions, respectively. The results revealed the spatial and temporal distribution characteristics of grassland degradation in Yili Prefecture and provided data basis and technical support for regional grassland resource management, degradation prevention and control and ecological restoration. Full article

The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research contains records with measurements of the air pollutants ozone (O₃) and carbon monoxide (CO), as well as meteorological parameters such as temperature (T), relative humidity (RH), and barometric pressure (BP). This dataset was collected using a set of low-cost sensors over a four-month study period (March to June) in 2024. The monitoring of air pollutants and meteorological parameters was conducted in a city with high industrial activity, heavy traffic, and close proximity to a petrochemical refinery plant. The data were subjected to a series of statistical analyses for visualization using plots that allow for the identification of their behavior. Finally, the dataset can be utilized for air quality studies, public health research, and the development of prediction models based on mathematical approaches or artificial intelligence algorithms. Full article

Dark skies, a vital natural and cultural resource, have been increasingly threatened by light pollution due to rapid urbanization, leading to ecological degradation and biodiversity loss. As a key strategy for sustainable regional development, dark sky parks (DSPs) not only preserve nocturnal environments but also enhance livability by balancing urban expansion and ecological conservation. This study develops a novel framework for evaluating DSP suitability, integrating ecological and socio-economic dimensions, including the resource base (e.g., nighttime light levels, meteorological conditions, and air quality) and development conditions (e.g., population density, transportation accessibility, and tourism infrastructure). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, we employ Delphi expert consultation, GIS spatial analysis, and multi-criteria decision-making to identify optimal DSP locations and prioritize conservation zones. Our key findings reveal the following: (1) spatial heterogeneity in suitability, with high-potential zones being concentrated in the GBA’s northeastern, central–western, and southern regions; (2) ecosystem advantages of forests, wetlands, and high-elevation areas for minimizing light pollution; (3) coastal and island regions as ideal DSP sites due to the low light interference and high ecotourism potential. By bridging environmental assessments and spatial planning, this study provides a replicable model for DSP site selection, offering policymakers actionable insights to integrate dark sky preservation into sustainable urban–regional development strategies. Our results underscore the importance of DSPs in fostering ecological resilience, nighttime tourism, and regional livability, contributing to the broader discourse on sustainable landscape planning in high-urbanization contexts. Full article

Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO₂), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO₂ concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article

Global warming and rapid urbanization have increased population exposure to heatwaves, with compound day- and night-time heatwaves (CDNH) posing greater health risks than individual heatwave events. Although air conditioning (AC) adaptation effectively mitigates heat-related impacts, its role in reducing CDNH exposure under climate change remains unknown. Using meteorological and socioeconomic data, this study quantified population exposure to CDNHs and the impacts that could be avoided through AC adaptation across China and its regional variations. Results show that CDNH exposure risks were particularly high in the middle–lower Yangtze–Huaihe Basin and south China, with an increasing trend observed over the period of 2001–2022. AC adaptation has reduced the exposure risk and its upward trend by 5.85% and 37.87%, respectively, with higher mitigating effects in urban areas. By breaking down the total exposure changes into climatic, demographic, and AC-driven changes, this study reveals that increased AC contributes 10.16% to exposure reduction, less than the effect of climate warming (59.80%) on the exposure increases. These findings demonstrate that expanding AC adaptation alone is insufficient to offset climate-driven increases in exposure, highlighting the urgent need for more effective adaptation measures to address climate change and thereby alleviate its adverse impacts on human beings. Full article

Heatwaves intensified by climate change increasingly threaten urban populations, especially the elderly. However, most existing studies have concentrated on short-term or single-scale analyses, lacking a comprehensive understanding of how land cover changes and urbanization affect the vulnerability of the elderly to extreme heat. This study aims to investigate the spatiotemporal distribution patterns of heat-related health risks among the elderly in Nanchang City and to identify their key driving factors within the context of rapid urbanization. This study employs Crichton’s risk triangle framework to the heat-related health risks for the elderly in Nanchang, China, from 2002 to 2020 by integrating meteorological records, land surface temperature, land cover data, and socioeconomic indicators. The model captures the spatiotemporal dynamics of heat hazards, exposure, and vulnerability and identifies the key drivers shaping these patterns. The results show that the heat health risk index has increased significantly over time, with notably higher levels in the urban core compared to those in suburban areas. A 1% rise in impervious surface area corresponds to a 0.31–1.19 increase in the risk index, while a 1% increase in green space leads to a 0.21–1.39 reduction. Vulnerability is particularly high in economically disadvantaged, medically under-served peripheral zones. These findings highlight the need to optimize the spatial distribution of urban green space and control the expansion of impervious surfaces to mitigate urban heat risks. In high-vulnerability areas, improving infrastructure, expanding medical resources, and establishing targeted heat health monitoring and early warning systems are essential to protecting elderly populations. Overall, this study provides a comprehensive framework for assessing urban heat health risks and offers actionable insights into enhancing climate resilience and health risk management in rapidly urbanizing regions. Full article

The Yangtze River Delta urban agglomeration, characterized by high population density, an advanced transportation system, and a concentration of industrial activity, is one of the regions severely affected by O₃ pollution in central and eastern China. Using data collected from 251 monitoring stations between 2015 and 2025, this paper analyzed the spatio-temporal variation of 8 h O₃ concentrations and instances of exceedance. On the basis of exploring the influence of meteorological factors on regional 8 h O₃ concentration, the potential source contribution areas of pollutants under the exceedance condition were investigated using the HYSPLIT model. The results indicate a rapid increase in the 8 h O₃ concentration at a rate of 0.91 ± 0.98 μg·m⁻³·a⁻¹, with the average number of days exceeding concentration standards reaching 41.05 in the Yangtze River Delta urban agglomeration. Spatially, the 8 h O₃ concentrations were higher in coastal areas and lower in inland regions, as well as elevated in plains compared to hilly terrains. This distribution was significantly distinct from the concentration growth trend characterized by higher levels in the northwest and lower levels in the southeast. Furthermore, it diverged from the spatial characteristics where exceedances primarily occurred in the heavily industrialized northeastern region and the lightly industrialized central region, indicating that the growth and exceedance of 8 h O₃ concentrations were influenced by disparate factors. Local human activities have intensified the emissions of ozone precursor substances, which could be the key driving factor for the significant increase in regional 8 h O₃ concentrations. In the context of high temperatures and low humidity, this has contributed to elevated levels of 8 h O₃ concentrations. When wind speeds were below 2.5 m·s⁻¹, the proportion of 8 h O₃ concentrations exceeding the standards was nearly 0 under almost calm wind conditions, and it showed an increasing trend with rising wind speeds, indicating that the potential precursor sources that caused high O₃ concentrations originated occasionally from inland regions, with very limited presence within the study area. This observation implies that the main cause of exceedances was the transport effect of pollution from outside the region. Therefore, it is recommended that the Yangtze River Delta urban agglomeration adopt economic and technological compensation mechanisms within and between regions to reduce the emission intensity of precursor substances in potential source areas, thereby effectively controlling O₃ concentrations and improving public living conditions and quality of life. Full article

Air pollution, particularly fine (PM_2.5) and coarse (PM₁₀) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. A five-year dataset (2020–2024), comprising 20 meteorological and pollution-related variables recorded by four air quality monitoring stations, was analyzed. The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. A deep Nonlinear AutoRegressive Moving Average with eXogenous inputs (Deep-NARMAX) model was employed for multi-step-ahead forecasting. The best-performing models achieved R² values of 0.85 for PM_2.5 and 0.89 for PM₁₀, with low RMSE and MAPE scores, demonstrating high accuracy and generalizability. The GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. These results highlight the potential of hybrid AI models for air quality management and provide a scalable framework for urban pollution monitoring, predicting, and forecasting. Full article

This study provides an assessment of BTEX compounds—benzene, toluene, ethylbenzene, and xylene isomers—in urban precipitation collected in the city of Novi Sad, Republic of Serbia, during autumn and winter 2024, analyzed by gas chromatography-mass spectrometry (GC-MS). By combining chemical analysis with meteorological observations and HYSPLIT backward trajectory modeling, the study considers the mechanisms of BTEX removal from the atmosphere via wet scavenging and highlights the role of local weather conditions and long-range atmospheric transport in pollutant concentrations. During the early observation period (September to late November), average concentrations were 0.45 µg/L benzene, 3.45 µg/L ethylbenzene, 4.0 µg/L p-xylene, 2.31 µg/L o-xylene, and 1.32 µg/L toluene. These values sharply dropped to near-zero levels in December for benzene, ethylbenzene, and xylenes, while toluene persisted at 1.12 µg/L. A pronounced toluene spike exceeding 6 µg/L on 28 November was likely driven by transboundary air mass transport from Central Europe, as confirmed by trajectory modeling. The environmental risks posed by BTEX deposition, especially from toluene and xylenes, underline the need for regulatory frameworks to include precipitation as a pathway for pollutant deposition. It should be clarified that the identified risk primarily concerns aquatic organisms, due to the potential for BTEX infiltration into surface waters and subsequent ecotoxicological impacts. Incorporating such monitoring into EU policies can improve protection of air, water, and ecosystems. Full article

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

Under the dual pressures of global climate warming and rapid urbanization, the Yangtze River Basin, as the world’s largest urban agglomeration, is facing intensifying thermal environmental stress. Although river ecosystems demonstrate significant thermal regulation functions, their spatial thresholds of cooling effects and multiscale driving mechanisms have remained to be systematically elucidated. This study retrieved land surface temperature (LST) using the split window algorithm and quantitatively analyzed the changes in the river cold island effect and its driving mechanisms in the Yangtze River Basin by combining multi-ring buffer analysis and the optimal parameter-based geographical detector model. The results showed that (1) forest land is the main land use type in the Yangtze River Basin, with built-up land having the largest area increase. Affected by natural, socioeconomic, and meteorological factors, the summer temperatures displayed a spatial pattern of “higher in the east than the west, warmer in the south than the north”. (2) There are significant differences in the cooling magnitude among different land types. Forest land has the maximum daytime cooling distance (589 m), while construction land has the strongest cooling magnitude (1.72 °C). The cooling effect magnitude is most pronounced in upstream areas of the basin, reaching 0.96 °C. At the urban agglomeration scale, the Chengdu–Chongqing urban agglomeration shows the greatest temperature reduction of 0.90 °C. (3) Elevation consistently demonstrates the highest explanatory power for LST spatial variability. Interaction analysis shows that the interaction between socioeconomic factors and elevation is generally the strongest. This study provides important spatial decision support for formulating basin-scale ecological thermal regulation strategies based on refined spatial layout optimization, hierarchical management and control, and a “natural–societal” dual-dimensional synergistic regulation system. Full article

Accurate detection of the atmospheric boundary layer (ABL) is important for weather forecasting, urban air quality monitoring, and agricultural and ecological protection. In this study, we propose a new method for enhancing ABL height detection accuracy by integrating multi-channel polarized lidar signals at 355 nm and 532 nm wavelengths. Radiosonde observations and ERA5 reanalysis are used to validate the lidar-derived results. By calculating the gradients of signals of different wavelengths and weighted fusion, the position of the top of the boundary layer is identified, and corresponding weights are assigned to signals of different wavelengths according to the signal-to-noise ratio of the signals to obtain a more accurate atmospheric boundary layer height. This method can effectively mitigate the influence of noise and provides more stable and accurate ABL height estimates, particularly under complex aerosol conditions. Three case studies of ABL height detection over the Beijing region demonstrate the effectiveness and reliability of the proposed method. The fused ABLHs were found to be consistent with the sounding data and ERA5. This research offers a robust approach to enhancing ABL height detection and provides valuable data support for meteorological studies, pollution monitoring, and environmental protection. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

This study compares high-frequency mobile observation data collected in the same area of Kunming under two different meteorological conditions—15 January 2020, and 8 January 2023—to analyze changes in the micro-scale urban thermal environment. Vehicle-mounted temperature and humidity sensors, combined with GPS tracking, were used to conduct real-time, high-resolution data collection across various urban functional areas. The results show that in the two tests, the maximum temperature differences were 10.4 °C and 16.5 °C, respectively, and the maximum standard deviations were 0.34 °C and 2.43 °C, indicating a significant intensification in thermal fluctuations. Industrial and commercial zones experienced the most pronounced cooling, while green spaces and water bodies exhibited greater thermal stability. The study reveals the sensitivity of densely built-up areas to cold extremes and highlights the important role of green infrastructure in mitigating urban thermal instability. Furthermore, this research demonstrates the advantages of mobile observation over conventional remote sensing methods in capturing fine-scale, dynamic thermal distributions, offering valuable insights for climate-resilient urban planning. Full article