Search Results (2,247)

Background: Greek islands face mounting pressures on their marine water resources due to tourism growth, agricultural runoff, climate change, and emerging pollutants. Safeguarding seawater quality is critical for ecosystem integrity, public health, and the sustainability of tourism-based economies. Objectives: This scoping review synthesizes and evaluates the existing research on seawater quality in the Greek islands, with emphasis on pollution sources, monitoring methodologies, and socio-environmental impacts, while highlighting the gaps in addressing emerging contaminants and aligning with sustainable development goals. Methods: A systematic literature search was conducted in Scopus, Google Scholar, ResearchGate, Web of Science, and PubMed for English- and Greek-language studies published over the last two to three decades. The search terms covered physical, chemical, and biological aspects of seawater quality, as well as emerging pollutants. The PRISMA-ScR guidelines were followed, resulting in the inclusion of 178 studies. The data were categorized by pollutant type, location, water quality indicators, monitoring methods, and environmental, health, and tourism implications. Results: This review identifies agricultural runoff, untreated wastewater, maritime traffic emissions, and microplastics as key pollution sources. Emerging contaminants such as pharmaceuticals, PFASs, and nanomaterials have been insufficiently studied. While monitoring technologies such as remote sensing, fuzzy logic, and Artificial Neural Networks (ANNs) are increasingly applied, these efforts remain fragmented and geographically uneven. Notable gaps exist in the quantification of socio-economic impact, source apportionment, and epidemiological assessments. Conclusions: The current monitoring and management strategies in the Greek islands have produced high bathing water quality in many areas, as reflected in the Blue Flag program, yet they do not fully address the spatial, temporal, and technological challenges posed by climate change and emerging pollutants. Achieving long-term sustainability requires integrated, region-specific water governance linked to the UN SDGs, with stronger emphasis on preventive measures, advanced monitoring, and cross-sector collaboration. Full article

Air pollution poses a threat to public health, ecosystem stability, and sustainable development. Accurate air quality prediction is essential for environmental protection and achieving sustainability. This study proposes a symmetry-driven hybrid framework that integrates an Improved Triangulation Topology Aggregation Optimizer (ITTAO) with a Stable Long Short-Term Memory (sLSTM) network and an attention mechanism to achieve high-precision air quality prediction. Three enhancement strategies are introduced to improve the optimization capability of the TTAO algorithm. Experiments with CEC2017 standard functions validate the ITTAO algorithm’s superior convergence and global search ability. ITTAO then optimizes the hyperparameters of the sLSTM-Attention model, resulting in the ITTAO-sLSTM-Attention model. Four air quality datasets from diverse regions in China verify the model’s performance, demonstrating that the proposed model outperforms seven swarm intelligence-optimized sLSTM-Attention models and six machine learning models. Compared to the LSTM model, ITTAO-sLSTM-Attention reduces RMSE by 23.47%, 13.23%, 19.69%, and 26.46% across four cities, confirming its enhanced accuracy and generalization. Finally, an interactive air quality prediction system based on the ITTAO-sLSTM-Attention model and PyQt is developed, offering a user-friendly tool for air quality prediction. Full article

Environmental policy helps policymakers and researchers understand the process and expected effects of policy before the policies are fully implemented. This study aims to estimate the effects of resource-conserving and environmentally friendly policy implemented in the Wuhan metropolitan area and Changsha–Zhuzhou–Xiangtan urban agglomeration. The synthetic control method is employed as an estimation method. The results show that policy has positive impacts on economic development and SO₂ emission reduction in the pilot regions but cannot improve wastewater treatment. Compared to large cities, medium-sized and small cities are more sensitive to policies since the large cities have transferred a large number of enterprises with high energy consumption and high emissions to the surrounding medium-sized and small cities. The study also finds that the Wuhan metropolitan area reduces pollution emissions through increasing environmental investment and the efficiency of resource allocation. In the Changsha–Zhuzhou–Xiangtan urban agglomeration, policy triggers green technology innovation to improve the environment and boost the economy. Full article

Air pollution is causing a global health, climate, and environmental crisis. Air quality (AQ) in hyper-arid regions, such as the Arabian Peninsula, remains under-explored, posing significant concerns for public health and the scientific community. Both long-term and short-term exposure to high pollutant levels, whether from anthropogenic or natural sources, can pose serious health risks. This paper offers a comprehensive review and meta-analysis of urban AQ literature published in the region over the past decade (2013–June 2025). We aim to provide guidance and highlight key directions for future research in the field. This paper examines key pollutants, emission sources, implications of urban sources, and the most studied countries, methodologies, limitations, and recommendations from different case studies. Our analysis reveals a significant research gap highlighting insufficient recent literature. Saudi Arabia was the most studied country with 20 papers, followed by the broader Arabian Peninsula (sixteen), Qatar (twelve), the United Arab Emirates and Iraq (seven each), Kuwait (four), Oman (three), Jordan, and Bahrain (one each). The primary methods employed included measurements and sampling (28%) and remote sensing (24%), with a focus on pollutants such as dust (23.1%), NO_x/NO₂/NO (17.2%), PM_2.5 (17.6%), and PM₁₀ (12%). Industrial emissions (27%) and natural dust (24%) were identified as significant emission sources. Monitoring methods included grab sampling (19%), integrated sampling (34%), and continuous monitoring (47%). Notably, 13.3% of AQ sensors were linked to a station, 27.6% were self-referenced, and 59.1% did not specify calibration methods. The findings highlight the need for further research, regular calibration of air quality monitors, and the integration of advanced modeling approaches. Moreover, we recommend exploring the links between air pollution and urban development to ensure cleaner air and contribute to the global dialogue on sustainable and cross-border AQ solutions. Full article

There is a growing global emphasis on reducing environmental pollution through innovative clean energy technologies, with photovoltaic systems gaining prominence as a sustainable solution. This study presents an integrated approach, combining advanced architectural modeling and dynamic energy simulation to evaluate the utilization of rooftop photovoltaic panels on a high-density higher educational building in Saudi Arabia. Utilizing detailed modeling involving Autodesk Revit and energy simulation through DesignBuilder to Level of Detail 3, the research provides unprecedented accuracy, validated against actual energy consumption data with a remarkable 92.28% precision. Notably, approximately 60% of the rooftop area is identified as suitable for photovoltaic installation, demonstrating a significant capacity to generate 1,028,494.50 kWh annually, covering 61.7% of the building’s energy needs. Financial analysis reveals robust economic benefits, including annual savings of USD 74,938.84, a payback period of under 7 years, and lifetime savings exceeding USD 1.87 million over 25 years. Seasonal variations and surplus energy during winter months are also detailed, highlighting the system’s resilience. Importantly, this study aligns with Saudi Arabia’s “Vision 2030” by showcasing the feasibility and strategic importance of rooftop photovoltaic solutions in urban educational settings within hot-climate regions, offering a pioneering contribution to sustainable urban energy planning. Full article

In this study, typical haze pollution influenced by biomass burning (BB) activities in Changsha in the autumn of 2024 was investigated through the mixing state and evolution process of BB particles via the real-time measurement of single-particle aerosol mass spectrometry (SPAMS). From the clean period to the haze period, the PM_2.5 concentration increased from 25 μg·m⁻³ at 12:00 to 273 μg·m⁻³ at 21:00 on 12 October, and the proportion of total BB single particles in the total detected particles increased from 17.2% to 54%. This indicates that the rapid increase in PM_2.5 concentration was accompanied by a concurrent increase in the contribution of particles originating from BB sources. The detected BB particles were classified into two types based on their mixing states and temporal variations: BB1 and BB2, which accounted for 71.7% and 28.3% of the total BB particles, respectively. The analysis of backward trajectories and fire spots suggested that BB1 particles originated from straw burning emissions at northern Changsha, while BB2 particles were primarily related to local nighttime cooking emissions in Changsha. In addition, a special type of K-containing single particles without K cluster ions was found closely associated with BB1 type particles, which were designated as secondarily processed BB particles (BB-sec). The BB-sec particles contained abundant sulfate and ammonium signals and showed lagged appearance after the peak of BB1-type particles, which was possibly due to the aging and formation of ammonium sulfate on the freshly emitted particles. In all, this study provides insights into understanding the substantial impact of BB sources on regional air quality during the crop harvest season and the appropriate disposal of crop straw, including conversion into high-efficiency fuel through secondary processing or clean energy via biological fermentation, which is of great significance for the mitigation of local haze pollution. Full article

The global increase in municipal and industrial wastewater generation has intensified the need for ecologically resilient and technologically advanced treatment systems. Although traditional biological treatment technologies are effective for organic load reduction, they often fail to remove recalcitrant xenobiotics such as pharmaceuticals, synthetic dyes, endocrine disruptors (EDCs), and microplastics (MPs). Engineered microbial consortia offer a promising and sustainable alternative owing to their metabolic flexibility, ecological resilience, and capacity for syntrophic degradation of complex pollutants. This review critically examines emerging strategies for enhancing microbial bioremediation in wastewater treatment systems (WWTS), focusing on co-digestion, biofilm engineering, targeted bioaugmentation, and incorporation of conductive materials to stimulate direct interspecies electron transfer (DIET). This review highlights how multi-omics platforms, including metagenomics, transcriptomics, and metabolomics, enable high-resolution community profiling and pathway reconstructions. The integration of artificial intelligence (AI) and machine learning (ML) algorithms into bioprocess diagnostics facilitates real-time system optimization, predictive modeling of antibiotic resistance gene (ARG) dynamics, and intelligent bioreactor control. Persistent challenges, such as microbial instability, ARG dissemination, reactor fouling, and the absence of region-specific microbial reference databases, are critically analyzed. This review concludes with a translational pathway for the development of next-generation WWTS that integrate synthetic microbial consortia, AI-mediated biosensors, and modular bioreactors within the One Health and Circular Economy framework. Full article

Addressing the need to optimize human settlement quality in arid and semi-arid regions under rapid urbanization, this study innovatively constructs an evaluation framework integrating greenness, thermal conditions, impervious surfaces, water bodies, and air transparency. Focusing on 12 prefecture-level cities in Inner Mongolia, Northern China, it systematically reveals the spatial differentiation characteristics and driving mechanisms of human settlement quality. Findings indicate the following: (1) Regional human settlement quality exhibits a spindle-shaped structure dominated by the medium grade (Excellent: 18.13%, High: 23.34%, Medium: 46.48%, Low: 12.04%), with Ulanqab City having the highest proportion of Excellent areas (25.26%) and Ordos City the lowest proportion of Low-grade areas (6.20%), reflecting a critical transition period for regional quality enhancement. (2) Spatial patterns show pronounced east-west gradients and functional differentiation: western arid zones display significant blue-green space advantages but face high-temperature stress and rigid water constraints, eastern humid zones benefit from superior ecological foundations with weaker heat island effects, the core Hetao Plain experiences strong heat island effects due to high impervious surface density, while industrial cities confront prominent air pollution pressures. Consequently, implementing differentiated strategies—strengthening ecological protection/restoration in High/Low-grade zones and optimizing regulation to drive upgrades in Medium-grade zones—is essential for achieving three sustainable pathways: compact development, blue-green space optimization, and industrial upgrading, providing vital decision-making support for enhancing human settlement quality and promoting sustainable development in ecologically fragile cities across northern China. Full article

This study delivers a pioneering, high-resolution hydrogeochemical assessment of surface waters in the Upper Velhas and Upper Paraopeba river basins within Brazil’s Iron Quadrangle—an area of critical socioeconomic importance marked by intensive mining and urbanization. Through a dense sampling network of 315 surface water points (one every 23 km²), the research generates an unprecedented spatial dataset, enabling the identification of contamination hotspots and the differentiation between lithogenic and anthropogenic sources of potentially toxic elements (PTEs). Statistical methods, including exploratory data analysis and cluster analysis, were applied to determine background and anomalous concentrations of potentially toxic elements (PTEs). Geospatial distribution maps were generated using GIS. The results revealed widespread contamination by As, Cd, Cr, Ni, Pb, and Zn, with many samples exceeding Brazilian, European, and global drinking water standards. Arsenic and cadmium anomalies in rural and peri-urban communities raise concerns due to the direct consumption of contaminated water. The innovative application of dense spatial sampling and integrated geostatistical methods offers new insights into the pathways and sources of PTE pollution, identifying specific lithological units (e.g., gold schists, mafic intrusions) and land uses (e.g., urban effluents, mining sites) associated with elevated contaminant levels. By establishing robust regional geochemical baselines and source attributions, this study sets a new standard for environmental monitoring in mining-impacted watersheds and provides a replicable framework for water governance, environmental licensing, and risk management in similar regions worldwide. Full article

Air pollution poses a significant threat to public health, particularly in urban and industrialized regions. This study investigates the relationship between air quality and the frequency of Emergency Medical Service (EMS) calls in the Małopolska Voivodeship of Poland between 2020 and 2023. Data from over 190 air quality sensors (PM₁₀) were spatially aggregated using both hexagonal grids and administrative boundaries, while EMS call records were filtered to focus on cardiovascular and respiratory incidents. During 2020–2023, a total of 305,142 EMS calls were analyzed, and months with PM₁₀ exceedances showed an average of 1.50 respiratory calls per 1000 residents compared to 1.19 in months without exceedances. Statistical analyses, including Kolmogorov-Smirnov tests and Pearson correlation, were applied to explore temporal and spatial associations. Results indicate a statistically significant increase in EMS calls during periods of elevated air pollution, with the strongest correlation observed for respiratory-related incidents. Comparative analyses between high- and low-pollution municipalities supported the observed relationships. Further analysis indicated that the COVID-19 pandemic may have partially confounded these associations, particularly for respiratory cases, though significant patterns remained even after accounting for pandemic peaks. While limitations related to data gaps and seasonal biases exist, the findings suggest that real-time air pollution data could inform better EMS resource allocation. This research highlights the potential of integrating environmental data into public health strategies to improve emergency response and reduce health risks in polluted regions. Full article

Amphibians are particularly vulnerable to hydric stress due to their permeable skin, biphasic life cycle, and strong dependence on aquatic and moist terrestrial environments. In the Northwestern Mediterranean Basin—one of Europe’s most climate-sensitive regions—the intensification of droughts associated with climate change poses a critical threat to amphibian populations. Increased aridification, either due to higher temperatures or to more frequent, prolonged, and severe drought episodes, can affect both aquatic and terrestrial life stages, directly altering breeding opportunities, larval development, post-metamorphic survival, and dispersal capacity. This review aims to gather and synthesize current knowledge on the ecological, physiological, and demographic impacts of drought on amphibians of the Northwestern Mediterranean across habitat types, including ephemeral ponds, permanent water bodies, lotic systems, and terrestrial landscapes, including a final section on possible mitigation actions. Drought-induced shifts in hydroperiod can drastically reduce reproductive success and accelerate larval development with fitness consequences while, on land, desiccation risk and habitat degradation could limit access to refugia and fragment populations by reducing structural connectivity. These environmental constraints are compounded by the interactions between drought and emerging infectious diseases. We discuss the current knowledge on how chytrid fungi (Batrachochytrium dendrobatidis and B. salamandrivorans) and ranaviruses may respond to temperature and moisture regimes, and how drought may affect their transmission dynamics, host susceptibility, and pathogen persistence. In these cases, microbiome disruption, pollutant concentration, and increased contact rates between species may amplify disease outbreaks under dry conditions, but a better understanding of the multifactorial effects of drought on amphibian biology and disease ecology is needed for predicting species vulnerability, identifying high-risk populations, and guiding future conservation and management strategies in Mediterranean environments. Full article

Atmospheric pollution constitutes one of the key environmental challenges hindering Atmospheric pollution is a key environmental challenge constraining the sustainable development of Gansu Province’s land-based Belt and Road corridor and its regional ecological barrier function. The spatiotemporal heterogeneity of aerosol optical depth (AOD) profoundly impacts regional environmental quality. Based on MODIS AOD, NCEP reanalysis, and emission data, this study employed trend analysis (Mann–Kendall test) and attribution analysis (multiple linear regression combined with LMG and Spearman correlation) to investigate the spatiotemporal evolution of AOD over Gansu Province during 2009–2019 and its meteorological and emission drivers. Key findings include the following: (1) AOD exhibited significant spatial heterogeneity, with high values concentrated in the Hexi Corridor and central regions; monthly variation showed a unimodal pattern (peak value of 0.293 in April); and AOD generally declined slowly province-wide during 2009–2019 (52.8% of the area showed significant decreases). (2) Following the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (2014–2019), AOD trends stabilized or declined in 99.8% of the area, indicating significant improvement. (3) Meteorological influences displayed distinct regional-seasonal specificity—the Hexi Corridor (arid zone) was characterized by strong negative correlations with relative humidity (RH2) and wind speed (WS) year-round, and positive correlations with temperature (T2) in spring but negative in summer in the north; the Hedong region (industrial zone) featured strong positive correlations with planetary boundary layer height (PBLH) in summer (r > 0.6) and with T2 in spring/summer; and the Gannan Plateau (alpine zone) showed positive WS correlations in spring and weak positive RH2 correlations in spring/autumn, highlighting the decisive regulatory role of underlying surface properties. (4) Emission factors (PM_2.5, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{NH}}_4^{+}$, OM, and BC) dominated (>50% relative contribution) in 80% of seasonal scenarios, prevailing in most regions (Hexi: 71–95% year-round; Hedong: 68–80% year-round; and Gannan: 69–72% in spring/summer). Key components included BC (contributing > 30% in 11 seasons, e.g., 52.5% in Hedong summer), ${\mathrm{NO}}_3^{-}$ + ${\mathrm{NH}}_4^{+}$ (>57% in Hexi summer/autumn), and OM (20.3% in Gannan summer, 19.0% province-wide spring). Meteorological factors were the primary driver exclusively in Gannan winter (82%, T2-dominated) and province-wide summer (67%, RH2 + WS-dominated). In conclusion, Gansu’s AOD evolution is co-driven by emission factors (dominant province-wide) and meteorological factors (regionally and seasonally specific). Post-2013 environmental policies effectively promoted regional air quality improvement, providing a scientific basis for differentiated aerosol pollution control in arid, industrial, and alpine zones. Full article

The upper reaches of the Yangtze River face the challenge of balancing livestock development and ecological protection. As a significant livestock production region in China, optimizing the livestock ecological efficiency (LEE) of Sichuan Province (SP) is of strategic importance for regional sustainable development. Livestock carbon emissions and related pollution indices were utilized as undesirable output indicators within the super-efficiency SBM model to measure SP’s LEE over the 2010–2022 period. Kernel density estimation was combined with the Theil index to analyze spatiotemporal variation characteristics. A STIRPAT model was constructed to explore the influencing factors of SP’s LEE, and a grey forecasting GM (1,1) model was employed for prediction. Key findings reveal the following: (1) LEE increased by 25.9%, with high-efficiency regions expanding from 19.0% to 57.1%; (2) regional disparities persist, driven by labor redundancy and environmental governance gaps; (3) per capita GDP, industrial agglomeration, and technology advancement significantly promoted efficiency, while government subsidies and carbon intensity suppressed it. Projections show LEE reaching 0.923 by 2035. Key recommendations include the following: (1) implementing region-specific strategies for resource optimization, (2) restructuring agricultural subsidies to incentivize emission reduction, and (3) promoting cross-regional technology diffusion. These provide actionable pathways for sustainable livestock management in ecologically fragile zones. Full article

Picea neoveitchii Mast., a critically endangered spruce species endemic to China, is classified as a national second-level key protected wild plant and listed as critically endangered (CR) on the International Union for Conservation of Nature (IUCN) Red List. Its habitat features complex forest light environments, and global climate change coupled with environmental pollution has increased regional nitrogen deposition, posing significant challenges to its survival. This study explores the effects of light–nitrogen interactions on the leaf functional traits of Picea neoveitchii Mast. seedlings by simulating combinations of light intensities (100%, 70%, and 40% full sunlight) and nitrogen application levels (0, 10, and 20 g N·m ⁻²·a⁻¹, where g N·m⁻²·a⁻¹ denotes grams of nitrogen applied per square meter per year). We examined changes in morphological traits, anatomical structures, photosynthetic physiology, and stress resistance traits. Results indicate that moderate shading (70% full sunlight) significantly enhances leaf morphological traits (e.g., leaf length, leaf area, and specific leaf area) and anatomical features (e.g., mesophyll tissue area and resin duct cavity area), improving light capture and stress resistance. Medium- to high-nitrogen treatments (10 or 20 g N·m⁻²·a⁻¹) under moderate shading further increase photosynthetic efficiency, stomatal conductance, and antioxidant enzyme activity. According to the comprehensive membership function evaluation, the L2N0 (70% full sunlight, 0 g N·m⁻²·a⁻¹) treatment exhibits the most balanced performance across both growth and stress-related traits. These findings underscore the critical role of light–nitrogen interactions in the growth and adaptability of Picea neoveitchii Mast. leaves, offering a scientific foundation for the conservation and ecological restoration of endangered plant populations. Full article

Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R² > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R² improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management. Full article