Search Results (3,564)

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

Quantifying the large-scale impact of combined ozone (O₃) and nitrogen dioxide (NO₂) pollution on terrestrial carbon sinks remains a major challenge. Here, we develop a parsimonious yet robust empirical framework that leverages high-resolution remote sensing datasets (CHAP O₃/NO₂ and MODIS NPP, 2008–2021) to characterize nonlinear threshold responses of terrestrial net primary productivity (NPP) across China’s diverse ecosystems. Our observational analysis identifies only associative temporal relationships between annual NPP variability and pollutant concentrations, with NPP positively correlated with O₃ (Pearson’s r = 0.714, p < 0.01) and negatively correlated with NO₂ (r = −0.599, p < 0.05). Notably, the ecosystem-specific threshold values (O₃: 28,324–34,391 μg m⁻³ yr⁻¹; NO₂: 3646–4968 μg m⁻³ yr⁻¹) are statistically derived from spatially aggregated pixel-level records across the full 14-year period, independent of the national annual time-series correlation analyses. Distinct from previous single-pollutant national evaluations, our study advances a novel analytical framework focusing on the interactive and combined impacts of O₃ and NO₂ co-exposure. The results demonstrate that NPP displays an increasing trend under low-level pollutant exposure but declines substantially once pollutant loads exceed the identified threshold ranges. Based on K-means clustering and segmented regression analyses, we estimate a national average NPP reduction of 17.4% per year (−0.68 Pg C yr⁻¹), resulting in a cumulative carbon loss of −9.48 Pg C over the 14-year study period—equivalent to 2.45 years of China’s total terrestrial carbon uptake. Among all ecosystem types, forestlands experience the largest cumulative carbon loss (−4.22 Pg C), with prominent loss hotspots concentrated on the Tibetan Plateau and Northwest China. This refined national-scale assessment of dual-pollutant impacts provides observation-based evidence of substantial terrestrial carbon sink degradation, underscoring the necessity of combined air pollution mitigation strategies to sustain ecosystem stability and climate mitigation targets. Full article

Microplastic contamination in coastal environments has become an increasing environmental concern due to its persistence, widespread distribution, and potential ecological impacts. However, comprehensive assessments that integrate microplastic abundance, polymer composition, and pollution indices for both coastal waters and sediments remain limited in the southern Black Sea. Therefore, this study presents an integrated assessment of microplastic contamination in surface waters and coastal sediments from eleven locations along the southern Black Sea coast by combining particle characterization, polymer identification, and pollution assessment. The mean microplastic abundance was determined as 138.6 ± 65.1 MP/L in surface waters and 195.8 ± 91.9 MP/kg dry weight in sediments. Fragments were the dominant particle type in both environmental compartments, where black and transparent particles constituted the most abundant colour categories, while smaller size fractions (<1000 µm) predominated in surface waters. ATR-FTIR analysis identified polyethylene and ethylene-vinyl acetate as the dominant polymers, followed by polyethylene terephthalate, acrylic polymers, polyvinyl stearate, and poly(α-methyl styrene). Spatial differences were evident among the sampling stations, with the highest microplastic abundance in surface water observed at Station S₁₁ and the highest sediment abundance at Station S₁, reflecting the influence of local anthropogenic activities and environmental conditions. Pollution index results revealed spatial variability among sampling locations, indicating differences in microplastic accumulation patterns and potential contamination levels associated with local anthropogenic pressures and coastal activities. Overall, the predominance of secondary microplastics and the spatial variation in pollution indices provide baseline information for future monitoring and support management strategies aimed at reducing plastic pollution along the southern Black Sea coast. Full article

The study presents an integrated assessment of water quality in wadis of the Bizerte Lagoon catchment (Northern Tunisia), using benthic diatom assemblages. Seasonal sampling was conducted between winter 2023 and winter 2024 in three wadis exposed to varying anthropogenic pressures. In each wadi, upstream and downstream stations were surveyed to assess spatial variability. Diatom assemblages showed clear responses to both environmental gradient and seasonal variation. Pollution-tolerant taxa, particularly Navicula veneta, Gomphonema parvulum, and Nitzschia inconspicua, were mainly recorded during winter in downstream stations of wadis most impacted by nutrient enrichment and organic pollution. halophilic and marine-related taxa, including Gyrosigma kuetzingii, Navicula salinarum, Navicula simulata, Halamphora coffeaeformis and Haslea ostrearia, dominated during warmer seasons in the high-conductivity wadi. Pollution-sensitive taxa, such as Encyonema minutum, Cymbella affinis and Gomphonema affine, were restricted to the least disturbed wadi. Diatom-based indices confirmed the predominance of hypertrophic conditions in all wadis and revealed a clear gradient of environmental degradation, with ecological status ranging from moderate to poor and bad quality. These findings highlight the strong potential of benthic diatoms and their ecological indices as valuable tools for monitoring eutrophication and salinization gradients in Mediterranean coastal wadis, although their adaptation and calibration to Tunisian freshwater ecosystems still require further validation. Full article

The global plastic pollution crisis demands a fundamental re-evaluation of materials systems beyond incremental improvements to fossil fuel-based polymers. Bioplastics, polymers derived from renewable biological feedstocks, biodegradable under defined conditions, or both, offer a chemically diverse and rapidly evolving platform for transitioning toward circular materials economies in which the value of carbon, energy, and material is retained across multiple use cycles. This review provides a comprehensive and critically organized account of the bioplastics field, spanning three generations of feedstock development from food crops through lignocellulosic residues to algae and waste streams; primary production pathways including microbial fermentation, ring-opening polymerization, and biosynthesis; forming processes from extrusion and injection molding to additive manufacturing; and the mechanical, thermal, and barrier properties that determine application fitness. Particular emphasis is placed on life cycle assessment, which reveals that bioplastics’ climate benefits are conditional on feedstock choice, land-use management, energy source at manufacturing, and end-of-life pathway, and that burden-shifting from greenhouse gas emissions to land use, water consumption, and eutrophication is a systematic risk requiring integrated LCA evaluation rather than single-metric optimization. The review further examines end-of-life recycling, composting, and biodegradation pathways; market applications across packaging, agriculture, automotive, biomedical, and electronics sectors; and the growing role of artificial intelligence and machine learning in accelerating materials design, process optimization, and lifecycle data management. Critical barriers to scale, such as cost premiums of 20–75% over conventional plastics, inadequate composting infrastructure, recycling stream contamination, regulatory fragmentation, and consumer labeling confusion, are systematically analyzed alongside mitigation strategies. The review concludes with a forward-looking discussion of emerging feedstocks, smart and functional bioplastics, and the policy and infrastructure investments required to translate the environmental promise of bio-based polymers into realized circular economy impact. Full article

Air pollution poses a major health risk, particularly in children, who spend considerable time in school. We monitored air pollution in school environments and investigated its impact on cardiovascular and neurocognitive outcomes. Seven elementary schools in Antwerp, Belgium, including one open-air school, participated in a longitudinal study. Indoor and outdoor particulate matter (PM) and outdoor NO₂ concentrations were measured. Endothelial function and attentional outcomes were assessed repeatedly (n = 3) in 138 healthy children (mean age of 10.3 ± 0.5 years, 55.8% male). Mixed effect models were used to investigate the association between PM and NO₂ exposure and endothelial and attentional outcomes. This study considered median PM concentrations during the 4 h of the study visits (direct exposure), those during the 24 h (acute exposure) and 1 week preceding the study visits (recent exposure), and monthly NO₂ concentrations. Models were adjusted for sex, age, BMI-z, and visit. Increased direct (4 h) indoor PM exposure was associated with reduced microvascular dilatation, while selective and sustained attention improved with direct and recent PM exposure, respectively. No associations were found with NO₂. Exclusion of the open-air school slightly altered the findings. Larger longitudinal studies with improved exposure assessment are needed to clarify the effects of PM exposure and the influence of (open-air) school environments on children’s learning and development. Full article

Air travel induces substantial landside traffic through the movement of passengers, employees, suppliers, and cargo between airports and their surrounding regions. While this airport-induced landside traffic has received growing attention within airport sustainability research, its associated external costs remain insufficiently quantified. This study develops a modular model to estimate traffic volumes and associated external costs of airport-induced landside traffic. It accounts for key behavioral and operational parameters, including modal split, trip distances, occupancy rates, and trip frequencies, differentiated across user groups and transport modes. The model is applied to Germany as a case study using national mobility statistics, airport data, and external cost factors from European transport studies. The assessment covers greenhouse gas emissions, air pollution, accidents, noise, habitat damage, and upstream fuel supply impacts. Results indicate that airport-induced landside traffic generated external costs of approximately EUR 1.43 billion in Germany in 2019, with passengers and airport employees accounting for the largest shares. Accident costs and greenhouse gas emissions dominate the overall impacts. Sensitivity analyses further show that moderate behavioral changes, such as modal shifts toward public transport and increased vehicle occupancy, can significantly reduce external costs. The findings highlight the importance of integrating landside access into environmental assessments and sustainable airport planning. Full article

In crisis times, traditional models of water quality assessment and water resources management lose their effectiveness. In the current conditions of local climate change, accidental pollution, emergencies or military operations, there is an urgent need to transition from traditional descriptive hydrochemical monitoring toward intelligent analysis of spatial-temporal datasets. In this paper, the integrated approach combining spatial cluster analysis, GIS-based visualization, and machine learning is proposed for assessing the surface water quality under conditions of limited and incomplete hydrochemical monitoring data. A geospatial assessment of nutrient impacts on surface water quality was conducted using 192 hydrochemical observations collected during the 2024–2025 monitoring period at eight state monitoring stations located in the basins of the Teteriv, Uzh, Irsha, Ubort, Sluch, Hnylopiat, and Voznia rivers, Polissia, Ukraine. Permutation feature importance analysis based on the Random Forest model showed that nitrate concentration accounted for approximately 75% of the total relative importance, whereas phosphate concentration contributed approximately 14%, indicating that these variables were the most informative predictors among the investigated hydrochemical parameters. The latter parameters are associated with dissolved oxygen variability among the analyzed hydrochemical parameters. According to the results of this study, three interpretable groups of monitoring stations were formed: Cluster 1, representing moderate water quality with increased nutrient pressure, Cluster 2, representing comparatively favourable background conditions, and Cluster 3, representing a nitrate-dominated hydrochemical type. The Random Forest model demonstrated limited predictive performance (R² = 0.154), indicating that nutrient-related variables alone explain only a small proportion of dissolved oxygen variability. Hence, additional factors, including hydrological conditions, water temperature, organic matter decomposition, biological productivity, and catchment-specific characteristics, also play an important role in shaping oxygen dynamics. The spatial visualization of cluster membership showed that geographical location alone does not fully determine the surface water quality patterns in Ukrainian Polissia. Instead, the local catchment characteristics and land-use conditions appear to exert a stronger influence on the formation of nutrient-related water quality differences. The authors propose to employ the spatial cluster analysis and machine learning as a basic supporting tool for the transition from retrospective interpretation of hydrochemical monitoring data to predictive and adaptive water resources management. The integration of geospatial analysis and machine learning provides a practical decision-support framework for the early detection of anomalies, identification of potential pollution sources, and prioritization of river sub-basins for implementing nature-based solutions. Full article

Environmental sustainability of natural and synthetic fibers used in textiles and composites depends on their impacts throughout production, use, and end-of-life (EoL) stages. Natural fibers are renewable and biodegradable but may require substantial water and agricultural inputs, whereas synthetic fibers contribute to fossil resource depletion, microplastic pollution, and persistent waste generation. Natural fibers are often regarded as more sustainable alternatives to synthetic fiber; however, evidence from a life cycle assessment (LCA) reveals a more nuanced reality. As demand for fiber-based materials increases across textile and composite applications, a deeper understanding of the environmental implications of both natural and synthetic options becomes essential. This review compares these fiber categories from a life cycle perspective, examining carbon footprint, energy demands, resource consumption, and EoL pathways. Natural fibers such as cotton, flax, jute, hemp, sisal, banana, coir, and emerging plant-based alternatives offer advantages including biodegradability and carbon sequestration during cultivation. Nevertheless, agricultural practices and subsequent industrial processing require substantial land, water, and chemical inputs. Synthetic fibers, predominantly derived from fossil resources, provide a long service life and consistent performance but are associated with high greenhouse gas (GHG) emissions, dependence on non-renewable feedstocks, microplastic pollution, and broader environmental impacts. By presenting a comprehensive life cycle-based comparison, this review identifies the conditions under which each fiber type may offer environmental benefits, supporting informed material selection for sustainable development. Full article

Artificial intelligence (AI) data centers are creating large, power-dense loads, often faster than transmission lines, substations, transformers, and grid interconnections can be expanded. This review assesses whether solid oxide fuel cells (SOFCs) can provide dependable on-site power during these grid delivery constraints and remain competitive after grid capacity becomes available. We critically synthesized evidence on AI electricity demand, competing power supply options, SOFC efficiency and durability, commercial deployments, environmental impacts, thermal and electrical integration, and hybrid SOFC–battery–grid systems. We also performed a screening-level levelized cost of electricity sensitivity analysis covering natural gas prices, carbon costs, stack replacement, grid electricity prices, and the avoided cost of delayed grid access. The evidence indicates that commercial SOFC systems can achieve approximately 50–60% net electrical efficiency and scale modularly from 325 kW units to a planned deployment of up to 2.45 GW. A nominal 100 MW installation would require approximately 308 such modules and at least 3600 m² of direct equipment area, excluding auxiliary systems and safety setbacks. However, multi-year durability targets of about 40,000 h, fuel and carbon price exposure, slow transient response, lifecycle methane emissions, and limited opportunities to use high temperature exhaust heat remain important constraints. The economic analysis indicates that avoided grid delay costs can justify SOFCs as bridge assets, whereas long-term retention requires competitiveness without this temporary benefit. SOFCs are therefore most suitable for sites that prioritize rapid access to firm power, modularity, reliability, and low local air pollutant emissions, rather than as a universal alternative to grid expansion. Full article

Concerns regarding poor air quality in communities experiencing disproportionately high levels of air pollution frequently motivate formation of community–academic partnerships. Low-cost air monitors can assess air pollution but require specific training. The RISE Communities program was created to support partnerships in air quality training and community-engaged research. The RISE Communities program in-person training took place in Cincinnati, OH, in summers 2023 and 2024. The training included hands-on workshops, lectures, and monthly webinars with continued expert guidance for one year following the in-person training. A mixed method evaluation included pre-, post-, and one-year follow-up surveys, webinar evaluation surveys, and focus groups at the conclusion of each cohort. Participants reported significantly increased confidence in describing the health impacts of air pollution and using low-cost air sensors. Subjective feedback commented on the need for more tailored and interactive webinars and data training. Participants agreed that the goals of the RISE Communities training program were met and skills were sustained. This mixed method evaluation study demonstrates that an immersive training program for academic and community partners resulted in significantly higher confidence levels related to community partnership, data analytics, data visualization, and achieving project planning outcomes. Full article

Nonylphenol (NP), a typical emerging pollutant, is widely detected in water bodies, yet its hepatotoxic mechanisms and mitigation strategies remain underexplored. This study evaluated the protective effects of a mixed probiotic (Lactobacillus acidophilus and Bacillus subtilis) against NP-induced hepatotoxicity in Silurus meridionalis by integrating transcriptomic and metabolomic analyses, with validation by RT-qPCR and ELISA. The results showed that NP exposure disrupted the arachidonic acid (AA) pathway (activating pro-inflammatory COX and LOX pathways while the suppressing anti-inflammatory CYP450 branch), promoted ferroptosis via iron dyshomeostasis and oxidative damage, and impaired triglyceride (TG) synthesis. Probiotic pretreatment reversed these toxic effects by modulating AA metabolism, suppressing COX (ptgs2a↓ → PGE₂↓) and LOX (alox12↓ → MDA↓) pathways, while upregulating the CYP450 pathway (cyp2j↑ → 11,12-EET↑). Probiotics also enhanced Fe³⁺ sequestration (steap4↑) and antioxidant defense (CAT↑, gpx4a↑), limiting Fenton reaction-mediated oxidative injury, and restored hepatic TG synthesis through upregulation of the DHAP-to-TG cascade (DHAP—(gpd1b↑) → G3P↑—(gpat3↑) → LPA—(agpat6↑) → PA—(plpp7↑) → DAG↑—(dgat2↑) → TG↑). These findings suggest that NP induces hepatotoxicity in S. meridionalis, involving ferroptosis, AA metabolism disruption and impaired TG synthesis as interconnected pathological events. Probiotics likely counteract this toxicity through systemic metabolic reprogramming. Collectively, these findings elucidate the hepatotoxic mechanisms of NP in S. meridionalis and offer toxicological data for its risk assessment, supporting the potential of probiotic-based strategies to mitigate emerging pollutant impacts in aquatic organisms. Full article

This study evaluates whether existing long-term monitoring data are sufficient to distinguish natural background concentrations from anthropogenic influences on chemical river water quality in Lake Sevan basin. A comprehensive dataset covering physicochemical parameters, nutrients, and trace metals was analyzed for nine major tributaries with different geological settings and land-use characteristics. Multivariate statistical analysis was applied to identify baseline conditions and deviations attributable to human activities. The results indicate that water chemistry is primarily controlled by lithology and hydrological regime, particularly in minimally impacted headwater regions. In contrast, elevated concentrations of nutrients (e.g., nitrate and phosphate) and selected trace elements were associated with agricultural runoff, urban discharge, and localized industrial inputs. Spatial patterns reveal clear gradients of increasing anthropogenic impact downstream and in densely populated sub-basins. The study also demonstrates that, while the current monitoring network is suitable for assessing the overall chemical status of rivers, it is less effective in defining natural background levels and quantifying individual pollution sources due to limited upstream reference conditions. Overall, this approach provides a scientific basis for improved water quality management and policy implementation in the Lake Sevan basin. The findings highlight the importance of integrating long-term monitoring data with statistical tools to support sustainable watershed management in vulnerable catchments. Full article

Microplastics are plastic particles smaller than 5 mm that have become a growing environmental concern due to their persistence and widespread distribution. They originate from diverse sources, including laundry detergents, vehicle tires, cosmetic microbeads, and the degradation of larger plastics through ultraviolet radiation and saline exposure. This systematic review was conducted following the PRISMA guidelines to summarize current knowledge on the environmental and human health effects of microplastics. A comprehensive literature search was performed in ScienceDirect, PubMed, Web of Science, and Google Scholar databases, yielding 2694 initial records. After applying exclusion criteria and removing duplicates, 111 studies were selected for full reading, and 54 articles were ultimately included in the analysis. The results reveal that microplastics can absorb and release pollutants, leading to the contamination of water and soil and enabling them to enter the food chain, thereby posing potential risks to both ecosystems and human health. However, significant discrepancies were found among the databases regarding the amount and quality of available data, highlighting the need for standardized research approaches. In conclusion, understanding the sources, distribution, and impacts of microplastics is crucial to developing strategies to mitigate their release, and further research is essential to assess their long-term effects and to guide environmental policy. Full article

Tropical coastal ecosystems are highly productive but increasingly threatened by anthropogenic pressures, making environmental monitoring tools essential to determine the health status of the environment. In this study, the health status of three sites along the Pacific coast of Panama (Río Santa María, Cuchilluyo, and Montijo) was assessed using the bivalve mollusc Anadara tuberculosa as a sentinel species. A multilevel biomarker approach was employed, combining chemical analyses with biochemical and histological biomarkers. Metal concentrations in whole soft tissues were generally low-to-moderate, except for arsenic, and polycyclic aromatic hydrocarbon (PAH) concentrations were low in all sites, being dominated by low-molecular-weight compounds. Biomarker responses were consistent with the chemical data, with cockles from Montijo exhibiting lower glutathione S-transferase (GST) activity and reduced oxidative DNA damage. Histological analyses revealed only mild alterations, including hemocytic infiltration in the connective tissue of digestive glands and atrophy, with no major reproductive impairments. Overall, results indicate low to moderate pollution levels in the studied areas, with Montijo representing the least impacted site. The study supports the suitability of A. tuberculosa as a sentinel species and the relevance of a multilevel biomarker approach for assessing environmental health in tropical coastal ecosystems. Full article