Search Results (17,156)

Heavy metal(loid) (HM) contamination in soils from smelting activities poses significant environmental and public health risks, as well as disruptions in microbial community dynamics and HM resistance gene profiles. This study investigates the microbial diversity, resistome, and physicochemical properties of soils from the abandoned Avalos smelter in Chihuahua, Mexico. Through soil analyses, we identified elevated concentrations of certain HMs, which pose serious environmental and health hazards. The metagenomic analysis of the microbial community, composed of bacteria, archaea, and fungi, was dominated by genera such as Streptomyces, Bradyrhizobium, Halobaculum, Nitrosocosmicus, Fusarium, and Aspergillus in rhizospheric soil. Furthermore, a diverse array of metal resistance genes (MRGs) were detected, associated with copper, arsenic, iron, lead, cadmium, zinc, and other HMs. Additionally, metagenome-assembled genomes (MAGs) revealed the presence of functional genes linked to HM resistance, providing deeper insights into the ecological roles and metabolic capabilities of microbial taxa. These findings highlight the significant impact of smelting-derived contamination on microbial diversity and functional potential, offering valuable insights for the development of bioremediation strategies in HM-contaminated environments. Full article

Landfill leachate, characterized by its high concentration of organic matter (high COD), elevated ammonia and nitrogen levels, high salinity, and toxicity, poses a significant challenge for environmental pollution control. In recent years, extensive research efforts have been dedicated to treating landfill leachate, resulting in the implementation of various engineering technologies. However, with the advancement of analytical techniques, an increasing number of emerging contaminants (ECs) have been detected in landfill leachate. These pollutants pose potential environmental and health risks, yet traditional wastewater treatment technologies struggle to effectively remove them, necessitating innovative upgrades to existing methods. This paper reviews the current research status of landfill leachate treatment technologies, compares the advantages and disadvantages of various techniques, and emphasizes the importance of technological innovation in treatment processes. Full article

Nitrate pollution in drinking water is a major environmental and health issue. High levels of nitrates in water sources present serious risks to both the environment and public health, highlighting the need for immediate research and management efforts to reduce pollution sources and safeguard water resources for sustainable growth. This study investigates the elemental associations with nitrate concentrations in groundwater across the northeastern region of Saudi Arabia, employing diverse analytical techniques to assess water quality and develop sustainable management strategies. Spatial variations in nitrate levels were observed in both deep and shallow wells using GIS-based interpolation, revealing distinct patterns influenced by geological, hydrological, and anthropogenic factors. A strong linear correlation with a high coefficient of determination (R² of 0.99) between electrical conductivity and dilution factor suggests the potential interchangeability of ion-selective electrode methods and conductivity meters for EC determination. The study identified a positive correlation between nitrate concentration and electrical conductivity in groundwater samples (R² of 0.70), indicating that conductivity measurements could potentially serve as a proxy for estimating nitrate levels. However, a very weak negative correlation between nitrate and pH suggests other factors may have a more significant impact on groundwater pH. The research also highlights the strong positive correlation between nitrate and nitrate-nitrogen concentrations, reflecting their close chemical association in water. These findings contribute to the understanding of nitrate dynamics in groundwater and emphasize the importance of comprehensive water quality assessments. Future research should focus on elucidating factors influencing nitrate distribution in groundwater systems and developing more robust predictive models based on readily measurable water quality parameters. Full article

The separation via the extractive distillation of 1,3-butadiene from C₄ hydrocarbon mixtures is an essential step in synthetic rubber and plastic production. Conventional extractive distillation methods rely on solvents such as N,N-dimethylformamide (DMF) and N-methyl-2-pyrrolidone (NMP), which, despite their efficiency, pose significant environmental and health risks. This study investigates the feasibility of replacing these hazardous solvents with 1,2-propylene carbonate (PC), a greener alternative that aligns with REACH restrictions and CEFIC recommendations. The adoption of green solvents closely follows the UN’s Sustainable Development Goals (SDGs). Indeed, by using green solvents, industries reduce pollution, enhance worker safety, and minimize their environmental impact, contributing to multiple SDGs, and thus fostering sustainable economic growth. Advanced solvent screening methodologies, based on thermodynamic solution models (NRTL-RK) and quantum-based approaches (COSMO-RS), were employed to evaluate PC’s viability. Aspen Plus^® simulations were conducted to evaluate the industrial feasibility of PC in the 1,3-butadiene separation process. The results indicate that PC achieves comparable 1,3-butadiene separation efficiency while offering economic, operational, and environmental benefits. These findings underscore the importance of integrating sustainable solvents into industrial processes, reducing reliance on hazardous chemicals, improving compliance with evolving regulatory frameworks, and supporting sustainable industrial development. Full article

This study investigated the occurrence of fibrous microplastics and natural and artificial cellulose microfibers in the gastrointestinal tracts of Mullus barbatus and Merluccius merluccius specimens from the Adriatic Sea (Central Mediterranean), an important hotspot for marine litter accumulation. Red mullet and European hake were chosen due to their roles as bioindicators of marine pollution in the Mediterranean, and their economic relevance as fishery resources. Microfibers were found in 72% of M. barbatus and 68% of M. merluccius, at levels ranging from 1 to 67 particles/individual. Most of the microfibers extracted were textile fibers that were blue (33.6%), clear (26.1%), and black (20.3%) in color, while the length distribution showed the prevalence of microfibers in the size range of 350–950 µm. This visual identification, corroborated by the micro-FTIR analysis of a sub-sample of microfibers, revealed that natural and artificial cellulose microfibers were more common (80%) than fibrous microplastics. The results confirmed that both of these fish species are susceptible to microfiber ingestion and indicated the high availability of natural and artificial cellulosic fibers in the Adriatic Basin. Despite the increased evidence of microfiber pollution in the marine ecosystem, only a limited number of studies examine natural/artificial microfiber contamination and ingestion by marine biota. Therefore, greater attention should be given to this new type of contaminant, considering its implications in terms of environmental health, food security, and food safety. Full article

The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine learning model was introduced which classifies enzymes by their toxin degradation ability. In this model, two different sets of data were used which include enzymes that can catalyze the toxin degradation as a positive dataset and non-toxin-degrading enzymes as a negative dataset. Further, a comparison of multiple classifiers was performed to find the best model and a Random Forest (RF) classifier was selected due to its strong performance. To enhance the accuracy, we combined RF with a Deep Neural Network (DNN), forming an ensemble model which effectively integrated both techniques. This combination achieved 95% precision, surpassing individual models. Our ensemble model not only ensures high prediction accuracy but also reliably differentiates toxin-degrading enzymes from non-degrading ones. This study highlights the power of combining classical machine learning with deep learning to advance prediction. Our model represents a significant step in enzyme classification and serves as a valuable resource for environmental biotechnology, food nutrition, and health applications. Full article

The inadequate collection and treatment of urban wastewater continue to pollute built environments, threaten public health, and contribute to epidemic outbreaks in many densely populated, underdeveloped regions. This study investigates whether algae-based wastewater treatment offers an optimal and efficient solution for drought-prone and underdeveloped cities. Given recent global challenges, such as the COVID-19 pandemic, nature-based wastewater treatment methods—particularly algae-based systems—have regained attention due to their feasibility, cost-effectiveness, and sustainability. Algae-based wastewater treatment presents an innovative approach to sustainable urban development, offering environmental, resource-efficient, energy-saving, and biodiversity benefits while supporting circular economy principles. This study evaluates recent advancements in wastewater treatment technologies and applies a case study methodology to Zahedan City, analyzing sewage canal networks, wastewater composition, and treatment feasibility. Three algae-based techniques were assessed, with waste stabilization ponds (WSPs) identified as the most suitable solution based on technical, economic, and environmental indicators. Key factors such as climate conditions, land-use policies, and cost-effectiveness were incorporated into the comparative analysis, enhancing the scientific rigor of this study compared to prior research. The findings provide actionable insights for urban planners, engineers, and policymakers to address simultaneous challenges in wastewater management, public health, and water scarcity. Full article

This study investigated the predictive performance of three regression models—Gradient Boosting (GB), Random Forest (RF), and XGBoost—in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. Utilizing a dataset encompassing air pollution metrics and socio-economic indices, the models were trained and tested to evaluate their accuracy and robustness. Performance was assessed using metrics such as coefficient of determination ($r^2$), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability ($r^2$ = 0.55, MAE = 0.17, and RMSE = 0.05). To further interpret the results, SHAP (SHapley Additive exPlanations) analysis was applied to the best-performing model to identify the most influential features driving mortality predictions. The analysis highlighted the critical roles of specific pollutants, including benzene and socio-economic factors such as life quality and instruction, in influencing mortality rates. These findings underscore the interplay between environmental and socio-economic determinants in health outcomes and provide actionable insights for policymakers aiming to reduce health disparities and mitigate risk factors. By combining advanced machine learning techniques with explainability tools, this research demonstrates the potential for data-driven approaches to inform public health strategies and promote targeted interventions in the context of complex environmental and social determinants of health. Full article

Nitrate pollution in aquatic environments poses significant environmental and public health issues, mostly due to industrial activities and agricultural runoff. Biological denitrification, the favored method for removing nitrates, typically needs an external carbon source to support microbial processes. Traditional electron donors like methanol, ethanol, and acetate are effective but introduce economic, environmental, and operational challenges such as cost variability, flammability hazards, and excessive residual organic material. Recently, solid-phase carbon sources—like biodegradable polymers and organic agricultural waste—have shown promise as alternatives because they allow for controlled carbon release, improved safety, and enhanced long-term sustainability. This review systematically examines the performance of solid-phase carbon in wastewater denitrification by analyzing peer-reviewed studies and experimental data. The findings suggest that solid-phase carbon sources, including polycaprolactone (PCL) and polyhydroxyalkanoates (PHA), offer stable and extended carbon release, ensuring consistent denitrification effectiveness. Nonetheless, challenges remain, including optimizing biofilm development, balancing carbon availability, and reducing operational costs. Furthermore, the review emphasizes the potential for integrating machine learning in process optimization and highlights the need for more research to enhance the economic viability of these materials. The findings confirm the practicality of solid-phase carbon sources for extensive wastewater treatment and their capability to sustainably address nitrate contamination. Full article

In the context of increasingly severe global climate change, both companies and investors are placing greater emphasis on investment philosophies centered around environmental protection, social responsibility, and corporate governance (ESG). This paper, based on data from 847 Chinese A-share listed companies over the period 2007–2022, employs a two-way fixed effects model to investigate the relationship between ESG controversies and firm investment efficiency. The results indicate that ESG controversies significantly reduce overall firm investment efficiency. Further analysis reveals that ESG controversies affect investment efficiency by exacerbating agency costs and reducing audit quality. Meanwhile, financing constraints and robust internal control quality mitigate these negative effects. Heterogeneity analysis shows that the impact is more pronounced for firms with higher pollution levels, non-state-owned enterprises, those with higher analyst coverage, and firms with lower levels of digitalization. The findings have significant implications for encouraging companies to fulfill their social responsibilities and promote high-quality economic development. Full article

Micro- and nanoplastics (MPs and NPs) are pervasive environmental pollutants detected in aquatic ecosystems, with emerging evidence suggesting their presence in airborne particles generated by water body motion. Inhalation exposure to airborne MPs and NPs remains understudied despite documented links between occupational exposure to these particles and adverse respiratory outcomes, including airway inflammation, oxidative stress, and chronic respiratory diseases. This study explored the effects of acute NP exposure on a fully differentiated 3D human airway epithelial model derived from 14 healthy donors. Airway epithelium was exposed to aerosolized 50 nm polystyrene NPs at concentrations ranging from 2.5 to 2500 µg/mL for three minutes per day over three days. Functional assays revealed no significant alterations in tissue integrity, cell survival, mucociliary clearance, or cilia beat frequency, suggesting intact epithelial function post-exposure. However, cytokine and chemokine profiling identified a significant five-fold increase in CCL3 (MIP-1α), a neutrophilic chemoattractant, in NP-exposed samples compared to controls. This was corroborated by increased neutrophil chemotaxis in response to conditioned media from NP-exposed tissues, indicating a pro-inflammatory neutrophilic response. Conversely, levels of interleukins (IL-21, IL-2, IL-15), CXCL10, and TGF-β were significantly reduced, suggesting immunomodulatory effects that may impair adaptive immune responses and tissue repair mechanisms. These findings demonstrate that short-term exposure to NP-containing aerosols induces a distinct pro-inflammatory response in airway epithelium, characterized by enhanced neutrophil recruitment and reduced secretion of key immune modulators. These findings underscore the potential for aerosolized NPs to induce oxidative and inflammatory stress, raising concerns about their long-term impact on respiratory health and redox regulation. Full article

The field of bio-nanotechnology has seen significant advancements in recent years, particularly in the synthesis and application of bio-nanoparticles (BNPs). This review focuses on the green synthesis of BNPs using biological entities such as plants, bacteria, fungi, and algae. The utilization of these organisms for nanoparticle synthesis offers an eco-friendly and sustainable alternative to conventional chemical and physical methods, which often involve toxic reagents and high energy consumption. Phytochemicals present in plant extracts, unique metabolic pathways, and biomolecules in bacteria and fungi, and the rich biochemical composition of algae facilitate the production of nanoparticles with diverse shapes and sizes. This review further explores the wide-ranging applications of BNPs in various fields like therapeutics, fuel cells, energy generation, and wastewater treatment. In therapeutics, BNPs have shown efficacy in antimicrobial, anti-inflammatory, antioxidant, and anticancer activities. In the energy sector, BNPs are being integrated into fuel cells and other energy generation systems like bio-diesel to improve efficiency and sustainability. Their catalytic properties and large surface area enhance the performance of these devices. Wastewater treatment is another critical area where BNPs are employed for the removal of heavy metals, organic pollutants, and microbial contaminants, offering a cost-effective and environmentally friendly solution to water purification. This comprehensive review highlights the potential of bio-nanoparticles synthesized through green methods. It highlights the need for further research to optimize synthesis processes, understand mechanisms of action, and expand the scope of their applications. BNPs can be utilized to address advantages and some of the pressing challenges in medicine, energy, and environmental sustainability, paving the way for innovative and sustainable technological advancements in future prospects. Full article

Aging is a multifactorial process influenced by genetic predisposition and lifestyle choices. Environmental exposures are too often overlooked. Environmental pollutants—ranging from airborne particulate matter and heavy metals to endocrine disruptors and microplastics—accelerate biological aging. Oxidative stress is a major molecular initiating event, driving inflammation and toxicity across biological levels. We detail the mechanisms by which pollutants enhance reactive oxygen species (ROS) production. This oxidative stress inflicts damage on DNA, proteins, and lipids, accelerating telomere shortening, dysregulating autophagy, and ultimately driving epigenetic age acceleration. For instance, exposure to polycyclic aromatic hydrocarbons, benzene, and pesticides has been associated with increased DNA methylation age. Early-life exposures and lifestyle factors such as tobacco and alcohol consumption further contribute to accelerated biological aging. The cumulative loss of healthy life years caused by these factors can conceivably reach between 5 and 10 years per person. Addressing pollutant-induced accelerated aging through regulatory measures, lifestyle changes, and therapeutic interventions is essential to mitigate their detrimental impacts, ultimately extending healthspan and improving quality of life in aging populations. Full article

This study evaluates the impact of phosphogypsum stacks on the chemical composition of rainwater in the Huelva metropolitan area, a metal-polluted area with high cancer and heart disease mortality rates. A total of 612 rainwater samples were collected using 17 rain gauges located around the study area between January 2021 and December 2022. The pH, conductivity, major ions, and trace metals were detected in the soluble fraction of rainwater. The results revealed spatial variability in the rainwater quality. The highest values of As, Ca²⁺, Cr, F⁻, NH₄⁺, Ni, PO₄³⁻, SO₄²⁻, Sr, and V were detected in rain-gauges near phosphogypsum stacks, exceeding the levels of pH, F⁻, and Ni according to the guideline values for drinking water quality from the WHO. Additionally, other pollution sources also contributed: a regional source (marine factors: Ca²⁺, Cl⁻, K⁺, Mg²⁺, and Na⁺) and a local source (chemical complexes emissions: Co, Cu, Pb, and Zn). A downward trend of most toxic metal(loid) concentrations in wet depositions was detected as the distance to the affected area increased. The findings revealed that phosphogypsum stacks are a relevant source of metal(loid)s with potentially adverse environmental and public health effects that, if replicated, could be relevant for environmental monitoring and policy making. Full article

Background/Objectives: Chronic Obstructive Pulmonary Disease (COPD) is one of the most common chronic non-communicable diseases in adults. The most critical risk factors are tobacco and air pollution. The familial aggregation of this disease and the fact that only 15–20% of smokers develop COPD demonstrate the existence of an individual susceptibility that would depend on genetic factors. The already-known susceptibility genomic variants explain only about 38% of the heritability of COPD. The present work analyzes the relationship between the percentage of Amerindian genomic ancestry of Chileans with morbidity and mortality of Chronic Obstructive Pulmonary Disease (COPD), adjusting for socioeconomic and environmental variables. Methods: We rely on the estimates of genomic ancestry percentages obtained in the Chilegenomico project in urban Chileans from 39 communes along eight regions of the country from north to south. From the public databases of the Departamento de Estadísticas e Información en Salud (DEIS) of the Chilean Ministry of Health, we obtained mortality rates and hospital discharge rates. We incorporated adjustment variables (communal data) obtained from other public databases. We performed correlation analyses and fitted negative binomial regression models to examine the association between Amerindian ancestries and COPD statistics. Results: A positive and significant association between Mapuche ancestry and hospital discharge and mortality rates for COPD was found in both simple and multiple analyses. In contrast, we found a negative and significant association between the percentage of Aymara genomic ancestry and COPD mortality rates. Conclusions: The levels of Mapuche and Aymara genomic ancestries have different and contrasting significant associations with COPD susceptibility and mortality in the Chilean mixed population. Full article