Search Results (4,022)

Microalgae-based wastewater treatment systems are an environmentally friendly technology for reuse of polluted water produced in livestock farming. Since pollution removal depends on light availability, the performance should be evaluated under different seasonal conditions, even in reduced lab scale systems. This study evaluates the treatment of livestock digestate in an experimental High-Rate Algae Pond (HRAP) that recreates outdoor conditions. Chemical and biological pollution removal were analyzed, as well as the response of photosynthetic activity of the culture. Pollutant removal varied between seasons, while summer was characterized by higher nitrogen and phosphorus removal (81 and 69%, respectively), on the other hand, winter presented higher elimination of organic matter (91%) and pathogens. In this sense, P. aeruginosa removal was notably higher in winter (100%) than in summer (50%). Higher light penetration and increased photosynthetic efficiency in winter, along with greater fluctuations in pH and dissolved oxygen concentrations, contributed to higher levels of pathogen decay. Photosynthetic response tests indicated higher oxygen production per unit biomass in winter, suggesting physiological adaptations to lesser light conditions. This adaptation was correlated with the relative high pH and dissolved oxygen values registered. The findings highlight the adaptation and robustness of algae cultures as a solution for wastewater treatment and reuse in the primary sector. Full article

To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM₁₀ and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management. Full article

Worldwide, the healthcare industry produces massive quantities of medical waste (MW), most of which is incinerated, releasing large quantities of dioxins, mercury, and other pollutants. Despite this, only a limited number of studies have explored the incorporation of MW into construction materials, with a special focus on cement-based construction materials (CB-CMs). However, to the best of the authors’ knowledge, no existing review formally structures, summarizes, correlates, and discusses the findings of previous studies on MW in CB-CMs to encourage further research and applications of this promising alternative. Therefore, the added value of this study lies in providing an innovative and critical analysis of existing research on the use of MW in CB-CMs, consolidating and evaluating dispersed findings through a systematic literature review, enhancing understanding of the topic, and identifying knowledge gaps to guide future research. A robust systematic literature review was conducted, encompassing 40 peer-reviewed research articles, retrieved from the Web of Science Core Collection database. The methodology involved a three-stage process: a descriptive analysis of the included articles, the identification and synthesis of key thematic areas, and a critical evaluation of the data to ensure a rigorous and systematic report. The selection criteria prioritized peer-reviewed research articles in English with full text availability published in the last 7 years, explicitly excluding conference papers, book chapters, short reports, and articles not meeting the language or accessibility requirements. The results indicate that the influence of MW in CB-CM varies significantly. For example, while the incorporation of face masks as fiber reinforcement in concrete generally enhances its mechanical and durability properties, the use of gloves is less effective and not always recommended. Finally, it was found that further research is needed in this field due to its novelty. Full article

Potentially toxic elements (PTEs) are the most important indicators of environmental pollution and represent a potential risk to the ecology and human health in industrial regions. Eight potentially toxic elements (Mn, Ni, Co, Cr, Pb, Zn, Cd, As) in soils formed on the territory of the industrial site of the Udachny Mining and Processing Division were considered in this study. The potential ecological risk index (RI) was calculated to determine environmental risks of soil contamination. The concentrations of PTEs decreased in the following order Mn > Ni > Zn > Co > Pb > Cr > As > Cd. In total, 19.51% of the sites in the study area exhibited a high potential ecological risk for Mn and Ni, while only 4.87% exhibited a low potential ecological risk for other PTEs. The greatest impacts on soil contamination are exerted by the areas of the Udachny and Zarnitsa pipes, tailings ponds, and the area’s highly mineralized water outlet. The results of correlation analysis (CA) and hierarchical cluster analysis (HCA) revealed that the same groups of elements were present: Co-Cr-Ni and Cd-Zn. The PMF findings demonstrate that the five main diverse sources of PTEs in this study area’s soils were natural, mining activities, transportation, and industrialization, as well as highly mineralized waters. Full article

Chronic kidney disease (CKD) is a noteworthy global health issue affecting 10% of the world’s populace. It is increasingly linked to environmental exposures; however, the interplay of toxic metals, per- and polyfluoroalkyl substances (PFAS), and essential elements has not been fully elucidated. This cross-sectional study analyzed 5800 out of the 9245 participants from the 2017–2018 NHANES dataset to evaluate the combined effect of PFAS, essential elements, and toxic metals on CKD using logistic regression and advanced environmental mixture models, namely, Bayesian Kernel Machine Regression (BKMR), quantile g-computation (qgcomp), and Weighted Quantile Sum (WQS) regression. Our results showed cadmium (Cd) emerging as a significant contributor to CKD (OR = 2.16, p = 0.023) from the logistic regression analysis. Mercury (Hg) demonstrated the highest contribution in mixtures (posterior inclusion probability = 0.908) from our BKMR analysis, with a non-linear U-shaped dose–response relationship. Essential elements like selenium (Se) and manganese (Mn) exhibited protective correlations but complex non-linear interactions, moderating toxic metal effects from our qgcomp and WQS regression. Notably, antagonistic interactions between essential elements and some pollutants reduced the overall mixture impact on CKD, showing an overall decreasing joint effect of the combined PFAS, toxic metals, and essential elements on CKD, from the 25th to the 75th quantile. This study highlights the role of environmental co-exposures in CKD risk and highlights the need for advanced statistical and machine learning approaches in studying complex environmental mixture interactions on human health. Full article

This study systematically investigates the dynamic evolution of chemical regimes in pulsed coaxial dielectric barrier discharge (DBD) plasma under atmospheric pressure using mass spectrometry. An innovative real-time mass spectrometric monitoring methodology was established, enabling the dynamic tracking of the formation and consumption processes of key reactive species such as ozone (O₃) and nitrogen oxides (NO_x). Energy density was the critical parameter governing the evolution of gaseous chemical components, with a quantitative elucidation of the regulatory mechanisms of air flow rate and control voltage on plasma chemical regime transition kinetics. Experimental results revealed significant parametric correlations: Under a constant control voltage of 140 V, increasing the gas flow rate from 0.5 to 5.5 L/min prolonged the transition duration from O₃-NO_x coexistence regime to a NO_x-dominant regime from 408 s to 1210 s. Conversely, at a fixed flow rate of 3.5 L/min, elevating the control voltage from 120 V to 140 V accelerated this transition, reducing the required time from 2367 s to 718 s. Parametric sensitivity analysis demonstrated that control voltage exerts approximately 3.3 times greater influence on transition kinetics than flow rate variation. Through comprehensive analysis of the formation and consumption mechanisms of N, O, O₃, and NO_x species, we established a complete plasma chemical reaction network. This scheme provides fundamental insights into reaction pathways while offering practical optimization strategies for DBD systems. For aerospace applications, this work holds particular significance by demonstrating that the identified control parameters can be directly applied to plasma-assisted treatment of propellant wastewater at launch sites, where the efficient removal of nitrogen-containing pollutants is crucial. These findings advance both the fundamental understanding of atmospheric-pressure plasma chemistry and the engineering applications of plasma-based environmental remediation technologies in aerospace operations. Full article

Understanding the spatiotemporal distribution of air pollution is critical for improving urban air quality. Advances in wireless sensor networks have made it possible to monitor air pollution across cities at higher spatiotemporal resolutions. The new spatial coverage allows the novel implementation of advanced statistical methods to detect spatially important, coherent patterns in environmental flows. In this study, we apply proper orthogonal decomposition to a spatial distribution derived from 34 particulate matter sensors, which collected data over 250 days across the Liverpool City Region in England, to identify a set of spatially orthogonal modes. The dominant mode exhibits a daily periodicity in the increases of particulate matter, with higher increases in residential areas interpreted as changes driven by daily commutes. The second mode highlights seasonal changes, and the third mode alludes to pollution transportation with simultaneous increases and decreases. In contrast with traditional time series and spatial analyses, proper orthogonal decomposition enables the elucidation of patterns that otherwise might remain hidden. Our findings highlight the benefits of urban wireless sensor networks and demonstrate the applicability of proper orthogonal decomposition in studying the movements of polluted areas and their correlations with meteorological variables and anthropogenic factors. Full article

This paper was concerned with individual difference correlates of preferences for three issues associated with ethical investing. Five hundred adults completed a long, 60-item, questionnaire concerning personal details, including demographic (sex, age, education) and ideological (political and religious beliefs), as well as a three-part measure of their investment attitudes: what investments to avoid, what general issues to consider when investing and what people issues to consider when investing. The results indicated that they most wanted to avoid investments concerning weapons, animal testing and fossil fuels. The most important issues when investing were thought to be pollution, deforestation and carbon footprint, which all have at heart the sustainability philosophy. With regards to workers, they noted child labour, wages and worker rights as the most important issues. Correlations showed relatively few demographic correlates, but there were a number of religious belief and political attitude correlates of investment preferences. The strongest relationship was between political beliefs and anything associated with global warming. Implications and limitations are acknowledged, in particular with respect to having rank-order data and not knowing important information about the respondents. Full article

This study investigates the photocatalytic degradation of methylene blue (MB) using strontium-doped SnO₂ nanofibers synthesized via electrospinning. The 1% Sr-doped SnO₂ nanofibers exhibited remarkable photocatalytic activity, achieving 84.74% MB degradation under visible light irradiation, substantially outperforming both undoped SnO₂ nanofibers (61%) and the same catalyst under UV light (69%) under identical experimental conditions. Comprehensive electrochemical investigations revealed that Sr doping fundamentally transformed interfacial charge transfer kinetics, with 1% Sr-doped nanofibers exhibiting a remarkable three-fold decrease in charge transfer resistance (404 Ω compared to 1350 Ω for undoped samples), a dramatic enhancement in charge carrier density (5.17 × 10²² versus 9.24 × 10¹⁹ for undoped samples), and an approximately eight-fold increase in diffusion coefficient (8.78 × 10⁻¹⁰ versus 1.13 × 10⁻¹⁰ cm²s⁻¹). These electrochemical improvements were corroborated by comprehensive structural characterization, which demonstrated that strategic Sr incorporation induced beneficial oxygen vacancies, reduced crystallite size, increased microstrain, and enhanced dislocation density, collectively contributing to superior surface reactivity and accelerated photocatalytic mechanisms. This work establishes a quantitative correlation between electrochemical characteristics and photocatalytic activity in Sr-doped SnO₂ nanofibers, revealing the fundamental mechanisms that transform the SnO₂ nanostructure from UV-dependent to efficient visible light-driven catalysts for organic pollutant degradation. Full article

The long-term nature of and heterogeneity in industrialization has led to high formaldehyde (HCHO) concentrations with seasonal and regional variation in North China, and this is highly influenced by changes in meteorological and population conditions. Here, we analyzed the spatial and temporal distribution characteristics of tropospheric HCHO VCD (vertical column density) and their key drivers in North China from 2019 to 2023 based on the HCHO daily dataset from TROPOMI. The results showed that the spatial distribution of tropospheric HCHO VCD in North China presented similar variation characteristics in the past 5 years, with the highest in the center, followed by the east and the lowest in the west. Seasonal variations were characterized, with the highest tropospheric HCHO VCD concentrations in summer and the lowest ones in spring. In addition, the effects of meteorological elements on HCHO VCD were analyzed based on the ERA5 dataset, and the correlation of HCHO VCD with temperature and wind was strong. In contrast, the correlation with precipitation and surface solar radiation was low, and the effects were different between the growing and non-growing seasons (the growing season, i.e., March–November, is defined as the period when the plant or a part of it actually grows and produces new tissues, while the non-growing season refers to December–the following February). Population density is directly proportional to tropospheric HCHO VCD. In this study, a higher-resolution spatial and temporal distribution model of tropospheric HCHO VCD in North China is obtained based on TROPOMI, which effectively characterizes the driving factors of HCHO VCD. Our study provides an important reference for developing of air pollution control measures in North China. Full article

Major global gulfs are essential for economic development but remain highly vulnerable to environmental pollutants. Despite progress in gulf environmental research, the extent of research investment in gulf environments across different regions worldwide remains unclear, which hinders the development of a unified global framework for bay environmental management. We aim to fill this gap by integrating GIS and Python-based methods to identify the ten largest gulfs globally and conducting a statistical analysis of research publications from 2000 to 2024 for these gulfs, based on the Web of Science core database. We find that the publication numbers show a correlation with the size of the gulf. However, the Gulf of Mexico and the Gulf of St. Lawrence show higher publication volumes, likely influenced by economic activities and major environmental incidents. In contrast, regions such as Hudson Bay and the Gulf of Carpentaria receive relatively less research attention. This suggests that scientific output in gulf regions may be attributed to economic activities and significant environmental events. Water quality research predominates, while sediment studies, particularly in high-latitude gulf areas (such as Hudson Bay), account for the lowest proportion, possibly due to sampling costs and challenges. Traditional pollutants, especially heavy metals (HMs) and persistent organic pollutants (POPs), are the primary focus of research. The investigation of emerging contaminants reveals significant regional disparities, emphasizing the necessity for further research and enhanced regulatory frameworks. This study provides scientific evidence for the unified governance of gulf environments. Full article

The assessment of air pollution is an important and relevant issue that requires continuous monitoring and control, especially in urban spaces. However, using instrumental air quality measurement techniques and deploying meters throughout the city is extremely expensive, so a biological alternative can be used—a bioindicator, i.e., a species whose vital functions or morphological structure can reveal the qualitative state of the environment. In this work, the lichen Hypogymnia physodes L. was used to analyze air pollution in areas of the provincial city of Opole, southern Poland. Microscope and chemotaxonomy methods were used in the laboratory to confirm field identification of lichens (atlases and keys). The selected elements, Mn, Fe, Ni, Cu, Zn, Cd, and Pb, were determined using atomic absorption spectrometry, and direct mercury analyzer was used to analyzed Hg concentration. Factor analysis (FA) was performed to associate elements with possible sources of air pollution. The highest concentrations of analytes were found at measurement points close to railway roads (Fe = 5131 mg/kg) and streets with heavy traffic (Pb = 101 mg/kg). Statistically significant differences (p < 0.001) were found between the concentrations of individual elements, which have positive correlation coefficients higher than 0.65. Based on the research carried out, different anthropogenic and traffic-related activities can be considered as one of the main sources of air pollution in Opole City based on the results of FA. Using an additional lichen scale, it can be concluded that the areas surveyed in the town of Opole can be classified as zone IV—characterized by an increase in the number of leaf lichens (additionally co-occurring lichens of the Polycauliona candelaria species), i.e., an area with an average level of air pollution (based also on contamination factor [CF] and pollution load index [PLI]). Accumulation concentrations of heavy metals in lichen were metal-specific and varied spatially, thus reflecting local differences in heavy metal deposition. The research presented here proves that low-cost passive biomonitoring can effectively support classical methods of assessing air pollution in urban spaces. Full article

This study addresses the need for effective, real-time monitoring of indoor air quality, a critical factor for health and environmental well-being. The aim is to develop an affordable, Arduino-based IoT sensor system capable of continuous measurement of key air pollutants, including CO₂, PM_2.5, NO₂, and VOCs. The system integrates multiple sensors and transmits data to an online server, where it is stored in a MySQL database for analysis and visualization. Validation studies conducted at Concordia University and Qatar University confirm the system’s accuracy and reliability, with discrepancies reduced to under 15% through calibration and adjustment techniques. Comparative analysis with commercial monitoring instruments reveals strong correlations and negligible deviations, supporting the system’s validity for real-time air quality monitoring. The system also includes a user-friendly interface that displays real-time data through intuitive charts and tables, along with an indoor air quality index to help users assess and address air pollution levels. The system demonstrates a 90% cost reduction versus commercial tools while maintaining a mean deviation of <15% across climatic extremes. Its combination of comprehensive sensors, data visualization tools, and an air quality index makes it an effective tool for environmental monitoring and decision-making. Full article

With the acceleration of urbanization, the diffusion mechanism of urban non-point source (NPS) pollution caused by extreme rainfall is not clear, which leads to high cost and difficulty in water environment treatment. In view of the shortcomings of dynamic diffusion simulations of mesoscale pollution, this paper proposes a simulation framework based on cellular automata, GIS geographic technology, and a two-dimensional shallow water model. Taking the 500 m × 500 m grid as the unit, we explore the migration laws of nitrogen and phosphorus pollutants and the response relationship between pollutant diffusion and land use under extreme rainfall scenarios. The results show that (i) the pollution risk increases significantly with diffusion, with the maximum pollution load in high-risk areas increasing by 181%, and the diffusion rate is positively correlated with the rate of change in rainfall intensity; (ii) forest land has the highest grid pollution load loss rate, whereas the water grid has the highest accumulation rate; (iii) this method can accurately identify the hot spots of pollution diffusion, providing a basis for the precise control of high-risk areas. This study can support the targeted governance of pollution sources and land planning optimization in urban storm and flood management, and help reduce environmental health risks in extreme climates. Full article

The vegetation restoration of contaminated sites plays a critical role in ensuring the sustained stability and functional integrity of natural ecosystems. However, during the natural revegetation process, the variations in habitat conditions, bacterial community structure, and metabolic functions in aged, polluted soil are still unclear. In the present study, we investigated aged, polycyclic aromatic hydrocarbon (PAH)-polluted soils at closed, abandoned oil well sites from the Yellow River Delta. Using gene amplification and real-time qPCR methods, the abundance, taxonomy, and diversity characteristics of indigenous bacterial communities and functional bacteria carrying C12O genes in both vegetated soils and bare soils were investigated. The results show that natural revegetation significantly changes the physicochemical parameters, PAH content, and bacterial community structure of aged, PAH-polluted soils. When comparing the abundance and components of PAH-degrading bacterial communities in vegetated and bare soils, the PAH-degrading potential was revealed to be stimulated by vegetation communities. Through correlation analysis, dual stress from soil salinity and PAH contamination in bacterial communities was revealed to be mediated through alterations in the soil’s physicochemical properties by local vegetation. The network analysis revealed that bacterial communities in vegetated soils have higher network connectivity. These results elucidate the alterations in habitat conditions, bacterial components, and PAH-degrading communities following vegetation restoration, providing critical insights for optimizing ecological rehabilitation strategies in salinized and contaminated ecosystems. Full article