Search Results (18,512)

This pilot study investigated an automated pilot plant for removing microplastics (MPs) from industrial wastewater that are generated during packaging production. MP removal is based on organosilane-induced agglomeration-fixation (clump & skim technology) followed by separation. The wastewater had high MP loads (1725 ± 377 mg/L; 673 ± 183 million particles/L) and an average COD of 7570 ± 1339 mg/L. Over 25 continuous test runs, the system achieved consistent performance, removing an average of 97.4% of MPs by mass and 99.1% by particle count, while reducing the COD by 78.8%. Projected over a year, this equates to preventing 1.7 tons of MPs and 6 tons of COD from entering the sewage system. Turbidity and photometric TSS measurements proved useful for process control. The approach supports water reuse—with water savings up to 80%—and allows recovery of agglomerates for recycling and reuse. Targeting pollutant removal upstream at the source provides multiple financial and environmental benefits, including lower overall energy demands, higher removal efficiencies, and process water reuse. This provides financial and environmental incentives for industries to implement sustainable solutions for pollutants and microplastic removal. Full article

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article

The accurate and effective prediction of PM2.5 concentrations is crucial for mitigating air pollution, improving environmental quality, and safeguarding public health. To address the challenge of strong temporal correlations in PM2.5 concentration forecasting, this paper proposes a novel hybrid model that integrates the Particle Optimization Algorithm (POA) and Variational Mode Decomposition (VMD) with the Long Short-Term Memory (LSTM) network. First, POA is employed to optimize VMD by adaptively determining the optimal parameter combination [k, α], enabling the decomposition of the original PM2.5 time series into subcomponents while reducing data noise. Subsequently, an LSTM model is constructed to predict each subcomponent individually, and the predictions are aggregated to derive hourly PM2.5 concentration forecasts. Empirical analysis using datasets from Beijing, Tianjin, and Tangshan demonstrates the following key findings: (1) LSTM outperforms traditional machine learning models in time series forecasting. (2) The proposed model exhibits superior effectiveness and robustness, achieving optimal performance metrics (e.g., MAE: 0.7183, RMSE: 0.8807, MAPE: 4.01%, R²: 99.78%) in comparative experiments, as exemplified by the Beijing dataset. (3) The integration of POA with serial decomposition techniques effectively handles highly volatile and nonlinear data. This model provides a novel and reliable tool for PM2.5 concentration prediction, offering significant benefits for governmental decision-making and public awareness. Full article

Plastic pollution in marine environments poses a new global threat. Microplastics (MPs) can bioaccumulate in marine organisms, leading to oxidative stress (OS). This study investigates MP accumulation and associated OS responses in six invertebrate species (Bivalvia, Gastropoda, and Malacostraca) and three key fish species of the Bulgarian Black Sea ecosystems. The target hydrobionts were collected from nine representative coastal habitats of the northern and southern aquatory. MPs were quantified microscopically, and OS biomarkers (lipid peroxidation, glutathione, and antioxidant enzymes) were analyzed spectrometrically in fish liver and gills and invertebrate soft tissues (STs). The specific OS (SOS) index was calculated as a composite indicator of the ecological impact, incl. MP effects. The results revealed species-specific MP bioaccumulation, with the highest concentrations in Palaemon adspersus, Rathke (1837) (0.99 ± 1.09 particles/g ST) and the least abundance in Bittium reticulatum (da Costa, 1778) (0.0033 ± 0.0025 particles/g ST). In Sprattus sprattus (Linnaeus, 1758), the highest accumulation of MPs was present (2.01 ± 2.56 particles/g muscle). The correlation analyses demonstrated a significant association between MP counts and catalase activity in all examined species. The SOS index varied among species, reflecting different stress responses, and this indicated that OS levels were linked to ecological conditions of the habitat and the species-specific antioxidant defense potential to overcome multiple stressors. These findings confirmed the importance of environmental conditions, including MP pollution and the evolutionarily developed capacity of marine organisms to tolerate and adapt to environmental stress. This study emphasizes the need for novel approaches in monitoring MPs and OS to better assess potential ecological risks. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

Chemical fertilizers pose significant risks to both human health and the environment. To investigate the effect of substituting nitrogen fertilizer with vermicompost tea on wheat yield, shoot chemical constituents, and grain quality under clay-loam soil conditions, two field experiments were conducted at the Faculty of Agriculture, Cairo University, Egypt, during the winter seasons of 2021–2022 and 2022–2023. A split-plot design in randomized complete blocks with three replications was employed. Vermicompost tea was assigned to the main plots, while wheat cultivars were assigned to the subplots. The cultivars were evaluated under four treatments involving partial substitution of mineral nitrogen (recommended dose of nitrogen (RDN%, 190 kg N ha⁻¹): a control (90% of RDN + 25 kg vermicompost tea), 80% of RDN + 37.5 kg vermicompost tea, and 70% of RDN + 50 kg vermicompost tea. Nitrogen fertilizer (RDN%) was applied at rates of 190 (control), 170 (90%), 150 (80%), and 130 (70%) kg N ha⁻¹. The results indicated that partially substituting mineral nitrogen with vermicompost tea significantly increased grain weight/Ha, chlorophyll A, chlorophyll B, carotenoids, nitrogen, phosphorus (P), and potassium (K) content in shoots, as well as ash, crude protein, crude fiber, total sugar, and N, P, and K content in wheat grains. The grain weight/Ha of the Sakha-95, Giza-171, and Sads-14 cultivars increased by 38.6%, 33.5%, and 39.3%, respectively, when treated with 70% RDN + 50 kg vermicompost tea. The combination of the Sads-14 cultivar and 70% RDN + 50 kg vermicompost tea resulted in the highest values for grain weight/ha (9.43 tons ha⁻¹), chlorophyll A (1.39 mg/g), chlorophyll B (1.04 mg/g), N (5.08%), P (1.63%), and P (2.43%) content in shoots. The same combination also improved ash (2.89%), crude fiber (2.84%), and K (6.05%) content in grains. In conclusion, the application of vermicompost tea in conjunction with chemical fertilizers offers a viable alternative to using chemical fertilizers alone, promoting sustainable agricultural practices and improving wheat production. It is recommended that mineral nitrogen fertilizer be partially replaced with vermicompost tea to enhance both the productivity and grain quality of wheat while minimizing environmental pollution. Full article

Complex wastewater matrices hinder the efficacy of conventional treatment methods due to the presence of various inorganic and organic pollutants, along with their intricate interactions. Leveraging the synergy between algae and bacteria, algal–bacterial symbiosis (ABS) systems offering an evolutionary and highly effective approach. The ABS system demonstrates 10–30% higher removal efficiency than conventional biological/physicochemical methods under identical conditions, especially at low C/N ratios. Recent advances in biology techniques and big data analytics have deepened our understanding of the synergistic mechanisms involved. Despite the system’s considerable promise, challenges persist concerning complex pollution scenarios and scaling it for industrial applications, particularly regarding system design, environmental adaptability, and stable operation. In this review, we explore the current forms and operational modes of ABS systems, discussing relevant mechanisms in various wastewater treatment contexts. Furthermore, we examine the advantages and limitations of ABS systems in treating complex wastewater matrices, highlighting challenges and proposing future directions. Full article

The two-spotted spider mite, Tetranychus urticae, is one of the polyphagous pests of several crops and forestry, resistant to numerous conventional chemicals. Due to the negative side effects of harmful chemical pesticides, such as environmental pollution, and risks to human health, the introduction of effective and low-risk alternatives is essential. The promising pesticidal effects of essential oils (EOs) isolated from Artemisia annua have been documented in recent studies. In the present study, the acaricidal effects of an A. annua EO, along with its two dominant monoterpenoids, 1,8-cineole and camphor, were investigated against adults of T. urticae. Artemisia annua EO, 1,8-cineole, and camphor, with 24 h-LC₅₀ values of 0.289, 0.533, and 0.64 µL/L air, respectively, had significant toxicity by fumigation against T. urticae adults. Along with lethality, A. annua EO and monoterpenoids had significant inhibitory effects on the activity of detoxifying enzymes, including α- and β-esterases, glutathione S-transferases, and cytochrome P-450 monooxygenase. According to the findings of the present study, A. annua EO and its two dominant monoterpenoids, 1,8-cineole and camphor, with significant toxicity and inhibitory effects on detoxifying enzymes, can be introduced as available, effective, and eco-friendly acaricides in the management of T. urticae. Full article

Degradable fluorescent sensors present a promising portable approach for heavy metal ion detection, aiming to prevent secondary environmental pollution. Additionally, the excessive intake of ferric ions (Fe³⁺), an essential trace element for human health, poses critical health risks that urgently require effective monitoring. In this study, we developed a thermally degradable fluorescent hydrogel sensor (Eu-CDs@DPPG) based on europium-doped carbon dots (Eu-CDs). The Eu-CDs, synthesized via a hydrothermal method, exhibited selective fluorescence quenching by Fe³⁺ through the inner filter effect (IFE). Embedding Eu-CDs into the hydrogel significantly enhanced their stability and dispersibility in aqueous environments, effectively resolving issues related to aggregation and matrix interference in traditional sensing methods. The developed sensor demonstrated a broad linear detection range (0–2.5 µM), an extremely low detection limit (1.25 nM), and rapid response (<40 s). Furthermore, a smartphone-assisted LAB color analysis allowed portable, visual quantification of Fe³⁺ with a practical LOD of 6.588 nM. Importantly, the hydrogel was thermally degradable at 80 °C, thus minimizing environmental impact. The sensor’s practical applicability was validated by accurately detecting Fe³⁺ in spinach and human urine samples, achieving recoveries of 98.7–108.0% with low relative standard deviations. This work provides an efficient, portable, and sustainable sensing platform that overcomes the limitations inherent in conventional analytical methods. Full article

Background/Objectives: This study examines how seasonality, pollution, and sample type (water and sediment) influence the presence and distribution of antibiotic resistance genes (ARGs), with a focus on antibiotic resistance genes (ARGs) located on plasmids (the complete set of plasmid-derived sequences, including ARGs) in a tropical urban river. Methods: Samples were collected from three sites along a pollution gradient in the Virilla River, Costa Rica, during three seasonal campaigns (wet 2021, dry 2022, and wet 2022). ARGs in water and sediment were quantified by qPCR, and metagenomic sequencing was applied to analyze chromosomal and plasmid-associated resistance profiles in sediments. Tobit and linear regression models, along with multivariate ordination, were used to assess spatial and seasonal trends. Results: During the wet season of 2021, the abundance of antibiotic resistance genes (ARGs) such as sul-1, intI-1, and tetA in water samples decreased significantly, likely due to dilution, while intI-1 and tetQ increased in sediments, suggesting particle-bound accumulation. In the wet season 2022, intI-1 remained low in water, qnrS increased, and sediments showed significant increases in tetQ, tetA, and qnrS, along with decreases in sul-1 and sul-2. Metagenomic analysis revealed spatial differences in plasmid-associated ARGs, with the highest abundance at the most polluted site (Site 3). Bacterial taxa also showed spatial differences, with greater plasmidome diversity and a higher representation of potential pathogens in the most contaminated site. Conclusions: Seasonality and pollution gradients jointly shape ARG dynamics in this tropical river. Plasmid-mediated resistance responds rapidly to environmental change and is enriched at polluted sites, while sediments serve as long-term reservoirs. These findings support the use of plasmid-based monitoring for antimicrobial resistance surveillance in aquatic systems. Full article

This research examines the compounding risks of global environmental change, including climate change, environmental law, biodiversity loss, and pollution, which threaten the stability of economic systems worldwide. While digital technology and global governance regulation are increasingly being proposed as solutions, their synergistic potential in advancing economic sustainability has been less explored. How can these technologies mitigate environmental risks while promoting sustainable and equitable development, aligning with the Sustainable Development Goals? We analyze policy global environmental data from the World Bank and the United Nations, as well as literature reviews on digital interventions, artificial intelligence, and smart databases. Global environmental change presents economic stability and rule of law threats, and innovative governance responses are needed. This study evaluates the potential for digital technology to be leveraged to enhance climate resilience and regulatory systems and address key implementation, equity, and policy coherence deficits. Policy recommendations for aligning economic development trajectories with planetary boundaries emphasize that proactive digital governance integration is indispensable for decoupling growth from environmental degradation. However, fragmented governance and unequal access to technologies undermine scalability. Successful experiences demonstrate that integrated policies, combining incentives, data transparency, and multilateral coordination, deliver maximum economic and environmental co-benefits, matching digital innovation with good governance. We provide policymakers with an action plan to leverage technology as a multiplier of sustainability, prioritizing inclusive governance structures to address implementation gaps and inform legislation. Full article

Detection of soil pollution by petroleum products is necessary to remedy threats to economic and human health. Pollution by hydraulic oil often occurs through leaks from forestry machinery such as harvesters. Electronic noses equipped with gas sensor arrays are promising tools for applications of pollution detection and monitoring. A self-made, low-cost electronic nose was used for differentiation between clean and polluted samples, with two types of oils and three levels of pollution severity. An electronic nose uses the TGS series of gas sensors, manufactured by Figaro Inc. Sensor responses to changes in environmental conditions from clean air to measured odor, as well as responses to changes in sensor operation temperature, were used for analysis. Statistically significant response results allowed for the detection of pollution by biodegradable oil, while standard mineral oil was difficult to detect. It was demonstrated that the TGS 2602 gas sensor is most suitable for the studied application. LDA analysis demonstrated multidimensional data patterns allowing differentiation between sample categories and pollution severity levels. Full article

Nano- and microplastic pollution, together with the ongoing rise in global temperatures driven by climate change, represent increasingly critical environmental challenges. Although these stressors often co-occur in the environment, their combined effects on plant systems remain largely unexplored. To test the hypothesis that their interaction may exacerbate the effects observed under each stressor individually, we investigated the response of seedlings of Triticum turgidum to treatments with fluorescent polystyrene nanoplastics under optimal (25 °C) and elevated (35 °C) temperature conditions. We evaluated seedling growth, photosynthetic pigment content, and oxidative stress markers using both biochemical and histochemical techniques. In addition, we assessed enzymatic and non-enzymatic antioxidant responses. The use of fluorescently labeled nanoplastics enabled the visualization of their uptake and translocation within plant tissues. Elevated temperatures negatively affect plant growth, increasing the production of proline, a key protective molecule, and weakly activating secondary defense mechanisms. Nanoplastics disturbed wheat seedling physiology, with these effects being amplified under high temperature conditions. Combined stress enhances nanoplastic uptake in roots, increases oxidative damage, and alters antioxidant responses, reducing defense capacity in leaves while triggering compensatory mechanisms in roots. These findings underscore a concerning interaction between plastic pollution and climate warming in crop plants. Full article

Effective management of fecal pollutants in rural sanitation is crucial for environmental health and public safety, especially in developing regions. In this study, temporal and regional variations in nutrient elements, heavy metals, pathogenic microorganisms (PMs), and antibiotic resistance genes (ARGs) of fecal samples from rural toilets in China were investigated. The moisture contents of the fecal samples average 92.7%, decreasing seasonally from 97.4% in summer to 90.6% in winter. The samples’ pH values range from 6.5 to 7.5, with a slight decrease in winter (6.8), while their electrical conductivity varies from 128.1 to 2150 μs/cm, influenced by regional diets. Chromium (9.0–49.7 mg/kg) and copper (31.9–784.4 mg/kg) levels vary regionally, with higher concentrations in Anhui and Guangxi Provinces due to dietary and industrial factors. Zinc contents range from 108.5 to 1648.9 mg/kg, with higher levels in autumn and winter, resulting from agricultural practices and Zn-containing fungicides, posing potential health and phytotoxicity risks. Seasonal and regional variations in PMs and ARGs were observed. Guangxi Province shows the high PM diversity in summer samples, while Jiangsu Province exhibits the high ARGs types in autumn samples. These findings highlight the need for improved waste management and sanitation solutions in rural areas to mitigate environmental risks and protect public health. Continued research in these regions is essential to inform effective sanitation strategies. Full article

Improving green innovation quality is a critical component for speeding green transformation and generating high-quality growth. This study examines the link between the pilot zone for green finance reform and innovations (PZGFRI) policy and the quality of green innovation in Chinese A-share listed firms from 2010 to 2020. This study demonstrates that the PZGFRI may greatly enhance the quality of enterprises’ green innovation. Additionally, by promoting environmental investment and reducing financial barriers, we use the mediating effect model to confirm that the PZGFRI improves the enterprises’ quality of green innovation. Meanwhile, the heterogeneity analysis demonstrates that the PZGFRI is more successful in raising the green innovation quality in state-owned, large-sized, and heavily polluting businesses. Our study’s findings offer a strong theoretical basis for improving the PZGFRI and encouraging businesses to undergo high-quality transformation. Full article