Journal of Environmental Science and Health Part A-toxic\/hazardous Substances & Environmental Engineering最新文献

Caffeine (CAF) is widely detected in aquatic environments, serving as an indicator of anthropogenic contamination. Its high consumption, and persistence raise environmental concerns. This study was to evaluate the chronic effects in terms of growth rate, weight, behavior, and biochemical parameters of environmental concentrations of CAF on adult zebrafish. Adult zebrafish were exposed, for 30 d, to 0, 0.5, 1.5, and 300 µg L^-1 CAF, with behavior (feeding latency, exploration, aggression, sociability, sound response) and biochemical endpoints (acetylcholinesterase (AChE), lactate dehydrogenase (LDH), and cortisol levels) assessed at the end of the exposure. CAF 0.5 µg L^-1 increased feeding latency time, while 300 µg L^-1 reduced growth and weight. Exposure to CAF affect fish behavior in terms of vertical exploration, aggressiveness, shoaling, and sound responses although were concentration specific. All concentrations tested increased social behavior, with fish swimming closer to the shoal. At a biochemical level, CAF exposed showed reduced AChE activity, while LDH activity, and cortisol levels increased at 300 µg L^-1. Low concentrations of CAF caused neurotoxicity in zebrafish which may compromise their feeding behavior, and social interactions in the wild. These changes suggest potential ecological impacts of chronic exposure to CAF, such as impaired feeding and stress responses.

Two-chamber microbial fuel cell (MFC) with biogas slurry (BS) of corn stover as the anode substrate and Chlorella as the cathode substrate was investigated to solve the problem of the accumulation of wastewater generated from biogas plants and to achieve low-cost separation of CO₂ from biogas. A simple two-compartment MFC was constructed using biocatalysis and inexpensive materials without expensive catalysts. The performance of MFC (X1-W, Y1-W, Z1-W) with different biogas solution concentrations as anode substrate and MFC (X2-C, Y2-C, Z2-C) with Chlorella as biocathode were compared, respectively. The MFCs (Z1-W,) can start quickly and maintain a stable power production (286.82 mV ± 184.59 mV). The growth rate of Chlorella at the MFCs (X2-C, Y2-C, Z2-C) biocathode was highly coincident with the output voltage. The MFC (Z2-C) has a maximum power density of 489.7 mW/m² when the external resistance is varied to 200 Ω. The removal rates of chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N) are 93.42% and 92.59%. The maximum cell growth (X_max) of Chlorella was 125.61 mg d^-1, biomass productivity (P_max) was 95.60 g L^-1 d^-1 and the maximum CO₂ biofixation rate (R_CO2) was 175.26 mg L^-1 d^-1. The microbial community analysis showed that the microorganisms in the anode solution were mainly from the biogas slurry and belonged to the hydrolytic bacteria. At the same time, the electroactive microbial community was mainly from anaerobic sludge. Therefore, MFCs can effectively degrade the organic matter in the biogas solution and generate electricity, and use Chlorella to absorb CO₂ from the biogas, providing a new method for the development of biogas industry.

There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, and supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. This study examined articles published since 2015 to understand better the current status, future possibilities, and capabilities of ML in supporting environmentally friendly development and BDM applications. Previous ML applications were classified into three categories according to their objectives: material and process design, performance prediction and sustainability evaluation. ML helps optimize BDMs systems, predict material properties and performance, reverse engineering, and solve data difficulties in sustainability evaluations. Ensemble models and cutting-edge Neural Networks operate satisfactorily on these datasets and are easily generalized. Ensemble and neural network models have poor interpretability, and there have not been any studies in sustainability assessment that consider geo-temporal dynamics; thus, building ML methods for BDM systems is currently not practical. Future ML research for BDM systems should follow a workflow. Investigating the potential uses of ML in BDM system optimization, evaluation and sustainable development requires further investigation.

This study investigates biochar as an attractive option for removing pharmaceuticals from wastewater streams utilizing data from various literature sources and also explores the sensitivity of the characteristics and implementation of biochar. ANN 1 was designed to determine the optimal biochar characteristics (Surface Area, Pore Volume) to achieve the maximum percentage removal of pharmaceuticals in wastewater streams. ANN 2 was developed to identify the optimal biomass feedstock composition, pyrolysis conditions (temperature and time), and chemical activation (acid or base) to produce the optimal biochar from ANN 1. ANN 3 was developed to investigate the effectiveness of the biochar produced in ANN 1 and 2 in removing dye from water. Biomass feedstock with a high lignin content and high volatile matter at a high pyrolysis temperature, whether using an acid or base, achieves a high mesopore volume and high surface area. The biochar with the highest surface area and mesopore volume achieved the highest removal percentage. Regardless of hydrophobicity conditions, at low dosages (0.2), a high surface area and pore volume are required for a high percent removal. And with a higher dosage, a lower surface area and pore volume is necessary to achieve a high percent removal.

This study proposes the use of diglycolamic acid-functionalized graphitic carbon nitride (HDGA-gCN) as an adsorbent for uranium removal. Our experiments showed that at pH 6.0, HDGA-gCN had a high adsorption capacity of 263.2 mg g^-1 and achieved equilibrium in 30 min. The adsorption isotherm was well-fitted by the Langmuir model, and the adsorption kinetics followed a pseudo-second-order equation. U(VI) adsorption on HDGA-gCN is due to electrostatic interactions between the amine, diglycolamic acid, and uranium species. The thermodynamic parameters indicate that adsorption is spontaneous and exothermic. The loaded U(VI) can be desorbed using 0.1 M Na₂CO₃, and HDGA-gCN exhibited an exceptional adsorption percentage for U(VI) compared to other coexisting ions. HDGA-gCN had faster kinetics, adsorption capacity, and reusability, making it suitable for U(VI) remediation.

Plant-mediated biosynthesis of nanoparticles is a green method that allows synthesis in one-pot process. Synthesis of gold nanoparticles with plant extracts has gained interest in the field of biomedicine due to its variety of applications. This study presents the synthesis via green chemistry of gold nanoparticles (AuNPs) using the methanol extract of Moringa oleifera seeds. The AuNPs were synthesized at room temperature. UV-Vis spectroscopy confirmed the formation of AuNPs by identifying the surface plasmon resonance located at 546 nm. TEM analysis shows spherical nanoparticles. FTIR analysis demonstrated the presence of specific bioactive molecules responsible for the Au³⁺ ion reduction process. The antioxidant activity of the nanoparticles was evaluated on the stabilization of the DPPH radical (1,1-diphenyl-2-picrylhydrazyl, 2,2-diphenyl-1-(2,4,6-trinitrophenyl) hydrazyl). The antimicrobial activity analysis was developed by broth microdilution method at different concentrations against Escherichia coli and Staphylococcus aureus. Minimum inhibitory concentration were 400 µg/mL and 200 µg/mL, respectively. A549 lung cancer cell proliferation was measured according to the MTT protocol, indicating a dose-dependent response and a IC₅₀ of 163.9 ± 13.27 µg/mL. The AuNPs synthesized using M. oleifera seeds showed promise as active materials for antimicrobial or anticancer products.

Removing hazardous organic pollutants, such as 4-nitrophenol (4-NP) and Congo red (CR) dyes from aqueous media and CO₂ from the atmospheric medium remains a significant challenge. Herein, we report a facile in-situ synthetic approach for fabricating CuO-ZnO heterostructure photocatalysts through the surfactant-assisted co-precipitation method. The catalytic results demonstrate that the Cu₁O-ZnO photocatalyst exhibits excellent activity under direct sunlight irradiation, owing to the heterostructure formation between the CuO and ZnO. The Cu₁O-ZnO photocatalyst showed higher reaction rate constant (k) values of 0.20 min^-1 for 4-NP and 0.09 min^-1 for CR compared to previous reports. Additionally, efficient CO₂ reduction was also achieved over Cu₁O-ZnO photocatalyst. The optical and structural characterization results indicate that the improved photocatalytic reduction and degradation observed for the Cu₁O-ZnO photocatalyst can be attributed to the strong synergistic interaction between p-type CuO and n-type ZnO and the construction of the p-n heterojunction. As a result, the absorption of visible light distinctly increased and inhibited the recombination rate of the photo-created electron-hole (e^-/h⁺). Furthermore, the Cu₁O-ZnO photocatalyst exhibited remarkable durability and recyclability, retaining high photoactivity (≥ 93%) after five cycles, demonstrating its potential for real-world applications in the photocatalytic reduction and degradation reactions under direct sunlight irradiation.

In indoor air the reaction of ozone (O₃) with terpenes may lead to the formation of irritating gas-phase products which may induce acute airway effects (i.e. sudden, short-term changes or symptoms related to the respiratory system). We aimed to perform an in vitro study on possible health effects of products from the O₃-initiated reaction of limonene with printer exhaust, representing real-life mixtures in offices. Human bronchial epithelial cells were exposed for 1 hour (h) to limonene and O₃, combined with printer exhaust. The resulting concentrations represented 34% and 6% of the generated initial concentrations of limonene (400 µg/m³) and O₃ (417 µg/cm³), respectively, which were in range of high end realistic indoor concentrations. We observed that the reaction of limonene with O₃ generated an increase of ultrafine particles within 1 h, with a significant increase of secondary reaction products 4-oxopentanal and 3-isopropenyl-6-oxo-heptanal at high end indoor air levels. Simultaneous printing activity caused the additional release of micron-sized particles and a further increase in reaction products. Relevant cellular endpoints to evaluate the possible induction of acute airway effects were measured. However, none of the test atmospheres representing office air was observed to induce these effects.

Bacterial source characterization and allocation are imperative to watershed planning and identifying best management practices. The Spatially Explicit Load Enrichment Calculation Tool (SELECT) has been extensively utilized in watershed protection plans to evaluate the potential bacteria loads and sources in impaired watersheds. However, collecting data, compiling inputs, and spatially mapping sources can be arduous, time-intensive, expensive, and iterative until potential bacteria loads are appropriately allocated to sources based on stakeholder recommendations. We developed a web-based decision support system (DSS), TXSELECT (https://tx.select.tamu.edu), providing a user-friendly interface to run the SELECT model on Texas watersheds. The DSS includes pre-determined watershed-specific inputs that can be readily adjusted within the interface based on user preference and stakeholder recommendations, obviating the necessity for expensive GIS tools and data extraction. To illustrate the applications of TXSELECT, we implemented it in the entire coverage area to identify the potential hotspots and source contributions for Escherichia coli at a regional scale. Median potential E. coli loads were significantly higher in subwatersheds not supporting recreation use. Overall, the large-scale application of SELECT has the potential to aid in prioritizing management measures in watersheds that are less frequently monitored but could have an elevated risk of impairment.

To meet wastewater treatment quality standards for reuse, integrating advanced oxidation processes (AOPs) with Decentralized Wastewater Treatment Systems (DEWATS) is promising. This study aimed to optimize AOPs (ozonolysis, UV photolysis, TiO₂ photocatalysis) for polishing anaerobic filter (AF) effluent from DEWATS, as an alternative to constructed wetlands. Metrics included pathogen reduction efficiency, post-disinfection regrowth, and effects on physical parameters (pH, EC, turbidity), organic matter (soluble COD, BOD, DOC, humic), and nutrient concentration (ammonium, nitrates, ortho-P). Ozonolysis and TiO₂ photocatalysis achieved a 6.4-log pathogen reduction, while UV photolysis achieved a 6-log. No pathogen regrowth occurred with ozonolysis, but TiO₂ photocatalysis showed E. coli and Total coliforms regrowth of 2.5-log and 2.7-log, respectively. UV photolysis showed 0.5-log and 2.2-log regrowth for E. coli and Total coliforms, respectively. TiO₂ photocatalysis significantly reduced BOD, soluble COD, humic substances, ortho-P, turbidity, and nitrates, while increasing pH, EC, ammonium, and DOC. Ozonolysis significantly lowered BOD, soluble COD, humics, and turbidity, but increased ortho-P, nitrates, pH, EC, ammonium, and DOC. UV-photolysis showed marginal reductions in BOD, nitrates, and turbidity, with increased EC, pH, ammonium, DOC, ortho-P, and humic levels. Ozonolysis emerged as the best AOP, demonstrating efficient effluent treatment with no pathogen regrowth.