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

2-ethylhexyl 4-methoxycinnamate (EHMC), a UV filter commonly used in sunscreen products, is also an emerging environmental pollutant that interferes with the structure and function of bovine serum albumin (BSA). A study was conducted on the interaction between EHMC and BSA when they coexist and are encapsulated within β-cyclodextrin (β-CD). Multiple spectra demonstrate, both qualitatively and quantitatively, that β-CD coexistence and encapsulation weaken the interaction between EHMC and BSA, resulting in a more difficult binding process between the two and inhibiting EHMC-induced conformational changes in BSA. Once encapsulated by β-CD, the inclusion complex (IC) was weakly bound to BSA (K_b=(7.63 ± 0.01)×10⁴ M^-1), and it had no significant impact on BSA's structure. Despite this, β-CD did not significantly alter EHMC's UV shielding ability.

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.

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.

Heavy metal ions are acknowledged to impact the environment and human health adversely. CNCs are effective materials for removing heavy metal ions in industrial applications and process innovations since they can be used in static and dynamic adsorption processes. Cost-effective, uncomplicated water treatment technologies must be developed using biodegradable polymers, namely, modified cellulose nanocrystals. Adaptive neuro-fuzzy inference systems (ANFISs) and artificial neural networks (ANNs) were used to evaluate and examine the efficacy of modified cellulose nanocrystals in removing lead(II) from wastewater. The research indicated that the maximum adsorption capacity attained was 260 mg/g at a pH of 6, an initial concentration of 200 mg/L, a contact duration of 300 min, and a 5 g/200 mL dose. Influence of four input variables on the Pb(II) adsorption capacity: The experimental data were juxtaposed with the outcomes from ANN and ANFIS to ascertain the pH, contact time, starting concentration, and dose. The correlations of 0.9916 for the created artificial neural network (ANN) and 0.9953 for the adaptive neuro-fuzzy inference system ANFIS indicate that the study data may be predicted with precision. ANFIS had a Pearson's chi-square value of 0.638, surpassing the ANN's score of 0.979.

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.

The influx of insufficiently purified or untreated domestic wastewater into aquatic ecosystems raises the question of the production of environmentally friendly detergents. The purpose of this work was to investigate the toxicity of phosphonate-containing and phosphate-free dishwashing detergents for dishwashers according to the phytotest with a garden cress (Lepidium sativum L.). Dishwashing detergents for dishwashers ("All in 1"), widely available in the retail network of Ukraine, were used in concentrations the aqueous solutions from 0.005% to 10.0% for their effect on test indicators of garden cress: energy of seed germination, seed germination and biometric and morphometric indicators of seedlings, which were processed statistically. Some phytotoxic indices were determined for the tested aqueous solutions. It was established that the studied solutions of both phosphonate-containing and phosphate-free detergents are extremely and high toxic according to the calculated indices. The obtained data indicate the danger of the investigated detergents for the hydrosphere, the need to replace with a less toxic agents, in particular, based on biosurfactants.

The ketamine (KET) and its analogs consumed by humans are becoming emerging contaminants (ECs), as they at present in surface waters after being carried through wastewater systems. Drugs in wastewater can be analyzed using the direct-injection method, a simple wastewater analysis (WWA) method that can provide objective, continuous and nearly to real-time findings. This article describes an ultra-high-pressure liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the simultaneous quantification and confirmation of seven KET-based ECs in wastewater by direct injection. After optimization of the UPLC-MS/MS and sample pretreatment conditions, the method was validated and applied to samples (n = 157) collected from several wastewater treatment plants (WWTPs) in southern China in which KET had the highest detection rate. The established direct-injection method was not only simple to perform but also had better sensitivity, shorter detection times, and analyzed more KET-based ECs than currently published methods, meeting the requirements for the monitoring and high-throughput analysis of common KET-based ECs. We also analyzed the fragmentation pathway of KET-based ECs to obtain product ion information on other unknown substances. Additional studies are needed to establish a comprehensive direct-injection screening method of ECs in wastewater on model-based assessment.

The distribution of antibiotic resistance genes (ARGs) in Laizhou Bay affects the local socio-economic development. The study aimed to investigate the distribution of ARGs in the rivers that flow into the sea around Laizhou Bay's southern shore. Water and sediment samples were collected from different typical sites of rivers entering the sea in Weifang, including Mi River, Bai Lang River, Yu River, Wei River, Jiaolai River, Xiaoqing River and Di River. The species and abundance of ARGs in the sediments were characterized and quantified by macro-genome high-throughput sequencing technology. The species distribution of ARGs was compared. In two sediment samples and seven water samples, 24 ARGs types and 1244 subtypes of ARGs were detected, in which multidrug-resistant class was the main ARGs type and FBJ murine osteosarcoma viral oncogene homolog B (fosB) was the dominant ARGs. The types of ARG in the top ten of these samples were the same, although the proportion was different. Dominant ARG subtypes accounted for more than 50% of all the nine samples. This article provides basic data support for pollution status and environmental risk assessment as well as remediation of ARGs in rivers entering the sea along the south coast of Laizhou Bay.

Cellulose was isolated from recycled pulp and paper sludge and used to synthesize cellulose nanocrystals. Response surface methodology and Box-Behnken design model were used to predict, improve, and optimize the cellulose isolation process. The optimal conditions were a reaction temperature of 87.5 °C, 180 min with 4% sodium hydroxide. SEM and TEM results revealed that the isolated cellulose had long rod-like structures of different dimensions than CNCs with short rod-like structures. The crystallinity index from XRD significantly increased from 41.33%, 63.7%, and 75.6% for Kimberly mill pulp sludge (KMRPPS), chemically purified cellulose and cellulose nanocrystals, respectively. The TGA/DTG analysis showed that the isolated cellulosic materials possessed higher thermal stability. FTIR analysis suggested that the chemical structures of cellulose and CNCs were modified by chemical treatment. The cellulose surface was highly hydrophilic compared to the CNCs based on the high water holding capacity of 65.31 ± 0.98% and 83.14 ± 1.22%, respectively. The synthesized cellulosic materials portrayed excellent properties for high-end industrial applications like biomedical engineering, advanced materials, nanotechnology, sustainable packaging, personal care products, environmental remediation, additive manufacturing, etc.

The leaching laboratory experiment uses the artificial neural network (ANN) to predict and evaluate copper and cobalt recovery. This study aimed to evaluate the efficacy of using the shrinking core model in conjunction with an artificial neural network (ANN) as part of a machine learning strategy to improve the leaching process of cobalt (II) and copper (II). The numerous factors in the leaching process, such as acid concentration, leaching time, temperature, soil-to-solution ratio, and stirring speed, are adjusted using an ANN with several layers, feed-forward, and back-propagation learning methods. These variables are in charge of the high cobalt recovery during the reduced sulfuric acid leaching procedure. The ANN algorithm has 10 hidden layers, 5 input variables describing the leaching parameters, and two neurons as output layers corresponding to copper and cobalt leaching recovery. The optimum conditions were found to be acid concentration of 100 g/L, leaching duration 120 min, temperature 55 °C, soil-to-solution ratio of 1:40 g/mL, and stirring speed 300 rpm. The optimized trained neural networks tested, trained, and validated steps are represented by R² values of 0.94, 0.99, 0.97, and 0.97, respectively, equating to 97.5% copper recovery and 95.4% cobalt recovery.