Journal of Environmental Health Science and Engineering最新文献

Mitigating the pollution of water by emerging contaminants (ECs) presents a critical environmental challenge that demands innovative, effective, cost-efficient, and sustainable strategies. In this study, the potential of TiO₂-modified activated carbon (AC) for the sequestration of ECs from water was evaluated through a combined experimental and in silico approach, using molecular modeling based on density functional theory (DFT). Unmodified AC removed 67.76–82.09% of ECs such as carbamazepine, flumequine, clarithromycin, azithromycin, and roxithromycin, and 44.54–52.27% of sulfamerazine, sulfamethoxazole, sulfamonomethoxine, trimethoprim, and levofloxacin. Incorporating TiO₂ and utilizing sunlight improved removal efficiencies to 93.09–99.91%. The hydrophobicity of contaminants significantly influenced adsorption. Kinetic and isotherm analyses indicated chemical interaction-driven, monolayer adsorption, with the Langmuir model fitting best (R² = 0.9856–0.9975). Textural analysis of TiO₂–AC (10% TiO₂) revealed a surface area of 557.72 m²·g⁻¹ and a pore volume of 0.317 cm³·g⁻¹, supporting its high adsorption potential. Fourier transform infrared spectroscopy and molecular modeling identified functional groups facilitating adsorption, while DFT provided insights into energetic and non-covalent interactions (NC-interaction) including hydrogen bonding, van der Waals forces (VDW-forces), and charge transfer that occur during the process. TiO₂-modified AC demonstrates high efficiency for pharmaceutical removal from water, highlighting great promise as a sustainable and advanced adsorbent material, offering practical solutions for tackling diverse water pollution challenges.

Alzheimer’s disease (AD) is a prevalent and severe neurodegenerative disorder influenced by both genetic and environmental factors—such as air pollution, toxic elements, pesticides, and infectious agents. In recent years, machine learning techniques have become essential in biomedical research, advancing fields like drug delivery and medical imaging through predictive modeling and pattern recognition. Functional connectivity derived from functional magnetic resonance imaging (fMRI) serves as a promising noninvasive biomarker for AD by mapping the brain’s connectome and revealing neural network disruptions. In this study, we employed the Robust Multitask Feature Extraction Method to evaluate six supervised machine learning algorithms logistic regression, naïve Bayes, support vector machine, random forest, XGBoost, and CatBoostmfor AD diagnosis. A dataset of 140 fMRI images from an equal number of AD patients and healthy individuals (mean age 67.3 ± 6.7 years) was analyzed. The XGBoost algorithm demonstrated exceptional performance, achieving an accuracy of 98.2%, a recall of 96.6%, perfect precision (100%), an F1-Score of 98.2%, and a Matthews correlation coefficient of 0.96 effectively minimizing false positives and negatives. Although CatBoost and Random Forest also yielded robust results, logistic regression and naïve Bayes showed lower reliability. Overall, XGBoost emerges as a robust solution for the early and precise prediction of Alzheimer’s disease, carrying significant implications for proactive patient care and treatment strategies. Beyond these findings, emerging research is exploring multimodal imaging techniques—such as PET and EEG and deeper neural network architectures to further enhance early diagnostic accuracy and treatment personalization in AD.

This study aims to evaluate the environmental performance of a hypothetical wastewater treatment plant (WWTP) with activated sludge modeling and life cycle assessment (LCA). In order to simulate the treatment performance of an A²O (anaerobic-anoxic-aerobic) process for low-, medium-, and high-strength municipal wastewaters, activated sludge model no.3 (ASM3) was employed. Simulation results were then used for performing LCA of wastewater treatment plant to assess the environmental impacts associated with wastewater treatment system. Additionally, net environmental benefit (NEB) approach that is useful for wastewater systems was also used to determine the eutrophication potential reduction of the hypothetical WWTP. The LCA results show that global warming, photochemical oxidation, and eutrophication potential impact categories were affected by characteristics of wastewater treated. The highest values of these impact categories (7.87E-01 kg CO₂-eq., 1.73E-04 kg C₂H₄-eq., and 1.28E-02 kg PO₄-eq./m³.treated wastewater; respectively) were determined for high-strength wastewater. Considering eutrophication potential reduction, the highest NEB value was found 0.042 kg PO₄-eq/m³.wastewater for high-strength wastewater, followed by medium-strength (0.027 kg PO₄-eq/m³.wastewater) and low-strength (0.013 kg PO₄-eq/m³.wastewater) wastewater. The results of the study is crucial to indicate that combining LCA with other decision support tools ensures achieving predictive and reliable results for proving the performance of WWTPs.

Comprehensive metabolomic profiling in reproductive medicine is sought to clarify the specific mechanisms underlying potential exposome-metabolome interactions in adverse pregnancy outcomes. Taking the advantages of longitudinal data, untargeted metabolomics, and machine learning coupled with traditional analysis, we aimed to study the associations between altered metabolome in the first and third trimesters of pregnancy and subsequent implications to explore causal associations. Totally, 201 pregnant women from a low- and middle-income community (LMIC), known for high levels of environmental pollution, were enrolled during their first trimester, 13 ended in pregnancy failure. Gas chromatography-mass spectrometry (GC-MS) was used to obtain untargeted metabolic profiles and to quantify relative levels of metabolome signatures in serum samples. Data processing and analysis were conducted to select features associated with adverse pregnancy outcomes (including miscarriage, stillbirth, preterm birth, and infant death), adjusting for participants’ occupational status, education level, smoking, and the season of conception. Metabolic network and pathway enrichment analyses were then conducted to explore metabolome-associated pregnancy failure. Statistical and machine learning methods were used to visualize the associations between metabolomic features and the risk of adverse pregnancy and neonatal outcomes, accounting for other covariates. The pattern of associations between maternal metabolome during pregnancy and birth outcomes revealed a clear separation of pregnancy failure cases from medically approved healthy-term births (p < 0.05). L-alanine, dioctyl phthalate, L-phenylalanine, L-threonine, cholesterol, L-serine, proline, L-isoleucine, L-valine, arabinofuranose and gluconic acid were upregulated in the pregnancy failure participants, while glycine, L-lactic acid, arachidonic acid, L-tryptophan, creatinine, palmitic acid, L-tyrosine, ornithine, glutamic acid, phosphate, 1,5-anhydrosorbitol, taurine, 3-hydroxybutyric acid, oxoproline, D-glucose, oleic acid and linoleic acid were less abundant. Specific metabolite patterns linked to pregnancy failure were discovered by machine learning methods over the course of pregnancy. Our analysis identified L-alanine, cholesterol, D-glucose, and urea as potential biomarkers for the early detection of pregnancy failure. While promising, further studies are needed to validate these findings and assess their clinical applicability, particularly in populations highly exposed to environmental pollutants.

Background

Adsorption is currently one of the promising technologies widely used in the clean-up of heavy metal ions in the aquatic environment due to its affordability, ease of use, and efficiency. The study investigated the efficacy of activated and functionalised carbon prepared from the stem bark of Persea Americana (C-PA) using ethylenediaminetetraacetic acid (EDTA), to obtain M-PA.

Methods

Both biosorbents were characterized using Fourier transform-infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The suitability of biosorbents for the clean-up of Cu²⁺ and Pb²⁺ contaminated aqueous solutions was investigated under various experimental conditions.

Results

The maximal percentage uptake by C-PA was 98.56% for Cu²⁺ and 67.78% for Pb²⁺, observed at optimal conditions of duration of 30 min contact time, pH of 5, temperature of 70 °C, dosage of 0.6 g, and initial metal concentrations of 30 mg/L (Cu²⁺) and 40 mg/L (Pb²⁺). Biosorption kinetic model for Cu²⁺ followed both pseudo-first order and intra-particle diffusion, while Pb²⁺ followed the pseudo-second order. Experimental data for both metal ions best fitted the Langmuir isotherm model. Studies conducted using M-PA under the above set optimal conditions enhanced the percentage uptake of Cu²⁺ (99.08%) and Pb²⁺ (99.60%).

Conclusion

Overall, both C-PA and M-PA showed remarkable potential as biosorbents for Cu²⁺ and Pb²⁺ clean-up in aqueous solutions.

A magnetic double-layer metal-organic framework composite (Fe₃O₄@ZIF-8@ZIF-67) was successfully synthesized through a facile layer-by-layer self-assembly method at room temperature and thoroughly characterized using various techniques. The composite Fe₃O₄@ZIF-8@ZIF-67 was explored as an adsorbent for the removal of two harmful organic pollutants, Congo red (CR) and tetracycline hydrochloride (TC). Some essential parameters, including initial concentration, adsorbent dose, contact time, pH, and temperature, were systematically optimized. Under optimal conditions, Fe₃O₄@ZIF-8@ZIF-67 demonstrated the maximum adsorption capacities of 276.77 mg/g for CR and and 356.12 mg/g for TC, respectively. The double-layer structure endowed Fe₃O₄@ZIF-8@ZIF-67 high adsorption efficiency for CR (99.44%) than the pristine Fe₃O₄@ZIF-8 (73.26%). Adsorption kinetics and isotherms studies revealed that the adsorption process followed pseudo-second-order kinetics and Langmuir model, indicating a monolayer chemisorption-dominated mechanism. Furthermore, the spent Fe₃O₄@ZIF-8@ZIF-67 was regenerated through a Fenton-like oxidative degradation reaction, maintaining a removal efficiency above 70% after three consecutive cycles. With its facile synthesis, cost-effectiveness, mild operating conditions, and high selectivity for anionic dyes, Fe₃O₄@ZIF-8@ZIF-67 emerges as a highly promising material for advanced wastewater treatment applications.

Background

Antibiotic contamination in aquatic systems demands advanced oxidation solutions. This study develops a nano zero-valent iron (nZVI)-activated peroxide system to address sulfadiazine (SDZ) persistence and associated ecological risks.

Methods

Structural properties of nZVI were analyzed by TEM/XRD. Process parameters were optimized through Box-Behnken design. Degradation mechanisms were investigated via radical quenching experiments, HPLC-MS analysis, and acute toxicity bioassays. ‌

Significant findings

The system achieved complete SDZ (20 mg L⁻¹) removal within 5 min under optimal conditions (pH 2.44, 0.12 g L⁻¹ nZVI, 0.009% H₂O₂), showing strong agreement with pseudo-first-order kinetics (k = 0.637 min ⁻¹, R²=0.998). Hydroxyl radicals dominated SDZ degradation, generating 12 transformation products through amino oxidation, hydroxylation, and sulfonamide bridge cleavage. Toxicity reduction (60–90% EC50 improvement) confirmed effective detoxification. This work establishes nZVI-driven peroxide activation as a viable strategy for antibiotic wastewater remediation.

Graphical abstract

The rising infiltration of microplastics (MPs) into aquatic environments is a complex and alarming threat jeopardizing marine biodiversity, destabilizing entire ecosystems, and endangering human health. Traditional methods for identifying and characterizing microplastics are often manual, requiring significant time and effort due to the small size, diverse shapes, and varying sources of microplastics. By integrating artificial intelligence (AI) with traditional environmental approaches, we can make significant progress in mitigating the influence of microplastics on aquatic ecosystems and health of humans. This review emphasizes the goals, benefits, results, and key insights of emerging robotics and various AI models across three critical areas: collection and sorting of microplastic waste, characterization of microplastic waste to determine its abundance, size and chemical composition and predicting and monitoring microplastic degradation. Several countries and organizations are using AI technologies to address microplastic pollution through innovative projects and supportive policies. The review aims to highlight these successful initiatives focused on monitoring, prevention, and cleanup of microplastics in aquatic environments. Further, challenges and future research opportunities on integrating robotics and AI technologies in mitigating microplastic pollution have also been discussed.

The carcinogenicity of air pollution has been well established and is considered a threat to humans worldwide. Researchers have concluded although the properties of particulate matter (PM) such as size, shape, and mass are of primary importance for the study of air quality, another parameter such as oxidation potential (OP) can be used to determine particle toxicity or the health consequences related to PM samples. Here, the present study examines the characteristics of PM_2.5 components and their associated oxidation potential in the ambient air of Tehran, Iran using the dithiothreitol (DTT) assay. This study also compares the values of OP, and chemical composition (e.g.; anions and cations, metalloids, and polycyclic aromatic hydrocarbons (PAHs)) in the ambient air of Tehran with other urban areas globally. Sampling was conducted for nine months during three seasons: spring, summer, and autumn, in the ambient air of Tehran city, the capital of Iran from 2021/4/17 to 2021/12/6. According to the US EPA’s Sampling Schedule, a high-volume air sampler (operating at a flow rate of 1.415 m³/min) was employed for PM_2.5 on fiberglass filters once every six days. The average value of DTTv was equal to 0.8 ± 0.3 (nmol.min⁻¹m⁻³). The average values of DTTm were equal to 0.017 ± 0.01 (nmol.min⁻¹ µg⁻¹). Although the values of DTTv and DTTm in Tehran were relatively tolerable compared to other parts of Asia, they were at a high level compared to European and American countries. Nonetheless, DTTv in autumn was significantly higher than in summer and spring, while DTTm was slightly higher in spring than summer.

Environmental pollution is increasingly negatively affecting our lives, requiring advanced methods and materials that are highly effective for pollutant treatment processes. This study proposes the synthesis of zero-valent iron nanoparticles (nZVIs) through a green chemistry approach, which were then encapsulated in calcium alginate (Alg) spheres for application in the treatment of Rhodamine B (RhB) and Methylene Blue (MB). The morphology and structure of the alginate particles encapsulating zero-valent iron nanoparticles (Alg-nZVIs) were characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The analytical results indicate that the material consists of alginate polymer particles with an average diameter of 2.5 mm, containing nZVIs with an average size of 50 nm. Factors affecting the treatment of RhB and MB, including the proportion of components in the material, pH, solution concentration, and treatment time, were studied and evaluated by UV–Vis method. This material showed high removal efficiency for RhB and MB. 0.08 ml nZVIs in 1 g of Alg-nZVIs beads treated 100 mL of RhB 5 mg/L at pH 7 for 180 min with an efficiency of over 90%. The same amount of material effectively treated 100 mL of MB 5 g/L at pH 3 for 120 min with an efficiency of over 90%. The prepared Alg-nZVIs spheres were easy to collect and reuse for up to 6 cycles with a decrease in removal efficiency of less than 15%. Alginate-nZVIs spheres are derived from readily available and natural materials through a clean, cost-effective, and economically sustainable technique.

Graphical Abstract