Journal of Environmental Health Science and Engineering最新文献

Background

As the global burden of glaucoma continues to rise, evidence suggests factors beyond intraocular pressure contribute to its pathogenesis. Environmental pollutants are increasingly implicated in ocular disease, yet the association between dichlorophenol exposure and glaucoma remains unexamined. This study addresses this gap by analyzing NHANES data to investigate their relationship, providing new evidence for disease prevention and environmental intervention.

Methods

The data originated from the National Health and Nutrition Examination Survey (NHANES, 2005–2008). To explore the connection between urinary dichlorophenol levels and glaucoma, we conducted weighted logistic regression analyses. Additionally, to examine potential nonlinear associations, restricted cubic splines (RCS) were utilized. We further performed subgroup analyses to investigate interaction effects.

Results

There were 1,742 participants. The risk of glaucoma was significantly associated with higher ln-transformed urinary 2,4-dichlorophenol levels (OR = 1.39; 95% CI: 1.11–1.74; P = 0.002), as were Ln-transformed urinary 2,5-dichlorophenol levels (OR = 1.28; 95% CI: 1.09–1.51; P = 0.001). Participants in the third tertile of 2,4-dichlorophenol were at a greater risk for glaucoma compared to the first tertile (OR = 2.11; 95% CI: 1.07–4.17; P for trend = 0.042), according to the completely adjusted model. Similar results were observed for the levels of 2,5-dichlorophenol (OR = 2.15; 95% CI: 1.03–4.48; P for trend = 0.039). RCS demonstrated the nonlinear connection between urinary dichlorophenol levels (2,4-dichlorophenol and 2,5-dichlorophenol) and glaucoma risk (P for nonlinearity < 0.05).

Conclusions

Our results demonstrated a strong nonlinear positive association between urinary dichlorophenol levels and glaucoma risk. Given the global prevalence of dichlorophenols and the universal burden of glaucoma, these findings provide a valuable reference for international counterparts investigating similar environmental-ocular health links.

Purpose

Following reports of an adverse link between glyphosate and bone mineral density (BMD), this cross-sectional study extended the investigation between glyphosate exposure and BMD to include osteoporosis, and fractures, with a focus on the high risk, post-menopausal female subgroup.

Methods

Cross-sectional data from 2,710 participants of the US National Health and Nutrition Examination Survey aged 8–59 years from 2013 to 2018 were used to assess associations between urinary glyphosate concentration (µg/L) and BMD (g/cm²) was measured using dual-energy X-ray absorptiometry (DXA), while osteoporosis and fractures were self-reported. Linear and logistic regression analyses were adjusted for age, race/ethnicity, BMI, smoking status, and income.

Results

There was a negative association between glyphosate and BMD in the entire population (Whole Body BMD slope = -0.018; 95% CI = -0.027, -0.010; p < 0.001; Lumbar Spine BMD slope = -0.019; 95% CI = -0.030, -0.007; p = 0.002). In the high-risk, post-menopausal female subgroup, there was an inverse relationship (Whole Body BMD slope = -0.044; 95% CI = -0.076, -0.012; p = 0.009; Lumbar Spine BMD slope = -0.048; 95% CI = -0.101, 0.004, p = 0.069). Additionally, post-menopausal females with higher glyphosate had greater odds of self-reported osteoporosis (adjusted odds ratio (AOR) = 2.57; 95% CI = 1.24, 5.34; p = 0.011) and of self-reported fracture (AOR = 2.14; 95% CI = 1.28, 3.58, p = 0.004).

Conclusions

Results are internally consistent and align with existing literature concerning toxicity of glyphosate on bone and extend it to include adverse impact on osteoporosis and fractures. Given the cross-sectional design and single point of sampling, longitudinal studies with repeated sampling are recommended to evaluate causation.

Per- and polyfluoroalkyl substances (PFAS) are a class of persistent and toxic contaminants that have been widely detected in aquatic matrices, necessitating the development of efficient and sustainable treatment strategies. Present systematic review examines the efficacy of Supercritical Water Oxidation (SCWO) for PFAS degradation, with a specific focus on the underlying degradation mechanisms and identifying critical future research directions to advance the technology. A literature review of studies that considered SCWO for PFAS destruction in aquatic environment, available in multiple international databases, was carried out following the PRISMA recommendations. The study investigates the effects of SCWO operation variables, including initial concentration, pressure, reaction temperature, residence time and oxidant on PFAS destruction. Co-contaminants’ impact, defluorination efficiency, and gas emission during the reaction are also assessed. The study highlights the advantages and disadvantages of SCWO for PFAS destruction in water matrices, providing valuable insights and guidance for the effective degradation of PFAS using SCWO. The findings of this research have significant implications for the development of sustainable and efficient technologies for PFAS remediation, contributing to the protection of human health and the environment.

High concentrations of air pollutants have adverse effects on our health. They cause various diseases related to the respiratory and circulatory systems, leading to premature deaths. This paper aims to assess changes in PM_2.5 and NO₂ concentrations and to estimate how many premature deaths were caused by the pollutants analysed. The analysis was performed for the period from 2010 to 2019, for the major cities of Poland. For the analysis, annual average concentrations of PM_2.5 and NO₂ calculated on the basis of measurements available from the National Air Quality Monitoring (JPOAT) service were used. Health exposure assessment was performed in the AirQ + tool, and factors for estimating health effects were used in accordance with the recommendation of the AirQ + tool authors. From 2010 to 2019, the average annual concentration of PM_2.5 in most studied cities decreased. During this period, the highest reduction in concentration occurred in Wrocław, Warsaw, and Poznań. In contrast, the annual average NO₂ concentrations remained similar in most analysed cities. The exceptions are Wroclaw, where a significant decrease occurred, and Katowice, where concentrations significantly increased. The highest number of premature deaths due to high PM_2.5 and NO₂ concentrations occurred in Katowice and Cracow. High concentrations of PM_2.5 in the analysed cities caused approximately 7–10% of premature deaths, while high concentrations of NO₂ accounted for approximately 5–8% of premature deaths. PM_2.5 concentrations showed a steady decreasing trend, while NO₂ concentrations showed a decreasing or increasing trend, depending on the city. PM_2.5 concentrations showed a much higher reduction than NO₂. The percentage of premature deaths due to long-term exposure to PM_2.5 has reduced by about 5% between 2010 and 2019, and due to NO₂ by about 2%, and has increased in some cities.

Although microplastics (MPs) have been widely detected in the atmosphere, their presence and deposition patterns within the human respiratory system remain poorly understood. This study aimed to investigate the occurrence and characteristics of MPs in bronchial lavage fluid (BLF) and bronchoalveolar lavage fluid (BALF) of the patients undergoing flexible bronchoscopy and to examine the relation between the amount of MPs and patient’s demographic characters. Results of this study revealed that MPs concentrations in BLF and BALF were 1.21 MPs/ml and 1.38 MPs/ml, respectively. Fiber constituting was the predominant morphology in the samples (BLF = 76%, BALF = 72%) and the longest fiber dimension was observed in BLF (2425 μm). Individuals employed in industrial and agriculture exhibited significantly higher MPs concentrations. Based on the µ-Raman results, seven distinct polymer types were identified, as polypropylene (PP), polycarbonate (PC), and polyamide (PA) were the most prevalent. Furthermore, SEM-EDS analysis detected the presence of some heavy metals associated with MPs, including Fe, Zn, and Cr. These results provide evidence for the presence of MPs within the human airway, although further research is warranted to elucidate the entry pathways, potential health impacts, and associated respiratory diseases.

The research conducted a pilot-scale comparative study of these four emerging aerobic sewage treatment technologies: R1 – Conventional MBBR (Moving Bed Biofilm Reactor), R2 – Conventional IFAS (Integrated Fixed Film Activated Sludge), R3 – Conventional AGS (Aerobic Granular Sludge), and R4 – hybrid configuration of AGS partly filled with high-performance biocarrier (AGS-BF). The reactors were compared in terms of biomass formation time, system performance and stability, area demand, energy consumption, and other engineering aspects. COD removal and nitrification were high for all reactors. However, total nitrogen removal was moderate for reactors R1 (48%) and R2 (54%), but values ​​above 70% were observed for R3 and R4. P removal was also possible, reaching values ​​below 52% for the MBBR (R1) and IFAS (R2) systems but with values ​​above 80% in the aerobic granulation systems R3 and R4. The inclusion of biocarrier in R4 significantly improved the effluent quality in terms of suspended solids, as well as the denitrification process. AGS reactors offer a 6% area reduction compared to MBBR and IFAS. However, the type of biocarrier used interferes with this direct comparison. In general terms, a reduction of 19% in the electrical energy demand of the aerobic granulation systems was achieved compared to MBBR and 30.5% to the IFAS. The results obtained in this research enable a more comprehensive comparative analysis of the various evaluated emerging aerobic technologies, providing valuable data for the design and operation of sewage treatment plants.

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.