Journal of Environmental Health Science and Engineering最新文献

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

Purpose

The Naseri Artificial Wetland was created by the discharge of agricultural drainage water, including effluent from the sugarcane development project. The continuous inflow of drainage water from the sugarcane development units has altered the natural regime of the wetland. Considering the high probability of herbicides entering agricultural runoff, this study was conducted to identify atrazine and to assess the health risks of it in this wetland.

Methods

Sixty water samples from the wetland and 15 samples from the drainage channel were collected in three seasons: June (summer), October (Autumn), and February(winter). Variables such as electrical conductivity, dissolved oxygen, temperature, and pH were also monitored at the sampling points.

Results

The average measured ATZ concentration in NAW was lowest in June (0.015 mg/L) and was 0.021 mg/L in October and 0.024 mg/L in February. The average ATZ concentration in the drainage channel in June, October, and February was 0.03, 0.04, and 0.05 mg/L, respectively. The average electrical conductivity and pH in June and October were 29,900 µS/cm, 28,544 µS/cm and 7.29 and 7.28, respectively. The maximum and minimum values ​​of temperature were 30.7 °C in June and 8.6 in February.

Conclusion

The health risk for children and adults, based on the HQ index, was calculated to be 0.12 and 0.014, respectively. Additionally, the carcinogenic risk, based on the ILCR index, was calculated to be 2.7E-2 and 3.2E-3, respectively, which indicates the risk of carcinogenic.

Predicting indoor air quality during infectious disease conditions relies on models simulating particle materials (PM)/bioaerosols distribution. Understanding the thermo-fluid properties of exhaled air is crucial for comprehending disease transmission dynamics. This study employs a computational fluid dynamics (CFD) model to simulate cough-induced particle dispersion in a closed space. Furthermore, the number of released particles and the presence of SARS-CoV-2 viral genomes by a cough were assessed (in eight COVID-19 patients). According to the CFD model, in the first 30 s of cough, the vertical height and lateral breadth of the particles’ dispersion were up to 138cm and 92cm, respectively. As the distance from the patient's respiratory zone increased, the lateral distribution width of particles expanded, reaching 1.3 m at 2.4 m away. Larger droplets (> 62.5µ) were deposited at shorter distances, while smaller particles remained airborne longer. The comparison of experimental and simulated results focused on particle dispersion at specific distances from the patient, particularly in the 2.5µ range. The distribution pattern of PM_2.5 and PM₁₀ at a distance of 1 and 2 m for women, not men, is similar to the distribution pattern of PM in CFD modeling. Viral genome detection was more prevalent in particles near the left side of the body, especially within the first 20 min post-cough, exhibiting a correlation with CFD predictions.

Purpose

The aim of this study was to investigate the impact of improved deep learning model on the predictive performance of PM_2.5 concentration.

Methods

We developed a new model combining one-dimensional convolutional neural network and bidirectional long short-term memory neural network to predict PM_2.5 concentrations at hourly intervals. The air pollution observation data from 2020 to 2022 collected at several national air quality monitoring stations in Shenyang (Liaoning province, China) were employed to train our model. The performance of the proposed model was boosted by connecting the layer of network calculated results with the PM_2.5 sequence data. Furthermore, data of most relevant air quality monitoring stations and PM_2.5 feature factors of the target station were screened. The spatial correlation of major air pollutant and the interaction between PM_2.5 and other pollutant factors were therefore considered to improve the accuracy of the model.

Results

The root mean square error, mean absolute error, mean absolute percentage error of the new method were reduced by 49%, 51%, 44% and the R² was improved by 4.6% respectively compared with the control group for the next hour prediction. The proposed improvement method can reduce the prediction error of the model in the next 6 h.

Conclusions

In this study, the proposed model improvement method can significantly reduce the error of the model in predicting PM_2.5 concentration. The proposed method can improve the model in the next 6 h prediction accuracy. This study provides a new perspective for establishing high-precision models for PM_2.5 prediction.

Bioenergy plays a crucial role in addressing the global energy crisis. The utilization of agricultural byproducts for biofuel production through fermentation is well-established. Among various pretreatment methods, breaking lignin and cellulose bonds under heat and pressure to release sugar moieties is the most predominant approach. This study focuses on enhancing sugar yield through the most economical, energy-efficient, and time-saving pretreatment of the highly underrated agricultural residue, cocoa pod shell (CPS), using green-synthesized FeO nanoparticles derived from CPS extract. The synthesized nanoparticles, ranging from 25 nm to 31 nm in size, exhibited an EDS spectrum confirming the atomic composition of C (30.01%), Fe (6.09%), O (59.76%), N (2.36%), P (0.79%), Cl (0.53%), and K (0.46%). FTIR analysis revealed the presence of O-H, C-H, C-Cl, and O = C = O stretching, indicating effective nanoparticle capping. The novel ex-situ hydrolysis process, coupled with induction heating, yielded 356.04 g/L of total sugars and 60.28 g/L of reducing sugars using 10% w/v biomass and 4% acid within just 30 min. RSM and ANN modeling were employed for process validation, yielding R² values of 0.91 and 0.92 for total and reducing sugars, respectively, while ANN modeling achieved R² values of 0.96 and 0.97. This energy-efficient hydrolysis process achieved a significant sugar yield in less time while requiring minimal raw material. It presents a scalable and reliable approach to the industries, providing a promising direction for biofuel production.