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

This study analyses the long-term variations in air quality at Amity University, Noida, Uttar Pradesh, India, from May 2017 to December 2024, focusing on the monthly mean Air Quality Index (AQI) and its key precursors. The specific objectives of the study are to: (i) characterize temporal trends in AQI; (ii) identify dominant pollutant drivers influencing seasonal air quality; and (iii) evaluate the relative contributions of anthropogenic and meteorological factors to observed variations. The average AQI during the period was 217, with peaks in winter due to temperature inversions and increased emissions, and improvements during monsoon months due to wet deposition. The highest AQI (487) was recorded in November 2017, while the lowest (40) was observed in July 2024. A notable reduction in AQI occurred during the COVID-19 lockdown in 2020, highlighting the impact of reduced anthropogenic activities. Particulate matter (PM_2.5 and PM₁₀) emerged as the primary contributor to high AQI, frequently exceeding the National Ambient Air Quality Standards (NAAQS) during winter. Nitrogen dioxide (NO₂) peaked in June 2023 (192 µg m^-³), while ammonia (NH₃) exhibited episodic spikes, mainly due to agricultural activities. Ground-level ozone (O₃) levels fluctuated, indicating variations in precursor emissions and photochemical processes. Correlation analysis revealed a strong relationship between AQI and PM_2.5 (r = 0.9) as well as PM₁₀ (r = 1.0), emphasizing particulate pollution as the dominant driver of poor air quality. Unlike studies that focus primarily on PM_2.5 and PM₁₀, this research gives equal attention to secondary pollutants and their role in shaping AQI trends. Local meteorological conditions play a critical role, and the associated emission sources were also examined to provide a comprehensive understanding of pollutant variability. The findings conclude that PM remains the most influential factor governing air quality in the region, and sustained improvement will require targeted emission control strategies addressing both primary particle sources and secondary pollutant formation pathways.

Heavy metal pollution in aquatic ecosystems demands sustainable, scalable remediation solutions. This study evaluated the long-term (three-month) phytofiltration potential of the aquatic moss Taxiphyllum barbieri (Cardot & Copp.) Z. Iwats, under controlled multi-metal exposure and in real cement factory effluent (CE). In simulated solutions, the moss achieved high removal efficiencies (>93%), particularly for Ni (99.2%) and Zn (99.1%), with substantial tissue accumulation (e.g., Cu: 221.86 mg kg^-1, Cd: 210.36 mg kg^-1). In cement effluent, removal efficiencies were lower (41-64%), yet bioconcentration factors (BCFs) increased dramatically, reaching 4523.9 for Zn and 4093.8 for Cd, indicating efficient hyper-concentration of bioavailable metal fractions. Physiological assessments revealed metal-specific stress responses, including antioxidant activation, significant proline accumulation (up to 328% under Ni), and modulated pigment profiles. Notably, exposure to CE stimulated moss growth (+23.37% RGR) and enhanced photosynthetic pigments, demonstrating physiological resilience under realistic, low-level mixed-metal stress. These findings confirm T. barbieri as a robust, adaptable phytoremediation agent capable of high metal removal and bioconcentration while maintaining physiological integrity. The results support its potential integration into engineered, low-energy wastewater treatment systems for sustainable mitigation of heavy metal contamination.

This study evaluates the ecological risks and pollution levels of nine heavy metals (As, Cr, Co, Cu, Mn, Ni, Pb, V, and Zn) in stream sediments of the Erbil Governorate in the Kurdistan Region of Iraq, bordered by the Upper and Lower Zab Rivers to the northwest and southeast, respectively. Average concentrations of heavy metals from 100 sediment samples collected across ten districts in Erbil Governorate were ranked as follows: Choman > Rawanduz > Soran > Erbil Plain > Koysinjaq > Shaqlawa > Mergasur > Khabat > Erbil Center > Makhmur. Notably, Ni, Cu, As, and Cr emerged as the primary contaminants, particularly in the northeastern areas of the Governorate, specifically Choman and Erbil Plain Districts. Enrichment factor analysis revealed slight to moderate pollution levels, except for Ni, which indicated moderate to heavy pollution. Principal component analysis (PCA) grouped the majority of metals (Cr, Co, Cu, Mn, Pb, Ni, V, and Zn) into three components, suggesting a natural origin. The fourth component indicated salinity effects related to cation and anion exchange processes that facilitate the leaching of other metals, while the fifth component, comprising arsenic, was associated with the application of arsenical pesticides in agricultural practices. Both As and Ni present significant concerns due to their toxicity, with as occurring at low to moderate levels and Ni at moderate to high concentrations. Overall, a low potential ecological risk index was calculated for soil samples from the Erbil Governorate.

Constructed wetlands are a potential alternative for treating aquaculture effluents, whose geographic characteristics (fluctuations in water quality and levels) make their treatment difficult. This study evaluates the performance of a subsurface flow constructed wetland (HSSCW) for treating effluents from shrimp farms, which were previously characterized in detail and are located in one of Latin America's most intense aquaculture zones. During the 90 operation days, the results indicated that HSSCWs can stably remove (≤10% variation) high organic matter and nutrient contents (up to 740 mg/L COD and 11.3 mg/L NH₃-N, respectively). The average removal efficiencies of the HSSCWs were 71.68, 63.76, 50.8, 61.3, and 40.7% of COD, NH₃-N, total phosphorus, phosphates, and TSS, respectively. The HSSCW system stabilized after 66 days of operation, with less than 5% variation in COD. Nevertheless, phosphorus and NH₃-N removal rates were proportional to the number of operation days, which correlated with the increase in plant biomass observed. In addition, the proportion of inorganic phosphorus was reduced to a minimum at the end of the operation due to the predominance of oxidizing conditions in the rhizospheric system. HSSCWs were technically feasible for treating aquaculture effluents and could be adapted to the local conditions of aquaculture practices.

Triclocarban (TCC), a widely used antimicrobial agent, may threaten ecosystems and human health via bioaccumulation, necessitating study of its protein interactions to understand molecular toxicity. In this paper, trypsin (TRY) was utilized as a model protein to explore its binding to TRY. The results revealed that the binding could result in a reduction of the enzymatic activity of TRY. Spectra analysis showed that TCC could heighten the quenching effect on the intrinsic fluorescence of TRY. The fluorescence quenching of TRY encompassed dynamic and static quenching mechanisms. The association constants (K_a) exhibited a high magnitude (∼10⁶) at both 293 and 313 K, indicating a robust affinity between the two entities. Molecular docking studies and thermodynamic parameters (ΔH < 0, ΔS < 0) suggested hydrogen bonds and van der Waals forces are necessary for TCC's binding to TRY. The formation of the TRY-TCC complex induced alterations in the secondary structure and local microenvironment of TRY, leading to a more relaxed skeletal structure. This paper will provide a fundamental basis for further studying the molecular toxicity of TCC in living organisms. Future in vivo studies will be essential to establish the physiological consequences of TCC-TRY binding in biological systems.

The problem of marine nitrogen pollution is becoming increasingly severe, necessitating the development of efficient and sustainable denitrification microbial technologies. Heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria have garnered significant attention in recent years due to their ability to efficiently convert ammonia nitrogen to gaseous nitrogen under aerobic conditions, making them a focus of research on marine water quality remediation. This review systematically examines the main classifications and ecological characteristics of marine HN-AD bacteria, analyzing their adaptability and nitrogen metabolism characteristics in typical marine environments with high salinity, high ammonia, and low C/N ratios. It also summarizes current methods for screening and isolating strains, with a particular focus on the impact mechanisms of key environmental factors such as carbon sources, salinity, heavy metals, DO, and carbon source concentrations on denitrification efficiency. This review aims to provide a theoretical foundation and research directions for the subsequent development of functional strains, process regulation mechanisms, and marine ecological remediation practices, thereby promoting the scientific transformation and wide application of HN-AD bacteria in marine environmental management.

Paints used as cosmetic and architectural surface coatings constitute essential structural components, however, they may also act as significant environmental pollutants due to abrasion and weathering processes. Following environmental disturbances such as earthquakes and landslides, these materials can contribute substantially to surface and groundwater contamination. Seven commercially available wall paints of different colors and formulation qualities were selected for analysis, including Sand White (P1), Beige (P2), Ceiling White (P3), Ivory (P4), Exterior White (P5), Anthracite (P6), and Red (P7), which were expected to contain distinct additive compositions. Structural characterization was performed using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and elemental analysis via energy-dispersive X-ray spectroscopy (EDX). Particle size distributions were determined with a Malvern Zetasizer Nano-ZS. Although all paints demonstrated a measurable potential to generate microplastics (MPs), no acute toxicity was observed in Danio rerio or Escherichia coli under the tested conditions.

This study assessed the impact of silica exposure on 145 mine workers in Mianwali, Punjab, Pakistan, compared to 45 non-exposed individuals. Pulmonary function tests revealed significantly reduced lung function in exposed workers (P < 0.05), with declines in Forced Expiratory Volume in one second (FEV₁), Forced Vital Capacity (FVC), FEV₁/FVC ratio, Peak Expiratory Flow, and Forced Expiratory Flow at 25-75% of FVC (FEF25-75). Radiological evaluations confirmed extensive lung damage (P < 0.05), including pleural effusion, reticular shadowing, and lung consolidation. Oxidative stress markers demonstrated increased lipid peroxidation, Fenton's Oxidative Stress, and Oxidative Stress Index (P < 0.05), along with reduced antioxidant enzyme activities, including Catalase, Superoxide Dismutase, Total Antioxidant Capacity, and Glutathione Peroxidase. Hematological analysis showed elevated White Blood Cells, Lymphocyte percentage, Hemoglobin, Hematocrit, Mean Corpuscular Volume, and Mean Corpuscular Hemoglobin (P < 0.05), reflecting systemic inflammation. Silica's piezoelectric properties contributed to oxidative stress and cellular damage, exacerbating pulmonary dysfunction. These findings highlight silica exposure as a severe occupational hazard, causing irreversible lung impairment and systemic oxidative imbalance. Implementing strict safety protocols, personal protective measures, and regular health monitoring is crucial to safeguarding workers.

Biochar is a promising, sustainable adsorbent for hydrogen sulfide (H₂S) removal, yet its adsorption capacity is governed by complex interactions among material properties, preparation conditions, and operating parameters. In this study, we develop and systematically compare a suite of machine learning (ML) models including Decision Tree, Random Forest, AdaBoost, K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), Support Vector Regression (SVR), and an Ensemble Learning scheme to predict the H₂S adsorption capacity of biochar. The models are trained on 277 experimental data points collected from the literature, using a comprehensive set of inputs that includes physicochemical properties (specific surface area, mass percentages of C, O, and N, C/N, O/N, (O + N)/C, total pore volume, and average pore diameter), pyrolysis conditions (temperature and time), and reaction conditions (gas humidity, adsorption temperature, H₂S concentration, gas flow rate, and breakthrough time). Model robustness is ensured through 5-fold cross-validation and rigorous outlier assessment using the Leverage (Williams) method, while SHapley Additive exPlanations (SHAP) are applied to interpret feature contributions. Among all algorithms, KNN emerges as the best-performing model, achieving the highest coefficient of determination (R² ≈ 0.94) and the lowest mean squared error and average absolute relative error on the full dataset. Sensitivity and SHAP analyses consistently identify breakthrough time as the dominant factor controlling adsorption capacity, followed by specific surface area, gas humidity, and oxygen-to-nitrogen ratio. These findings demonstrate that combining diverse ML architectures with robust statistical validation provides an accurate, interpretable, and computationally efficient alternative to conventional experimental determination of H₂S adsorption capacity, facilitating rapid screening and optimization of biochar-based gas purification systems.