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

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.

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.

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.

Lakes serve as critical freshwater resources that sustain biodiversity, support recreational activities, and contribute to regional tourism. Maintaining their water quality is essential to avoid ecological degradation. Considering the ubiquity of various emerging contaminants, lakes in the Indore district of Madhya Pradesh (Central India) were examined for the presence of microplastics. A total of 3 lakes were taken into consideration and water sampling was done followed by analysis and risk assessment. Microplastics were found in all the lakes with concentration varying from 6.7 items/L to12.3 items/L. Most of the obtained microplastic items were fibers, with presence of fragments, sheet, and foam as well. Chemical characterization analysis revealed the highest presence of cellulose and its derivatives (70%), while polyethylene, polyamide, and polyvinyl stearate were also found. Presence of cellulosic fibers was majorly attributed to textile industries; while, plastics originated from packaging materials and household discharge were considered to be the source of other microplastic items. Since, a significant fraction of the obtained microplastic items was biodegradable cellulose and its derivatives, the risk imposed was very low; however, to mitigate long-term impacts, strategic interventions focusing on source reduction and improved plastic waste management are imperative.

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.

Green synthesis is a prominent procedure used to easily and environmentally friendly fabricate nanostructures and nanoparticles (NPs), without complex instrumentation, intricate procedures, and harmful chemicals. This study investigated the green synthesis of silver NPs (AgNPs), with Aronia melanocarpa fruit extract as the reducing agent. Following synthesis optimization, AgNPs were characterized using a range of analytical techniques, including UV-Vis, XRD, SEM-EDX, BET, and TEM analyses. Based on the characterization results, an Ag/AgCl hybrid structure was formed. The Ag/AgCl NPs obtained were utilized as adsorbents to remove Cd(II) and Pb(II) heavy metals. The experimental factors that were optimized included pH (5.5), sample volume (5 mL), eluent type and concentration (2 M HNO₃), adsorbent amount (5 mg), and extraction time (30 min). Under optimized conditions, the limits of detection were determined to be 9.6 and 4.8 µg/L for Pb(II) and Cd(II), respectively. These limits were established within a concentration range of 50-3,000 µg/L.

In rural India, fluoride and arsenic contaminated groundwater causes widespread fluorosis and arsenicosis. To remove these contaminants, this study describes the design, synthesis, and practical implementation of novel domestic water filtration system (non-electric) that uses nano alumina as a key adsorbent material and efficiently process up to 3,500 L of water with fluoride and arsenic concentrations exceeding 2.5 mg/L and 300 μg/L respectively at a flow rate of 4-5 L h^-1. The nano alumina used was laboratory synthesized and characterized using FTIR, SEM, and XRD. Field studies were carried out in two distinct rural areas: Karkatpur (Uttar Pradesh) and Molu Khedi (Madhya Pradesh) in India. Extensive water quality measurements showed a significant decrease up to 100% in arsenic concentrations and over 95% in fluoride concentrations during early cycles while the filtration system kept physicochemical parameters within allowable bounds. Upon adsorbent saturation, an economical and user-friendly chemical regeneration procedure was used to successfully restore the device's function, proving its operational longevity and viability. Collectively, these findings highlighted the development of nano alumina based filtering technology as a viable, expandable, and affordable way to reduce the health hazards associated with fluoride and arsenic exposure in susceptible rural populations.