Clean-soil Air Water最新文献

Nano zero-valent iron (nZVI) has become one of the most widely studied and applied materials for the treatment of a wide range of contaminants in water and soil over the years. Concurrently, micro- and nanoplastics (MNPs) are considered among the top emerging pollutants detected in all environmental compartments. Understanding the interactions between these materials is crucial given the significant interest in nZVI for environmental remediation and the increasing MNP abundance. This article reviewed current literature focusing on the impact of MNPs on contaminant removal by nZVI in water and soil matrices. Most of the studies suggest that MNPs can inhibit the contaminant removal efficiency through mechanisms such as aggregation, blockage of reactive sites, and premature aging. Critically, modification approaches showed promise: Sulfidated nZVI (S-nZVI) was significantly resistant to MNP detrimental effects compared to pristine nZVI and could offset inhibition caused by MNPs to achieve higher contaminant removal. Several factors affecting the interaction between MNPs and nZVI were critically reviewed, including the type and properties of MNPs (e.g., polymer type and surface charge), the type of nZVI (e.g., sulfidated and modified nZVI), and environmental conditions (e.g., pH, ionic strength, dissolved oxygen, and organic matter). It was concluded that further efforts are needed to improve the performance of nZVI–based remediation technologies in the presence of MNPs.

PM_2.5 are highly polluting atmospheric particles, especially in urban areas where they endanger the health of their inhabitants. Their retention by trees can reduce their concentration. This process also brings economic benefits by reducing the incidence of pollution-related diseases. This study evaluated PM_2.5 removal by urban trees and its economic valuation in the Parque Metropolitano Bicentenario (PMB), Toluca City, Mexico, using the i-Tree Eco model, which integrates tree dasometric data and meteorological conditions from the Nueva Oxtotitlán-15211 station, located 3.4 km from the study area. PM_2.5 concentration was obtained from the Toluca Centro monitoring station, located 0.87 km from the PMB, and was analyzed using Fisher's LSD method with a 95% confidence level. Trees retained a total of 120.93 kg year⁻¹ of PM_2.5 in PMB, valued at MXN 1 244 611.34. Hesperocyparis lusitanica retained 102.11 kg year⁻¹ (MXN 1 050 808.68), followed by Pinus montezumae (7.60 kg year⁻¹, MXN 78 176.07), mainly due to their persistent foliage, waxy cuticle, and high canopy cover (52.54%). The highest retention occurred in May–July (50 719 kg year⁻¹, MXN 521 973.45), coinciding with peak leaf area index (LAI 7.07) and biomass (202.63 t). Seasonal changes influenced the dispersion and deposition of PM_2.5. This highlights the importance of evergreen species and canopy structure in maximizing PM_2.5 air pollution mitigation and their economic value under changing climatic conditions.

In the current investigation, geostatistical application and the Water Quality Index (WQI) method were employed to understand the geochemical characteristics, nitrate dynamics, and the susceptibility of different human groups to nitrate pollution in the hard rock aquifers of the Bargarh block in Western Odisha, India. Forty groundwater samples were collected and examined for various physicochemical properties. The ionic composition of the groundwater was found to follow the trend Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ = SO₄²⁻ > HCO₃⁻ > Cl⁻ > NO₃⁻ > F⁻. Geochemical interpretations indicated that inverse ion exchange governs the groundwater chemistry in the region. Nitrate concentrations ranged from 1.1 to 280.03 mg/L, with 45% of the samples exceeded the WHO drinking water limit. The Nitrate Pollution Index ranged from clean to heavily polluted categories. A Human Health Risk Assessment highlighted that children and females are at a higher risk of experiencing adverse non-cancerous health effects from nitrate exposure than males. The primary source of nitrate was widespread agricultural runoff caused by over-application of fertilizers and pesticides, with domestic waste as a secondary contributor. Furthermore, about 38% of the groundwater samples were undrinkable, while the WQI rated more than 40% of the groundwater as poor to very poor quality. Despite this, groundwater was considered appropriate for irrigation based on Sodium Adsorption Ratio, Percent Sodium, Residual Sodium Carbonate, and Kelly Ratio. The findings call for targeted nitrate mitigation, including precision fertilizer use, improved wastewater handling, and long-term monitoring to ensure safe groundwater use for future generations.

Atmospheric visibility serves as a practical indicator of air quality, yet it is often overlooked in environmental evaluation. Therefore, this study investigates long-term visibility trends in Ulsan, a multi-industrial city of South Korea, from 2015 to 2020, and explores major influencing factors. Hourly visibility data were analyzed alongside concentrations of criteria air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃) and meteorological parameters (temperature, relative humidity, and wind speed). The mean visibility in Ulsan was 16.9 ± 2.04 km, with a slight but statistically significant increase of 0.12 km/year. Correlation and mutual information analyses identified relative humidity and concentrations of PM_2.5 and PM₁₀ as the dominant drivers of visibility reduction. Visibility was persistently enhanced in all seasons except winter despite reductions in PM_2.5 and PM₁₀ concentrations. Three machine learning models, including random forest (RF), multiple linear regression (MLR), and multilayer perceptron (MLP), were developed to improve visibility forecasting. Among these, the RF model achieved the highest predictive accuracy (r² = 0.74), outperforming MLR and MLP. The study highlights the need for further investigation into the complex interplay between air quality and meteorological conditions and demonstrates the potential of machine learning for accurate predictions of visibility.

Heavy metals in water can pose a serious threat to biotic health and the environment. Herein, we describe the synthesis and characterization of magnetic graphene oxide (MGO) composites, followed by its application for nickel removal from aqueous solutions, which could help in the mitigation of its negative environmental impacts. The graphene oxide (GO) was prepared through Hummer's method followed by its magnetization to obtain MGO. Its synthesis was confirmed using various techniques such as FTIR, SEM, and EDX. The maximum of 94 ± 4% nickel removal was achieved at the optimized values of pH, contact time, and adsorbent doses that were found to be 7.00, 30 min, and 50 mg with 2 mL adsorbate volume, respectively. The MGO adsorption data were found well suited to the Langmuir isotherm, that revealed the maximum adsorption capacity of 83.69 mg/g for Ni(II) ions. The recyclability experiment resulted in a decrease of 13 ± 3% Ni(II) removal after three consecutive sorption–desorption procedures, reflecting the sustainability and better applicability of the prepared MGO. Our findings demonstrated excellent performance of MGO as compared to GO, proving to be an effective adsorbent and can be utilized to reduce Ni(II) related toxicities from aqueous systems.

This study aimed to evaluate chickpea husk (CH) (Cicer arietinum), an agricultural waste, as a sustainable adsorbent for the removal of cationic thionine (TH) dye from aqueous solution. CH was utilized in raw powder form and after pyrolysis at 300°C (CH@300) and 800°C (CH@800). All materials were characterized using Fourier transform infrared spectroscopy, x-ray diffraction (XRD), surface area analysis Brunauer–Emmett–Teller (BET), scanning electron microscopy (SEM), energy-dispersive x-ray spectroscopy (EDX), and zeta potential. BET surface area analysis revealed that CH has a specific surface area 3.48 m² g⁻¹, whereas CH@300 and CH@800 exhibit 1.78 and 0.082 m² g⁻¹, respectively, indicating reducing in surface area with higher pyrolysis temperature. Among the three adsorbents (CH, CH@300, and CH@800), the raw CH exhibited the highest adsorption efficiency, achieving ∼90% removal of 10 ppm TH dye at 8.5 pH and room temperature, which is higher than of its pyrolyzed samples. The adsorption kinetics of CH adsorbent was fitted best with both linear and nonlinear pseudo-second-order with (>0.99) R² value for. The CH adsorbent has also shown referability up to three successive cycles. The finding demonstrates that CH is an efficient and sustainable biosorption for TH dye removal. Its high adsorption efficiency and good regeneration ability highlight its potential as a low-cost material for wastewater application.

This study integrates land-use/land cover (LULC) dynamics with assessments of irrigation water, soil, and groundwater quality in reclaimed lands west of Mallawi, El Minya Governorate, Egypt. Sixteen groundwater and 16 water-saturated soil samples were analyzed for physicochemical properties, including electrical conductivity (EC), major ions, and salinity–sodicity indices. LULC changes from 2016 to 2025 were evaluated using remote sensing and modeling, while multivariate analyses explored relationships among quality indicators. Groundwater was generally suitable for irrigation, with an average irrigation water quality index (IWQI) of 3.0, EC of 1817 µS/cm, and total salts of 219 ppm—all within FAO limits. However, soil quality showed marked deterioration: EC averaged 8754 µS/cm (maximum 32 400 µS/cm), sodium reached 1132 ppm, and salinity indices were elevated (sodium adsorption ratio [SAR] = 15.1; residual sodium bicarbonate [RSBC] = 9.6; potential salinity [PS] = 21.2 meq/L). Principal component analysis identified EC, Na⁺, Cl⁻, Ca²⁺, and Mg²⁺ as dominant salinization factors, explaining 82% and 69.7% of total variance in water and soil, respectively. Multiple linear regression models accurately predicted IWQI (R² ≈ 1), with EC, Ca, Mg, and Na as key predictors. The mean soil IWQI (3.7) reflected cumulative degradation compared to groundwater. LULC analysis indicated agricultural expansion and agroforestry growth consistent with reclamation policies, alongside urban encroachment and fallow persistence. Without intervention, salinization risks may intensify. The study emphasizes integrated land–water management, including gypsum application, improved drainage, leaching, and salt-tolerant crops, to sustain productivity in reclaimed areas.

The present study aims at determining water pollution due to industrial effluents, exceeding the self-purification capacity of water bodies, compounded by groundwater overexploitation. This study focuses on Nagda's industrial area in Madhya Pradesh, India. Water samples were collected from tube wells, rivers, and surface water from fields during January–June. Key parameters analyzed included potential of hydrogen (pH), total dissolved solids (TDS), chlorides, sulfates, sodium, sodium adsorption ratio (SAR), Kelly's ratio, chemical oxygen demand (COD), heavy metals, and most probable number (MPN). Results show effluent-contaminated water has elevated TDS, chlorides, sulfates, sodium, SAR, and COD, rendering it unfit for irrigation. Groundwater near polluted sites also exhibited high contamination and bacterial presence, whereas unaffected villages in Kharod Block remained suitable for use. Soil in impacted areas showed 2–3 times higher pH and electrical conductivity than normal, becoming sticky when wet and crusty when dry. High SAR levels reduced soil permeability, degrading its texture. The study underscores the need for effluent treatment to mitigate ecological and agricultural damage.

This study evaluates the bioindicator potential of tree species in response to traffic-related air pollution in Urmia, Iran. Nine intersections with varying traffic congestion (low, moderate, and high) were selected, along with the Nazlu campus of Urmia University as a control site. Leaf and soil samples of Cupressus arizonica Greene. (Cupressaceae), Pinus nigra Arnold. (Pinaceae), and Robinia pseudoacacia “Umbraculifera” DC. (Fabaceae) were collected and immediately transferred to the laboratory for morphological, physiological, and biochemical analysis. Results indicated that traffic-induced air pollution reduced leaf area, stomatal density, cuticle layer thickness, and total chlorophyll content, while electrolyte leakage increased across all investigated tree species. The concentration of heavy metals, particularly Pb, in soil and leaf samples increased in correlation with rising traffic congestion levels. Overall, all the studied tree species responded to traffic-related pollution. However, the intensity of their responses varied, indicating that species-specific traits influence both leaf and soil properties under pollution stress. These results confirm the potential of C. arizonica, P. nigra, and R. pseudoacacia as good bioindicators of urban air pollution. The Metal Accumulation Index (MAI) indicated that C. arizonica had the highest capacity to absorb and accumulate heavy metals. This finding highlights C. arizonica as the most tolerant and resilient species to traffic-related pollution among the studied trees, making it an excellent candidate for planting in high-traffic areas in Urmia. These findings offer practical guidance for sustainable urban planning, urban greening, and air quality monitoring, contributing to effective pollution mitigation strategies.