Clean-soil Air Water最新文献

E-waste management is a critical global issue due to its environmental and human health impacts. This study employs multicriteria decision-making (MCDM) methods, specifically the analytical hierarchy process (AHP) and fuzzy AHP, to evaluate and prioritize e-waste management strategies in Markazi Province, Iran. The research follows a structured methodological framework, beginning with an extensive literature review and expert consultations to define key evaluation criteria, including feasibility, cost, environmental policies, public acceptance, and process complexity. AHP and fuzzy AHP methodologies were applied to analyze expert survey data, collected from 60 specialists across academia, government, and industry. The results reveal that recycling (0.34) and reuse (0.31) are the most effective strategies, followed by incineration (0.18) and electronic waste management (EWM) (0.15). The study underscores the importance of structured decision-making frameworks in environmental policy and highlights the necessity of sustainable e-waste management practices to mitigate adverse environmental effects. By integrating qualitative expert judgments with quantitative decision analysis, this research contributes to the global discourse on e-waste governance and offers insights applicable to developing regions facing similar challenges.

Over the last few years, the KrCl* excilamp (222 nm) has emerged as a promising Far-UVC source to drive advanced oxidation processes (AOPs) for degrading organic contaminants in water. In the present study, we explored the effect of common anions (Cl⁻, SO₄²⁻, NO₃⁻, HCO₃⁻) in the concentration range of 1–100 mM on the kinetics of degradation and mineralization of ceftriaxone (CEF) by Far-UVC/H₂O₂ process using excilamp in synthetic (deionized water + anions) and real (tap) water. Nitrate at 1 mM and other anions at all concentrations exhibited a promotion effect by accelerating the degradation rate. It can be attributed to the contribution of secondary radicals generated by anions under Far-UVC exposure. However, the mineralization in terms of total organic carbon (TOC) removal decreased in the presence of HCO₃⁻ and NO₃⁻. When reducing the H₂O₂ dose, chloride ions provided significant process intensification by increasing the rate constant by 3.5 times and reaching 30% TOC removal along with complete CEF degradation. Examining tap water revealed the high performance of the Far-UVC/H₂O₂ process for degrading and mineralizing CEF without the inhibition effect of matrix components. Results demonstrate that anions as radical precursors tend to promote the oxidation processes rather than inhibit them. This opens up good prospects for practical application of KrCl* excilamp in water treatment.

Using waste materials as adsorbents helps in attaining the goal of the circular economy for sustainable environmental development. Numerous wastes have undergone testing for their potential for the adsorption of contaminants and toxicants. The motive of the presented work was to appraise the phosphorus (P) adsorption capacities and physicochemical characteristics of brick and tile waste for use as a substrate in the constructed wetlands (CWs). The data showed that the P adsorption capacity for materials increased with the initial concentration. The nonlinear regression analysis of the experimental data revealed that the Langmuir isotherm was more suitable for the brick waste and tile waste study. For brick waste, the maximum adsorption was 1.86 mg/g, and for tile waste, it was 0.65 mg/g. The P adsorption capacity of both materials was reduced in batch studies with domestic wastewater. The increase in pH of the solution greatly reduced the adsorption capacity of brick waste, whereas the reduction was marginal for tile waste. The microstructure of both materials showed the presence of pores on the uneven surface, which increased the specific surface area and facilitated adsorption. The ability of both materials to adsorb in column studies was less compared to the Langmuir isotherm adsorption value due to the dynamic environment setting. Both materials showed the potential for use as substrates in the CW for the elimination of P.

In this study, the degradation of synthetic micropolyester effluent (SMPE) was investigated using the microbubble ozonation technique (MOz). A novel approach for quantifying microplastics based on chemical oxygen demand (COD) was adopted in this study. The degradation efficiency under varying process parameters, like pH, contact time, and initial concentration, was evaluated on the basis of the one factor at a time (OFAT) method. Response surface methodology (RSM) using the Box–Behnken design (BBD) was used to optimize the parameters further. Overall, 86% degradation at an optimum pH of 9, a contact time of 60 min, and an ozone flow rate of 1.42 × 10⁻⁵ m³/s was achieved. Kinetic analysis revealed pseudo-first-order reaction behavior. Fourier transform infrared (FTIR) and GC–MS analyses confirmed the breakdown of complex microplastic additives into smaller, oxidized, and potentially biodegradable compounds. Thus, the MOz process effectively reduced the toxicity of SMPE by transforming persistent compounds into less hazardous by-products, enhancing environmentally safe treated effluent. This study establishes a foundation for employing COD as a reliable parameter for measuring the degradability of microplastic, also highlighting the potential of MOz in microplastic effluent treatment.

In the current study, we present two main components: (1) A detailed survey based on a structured questionnaire answered by farmers of Purba Medinipur district of West Bengal, India, and (2) the bioremediation of chlorpyrifos and its metabolite 3,5,6-trichloro-2-pyridinol (TCP) by Bacillus paramycoides. The survey findings indicated that out of 51 reported pesticides, herbicides were most commonly used (76%), with 9% classified as carcinogenic. Notably, 65% respondents reported health-related issues connected to pesticide use. We isolated a bacterium from paddy leaves, identified as B. paramycoides through 16S rRNA gene sequencing. In controlled batch assays, this bacterium effectively degraded chlorpyrifos (at a concentration of 120 mg/L) by 83.08% within 9 days under stationary conditions at a temperature of 35°C ± 2°C and pH 7.5 in M9 minimal medium. Additionally, TCP, a persistent and toxic metabolite of chlorpyrifos, was degraded by approximately 50% over 12 days of treatment. Both chlorpyrifos and TCP demonstrated statistically significant efficacy concerning the duration of microbial treatment (p < 0.05). Further, pot experiments involving Vigna radiata (mung beans) indicated enhanced seedling growth when amended with the degraded products of chlorpyrifos and TCP. This mineralization process contributed to better plant growth compared to the control and those treated solely with pesticides. Biocompatibility assessments in fish erythrocytes showed that the hemolysis caused by chlorpyrifos was significantly reduced when treated with the bacterium. These findings suggest the potential application of B. paramycoides in soil amendment for the detoxification and mineralization of chlorpyrifos, ultimately enhancing soil fertility.

This study investigates the potential of cabbage (Brassica oleracea var. capitata f. alba) leaves as a low-cost, eco-friendly adsorbent for the removal of heavy metals—Cu(II), Pb(II), and Cd(II)—from aqueous solutions. The research aims to evaluate single, binary, and ternary metal systems under experimental conditions with initial metal concentrations (20–200 mg/L) and contact time (10–180 min). Adsorption efficiency increased with initial metal concentration, reaching saturation at 40 ppm for Cu and Pb. In competitive binary and ternary systems, Cu exhibited the highest adsorption capacity, followed by Pb and Cd (Cu > Pb > Cd), likely due to differences in ionic radius and hydration energy. The adsorption mechanism predominantly followed chemisorption, as indicated by the pseudo-second-order kinetic model. Kinetic studies revealed that the adsorption process follows the pseudo-second-order model more closely, whereas equilibrium data fitted well with the Langmuir isotherm, indicating monolayer adsorption. The maximum adsorption capacities (q_max) for Cu(II), Pb(II), and Cd(II) were found to be 42.5, 56.8, and 33.2 mg/g, respectively. The results demonstrate the effectiveness of cabbage leaves in treating heavy metal contaminated water and highlight their potential application in sustainable wastewater treatment technologies.

Studies on risks associated with atrazine have largely focused on a single exposure pathway, paying less attention to potential integrated risks from multiple avenues. Health risks associated with exposure to atrazine residues in drinking water, soil, and cassava from three farm settlements in Ago-Iwoye, Nigeria, were evaluated. Drinking water, soil, and cassava tubers collected from each farm settlement were analyzed for atrazine residues using a standard method. The mean values of atrazine obtained were used to evaluate carcinogenic and non-carcinogenic risks associated with its exposure in adults and children. Atrazine in soil ranged from 0.120 to 0.310 mg/kg. Stream and well water recorded a range of 0.020–0.070 mg/L, but cassava recorded a range of 0.003–0.005 mg/kg. The hazard index for children and adults exposed to water and soil was below the risk limit. Although the incremental lifetime cancer risk for soil was below the threshold risk limit in adults and children, it was slightly above the limit for water. The human risk index associated with cassava consumption was below the threshold values for adults (0.35), but not for children (1.65). Water or cassava exposure, excluding soil, from the farm settlements may pose high risks, especially to children.

Situated within the metallogenically critical Nanling metallogenic belt of Hunan Province, the Sixi River basin exemplifies subtropical watersheds experiencing compounded anthropogenic pressures from historic tin mining and intensive agriculture. This hydrogeochemical investigation examines heavy metal contamination dynamics across aquatic matrices in this Pearl River tributary. Field analyses reveal severe Hg (20× WHO guidelines) and As exceedances with distinct spatial stratification: contamination frequencies follow tailings dams (87.61%) > ponds (81.86%) > rivers (67.64%) > wells (71.76%), posing significant neurotoxic and carcinogenic risks. Dominant HCO₃–Ca·Mg hydrochemical facies reflect carbonate-granite weathering regimes, with ionic concentrations declining from tailings (12.01 mg/L) to wells (7.40 mg/L). Pollution indices demonstrate pH-dependent metal mobility, where alkaline conditions (pH > 8.5) exacerbate Hg/As dissolution in lotic systems. Principal component analysis delineates dual pollution pathways: PC1 (33.3% variance, As–Hg–Cu) traces agricultural inputs in alluvial plains, whereas PC2 (19.9%, Tl–Pb–Sn–Mn) aligns with fault-controlled sulfide mineralization in the Bailashui tin belt. Critically, anthropogenic loading from fertilizer-enriched runoff exerts greater influence on basin-wide degradation than mining effluents, underscoring the lithogenic–anthropogenic interface in subtropical mining watersheds.

The Loess Plateau, a vital ecological region in China, suffers from severe soil pollution and erosion. The soil fertility index (SFI) is a key indicator for assessing soil conditions, and understanding its spatial distribution and influencing factors is crucial for effective soil management. Machine learning methods, capable of analyzing complex and high-dimensional data, offer potential for large-scale SFI prediction. This study focuses on Lanzhou, a representative city on the Loess Plateau, using soil samples and the data of five key factors screened from environmental big data to train three machine learning models (random forest [RF], LightGBM, and XGBoost) for SFI prediction. The results show that all models effectively matched reference data trend, with XGBoost achieving the highest performance (R² > 0.81). Notably, normalized difference vegetation index (NDVI) and soil organic carbon density (SOCD) emerged as the dominant predictors, collectively contributing over 80% to SFI prediction accuracy. Predicted SFI values in Lanzhou ranged from 0.09 to 0.91, with medium and lower quality soils predominantly located in central and north-central regions, highlighting the need for soil quality improvement. This study provides a theoretical basis and scientific support for large-scale SFI prediction.