环境•农林最新文献

In order to study the effects of common ionic components in wastewater on the catalytic performance and salt resistance of the Fe-Bi@γ-Al₂O₃ catalyst, hydroquinone was selected as the target organic pollutant. Five factors, namely cation species, anion species, total hardness, total alkalinity, and TDS were studied to investigate the effects of different ionic components on the degradation of hydroquinone by the Fe-Bi@γ-Al₂O₃ catalyst. K⁺ and Na⁺ had basically no effect on the COD removal rate, and the COD removal rates were 81.43% and 83.81%, respectively, with no significant change from the COD removal rate from raw water (85.24%), Cu²⁺ and Al³⁺ had some inhibitory effect on the COD removal rate, and the COD removal rate was 68.57% and 70.00%, respectively. While, the presence of Fe³⁺, Cl⁻, Br⁻ and SiO₃²⁻ had a significant inhibitory effect on the COD removal rate, and the COD removal rate was 61.90%, 51.90%, 55.71% and 60.48%. The concentration of Ca²⁺ was 50 mg/L and Mg²⁺ was 200 mg/L, the COD removal rate was 57.62% and 60.48%, respectively due to water hardness. The alkalinity had an inhibitory effect on the treatment effect of simulated waste water, when the OH concentration was 1500 mg/L, the COD removal rate was 49.05%. The higher the TDS concentration, the more obvious was the inhibitory effect on the COD removal rate, and the COD removal rate was 41.43% when the TDS was 50,000 mg/L. The intermediates and possible degradation mechanisms after catalytic ozone oxidation of hydroquinone-simulated wastewater by Fe-Bi@γ-Al₂O₃ were investigated by UV spectroscopy scanning, 3D fluorescence spectroscopy scanning, and GC–MS scanning.

This study focuses on assessing the contamination of surficial sediments in the Shadegan International Wetland, southwest of the Persian Gulf, identifying heavy metal concentrations, evaluating pollution levels using multi-element indices, and determining pollution sources through statistical analyses. Sediment samples were collected from fifteen sites across the wetland and analyzed for the concentrations of six heavy metal(loid)s including Arsenic (As), Chromium (Cr), Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn). The results revealed moderate to considerable contamination of sediments with heavy metals such as Cr, Ni, Pb, and Zn, with concentrations exceeding local background levels. Multivariate statistical techniques, including Correlation Matrix and Principal Component Analysis, identified common pollution sources, such as municipal wastewater, industrial effluents, and agricultural runoff. Pollution indices, including the Modified Pollution Index (MPI), the Aggregative Toxicity Index (ATI), the Ecological Contamination Index (ECI), the Contamination Severity Index (CSI), the Toxic Risk Index (TRI), and the Total Toxic Units (STU), were employed to assess pollution severity, revealing moderate to heavy pollution levels in certain areas, primarily attributed to anthropogenic activities. The Safe-Heart indicator, a novel visualization tool introduced in this study, provided a comprehensive overview of sediment pollution, highlighting areas of high contamination and underscoring the urgent need for remediation measures to safeguard the wetland ecosystem and aquatic organisms.

This study presents a comprehensive analysis of hydrocarbon pollution in Ghana's coastal sediments, with a focus on aliphatic hydrocarbons and polycyclic aromatic hydrocarbons. The primary objectives were to identify the sources of hydrocarbon pollution, assess its extent, and understand its implications for environmental management and policy. A total of 15 samples were collected from 5 sampling spots. Soxhlet extraction technique was applied. Analysis was conducted by gas chromatography/flame ionization detector for aliphatic hydrocarbons and gas chromatography/mass spectrometry for polycyclic aromatic hydrocarbons. Isomeric ratios, such as the carbon preference index, low molecular weight to high molecular weight n-alkanes, etc., were used to infer the sources of n-alkanes. Polycyclic aromatic hydrocarbons diagnostic ratios, including Benzo[b + k]fluoranthene/Benzo[a]pyrene, Phenanthrene/Anthracene, etc., were used to predict PAHs sources into petrogenic and pyrogenic sources. The study also utilized statistical tools like principal component analysis-absolute principal component scores-multiple linear regression for a detailed source appointment. The type of aliphatic hydrocarbon detected in samples ranged from C₁₀H₂₂ to C₃₃H₆₈. Concerning aliphatic hydrocarbon, C₂₁H₄₄ has the highest average presence at 5.224 μg/kg of dry mass in sediment samples whereas, C₁₀H₂₂ shows the lowest mean concentration of 1.953 µg/kg of dry mass. The mean concentrations of the polycyclic aromatic hydrocarbons detected in samples ranged from 0.544 µg/kg for Anthracene to 2.168 µg/kg for Acenaphthene. Primary findings revealed a mix of petrogenic and pyrogenic sources in the coastal sediments, evidenced by the varying aliphatic hydrocarbons and polycyclic aromatic hydrocarbons ratios. Notably, the presence of carcinogenic PAHs highlighted potential health risks. The APCS-MLR analysis identified specific sources influencing hydrocarbon pollution. These include crude oil, urban runoff, atmospheric deposition, etc. This research contributes to a better understanding of coastal sediment pollution, serving as a foundation for future environmental policies and sustainable coastal management strategies in Ghana.

In this work, a novel Ca-Fe-Si-S composite was prepared from thermal desorption residue through FeSO₄ impregnation-pyrolysis. The characteristics and modification process of the composite were specified through a series of analytical methods. And stabilization experiments were conducted to investigate the performance of prepared materials. The optimal Ca–Fe–Si–S composite was obtained at impregnation ratio of 10%, pyrolysis temperature of 900℃ and pyrolysis time of 60 min. The modification process included the dissolution of calcium hydroxide, the formation of gypsum and ferrous silicate, the dehydration of gypsum, the reduction decomposition of calcium sulfate, the decomposition of calcium carbonate and the solid reaction at high temperature. The obtained optimal Ca–Fe–Si–S composite was a multifunctional material mainly composed of high contents of Ca, Fe, Si, S, which corresponding to FeS, CaS, Ca₂SiO₄, Ca₃Al₂(SiO₄)₃, Ca₃Fe₂(SiO₄)₃, Ca₅(SiO₄)₂SO₄. The application of 5% optimal Ca–Fe–Si–S composite successfully lowed the leaching concentrations of As, Zn, Cu, Cd in arsenic slag to meet the discharging standard. Meanwhile, the non-specifically bound and specifically bound of As totally decreased by 3.72%, and the acid extractable species of Zn, Cu, Cd reduced by 11.20%, 29.37%, 2.76% respectively. The distribution of stable species for heavy metals significantly increased as united results of surface complexation, chemical precipitation and ion/anion exchange reactions between the prepared composite and heavy metals. The findings of this research provide an effective material for the simultaneous stabilization of multiple heavy metals.

For effective and sustainable water management, assessing the water quality and identifying potential sources that threaten the river system are crucial steps. In the present study, spatiotemporal variation of 20 hydrochemical variables, water quality indices, and multivariate statistics were applied to evaluate the quality of Yamuna River water. In the middle and lower stretch, the levels of electric conductivity (EC), total dissolved solids (TDS), turbidity, dissolved organic matter (DOM), chemical oxygen demand (COD), and nutrients were higher than in the upper stretch. Based on the trophic state index, the upper, middle, and lower stretches were mesotrophic, moderate, and low eutrophic in nature, respectively. In the drinking water category, the water quality index (WQI) ranged from almost good (upper stretch) to inappropriate (middle and lower stretch). Nemerow pollution index (PI_Nemerow) and the comprehensive pollution index (CPI) indicated that most sites were strongly and moderately polluted, respectively. Various point and nonpoint sources of pollution deteriorated the quality of Yamuna water. Spatial cluster analysis divided eleven stations into three groups based on water variables similarity. Discriminate analysis indicated that water temperature, flow, turbidity, pH, dissolved oxygen (DO), magnesium hardness (Mg-H) and COD were the most influencing variables seasonally, while water flow, pH, chloride (Clˉ), DO, Mg-H, and nitrate–N were for spatial variation in Yamuna water quality. Five potential sources were identified using principal component analysis (PCA); anthropogenic, natural, agricultural non-point sources, metrological, and seasonal factors. This study emphasizes the importance of using multivariate statistical techniques to identify variability patterns and develop management plans to improve river water quality by identifying the key variables responsible for maximum deterioration.

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