Clean-soil Air Water最新文献

In this work, a composite (CuBTC/superparamagnetic iron oxide nanoparticles [SPION]) based on copper, benzene-1,3,5-tricarboxylic acid (CuBTC) and SPION was synthesized by electrochemical method for the magnetic separation of methylene blue (MB) from aqueous solutions. The synthesis of the proposed composite was carried out under various experimental conditions from 1.4 to 5.4 V for 1–5 h and subsequently studied using different techniques. Scanning electron microscopy showed a granular structure, whereas Brunauer–Emmett–Teller results revealed a well-developed surface area of around 182 m² g⁻¹. Fourier transform infrared confirmed the presence of functional groups characteristic to CuBTC and Fe₃O₄, whereas X-ray diffraction revealed the phase structure of CuBTC 1D, CuBTC 3D, and Fe₃O₄ in the obtained composite. Based on the experimental results, the sample synthesized under a potential of 1.4 V for 5 h was selected for MB adsorption studies in the function of adsorbent mass, contact time, solution pH, ionic strength, initial concentration, and temperature. The maximum adsorption capacity was 681 mg g⁻¹, and the adsorption undergoes the Redlich–Peterson and Sips isotherm model. The results obtained for CuBTC/SPION indicate that the nanocomposite is a promising adsorbent for removing MB in synthetic dye water and wastewater.

In the last few years, urban trees have emerged as an effective nature-based solution to mitigate increasing air pollutant levels due to urbanization and industrialization. This study aims to assess the synergistic effect of urban trees on improving air quality by combining real-time PM_2.5 monitoring with the i-Tree Eco model. The monitoring was conducted during rush hours with high traffic volume and during non-rush hours, in both the tree alley and a non-tree road section within the industrial areas of the north-west region of the National Capital Territory of Delhi, India. The i-Tree Eco model was run using the diameter at breast height values of tree species present in the study area, and the PM_2.5 reduction ability of the trees was quantified. The results from both approaches indicated that urban trees can significantly reduce the traffic-fed PM_2.5 concentrations. Therefore, it is suggested that tree plantations be integrated into air pollution management strategies in urbanized regions with high traffic volumes. Although this study explores the initial link between trees and air quality in Delhi, further research incorporating local wind speed and direction measurements would provide a more comprehensive understanding of how trees influence air quality in any highly polluted urban setting.

Leachate from municipal waste contains volatile organic compounds and potentially toxic metals. The leaching of which into water sources also jeopardizes access to clean water. Therefore, reducing the concentration of pollutants in leachate is important to reduce health risks and environmental pollution. In this study, the efficacy of granulated organic leonardite added to leachate from municipal waste in reducing the toxic concentrations of the leachate for different time points (30, 60, 90, and 120 min) at a shaking speed of 200 rpm was investigated. Results demonstrated that leonardite significantly removed various contaminants, including organic acids (71.16%), alcohols (74.31%), aldehydes (68.01%), esters (78.28%), ethers (81.03%), ketones (68.52%), hydrocarbons (84.25%), N compounds (78.56%), S compounds (80.67%), organic N (86.01%), total Kjeldahl nitrogen (93.26%), NH₄-N (84.83%), NO₃-N (89.30%), SO₄ (76.62%), PO₄ (73.85%), organic C (50.07%), Hg (96.80%), Pb (95.99%), Cu (82.68%), Al (65.56%), total Cr (98.11%), Cd (99.28%), Li (96.31%), Ni (97.27%), and As (67.79%). The leonardite granules used in this study showed high adsorption and removal performance for organic/inorganic and volatile compounds in landfill leachate. These results indicate that leonardite can be a suitable adsorption material for leachate pretreatment. However, it is necessary to perform a durability test to use the material in the long term as a covering on landfills.

The aim of this research was to assess the efficacy of different microbial strains in the decolorization of anthraquinone dyes. Strain R81 was obtained from a textile company's wastewater discharge for its remarkable ability to decolorize reactive blue 19 (RB19). By employing physiological and biochemical analyses, along with 16S rRNA gene sequencing, strain R81 was determined to be Brevibacillus laterosporus. After optimization, the decolorization rate achieved a peak of 86.24% over a 48-h timeframe, utilizing an initial dye concentration of 100 mg L^–1. The decolorization capacity of strain R81 was observed to be impeded by heightened levels of salt and temperature in culture solutions, yet remained unaltered when R81 cells were directly introduced into dye solutions. Furthermore, cells that were induced through prior cultivation in a medium containing RB19 demonstrated enhanced efficacy in decolorization compared to noninduced cells. Subsequent analysis indicated that the development of biofilms and the synthesis of polysaccharides by strain R81 were augmented in a concentration-dependent fashion by RB19. Nevertheless, the decolorization efficacy of R81 was impeded by the existence of sodium dodecyl sulfate (SDS) and cetyltrimethylammonium bromide (CTAB), both of which possess the capacity to eliminate polysaccharides. The decolorization capabilities were reinstated by the SDS or CTAB eluent containing polysaccharides, suggesting a reliance on the presence of polysaccharides. The employment of stepwise diethylaminoethyl (DEAE)-cellulose chromatography and decolorization experiments elucidated the importance of a specific polysaccharide in the decolorization process. This study proposes a bacteria-derived polysaccharide as a promising remedy for treating dyeing wastewater contaminated with anthraquinones.

The aim of this study was to evaluate the impact of pandemic-related lockdown on Turkey's air quality throughout time and space. For this purpose, statistical techniques were used to assess daily particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen oxides and nitrogen dioxide (NO_x and NO₂), ozone (O₃), and carbon monoxide (CO). The study's findings showed that, while the lockdown improved air quality in terms of air pollutant emissions, the most notable reduction was in NO₂ and NO_x emissions. When comparing the months prior to the pandemic (November 2019 to January 2020) with the months during the pandemic (November 2020 to January 2021), the declines in NO₂ were 20%, 3%, and 0.5%, respectively. NO_x emissions decreased by an average of 19% and 5% in November and December, respectively, and increased by an average of 16% in January during the pandemic. When the data for the 33 days of lockdown were compared to the data for the same 33 days the previous year, significant differences were determined at several Clean Air Centers, which were two for PM₁₀, two for SO₂, seven for NO_x, four for NO₂, two for CO, and three for O₃, respectively. In this study, pollutant concentrations were found in the following ranges from November 2019 to January 2021: PM₁₀: 3–208 µg m^–3, SO₂: 1–56 µg m^–3, NO_x: 6–600 µg m^–3, NO₂: 4–155 µg m^–3, CO: 1–3921 µg m^–3, and O₃: 2–119 µg m^–3. There were days that exceeded the limit values for PM₁₀.

Due to the critical impacts of microplastic (MP) aggregation on their fate, mobility, and bioavailability, this study developed a simple approach to examine their aggregation under varying water chemistry and MPs’ surface aging conditions. An accelerated photodegradation experiment was conducted for 6 weeks. The water chemistry conditions varied by altering pH, using natural organic matter (NOM), and conducting experiments in ultrapure water and synthetic stormwater. The surface chemistry analysis of photodegraded MPs revealed the formation of carbonyl and vinyl functional groups. Zeta potential measurements revealed a more negative surface charge for photodegraded MPs compared to new MPs. The aggregation kinetics of MPs were studied by comparing the number of MP clusters formed over time after intense dispersion in water. The results showed that the presence of NOMs reduces the aggregation tendency of new low-density polyethylene MPs due to enhanced steric hindrance and electrostatic repulsion. However, variations of pH and utilizing synthetic stormwater versus ultrapure water did not alter the aggregation kinetics of new MPs. The aggregation behavior of photodegraded MPs was significantly different from new MPs. A greater tendency for aggregation of photodegraded MPs was found in the stormwater compared to the ultrapure water. This study contributes to a better understanding of the transport and fate of MPs within the aqueous environment and their subsequent environmental risks.

Air pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short-term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO₂) and particulate matter (PM₁₀), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey–Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short-term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region.