Environmental Nanotechnology, Monitoring and Management最新文献

Nanotechnology has rapidly expanded across various fields, yet its application in agriculture remains underexplored. This study investigates the impact of zinc oxide (ZnO) and magnesium oxide (MgO) nanoparticles on maize cultivation, comparing commercial samples with those synthesized by combustion reaction. Synthesized ZnO and ZnO/MgO (1:1 by mass) were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and transmission electron microscopy (TEM) to determine particle size and morphology. The experimental design assessed the effects of different treatments on magnesium and zinc uptake in maize roots and leaves, using atomic absorption spectrometry (AAS) for analysis. Results indicate that commercial ZnO significantly increased Zn absorption compared to synthesized samples and the control group, highlighting the influence of particle size and surface area on nutrient uptake. This study provides valuable insights into the potential of nanomaterials into the plant’s absorption mechanism as well as show that the availability of Zn NP synthesized contributes to the absorption of zinc by the plant without competing with Mg. On the other hand, when in Zn commercial, Mg absorption may be impaired.

Emerging contaminants (ECs) are a diverse group of chemicals that have recently been identified as potential threats to human health and the environment. ECs are typically found at low concentrations (ng/L to ug/L) in water and wastewater, but they can bioaccumulate and biomagnified in the food chain, posing a risk to aquatic life and humans. Sources of these contaminants are diverse, with pharmaceuticals and personal care products entering the environment through human excretion, while industrial chemicals and pesticides are introduced through manufacturing processes and agricultural runoff. Wastewater treatment plants (WWTPs) are often unable to remove ECs effectively so that they can increase in surface water, groundwater, and drinking water. The fate of ECs in the environment is complex. It depends on various factors, including the chemical properties of the EC, the environmental conditions, and the presence of other chemicals. ECs can be transported long distances in water and persist in the environment for years or even decades.

Developing countries like India have limited information about most of the ECs. The ecological risks of ECs are not fully understood, but there is growing concern that they can have a negative impact on aquatic life and human health. Furthermore, the EC has undergone a detailed risk assessment examination, and the risk quotient (RQ) for different aquatic species with respect to corresponding contaminants is also calculated. Results imply that Paracetamol and Bisphenol-A have high RQ values for algae, fish and daphnia. Algae have shown substantially greater resilience to the action of ECs among the selected aquatic species.

This work aims to investigate the atrazine (ATZ) mitigation by an advanced oxidative process. Atrazine is one an effective herbicide which has been detected in water sources, causing contamination problems. To address the persistent issue of contamination, ATZ degradation and mineralization were studied by ozonation. In addition, the eco-toxicity of the possible degradation byproducts was also evaluated by the Quantitative Structure-Activity Relationship (QSAR) OECD toolbox. To evaluate the influence and predict the optimum conditions of the ozonation process and the reaction time on the degradation of ATZ, as well as, the percentage of mineralization, an experimental design was performed based on factorial design 23 methodology with center-point analysis. Total organic carbon (TOC) analyses and High-Performance Liquid Chromatography (HPLC) were employed to evaluate the efficiency of ATZ mitigation. The optimal conditions were achieved at an ozone flow rate of 0.4 mL/min, oxidation time = 30 min, and pH=8 where 100 % of ATZ was degraded and the highest percentage of mineralization was obtained (25.61 %). The potential toxicity of the residual concentration of ATZ was obtained by comparing with the values predicted by the QSAR tool, by comparing the outcomes. It was possible to come to the conclusion that the approach had positive implications for environmental safety. The values obtained are below the values considered toxic in aquatic environments, in almost all experiments. Low-concentration byproduct formation suggests that the degradation routes lead to low-hazardous concentrations of compounds for the environment. This implies the ozone treatment strategy might offer a long-term remedy for the ATZ.

In recent times, there has been a growing trend in utilizing medicinal plant extracts for the fabrication of fluorescent nanomaterials. In this work, Tinospora cordifolia-copper nanoclusters (T. cordifolia-CuNCs) were produced by employing Tinospora cordifolia (common name is “giloy”), a medicinal plant. A green chemistry approach was employed to generate blue fluorescent T. cordifolia-CuNCs, displaying λ_Em at 430 nm when λ_Ex at 330 nm, which shows a good quantum yield (QY) of 26.67 %. Sulfosulfuron pesticide was able to quench the fluorescence intensity of T. cordifolia-CuNCs via a “turn-off” mechanism. It was noticed that T. cordifolia-CuNCs could be used for the detection of sulfosulfuron pesticide in the range of 0.025–90 µM with a detection limit of 6.52 nM. Furthermore, a cellulose-based paper strip sensor was created for the visual detection of sulfosulfuron pesticide. Moreover, T. cordifolia-CuNCs-based fluorescence method was applied to quantify sulfosulfuron pesticide in apple, tomato, and rice samples, showing good recoveries, which demonstrates that this probe offers great potentiality for sensing of sulfosulfuron pesticide in food and environmental samples.

The present work includes a facile and economic microwave assisted hydrothermal synthesis of Zinc Oxide (ZnO), Diethylene Triamine Pentaacetic Acid (DTPA) stabilized Zinc Oxide (ZD) and DTPA stabilized Silver doped Zinc Oxide (ZAD) nanostructures using Zn from spent alkaline batteries. The synthesised nanostructures were well characterised using electronic, vibrational and X-Ray spectroscopic techniques as well as thermal and microscopic techniques revealing the successful stabilisation of DTPA in ZD and doping of Ag in ZAD. The Fourier Transform Infrared Spectroscopy (FTIR) spectra showed peaks characteristic to the presence of ZnO in the fingerprint region and those to the presence of DTPA. The X-Ray Diffraction Spectroscopy (XRD) pattern of ZnO, ZD and ZAD indicated the hexagonal wurtzite structure of ZnO and face centred cubic metallic Ag in ZAD. The Transmission Electron Microscopy (TEM) images revealed rod shaped morphology for ZnO and spherical morphologies for ZD and ZAD. The nanostructures proved to be efficient catalysts to achieve 100 % degradation of Malachite Green, Crystal Violet and Reactive Blue-21 and their binary mixtures under ambient conditions in presence of Hydrogen peroxide (H₂O₂). ZAD exhibited relatively rapid degradation with rate constants 0.754 min⁻¹, 0.187 min⁻¹ and 0.0150 min⁻¹ for MG, CV, and RB-21 respectively as well as 99 % reduction in Chemical Oxygen Demand (COD) value of the dye solutions. Scavenging studies and Electron Paramagnetic Resonance (EPR) studies using different spin trapping agents revealed the involvement of singlet oxygen species, hydroxyl radicals (OH^.) and superoxide radicals (O₂^.-) in the degradation process. This work aligns with Sustainable Development Goals 6, 12 and 13.

Engineered nanoparticles (ENPs) are of particular concern due to their ubiquitous occurrence and potential to cause adverse effects on aquatic biota. Consequently, a comprehensive understanding of ENP interactions and the mechanisms that underpin their fate and behaviour in the aquatic system is important to support their long-term applications and protection of ecology. However, due to a wide range of physicochemical parameters, as well as possible dynamic interactions with natural colloid particles, it is not practical to undertake experimental testing for each variation of ENPs using different aquatic permutations. This study describes machine learning (ML) algorithms for prediction of nZnO dissolution in aquatic systems using experimental data. The input parameters with the highest correlation were size and pH. On the contrary, categorical input variables such as coating, coating type, salt, and NOM type had a low correlation. The random forest regression and the extreme gradient boost algorithms performed remarkably well, with coefficients of determination (R²) of 0.85 and 0.92, respectively. The least effective method was multiple linear regression, which had a root mean square error of 0.15 and an R² of 0.31. ML offers a convenient and low-cost approach for screening nZnO dissolution in aquatic systems.