International Journal of Environmental Research最新文献

In the current work, an efficient co-precipitation technique was used to develop a new Pr₂O₃/ZnO/g-C₃N₄ ternary nanocomposite. Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) were used for vibrational, structural, morphological, and compositional analysis, respectively. The FTIR spectrum confirmed the presence of characteristics vibrational bands associated with the Pr–O, Zn–O, at 603 cm⁻¹ and 546 cm⁻¹, respectively. The XRD pattern revealed the presence of diffraction peaks related to ZnO (hexagonal), Pr₂O₃ (hexagonal) and gC₃N₄ (tetragonal) in the nanocomposite. SEM images revealed that the nanocomposite has porous type morphology with high agglomeration. The photocatalytic activity of nanocomposite was assessed against organic dye pollutant Methylene blue (MB) and antibiotic Ciprofloxacin (CIP) under the sunlight irradiation. The maximum degradation efficiency of nanocomposite for MB 93% at pH = 11 and CIP 97% at pH = 6. The scavenger's experiment was carried out, and the findings demonstrate that the photocatalytic activity of MB and CIP is strongly dependent on the (•OH) and (O₂•-) species. The antimicrobial activity was also analyzed against bacterial strains such as GrampositiveBacillus subtilis, Staphylococcus aureus, and Gram negative Klebsiella pneumoniae, Escherichia colispecies. The results showed higher inhibition zones against Escherichia coli.

The biodegradable film that exhibited excellent physical barrier properties and the function to degrade smelly organic pollutants (SOP) had been successfully synthesized in this study. The organic intercalation-modified bentonite that loaded with CaO₂ (OBT@CaO₂) was synthesized initially, which would serve as a layered nano-filler to modify the long-chain (polybutylene adipate-co-terephthalate) branched polylactic acid (PLA/PBAT), to produce the SOP diffusion control film (OBT@CaO₂-PLA/PBAT, OCPP). OBT@CaO₂ could improve the initial tensile strength (46.5 ± 6.53 MPa) and elongation at break (412.8 ± 9.25%) of OCPP film, and the O₂ transmission rate was as low as 44.89 cc/m²∙day. Although the saturated adsorption capacity of SOP on OBT@CaO₂ decreased with olfactory thresholds, the penetration time correspondingly increased due to reduced degradation difficulty. OCPP film showed excellent SOP control performance in laboratory and large-scale tests at actual pesticide industrial contaminated sites (PICSs). The diffusion fluxes of SOP exhibited a reduction of at least 84.0% within 14 days, and the effectiveness of SOP control increased over time. This improvement is attributed to the gradual release of H₂O₂ by OBT@CaO₂, enabling the effective degradation of SOP that adsorbed on it, thereby continuously providing adsorption sites. The research can provide a low-cost and green approach for long-term diffusion control of SOP at contaminated sites that are not limited to PICSs.

Graphical Abstract

Airborne microplastics have become invisible global threats to all living organisms today. This study was designed for the first time to monitor atmospheric microplastic pollution in the city of Istanbul (Turkiye) through lichens, known as air pollution biomonitors. Epiphytic foliose lichen Xanthoria parietina was sampled from forested areas in 8 different districts on the Asian side of megacity, and searched for clues of microplastics through chemical characterization and microscopic examination. Twelve compounds (aldehyde, alkene, amine, carboxylic acid, ether, hydrocarbon, hydroxide, ketone, methyl, methylene, nitrogen dioxide, and sulfur dioxide) were identified as microplastic components in urban lichen samples taken from all localities with the FT-IR technique used in polymer identification. The most accumulated compound in lichen samples was amine, which is formed as a result of the chemical degradation of plastics. Building blocks of microplastic particles (MPs) such as aldehydes, carboxylic acid and methylene, as well as air pollutants such as SO₂ and NO₂ were also detected. Analysis data were supported by microscopic observations made by applying fluorescent staining method to lichen thalli and MPs were also detected visually. The highest number of MPs seen in the lichen thalli was detected in samples taken from touristic areas in Üsküdar district. Based on the results, in addition to human impact, intense atmospheric microplastic compounds identified by lichen monitoring on the Asian side of Istanbul suggest that these pollutants may have been transported from local plastic waste or industrial areas. This study shows that biomonitoring studies of airborne organic pollutants such as microplastics can be done through lichens.

Graphical Abstract

The efficacy of oxalate–phosphate–amine metal–organic frameworks (OPA-MOFs) as highly efficient slow-release fertilizers in acidic soils is well established. However, a research gap exists concerning the impact of OPA-MOFs on soils with high levels of calcium carbonate. Thus, two distinct types of OPA-MOFs with enhanced specific surface areas were synthesized and utilized in field experiments. A randomized complete block design was employed to evaluate the effect of five different fertilizers on nitrogen (N) and phosphorus (P) contents in wheat at various growth stages cultivated in calcareous soil. The treatments comprised OPA-MOF1, OPA-MOF2, IF1 (urea and triple superphosphate fertilizer), IF2 (ammonium nitrate and triple superphosphate fertilizer), and a control, each with three replications. N and P contents of the wheat plants were assessed at Zadoks growth stages 13 and 40, as well as in the wheat grains. The findings indicated that the highest grain yield was obtained with IF1 (1439.86 kg ha⁻¹), followed by IF2 (1146.83 kg ha⁻¹). In contrast, the OPA-MOF treatments yielded lower grain yields (OPA-MOF1: 1020.64 kg ha⁻¹, OPA-MOF2: 845.06 kg ha⁻¹). Moreover, OPA-MOF1 exhibited a more pronounced effect on the slow release of N and P elements due to its more regular structure, resulting in higher N content during the middle growth stage and a greater grain yield compared to OPA-MOF2.

The decomposition of returned straw is increasing facing the negative impacts by metal nanoparticles (NPs), however, which may be modulated by soil fauna and this modulation effect is unclear. Here, the interactive effects of ZnO NPs with soil fauna on wheat straw decomposition were investigated in a potted rice cropping system. The results showed that ZnO NP below middle concentrations did not significantly influence straw decomposition, and mass loss was mainly driven by microfauna and microbes. High concentrations of ZnO NPs significantly impeded decomposition, mainly by reducing the complexity of fungal communities. This negative effect was ascribed to the promotion of Zn solubilization by bacterial taxa such as unclassified Acidobacteria, Bacteroidetes and Gemmatimonadetes. ZnO NPs had a greater impact on soil microorganisms than on fauna, reduced microbial activity, promoted the released straw nutrients entering into the soil by damaging nutrient transferring microorganisms and dominated the effects on soil stoichiometry. However, soil fauna significantly increased the activities of C- and N-releasing enzymes, decreased the activity of P-releasing enzymes, regardless of ZnO NP concentration, and promoted straw C decomposition. ZnO NPs altered soil microbial community composition, but these changes were modulated by soil fauna. Nonetheless, nutrient transport by fungi such as Ascomycota and Zygomycota and grazing by fauna were the predominant modulators on straw stoichiometry. The results of this study revealed that rational control of soil fauna will be helpful for promoting straw decomposition and efficient recycling of straw nutrients by crops under ZnO NP contamination.

Two bacteria Providencia vermicola IITG20 and Pseudomonas aeruginosa IITG21 were isolated from the local refinery waste sludge and contaminated soil. They were found to be able to utilise different hydrocarbons such as diesel, petrol, kerosene and engine oil for their growth. The effect of process parameters was studied and most suitable conditions for degradation by the selected strains and their mixed culture were found to be 4% v/v initial diesel concentration, 37 °C temperature, pH 7, and 1% v/v initial inoculum concentration. The highest growth was observed for mixed culture. The lowest growth was observed for the pure culture of Pseudomonas aeruginosa IITG21. The degradation of 4% v/v diesel at 37 °C in 15 days was found to be 70 and 76% by the pure culture of Pseudomonas aeruginosa IITG21 and Providencia vermicola IITG20, respectively. Their mixed culture gave 85% degradation. The rate of degradation for diesel was highest for mixed culture compared to pure cultures. The rate constant for first order degradation was 0.154 day⁻¹ for mixed culture. It was higher compared to that of the pure culture of Providencia vermicola IITG20 (0.098 day⁻¹) and Pseudomonas aeruginosa IITG21 (0.83 day⁻¹). Accordingly, the half-life for mixed culture was lower (4.5 days) than that for pure cultures of both bacteria (7.1 and 8.3 days). These findings highlight the potential of mixed bacterial culture of Providencia vermicola IITG20 and Pseudomonas aeruginosa IITG21 for more efficient hydrocarbon degradation in petroleum hydrocarbon contaminated environments, offering promising implications for bioremediation strategies.

Climate change poses challenges to maize production and productivity in eastern Ethiopia. Impact assessment using climate predictions is the prime step to design adaptation strategies. Crop Environment Resource Synthesis (CERES)-Maize model in DSSAT (Decision Support System for Agrotechnology Transfer) was calibrated using GenCalc software. The model was used to simulate change in maize yield in the baseline (1988–2017) and future climate periods (2030s and 2050s) under (Representative Concentration Pathways) RCP4.5 and RCP8.5 scenarios using 17 CMIP5 (Coupled Model Inter-comparison Project Phase Five) GCMs (Global Circulation Models). During calibration and evaluation of the model excellent agreement of measured and simulated anthesis, and days to physiological maturity for all the cultivars with normalized root mean square error (nRMSE) of less than 10% and R² value of 0.99 was obtained. The seasonal leaf area index (LAI) and top weight progressions were also predicted well by the model with d-index of 0.96 and 0.99, respectively. Excellent (nRMSE < 10) to good (nRMSE 10–20) predictions were also obtained for grain yield and tops weight. The average annual temperature would increase by (1.90 ± 0.36) ^oC, (2.45 ± 0.53) ^oC and rainfall would increase (8 ± 5) %, (12 ± 8) % under RCP 4.5 in 2030s and 2050s, respectively across GCMs compared to baseline in the study area. As a result in 2030s yield reduction, – 10.6% to – 15.4% and – 7.4% to – 9.3% in 2050s of maize cultivar was projected across GCMs. In 2030s and 2050s under RCP4.5 and RCP8.5 long maturing cultivars (BH661) on 15th May planting with 130.5 kg N ha^–1 application predicted the highest maize grain yield.

Around 70% of surface in Extremadura, Spain, faces a critical risk of degradation processes, highlighting the necessity for regional-scale soil property mapping to monitor degradation trends. This study aimed to generate the most reliable soil property maps, employing the most accurate methods for each case. To achieve this, six different machine learning (ML) techniques were tested to map nine soil properties across three depth intervals (0–5, 5–10 and > 10 cm). Additionally, 22 environmental covariates were utilized as inputs for model performance. Results revealed that the Random Forest (RF) model exhibited the highest precision, followed by Cubist, while Support Vector Machine showed effectiveness with limited data availability. Moreover, the study highlighted the influence of sample size on model performance. Concerning environmental covariates, vegetation indices along with selected topographic indices proved optimal for explaining the spatial distribution of soil physical properties, whereas climatic variables emerged as crucial for mapping the spatial distribution of chemical properties and key nutrients at a regional scale. Despite providing an initial insight into the regional soil property distribution using ML, future work is warranted to ensure a robust, up-to-date, and equitable database for accurate monitoring of soil degradation processes arising from various land uses.