环境•农林最新文献

Microplastics (MPs) are omnipresent in all ecosystems, and sediments are considered as their ultimate sink in marine environment. This study focused on the occurrence, characteristics, and risk of MPs in the beach sediments of Cox’s Bazar, Bangladesh. Sediment samples were collected from a total of 17 sites from four study areas hosting various types of aquacultural, agricultural, animal agricultural, and tourism activities during the peak tourist season. Stereomicroscopy, micro-Fourier transform infrared spectroscopy (μ-FTIR), and scanning electron microscopy (SEM) were used for qualitative and quantitative characterization of MPs. MPs were detected in all beach sediment samples, with abundance ranging from 280 to 1060 items/kg. Overall, small sizes (< 250 μm), fibrous and granular shapes, white and transparent color were the major characteristics for MPs in the beach sediments. The dominant polymer types for the MPs were PP (24.89%), LDPE (21.85%), and HDPE (18.06%). The risk of MPs in the beach sediments was quantitatively assessed with the pollution load index (PLI, 1.0–3.78), polymeric hazard index (PHI, 49.5–70.0), and ecological risk index (ERI, 54.3–232). MPs in the sediments of Kolatoli sea beach had the highest average PLI (2.32), which is indicative of significant pollution, while those in the sediments of Darianagar beach point had the lowest average PLI (1.07). The average PHI values of MPs in the four study areas (57.2 to 63.4) were within the medium range, while the average ERI values (61.1 to 135) were indicative of low to medium ecological risk. While the ecological risk of MPs in the beach sediments of Cox’s Bazar is relatively low due to the dominance of polymers with low risk scores, efforts devoted to the management of plastic waste should be made to prevent further accumulation of MPs.

The monitoring of ions in natural water provides valuable insights into environmental processes and impacts. Liquid ion chromatography (LIC) is a widely utilised method for the analysis of ionic species in water, facilitating the identification of pollution and environmental assessment. Despite its accuracy and reliability, LIC methods are not widely adopted, with outdated methods such as flame photometry and titrimetry still being prevalent. In this study, we employed LIC to assess the concentrations of cations (Na⁺, K⁺, Mg²⁺, Ca²⁺) and anions (Cl⁻, SO₄²⁻) in the surface water of the Styr river, Ukraine. The LIC measurements were conducted using Dionex ICS- 1500 and ICS- 2000 devices, with anion and cation exchange columns and appropriate eluents. Samples were collected on a weekly basis throughout 2023, subsequently stored and handled in accordance with standardised protocols. The analysis of the data was conducted using Pearson's correlation and principal component analysis (PCA), with the objective of identifying underlying relationships between parameters. The greenness of the LIC method was then compared with that of traditional methods using the AGREE software metric. The ion concentrations present in the Styr river exhibited a specific order of abundance: Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ for cations and SO₄²⁻ > Cl⁻ for anions. Pearson's correlation and PCA identified five principal components representing different aspects of the water's ionic composition. The greenness assessment using AGREE indicated that the LIC method is more sustainable compared to traditional methods. The comprehensive evaluation of irrigation suitability, based on various indices, confirmed that the water of the Styr river is suitable for irrigation. The adoption of LIC, coupled with green analytical practices, can significantly enhance water quality assessment, promoting sustainable environmental management.

Graphic Abstract

Extracellular polymeric substances (EPS) are a collection of organic molecules from microbial metabolism, secreted outside the cell to counteract effects of harsh environment, a process that has enabled the application of the biopolymer for wastewater treatment. Reduction in concentration of lead II (Pb²⁺) and chromium VI (Cr⁶⁺) together with other physicochemical parameters from tannery wastewater by EPS from Penicillium expansum was studied. The maximum removal of Pb²⁺ and Cr⁶⁺ were 23.13% and 17.83% respectively under optimized removal conditions of 180 rpm agitation, 90 mg/L EPS concentration, 5.0 pH at  40 °C using a 2-h residence time. However, this low percentage removal of Pb²⁺ and Cr⁶⁺ in the tannery wastewater called for more batches of removal process under optimized conditions. After subjecting the EPS to four batches of removal process, 100% and 89.75% removal of Pb²⁺ and Cr⁶⁺ respectively were achieved in less than 8 h. Based on this, FTIR Analyses showed frequency shifts in alkenyl (C = C), hydroxyl (OH) and carbonyl (C = O) functional groups, and molecular overlapping in the EPS was observed on scanning electron micrograph. Physicochemical parameters in the tannery wastewater were significantly (p < 0.05) reduced by 50% or more with the exception of pH after the duration of the biosorption process. The findings in this study showed that EPS produced by P. expansum could significantly (p < 0.05) decrease the concentrations of Pb²⁺, Cr⁶⁺ and other heavy metals as well as other tannery wastewater physicochemical properties that contributed to its polluting effect.

The prediction of methane (CH₄) concentration is important for pig farming due to its environmental impact on pigs and farm workers. This study examined the utilization of machine learning algorithms, specifically multiple linear regression (MLR), XGBoost regression (XGB), and random forest regression (RFR), to predict CH₄ concentrations in pig barns during the growing-finishing stage of pigs. The dataset included five key input biophysical variables: feed intake (FI), pig mass (MP), carbon dioxide (CO₂) levels, temperature (T), and relative humidity (RH). Data was collected from three experimental pig barns during 2022 and 2023 to train and test the machine learning models. Among the three machine learning models, the RFR consistently outperformed both MLR and XGB in predicting CH₄ concentrations. The results demonstrated better performance by the RFR model in testing (R² > 0.81), with improvements in R² of up to 1.92% and 10.46%, as well as decreases in RMSE of up to 5.74% and 20.51%, compared to the XGB and MLR across the three input datasets. In terms of stability, MLR exhibited the maximum stability, followed by RFR and XGB. Sensitivity analysis found FI to be the most influential input variable for CH₄ concentration prediction, with the impact ranking being FI > MP > CO₂ > T > RH. This study emphasized the potential of machine learning models, particularly RFR, in predicting CH₄ concentrations using relevant input variables. These findings enhance understanding of CH₄ concentration, providing useful insights into pig production and environmental management.

As freshwater resources become increasingly scarce, safe drinking water is critical to public health. However, emergent pollutants and pathogenic microorganisms threaten this goal, posing direct and indirect water quality risks. Photocatalysis offers a promising alternative disinfection strategy, overcoming many limitations of conventional methods by generating reactive oxygen species (ROS) capable of effectively deactivating microbial contaminants through targeted redox reactions. This review explores the photocatalytic disinfection process, detailing microbial inactivation mechanisms, and provides a comparative analysis of commonly used photocatalysts and their composites. Sources of microbial contamination, including bacteria, viruses, algae, fungi, and protozoa, are discussed to contextualize the challenges faced in water treatment. Key operational parameters influencing photocatalytic efficiency and an overview of practical bioreactor types optimized for varied disinfection needs are also addressed. This comprehensive review highlights the advancements and potential of photocatalytic bioreactors in safeguarding water quality and expanding accessible, efficient, and eco-friendly disinfection technologies.

The Reghaïa Nature Reserve (Algeria) comprises a freshwater lake surrounded by natural soils covered by spontaneous vegetation with a habitat for wildlife. It is exposed to 3 mains sources of pollution i.e. an industrial zone, agricultural lands and the urban area. Abnormal high levels of metallic trace elements (MTEs) have been already reported in lake’s water and natural topsoil. In the present study, we explore the tolerance and phytoremediation capacity of Oxalis pes-caprae L., growing spontaneously on Reghaïa lake’s bank, toward Pb, Zn and Cd, the 3 main metal pollutants from anthropogenic activities. Sampling was carried out over 4 months, for Oxalis pes-caprae L. plants and rhizosphere soils, harvested in 3 sites along a gradient of proximity to lake’s water. Firstly, we estimated the degree of contamination of the sampled sites by Pb, Zn and Cd and we determined soils parameters which influence metals distribution in soil and their absorption by O. pes-caprae L. roots. Secondly, we examined the distribution and the accumulation of Pb, Zn and Cd within O. pes-caprae shoots and roots. The results revealed that Pb and Cd were the main soil pollutants and that O. pes-caprae L. adopted different remediation strategies such as an exclusion strategy for Pb in soil and a phytoextraction strategy for Cd.