环境•农林最新文献

Habitat selection by spiders is strongly influenced by biotic factors such as the availability and diversity of prey and abiotic factors such as temperature, humidity, and the structural complexity of the habitat. Structural complexity is an aspect that intensely affects species persistence, population stability, and the coexistence of interacting species. Trees comprise a complex set of microhabitats due to their large biomass and heterogeneity of the architectural components of their trunk surface and branches. Spider species that live on trunks have diversified physiological or morphological adaptations that confer advantages in this environment. In this study, we experimentally examined the habitat choice by the tree-dwelling spiders Selenops cocheleti (Selenopidae), Corinna rubripes (Corinnidae), and Loxosceles gaucho (Sicariidae). We found that microhabitat specialization was restricted to trunk architectural characteristics rather than plant taxonomy. Selenops cocheleti and C. rubripes significantly preferred loose barks and holes in the trunks, respectively, showing that both spider species can evaluate the physical structure of the microhabitat on a fine scale. On the other hand, L. gaucho selected crevices and holes near the base of the trunk without giving much importance to the physical characteristics of the microhabitat per se (e.g., depth, height, length). Our findings indicate that for generalist predators like spiders, coexistence relies heavily on spatial segregation driven by distinct habitat preferences, irrespective of their method for capturing prey.

Climate-smart farming practices are increasingly essential to address the challenges of food security and water scarcity amidst changing environmental conditions. Earthworms play a pivotal role in enhancing soil health and resilience, contributing to sustainable agricultural production. Their activities improve soil structure, facilitate water infiltration, and enhance nutrient cycling, promoting plant growth and development. By sequestering carbon in the soil, earthworms contribute to mitigating climate change. Additionally, they help to reduce soil erosion and improve water retention, leading to more efficient water use and reduced reliance on external inputs. Furthermore, earthworms can help to mitigate the negative impacts of air pollution by reducing the release of harmful gases. Integrating earthworms into agricultural systems can be a promising strategy for adapting to climate change. However, further research is needed to optimize their use and fully understand their potential benefits. By harnessing the ecological services provided by earthworms, we can promote sustainable agriculture and ensure food security in a changing climate.

The temporal and spatial prediction and early warning of soil heavy metal pollution are crucial for preventing and controlling soil environmental contamination and optimizing the utilization of regional soil resources. This study investigates the spatiotemporal prediction and early warning of soil heavy metal pollution in a lead–zinc mining area in Chifeng City, Inner Mongolia. Soil samples were collected at various depths and times across the mining area and its surroundings. A combination of BP neural network and grey prediction models was used to forecast the distribution of heavy metals, providing a basis for soil pollution control and remediation. The BP neural network model showed that As, Cu, Zn, and Cd concentrations exceeded the risk screening values set by the Soil Environmental Quality Risk Control Standard for Agricultural Land (GB15618-2018), with significant enrichment of As and Cd. Pb showed slight contamination. Spatial analysis indicated that contamination was most severe near the mine and decreased with distance and depth. Grey prediction results suggested that As and Cu levels in the mine restoration area would decline over the next three years, with Cu potentially falling below risk levels by 2024. However, As and Cu levels are expected to increase in surrounding agricultural and unremediated areas. The study concludes that the combined use of BP neural network and grey prediction models is effective for predicting and managing soil heavy metal contamination, supporting targeted remediation efforts in mining regions.

Bioremediation is a promising environmental friendly strategy for the treatment of oil drilling waste, which is particularly challenging in arid areas concerned by high petroleum production activities. Microbial consortia adapted to such environmental conditions are required for the implementation of bioaugmentation treatments. Here four metal(loid)s-resistant hydrocarbon-degrading microbial consortia growing at 40 °C were obtained from oil drilling waste maintained in different phyto-management conditions. The microbial consortia exhibited different microbial compositions with the capacity to degrade 15 to 35% of total petroleum hydrocarbon in 15 days. The hydrocarbon degradation resulted in different hydrocarbon fraction profiles underpinned by the presence of 14 specific OTUs revealed by linear discriminant analysis effect size (LEfSe). Each consortium was characterized by the presence of genera groups, defined according to their correlation with the hydrocarbon fractions, explaining their different degradation capacity and the resulting hydrocarbon fraction profiles. Thus, these consortia can be used in combination or successively to implement bioremediation strategies for the treatment of multi-contaminated oil drilling waste in arid environments.

Microplastics (MPs) are plastic debris having a size ≤ 5 mm. The detrimental impact of MPs on the environment and, consequently, their dangerous effects on human health (emerging risk) have attracted much attention in recent years. Contamination by microplastics is difficult to measure, due to the non-standardization of collection, detection, and analysis techniques. This work consists of a bibliographic review of the analysis of the pros and cons of the various existing legislations at the international level, identifying the possible legislative gaps, intending to improve the efficiency of implementation of new policies on plastics and microplastics, offering the possible recommendations to address potential human and environmental health hazards caused by MPs pollution. Future studies on the mentioned subject should focus on a uniformity of methodology for the determination of microplastics and at the same time, offer help to governments, to write a legislative policy on plastics that is valid at the international level, to help the green earth and completely avoid the risk to human health.

Enhancing the efficacy of semiconductor photocatalysts through the doping of rare-earth ions is a viable approach for regulating their behaviour. The current study employs a solvothermal method followed by calcination to produce Bi₅O₇I photocatalysts doped with rare earth elements (Sm, Nd, and Dy). Ciprofloxacin was used as the target pollutant for all produced catalysts. Among all, Sm-doped Bi₅O₇I exhibited optimal degradation efficiency against ciprofloxacin. Sm doping was identified to be responsible for increased visible light absorption and enhanced separation of light-induced carriers, leading to increased performance in photocatalysis. The Sm doped Bi₅O₇I also showed good adaptation to higher initial ciprofloxacin concentrations and the requisite photodegradation stability after four cycles. Furthermore, the up-conversion luminescence feature of Sm increased the catalyst's visible light usage range. The scavenging experiment identified ·O₂⁻, h⁺, and ¹O₂ as active chemicals in the photocatalytic degradation of ciprofloxacin. Based on this fact, a possible degradation mechanism was postulated. This work may serve as a guide for creating doped bismuth-rich halides for waste water remediation.

Gorgan Bay is located along the southeast Caspian Sea and surrounded with significant agricultural and urban areas. Plastic pollution is a significant issue that affects aquatic ecosystems globally. The accumulation and degradation of plastics into microplastics in aquatic ecosystems highlight the importance of studying them to assess pollution risks. So, an investigation was conducted for the assessment of MicroPlastics pollution (MPs) in water and sediment of this ecosystem. The study involved collecting water and sediment samples from 40 stations within the Bay. Microplastics (MPs) extracted from these samples were identified using microscopic detection methods, specifically visual observation under polarized light to SEM–EDX, and µ-Raman. A total of 16,360 MP particles per kilogram of sediment, and 211 particles per liter of water were detected. The research demonstrated that the river inlets situated within agriculturally intensive regions of the watershed exhibited the highest levels of microplastics (MPs) in both water and sediment samples. Fiber MPs were the most frequent (> 50%) shape in sediment and water. The size of mostly MPs (> 90%) was smaller than 1,000 µm. The dominant polymer within MPs in Gorgan Bay sediment identified as polypropylene (PP), polyethylene (PE), and polystyrene (PS), while polypropylene (PP) and polyethylene (PE) were the frequent polymer in water, respectively. The most amount of MPs was found in the areas close to the rivers and agricultural fields (including stations S4, S12, S13, S14, S22).