Environmental changes and heavy metal pollution are some of the consequences of anthropogenic activities. Many ecosystems, including edaphic ecosystems, suffer from the effects of pollution. The accurate assessment of soil heavy metal contamination leads to better approaches for remediating soils. The exploration of different ways, including biological methods, to conduct environmental monitoring is still ongoing. Here, we focus on reviewing the potential of ectomycorrhizal fungi as a natural indicator of soil heavy metal pollution. Mycorrhizal fungi fulfill basic criteria required as natural bio-indicators for heavy metal contamination. These fungi use different mechanisms such as avoidance and tolerance to survive in metalliferous soils. Thus, we promote ectomycorrhizal fungi as natural indicators. This review also synthesizes existing research on ectomycorrhizal mushrooms as natural bio-indicators for heavy metal pollution and the elaboration of mechanisms, by which ectomycorrhizal fungi meet the criteria required for a successful bio-indicator.

Polyurethanes (PU) are integral to many aspects of our daily lives. Due to the extensive use of and difficulties in recycling or reusing PU, it mostly accumulates as waste. Various bacteria and fungi have been reported to degrade PU. We examined the fungus Aspergillus flavus G10 in that regard, after isolating it from the guts of Gryllus bimaculatus , a common cricket species. We observed surficial and chemical changes of PU with atomic force microscopy, scanning electron microscopy, and attenuated total reflectance Fourier-transform infrared spectroscopy. We measured physical changes as loss in tensile stress, stretching force, and weight of PU after incubations. Fungal hydrolysis of urethane bonds in the polymer backbone was demonstrated by detecting the formation of methylene di-aniline during incubations. Trapped CO ₂ during incubations equaled 52.6% of PU carbon. Biodegradation of PU was maximal by fungi cultured on a malt extract medium at 25 °C, pH 12, and 14:10 hrs light to dark ratio. Pretreating PU films with UV light or 1% FeSO ₄ or NaCl solutions further enhanced the rate of biodegradation. A range of techniques are needed to fully characterize the degradation of PU or other plastic polymers and to optimize conditions for their microbial degradation.

Yunnan Province, as a critical ecological security barrier in China, has long been highly susceptible to drought events. Characterizing the spatiotemporal distributions of drought and identifying its driving factors is crucial. Due to the complexity of drought occurrence, linear correlation analysis alone is insufficient to quantify drought drivers and their interactions. This study used the Standardized Precipitation Evapotranspiration Index (SPEI) as a drought indicator to analyze the spatiotemporal trends of drought across Yunnan and its six major river basins. The geographic detector model (GDM) and random forest (RF) were utilized to quantify the impacts of meteorological, topographical, soil, and human activities on drought, as well as the interactions among these factors. The results showed that 63.61% of the study area exhibits a significant drying trend (p-value < 0.05), with the Jinsha River Basin (JSRB) experiencing the highest frequency of extreme drought events. Precipitation (PRE), temperature, potential evapotranspiration (PET), vapor pressure deficit (VPD), and relative humidity (RH) were identified as the primary controlling factors of drought, with factor interactions displaying nonlinear enhancement effects. PRE plays a dominant role in driving drought across Yunnan, whereas elevation primarily influenced drought severity in the JSRB, Lancang River Basin (LCRB), and Nujiang River Basin (NJRB). The RF-based SPEI prediction model demonstrated superior performance in simulating short-term drought (SPEI_1, R² > 0.931, RMSE < 0.279), particularly in the JSRB (R² = 0.947 RMSE = 0.228). These findings provide a scientific basis for regional water resource management applications and drought early warning systems, offering a robust framework for understanding and mitigating drought impacts in ecologically sensitive regions.