The Tibetan Plateau (TP), often referred to as the 'Asian Water Tower', plays a critical role in regulating the hydrological cycle and influencing global climate patterns. Its unique topography and climatic conditions result in pronounced seasonal freeze-thaw (FT) dynamics of the land surface, which are critical for understanding permafrost ecosystem responses to climate change. However, existing studies on FT dynamics over the TP are limited by either short observational periods or deficiency in accuracy, failing to capture the long-term FT processes comprehensively. This study presents a novel satellite-based approach for monitoring the FT dynamics over the TP from 1979 to 2022, utilizing passive microwave observations. We developed a new algorithm that integrates discriminant function algorithm (DFA) with a seasonal threshold algorithm (STA), employing the freeze-thaw index (FTI) as the classification variable to determine optimal FT thresholds. The strong performance of the algorithm was confirmed by in-situ validation, with an overall accuracy of 91.46%, a Kappa coefficient of 0.83, and an F1-score of 0.92, outperforming other remote sensing-derived FT products such as SMAP (OA = 89.44%, Kappa = 0.79, F1 = 0.89). Results reveal significant changes in surface freeze-thaw dynamics over the past four decades. Between 1988-2022, frozen days exhibited a significant decreasing trend of -0.19 daysyear(-)(1), primarily attributed to the delayed freeze onset (0.19 daysyear(-)(1)), while thaw onset showed no significant trend. Spatially, permafrost regions experienced a more pronounced decrease in frozen days and earlier thaw onset compared to seasonally frozen regions. Moreover, marked interannual trend differences in FT processes were observed across elevation gradients, with higher elevations showing more negative trends in frozen days and thaw onset. This study provides a reliable and up-to-date analysis of surface FT process changes over the TP, informed by long-term satellite-based observational perspectives. These analyses revealed marked spatial heterogeneity in surface FT dynamics across the TP region, underscoring the impacts of climate change on the cryosphere and hydrology.

Soil moisture is a vital parameter for a variety of applications including hydrological modelling and climate change studies, particularly in permafrost regions where freeze-thaw processes and complex terrain pose significant monitoring challenges. This study evaluates the accuracy of seven surface soil moisture (SSM) products (SMOS-IC, ESA CCI, AMSR2 LPRM, SMAP-L3, SMAP-L4, ERA5-Land, GLDAS-Noah) and three root-zone soil moisture (RZSM) products (SMAP-L4, ERA5-Land, GLDAS-Noah) using in situ observations from 19 stations in the permafrost region of the Heihe River Basin, China, from 2012 to 2020. Focusing on the thawing season (July-October), the analysis employs statistical metrics including Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), bias, and slope. Results indicate that SMAP-L3 and SMAP-L4 exhibit the highest SSM accuracy (R = 0.24 and 0.23, respectively) with low ubRMSE (0.037-0.038), while ERA5-Land shows the best RZSM correlation (R = 0.43) but may indicate the presence of systematic biases, nonlinear responses, or limitations in dynamic range, among other issues (slope = 0.01). Environmental factors such as precipitation, land surface temperature, and normalised difference vegetation index significantly influence accuracy. Spatial variability and scale mismatches highlight the need for improved land surface models and data assimilation. This study provides critical insights for selecting and refining soil moisture products to enhance hydrological and climate research in permafrost regions.

The High Arctic deserts of remote northern Greenland are expected to become warmer and wetter due to climate change. Precipitation changes will increase fluctuations in surface soil salinity, and the same happens for thawed permafrost soil where stable salt concentrations are replaced with fluctuating salinity during annual freeze-thaw cycles. Both have unknown effects on the microbial communities and their emissions of microbial volatile organic compounds (MVOCs). These compounds are produced from various pathways mainly as secondary metabolites and have ecological and climatic implications when released into the environment and the atmosphere. Thus, it is important to explore the effects of environmental changes, such as changes in salinity, on soil microbial communities and their MVOC emissions. Here, we characterize the MVOC production of three novel bacterial isolates from northern Greenland throughout their growth period under low, moderate, and high salt concentrations. We demonstrate that salinity significantly alters both the quantity and composition of MVOCs emitted by all three strains, including changes in the emissions of sulphur- and nitrogen-containing compounds, potentially leading to ecosystem nutrient loss. The observed changes in MVOC profiles suggest that changes in soil salinity due to climate change could alter microbial metabolism and MVOC emissions, with potential implications for Arctic nutrient cycling and atmospheric chemistry. Novel Arctic bacterial isolates were found to produce diverse microbial volatile organic compounds, including sulphur- and nitrogen-containing gases, with emissions strongly shaped by changing soil salinity

Aim Alaska's boreal forest is experiencing increasingly severe fires, droughts, and pest attacks that may destabilize carbon sequestration. Our aim was to understand boreal forest resilience to changing wildfire regimes using remote-sensed datasets validated with ground-truthing (GT).Location Five recently burned boreal forest sites (2010-2019) near Fairbanks, Alaska.Methods We used four AVIRIS-NG hyperspectral image datasets (425 spectral bands at 5-nm intervals; 3.5 x 43 km average swath) imaged by NASA in 2017-2018 during the Arctic-Boreal Vulnerability Experiment (ABoVE). Spectral analysis included fire fuel loads and random forest (RF) models constructed from key bands to describe common pre- and postburned vegetation classes. Models were validated with 89 GT plots inside the AVIRIS scenes. GT included tree stem densities, understory cover, soil characteristics, radial growth of 51 spruce trees from cores, and visual damage assays of 668 conifers and deciduous trees.Results Spectral evidence of high fuel loads in 2017 pre-dated a 2019 wildfire. Post-GT local models described vegetation more accurately than pre-GT, but accuracy decreased when spectral rulesets were broadened to increase overall classification. Soil temperature, basal area, slope, elevation, and tree density varied widely; thaw depth, soil moisture, moss cover, and canopy height varied mainly by vegetation class. Invasive species and thermokarst were insignificant. Deciduous seedlings were abundant in postburned sites; however, conifer seedling densities were similar to unburned forest. Upland spruce radial growth showed earlier drought sensitivity than lowland spruce.Conclusion Spectral analysis revealed fire vulnerability in some areas; however, local and temporal spectral variation presented challenges to accurately classify vegetation in AVIRIS scenes. GT suggests that recovering forests near Fairbanks may lack sufficient conifer recruitment to replace existing stands. Sites with stable seasonal thaw may offset drought stress under global warming.

Objective Absorbing aerosols, particularly black carbon (BC), exerts significant influence on the Earth's radiation budget by modifying both the amount and vertical distribution of solar radiation. Their climatic effects are especially pronounced in regions characterized by concentrated fossil fuel activities, such as large-scale coal mining areas. However, the spatial and temporal variability of their microphysical and optical properties introduces considerable uncertainty into regional radiative forcing assessments. The Zhundong Coalfield, located in eastern Xinjiang, China, is one such region where BC emissions from coal extraction and associated industrial activity are persistent yet under-characterized from a radiative perspective. This study aims to construct a rapid estimation framework for aerosol radiative forcing (ARF) over this region by integrating multi-band satellite observations with physically based scattering and radiative transfer models. The primary goal is to evaluate how aerosol optical depth (AOD), single scattering albedo (SSA), and particle size influence shortwave ARF at the top of the atmosphere (TOA), bottom of the atmosphere (BOA), and within the atmospheric column (ATM), and how ultraviolet-band data enhances the reliability of this estimation. Methods The research adopts a modular approach comprising aerosol property inversion and radiative transfer modeling. The aerosol inversion is based on a Mie scattering model incorporating a core-shell structure assumption, where BC forms the absorbing core and is coated by non-absorbing substances such as sulfate and nitrate. Satellite-derived aerosol products are used to constrain the model: MODIS provides AOD and SSA at visible wavelengths, while OMI contributes ultraviolet (UV) -band SSA and AOD information. Two experimental configurations are established-one based solely on MODIS data, and another integrating both MODIS and OMI-to assess the role of UV spectral information in constraining aerosol characteristics. Following inversion, the retrieved aerosol size and optical parameters are used as input to the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model to simulate instantaneous ARF at TOA, BOA, and ATM under clear-sky conditions. Radiative forcing is calculated as the difference in net shortwave flux with and without aerosols. Multiple linear regression models are then constructed using different combinations of AOD, SSA, and core radius to quantify the relationship between these parameters and simulated ARF. Regression performance is evaluated using R (2) and RMSE statistics across both single-source and combined-source scenarios. Results and Discussions First, the inclusion of OMI UV-band data significantly improves the inversion accuracy of aerosol particle size characteristics. When only MODIS data are used, the retrieved BC core sizes are relatively narrow, mostly centered around 120 nm, and the shell diameters exhibit limited variation. However, when OMI UV observations are incorporated, the core size distribution broadens, capturing particles ranging from 90 to 160 nm, while the shell diameter spans a wider interval of 300?700 nm. This improved resolution stems from the stronger sensitivity of UVs to absorption by fine-mode particles, which enhances the model's ability to distinguish subtle differences in particle morphology. The resulting total particle size distributions-core plus shell-are more consistent with reported field measurements in coal-intensive regions. These results confirm that UV data not only improve inversion detail but also reduce the uncertainty in the wavelength in the representation of aerosol mixing states. Second, the quantitative relationship between optical parameters and ARF demonstrates clear physical consistency across TOA, BOA, and ATM layers. In both MODIS-only and MODIS-OMI configurations, AOD exhibits a strong negative correlation with TOA and BOA radiative forcing (R=-0.77 and -0.78, respectively), indicating a cooling effect due to enhanced scattering and absorption of incoming solar radiation. SSA also shows a strong negative correlation with TOA and BOA forcing (R=-0.78 and -0.62, respectively), suggesting that as the aerosol becomes more scattering-dominant, its net radiative cooling effect intensifies. Conversely, AOD shows weaker but positive correlations with ATM forcing (R=0.43), suggesting an increase in atmospheric heating when aerosol loading or absorption increases. This pattern aligns with physical expectations: absorbing aerosols like BC trap energy in the atmosphere, contributing to vertical energy redistribution. The analysis confirms that SSA has a stronger explanatory power than AOD, emphasizing its role as a key driver of radiative uncertainty forcing. Third, regression model performance improves markedly with the inclusion of SSA and core size as input parameters. Under the MODIS-only scenario, models using AOD alone yield limited explanatory power, withR (2) values of 0.59 (TOA), 0.61 (BOA), and 0.18 (ATM). Adding SSA improves the fits substantially, increasingR (2) to 0.78 (TOA) and 0.67 (BOA), and to 0.21 in the ATM. Incorporating core radius into the model yields additional gains, raisingR (2) in the ATM layer to 0.23 and lowering RMSE values across all layers. In the MODIS-OMI fusion scenario, even though the number of valid observation days decreases significantly (eg, from 2589 to 954 days at the Wucaiwan site), model performance continues to improve. For example,R (2) for ATM forcing increases from 0.18 to 0.29, and RMSE decreases from 2.04 to 1.85. These results suggest that high-spectral-resolution UV data provide greater constraint on aerosol absorption properties, thereby enabling more physically consistent radiative forcing estimates, even with reduced samples. This finding supports the robustness of UV-enhanced satellite inversion strategies in regional ARF modeling. Conclusions This study presents a data-model integration framework for estimating ARF over coal mining regions using multi-source satellite observations and physically based scattering and radiative transfer models. The combination of MODIS visible and OMI ultraviolet aerosol products improves the inversion of absorbing aerosol particle size distributions and enhances the retrieval of SSA, especially under complex mixing conditions. The constructed regression models reveal that SSA exerts a greater influence on radiative forcing than AOD, and that including particle size parameters further strengthens model reliability. Despite a reduction in observational frequency due to OMI's narrower sampling, the incorporation of UV-band information leads to consistently improved model performance across all atmospheric layers, particularly in the atmospheric column. These results highlight the critical role of spectral diversity in satellite remote sensing for accurately characterizing the radiative impacts of absorbing aerosols, and demonstrate the feasibility of applying such approaches to high-emission, data-scarce environments like the Zhundong Coalfield.

The freeze-thaw erosion zone of the Tibetan Plateau (FTZTP) maintains an ecologically fragile system with enhanced thermal sensitivity under climate warming. Vegetation phenology in this cryosphere-dominated environment acts as a crucial biophysical indicator of climate variability, showing potentially amplified responses to environmental changes relative to other ecosystems. To investigate vegetation phenological characteristics and their climate responses, we derived key phenological parameters (the start, end and length of growing season-SOS, EOS, LOS) for the FTZTP from 2001 to 2021 using MODIS EVI data and analysed their spatiotemporal patterns and climatic drivers. Results indicated that the spatial distribution of phenology was highly heterogeneous, influenced by local climate, complex topography and diverse vegetation. SOS generally exhibited a delayed trend from east to west, while EOS was progressively later from the central plateau towards the southeast and southwest. Consequently, LOS shortened along both east-west and south-north gradients. Under sustained warming and wetting, the region experienced intensified freeze-thaw cycles, characterised by a delayed freeze-start, advanced thaw-end and shortened freeze-thaw duration. Both climate warming and freeze-thaw changes drove an overall significant advancement of SOS (-3.1 days/decade), delay of EOS (+2.2 days/decade) and extension of LOS (+5.3 days/decade) over the 21-year period. Notably, an abrupt phenological shift occurred around 2015. Prior to 2015, both SOS and EOS advanced, whereas afterward, SOS transitioned to a delaying trend and EOS exhibited a markedly stronger delay, leading to a pronounced extension of LOS. This regime shift was primarily attributed to changes in hydrothermal conditions controlled by climate warming and evolving freeze-thaw dynamics, with temperature being the dominant factor and precipitation exerting seasonally differential effects. Our findings elucidate the complex responses of alpine cryospheric ecosystems to climate change, revealing freeze-thaw processes as a key modulator of vegetation phenology.

Highlights What are the main findings? Permafrost in the Muri area responded to human disturbance without significant spatial expansion during 2000-2024. The semi-arid climate, rough terrain, thin root zone and gappy vertical structure underneath were the major factors. What are the implications of the main findings? Annual ALT estimated from 2000 to 2024 filled the data gap of high-resolution ALT in the Muri area. Knowledge was provided for a better understanding of alpine permafrost development.Highlights What are the main findings? Permafrost in the Muri area responded to human disturbance without significant spatial expansion during 2000-2024. The semi-arid climate, rough terrain, thin root zone and gappy vertical structure underneath were the major factors. What are the implications of the main findings? Annual ALT estimated from 2000 to 2024 filled the data gap of high-resolution ALT in the Muri area. Knowledge was provided for a better understanding of alpine permafrost development.Abstract Alpine permafrost plays a vital role in regional hydrology and ecology. Alpine permafrost is highly sensitive to climate change and human disturbance. The Muri area, which is located in the headwaters of the Datong River, northeast of the Tibetan Plateau, has undergone decadal mining, and the permafrost stability there has attracted substantial concerns. In order to decipher how and to what extent the permafrost in the Muri area has responded to the decadal mining in the context of climate change, daily MODIS land surface temperatures (LSTs) acquired during 2000-2024 were downscaled to 30 m x 30 m. The active layer thickness (ALT)-ground thaw index (DDT) coefficient was derived from in situ ALT measurements. An annual ALT of 30 m x 30 m spatial resolution was subsequently estimated from the downscaled LST for the Muri area using the Stefan equation. Validation of the LST and ALT showed that the root of mean squared error (RMSE) and the mean absolute error (MAE) of the downscaled LST were 3.64 degrees C and -0.1 degrees C, respectively. The RMSE and MAE of the ALT estimated in this study were 0.5 m and -0.25 m, respectively. Spatiotemporal analysis of the downscaled LST and ALT found that (1) during 2000-2024, the downscaled LST and estimated ALT delineated the spatial extent and time of human disturbance to permafrost in the Muri area; (2) human disturbance (i.e., mining and replantation) caused ALT increase without significant spatial expansion; and (3) the semi-arid climate, rough terrain, thin root zone and gappy vertical structure beneath were the major controlling factors of ALT variations. ALT, estimated in this study with a high resolution and accuracy, filled the data gaps of this kind for the Muri area. The ALT variations depicted in this study provide references for understanding alpine permafrost evolution in other areas that have been subject to human disturbance and climate change.

Soil chemical washing has the disadvantages of long reaction time, slow reaction rate and unstable effect. Thus, there is an urgent need to find a cost-effective and widely applicable alternative power to facilitate the migration of washing solutions in the soil, so as to achieve efficient removal of heavy metals, reduce the risk of soil compaction, and mitigate the damage of soil structure. Therefore, the study used a combination of freeze-thaw cycle (FTC) and chemical washing to obtain three-dimensional images of soil pore structure using micro-X-ray microtomography, and applied image analysis techniques to study the effects of freeze-thaw washing on the characteristics of different pore structures of the soil, and then revealed the effects of pore structure on the removal of heavy metals. The results showed that the soil pore structure of the freeze-thaw washing treatment (FT) became more porous and complex, which increased the soil imaged porosity (TIP), pore number (TNP), porosity of macropores and irregular pores, permeability, and heavy metal removal rate. Macroporosity, fractal dimension, and TNP were the main factors contributing to the increase in TIP between treatments. The porous structure resulted in larger effective pore diameters, which contain a greater number of branching pathways and pore networks, allowing the chemical washing solutions to fully contact the soil, increasing the roughness of the soil particle surface, mitigating the risk of soil compaction, and decreasing the contamination of heavy metals. The results of this study contribute to provide new insights into the management of heavy metal pollution in agricultural soils.

The vadose zone acts as a natural buffer that prevents contaminants such as arsenic (As) from contaminating groundwater resources. Despite its capability to retain As, our previous studies revealed that a substantial amount of As could be remobilized from soil under repeated wet-dry conditions. Overlooking this might underestimate the potential risk of groundwater contamination. This study quantified the remobilization of As in the vadose zone and developed a prediction model based on soil properties. 22 unsaturated soil columns were used to simulate vadose zones with varying soil properties. Repeated wet-dry cycles were conducted upon the As-retaining soil columns. Consequently, 13.9-150.6 mg/kg of As was remobilized from the columns, which corresponds to 37.0-74.6 % of initially retained As. From the experimental results, a machine learning model using a random forest algorithm was established to predict the potential for As remobilization based on readily accessible soil properties, including organic matter (OM) content, iron (Fe) content, uniformity coefficient, D30, and bulk density. Shapley additive explanation analyses revealed the interrelated effects of multiple soil prop-erties. D30, which is inter-related with Fe content, exhibited the highest contribution to As remobilization, fol-lowed by OM content, which was partially mediated by bulk density.

The environmental threat, pollution and damage posed by heavy metals to air, water, and soil emphasize the critical need for effective remediation strategies. This review mainly focuses on microbial electrochemical technologies (MET) for treating heavy metal pollutants, specifically includes Chromium (Cr), Copper (Cu), Zinc (Zn), Cadmium (Cd), Lead (Pb), Nickel (Ni), and Cobalt (Co). First, it explores the mechanisms and current applications of MET in heavy metal treatments in detail. Second, it systematically summarizes the key microbial communities involved, analyzing their extracellular electron transfer (EET) processes and summarizing strategies to enhance the EET efficiencies. Next, the review also highlights the synergistic microbial interactions in bioelectrochemical systems (BES) during the recovery and removal (remediation) processes of heavy metals, underscoring the crucial role of microorganisms in the transfer of the electrons. Then, this paper discussed how factors including pH values, applied voltages, types and concentrations of electron donors, electrode materials, biofilm thickness and other factors affect the treatment efficiencies of some specific metals in BES. BES has shown its great superiority in treating heavy metals. For example, for the treatments of Cr6+, under low pH conditions, the recovery and removal rate of Cr-6(+) by double chambers microbial fuel cell (DCMFC) can generally reach 98-99%, with some cases even achieving 100% (Gangadharan & Nambi, 2015). For the treatments of heavy metal ions such as Cu2+, Zn2+ and Cd2+, BES can also achieve the rates of treatments of more than 90% under the corresponding conditions of appropriate pH values and applied voltages(Choi, Hu, & Lim, 2014; W. Teng, G. Liu, H. Luo, R. Zhang, & Y. Xiang, 2016; Y. N. Wu et al., 2019; Y. N. Wu et al., 2018). After that, the review outlines the future challenges and the research opportunities for understanding the mechanisms of BES and microbial EET in heavy metal treatments. Finally, the prospect of future BES researches are pointed out, including the combinations with existing wastewater treatment systems, the integrations with the wind energy and the solar energy, and the application of machine learning (ML) in future BES. This article has certain significance and value for readers to better understand the working principles of BES and better operate and control BES to deal with heavy metal pollutants.