The extensive utilization of agricultural machinery in China has made it a prominent contributor to particulate matter (PM). However, there still exist significant knowledge gaps in understanding optical characteristics and molecular composition of chromophores of brown carbon (BrC) in PM emitted from agricultural machinery. Therefore, BrC in PM from six typical agricultural machines in China were measured to investigate the light absorption, chromophore characteristics, and influencing factors. Results showed that the average emission factors of methanol-soluble organic carbon (MSOC) and water-soluble organic carbon (WSOC) were 0.96 and 0.21 g (kg fuel)-1, respectively, exhibiting clear decreasing trends with increasing engine power and improving emission standards. Despite the light absorption coefficient of methanol-extracted BrC (Abs365,M) being approximately 2.2 times higher than that of water (Abs365,W), mass absorption efficiency of water-extracted BrC (MAE365,W) exhibited significantly greater values than MAE365,M. Among the detected chromophores, nitro-aromatic compounds (NACs) exhibited the highest contribution to light absorption that was about 14.5 times more than to total light absorption compared to their mass contributions to MSOC (0.04%), followed by polycyclic aromatic hydrocarbons (PAHs) and oxygenated PAHs (OPAHs). Besides, the average integrated simple forcing efficiency values were estimated to be 1.5 W g-1 for MSOC and 3.7 W g-1 for WSOC, indicating significant radiative forcing absorption of agricultural machinery. The findings in this study not only provide fundamental data for climate impact estimation of but also propose effective strategies to mitigate BrC emissions, such as enhancing emission standards and promoting the adoption of high-power agricultural machinery.

Brown carbon (BrC) is the ubiquitous part of the atmospheric organic carbon. It absorbs solar lights and greatly impacts the Earth's radiative balance. This study examines the spectral characteristics of BrC and its radiative effect in the Dhaka South (DS) site and Dhaka North (DN) site from July 2023 to January 2024 with a high-volume particulate matter sampler on quartz filters. Spectral characteristics such as absorption coefficient (babe,), mass absorption efficiency (MAE), absorption angstrom exponent (AAE), and refractive index (Kabs-x) were determined by using a UV -visible spectrophotometer, and fluorescence emission spectra were analyzed in different pH by the fluorescent spectrophotometer. The concentrations of BrC and black carbon (BC) were determined by an aethalometer. The mean concentrations of BrC and BC in Dhaka city were 18.63 +/- 3.84 mu g 111-3 and 17.93 +/- 3.82 pg M-3, respectively. The AAE values lie in the range of 3.20-4.01 (DN) and 3.27-4.53 (DS), and the radiative forcing efficiency of BrC was obtained at 4.43 +/- 1.02 W g-1 in DN and 3.93 +/- 0.74 W g-1 in DS, indicating the presence of highly light-absorbing BrC in these locations. Average MAE and Kabs_k values were 1.55 +/- 0.45 m2g1 and 0.044 + 0.013, respectively, in DS, alternatively 1.84 +/- 0.59 m2g1 and 0.052 +/- 0.016 in DN. The fluorescence excitation-emission spectra confirmed the presence of a polyconjugate cyclic ring with multifunctional groups in the structure of BrC. Light absorption properties and fluorescence emission spectra were varied with the change of pH. As the pH increased (2-8), the AAE value decreased and MAEB,c_365 increased due to protonation or deprotonation. This study highlights that the BrC has a significant impact on the air quality as well as the Earth's radiative balance, emphasizing its strong light-absorbing properties and variability with environmental factors.

Soil organic carbon (SOC) in the active layer (0-2 m) of the Tibetan Plateau (TP) permafrost region is sensitive to climate change, with significant implications for the global carbon cycle. Environmental factors-including parent material, climate, vegetation, topography, soil, and human activities-inevitably drive SOC variations. However, vegetation and climate are likely the two most influential factors impacting SOC variations. To test this hypothesis, we conducted experiments using 31 environmental variables combined with the recursive feature elimination (RFE) algorithm. These experiments showed that RFE retained all vegetation variables [Land cover types (LCT), normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)] as well as two climate variables [Moisture index (MI) and drought index (DI)], supporting our hypothesis. We then analyzed the relationship between SOC and the retained vegetation and climate variables using random forest (RF), Shapley additive explanations (SHAP), and GeoDetector models to quantify the independent and interactive drivers of SOC distribution and to identify the optimal conditions for SOC accumulation. The RF model explained 68% and 42% of the spatial variability in SOC at depths of 0-1 m and 1-2 m, respectively, with SOC stocks higher in the southeast and lower in the northwest. Additionally, SOC stock at 0-1 m was significantly higher (p 0.05). Spearman correlation coefficients results indicated that NDVI, LAI, GPP, and MI had highly significant positive correlations with SOC (p < 0.01), whereas DI had a highly significant negative correlation with SOC (p < 0.01). SHAP analysis revealed environmental thresholds for SOC variations, with notable shifts at NDVI (0.40), LAI (7), GPP (250 g C m(-)(2) year(-)(1)), MI (0.40), and DI (0.50). The spatial distribution of these thresholds aligns with the 400 mm equivalent precipitation line. Additionally, GeoDetector results emphasized that interactions between climate and vegetation factors enhance the explanatory power of individual variables on SOC variations. The swamp meadow type, with an NDVI range of 0.73-0.84, LAI range of 11.06-15.94, and MI range of 0.46-0.56, was identified as the most favorable environment for SOC accumulation. These findings are essential for balancing vegetation and climate conditions to sustain SOC levels and mitigate climate change-driven carbon release.

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0 degrees C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation-factors associated with energy balance-were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

The fine-scale controls of active layer dynamics remain poorly understood, particularly at the southern boundary of continuous permafrost. We examined how environmental conditions associated with upland tundra heath, open graminoid fen, and palsa/peat plateau landforms affected active layer thermal regime (timing, magnitude, and rate of thaw) in a subarctic peatland in the Hudson Bay Lowlands, Canada. A significant increase in active layer thaw depth was evident between 2012 and 2024. Within-season thaw patterns differed among landforms, with tundra heath exhibiting the highest thaw rates and soil temperatures, succeeded by fen and palsa. Air temperature mediated by soil properties, topography, and vegetation affected thaw patterns. The increased thermal conductivity of gravel/sandy tundra heath soils exerted a more pronounced influence on thaw patterns relative to fens and palsas, both of which had a thicker organic layer. Near-surface soil moisture was the lowest in tundra, followed by palsas, and fens. Increased soil moisture impeded active layer thaw, likely due to a combination of soil surface evaporation and meltwater percolation. These findings elucidate the relationship between the biophysical properties of landform features and climate, revealing their role in influencing active layer thaw patterns in a subarctic ecosystem.

The paper presents the strategic project of Tomsk State University devoted to studying the carbon cycle in the arctic land-shelf system. The obtained carbon cycle characteristics should be used for global climate model correction. The main objective of the consortium is to obtain new data on the variability of climatic and biological factors of various ecosystems, monitor them, and create archives of data on their dynamics. The area of the project includes the basins of the Great Siberian Rivers, and the shelf of the adjacent Arctic seas. A consortium of approximately twenty universities and research institutions was formed to study the carbon cycle in various environments, including seas, rivers, wetlands, and permafrost. In addition to studying the carbon cycle, the project also aims to develop methods for carbon sequestration and ecosystems remediation. One of such methods was developed for the assessment and cleanup of bottom sediments from oil and petroleum products as well as other hydrophobic contaminants and has been patented and tested in a series of field trials. Several special monitoring methods are described, such as novel sampling and sample laboratory processing techniques to assess microplastics in the environment; and holographic methods for underwater monitoring of the plankton behavior for early bioindication of hazards in the water area. This is particularly relevant for areas with dangerous objects, such as nuclear power plants, oil platforms, and gas pipelines. The methods of math modeling of the impact of climate change and anthropogenic factors on indigenous and local population lives were used.

The Arctic experiences rapid climate change, but our ability to predict how this will influence plant communities is hampered by a lack of data on the extent to which different species are associated with particular environmental conditions, how these conditions are interlinked, and how they will change in coming years. Increasing temperatures may negatively affect plants associated with cold areas due to increased competition with warm-adapted species, but less so if local temperature variability is larger than the expected increase. Here we studied the potential drivers of vegetation composition and species richness along coast to inland and altitudinal gradients by the Nuuk fjord in western Greenland using hierarchical modelling of species communities (HMSC) and linear mixed models. Community composition was more strongly associated with random variability at intermediate spatial scales (among plot groups 500 m apart) than with large-scale variability in summer temperature, altitude or soil moisture, and the variation in community composition along the fjord was small. Species richness was related to plant cover, altitude and slope steepness, which explained 42% of the variation, but not to summer temperature. Jointly, this suggests that the direct effect of climate change will be weak, and that many species are associated with microhabitat variability. However, species richness peaked at intermediate cover, suggesting that an increase in plant cover under warming climatic conditions may lead to decreasing plant diversity.

Global warming due to climate change has substantial impact on high-altitude permafrost affected soils. This raises a serious concern that the microbial degradation of sequestered carbon can result in alteration of the biogeochemical cycles. Therefore, the characterization of permafrost affected soil microbiomes, especially of unexplored high-altitude, low oxygen arid region, is important for predicting their response to climate change. This study presents the first report of the bacterial diversity of permafrost-affected soils in the Changthang region of Ladakh. The relationship between soil pH, organic carbon, electrical conductivity, and available micronutrients with the microbial diversity was investigated. Amplicon sequencing of permafrost affected soil samples from Jukti and Tsokar showed that Proteobacteria and Actinobacteria were the dominant phyla in all samples. The genera Brevitalea, Chthoniobacter, Sphingomonas, Hydrogenispora, Clostridium, Gaiella, Gemmatimonas were relatively abundant in the Jukti samples whereas the genera Thiocapsa, Actinotalea, Syntrophotalea, Antracticibcterium, Luteolibacter, Nitrospirillum dominated the Tsokar sample. Correlation analyses highlighted the influence of soil geochemical parameters on the bacterial community structure. PCoA analyses showed that the bacterial beta diversity varied significantly between the sampling locations (PERMANOVA test (F-value: 2.3316; R2 = 0.466, p = 0.001) and similar results were also obtained while comparing genus abundance data using the ANOSIM test (R = 0.345, p = 0.007).

Freeze-thaw-induced N2O pulses could account for nearly half of annual N2O fluxes in cold climates, but their episodic nature, sensitivity to snow cover dynamics, and the challenges of cold-season monitoring complicate their accurate estimation and representation in global models. To address these challenges, we combined in situ automated high-frequency flux measurements with cross-ecoregion soil core incubations to investigate the mechanisms driving freeze-thaw-induced N2O emissions. We found that deepened snow significantly amplified freeze-thaw N2O pulses, with these similar to 50-day episodes contributing over 50% of annual fluxes. Additionally, freeze-thaw-induced N2O pulses exhibited significant spatial heterogeneity, ranging from 3.4 to 1184.1 mu g N m(-2) h(-1) depending on site conditions. Despite significant spatiotemporal variation, our results indicated that 68%-86% of this variation can be explained by shifts in controlling factors: from water-filled pore space (WFPS), which drove anaerobic conditions, to microbial constraints as snow depth increases. Below 43% WFPS, soil moisture was the overwhelmingly dominant driver of emissions; between 43% and 66% WFPS, moisture and microbial attributes (including denitrifying gene abundance, nitrogen enzyme kinetics, and microbial biomass) jointly triggered N2O emissions pulses; above 66% WFPS, microbial attributes, particularly nitrogen enzyme kinetics, prevailed. These findings suggested that maintaining higher soil moisture served as a trigger for activating microbial activity, particularly enhancing nitrogen cycling. Furthermore, we showed that hotspots of freeze-thaw-induced N2O emissions were linked to high root production and microbial activity in cold and humid grasslands. Overall, our study highlighted the hierarchical control of WFPS and microbial processes in driving freeze-thaw-induced N2O emission pulses. The easily measurable WFPS and microbial attributes predictable from plant and soil properties could forecast the magnitude and spatial distribution of N2O emission hot moments under changing climate. Integrating these hot moments, particularly the dynamics of WFPS, into process-based models could refine N2O emission modeling and enhance the accuracy of global N2O budget prediction.

Soil thermal conductivity (STC) plays a crucial role in regulating the energy distribution of both the surface and underground soil layers. It is widely applied in various fields, including engineering design, geothermal resource development and climate change research. A rapid and accurate estimation of STC remains a key focus in the study of soil thermodynamic parameters. However, the methods for estimating STC and their distinct characteristics have yet to be systematically reviewed. In this study, we used bibliometrics to comprehensively and systematically review the literature on STC, focusing on knowledge graph characteristics to analyze the development trend of calculation schemes. The main conclusions drawn from the study are as follows: (1) In recent years, most studies have been focused on soil thermal characteristics and their main contributing factors, the soil hydrothermal process in the Qinghai-Tibet Plateau, geothermal equipment and numerical simulations, and the exploration of geothermal resources. (2) A systematic review of various schemes indicates that no single scheme is universally applicable to all soil types. Moreover, a single parameterization scheme fails to meet the practical requirements of land surface process models. We evaluated the advantages and disadvantages of the traditional heat conduction schemes, parameterization schemes, and machine learning-based schemes and the findings suggest that a comprehensive scheme that integrates these three different schemes for STC simulations should be urgently developed.