Air pollution is a global health issue, and events like forest fires, agricultural burning, dust storms, and fireworks can significantly worsen it. Festivals involving fireworks and wood-log fires, such as Diwali and Holi, are key examples of events that impact local air quality. During Holi, the ritual of Holika involves burning of biomass that releases large amounts of aerosols and other pollutants. To assess the impact of Holika burning, observations were conducted from March 5th to March 18th, 2017. On March 12th, 2017, around 1.8 million kg of wood and biomass were openly burned in about 2250 units of Holika, located in and around the Varanasi city (25.23 N, 82.97 E, similar to 82.20 m amsl). As the Holika burning event began the impact on the Black Carbon (BC), particulate matter 10 & 2.5 (PM10 and PM2.5), sulphur dioxide (SO2), oxides of nitrogen (NOx), ozone (O-3) and carbon monoxide (CO) concentration were observed. Thorough optical investigations have been conducted to better comprehend the radiative effects of aerosols produced due to Holika burning on the environment. The measured AOD at 500 nm values were 0.315 +/- 0.072, 0.392, and 0.329 +/- 0.037, while the BC mass was 7.09 +/- 1.78, 9.95, and 7.18 +/- 0.27 mu g/m(3) for the pre-Holika, Holika, and post-Holika periods. Aerosol radiative forcing at the top of the atmosphere (ARF-TOA), at the surface (ARF-SUR), and in the atmosphere (ARF-ATM) are 2.46 +/- 4.15, -40.22 +/- 2.35, and 42.68 +/- 4.12 W/m(2) for pre-Holika, 6.34, -53.45, and 59.80 W/m(2) for Holika, and 5.50 +/- 0.97, -47.11 +/- 5.20, and 52.61 +/- 6.17 W/m(2) for post-Holika burning. These intense observation and analysis revealed that Holika burning adversely impacts AQI, BC concentration and effects climate in terms of ARF and heating rate.

Permafrost is undergoing rapid changes due to climate warming, potentially exposing a vast reservoir of carbon to be released to the atmosphere, causing a positive feedback cycle. Despite the importance of this feedback, its specifics remain poorly constrained, because representing permafrost dynamics still poses a significant challenge for Earth System Models (ESMs). This review assesses the current state of permafrost representation in land surface models (LSMs) used in ESMs and offline permafrost models, highlighting both the progress made and the remaining gaps.We identify several key physical processes crucial for permafrost dynamics, including soil thermal regimes, freeze-thaw cycles, and soil hydrology, which are underrepresented in many models. While some LSMs have advanced significantly in incorporating these processes, others lack fundamental elements such as latent heat of freeze-thaw, deep soil columns, and Arctic vegetation dynamics. Offline permafrost models provide valuable insights, offering detailed process testing and aiding the prioritization of improvements in coupled LSMs.Our analysis reveals that while significant progress has been made in incorporating permafrost-related processes into coupled LSMs, many small-scale processes crucial for permafrost dynamics remain underrepresented. This is particularly important for capturing the complex interactions between physical and biogeochemical processes required to model permafrost carbon dynamics. We recommend leveraging advancements from offline permafrost models and progressively integrating them into LSMs, while recognizing the computational and technical challenges that may arise in coupled simulations. We highlight the importance of enhancing the representation of physical processes, including through improvements in model resolution and complexity, as this is a fundamental precursor to accurately incorporate biogeochemical processes and capture the permafrost carbon feedback.

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 Tibetan Plateau (TP) covers the largest regions under low- and mid-latitude permafrost. The evolution of permafrost has significantly affected the hydrology, biogeochemistry, and infrastructure of Asia. However, model reconstructions of long-term permafrost evolution with high accuracy and reliability are insufficient. Here, spatial changes in mean annual ground temperature at the depth where the annual amplitude is zero (MAGT) on the TP since 1981 were modeled and validated based on temperature records from 155 boreholes, and future changes were predicted under scenarios from the Climate Model Intercomparison Project 6 (CMIP6). The results indicated that the MAGT on the TP was approximately 1.5 degrees C (2010 - 2018), and the corresponding permafrost extent on the TP is estimated to be approximately 1.03 x 106 km2, which is projected to decrease to 0.77 x 106, 0.50 x 106, 0.30 x 106, and 0.17 x 106 km2 under the scenarios of shared socioeconomic pathway (SSP)126, SSP245, SSP370, and SSP585, respectively, by 2100. As predicted in the SSP585 scenario, permafrost is predicted to largely disappear from many basins of major Asian rivers, such as the Yarlung Zangpo-Brahmaputra, NuSalween, and Lancang-Mekong Rivers, between 2041 and 2060, followed by the Yellow and Yangtze Rivers between 2061 and 2080. Moreover, the original stable permafrost in the West Kunlun Mountains will change to transitional and unstable conditions. Our study offers comprehensive datasets of year-to-year ground temperatures and permafrost extent maps for the TP, which can serve as a fundamental resource for further investigations on the hydrogeology, engineering geology, ecology, and geochemistry of the TP.

Alpine treelines ecotones are critical ecological transition zones and are highly sensitive to global warming. However, the impact of climate on the distribution of treeline trees is not yet fully understood as this distribution may also be affected by other factors. Here, we used high-resolution satellite images with climatic and topographic variables to study changes in treeline tree distribution in the alpine treeline ecotone of the Changbai Mountain for the years 2002, 2010, 2017, and 2021. This study employed the Geodetector method to analyze how interactions between climatic and topographic factors influence the expansion of Betula ermanii on different aspect slopes. Over the past 20 years, B. ermanii, the only tree species in the Changbai Mountain tundra zone, had its highest expansion rate from 2017 to 2021 across all the years studied, approaching 2.38% per year. In 2021, B. ermanii reached its uppermost elevations of 2224 m on the western aspects and 2223 m on the northern aspects, which are the predominant aspects it occupies. We also observed a notable increase in the distribution of B. ermanii on steeper slopes (> 15 degrees) between 2002 and 2021. Moreover, we found that interactions between climate and topographic factors played a more significant role in B. ermanii's expansion than any single dominant factor. Our results suggest that the interaction between topographic wetness index and the coldest month precipitation (Pre(1)), contributing 91% of the observed variability, primarily drove the expansion on the southern aspect by maintaining soil moisture, providing snowpack thermal insulation which enhanced soil temperatures, decomposition, and nutrient release in harsh conditions. On the northern aspect, the interaction between elevation and mean temperature of the warmest month explained 80% of the expansion. Meanwhile, the interaction between Pre(1) and mean temperature of the growing season explained 73% of the expansion on the western aspect. This study revealed that dominant factors driving treeline upward movement vary across different mountain aspects. Climate and topography play significant roles in determining tree distribution in the alpine treeline ecotone. This knowledge helps better understand and forecast treeline dynamics in response to global climate change.

Influenced by a warm and humid climate, the permafrost on the Qinghai-Tibet Plateau is undergoing significant degradation, leading to the occurrence of extensive thermokarst landforms. Among the most typical landforms in permafrost areas is thaw slump. This study, based on three periods of data from keyhole images of 1968-1970, the fractional images of 2006-2009 and the Gaofen (GF) images of 2018-2019, combined with field surveys for validation, investigates the distribution characteristics and spatiotemporal variation trends of thaw slumps in the Hoh Xil area and evaluates the susceptibility to thaw slumping in this area. The results from 1968 to 2019 indicate a threefold increase in the number and a twofold increase in total area of thaw slumps. Approximately 70% of the thaw slumps had areas less than 2 x 104 m2. When divided into a grid of 3 km x 3 km, about 1.3% (128 grids) of the Hoh Xil region experienced thaw slumping from 1968 to 1970, while 4.4% (420 grids) showed such occurrences from 2018 to 2019. According to the simulation results obtained using the informativeness method, the area classified as very highly susceptible to thaw slumping covers approximately 26% of the Hoh Xil area, while the highly susceptible area covers about 36%. In the Hoh Xil, 61% of the thaw slump areas had an annual warming rate ranging from 0.18 to 0.25 degrees C/10a, with 70% of the thaw slump areas experiencing a precipitation increase rate exceeding 12 mm/10a. Future assessments of thaw slump development suggest a possible minimum of 41 and a maximum of 405 thaw slumps occurrences annually in the Hoh Xil region. Under rapidly changing climatic conditions, apart from environmental risks, there also exist substantial potential risks associated with thaw slumping, such as the triggering of large-scale landslides and debris flows. Therefore, it is imperative to conduct simulated assessments of thaw slumping throughout the entire plateau to address regional risks in the future.

The Net Ecosystem Carbon Balance (NECB) is a crucial metric for understanding integrated carbon dynamics in Arctic and boreal regions, which are vital to the global carbon cycle. These areas are associated with significant uncertainties and rapid climate change, potentially leading to unpredictable alterations in carbon dynamics. This mini-review examines key components of NECB, including carbon sequestration, methane emissions, lateral carbon transport, herbivore interactions, and disturbances, while integrating insights from recent permafrost region greenhouse gas budget syntheses. We emphasize the need for a holistic approach to quantify the NECB, incorporating all components and their uncertainties. The review highlights recent methodological advances in flux measurements, including improvements in eddy covariance and automatic chamber techniques, as well as progress in modeling approaches and data assimilation. Key research priorities are identified, such as improving the representation of inland waters in process-based models, expanding monitoring networks, and enhancing integration of long-term field observations with modeling approaches. These efforts are essential for accurately quantifying current and future greenhouse gas budgets in rapidly changing northern landscapes, ultimately informing more effective climate change mitigation strategies and ecosystem management practices. The review aligns with the goals of the Arctic Monitoring and Assessment Program (AMAP) and Conservation of Arctic Flora and Fauna (CAFF), providing important insights for policymakers, researchers, and stakeholders working to understand and protect these sensitive ecosystems.

Glaciers playa vital role in providing water resources for drinking, agriculture, and hydro-electricity in many mountainous regions. As global warming progresses, accurately reconstructing long-term glacier mass changes and comprehending their intricate dynamic relationships with environmental variables are imperative for sustaining livelihoods in these regions. This paper presents the use of eXplainable Machine Learning (XML) models with GRACE and GRACE-FO data to reconstruct long-term monthly glacier mass changes in the Upper Yukon Watershed (UYW), Canada. We utilized the H2O-AutoML regression tools to identify the best performing Machine Learning (ML) model for filling missing data and predicting glacier mass changes from hydroclimatic data. The most accurate predictive model in this study, the Gradient Boosting Machine, coupled with explanatory methods based on SHapley Additive eXplanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) analyses, led to automated XML models. The XML unveiled and ranked key predictors of glacier mass changes in the UYW, indicating a decrease since 2014. Analysis showed decreases in snow water equivalent, soil moisture storage, and albedo, along with increases in rainfall flux and air temperature were the main drivers of glacier mass loss. A probabilistic analysis hinging on these drivers suggested that the influence of the key hydrological features is more critical than the key meteorological features. Examination of climatic oscillations showed that high positive anomalies in sea surface temperature are correlated with rapid depletion in glacier mass and soil moisture, as identified by XML. Integrating H2OAutoML with SHAP and LIME not only achieved high prediction accuracy but also enhanced the explainability of the underlying hydroclimatic processes of glacier mass change reconstruction from GRACE and GRACE-FO data in the UYW. This automated XML framework is applicable globally, contingent upon sufficient high-quality data for model training and validation.

Reanalysis is a valuable potential data source for permafrost studies. The latest-generation reanalysis of the Japanese Reanalysis for three quarters of a century (JRA-3Q) benefits from improved snow and soil schemes and demonstrates encouraging performance for soil temperature in permafrost regions compared to its predecessor, JRA-55, and other state-of-the-art reanalyses. We find JRA-3Q to have an overall mean annual air temperature bias of-0.17 degrees C, with-0.55 degrees C in permafrost regions. The snow depth was underestimated by-5.5 cm. In permafrost regions, the mean annual ground temperature bias was about-0.09 degrees C. The estimated permafrost area from JRA-3Q is between 10.8 and 15.8 x 106 km2. The active layer thickness is substantially overestimated by about 0.65 m. The JRA-3Q soil temperature exhibits a pronounced warm bias in Alaska, which is very likely due to the overestimated snow insulation and simplified soil organic content. The decoupled energy conservation parameterization (DECP) method employed in the JRA-3Q soil scheme restricts its suitability for the interpretation of detailed permafrost phenomena, such as zero-curtain effects. This DECP method is used in many stateof-the-art land surface models; our results demonstrate the need for additional contributions to improve the representation of permafrost-specific processes.

In the context of global climate change, changes in unfrozen water content in permafrost significantly impact regional terrestrial plant ecology and engineering stability. Through Differential Scanning Calorimetry (DSC) experiments, this study analyzed the thermal characteristic indicators, including supercooling temperature, freezing temperature, thawing temperature, critical temperature, and phase-transition temperature ranges, for silt loam with varying starting moisture levels throughout the freezing and thawing cycles. With varying starting moisture levels throughout the freezing and thawing cycles, a model describing the connection between soil temperature and variations in unfrozen water content during freeze-thaw cycles was established and corroborated with experimental data. The findings suggest that while freezing, the freezing and supercooling temperatures of unsaturated clay increased with the soil's starting moisture level, while those of saturated clay were less affected by water content. During thawing, the initial thawing temperature of clay was generally below 0 degrees C, and the thawing temperature exhibited a power function relationship with total water content. Model analysis revealed hysteresis effects in the unfrozen water content curve during freeze-thaw cycles. Both the phase-transition temperature range and model parameters were sensitive to temperature changes, indicating that the processes of permafrost freezing and thawing are mainly controlled by ambient temperature changes. The study highlights the stability of the difference between freezing temperature and supercooling temperature in clay during freezing. These results offer a conceptual framework for comprehending the thawing mechanisms of permafrost and analyzing the variations in mechanical properties and terrestrial ecosystems caused by temperature-dependent moisture changes in permafrost.