Black carbon (BC) is a major short-lived climate pollutant (SLCP) with significant climate and environmentalhealth impacts. This review synthesizes critical advancements in the identification of emerging anthropogenic BC sources, updates to global warming potential (GWP) and global temperature potential (GTP) metrics, technical progress in characterization techniques, improvements in global-regional monitoring networks, emission inventory, and impact assessment methods. Notably, gas flaring, shipping, and urban waste burning have slowly emerged as dominant emission sources, especially in Asia, Eastern Europe, and Arctic regions. The updated GWP over 100 years for BC is estimated at 342 CO2-eq, compared to 658 CO2-eq in IPCC AR5. Recent CMIP6-based Earth System Models (ESMs) have improved attribution of BC's microphysics, identifying a 22 % increase in radiative forcing (RF) over hotspots like East Asia and Sub-Saharan Africa. Despite progress, challenges persist in monitoring network inter-comparability, emission inventory uncertainty, and underrepresentation of BC processes in ESMs. Future efforts could benefit from the integration of satellite data, artificial intelligence (AI)assisted methods, and harmonized protocols to improve BC assessment. Targeted mitigation strategies could avert up to four million premature deaths globally by 2030, albeit at a 17 % additional cost. These findings highlight BC's pivotal roles in near-term climate and sustainability policy.

The retreat of glaciers due to climate change is reshaping mountain landscapes and biodiversity. While previous research has documented vegetation succession after glacier retreat, our understanding of functional diversity dynamics is still limited. In this case study, we address the effects of glacier retreat on plant functional diversity by integrating plant traits with ecological indicator values across a 140-year chronosequence in a subalpine glacier landscape. We reveal that functional richness and functional dispersion decrease with glacier retreat, while functional evenness and functional divergence increase, suggesting a shift toward more specialized and competitive communities. Our findings highlight the critical role of ecological factors related to soil moisture, soil nutrients and light availability in shaping plant community dynamics. As years since deglaciation was a key factor in regression and machine learning models, encapsulating time-lagged, spatial and historical processes, we highlight the need of including time into phenomenological or mechanistic models predicting biodiversity change following glacier retreat. The integrative approach of this case study provides novel insights into the potential response of alpine plant communities to climate change, offering a deeper understanding of how to predict and anticipate the effects of glacier extinction on biodiversity in rapidly changing environments. (sic)(sic): (sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)140(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).

Studying permafrost changes under different (e.g., glacial/interglacial) and changing (e.g., current various scenarios) climates can potentially advance our understanding of permafrost's responses to climate change and further enable informed policy making for mitigating impacts from permafrost changes. Despite existing studies generally focusing on permafrost change during certain periods, here, we have synthetically examined the changes of the Northern Hemisphere near-surface permafrost during the six periods (Last Glacial Maximum (LGM, similar to 21 ka), mid-Holocene (MH, similar to 6 ka), preindustrial (PI, ca 1850), future 1.5 degrees C and 2.0 degrees C global warming periods, and end of the 21st century), using the surface frost index (SFI) model and outputs of six climate models. Simulated climate anomalies plus present-day observed climatology are used to drive the SFI model in this study. This helps correct systematic biases in permafrost change simulations.The results show that multi-model ensemble extent of present-day near-surface permafrost in the Northern Hemisphere agree well with the observations, with an area bias of 0.27x106 km2 in area (1.8% of the total observed area). Minor deviations (1.55x106 km2) in the simulated present-day permafrost extents across the climate models indicate a low inter-model diversity. In response to changes in annual mean surface air temperature of -10.3 +/- 2.3 degrees C (LGM), +0.1 +/- 0.5 degrees C (MH), +2.6 +/- 0.7 degrees C (1.5 degrees C global warming, RCP4.5), +3.6 +/- 1.0 degrees C (2.0 degrees C global warming, RCP4.5), and +5.0 +/- 1.3 degrees C (end of the 21st century, RCP4.5) in present-day permafrost regions relative to the PI, the changes in near-surface permafrost area are +33%+/- 30% (LGM), -13%+/- 6% (MH), -25%+/- 8% (1.5 degrees C warming, RCP4.5), -35% +/- 10% (2.0 degrees C warming, RCP4.5), and -55%+/- 12% (end of the 21st century, RCP4.5), respectively. From the LGM to the future, near-surface permafrost extent substantially decreases, underlining its high sensitivity to climate change and implying its potentially profound impacts in a warming future.

The Arctic has been warming much faster than the global average, known as Arctic amplification. The active layer is seasonally frozen in winter and thaws in summer. In the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign, airborne L- and P- band synthetic aperture radar (SAR) was used to acquire a dataset of active layer thickness (ALT) and vertical soil moisture profile, at 30 m resolution for 51 swaths across the ABoVE domain. Using a thawing degree day (TDD) model, ALT=K root TDD, we estimated ALT along the ABoVE swaths employing the 2-m air temperature from ERA5. The coefficient (K) calibrated has an R2=0.9783. We also obtained an excellent fit between ALT and K root(TDD/theta) where theta is the soil moisture from ERA5 (R2=0.9719). Output based on shared-social economic pathway (SSP) climate scenarios SSP 1-2.6, SSP 2-4.5, and SSP 5-8.5 from seven global climate models (GCMs), statistically downscaled to 25-km resolution, was used to project the impacts of climate warming on ALT. Assuming ALT=K root TDD, the projections of UKESM1-0-LL GCM resulted in the largest projected ALT, up to about 0.7 m in 2080s under SSP5-8.5. Given that the mean observed ALT of the study sites is about 0.482 m, this implies that ALT will increase by 0.074 to 0.217 m (15% and 45%) in 2080s. This will have substantial impacts on Arctic infrastructure. The projected settlement Iset (cm) of 1 to 7 cm will also impact the infrastructure, especially by differential settlement due to the high spatial variability of ALT and soil moisture, given at local scale the actual thawing will partly depend on thaw sensitivity of the material and potential thaw strain, which could vary widely from location to location.

This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial-spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light-canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R2=0.91, RMSE=0.42 m2/m2), leaf chlorophyll content (LCC, R2=0.61, RMSE=3.89 mu g/cm2), leaf nitrogen content (LNC, R2=0.86, RMSE=1.13 x 10-2 mg/cm2), leaf dry matter content (LMA, R2=0.84, RMSE=0.15 mg/cm2), and leaf water content (LWC, R2=0.78, RMSE=0.88 mg/cm2). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.

Land-cover changes and new ecosystem trajectories in Interior Alaska have altered the structure and function of landscapes, with regional warming trends altering carbon and water cycling. Notably, these changes include the increased distribution of tall woody vegetation, trees and shrubs, in landscapes that historically only supported low shrub vegetation cover. In Denali National Park, Alaska, this phenomenon has altered primary succession pathways towards tundra ecosystems with the establishment and expansion of balsam poplar (Populus balsamifera) trees. In this study, we examine how snow, soil, and vegetation processes interact within this altered successional pathway towards further landscape change following glacial recession. In a sequence of outflow terraces, we found that variations in snow depth, functional soil depth, leaf area index, overstory height, and understory height were all significantly correlated with each other, with those effects largely explained by the presence of poplar. Poplar-dominated plots had deeper snowpacks, deeper functional soil depths, taller overstory and shrub heights, and greater LAI than in non-poplar plots of the same landscape age. These findings suggest a feedback cycle where the establishment of taller vegetation (here, poplar) alters ecosystem processes in the following notable ways: taller vegetation is able to trap more snow by reducing wind exposure and limiting sublimation; this snow provides water through additional snowmelt and insulation, keeping soils warmer and lessening permafrost development, leading to deeper functional soil depths. This feedback demonstrates poplar's ability to modify the environment as an ecosystem engineer, engineering a trajectory away from the otherwise expected permafrost-underlain tundra.

Bats are indispensable members of the natural world, supporting its delicate balance. Bats have vital roles in controlling insect populations and enhancing soil fertility. They also help in the harvesting and dispersal of seeds, pollination in plants, and nutrient recycling and distribution. However, through evolution over millions of years, they have also adapted their immune system so that they may carry numerous types of pathogens, the majority of which are viruses, without these pathogens having any serious ill effects on bats themselves. Their anatomical adaptation to flight and the reduced immune response to DNA damage during flight have also contributed to bats becoming reservoirs of deadly pathogenic diseases. This review discusses the different adaptations of bats with a special focus on the immune system that have helped them evolve as a reservoir for various viruses. The study also enumerates how the increase in global warming, the consequent changes in climatic conditions, habitat destruction, and bushmeat consumption increase the chances of an outbreak of novel zoonotic disease when humans come in contact with bats.

With Arctic amplification, hydrological conditions in Arctic permafrost regions are expected to change substantially, which can have a strong impact on carbon budgets. To date, detailed mechanisms remain highly uncertain due to the lack of continuous observational data. Considering the large carbon storage in these regions, understanding these processes becomes crucial for estimating the future trajectory of global climate change. This study presents findings from 8 years of continuous eddy-covariance measurements of carbon dioxide (CO2) and methane (CH4) fluxes over a wet tussock tundra ecosystem near Chersky in Northeast Siberia, comparing data between a site affected by a long-term drainage disturbance and an undisturbed control site. We observed a significant increasing trend in roughness lengths at both sites, indicating denser and/or taller vegetation; however, the increase at the drained site was more pronounced, highlighting the dominant impact of drainage on vegetation structure. These trends in aboveground biomass contributed to differences in gross primary production (GPP) between the two sites increasing over the years, continuously reducing the negative effect of the drainage disturbance on the sink strength for CO2. In addition, carbon turnover rates at the drained site were enhanced, with ecosystem respiration and GPP consistently higher compared to the control site. Because of the artificially lower water table depth (WTD), CH(4 )emissions at the drained site were almost halved. Furthermore, drainage altered the ecosystem's response to environmental controls. Compared to the control site, the drained site became slightly more sensitive to the global radiation (R-g), resulting in higher CO(2 )uptake under the same levels of R-g. Meanwhile, CH(4 )emissions at the drained site showed a higher correlation with deep soil temperatures. Overall, our findings from this WTD manipulation experiment show that changing hydrological conditions will significantly impact the Arctic ecosystem characteristics, carbon budgets, and ecosystem's response to environmental changes.

Coal mining has significant economic and environmental implications. The extraction and combustion of coal release harmful chemicals and dust, impacting air, soil, and water quality, as well as natural habitats and human health. This study aimed to investigate the association between global DNA methylation, DNA damage biomarkers (including telomere length), and inorganic element concentrations in the blood of individuals exposed to coal mining dust. Additionally, polycyclic aromatic hydrocarbons were analyzed. The study included 150 individuals exposed to coal mining and 120 unexposed controls. Results showed significantly higher global DNA hypermethylation in the exposed group compared to controls. Moreover, in the exposed group, micronucleus frequency and age showed a significant correlation with global DNA hypermethylation. Blood levels of inorganic elements, including titanium, phosphorus, sodium, aluminum, iron, sulfur, copper, chromium, zinc, chlorine, calcium, and potassium, were potentially associated with DNA methylation and oxidative damage, as indicated by comet assay results. Furthermore, exposure to polycyclic aromatic hydrocarbons such as fluoranthene, naphthalene, and anthracene, emitted in mining particulate matter, may contribute to these effects. These findings highlight the complex interplay between genetic instability, global DNA hypermethylation, and environmental exposure in coal mining areas, emphasizing the urgent need for effective mitigation strategies.

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.