Atmospheric particulate matter (PM) as light-absorbing particles (LAPs) deposited to snow cover can result in early onset and rapid snow melting, challenging management of downstream water resources. We identified LAPs in 38 snow samples (water years 2013-2016) from the mountainous Upper Colorado River basin by comparing among laboratory-measured spectral reflectance, chemical, physical, and magnetic properties. Dust sample reflectance, averaged over the wavelength range of 0.35-2.50 mu m, varied by a factor of 1.9 (range, 0.2300-0.4444) and was suppressed mainly by three components: (a) carbonaceous matter measured as total organic carbon (1.6-22.5 wt. %) including inferred black carbon, natural organic matter, and carbon-based synthetic, black road-tire-wear particles, (b) dark rock and mineral particles, indicated by amounts of magnetite (0.11-0.37 wt. %) as their proxy, and (c) ferric oxide minerals identified by reflectance spectroscopy and magnetic properties. Fundamental compositional differences were associated with different iron oxide groups defined by dominant hematite, goethite, or magnetite. These differences in iron oxide mineralogy are attributed to temporally varying source-area contributions implying strong interannual changes in regional source behavior, dust-storm frequency, and (or) transport tracks. Observations of dust-storm activity in the western U.S. and particle-size averages for all samples (median, 25 mu m) indicated that regional dust from deserts dominated mineral-dust masses. Fugitive contaminants, nevertheless, contributed important amounts of LAPs from many types of anthropogenic sources.

We present an innovative approach to understanding permafrost degradation processes through the application of new environment-based particle image velocimetry (E-PIV) to time-lapse imagery and correlation with synchronous temperature and rainfall measurements. Our new approach to extracting quantitative vector movement from dynamic environmental conditions that can change both the position and the color balance of each image has optimized the trade-off between noise reduction and preserving the authenticity of movement data. Despite the dynamic polar environments and continuous landscape movements, the E-PIV provides the first quantitative real-time associations between environmental drivers and the responses of permafrost degradation mechanism. We analyze four event-based datasets from an island southwest of Tuktoyaktuk, named locally as Imnaqpaaluk or Peninsula Point near Tuktoyaktuk, NWT, Canada, spanning a 5-year period from 2017 to 2022. The 2017 dataset focuses on the interaction during a hot dry summer between slope movement and temperature changes, laying the foundation for subsequent analyses. In 2018, two datasets significantly expand our understanding of typical failure mechanisms in permafrost slopes: one investigates the relationship between slope movement and rainfall, while the other captures an overhang collapse, providing a rare quantitative observation of an acute landscape change event. The 2022 dataset revisits the combination of potential rain and air temperature-related forcing to explore the environment-slope response relationship around an ice wedge, a common feature of ice-rich permafrost coasts. These analyses reveal both a direct but muted association with air temperatures and a detectable delayed slope response to the occurrence of rainfall, potentially reflective of the time taken for the warm rainwater to infiltrate through the active layer and affect the frozen ground. Whilst these findings also indicate that other factors are likely to influence permafrost degradation processes, the associations have significant implications given the projections for a warmer, wetter Arctic. The ability to directly measure permafrost slope responses offers exciting new potential to quantitatively assess the sensitivity of different processes of degradation for the first time, improving the vulnerability components of hazard risk assessments, guiding mitigation efforts, and better constraining future projections of erosion rates and the mobilization of carbon-rich material.

In permafrost regions, vegetation growth is influenced by both climate conditions and the effects of permafrost degradation. Climate factors affect multiple aspects of the environment, while permafrost degradation has a significant impact on soil moisture and nutrient availability, both of which are crucial for ecosystem health and vegetation growth. However, the quantitative analysis of climate and permafrost remains largely unknown, hindering our ability to predict future vegetation changes in permafrost regions. Here, we used statistical methods to analyze the NDVI change in the permafrost region from 1982 to 2022. We employed correlation analysis, multiple regression residual analysis and partial least squares structural equation modeling (PLS-SEM) methods to examine the impacts of different environmental factors on NDVI changes. The results show that the average NDVI in the study area from 1982 to 2022 is 0.39, with NDVI values in 80% of the area remaining stable or exhibiting an increasing trend. NDVI had the highest correlation with air temperature, averaging 0.32, with active layer thickness coming in second at 0.25. Climate change plays a dominant role in NDVI variations, with a relative contribution rate of 89.6%. The changes in NDVI are positively influenced by air temperature, with correlation coefficients of 0.92. Although the active layer thickness accounted for only 7% of the NDVI changes, its influence demonstrated an increasing trend from 1982 to 2022. Overall, our results suggest that temperature is the primary factor influencing NDVI variations in this region.

Alpine meadows are vital ecosystems on the Qinghai-Tibet Plateau, significantly contributing to water conservation and climate regulation. This study examines the energy flux patterns and their driving factors in the alpine meadows of the Qilian Mountains, focusing on how the meteorological variables of net radiation (Rn), air temperature, vapor pressure deficit (VPD), wind speed (U), and soil water content (SWC) influence sensible heat flux (H) and latent heat flux (LE). Using the Bowen ratio energy balance method, we monitored energy changes during the growing and non-growing seasons from 2022 to 2023. The annual average daily Rn was 85.29 W m-2, with H, LE, and G accounting for 0.56, 0.71, and -0.32 of Rn, respectively. Results show that Rn is the main driver of both H and LE, highlighting its crucial role in turbulent flux variations. Additionally, a negative correlation was found between air temperature and H, suggesting that high temperatures may suppress H. A significant positive correlation was observed between soil moisture and LE, further indicating that moist soil conditions enhance LE. In conclusion, this study demonstrates the impact of climate change on energy distribution in alpine meadows and calls for further research on the ecosystem's dynamic responses to changing climate conditions.

The abrupt warming events punctuating the Termination 1 (about 11.7-18 ka Before Present, BP) were marked by sharp rises in the concentration of atmospheric methane (CH4). The role of permafrost organic carbon (OC) in these rises is still debated, with studies based on top-down measurements of radiocarbon (14C) content of CH(4 )trapped in ice cores suggesting minimum contributions from old and strongly C-14-depleted permafrost OC. However, organic matter from permafrost can exhibit a continuum of C-14 ages (contemporaneous to >50 ky). Here, we investigate the large-scale permafrost remobilization at the Younger Dryas-Preboreal transition (ca. 11.6 ka BP) using the sedimentary record deposited at the Lena River paleo-outlet (Arctic Ocean) to reflect permafrost destabilization in this vast drainage basin. Terrestrial OC was isolated from sediments and characterized geochemically measuring delta C-13, Delta C-14, and lignin phenol molecular fossils. Results indicate massive remobilization of relatively young (about 2,600 years) permafrost OC from inland Siberia after abrupt warming triggered severe active layer deepening. Methane emissions from this young fraction of permafrost OC contributed to the deglacial CH4 rise. This study stresses that underestimating permafrost complexities may affect our comprehension of the deglacial permafrost OC-climate feedback and helps understand how modern permafrost systems may react to rapid warming events, including enhanced CH4 emissions that would amplify anthropogenic climate change.

Sources and implications of black carbon (BC) and mineral dust (MD) on two glaciers on the central Tibetan Plateau were estimated based on in situ measurements and modeling. The results indicated that BC and MD accounted for 11 +/- 1% and 4 +/- 0% of the albedo reduction relative to clean snow, while the radiative forcing varied between 11 and 196 and 1-89 W m(-2), respectively. Assessment of BC and MD contributions to the glacier melt can reach up 88 to 434 and 35 to 187 mm w.e., respectively, contributing 9-23 and 4-10% of the total glacier melt. A footprint analysis indicated that BC and MD deposited on the glaciers originated mainly from the Middle East, Central Asia, North China and South Asia during the study period. Moreover, a potentially large fraction of BC may have originated from local and regional fossil fuel combustion. This study suggests that BC and MD will enhance glacier melt and provides a scientific basis for regional mitigation efforts.

Deposition of ambient black carbon (BC) aerosols over snow-covered areas reduces surface albedo and accelerates snowmelt. Based on in-situ atmospheric BC data and the WRF-Chem model, we estimated the dry and wet deposition of BC over the Yala glacier of the central Himalayan region in Nepal during 2016-2018. The maximum and minimum BC dry deposition was reported in pre- and post-monsoon respectively. Approximately 50% of annual dry deposition occurred in the pre-monsoon season (March to May) and 27% of the annual dry deposition occurred in April. The total dry BC deposition rate was estimated as -4.6 mu g m- 2 day- 1 providing a total deposition of 531 mu g m- 2 during the pre-monsoon season. The contribution of biomass burning and fossil fuel sources to BC deposition on an annual basis was 28% and 72% respectively. The annual accumulated wet deposition of BC was 196 times higher than the annual dry deposition. The ten months of observed dry deposition of BC (October 1, 2016 to August 31, 2017 - except December 2016) was -39% lower than that of WRFChem's estimated annual dry deposition from September 1, 2016 to August 31, 2017 partially due to model bias. The deposited content of BC over the snow surface has an important role in albedo reduction, therefore snow samples were collected from the surface of the Yala Glacier and the surrounding region in April 2016, 2017, and 2018. Samples were analyzed for BC mass concentration through the thermal optical analysis and single particle soot photometer method. The BC calculated via the thermal optical method was in the range of 352-854 ng g- 1, higher than the BC calculated through the particle soot photometer method and estimated BC in 2 cm surface snow (imperial equation). The maximum surface snow albedo reduction due to BC was 8.8%, estimated by a widely used snow radiative transfer model and a linear regression equation.

Vehicle -emitted fine particulate matter (PM 2.5 ) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic -related exhaust emissions (TREE) to observed PM 2.5 using a novel factorization framework. Specifically, co -measured nitrogen oxides (NO x ) concentrations served as a marker of vehicle -tailpipe emissions and were integrated into the optimization of a Non -negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012 - 2019) PM 2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co -measured black carbon (BC) and PM 2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM 2.5 concentrations at an AQM station from the first location showed close agreement ( R 2 = 0 .79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM 2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7 - 11 % of the observed PM 2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60 - 78 % of the traffic -related PM 2.5 concentrations could be attributed to particulate traffic -exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM 2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic -related air pollution abatement.

Recent research on the Himalayan cryosphere has increasingly been focused on climate uncertainty and regional variations, considering features such as glacier recession, lake expansion, outburst floods, and regional hazards. The Bhilangana river basin, located in the central Himalayas, is predominantly characterized by increased elevation-dependent warming and declining seasonal precipitation. Our study shows that high-elevation temperature increased from 2000 to 2022 (0.05(degrees)C/year, p = 20 m/sec). Quantification of the regional hazard reveals potentially severe downstream challenges for low-to-medium-scale hydropower stations, local settlements, and road and railway bridges near Devling and Ghuttu villages.

Carbonaceous particles have been confirmed as major components of ambient aerosols in urban environments and are related to climate impacts and environmental and health effects. In this study, we collected different-size particulate matter (PM) samples (PM1, PM2.5, and PM10) at an urban site in Lanzhou, northwest China, during three discontinuous one-month periods (January, April, and July) of 2019. We measured the concentrations and potential transport pathways of carbonaceous aerosols in PM1, PM2.5, and PM10 size fractions. The average concentrations of OC (organic carbon) and EC (elemental carbon) in PM1, PM2.5, and PM10 were 6.98 +/- 3.71 and 2.11 +/- 1.34 mu g/m(3), 8.6 +/- 5.09 and 2.55 +/- 1.44 mu g/m(3), and 11.6 +/- 5.72 and 4.01 +/- 1.72 mu g/m(3). The OC and EC concentrations in PM1, PM2.5, and PM10 had similar seasonal trends, with higher values in winter due to the favorable meteorology for accumulating pollutants and urban-increased emissions from heating. Precipitation played a key role in scavenge pollutants, resulting in lower OC and EC concentrations in summer. The OC/EC ratios and principal component analysis (PCA) showed that the dominant pollution sources of carbon components in the PMs in Lanzhou were biomass burning, coal combustion, and diesel and gasoline vehicle emissions; and the backward trajectory and concentration weight trajectory (CWT) analysis further suggested that the primary pollution source of EC in Lanzhou was local fossil fuel combustion.