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.

China experiences severe particulate matter (PM) pollution. Although a monitoring network for PM2.5 (diameter < 2.5 mu m) has been set up in more than 100 major Chinese cities, insufficient spatial coverage of observations limits the study of the temporal and spatial characteristics, influencing factors, and component of PM2.5. In this study, we conducted a one year air quality simulation using a regional climate-chemistry model and evaluated the simulation's performance based on in situ observations concerning meteorological elements and PM2.5 concentrations. The simulated results showed that, higher PM2.5 concentrations appeared in northern China and the Sichuan Basin, and the maximal value occurred in winter. Furthermore, Vertical PM2.5 concentrations presented a gradual decreasing trend from the surface, whereas in southern coastal cities the profiles were unsteady with a secondary peak in the lower layer. Meteorological conditions were conducive to both pollutant diffusion and removal in summer, whereas stagnant conditions appeared in winter, characterized by high sea level pressure (SLP), the lowest planetary boundary layer height (PBLH), and 2-m temperature (T2). In provincial capital cities, PM2.5 was positively correlated with residential emissions but negatively correlated with precipitation, 10-m wind speed, T2, PBLH, and industrial emissions. Finally, we utilized the simulation results to investigate the component variations of PM2.5. Results indicated that primary PM2.5 components had significantly higher concentrations in northern China where residential heating is the major source of PM2.5 emissions, whereas they had lower concentrations in southern China. Secondary components played a crucial role in PM2.5 mass in eastern China. This study provided a clear perspective of seasonal variations, horizontal and vertical distributions of PM2.5 and its components and influence factors, which could be used in subsequent studies to investigate the formation mechanism and emission sources of PM2.5.

To address data scarcity on long-term glacial discharge and inadequacies in simulating and predicting hydrological processes in the Tien Shan, this study analysed the observed discharge at multiple timescales over 1980s-2017 and projected changes within a representative glacierized high-mountain region: eastern Tien Shan, Central Asia. Hydrological processes were simulated to predict changes under four future scenarios (SSP1, SSP2, SSP3, and SSP5) using a classical hydrological model coupled with a glacier dynamics module. Discharge rates at annual, monthly (June, July, August) and daily timescales were obtained from two hydrological gauges: Urumqi Glacier No.1 hydrological station (UGH) and Zongkong station (ZK). Overall, annual and summer discharge increased significantly ( p < 0.05) at both stations over the study period. Their intra-annual variations mainly resulted from differences in their recharge mechanisms. The simulations show that a tipping point in annual discharge at UGH may occur between 2018 and 2024 under the four SSPs scenarios. Glacial discharge is predicted to cease earlier at ZK than at UGH. This relates to glacier type and size, suggesting basins with heavily developed small glaciers will reach peak discharge sooner, resulting in an earlier freshwater supply challenge. These findings serve as a reference for research into glacial runoff in Central Asia and provide a decision-making basis for planning local water-resource projects.

Permafrost is a potential mercury (Hg) pool released by thawing, which can raise the risk of Hg pollution under global warming. Tree rings are useful archives of environment-specific Hg exposure over long periods. We determined Hg concentrations in tree rings of two dominant tree species (Larix gmelinii Rupr. and Pinus sylvestris var. mongolica) at permafrost sites in northeastern China. The biweighted mean Hg concentrations ranged from 0.36 to 3.96 ng g(-1) from 1840 to 2014. The tree-ring width had no significant influence on the Hg concentration. Larch Hg increased slightly before the 1970s and peaked in the 1990s. However, the pine Hg concentration increased continuously until the 1930s, decreased rapidly until the 1970s, then rose to a peak in the late 1980s. The change of Hg concentrations in larch and pine revealed a time offset of 4 to 5 years, which implied possibly high mobility of Hg in pine tree rings. Higher Hg concentrations from 1920 to 1960 and subsequent decreases in isolated permafrost forests revealed the local geographical Hg cycling history. Lower Hg concentrations and faster increases in larch suggest the role of additional winter Hg loading for the evergreen pine and species-specific differences in root absorption in response to melting permafrost. Our results highlight possible geographical impacts on tree-ring Hg records, improve understanding of Hg cycles in permafrost forest, and suggest a need to sample additional species in a range of permafrost environments.

To understand the characteristics of particulate matter (PM) and other air pollutants in Xinjiang, a region with one of the largest sand-shifting deserts in the world and significant natural dust emissions, the concentrations of six air pollutants monitored in 16 cities were analyzed for the period January 2013-June 2019. The annual mean PM2.5, PM10, SO2, NO2, CO, and O-3 concentrations ranged from 51.44 to 59.54 mu g m(-3), 128.43-155.28 mu g m(-3), 10.99-17.99 mu g m(-3), 26.27-31.71 mu g m(-3), 1.04-1.32 mg m(-3), and 55.27-65.26 mu g m(-3), respectively. The highest PM concentrations were recorded in cities surrounding the Taklimakan Desert during the spring season and caused by higher amounts of wind-blown dust from the desert. Coarse PM (PM10-2.5) was predominant, particularly during the spring and summer seasons. The highest PM2.5/PM10 ratio was recorded in most cities during the winter months, indicating the influence of anthropogenic emissions in winters. The annual mean PM2.5 (PM10) concentrations in the study area exceeded the annual mean guidelines recommended by the World Health Organization (WHO) by a factor of ca. similar to 5-6 (similar to 7-8). Very high ambient PM concentrations were recorded during March 19-22, 2019, that gradually influenced the air quality across four different cities, with daily mean PM2.5 (PM10) concentrations similar to 8-54 (similar to 26-115) times higher than the WHO guidelines for daily mean concentrations, and the daily mean coarse PM concentration reaching 4.4 mg m(-3). Such high PM2.5 and concentrations pose a significant risk to public health. These findings call for the formulation of various policies and action plans, including controlling the land degradation and desertification and reducing the concentrations of PM and other air pollutants in the region. (C) 2020 Elsevier Ltd. All rights reserved.

The optical properties and sources of brown carbon (BrC) have been poorly constrained in climate models due to the variability of spatiotemporal characteristics, impeding the accurate understanding of its impact on air quality and climate. In this study, daily PM2.5 samples, which were collected from January to November 2021 in urban Taipei, Taiwan, and seasonal variations of optical properties of water-soluble and methanol-soluble organic carbon (WSOC and MSOC) were evaluated. The light absorption coefficients at 365 nm (Abs(365)) of both extracts, which strongly correlated with WSOC and MSOC mass concentrations, displayed distinct seasonal variations with the highest in winter and the lowest in summer. The Absorption Angstrom Exponent of WSOC and MSOC ranged from 4.16 to 7.75 and 4.03-6.83, with averages of 6.05 +/- 0.56 and 5.29 +/- 0.61, respectively. The mass absorption efficiency (MAE(365)), which normalizes the Abs(365) of both extracts to the mass of WSOC and MSOC, showed significant seasonal difference with the high MAE(365), (WSOC) of 0.96 +/- 0.29 m(2) g(-1) in winter and the lowest in summer of 0.49 +/- 0.07 m(2) g(-1), whereas contrasting with the largest MAE(365), (MSOC) of 0.99 +/- 0.46 m(2) g(-1) in summer and the lowest in winter of 0.66 +/- 0.28 m(2) g(-1), respectively. Fossil fuel combustion, such as traffic emission, and biomass burning, such as crematorium, were identified to be important contributors to light-absorbing substances. The estimated fractional radiative forcing by WSOC and MSOC to elemental carbon was most significant during winter (8.15 +/- 3.77%) and spring (13.90 +/- 4.38%), respectively, which may greatly affect the atmospheric photochemistry and climate. This study suggests that the impact of BrC in Taiwan on the local and regional air quality and climate is non-negligible.

Surface albedo is an important driver of surface processes that promote glacier melting and is a key variable influencing glacier surface melt. Despite much focus in the literature on variations in albedo and its influence on snow surfaces, little attention has been paid to dust and its impact on bare-ice albedo with respect to glacier melting surfaces. In this paper, spatial changes in glacier albedo were investigated using three Landsat images taken during the ablation season in 2006; temporal variations in albedo were measured by an automatic weather station (AWS) in the ablation zone between 26 June and 1 August 2007 at Urumqi Glacier No. 1 in Tien Shan. Ice and snow samples and reflection spectra at 325-1050 nm were collected in August, 2007 at Urumqi Glacier No. 1. The data suggested that spatial changes in glacier albedo are not prominent after snowfall; however, once ice becomes exposed, glacier albedo varies remarkably and generally increases with elevation, especially around the snow line. Temporal variations are characterized by a large range and high frequency, and most are induced by snowfall, changes in cloud conditions, and surface dust; snowfall and cloud increase glacier albedo. Furthermore, the response of snow albedo is more sensitive to cloud compared with the response of ice albedo. Over a bare ice surface, the albedo generally decreases as the concentration of surface dust increases. Organic matter is a primary factor in reducing the albedo over ice.

In Arctic soils, warming accelerates decomposition of organic matter and increases emission of greenhouse gases (GHGs), contributing to a positive feedback to climate change. Although microorganisms play a key role in the processes between decomposition of organic matter and GHGs emission, the effects of warming on temporal responses of microbial activity are still elusive. In this study, treatments of warming and precipitation were conducted from 2012 to 2018 in Cambridge Bay, Canada. Soils of organic and mineral layers were collected monthly from June to September in 2018 and analyzed for extracellular enzyme activities and bacterial community structures. The activity of hydrolases was the highest in June and decreased thereafter over summer in both organic and mineral layers. Bacterial community structures changed gradually over summer, and the responses were distinct depending on soil layers and environmental factors; water content and soil temperature affected the shift of bacterial community structures in both layers, whereas bacterial abundance, dissolved organic carbon, and inorganic nitrogen did so in the organic layer only. The activity of hydrolases and bacterial community structures did not differ significantly among treatments but among months. Our results demonstrate that temporal variations may control extracellular enzyme activities and microbial community structure rather than the small effect of warming over a long period in high Arctic soil. Although the effects of the treatments on microbial activity were minor, our study provides insight that microbial activity may increase due to an increase in carbon availability, if the growing season is prolonged in the Arctic.

Long-term variations in aerosol optical properties, types, and radiative forcing over the Sichuan Basin (SCB) and surrounding regions in Southwest China were investigated based on two-decade data (2001-2020) from the Moderate Resolution Imaging Spectroradiometer, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, and the Santa Barbara DISORT Atmospheric Radiative Transfer model. The results showed that the aerosol optical depth (AOD550nm) in the SCB, a major polluted region in Southwest China, experienced an increasing tendency at a rate of +0.052 yr-1 during 2001-2006; thereafter, it decreased speedy up from -0.020 to -0.058 yr-1 over recent years, whereas the interannual variation in angstrom ngstrom exponent (AE470-660nm) presented a persistently increasing trend during 2001-2020, with a rate of +0.014 yr-1. An improved atmospheric environment but an enhanced fine particle contribution to regional aerosols in the SCB was observed. Over the polluted SCB region, the dominant aerosol types were biomass burning/urban industrial and mixedtype aerosols with the proportions of 80.7%-87.5% in regional aerosols, with a higher frequency of clean aerosols in recent years, reflecting an effect of controlling anthropogenic emission in the SCB owing to governmental regulation. By contrast, few changes were observed in the aerosol types and amounts in the eastern Tibetan Plateau (ETP), where clean continental aerosols dominate with high proportion of 93.7% in the clean atmospheric environment. A significant decline in polluted anthropogenic aerosols was observed below 3 km over the SCB, resulting in the regional aerosol extinction coefficients at 532 nm (EC532nm) were declined by -0.22 km-1 from 2013 to 2020. Notably, the decreases in aerosol radiative forcing within the atmosphere were found in

Climate change effects, such as melting of glaciers and sea ice in response to rising temperatures, may lead to an increase in global water availability and thus in precipitation. In Central Yakutia, as one of the possible options for climate change, an increase in rainfall is possible, which makes up more than 60% of the annual precipitation. Rainfall is a highly variable meteorological parameter both spatially and temporally. In order to assess its effect on the ground temperature regime in Central Yakutia, we conducted manipulation and numerical experiments with increased rainfall. The manipulation experiment results suggest that a significant (three-fold) increase in rainfall can lower the mean annual ground temperatures locally. The long-term simulation predicts that a 50% increase in rainfall would have a warming effect on the ground thermal regime on a regional scale. For Central Yakutia, infiltration of increased precipitation has been shown to have both warming and cooling effect depending on the area affected.