Atmospheric Brown Carbon (BrC) with strong wavelength-dependence light-absorption ability can significantly affect radiative forcing. Highly resolved emission inventories with lower uncertainties are important premise and essential in scientifically evaluating impacts of emissions on air quality, human health and climate change. This study developed a bottom-up inventory of primary BrC from combustion sources in China from 1960 to 2016 with a spatial resolution at 0.1 degrees x 0.1 degrees, based on compiled emission factors and detailed activity data. The primary BrC emission in China was about 593 Gg (500-735 Gg as interquartile range) in 2016, contributing to 7% (5%-8%) of a previously estimated global total BrC emission. Residential fuel combustion was the largest source of primary BrC in China, with the contribution of 67% as the national average but ranging from 25% to 99% among different provincial regions. Significant spatial disparities were also observed in the relative shares of different fuel types. Coal combustion contribution varied from 8% to 99% across different regions. Heilongjiang and North China Plain had high emissions of primary BrC. Generally, on the national scale, spatial distribution of BrC emission density per area was aligned with the population distribution. Primary BrC emission from combustion sources in China have been declined since a peak of similar to 1300 Gg in 1980, but the temporal trends were distinct in different sectors. The high-resolution inventory developed here enables radiative forcing simulations in future atmospheric models so as to promote better understanding of carbonaceous aerosol impacts in the Earth's climate system and to develop strategies achieving co-benefits of human health protection and climate change.

The negative effects of PM2.5 concentration in urban development are becoming more and more prominent. Bernaola-Galvan Segmentation Algorithm (BGSA) and wavelet analysis are powerful tools for processing non-linear and non-stationary signals. First, we use BGSA that reveals there are 41 mutation points in the PM2.5 concentration in Guiyang. Then, we reveal the multi-scale evolution of PM2.5 concentration in Guiyang by wavelet analysis. In the first part, we performed one-dimensional continuous wavelet transform (CWT) on the eight monitoring points in the study area, and the results showed that they have obviously similar multi-scale evolution characteristics, with a high-energy and significant oscillation period of 190-512 days. Next, the wavelet transform coherence (WTC) reveals the mutual relationship between the PM2.5 concentration and the atmospheric pollutants and meteorological factors. PM2.5 concentration variation is closely linked to that of PM10 concentration. But, it is not to be ignored that the increase in the SO2 and NO2 concentrations will cause the PM2.5 concentration to rise on different scales. Lastly, the variation of the PM2.5 concentration can be better explained by the combination of multiple factors (2-4) using the multiple-wavelet coherence (MWC). Under the combination of the two factors, the average temperature (Avgtem) and relative humidity (ReH) have the highest AWC and PASC. In the case of the combination of four factors, CO-Avgtem-Wind-ReH plays the largest role in determining PM2.5 concentration.

Soil freeze-thaw cycles play a critical role in ecosystem, hydrological and biogeochemical processes, and climate. The Tibetan Plateau (TP) has the largest area of frozen soil that undergoes freeze-thaw cycles in the low-mid latitudes. Evidence suggests ongoing changes in seasonal freeze-thaw cycles during the past several decades on the TP. However, the status of diurnal freeze-thaw cycles (DFTC) of shallow soil and their response to climate change largely remain unknown. In this study, using in-situ observations, the latest reanalysis, machine learning, and physics-based modeling, we conducted a comprehensive assessment of the spatiotemporal variations of DFTC and their response to climate change in the upper Brahmaputra (UB) basin. About 24 +/- 8% of the basin is subjected to DFTC with a mean frequency of 87 +/- 55 days during 1980-2018. The area and frequency of DFTC show small long-term changes during 1980-2018. Air temperature impacts on the frequency of DFTC changes center mainly around the freezing point (0 degrees C). The spatial variations in the response of DFTC to air temperature can primarily be explained by three factors: precipitation (30.4%), snow depth (22.6%) and seasonal warming/cooling rates (14.9%). Both rainfall and snow events reduce diurnal fluctuations of soil temperature, subsequently reducing DFTC frequency, primarily by decreasing daytime temperature through evaporation-cooling and albedo-cooling effects, respectively. These results provide an in-depth understanding of diurnal soil freeze-thaw status and its response to climate change. Freeze-thaw transitions of terrestrial landscapes are a common phenomenon in cold regions. The seasonal and diurnal freeze-thaw cycles (DFTC) of shallow soil exhibit substantial differences in response to climate. Understanding of the spatiotemporal patterns of DFTC and their response to climate change remains limited over the Tibetan Plateau (TP), which is characterized by the largest areas of freeze-thaw terrain in the mid- and low-latitudes of the world. We found the frequency and area of DFTC show a slight increase trend in a significantly warming climate in upper Brahmaputra (UB) basin, the largest river basin of the TP. The variation of DFTC depends on climatic conditions, with soils near the freezing point (0 degrees C) being more susceptible to changes in DFTC. Precipitation, snow depth and seasonal warming/cooling rates are the top three factors influencing the response of DFTC to air temperature changes. Snowfall plays a more important role in the temporal variability of DFTC frequency than rainfall. The number of diurnal freeze-thaw cycles (DFTC) in shallow soil increase slightly during the period 1980-2018 in the upper Brahmaputra (UB) basin Air temperature effects on the changes in DFTC frequency center on the freezing point Snowfall plays a more important role in the temporal variability of DFTC than rainfall

In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing'an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

Global warming has shown an Arctic amplification effect in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land-atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 degrees C, which is a 42.95---53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 degrees C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land-atmosphere energy balance in pan-Arctic.

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.

Permafrost temperature is a vital indicator of climate and permafrost changes, benefiting ecosystem development and informing local climate strategies. Alpine grasslands impact moisture and heat exchange between the surface and atmosphere, thereby affecting the thermal state of underlying permafrost. This study analyzed permafrost temperatures (2004-2019) from various alpine grasslands (including alpine meadow, alpine steppe, alpine desert grassland, and barren land) in the Beiluhe region of the Tibetan Plateau and revealed their connections to climate change and controlling factors, using time-frequency analysis. The findings revealed that in the time-frequency domain, permafrost temperatures exhibited multiple time scales characteristics, driven by climate fluctuations. Changes in the active layer closely followed monthly climate variations, while permafrost dynamics responded to annual climate changes. Significant oscillations with periods of 10-11, 8-9, and 14 years were observed in the surface, permafrost table, and deep permafrost layers, respectively. Among the different types of alpine grasslands, alpine meadows proved to be the most sensitive to climate change, with the intensity of periodic fluctuations initially decreasing and then increasing with depth in alpine meadows, while it consistently decreased with depth in the other three alpine grasslands. The impact of air temperature, precipitation, and wind speed on permafrost dynamics exhibited depth-dependent variations in the time-frequency domain, contrasting with the time domain where permafrost temperature changes were predominantly associated with air temperature across all depths.

Debris cover either enhances or reduces glacier melting, thereby modulating glacier response to increasing temperatures. Debris cover variation and glacier recession were investigated on five glaciers; Pensilungpa (PG), Drung Drung (DD), Haskira (HK), Kange (KG) and Hagshu (HG), situated in the topographically and climatically similar zone in the Zanskar Himalaya using satellite data between 2000 and 2020. Analyses reveals that the HK, KG, and HG had a debris-covered area of similar to 24% in 2020, while PG and DD had a debris cover of <10%. Comparing PG to the other four glaciers, it had the highest shrinkage (5.7 +/- 0.3%) and maximum thinning (1.6 +/- 0.6 m a(-1)). Accordingly, detailed measurements of PG's debris cover thickness, temperature and ablation were conducted for eleven days in August 2020. The results indicated a significant variation of temperature and the highest melting was observed near dirty and thin debris-covered ice surface. Thermal conductivity of 0.9 +/- 0.1 Wm(-1) K-1 and 1.1 +/- 0.1 Wm(-1) K-1 was observed at 15 cm and 20 cm debris-depth, respectively. The ablation measurements indicated an average cumulative melting of 21.5 cm during eleven days only. Degree-day factor showed a decreasing trend towards debris cover depth with the highest value (4.8 mm w.e.degrees C-1 d(-1)) found for the dirty ice near the glacier surface and the lowest value (0.4 mm w.e.degrees C-1 d(-1)) found at 30 cm depth. The study highlights the importance of in-situ debris cover, temperature and ablation measurements for better understanding the impact of debris cover on glacier melting.

The Qinghai-Tibet Plateau (QTP) has experienced rapid environmental changes, including climate warming and wetting, since the 1980s. These environmental changes significantly impact the shallow soil hydrothermal conditions, which have key roles in land-atmosphere feedback and ecosystem functions. However, the spatial variations and responses of soil hydrothermal conditions to environmental changes over the QTP with permafrost (PF) and seasonal frost (SF) remain unclear. In this study, we investigated the spatial variations in soil temperature (ST) and soil moisture (SM) changes over the QTP from 2000 to 2020 using 99 in-situ sites with observations at 4 depths (i.e. 10, 40, 100 and 200 cm). The main environmental controlling factors were further identified using a calibrated statistical model. Results showed that significant ( p < 0.05) soil warming occurred at multiple soil layers during 2000-2020 with a wide variation (i.e. 0.033-0.039 degrees C per year on average), whereas the warming rates at PF sites were two times greater than those at SF sites. In addition, the soil wetting rate was high over the SF region, whereas the soil wetting rate was low over the PF region. Aside from air temperature, changes in thawing degree days and solar radiation (Srad) contributed most to soil warming in the PF region, whereas changes in rainfall, Srad and evaporation (EVA) have been identified as the key factors in the SF region. As for soil wetting, changes in snowfall, freezing degree days and vegetation have noticeable nonlinear effects over the PF region, whereas changes in EVA, Srad and rainfall highlighted distinct linear and nonlinear effects in the SF region. These findings enhance our understanding of the hydrothermal impacts of future environmental changes over the QTP.

In this study, in situ observations were conducted for six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O-3) at 23 sites in western China for 1 year. Subsequently, the detailed Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) results for the pollutants were determined. The WRF-Chem model provided a clear perspective on the spatiotemporal distribution of air pollutants. High pollutant concentrations were mainly observed over highly populated mega-city regions, such as Sichuan and Guanzhong basins, whereas low concentration levels were observed over the Tibetan Plateau (TP). The TP also showed an increased concentration of O-3. Seasonally, all six pollutants except O-3 exhibited high concentration values during winter and low values during summer. O-3 concentrations exhibited an opposite seasonal variation in low-altitude regions. Unlike other pollutants that exhibited gradually decreasing concentrations with an increase in altitude, O-3 concentrations revealed an increasing trend. Furthermore, NO2 concentrations gradually increased in the upper atmosphere possibly due to lighting and stratospheric transmission. Atmospheric pollution is closely related to emissions and meteorological variations in western China. Meteorological conditions in the summer are conducive to pollutant dispersion and wet scavenging; however, unfavourable weather conditions (high pressure as well as a low planetary boundary layer height and precipitation level) in the winter can further worsen air pollution. Atmospheric pollutants from various emission sectors generally exhibited varying monthly profiles. In six typical cities, pollutants were positively correlated with multiple emission sources except for industrial emissions. Further sensitivity simulations indicated that eliminating residential emissions resulted in the largest decrease (up to 70%) in PM2.5 and PM10 concentrations. The most significant reductions in the concentrations of SO2 and NO2 were achieved by eliminating industrial and transportation emissions, respectively. The outcomes of this study could be helpful for future studies on pollution formation mechanisms as well as environmental and health risk assessments in western China. (C) 2019 Elsevier Ltd. All rights reserved.