As a key component of the cryosphere, permafrost is sensitive to climate change, but mapping permafrost, especially in the Tibetan Plateau, has been challenging due to the heterogeneous mountainous landscape and limited representativeness of ground observations. Using 155 compiled ground observations and more than 20,000 rock glacier records, we developed a machine learning model to map the distribution of permafrost and produce an improved permafrost zonation index (PZI) map. The model was applied by incorporating several control variables, including terrain (elevation and relief), soil (bulk density, clay, coarse fragments, sand, and silt), and temperature (MAAT, FDD, and TDDT) to estimate the PZI at a 1-km resolution in the southern Tibetan Plateau. Excluding glaciers and lakes, the area of permafrost estimated by the new map is approximately 103.5 x 103 km2, accounting for 47.8% of the total area of the region. The result was assessed with various datasets and compared with existing permafrost maps and achieved higher accuracy compared with previous studies. The overall classification accuracy was 96.1% in high plain areas and 84.4% in mountain areas. The results demonstrated the substantial potential for improving mapping permafrost and understanding the periglacial environment with rock glacier inventories and machine learning, especially in complex terrain and climate.

Heavy metals, one of the most toxic classes of pollutants, are resistant to degradation and harmful to the biological environment. The lakes that have developed on the Tibetan Plateau are ideal regions to investigate historic heavy metal pollution, particularly through the use of the reliable(210)Pb dating technique. Environmental magnetism has been successfully applied to estimate heavy metal pollution in different environmental systems due to its characteristics of simple processing steps, good sensitivity, and non-destructibility. However, it has not yet been applied to assess heavy metal pollution in lake sediments on the Tibetan Plateau. A series of environmental magnetic investigations of Qiangyong Co Lake sediments (southern Tibetan Plateau) was therefore conducted to explore the relationship between magnetic minerals and mercury (Hg) concentrations. The results showed that the magnetic mineral species in lake sediments remained stable, with similar levels of four different components from 1899 to 2011. However, the proportion of component 1 (C1, hematite) increased continuously with the corresponding decrease in the proportion of C2 (goethite), while the proportions of C3 and C4 (magnetite) did not change significantly. As a result, the bulk magnetic signals (e.g., SIRM and chi(lf)) were unsuitable for the evaluation of the Hg concentration; however, the proportion of hematite had a strong positive correlation with the Hg concentration. It is possible that the Qiangyong Glacier (the main water supply for Qiangyong Co Lake) has experienced faster melting with global and local warming, and the Hg trapped in cryoconite and ice was released. Hematite, with a large specific surface area, has a strong capacity for absorbing Hg, and both materials are ultimately transported to Qiangyong Co Lake. The proportion of hematite in a sample is therefore a suitable semi-quantitative proxy that can be used to evaluate the Hg concentration in Qiangyong Co Lake sediments. This study confirmed that the variation of magnetic minerals can provide a new method to estimate the variation of Hg concentrations and to study the process of Hg deposition in lakes in the southern Tibetan Plateau on the basis of a detailed environmental magnetic analysis.

The major ions in precipitation can reflect the conditions of the atmosphere, while stable isotopic characteristics provide information on the moisture source. In order to understand the local hydro-chemical features and regional geochemical cycle, it is essential to assess the chemical composition of precipitation and the associated sources. Therefore, a total of 57 precipitation samples (2016 to 2017) for major ions and 178 samples (2013 to 2017) for stable isotopes were collected from the Wengguo station and analyzed to explore the major ionic deposition and stable isotopic characteristics in the northern slopes of the Himalayas. The average pH and electrical conductivity were 6.82 +/- 0.45 and 15.36 +/- 11.67 mu S cm(-1), respectively. Ca2+ followed by K+ and Mg2+ played a crucial role in neutralizing the precipitation acidity. The major ionic sources in the region were terrigenous (Ca2+, HCO3-, and Mg2+) and sea salt (Na+, Cl-, and Mg2+), as well as anthropogenic emissions (SO42- and NO3-) and biomass burning (K+). The total deposition flux of the major ions was higher in 2016 than in 2017 and was influenced by the higher precipitation. The average values of delta O-18 and delta D in precipitation were - 15.22 +/- 5.17 parts per thousand and - 116.01 +/- 41.31 parts per thousand, respectively. The precipitation stable isotopes were not significantly correlated to the local air temperature but the precipitation amount. Moreover, the variation in stable isotopes, local meteoric water line, and d-excess indicated the existence of continental and monsoon moisture transport systems. The transport of chemicals over the high elevation region from polluted cities in South Asia via moisture originating in the Bay of Bengal and the Arabian Sea was determined based on the source identification, clusters of air mass backward trajectory analysis, and the National Center for Environmental Prediction Final dataset. Thus, the ionic concentrations and stable isotopic characteristics of the precipitation from this study provided a valuable dataset to assess the atmospheric environment in the northern slopes of the Himalayas at Southern Tibetan Plateau.