Long-range transport of black carbon from South Asia to the Tibetan plateau and its deposition on glaciers directly enhances glacier melt. Here we find South Asian black carbon also has an indirect effect on the plateau’s glaciers shrinkage by acting to reduce the water supply over the southern Tibetan plateau. Black carbon enhances vertical convection and cloud condensation, which results in water vapor depletion over the Indian subcontinent that is the main moisture flux source for the southern Tibetan plateau. Increasing concentrations of black carbon causes a decrease in summer precipitation over the southern Tibetan plateau, resulting in 11.0% glacier deficit mass balance on average from 2007 to 2016; this loss rises to 22.1% in the Himalayas. The direct (accelerated melt) and indirect (mass supply decrease) effects of black carbon are driving the glacial mass decline of the so-called “Asian Water Tower”.

Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers.

Mycotoxins and heavy metals extensively contaminate grains and grain products, posing severe health risks. This work implements validated ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and inductively coupled plasma mass spectrometry (ICP-MS) methods to quantify the concentration of 12 mycotoxins and five heavy metals in rice, maize, soybeans, and wheat flour samples marketed in Shanghai. The mixed contamination characteristics were analyzed using correlation cluster analysis and co-contamination index, and the probabilities of all cross combinations of contaminations were analyzed using a self-designed JAVA language program. The results showed that grains and grain products were frequently contaminated with both mycotoxins and heavy metals, mostly with deoxynivalenol (DON), 3-acetyl-deoxynivalenol (3-ADON), 15-acetyl-deoxynivalenol (15-ADON), ochratoxin A (OTA), aflatoxins, fumonisin B1 (FB1), fumonisin B2 (FB2), fumonisin B3 (FB3), arsenic (As), chromium (Cr) and cadmium (Cd). All the samples (100 %) were contaminated with two or more contaminants, and 77.3 % of the samples were co-contaminated with more than four contaminants. In cereals and cereal products, the following combinations were closely associated: (FB3 +3-ADON), (FB1 +As), (FB1 +FB2), (DON+FB1), (DON+Cd), (As+Cd), (DON+Cd+As), (FB1 +FB2 +As), and (DON+3-ADON+15-ADON). The results indicated that mycotoxins and heavy metals frequently co-occurred in Shanghai grains and grain products, and they provided primary data for safety assessments, early warnings, and regulatory measures on these contaminants to protect public health.

Vegetation cover is important to the stability of regional ecosystems and is a focus of research on the relationship between natural and human environments. Although some studies have investigated the association between changes in vegetation cover and various influencing factors, these have shortcomings in quantifying direct and indirect effects. In this study, MOD13Q1 enhanced vegetation index (EVI) data for Anhui Province, China, were acquired between 2000 and 2020. The univariate linear regression, coefficient of variation and Hurst index methods were used to analyze spatial and temporal trends and fluctuations in the EVI between 2000 and 2020 and predict future trends. The impact of land-use change on EVI change was explored using 2000 and 2020 land-use data. Finally, a structural equation model (SEM) was used to quantify the effects of topography, annual average temperature, annual precipitation and human activity changes on EVI variation in Anhui Province. The results show that (1) from 2000 to 2020, the overall EVI in Anhui Province showed a fluctuating trend that increased at a rate of 0.0181·10a⁻¹, and 67.1% of the study area showed a greening trend. The EVI was relatively stable in most regions, with regions of fluctuating EVI being mostly affected by urbanization. For a period after 2020, the overall EVI change will exhibit anti-sustainability and will likely decrease. (2) Among the regions of EVI increase, 72.2% had no change in land-use type, while 10.8% and 6.6% changed to farmland and woodland land uses, respectively. Among the regions where EVI decreased, 69.9% had no change in land-use type, while 13.7% changed from farmland to construction land. (3) Overall, human activity change was the main influence on EVI change, which was mainly reflected in the negative impacts of accelerated urbanization. Topography had direct and indirect effects on EVI variations in Central and Southern Anhui. Annual precipitation change had a stronger impact on EVI variation in Northern and Central Anhui than in Southern Anhui, while annual average temperature change had a small impact in the entire province. Compared with other study methods, SEM provides a new approach to quantifying the influences of vegetation cover dynamics. In addition, the results of this study have important implications for ecological environmental protection and sustainable development in Anhui Province.

The Pan‐Third Pole contains the largest number of glaciers outside the polar region that plays a crucial role in atmospheric circulation and the hydrological cycle. However, this pristine region has undergone rapid change through complex interactions including the black carbon (BC) enhanced warming effect and glacier melting. Study shows, Weather Research and Forecasting coupled with Chemistry (WRF‐Chem) simulation is able to capture distinctive seasonal variability of BC. The result from our sensitivity experiments revealed that South Asia (SA; 60.7%) and East Asia (EA; 32.9%) contributed more toward the Tibetan Plateau (TP). Our analysis on aerosol‐boundary feedback interaction revealed BC expand planetary boundary layer height by 5.0% and 4.8% over SA and EA, respectively, which facilitates BC dispersion and transportation. Whereas, we also found that under the influence of different wind regimes the significant BC transport flux aloft over the TP and the upper troposphere and lower stratosphere. Additionally, mountain‐valley channel and synoptic and local meteorological processes also facilitated BC transport to the TP. This study also evaluated the effect of BC on direct radiative forcing and calculated subsequent temperature changes. A strong dimming effect of BC corroborated with the following negative surface temperature changes. However, enhanced BC concentration during winter and spring caused the increase in temperature over the TP. Here, the WRF‐Chem model, synergy on aerosol‐boundary feedback, BC transport flux, and source‐receptor methods confirmed the significant BC contribution and transportation, and notable BC‐induced warming over TP. Such trans‐Himalayan BC transport and associated warming could grim glacier melt and water availability in the region.