The objectives of this work were to identify the best possible method to estimate soil erodibility (K) and understand the influencing factors of soil erodibility. In this study, 151 soil samples were collected during soil surveys in the Ansai watershed of the Loess Plateau of China. The K values were estimated by five methods: erosion-productivity impact model (EPIC), nomograph equation (NOMO), modified nomograph equation (M-NOMO), Torri model and Shirazi model. The main conclusions of this paper are (1) K values in the Ansai watershed ranged between 0.009 and 0.092 t ⋅ hm2 ⋅ h/(MJ ⋅ mm ⋅ hm2), and the maximum values were 1.9–7.3 times larger than the corresponding minimum values, and the Shirazi and Torri models were considered the optimal models for the Ansai watershed. (2) Different land use types had different levels of importance; the principal components (PCs) accounted for 100 % (native grassland), 48.88 % (sea buckthorn), 62.05 % (Caragana korshinskii), and 53.61 % (pasture grassland) of the variance in soil erodibility. (3) The correlations between soil erodibility and the selected environmental variables differed among different vegetation types. For native grasslands, soil erodibility had significant correlations with terrain factors. For the most artificially managed vegetation types (e.g., apple orchards) and artificially restored vegetation types (e.g., sea buckthorn), soil erodibility had significant correlations with the growing conditions of vegetation. Soil erodibility had indirect relationships with both environmental factors (e.g., elevation and slope) and human activities, which potentially altered soil erodibility.

Grassland is one of the most widely distributed ecosystems on the Tibetan Plateau (TP) accounting for about 60% of the total area. The grassland degradation has spread throughout the TP, and the scope and degree are increasing. The inconsistency of multi-source data poses a great challenge to accurately obtaining information about grassland degradation on the TP. This study used five land cover products and six vegetation indexes to analyze the spatial-temporal change in grassland area and quality at the pixel level across the TP from 2000 to 2020. Then, 279 observed grassland degradation points that were collected from 86 published papers were used to verify the grassland degradation information. The grassland fusion product demonstrated that the grassland area increased by 8.84% from 2000 to 2020, and the rate of grassland degradation exceeded the rate of grassland greening during 2010–2020. The superimposed six vegetation indexes showed that 25.88% of the grassland quality has been degraded on the TP from 2000 to 2020. In Changdu City, Ganzi Tibetan Autonomous Prefecture, Gannan Tibetan Autonomous Prefecture, Yushu Tibetan Autonomous Prefecture, Aba Tibetan and Qiang Autonomous Prefecture, Rikaze City, Shannan City and Nagqu City, the grassland quality degraded by more than in 20% and the degraded grassland area exceeded 2000 km². The observed grassland degradation points were mainly distributed in the northeastern and central parts of the TP. The consistency of six vegetation indexes with the observed grassland degradation points on the TP was 56.63%, with solar-induced chlorophyll fluorescence (SIF) being more effective than other vegetation indexes for monitoring grassland degradation on the TP. In general, the degradation of grassland on the TP has been a looming problem in recent decades.

The increased production and environmental release of graphene nanoparticles has raised concerns about its environmental impact, but the effects of graphene on living organisms at the metabolic level remain unknown. In this study, we used matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI)-based untargeted metabolomics to investigate the metabolic response of juvenile earthworms (Eisenia fetida) to graphene exposure in soil tests for the first time. Our results reveal that graphene-exposure significantly disturbs earthworm metabolome, and graphene toxicity on earthworm shows non-concentration-dependent effect. Alanine, phenylalanine, proline, glutamate, arginine, histidine, maltose, glucose, malate, succinate, myo-inositol, and spermidine were successfully screened as significantly change compounds in earthworms for the exposure of graphene. The heterogeneous distributions of these metabolites in earthworm were also clearly imaged by MALDI-MSI. Our MSI results fully showed that the metabolite expression levels in juvenile earthworms significantly changed (up-/down-regulation) after exposure to graphene nanoparticles. This work improves our understanding of graphene nanoparticle toxicity to juvenile earthworms and also enables the continued progression of MALDI-MSI-based metabolomics as an emerging, reliable, and rapid ecotoxicological tool for assessing contaminant toxicity.