Quantifying soil pH at manifold spatio-temporal scales is critical for examining the impacts of global change on soil quality. It is still unclear whether meteorological data and normalized difference vegetation index (NDVI) can be used to quantify soil pH in grasslands. Here, nine methods (i.e., RF: random-forest, GLR: generalized-linear-regression, GBR: generalized-boosted-regression, MLR: multiple-linear-regression, ANN: artificial-neural-network, CIT: conditional-inference-tree, SVM: support-vector-machine, eXGB: eXtreme-gradient-boosting, RRT: recursive-regression-tree) were applied to quantify soil pH. Three independent variables (i.e., AP: annual precipitation, AT: annual temperature, ARad: annual radiation) were used to quantify potential soil pH (pHp), and four independent variables (i.e., AP, AT, ARad and NDVImax: maximum NDVI during growing season) were applied to quantify actual soil pH (pHa). Overall, the developed eXGB models performed the worst (linear regression slope < 0.60; R2 = 0.99; relative deviation ≤ –43.54%; RMSE ≥ 3.14), but developed RF models performed the best (linear regression slope: 0.99–1.01; R2 = 1.00; relative deviation: from –1.26% to 0.65%; RMSE ≤ 0.28). The linear regression slope, R2, absolute value of relative deviation and RMSE between modelled and measured soil pH were 0.96–1.03, 0.99–1.00, ≤ 3.87% and ≤ 0.88 for the other seven methods, respectively. Accordingly, except the developed eXGB approach, the developed other eight methods can have relative greater accuracies in quantifying soil pH. However, the developed RF had the uppermost quantification accuracy for soil pH. Whether or not meteorological data and normalized difference vegetation index can be used to quantify soil pH was dependent on the chosen models. The RF developed by this study can be used to quantify soil pH from measured meteorological data and NDVImax, and may be conducive to scientific studies related to soil quality and degradation (e.g., soil acidification and salinization) at manifold spatial-temporal under future globe change.

Forage nutrient storages can determine livestock size and husbandry development. There is insufficient research on the response of forage nutrient storages to grazing and related driving mechanisms in alpine grasslands, especially on the Tibetan Plateau. This study conducted a grazing experiment in three alpine grassland sites along an elevation gradient (two warm-season pastures and one cold-season pasture; two alpine steppe meadow sites and one alpine meadow) of Northern Tibet. Different types of alpine grassland ecosystems, at least for forage nutrient storages, may have different responses to grazing. Warm-season grazing significantly reduced crude protein (CP) storage, acid detergent fiber (ADF) storage, and neutral detergent fiber (NDF) storage of high-quality forage by 53.29, 63.82, and 63.26%, respectively, but cold-season grazing did not significantly alter the CP, ADF and NDF storages of high-quality forage. Warm-season grazing significantly reduced CP, ADF, NDF, crude ash (Ash), ether extract (EE) and water-soluble carbohydrate (WSC) storages of the plant community by 46.61, 62.47, 55.96, 64.94, 60.34, and 52.68%, and forbs by 62.33, 77.50, 73.69, 65.05, 57.75, and 62.44% in the alpine meadow site but not the alpine steppe meadow site, respectively. Plant species and phylogenetic diversity had different relationships with forage nutrient storages. The elevation distribution of forage nutrient storages under fencing conditions were different from those under grazing conditions. Therefore, cold-season grazing can have lower negative effects on forage nutrient storages than warm-season grazing. Combined plant species with phylogenetic diversity and composition can be better in predicting forage nutrient storages. Grazing can restructure the elevation distribution of forage nutrient storages in alpine grasslands.

We studied Tibet’s Qamdo City, which currently hosts the most serious prevalence of Kashin-Beck osteoarthropathy (KB) in China. This study utilizes the geographical detector (GeoDetector) algorithm to measure the individual and interactive influences of risk factors on KB and to quantify the highest potential risk subzones of each principal factor. With a comprehensive consideration of 13 possible related factors, namely, the tectonic division, stratum, moisture index, gross domestic product, mean annual precipitation, soil type, groundwater type, elevation, mean annual temperature, vegetation type, geomorphic type, slope degree and slope aspect, our results indicate that the main exposure factors for KB in Qamdo City are geological factors (tectonic division and stratum), wetting factors (moisture index and mean annual precipitation), and an economic factor (gross domestic product). In contrast, other factors have little effect on the prevalence of KB in Qamdo City. All 13 factors either nonlinearly or bivariately enhance each other, and the interactions between these factors can increase the prevalence of KB. Consequently, it can be inferred that KB in Qamdo City is caused primarily by a set of multiple and interrelated disease risk factors.

Abstract:The contents of major and trace elements were analyzed in 204 different types of water samples in 138 villages across 51 counties and cities of Tibet. The average concentrations of arsenic (As), selenium, and fluorine for each water category decreased in the following order: arsenic (in µg/L: hot spring 241.37 > lake 27.46 > stream 22.11 > shallow well 11.57 > deep well 6. .52, 2.10, 1.68, and 1.51 µg/L in the prefectures of Shigatse, Nagchu, Lhasa, Lhoka, and Nyingchi, respectively. Carbonatite is a major source of elements in these waters. The non-carcinogenic risk in Tibet caused by heavy metals in drinking water is low overall, except in Ali prefecture's surface and shallow ground waters, which contain high levels of As. Thus, deep well water in Tibet is safe to drink.