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 6.22), selenium (in μg/L: deep well 0.85 > shallow well 0.68 > stream 0.62 > hot spring 0.39 > lake 0.36), and fluorine (in mg/L: hot spring 2.10 > lake 1.06 > deep well 0.45 > stream 0.20 > shallow well 0.15). The distribution of arsenic in Tibetan waters ranged between 77.35 μg/L in Ali prefecture and 1.17 μg/L in Chamdo prefecture, with intermediate values of 4.39, 2.52, 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.

Sixty water samples (35 groundwater samples, 22 surface water samples and three hot-spring water samples) were collected at 36 points from villages and towns in Lhasa city, Nagchu (Nagqu) prefecture, Ali (Ngari) prefecture and Shigatse (Xigaze) prefecture (Tibet) in 2013 to study the hydrochemical characteristics and element contents of natural waters. The concentrations of elements were determined in the water samples and compared with the concentrations in water samples from other regions, such as southeast Qinghai, south Xinjiang, east Sichuan and west Tibet. The hydrochemical species in different areas were also studied. Water in most parts of Tibet reaches the requirements of the Chinese national standard and the World Health Organization international standard. The pH values of the water samples ranged from 6.75 to 8.21 and the value for the mean total dissolved solids was 225.54 mg/L. The concentration of arsenic in water from Ali prefecture exceeded the limit of both the Chinese national standard and the international standard and the concentration of fluoride in water from Shuanghu exceeded the limit of both the Chinese national standard and the international standard. The main hydrochemical species in water of Tibet is Ca (HCO₃)₂. From south to north, the main cation in water changes from Ca²⁺ to Na⁺, whereas the main anions in water change from HCO₃ ⁻ to Cl⁻ and SO₄ ²⁻. The chemistry of river water and melt water from ice and snow is dominated by the rocks present at their source, whereas the chemistry of groundwater is affected by many factors. Tectonic divisions determine the concentrations of the main elements in water and also affect the hydrochemical species present.

Quantitative plant species α-diversity of grasslands at multiple spatial and temporal scales is important for investigating the responses of biodiversity to global change and protecting biodiversity under global change. Potential plant species α-diversity (i.e., SR_p, Shannon_p, Simpson_p and Pielou_p: potential species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by climate data (i.e., annual temperature, precipitation and radiation) and actual plant species α-diversity (i.e., SR_a, Shannon_a, Simpson_a and Pielou_a: actual species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by normalized difference vegetation index and climate data. Six methods (i.e., random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees) were used in this study. Overall, the constructed random forest models performed the best among the six algorithms. The simulated plant species α-diversity based on the constructed random forest models can explain no less than 96% variation of the observed plant species α-diversity. The RMSE and relative biases between simulated α-diversity based on the constructed random forest models and observed α-diversity were ≤1.58 and within ±4.49%, respectively. Accordingly, plant species α-diversity can be quantified from the normalized difference vegetation index and climate data using random forest models. The random forest models of plant α-diversity build by this study had enough predicting accuracies, at least for alpine grassland ecosystems, Tibet. The proposed random forest models of plant α-diversity by this current study can help researchers to save time by abandoning plant community field surveys, and facilitate researchers to conduct studies on plant α-diversity over a long-term temporal scale and larger spatial scale under global change.

This work reviewed how ionic liquids support hydrogen energy technologies for production, storage and utilization.

The allocation of net primary production (NPP) between above- and belowground components is a key step of ecosystem material cycling and energy flows, which determines many critical parameters, e.g., the fraction of below ground NPP (BNPP) to NPP (f_BNPP) and root turnover rates (RTR), in vegetation models. However, direct NPP estimation and partition are scarcely based on field measurements of biomass dynamics in the alpine grasslands on the Northern Tibetan Plateau (NTP). Consequently, these parameters are unverifiable and controversial. Here, we measured above- and belowground biomass dynamics (monthly from May to September each year from 2013 to 2015) to estimate NPP dynamics and allocations in four typical alpine grassland ecosystems, i.e., an alpine meadow, alpine meadow steppe, alpine steppe and alpine desert steppe. We found that NPP and its components, above and below ground NPP (ANPP and BNPP), increased significantly from west to east on the NTP, and ANPP was mainly affected by temperature while BNPP and NPP were mainly affected by precipitation. The bulk of BNPP was generally concentrated in the top 10 cm soil layers in all four alpine grasslands (76.1% ± 9.1%, mean ± SD). Our results showed that f_BNPP was significantly different among these four alpine grasslands, with its means in alpine meadow (0.93), alpine desert steppe (0.92) being larger than that in the alpine meadow steppe (0.76) and alpine steppe (0.77). Both temperature and precipitation had significant and positive effects on the f_BNPP, while their interaction effects were significantly opposite. RTR decreased with increasing precipitation, but increased with increasing temperature across this ecoregion. Our study illustrated that alpine grasslands on the NTP, especially in the alpine meadow and alpine desert steppe, partitioned an unexpected and greater NPP to below ground than most historical reports across global grasslands, indicating a more critical role of the root carbon pool in carbon cycling in alpine grasslands on the NTP.