China's coal-fired power plants impose pressure on water resources

Coal is the dominant fuel for electricity generation around the world. This type of electricity generation uses large amounts of water, increasing pressure on water resources. This calls for an in-depth investigation in the water-energy nexus of coal-fired electricity generation. In China, coal-fired power plants play an important role in the energy supply. Here we assessed water consumption of coal-fired power plants (CPPs) in China using four cooling technologies: closed-cycle cooling, once-through cooling, air cooling, and seawater cooling. The results show that water consumption of CPPs was 3.5 km3, accounting for 11% of total industrial water consumption in China. Eighty-four percent of this water consumption was from plants with closed-cycle cooling. China's average water intensity of CPPs was 1.15 l/kWh, while the intensity for closed-cycle cooling was 3-10 times higher than that for other cooling technologies. About 75% of water consumption of CPPs was from regions with absolute or chronic water scarcity. The results imply that the development of CPPs needs to explicitly consider their impacts on regional water resources

Refining soil organic carbon stock estimates for China’s palustrine wetlands

Palustrine wetlands include all bogs, fens, swamps and marshes that are non-saline and which are not lakes or rivers. They therefore form a highly important group of wetlands which hold large carbon stocks. If these wetlands are not protected properly they could become a net carbon source in the future. Compilation of spatially explicit wetland databases, national inventory data and in-situ measurement of soil organic carbon (SOC) could be useful to better quantify SOC and formulate long-term strategies for mitigating global climate change. In this study, a synergistic mapping approach was used to create a hybrid map for palustrine wetlands for China and to estimate their SOC content. Total SOC storage in palustrine wetlands was estimated to be 4.3±1.4 Pg C, with a SOC density of 31.17 (±10.55) kg C m-2 in the upper 1 m of the soil layer. This carbon stock is concentrated in Northeast China (49%) and the Qinghai-Tibet Plateau (41%). Given the large pool of carbon stored in palustrine wetlands compared to other soil types, we suggest that urgent monitoring programmes on SOC should be established in regions with very few datasets, but where palustrine wetlands appear to be common such as the Tibet region and Northwest China

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

This study was supported by the NSF China Programs (Grant No. 31300539 and 31570629) and the Public Welfare Technology Application Research Program of Zhejiang province (Grant No. 2015C31004).Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.Yeshttp://www.plosone.org/static/editorial#pee

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

Genome Characterization of the Oleaginous Fungus Mortierella alpina

Mortierella alpina is an oleaginous fungus which can produce lipids accounting for up to 50% of its dry weight in the form of triacylglycerols. It is used commercially for the production of arachidonic acid. Using a combination of high throughput sequencing and lipid profiling, we have assembled the M. alpina genome, mapped its lipogenesis pathway and determined its major lipid species. The 38.38 Mb M. alpina genome shows a high degree of gene duplications. Approximately 50% of its 12,796 gene models, and 60% of genes in the predicted lipogenesis pathway, belong to multigene families. Notably, M. alpina has 18 lipase genes, of which 11 contain the class 2 lipase domain and may share a similar function. M. alpina's fatty acid synthase is a single polypeptide containing all of the catalytic domains required for fatty acid synthesis from acetyl-CoA and malonyl-CoA, whereas in many fungi this enzyme is comprised of two polypeptides. Major lipids were profiled to confirm the products predicted in the lipogenesis pathway. M. alpina produces a complex mixture of glycerolipids, glycerophospholipids and sphingolipids. In contrast, only two major sterol lipids, desmosterol and 24(28)-methylene-cholesterol, were detected. Phylogenetic analysis based on genes involved in lipid metabolism suggests that oleaginous fungi may have acquired their lipogenic capacity during evolution after the divergence of Ascomycota, Basidiomycota, Chytridiomycota and Mucoromycota. Our study provides the first draft genome and comprehensive lipid profile for M. alpina, and lays the foundation for possible genetic engineering of M. alpina to produce higher levels and diverse contents of dietary lipids