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China is confronted with an unprecedented water crisis regarding its quantity and quality. In this study, we quantified the dynamics of China?s embodied water use and chemical oxygen demand (COD) discharge from 2010 to 2015. The analysis was conducted with the latest available water use data across sectors in primary, secondary and tertiary industries and input?output models. The results showed that (1) China?s water crisis was alleviated under urbanisation. Urban consumption occupied the largest percentages (over 30%) of embodied water use and COD discharge, but embodied water intensities in urban consumption were far lower than those in rural consumption. (2) The ?new normal? phase witnessed the optimisation of China?s water use structures. Embodied water use in light-manufacturing and tertiary sectors increased while those in heavy-manufacturing sectors (except chemicals and transport equipment) dropped. (3) Transformation of China?s international market brought positive effects on its domestic water use. China?s water use (116?80 billion tonnes (Bts))(9) and COD discharge (3.95?2.22 million tonnes (Mts)) embodied in export tremendously decreased while its total export values (11?25 trillion CNY) soared. Furthermore, embodied water use and COD discharge in relatively low-end sectors, such as textile, started to transfer from international to domestic markets when a part of China?s production activities had been relocated to other developing countries.

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models' ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output. Environmental Research Letters, 13 (6) ISSN:1748-9326 ISSN:1748-9318

China's household energy demands' life-cycle water uses from 2002 to 2015 are quantified with an Input-Output analysis disaggregating rural and urban impacts. 9.73 and 1.60 km3 of water was withdrawn and consumed respectively in the life cycle of Chinese household energy demands in 2015, which was dominated by power and heat uses. An average urbanite's household energy uses, including coal, gas, petroleum products, power and heat, require about four times of life-cycle water uses than its rural counterpart. Among all upstream sectors, while agricultural sectors accounted for the largest shares for all energy uses, oil and gas extraction made significant contributions to petroleum products and gas consumption. A Structural Decomposition Analysis is conducted to disentangle the impacts of four driving factors, i.e. population, demand, economic structure and technology. Population change reduced energy consumption's life-cycle water use for rural households but increased that for urban households. Each economic sector's water intensity decreases, which represent technology advancement, played the dominant role curbing household energy consumption's life-cycle water uses. While power and heat dominates the household energy use profile, urbanization is accompanied by household consumption shifting from coal to gas and petroleum products. In order to reduce household energy consumption's impacts and reliance on water resources, it is imperative to reduce energy production's water use by adopting water-saving technologies, such as air cooling, as well as to reduce upstream sectors' water intensities, such as by promoting drip irrigation.

Large waste water treatment plants (WWTP) often operate nitrification in two different process environments: the cold-dilute sewage is treated in the mainstream nitrification/denitrification system, while the high strength ammonia liquors from sludge dewatering are treated in a separate high temperature reactor (SBR). This study investigates transfer from nitrifier biomass into a two-stage WWTP, commonly referred to as bioaugmentation. Besides the quantitation of ammonia oxidising bacteria (AOB), community differences were analysed with two techniques, denaturing gradient gel electrophoresis and real-time PCR melt curve analysis. It was shown that, without bioaugmentation, two distinct AOB communities establish in the mainstream and in the SBR, respectively. A gradual shift of the two AOB communities with increasing pump rates between the systems could be demonstrated. These molecular findings support process engineering experience, that cycling of waste activated sludge improves the ability of AOB to adapt to different process environments.

AbstractNitrogen is a critical component of the economy, food security, and planetary health. Many of the world's sustainability targets hinge on global nitrogen solutions, which, in turn, contribute lasting benefits for (i) world hunger; (ii) soil, air, and water quality; (iii) climate change mitigation; and (iv) biodiversity conservation. Balancing the projected rise in agricultural nitrogen demands while achieving these 21st century ideals will require policies to coordinate solutions among technologies, consumer choice, and socioeconomic transformation.

Alpine lakes are considered pristine freshwater ecosystems and sensitive to direct and indirect changes in water temperature as induced by climate change. The bacterial plankton constitutes a key component in the water column and bacterial metabolic activity has direct consequences for water quality. In order to understand bacterial response to global temperature rise in five alpine lakes located in the Austrian Alps (1700-2188 m a.S.L.) water temperature was compared within a decadal period. Depth-integrated samples were characterized in community composition by 16S rDNA deep-amplicon sequencing early [56 ± 16 (SD) days after ice break up] and later (88 ± 16 days) in the growing season. Within the 10 years period, temperature rise was observed through reduced ice cover duration and increased average water temperature. During the early growing season, the average water temperature recorded between circulation in spring until sampling date (WAS), and the day of autumn circulation, as well as chemical composition including dissolved organic carbon influenced bacterial community composition. In contrast, only nutrients (such as nitrate) were found influential later in the growing season. Metabolic theory of ecology (MTE) was applied to explain the dependence of taxonomic richness on WAS in mathematical terms. The calculated activation energy exceeded the frequently reported prediction emphasizing the role of WAS during early growing season. Accordingly, the relative abundance of predicted metabolism related genes increased with WAS. Thus, the dominant influence of temperature after ice break up could be explained by overall climate change effects, such as a more intense warming in spring and an overall higher amplitude of temperature variation.