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The responses of macroalgae to ocean acidification could be altered by availability of macronutrients, such as ammonium (NH4+). This study determined how the opportunistic macroalga, Ulva australis responded to simultaneous changes in decreasing pH and NH4+ enrichment. This was investigated in a week-long growth experiment across a range of predicted future pHs with ambient and enriched NH4+ treatments followed by measurements of relative growth rates (RGR), NH4+ uptake rates and pools, total chlorophyll, and tissue carbon and nitrogen content. Rapid light curves (RLCs) were used to measure the maximum relative electron transport rate (rETRmax) and maximum quantum yield of photosystem II (PSII) photochemistry (Fv/Fm). Photosynthetic capacity was derived from the RLCs and included the efficiency of light harvesting (α), slope of photoinhibition (β), and the light saturation point (Ek). The results showed that NH4+ enrichment did not modify the effects of pH on RGRs, NH4+ uptake rates and pools, total chlorophyll, rETRmax, α, β, Fv/Fm, tissue C and N, and the C:N ratio. However, Ek was differentially affected by pH under different NH4+ treatments. Ek increased with decreasing pH in the ambient NH4+ treatment, but not in the enriched NH4+ treatment. NH4+ enrichment increased RGRs, NH4+ pools, total chlorophyll, rETRmax, α, β, Fv/Fm, and tissue N, and decreased NH4+ uptake rates and the C:N ratio. Decreased pH increased total chlorophyll content, rETRmax, Fv/Fm, and tissue N content, and decreased the C:N ratio. Therefore, the results indicate that U. australis growth is increased with NH4+ enrichment and not with decreasing pH. While decreasing pH influenced the carbon and nitrogen metabolisms of U. australis, it did not result in changes in growth.

To solve the problem of water management subsystem fault diagnosis in a proton exchange membrane fuel cell (PEMFC) system, a novel approach based on learning vector quantization neural network (LVQNN) and kernel principal component analysis (KPCA) is proposed. In the proposed approach, the KPCA method is used for processing strongly coupled fault data with a high dimension to reduce the data dimension and to extract new low-dimensional fault feature data. The LVQNN method is used to carry out fault recognition using the fault feature data. The effectiveness of the proposed fault detection method is validated using the experimental data of the PEMFC power system. Results show that the proposed method can quickly and accurately diagnose the three health states: normal state, water flooding failure and membrane dry failure, and the recognition accuracy can reach 96.93%. Therefore, the method proposed in this paper is suitable for processing the fault data with a high dimension and abundant quantities, and provides a reference for the application of water management subsystem fault diagnosis of PEMFC.

Ocean manipulation to mitigate climate change may harm deep-sea ecosystems

Fine-grained natural gas hydrate (NGH) reservoirs are widely distributed across the world and bear more than 90% of global NGH. However, it is difficult to exploit this kind of NGH reservoir economically and environmentally using conventional methods. Water-jet cutting is an efficient and environmentally friendly technique for mining such hydrate reservoirs, as the production process does not depend on mass and heat transfer within the formations. In this work, a series of physical experiments were conducted to clarify the erosion performance of marine hydrate-bearing sediment (HBS) impacted by water jets. The results show that the accumulation of sediment particles and hydrate particles at the bottom of erosion hole severely inhibits the vertical erosion of HBS by water jet. For a particular jet flow rate, the jet distance has an optimal value, which is between 4 mm and 28 mm. Moreover, the upwelling flow containing solid particles has a significant impact on the erosion of the hole top. In reservoirs with a low hydrate saturation (20–40%) and reservoirs with a high hydrate saturation (60–80%), the erosion holes exhibit a gourd shape and a bamboo shape, respectively. In addition, the volume erosion efficiency and the depth erosion efficiency are more sensitive to the variation in jet flow rate than jet distance and hydrate saturation. This study can provide theoretical and technical support for the application of water-jet cutting in the exploitation of marine HBS.

Due to the irregular change of gas void fraction (GVF) in multiphase pumps, the pressure distribution in the pump is often uneven, which leads to the formation of low-pressure area and thus the occurrence of cavitation. In order to study the gas phase and cavitation distribution in the impeller region of a multiphase pump under different cavitation stages and GVF conditions, this study used numerical calculations as the main method and experimental verification as a secondary method to investigate the cavitation phenomenon in the pump under different stages and GVF conditions. The results showed that at different stages, both the volume fraction and the covering area of the gas phase were reduced to a certain extent with the increase in blade height, and the distribution law of the gas phase on the blade changed with the development of the cavitation stage, especially on the blade surface. At different GVFs, cavitation first occurred at the inlet of the blade SS and then extended along the blade streamline from the inlet to outlet, with the volume fraction and distribution of cavitation gradually increasing and then extending to the blade PS. The results showed that the presence of the gas phase inhibited the development of cavitation in the multiphase pump to some extent, and the cavitation performance of the multiphase pump was better in the presence of the gas phase than in pure water conditions. The results of this study provide a theoretical basis for improving the cavitation performance of multiphase pumps.

With the growth of intermittent renewable energy generation in power grids, there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability. The flexibility of demand response (DR) resources has become a valuable solution to this problem. However, existing research indicates that problems on flexibility prediction of DR resources have not been investigated. This study applied the temporal convolution network (TCN)-combined transformer, a deep learning technique to predict the aggregated flexibility of two types of DR resources, that is, electric vehicles (EVs) and domestic hot water system (DHWS). The prediction uses historical power consumption data of these DR resources and DR signals (DSs) to facilitate prediction. The prediction can generate the size and maintenance time of the aggregated flexibility. The accuracy of the flexibility prediction results was verified through simulations of case studies. The simulation results show that under different maintenance times, the size of the flexibility changed. The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

Microalgae-based biodiesel is increasingly recognized as an alternative to crop-based biodiesel. In this study, 10 local strains of dinoflagellates collected from Hong Kong waters, including a monoculture and field sample of Scrippsiella sp. isolated from an algal bloom, were evaluated against the performance of green alga Tetraselmis suecica. The specific growth rate, biomass production, lipid productivity, and fatty acid profile were investigated. The total lipid content of isolated strains ranged from 16.2% to 32.2% of the total dry biomass, whereas palmitic acid (C16:0) and docosahexaenoic acid (DHA, C22:6n3) were dominant in the fatty acid profile. Scrippsiella sp. has a high lipid productivity (47.3 mg/L/day) and fatty acid methyl esters (FAME) content (55.2–73 mg/g dry weight (dw)), which were comparable to that in green alga T. suecica. Further, monoculture and field sampled blooming Scrippsiella sp. showed no significant difference in most parameters, suggesting the possibility of harvesting a natural algal bloom population as a mitigation strategy to harmful algal bloom and to use as biodiesel feedstock. Overall, dinoflagellate species showed a slower growth rate (0.04–0.57 day−1) than most compared species (0.07–1.34 day−1), likely due to a large genome size and low chlorophyll to carbon ratio. Notably, most investigated dinoflagellates were not ideal for mass biodiesel production due to the low growth rate and lipid productivity. However, a high level of polyunsaturated fatty acids (PUFA) in dinoflagellates are prospective for further studies in other biotechnological applications. Though effectively harvesting algal blooming biomass can be complex, it can be further explored as a strategy for algal bloom mitigation and potentially creating values at the advantage of natural bloom when applying harvested biomass for biodiesel and bioactive compounds extraction.

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use and land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2008–2017), EFF was 9.4±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.7±0.02 GtC yr−1, SOCEAN 2.4±0.5 GtC yr−1, and SLAND 3.2±0.8 GtC yr−1, with a budget imbalance BIM of 0.5 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in EFF was about 1.6 % and emissions increased to 9.9±0.5 GtC yr−1. Also for 2017, ELUC was 1.4±0.7 GtC yr−1, GATM was 4.6±0.2 GtC yr−1, SOCEAN was 2.5±0.5 GtC yr−1, and SLAND was 3.8±0.8 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.7 % (range of 1.8 % to 3.7 %) based on national emission projections for China, the US, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. The analysis presented here shows that the mean and trend in the five components of the global carbon budget are consistently estimated over the period of 1959–2017, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations show (1) no consensus in the mean and trend in land-use change emissions, (2) a persistent low agreement among the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models, originating outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018, 2016, 2015a, b, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018.