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AbstractThe integration of power plants and desalination systems has attracted increasing attention over the past few years as an effective solution to tackle sustainable development and climate change issues. In this light, this paper introduces a novel modelling and optimization approach for a combined-cycle power plant (CCPP) integrated with reverse osmosis (RO) and multi-effect distillation (MED) desalination systems. The integrated CCPP and RO–MED desalination system is thermodynamically modelled utilizing MATLAB and EES software environments, and the results are validated via Thermoflex software simulations. Comprehensive energy, exergic, exergoeconomic, and exergoenvironmental (4E) analyses are performed to assess the performance of the integrated system. Furthermore, a new multi-objective water cycle algorithm (MOWCA) is implemented to optimize the main performance parameters of the integrated system. Finally, a real-world case study is performed based on Iran's Shahid Salimi Neka power plant. The results reveal that the system exergy efficiency is increased from 8.4 to 51.1% through the proposed MOWCA approach, and the energy and freshwater costs are reduced by 8.4% and 29.4%, respectively. The latter results correspond to an environmental impact reduction of 14.2% and 33.5%. Hence, the objective functions are improved from all exergic, exergoeconomic, and exergoenvironmental perspectives, proving the approach to be a valuable tool towards implementing more sustainable combined power plants and desalination systems.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CAS.FGOALS-g3.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-g3 climate model, released in 2017, includes the following components: atmos: GAMIL3 (180 x 80 longitude/latitude; 26 levels; top level 2.19hPa), land: CAS-LSM, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

1. Tidal stream energy converters (turbines) are currently being installed in tidally energetic coastal sites. However, there is currently a high level of uncertainty surrounding the potential environmental impacts on marine mammals. This is a key consenting risk to commercial introduction of tidal energy technology. Concerns derive primarily from the potential for injury to marine mammals through collisions with moving components of turbines. To understand the nature of this risk, information on how animals respond to tidal turbines is urgently required. 2. We measured the behaviour of harbour seals in response to acoustic playbacks of simulated tidal turbine sound within a narrow coastal channel subject to strong, tidally induced currents. This was carried out using data from animal-borne GPS tags and shore-based observations, which were analysed to quantify behavioural responses to the turbine sound. 3. Results showed that the playback state (silent control or turbine signal) was not a significant predictor of the overall number of seals sighted within the channel. 4. However, there was a localised impact of the turbine signal; tagged harbour seals exhibited significant spatial avoidance of the sound which resulted in a reduction in the usage by seals of between 11 and 41% at the playback location. The significant decline in usage extended to 500 m from the playback location at which usage decreased by between 1 and 9% during playback. 5. Synthesis and applications: This study provides important information for policy makers looking to assess the potential impacts of tidal turbines and advise on development of the tidal energy industry. Results showing that seals avoid tidal turbine sound suggest that a proportion of seals encountering tidal turbines will exhibit behavioural responses resulting in avoidance of physical injury; in practice, the empirical changes in usage can be used directly as avoidance rates when using collision risk models to predict the effects of tidal turbines on seals. There is now a clear need to measure how marine mammals behave in response to actual operating tidal turbines in the long term to learn whether marine mammals and tidal turbines can co-exist safely at the scales currently envisaged for the industry. JApEcol_Hastie_etal_observation_data_DryadLand based observer data (.xlsx) used in the analysis of seal responses to tidal turbine sounds. This is effectively counts of seals observed in the water during acoustic playbacks of tidal turbine sound and silent controls. Data were collected by a series of observers located on a clifftop overlooking the study area (Kyle Rhea, Isle of Skye, Scotland) README file is provided as a tab in the file.JApEcol_Hastie_etal_seal_telemetry_data_DryadHarbour seal telemetry data (.xlsx) used in the analysis of changes in usage with distance from the location of playbacks of tidal turbine sound. The data are regularised lat-lon locations from 10 individual harbour seals tagged with GPS telemetry devices. README is provided as a tab in the file.STIMweighted_J11_1hour_withRampSound file (.wav) used during playbacks of simulated tidal turbine sound to harbour seals to investigate avoidance responses. The file has a 10 second ramp at the start and end of the file, and is frequency weighted for use with a J11 underwater speaker.

Owing to the storage and transportation problems of hydrogen fuel, exploring new methods of the realtime hydrogen production from ammonia becomes attractive. In this paper, non-thermal arc plasma (NTAP) combining with NiO/Al2O3 catalyst is developed to produce hydrogen from ammonia with high efficiency and large scale. The effects of ammonia gas flow rate and discharge power on the gas temperature, electron density, the hydrogen production rate, and energy efficiency were investigated. Experimental results show that the optical emission spectrum of NTAP working with pure ammonia medium was dominated by the atom spectrum of Hα, Hβ, and molecular spectrum of NH component. Under the optimum experimental condition of plasma discharge, the highest energy efficiency of hydrogen production reached 783.4 L/kW·h at NH3 gas flow rate of 30 SLM. When the catalyst was added, and heated by the NTAP simultaneously, the energy efficiency further increased to 1080.0 L/kW·h. Owing to the storage and transportation problems of hydrogen fuel, exploring new methods of the realtime hydrogen production from ammonia becomes attractive. In this paper, non-thermal arc plasma (NTAP) combining with NiO/Al2O3 catalyst is developed to produce hydrogen from ammonia with high efficiency and large scale. The effects of ammonia gas flow rate and discharge power on the gas temperature, electron density, the hydrogen production rate, and energy efficiency were investigated. Experimental results show that the optical emission spectrum of NTAP working with pure ammonia medium was dominated by the atom spectrum of Hα, Hβ, and molecular spectrum of NH component. Under the optimum experimental condition of plasma discharge, the highest energy efficiency of hydrogen production reached 783.4 L/kW·h at NH3 gas flow rate of 30 SLM. When the catalyst was added, and heated by the NTAP simultaneously, the energy efficiency further increased to 1080.0 L/kW·h.

Spatio-temporal variations in extreme drought in China during 1961–2015 Spatio-temporal variations in extreme drought in China during 1961–2015

Including solar power geneartion from 2018 to 2019 at a 3kW rooftop pv plant in the university of macau, with a resolation of 30 s, and the public weather report of Macau with a resolution of 1 hour.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CAMS.CAMS-CSM1-0.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

This dataset contains modelled vegetation carbon output from the land surface model JULES, along with the temperature and rainfall outputs (which were originally inputted) at a monthly, 1.5km resolution. There are four different JULES simulations, using two different climate projections (global climate sensitivity of 3.5K and highest global climate sensitivity of 7.1K) under a constant, present day atmospheric CO2 and a CO2 pathway that follows the SRES (Special Report on Emissions Scenarios) A1B scenario. JULES is a community developed land surface model, led by the UK Met Office and Centre for Ecology and Hydrology and is available for use after registering on the JULES repository (https://code.metoffice.gov.uk/trac/jules). The data produced using JULES was model version vn4.9 and the model configuration can be found on the Rose suite u-ao645 under the branch ‘transient_25km_drive’, available from https://code.metoffice.gov.uk/trac/roses-u (registration required).

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NUIST.NESM3.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The NUIST ESM v3 climate model, released in 2016, includes the following components: atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa), land: JSBACH v3.1, ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4.1. The model was run by the Nanjing University of Information Science and Technology, Nanjing, 210044, China (NUIST) in native nominal resolutions: atmos: 250 km, land: 2.5 km, ocean: 100 km, seaIce: 100 km.

Positive forced aeration is widely used in industrial composting plants to supply sufficient oxygen, accelerating compost maturity. However, this technology results in significant gaseous emission, especially NH3 and GHGs emissions. To reduce gaseous emissions and investigate aeration efficiency, negative pressure aeration was used during cattle manure þ corn stalk composting in 50 L-scale reactors. Composting with negative pressure aeration at three different flow rates (0.25, 0.50 and 0.75 L/min/kg dry weight, named Negative-L, Negative-M and Negative-H treatments) were conducted. Treatment with positive pressure aeration was set as a control (Positive-M, with flow rate at 0.50 L/min/kg dry weight). The results showed that negative pressure aeration changed the temporal distribution of oxygen and temperature. With the same flow rate, the Negative-M treatment maintained a longer thermophilic period, accelerating organic matter degradation (47.6% in treatment Negative-M and 41.4% in Positive-M) and the maturity of feedstock (germination index was 105.9% in Negative-M and 58.5% in Positive-M). Ammonia emissions were significantly reduced by composting with negative pressure aeration. During composting, 36.7%, 15.8%, 16.8% and 16.0% of the initial total nitrogen was lost via NH3 volatilizations in the Positive-M, Negative-L, Negative-M and Negative-H treatments, respectively, indicating NH3 emissions were reduced by ~55% compared to the positive pressure aeration treatment. Even though both CH4 and N2O emission were greater from the negative pressure aeration treatments, the global warming potential was significantly reduced in treatments with negative pressure aeration because of the lower NH3 emission (an indirect N2O source). This indicates the benefit of NH3 emission mitigation was larger than the increase in CH4 and N2O emissions. Positive forced aeration is widely used in industrial composting plants to supply sufficient oxygen, accelerating compost maturity. However, this technology results in significant gaseous emission, especially NH3 and GHGs emissions. To reduce gaseous emissions and investigate aeration efficiency, negative pressure aeration was used during cattle manure þ corn stalk composting in 50 L-scale reactors. Composting with negative pressure aeration at three different flow rates (0.25, 0.50 and 0.75 L/min/kg dry weight, named Negative-L, Negative-M and Negative-H treatments) were conducted. Treatment with positive pressure aeration was set as a control (Positive-M, with flow rate at 0.50 L/min/kg dry weight). The results showed that negative pressure aeration changed the temporal distribution of oxygen and temperature. With the same flow rate, the Negative-M treatment maintained a longer thermophilic period, accelerating organic matter degradation (47.6% in treatment Negative-M and 41.4% in Positive-M) and the maturity of feedstock (germination index was 105.9% in Negative-M and 58.5% in Positive-M). Ammonia emissions were significantly reduced by composting with negative pressure aeration. During composting, 36.7%, 15.8%, 16.8% and 16.0% of the initial total nitrogen was lost via NH3 volatilizations in the Positive-M, Negative-L, Negative-M and Negative-H treatments, respectively, indicating NH3 emissions were reduced by ~55% compared to the positive pressure aeration treatment. Even though both CH4 and N2O emission were greater from the negative pressure aeration treatments, the global warming potential was significantly reduced in treatments with negative pressure aeration because of the lower NH3 emission (an indirect N2O source). This indicates the benefit of NH3 emission mitigation was larger than the increase in CH4 and N2O emissions.