Quantitative analysis of the impact factors of conventional energy carbon emissions in Kazakhstan based on LMDI decomposition and STIRPAT model

Quantitative analysis of the impact factors in energy-related CO₂ emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO₂ emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO₂ emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO₂ emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO₂ emissions, which produced 110.86 Mt CO₂ emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO₂ emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused–110.90 Mt CO₂ emissions with a contribution rate of–43.94%, followed by the energy carbon structure effect resulting in–18.76 Mt CO₂ emissions with a contribution rate of–7.43%. In order to provide an in-depth examination of the change response between energy-related CO₂ emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO₂ emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.     

The unusual X-ray light curve of GRB 080307: the onset of the afterglow?

Swift -detected GRB 080307 showed an unusual smooth rise in its X-ray light curve around 100 s after the burst, at the start of which the emission briefly softened. This 'hump' has a longer duration than is normal for a flare at early times and does not demonstrate a typical flare profile. Using a two-component power-law-to-exponential model, the rising emission can be modelled as the onset of the afterglow, something which is very rarely seen in Swift -X-ray light curves. We cannot, however, rule out that the hump is a particularly slow early-time flare, or that it is caused by upscattered reverse shock electrons.     

Coordinated Study on Solar Wind Turbulence During the Venus-Express, ACE and Ulysses Alignment of August 2007

At the end of August 2007, Venus, Earth and Ulysses were aligned within a few degrees. This unusual event gives the opportunity to attempt a coordinated study on the radial evolution of solar wind turbulence and coronal transients like CMEs between 0.7 and 1.4 AU. Interplanetary magnetic field data and moments of proton velocity distribution function such as density, speed and temperature are required for this programme and will be provided by ACE at Earth, Venus Express at Venus and Ulysses at 1.4 AU. This project has been recently proposed as a Coordinated Investigation Programme (CIP35) for the International Heliophysical Year.     

岩石圈中水的临界奇异性与断裂耦合触发地震

以等压热容为例,研究岩石圈中水的临界奇异性及出现的条件,探讨岩石圈中水的临界奇异性与断裂耦合作用触发地震的可能性。岩石圈中水存在一、二级相变,相变时水的物理化学性质将发生明显改变,在临界点处表现出奇异性,如热容、膨胀系数和压缩系数等将趋于无穷大,按照Mie-Grneisen方程((P/T)V=γρCV),热容无穷大时将导致热压也趋于无穷大,压力将瞬时剧增。但水的临界奇异性出现的条件是温、压同时趋于临界值,在岩石圈中温、压是相互关联的,按照正常的地温梯度(15～35℃/km)和地压梯度(0.025～0.03GPa/km)计算,达到水的临界温度和压力的深度分别是24～10km和0.88～0.73km,即在岩石圈中若没有断裂的存在时则不可能同时达到水的临界温度和临界压力,不可能出现临界奇异性。断裂使压力降至水的临界压力,使得在岩石圈中某处温度和压力可同时达到水的临界值而出现奇异性,水的临界奇异性和断裂的耦合导致热压剧增,可能触发地震     

Large aftershocks triggering by Coulomb failure stress following the 2001<Emphasis Type="Italic">M</Emphasis><Subscript>S</Subscript>=8.1 great Kunlun earthquake

The great Kunlun earthquake occurred on Nov. 14, 2001 in Qinghai Province, China. Five large aftershocks with magnitude larger than 5.0 occurred near the Kunlun fault after main shock. Calculations of the change in Coulomb failure stress reveal that 4 of 5 large aftershocks occurred in areas with Δσ_f>0 (10^?2–10^?1 MPa) and one aftershock occurred in an area with Δσ_f=?0.56 MPa. It is concluded that the permanent fault displacement due to the main shock is the main cause of activity of large aftershocks, but not the whole cause.     

Treatment of Malting Wastewater in a Granular Sludge Sequencing Batch Reactor (SBR)

Aerobic granular sludge was successfully cultivated in a sequencing batch reactor (SBR) treating wastewater from the malting process with a high content of particulate organic matter. At an organic loading rate of 3.2 kg/(m³ d) COD_total and an influent particle concentration of 0.95 g/L MLSS an average removal of 50% in COD_total and 80% in COD_dissolved could be achieved. A comparison of granular and flocculent sludge grown under the same operating conditions showed no significant difference in removal efficiency although granules exhibited a higher metabolic activity in terms of specific oxygen uptake rate (r_{O2, X}). Two distinct mechanisms of particle removal were observed for granular sludge: during initial granule formation, particles were incorporated into the biofilm matrix. For mature granules, a high level of protozoa growth on the granule surface accounted for the ability to remove particulate COD. Combined evaluation of the development in MLSS content and sludge bed settling rate (i.e., mean derivative of the normalized sludge volume) was found to be an adequate method for monitoring the characteristic settling properties of a granulizing sludge bed. By means of this method, a distinct substrate gradient out of several operating conditions was concluded to have the biggest impact on the formation of aerobic granular sludge.     

Open and coastal seas interactions south of Japan represented by an ensemble Kalman filter

We investigated the feasibility of the ensemble Kalman filter (EnKF) to reproduce oceanic conditions south of Japan. We have adopted the local ensemble transformation Kalman filter algorithm based on 20 members’ ensemble simulations of the parallelized Princeton Ocean Model (the Stony Brook Parallel Ocean Model) with horizontal resolution of 1/36°. By assimilating satellite sea surface height anomaly, satellite sea surface temperature, and in situ temperature and salinity profiles, we reproduced the Kuroshio variation south of Japan for the period from 8 to 28 February 2010. EnKF successfully reproduced the Kuroshio path positions and the water mass property of the Kuroshio waters as observed. It also detected the variation of the steep thermohaline front in the Kii Channel due to the intrusion of the Kuroshio water based on the observation, suggesting efficiency of EnKF for detection of open and coastal seas interactions with highly complicated spatiotemporal variability.     

The Wadden Sea in transition - consequences of sea level rise

The impact of sea level rise (SLR) on the future morphological development of the Wadden Sea (North Sea) is investigated by means of extensive process-resolving numerical simulations. A new sediment and morphodynamic module was implemented in the well-established 3D circulation model GETM. A number of different validations are presented, ranging from an idealized 1D channel over a semi-idealized 2D Wadden Sea basin to a fully coupled realistic 40-year hindcast without morphological amplification of the Sylt-Rømøbight, a semi-enclosed subsystem of the Wadden Sea. Based on the results of the hindcast, four distinct future scenarios covering the period 2010–2100 are simulated. While these scenarios differ in the strength of SLR and wind forcing, they also account for an expected increase of tidal range over the coming century. The results of the future projections indicate a transition from a tidal-flat-dominated system toward a lagoon-like system, in which large fractions of the Sylt-Rømøbight will remain permanently covered by water. This has potentially dramatic implications for the unique ecosystem of the Wadden Sea. Although the simulations also predict an increased accumulation of sediment in the back-barrier basin, this accumulation is far too weak to compensate for the rise in mean sea level.     

Estimation of spawning area of <Emphasis Type="Italic">Pisodonophis sangjuensis</Emphasis> Ji and Kim, 2011 (Pisces: Ophichthidae) based on leptocephali size and distribution

Pre-metamorphic leptocephali of Pisodonophis sangjuensis (n = 91, 10.4?90.2 mm in total length, TL) were collected in the East China Sea for the first time. Pre-metamorphic leptocephali of P. sangjuensis, which were identified using mitochondrial DNA cytochrome c oxidase subunit I gene (mtDNA COI), are characterized by various combinations of morphological characters: 8 moderate to pronounced gut loops with the kidney terminating on the 6-7th loops; and 8 subcutaneous pigment patches on the tail just ventral to the notochord. Pisodonophis sangjuensis leptocephali were more numerously collected offshore than inshore around Jeju Island. The smallest leptocephali (< 15.0 mm TL) were collected from the south far from Jeju Island, and the largest leptocephali (> 100.0 mm TL) were collected from around Jeju Island and the southern coast of Korea. Our findings indicate that P. sangjuensis spawns offshore south of Jeju Island that is an area associated with high water temperature, and then the hatched leptocephali are transported to Jeju Island or the southern coast of Korea by the Tsushima Warm Current.     

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α _k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.