Carbon dioxide emission reduction quota allocation study on Chinese provinces based on two-stage Shapley information entropy model

Chinese central government made a commitment to achieve a 40–45% reduction in carbon dioxide (CO₂) per unit of GDP by 2020 compared with 2005. This targeted reduction was allocated averagely among all the provinces rather than individually according to different situations of each province. Though some research has been done regarding this rough allocation, two shortcomings in previous studies exist: Firstly, CO₂ marginal abatement cost (MAC) has been ignored as one of the CO₂ emission reduction allocation indexes. Secondly, either subjective or objective method has been used rather than comprehensively of both subjective and objective method to calculate the weight of each index in the previous studies. In order to fill the gaps, this paper builds a two-stage Shapley information entropy model to allocate CO₂ emission reduction quota among the Chinese provinces based on the equity and efficiency principles. Afterward, three CO₂ emission reduction quota allocation scenarios have been proposed. The results show that the CO₂ MAC is an indispensable index in CO₂ emission reduction quota allocation, because its value of CO₂ Shapley information entropy is the highest among five indexes. CO₂ emission reduction quota of lower-MAC provinces should be allocated larger, while the quota of higher-MAC provinces should be allocated smaller. Therefore, two suggested policies have been proposed: First, differential CO₂ emission reduction quota allocation should be proposed. Second, synergetic development should be promoted.     

Estimation of the methane flux from mud volcanoes along Chishan Fault,southwestern Taiwan

Because of the increasing public awareness of the greenhouse effect, geological emissions of methane (GEM) have gained more attention. The Intergovernmental Panel on Climate Change included GEM in the natural sources of methane category in the Fourth Assessment Report in 2007. The methane flux from mud volcanoes (MVs) is the second leading source within GEM. However, given that gas flux from MVs has been measured directly only in Sicily and Azerbaijan, the global methane estimation is still a conservative assumption based on limited data. The behavior of MV activities is seldom reported. This study attempts to estimate annual methane emissions through MVs by using a video recorder to record MV activities along the Chishan Fault and determining the bubble size and flux rate of each MV. Observation results, after a 1 year observation period, indicate that the annual emissions of the 19 MVs along the Chishan Fault are 0.1–0.2% of the global methane flux from MVs, highlighting that the methane emission from these MVs should not be neglected. In addition, the continuous monitoring of a particular MV disclosed periodic characteristics of MV activities. These results manifest the spatial and temporal variations of MV activities along the Chishan Fault. The spatial variations are related to the extensional kinematics of the Chishan fault, while the temporal variation relates to the earth-tide-related MV activities. The estimated annual methane emissions from the study are far more than those of previous studies and are still considered conservative. Results of this study may provide valuable information for researchers attempting to estimate the quantity of GEM.     

Periodical mixing of MORB magmas near East Pacific Rise 13°N: Evidence from modeling and zoned plagioclase phenocrysts

Thirty-six basalt samples from near East Pacific Rise 13°N are analyzed for major and trace elements. Different types of zoned plagioclase phenocrysts in basalts are also backscatter imaged, and major element profiles scanned and analyzed for microprobe. Basalts dredged from a restricted area have evolved to different extents (MgO=9.38wt%—6.76wt%). High MgO basalts are modeled for crystalliza-tion to MgO of about 7wt%, and resulted in the Ni contents (≈28 ppm) that are generally lower than that in observed basalts (>60 ppm). It suggests that low MgO basalts may have experienced more intensive magma mixing. High MgO (9.38wt%) basalt is modeled for self-"mixing-crystallization", and the high Ni contents in low MgO basalts can be generated in small scale and periodical self-mixing of new magma (high MgO). "Mixing-crystallization" processes that low MgO magmas experienced accord with recent 226Ra/230Th disequilibria studies for magma residence time, in which low MgO magmas have experi-enced more circles of "mixing-crystallization" in relatively longer residence time. Magma mixing is not homogeneous in magma chamber, however, low MgO magmas are closer to stable composition pro-duced by periodical "mixing-crystallization", which is also an important reason for magma diversity in East Pacific Rise. Zoned plagioclase phenocrysts can be divided into two types: with and without high An# cores, both of which have multiple reversed An# zones, suggesting periodical mixing of their host magmas. Cores of zoned plagioclase in low MgO (7.45wt%) basalt differ significantly with their mantle in An#, but are similar in An# with microlite cores (products of equilibrium crystallization) in high MgO (9.38wt%) basalt, which further shows that plagioclase phenocryst cores in low MgO basalts may have formed in their parental magmas before entering into the magma chamber.     

Microsimulation of Hurricane Evacuation Strategies of Galveston Island

This article investigates the effectiveness of simultaneous and staged evacuation strategies for hurricane evacuations of Galveston Island using agent-based microsimulation techniques. In the simultaneous strategy the entire population is informed to evacuate simultaneously, whereas in a staged evacuation strategy, people are informed to evacuate in a sequence. The results suggest that (1) the most efficient staged evacuation strategy can help reduce the evacuation time for Galveston Island by approximately one hour, (2) previous studies might have underestimated the evacuation time of Galveston, and (3) an evacuation under the rapid response assumption does not necessarily lead to an effective evacuation.     

高精度数字扫描仪的最新进展

高精度扫描是数字摄影测量生产工作流程的一个重要部分。评定性能优良扫描仪的一个重要的条件是:在一定要求下连续高速的进行高光学分辨率的胶卷扫描。目前徕卡公司在试图改善图像的质量和扫描平台控制的同时,将在DSW彩色扫描仪上提供这种快速的扫描功能。徕卡公司为了这些新的性能,已经进行了许多针对性的革新,这些新的革新技术构成了一个新的型号DSW700。     

Trace metals in seawater and copepods in the ocean outfall area off the northern Taiwan coast

The distribution, partitioning and concentrations of trace metals (Cd, Cr, Cu, Fe, Mn, Pb and Zn) in seawater, including dissolved and particulate phases, and in copepods in the ocean outfall area off the northern coast of Taiwan were investigated. Normalization of metal concentrations to the background metal concentration to yield relative enrichment factors (EF), which were used to evaluate the contamination of dissolved and particulate trace metals in seawater around the ocean outfall. The EF results indicated that the outfall area was significantly contaminated by dissolved Fe and Zn, and by particulate Fe, Cr, Cu, Pb and Zn. In addition, most trace metals were chiefly in the particulate phase. The average percentage of total metal concentrations (dissolved plus particulate phases) bound by suspended particulate matter followed the sequence Al(95%) = Mn(95%) > Pb(88%) > Cu(86%) > Fe(72%) > Zn(32%) > Cr(17.5%) > Cd(3.4%). Therefore, metal contamination is better evaluated in solid phase than in the dissolved phase. The concentration ranges of trace metals in the copepods, Temora turbinata, Oncaea venusta and Euchaeta rimana, near the outfall were: Cd, 0.23-1.81 microg g(-1); Cr, 16.5-195 microg g(-1); Cu, 14-160 microg g(-1); Fe, 256-7255 microg g(-1); Mn, 5.5-80.8 microg g(-1); Pb, 2.6-56.2 microg g(-1); Zn, 132-3891 microg g(-1); and Al, 0.21-1.13%. Aluminum, and probably Fe, seemed to be the major elements in copepods. The concentrations of trace metals in copepods, especially Temora turbinata, near the outfall were generally higher than those obtained in the background station. The mean increase in bioconcentration factor of metals in copepods ranged from 4 to 7 and followed the sequence Al(6.4) > Cu(6.2) > Fe(6.0) > Zn(5.7) > Pb(5.6) > Cr(5.5) > Cd(5.1) > Mn(4.7). Therefore, marine copepods in the waters of northern Taiwan can accumulate trace metals over background concentrations and act as contamination indicators.     

Predicting Sea Surface Salinity Using an Improved Genetic Algorithm Combining Operation Tree Method

The purpose of this study is to demonstrate the use of an improved genetic algorithm combining operation tree method (IGAOT) and apply it to monitor the salinity of the Taiwan Strait by using remote-sensing data. The genetic algorithm combining operation tree (GAOT) is a data mining method used to automatically discover relationships among nonlinear systems. Based on genetic algorithms (GAs), the relationships between input and output can be expressed as parse trees. The GAOT method typically has the disadvantages of premature convergence, which means it cannot produce satisfying solutions and performs satisfactorily when applied to only low-dimensional problems. Therefore, the GAOT method is enhanced using an automatic incremental procedure to improve the search ability of the method and avoid trapping in a local optimum. In this case study, an IGAOT is used to determine the relationship between the in situ data on the salinity of the Taiwan Strait and the data on the spectral parameters, seven wavebands, of a Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The results indicate that the IGAOT model performs more favorably than do the GAOT and linear regression (LR₁ and LR₂) models, exhibits higher correlation coefficients, and involves fewer estimating errors. The results of this study indicate that the proposed technique is useful for estimating the Taiwan Strait salinity.     

Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis

The existing spatiotemporal analysis methods suppose that the involved time series are complete and have the same data interval. However missing data inevitably occur in the position time series of Global Navigation Satellite Systems networks for many reasons. In this paper, we develop a modified principal component analysis to extract the Common Mode Error (CME) from the incomplete position time series. The principle of the proposed method is that a time series can be reproduced from its principle components. The method is equivalent to the method of Dong et al. (J Geophys Res 111:3405–3421, 2006) in case of no missing data in the time series and to the extended ‘stacking’ approach under the assumption of a uniformly spatial response. The new method is first applied to extract the CME from the position time series of the Crustal Movement Observation Network of China (CMONOC) over the period of 1999–2009 where the missing data occur in all stations with the different gaps. The results show that the CMEs are significant in CMONOC. The size of the first principle components for the North, East and Up coordinates are as large as 40, 41 and 37 % of total principle components and their spatial responses are not uniform. The minimum amplitudes of the first eigenvectors are only 41, 15 and 29 % for the North, East and Up coordinate components, respectively. The extracted CMEs of our method are close to the data filling method, and the Root Mean Squared error (RMS) values computed from the differences of maximum CMEs between two methods are only 0.31, 0.52 and 1.55 mm for North, East and Up coordinates, respectively. The RMS of the position time series is greatly reduced after filtering out the CMEs. The accuracies of the reconstructed missing data using the two methods are also comparable. To further comprehensively test the efficiency of our method, the repeated experiments are then carried out by randomly deleting different percentages of data at some stations. The results show that the CMEs can be extracted with high accuracy at the non missing-data epochs. And at the missing-data epochs, the accuracy of extracted CMEs has a strong dependence on the number of stations with missing data.     

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.