基于多元统计分析的渔山列岛潮间带软体动物功能群和次级生产力比较

渔山列岛于2008年成为国家级海洋生态保护区,为了解渔山列岛保护区内潮间带软体动物次级生产力的时空变化。于1982年和2010年的3个季节(春、夏、冬)在渔山列岛进行潮间带软体动物调查,采用多元统计分析软体动物时空差异,以Brey经验公式计算分析软体动物次级生产力(P值,P/B值)。结果表明,年际间物种食性结构发生了明显的改变,1982年潮间带软体动物对次级生产力贡献率最大的物种有单齿螺(Monodonta labio)、锈凹螺(Chlorostoma rustica),占软体动物总次级生产力的42.88%,2010年潮间带软体动物对次级生产力贡献率最大的物种有覆瓦小蛇螺(Serpulorbis imbricata)、条纹隔贻贝(Septifer virgatus),占软体动物总次级生产力的75.96%。1982年,年均栖息密度为2758ind/m2,年均生物量为2100.26g/m2,年均次级生产力为240.04g/(m2·a),年均P/B值为0.63a–1;2010年,年均栖息密度为699ind/m2,年均生物量为1101.85g/m2,年均次级生产力为94.82g/(m2·a),年均P/B值为0.55a–1。多因素方差分析表明软体动物次级生产力年际间差异显著(F=5.761,P0.05),季节间和潮位间差异都不显著(F=0.135,P0.05;F=2.076,P0.05),P/B的值较低,表明该海域软体动物世代更替速度较慢,2010年的P/B值(0.55a–1)低于1982年的P/B值(0.63a–1),表明近30年来群落结构受到影响。     

A New Retracking Technique for Brown Peaky Altimetric Waveforms

A new Brown-Peaky (BP) retracker has been developed for peaky waveforms that usually appear within ～10 km to the coastline. The main feature of the BP is that it fits peaky waveforms using the Brown model without introducing a peak function. The retracking strategy first detects the peak location and width of a waveform using an adaptive peak detection method, and then estimates retracking parameters using a weighted least squares (WLS) estimator. The WLS assigns a downsized weight to corrupted waveform gates, but an equal weight to other normal waveform gates. The BP retracker has been applied to 4-year Jason-1 waveform (2002–2006) in two Australian coastal zones. The results retracked by BP, MLE4 and ALES retrackers have been validated against tide-gauge observations located at Burnie, Lorne and Broome. The comparison results show that three retrackers have similar performance over open oceans with the correlation coefficient (～0.7) and RMSE (～13 cm) between altimetric and tide-gauge sea levels for distance >7 km offshore. The main improvement of BP retracker occurs for distance ≤7 km to the coastline, where validation results indicate that data retracked by BP are more accurate (15–21 cm) than those by ALES (16–24 cm) and MLE4 (19–37 cm).     

Spatial Distribution Assessment and Source Apportionment of Land-Based Pollutant Yield from Qingdao,China

The spatial distribution of chemical oxygen demand(COD) and total nitrogen(TN) yield from Qingdao are studied by comparing pollutant yield amount, densities and spatial aggregation(Getis-Ord indexes) among the land-based pollutant source regions(PSRs) entering the three sub-seas(i.e. the Jiaozhou Bay(JZB), other coastal area in the Yellow Sea(OCAYS) and Laizhou Bay(LZB), respectively). Industrial composition of the loads are also studied by comparing pollutant yield among the sources of agriculture, rural domesticity, industry, urban domesticity and service, and calculation of Gini coefficient. Results show that spatial distribution of COD and TN yield from Qingdao are extremely unbalanced. The JZB, with less than 3% of the total coastal sea area of Qingdao, received 62% COD load and 65% TN yield from Qingdao, while the OCAYS, with more than 97% area, only received 23% COD and 20% TN, which consist with the much worsen water quality of JZB than that of OCAYS. On the other hand, the source apportionment of COD and TN loads in the PSRs entering JZB and the OCAYS was similar. The agricultural and domestic sources with high pollution intensity account for more than 80%, while the industrial and service sources with low pollution intensity account for less than 20%. While Gini coefficients, COD 0.81 and TN 0.84 which are much higher than the ‘imbalance' threshold of 0.4, show the uneven industrial structure of Qingdao. These results may be useful in the determination of land-based pollution total amount control at the PSR level.     

一种自然邻近关系查询的空间索引结构

自然邻近关系在空间数据库管理系统中具有十分重要的地位。为了克服Voronoi图受空间目标位置的影响,本文采用约束Delaunay三角网,针对在二维欧式空间中离散面目标的自然邻近关系查询问题。首先对离散面进行三角剖分,然后将代表相同邻近关系的三角形合并成一个union,进而建立面目标自然邻近关系表达结构-Unions Delaunay。通过与Unions Delaunay相结合建立空间索引结构-R-tree Gridfile来实现对空间目标自然邻居完备的候选集提取以及索引结构的更新。对比实验结果表明R-tree Gridfile索引结构能够有效支持空间目标的自然邻近关系查询。     

南极中山站全天信息采集系统

南极高原拥有独特的天文观测优势,为了对南极中山站夜天文观测条件进行实测研究,中国科学院云南天文台专门研制了一套具有耐低温、自动除雪除霜等适应南极气候特征的全自动全天信息采集系统,该系统可以提供实时的全天云量、天光背景和全天图像,并将信息推送到网页实时显示。介绍了系统的研制及为适应南极气候进行的耐低温实验,统计分析了中山站2016~2017年的全天信息数据,结果显示,中山站2016和2017年的可观测时间为772.21 h和437.38 h,可观测夜数为93 d和51 d,天光背景最大真实值为22.05 Mag/arcsec 2,年平均气温为-10.6℃,最高气温19.1℃,最低气温为-44℃,2016年平均相对湿度为55.2%。     

Fast adsorption of microcystin-LR by Fe(III)-modified powdered activated carbon

Journal of Oceanology and Limnology - Microcystins (MCs) are cyclic hepatotoxic peptides produced by the bloom-forming cyanobacterium Microcystis and present a public health hazard to humans and...     

印度尼西亚北苏拉威西蓝碧海峡浮游动物种类组成及分布

Based on oceanographic survey data in June 2012 in the Lembeh Strait, the zooplankton ecological characteristics such as species composition, individual abundance, dominant species and distribution were analyzed. The results showed that 183 species(including 4 sp.) had been recognized, most of them belonged to copepoda.Cnidaria followed with 43 species(including 1 sp.) were identified. The average abundance of zooplankton was(150.47±58.91) ind./m~3. As to the horizontal distribution, the abundance of the zooplankton was higher in the southern waters than in the northern waters. The dominant species in the study area were Lensia subtiloides,Sagitta enflata, Lucifer intermedius, Oikopleura rufescens, Diphyes chamissoni, Creseis acicula, Subeucalanus subcrassus, Temora discaudata, Aglaura hemistoma, Doliolum denticulatum, Canthocalanus pauper, Oikopleura longicauda and Nanomia bijuga. Zooplankton biodiversity indexes were higher in study area than previous study in the other regions. The findings from this study provide important baseline information for future research and monitoring programs.     

Smectite formation in metalliferous sediments near the East Pacific Rise at 13°N

A 43 cm long E271 sediment core collected near the East Pacific Rise(EPR) at 13°N were studied to investigate the origin of smectite for understanding better the geochemical behavior of hydrothermal material after deposition.E271 sediments are typical metalliferous sediments. After removal of organic matter, carbonate, biogenic opal,and Fe-Mn oxide by a series of chemical procedures, clay minerals(2 μm) were investigated by X-ray diffraction,chemical analysis and Si isotope analysis. Due to the influence of seafloor hydrothermal activity and close to continent, the sources of clay minerals are complex. Illite, chlorite and kaolinite are suggested to be transported from either North or Central America by rivers or winds, but smectite is authigenic. It is enriched in iron, and its contents are highest in clay minerals. Data show that smectite is most likely formed by the reaction of hydrothermal Fe-oxyhydroxide with silica and seawater in metalliferous sediments. The Si that participates in this reaction may be derived from siliceous microfossils(diatoms or radiolarians), hydrothermal fluids, or detrital mineral phases. And their δ30 Si values are higher than those of authigenic smectites, which implies that a Si isotope fractionation occurs during the formation because of the selective absorption of light Si isotopes onto Feoxyhydroxides. Sm/Fe mass ratios(a proxy for overall REE/Fe ratio) in E271 clay minerals are lower than those in metalliferous sediments, as well as distal hydrothermal plume particles and terrigenous clay minerals. This result suggests that some REE are lost during the smectite formation, perhaps because their large ionic radii of REE scavenged by Fe-oxyhydroxides preclude substitution in either tetrahedral or octahedral lattice sites of this mineral structure, which decreases the value of metalliferous sediments as a potential resource for REE.     

日照市露天开采矿山地质环境问题及恢复治理措施

通过1∶5万矿山地质环境调查和遥感解译手段,对日照市矿山进行了全面的调查,查明各类矿山546处,主要集中在莒县和五莲县境内。除莒县刘官庄煤矿外,其余全部为露天开采矿山,主要选用直接开挖的方式,不仅破坏了地表环境,而且矿山生产活动产生的固体废弃物占用了大面积的土地。该文根据矿山地理位置及开采方式的不同,将相同类型的多个矿山划分为同一治理区,针对不同治理区的特点,采取科学有效的治理方法,建立健全矿山地质环境监督管理机制,切实解决矿山地质环境问题,改善生态环境。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.