2007-2017年中国沙尘气溶胶的三维分布特征及输送过程

利用最新发布的CALIPSO产品，构建了2007-2017年中国沙尘气溶胶的三维分布，并结合HYSPLIT-4模式和再分析数据，探讨了沙尘的三维输送过程。结果表明：中国的沙尘排放源区主要是塔克拉玛干沙漠和巴丹吉林沙漠，沙尘气溶胶出现频率分别为60%和35%。塔克拉玛干沙漠排放的沙尘主要（50%~70%）停留在源地0~6 000 m高度，少部分向东输送至甘肃和内蒙古；巴丹吉林沙漠排放的沙尘则主要向东输送。中国沙尘排放量在春季最大，向东输送最强；夏季，东亚夏季风限制了沙尘向东输送；秋季，沙尘排放减弱，输送强度和夏季相当；沙尘排放量在冬季最小，输送最弱。夏季，沙尘在输送过程中可被抬升至高度5 000 m以上，春季次之，秋、冬季的沙尘主要在低层大气输送。沙尘在向东输送的过程中被抬升并和当地人为污染物混合变为污染性沙尘，华北地区污染性沙尘出现频率高达30%；输送到海洋的沙尘也会与洋面上（0~3 000 m高度）的海盐气溶胶混合，出现频率约为10%。     

An Early Cretaceous slab window beneath central Tibet,SW China: Evidence from OIB‐like alkaline gabbros in the Duolong area

The geodynamic mechanism of the late Early Cretaceous magmatic flare‐up in the collisional zone between the Lhasa and Qiangtang terranes in Tibet is controversial because of a scarcity of robust evidence. To address this problem, we report geochronological, geochemical and Hf isotopic data for the newly discovered Gufeng gabbros from the Duolong Cu–Au mineral district of the western Bangong–Nujiang Suture Zone (BNSZ). The gabbro samples, dated at 126.3 ± 1.8 Ma, show geochemical similarities to typical ocean island basalt (OIB) and have positive εHf(t) values of +3.3 to +6.9. The gabbros were generated by decompression melting of deep upwelling asthenosphere. This event is best explained by slab break‐off and the resultant development of a slab window beneath central Tibet.     

Extreme Flood Events over the Past 300 Years Inferred from Lake Sedimentary Grain Sizes in the Altay Mountains,Northwestern China

Understanding the temporal variations of extreme floods that occur in response to climate change is essential to anticipate the trends in flood magnitude and frequency in the context of global warming. However, long-term records of paleofloods in arid regions are scarce, thus preventing a thorough understanding of such events. In this study, a reconstruction of paleofloods over the past 300 years was conducted through an analysis of grain sizes from the sediments of Kanas Lake in the Altay Mountains of northwestern China. Results showed that grain parameters and frequency distributions can be used to infer possible abrupt environmental events within the lake sedimentary sequence, and two extreme flood events corresponding to ca. 1736–1765 AD and ca. 1890 AD were further identified based on canonical discriminant analysis(CDA) and coarse percentile versus median grain size(C-M) pattern analysis, both of which occurred during warmer and wetter climate conditions by referring to tree-ring records. These two flood events are also evidenced by lake sedimentary records in the Altay and Tianshan mountains. Furthermore, through a comparison with other records, the flood event from ca. 1736–1765 AD in the study region seems to have occurred in both the arid central Asia and the Alps in Europe, and thus may have been associated with changes in the North Atlantic Oscillation(NAO) index.     

张掖盆地地下水硝酸盐污染与人体健康风险评价

地下水是张掖盆地的重要水资源，其硝酸盐污染尚未得到足够重视。对张掖盆地2004、2015年地下水硝酸盐浓度进行了系统分析，并采用美国环境保护署（USEPA）推荐的健康风险评价模型评估了地下水硝酸盐的健康风险。结果表明：自2004年以来张掖盆地地下水硝酸盐污染日趋严重。2015年硝酸盐浓度最高已达到283.32 mg·L^-1，17.61%的采样点硝酸盐氮浓度超过GB5749-2006《生活饮用水卫生标准》中饮用地下水限量值（20 mg·L^-1）。研究区人群经皮肤接触途径摄入硝酸盐的健康风险在可接受水平，而饮水摄入硝酸盐的健康风险较高，总风险中饮水途径引起健康风险的贡献率占99.40%，远大于皮肤接触途径。儿童经饮水摄入和皮肤接触两种途径的健康风险均显著高于成人，分别为成人的1.544倍和1.039倍。32.39%的采样点地下水硝酸盐对儿童的健康风险超出了可接受水平，14.79%的采样点地下水硝酸盐对成人的健康风险不可接受。甘州区城区、临泽县北部边缘及高台县城区周围硝酸盐浓度最高，这些区域内所有人群都面临硝酸盐引发的高健康风险，其余区域硝酸盐引发的健康风险相对较低。     

辽宁省海洋文化景观概述

文章介绍海洋文化景观的属性、定义和构成;将辽宁省海洋文化景观资源分为海洋物质文化景观和非物质文化景观2个大类,其中海洋物质文化景观包括海洋自然文化景观、海洋遗迹文化景观、海洋聚落文化景观、海洋宗教文化景观、海洋旅游文化景观和海洋设施景观,海洋非物质文化景观包括海洋历史文化景观、海洋民俗文化景观和海洋艺术文化景观,分别概述具体类别资源的代表内容;提出可采用模糊综合评价法评价辽宁省海洋文化景观,研究海洋文化景观对于海洋文化建设和提高海洋意识等具有重要意义。     

Assessment of alternative adsorption models and global sensitivity analysis to characterize hexavalent chromium loss from soil to surface runoff

We investigate our ability to assess transfer of hexavalent chromium, Cr(VI), from the soil to surface runoff by considering the effect of coupling diverse adsorption models with a two‐layer solute transfer model. Our analyses are grounded on a set of two experiments associated with soils characterized by diverse particle size distributions. Our study is motivated by the observation that Cr(VI) is receiving much attention for the assessment of environmental risks due to its high solubility, mobility, and toxicological significance. Adsorption of Cr(VI) is considered to be at equilibrium in the mixing layer under our experimental conditions. Four adsorption models, that is, the Langmuir, Freundlich, Temkin, and linear models, constitute our set of alternative (competing) mathematical formulations. Experimental results reveal that the soil samples characterized by the finest grain sizes are associated with the highest release of Cr(VI) to runoff. We compare the relative abilities of the four models to interpret experimental results through maximum likelihood model calibration and four model identification criteria (i.e., the Akaike information criteria [AIC and AIC_C] and the Bayesian and Kashyap information criteria). Our study results enable us to rank the tested models on the basis of a set of posterior weights assigned to each of them. A classical variance‐based global sensitivity analysis is then performed to assess the relative importance of the uncertain parameters associated with each of the models considered, within subregions of the parameter space. In this context, the modelling strategy resulting from coupling the Langmuir isotherm with a two‐layer solute transfer model is then evaluated as the most skilful for the overall interpretation of both sets of experiments. Our results document that (a) the depth of the mixing layer is the most influential factor for all models tested, with the exception of the Freundlich isotherm, and (b) the total sensitivity of the adsorption parameters varies in time, with a trend to increase as time progresses for all of the models. These results suggest that adsorption has a significant effect on the uncertainty associated with the release of Cr(VI) from the soil to the surface runoff component.     

Geophysical Signature of the Shallow Water Flow in the Deepwater Basin of the Northern South China Sea

Shallow water flow (SWF), a disastrous geohazard in the continental margin, has threatened deepwater drilling operations. Under overpressure conditions, continual flow delivering unconsolidated sands upward in the shallow layer below the seafloor may cause large and long-lasting uncontrolled flows; these flows may lead to control problems and cause well damage and foundation failure. Eruptions from over-pressured sands may result in seafloor craters, mounds, and cracks. Detailed studies of 2D/3D seismic data from a slope basin of the South China Sea (SCS) indicated the potential presence of SWF. It is commonly characterized by lower elastic impedance, a higher Vp/Vs ratio, and a higher Poisson’s ratio than that for the surrounding sediments. Analysis of geological data indicated the SWF zone originated from a deepwater channel system with gas bearing over-pressured fluid flow and a high sedimentation rate. We proposed a fluid flow model for SWF that clearly identifies its stress and pressure changes. The rupture of previous SWF zones caused the fluid flow that occurred in the Baiyun Sag of the northern SCS.     

两株南极枝孢霉属真菌Cladosporium sp.NJF4和NJF6的次级代谢产物研究

南极因其独特的自然环境成为潜在、重要的微生物资源库,是产生新型生物活性物质和先导化合物菌株的潜在种源地,南极微生物正在成为创新药物研究新的重要资源。虽然近年来对南极微生物次级代谢产物的研究逐渐增加,但与温带和热带微生物研究相比仍处于初级阶段。对从南极普里兹湾海洋沉积物中获得的两株枝孢霉属真菌Cladosporium sp. NJF4和NJF6进行次级代谢产物分离及结构鉴定,获得20个化合物。化合物结构类型包括甾醇(1)、倍半萜类(7—8)、生物碱类(9—14)、二酮哌嗪(2—5、15—17)、芳香酸(6、18—19)等,其中倍半萜类(7—8)为首次从枝孢霉属真菌中分离得到,以上研究将为丰富南极微生物次级代谢产物库奠定一定的研究基础。     

Fe3O4/Mg(OH)2复合材料的制备及形貌控制

通过种子沉积法制备出Fe_3O_4/Mg(OH)_2复合材料并进行XRD、SEM测试分析。探究了氨水的浓度与加入速度、搅拌速度及等因素对磁性复合材料形貌的影响。氨水浓度与注入速度的降低,对氢氧化镁基体形貌的影响是相同的,但要使合成磁性复合材料形貌和四氧化三铁粒子在氢氧化镁中分散性均匀,应控制氨水浓度(ω=2.5%)或加入速度在一个合理的较低值。     

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.