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811.
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矿山环境中苔藓植物重金属元素的地球化学特征——以大冶铜绿山为例 总被引:4,自引:0,他引:4
通过系统的野外采样并结合室内分析,将研究区分为3个区域:靠近污染源的铜绿山矿区附近、受矿区影响明显的大冶市区以及相对清洁的武汉市中国地质大学校园,研究了苔藓中和降尘中重金属元素质量分数分布特征和来源,进而进行了对应性分析,最后将研究区域与国内外其他地区进行了对比,结果表明:①铜、铁等元素的污染受矿源的影响较大,而铅、锌的污染则与交通等人类活动有较大关系;②苔藓植物对Cu和Zn有较强的吸附能力;③与其他地区相比,铜污染是大冶市有别于其他地区的首要污染特征;④苔藓植物可以较好地指示大气中的污染物来源. 相似文献
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利用日本高知大学提供的逐小时分辨率静止卫星云顶黑体亮温(TBB)资料,使用模式匹配算法对2000~2016年(2005年除外)暖季(5~9月)青藏高原东部的两类中尺度对流系统(MCS)进行了识别和追踪,并利用人工验证订正了结果。基于此,利用NOAA的CMORPH(Climate Prediction Center Morphing)降水资料和NCEP的CFSR(Climate Forecast System Reanalysis)再分析资料对高原东部两类MCS进行了统计和对比研究。研究发现,7月和8月是高原东部MCS生成最活跃的季节,然而,此两个月能够东移出高原MCS的比例最小;5月虽然MCS生成数最少,但是移出率高达近40%。对比表明,能够东移出高原的MCS(V-MCS)比不能移出的MCS(N-MCS)生命史更长,触发更早,短生命史个例占比更低。暖季各个月份,相比于N-MCS,V-MCS的对流更旺盛且发展更快,然而,由于其发生频数远低于N-MCS,总体而言,V-MCS对高原东部的降水贡献率仅为15%左右,是N-MCS相应数值的一半左右。高原东部两类MCS的环流特征差异显著,有利于V-MCS发生、维持和东移的因子主要位于对流层中低层(西风带短波槽、西风引导气流、低层风场切变),而在对流层高层,N-MCS拥有更好的高空辐散条件(其对应的南亚高压更强)。 相似文献
817.
C. Qin A.‐X. Zhu T. Pei B. Li C. Zhou L. Yang 《International journal of geographical information science》2013,27(4):443-458
Most multiple‐flow‐direction algorithms (MFDs) use a flow‐partition coefficient (exponent) to determine the fractions draining to all downslope neighbours. The commonly used MFD often employs a fixed exponent over an entire watershed. The fixed coefficient strategy cannot effectively model the impact of local terrain conditions on the dispersion of local flow. This paper addresses this problem based on the idea that dispersion of local flow varies over space due to the spatial variation of local terrain conditions. Thus, the flow‐partition exponent of an MFD should also vary over space. We present an adaptive approach for determining the flow‐partition exponent based on local topographic attribute which controls local flow partitioning. In our approach, the influence of local terrain on flow partition is modelled by a flow‐partition function which is based on local maximum downslope gradient (we refer to this approach as MFD based on maximum downslope gradient, MFD‐md for short). With this new approach, a steep terrain which induces a convergent flow condition can be modelled using a large value for the flow‐partition exponent. Similarly, a gentle terrain can be modelled using a small value for the flow‐partition exponent. MFD‐md is quantitatively evaluated using four types of mathematical surfaces and their theoretical ‘true’ value of Specific Catchment Area (SCA). The Root Mean Square Error (RMSE) shows that the error of SCA computed by MFD‐md is lower than that of SCA computed by the widely used SFD and MFD algorithms. Application of the new approach using a real DEM of a watershed in Northeast China shows that the flow accumulation computed by MFD‐md is better adapted to terrain conditions based on visual judgement. 相似文献
818.
Tao Pei A-Xing Zhu Baolin Li Chengzhi Qin 《International journal of geographical information science》2013,27(6):925-948
In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process. 相似文献
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