Analysis of Temporal and Spatial Differences

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Spatio-Temporal Questions

S PACE-T IME C HARACTERIZATION OF L AND C OVER C HANGEDaniel G. BrownEnvironmental Spatial Analysis LabSchool of Natural Resources and EnvironmentThe University of MichiganPosition Paper for Workshop on Spatio-temporal Data Models for Biogeophysical Fields March 22, 2002Spatio-Temporal QuestionsMy work has been focusing on describing, understanding, and modeling the processes by which landscape patterns are generated. Land cover change is driven by both biophysical and socioeconomic processes. Land cover changes have important local hydrological and ecological impacts, but some also have cumulative and important global impacts on biogeochemical cycles and climate. Understanding, and in some cases forecasting, these changes can help in developing land cover scenarios that can serve in environmental and impact assessment activities. The core goals involve identifying the processes that can explain the amounts, locations, and patterns of observed land cover changes. To do this requires, at least, relating observed patterns in space and time to patterns of driving variables. This work needs to also consider the spatial and temporal autocorrelatoin in these processes that might arise from spatial interactions between places and temporal lags.The DataThe primary source of land cover observations is multi-temporal aerial and satellite-based imagery. The representations are affected by issues of spatial, temporal, spectral and thematic detail and quality. The record is largely limited to the latter half of the 20th century and beyond. Typical representations are raster-based snapshots, some of whichare multi-spectral images from which land cover and land cover changes have yet to be identified, and some of which are classified to particular land cover types or to changes.InstantTime Period LocationObject identification from the imagery is animportant step in identifying land cover change. The objects can refer to an instant in time or a time period and can refer to places or the relationships between places (Figure 1). The distinction betweenSpatialRelationFigure 1: Typology of land cover object types defined in time and space.image-based change detection and post-classification change detection (e.g., Jensen, 1995) refers to when the temporal relationships are examined relative when the objects are identified. Both of these common approaches to land cover change focus on identifying objects of Type b (Figure 1), but differ in whether or not they first produce objects of Type a. The remote sensing literature does not address well the identification of boundary or gradient changes, which probably first requires identification of multi-temporal boundaries development of a movement model of some sort.Spatial-Temporal Data ModelsBy far the most common data model used in land cover change work is the snapshot, i.e., multiple spatial representations created for different points in time. A good rationale for this model is that the data are collected in essentially this way, i.e., complete spatial images taken at instances that are separated by time intervals. This suggests a case where good (often complete) spatial coverage exists for a fairly limited number of times (though this is getting better). This model is good for representing objects of Types a and c in Figure 1, but not for Types b and d. Once we have identified locations and types of change, we don't have a good working data model within which to structure those changes to include time (i.e., when they occurred and the intervals they represent). This is particularly problematic because the time intervals are often not constant, and this needs to be represented somehow.Interface to Spatial-Temporal Process ModelsIn order to relate observed changes to processes, which much of this work is ultimately aimed towards, we are inevitably faced with comparing or interfacing the representations of change (i.e., the data models) with the representations of process (i.e., process models). We are working with two broad types of land cover change process models. The first, which I will describe in more detail here, are what I'm calling top-down models. We are using geostatistical methods to characterize the space-time patterns inherent in observations of land cover change. These patterns can be related to space-time patterns in variables that represent various driving forces. The second type of model we are working on is bottom-up models, so-called because they develop a detailed agent-based description of how people make decisions about land cover change, and simulate the space-time patterns of land cover that emerge through the collective effects of those individual decisions. Ultimately, we seek strengthen our understanding of land cover change processes through the comparative contributions of both top-down and bottom-up models.To accomplish the top-down modeling we employ geostatistics, which provide a probabilistic framework for data analysis that builds on the joint spatial and temporal dependence between observations (Brown et al., In Review). The model of change thatwe employ is calibrated to land cover changes observed in a pair of images and involves (1) an initial map of land cover, (2) description of the change probabilities at locations, and (3) description of the spatial pattern of observed changes. The distribution of change probabilities is described using a statistical model that associates where changes occur with the characteristics of places on a number of suspected driving variables. The spatial patterns of change are described through indicator variograms describing each type of change and indicator cross-variograms describing the spatial interactions between changes. Reducing the observed changes to several parameters, which are part of the statistical model of change locations and the geostatistical description of change patterns, facilitates evaluations of spatial and temporal stationarity in the change process, comparisons based on hypothesized driving variables, and simulation of change for spatial-temporal interpolation or forecasting purposes. Because the framework facilitates simulation, it can also be used in the evaluation of how uncertainty propogates through the change processes observed, for example following approaches described by Goovaerts (1997) and Huevelink (1998).Final ThoughtsReasoning about space-time processes requires that we work with representations of both phenomena (entities and events) and processes (cause-effect linkages, feedbacks, etc.). One of the more fundamental questions we face is how we reconcile our observations of empirical reality, which rarely offer the reasoning power of controlled experiments, with our models of process, which are necessarily simplified representations of complex processes. We need to decide what are the characteristics of the observations that we think need to be well reproduced by our models. For this purpose, data mining to create reduced descriptions of space-time patterns is critical. Further, summarization of model output and the search of space-time data that match these summaries will facilitate the process of model validation. For these reasons, I see the development of intuitive and robust interfaces between models of data and models of process as an important research agenda item in the context of this workshop. ReferencesGoovaerts, P. 1997. Geostatistics for Natural Resources Evaluation. New York: Oxford.Heuvelink, G.B.M. 1998. Error Propagation in Environmental Modeling with GIS. New York: Taylor and Francis.Jensen, J.R. 1995. Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, NJ: Prentice Hall.Brown, D.G., Goovaerts, P., Burnicki, A.C., and Li, M.-Y. n.d. Stochastic simulation of land-cover change using geostatistics and generalized additive models. In Review.。

INTRODUCTION TO SPATIAL ECONOMETRICS USING R

Caveats
Modifiable areal unit problem (Openshaw and Taylor, 1979)
The choice of spatial weight matrix
The link between spatial modeling and social theories
A spatial perspective better reflects the real world as people are not confined by administrative boundaries.
How Do We Analyze Spatial Data?
Exploratory spatial data analysis (ESDA):
County-level mortality data (1998-2002) Independent variables drawn from 2000 Census
Tasks:
Load necessary R packages Read the shapefile containing data Visualize the dependent variable and save it as a figure Generate spatial weight matrix using the shapefile Test spatial dependence (both global and local) Examine if a spatial perspective is better Implement spatial econometrics models Conduct model comparisons

spatio-temporall...

Spatio-Temporal LSTM with Trust Gates for3D Human Action Recognition817 respectively,and utilized a SVM classiﬁer to classify the actions.A skeleton-based dictionary learning utilizing group sparsity and geometry constraint was also proposed by[8].An angular skeletal representation over the tree-structured set of joints was introduced in[9],which calculated the similarity of these fea-tures over temporal dimension to build the global representation of the action samples and fed them to SVM forﬁnal classiﬁcation.Recurrent neural networks(RNNs)which are a variant of neural nets for handling sequential data with variable length,have been successfully applied to language modeling[10–12],image captioning[13,14],video analysis[15–24], human re-identiﬁcation[25,26],and RGB-based action recognition[27–29].They also have achieved promising performance in3D action recognition[30–32].Existing RNN-based3D action recognition methods mainly model the long-term contextual information in the temporal domain to represent motion-based dynamics.However,there is also strong dependency between joints in the spatial domain.And the spatial conﬁguration of joints in video frames can be highly discriminative for3D action recognition task.In this paper,we propose a spatio-temporal long short-term memory(ST-LSTM)network which extends the traditional LSTM-based learning to two con-current domains(temporal and spatial domains).Each joint receives contextual information from neighboring joints and also from previous frames to encode the spatio-temporal context.Human body joints are not naturally arranged in a chain,therefore feeding a simple chain of joints to a sequence learner can-not perform well.Instead,a tree-like graph can better represent the adjacency properties between the joints in the skeletal data.Hence,we also propose a tree structure based skeleton traversal method to explore the kinematic relationship between the joints for better spatial dependency modeling.In addition,since the acquisition of depth sensors is not always accurate,we further improve the design of the ST-LSTM by adding a new gating function, so called“trust gate”,to analyze the reliability of the input data at each spatio-temporal step and give better insight to the network about when to update, forget,or remember the contents of the internal memory cell as the representa-tion of long-term context information.The contributions of this paper are:(1)spatio-temporal design of LSTM networks for3D action recognition,(2)a skeleton-based tree traversal technique to feed the structure of the skeleton data into a sequential LSTM,(3)improving the design of the ST-LSTM by adding the trust gate,and(4)achieving state-of-the-art performance on all the evaluated datasets.2Related WorkHuman action recognition using3D skeleton information is explored in diﬀerent aspects during recent years[33–50].In this section,we limit our review to more recent RNN-based and LSTM-based approaches.HBRNN[30]applied bidirectional RNNs in a novel hierarchical fashion.They divided the entire skeleton toﬁve major groups of joints and each group was fedSpatio-Temporal LSTM with Trust Gates for3D Human Action RecognitionJun Liu1,Amir Shahroudy1,Dong Xu2,and Gang Wang1(B)1School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore,Singapore{jliu029,amir3,wanggang}@.sg2School of Electrical and Information Engineering,University of Sydney,Sydney,Australia******************.auAbstract.3D action recognition–analysis of human actions based on3D skeleton data–becomes popular recently due to its succinctness,robustness,and view-invariant representation.Recent attempts on thisproblem suggested to develop RNN-based learning methods to model thecontextual dependency in the temporal domain.In this paper,we extendthis idea to spatio-temporal domains to analyze the hidden sources ofaction-related information within the input data over both domains con-currently.Inspired by the graphical structure of the human skeleton,wefurther propose a more powerful tree-structure based traversal method.To handle the noise and occlusion in3D skeleton data,we introduce newgating mechanism within LSTM to learn the reliability of the sequentialinput data and accordingly adjust its eﬀect on updating the long-termcontext information stored in the memory cell.Our method achievesstate-of-the-art performance on4challenging benchmark datasets for3D human action analysis.Keywords:3D action recognition·Recurrent neural networks·Longshort-term memory·Trust gate·Spatio-temporal analysis1IntroductionIn recent years,action recognition based on the locations of major joints of the body in3D space has attracted a lot of attention.Diﬀerent feature extraction and classiﬁer learning approaches are studied for3D action recognition[1–3].For example,Yang and Tian[4]represented the static postures and the dynamics of the motion patterns via eigenjoints and utilized a Na¨ıve-Bayes-Nearest-Neighbor classiﬁer learning.A HMM was applied by[5]for modeling the temporal dynam-ics of the actions over a histogram-based representation of3D joint locations. Evangelidis et al.[6]learned a GMM over the Fisher kernel representation of a succinct skeletal feature,called skeletal quads.Vemulapalli et al.[7]represented the skeleton conﬁgurations and actions as points and curves in a Lie group c Springer International Publishing AG2016B.Leibe et al.(Eds.):ECCV2016,Part III,LNCS9907,pp.816–833,2016.DOI:10.1007/978-3-319-46487-950。

1986—2020_年山东省地表水时空变化特征分析李艺,范

第38卷第1期2024年1月山东理工大学学报(自然科学版)Journal of Shandong University of Technology(Natural Science Edition)Vol.38No.1Jan.2024收稿日期:20221128基金项目:国家自然科学基金项目(42171413);山东省自然科学基金项目(ZR2020MD015);山东理工大学青年教师发展支持计划项目(4072-115016)第一作者:李艺,女,20507020764@;通信作者:范俊甫,男,fanjf@文章编号:1672-6197(2024)01-0001-071986 2020年山东省地表水时空变化特征分析李艺,范俊甫,张志锟,左吉伟,时宗闻,高宇(山东理工大学建筑工程与空间信息学院,山东淄博255049)摘要:针对地表水提取过程中部分细小河流和半干涸河道提取较困难的问题,以1986 2020年的山东省内陆地表水为研究对象,基于改进的归一化水体指数(MNDWI )模型,引入建筑物指数模型和植被指数模型,提出了一种新型水体指数(GMNDWI ),较明显地提高了地表水体的提取精度㊂在此基础上,运用空间网格化处理与分区统计㊁动态度等方法分析了山东省内陆地表水的时空变化特征,并通过相关性分析对选取的影响因子进行了讨论㊂结果表明:研究区内地表水时空分布不均衡,旱雨季水面积之比稳定在3ʒ5,均呈现先增加后减少的变化趋势;降雨量和耕地面积与地表水面积的相关系数明显高于其他因素,表明二者的共同作用是导致研究区地表水面积变化的关键因素㊂关键词:Landsat ;遥感;地表水提取;水体指数;精度分析;相关性分析中图分类号:TB532.1;TB553文献标志码:AAnalysis of temporal and spatial variation characteristics of surfacewater resources in Shandong Province from 1986to 2020LI Yi,FAN Junfu,ZHANG Zhikun,ZUO Jiwei,SHI Zongwen,GAO Yu(School of Architectural Engineering and Spatial Information,Shandong University of Technology,Zibo 255049,China)Abstract :In order to solve the difficulty of extracting some small rivers and semi-dry channels in theprocess of surface water extraction,the inland surface water of Shandong Province from 1986to 2020was taken as the research object and a new water index (GMNDWI)based on the MNDWI water index model was proposed by introducing the building index model and the vegetation index model,which significantly improved the extraction accuracy of surface water.On this basis,the spatio-temporal variation character-istics of inland surface water in Shandong Province were analyzed using spatial grid processing,zonal sta-tistics and dynamic attitude methods,and the selected influencing factors were discussed by correlation a-nalysis.The results show that the spatial and temporal distribution of surface water in the study area is uneven,and the ratio of water area in the dry and rainy seasons is stable at 3ʒ5,showing a trend of in-creasing first and then decreasing.The correlation coefficients of rainfall and cultivated land area with surface water area are significantly higher than other factors,indicating that the combined action ofrainfall and cultivated land area is the key factor leading to the change of surface water area in the study area.Keywords :Landsat;remote sensing;surface water extraction;water index;precision analysis;corre-lation analysis㊀㊀㊀地表水是维持区域生态平衡㊁促进经济社会发展的关键因素[1],其作为日益短缺的关键性基础资源,近几年在山东省的总量降幅明显,季节变化显著,供需矛盾日益突出[2]㊂作为中国典型的沿海省份,目前学者们对该区域的地表水开发利用[3]㊁水资源管理[4]和水保护[5]已做了大量的研究,开展了一系列基于水文气象站点㊁雨量数据等资料的定量分析[6],但由于数据的来源渠道多样㊁标准不规范,且缺乏连续性,因此较难反映研究区地表水的整体时空演化特征㊂卫星遥感数据能大区域㊁长时段㊁高效率监测地表水状态[7],为实现对区域地表水的长时间跨度连续观测提供高可靠的数据源;然而目前研究多局限于局部典型水域或较短时间序列,缺乏对山东省整体地表水长期连续动态变化的研究,且未对地表水的季节性变化特征进行分析比较㊂从导致地表水面积变化的驱动力方面来看,山东省地表水季节性动态变化受到大气环境影响和社会经济影响等诸多因子的综合影响比较明显,因此单个季度的地表水监测并无法全面反映地表水变化㊂获取地面大范围㊁大尺度的地面信息主要是通过遥感影像,国内外学者在遥感影像提取水体方面已做了大量研究㊂目前Landsat影像提取方法可大致分为两大类:一类是基于影像波段信息,通过原始波段处理或波段间相组合所构成的指数模型,如McFeeters[8]提出的归一化水体指数(NDWI),虽能提取水体信息但也掺杂大量背景噪音,徐涵秋[9]通过波段变换的方式提出改进后的归一化水体指数(MNDWI),在抑制植被等无效信息方面取得良好效果;另一类是基于图像显示的特征,通过特征分类别判定的分类器法,如Kalke等[10]通过支持向量机的方法对河流水体进行提取,但其中核函数参数的选取有一定的难度,薛源等[11]利用水体指数和决策树结合DEM河网实现对山区河流的自动提取㊂目前深度学习算法也开始被应用在水体的分类提取当中,但其需要大量准确样本支持的特性是提取工作的痛点[12],因此,高效准确的指数模型法因其普适性依然是当今大范围数据提取工作的首选[13]㊂本文基于1986 2020年长时序的Landsat遥感影像数据,通过水体指数模型提取水体以分析山东省地表水的动态度㊁变化率,揭示了多年山东省地表水的时空演变特征,结合自然资源数据和社会经济数据,对影响地表水面积变化的影响因子进行了相关性分析㊂该研究旨在揭示山东省地表水时空演变规律及其与气候变化和人类活动的关系,为山东正在实施的区域协调发展战略[14]的顺利实施提供科学的参考依据,为中国沿海地区地表水资源的保护及合理开发利用提供科学参考㊂1㊀研究区与数据来源1.1㊀研究区概况山东省地处中国东部沿海,位于世界公认的黄金纬度的海岸线上(34ʎ25ᶄN~38ʎ23ᶄN,114ʎ36ᶄE~122ʎ43ᶄE)㊂地处温带季风型气候,雨热同期,降水集中且季节分配不均,易发生严重春旱和夏涝;加之利用效率低㊁过度开发等人为因素综合作用,地表水资源短缺逐渐成为常态,给各行业发展和人民的生产生活带来显著影响,成为严重制约工业和农业持续健康发展的瓶颈[15]㊂1.2㊀数据来源与预处理Landsat系列卫星遥感影像数据来源于美国地质调查局官网;人口和GDP数据来自中国国家统计局;降雨量和用水量数据来自山东省水利厅;耕地数据来自山东省统计局和公开文献[16];气温数据来自欧洲中期数值预报中心的气候再分析数据㊂选择1986年㊁1995年㊁2004年㊁2013年㊁2020年的旱季㊁雨季共10期Landsat卫星遥感数据作为数据源㊂旱季选取一月份至三月份㊁雨季选取八月份中旬至九月下旬,且云量小于5%的数据㊂为消除差异影响,根据影像特征确定合适的参数和大气模型,对遥感影像进行辐射定标和大气校正等处理后得到可满足研究要求的数据集,相关操作在ENVI 5.3软件中完成㊂1.3㊀研究方法1.3.1㊀提取方法将文献[17]中现有的22种水体指数模型分别应用在研究区地表水提取中,对实验结果进行对比分析发现,MNDWI是分离度最高㊁提取效果最好的水体指数模型,这与屈慧慧等[18]研究结果一致,其计算原理见式(1)[9]㊂MNDWI=Green-MIR1Green+MIR1,(1)式中:MNDWI为水体指数模型,Green为绿光波段的像元值,MIR1为中红外波段的像元值㊂该方法能准确提取绝大部分大面积水体,但部分细小河流未被成功提取㊂采用调整最佳阈值的方法以解决该问题,将初始阈值减小0.01后,水体未被提取的情况得到改善,但部分建筑物㊁植被等干扰物也被归为水2山东理工大学学报(自然科学版)2024年㊀体㊂针对该问题,本研究引入提取植被效果较好的植被指数模型NDVI[19](式(2))和提取建筑物较为完善的建筑物指数模型NDBI[19](式(3)),分别二值化后构建新型的水体指数GMNDWI,其计算公式见式(4)㊂NDVI=NIR-REDNIR+RED,(2)式中:NDVI为植被指数模型,NIR为近红外波段的像元值,RED为红光波段的像元值㊂NDBI=MIR1-NIRMIR1+NIR,(3)㊀GMNDWI=MNDWI-NDVI-NDBI㊂(4) 1.3.2㊀时空分布特征分析时空分布特征分析利用空间网格化处理和分区统计来完成㊂空间网格化处理就是用规定大小的格网将一定范围的平面进行分割,从而得到单元数据的过程㊂通过ArcGIS软件,采用1kmˑ1km的格网将研究区内旱季水和雨季水进行分割,经过分区统计得到水体面积占各网格的位置及比例,从而分析讨论研究区内旱季水和雨季水的空间分布和时间变化特征㊂1.3.3㊀变化趋势分析通过动态度计算来反映地表水面积变化的剧烈程度,分析地表水面积变化的趋势,其计算方法为研究区内研究期始末地表水的面积变化量占初始期地表水面积的比重,并与研究时段的比值㊂动态度的绝对值越大,表明在一段时间内地表水的面积变化越剧烈㊂此外,为直观展现研究区地表水面积变化在空间上的分布,采用格网法将研究区分割成1km ˑ1km的空间格网,用研究期始末地表水面积占该格网面积的比重变化反映该地区地表水面积变化的趋势,若其值为正值,表明呈增加趋势,反之则为减少趋势㊂1.3.4㊀相关性分析为探究各影响因子对研究区内旱季水和雨季水的影响程度,分析地表水区域差异特征,运用皮尔森相关系数法将每年地表旱季水和雨季水面积与各影响因子进行相关性分析,通过该系数反映地表水面积与各影响因子的紧密程度㊂2㊀精度评价与结果2.1㊀提取结果近几年全球海平面逐年上升[20],加上研究区北部大面积的养殖池扩建和盐田开发导致海岸线边界不断扩展,海岸侵蚀导致滩涂下边界向内陆推进[21],故本研究将海水从GMNDWI指数提取的研究区地表水的实验结果中进行剔除处理㊂参照山东省海洋局发布的‘海岸线调查技术规范“(山东省地方标准)[22],结合行政区划矢量数据目视解译2020年Landsat遥感影像的海岸线边界,将海水剔除㊂2.2㊀精度评价先以JRC数据集为参考,验证本文水体指数模型方法的精度,利用ArcGIS软件在提取的矢量水体数据内选取随机点,使用eCognition软件对水体提取结果进行基于对象样本的混淆矩阵法精度评价㊂选取研究区境内的黄河流域对比高分辨率的遥感影像进行人工目视解译,作为精度评价的验证样本,以50m为最小允许距离,随机选取653个样本点对水体提取结果进行Kappa系数精度㊁总体精度和用户精度验证,验证结果见表1㊂㊀㊀㊀㊀㊀㊀表1㊀精度验证结果㊀㊀㊀㊀㊀㊀㊀单位:%项目精度评价Kappa系数精度总体精度用户精度水体指数96.5078.7195.1195.11 JRC数据集75.0077.0379.3679.36㊀㊀经精度验证发现,本研究所提出方法的精度评价㊁Kappa系数㊁总体精度㊁用户精度均比JRC数据集精度有提升,充分证明了这种水体指数方法精度的提高㊂3㊀研究区地表水时空演变特征3.1㊀研究区域地表水空间区划为更直观地反应研究区内地表水面积的时空变化,参考水利部水利水电规划设计总院发布的‘中国水功能区划“[23]及山东省水利部门发布的‘山东省水资源公报“[24],将研究区划分为四部分分别为徒骇马颊河区㊁花园口以下区㊁沂沭泗河区和山东半岛诸河区,如图1所示㊂3.1.1㊀地表水空间变化特征结合图2,从地表水面积的空间占比来看,研究区内永久水(即全年维持水域状态的地表水)和季节水(即随季节变动的地表水)在空间分布上的疏密程度不均,永久水在花园口以下区和山东半岛诸河区的面积占比高于季节水,而徒骇马頬河区和沂沭泗河区内的永久水和季节水占比均等㊂1986 2020年间永久水的占比有所下降,但仍高于季节水3第1期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀李艺,等:1986 2020年山东省地表水时空变化特征分析图1㊀地表水分区(a)1986年(b)2020年图2㊀地表水的面积空间占比占比㊂3.1.2㊀地表水时间变化特征整体来看,1986 2020年间研究区旱季水面积和雨季水面积的变化趋势大体一致,均为先增加后减少㊂雨季水面积呈现出波动增加的趋势,面积增加14.2%;旱季水面积呈现出波动减少的趋势,面积减少7.1%;雨季水在1986年时,面积最小为3238km 2;旱季水在2020年面积最小为2223km 2㊂在2004年旱季水面积和雨季水面积均达到顶峰,分别为4474km 2和2847km 2㊂根据地表水区域划分,本文分别统计了各时期各分区内旱雨季水的面积,其变化趋势如图3所示,由图可知,徒骇马頬河区旱季水面积稳步增加,雨季水面积增加后趋于稳定,花园口以下区的旱雨季水面积均呈波动减少的态势,沂沭泗河区与山东半岛诸河区旱雨季水的面积均呈先增加后减少的趋势㊂图3㊀分区地表水面积3.2㊀地表水面积测度动态变化3.2.1㊀地表水面积变化率分别计算旱雨季水在四个研究时段的变化率及1986 2020年均变化率,以此来分析研究区内地表水面积的变化程度㊂由图4可知,旱季水增加面积的变化率呈现先减少后增加的趋势,特别是在2004 2013年时间段变化率最小为10%;而旱季水减少面积的变化率在各研究时段较为平稳,保持在15%左右㊂由此可见,旱季水总体变化率与增加面积的变化率基本保持一致㊂另外,雨季水增加面积的变化率呈现先减少后增加的趋势,尤其是在2004 2013年时间段变化率最小为6.7%,成为在各研究期变化最为平稳的地表水类型㊂雨季水减少面积的变化率在前三个研究时间段保持了平稳变化,而在2013 2020年时间段雨季面积减少的幅度高达39.87%,成为在各研究期变化最剧烈的地表水类型㊂图4㊀分区地表水的面积变化率总体来看,在1986 1995年㊁1995 2004年㊁2004 2013年三个研究期时段内,旱季水的总体变化率高于雨季水的总体变化率,说明在该时段内旱季水变化较为剧烈,而在2013 2020年时间段内雨季水的变化剧烈程度高于旱季水,详见表2㊂4山东理工大学学报(自然科学版)2024年㊀表2㊀研究区地表水面积变化率㊀单位:%地表水情况1986 1995年1995 2004年2004 2013年2013 2020年平均变化率旱季水增加18.0617.5710.4112.1714.55减少15.9115.5116.1715.7015.82总计33.9733.0826.5827.8730.37雨季水增加18.3515.81 6.7013.5613.61减少10.207.819.3839.8716.82总计28.5523.6216.0853.4330.433.2.2㊀地表水面积动态度从地表水面积变化动态度的空间分布(图5)来看,1986 2020年旱雨季水动态度均整体表现为负,其中旱季水动态度在徒骇马頬河区的中部㊁沂沭泗河区的东部及微山湖水域西部和南部㊁山东半岛诸河区东北部均表现为负,而在动态度表现为正的区域较为集中如徒骇马頬河区的西北部㊁山东半岛诸河区的峡山水库和淮河水域㊁黄河流域花园口以下区的大汶河流域以及沂沭泗河区的沂河流域和微山湖水域㊂雨季水动态度表现为负的地区包括黄河三角洲地区㊁沂沭泗河区的微山湖水域西部㊁山东半岛诸河区的青岛胶州湾等地,而微山湖水域㊁黄河流域㊁大汶河流域㊁潍河流域等地的雨季水动态度则表(a)旱季水(b)雨季水图5㊀1986—2020年地表水面积动态度现为正㊂整体来看,内陆地表水总体面积呈减少趋势㊂3.3㊀地表水变化的驱动力分析3.3.1㊀影响因子分析1)气候变化㊂山东省地区属温带季风气候,雨热同期,因此选取气温和降雨量2个重要的气候因子分析气候变化对地表水的影响,如图6所示㊂图6㊀气候影响因子年际变化2)人类活动㊂综合前人研究,选取人口㊁GDP ㊁耕地面积㊁用水量4个社会因子作为指标,用于分析人类活动对山东地表水面积的影响程度㊂从各指标的变化趋势来看,人口㊁耕地面积和GDP 呈现增长趋势;用水量趋势不明显,在220~260亿m 3之间波动,如图7所示㊂3.3.2㊀相关性分析由表3可知,旱雨季地表水的面积变化受降雨量㊁GDP ㊁人口及耕地面积等因素的综合影响作用㊂从雨季水的影响因素看,降雨量对其面积变化的影响效果最为显著,相关系数达到0.895,造成该现象的主要原因是研究区内雨季降水较为集中㊂旱季水面积变化的影响因素中,耕地面积的影响程度最大,其相关系数为-0.997,其原因是受到研究区农业结5第1期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀李艺,等:1986 2020年山东省地表水时空变化特征分析(a)人口和GDP(b)耕地和用水量图7㊀社会影响因子年际变化构的影响,冬季大量种植高需水量的小麦㊁油菜等作物㊂由于研究区范围较大㊁地形复杂㊁人口分布不均匀,其余因素与地表水面积变化的相关性较低,因此其余单一影响因素对地表水面积变化的影响并非是简单的线性关系,其影响效果并不显著㊂表3㊀各影响因子与地表水面积相关性㊀㊀注:∗在0.05级别(双尾)相关性显著㊂4㊀总结与讨论通过分析新指数方法对研究区地表水面积的提取结果,得出结论如下:1)从地表水时空分布来看,研究区地表水空间上时空分布不均衡,大致表现出东密西疏的特征㊂研究区每期旱季水和雨季水面积之比均大致稳定在3ʒ5㊂2)从地表水测度动态变化来看,研究区1986 2020年地表水面积整体呈先增加再减少的趋势㊂除徒骇马頬河区西北部沿海地区地表水面积增加外,其他地区地表水面积明显减少㊂地表水总体面积从2004年开始呈减少趋势㊂3)地表水面积与降雨量和耕地面积的相关系数明显高于其他因素,表明二者的共同作用是导致旱雨季地表水面积变化的关键因素㊂总体来说,本研究提出的指数(GMNDWI )对于大多数水体提取效果都较为准确,能基于Landsat 遥感影像对研究区地表水进行精细提取,尤其对于面积较大的水体,提取边界极其吻合,准确度高;但影像重访周期较长,无法及时捕捉短期内暴发的洪水,极端天气也一定程度地影响着地表水提取精度㊂通过本文所用相关性分析指数的结果发现,雨季水增加主要是因为降雨量,而旱季水减少与耕地面积相关性极高,考虑主要是因为农业用水导致㊂在丰水期,降雨量显著增加,同时由于雨季的灌溉,农业用水量减少,进一步增加了地表水资源量,加上河流湖泊汛期到来,此时易发生洪涝灾害使得人民的生命财产受到损失㊂在枯水期,由于降水量减少和耕地开发,使得农业用水量显著增加,此时由于水资源的短缺,造成农产品的价格波动,同样会对社会经济的发展造成不利影响㊂根据本文研究结果中研究区冬末初春降雨稀少㊁河流干旱,而夏末雨量充沛㊁洪涝灾害频发的特点,应分周期种植适宜生长的农作物,在夏季重点做好黄河流域㊁小清河流域和微山湖周边的水资源调节工作以及旱涝灾害的应急预案㊂根据雨季丰水期地表水时空变化规律和分布规律,建议着重在黄河流域加固堤坝以保障财产安全㊂在枯水期做好防旱工作,适当增加人工降雨㊂山东省地域辽阔,由沿海向内陆延伸的过程中气候及地域差异明显,加上各地区经济水平发展差异较大,因此地表水存在很大的空间差异,基于此本文提出地表水分区分级的治理方案,在一级分区中按流域划分为四个区,实现研究区内地表水的整体调控和方案部署㊂在此条件下可按行政区划㊁经济发展情况㊁流域分布进行二级划分,在该级别中做好水资源的调配,做好灾情的预警㊁灾时的救援以及灾后的重建㊂对易发生灾情的湖泊㊁河流实行三级划分,由当地政府施行专案措施,采取改进传统的灌溉方式㊁保护和修复水生态等因地制宜的措施,以促进当地经济的可持续发展㊂6山东理工大学学报(自然科学版)2024年㊀此外,本文侧重于山东省地表水的宏观研究,在小区域地表水体提取方面存在诸多不足,今后将进一步加强联合分析,开展地方地表水综合研究和区域发展规划,促进地表水资源的合理开发利用㊂参考文献:[1]赵文津.我国西北地区水资源开发利用对策的建议[J].中国工程科学,2004(8):21-27.[2]杨凡.山东省地市灰水足迹测度与空间格局分析[J].节水灌溉, 2017(2):69-75.[3]邵金花,刘贤赵.山东省水资源开发利用程度综合评价[J].人民黄河,2007(3):39-41.[4]ZHANG H,ZHENG 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固体物理名词解释

名词解释：波粒二象性:wave-particle duality,是指同时具有波和粒子的特征,一切微观粒子都具有波粒二象性,满足 , ,其中为能量, 为频率, 为动量, 为波长。

Bohr原子模型：Bohr's model of atom，是指通过将围绕原子核周围旋转的电子的角动量量子化，各元素的电子均获得各自既定能量轨道的原子模型。

波函数：wavefunction(Ψ)- a wave representing the spatial distribution of a “particle”. 波函数 - 代表“粒子”空间分布的波，是量子力学中描写微观系统状态的函数。

物质波：matter wave，又称德布罗意波，是指物质在空间中某点某时刻可能出现的几率，其中概率的大小受波动规律的支配。

晶格: lattice,由原子或原子团周期性排列组成,可以在空间中无限延伸.格点: lattice point,在空间中具有相同环境的点.密堆: close packing, 也称最密堆积,是原子的一种排列方式，在最密堆积中，许多等径球并置在一起，其空间利用率达到最大。

配位数: coordination number (CN),中央原子相邻原子的总数.初级平移矢量: primitive translation vectors, 是坐标系的三个坐标轴的单位矢量,即:T ( 1, 0, 0 ) = a1;T ( 0, 1, 0 ) = a2;T ( 0, 0, 1 ) = a3.分数坐标:fractional coordinates,以晶胞的3个轴作为坐标轴,表示基元的位置r j =x j a1+y j a2+z j a3，其中0≤(x j y j z j) ≤1.晶系: crystal systems,晶体按其几何形态的对称程度。

可将其划分为七类，即三斜晶系、单斜晶系、正交晶系、四角晶系、立方晶系、三角晶系和六角晶系。

SPSS术语中英文对照

SPSS术语中英文对照【常用软件】SPSS术语中英文对照Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA （analysis of variance）, 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interac tion Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 一般线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层（复数）Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。

Temporal and spatial variations of the particle size distribution of PAHs and their dry deposition f

Atmospheric Environment 36 (2002) 5491–5500
Temporal and spatial variations of the particle size distribution of PAHs and their dry depositiont al., 1984), which can be deposited in the respiratory tract, hence increasing the potential health effects. In this work, measurements of the size distributions between 0.1 and 100 mm in diameter and dry deposition ﬂuxes of particulate PAHs at four sites, Korea in February and May 2000 were reported. The objective of this study is to understand the characteristics of temporal and spatial variations of the particle size distribution of PAHs and their dry deposition ﬂuxes in Korea.
*Corresponding author. Tel.: +822-3277-2832; fax: +8223277-3275. E-mail address: yong@ewha.ac.kr (Y.P. Kim). 1 Present address: Graduate School of Public Health, Seoul National University, Korea.

近10年中国耕地变化的区域特征及演变态势

第37卷第1期农业工程学报V ol.37 No.12021年1月Transactions of the Chinese Society of Agricultural Engineering Jan. 2021 267 近10年中国耕地变化的区域特征及演变态势袁承程1，张定祥2，刘黎明1※，叶津炜1（1. 中国农业大学土地科学与技术学院，北京100193；2. 中国国土勘测规划院，北京100035）摘要：随着工业化、城市化进程推进,中国耕地在数量和质量方面均发生了显著变化。

通过分析2009－2018年中国耕地的时空变化，掌握中国耕地变化的区域特征与变化态势，有助于制定差别化的区域耕地保护政策与管理策略，为保障粮食安全提供科学依据。

该研究基于2009－2018年土地调查格网数据，利用GIS空间分析、数学指数模型等方法，从耕地数量、空间以及立地条件等方面研究近10年来中国的耕地时空变化特征。

研究表明：1）2009－2018年间中国耕地数量总体稳定，但是耕地数量变化的区域差异较大。

全国耕地共减少39.37万hm2，减少幅度为0.29%。

2）从市域尺度分析，呈现以“哈尔滨-郑州-昆明”带为中心的东-中-西分异特征，该中心带内耕地净减少面积与全国耕地净减少总量基本持平，而该中心带以东地区的耕地净减少量与中心带以西地区的耕地净增加量相近。

3）耕地空间变化率在长江以北的长江中下游平原区、黄淮海平原区以及四川盆地及其周边地区相对较高，表明这些区域人为调整耕地空间布局的强度较大，但其市域内净增加耕地面积总量却不大。

4）耕地减少主要分布在距离主要城市中心30 km以内的区域，而耕地增加主要发生在离城市中心40 km以外区域，这进一步说明城市化发展仍然是当前耕地减少的主导因子。

此外，石嘴山、延安、雅安、榆林、张家口、丽水和泉州等地的耕地平均海拔增加较大，说明这些地区耕地“上山”现象较为严重。

因此，今后应根据耕地变化“热点地区”的动态识别，提升自然资源管理和督察的精准定位和因地施策的能力。

黔东南州近60年气温时空变化特征分析

Climate Change Research Letters 气候变化研究快报, 2020, 9(5), 508-514Published Online September 2020 in Hans. /journal/ccrlhttps:///10.12677/ccrl.2020.95055黔东南州近60年气温时空变化特征分析陈晓丹1，吴树炎1*，王敏敏1，蒋汉开2，李舒颖11贵州省黎平县气象局，贵州黎平2贵州黔东南气象局，贵州凯里收稿日期：2020年9月2日；录用日期：2020年9月17日；发布日期：2020年9月24日摘要利用1960~2019年黔东南全州16个县市台站气温资料，使用气候变化分析方法如M-K突变检验、小波分析等，分析了黔东南州近60年来的气温时空变化特征。

结果表明：黔东南州各县市年平均气温在14.7℃~18.5℃之间，全州年平均气温从1960年至今整体缓慢上升，M-K突变检验表明黔东南州气温突变点为2006年，且2009年以后气温的上升趋势十分显著。

黔东南州年平均气温存在着准4年周期，在60年代有显著的3年左右的年际振荡周期，在1986~2010年有显著的4年振荡周期。

黔东南州最冷月(1月)各县市月平均气温均在0℃以上，全州年平均最低气温≤ 0℃的日数在州西部为24~28 d，州南部仅为4 d左右；黔东南州最热月(7月)各县市月平均气温呈东部向西部递减的趋势，州西部较东部凉爽，北部较南部凉爽，全州年平均最高气温≥ 35℃日数南部年平均出现20 d以上，西部为0 d。

关键词黔东南，气温，时空变化，高温Analysis of Temporal and Spatial VariationCharacteristics of Temperature inQiandongnan Prefecture in thePast 60 YearsXiaodan Chen1, Shuyan Wu1*, Minmin Wang1, Hankai Jiang2, Shuying Li11Liping Count Meteorological Bureau of Guizhou Province, Liping Guizhou2Qiandongnan Prefecture Meteorological Bureau, Kaili GuizhouReceived: Sep. 2nd, 2020; accepted: Sep. 17th, 2020; published: Sep. 24th, 2020*通讯作者。

宿州市耕地土壤养分时空变化特征分析

Temporal and spatial characteristics of soil nutrients in cultivated land in Suzhou CityDING Qixun 1,ZHAN Xuejie 1,ZHANG Tian′en 1,XU Nuo 2,MA Xiuting 3,ZHANG Changkun 3,MA Youhua 1*（1.Key Laboratory of Farmland Ecological Conservation and Pollution Control of Anhui Province,College of Resources and Environment,Anhui Agricultural University,Hefei 230036,China;2.Suzhou Agriculture and Rural Affairs Bureau,Suzhou 234000,China;3.Anhui Huacheng Seed Co.,Ltd.,Suzhou 234000,China ）Abstract ：Analyzing the temporal and spatial evolution of soil nutrients is a prerequisite for implementing precision agriculture and sustainable soil management.The spatial and temporal variation characteristics of soil organic matter,total N,available P,and available K in arable soil in Suzhou City in 2010and 2019were analyzed by inverse distance weighted spatial interpolation analysis method.The results showed that the soil nutrients of arable soil in Suzhou increased slightly in 2019compared with 2010.The soil organic matter of arable soil was relatively scarce in Dangshan County,Xiaoxian County,and Sixian County,and abundant in the middle towns of YongqiaoDistrict,with an average value of 17.95g·kg -1,an increase of 6.15%.The area with intermediate soil organic matter content accounted for76.00%of the total cultivated land area;the soil total N content was the same,with an average value of 1.06g·kg -1.The area with medium宿州市耕地土壤养分时空变化特征分析丁琪洵1，詹雪洁1，张天恩1，许诺2，马秀婷3，张长坤3，马友华1*（1.农田生态保育与污染防控安徽省重点实验室，安徽农业大学资源与环境学院，合肥230036；2.宿州市农业农村局，安徽宿州234000；3.安徽华成种业股份有限公司，安徽宿州234000）收稿日期：2021-11-26录用日期：2022-03-02作者简介：丁琪洵（1997—），女，江苏泰州人，硕士研究生，主要从事耕地质量评价与提升研究。

【托福英语】地质学

TPO1-L2 确定石头年代的方法TPO4 L3 ＊＊沙漠类生词预览1.Death Valley死谷：加利福尼亚州东部和内华达州西部的干旱盆地。

西半球低于海平面86米(282英尺）的最低点位于该谷2.bulldozer ['buldәuzә] n. 推土机3.slippery ['slipәri] a. 滑的, 光滑的, 靠不住的, 圆滑的, 不稳固的4.vibrate ['vaibreit] vi. 振动, 颤动, 激动, 摇摆, 踌躇5.tilt [tilt] n. 倾斜, 倾向, 船篷, 车篷vt. 使倾斜6.magnetic [mæg'netik] a. 有磁性的, 有吸引力的, 催眠术的7.eliminate [i'limineit] vt. 除去, 排除, 剔除, 消除8.meteorology [,mi:tiә'rɒlәdʒi] n. 气象学, 气象状态9.prospective [prәs'pektiv] a. 预期的, 将来的10.physics ['fiziks] n. 物理学, 物理过程, 物理现象6. What does the professor mainly discuss?A. His plans for research involving moving rocks.B. A difference between two geological forces that cause rocks to move.C. Theories about why desert rocks move.D. Reasons why geologists should study moving rocks.7. According to the professor, what have the researchers agreed on?A. The rocks cannot move after ice storms.B. The rocks do not move at night.C. The rocks never move in circles.D. The rocks are not moved by people.8. The professor mentions experiments on the wind speed necessary to move rocks. What is the professor’s attitude toward theexperiments?A. Their results were decisive.B. They were not carried out carefully.C. They were not continued long enough to reach a conclusion.D. The government should not have allowed the experiments.9. What important point does the professor make about the area where the rocks are found?A. It has been the site of Earth’s highest wind speeds.B. It is subject to laws that restrict experimentation.C. It is accessible to heavy machinery.D. It is not subject to significant changes in temperature.10. What is the professor’s purpose in telling the students about moving rocks?A. To teach a lesson about the structure of solid matter.B. To share a recent advance in geology.C. To give an example of how ice can move rocks.D. To show how geologists need to combine information from several fields.11. Replay: What does the professor imply when he says this:A. The movement pattern of the rocks was misreported by researchers.B. The rocks are probably being moved by people.C. The movement pattern of the rocks does not support the wind theory.D. There must be differences in the rocks’ composition.TPO6 L4 ＊＊＊1.Sahara Desert 撒哈拉沙漠：北非的一个大沙漠，东至大西洋沿岸，西至尼罗河河谷，南至亚特拉斯山脉，北至苏丹境内。

Spatial and temporal distribution of PAHs in sediments from Daya Bay

Spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs)in sediments from Daya Bay,South ChinaWen Yan a ,*,Jisong Chi b ,Zhiyuan Wang a ,Weixia Huang a ,Gan Zhang caCAS Key Laboratory of Marginal Sea Geology,South China Sea Institute of Oceanology,Chinese Academy of Sciences,164West Xingang Road,Haizhu,Guangzhou 510301,China bGuiyang Environmental Monitoring Central Station,Guiyang 550002,China cState Key Laboratory of Organic Geochemistry,Guangzhou Institute of Geochemistry,CAS,Guangzhou 510640,ChinaA survey of sediments from Daya Bay serves as a baseline study for levels,distributions and possible sources of PAHs in surface sediments and both core sediments.a r t i c l e i n f oArticle history:Received 14October 2008Received in revised form 22January 2009Accepted 25January 2009Keywords:PAHsSediment Daya Baya b s t r a c tThe spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs)has been investigated in Daya Bay,China.The total concentration of the 16USEPA priority PAHs in surface sediments ranged from 42.5to 158.2ng/g dry weight with a mean concentration of 126.2ng/g.The spatial distribution of PAHs was site-speciﬁc and combustion processes were the main source of PAHs in the surface sediments.Total 16priority PAH concentration in the cores 8and 10ranged from 77.4to 305.7ng/g and from 118.1to 319.9ng/g respectively.The variation of the 16PAH concentrations in both cores followed the economic development in China very well and was also inﬂuenced by input pathways.Some of the PAHs were petrogenic in core 8while pyrolytic source was dominant in core 10.In addition,pyrolytic PAHs in both cores were mainly from the coal and/or grass and wood combustion.Ó2009Elsevier Ltd.All rights reserved.1.IntroductionPolycyclic aromatic hydrocarbons (PAHs)are an important class of persistent organic pollutants (POPs).They are primarily derived from incomplete combustion of fossil fuels and burning of vege-tation and other organic materials (Yunker et al.,2002).The derivatives from crude oil seepage and diagenesis of organic matter in anoxic sediments are also the important PAHs’sources (Lima et al.,2005).PAHs are introduced into the environments via various routes and are ubiquitous environmental pollutants.They have been detected widespreadly in various environmental media,such as organism (Liang et al.,2007),atmosphere (Qi et al.,2001),water (Zhou and Maskaoui,2003),soils (Mielke et al.,2001),and sedi-ments (McCready et al.,2000).Because of their potentially hazardous properties,persistence and prevalence in the environ-ments,the efforts have been made to reduce PAH emission in many countries,for example,16of PAHs have been listed as priority control pollutants by the Environmental Protection Agency of the USA (Manoli et al.,2000).The marine sediment is one of the most important reservoirs of environmental pollutants (Voorspoels et al.,2004;Yang et al.,2005).Contaminated sediments can directly affect bottom-dwelling organisms.Moreover,once disturbed,the sediment can be resuspended and the contaminants would reenter the marine aquatic environment and circulate in ecosystems,resulting in second contamination (Zeng and Venkatesan,1999).Thus,the contaminated sediments represent a continuing source for toxic substances in aquatic environments that may affect wildlife and humans via the food chain (Kannan et al.,2005).Therefore,the distribution and fate of contaminants,such as heavy metals,PAHs,OCPs,and PCBs in coastal sediments have provoked considerable concern and have been largely documented.In recent decades,the Pearl River Delta,located in Southern China,has become one of the rapidest developing regions in China.The rapid economic development,however,has caused serious pollution problems,which have adversely affected the air (Qi et al.,2001)and water quality (Yang et al.,1997)in the region.The persistent organic pollutants in environment of the Pearl River Delta have been well documented (Fu et al.,2003).As one of the largest Bays in the South Sea,Daya Bay is located in the region and is one of the main aquacultural areas in the Guangdong Province thanks to its rich biological resources.In order to understand and assess the impact of contaminants on the aquatic ecosystem of Daya Bay,constant efforts are much needed to determine the distribution and fate of possible pollutants in the Bay.There are several studies which have analyzed levels of POPs in the water,*Corresponding author.Tel.:þ862089023150;fax:þ862089023121.E-mail address:wyan@ (W.Yan).Contents lists available at ScienceDirectEnvironmental Pollutionjournal homepage:/locate/envpol0269-7491/$–see front matter Ó2009Elsevier Ltd.All rights reserved.doi:10.1016/j.envpol.2009.01.023Environmental Pollution 157(2009)1823–1830surface sediment,and aquatic organisms of Daya Bay (Zhou et al.,2001;Zhou and Maskaoui,2003).A previous survey indicated that the mean concentration of total PAHs in surface sediment was 481ng/g (Zhou and Maskaoui,2003).However,the samples collected from the sediment in that survey were only focused on surface sediment,so the information for wholly assessing the contamination levels of the sediment of Daya Bay arose by PAHs was limited.The present study aimed to carry out a survey of sediments of Daya Bay to determine the concentration levels and distribution of selected PAHs.So fourteen surface sediment samples were collected for analysis to demonstrate the spatial distribution of PAHs in Daya Bay,and two sedimentary cores were collected for analysis to examine the temporal distribution of PAHs and to evaluate and reconstruct historical records of PAHs in recent decades.2.Materials and methods 2.1.Chemicals and reagentsA standard solution of the 16USEPA priority PAHs [naphthalene (Nap),ace-naphthylene (Acy),acenaphthene (Ace),ﬂuorine (Fl),phenanthrene (P),anthracene (Ant),ﬂuoranthene (Flu),pyrene (Pyr),benzo[a]anthracene (BaA),chrysene (Chr),benzo[b]ﬂuoranthene (BbF),benzo[k]ﬂuoranthene (BkF),benzo[a]pyrene (BaP),indeno[1,2,3-c,d]pyrene (InP),dibenzo[a,h]anthracene (DBA),and benzo[g,h,i]per-ylene (BgP)],and a mixture solution of the surrogate standards perdeuterated PAHs (naphthalene-d 8,acenaphthene-d 10,phenanthrene-d 10,chrysene-d 12,and perylene-d 12)were purchased from Ultra Scientiﬁc,Inc.(North Kingstown,RI,USA).Neat (99%)hexamethylbenzene was obtained from Aldrich Chemical Company (Mil-waukee,WI,USA).A standard reference material (SRM 1941)was purchased from National Institute of Standards and Technology (NIST,Gaithersburg,MD,USA).All solvents used for sample processing and analyses (dichloromethane,acetone,hexane and methanol)were analytical grade and redistilled twice before use.The Silica gel (80–100mesh)and alumina (120–200mesh)were extracted for 72h in a Soxhlet apparatus,activated in the oven at 150 C and at 180 C for 12h,respec-tively,and then deactivated with distilled water at a ratio of 3%(m/m).Deionised water was taken from a Milli-Q system.2.2.Environmental sample collectionSurface sediment samples were taken with a grab sampler in November of 2003and the locations of sampling stations are shown in Fig.1.The top 1-cm layers were carefully removed with a stainless steel spoon for subsequent analysis.Two sedi-ment cores of about 40cm were also collected at sites 8and 10in the same time,and then sliced at 1-cm intervals.A stainless steel static gravity corer (8cm i.d.)was employed to minimize the disturbance of the surface sediment layer.All the samples were packed into aluminum boxes and immediately stored at À20 C until required.2.3.Measurement of TOC of the sedimentsFreeze-dried samples were ground,and then carbonate was removed by treat-ment of sample with 10%(v/v)HCl.After the samples were dried at 60 C in an oven the content of total organic carbon (TOC)of sediment was measured by an Ele-mentar Vavio EL III elemental analyzer (Hanau,Germany).2.4.Dating of the sedimentary coresThe procedure of sediment dating has been described in detail elsewhere (Zhang et al.,2002).In summary,the 210Pb activities in sediment subsamples were deter-mined by analysis of the a -radioactivity of its decay product 210Po,on the assumption that the two are in equilibrium.The Po was extracted,puriﬁed,and self-plated onto silver disks at 75–80 C in 0.5M HCl,with 209Po used as yield monitor and tracer in quantiﬁcation.Counting was conducted by computerized multi-channel-spectrometry with gold–silicon surface barrier detectors.Supported 210Po was obtained by indirectly determining the a -activity of the supporting parent 226Ra,which was carried by coprecipitated BaSO 4.A constant activity model of the 210Pb-dating method was applied to give average sedimentation rates for the sedi-mentary cores (Allen et al.,1993).114°30'114°35'114°40'114°45'114°50'E22°30'22°35'22°40'22°45'22°50'NN121079611812515414133D a y a B a yFig.1.Map of Daya Bay showing the locations where samples weretaken.Table 1Total 16EPA priority PAHs.W.Yan et al./Environmental Pollution 157(2009)1823–183018242.5.Extraction procedureSediment samples were homogenized and freeze-dried before extracting.About 5g of dried and homogenized sediment samples were extracted for 72h in a Soxhlet apparatus with 150ml dichloromethane.A mixture of deuterated PAH compounds (naphthalene-d 8,acenaphthene-d 10,phenanthrene-d 10,chrysene-d 12,and perylene-d 12)as recovery surrogate standards was added to all the samples prior to extraction.Activated copper granules were added to the collection ﬂask to remove elemental sulfur.After extraction,the extract was concentrated up to a volume of about 2–3ml and solvent-exchanged into 10ml n-hexane which further reduced to approxi-mately 1–2ml with a rotary vacuum evaporator.A 1:2alumina/silica gel column was used to clean-up and fractionate the extract.The ﬁrst fraction,containing aliphatic hydrocarbons,was eluted with 15ml of hexane.The second fraction containing PAHs was collected by eluting 60ml of hexane/dichloromethane (1:1).The PAH fraction was then concentrated up to 1ml by rotary vacuum evaporator and further to 0.2ml under a gentle gas stream of puriﬁed nitrogen.A known quantity of hex-amethylbenzene was added as an internal standard prior to gas chromatography–mass spectrometer (GC–MS)analysis.2.6.GC–MS analysisGC–MS analysis was carried out on a Hewlett–Packard 5890series gas chro-matograph/5972mass spectrometer in the selective ion monitoring (SIM)mode or in scanning mode.An HP-5fused silica capillary column (50m,0.32mm,0.17m m)was used for separation.Helium was used as carrier gas at a ﬂow rate of 2ml/min with a head pressure of 12.5psi,and a linear velocity of 39.2cm/s at 290 C.The injection and interface temperature were maintained at 290 C.Oven temperaturewas initially isothermal at 80 C for 5min,and then ramped from 80to 290 C at a rate of 3 C/min,and then kept isothermal at 290 C for 30min.A 1m l sample was manually injected in the splitless injector with a 1min solvent delay.Mass spectra were acquired at electron impact (EI)mode under 70eV.The mass scanning ranged between m /z 50and m /z 500.2.7.Quality control and quality assuranceAll analytical data were subject to strict quality control.Method blanks (solvent),spiked blanks (standards spiked into solvent),sample duplicates,and a National Institute of Standards and Technology (NIST)standard reference material (SRM 1941)sample were processed.PAHs were quantiﬁed using the internal calibration method based on ﬁve-point calibration curves for individual compounds.The reported results were corrected with the recoveries of the surrogate standards.The surrogate recoveries were 53.26Æ7.47%for naphthalene-d 8,75.9Æ10.66%for ace-naphthene-d 10,89.42Æ8.78%for phenanthrene-d 10,96.75Æ9.61%for chrysene-d 12,and 89.56Æ12.97%for perylene-d 12with surface sediment samples,and were 64.12Æ15.6%for naphthalene-d 8,73.1Æ16.8%for acenaphthene-d 10,90.5Æ16.9%for phenanthrene-d 10,87.7Æ21.56%for chrysene-d 12,and 95.76Æ15.48%for per-ylene-d 12with sediment core 8samples,and were 43.08Æ18.56%for naphthalene-d 8,67.89Æ19.25%for acenaphthene-d 10,102.5Æ6.19%for phenanthrene-d 10,94.58Æ10.87%for chrysene-d 12,and 85.95Æ13.78%for perylene-d 12with sediment core 10samples.Recoveries of all the PAHs in the NIST 1941sample were between 80and 120%of the certiﬁed values.Nominal detection limits ranged from 0.2to 2.0ng/g.3.Results and discussionThe results of TOC levels and sedimentation rates were pre-sented in the Supplementary information .ThefollowingTable 20%20%40%60%80%100%12345678910111213146-rings5-rings 4-rings 3-rings 2-ringsStationA b u n d a n c eFig.2.Distribution of 2-,3-,4-,5-,6-ring in the surﬁcial sediments from DayaBay.L M W /H M W1.52.0MP/P0.51.01.52.0Petrogenic sourcesPyrolytic origins0.51.0Fig.3.Plot of the isomeric ratios LWM/HWM vs MP/P.W.Yan et al./Environmental Pollution 157(2009)1823–18301825interpretation and discussion will be focused on the distribution and sources of PAHs in the surface sediments and sediment cores.3.1.Total concentrations and distribution of PAHs in the surface sediments in Daya BaySurface sediments can reﬂect the current sediment contaminant status.The concentrations of the polycyclic aromatic hydrocarbons (PAHs)in surface sediments are summarized in Table 1.As shown in Table 1,the total concentration of the 16USEPA priority PAHs in surface sediments ranged from 42.5to 158.2ng/g dry weight with a mean concentration of 126.2ng/g.The total concentrations of PAHs at most stations are of the same order of magnitude except the stations 7and 14,at which the lowest PAHs’concentration was found.The spatial distribution of PAHs in the surface sediment was site-speciﬁc.Station 7is located in the middle east of the Daya Bay and far away from coast,whilst those stations at which relatively high concentrations of PAHs were found are located on aquaculture area and near densely polluted area (stations 3,8and 13),or close to the Daya nuclear power station (stations 5and 6)or to Yihe harbour (station 10).This indicated that the amount of PAHs detected is possibly related to urban runoffs,and sewagedischarges.In addition,the TOC is also one important factor that controls the levels of PAHs in the sediments.The relatively low concentrations of PAHs at stations 7and 14could also be related to the low TOC there.A linear regression analysis showed that the total concentrations of PAHs in the surface sediments were corre-lated to the sediment organic carbon contents with p ¼0.56.If we excluded stations 1and 9,a signiﬁcantly positive correlation (p ¼0.87)between PAH concentrations and TOC was obtained.The relatively high concentration of PAHs at stations 1and 9with lower TOC might suggested that there were other factors,such as non-point sources that affected the levels of PAHs pared with the previous studies in Daya Bay when the total concentrations of 16PAHs in sediment ranged from 115ng/g to 1134ng/g,with a mean concentration of 481ng/g (Zhou and Maskaoui,2003),the decreased levels suggest possible decreased inputs of PAHs from sources such as urban runoffs,sewage discharges.A comparison of PAHs’concentrations in surface sediment collected from different estuaries and bays is given in Table 2.The PAH concentrations in surface sediment from Daya Bay in the study are similar to those detected in Kyeonggi Bay,Korea (Kim et al.,1999),Northwestern Black Sea (Maldonado et al.,1999),Shenzhen Bay (Connell et al.,1998),South China Sea (Yang,2000),and Todos Santos Bay,Mexico (Macias-Zamora et al.,2002),but lower than others.To place the current concentrations of PAHs into an ecological perspective,we compared threshold effect concentrations (Long et al.,1995)with the surface sediment concentrations determined for the Bay.Concentrations of total PAHs in surface sediments of Daya Bay were far less than the threshold concentrations,sug-gesting that the probability of negative toxic effective caused by PAHs alone would be low.3.2.PAH composition and sources in the surface sedimentsThe composition pattern of PAHs by ring size in the surface sediment is shown in Fig.2.As shown in Fig.2,4-ring PAHs are most abundant,which is consistent with previous observation (Zhou and Maskaoui,2003).In addition,5-ring PAHs take second ually,high-molecular-weight PAHs predominated in sediment samples.The higher concentration of high-molecular-A n t /178Ant/178CombustionPetroleumPetroleum Petroleum Grass, Wood & CoalB a A /228CombustionMixed SourcesPetroleum00.10.20.30.10.20.30.40.5Flu/Flu+Pyr0.10.30.50.7I n P /I n P + B g PInP/InP + BgP Grass, Wood & CoalCombustionPetroleum CombustionPetroleumFig.4.Plot of isomeric ratios BaA/228,InP/InP þBgP,and Ant/178vs Flu/Flu þPyr.Table 3Total 16EPA priority PAHs.W.Yan et al./Environmental Pollution 157(2009)1823–18301826weight PAHs than that of low-molecular weight PAHs has been commonly observed in sediments from river and marine environ-ments (Magi et al.,2002;Guo et al.,2007a ).Based on characteristics in PAH composition and distribution pattern,the sources of anthropogenic PAHs,which are formed mainly via combustion processes and release of uncombusted petroleum products,can be distinguished by ratios of individual PAH compounds.Of anthropogenic PAHs,the lower-molecular-weight parent PAHs and alkylated PAHs have both petrogenic and combustion (low-temperature pyrolysis)sources,whereas the high-molecular parent PAHs have a predominantly pyrolytic source (Mai et al.,2002).Therefore,lower LWM/HWM (low-molecular-weight parent PAHs (2and 3rings PAHs)/high-molecular-weight parent PAHs (4,5,and 6ring PAHs except perylene))and MP/P (methylphenanthrene/phenanthrene)ratio are observed in the pyrolytic source.In general,a ratio of LWM/HWM <1suggests a pollution of pyrolytic origin (Magi et al.,2002;Soclo et al.,2000).An MP/P ratio less than 1is generally found in combustion mixtures,and a ratio between 2and 6presents in unburned fossil fuel mixtures (Zakaria et al.,2002;Youngblood and Blumer,1975).Besides the ratio of LWM/HWM and MP/P,PAH isomer pairs’ratios,such as Ant/178,Flu/Flu þPyr,BaA/228,and InP/BgP,have been developed for interpreting PAH composition and inferring possiblesources (Katsoyiannis et al.,2007;Bra¨ndli et al.,2007;Yunker et al.,2002).An Ant/178ratio <0.1usually is taken as an indication of petroleum while a ratio >0.1indicates a dominance ofcombustion;5,6-ring PAHs4-ring PäHs2,3-ring PAHsTotal PAHsperyleneY e a r1945195519651975198519952005Y e a r1945195519651975198519952005Fig.5.Down-core concentration variations of total PAHs,2,3-ring PAHs,4-ring PAHs,5,6-ring PAHs,and perylene in both cores 8and 10.W.Yan et al./Environmental Pollution 157(2009)1823–18301827Flu/Flu þPyr ratio <0.4is attributed to petrogenic source,ratio >0.5is suggested wood and coal combustion,while between 0.4and 0.5is characteristic of petroleum combustion;ratio of BaA/228<0.2implies petroleum,from 0.2to 0.35indicates either petroleum or combustion,and >0.35means pyrolytic origin;InP/BgP ratio less than 0.2is corresponded to petroleum pollution,higher than 0.5grass,wood or coal combustion,and between 0.2and 0.5petroleum combustion (Bra¨ndli et al.,2007;Yunker et al.,2002).In order to survey the sources of PAHs in the surface sediments from Daya Bay,LWM/HWM against MP/P (Fig.3),and BaA/228,InP/BgP,and Ant/178against Flu/Flu þPyr were plotted (Fig.4).As shown in Fig.3,the ratios of LWM/HWM and MP/P were below 1,suggesting a pyrolytic origin.This kind of source is conﬁrmed by three other parameters,BaA/228(BaA/BaA þChr),Flu/Flu þPyr and InP/InP þBgP,which were ranged from 0.39to 0.46,from 0.56to 0.69,and from 0.56to 0.66respectively (Fig.4).However,some Ant/178ratios were less than 0.1,suggesting that the surface sediments were also contaminated by petrogenic PAHs.Normally,pyrolytic PAHs were mainly from the coal,grass and wood combustion and/or petroleum combustion.As shown in Fig.4,the ratios of Flu/Flu þPyr and InP/InP þBgP were all higher than 0.5,indicating biomass and coal combustion sources of pyrolyticPAHs.01219451955196519751985199520050.10.250.250.350.75MP/P Ant/178BaA/228LWM/HWMFlt/Flt+PyrInP/InP+BgPY e a rcore-81945195519651975198519952005MP/P Ant/178BaA/228LWM/HWMFlt/Flt+PyrInP/InP+BgPY e a rcore-10Fig.6.LWM/HWM,MP/P,BaA/228,InP/InP þBgP,Ant/178and Flu/Flu þPyr proﬁles for source identiﬁcation in both cores 8and 10.I:combustion;II:petroleum;III:petroleum combustion;IV:grass,wood &coal combustion;V:mix.W.Yan et al./Environmental Pollution 157(2009)1823–18301828Besides anthropogenic PAHs,natural PAH(perylene)was also found widely in a variety of marine,lacustrine,riverine sediments (Luo et al.,2006;Chen et al.,2006;Liu et al.,2008).Perylene is a diagenetic product derived from its natural precursors during early diagenesis,while only small amounts of perylene are produced during combustion(Silliman et al.,1998;Luo et al.,2006). Relative concentrations of perylene higher than10%of the total penta-aromatic isomers suggest a probable diagenetic input, otherwise a probable pyrolytic origin of the compound is indicated (Baumard et al.,1998b)In the present study,perylene occurred at elevated levels(10.14–82.68ng/g)and was the most predominant component of PAHs in our study area.The percentage of perylene over the penta-aromatic isomer was from41%to79%,indicatinga diagenetic input of perylene in sediments.3.3.Concentrations and time trends of PAHs in the sediment proﬁlesAnalytical results of PAH concentrations for cores8and10 sediments are summarized in Table3.Total16priority PAH concentrations in the core8sediments in this study ranged from 77.4to305.7ng/g with a mean value of92.1ng/g,while in the core 10sediments ranged from118.1to319.9ng/g with a mean value of 210.2ng/g.In terms of individual PAH composition,the compound of Phe is the most abundant in both cores.Other compounds,such as Nap,Flu and B(b)Flu,are posteriorly abundant.Perylene, a natural PAH,is also very abundant in the core8and core10,and its concentrations ranged from27.7to51.0ng/g with a mean value of41.2ng/g in the core8sediments,ranged from29.5to92.2ng/g with a mean value of67.4ng/g in the core10sediments.Fig.5shows the down-core concentration variations of total PAHs,2,3-ring,4-ring,5,6-ring(excluding perylene),and perylene in both sediment cores collected from Daya Bay.In core8,the total PAHs’concentration experienced two obvious peak-time periods in the1950s and1990s respectively.From the early1960s to the mid-1980s,the values of the total PAHs’concentrations showed widely ﬂuctuating.In core10,the concentrations of PAHs generally showed relatively constant with a sharp rebound in the surﬁcial slice.It is noticeable that an obvious peak-time period was observed in the late1980s.In the meantime,two high concentra-tions of total PAHs were identiﬁed in the1950s and1960s.The variation of the16PAHs’concentrations in cores8and10 from the founding of the People’s Republic of China in1949fol-lowed the economic development in China very well.The PAHs are good indicators of anthropogenic activities.Researchers have reported that the concentration of PAHs is proportional to the socioeconomic status of the country/region from which the samples were taken(Liu et al.,2005;Guo et al.,2007b).Theﬁrst peak-time period in1950s may correspond to the rapid economic development in theﬁrst Five-Year-Plan(1951–1955)after the founding of the People’s Republic of China,and the second peak-time period in late1980s or1990s may reﬂect the rapid economic growth and urbanization since the economic reform in the country in the late1970s(Liu et al.,2005).Theﬂuctuation of the total PAHs’concentrations in the1960s and1970s could be attributed to the social turbulence and confusion which let to theﬂuctuation of the country’s agricultural and industrial production.It is also noticeable that the sedimentary record of PAHs in sediment core8is more ﬂuctuating than that in sediment core10,which indicates that the localization of the input sources played a very important role in PAH contamination.As stated above,the station8is close to the coast and near densely populated area,while the station10is located far away from the coast.So it is considered that much more sewage efﬂuents and surface runoff were inputted into the area around the station8than into the station10.In the mean while,it is observed that the variation of the TOC content in the sediment core 10corresponded to the vertical distribution of PAH contamination level.This implies that the properties of the sediment such as organic carbon would also inﬂuence the vertical distribution and concentration of PAHs in sediment core10.3.4.Source of PAHs in sediment coresFig.6illustrates proﬁles of some PAH indicators including LWM/ HWM,MP/P,Ant/178,Flu/FluþPyr,BaA/228,InP/BgP in sediment cores collected from Daya Bay.In core8,ratios of LWM/HWM and Ant/178were0.66–1.96and0.06–0.20respectively,indicating that some of the PAHs were petrogenic(Magi et al.,2002;Soclo et al., 2000;Yunker et al.,2002).However,most values of MP/P in the core were below1,suggesting that pyrolytic source was dominant (Zakaria et al.,2002;Youngblood and Blumer,1975).In core10, ratios of LWM/HWM and MP/P were less than1,and most of An/178 values were less than0.1,suggesting that pyrolytic source was dominant in the site(Magi et al.,2002;Soclo et al.,2000;Zakaria et al.,2002;Youngblood and Blumer,1975).In addition,as shown in Fig.6,most of the values of Flu/FluþPyr,BaA/228and InP/InPþBgP were higher than0.5,0.35and0.5respectively,implying that pyrolytic PAHs in both cores were mainly from the coal and/or grass and wood combustion(Katsoyiannis et al.,2007;Bra¨ndli et al., 2007;Yunker et al.,2002).AcknowledgementsThe research work wasﬁnancially supported by Science Foun-dation of Guangdong Province(Grant No.06023662),and Planning Project of Guangdong Province(Grant No.2003C32804and No. 2006B36601005).The authors wish to thank Linli Guo,Guoqing Liu, Xiang Liu,Shichun Zhou and Kechang Li for their kindly help in sample collection and treatment and GC–MS analysis. Appendix.Supplementary dataSupplementary data associated with this article can be found,in the online version,at doi:10.1016/j.envpol.2009.01.023. ReferencesAllen,J.R.L.,Rae,J.R.,Longworth,G.,Hasler,S.E.,Ivanovich,H.,1993.A comparison of the210Pb dating technique with three other independent dating methods in an oxic estuarine salt-marsh sequence.Estuaries16,670–677.Baumard,P.,Budzinski,H.,Garrigues,P.,1998a.PAHs in Arcachon Bay,France:origin and biomonitoring with caged organisms.Marine Pollution Bulletin36,577–586.Baumard,P.,Budzinski,H.,Mchin,Q.,Garrigues,P.,Burgeot,T.,Bellocq,J.,1998b.Origin and bioavailability of PAHs in the Mediterranean Sea from mussel and sediment records.Estuarine,Coastal and Shelf Science47,77–90.Bra¨ndli,M.,Bucheli,T.D.,Kupper,T.,Mayer,J.,Stadelman,F.X.,Taradellas,J.,2007.Fate of PCBs,PAHs and their source characteristic ratios during composting and digestion of source-separated organic waste in full-scale plants.Environmental Pollution148,520–528.Chen,S.,Luo,X.,Mai,B.,Sheng,G.,Fu,J.,Zeng,E.Y.,2006.Distribution and mass inventories of polycyclic aromatic hydrocarbons and organochlorine pesticides in sediments of the Pearl River Estuary and the Northern South China Sea.Environmental Science and Technology40,709–714.Connell,D.W.,Wu,R.S.S.,Richardson,B.J.,1998.Occurrence of persistent organic contaminants and related substances in Hong Kong marine area:an overview.Marine Pollution Bulletin36,376–384.Fu,J.,Mai,B.,Sheng,G.,Zhang,G.,Wang,X.,Peng,P.,Xiao,X.,Ran,Y.,Cheng,F., Peng,X.,Wang,Z.,Tang,U.W.,2003.Persistent organic pollutants in environ-ment of the Pearl River Delta,China:an overview.Chemosphere52,1411–1422. Fu,J.,Wang,Z.,Mai,B.,Kang,Y.,2001.Field monitoring of toxic organic pollution in the sediments of Pearl River Estuary and its tributaries.Water Science and Technology43,83–89.Guo,W.,He,M.,Yang,Z.,Lin,C.,Quan,X.,Wang,H.,2007a.Distribution of polycyclic aromatic hydrocarbons in water,suspended particulate matter and sediment from Daliao River watershed,China.Chemosphere68,93–104.Guo,Z.,Lin,T.,Zhang,G.,Zheng,M.,Zhang,Z.,Hao,Y.,Fang,M.,2007b.The sedi-mentaryﬂuxes of polycyclic aromatic hydrocarbons in the Yangtze RiverW.Yan et al./Environmental Pollution157(2009)1823–18301829。

面向疾病的空间聚集性与影响因素分析方法

2097-3012(2024)01-0065-09 Journal of Spatio-temporal Information 时空信息学报收稿日期: 2022-06-30；修订日期: 2023-12-15 基金项目: 国家自然科学基金（42201490）作者简介: 胡涛，研究方向为时空大数据分析与可视化。

E-mail:*****************通信作者: 王丽娜，研究方向为地理信息可视化、疾病制图。

E-mail:***************面向疾病的空间聚集性与影响因素分析方法胡涛1，王丽娜2，李响1，张正斌3，俞鑫楷11. 信息工程大学地理空间信息学院，郑州450052；2. 郑州轻工业大学计算机科学与技术学院，郑州 450001；3. 武汉市结核病防治所结核病控制办公室，武汉430030摘要：疾病的发生与自然环境、社会环境和人群特点密切相关，其发生与流行通常具有一定的空间分布特征。

目前在疾病空间聚集特征与影响因素的已有研究中缺少两者关联关系的探讨，以及空间尺度多集中于省、市和县域，因此，本研究提出一种面向疾病空间聚集性与影响因素分析的方法。

以武汉市的历史肺结核数据为例，进行基于乡镇尺度的肺结核发病率数据及影响因素数据的处理与整合，基于空间自相关方法分析2011年、2013年和2015年肺结核空间聚集情况；并运用地理探测器探测肺结核发病率空间分布的影响因素及交互作用，探究肺结核空间聚集的成因。

结果表明：肺结核热点聚集乡镇主要分布在新洲区、江夏区和蔡甸区，冷点聚集乡镇主要分布在洪山区；植被指数、人口密度、人均GDP 及五类兴趣点密度（医疗保健类、生活服务类、餐饮类、住宅类和农林牧渔类）为肺结核发病率空间分布的主要影响因素，其交互作用对肺结核发病率影响显著增强。

研究成果可为武汉市肺结核防治提供科学参考。

关键词：肺结核；空间聚集性；空间自相关；地理探测器；兴趣点引用格式：胡涛, 王丽娜, 李响, 张正斌, 俞鑫楷. 2024. 面向疾病的空间聚集性与影响因素分析方法. 时空信息学报, 31(1): 65-73Hu T, Wang L N, Li X, Zhang Z B, Yu X K. 2024. Analysis method for disease-oriented spatial clustering and influencing factors. Journal of Spatio-temporal Information, 31(1): 65-73, doi: 10.20117/j.jsti.2024010091 引言计算机科学、地理信息系统和空间分析技术快速发展，为挖掘多维、海量的疾病数据提供了坚实的技术基础，并广泛应用于流行病的预警、聚类分析、疾病制图等方面（施迅和王法辉，2016；李杰等，2020；陈曦和闫广华，2021）。

时空依赖英语表述

时空依赖英语表述Temporal and Spatial Dependencies.Temporal and spatial dependencies are two fundamental concepts that underlie our understanding of the interconnectedness and evolving nature of phenomena in various domains, ranging from physics to social sciences. These dependencies refer to the relationships between events or objects that are influenced by time and space, respectively.Temporal dependency is the relationship between events or observations that occur at different points in time. It encapsulates the idea that what happens at one time can influence what happens at another time. This is a crucial consideration in areas like meteorology, where the weather patterns of today can inform predictions for tomorrow. In the realm of finance, temporal dependencies are essential for understanding how market trends evolve over time, influencing investment decisions. Similarly, inneuroscience, temporal dependencies underlie our understanding of how neural activity patterns change over time, leading to the perception of motion or the processing of information.Spatial dependency, on the other hand, refers to the relationships between events or objects that are influenced by their physical proximity or location. This concept is central to fields like geography, where spatial patterns of population distribution, resource availability, and environmental factors influence regional development. In ecology, spatial dependencies are key to understanding how species interactions and habitats are distributed across landscapes. Urban planning also relies heavily on spatial dependencies, as they determine how cities grow, the flow of traffic, and the distribution of services.Temporal and spatial dependencies often coexist and intersect in complex systems. For instance, in climate science, changes in temperature and precipitation patterns over time are influenced by spatial factors like the distribution of land masses, ocean currents, and elevation.In social networks, the spread of information or trends can be influenced by both temporal factors like the time of day or week and spatial factors like the geographic location of users.The analysis of temporal and spatial dependencies requires sophisticated statistical techniques and models. Time series analysis, for instance, is a widely used method for studying temporal dependencies by examining how variables change over time. Spatial analysis techniques, such as geographic information systems (GIS) and spatial statistics, allow researchers to identify patterns and relationships between events or objects based on their spatial arrangement.In conclusion, temporal and spatial dependencies are fundamental to our understanding of the world. They underlie the interconnectedness of events and objects, shaping the evolution of systems and influencing our decisions and actions. As we continue to explore and model these dependencies, we gain deeper insights into thecomplexity of the world and the ability to make more informed predictions and decisions.。

EI_05981000

Analyzing Spatio-temporal Distribution of Crime Hot-spots and Their Related Factorsin Shanghai, ChinaZhanhong Wang, Jianping Wu, Bailang Yu*Key Laboratory of Geographical Information Science,East China Normal University,Shanghai, China*Corresponding author: blyu@Abstract—As an analysis method for temporal and spatial variations, the hot spot analysis is an effective way to reveal the implied relationship among events. Crime hot spots analysis is fundamentally important for the public safety, and can contribute to combat and prevent crime. In this study, the monthly hotspots of thefts and robberies in Shanghai in 2009 are analyzed and mapped by using the hotspot analysis tool of ArcGIS 9.3. The spatio-temporal variations of hotspots for those two types of crime are identified. In order to find their related factors, the Principal Component Analysis (PCA) is adopted to investigate the 18 indicators (e.g. resident population density and floating population density) involved in the crime distribution. The main factors related to the crime hot spots are discussed. The spatio-temporal variations of crime hot spots would benefit the decision-making for combating and preventing urban crime.Keywords-crime hot spot; hot spot analysis; spatial-temporal distribution; principal component analysisI.I NTRODUCTIONIn a specific urban region, crime is not distributed randomly, but showed the spatial and temporal distribution characteristics owing to the inevitable connection with region population, environment, economic, policy, and social factors. Controlling the spatial-temporal distribution is fundamentally important to fight against crime and improve the public trust on the social security [1]. Among the numerous spatial-temporal analyses, the hot spot analysis is an effective tool to understand the implied relationship among events. Using the hot-spot analysis we can make a regression analysis and a prognosis of events. Therefore, researchers will draw a scientific conclusion about crime control and prevention combined with the hot spot analysis.At the present time, research of the hot spot analysis has been based on the following aspects: grid [2], division of technology [3], density technology [4], space scanning technology [5], support vector machine technology [6], hierarchical clustering [7] and spatial autocorrelation. All of these methods have their own characteristics, especially the spatial autocorrelation, which can not only detect hot spots, but also reveal the links between events, so this study used self-correlation analysis method.In this paper, the monthly hotspots of thefts and robberiesin Shanghai in 2009 are analyzed and mapped, and then the Principal Component Analysis (PCA) are adopted to investigate the 18 indicators (e.g. resident population densityand floating population density) to find the main related factorsof the crime hot spots.For the purpose of this study, we hope it will be helpful to provide the decision-making guides and references for the rational distribution of crime prevention measures in space.Thus we can change the previous passive situation of crime prevention and strengthen the management on key areas andkey indicators initiatively.II.S TUDY A REA AND M ETHODSA.Study Area and DataShanghai, which is located in the Yangtze River delta front,is the economic, financial, trade and shipping center of China.The whole city total area are about 6340 square kilometers, the resident population is more than 1900 million. It has a total of19 districts and counties, including the Central City Core Area (Huangpu, Jingan, Luwan, Hongkou), the Central City EdgeZone (Yangpu, Zhabei, Putuo, Xuhui, Changning), the Suburban Areas (Qingpu, Songjiang, Jiading, Minhang, Baoshan, Pudong), and the Outer Suburbs (Jinshan, Fengxianand Chongming). The unit of data processing and statistics isthe police station area. There are 441 police station areas in Shanghai totally.Spatial data are from the Shanghai Police G eographic Information System, including the district and police station range. Cases data is from the Shanghai Police Cases Online Information System. Based on the study purpose, we focus onthe data of robberies and thefts in 2009, and count them according to the police station. Population and housing data isfrom the Shanghai Population Information Management System, including the household population, floating population, foreign Population, rental housing, spare rooms, room, dormitories, and campsites. Entertainment and leisure places data is from the Shanghai Police Station Integrated Information System. The above data is extracted, transformed,and loaded through the ETL tool.978-1-61284-848-8/11/$26.00 ©2011 IEEEB.Research Methods 1)Hotspot analysisWe calculated the Getis-Ord G *i for each element of data set using the hotspot analysis tool of ArcG IS9.3. Then the positions - those elements with high or low value occurred clustering -were obtained [8].Getis-Ord local statistic can be expressed as follows:nni,jj i,j*iwx X w G¦¦x j is the property value of j,w i,j is Spatial Weight between the i and j ,n is the total number of elements,and:njj=1xX=n¦ (2)S(3)2)Principal component analysisPCA is a statistical analysis to grasp the major contradiction of things.It can analyze the main factors from multiple things, reveal the essence of things, and simplify complex issues [9].In this study, we used PCA tools of SPSS to analysis the 18indicators of the crime hot spots from January to December2009. Those indicators include the resident population density,floating population density, foreign population density, numberof rental housing, spare rooms, their own room, dormitories, campsites, hotels, entertainment, leisure venues, dance halls,Internet cafes, sauna, game room, billiards room, bar, coffeebar and tea room.III.RESULTS ANDDISCUSSIONA.The Spatial-temporal Distribution of Crime Hot SpotsFigure 1. The hot/cold spatial-temporal distribution maps of theft in January to December 2009,in Shanghai.Figure 2. The hot/cold spatio-temporal distribution maps of robbery in January to December 2009,in Shanghai.By calculating, we obtained the Z, P value of each policestation for theft and robbery.When the P value is less than0.05, Z value has two intervals, i.e. Z> 2.58, Z <-2.58. It showsthat the two kinds of cases have significant spatial clusteringcharacteristics, namely have hot and cold spots. Fig. 1 and Fig.2 are the hot/cold spatial-temporal distribution maps of theft and robbery cases in Shanghai. Hot spots are shown in bright red. Cold spots, or areas of low crime, are shown in dark blue.Fig. 1 shows theft crime hot spots to the “central city core area”as the center, and changed dynamically with the seasons, extending to the east-west direction in the first three quarters, extending to the north-south direction in the fourth quarter, while the “outer suburbs”is basically a crime cold spots shows robbery crime hot spots mainly in“Central City Edge Zone”and “suburban areas”, and the“central city core area”of the city is a crime cold spot in most of the seasons. TABLE I. T HE T OTAL V ARIANCE E XPLAINED OF T HEFT IN SEPTEMBERTABLE II. C OMPONENT M ATRIX OF T HEFT IN S EPTEMBERTABLE III. T HE T OTAL V ARIANCE E XPLAINED OF R OBBERY INS EPTEMBERB.The Principal Component Analysis of Crime Hot SpotsThe results showed that were consistent based on similar cases at different times of the principal component analysis, but the results still have large differences between theft and robbery. TABLE I and TABLE II are the results of theft principal component analysis of Shanghai in September 2009, the components are excluded the cumulative contribution of more than 85% after the composition in TABLE Ċ.TABLE III, TABLE IV are the results of robbery principal component analysis of Shanghai in September 2009, the components are excluded the cumulative contribution of more than 85% after the composition in TABLE IV.Based on the results and the actual survey, we found that the main factors of theft are entertainment, leisure venues, dance halls, game rooms and saunas, while the main factors affect the robbery are floating population density and the number of rental housing.IV.C ONCLUSIONThrough the hot spot analysis of theft and robbery in Shanghai from the January to December 2009, we found the overall trend and the spatio-temporal variations of crime hot spots, and by the principal component analysis, we analyzed the main factors of crime hot spots related to the theft and robbery.TABLE IV. C OMPONENT M ATRIX OF R OBBERY IN SEPTEMBERTherefore, we propose that law enforcement agencies should strengthen the management of entertainment,leisure venues, dance halls, game room, sauna and other areas in the downtown, the floating population and rental housing in the suburban. Meanwhile, patrols also should be emphasized in the suburban.R EFERENCES[1]H. Zhong, J. Yin, J. Wu, S. Yao, Z. Wang, Z. Lv, and B. Yu, "SpatialAnalysis for Crime Pattern of Metropolis in Transition Using Police Records and G IS: a Case Study of Shanghai, China," International Journal of Digital Content Technology and its Applications, vol. 5, 2011,pp. 93-105.[2] E Schikuta, and M Erhart, The BANG-clustering system: grid-baseddata analysis, ser. Lecture Notes in Computer Science, Berlin/Heidelberg: Springer, vol. 1280,1997.[3]J.B. MacQueen, “Some Methods for Classification and Analysis ofMultivariate Observations,”In Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297, 1967.[4] A. Azzalini and N. Torelli, “Clustering via nonparametric densityestimation,” Statistics and Computing, vol. 17(1), 2007,pp. 71-80. [5]M. Kulldorff, “A Spatial Scan Statistic,” Communications in Statistics:Theory and Method, vol. 26(6), 1997,pp.1481-1496.[6] D. Zeng, W. Chang, and H. Chen, “A comparative study of spatio-temporal hotspot analysis techniques in security informatics,”in Proc.IEEE Int. Conf. Intelligent Transportation Systems, pp. 112–117, 2004 [7]S.C. Johnson, “Hierarchical Clustering Schemes,” Psychometrika, vol.2,1967,pp.241–254.[8]ESRI, “ArcGIS Resource Center,”/zh-cn/arcgisdesktop/10.0/help/index.html, accessed at December 17, 2010.[9] Baidu, “Baidu Encyclopedia,”/view/852194.htm,accessed at December 18, 2010.。

2015—2020_年植被吸收光合有效辐射的时空特征及影响因素分析

第43卷第2期生态科学 43(2): 211–2222024年3月 Ecological Science Mar. 2024收稿日期: 2021-11-03; 修订日期: 2021-12-30基金项目: 福建省社会发展产学研合作项目(2018Y0054); 国家自然科学基金项目(41771423)作者简介: 林婷敏(1994—), 女, 福建漳州人, 硕士生, 主要从事地理信息服务研究,E-mail:**********************通信作者: 陈楠(1975—), 男, 福建厦门人, 博士，研究员, 主要从事数字地形分析,E-mail:***************.cn林婷敏, 陈楠, 林偲蔚. 2015—2020年植被吸收光合有效辐射的时空特征及影响因素分析[J]. 生态科学, 2024, 43(2): 211–222. LIN Tingmin, CHEN Nan, LIN Siwei. Analysis of spatial-temporal characteristics and influence indices of photosynthetically active radiation for vegetation absorption from 2015 to 2020[J]. Ecological Science, 2024, 43(2): 211–222.2015—2020年植被吸收光合有效辐射的时空特征及影响因素分析林婷敏1,2,3, 陈楠1,2,*, 偲林蔚1,2,41. 福州大学, 空间数据挖掘与信息共享教育部重点实验室, 福州3501082. 福州大学, 数字中国研究院(福建), 福州3501083. 漳州市土地收购储备中心, 漳州3630004. 南京大学地理与海洋科学学院，南京 210023【摘要】植被吸收光合有效辐射(Absorbed Photosynthetically Active Radiation, APAR)是植被进行光合作用中实际吸收的太阳辐射量, 是植被净第一性生产力的重要指标, 也是生态系统的功能模型、作物生长模型、净初级生产力模型、气候模型等的重要参数。

大入射余角实测高分辨海杂波数据幅度统计特性

大入射余角实测高分辨海杂波数据幅度统计特性宋杰;于家伟;丁昊;刘宁波【摘要】The Ku-band, high-resolution sea clutter data at high grazing angles was used to fit the common theoretical models, including rayleigh distribution, Weibull distribution, logarithmic normal distribution, K distribution and KK distri?bution. The fitting effect of these distribution methods was analyzed in this paper and the results showed that the amplitude of sea clutter was close to rayleigh distribution in the case of large grazing angle and tail of the amplitude distribution curve of the sea clutter in some distance units deviates from the rayleigh distribution while the K and KK distributions couldfit better.%基于Ku波段高分辨大入射余角(擦地角)海杂波数据,采用瑞利分布、韦布尔分布、对数正态分布、K分布和KK分布进行仿真,并与实测数据对比,分析了这些分布方式的拟合效果.结果表明,海杂波的幅度在大入射余角情况下基本还是逼近瑞利分布的,海杂波在某些距离单元上的幅度分布曲线尾部偏离瑞利分布,此时K和KK分布可在拖尾处达到更好的拟合效果.【期刊名称】《海军航空工程学院学报》【年(卷),期】2017(032)002【总页数】6页(P187-191,204)【关键词】大入射余角;分布;海杂波;实测数据【作者】宋杰;于家伟;丁昊;刘宁波【作者单位】海军航空工程学院信息融合技术研究所,山东烟台264001;西南交通大学电气工程学院,成都611756;海军航空工程学院信息融合技术研究所,山东烟台264001;海军航空工程学院信息融合技术研究所,山东烟台264001【正文语种】中文【中图分类】TN953在海杂波幅度统计分布特性研究中，低分辨率下海杂波经常采用的统计分布模型主要包括：瑞利分布、韦布尔分布、对数正态分布和K分布等[1-2]。

基于地貌分区的近30年中国粮食生产空间分异研究

作物学报ACTA AGRONOMICA SINICA 2021, 47(12): 2501 2510 / ISSN 0496-3490; CN 11-1809/S; CODEN TSHPA9E-mail: zwxb301@DOI: 10.3724/SP.J.1006.2021.03068基于地貌分区的近30年中国粮食生产空间分异研究王凯澄韩桐臧华栋陈阜薄晓智褚庆全*中国农业大学农学院 / 农业农村部农作制度重点实验室, 北京 100193摘要: 研究并揭示中国粮食生产时空变化特征及其与耕地等农业资源的匹配性, 对于合理利用土地和提高土地综合生产能力, 保障国家粮食安全和生态安全具有重要意义。

本文采用ArcGIS空间分析方法, 利用1985—2015年中国县域粮食生产数据, 定量分析了基于地貌分区的中国粮食生产时空分异特征。

结果表明: (1) 1985—2015年, 平原区以占全国22.9%的国土面积生产了平均占全国42.7%的粮食, 其次为山地、台地、丘陵, 分别占全国的25.5%、17.2%、14.7%, 且粮食生产有向平原和台地集中的趋势, 近30年平原区粮食产量集中度上升了3.8%, 而山地下降了4.4%。

(2) 在4种地貌分区中, 平原区近30年平均生产水稻、小麦、玉米和大豆占比30.6%、63.3%、46.9%和40.7%的, 而山地生产马铃薯达到了54.1%。

水稻、小麦、玉米三大粮食作物的生产均呈现向平原集中的趋势, 而大豆和马铃薯在丘陵、山地的集中度增加。

(3) 近30年不同地貌分区的粮食产量变化差异要大于播种面积变化差异, 不同地貌分区的粮食生产差异加大。

因此, 在未来进行区域作物布局优化以及制定区域粮食生产技术策略方面, 不仅要考虑到气候资源、社会经济条件的变化, 也要考虑地形地貌的变化和其对农机、土壤耕作等技术的不同需求。

关键词:粮食生产; 空间分异; 地貌分区; 集中度; 中国Spatial distribution of Chinese grain production in the past 30 years based on geomorphological divisionWANG Kai-Cheng, HAN Tong, ZANG Hua-Dong, CHEN Fu, BO Xiao-Zhi, and CHU Qing-Quan*Key Laboratory of Farming System, Ministry of Agriculture and Rural Affairs / College of Agronomy and Biotechnology, China AgriculturalUniversity, Beijing 100193, ChinaAbstract: Revealing the temporal and spatial distribution characteristics of grain production in China and its matching with arableland and agricultural resources was of great significance to rationally utilize land and improve the comprehensive land productioncapacity of land, and guarantee the national food security and ecological security. In this study, we quantitatively analyzed thetemporal and spatial distribution characteristics of Chinese grain production based on geomorphological division using the grainproduction data of county regions via ArcGIS spatial analysis method from 1985 to 2015 in China. The results were as follows: (1)From 1985 to 2015, the plain areas produced an average of 42.7% of the country’s grain with 22.9% of the country’s land area. Itwas followed by mountains, terraces, and hills, accounting for 25.5%, 17.2%, and 14.7% of the country’s grain on average, re-spectively. In addition, grain production tended to be concentrated in plain and platform areas. In the past 30 years, the grain pro-duction concentration in plain areas had increased by 3.8%, while that in mountainous areas had decreased by 4.4%. (2) In thefour geomorphological divisions, the average production of rice, wheat, maize, and soybean in the plain area accounted for 30.6%,63.3%, 46.9%, and 40.7% in recent 30 years, while potato production in the mountainous areas reached 54.1%. The production ofrice, wheat, and maize all showed a tendency to concentrate in the plain areas, while the concentration of soybean and potato inthe hills and mountain was increased. (3) In the past 30 years, the magnitude of the change for grain production among differentgeomorphological divisions was greater than that of planting area, and the variation of grain production in difference geomor-phological divisions increased. In conclusion, in the future optimization of regional crop layout and the formulation of regionalgrain production technology strategy, not only the change of climate resources and social and economic conditions should be con-本研究由国家重点研发计划项目(2016YFD0300201)资助。

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Water Resour Manage(2010)24:1089–1105DOI10.1007/s11269-009-9487-1Analysis of Temporal and Spatial Differencesin Eco-environmental Carrying Capacity Relatedto Water in the Haihe River Basins,ChinaYonghua Zhu·Sam Drake·Haishen Lü·Jun XiaReceived:24April2008/Accepted:17July2009/Published online:4August2009©Springer Science+Business Media B.V.2009Abstract With overly-rapid socio-economic development and population increases, water abstraction for agricultural,industrial and municipal use increases rapidly, while the water left for ecological maintenance decreases greatly.At the same time, large amounts of polluted water are discharged into rivers because puriﬁcation plants are inadequate or not built in time,causing serious eco-environmental problems in the Haihe river basins which make regional development unsustainable.Estimating eco-environmental carrying capacity related to water is a key to curbing overuse of water and resolving eco-environmental problems.Because of different trends in water resources development and resultant eco-environmental problems in different sub-basins of the Haihe river,there are different water-related eco-environmental carrying capacities(EECCs)in these sub-basins.Time-series and multi-objective optimization methods are used to determine the EECC in various eco-environmental regions of the Haihe river basins,China.The results show that the entirety of the Y.Zhu(B)·H.LüState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,College of Water Resources and Environment,Hohai Univesity,Naijing,210098,Chinae-mail:Yonghua321@S.DrakeArizona Remote Sensing Center,Ofﬁce of Arid Lands Studies,University of Arizona,1955E.6th Street,Tucson,AZ85719,USAH.LüDepartment of Applied Mathematics,Hohai University,Naijing,210098,ChinaJ.XiaKey Laboratory of Water Cycle&Relative Land Surface Processes,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing,100101,China1090Y.Zhu et al. Haihe river basins will not reach a stable,sustainable state until about2033,through gradual amelioration of eco-environmental problems.The various eco-regions of the sub-basins will need different lengths of time to reach their own stable states because of different available water resources,eco-environmental problems and social and economic development.Keywords Multi-objective optimization method·Time-series·EECC·Haihe river basins1IntroductionThe Haihe river basin holds special importance for China’political and economic functions because the capital,Beijing,is located there,as well as the third-largest city,Tianjin,and other large and medium-sized municipalities.Since the People’s Republic of China was founded,society and economy have developed perhaps too rapidly in this region,and population has increased sharply.This has led to substantial abstraction of water from the river system for agricultural,industrial and municipal uses,leaving a meager and shrinking share for in-streamﬂow and ecological maintenance of the riparian system.Moreover,returns of polluted water from industry and inadequate sewage treatment facilities have exacerbated eco-logical degradation.Environmental protection has not kept pace with economic development.Looming shortages of water and serious eco-environmental problems now threaten continued growth and sustainable use of the region.Eco-environmental restoration is demanded by government in the Haihe river basins.To augment local water resources,water will be diverted into the Haihe river basin by the South-to-North Water Transfer Project.From2010on,79.9×108m3/year of water will be diverted into the Haihe river basins from the Yangtse river by an eastern route,and from2030on,a total of108.4×108m3/year will be diverted into the Haihe from the Yangtse by the eastern route and an added middle route.Based on the South-to-North Water Transfer Project,a program of eco-environmental restoration in the Haihe river basins will be carried out by the Haihe Water Conservancy Committee,State Water Conservancy Ministry,in order to mitigate eco-environmental problems related to water.The study of carrying capacity of eco-environments in the Haihe river basins is an important part of the program. Based on the South-to-North Water Transfer Project functioning as planned,and on assumptions about future socioeconomic development,this study is intended to answer:(1)What is the status quo of sub-basin eco-environments related to water;(2) How many years are needed for the Haihe river basins to reach a critical sustainable state of environment-society-economy;and(3)What is the largest population that can be carried by the river basins when they reach their critical sustainable state.EECC is deﬁned as the largest population and economic scale(usually expressed as gross domestic product,GDP)that can be sustained in a particular region,over a speciﬁed time,under a certain environmental protection standard and social welfare level,when the eco-environments,society and economy of the region reach a critical acceptable state by using the local(or diverted from outside)water and other resources available.In the context of the Haihe river basins,EECC is put forwardAnalysis of Temporal and Spatial Differences in Haihe River Basin1091 as a basis forﬁnding a way to resolve the eco-environmental problems related to water.It is the capacity of the eco-environmental system(water resources,soil resources and ecological services such as water quality restoration)to support a society-economy system restrained by a certain technology level,welfare level and environmental quality standards.The carrying capacity is expressed by population and GDP.The word“carrying capacity”wasﬁrst put forward in ecology,to mean the largest number of a certain kind of living thing that can be carried by a regional ecological system(Odum1972;Long and Jiang2003).With human population increase,economic growth and technological advancement,the human appropriation of resources and environments becomes higher,and the phenomenon of over-use of resources occurs.Non-harmonious relationships among resources,environments, population and economy arise and can hamper sustainable development.The word “carrying capacity”no longer belongs only to ecology(e.g.Retzer and Reudenbach 2005;Downsa et al.2008);it can be applied to economics,eco-economies,geogra-phy,environmental science or other sciences.Therefore,appeared environmental carrying capacity(Mcleod1997;Tang et al.1997;Guo et al.2000;Liu et al.2009), soils(Feng1994;Li1996),water resources(Wei and Zhang1995;Xu et al.1997; Jia et al.1998,2000;Xu1999;Qu and Fan2000;Li et al.2000;Xu and Cheng2000, 2002;Jiang et al.2001;Cheng2002;Liu et al.2004;Feng and Huang2008;Gong and Jin2009),water-environmental carrying capacity(Guo et al.1994;Wang1996; Zen and Cheng1997;Cui1998;Jiang et al.2001;Long and Jiang2003;Zhao et al. 2005;Zhang and Zhao2007;Weng et al.2009),parks and wilderness(Manning and Lawson2002;Lawson et al.2003;Wang et al.2007;Dong et al.2008),and other synthetic carrying capacities(Arrow et al.1995;Zhang et al.2002;Zhang and Fang 2002;Simón et al.2004;Zhu et al.2005a,b;Zheng et al.2007;Wang et al.2008; Prato2009).Except for the studies of Zhu et al.(2005a,b),among previous studies of carrying capacity related to water,water-related environmental problems refer only to water pollution,other environmental problems related to water,such as river de-watering,falling water tables,urban rivers and lakes drying,wet lands shrinking, sea outfall decreasing,erosion and soil loss,were not considered in combination (e.g.Xu et al.1997;Xu1999;Xu and Cheng2000,2002;Li et al.2004).Previous carrying capacity studies have not investigated the time required for a river basin to reach a stable,sustainable state in the context of eco-environmental restoration processes,nor considered large-scale spatial and temporal variations in carrying capacity across a basin.In this paper,the stable-state points to a state as LI(the eco-environmental quality estimating index)is equal to0.8and at the same time,the maximum sustainable population is greater than actual population.In this paper,the carrying capacity of seven Haihe river sub-basins is studied in the context of system theory,through multi-objective optimization and time-series analysis.The most frequently seen environmental problems related to water in the Haihe sub-basins are considered together(synthetically)in a water resources–environments–social-economy interactional model.The current status and future changes in the carrying capacity of eco-environments in the Haihe basins are examined.The time required for each sub-basin,and the Heihe basin as a whole, to reach a stable,sustainable state is calculated,and their maximum population and GDP in2010,2020,2030and2040are stly,whether or not the results are rational is analyzed.1092Y.Zhu et al. 2Materials and Methods2.1General Situation of the Haihe River BasinsThe Haihe river basins(Fig.1)are located in northeastern China,at35◦–43◦N and 122◦–120◦E,bordered by the Bo sea in the east,Liaoning Province in the northeast, the Inner Mongolia Autonomous Region in the northwest,Shandong Province in theFig.1Location of seven eco-environmental regions in the Haihe river basins.I Mountainous area of Luan river and eastern Hebei Province shoreward.II Plains area of Luan river and eastern Hebei Province shoreward.III Mountainous area of northern river systems of the Haihe.IV Plains area of northern river systems of the Haihe.V Mountainous area of southern river systems of the Haihe. VI Plains area of southern river systems of the Haihe.VII Plains area of the Tuhaimajia riverAnalysis of Temporal and Spatial Differences in Haihe River Basin1093 southeast,and Shanxi Province in the west.Included in this area are Beijing,Tianjin Municipality,most of Hebei Province,eastern Shanxi Province,northern parts of Henan and Shandong Provinces,a small part of the Inner Mongolia Autonomous Region and Liaoning Province.The total area is318,000km2,of which hills and plateaus make up189,000km2or60%,while lowland plains comprise129,000km2 or40%of the area.The Haihe basins lie in the temperate zone of semi-moist and semi-arid continental monsoon climate.Mean annual precipitation is539mm, mean annual free water surface evaporation is1,100mm,and mean annual land surface evaporation is470mm.In1998(latestﬁgures available),the total population was122million,nearly10%of the total population of China.Urban population was33,650,000according to the census register,and the rate of urbanization was 28%.The mean population density of upland regions was384persons per square kilometer,and608persons per square kilometer in the plains.Within the Haihe river system are the Luan river,Hai river and Tuhaimajia river from north to south.The Haihe basin is divided into seven eco-environmental regions according to river system and topography(Fig.1).In the seven eco-environmental regions,there are different eco-environmental problems related to water(Table1). In mountainous regions,the primary problem is serious water and soil loss,while in plains regions most of the observed problems are present:water contaminants, perennial river segment shortening or river water drying,water table dropping down, wetlands shrinking,volume of sea outfall decreasing,etc.2.2Calculation of Eco-environmental Carrying Capacity2.2.1Model DescriptionThe model includes objective functions and restraining conditions.Objective Functions Our deﬁnition of EECC is the maximum population and GDP in a certain time period,when the eco-environmental quality and social economy levels are simultaneously maximized.Thus EECC is a multi-objective function. Table1Main eco-environmental problems related to water in seven eco-environmental regions in the Haihe river basinsRegion Eco-environmental problems related to waterI Water and soil loss,polluted water dischargeII Groundwater over-extraction,water quality pollution,river channel shortening or river water drying and the volume of water reaching sea outfall decreasingIII Water and soil loss,polluted water dischargeIV Water quality pollution,groundwater over-extraction,river channel shortening or river water drying,wetlands shrinking and the volume of water reaching seaoutfall decreasingV Water and soil loss,polluted water dischargeVI Groundwater serious over-extraction,water quality pollution,wetlands shrinking, the volume of water reaching sea outfall decreasing and river channel shorteningor river water dryingVII Water quality pollution,wetlands shrinking,the volume of water reaching sea outfall decreasing,groundwater over-extraction,and river channel shortening or riverwater drying1094Y.Zhu et al.During EECC calculation,in order to change the multi-objective function to single-objective,a synthetic index,ES,is developed,called the eco-environmental equality–social-economy level synthetic estimating index,which is expressed in Eqs.1,2,and 3:ES (T )=EG T β1LI T β2(1)EG T =m i =1U i T a i (2)LI T =n j =1H j T b j (3)In Eqs.1,2,and 3,ES (T )is the synthetic value of eco-environmental quality and social-economy level evaluated during the period T ;and EG (T )and LI (T )express the value of the social-economy level and that of eco-environmental quality during the period T ,respectively.EG (T )is called the social-economy level estimating index and LI (T )is called the social-economy level estimating index during the period.β1is the weight of the social economy level in EG (T ),and β2is the weight of eco-environmental quality in EG (T ).U i (T )expresses the subordination degree value of the social-economy level index i during the period T ,H j (T )expresses the subordination degree value of the eco-environmental quality index j during the period T;m and n express the number of the social-economy level index and eco-environmental quality index,respectively;a i is the weight of the social-economy level index i in EG (T );b j is the weight of the eco-environmental quality index j in LI (T ).All the weights are determined by stratiﬁcation analysis (analytic hierarchy process),the speciﬁc determination is similar to the method used by Miao et al.(2006).In year N ,the objective function is expressed in Eq.4:BTI =Max N T =1ES T 1N (4)BTI is called the measuring index of sustainable development of the eco-environments–social-economy compound system.In the Haihe river basins,the indexes of social-economy level,chosen by their importance in the social-economy system,are:per capita GDP,percentage of tertiary industry to GDP,urbanization ratio and per capita grain yield.The indexes of eco-environmental quality,chosen according to the main eco-environmental problems in the Haihe river basins,are:area ratio of water and soil loss,the discharge amount of chemical oxygen demand (COD),groundwater extraction coefﬁcient,length ratio of perennial river segment shortening,area ratio of wet-lands,area ratio of urban rivers and lakes,and volume of water reaching sea outfall.The indexes of social-economy level are the same in the seven eco-environmental regions,but the indexes of eco-environmental quality are different in regions with different eco-environmental problems (Table 2).Analysis of Temporal and Spatial Differences in Haihe River Basin1095Table2Eco-environmental indexes in seven eco-environmental regionsRegion Eco-environmental indexes expressing LII Area ratio of water and soil loss,area ratio of urban river or lake and dischargeamount of CODII Discharge amount of COD,groundwater extraction coefﬁcient,length ratio of river channel shortening,area ratio of urban river or lake and volume of water reachingsea outfallIII Discharge amount of COD,area ratio of water and soil loss and area ratio of urban river or lakeIV Discharge amount of COD,groundwater extraction coefﬁcient,length ratio of river channel shortening,area ratio of wetlands,area ratio of urban river or lake andvolume of water reaching sea outfallV Discharge amount of COD,area ratio of water and soil loss and area ratio of urban river or lakeVI Discharge amount of COD,groundwater extraction coefﬁcient,length ratio of river channel shortening,area ratio of wetlands,area ratio of urban river or lake andvolume of water reaching sea outfallVII Discharge amount of COD,groundwater extraction coefﬁcient,length ratio of river channel shortening,area ratio of wetlands,area ratio of urban river or lake andvolume of water reaching sea outfallConstraints1.Interactional relationship among the water resources–eco-environments–socialeconomy compound systemThe model of the water–eco-environments–social economy compound system interactional relationship includes a water budget sub-model,social economy–water relationship sub-model,environments–water relationship sub-model and society–economy prediction sub-model.The model is indispensable and it con-nects the other indexes in the calculating model of carrying capacity functions with water.Thus by optimization we can obtain the maximum sustainable population and GDP under the conditions of available water consumption.The result is a beneﬁt to decision-makers.(a)Water budget sub-model.It is expressed by Eq.5P+W in=R+E+W out+ WW t =P+W in−W prod+W life+W eco−W out(5)Where,P,R,E is the precipitation,runoff and evporation,respectively;W is the change of water storage in the studied region,positive as increasing;t is the time;W in is the water volume running into or diverted into the studied region;W out is the water volume running out of the studied region;W prod is the water volume used by production including Industry and Agriculture;W life is the water volume used by life and W eco is the water volume used by eco-environments.(b)Social economy-water relationship sub-model.It is expressed by the func-tions between social economy indexes and various water volumes in water1096Y.Zhu et al.budget sub-model.The social economy indexes used are domestic productof Industry and Agriculture;GDP;crop yield and population,respectively(c)Environments–water relationship sub-model.It is expressed by the func-tions between environmental indexes related to water and the correspond-ing ecological water usage.The environmental indexes related to waterare area ratio of water and soil loss,groundwater extraction coefﬁcient,length ratio of perennial river segment shortening,area ratio of wet-lands,area ratio of urban rivers and lakes,and the discharge amount of chemicaloxygen demand(COD),respectively.The corresponding ecological waterusages are ecological water usage for water and soil conservation,thesupplied water volume for groundwater,the ecological water usage by river,the ecological water usage by urban rivers and lakes.(d)Sociey–economy prediction sub-model.The sub-model is used to predictthe state of social-economic development for the studied region in thefuture.It is expressed byP t=P t−1(1+k P)(6)GDP t=GDP t−1(1+k GDP)(7) Where,P t,GDP t,are the population and GDP in the Year t,respectively.P t−1,are the population and GDP in the Year t−1,respectively.k p,k GDPare the increasing rate of population and GDP,respectively.2.Water resources constraintThe water resources constraint is expressed in the following:W usable≥W ind.+W agr.+W eco+W lif.W usable=W sur f ace+W ground+W sea−inf lo w+W cleaned polluted−w ater+W brackish+W de−salinated(8) where W usable is the total usable water volume of the studied region during the calculated period,which includes surface water(W sur f ace),groundwater(W ground),sea inﬂow(W sea−inf lo w),cleaned polluted water(W cleaned polluted−w ater), brackish water(W brackish)and de-salinated water(W de−salinated).W ind.is the water volume to be used by industry;W agr.is the water volume to be used by agriculture;W eco.is the water volume to be used for ecological maintenance (water and soil conservation,groundwater recharge,wetlands and perennialﬂow preservation,urban river or lake ecology and for sea outfall ecology);W lif.is the water volume to be used for human life,municipal consumption.3.The constraint of environments related to waterPolluted water discharge is expressed as:PW ind.+PW lif.≤B(9) where PW ind.is waste water from industry,and PW lif.is polluted municipal water.B is the polluted water volume permitted to discharge during the calculated time,including the volume of polluted water that can be cleaned and the runoff self-puriﬁcation capacity of the Haihe river basins.Analysis of Temporal and Spatial Differences in Haihe River Basin1097 Each of the different types of pollutants present in the Haihe river system must be considered over the calculated period.Here COD(chemical oxygen demand) in water is taken as an example:COD ind.+COD lif.≤B1(10) COD ind.and COD lif.are the amounts of COD in waste water from industry and in polluted water from human use,respectively.B1is the COD amount permitted to discharge during the calculated time,including the amount that can be cleaned and the runoff self-puriﬁcation capacity of the Haihe river basins.A groundwater exploitation coefﬁcient and length ratio of perennial river seg-ment shortening are included as:C k≤A k(K=3,4)(11)If k=3,then C3and A3are the actual groundwater exploitation coefﬁcient and the maximum sustainable coefﬁcient(Table3),respectively.If k=4,C4and A4are the actual length ratio of perennial river segment shortening and the maximum permitted,respectively.First-to third-order rivers are considered.The area ratio of wetlands,area ratio of urban rivers and lakes,and volume of water reaching sea outfall are included:C k≥A k(K=5,6,7)(12)If k=5,C5and A5are the area ratio of actual wetlands and the minimum demanded in the Haihe river basins,respectively.If k=6,C6and A6are theTable3The input parameters for the carrying capacity calculation schemeParameter The status value The programmed value199820102030 Usable water resources(108m3)503.80537.00Actual water abstraction(108m3)432.30Water volume from South-to-North Water79.90108.40 Transfer Project(108m3)*Sewage treatment ratio(%)13.0045.0060.00 Urbanization ratio(%)27.6027.6046.30 Percentage of tertiary industry to GDP(%)33.0033.0046.00 COD discharge amount(104t)128.10≤B1Waste water discharge amount(104m3)60.30≤BPolluted water discharge ratio from life(%)16.716.716.7 Waste water discharge ratio from industry(%)56.751.045.3Per capita grain yield(kg/person)a438≥350≥300Per capita GDP(Yuan/person)7,92217,96342,941 Water-used quota in agriculture(m3/104Yuan)2,3772,1401,902 Water-used quota in industry(m3/104Yuan)143.771.950.3 Minimum ecological water requirement(108m3)121.3*Data points to the water volume from South-to-North Water Transfer project included in the total usable water volumea The programmed values of per capita grain yield in2010,2030is given on the low side,because of the thought that the Haihe river basins have the special regional locations,which lie in Capital Cycle and Beijing,Tianjing and Tangshan Economic Regions,and their grains need not to be autarkic,can be supplied by trade and other ways1098Y.Zhu et al.area ratio of urban rivers and lakes and the minimum demanded.If k=7,C7and A7are the volume of water reaching sea outfall and the minimum permissible.4.Social-economy constraintsThe primary social-economy constraint is per capita GDP:GDP rj≥A GDP rj(13)where GDP rj and A GDPrj are per capita GDP and the minimum regional percapita GDP,respectively.Also included is per capita grain yield:G≥A grain(14) where G and A grain are per capita grain yield and the minimum regional per capita grain stock(kg)required for sustenance.5.Sustainable development constraintThe sustainable development constraint is stated as:ES(T)≥ES(T−1)(15) Because the course described by the model will affect the later course after period T,this does not accord with the essential nature of dynamic programming in operations analysis(no after-effect).2.2.2Analysis of Maximum Eco-environmental LoadingIn the Haihe river basins,whether or not the social-economy system is sustainably carried by the eco-environmental system is decided by an eco-environmental quality estimating index LI and predicted human population,including natural increase and migration(Report of Water Resources Layout in Haihe River Basins,Haihe Water Conservancy Committee,State Water Conservancy Ministry,2000).By deﬁnition, when LI≥0.8and the maximum sustainable population is greater than actual population,the social-economy system can be carried by the eco-environmental system.LI is a combination of various eco-environmental quality indexes,and LI= 0.8is the minimum value of the compound index endured by man.2.2.3Calculation Scheme and Material SourceThe calculation scheme contains the assumptions that the South-to-North Water Transfer Project functions as planned,and that local society and economy develop in a usual style,as determined by the report on the program of water resources in the Haihe river basins(Haihe Water Conservancy Committee,State Water Conservancy Ministry,2000).The input parameters for the carrying capacity calculation scheme are shown in Table3.The baseline year for calculations is1998.The materials needed are those on water and soil resources,eco-environments and the economy, except the programmed values of sewage treatment ratio and percentage of tertiary industry to GDP;other materials are from the report of water resources pro-gramme in Haihe river basins(Haihe Water Conservancy Committee,State Water Conservancy Ministry,2000)and the Statistical Yearbook of Peking Municipality, Tianjin Municipality,Hebei,Shanxi,Shandong,Henan Province and the Inner Mongolia Autonomous Region(1998–2003).The programmed value of sewage treat-ment ratio is determined by the proportion of capital investment for environmentalTable4The status quo(1998)of eco-environments and social-economy in the Haihe river basin Region LI EG BTI Sewage treatmentratio(%)I0.030.780.16 3.50II0.140.900.3525.00III0.170.900.3949.00IV0.150.950.3854.80V0.140.800.33 3.20VI0.080.900.27 5.00VII0.090.770.2715.00EG The measuring index of social-economic level,BTI the measuring index of sustainable develop-ment of eco-environments–social-economy compound systemprotection in GDP,and that of percentage of tertiary industry to GDP is determined by the corresponding industrial structure relationship theory on various economic development phases developed by H.Qiannule,the famous USA economist,and revised by the development centre of the State Council in2001,according to conditions in China.2.2.4Calculation Process of the Carrying CapacityThe calculation process of the carrying capacity are the following:Firsty,the total usable water is distributed in a ratio of1998,the initial year of the calculation,to be the water volume used by production including Industry and Agriculture,the water volume used by life and the water volume used by eco-environments;secondly,BTI is calculated by running the carrying capacity model under various kinds of constraints; thirdly,the results are outputted.The speciﬁc contents is seen in the paper of Zhu et al.(2005b).2.2.5Results of Carrying Capacity CalculationResults obtained from the carrying capacity model are presented in Tables4,5and6. Table4shows the current status of eco-environments and social-economy in the Haihe river basins.Table5shows the time to reach a stable state for seven sub-regions and for the whole Haihe river basin.Table6shows the population in1998, 2010,2020and2030of the seven regions.Table5Time to reach a stable state for seven sub-regions and for the whole Haihe river basin Region Time to reach Number of yearsstable state to reach stablestate from2004 I203734II203532III203532IV203128V204037VI203936VII202522Whole river basin203330。