2011~2017年中国PM2.5多尺度时空分异特征分析

PM_2.5时空分异特征认知对大气污染联防联控意义重大,本文从空间多尺度的视角出发,利用空间模式分析方法与地理探测器,对2011~2017年中国大陆地区PM_2.5年均浓度时空分布格局及成因进行探究,从而揭示PM_2.5多尺度时空分异特征.结果表明：①2011~2017年PM_2.5年均浓度相对稳定,无明显趋势,国家与区域尺度PM_2.5变化特征基本一致,呈现"W"型变化,整体上看,污染程度由高到低依次为：中部、东部、西部与东北.②由空间模式分析结果可知,高值聚集区主要位于中国的东部、中部以及新疆的西南地区,低值聚集区则集中在青藏、云贵高原以及大兴安岭地区.③地理探测器分析结果证实：城市化因素中人口密度是国家与区域尺度上PM_2.5时空分异的主导因素,同时,产业、能耗与交通因素对PM_2.5分布格局存在不同程度影响.在区域尺度上,除了人口密度因素之外,工业用电量与公车总量对中部地区PM_2.5年均浓度影响较大,东部地区是工业烟粉尘排放量与道路面积,东北地区则为第二产业产值占比与城市绿地率,社会经济因素对西部地区的PM_2.5年均浓度影响不显著.     

基于遥感影像的江西省水体资源和水产养殖结构空间异质性分析

为系统掌握江西省水体资源结构和水产养殖基本特征,论文通过遥感技术获取了2012年各县域的水体面积,并划分为不同的水体类型。通过全局和局域空间自相关,分析了89个县（市）水产养殖典型要素的空间异质性以及空间差异程度。针对空间关联及非均质性的现象,运用地理加权回归揭示了水体资源大小、养殖方式、平均单产水平和渔业专业养殖人员数量对养殖产量的影响程度。结果表明：江西省水体资源类型以大水面为主,占总面积的70%,其次为水库山塘,占21.26%,小型池塘面积占9%。水资源结构地区差异明显,北部区域以大水面为主,南部以山塘水库为主。养殖产量和面积表现出空间集聚分布特征（P<0.01）,但各县域的聚集性分布也表现出不同的模式,彭泽县、都昌县、鄱阳县、南昌市辖区、南昌县、丰城市、进贤县和余干县8个县域,均为高-高分布类型,养殖面积和产量分别占到总量的34.55%、35.42%。从养殖品种结构上,特种养殖产量主要集中在上饶市、九江市、南昌市和宜春市,占69.84%,但特种养殖产量所占比例与水体资源大小的相关性下降,水资源较少的东部山区丘陵地带依据区位特征发展鳖、蛙产业,特种养殖比例较高。从水产养殖驱动力分析,水体面积对养殖产量的影响程度最大（中位值为0.46）,其次是小型池塘面积（中位值为0.36）、专业养殖人口数量（中位值为0.26）,3个因素与养殖产量均为正相关,其中水体面积和小型池塘面积对养殖产量的影响程度从南向北递减,专业养殖人口对养殖产量的影响程度从南向北递增。影响程度的空间异质性变化可能与养殖方式和品种结构有关。平均单产水平对养殖产量的贡献较弱（中位值为 -0.003）,各地区影响强度差别不明显。分析结果有助于系统了解江西省水体资源分布和养殖结构,对优化产业规划布局有重要的指导意义。     

中国灌区粮食生产水足迹及用水评价

以1998、2005 及2009 年459 个灌区资料为基础,从区域尺度计算粮食生产水足迹,建立粮食生产用水水平综合评价指标I对灌区粮食生产与水资源利用之间的关系进行评价。结果显示:粮食生产水足迹及其组成的区域间差异均较大,大值省区主要分布于东北、华南,小值省区以黄淮海平原为核心集中分布;全国粮食生产水足迹多年均值为1.336 m³/kg,蓝、绿水比例分别为63.7%和35.8%,有35.1%的水足迹损失于粮食生产过程。空间自相关分析表明,粮食生产用水水平综合评价指标I相似的省区在空间上显著地聚集,局部自相关属性为HH和LL的省区超过了20 个,不同年份总体和局部的空间分异特征随时间变化均不明显;省区间粮食生产综合用水水平差异大,I值介于0.079~0.889 之间,变异系数为0.59,黄河中下游省区用水水平较高而东北及西北大部分省区则相反。结合人均粮食占有量与I值在全国的地位,对各省区粮食生产与农业用水发展方向进行了讨论。     

基于空间网格尺度的中国PM2.5污染健康效应空间分布

以全国城市空气质量实时发布平台的监测数据为基础，运用空间插值法模拟中国PM_2.5在10kmx10km空间网格尺度上的暴露水平，利用BenMap工具估计2017年中国PM_2.5污染的健康损失，在城市尺度上对PM_2.5污染的健康经济损失进行空间分析.结果表明，在统计意义层面上，2017年PM_2.5污染共计造成我国321435例早逝、746078例住院、14877551例患病，健康经济损失约为12625亿元，占当年全国GDP的1.53%.从城市尺度来看，健康效应呈现出一定的空间聚集效应，京津冀地区城市较为严重.在空间分布上，主要以“高-高”型和“低-低”型分布为主，即健康经济损失高值城市相互聚集、健康经济损失低值城市也相互聚集，并且存在高值城市之间相互影响并逐渐向四周扩散，进而影响周围低值城市的现象.     

Neighborhood effects in bird distributions,Navarre, Spain

This study examines the role of neighborhood effects in the spatial distributions of selected bird species in Navarre, Spain. We employed a geographic information system (GIS) to organize the data on bird distributions and relevant environmental variables and to analyze their spatial patterns. Three bird species were selected for analysis: the European honey-buzzard (Pernis apivorus), the Eurasian hobby (Falco subbuteo), and the European pied flycatcher (Ficedula hypoleuca). Selected environmental variables of the study area were digitized to create a comprehensive data base and logistic regression models were used to evaluate the significance of each variable in the spatial distribution. The spatial patterns of bird distributions were used to extract topological relationships and to identify neighborhood effects. Although all the selected species illustrate a pattern of positive spatial autocorrelation in their distributions, the significance of neighborhood effects varies from species to species. Among the selected species, neighborhood effects are most evident in the distribution of the European pied flycatcher and are significant for the Eurasian hobby. The distribution of the European honey-buzzard is not much affected by neighborhood effects. The results suggest that examination of neighborhood effects is a prerequisite for modeling bird distributions.     

基于AWRSEI的岱海流域生态环境质量时空演变及驱动因子分析

流域是重要的生态安全屏障和社会经济发展区域,为了更加客观准确地评价干旱和半干旱区流域生态环境质量,基于遥感生态指数（RSEI）引入盐度指标构建适用于干旱和半干旱区流域的遥感生态指数（AWRSEI）并以岱海流域为例分析其适用性.基于AWRSEI模型,采用4期Landsat TM/OLI合成影像,对2001~2020年岱海流域的生态环境质量进行定量评价,通过变异系数和空间自相关分析岱海流域生态环境质量时空演变规律,并通过地理探测器进行成因分析和驱动因子解释力分析.结果表明：①AWRSEI与各生态因子的平均相关系数为0.860,比单个指标分量更具代表性;第一主成分的荷载正负值和排序与RSEI一致,特征值贡献率比RSEI高3.69%,评价结果与真实地表情况更为接近,适合用于干旱区流域的生态环境质量评价.②岱海流域2001~2020年AWRSEI指数的年均值为0.427,生态环境质量基础较差,期间AWRSEI的均值呈现波动上升的趋势,并且在2020年均值达到最高0.502.整体生态环境质量明显好转,恶化区域减少了20.51%,改善区域增加了12.71%.空间分布上,岱海流域南部和西北部高海拔地区的林地生态环境质量优,北部和南部中海拔地区生态环境质量较差,湖区北侧生态环境质量优于南侧.③岱海流域AWRSEI变异系数平均值为0.280,生态环境质量状况稳定,整体变化波动较小,高波动主要集中在湖区南侧和居民点区域.岱海流域生态环境质量存在显著的空间自相关性,高-高集聚区主要分布在高海拔的林地区域和低海拔耕地区域;低-低集聚区零星分布在中海拔区域.④2001~2020年岱海流域生态环境的提升主要由于NDVI提高,NDBSI和NDSI降低.NDVI和NDBSI是交互作用最强的组合,对生态环境的解释力最强.土地利用是AWRSEI的主导因子,解释力最强.土地利用和气象因子的组合是交互作用最强,且各驱动因子之间均为增强关系.     

1990~2020年阿克苏河流域土地利用碳排放时空轨迹与影响因素

土地利用变化会导致不同类型碳源和碳汇功能变化,是碳排放的关键来源.从土地利用变化的角度开展阿克苏河流域碳排放及其影响因素研究,对于促进流域山水林田湖草沙冰一体化保护修复、助力碳达峰与碳中和目标实现具有重要意义.基于1990~2020年的4期土地利用数据与同期社会经济数据,测算土地利用碳排放总量,探究土地利用碳排放时空轨迹及其影响因素.结果表明:①1990~2020年,耕地、林地、建设用地和未利用地整体呈增加趋势,草地和水域呈减少态势.土地利用类型空间变化特征主要表现为草地、未利用地转换为耕地;②1990~2020年流域净碳总排放量呈现持续上升趋势,累计增加了14.78×10⁴ t,耕地面积增加是引起流域净碳排放量增长的关键因素;③流域土地利用碳排放量空间上呈中间高四周低的分布格局,净碳排放量显著变化区域主要分布在温宿县南部、阿克苏市、阿瓦提县及阿拉尔市;④人类活动对土地利用碳排放驱动作用最强且其影响由东部向西部逐渐增大,年均气温对土地利用碳排放影响贡献主要集中在阿克苏市东部和阿瓦提县北部,年均降雨量对温宿县北部和阿合奇县西部的抑制作用较强.     

取代芳烃的自相关拓扑与其生物活性、性质的关系研究

本文提出表征原子生物活性的特征值（Ａｉ）,由Ａｉ建构自相关拓扑指数。其中１阶指数＾１Ｆ与氯代苯的８种理化性质的相关性优于Ｋｉｅｒ的＾１ｘ＾ｖ,＾１Ｆ与１７种取代芳烃ｌｇＫＯＷ的相关系数为０．９９４９,计算值与实验值非常吻合,取代芳烃对水生生物急性毒性与＾１Ｆ的线性回归方程如下：发光菌：－ｌｇＥＣ５０＝２．６７７＋０．０１５＾１ｆ,ｎ＝３０,ｒ＝０．９４３,ｓ＝０．２０５,保鲦鱼：－ｌｇＬＣ５０＝２     

Terraced fields and Mediterranean landscape structure: An analytical case study from Antikythera, Greece

Terraces are important capital investments in a range of agricultural landscapes worldwide, typically enduring well beyond any single farming cycle and over many human generations. This paper begins by emphasising that, while human population growth may often be loosely linked to terrace construction efforts, the association is by no means a straightforward one. We then argue that the choice of which parts of the landscape to terrace is driven by a range of cultural and environmental priorities that are most usefully explored by a combination of global, local and auto-correlative modelling, as well as via simulation-based methods. The results demonstrate that surficial geology, terrain slope, pre-existing terraces and pre-existing patterns of human habitation are all important structuring features. We also consider terraces as method for soil conservation, question the uncritical use of meso-scale erosion models and argue that patterns of catastrophic soil loss are often overstated in Mediterranean contexts. However, erosion modelling can, if deployed cautiously and comparatively, nonetheless be used to explore ways in which terraces do indeed manage localised soil movement in agriculturally favoured parts of the landscape, with our results suggesting that a substantial proportion of the erosion in this regions is indeed ameliorated by such measures.     

Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling

Nowadays, species are driven to extinction at a high rate. To reduce this rate it is important to delineate suitable habitats for these species in such a way that these areas can be suggested as conservation areas. The use of habitat suitability models (HSMs) can be of great importance for the delineation of such areas. In this study MaxEnt, a presence-only modelling technique, is used to develop HSMs for 223 nematode species of the Southern Bight of the North Sea. However, it is essential that these models are beyond discussion and they should be checked for potential errors. In this study we focused on two categories (1) errors which can be attributed to the database such as preferential sampling and spatial autocorrelation and (2) errors induced by the modelling technique such as overfitting, In order to quantify these adverse effects thousands of nulls models were created. The effect of preferential sampling (i.e. some areas where visited more frequenty than others) was investigated by comparing model outcomes based from null models sampling the actual sampling stations and null models sampling the entire mapping area (Raes and ter Steege, 2007). Overfitting is exposed by a fivefold cross-validation and the influence of spatial autocorrelation is assessed by separating test and training sets in space. Our results clearly show that all these effects are present: preferential sampling has a strong effect on the selection of non-random species models. Crossvalidation seems to have less influence on the model selection and spatial autocorrelation is also strongly present. It is clear from this study that predefined thresholds are not readily applicable to all datasets and additional tests are needed in model selection.