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     

基于多站混合受体模型的临汾市PM2.5潜在源分析

利用重污染城市临汾多个站点2018~2019年的PM_2.5浓度监测数据,分析了不同季节临汾市PM_2.5污染特征及其空间自相关度和集聚模式,最后引入多站受体模型分析临汾市PM_2.5潜在源区.研究发现,临汾市的PM_2.5污染主要集中在临汾盆地内的8个区县,包括尧都、襄汾、洪洞、霍州、侯马、古县、曲沃和翼城,这8个区县的PM_2.5年平均浓度均超过50μg/m³,冬季平均浓度均超过100μg/m³.PM_2.5空间分布特征与地形关系密切,临汾盆地内的8个站点空间自相关度很高,PM_2.5高浓度区（高-高聚类）主要集中在盆地内部,说明邻近区县污染是临汾市主城区PM_2.5浓度居高不下的重要原因.结合多站混合受体模型（MS-PSCF和MS-CWT）分析临汾PM_2.5潜在源区,发现临汾市春季的潜在源主要集中在东北、西南和东南部,大部分为中远距离传播;在夏季,潜在源影响明显低于其他3个季节,主要在东部;秋季的潜在源主要集中在西南方向的一些地区;冬季的潜在源主要集中在东南和西南方向以及临汾市北部近距离区域.除夏季外,其他3季共同的潜在源区是陕西中南部地区（位于西南方向）,且PSCF值均超过了0.7,说明在西南风时,临汾市发生污染的概率超过70%.     

2015-2019年成渝城市群臭氧浓度时空变化特征及人口暴露风险评价

近年来O₃污染给人类健康带来了很大威胁.本文利用2015—2019年中国环境监测总站的O₃地表监测数据,通过Global Moran''s I和Getis-Ord G^*指数等方法,分析了成渝城市群O₃浓度的时空变化特征,并利用空间插值和LandScan人口格网分布数据,基于人口暴露风险模型对该地区的O₃人口暴露风险进行了评价.结果表明：①2015—2019年成渝城市群O₃浓度总超标比例为6.9%,年际变化呈先上升后下降趋势,逐月变化呈"双峰型",5月和8月达到峰值,12月最低,季节变化表现为夏季>春季>秋季>冬季,日变化呈"单峰型",8：30左右开始升高,16：00左右达到峰值;②2015—2019年成渝城市群O₃浓度呈现出由成都及周边城市为污染中心向以成都市和重庆市为首尾的"带状"污染空间格局发展的趋势,且空间自相关性较强,逐步形成以成都市和重庆市为双中心的高浓度集聚特征;③2015—2019年成渝城市群平均O₃人口暴露风险指数处于较低风险,但空间分布差异较大,人口暴露高风险地区主要集中于成都市、内江市、自贡市、德阳市、泸州市北部及重庆市主城区,低风险地区主要集中于东西片区、绵阳市北部及成渝城市群边界,中部高风险区域有向西南方向转移的趋势,重庆市南部有高风险向极高风险转化的趋势,同时,成都市和重庆市存在显著的高风险指数集聚特征.     

汾渭平原PM2.5浓度的影响因素及空间溢出效应

基于实时监测和遥感反演数据,利用空间自相关分析和空间回归分析等方法,探讨了汾渭平原2015~2017年PM_2.5浓度时空变化规律和影响因素,揭示了各因素的空间溢出效应.结果表明：（1）2015~2017年汾渭平原PM_2.5浓度逐年上升,主要由采暖期（11月~次年3月）的快速上升引起,非采暖期（4~10月）年际变化不大.（2）PM_2.5月均浓度变化曲线呈底部宽缓的U型,采暖期PM_2.5污染明显高于非采暖期,超标天数占全年总超标天数比例由2015年的75.0%上升到2017年的83.4%.（3）2015~2017年,除铜川和三门峡外,各城市PM_2.5浓度都有不同程度的上升.咸阳至运城间的平原地区和洛阳盆地污染最严重,已形成连片的高污染区域,且区域内城乡差异小.临汾及其上游平原地区其次,但主要分布在城镇,城乡差异较大.（4）空间回归分析表明,汾渭平原PM_2.5浓度有显著的空间溢出效应.年均气温、城镇化率、能源消费指数和年均人口不仅与本地PM_2.5浓度有显著的正相关,而且会加重邻近地区PM_2.5污染.年降水量和地形起伏度则不仅与本地PM_2.5浓度有显著的负相关,而且能降低邻近地区PM_2.5浓度.风的传输作用能加重本地PM_2.5污染,植被覆盖度能消减本地PM_2.5浓度,但其间接效应都不显著.     

基于景观指数和空间自相关的吉林大安市景观格局动态研究

以松嫩平原西部盐碱化严重的典型区域大安市为研究对象,基于2000和2010年两个时期的土地利用和增强植被指数（EVI）数据,利用移动窗口法和局部空间自相关分析,研究大安市景观格局的变化。结果表明,大安市优势景观类型是农田、草地和盐碱地,2000年农田、草地和盐碱地分别占景观类型总面积的40.20%、19.09%和19.26%,2010年分别占景观类型总面积的41.69%、18.16%和19.94%。2000-2010年,农田和盐碱地面积增加,最大斑块指数增加,景观形状变得复杂,并且盐碱地的景观连通性增强,而草地的面积减少,最大斑块指数降低,景观形状变得复杂。大安市景观格局空间分布特征明显,优势度较高的景观类型分布的区域包括草地、农田和盐碱地,景观连通性强,景观破碎化程度和异质化程度低。而各种景观类型交错分布的区域景观破碎化程度较高,景观异质性较强。大安市2000和2010年EVI的局部Moran’s I分别为0.73和0.75,在空间分布上呈现出明显的空间自相关性。2000和2010年EVI呈高-高自相关的地区大多为农田,这些区域的植被覆盖较好,EVI呈低-低自相关的地区大多为盐碱地,植被覆盖较差。大安市不同景观类型的Moran’s I和斑块密度（PD）以及斑块形状指数（LSI）呈负相关关系,和最大斑块指数（LPI）以及蔓延度指数（CONTAG）呈正相关关系。移动窗口法和空间自相关法的结合分析,有助于了解大安市景观格局的空间变化特征及植被覆盖的空间聚集规律,从而为该地区生态环境保护提供依据。     

塔里木盆地东南缘绿洲区土壤砷空间分布及农作物砷富集特征

以新疆塔里木盆地东南缘的巴音郭楞蒙古自治州的若羌县、且末县和和田地区的民丰县、于田县的绿洲区为研究区,采集表层土壤3487组、土壤剖面采样点35组,农产品及根系土壤采样点93组,综合采用数理统计方法、地统计学和GIS技术,研究土壤As的空间分布特征,探讨各类农产品对As富集的影响,比较空间自相关性显著区域和空间自相关性无显著区域内农产品和根系土壤中As的含量特征.结果表明土壤As含量较低,农用地和非农用地土壤As含量均值分别为9. 81mg·kg^-1和7. 94 mg·kg^-1.表层土壤As含量超过新疆土壤背景值的采样点个数为568个,占总取样点数的16. 3%;超过风险筛选值的采样点个数为5个.土壤As空间自相关的莫兰指数均大于0,空间聚集类型主要以高-高型和低-低型为主,其中高-高型聚集区主要位于各县农用地范围内.GIS空间分布显示,土壤As含量高值区呈片状集中或岛状零散分布.标准差椭圆显示,若羌县土壤As含量变化趋势方向为南北方向,且末县和民丰县土壤As含量变化趋势方向为西南-东北方向,于田县土壤As含量变化趋势方向为西北-东南方向.若...     

自相关拓扑指数与有机物生物活性的定量关系

采用自相关拓扑指数作为分子描述符，研究有机污染物的遗传毒性和急性毒性与分子结构的定量关系，建立了良好的构效关系方程。     

北方农牧交错带干旱脆弱性时空格局演变

干旱脆弱性研究为北方农牧交错带人地系统的“整合”研究提供了新思路与分析框架。论文运用VSD脆弱性评估框架,将系统脆弱性分解为暴露度、敏感性、适应能力3个维度,以北方农牧交错带112个县域为研究单元,以2000、2008、2015年社会经济统计数据、气象数据、遥感影像数据为研究基础,运用模糊层次分析、空间热点探测分析和变异系数分析等方法,对各县域干旱脆弱性时空演变特征进行分析与总结。研究结论：1）研究区干旱脆弱性指数逐步降低,整体呈现东北地区低脆弱、西南地区高脆弱的空间格局。2）研究区暴露度指数先上升、后下降,且由东北向西南逐渐增高;敏感性指数先下降、后上升,呈现空间碎片化特征;适应能力指数逐步提高,呈现中部高度适应、东部中度适应、西部低度适应的格局。3）研究区干旱脆弱性差异度先增大、后减小,且呈现西、南部差异度大,东、北部差异度小的格局。4）研究区干旱脆弱性冷、热点个数增加明显,2000—2008年冷热点个数增加最明显;空间上,热点先由中部向西部蔓延,冷点由东部边界区向西部、南部扩展,形成了东部边界冷点集聚带和中、西部热点集聚区。     

Thresholds in Songbird Occurrence in Relation to Landscape Structure

Abstract:  Theory predicts the occurrence of threshold levels of habitat in landscapes, below which ecological processes change abruptly. Simulation models indicate that below critical thresholds, fragmentation of habitat influences patch occupancy by decreasing colonization rates and increasing rates of local extinction. Uncovering such putative relationships is important for understanding the demography of species and in developing sound conservation strategies. Using segmented logistic regression, we tested for thresholds in occurrence of 15 bird species as a function of the amount of suitable habitat at multiple scales (150–2000-m radii). Suitable habitat was defined quantitatively based on previously derived, spatially explicit distribution models for each species. The occurrence of 10 out of 15 species was influenced by the amount of habitat at a landscape scale (≥500-m radius). Of these species all but one were best predicted by threshold models. Six out of nine species exhibited asymptotic thresholds; the effects of habitat loss intensified at low amounts of habitat in a landscape. Landscape thresholds ranged from 8.6% habitat to 28.7% (     = 18.5 ± 2.6%[95% CI]). For two species landscape thresholds coincided with sensitivity to fragmentation; both species were more likely to occur in large patches, but only when the amount of habitat in a landscape was low. This supports the fragmentation threshold hypothesis. Nevertheless, the occurrence of most species appeared to be unaffected by fragmentation, regardless of the amount of habitat present at landscape extents. The thresholds we identified may be useful to managers in establishing conservation targets. Our results indicate that findings of landscape-scale studies conducted in regions with relatively high proportions of habitat and low fragmentation may not be applicable in regions with low habitat proportions and high fragmentation.     

The importance of spatial autocorrelation,extent and resolution in predicting forest bird occurrence

Concerns about declines in forest biodiversity underscore the need for accurate estimates of the distribution and abundance of organisms at large scales and at resolutions that are fine enough to be appropriate for management. This paper addresses three major objectives: (i) to determine whether the resolution of typical air photo-derived forest inventory is sufficient for the accurate prediction of site occupancy by forest birds. We compared prediction success of habitat models using air photo variables to models with variables derived from finer resolution, ground-sampled vegetation plots. (ii) To test whether incorporating spatial autocorrelation into habitat models via autologistic regression increases prediction success. (iii) To determine whether landscape structure is an important factor in predicting bird distribution in forest-dominated landscapes. Models were tested locally (Greater Fundy Ecosystem [GFE]) using cross-validation, and regionally using an independent data set from an area located ca. 250 km to the northwest (Riley Brook [RB]). We found significant positive spatial autocorrelation in the residuals of at least one habitat model for 76% (16/21) of species examined. In these cases, the logistic regression assumption of spatially independent errors was violated. Logistic models that ignored spatial autocorrelation tended to overestimate habitat effects. Though overall prediction success was higher for autologistic models than logistic models in the GFE, the difference was only significantly improved for one species. Further, the inclusion of spatial covariates did little to improve model performance in the geographically discrete study area. For 62% (13/21) of species examined, landscape variables were significant predictors of forest bird occurrence even after statistically controlling for stand-level variability. However, broad spatial extents explained less variation than local factors. In the GFE, 76% (16/21) of air photo and 81% (17/21) of ground plot models were accurate enough to be of practical utility (AUC > 0.7). When applied to RB, both model types performed effectively for 55% (11/20) of the species examined. We did not detect an overall difference in prediction success between air photo and ground plot models in either study area. We conclude that air photo data are as effective as fine resolution vegetation data for predicting site occupancy for the majority of species in this study. These models will be of use to forest managers who are interested in mapping species distributions under various timber harvest scenarios, and to protected areas planners attempting to optimize reserve function.