综合拉底、扩漏和出矿全过程的底部结构安全性颗粒流模拟技术研究

为探明自然崩落法从拉底崩落至出矿全过程中底部结构节理连续扩展过程与破坏过程规律，采用PFC2D软件，结合RocLab软件反演节理岩体参数，建立229 m×129 m的岩体扩大模型，对拉底崩落至出矿过程进行连续综合计算。研究结果表明:模拟结果及破坏现象与现场监测结果相近，建模与细观参数反演方法适用于该类岩体工程研究；裂纹扩展贯通导致岩体失稳破坏，破坏过程与裂纹扩展具有相对应的阶段特征，裂纹扩展分为崩落前稳定扩展期，崩落与聚矿槽开挖过程加速扩展期和后续持续扩展期；结构破坏分为蕴育过程，扰动失稳过程和宏观破坏过程；较大范围的宏观破坏主要在出矿阶段。该模拟方法与结果可为底部结构维护以及节理岩体长期稳定性研究提供参考。     

广东省“十二五”生态环境时空变化趋势及影响因素

基于2011—2015年Landsat7、Landsat8等卫星遥感影像,结合土壤侵蚀面积、水资源量、降雨量、污染物排放量等统计数据,按照《生态环境状况评价技术规范》(HJ 192—2015),研究评价广东省"十二五"期间生态环境状况及其时空变化趋势,并对其影响因素进行综合分析。结果表明,广东省的生态环境状况总体为优,各市生态环境状况均属优、良,粤北生态环境状况整体最好;广东省及各市生态环境状况稳中趋好,但呈现温和地波动变化;主要污染物排放量下降和水资源总量提高是促进生态环境状况改善的主要原因。     

北京市PM5.0可吸入颗粒物反演及其时空分布分析

分析城市可吸入颗粒物的空间特征及影响因素对于制定更为有效的可吸入颗粒物污染控制政策具有重要的现实意义.本研究在实际采样和遥感数据处理基础上,构造差值植被指数(DVI),通过建立TM影像与对应PM5.0颗粒物实测值之间的相关关系模型,对北京市2008~2010年3 a的PM5.0的分布进行了反演计算和精度分析;研究了NDBI和NDMI对PM5.0浓度的影响,进一步揭示了北京五环区域范围内可吸入颗粒物的时空分布特征.结果表明,采用DVI反演PM5.0分布的方法可行,反演精度在可接受范围内;整体上研究区2008年PM5.0颗粒物污染最轻,西南三环和四环以及东南三四环之间为颗粒物污染的高值区,西北五环附近颗粒物污染较轻;NDBI(归一化建筑指数)、NDMI(归一化水汽指数)与PM5.0可吸入颗粒物的分布分别呈现显著的负相关和正相关关系,且二者对PM5.0的影响相近.     

生态环境变化遥感评价指数的应用研究——以敖江流域为例

利用1994年5月12日与2009年6月6日的Landsat TM和2001年5月23日的Landsat ETM＋卫星影像,选用遥感生态指数（RSEI）,结合流域的植被覆盖度状况,定量评价了15年间敖江流域的生态环境变化与植被覆盖度变化情况,并对二者的关系进行了简要分析.结果表明：15年间,敖江流域生态等级为优所占的面积比例从13.48％上升到24.90％,增加了304.29 km2;植被覆盖度等级为极高的面积增加比例为29.31％.总体看来,敖江流域生态环境状况和植被覆盖状况均有明显的提高,两者具有较好的对应关系.     

利用FLEXPART模式反演中国区域SF6排放量

六氟化硫(SF_6)是一种长寿命卤代温室气体,被列为《京都议定书》限排物种.随着经济高速发展,中国的SF6排放量受到世界各国的关注.采用传统的"自下而上"清单方法估算SF6排放量时,所需排放因子、活动水平数据的准确性和时效性存在较大局限.因此,本文利用拉格朗日粒子扩散模式FLEXPART,结合2009年北京上甸子区域大气本底站SF6浓度观测资料,尝试建立中国区域SF6排放量的反演方法.结果表明,初步反演的2009年中国区域SF6排放量为1.25×103(0.53×103~1.97×103)t·a-1,与文献结果相当,源排放量的不确定性从1.05×103t·a-1减小到0.72×103t·a-1.与先验源相比,反演源的相关系数从0.37提高到0.43,均方根误差减小了2.64%.     

大渡河上游丹巴地区地质灾害的地貌分维特征解析

地貌对地质灾害的分布、类型、规模与危害等特征具有重要的影响,宏观及微观地貌指标均能在一定程度上刻画其作用特征。应用高分辨率遥感影像详细解译崩塌和滑坡灾害,采用高精度DEM和滑动窗口技术提取地貌分维特征,并用其解析灾害分布的地貌特征。结果表明:灾害体的内部与后壁、前缘、侧壁和邻区均表现出较为明显的分异性,崩塌的微地貌分异特征较之滑坡显著,大型、厚层、老(古)崩塌及滑坡分异特征更为突出。计算地貌分维值可有效识别与其同时期的或更早的地质灾害,也可间接挖掘新发生灾害的微地貌复杂性和离散性。     

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain‐referral survey. We then used a workshop‐based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real‐time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing‐derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?     

AHLs群体感应信号分子对硝化污泥附着生长及硝化效果的影响

污水生物脱氮系统中的硝化菌生长慢、易流失,人为添加N-酰基高丝氨酸内酯类(AHLs)群体感应信号分子,可能会强化硝化菌生物膜的形成,从而有助于富集硝化菌,提高硝化效率.本研究以采用低碳氮比(C∶N=8)人工配置废水驯化100 d后的硝化活性污泥为菌源,人为外加2μmol·L-1的AHLs信号分子(C8-HSL或OHHL),分析了两种信号分子对硝化污泥静态附着、氨氮动态降解及微生物生长速率的影响.研究结果表明,信号分子OHHL能快速强化微生物附着生长,且能在一定时间内持续发挥作用,有助于硝化生物膜的形成;而信号分子C8-HSL则能明显提高硝化污泥的氨氮降解速率;两种信号分子都能促进硝化污泥生长,提高微生物生长速率,增强硝化污泥活性,加速硝化污泥生物量累积.人为添加C8-HSL或OHHL信号分子,不仅能保证氨氮降解效率还能降低出水硝氮浓度,减轻氮污染.     

Calibrating a Basin‐Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ET_g) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ET_g. An iterative approach is provided that calibrates GWM to ET_g derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11‐year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ET_g to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM‐grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first‐order second moment analysis shows ET_g to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ET_g despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ET_g appears to bias ET_g during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness.