深黄伞形霉(Umbelopsis isabellina)华2-1产油发酵培养条件优化

为开发微生物油脂资源,从富含油脂的土样中分离获得一株产油脂微生物——深黄伞形霉(Umbelopsisisabellina)华2-1,油脂含量达48.60%.采用单因素和正交实验方法,对深黄伞形霉华2-1的发酵培养条件进行优化研究.优化的培养条件为:葡萄糖100 g/L,酵母粉3 g/L,接种量为20%,初始pH值为5.0～6.0,MgSO4.7H2O和KH2PO4的添加浓度分别为0.5 g/L和2 g/L,培养温度为31℃,最佳发酵培养时间为168 h.华2-1在优化发酵条件下可获得菌体生物量、油脂产量和油脂含量分别为45.86 g/L、24.47 g/L和59.53%,较优化前分别提高了21.48%、33.42%和22.49%.因此,深黄伞形霉华2-1作为微生物油脂生产的新资源具有广阔的应用前景.     

基于环境一号卫星的大洋河河口海域营养盐遥感反演

综合利用大洋河河口海域水体调查的实测数据及环境一号CCD影像数据,进行了该区域营养盐反演研究.结果表明,对数函数拟合NO2--N与Rrs(Band2)效果最佳,而线性函数拟合NH4+-N与Rrs(Band2)、NO3--N与Rrs(Band1)、TIN与Rrs(Band1)效果最佳,相关性R2分别可达0.939、0.935、0.945、0.970,未发现较佳的PO43--P反演模型;营养盐在可见光波段并非光敏感性物质,其与等效遥感反射率存在相关性的主要原因在于河口海域陆源输入物质典型的扩散特征,使得营养盐与某些水色参数存在较高的相关性,NO2--N、NH4+-N、NO3--N以及TIN与有色溶解性有机物吸收系数ag(400)的相关性R2分别可达0.836、0.808、0.786、0.854,这决定了在河口海域进行营养盐遥感反演的可行性;从卫星产品专题图可看出,大洋河河口海域NO2--N、NH4+-N、NO3--N以及TIN都存在一个明显的高值区,该高值区营养盐浓度由河口向外逐渐降低,直至外海最低.     

国内引进催化裂化再生烟气脱硫装置存在问题及对策

对国内引进杜邦Belco公司EDV湿法洗涤脱硫系统治理催化裂化再生烟气运行过程进行分析,指出EDV湿法洗涤脱硫系统存在的问题;并结合中国石油化工研究院开发的10000m3/h催化裂化烟气脱硫、脱硝、除尘一体化技术提出了解决措施,以满足《石油炼制企业污染物排放标准》中催化裂化催化剂再生烟气排放限值。     

基于高分1号卫星和地面实测数据的杭州湾河口湿地植物物种多样性研究

遥感监测河口湿地植物物种多样性对于湿地生态环境的保护具有重要意义,利用遥感卫星准确反演湿地植物物种多样性一直是研究的难点。研究以杭州湾河口湿地植物为研究对象,利用地面实测数据,采用植物物种丰富度指数和Simpson指数分析了该湿地植物物种多样性水平。结合高分1号卫星影像,运用半变异函数对该区域植物物种多样性进行了空间异质性分析,探讨了高分1号卫星归一化植被指数（NDVI）用于湿地植物物种多样性监测的最佳遥感尺度,分析该植被指数与植物物种多样性指数的相关性,构建和应用了杭州湾河口湿地植物物种多样性遥感监测模型。结果表明：高分1号卫星NDVI监测湿地植物物种多样性的最佳尺度为19×19像元,利用该尺度及构建的遥感监测模型所得到的杭州湾河口湿地植物物种多样性水平与实际情况相吻合。研究证明利用高分1号卫星可快速有效地监测杭州湾河口湿地植物物种多样性水平。     

基于植被指数的GF-1与Landsat-OLI石漠化识别能力对比评价

植被指数是运用多源遥感影像提取石漠化过程中的主要参考指标之一。为了在石漠化提取中选择最优植被指数,论文以GF-1和Landsat-OLI为源数据,运用欧氏距离对多种植被指数在石漠化提取过程中的可分性和类型识别能力进行了定量的对比评价。结果表明：Landsat-OLI在石漠化与非石漠化、不同等级石漠化信息提取的可分性上略优于GF-1,共有71个参数的欧氏距离大于等于阈值1.56;通过植被指数光谱特征,可以对非岩溶区与石漠化地区进行较好的区分,其类型间欧氏距离普遍高于阈值;然而由于相邻等级石漠化之间植被覆盖率存在渐近式过渡关系,在遥感影像上光谱反射率接近,比间隔等级石漠化更加难于区分。在石漠化类型识别能力方面,波段差和比方法优于单一光谱指数。对于GF-1和Landsat-OLI而言,石漠化信息提取中推荐使用的最优植被指数均为NDVI,其次为GRNDVI。     

A generalized approach for producing,quantifying, and validating citizen science data from wildlife images

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org , more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.     

Characterizing Spatial Uncertainty when Integrating Social Data in Conservation Planning

Recent conservation planning studies have presented approaches for integrating spatially referenced social (SRS) data with a view to improving the feasibility of conservation action. We reviewed the growing conservation literature on SRS data, focusing on elicited or stated preferences derived through social survey methods such as choice experiments and public participation geographic information systems. Elicited SRS data includes the spatial distribution of willingness to sell, willingness to pay, willingness to act, and assessments of social and cultural values. We developed a typology for assessing elicited SRS data uncertainty which describes how social survey uncertainty propagates when projected spatially and the importance of accounting for spatial uncertainty such as scale effects and data quality. These uncertainties will propagate when elicited SRS data is integrated with biophysical data for conservation planning and may have important consequences for assessing the feasibility of conservation actions. To explore this issue further, we conducted a systematic review of the elicited SRS data literature. We found that social survey uncertainty was commonly tested for, but that these uncertainties were ignored when projected spatially. Based on these results we developed a framework which will help researchers and practitioners estimate social survey uncertainty and use these quantitative estimates to systematically address uncertainty within an analysis. This is important when using SRS data in conservation applications because decisions need to be made irrespective of data quality and well characterized uncertainty can be incorporated into decision theoretic approaches.     

Trends in Deforestation and Forest Degradation after a Decade of Monitoring in the Monarch Butterfly Biosphere Reserve in Mexico

We used aerial photographs, satellite images, and field surveys to monitor forest cover in the core zones of the Monarch Butterfly Biosphere Reserve in Mexico from 2001 to 2012. We used our data to assess the effectiveness of conservation actions that involved local, state, and federal authorities and community members (e.g., local landowners and private and civil organizations) in one of the world's most iconic protected areas. From 2001 through 2012, 1254 ha were deforested (i.e., cleared areas had <10% canopy cover), 925 ha were degraded (i.e., areas for which canopy forest decreased), and 122 ha were affected by climatic conditions. Of the total 2179 ha of affected area, 2057 ha were affected by illegal logging: 1503 ha by large‐scale logging and 554 ha by small‐scale logging. Mexican authorities effectively enforced efforts to protect the monarch reserve, particularly from 2007 to 2012. Those efforts, together with the decade‐long financial support from Mexican and international philanthropists and businesses to create local alternative‐income generation and employment, resulted in the decrease of large‐scale illegal logging from 731 ha affected in 2005–2007 to none affected in 2012, although small‐scale logging is of growing concern. However, dire regional social and economic problems remain, and they must be addressed to ensure the reserve's long‐term conservation. The monarch butterfly (Danaus plexippus) overwintering colonies in Mexico—which engage in one of the longest known insect migrations—are threatened by deforestation, and a multistakeholder, regional, sustainable‐development strategy is needed to protect the reserve. Tendencias en la Deforestación y la Degradación de Forestal después de una Década de Monitoreo en la Reserva de la Biósfera de la Mariposa Monarca en México     

Foundations of Resilience Thinking

Through 3 broad and interconnected streams of thought, resilience thinking has influenced the science of ecology and natural resource management by generating new multidisciplinary approaches to environmental problem solving. Resilience science, adaptive management (AM), and ecological policy design (EPD) contributed to an internationally unified paradigm built around the realization that change is inevitable and that science and management must approach the world with this assumption, rather than one of stability. Resilience thinking treats actions as experiments to be learned from, rather than intellectual propositions to be defended or mistakes to be ignored. It asks what is novel and innovative and strives to capture the overall behavior of a system, rather than seeking static, precise outcomes from discrete action steps. Understanding the foundations of resilience thinking is an important building block for developing more holistic and adaptive approaches to conservation. We conducted a comprehensive review of the history of resilience thinking because resilience thinking provides a working context upon which more effective, synergistic, and systems‐based conservation action can be taken in light of rapid and unpredictable change. Together, resilience science, AM, and EPD bridge the gaps between systems analysis, ecology, and resource management to provide an interdisciplinary approach to solving wicked problems. Fundamentos del Pensamiento sobre Resiliencia     

基于PLS1的哈尔滨市PM2.5与空气污染物相关性分析

以往对PM2.5的研究多集中在气象因子或单一空气污染物对PM2.5质量浓度变化的影响,未考虑多种空气污染物对PM2.5质量浓度的协同作用。通过哈尔滨市环保局发布的2014年1月份（共31 d）市区内主要空气污染物SO2、NO2、PM10、CO、O3、PM2.5的质量浓度数据,运用相关性分析、PLS1和通径分析方法,研究哈尔滨市区内主要空气污染物对PM2.5质量浓度变化的直接影响、通过其他空气污染物的间接影响及污染物之间的协同作用。结果表明,SO2、NO2、PM10、CO 与 PM2.5质量浓度显著性相关,O3与 PM2.5质量浓度相关性不显著,SO2、NO2、PM10、CO 之间存在严重的复相关性。依据相关性分析结果,建立了SO2、NO2、PM10、CO对PM2.5质量浓度的PSL1模型,模型的拟合优度r2为0.852,模型拟合良好。对所建立的模型进行通径分析,结果显示,SO2、NO2、PM10、CO对PM2.5质量浓度变化的直接作用分别为0.005、-0.142、-0.140、1.191,CO对PM2.5质量浓度变化的直接影响作用最大。SO2、NO2、PM10通过CO对PM2.5质量浓度变化的间接作用分别为0.706、1.011、1.118均大于它们对PM2.5质量浓度变化的直接作用。SO2、NO2、PM10、CO对PM2.5质量浓度变化的总决定系数为85.9%。CO是主要空气污染物中影响PM2.5质量浓度变化的主要因素,降低冬季煤炭供暖期CO的排放量,有利于提高空气环境质量,降低对人体的健康危害。