Translating habitat class to land cover to map area of habitat of terrestrial vertebrates

Area of habitat (AOH) is defined as the “habitat available to a species, that is, habitat within its range” and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.     

基于AHP-PSO的滇池流域生态补偿标准评估

通过AHP（层次分析法）在决策目标、影响指标及评估方案3层要素之间,选取湖泊环境、社会因素及经济因素3个维度的25个基层指标,建立了湖泊生态补偿标准评估指标体系。采用MATLAB软件编写了2个程序,分别用于求解初始评分矩阵、计算评估方案权重和辅助PSO（粒子群优化算法）修正指标体系中不满足一致性要求的初始评分矩阵、优化层次分析结果。经计算,生态系统服务价值理论与污染治理费用法的权重分别为38.16%、61.84%。选用生态系统服务价值理论与污染治理费用法两种评估方案,并结合市场价值法、成果参照法、影子工程法、Vollenweider模型及完全混合模型,通过实证研究检验评估标准的合理性。计算得出滇池流域的生态补偿标准总额为32.630 2亿元/a,流域内单位面积的生态补偿标准为5 207元/（a·hm²）。     

不同标准对城市土壤重金属质量分数的评价

对广州市某区城市土壤重金属污染现状进行调查,共采集土壤样品34个,分析样品中As,Hg,Cr,Cd,Ni和Pb的全量及有效态质量分数,探讨重金属元素全量和有效态质量分数之间的关系,并采用4种不同标准对结果进行了评价. 结果表明:研究区工业用地、城区和农村居住区的土壤已受到一定程度的重金属污染,且为2种或3种重金属元素的复合污染;As,Hg,Cr,Cd,Ni和Pb有效态质量分数平均值分别为0.09,0.004,0.09,0.18,0.40和32.80 mg/kg,Cd和Pb的有效态质量分数占全量的比例较高. 4种标准综合评价结果中,轻度、中度和重度污染样点数占样点总数的比例依次为国标(GB15618—1995)二级标准评价(62%)>有效态污染起始值评价(29%)>全量污染起始值评价(24%)>国标三级标准评价(21%).      

Uncertainty propagation in vegetation distribution models based on ensemble classifiers

Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannon’s entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.     

大气颗粒物“二重源解析”技术的误差分析

"二重源解析"模型计算结果的误差是采样误差、样品处理误差、化学组分分析误差、数据处理误差以及数学模型误差等所有误差的积累。提出了"二重源解析"解析结果的相对误差和标准偏差表达式,并用之计算了某市利用"二重源解析"模型计算的源贡献值的相对误差和标准偏差,还针对从源排放出来的初始态颗粒物在传输过程中发生的扬尘态变化提出了扬尘转化率的概念和计算方法。     

间位羟基/甲氧基多溴联苯醚的合成

本文设计了3种合成路线,通过偶合反应、Baeyer-Villiger氧化水解、硝基还原、加溴反应、去甲基化等一系列反应,合成了多种间位羟基/甲基多溴联苯醚.产物经过气相色谱-质谱仪(GC-MS)和核磁共振测试表明实验所合成得到的产物与设计的目标产物结构完全一致,其纯度达到标样要求.     

燃煤电厂应对新标准的氮氧化物控制策略研究

介绍了我国燃煤电厂NOx排放标准及其控制技术发展状况,分析了NOx的排放现状及我国燃煤电厂NOx控制技术的特点及存在问题。结合新标准,提出在环境约束条件下效益―成本最大化的NOx控制策略。     

发电集团应对新火电厂大气污染物排放标准的思考

新的火电厂大气污染排放标准重点加大了对火电厂大气污染物中NOx的控制力度,收严了SO2、烟尘等污染物排放限值,增设了汞及其化合物控制指标。该标准的实施对发电企业的生存和发展带来一定的影响,为了落实新标准,发电集团应在法律法规政策及技术产业方面采取相应措施,加快除尘、脱硫、脱硝、脱汞等新技术的研究和应用,从单一污染物的总量控制向多污染物协同控制转变,积极探索和思考发电集团绿色低碳发展的道路。     

On Assessing Decomposition Rates of Plant Debris and Standard Cellulose Samples in Tundra Communities

Russian Journal of Ecology -     

区域高时空分辨率VOC天然源排放清单的建立

将中尺度气象模式MM5应用于估算VOC天然源排放的研究,建立了高时空分辨率VOC天然源排放清单的估算方法.根据方法需要,确定了我国部分树木排放异戊二烯和萜烯的标准排放因子,各植被类型排放各种VOC的标准排放因子,以及各植被类型季节平均的叶生物量密度.应用该方法估算了华南地区满足区域空气质量数值模拟要求的高时空分辨率VOC天然源排放清单.结果表明,华南地区夏季典型日的VOC天然源排放总量约1.12×10⁴t,VOC排放速率具有明显的时空分布,其中地理分布取决于植被类型及其分布,日变化规律则依赖于太阳辐射和温度的高低.并讨论了VOC天然源排放估算过程中误差的来源