Landscape trajectory of natural boreal forest loss as an impediment to green infrastructure

Loss of natural forests by forest clearcutting has been identified as a critical conservation challenge worldwide. This study addressed forest fragmentation and loss in the context of the establishment of a functional green infrastructure as a spatiotemporally connected landscape-scale network of habitats enhancing biodiversity, favorable conservation status, and ecosystem services. Through retrospective analysis of satellite images, we assessed a 50- to 60-year spatiotemporal clearcutting impact trajectory on natural and near-natural boreal forests across a sizable and representative region from the Gulf of Bothnia to the Scandinavian Mountain Range in northern Fennoscandia. This period broadly covers the whole forest clearcutting period; thus, our approach and results can be applied to comprehensive impact assessment of industrial forest management. The entire study region covers close to 46,000 km² of forest-dominated landscape in a late phase of transition from a natural or near-natural to a land-use modified state. We found a substantial loss of intact forest, in particular of large, contiguous areas, a spatial polarization of remaining forest on regional scale where the inland has been more severely affected than the mountain and coastal zones, and a pronounced impact on interior forest core areas. Salient results were a decrease in area of the largest intact forest patch from 225,853 to 68,714 ha in the mountain zone and from 257,715 to 38,668 ha in the foothills zone, a decrease from 75% to 38% intact forest in the inland zones, a decrease in largest patch core area (assessed by considering 100-m patch edge disturbance) from 6114 to 351 ha in the coastal zone, and a geographic imbalance in protected forest with an evident predominance in the mountain zone. These results demonstrate profound disturbance of configuration of the natural forest landscape and disrupted connectivity, which challenges the establishment of functional green infrastructure. Our approach supports the identification of forests for expanded protection and conservation-oriented forest landscape restoration.     

Fault detection and diagnostics of a three-phase separator

A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator.     

The value of virtual conferencing for ecology and conservation

The objectives of conservation science and dissemination of its research create a paradox: Conservation is about preserving the environment, yet scientists spread this message at conferences with heavy carbon footprints. Ecology and conservation science depend on global knowledge exchange—getting the best science to the places it is most needed. However, conference attendance from developed countries typically outweighs that from developing countries that are biodiversity and conservation hotspots. If any branch of science should be trying to maximize participation while minimizing carbon emissions, it is conservation. Virtual conferencing is common in other disciplines, such as education and humanities, but it is surprisingly underused in ecology and conservation. Adopting virtual conferencing entails a number of challenges, including logistics and unified acceptance, which we argue can be overcome through planning and technology. We examined 4 conference models: a pure‐virtual model and 3 hybrid hub‐and‐node models, where hubs stream content to local nodes. These models collectively aim to mitigate the logistical and administrative challenges of global knowledge transfer. Embracing virtual conferencing addresses 2 essential prerequisites of modern conferences: lowering carbon emissions and increasing accessibility for remote, time‐ and resource‐poor researchers, particularly those from developing countries.     

Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

Effectiveness of Scat‐Detection Dogs in Determining Species Presence in a Tropical Savanna Landscape

Abstract: Most protected areas are too small to sustain populations of wide‐ranging mammals; thus, identification and conservation of high‐quality habitat for those animals outside parks is often a high priority, particularly for regions where extensive land conversion is occurring. This is the case in the vicinity of Emas National Park, a small protected area in the Brazilian Cerrado. Over the last 40 years the native vegetation surrounding the park has been converted to agriculture, but the region still supports virtually all of the animals native to the area. We determined the effectiveness of scat‐detection dogs in detecting presence of five species of mammals threatened with extinction by habitat loss: maned wolf (Chrysocyon brachyurus), puma (Puma concolor), jaguar (Panthera onca), giant anteater (Myrmecophaga tridactyla), and giant armadillo (Priodontes maximus). The probability of scat detection varied among the five species and among survey quadrats of different size, but was consistent across team, season, and year. The probability of occurrence, determined from the presence of scat, in a randomly selected site within the study area ranged from 0.14 for jaguars, which occur primarily in the forested areas of the park, to 0.91 for maned wolves, the most widely distributed species in our study area. Most occurrences of giant armadillos in the park were in open grasslands, but in the agricultural matrix they tended to occur in riparian woodlands. At least one target species occurred in every survey quadrat, and giant armadillos, jaguars, and maned wolves were more likely to be present in quadrats located inside than outside the park. The effort required for detection of scats was highest for the two felids. We were able to detect the presence for each of five wide‐ranging species inside and outside the park and to assign occurrence probabilities to specific survey sites. Thus, scat dogs provide an effective survey tool for rare species even when accurate detection likelihoods are required. We believe the way we used scat‐detection dogs to determine the presence of species can be applied to the detection of other mammalian species in other ecosystems.     

滇西北高原湖泊剑湖演变过程及其生态环境效应分析

以滇西北高原湖泊剑湖为研究区,1974—2015年间8期遥感影像为数据源,运用遥感和地理信息系统技术,研究剑湖湖泊近40年间的时空演变过程,探析剑湖湖泊的入湖污染物及其生态环境效应变化。结果表明:近40年来剑湖湖泊面积呈现先明显减少再增加后缓慢减少的变化规律,由1974年的504.47 hm~2减至2015年的451.72 hm~2;目前剑湖水体中COD含量较低,符合地表水环境质量Ⅱ类标准;TP含量满足Ⅲ类标准;TN含量仅可满足IV类标准;剑湖入湖的主要污染源是农村生活污染和农业面源污染;随着时间的演变剑湖湖泊TN、TP污染物浓度呈上升趋势,剑湖湖泊生态系统受到极大干扰。     

基于质谱技术的在线大气颗粒物来源与污染过程解析研究——以江苏省淮安市为例

通过大气细颗粒物实时在线源解析技术(质谱直接测量法)采集录入淮安市市区部分特征污染源排放成分谱,对淮安市大气细颗粒物进行在线源解析,获取高时间分辨的颗粒物源解析结果,分析颗粒物来源、季度变化和重污染天气的污染过程,评估形成重污染过程的污染影响因子和影响程度,为区域大气污染综合整治提供重要目标指向,同时也为淮安环境空气PM2.5重度污染实时预警与监控、大气污染源减排方案评估、污染事件监控等提供有效的在线监测技术平台.     

单光子/光电子在线质谱实时分析聚氯乙烯热分解/燃烧产物

实验室研制了低能光电子磁场增强电离(MEPEI)和单光子电离(SPI)复合电离源,该电离源具有电离化合物范围宽、碎片离子少、质谱图简单等优点,结合飞行时间质谱(TOF-MS)实现了聚氯乙烯(PVC)热分解/燃烧产物的在线分析.针对不同的目标化合物,MEPEI/SPI复合电离源通过调节电离区电场强度,可以快速在SPI和SPI-MEPEI之间切换.PVC热分解/燃烧产物中电离能大于光子能量(10.60 eV)的HCl和CO2(12.74 eV、13.77 eV)利用MEPEI模式电离;而烯烃、二氯乙烯、苯系物、氯苯、苯乙烯、茚满及萘系物等可通过SPI-MEPEI复合模式电离.通过实时监测PVC主要燃烧产物的信号强度随温度的变化趋势可推断出,PVC燃烧产物主要是通过两种机制生成:①250～370℃时,PVC发生脱氯及分子内环化反应,产生大量的HCl、苯和萘;②380～510℃时,PVC发生分子间交联反应,生成烷基芳烃,如甲苯和二甲苯/乙苯等.实验结果表明,SPI/SPI-MEPEI复合离子源TOF-MS分析速度快,应用范围广,在在线分析中具有广泛的应用前景.     

原子吸收火焰法次灵敏线测定污水和电镀污泥中高含量的锌

对采用原子吸收火焰法次灵敏线测定污水和电镀污泥中高含量的锌进行了研究。试验结果表明：在锌浓度50．0～600．0mg/L之间,溶液锌含量与吸光度呈线性关系。线性回归方程为y＝0．000661x-0．0007,相关系数γ＝0．9998。方法检出限为25．0mg/L,相对标准误差（RSD）＜2．96％,加标回收率为92．5％～105％,与标准法的相对误差＜2．76％。     

在线气相色谱测定园区中特征污染因子研究

近两年来,随着科学技术的不断发展,在线气相色谱分析仪已逐步在国内监测实际中得到应用,在线气相色谱的应用使挥发性有机物的实时、连续监测成了可能,有效改善了实验室监测数据的滞后性,减少了样品采集、储存、运输过程中产生的误差,结合常州市某化工园区大气特征污染因子预警系统的建设情况,简单介绍了在线气相色谱的工作原理和过程。通过特征污染因子标准曲线的建立和比对试验,说明了在线气相色谱环境监测应用的可行性。