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.     

国家历史文化名城地区农业与水文地质的关系

国家历史文化名城地区记录了华夏农耕文明更多的生活经验,论文以这些城市地区为研究对象,通过在全国尺度选取典型群体110 个国家历史文化名城地区为样本,并定义农业水文地质适宜度指标,对各类水文地质与农业活动的空间关系进行了量化。从研究结果来看：各富水程度的松散岩类孔隙含水岩组是农业适宜性较强的水文地质类型;碳酸盐岩类含水岩组的农业适宜性也较强;而岩浆岩类含水岩组以及变质岩类含水岩组等则是农业适宜性较弱的水文地质类型,其中松散岩类孔隙含水岩组的农业水文地质适宜度是岩浆岩类含水岩组以及变质岩类含水岩组的2~3 倍。     

基于HJ-1B与TM热红外数据的大亚湾核电基地温排水遥感监测

选取同天过境的Landsat5TM和HJ-1B红外相机数据,在获取3个大气参数后,结合辐射传输模型分别对大亚湾核电基地附近海域海表温度和温排水温升区的分布情况进行遥感监测,对比结果显示,环境卫星遥感监测数据与TM数据监测所得温排水在空间分布上具有较高一致性,二者反演出的绝对温度相差不超过1.1℃,相同温升等级范围内的相对温度均值也不超过0.4℃.因此,HJ-1B红外相机可满足核电站热污染的监测需求.     

人工神经网络模型在水质预警中的应用研究进展

水质预警模型是大数据时代构建环境智能决策与管理体系的关键技术.近年来,水质自动化监测能力的提升以及测管协同对环境模型的强烈需求,激发了研究人员探索新的建模方法并努力提高模型预测性能.其中,人工神经网络(Artificial Neural Network, ANN)模型发展迅速.本文综述了3大类ANN模型的发展历史和模型结构特点,梳理了ANN模型在水质数据软测量、数据异常检测和时间序列预测等方面的研究进展,归纳了一般建模流程、技术建议和常用的模型性能指标,发现ANN模型的应用依赖于监测数据质量,存在模型可解释性差、模型运行硬件资源要求较高等不足,提出未来水质预警模型的研发思路和重点,需要加快推进水环境监测技术与预警模型的协同发展和业务化应用,通过多种应用场景检验实现技术迭代,形成大数据驱动的水质在线监测-智能预警-应急管理支撑体系,助力我国环境治理能力现代化.     

Adapting to new policy environment – past pattern and future trend in us-sino waste plastic trade flow

Plastics are one of the most used materials in human activities, where consumer consumption and industrial production together has imposed vast rise in demand for this material in last century. While plastic is ideally derived from crude oil as a primary source from manufacturers’ perspective, varying crude oil prices are driving manufacturers economically to seek for alternative sources for plastics production. Waste plastic recovered from obsolete consumer products thus becomes an economic substitution for virgin plastics, which is further intensified with the possibility of international waste plastic trading. This study focuses on waste plastic trade between the US and mainland China by performing a correlation analysis of trade data. It is suggested in this study that although waste plastics are traded from the US to mainland China in general, as many of us believes, the route is gradually shifting in the past years. With tightening Chinese customs regulations, waste plastic from the US now tends to take a transit in a third destination (Hong Kong SAR for instance) for preliminary treatment to bypass Chinese customs inspection. Such phenomenon is worth noting, as a complication in waste plastic trading route hinders waste plastic transboundary movement monitoring. Furthermore, it will have adverse consequent consumer, industrial, and environmental impacts. It is thus necessary for national competent authorities to strengthen cooperative study and communication capacity in the future as a response to the changing waste plastic trade pattern.     

How to address data privacy concerns when using social media data in conservation science

Social media data are being increasingly used in conservation science to study human–nature interactions. User-generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user-generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well-being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects’ rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk-based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.     

永久基本农田保护的实施评价与政策启示--以我国东部地区XX地级市为例

基于“三调”数据分析我国东部地区XX地级市永久基本农田保护变化,从流失去向与流失空间分布两个方面深入分析了其真实境况,从而提出切实的保护政策建议。结果表明:①流失去向包括农业结构调整、建设占用和自然损毁,其中农业结构调整占用为主导。②城市圈内比圈外占用普遍严重,尤其是城乡建设占用了大量基本农田。③永久基本农田存在空间错配现象,未做到“应划尽划”。据此,提出构建农户补偿机制、县级保护任务量与存量建设指标相挂钩、协同基本农田—生态—城镇空间布局,实施圈内基本农田动态监测机制等系列措施,以提升保护效果。     

非稳定流巷道火灾试验系统的研制

介绍了巷道火灾试验系统的构成,测定燃烧区膨胀热阻时测点位置的确定方法,火灾时期烟气温度、壁温及岩体温度、烟流中CO2及O2体积分数、沿程压力和风速等的测试方法及测试仪表精度,系统抗干扰方法,数据采集和处理软件的功能以及测试系统的调试方法.系统采用无补偿导线经济地解决了巷道燃烧区测温热电偶冷端温度补偿的方法,使测温系统误差由过去的10%左右下降到3%.     

Technical efficiency analysis of the conversion of cropland to forestland program in Jiangxi,Shaanxi, and Sichuan

Conversion of Cropland to Forestland Program (CCFP) has greatly impacted China’s agricultural sector, and more specifically rural farmers. While changes in living standards as a result of the implementation of the CCFP have been analyzed, little research has been conducted regarding the impacts of such policies on farming operations. As agriculture contributes nearly 10% of national GDP, it is important to analyze the implications of policies on a national industry. An input-oriented data envelopment analysis (DEA) model was used to examine the technical efficiency of farming operations following implementation of the CCFP, using survey data from farmers in Jiangxi, Shaanxi, and Sichuan provinces. Additionally, the impact of factors such as urbanization, age and education, and land fragmentation was examined with respect to farming operational efficiency. Scale inefficiency was found to have the greatest effect on overall inefficiency in farming operations in comparison to pure technical inefficiency, which was largely influenced by the presence and degree of land fragmentation of land holdings. Findings can be used to inform national land-use policies facilitating land fragmentation in China and address gaps in existing broader level studies that utilize non-parametric approaches to examine the technical efficiency of Chinese farmers affected by the CCFP.     

Applying habitat and population-density models to land-cover time series to inform IUCN Red List assessments

The IUCN (International Union for Conservation of Nature) Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends, and structure of species’ distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification or classification as data deficient. We devised an approach that combines data on land-cover change, species-specific habitat preferences, population abundance, and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence predict IUCN Red List categories for species. We applied our approach to nonpelagic birds and terrestrial mammals globally (∼15,000 species). The predicted categories were fairly consistent with published IUCN Red List assessments, but more optimistic overall. We predicted 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed and 20.2% of data deficient species (10 birds and 114 mammals) to be at risk of extinction. Incorporating the habitat fragmentation subcriterion reduced these predictions 1.5–2.3% and 6.4–14.9% (depending on the quantitative definition of fragmentation) for threatened and data deficient species, respectively, highlighting the need for improved guidance for IUCN Red List assessors on the application of this aspect of the IUCN Red List criteria. Our approach complements traditional methods of estimating parameters for IUCN Red List assessments. Furthermore, it readily provides an early-warning system to identify species potentially warranting changes in their extinction-risk category based on periodic updates of land-cover information. Given our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment.