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401.
Jeremy R. Brammer Nicolas D. Brunet A. Cole Burton Alain Cuerrier Finn Danielsen Kanwaljeet Dewan Thora Martina Herrmann Micha V. Jackson Rod Kennett Guillaume Larocque Monica Mulrennan Arun Kumar Pratihast Marie Saint‐Arnaud Colin Scott Murray M. Humphries 《Conservation biology》2016,30(6):1277-1287
Many argue that monitoring conducted exclusively by scientists is insufficient to address ongoing environmental challenges. One solution entails the use of mobile digital devices in participatory monitoring (PM) programs. But how digital data entry affects programs with varying levels of stakeholder participation, from nonscientists collecting field data to nonscientists administering every step of a monitoring program, remains unclear. We reviewed the successes, in terms of management interventions and sustainability, of 107 monitoring programs described in the literature (hereafter programs) and compared these with case studies from our PM experiences in Australia, Canada, Ethiopia, Ghana, Greenland, and Vietnam (hereafter cases). Our literature review showed that participatory programs were less likely to use digital devices, and 2 of our 3 more participatory cases were also slow to adopt digital data entry. Programs that were participatory and used digital devices were more likely to report management actions, which was consistent with cases in Ethiopia, Greenland, and Australia. Programs engaging volunteers were more frequently reported as ongoing, but those involving digital data entry were less often sustained when data collectors were volunteers. For the Vietnamese and Canadian cases, sustainability was undermined by a mismatch in stakeholder objectives. In the Ghanaian case, complex field protocols diminished monitoring sustainability. Innovative technologies attract interest, but the foundation of effective participatory adaptive monitoring depends more on collaboratively defined questions, objectives, conceptual models, and monitoring approaches. When this foundation is built through effective partnerships, digital data entry can enable the collection of more data of higher quality. Without this foundation, or when implemented ineffectively or unnecessarily, digital data entry can be an additional expense that distracts from core monitoring objectives and undermines project sustainability. The appropriate role of digital data entry in PM likely depends more on the context in which it is used and less on the technology itself. 相似文献
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403.
Derek Vollmer Maíra Ometto Bezerra Natalia Acero Martínez Octavio Rodríguez Ortiz Ivo Encomenderos Maria Clara Marques Lina Serrano-Durn Isabelle Fauconnier Raymond Yu Wang 《Ambio》2021,50(4):870
Quantitative assessments have long been used to evaluate the condition of the natural environment, providing information for standard setting, adaptive management, and monitoring. Similar approaches have been developed to measure environmental governance, however, the end result (e.g., numeric indicators) belies the subjective and normative judgments that are involved in evaluating governance. We demonstrate a framework that makes this information transparent, through an application of the Freshwater Health Index in three different river basins in Latin America. Water Governance is measured on a 0–100 scale, using data derived from perception-based surveys administered to stakeholders. Results suggest that water governance is a primary area of concern in all three places, with low overall scores (Guandu-26, Alto Mayo-38, Bogotá-43). We conclude that this approach to measuring governance at the river basin scale provides valuable information to support monitoring and decision making, and we offer suggestions on how it can be improved.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01407-8) contains supplementary material, which is available to authorized users. 相似文献
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中国城市热岛时空特征及其影响因子的分析 总被引:9,自引:6,他引:3
全球气候变暖背景下,城市热岛效应会加重城市地区的热胁迫,对人类健康和生存发展提出严峻挑战.近年来我国雾-霾污染情况严重,但雾-霾对城市热岛影响的认识仍较匮乏.本研究基于MODIS遥感卫星地表温度数据,明确了我国2003~2013年白天、夜间以及四季城市热岛的空间变化,并从生物物理学和生物化学角度定量分析其控制机制.结果表明,影响我国白天城市热岛强度的主要因素为人口、农田灌溉和植被活动.纬度、降水量、反照率以及气溶胶浓度是夜间城市热岛强度的主控因子.从对比城乡粗糙度、反照率等生物物理学属性的角度,揭示了乡村背景环境对城市热岛分析的重要影响.结果表明,雾-霾治理可以缓解我国夜间城市热岛现象和热胁迫,有利于缓解区域甚至全球气候变化. 相似文献
406.
不同国家基于健康风险的土壤环境基准比较研究与启示 总被引:15,自引:7,他引:8
20世纪90年代以来,许多发达国家颁布了基于健康风险的土壤环境标准及基准背景技术文件,为发展中国家启动标准制订和开展基准研究提供了重要的方法学参考.然而,由于各国在环境立法框架、基准推导过程、环境气候特征、土壤类型、人群生活方式与习惯等方面的不同,导致其基准名称、基准功能、保护受体以及基准取值等存在较大的差异.本文在综述基于健康风险的土壤环境基准科学内涵与基本特征的基础上,对不同国家场地土壤环境基准的名称、功能及基准值的差异进行了比较研究,从土地利用类型划分、暴露情景和暴露途径设定、致癌物可接受风险水平选择以及人体暴露参数确定等4个方面,对不同国家制定场地土壤环境基准的关键技术和影响因素进行了总结分析,探讨了借鉴国外经验考虑区域差异制定我国土壤环境基准的对策与方略,并指出了当前我国制定场地土壤环境基准和标准面临的困难和挑战. 相似文献
407.
Jesus D. Fernandez-Bayo Juliano Toniato Blake A. Simmons Christopher W. Simmons 《环境科学学报(英文版)》2017,29(6):164-168
Food processing facilities often use antimicrobial quaternary ammonium compound (QAC) sanitizers to maintain cleanliness. These QACs can end up in wastewaters used as feedstock for anaerobic digestion. The aim of this study was to measure the effect of QAC contamination on biogas production and structure of microbial communities in thermophilic digester sludge. Methane production and biogas quality data were analyzed in batch anaerobic digesters containing QAC at 0, 15, 50, 100 and 150 mg/L. Increasing sanitizer concentration in the bioreactors negatively impacted methane production rate and biogas quality. Microbial community composition data was obtained through 16S rRNA gene sequencing from the QAC-contaminated sludges. Sequencing data showed no significant restructuring of the bacterial communities. However, significant restructuring was observed within the archaeal communities as QAC concentration increased. Further studies to confirm these effects on a larger scale and with a longer retention time are necessary. 相似文献
408.
409.
基于地理信息系统GIS和土地利用回归LUR模型,模拟西安市PM2.5浓度空间动态分布,结果表明:与PM2.5浓度相关性最高的分别为缓冲区为2 km的水域面积、人口密度和距离水域距离,R 2分别为0.501,0.393和0.280;与PM2.5浓度相关性最低的分别为缓冲区为4 km的水域面积、未利用地面积和耕地面积,R 2分别为0.039、0.021和0.017.未考虑风速建立的LUR模型多元回归的相关系数为0.856,R 2为0.733,考虑风速的相关系数为0.892,R 2为0.796,表明风速对于污染物的分布影响较大,LUR模型模拟效果较好.模拟的PM2.5年均浓度高风险区分布于中部,中风险区分布于中西部,低风险区分布于东南部和西部. 相似文献
410.