The role of digital data entry in participatory environmental monitoring

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.     

大庆市草原和湖泊生态保护建设研究

通过对大庆草甸草原和湖泊湿地发生发展自然生态过程的分析,以大庆生态环境的整体性特征为基础,提出草甸草原是大庆生态环境的基础,湖泊湿地是大庆生态环境的特色.以保护和恢复为主巩固草原生态基础地位,以生态重建为主突出湖泊湿地特色.对生态功能正常发挥的草原,以生态保护为主,对生态功能退化草原,以生态恢复为主.对油田生产区的草原,重视生态功能和景观效应.以生态重建为主突出湖泊湿地特色,通过地表水结构的调整增加生态系统的活力和生态服务功能.草甸草原和湖泊湿地的保护与利用要扬长补短、协同并进.     

东北三省畜禽养殖类固醇激素排放及其潜在污染风险

以东北三省畜禽养殖数量统计数据和养殖结构分析为基础,对黑龙江、吉林和辽宁省畜禽养殖类固醇激素排放量进行了估算,并初步探讨了畜禽粪便中类固醇激素对当地水体的潜在污染风险.结果表明,由于养殖数量的增加,东北三省畜禽粪便所排放的类固醇激素量均持续增大.在1998～2008年的10 a间,辽宁、吉林和黑龙江三省的类固醇激素排放量分别增长了2 272.7、1 951.1和1 634.8 kg,并且省会城市沈阳、长春和哈尔滨的增长最为明显.东北三省各城市中畜禽粪便类固醇雌激素排放量最大的是长春,达到874.7 kg;哈尔滨和沈阳的排放量在各自省份中也是最大,分别为652.7 kg和603.1 kg·辽宁省的畜禽类固醇激素排放总量在东北三省为最多,所排放类固醇雌激素的雌二醇等当量浓度EEQs预测值也最高,达到17.1ng·L-1.如果以英国环保署(Environment Agency,United Kingdom)建议的最低可观测效应浓度10 ng·L-1为参考依据,辽宁省在目前的畜禽养殖规模和结构下,所排放的粪便类固醇雌激素极有可能会对地表水体中的水生生物产生内分泌干扰效应.     

咸潮预警值设定的研究

为研究咸潮预警,上海某饮用水源地安装了蓝色卫士水质综合预警系统。采用实验室配制人工海水模拟减潮,通过动态实验研究蓝色卫士的成潮预警值。试验证明,成潮预警值设定范围为1．6～1．8较好。经过连续1a自动监测,蓝色卫士共准确预警预报两次成潮,成功保障了该水源地的饮水安全。     

中国煤炭资源不同区带的生态环境特征

中国煤炭的“井”字型分布格局除了是对煤炭资源地质的、地理的、社会经济的区域分异现象,同时也基本是对不同煤炭资源发展区的生态环境特征进行区域上的归结。论文总结了不同煤炭资源发展区的生态环境特征和存在的主要生态环境问题。     

辽宁省菱镁矿区生态修复研究

针对辽宁省菱镁矿区资源状况与环境问题,提出菱镁矿区土地生态修复技术对策,包括土壤污染等级分类对策、不同立地条件土地复垦适宜性评价与对策、板结土壤复垦技术与对策和排土场贫瘠土壤肥力改良修复对策,进行适宜性复垦植物筛选与复垦效果评价。     

pH值对凯氏蒸馏法氨氮检出量的影响分析研究

土壤、肥料中的氨氮测定常用2mol·L^-1KC浸提一蒸馏法一酸滴定的方法进行测定,其中蒸馏是最关键和最重要的一个环节。以污泥堆肥样品为例,利用统计学原理,研究了汽提蒸馏过程中pH对氨氮检出量的影响。结果显示：pH值在6．5—11．0范围内与氨氮的检出系数是显著的正相关关系（相关系数r=0．9891）;回归方程在0．05的水平下是非常显著的,拟合度很好。进一步做残差和预测分析可以得出：pH值在6．5～10．0范围内,预测值和测定值的符合度更好。     

白洋淀湿地生态环境监测方法研究

随着人口的增长和社会经济的发展,越来越频繁的人类活动使白洋淀湿地生态功能逐渐减退,面临着入淀水量减少,干淀频繁、泥沙淤积、生物多样性减少、污染加剧等严重的生态环境问题,大大降低了其调节气候、调蓄洪水、净化水体的功能。针对目前白洋淀湿地生态环境现状,结合湿地环境监测现状,探讨了白洋淀湿地生态环境监测技术方法,以此来推动白洋淀生态环境监测的进一步发展。针对目前白洋淀湿地生态环境现状,结合湿地环境监测现状,探讨了白洋淀湿地生态环境监测技术方法,以此来推动白洋淀生态环境监测的进一步发展。     

论太湖流域生态补偿机制存在环境监测瓶颈

介绍了太湖流域生态补偿现状,对太湖流域生态补偿的理论及存在的问题进行分析和探讨,提出太湖流域生态补偿关键要建立生态补偿法规、实施垂直统一的环境监测体系及对水质自动监测设施监管,为我国环境资源区域补偿实施提供借鉴。     

基于BP神经网络的上市公司CEO报酬实证研究

近年来的研究表明,CEO报酬不仅受公司业绩的影响,还受到其他诸多因素的影响,而且,CEO报酬与其决定因素之间往往存在着非线性关系。本文以2003-2005年沪深股市的A股上市公司为样本,采用BP神经网络对CEO总报酬、CEO年薪、CEO持股价值及其决定因素分别进行训练和学习,结果表明：（1）网络训练输出值与实际值的拟合度分别达到91.09%、97.23%和78.44%;（2）网络的预测能力相对于传统的线性回归模型分别提高了92.72%、92.08%和53.89%。因此,本文认为在分析和确定CEO报酬水平时引入神经网络模型是可行的。