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.     

Evaluating the Quality of Citizen‐Scientist Data on Pollinator Communities

Abstract: Concerns about pollinator declines have grown in recent years, yet the ability to detect changes in abundance, taxonomic richness, and composition of pollinator communities is hampered severely by the lack of data over space and time. Citizen scientists may be able to extend the spatial and temporal extent of pollinator monitoring programs. We developed a citizen‐science monitoring protocol in which we trained 13 citizen scientists to observe and classify floral visitors at the resolution of orders or super families (e.g., bee, wasp, fly) and at finer resolution within bees (superfamily Apoidea) only. We evaluated the protocol by comparing data collected simultaneously at 17 sites by citizen scientists (observational data set) and by professionals (specimen‐based data set). The sites differed with respect to the presence and age of hedgerows planted to improve habitat quality for pollinators. We found significant, positive correlations among the two data sets for higher level taxonomic composition, honey bee (Apis mellifera) abundance, non‐Apis bee abundance, bee richness, and bee community similarity. Results for both data sets also showed similar trends (or lack thereof) in these metrics among sites differing in the presence and age of hedgerows. Nevertheless, citizen scientists did not observe approximately half of the bee groups collected by professional scientists at the same sites. Thus, the utility of citizen‐science observational data may be restricted to detection of community‐level changes in abundance, richness, or similarity over space and time, and citizen‐science observations may not reliably reflect the abundance or frequency of occurrence of specific pollinator species or groups.     

广州市环境监测管理信息化建设中若干问题探讨

以广州市环境监测中心站推进信息化的实践为例，重点对环境监测管理信息化规划、开发和应用3个环节所涉及的信息化进程路线、应用系统开发的需求分析和推广应用等问题进行了一些探讨，提出切合自身的解决思路或办法。     

某货车侧翻水污染事件的环境损害评估方法探索

随着我国进入突发水污染事件高发期,面临的水环境形势日益严峻.为了震慑环境污染行为,保证受损的环境资源得到恢复和补偿,量化突发性水污染事件造成的经济损失显得至关重要.本文以重庆市某货车侧翻污染事件为例,构建了一套突发水污染事件环境损害的量化评估方法,并用该方法从财产损害、生态环境资源损害、应急处置行政事务投入费用和调查评估费用这4个方面量化了该事件造成的环境污染损害.     

NABat: A top-down,bottom-up solution to collaborative continental-scale monitoring

Collaborative monitoring over broad scales and levels of ecological organization can inform conservation efforts necessary to address the contemporary biodiversity crisis. An important challenge to collaborative monitoring is motivating local engagement with enough buy-in from stakeholders while providing adequate top-down direction for scientific rigor, quality control, and coordination. Collaborative monitoring must reconcile this inherent tension between top-down control and bottom-up engagement. Highly mobile and cryptic taxa, such as bats, present a particularly acute challenge. Given their scale of movement, complex life histories, and rapidly expanding threats, understanding population trends of bats requires coordinated broad-scale collaborative monitoring. The North American Bat Monitoring Program (NABat) reconciles top-down, bottom-up tension with a hierarchical master sample survey design, integrated data analysis, dynamic data curation, regional monitoring hubs, and knowledge delivery through web-based infrastructure. NABat supports collaborative monitoring across spatial and organizational scales and the full annual lifecycle of bats.     

基于移动监测和土地利用回归模型的上海市近地面黑碳浓度空间模拟

黑碳(BC)是大气污染物的重要组成部分,对空气质量与人类生活健康产生重要的影响.本研究采用移动样带手段开展上海市近地面BC浓度监测,分析其基本统计特征和空间分异性.在此基础上,利用土地利用回归模型(LUR),探讨人口密度、经济产值和交通道路网密度等因素对上海市近地面BC浓度空间分异的影响.结果表明上海市近地面BC平均浓度为(9.86±8.68)μg·m~(-3),空间差异明显,郊区[(10.47±2.04)μg·m~(-3)]比市中心地区[(7.93±2.79)μg·m~(-3)]高32.03%(2.54μg·m~(-3)).气象要素(风速和相对湿度)和交通道路变量(路网长度、省道距离、高速距离等)显著影响上海市近地面BC浓度(r为0.5~0.7,P0.01).基于气象和交通道路变量的LUR模型能较好模拟上海近地面BC浓度(调整后R2为0.62~0.75,交叉验证R2为0.54~0.69,RMSE为0.15~0.20μg·m~(-3)),其中100 m和5 km缓冲距离的LUR模型相对较优,在一定程度上表明上海市近地面BC浓度主要受气象要素和交通源的影响.本研究有利于加深对上海市BC浓度空间分布格局及其影响因素的客观认识,可为模拟和预测BC对人类活动和自然环境的响应机制提供科学依据和理论支撑.     

坡岸截留强化处理设施在不同运行条件下对农业面源污染物去除效果

对坡岸截留强化处理设施在不同温度、不同雨强、不同降雨间隔条件下处理农业面源污染进行中试试验研究.结果表明,水温从20℃下降至4.3℃,COD、TN、NH+4-N、TP去除率下降了15.58%、48.93%、42.26%、57.75%;降雨强度由0.65mm·min-1增加1倍至1.30 mm·min-1,COD的平均去除率低了15.19%,TN、NH+4-N的去除效率相当,TP的去除效率提高了11.21%,但COD、TN、NH+4-N、TP单位面积去除的总量却提高了26.27%、68.3%、32.6%、104.2%;降雨间隔从2 d增加到4 d时对COD去除的影响不大,但使NH+4-N、TN的处理效率提高19.31%、13.95%,对TP的单位面积去除量提高71.43%,但去除总量有限.     

典型生活垃圾处理设施恶臭排放特征及污染评价

为研究生活垃圾处理设施恶臭污染排放特征,分别在北京市生活垃圾填埋、焚烧、堆肥3种工艺的前处理车间采集恶臭样品,利用冷阱富集-气质联用(GC/MS)技术对恶臭气体进行分析.结果表明,检测到的恶臭物质主要分为6大类:芳香烃化合物、硫化物、卤代物、烯烃、烷烃和含氧有机物.其中,填埋工艺的前处理车间共检测出50种物质,质量浓度为100.069mg·m~(-3),理论臭气浓度OU_T为350.611,综合臭气指数N为25.448.焚烧工艺的前处理车间共检测出55种物质,质量浓度为36.052 mg·m~(-3),理论臭气浓度OU_T为141.434,综合臭气指数N为21.506.堆肥工艺的前处理车间共检测出34种物质,质量浓度为25.382 mg·m~(-3),理论臭气浓度OU_T为27.547,综合臭气指数N为14.401.利用阈稀释倍数的恶臭贡献值计算方法,初步识别填埋工艺的前处理车间特征恶臭污染物质为:二甲二硫醚、乙酸丁酯、对二乙苯和乙醇;焚烧工艺的前处理车间特征恶臭污染物质为:甲硫醇、二甲二硫醚、乙醇和柠檬烯;堆肥工艺的前处理车间特征恶臭污染物质为:乙醇、二甲二硫醚、乙酸丁酯和柠檬烯.     

南京江北核心区气候环境宜居性评价

气候环境条件是影响宜居的一个重要因素。运用多层次评价模型,从气象灾害、大气环境、人体健康及生态气象等4个层面筛选出20项指标,构建区域气候环境宜居性的评价体系,基于层次分析法(AHP)确定各指标的权重,并以南京江北核心区为例,对其宜居水平进行评价。结果表明:在青奥生态建设期间(2011—2014年),研究区的宜居水平整体呈上升趋势,属于宜居范畴,接近非常宜居的标准。这反映青奥会期间的环境整治与灾害防治工作初见成效,对江北核心区的宜居性起正效应作用。     

β射线法与重量法监测环境空气PM_(2.5)的应用比较

重量法是测量PM_(2.5)的传统手工经典方法,而β射线法加动态加热系统联用光散射法作为自动在线监测技术的常用方法被广泛使用。将5030-SHARP型β射线法加动态加热系统联用光散射法PM_(2.5)自动监测设备监测结果与手工法测量结果进行比对、分析,结果表明:β射线法与重量法监测结果具有一致性,测量结果不存在显著性差异;β射线法与重量法相对偏差值与温度、相对湿度、风速等气象因子的相关性不明显;β射线法与重量法监测结果的差异性与PM_(2.5)质量浓度、季节等条件有关;随着PM_(2.5)质量浓度逐渐升高,β射线法与重量法相对偏差的绝对值|RD|也随之增大,不同季节的|RD|平均值差异较大,春季、冬季的|RD|平均值大于夏季、秋季。