Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

River-Stream Connectivity Affects Fish Bioassessment Performance

Stream fish bioassessment methods assume that fish assemblages observed in sample sites reflect responses to local stressors, but fish assemblages are influenced by local factors as well as regional dispersal to and from connected streams. We hypothesized that fish movement to and from refugia and source populations in connected rivers (i.e., riverine dispersal) would weaken or decouple relations between fish community metrics and local environmental conditions. We compared fish-environment relations between streams that flow into large rivers (mainstem tributaries) and streams that lack riverine confluences (headwater tributaries) at multiple spatial grains using data from the USEPA's Environmental Monitoring and Assessment Program in the mid-Atlantic highlands, USA (n = 157 sites). Headwater and mainstem tributaries were not different in local environmental conditions, but showed important differences in fish metric responses to environmental quality gradients. Stream sites flowing into mainstem channels within 10 fluvial km showed consistently weaker relations to local environmental conditions than stream sites that lacked such mainstem connections. Moreover, these patterns diminished at longer distances from riverine confluences, consistent with the hypothesis of riverine dispersal. Our results suggest that (1) the precision of fish bioassessment metrics may be improved by calibrating scoring criteria based on the spatial position of sites within stream networks and (2) the spatial grain of fish bioassessment studies may be manipulated to suit objectives by including or excluding fishes exhibiting riverine dispersal.     

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.     

基于神经网络的洪水预报研究

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理,具有自学习和自适应等一系列优点,因而用它来进行洪水预报是可行的.对洪水预报问题,初步建立了基于神经网络的洪水预报系统,给出了应用实例.     

对重庆城镇网络建设的探讨

在分析重庆直辖市城镇化现状的基础上,针对重庆城镇网络中存在的问题,依据重庆发展城镇网络的优势,提出了建立新的城镇网络扔基本思路和构想,相关的对策与建议。     

Optimal Design of a Multi-Pollutant Air Quality Monitoring Network in a Metropolitan Region Using Kaohsiung, Taiwan as an Example

An air quality monitoring network (AQMN) usually performs the basic function of assessment of regional air quality and demonstration of compliance with ambient air quality standards in an urban area. Different pollutants, however, may present different characteristic variabilities due to their specific emission patterns, rates of diffusion, and transport and transformation behaviors. But the costs of siting in a pollutant-specific monitoring network would be higher than that for a common network with respect to several pollutants monitored simultaneously. This paper presents a survey of multi-pollutant design principles and optimal searches for siting patterns of an AQMN using both simulation and optimization models as a combined tool. While conservative, quasi-stable, and reactive pollutants are considered in the design principles, cost, coverage effectiveness, and spatial correlation characteristics are included in the multi-criteria decision making process. For illustrative purpose, a series of technical settings and two types of objectives were examined in the case study for the city of Kaohsiung in Taiwan.     

系统操作者与管理者行为的博弈演化与收益分析*

为研究工程项目系统中,操作者安全行为与管理者奖惩行为的相互博弈情况,确定操作者和管理者收益,提出博弈演化与收益分析方法。操作者代表系统实际使用者,行为包括安全和不安全行为;管理者代表系统管理者和所有者,行为包括奖励和惩罚行为。确定方法的基本参数;研究博弈演化过程的博弈逻辑关系,从悲观和乐观角度研究二者不同行为相互作用后的收益关系,给出操作者收益和管理者收益的逻辑表达式。结果表明:方法能得到博弈过程演化结果,同时能根据收益结果表达式判断博弈胜出方。     

课堂、网络、宿舍三位一体的商务英语实践环境建设

对高职院校商务英语实践环境现状进行了分析,并结合高职商务英语教学的培养目标及中国环境管理干部学院商务英语专业建设实际,提出建设高职商务英语专业校园立体化实践环境模式。立体化实践环境主要包括课堂、网络和宿舍三个方面,并以网络交流平台为重点及主要特色。     

生物炭对施粪肥土壤中根际真菌群落多样性及相互作用的影响

为了研究生物炭对施粪肥土壤中根际真菌群落结构及相互作用的影响,通过黑麦草盆栽试验,比较了添加2%生物炭处理和仅施粪肥条件下,根际真菌的群落演替及分子生态网络. Illumina MiSeq测序结果表明,添加生物炭处理与不添加生物炭的对照处理中真菌的α多样性指数(Shannon指数)无显著差异.两种处理情况下,子囊菌门(Ascomycota,59. 64%～84. 80%)、担子菌门(Basidiomycota,1. 90%～5. 87%)和接合菌门(Zygomycota,4. 34%～16. 11%)均为主要菌群.分子生态网络分析表明,相比不添加生物炭的对照组,添加生物炭后的土壤真菌群落具有更复杂的联系且显著增强了种间积极的相互作用(P 0. 05). Mantel检验分析表明,添加生物炭处理中植物根系与真菌丰度和种间相互作用显著相关(P=0. 001).植物根系是影响真菌丰度和相互作用关系的最重要的因素.     

基于卷积神经网络的冲击地压预警方法研究*

为探究深度学习在冲击地压预警方面的应用前景,以新疆某冲击地压矿井为研究背景,将深度学习和专家评判运用到微震数据分析中,基于卷积神经网络构建冲击地压预警模型。充分利用一维卷积神经网络对时序数据有较强特征提取能力的优势,以微震数据及其特征参数作为输入,以专家评判值作为标签,借助Python-Keras框架实现冲击地压预警模型的构建和训练。研究结果表明:模型预警效果并不随着训练迭代次数的增加而逐渐最优,存在最优迭代次数,对于所建模型当迭代次数为30时测试集的冲击危险预测结果与专家评判结果基本吻合,同时说明模型可以较好地学习专家评判经验实现冲击地压预警。研究表明所建模型对研究时段内发生的5次大能量矿震事件均进行预警,其准确度较高,具有现场实际应用价值。