Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope(SEM)micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found(1) Rosa xanthina Lindl(shrub species) had greater retention efficiency than Broussonetia papyrifera(broadleaf species) and Pinus bungeana Zucc.(coniferous species), in terms of particle number and particle area cover.(2) 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical.(3) 32.4%–74.1% of the particles were generated from bare soil and construction activities, and 15.5%–23.0% were mainly from vehicle exhaust and cooking fumes.     

海域有机污染物优先排序和风险分类模糊评判系统

本文提出了对区域性有机污染物优先排序和风险分类和定量评判系统：模糊评判系统。该评判系统以反映污染物的毒性，暴露水平和环境化学性质的７项碜数为指标，通过指标参数的标准化及模糊运算，获得有机污染优先排序和风险分类的定量结果。     

Soil erosion and non-point source pollution impacts assessment with the aid of multi-temporal remote sensing images

Soil erosion associated with non-point source pollution is viewed as a process of land degradation in many terrestrial environments. Careful monitoring and assessment of land use variations with different temporal and spatial scales would reveal a fluctuating interface, punctuated by changes in rainfall and runoff, movement of people, perturbation from environmental disasters, and shifts in agricultural activities and cropping patterns. The use of multi-temporal remote sensing images in support of environmental modeling analysis in a geographic information system (GIS) environment leading to identification of a variety of long-term interactions between land, resources, and the built environment has been a highly promising approach in recent years. This paper started with a series of supervised land use classifications, using SPOT satellite imagery as a means, in the Kao-Ping River Basin, South Taiwan. Then, it was designed to differentiate the variations of eight land use patterns in the past decade, including orchard, farmland, sugarcane field, forest, grassland, barren, community, and water body. Final accuracy was confirmed based on interpretation of available aerial photographs and global positioning system (GPS) measurements. Finally, a numerical simulation model (General Watershed Loading Function, GWLF) was used to relate soil erosion to non-point source pollution impacts in the coupled land and river water systems. Research findings indicate that while the decadal increase in orchards poses a significant threat to water quality, the continual decrease in forested land exhibits a potential impact on water quality management. Non-point source pollution, contributing to part of the downstream water quality deterioration of the Kao-Ping River system in the last decade, has resulted in an irreversible impact on land integrity from a long-term perspective.     

都江堰市红色村干沟泥石流活动特征及危险性评估

"5·12"四川汶川大地震导致灾区地质灾害危险性大大增强,严重影响了周边居民的生命财产安全。都江堰市虹口乡红色干沟泥石流灾害隐患点是其中之一。根据泥石流的成因、类型、规模,以及泥石流的活动特征,对泥石流活动进行了危险性评估,指出泥石流防治工程方案应采用以排为主,适当拦挡的原则,建拦挡坝用以消能、调节泥石流流量;建防护堤用以限制泥石流漫流,对居民区进行有效防护。     

水上溢油环境风险源确定研究

依据泄漏进水域的最大油品量、水域环境敏感性等两个主要因素,研究确定了江苏油田水上(临水)设施环境风险源等级,并针对不同的风险源阐述了降低风险等级的技术方法和预防措施,为加强江苏油田环境风险防患、提高应急处理能力提供了依据。     

北京垃圾分类工作现存问题与对策建议

《北京市生活垃圾管理条例》实施以来,北京垃圾分类工作取得"六升两降"八项成效.基于现阶段北京垃圾分类工作推进情况与存在的痛点、难点问题,提出通过购买第三方服务、借助科技手段与工具、多种渠道多种方式相结合、建立长效监督评价机制等方式,为顺利推进北京垃圾分类工作提供参考.     

基于颗粒物浓度的沙尘天气分级标准研究

沙尘天气在近年对我国城市空气质量造成明显影响。目前我国沙尘天气分级仍然按照气象标准 ,主要是以水平能见度进行分级。文章参考国外的有关标准 ,通过统计近年来我国沙尘天气过程中的颗粒物浓度 ,结合我国沙尘天气的发生情况和特点 ,提出了基于沙尘浓度的沙尘天气分级标准     

An Automated Technique for Delineating and Characterizing Valley-Bottom Settings

Accurate delineation and characterization of valley-bottom settings is crucial to the assessment of the biological and geomorphological components of riverine systems; yet, to date, most valley-bottom mapping endeavors have been done manually. To improve this situation, we developed automated techniques in a Geographic Information System (GIS) for delineating and characterizing valley-bottom settings in river basins ranging in size from approximately 1,000–10,000 km². All procedures were developed with ARC/INFO GIS software and fully automated in Arc Macro Language (AML). The GRID module is required for valley-bottom delineation and slope calculations; whereas characterization (i.e., measuring the width of the valley-bottom zone) requires Coordinate Geometry (COGO) in the ARCEDIT module. The process requires three inputs: a polygon coverage of the analysis area; an arc coverage of its hydrography, and a grid representing its digital elevation. The AML is designed to operate within a wide range of computer memory/disk space options, and it allows users to customize several procedures to match the scale and complexity of a given analysis area with available computer hardware.     

河南省煤化工行业环境风险等级划分研究

基于目前国家建立环境污染责任保险制度的规划需求和河南省当前新发展形势需要,选择以河南省典型高污染、高风险行业(即煤化工行业)为研究对象.在对相关理论进行分析的基础上,识别影响河南省煤化工行业的环境风险因子,并确定指标的评估标准(权重及评分结果).遵照指标体系的建立原则,采用层次分析法,从内在性和外在性两方面因素构建煤化工行业环境风险等级划分指标体系,进一步将评分结果与环境风险等级比对,最终将煤化工行业的环境风险等级划分为5个等级,为河南省环境污染责任保险制度的建立奠定基础.     

基于机器学习分类算法解析EIS数据的有机涂层性能评价方法

目的 基于机器学习分类算法快速评估有机涂层的防腐性能。方法 通过实验室加速试验模拟涂层真实的退化过程,并根据测得的电化学数据,分析不同退化阶段的等效电路元件参数。随后,采用随机抽样方法获取大量数据,用于机器学习模型训练。通过对比支持向量机(SVM)、k最近邻(k-NN)和随机森林(RF)3种不同的机器学习算法,以及多种输入特征集训练的涂层性能分类器模型的准确率,分析最适合用于涂层性能快速评估的机器学习算法和电化学特征。结果 根据不同输入特征训练的k-NN和RF模型均表现出良好的预测效果,而SVM模型的预测效果相对较差。根据不同频率范围训练的分类器模型中,在低频区表现最佳,而在高频区表现较差。结论 基于阻抗虚部、虚部+实部和阻抗模值3种输入特征训练的RF分类器模型的预测效果最准确。不同频率区间内,低频区的阻抗特征更能准确表征涂层性能。