Ozone patterns for three metropolitan statistical areas in North Carolina, USA

As part of an effort by the state of North Carolina to develop a State Implementation Plan (SIP) for 1-h peak ozone control, a network of ozone stations was established to monitor surface ozone concentrations across the state. Between 19 and 23 ozone stations made continuous surface measurements between 1993 and 1995 surrounding three major metropolitan statistical areas (MSAs): Raleigh/Durham (RDU), Charlotte/Mecklenburg (CLT), and Greensboro/High Point/Winston-Salem (GSO). Statistical averages of the meteorological and ozone data were performed at each Metropolitan Statistical Area (MSA) to study trends and/or relationships on high ozone days (days in which one of the MSA sites measured an hourly ozone concentration90.0 ppbv). County emission maps of precursor gases, wind roses, total area averages of ozone, total downwind averages of ozone deviations, upwind averages of ozone, and a modified delta ozone analysis were all obtained and analyzed. The results of this study show a reduction in the delta ozone relative to an earlier study at RDU, but no average significant change at CLT (no comparison can be made for GSO). The statistical data analyses in this study are used to quantify the importance of local contributions and regional transport, to ozone air pollution in the MSAs.     

基于模糊物元的工业园区空气质量评价模型

针对工业园区空气质量的复杂性和模糊性,利用模糊物元分析理论,结合海明贴近度的概念,建立了一种基于模糊物元的空气质量评价模型。以金山二工区为例,选取《环境空气质量标准》规定的6种主要污染物SO2、NO2、CO、O3、PM10、PM2.5作为评价指标,分析了园区内5个自动监测站2016年7月1日的环境空气质量状况,并与空气质量指数(AQI)技术分析结果作比较。两种方法的评价结果基本一致,与实际状况相符。     

静脉产业类生态工业园区的建设方法分析

静脉产业类生态工业园区运用先进的技术,将生产和消费过程中产生的废物资源化,是中国循环经济建设的重点领域之一。但是中国现有的静脉产业类生态工业园区尚处于创建阶段,没有形成规模化发展,因此可以在建设过程中借鉴日本、德国等发达国家的成功经验,探索适合中国国情的建设模式。本文通过分析静脉产业类生态工业因区在生产原料、生产过程和产品销售方面的主要影响因素以及其他国家采取的相应措施,为中国静脉产业类生态工业园区的建设提出建议。     

基于神经网络的城市夜间环境监控与预测

因夜间天空亮度分布具有非线性变化特点,故引入神经网络算法,建立基于时间序列的夜天空亮度预测模型,夜天空亮度预测模型可为城市光污染防治提供评价依据.文章对神经网络的原理进行了论述,建立了基于时间序列预测模型.以测试数据为训练样本集,基于MATLAB(矩阵实验室,Matrix Laboratory的简称),采用改进的BP算法(误差反向传播算法)对网络进行学习训练,并对存在的误差进行了分析.基于时间序列BP神经网络的夜天空预测模型,当隐含层神经元数目为5,训练函数为L-M优化算法(trainlm)时,最大绝对误差可达到0.003 6 cd/m2,最大相对误差达到2.361 4%.结果表明,模型的运行结果与试验数据比较吻合,输出与目标矢量之间相关性也较好.     

The method for leakage detection of urban natural gas pipeline based on the improved ITA and ALO

To solve the problems of the difficulty in early leakage monitoring and larger positioning error for urban hazardous chemicals pipelines, the optimized method based on the improved Inverse Transient Analysis (ITA) and Ant Lion Optimizer (ALO) was proposed. Firstly, based on the obtained experiment's results of leakage of natural gas in the non-metallic pipeline, the segment classification method was incorporated into the pressure gradient calculation. The modified method can adapt to the multi-node characteristics of urban pipe networks and help to obtain the preliminary positioning calculation results after optimization. Then the calculation results were embedded in the ITA calculation model. The input parameters of the gas pipeline such as boundary conditions, leakage rate and friction coefficient were used to establish the characteristic linear equations. Then the objective function of the least-squares criterion was defined, and the improved ITA model suitable for leakage detection of urban natural gas pipeline networks was constructed. Finally, the ALO was used to optimize the calculation process of the improved ITA model, and iteratively optimize the optimal friction coefficient and its corresponding minimum objective function (OF) value. As a result, a more precise location of the leakage source was calculated. The validation of the modified method is conducted by comparing the calculated values with the experiment's results. The results show that the method can accurately predict the location where the pipeline leakage occurs. The minimum error is 3.17%. Compared with the traditional ITA, this method not only accelerates the convergence speed of the objective function, but also improves the accuracy of location calculation.     

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.     

中国城市空气质量分级管理策略探讨

本文在充分剖析我国现阶段城市环境空气质量及相关管理工作存在的主要问题的基础上,探讨了以改善城市空气质量兼顾城市群效应控制为重点的城市空气质量分级管理策略。研究提出,为适应新的污染特征和形势,国家应针对不同地区、不同污染来源特征、不同污染程度的各类城市实行分类指导和分级管理的思路。     

我国城市垃圾处理现状与分析

城市垃圾已经成为我国面临的严重问题。本文对我国垃圾处理的现状进行了介绍,通过对其进行分析,提出了几点建议。     

Composition of extractable organic matter in aerosols from urban areas of Rio de Janeiro city, Brazil

The hydrocarbon compositions of atmospheric particulate matter from urban areas of Rio de Janeiro city have been studied to assess the different pollution levels. Samples were acquired using a standard high-volume air sampler (Hi-Vol), extracts were prepared and fractionated into aliphatic and aromatic compounds. High-resolution gas chromatography and GC coupled to mass spectrometry (GC–MS) were used for the analysis of the organic matter. The results show that all samples contain n-alkanes, but the distributions are different for each sample, reflecting both the biogenic (vascular plant wax input) and fossil fuel contamination sources (vehicular exhaust). The fossil fuel biomarkers, hopanes and steranes, were also observed in all samples except in the Tijuca Forest, which is a mountain forest in the midst of the sea-level city. A decrease in the level of pollution was observed in the sequence for Rebouças Tunnel>Cinelândia (downtown)>Quinta da Boa Vista Park>Tijuca Forest, as expected from the traffic density. Unfortunately, all sites are polluted mainly from vehicular emissions, but at different degrees, with the lowest levels in Tijuca Forest.