Particulate matter exchange between atmosphere and roads surfaces in urban areas

The deposition and the re-suspension of particulate matter (PM) in urban areas are the key processes that contribute not only to stormwater pollution, but also to air pollution. However, investigation of the deposition and the re-suspension of PM is challenging because of the difficulties in distinguishing between the resuspended and the deposited PM. This study created two Bayesian Networks (BN) models to explore the deposition and the re-suspension of PM as well as the important influential factors. The outcomes of BN modelling revealed that deposition and re-suspension of PM10 occurred under both, high-traffic and low-traffic conditions, and the re-suspension of PM2.5 occurred under low-traffic conditions. The deposition of PM10 under low-volume traffic condition is 1.6 times higher than under high-volume traffic condition, which is attributed to the decrease in PM10 caused by relatively higher turbulence under high-volume traffic conditions. PM10 is more easily resuspended from road surfaces compared to PM2.5 as the particles which larger than the thickness of the laminar airflow over the road surface are more easily removed from road surfaces. The increase in wind speed contributes to the increase in PM build-up by transporting particulates from roadside areas to the road surfaces and the airborne PM2.5 and PM10 increases with the increase in relative humidity. The study outcomes provide a step improvement in the understanding of the transfer processes of PM2.5 and PM10 between atmosphere and urban road surfaces, which in turn will contribute to the effective design of mitigation measures for urban stormwater and air pollution.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Ambient Air Quality Monitoring Network Design for Assessing Human Health Impacts from Exposures to Airborne Contaminants

Existing methods of establishing ambientair quality monitoring networks typically evaluateonly parameters related to ambient concentrations ofthe contaminant(s) of interest such as emissionsource characteristics, atmospheric transport anddispersion, secondary reactions, depositioncharacteristics, and local topography. However,adverse health risks from exposures to airbornecontaminants are a function of the contaminant andthe anatomic and physiologic characteristics of theexposed population. Thus, ambient air qualitymonitoring networks designed for the protection ofpublic health or for epidemiological studiesevaluating adverse health impacts from exposures toambient air contaminants should account for bothcontaminant characteristics and human healthparameters. A methodology has been establishedwhich optimizes ambient air quality monitoringnetworks for assessments of adverse human healthimpacts from exposures to airborne contaminants byincorporating human health risk assessmenttechniques. The use of risk assessment techniquesas the basis for designing ambient air qualitymonitoring networks will help to target limitedfinancial and human resources to evaluate humanhealth risks from exposures to airbornecontaminants.     

Determining Ecoregions for Environmental and GMO Monitoring Networks

A representative environmental monitoring network at the regional scale cannot use raster-based or random sampling designs, but requires a stratified sampling procedure integrating different information layers, and it has to occur in ecologically differing homogeneous regions (ecoregions). These we have determined using a set of spatial strata with ecological variables which we analysed with classification and regression trees (CART). We present a framework for environmental monitoring, that covers different scales, and we transfer the framework to a potential GMO (genetically modified organisms) monitoring network. We use ecoregion and other environmental strata together with existing environmental monitoring networks to determine GMO monitoring sites more precisely.     

高等院校教育技术的发展思路探讨

本文阐述了高等院校教育技术的发展现状,分析了高等院校教育技术发展的特点,对高等院校教育技术的发展进行理性思考。认为高等院校应该在重视教育技术实践性和支持性研究的基础上,立足现实,更全面审视机构整合、教育信息化、信息资源库建设和教师培训这些热点问题。     

唤起“求知欲”，治疗“厌学症”

本文分析了求知欲的由来及消退原因，“厌学症”的形成及治疗方案，试图唤起学生的求知欲，治疗他们的“厌学症”。     

Molecular behavior and interactions with microbes during anaerobic degradation of bio-derived DOM in waste leachate

It is the key to control bio-derived dissolved organic matters (DOM) in order to reduce the effluent concentration of wastewater treatment, especially for waste leachate with high organic contaminants. In the present study, the anaerobic degradation of aerobically stabilized DOM was investigated with DOM substrate isolated through electrodialysis. The degradation of bio-derived DOM was confirmed by reduction of 15% of total organic carbon in 100 days. We characterized the molecular behavior of bio-derived DOM by coupling molecular and biological information analysis. Venn based Sankey diagram of mass features showed the transformation of bio-derived DOM mass features. Occurrence frequency analysis divided mass features into six categories so as to distinguish the fates of intermediate metabolites and persistent compounds. Reactivity continuum model and machine learning technologies realized the semi-quantitative determination on the kinetics of DOM mass features in the form of pseudo-first order, and confirmed the reduction of inert mass features. Furthermore, network analysis statistically establish relationship between DOM mass features and microbes to identify the active microbes that are able to utilize bio-derived DOM. This work confirmed the biological technology is still effective in controlling recalcitrant bio-derived DOM during wastewater treatment.     

活性污泥系统动力学模拟方法的综合分析

活性污泥法的应用现状和污水中氮磷排放标准的日益严格，使得传统数学模型已满足不了目前的要求，需要对活性污泥系统复杂的动力学规律进行有效模拟。文章在综合分析活性污泥动态模型国内外研究现状的基础上，介绍了3种占主流地位的模型：活性污泥数学模型、神经网络模型和混合模型。这3种模型在污水处理的设计、运行控制和工艺优化等方面各有其独到之处。     

Rural landscape planning case study of Nanhua State Farm

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