Deployment of parabolic trough concentrated solar power plants in North Africa – a case study for Libya

The study addresses the potential of using concentrated solar power plants (CSPs) as a sustainable alternative of clean energy generation in the Mediterranean region and, in particular, in its North Africa shore. This location presents attractive conditions for the installation of CSPs, in particular high solar irradiation, good manpower concentration, and proximity and availability of water resources for condenser cooling. Energetic, exergetic, and economic analyses were conducted taking into consideration a particular type of CSPs - the parabolic trough concentrated solar power plant, which incorporates the most proven technology and it is already used in Southern Europe (Spain). In addition, the study considered the impact of project financing and incentives on the cost of energy. The combination of higher values for performance and potentially lower levelized cost of electricity (LCE) for the North Africa Mediterranean Rim than the South of Spain region can yield a very favorable return for the invested capital. Tripoli compared to Almeria presented superior performance and potentially lower LCE values ($0.18/kWh versus $0.22/kWh). This is significant, even when it is taken into consideration the fact that the plant in Tripoli, despite a relatively modest capacity factor of 34%, has a large gross power output of 173,886 MWhe. In addition, the implementation at the Tripoli location of a plant similar to the Anadsol plant has a slight advantage (2–3%) in terms of overall efficiency.     

洪涝灾害条件下疏散交通生成预测方法

为有助于有关部门更准确预测洪涝灾害受灾民众的疏散量,结合非集计数据和集计数据的优点,提出分区集计数据的概念,设计了受灾区域分区方法,并通过意向偏好(SP)调查法对我国居民在洪涝条件下疏散交通需求数据进行调查。在此基础上,引入BP神经网络建立基于分区集计数据的疏散交通生成预测模型。利用调查数据进行实证分析发现,所设计方法取得了较好的预测效果,鲁棒性较好,平均相对预测误差仅为1.8%,其预测效果明显优于现有的非集计和整集计模型。     

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.     

水动力对沉积物-水界面氧通量产生机制的影响

选取三峡库区支流御临河为研究对象,测量了5个水动力条件（平均流速为0.00,0.03,0.07,0.12,0.20m/s）下沉积物-水界面（SWI）氧通量的变化及水动力条件对SWI氧通量产生机制的影响.结果表明,随着平均流速的升高,SWI氧通量增加,由0.00m/s时的1.197mmol/（m²·h）增加为0.20m/s时的43.981mmol/（m²·h）,溶解氧穿透深度增加,氧进入沉积物更深处并被微生物和还原性物质所利用,沉积物耗氧量上升;当平均流速较低时,沉积物耗氧量以生物耗氧量为主,在0.00m/s与0.03m/s时生物耗氧量为氧通量的85.3%与57.7%;当水体平均流速较高,化学耗氧量与其他耗氧量中的化学过程耗氧量在氧通量中的比重逐步提高.     

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.     

大理市生活垃圾焚烧处置初探

针对大理生活垃圾污染现状,提出采用高温焚烧方法处理大理市产生的城市生活垃圾的必要性,并用垃圾焚烧产生的热能发电.采取相应的措施防治燃烧过程中产生的污染物,可最终实现垃圾处理的减量化、无害化、资源化要求.     

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。