三峡工程与长江中游沿江产业带建设

讨论了长江中游沿江产业带发展条件、限制因素和未来发展，指出三峡工程的建设将对长江中游产业的布局和经济发展带来深刻的变化；防洪作用促进中游两岸大型工业项目的兴建，调整现有沿江城镇的布局，使之结构趋于合理化；电力供应经解能源的紧张状态，改善航运条件，发挥长江黄金水道的综合效益。当然，由于坝下河道冲刷，对现有的码头带来一定的影响，由于冬季水位抬升引起两岸低湖田地下水位上升，加剧两岸土壤潜育化和沼泽化。     

基于土地利用视角的乡村居业协同多尺度分析——以徐州市为例

“乡村振兴”战略是解决城乡发展不协调、乡村发展不充分问题的重要抓手。居业协同作为乡村人居融合发展的高级形态,其程度能够反映乡村振兴的质量与成效。采用土地利用数据从结构和利用两个维度探索淮海经济区中心城市徐州三个尺度下的居业协同格局,结果表明：（1）徐州市土地在供给方面表现出居业失衡,宅基地供给相对于经营性建设用地优势明显;但由于使用中宅基地的空废,二者利用状态表现得更协同。（2）不同空间尺度下,居业协同状态有所差异,其中乡镇尺度居业空间关系较为稳定,基本维持相对平衡,本文认为乡镇是土地资源配置、实施乡村振兴战略的最佳尺度。（3）后续研究中,居业用地适宜的比例及农用地对居业协同的影响都需要进一步探索。     

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.     

C OD在线分析仪比对中应注意的问题

分析了COD在线分析仪和实验室比对结果人为差异的原因是样品不均匀,在线分析仪取样装置滤过,取样层面和采样方式以及沉淀消除的方法不同造成的.提出应在相同的地点、层面和同一容器取样,用相同沉淀消除方法去除样品中氯离子干扰,保证两方法比对中使用水样的一致均匀,使测试结果正确可信.     

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.     

Using chemical process simulation to design industrial ecosystems

Chemical process simulation (CPS) software has been widely used by chemical (process) engineers to design, test, optimise, and integrate process plants. It is expected that industrial ecologists to bring these same problem-solving benefits to the design and operation of industrial ecosystems can use CPS. This paper provides industrial ecology researchers and practitioners with an introduction to CPS and an overview of chemical engineering design principles. The paper highlights recent research showing that CPS can be used to model industrial ecosystems, and discusses the benefits of using CPS to address some of the technical challenges facing companies participating in an industrial ecosystem. CPS can be used to (i) quantitatively evaluate and compare the potential environmental and financial benefits of material and energy linkages; (ii) solve general design, retrofit, or operational problems; (iii) help to identify complex and often counter-intuitive solutions; and (iv) evaluate what-if scenarios. CPS should be a useful addition to the industrial ecology toolbox.     

煤烟型大气污染解决方法

重点介绍了新型燃煤工艺——水平火焰无烟工业炉及其特点，并提出排烟尾气处理的资源化方案。     

生态工业园区的绿色招商指标体系的研究

生态工业园区是一种以追求更高物质利用率和能量转化效率,更少废物排放甚零排放为目标的企业地域分布形式,环境保护意义和生态绿色概念的工业园区.为了能够获得最大的生态效益,就要从源头开始提高招商引资的质量,制定一个绿色招商的指标体系,设置绿色门槛,把好环保关,为园区的可持续发展奠定良好的基础.