安徽省淮河流域水污染分析与环境目标可达性探讨

介绍了安徽省淮河流域水污染的历史与现状,分析了淮河流域水污染的特点,对实现《淮河流域水污染防治“十五”计划》的可能性进行了探讨。     

淮河流域污染"久治不愈"原因浅析及治理措施建议

根据淮河流域水质和产业结构现状、近几年来主要污染物排放量变化以及流域内污水处理情况,初步分析了淮河流域污染反弹“久治不愈”的几个原因并提出了几点相应的建议。     

雨天硫酸盐化速率样品扩散分析

选择采样期不同降雨天数的碱片，测定余边的硫酸盐化速率含量，其结果为：采样期全月无雨时，余边上的硫酸盐均未检出；半月以上至全月有雨时检出率为１００％，检出量占样品量的２．７０～１５．９５％。经扩散试验，查清了余边上的硫酸盐主要是有效面积上采集的样品的扩散，并提出了相应的修正方法。     

经济手段防治白色污染的博弈分析

经济手段在环境管理中的作用及涉及到政府与各利益关系人之间的博弈过程。本文就政府与一次性餐具生产者，销售者之间的博弈关系，分析如何应用经济手段使其在防治“白色污染”中发挥更好的作用。     

新疆河流洪水水污染分析

洪水期间的水污染主要来源于面源。其特点是污染物的浓度高、负荷量大。给工业、渔业和人民生活都会带来不同程度的危害。悬浮物是洪水过程中的主要面污染产物，同时还作为载体吸附和携带大量的有机物、无机盐、重金属类和微生物进入水体，致使河流水质下降。在研究洪水灾害的同时。不应忽略洪水期间的水污染问题。     

阿图什市空气质量变化趋势分析

分析了阿图什市“八五”至“九五”期间空气质量监测数据不同年度、不同季节及不同污染因子的动态变化趋势．结合当地能源结构、气候特征、城市环境综合发展水平，指出影响阿图什市空气质量的主要因素，为防治和减轻阿图什市的空气污染提供了科学依据。     

乌鲁木齐市餐饮业油烟污染的调查及防治初探

通过对乌鲁木齐市20家餐饮业油烟污染的调查分析，结果表明，乌鲁木齐市餐饮业油烟污染严重，并提出了防治措施。     

应急监测案例分析

叙述了某突发性不明污染源的应急监测经过,探讨了对此类应急监测应采取的措施,并阐述了相应的意见和看法。     

兰州市功能区大气细菌污染和城市绿地系统杀菌效应分析

在测定兰州市城市不同功能区和不同类型绿地系统空气细菌数量的基础上,分析了各功能区空气细菌污染状况,以及人流量和车流量与空气细菌数量之间的关系;探讨了不同绿地系统的杀菌效应,指出各绿地系统杀菌能力:乔、灌、草>乔、灌>草、灌>草坪。     

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.