锅炉环境影响评价系统软件研究

用Ｃ语言作为开发语言，研制开发了锅炉环境影响评价系统软件，软件充分利用Ｃ语言模块化，过程化的优点，源程序由１２个模块组成。经编译连接生成一个可执行程序，用户只需输入１５个必需的原始参数，软件即可自动查询，计算大量的中间参数，并立即给出污染源强及污染物在周围环境中分布的预测和评价结果。     

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。     

A pilot project to detect and forecast harmful algal blooms in the northern Gulf of Mexico

More timely access to data and information on the initiation, evolution and effects of harmful algal blooms can reduce adverse impacts on valued natural resources and human health. To achieve this in the northern Gulf of Mexico, a pilot project was initiated to develop a user-driven, end-to-end (measurements to applications) observing system. A key strategy of the project is to coordinate existing state, federal and academic programs at an unprecedented level of collaboration and partnership. Resource managers charged with protection of public health and aquatic resources require immediate notice of algal events and a forecast of when, where and what adverse effects will likely occur. Further, managers require integrated analyses and interpretations, rather than raw data, to make effective decisions. Consequently, a functional observing system must collect and transform diverse measurements into usable forecasts. Data needed to support development of forecasts will include such properties as sea surface temperature, winds, currents and waves; precipitation and freshwater flows with related discharges of sediment and nutrients; salinity, dissolved oxygen, and chlorophyll concentrations (in vivo fluorescence); and remotely-sensed spatial images of sea surface chlorophyll concentrations. These data will be provided via a mixture of discrete and autonomous in situ sensing with near real-time data telemetry, and remote sensing from space (SeaWiFS), aircraft (hyperspectral imagery) or land (high-frequency radar). With calibration across these platforms, the project will ultimately provide a 4-dimensional visualization of harmful algae events in a time frame suitable to resource managers.     

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.     

Benchmarking for best practice environmental management

Benchmarking of environmental performance to demonstrate theachievement of best practice environmental management is acomponent of a new form of licensing of industrialdischarges in Western Australia. The paper describes theapproaches to benchmarking for the critical environmentalissues for an alumina refinery and wastewater treatmentplant. It also describes the lessons learnt from thebenchmarking process on appropriate methods, the benefitsand difficulties in the benchmarking process, and changesthat would assist benchmarking for best practiceenvironmental management.