香港公共出租车转换站内污染物演变模式识别及预测(英文)  

Characteristics Recognition of Pollutant Levels in Public Taxi Transfer Interchanges in Hong Kong

作　　者：卢伟真[1] 王协康[2] 

机构地区：[1]香港城市大学建筑系 [2]四川大学高速水力学国家重点实验室,四川成都610065

出　　处：《华南理工大学学报（自然科学版）》2003年第z1期18-22,40,共6页Journal of South China University of Technology(Natural Science Edition)

基　　金：香港城市大学战略发展研究基金资助项目(7001371(BC),7001463(BC)~~

摘　　要：对香港公共出租车转换站(TTI)内的污染物扩散和分布情况进行了研究。影响TTI内污染物水平的因素有:出租车流、环境条件、人们的活动以及TTI的结构等。对TTI内的悬浮粒子进行了现场监测,通过分析前述因素对悬浮粒子水平的影响,提出用人工神经网络来进行研究。基于所获得的不同时间段的数据,训练了适用于此类问题的神经网络模型,对该模型的性能进行了分析和验证。文中还对TTI内空气污染物的积聚模式及演变趋势进行了模拟预测和实测对比,对比结果表明,文中提出的TTI内污染物的预测方法是可行和可靠的。An investigation on pollutant dispersion and distribution inside public taxi transfer interchange (TTI) is reported. The pollutant levels inside 111 are affected by many factors, e.g. , taxi flow-rate, environmental conditions, human activities, and geometrical layout of TTI, etc.. A site measurement of respirable suspended particulate (RSP) level is carried out in a typical TTI in Hong Kong. After analyzing the effect of above factors on RSP level, the authors propose to use artificial neural network (ANN) to study such phenomena. The recorded data within different time periods inside the selected TTIs are used as the test data set to train the proposed neural network model. The recovery performance of ANN model is analyzed and justified. In the study, the authors compare the forecasting results and the measured data of RSP in morning and afternoon sessions respectively. The results show the feasibility and reliability of the proposed approach for forecasting pollutant levels inside TTI.

关 键 词：人工神经网络 污染物水平 出租车转换站 可呼吸悬浮粒子 

分 类 号：X8[环境科学与工程—环境工程] TP183[自动化与计算机技术—控制理论与控制工程]