大气细颗粒物（PM2.5）高浓度污染预测技术方法研究

项目名称： 大气细颗粒物（PM2.5）高浓度污染预测技术方法研究

项目编号： No.51208010

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 建筑环境与结构工程学科

项目作者： 陈东升

作者单位： 北京工业大学

项目金额： 25万元

中文摘要： 大气细颗粒物（PM2.5）因其对人体健康危害的严重性，一直受到国内外环境管理部门和学术界的高度重视，但由于其形成和传输规律的复杂性，给PM2.5的预测特别是不利天气条件下高浓度污染的准确预测带来很大的难度。本项目拟在充分收集气象要素与空气质量数据等重要资料的基础上，以污染气象学、大气环境学等理论为基础，采用数理统计分析、模糊聚类、数值模拟等多种方法，对大气细颗粒物高浓度污染形成机制与传输规律进行研究，系统识别PM2.5高浓度污染过程中空气质量与天气背景、各类气象要素的响应关系，并选取典型污染过程进行诊断分析，识别细颗粒物高浓度污染过程的生成、发展和消散规律，研究建立气象-空气质量关系模型，形成大气细颗粒物高浓度污染预测技术方法，并以北京市为目标地点进行预测效果检验与应用示范，有效提升PM2.5高浓度污染预测技术水平和准确率。

中文关键词： PM2.5；高浓度污染；预测方法；；

英文摘要： Because of the seriousness of the hazards to human health, the airborne fine particulate matter (PM2.5) has always been attached great importance to domestic and international environmental management sector and academia. However, the PM2.5 forecasting especially in adverse weather conditions is facing a great deal of difficulty because of its complexity. This study base on the full collection of meteorological and air quality data, as well as the theory of pollution meteorology, atmospheric and environmental sciences, by the method of statistical analysis, fuzzy clustering and numerical simulation to study the formation and the transmission mechanism of PM2.5. The study identifies the response relationship between the air quality in the process of high PM2.5 concentrations and weather background, and selects the typical pollution process for diagnostic analysis. Though identifying the formation, development and dissipation of the high PM2.5 concentration period, establish the weather-air quality relational model and the high concentrations of airborne fine particulate matter pollution forecasting techniques. Select Beijing as the target application location and the study will effectively enhance the predicting ability and accuracy of PM2.5.

英文关键词： PM2.5；High concentrations of pollution；Forecasting methods；；