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| 基于响应面模型的区域大气污染控制辅助决策工具研发 | |
| 引用本文： | 劳苑雯,朱云,CareyJang,CheJenLin,邢佳,陈志润,谢俊平,王书肖,JoshuaFu.基于响应面模型的区域大气污染控制辅助决策工具研发[J].环境科学学报,2012,32(8):1913-1922. |
| 作者姓名： | 劳苑雯 朱云 CareyJang CheJenLin 邢佳 陈志润 谢俊平 王书肖 JoshuaFu |
| 作者单位： | 1. 华南理工大学环境科学与工程学院广东省大气环境与污染控制重点实验室,广州,510006 2. 华南理工大学环境科学与工程学院广东省大气环境与污染控制重点实验室,广州510006 USEPA/Office of Air Quality Planning ＆ Standards, RTP, NC27711, USA 3. 华南理工大学环境科学与工程学院广东省大气环境与污染控制重点实验室,广州510006 Department of Civil Engineering, Lamar University, Beaumont, TX 77710-0024, USA 4. 清华大学环境学院,北京,100084 5. Department of Civil ＆ Environmental Engineering, University of Tennessee, Knoxville, TN 37996-2010, USA |
| 基金项目： | 中美合作项目(No.OR13810-001.04);国家高技术研究发展计划项目(No.2006AA06A309) |
| 摘 要： | 基于CMAQ模型结果,利用高维克里金插值算法,建立了排放控制因子与污染物环境浓度的响应面模型(RSM),实现了大气污染可控源排放与复合污染水平的实时函数响应.研究结果显示,RSM对PM2.5的预测结果与CMAQ实际模拟结果的误差在容许范围内(最大误差小于0.20μg.m-3,3.89%).基于所建立的RSM,开发了RSM-VAT区域大气污染控制可视化辅助决策工具.使用RSM-VAT对美国8个典型城市的PM2.5污染状况进行了控制情景分析,通过"可视化展示"和"图表分析"二大模块,输出不同控制情景下的环境污染物浓度的实时响应图、可视化展示和数据分析图表等结果. |
| 关 键 词： | 大气污染控制 响应面模型 辅助决策工具 模型数据可视化 |
| 收稿时间： | 2011/10/19 0:00:00 |
| 修稿时间： | 2011/11/23 0:00:00 |
Research and development of regional air pollution control decision support tool based on response surface model |
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| LAO Yuanwen,ZHU Yun,Carey Jang,Che Jen Lin,XING Ji,CHEN Zhirun,XIE Junping,WANG Shuxiao and Joshua FU.Research and development of regional air pollution control decision support tool based on response surface model[J].Acta Scientiae Circumstantiae,2012,32(8):1913-1922. | |
| Authors: | LAO Yuanwen ZHU Yun Carey Jang Che Jen Lin XING Ji CHEN Zhirun XIE Junping WANG Shuxiao and Joshua FU |
| Institution: | Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006;Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006;1. Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006;2. USEPA/Office of Air Quality Planning & Standards, RTP, NC27711, USA;1. Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006;2. Department of Civil Engineering, Lamar University, Beaumont, TX 77710-0024, USA;School of Environment, Tsinghua University, Beijing 100084;Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006;Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006;School of Environment, Tsinghua University, Beijing 100084;Department of Civil & Environmental Engineering, University of Tennessee, Knoxville, TN 37996-2010, USA |
| Abstract: | Using CMAQ simulation results obtained from a multivariate design of experiments, a response surface model (RSM) describing the relationship between air pollution and emission control factors is built using the high dimensional Kriging Interpolation Algorithm. The RSM, coupled with a visualization/decision support tool (RSM-VAT), facilitates real-time visualization of 3-D air quality model data under a wide variety of emission control scenarios and supports policy making through a user-friendly graphical interface linking emission control measures to the concentrations of multiple air pollutants. Verification of RSM prediction against CMAQ simulation results of PM2.5 shows acceptable model performance of RSM (deviation of < 0.20 mg·m-3 or < 3.89%). Using the developed RSM-VAT, a case study predicting PM2.5 concentration changes corresponding to different emission control scenarios in eight US metropolitan areas is demonstrated. |
| Keywords: | air pollution control response surface model decision support tool model data visualization |
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