报告题目：Top-down and bottom-up emissions forecasting for dynamic air quality management
报告人： 胡泳涛博士，美国佐治亚理工学院（Georgia Institute of Technology）
报告时间：2016年3月16日9：30 -10：30 (星期三)
报告地点：浙江大学农生环大楼C216会议室
报告人简介： Dr. Yongtao Hu is a senior research scientist of the School of Civil and Environmental Engineering of Georgia Institute of Technology. He had over 18 years’ research and consulting experiences in the following fields: air pollution meteorology and atmospheric chemistry, meso-scale meteorological modeling and forecasting, regional air quality modeling and forecasting, as well as atmospheric composition data assimilation and inverse modeling. Dr. Hu has incorporated WRF, SMOKE and CMAQ into the operational HiRes and HiRes2 regional air quality forecasting system that has been serving the metropolitan areas in Georgia since 2006. He has also worked on dozens of projects that employed MM5/WRF, SMOKE, CMAQ/CAMx to study regional/urban-scale ozone and fine particulate matter issues and to assess air quality impacts from various sources. His expertise also extends to innovative hybrid method that utilizing strengths from both the CTM models and receptor models for source apportionment purpose that will aid health studies with regional observation-verified results at high spatial and temporal resolution.
报告摘要：The governmental interventions from long-term air quality management (AQM) actions typically do not take advantage of the knowledge of short-term factors that influence air quality. However, unhealthy air pollution levels, which frequently happens in cold seasons in China, are often caused by short-term changes such as those in meteorological conditions and fluctuations in emissions. Unlike long-term AQM, dynamic AQM (DAQM) provides potential opportunities to more quickly react to changing conditions and the resulting adverse impacts on air quality. This talk reports operational air quality and source impacts forecasting efforts in Georgia, USA for the purpose of assisting DAQM. The HiRes2 system forecasts air quality as well as source impacts on air quality from traffic and power plant emissions and from prescribed burn emissions. To improve the forecasting accuracy, the emissions inputs are subject to online adjustments through inverse modeling by utilizing near-real time observations. This talk focuses on prescribed burning forecasting and the evaluation. The techniques reported in this talk can be useful for the DAQM practice in China.
浙江大学环境与资源学院
2016年3月12日