题目： An Analytical Method for Understanding the Trend and Remaining Life of Haze in China
主讲人： Zhaojun ‘Steven’ Li
时间：2017年11月20日 9：30—10：30
地点：主楼 418
主讲人介绍：  Dr. Zhaojun ‘Steven’ Li is professor with the Department of Industrial Engineering and Engineering Management, Western New England University in Springfield, MA, USA. Dr. Li’s research interests focus on Reliability, Quality, and Safety Engineering, Applied Statistics and Operations Research, Data Science and Analytics, and Diagnostics and Prognostics of Complex Engineered Systems. He earned his PhD in Industrial Engineering from the University of Washington. He is an ASQ certified Reliability Engineer, and Caterpillar Six Sigma Black Belt. Dr. Li’s most recent industry position was a reliability lead with Caterpillar to support the company’s New Engine Development. He is currently serving as Associate Editor for IEEE Transactions on Reliability and the Co-EiC for the International Journal of Performability Engineering. He is also serving on the Boards for the IISE QCRE division and the IEEE Reliability Society AdCom.

内容介绍：
        In this paper, the patterns and trends of the concentration of particulate matters 2.5 or simply PM2.5 over the past three years in China are investigated. The PM2.5 data is collected for five cities, i.e., Beijing, Shanghai, Chengdu, Tianjin and Qingdao. Multiple linear regression analysis is applied to understand the trend of PM2.5 changes over time. Through regression analysis, it is observed that temperature and CO concentration are highly correlated to the PM2.5 when the predictive variables are selected to be natural factors (temperature, humidity, wind levels, SO2, CO, NO2, and O3) and one energy factor (the society electricity consumption). In addition, the temperature is negatively correlated with PM2.5 concentration, and the CO concentration is positively correlated with PM2.5 concentration. Time series analyses of PM2.5 concentration in five cities are carried out to predict the long-term trend with seasonal effects consideration, which shows that PM2.5 concentration has been decreasing over the past years. It is also observed that the highest PM2.5 concentration usually appears in January, and the lowest PM2.5 concentration appears in October. The times of the lower limit of PM2.5 prediction becoming below the standard value of 75 in four cities is also predicted. The impact of public policies on air quality control for PM2.5 reduction has been taken into consideration, and we observe that the policies have positive influences on PM2.5 concentration reduction since 2013.

（承办：管理科学与物流系，科研与学术交流中心）