title: Meteorological influence on predicting surface SO2 concentration from satellite remote sensing in Shanghai, China.
abstract: In this study, we explored the potential applications of the Ozone Monitoring Instrument (OMI) satellite sensor in air pollution research. The OMI planetary boundary layer sulfur dioxide (SO2_PBL) column density and daily average surface SO2 concentration of Shanghai from 2004 to 2012 were analyzed. After several consecutive years of increase, the surface SO2 concentration finally declined in 2007. It was higher in winter than in other seasons. The coefficient between daily average surface SO2 concentration and SO2_PBL was only 0.316. But SO2_PBL was found to be a highly significant predictor of the surface SO2 concentration using the simple regression model. Five meteorological factors were considered in this study, among them, temperature, dew point, relative humidity, and wind speed were negatively correlated with surface SO2 concentration, while pressure was positively correlated. Furthermore, it was found that dew point was a more effective predictor than temperature. When these meteorological factors were used in multiple regression, the determination coefficient reached 0.379. The relationship of the surface SO2 concentration and meteorological factors was seasonally dependent. In summer and autumn, the regression model performed better than in spring and winter. The surface SO2 concentration predicting method proposed in this study can be easily adapted for other regions, especially most useful for those having no operational air pollution forecasting services or having sparse ground monitoring networks.
authors.affiliation: School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China, xuedan@shu.edu.cn.
authors.forename: Dan
authors.initials: D
authors.lastname: Xue
authors.forename: Jingyuan
authors.initials: J
authors.lastname: Yin
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis / Q000706:statistics & numerical data; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D008685:Meteorological Concepts; D010126:Ozone / Q000032:analysis; D058998:Remote Sensing Technology; D063809:Satellite Imagery; D012621:Seasons; D018531:Spacecraft; D013458:Sulfur Dioxide / Q000032:analysis*; D013696:Temperature; D014919:Wind
chemical_list: D000393:Air Pollutants; D013458:Sulfur Dioxide; D010126:Ozone