Volume 14, No. 1, February 2014, Pages 396-405 PDF(790 KB)
Multi-Model Analyses of Dominant Factors Influencing Elemental Carbon in Tokyo Metropolitan Area of Japan
Satoru Chatani1, Yu Morino2, Hikari Shimadera3, Hiroshi Hayami3, Yasuaki Mori4, Kansuke Sasaki4, Mizuo Kajino5,6, Takeshi Yokoi7, Tazuko Morikawa8, Toshimasa Ohara2
1 Toyota Central Research and Development Laboratories, 41-1 Yokomichi, Nagakute, Aichi 480-1192, Japan
2 National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
3 Central Research Institute of Electric Power Industry, 1646 Abiko, Abiko, Chiba 270-1194, Japan
4 Japan Weather Association, 3-1-1 Higashi-Ikebukuro, Toshima-ku, Tokyo 170-6055, Japan
5 Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan
6 Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
7 National Maritime Research Institute, 6-38-1 Shinkawa, Mitaka, Tokyo 181-0004, Japan
8 Japan Automobile Research Institute, 2530 Karima, Tsukuba, Ibaraki 305-0822, Japan
The first phase of the Urban air quality Model InterComparison Study in Japan (UMICS) has been conducted to find ways to improve model performance with regard to elemental carbon (EC). Common meteorology and emission datasets are used with eight different models. All the models underestimate the EC concentrations observed in Tokyo Metropolitan Area in the summer of 2007. Sensitivity analyses are conducted using these models to investigate the causes of this underestimation. The results of the analyses reveal that emissions and vertical diffusion are dominant factors that affect the simulated EC concentrations. A significant improvement in the accuracy of EC concentrations could be realized by applying appropriate scaling factors to EC emissions and boundary concentrations. Observation data from Lidar and radiosonde suggest the possible overestimation of planetary boundary layer height, which is a vital parameter representing vertical diffusion. The findings of this work can help to improve air quality models to that they can be used to develop more effective strategies for reducing PM2.5 concentrations.
Air quality model; Model intercomparison; PM2.5; EC; Sensitivity analyses.