Aerosol and Air Quality Research, 14: 396–405, 2014 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2013.02.0035 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 ABSTRACT 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. Keywords: Air quality model; Model intercomparison; PM2.5; EC; Sensitivity analyses. INTRODUCTION reactions in the atmosphere, is becoming important (Minoura et al., 2006). To seek effective strategies for reducing Fine particulate matter adversely affects human health PM2.5 concentration including secondary components, it is (Pope and Dockery, 2006). The Japanese government has essential to use air quality models that represent physical set 15 (annual mean) and 35 (daily mean) micrograms per and chemical processes in the atmosphere, such as emission, cubic meter as the Environmental Quality Standard (EQS) advection, diffusion, photochemical reactions, and deposition. for fine particulate matter smaller than 2.5 micrometers However, a single model may bring inappropriate results (PM2.5) since 2009. The current PM2.5 concentration is owing to possible errors made by users and/or problems likely above the EQS in most parts of Japan (Ministry of intrinsic to models and input data. Model intercomparison the Environment of Japan, 2012). PM2.5 over Japanese is a promising way for evaluating the performance of urban areas mainly consists of elemental carbon (EC), multiple models and sorting problems that are inevitable organic carbon (OC), sulfate, ammonium, and nitrate. EC among the state-of-the-art models or are confined to a concentration exhibits a decreasing trend, and the contribution single model. The results obtained could also be useful for of the remaining components, which are mainly produced further improvement of models. from gaseous precursors via complex photochemical Intercomparisons of regional air quality models have been carried out for a broad range of spatial scales. The models in the United States and Europe participated in Air Quality Model Evaluation International Initiative II (AQME * Corresponding author. II), and their results were evaluated in both continents Tel.: 81-561-71-7724; Fax: 81-561-63-6559 (Solazzo et al., 2012). In the Model Intercomparison Study E-mail address: [email protected] for Asia Phase II (MICS-Asia II), several models were Chatani et al., Aerosol and Air Quality Research, 14: 396–405, 2014 397 applied to the domain covering East and Southeast Asian models or deviations from observations. The results countries, and their results were compared from various gathered in the project could provide information valuable aspects (Carmichael et al., 2008). Although CityDelta focused for improving air quality models. on evaluating the effects of emission reduction strategies The first phase of UMICS focuses on EC in Tokyo on air quality in selected European cities (Cuvelier et al., Metropolitan Area. EC is not directly affected by 2007), the results of the participating models were also photochemical reactions in the atmosphere. Therefore, it is compared (Vautard et al., 2007). All of these works revealed suitable to evaluate only the physical processes represented the performance characteristics and limitations of the in the models as the first step of the project. Inorganic and participating models in respective scales and regions. organic aerosol components including secondary products As the first trial of the model intercomparison in Japan, will be considered in the forthcoming phases of this project the performance characteristics of four models on ambient (Shimadera et al., 2013a). concentrations of ozone and PM2.5 components were evaluated and mutually compared (Morino et al., 2010a). METHODOLOGY Morino et al. (2010b) also indicated that the ensemble average of the four models was effective for evaluating ozone Participating Models and inorganic aerosol components. One of the issues found in Eight models of seven groups participated in the first their studies was that the four models used different domains, phase of the project. Their configurations are shown in meteorological fields, boundary concentrations, and emission Table 1. The models are labeled M1-M8 in this paper. The datasets, which made it difficult to identify the causes of the models except for M8 are different versions of the differences observed among the modeling results. Community Multiscale Air Quality (CMAQ) Modeling The Urban air quality Model InterComparison Study in System (Byun and Schere, 2006) with different adopted Japan (UMICS) is a model intercomparison project designed modules. They reflect the situation that CMAQ is widely using the experiences described above. The target of applied in Japan. CMAQ is a community model and has UMICS is to make models suitable for considering effective multiple choices of modules for physical and chemical strategies for reducing PM2.5. Most of major modeling groups processes which are periodically updated. Therefore, different in Japan have participated in this project. Common domains versions of CMAQ with different modules could cause large are specified, and datasets of common meteorological fields, differences. M8 is Regional Air Quality Model 2 (RAQM2) emissions, and boundary concentrations are provided to developed by Kajino et al. (2012), and its treatment of them. The participants are requested to conduct simulations physical and chemical processes is distinct from that of in their usual model configurations to evaluate a variety of CMAQ. Participating models were requested to submit the configurations which may be applied for considering simulated results of concentrations and dry deposition rates strategies in Japan. The performance and consistency of of EC. their simulation results are evaluated. In addition, UMICS serves as an efficient comprehensive sensitivity analysis Observation Data which is difficult to carry out for a single user. Participants The observation data obtained during the field monitoring conduct sensitivity runs in their fields of expertise to campaign called Fine Aerosol Measurement and Modeling examine the causes of the differences observed among the in Kanto Area (FAMIKA) (Hasegawa et al., 2008; Fushimi Table 1. Configurations of participating models. Model Advection Vertical diffusion Horizontal diffusion M1 CMAQ ver.4.6 Yamartino acm2 Byun and Schere M2 CMAQ ver.4.7 Yamartino eddy Byun and Schere M3 CMAQ ver.4.7 Yamartino eddy Byun and Schere M4 CMAQ ver.4.6 Yamartino eddy Byun and Schere M5 CMAQ ver.4.6 Yamartino eddy Byun and Schere M6 CMAQ ver.4.7.1 Yamartino acm2_inline Byun and Schere M7 CMAQ ver.4.7.1 Yamartino acm2_inline Byun and Schere M8 RAQM2 Walcek and Aleksic (1998) 1.5-order TKE Smagorinsky Reaction solver Aerosol Dry deposition Aqueous M1 ros3 aero4 aero_depv2 cloud_radm M2 ebi_saprc99_ae5 aero5 aero_depv2 cloud_acm_ae5 M3 ebi_saprc99_ae5 aero5 aero_depv2 cloud_acm_ae5 M4 ebi_saprc99 aero3 aero_depv2 cloud_acm M5 ebi_saprc99 aero3 aero_depv2 cloud_radm M6 ebi_saprc99_ae5 aero5 aero_depv2 cloud_acm_ae5 M7 ebi_saprc99 aero4 aero_depv2 cloud_acm M8 saprc99 Kajino et al. (2012) Zhang et al. (2001, 2003)1 cloud_acm 1 Modified by Kajino et al. (2012). 398 Chatani et al., Aerosol and Air Quality Research, 14: 396–405, 2014 et al., 2011) was compared with modeling results. Ambient layers are set following the sigma-P coordinates from the PM2.5 was collected on quartz fiber filters for six hours surface to 100 hPa, and the height of the bottom layer is during