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 three periods (July 31st to August 3rd, August 6th to approximately 57 m. The vertical layers of M8, which 10th, and August 13th to 16th, 2007) at four monitoring follow the sigma-Z coordinates, were adjusted to minimize stations (Komae, Kisai, Maebashi, and Tsukuba) shown in differences in both coordinates. The United States Geological Fig. 1 in Tokyo Metropolitan Area. The samples obtained Survey (USGS) land use data (30-seconds resolution) were were analyzed with the DRI Model 2001 Thermal/Optical used to prepare geological inputs. The National Centers for Carbon Analyzer (Atmoslytic Inc.) using the Interagency Environmental Prediction (NCEP) Final Analysis data (1- Monitoring of Protected Visual Environments (IMPROVE) degree resolution, every 6 hours) were fed as the initial and protocol to quantify EC concentration. Its relative error is boundary conditions. No grid nudging was applied because 5.3% (Fushimi et al., 2011). nudging to analysis data which is too coarser than grids of The participating models showed similar performances the target domains does not always result in better results. for three observation periods. The simulation results from The Monin-Obukhov (Janjic) scheme, Noah-MP land- August 6th to 10th, 2007 among them are discussed in this surface model and Mellor-Yamada-Janjic TKE scheme paper. Tokyo Metropolitan Area was covered by a Pacific were adopted for the options of the surface layer, land high-pressure system throughout the period after a typhoon surface, and planetary boundary layer, respectively. These passed through the west part of Japan on August 3rd. Wind choices realized the best and reasonable performance among speed was high in daytime and low at nighttime. Wind various configurations without nudging (Mori et al., 2011). direction was southerly throughout the period at Komae and Tsukuba, while it turned to northerly at nighttime at Emission and Boundary Concentration Kisai and Maebashi owing to the circulation of land-sea Dataset of common emissions and boundary concentrations and mountain-valley winds. No precipitation was observed were provided to the participating models. The emissions within Tokyo Metropolitan Area. are based on the database described in detail by Chatani et al. (2011). Emissions from large point sources and vessels Meteorological Field were derived from the East Asian Air Pollutant Emissions The common meteorological field was generated with Grid Database (EAGrid2000-Japan, Kannari et al., 2007), Weather Research and Forecasting (WRF) - Advanced and those from the remaining anthropogenic sources other Research WRF (ARW) model (Skamarock et al., 2008) than vehicles were estimated with the Georeference-Based version 3.1.1 for nineteen days from July 29th to August Emission Activity Modeling System (G-BEAMS, Nansai 16th, 2007 covering the entire period of FAMIKA as well et al., 2004). Vehicle emissions were estimated using the as a few spin-up days. The target domains are shown in Japan Auto-Oil Program (JATOP) vehicle emissions estimate Fig. 1. The two domains (Domains 1 and 2) are nested model. The Model of Emissions of Gases and Aerosols over the middle of Japan, and Domain 2 covers Tokyo from Nature (MEGAN, Guenther et al., 2006) version 2.04 Metropolitan Area. Grids in Domains 1 and 2 are 15 × 15 with the common meteorological fields described in the km and 5 × 5 km in size, respectively. Thirty vertical previous section was utilized to estimate hourly biogenic

Maebashi Kisai Tsukuba Komae

Domain 2

Domain 1 Domain 2

Fig. 1. Common target domains of simulation (left) and EC emission rates in Domain 2 (right). The locations of the four monitoring stations of FAMIKA are also shown in the right figure.

Chatani et al., Aerosol and Air Quality Research, 14: 396–405, 2014 399 emissions. The distribution of the EC emission rates in simulated values for the same period are found only at Domain 2 is shown in Fig. 1. EC emission rates are high at Komae. The simulated values tend to increase in the the heart of Tokyo, in the industrial areas along Tokyo morning and evening, and to decrease in the daytime and Bay, along major highways, and from isolated large point at night particularly at Komae and Maebashi whereas the sources. Boundary concentrations were set by the default decreases in observed values during the daytime are not values of CMAQ, but the values of sulfate were determined clearly found. The values simulated by M8 are the highest using the observation data obtained in FAMIKA. Emissions among the eight models. The differences in the values and boundary concentrations were divided into SAPRC99 simulated by the seven CMAQ are small. The versions and (Carter, 2000) species groups and aerosol components adopted modules of CMAQ which are widely used in (sulfate, nitrate, EC, OC, and others in fine fraction and Japan shown in Table 1 cause negligible differences. nonspeciated coarse fraction), and were provided to the Fig. 3 shows the vertical profiles of the EC concentrations participating models. simulated by M7 and M8 at Komae and Kisai at 6 AM on August 10th, 2007. The EC concentration simulated by M8 RESULTS is higher than those simulated by M7 within the bottom layer. M8 uses the 1.5-order TKE module for vertical Fig. 2 shows the time series of the observed surface EC diffusion, which is different from the modules applied in concentration for every six hours and the hourly surface EC CMAQ. The different treatment of vertical diffusion could concentration simulated by the eight participating models at be one of the reasons for the higher surface EC concentrations Komae, Kisai, Maebashi, and Tsukuba for August 6th to simulated by M8. Fig. 4 shows the time series of the 10th, 2007. The simulated values are lower than the observed hourly EC dry deposition rates simulated by M7 and M8 at values at all stations for most of the time. The observed Komae, Kisai, Maebashi, and Tsukuba from August 6th to values increase in the latter days, although the increases in 10th, 2007. All of the simulated values are high in daytime

Komae Kisai 3 4

3 ) 3 2

g/m 3

 2 Obs. 1 M1 EC ( EC 2.5 1 M2 2 ) 0 0 3 M3

g/m 1.5  M4 Maebashi Tsukuba EC ( M5 5 5 1 M6 4 4 0.5 M7 ) 3 3 3 0 M8

g/m 8/68/78/88/988……  2 2 EC ( EC 1 1

0 0 8/6 8/7 8/8 8/9 8/10 8/11 8/6 8/7 8/8 8/9 8/10 8/11 Date in 2007 Date in 2007 Fig. 2. Time series of observed surface EC concentration for every six hours and hourly surface EC concentration simulated by eight participating models at Komae, Kisai, Maebashi, and Tsukuba from August 6th to 10th, 2007.

Komae Kisai 2000 2000 1800 1800 1600 1600 1400 1400 1200 1200 M7 1000 1000 800 800 Height (m) Height (m) M8 600 600 400 400 200 200 0.20.400.60.81.21 0 0 0 0.2 0.4 0.6 0.8 1 1.2 0 0.2 0.4 0.6 0.8 EC (g/m3) EC (g/m3) Fig. 3. Vertical profiles of EC concentration simulated by M7 and M8 at Komae and Kisai at 6 A.M. on August 10th, 2007.

400 Chatani et al., Aerosol and Air Quality Research, 14: 396–405, 2014

Komae Kisai 7 2.5 6 2 5

/hr) 1.5 2 4

g/m 3 1  ( 2 8 0.5 1

EC dry depotision rate rate depotision dry EC 6 M7 0 0 4 Maebashi Tsukuba 2 M8 3 3.5 (mg/m3) EC 3 0 888888 2.5 2 /hr)

2 2 1.5 g/m 

( 1 1 0.5 EC dry depotision rate rate depotision dry EC 0 0 8/6 8/7 8/8 8/9 8/10 8/11 8/6 8/7 8/8 8/9 8/10 8/11 Date in 2007 Date in 2007 Fig. 4. Time series of hourly EC dry deposition rates simulated by M7 and M8 at Komae, Kisai, Maebashi, and Tsukuba from August 6th to 10th, 2007. and low at nighttime. However, the amplitude of temporal Their effects are high in daytime and low at nighttime. The variation is significantly smaller in the values simulated by absolute effect of vertical diffusion is comparable or slightly M8. M8 represents aerosol size distributions with multiple larger than that of emission in daytime. They result in the modes (Kajino, 2011) similarly to CMAQ (Binkowski and decreases of the simulated surface EC concentrations in Roselle, 2003). However, M8 explicitly treats various mixing daytime shown in Fig. 2. The effects of advection follow states of aerosols (Kajino and Kondo, 2011), while only those of emission and vertical diffusion. The negative change internal mixing is assumed in CMAQ. Differences in the rates caused by advection that are comparable to those of treatment of aerosol dynamics and resulting diameters could vertical diffusion are observed in the evening at Maebashi, cause significant differences in dry deposition rates. which is located in the north of Tokyo Metropolitan Area, when the direction of the land sea breeze is northerly. SENSITIVITY ANALYSES The simulated change rates of the surface EC concentrations caused by horizontal diffusion, dry deposition, The deviation of the simulated values from the observed and aqueous processes were small. Although the effect of ones cannot be explained only by uncertainties embedded aqueous process is negligible because of the absence of in the models. The reasons for this deviation and any precipitation within Tokyo Metropolitan Area during this possible ways to improve models were further explored via period, it could be a dominant removal process for EC sensitivity analyses conducted by the participants of the under rainy conditions. The small effect of dry deposition project who applied CMAQ. means that the difference in dry deposition rates shown in Fig. 4 is not a major cause of differences in the EC Process Analyses concentrations simulated by CMAQ and RAQM2. As A capability of the integrated process rate (IPR) analysis shown in Figs. 2 and 4, surface EC concentration is on the is incorporated into CMAQ. IPR is widely used to quantify order of a few micrograms per cubic meter, and EC dry the effects of all the physical and chemical processes on deposition rate is on the order of a few micrograms per model predictions (e.g., Khiem et al., 2010). To understand square meter per hour. Moreover, the EC emission rates at the effects of the physical and chemical processes on the the four stations are on the order of several tens of surface EC concentrations simulated by the participating micrograms per square meter per hour, as shown in Fig. 1. models, the simulated change rates of the EC concentrations Therefore, only a tiny fraction of EC in the bottom layer of caused by advection, vertical diffusion, emission, horizontal 57 meters depth is removed by dry deposition. Therefore, diffusion, dry deposition, and aqueous processes were the effects of the differences in dry deposition rates shown derived by the IPR analysis. in Fig. 4 are almost negligible against the surface EC Fig. 5 shows the simulated change rates of the surface concentration and emission rate. Further investigation on EC concentrations caused by advection, vertical diffusion, the differences in dry deposition rates of CMAQ and emission, and all the other processes at Komae, Kisai, RAQM2 is outside the scope of the current project. Maebashi, and Tsukuba from August 6th to 10th, 2007. The sensitivities of emission and vertical diffusion to the Emission and vertical diffusion are dominant processes that simulated EC concentration were further examined in the increase and decrease the simulated values, respectively. following subsections.

Chatani et al., Aerosol and Air Quality Research, 14: 396–405, 2014 401

Komae Kisai 3 1 2

/hr) 0.5 3 1

g/m 0  0 -0.5 -1 3 ADV -2 -1 2 d[EC]/dt ( -3 -1.5 1 VDIF 0 Maebashi 8/8/8/8/Tsukuba8/8/ EMIS -1 67891011 2 1.5 (mg/m3) EC 1.5 -2 Other 1 /hr) 1

3 -3 0.5 0.5 g/m

 0 0 -0.5 -1 -0.5 -1.5 -1 d[EC]/dt ( d[EC]/dt -2 -2.5 -1.5 8/6 8/7 8/8 8/9 8/10 8/11 8/6 8/7 8/8 8/9 8/10 8/11 Date in 2007 Date in 2007 Fig. 5. Simulated change rates of surface EC concentration caused by advection (ADV), vertical diffusion (VDIF), emission (EMIS), and all the other processes (Other) at Komae, Kisai, Maebashi, and Tsukuba from August 6th to 10th, 2007.

Emission and Boundary Concentration were estimated by multiple regression analysis utilizing pairs The differences between the observed and simulated of observed and simulated surface EC concentrations for values may be partly caused by the underestimation of the all periods at the four stations. The estimated multiplying EC emissions and boundary concentrations. A simple factors were 4.0, –0.8, 41.7, and 22.4 for the EC emissions of approach similar to that of Hu et al. (2009) was applied to vehicles, other anthropogenic sources, and open agricultural investigate how much EC emissions and boundary burning, and for EC boundary concentrations, respectively, concentrations need to be increased to match the simulated and the multiple correlation coefficient was 0.90. values to the observed ones. An additional run (named as posteriori) was executed in First, the hourly sensitivities of the EC emissions of three which the EC emissions of vehicles and open agricultural sources (vehicles, other anthropogenic sources, and open burning, and EC boundary concentrations were multiplied agricultural burning) and the EC boundary concentrations by 4, 40, and 20, respectively, based on the results of to the simulated hourly EC concentration were calculated multiple regression analysis. The time series of the observed by the so-called “brute force method”, in which gaps of and simulated surface EC concentrations of the two runs at simulated EC concentrations in two runs with and without Komae, Kisai, Maebashi, and Tsukuba from August 6th to the EC emission of each source or the EC boundary 10th, 2007 are shown in Fig. 6. The model performance was concentrations are considered as their sensitivities. The significantly improved in terms of not only the absolute sensitivities of vehicles and other anthropogenic sources value of EC concentration but also the temporal variation. were dominant, and those of open agricultural burning and Although no evidences for such high multiplying factors are boundary concentrations were only a few percents. currently available, the results imply that the EC emissions Then, it was assumed that the differences between the not originating from urban areas such as those from the open observed and simulated values are explained by the agricultural burning and the transport of EC from the outside sensitivities and multiplying factors as expressed in of Tokyo Metropolitan Area, should be paid more attention. Shimadera et al. (2013b) conducted a simulation including ECobs,i – ECsim,i the same period as in this study. Their simulation well = (fveh-1)Sveh,i + (fotr-1)Sotr,i + (fbng-1)Sbng,i + (fbdy-1)Sbdy,i, (1) reproduced the surface EC concentration observed at Chichi- jima, which is located at about 1000 km south of the center where ECobs and ECsim are the observed and simulated of Tokyo in the Pacific Ocean. They also indicated that the surface EC concentrations, Sveh, Sotr, Sbng, and Sbdy are the contribution of the emissions outside of Japan to the sensitivities of EC emissions (vehicles, other anthropogenic simulated EC concentration in Tokyo Metropolitan Area sources, and open agricultural burning) and EC boundary was negligible during the same period. On the basis of their concentrations obtained by the brute force method, and fveh, results, the effect of EC transported from regions far away fotr, fbng, and fbdy are the multiplying factors of the EC from Tokyo Metropolitan Area may be small. Effects of emissions of the three sources and EC boundary emission sources outside of Tokyo Metropolitan Area but concentrations, respectively. The subscript i represents within Japanese islands and surrounding oceans as well as each hour and station. Any spatial or temporal variation in within Tokyo Metropolitan Area should be examined further. multiplying factors was ignored. Suitable multiplying factors Fushimi et al. (2011) indicated that a few tens percent of

402 Chatani et al., Aerosol and Air Quality Research, 14: 396–405, 2014

Komae Kisai 5 8 7 4 6 ) 3 3 5 g/m

 4 2 3 5 EC ( EC 2 1 1 4 Obs. ) 0 0 3 3 Base g/m  Maebashi 2 Tsukuba 6 6 Posteriori EC ( EC 5 5 1 ) 3 4 4 0 g/m

 3 3 2 2 EC ( EC 1 1 0 0 8/6 8/7 8/8 8/9 8/10 8/11 8/6 8/7 8/8 8/9 8/10 8/11 Date in 2007 Date in 2007 Fig. 6. Time series of observed and simulated surface EC concentrations of two runs in which multiplying factors are applied (posteriori) and not applied (base) at Komae, Kisai, Maebashi, and Tsukuba from August 6th to 10th, 2007. modern carbon not originating in fossil fuels are included temperature observed by the radiosonde exceeds 6 K per in the total carbon collected during FAMIKA which is much kilometer (Kuribayashi et al., 2011). Fig. 8 shows the PBL higher than the sensitivity of open agricultural burning height determined from the two observation data and the obtained in the simulation. It implies possible underestimation values simulated in the common meteorological field at of EC emissions from open agricultural burning. Tsukuba from August 5th to 11th, 2007. The values of the PBL height determined by the two observation data Vertical Diffusion coincide; thus the two methodologies used to determine Vertical diffusion coefficients control the strength of the PBL height appear to be reliable. Although the PBL is vertical diffusion in the simulation. They are calculated low at nighttime and high in daytime in both of the observed using the equations of Holstlag and Boville (1993) and Liu and simulated values, the simulated values in daytime are and Carroll (1996) below and above the planetary boundary higher than the observed ones. These results imply a layer (PBL) height, respectively, within the acm2 scheme possibility that the simulated PBL height in daytime is of CMAQ. The former tends to produce higher values and overestimated. also utilizes a value of the PBL height itself in the equation. It must be noted that the simulated EC concentrations do Therefore, the vertical diffusion coefficients calculated in not reach the observed values even if the PBL height is set the simulation are highly affected by the PBL height. to zero. The deviation of the simulated values from the Three sensitivity runs in which the values of the PBL observed ones cannot be attributed only to vertical diffusion. height were changed to zero, half and twice the original value were performed. The PBL height is tightly linked to SUMMARY other meteorological parameters like winds in the real atmosphere, but only the values of the PBL height were A model intercomparison project in Japan called UMICS changed here to evaluate its importance. The time series of has been conducted. The first phase of UMICS focused on the observed and simulated surface EC concentrations of model performance characteristics and sensitivities to EC the four runs at Komae, Kisai, Maebashi, and Tsukuba for in Tokyo Metropolitan Area because it is not directly August 6th to 10th, 2007 are shown in Fig. 7. The simulated affected by photochemical reactions in the atmosphere and values are significantly affected by the PBL height. They is suitable for evaluating only the physical processes tend to increase in daytime when smaller values of the represented in the models. Common meteorological field PBL height are used because lower vertical diffusion and emission datasets were provided to eight participating coefficients are applied to more vertical layers. models. The simulated EC concentrations were compared We attempted to roughly estimate the PBL height by with each other and with the observation data obtained in using the two types of available observation data (lidar and FAMIKA. All of the participating models underestimated radiosonde) at Tsukuba. The PBL height was determined the surface EC concentration. as the altitude at which the gradient of the spherical particle Sensitivity analyses were performed to investigate the extinction coefficients higher than 5.0 × 10–5 for 532 nm reasons for the deviation of the simulated values from the measured by the lidar falls below –8% per 30 meters, and observation data. Emission and vertical diffusion were as the altitude at which the gradient of the potential identified as the two dominant factors that induce the

Chatani et al., Aerosol and Air Quality Research, 14: 396–405, 2014 403

Komae Kisai 3.5 4 3 3.5 3 )

3 2.5 2.5 2 g/m

 2 1.5 1.5 5 EC ( EC 1 1 0.5 0.5 4 Obs. ) 0 0 3 PBLx0 3 g/m

 PBLx0.5 Maebashi 2 Tsukuba 4.5 5 Base EC ( EC 4.5 4 1 PBLx2 3.5 4 ) 3 3 3.5 0 2.5 3 g/m

 2.5 2 2 1.5 EC ( EC 1.5 1 1 0.5 0.5 0 0 8/6 8/7 8/8 8/9 8/10 8/11 8/6 8/7 8/8 8/9 8/10 8/11 Date in 2007 Date in 2007 Fig. 7. Time series of observed and simulated surface EC concentrations of three runs, in which PBL height was changed to zero, half and twice, as well as base case at Komae, Kisai, Maebashi, and Tsukuba from August 6th to 10th, 2007.

Fig. 8. PBL heights determined from observation data of lider and radiosonde, and that simulated in common meteorological field (MCIP) at Tsukuba from August 5th to 11th, 2007. The values under clear sky (no-cld) and cloudy (cld) conditions are represented by different symbols. variation in simulated EC concentration via the process has tight linkages with the observation and emission research analyses. The performance of the simulation was significantly groups. The remaining issues described above will be improved when multiplying factors obtained by multiple reflected in their next observation campaign not only in regression analysis were applied to EC emissions as well summer and emission improvement, and then their results as to EC boundary concentrations. They implied that the will be utilized in the second and following phases of EC emissions not originating from urban areas should be UMICS. Such iteration should contribute to the improvement paid more attention. The PBL height estimated from the of air quality models. observation data of lidar and radiosonde implied that the The effectiveness of the regional air quality model simulated PBL height was possibly underestimated. These intercomparison designed in UMICS has been strongly outcomes are useful to improve air quality models further recognized by the participants. Further valuable results are and to make models suitable for considering effective expected in the forthcoming phases of UMICS on secondary strategies for reducing PM2.5 concentration. aerosol components (Shimadera et al., 2013a) as well as The target period for the first phase of UMICS was focused the remaining issues for EC described in this paper. on the summertime in which major winds are southerly and photochemical reactions are active. Large-scale transport is ACKNOWLEDGMENTS significant in spring and autumn, and stagnant air in winter causes heavy air pollution in winter. Therefore, model This research was supported by the Environment performance characteristics also vary with season. UMICS Research and Technology Development Fund (C-1001) of

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