Advances in Meteorology

Advances in Meteorology / 2016 / Article

Research Article | Open Access

Volume 2016 |Article ID 5694251 | https://doi.org/10.1155/2016/5694251

Hikari Shimadera, Tatsuya Kojima, Akira Kondo, "Evaluation of Air Quality Model Performance for Simulating Long-Range Transport and Local Pollution of PM2.5 in Japan", Advances in Meteorology, vol. 2016, Article ID 5694251, 13 pages, 2016. https://doi.org/10.1155/2016/5694251

Evaluation of Air Quality Model Performance for Simulating Long-Range Transport and Local Pollution of PM2.5 in Japan

Academic Editor: Gabriele Curci
Received02 Apr 2016
Revised11 Jun 2016
Accepted15 Jun 2016
Published14 Jul 2016

Abstract

The Community Multiscale Air Quality Model (CMAQ) v5.0.2 was applied to PM2.5 simulation in Japan, which is strongly affected by long-range transport (LRT) from anthropogenic sources in the Asian Continent, for one year from April 2010 to March 2011. The model performance for LRT and local pollution (LP) of PM2.5 was evaluated to identify the model processes that need to be improved. CMAQ well simulated temporal and spatial variation patterns of PM2.5 but underestimated the concentration level by 15% on average. The contribution of LRT was estimated from the difference between the baseline simulation case and a zero-emission case for anthropogenic emissions in the continent. The estimated LRT contribution to PM2.5 was 50% on average and generally higher in the western areas of Japan (closer to the continent). Days that were dominantly affected by LRT or LP were determined based on the contribution of LRT to sulfate, which was fairly well simulated and strongly affected by LRT among major PM2.5 components. The underestimation of PM2.5 was larger in LP days (by 26% on average) than LRT days (by 10% on average). Therefore, it is essential to improve local emissions, formation, and loss processes of precursors and PM2.5 in Japan.

1. Introduction

Particulate matter (PM) with aerodynamic diameter less than 2.5 μm (PM2.5) is an atmospheric pollutant that mainly consists of several major components, such as organic aerosol (OA), elemental carbon (EC), sulfate, nitrate, and ammonium. Primary OA (POA) and EC are emitted directly through combustions of fossil fuel and biomass [1]. Secondary OA (SOA) is formed from various volatile organic compounds (VOC) and the corresponding processes are highly complex and varied [2]. Sulfate and nitrate are typically secondary pollutants produced through oxidations of sulfur dioxide (SO2) and nitrogen oxides (NOX), respectively. Ammonium is produced from ammonia (NH3) and generally a counter ion of sulfate and nitrate.

The Ministry of the Environment of Japan (MOE) introduced an air quality standard (AQS) for PM2.5 concentration (35 μg m−3 for daily mean and 15 μg m−3 for annual mean) in 2009. However, the AQS has not been attained in most urban areas in Japan. Although it is essential to use air quality models (AQMs) to design effective PM2.5 control strategies, the performance of current AQMs for PM2.5 simulation is not adequate for the purpose. Therefore, in order to improve AQM performance, a variety of studies have been conducted, including AQM intercomparisons, sensitivity analyses, and revisions of AQM subprocesses [35].

In East Asia, the rapid growth in economic activity and energy consumption in recent decades have resulted in increased anthropogenic emissions of air pollutants [6, 7]. Since air pollutants from the Asian Continent are efficiently transported to the Pacific region by eastward traveling high- and low-pressure systems because of the Westerlies in spring and autumn and by northwesterly monsoon in winter, long-range transport (LRT) of air pollutants from the continent in these seasons has been a field of scientific interests [810]. In addition, even in summer, LRT can strongly contribute to sulfate concentration in Japan [11]. Therefore, AQMs applied to air quality management in Japan have to properly simulate both local pollution (LP) and LRT.

In this study, one-year air quality simulations by the Community Multiscale Air Quality Model (CMAQ) [12] were conducted in regions from East Asia to Japan. The model performance for simulating LRT and LP of PM2.5 in Japan was evaluated to identify aspects of AQM processes that need to be improved.

2. Materials and Methods

Air quality simulations were conducted by using CMAQ v5.0.2 from April 2010 to March 2011 (Japanese fiscal year 2010: JFY2010) with an initial spin-up period of 22–31 March 2010. The study period was selected because nation-wide PM2.5 observations were launched in JFY2010 in Japan. Figure 1 shows the modeling domains for the CMAQ simulations including domain 1 (D1) covering a wide area of Northeast Asia and domain 2 (D2) covering almost the entire area of Japan. The horizontal resolutions and the number of grid cells are 64 km and 76 × 76 grid cells for D1 and 16 km and 92 × 104 grid cells for D2, respectively. The vertical layers consist of 30 sigma-pressure coordinate layers from the surface to 100 hPa with the middle height of the first layer being approximately 28 m.

Meteorological fields were produced by the Weather Research and Forecasting Model (WRF) [13] v3.5.1. The physics options and objective analysis data were the same as those used by Shimadera et al. [14]: the Yonsei University planetary boundary layer (PBL) scheme [15], the Kain-Fritsch cumulus parameterization [16], the WRF single-moment 6-class microphysics scheme [17], the Noah land surface model [18], the rapid radiative transfer model [19] for the long wave radiation, and the shortwave radiation scheme of Dudhia [20], the real time, global analysis, high-resolution sea surface temperature (RTG_SST_HR) data by the US National Centers for Environmental Prediction (NCEP), the mesoscale model grid point value (MSM GPV) data by the Japan Meteorological Agency (JMA), and the final analysis (FNL) data by NCEP. Grid nudging was applied to horizontal wind components, temperature, and humidity with a nudging coefficient of 3.0 × 10−4 s−1 in the entire simulation domain and period. Shimadera et al. [14] showed that WRF successfully produced meteorological fields for air quality simulations in JFY2010 in Japan.

CMAQ was configured with the Carbon Bond mechanism developed in 2005 (CB05) [21] for the gas-phase chemistry, the sixth generation CMAQ aerosol module (AERO6) for the aerosol process. The hourly WRF results were processed using the Meteorology-Chemistry Interface Processor (MCIP) v4.2 for CMAQ. Initial and boundary concentrations in D1 were derived from the Model for Ozone and Related Chemical Tracers v4 (MOZART-4) [22].

Various datasets were used to produce emission data for the CMAQ simulations. Anthropogenic emissions in Japan were derived from the Japan Auto-Oil Program (JATOP) Emission Inventory-Data Base (JEI-DB) in the year 2010 developed by Japan Petroleum Energy Center (JPEC) [23] for vehicles, an emission inventory in the year 2005 developed by the Ocean Policy Research Foundation (OPRF) [24] for ships, and an emissions inventory in the year 2010 called EAGrid2010-JAPAN [25] for the other sectors. Anthropogenic emissions in East Asia except Japan were derived from an emission inventory for Asia in the year 2006 to support the Intercontinental Chemical Transport Experiment phase B (INTEX-B) [7] v1.2. Because of a lack in the INTEX-B emission data, NH3 emissions were derived from the Regional Emission inventory in Asia (REAS) [6] v1.11. Daily emissions from open biomass burning were derived from a fire inventory of Wiedinmyer et al. [26]. Biogenic emissions were estimated with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) [27] v2.04. Volcanic SO2 emissions were derived from the Aerosol Comparisons between Observations and Models (AEROCOM) data [28].

The CMAQ simulations were conducted in the following two cases: a baseline simulation case with all the emission data (EBase) and a zero-emission case for anthropogenic emissions outside Japan (EJapan). Results in EJapan roughly indicate the contribution of LP in Japan and differences between results of EBase and EJapan indicate the contribution of LRT from anthropogenic sources in the Asian Continent to Japan. Note that emission data in EJapan includes the entire natural emissions such as biomass burning and biogenic and volcanic emissions.

Figure 2 shows spatial distributions of mean SO2, NOX, NH3, and primary PM2.5 emissions. Major anthropogenic emission sources of SO2 are large point sources, industrial areas, and ships. Major sources of NOX and primary PM2.5 are vehicles in addition to those of SO2. Major sources of NH3 are agricultural sources such as volatilization from livestock wastes and fertilized fields. In China, these emissions were particularly large in and around Beijing, and, in Japan, emissions were particularly large in and around Tokyo, followed by Osaka. The total SO2, NOX, NH3, and primary PM2.5 emissions in D1 were 37.5, 23.3, 15.7, and 11.6 Tg yr−1 in EBase and 8.2, 3.3, 0.6, and 1.6 Tg yr−1 in EJapan, respectively.

Results of the CMAQ simulation in EBase were compared with ground-level concentration data at observation sites shown in Figure 1. The observation sites include ambient air pollution monitoring stations conducting PM2.5 observations in JFY2010 and national monitoring stations in Japan (49 stations in total) [29], the US embassy in Beijing for PM2.5 [30], and 24 h filter sampling sites for PM2.5 components (Fukuoka City in Fukuoka Prefecture, Hyuga in Miyazaki, Kurashiki in Okayama, Kobe in Hyogo, Sakai in Osaka, Joetsu in Niigata, Ichikawa in Chiba, Hasuda in Saitama, Itabashi in Tokyo, Toride in Ibaraki, Sendai in Miyagi, and Sapporo in Hokkaido in order from west to east) [31]. At the 12 sampling sites, concentration data of the five major PM2.5 components (OA, EC, sulfate, nitrate, and ammonium) are available for two weeks in each season in JFY2010 (spring: May 14–27, 2010, summer: July 26-August 11, 2010, autumn: November 5–18, 2010, and winter: January 26-February 10, 2011). The model performance was evaluated using statistical measures including Pearson’s correlation coefficient (r), the mean bias error (MBE), the root mean square error (RMSE), MBE normalized by the mean observed value (NMB), RMSE normalized by the mean observed value (NRMSE), the proportion of simulated values within a factor 2 of observation (PF2), and the index of agreement (IA).

3. Results and Discussion

3.1. Model Performance and LRT for SO2, NOX, and PM2.5

Table 1 shows statistical comparisons of the observations and the CMAQ simulation in EBase for daily mean SO2, nitrogen dioxide (NO2), and PM2.5 concentrations at the ambient air pollution monitoring stations in JFY2010. For SO2, while CMAQ approximately captured the mean concentration level, relatively small r, PF2, and IA and large NRMSE indicate that the model performance was inferior to that for NO2 and PM2.5. For NO2, although the IA value indicates better agreement between the observation and simulation compared to SO2, the model underestimated the mean concentration level. For PM2.5, the model better simulated daily mean concentrations than those of its precursors, SO2 and NO2. Most of the simulated daily PM2.5 concentrations were within a factor 2 of the observed values in both Japan and Beijing. In addition, the values of and IA were the highest among the three pollutants. Therefore, CMAQ well simulated their temporal and spatial variation patterns of PM2.5. However, the model moderately underestimated the mean PM2.5 concentration in Japan. The CMAQ performance is further discussed through the following comparisons on temporal and spatial variations.


JapanBeijing
SO2NO2PM2.5PM2.5

Sample number150531613315315323
Mean obs.2.6 (2.1)13.3 (8.1)15.0 (10.2)102.2 (85.3)
Mean sim.2.2 (2.0)9.7 (8.4)12.7 (9.3)110.4 (65.7)
0.400.710.750.83
MBE−0.4−3.7−2.28.3
RMSE2.37.37.348.8
NMB−14%−27%−15%8%
NRMSE88%55%49%48%
PF255%63%81%83%
IA0.620.800.850.89

Note: parenthetical values show standard deviations. Units of mean, MBE, and RMSE values are ppbv for SO2 and NO2 and μg m−3 for PM2.5.

Figure 3 shows time series of daily mean observed and simulated SO2, NO2, and PM2.5 concentrations averaged for all the ambient air pollution monitoring stations in D2 in JFY2010. For SO2, CMAQ well captured day-to-day variation averaged for the monitoring stations in Japan. For NO2, while both the observed and simulated concentrations tended to be high in the cool season from November 2010 to March 2011, relatively large underestimations frequently occurred in the period. For PM2.5, the model fairly well simulated day-to-day variation of PM2.5 concentration including occurrence of several high concentration peaks. The temporal variation pattern was similar to that of SO2, but the variation range was larger than that of SO2.

Figure 4 shows spatial distributions of simulated annual mean ground-level SO2, NO2, and PM2.5 concentrations in JFY2010. Figure 5 shows observed and simulated annual mean SO2, NO2, and PM2.5 concentrations at the ambient air pollution monitoring stations in D2. For SO2, the simulated concentration in China was much higher than that in Japan. The bias between the observation and simulation considerably varied with the monitoring stations from large positive to large negative. Because CMAQ captured the temporal variation of SO2 averaged for the monitoring stations (Figure 3(a)), the inferior model performance (Table 1) was attributed to the difficulty in reproducing the spatial variability. For NO2, the simulated concentration in large metropolitan areas in Japan, such as Tokyo and Osaka, was comparable to that in Beijing. While CMAQ well captured the NO2 concentration level in such metropolitan areas and coastal industrial areas with large emissions (Figure 2(b)), the model underestimated it at the monitoring stations in small cities, which resulted in the underestimation of the mean concentration (Table 1). For PM2.5, the spatial gradient of the simulated concentration from the Asian Continent to Japan indicates a large contribution of LRT to Japan. Although there was a decreasing trend from west to east in both the observed and simulated concentrations in D2, CMAQ tended to underestimate the concentration at most of the monitoring stations.

As shown in Figures 3 and 5, the contribution of LRT from anthropogenic emissions in the Asian Continent to Japan was substantial for PM2.5 concentration and negligible for NO2 concentration. The high concentration peaks of SO2 and PM2.5 concentrations were generally affected by LRT (Figures 3(a) and 3(c)), particularly for PM2.5 in spring and winter. Annual mean estimated contribution ratios of LRT to NO2, SO2, and PM2.5 concentrations averaged for the monitoring stations were 1, 13, and 50%, respectively. The contribution ratios were generally higher in the western areas of Japan, which are closer to the Asian Continent and have smaller emissions compared to the metropolitan areas. The contribution ratios to NO2, SO2, and PM2.5 concentrations for the individual stations were up to 5, 43, and 73%, respectively. The contribution of LRT explains the decreasing trend from west to east in PM2.5 concentration (Figure 5(c)). The higher LRT contribution ratio to PM2.5 compared to SO2 and NO2 indicate that these precursors reacted to form sulfate and nitrate during the LRT process before arriving at Japan, and LP controlled the concentration level of these precursors in Japan.

Considering the findings described above, the discrepancies between the observation and simulation for the spatial variabilities of SO2 and NO2 concentrations (Figures 4 and 5) were mainly attributed to deficiencies in simulating LP in Japan. Therefore, local emission inventories still need to be revised.

In addition, a coarse horizontal grid resolution may be partly responsible for the limitation of LP simulation in Japan as pointed out by Morino et al. [5]. For example, in an air quality simulation using 16 km grid cells like this study, LP can be overestimated at a monitoring station located at 15 km from a large point source and can be underestimated at a station located at 1 km from a major arterial road, particularly if it is a dominant emission source within a grid cell.

3.2. Model Performance and LRT for Major PM2.5 Components

Table 2 shows statistical comparisons of the observations and the CMAQ simulation in EBase for daily mean concentrations of PM2.5 and its major components at the observation sites for PM2.5 components for the four seasons (May 14–27, 2010, July 26-August 11, 2010, November 5–18, 2010, and January 26-February 10, 2011) in JFY2010. Figure 6 shows scatter plots for observed and simulated seasonal mean concentrations of PM2.5 and its major components at the observation sites for each season. Because high PM2.5 concentration peaks were observed in the reference period (Figure 3(c)), the mean PM2.5 concentration of the four seasons was higher than that of the entire simulation period (Table 1). For EC, sulfate, and ammonium, the values of and IA were particularly high, and most of the simulated daily concentrations were within a factor 2 of the observed values. These results indicate that CMAQ successfully captured the temporal and spatial variation patterns of these three components. For OA, the model clearly and consistently underestimated the concentration. Because CMAQ-simulated OA is dominated by POA even in summer [4], the underestimation of OA in warm season is likely caused by underestimation of SOA. The volatility basis set (VBS) approach may improve SOA formations in warm season [5]. Among the four seasons, the simulated OA concentration was the closest to the observed value in winter, in which POA was assumed to be dominant in the observation as well as the simulation. The observed OA concentration and the magnitude of underestimation were the largest in autumn that was harvest season in Japan. Therefore, it is essential to better estimate emissions from irregular open field burning after harvest. In addition, local emissions of condensable organic compounds (COC) that are not considered in the existing emission data can be a key factor for mitigating the underestimation [4]. For nitrate, the model clearly overestimated the concentration, particularly in warm season. Note that the overestimation in warm season may be partly attributed to a negative artifact in the observed data because of the volatilization from filter during 24 h sampling. As fine particulate nitrate is dominated by ammonium nitrate and the formation is highly sensitive to NH3 [32], emission and deposition processes of NH3 can be key factors for improving the model performance [3, 33]. In addition, the underestimation of NO2 and the overestimation of nitrate may be improved by incorporating a heterogeneous reaction of nitric acid to NOX [34].


PM2.5OAECSulfateNitrateAmmonium

Sample number674674674674601671
Mean obs.19.9 (15.0)5.0 (3.3)1.3 (0.9)5.1 (4.7)2.0 (3.0)2.3 (2.2)
Mean sim.17.1 (14.8)2.0 (2.2)1.2 (0.9)4.6 (4.5)3.1 (4.0)2.5 (2.5)
0.860.630.820.870.790.88
MBE−2.8−3.0−0.1−0.51.10.3
RMSE8.43.90.62.42.71.2
NMB−14%−59%−8%−9%53%12%
NRMSE42%79%44%47%133%55%
PF286%24%86%87%50%80%
IA0.920.630.900.930.850.93

Note: parenthetical values show standard deviations. Units of mean, MBE, and RMSE values are μg m−3.

Figure 7 shows observed and simulated mean concentrations of PM2.5 and its major components and corresponding contributions of LRT at the observation sites for PM2.5 components for the four seasons in JFY2010. For sulfate and ammonium, there was a clear decreasing trend from west to east in both the observed and simulated concentrations, which indicated a strong influence of LRT. Meanwhile, for OA, EC, and nitrate, the observed concentrations in and around Tokyo were comparable to or higher than those in the western areas of Japan that was more affected by LRT. Therefore, LP has a large impact on OA, EC, and nitrate in Japan. Mean estimated contribution ratios of LRT averaged for the observation sites for the four seasons were 57% for PM2.5 concentration, 54% for OA, 29% for EC, 58% for sulfate, 55% for nitrate, and 58% for ammonium. There are large uncertainties in the estimated LRT contribution for OA and nitrate because the model performance for these two components are inferior to that for the other three components (Table 2), and, in addition, nitrate formation is strongly affected by nonlinear processes such as the formation though reactions of transported precursors and local precursors [35].

3.3. Model Performance for LP and LRT of PM2.5

In order to evaluate the model performance for LRT and LP of PM2.5, the simulation period was classified into LP and LRT days. Because CMAQ fairly well simulated the temporal and spatial variations of sulfate that was strongly affected by LRT, the contribution ratio of LRT to sulfate was used as the index for LRT:Among days with the observed PM2.5 concentration >15 μg m−3 (AQS for annual mean PM2.5), days with the daily LRT index being top and bottom 30 values were, respectively, classified into LRT and LP days at each of the ambient air pollution monitoring stations. Figure 8 shows time series of station numbers counted as LP and LRT days in JFY2010 among the ambient air pollution monitoring stations in D2. LP days were frequent in summer and LRT days were frequent in spring and winter. Most of the monitoring stations were counted as LRT days during the period of the highest PM2.5 peak in February 2011 (Figure 3(c)).

The LP and LRT days classified with the LTR index were qualitatively verified through comparisons with backward trajectory analyses, which were conducted with the same method as that used by Shimadera et al. [11]. Figure 9 shows spatial distributions of simulated ground-level PM2.5 concentrations with horizontal wind vectors and backward trajectories arriving at 300 m above Kokusetsu-Osaka station (135.535°E, 34.680°N) on July 24, 2010, and February 6, 2011, which are, respectively, typical LP and LRT days (Figure 8). The LP days in the latter part of July were characterized by prevailing southerly wind and local circulations under the pacific high-pressure system. The LRT days in the early part of February were caused by eastward traveling high-pressure systems that transported a large amount of PM2.5 from the Asian Continent over a few days. The combination of the LRT index and the backward trajectory analysis enables detailed analysis of the atmospheric transport.

Figure 10 shows comparisons of observed and simulated PM2.5 concentrations averaged for LP and LRT days in JFY2010 at the ambient air pollution monitoring stations in D2. While the observed concentrations averaged for LP days were almost spatially uniform, the observed and simulated concentrations averaged for LRT days similarly decreased from west to east. The PM2.5 concentrations averaged for LRT days were generally higher than those for LP days, particularly in the western areas. The underestimation of PM2.5 was larger in LP days (MBE = −μg m−3 and NMB = −%) than LRT days (MBE = −μg m−3 and NMB = −%). The results indicate that it is still essential to improve local emissions of precursors and primary PM2.5 and local AQM subprocesses including formation and loss processes of precursors and PM2.5 in Japan. In addition, because of the larger underestimation of PM2.5 concentration in LP days than LRT days, the estimated contribution ratio of LRT to PM2.5 (50%) was likely somewhat overestimated in this study.

4. Conclusion

CMAQ v5.0.2 driven with WRF v3.5.1 was applied to air quality simulations in Japan, which is strongly affected by LRT from anthropogenic sources in the Asian Continent, for one year from April 2010 to March 2011. The CMAQ performance for simulating LRT and LP of PM2.5 in Japan was evaluated to provide information for future improvement of AQM performance.

The CMAQ performance for simulating PM2.5 was better than that of its precursors, SO2 and NO2. The model well captured temporal and spatial variation patterns of PM2.5 concentration, including the occurrence of several high concentration peaks and a decreasing trend from west (closer to the continent) to east in Japan. However, the model tended to underestimate the concentration level (by 15% on average). For major PM2.5 components, the model successfully simulated EC, sulfate, and ammonium but underestimated OA by 59% and overestimated nitrate by 53%. The AQM processes that should be preferentially improved include SOA formations in warm season, POA emissions in cool season, and the atmospheric behavior and emissions of NH3.

The contribution of LRT was estimated from the difference between the results of the EBase and EJapan cases. Annual mean estimated contribution ratios of LRT to NO2, SO2, and PM2.5 concentrations in Japan were, respectively, 1, 13, and 50%, with the contribution ratios being generally higher in the western areas of Japan. The contribution of LRT explained most of high PM2.5 concentration peaks and the decreasing trend from west to east. The higher LRT contribution ratio to PM2.5 compared to SO2 and NO2 indicated that these precursors reacted to form sulfate and nitrate during the LRT process before arriving at Japan, and LP controlled the concentration level of these precursors in Japan.

In order to evaluate the CMAQ performance for LRT and LP of PM2.5, days that were dominantly affected by LRT or by LP were determined based on the contribution of LRT to sulfate, which was fairly well simulated and strongly affected by LRT. LP days were frequent in summer and LRT days were frequent in spring and winter. The underestimation of PM2.5 was larger in LP days (by 26% on average) than LRT days (by 10% on average). Therefore, it is essential to improve local emissions of precursors and primary PM2.5 and local AQM subprocesses including formation and loss processes of precursors and PM2.5 in Japan. Because of the larger underestimation of LP in Japan, the estimated contribution ratio of LRT to PM2.5 was likely somewhat overestimated in this study.

Competing Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This research was supported by JSPS KAKENHI Grants nos. 26420581 and 26740038.

References

  1. T. C. Bond, D. G. Streets, K. F. Yarber, S. M. Nelson, J.-H. Woo, and Z. Klimont, “A technology-based global inventory of black and organic carbon emissions from combustion,” Journal of Geophysical Research D: Atmospheres, vol. 109, no. 14, p. D14203, 2004. View at: Publisher Site | Google Scholar
  2. M. Hallquist, J. C. Wenger, U. Baltensperger et al., “The formation, properties and impact of secondary organic aerosol: current and emerging issues,” Atmospheric Chemistry and Physics, vol. 9, no. 14, pp. 5155–5236, 2009. View at: Publisher Site | Google Scholar
  3. H. Shimadera, H. Hayami, S. Chatani et al., “Sensitivity analyses of factors influencing CMAQ performance for fine particulate nitrate,” Journal of the Air and Waste Management Association, vol. 64, no. 4, pp. 374–387, 2014. View at: Publisher Site | Google Scholar
  4. H. Shimadera, H. Hayami, S. Chatan et al., “Comprehensive sensitivity analyses on air quality model performance for PM2.5 simulation,” in Proceedings of the 16th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (HARMO '14), pp. 119–123, September 2014. View at: Google Scholar
  5. Y. Morino, T. Nagashima, S. Sugata et al., “Verification of chemical transport models for PM2.5 chemical composition using simultaneous measurement data over Japan,” Aerosol and Air Quality Research, vol. 15, no. 5, pp. 2009–2023, 2015. View at: Publisher Site | Google Scholar
  6. T. Ohara, H. Akimoto, J. Kurokawa et al., “An Asian emission inventory of anthropogenic emission sources for the period 1980–2020,” Atmospheric Chemistry and Physics, vol. 7, no. 16, pp. 4419–4444, 2007. View at: Publisher Site | Google Scholar
  7. Q. Zhang, D. G. Streets, G. R. Carmichael et al., “Asian emissions in 2006 for the NASA INTEX-B mission,” Atmospheric Chemistry and Physics, vol. 9, no. 14, pp. 5131–5153, 2009. View at: Publisher Site | Google Scholar
  8. G. R. Carmichael, G. Calori, H. Hayami et al., “The MICS-Asia study: model intercomparison of long-range transport and sulfur deposition in East Asia,” Atmospheric Environment, vol. 36, no. 2, pp. 175–199, 2002. View at: Publisher Site | Google Scholar
  9. M. Zhang, I. Uno, Y. Yoshida et al., “Transport and transformation of sulfur compounds over East Asia during the TRACE-P and ACE-Asia campaigns,” Atmospheric Environment, vol. 38, no. 40, pp. 6947–6959, 2004. View at: Publisher Site | Google Scholar
  10. M. Lin, T. Holloway, G. R. Carmichael, and A. M. Fiore, “Quantifying pollution inflow and outflow over East Asia in spring with regional and global models,” Atmospheric Chemistry and Physics, vol. 10, no. 9, pp. 4221–4239, 2010. View at: Publisher Site | Google Scholar
  11. H. Shimadera, H. Hayami, Y. Morino et al., “Analysis of summertime atmospheric transport of fine particulate matter in Northeast Asia,” Asia-Pacific Journal of Atmospheric Sciences, vol. 49, no. 3, pp. 347–360, 2013. View at: Publisher Site | Google Scholar
  12. D. W. Byun and K. L. Schere, “Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality (CMAQ) modeling system,” Applied Mechanics Reviews, vol. 59, no. 2, pp. 51–77, 2006. View at: Publisher Site | Google Scholar
  13. W. C. Skamarock and J. B. Klemp, “A time-split nonhydrostatic atmospheric model for weather research and forecasting applications,” Journal of Computational Physics, vol. 227, no. 7, pp. 3465–3485, 2008. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
  14. H. Shimadera, T. Kojima, A. Kondo, and Y. Inoue, “Performance comparison of CMAQ and CAMx for one-year PM2.5 simulation in Japan,” International Journal of Environment and Pollution, vol. 57, no. 3-4, pp. 146–160, 2015. View at: Publisher Site | Google Scholar
  15. S.-Y. Hong, Y. Noh, and J. Dudhia, “A new vertical diffusion package with an explicit treatment of entrainment processes,” Monthly Weather Review, vol. 134, no. 9, pp. 2318–2341, 2006. View at: Publisher Site | Google Scholar
  16. J. S. Kain and J. Kain, “The Kain-Fritsch convective parameterization: an update,” Journal of Applied Meteorology, vol. 43, no. 1, pp. 170–181, 2004. View at: Publisher Site | Google Scholar
  17. S.-Y. Hong and J.-O. J. Lim, “The WRF single-moment 6-class microphysics scheme (WSM6),” Journal of the Korean Meteorological Society, vol. 42, pp. 129–151, 2006. View at: Google Scholar
  18. F. Chen and J. Dudhia, “Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model implementation and sensitivity,” Monthly Weather Review, vol. 129, pp. 569–585, 2001. View at: Google Scholar
  19. E. J. Mlawer, S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, “Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave,” Journal of Geophysical Research D: Atmospheres, vol. 102, no. 14, pp. 16663–16682, 1997. View at: Publisher Site | Google Scholar
  20. J. Dudhia, “Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model,” Journal of the Atmospheric Sciences, vol. 46, no. 20, pp. 3077–3107, 1989. View at: Publisher Site | Google Scholar
  21. G. Yarwood, S. Rao, M. Yocke, and G. Z. Whitten, “Updates to the carbon bond chemical mechanism: CB05,” Final Report to the US EPA RT-0400675, 2005. View at: Google Scholar
  22. L. K. Emmons, S. Walters, P. G. Hess et al., “Description and evaluation of the model for ozone and related chemical tracers, version 4 (MOZART-4),” Geoscientific Model Development, vol. 3, no. 1, pp. 43–67, 2010. View at: Publisher Site | Google Scholar
  23. JPEC, “Technical report of the Japan Auto-Oil Program: emission inventory of road transport in Japan,” Tech. Rep. JPEC-2011AQ-02-06, 2012 (Japanese). View at: Google Scholar
  24. OPRF, “Report for comprehensive study for environmental impact lead by the establishment of emission control area in Japan,” Tech. Rep., 2012 (Japanese). View at: Google Scholar
  25. T. Fukui, K. Kokuryo, T. Baba, and A. Kannari, “Updating EAGrid2000-Japan emissions inventory based on the recent emission trends,” Journal of Japan Society for Atmospheric Environment, vol. 49, no. 2, pp. 117–125, 2014 (Japanese). View at: Google Scholar
  26. C. Wiedinmyer, S. K. Akagi, R. J. Yokelson et al., “The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning,” Geoscientific Model Development, vol. 4, no. 3, pp. 625–641, 2011. View at: Publisher Site | Google Scholar
  27. A. Guenther, T. Karl, P. Harley, C. Wiedinmyer, P. I. Palmer, and C. Geron, “Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature),” Atmospheric Chemistry and Physics, vol. 6, no. 11, pp. 3181–3210, 2006. View at: Publisher Site | Google Scholar
  28. T. Diehl, A. Heil, M. Chin et al., “Anthropogenic, biomass burning, and volcanic emissions of black carbon, organic carbon, and SO2 from 1980 to 2010 for hindcast model experiments,” Atmospheric Chemistry and Physics Discussions, vol. 12, no. 9, pp. 24895–24954, 2012. View at: Publisher Site | Google Scholar
  29. National Institute for Environmental Studies of Japan, “Environmental Numerical Databases,” http://www.nies.go.jp/igreen/index. View at: Google Scholar
  30. US Department of State, “Mission China, Beijing-PM2.5,” http://www.stateair.net/web/post/1/1.html. View at: Google Scholar
  31. MOE, “Measurement data of PM2.5 in 2010,” https://www.env.go.jp/air/osen/pm/monitoring/data/h22.html. View at: Google Scholar
  32. R. Gonzalez-Abraham, S. H. Chung, J. Avise et al., “The effects of global change upon United States air quality,” Atmospheric Chemistry and Physics, vol. 15, no. 21, pp. 12645–12665, 2015. View at: Publisher Site | Google Scholar
  33. A. Backes, A. Aulinger, J. Bieser, V. Matthias, and M. Quante, “Ammonia emissions in Europe, part I: development of a dynamical ammonia emission inventory,” Atmospheric Environment, vol. 131, pp. 55–66, 2016. View at: Publisher Site | Google Scholar
  34. J. Li, H. Dong, L. Zeng et al., “Exploring possible missing sinks of nitrate and its precursors in current air quality models—a case simulation in the Pearl River Delta, China, using an observation-based box model,” Scientific Online Letters on the Atmosphere, vol. 11, pp. 124–128, 2015. View at: Publisher Site | Google Scholar
  35. T.-F. Chen, K.-H. Chang, and C.-Y. Tsai, “Modeling direct and indirect effect of long range transport on atmospheric PM2.5 levels,” Atmospheric Environment, vol. 89, pp. 1–9, 2014. View at: Publisher Site | Google Scholar

Copyright © 2016 Hikari Shimadera et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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