Sensitivities of Simulated Source Contributions and Health Impacts of

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Sensitivities of Simulated Source Contributions and Health Impacts of PM2.5 to Aerosol Models Yu Morino, Kayo Ueda, Akinori Takami, Tatsuya Nagashima, Kiyoshi Tanabe, Kei Sato, Tadayoshi Noguchi, Toshinori Ariga, Keisuke Matsuhashi, and Toshimasa Ohara Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04000 • Publication Date (Web): 24 Nov 2017 Downloaded from http://pubs.acs.org on November 29, 2017

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Environmental Science & Technology

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Sensitivities of Simulated Source Contributions and Health Impacts of PM2.5 to

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Aerosol Models

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Yu Morino,†,* Kayo Ueda,‡ Akinori Takami,† Tatsuya Nagashima,† Kiyoshi Tanabe,† Kei

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Sato,† Tadayoshi Noguchi,† Toshinori Ariga, || Keisuke Matsuhashi, || and Toshimasa

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Ohara¶

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†

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Studies, 16-2, Onogawa, Tsukuba, Ibaraki, 305-8506, Japan

Center for Regional Environment Research, National Institute for Environmental

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‡

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Nishikyo-ku, Kyoto, 615-8530, Japan

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||

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Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki, 305-8506, Japan

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¶

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Fukushima-shi, Fukushima, 960-8670, Japan

Graduate School of Engineering, Kyoto University, Kyoto daigaku-katsura,

Center for Social and Environmental Systems Research, National Institute for

Fukushima Branch, National Institute for Environmental Studies, 2-16, Sugitsumacho,

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Submitted to Environmental Science and Technology

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Last revised on Oct. 22, 2017.

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ACS Paragon Plus Environment


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ABSTRACT: Chemical transport models are useful tools for evaluating source

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contributions and health impacts of PM2.5 in the atmosphere. We recently found that

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concentrations of PM2.5 compounds over Japan were much better reproduced by a

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volatility basis set model with an enhanced dry deposition velocity of HNO3 and NH3

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compared with a two-product yield model. In this study, we evaluated the sensitivities

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to organic aerosol models of the simulated source contributions to PM2.5 concentrations

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and of PM2.5-related mortality. Overall, the simulated source contributions to PM2.5

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were similar between the two models. However, because of the improvements

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associated with the volatility basis set model, the contributions of ammonia sources

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decreased, particularly in winter and spring, and contributions of biogenic and

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stationary evaporative sources increased in spring and summer. The improved model

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estimated that emission sources in Japan contributed 35%–48% of the PM2.5-related

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mortality in Japan. These values were higher than the domestic contributions to average

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PM2.5 concentrations in Japan (26%–33%) because the domestic contributions were

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higher in higher population areas. These results indicate that control of both domestic

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and foreign emissions is necessary to reduce health impacts due to PM2.5 in Japan.

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Keywords: PM2.5, mortality, VBS, source contributions, organic aerosols

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TOC Art Source contributions of PM2.5-related mortality over Japan 1.0

20

15

0.6 10 0.4

40

Winter

Spring

AERO6

AERO6VBS

AERO6

AERO6VBS

AERO6VBS

0 AERO6

0.0

Summer

-1

5

0.2

Mortality (death day )

Contributions (-)

0.8

Sources from Japan / Asia Others NH3 sources Biogenic Biomass burning Evaporative Energy sector Industrial sector Transport sector

Asia+Japan

Volcano Marine shipping

Mortality

41



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INTRODUCTION

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Because atmospheric aerosols can have large impacts on human health,1,2

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environmental standards for PM2.5 have been set in many countries. In Japan,

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environmental standards for PM2.5 were enacted in 2009 (annual average, 15 µg m−3;

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daily average, 35 µg m−3), but these limits have not been met at many observational

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stations in western Japan or in the Tokyo metropolitan area (TMA).3 To devise effective

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control strategies to reduce ambient PM2.5 concentrations, accurate knowledge of the

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contributions of PM2.5 sources is crucial.

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Chemical transport models (CTMs) are useful tools for evaluating the source

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contributions and health impacts of PM2.5 in the atmosphere. However, model results

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generally include large uncertainties because of problems with input data (e.g.,

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meteorological, boundary, and emissions data), the parameterization of each process,

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and missing science elements. Recently, we used PM2.5 chemical composition data

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measured simultaneously over several regions of Japan in the winter, spring, and

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summer of 2012 to evaluate simulation results based on a secondary organic aerosol

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(SOA) yield model and a volatility basis set (VBS) model.4 We found that

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concentrations of organic aerosol were better reproduced by a VBS model than a

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two-product SOA yield model, consistent with the results of previous studies (e.g.,

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Ahmadov et al.5). In addition, concentrations of aerosol nitrate were better reproduced

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by a model with dry-deposition velocities of nitric acid and ammonia enhanced by a

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factor of five, as was the case in a previous study.6

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In Japan, chemical transport models have been applied to estimate source

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contributions7,8 and health impacts.9 However, all of these estimates have been based on

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a two-product SOA yield model that underestimated the organic aerosol (OA)


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concentrations.10 Because OA makes a large contribution to PM2.5,11,12 source

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contributions and health impacts should be evaluated using a VBS model, which better

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reproduces OA concentrations over Japan.4

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In this study, we evaluated the sensitivities to aerosol models of simulated source

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contributions and health impacts of PM2.5. We first evaluated the source contributions to

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emission rates of atmospheric pollutants and to PM2.5 concentrations over Japan.

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Because the spatial and temporal distributions differ among PM2.5 chemical compounds,

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we also assessed the source contributions to PM2.5 chemical compounds. We then

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estimated the premature mortality associated with PM2.5 exposure over the long term

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and short term. In addition, we assessed the source contributions to PM2.5-related

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mortality. We then briefly discuss the relationship between source contributions to

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PM2.5 concentrations and PM2.5-related mortality. These analyses elucidated the impact

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of model uncertainty on estimates of PM2.5 sources and health impacts.

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METHODOLOGY

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Model system and sensitivity simulations.

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We simulated the distributions of gaseous and particulate species by using a

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three-dimensional CTM, the Models-3 Community Multiscale Air Quality (CMAQ,

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v5.0.2) modeling system developed by the U.S. Environmental Protection Agency.13

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Because the model setups were the same as those of Morino et al.,4 we have not

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included the details of the model here. In this study, we analyzed results of two

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sensitivity simulations, as summarized in Table S1 of the Supporting Information (SI).

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In both simulations, the chemical mechanism was based on the Carbon Bond



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Mechanism 05 (CB05) model of Yarwood et al..14 We used the sixth-generation aerosol

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module of CMAQ (AERO6, which is based on a SOA yield model), as well as the

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AERO6 coupled with a VBS model (AERO6VBS). The AERO6VBS simulation

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considered an aging reaction between OH radicals and semi-volatile organic compounds

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(SVOCs) produced from both anthropogenic and biogenic non-methane volatile organic

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compounds (NMVOCs) with a reaction rate of 2 × 10−11 cm3 molecule−1 s−1. In addition,

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in the AERO6VBS simulation, dry-deposition velocities for nitric acid (HNO3) and

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ammonia (NH3) were enhanced by a factor of 5, as in Shimadera et al..6 Meteorological

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fields were calculated with the Weather Forecast Research (WRF) model version 3.3.15

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For analysis nudging, we used the three-dimensional meteorological fields from the

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National Centers for Environmental Prediction Final Analysis datasets. Data for

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emissions from anthropogenic and natural sources in the simulation domains are

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summarized in Table S2 of SI. The two simulation domains of this study are shown in

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Figure 1. Domain 1 covered East Asia with a horizontal resolution of 60 km, and

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Domain 2 covered Japan with a horizontal resolution of 15 km. Monthly averaged data

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from the global chemical climate model, Chemical AGCM for Study of Atmospheric

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Environment and Radiative Forcing (CHASER)16 were used as the lateral boundary

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conditions for Domain 1. Interior data for Domain 1 were used as the lateral boundary

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conditions for Domain 2, which was 1-way nested within Domain 1.

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Morino et al. 4 conducted a comprehensive model evaluation of PM2.5 chemical

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composition. That study showed that model performance was significantly improved in

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the AERO6VBS simulation compared with the AERO6 simulation (Table 4 of Morino

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et al. 4). Simulation models have been shown to fail to reproduce concentrations of

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NO3– and OA in Japan6,10,17. As was the case in these previous studies, the AERO6


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simulation largely underestimated OA concentrations. The underestimation, which was

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a factor of ~3 in winter and summer, was resolved by the AERO6VBS simulation in

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summer, when the mean simulated-to-observed OA concentration ratio was 1.16. The

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AERO6 simulation also overestimated NO3– concentrations by factors of 3 (summer) to

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15 (winter). This overestimation was reduced by the AERO6VBS simulation, though

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NO3– in winter was still overestimated by a factor of 4. Although the AERO6VBS

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simulation somewhat underestimated SO42– and OA concentrations in winter and spring,

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it reproduced well the chemical composition of PM2.5. These improvements in model

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performance suggest that the AERO6VBS simulation will provide better estimates of

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the source contributions to PM2.5. We should note that unspeciated mass makes an

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important contribution to PM2.5.18 Because the chemical composition of this mass is

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unknown, the simulated concentration of this mass could not be verified. Thus, we did

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not focus on the evaluation of this unspeciated PM2.5 mass.

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To estimate the source contributions of individual emission sectors, we conducted 18

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sensitivity simulations. For 10 emission sectors (transport (TRAN), navigation (NAVI,

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marine shipping), industry (INDS), power plant (POWP), stationary evaporative sources

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(EVAP), biomass burning (BIOB), biogenic sources (BIOG), other NH3 (ONH3),

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volcano (VOLC), and others (OTHR)), we conducted sensitivity simulations with the

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emissions from these individual sectors reduced by 20%. Ikeda et al.7 have noted that

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sensitivity simulations using 20% perturbations and 50% perturbations implied very

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similar source contributions (agreement of annual mean PM2.5 concentrations within a

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few percent), the suggestion being that the effect of non-linearity was not large and that

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this sensitivity analysis provided reasonable results. The correspondence between the 10

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sector groups and the original categories of the emission inventories is summarized in



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Table S3 of SI. Because these sensitivity simulations were computationally expensive,

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we conducted these sensitivity simulations for 1 January to 29 February 2012 (winter),

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1 April to 31 May 2012 (spring), and 1 July to 31 August 2012 (summer), with a spin-up

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calculation of 10 days. To roughly evaluate the seasonality of source contributions to

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PM2.5 concentrations over Japan, we also conducted three year-long sensitivity

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simulations based on the AERO6VBS model. In these simulations, emissions were

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reduced by 20% from Japan, other countries, or marine shipping plus volcanoes. A

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year-long standard simulation was also conducted to estimate mortality associated with

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PM2.5.

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Estimation of health impacts due to PM2.5. Mortality associated with PM2.5 pollution was estimated with the following equation19,20

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PM2.5-related Mortality = Baseline mortality × {1 — exp(—∆[PM2.5] βPM2.5).

(1)

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In eq. 1, ∆[PM2.5] represents the enhanced PM2.5 concentration and βPM2.5 represents

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the concentration–response (C–R) function for PM2.5 (βPM2.5 = ln(RRPM2.5)/∆[PM2.5],

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where RRPM2.5 represents the relative risk due to PM2.5 exposure). In previous estimates

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of PM2.5-related mortality using regional models19,21,22, βPM2.5 was mostly taken from

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epidemiological analyses based on cohort studies in the United States.23-25 To our

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knowledge, a C–R function for long-term PM2.5 exposure has not been assessed in

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Asian countries. Therefore, we used the RRPM2.5 data of Pope et al.23 (RRPM2.5 = 1.04,

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95% confidence intervals 1.01–1.08 per 10 µg m–3 increase in PM2.5) and Laden et al.24


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(RRPM2.5 = 1.16, 95% confidence intervals 1.07–1.26 per 10 µg m–3 increase in PM2.5) as

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lower and upper limits, respectively.

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The C–R function for short-term PM2.5 exposure has been assessed in many countries,

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including Japan27,28. In this study, we also estimated short-term health impacts of PM2.5

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using the βPM2.5 estimated from mortality and the PM2.5 data in 20 areas of Japan during

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2002–2004 28 (RRPM2.5 = 1.0053 ± 0.0019 per 10 µg m–3 increase in PM2.5). These data

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were used to estimate PM2.5-related mortality in each season. The RRPM2.5 for long-term

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PM2.5 exposure was larger than that for short-term exposure, because short-term

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mortality impacts are only a small part of the impact of total mortality.29

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In previous modeling studies, it has often been assumed that there is no threshold

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PM2.5 concentration in the C–R function22,30. However, recent studies have suggested

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that threshold PM2.5 values exist in the C–R function, a reflection of the fact that there is

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no excess risk associated with exposure below the counterfactual low-exposure

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level.31-33 To take account of this uncertainty, we evaluated long-term PM2.5-related

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mortality by setting the threshold value to either 0 µg/m3,22,30 2.4 µg m–3, 33 or 5.8 µg m–

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3 34

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Burden of Diseases study,33 lower thresholds were used in order to check sensitivities of

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the mortality estimates to three different thresholds. To our knowledge, threshold values

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have not been proposed for the C–R function in the case of short-term PM2.5 exposure.

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We therefore set the threshold to zero for the assessment of impacts due to short-term

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PM2.5 exposure. As noted earlier, in this study, reproduction of total PM2.5 concentration

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was not intended due to lack of knowledge about unspeciated PM2.5. Introduction of

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threshold values was therefore another source of uncertainty because of the nonlinearity

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of the C–R function. The effect of the nonlinear relationship between mortality and

. Although thresholds of 2.4–5.9 µg m–3 have been recommended in the latest Global



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concentrations should be further evaluated after unspeciated PM2.5 has been better

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identified.

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Baseline mortality was the sum of the 5-year age-specific number of deaths of

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persons older than 30 years; it was calculated by multiplying the number of persons in

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each age group in 2010 by the baseline mortality rate for that age group. We used the

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age-specific population data per grid obtained from the Statistics Bureau, Ministry of

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Internal Affairs and Communications in Japan. Data on age-specific mortality rates

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were obtained from the Ministry of Health Labour and Welfare. Table S4 and Figure S1

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of SI summarize the estimated populations and baseline mortalities in nine regions of

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Japan.

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RESULTS AND DISCUSSION Contributions of individual source sectors to emissions.

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Contributions of individual source sectors to emission rates of atmospheric pollutants

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are summarized in Figure 2. Over Domain 1, anthropogenic combustion sources made

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the largest contributions to emission rates of NOx, SO2, PM2.5, and CO, although their

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relative contributions differed among compounds. TRAN, POWP, and INDS sources

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made the largest contributions (70%–80% of the total) to NOx emissions, whereas INDS

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and POWP were major contributors to SO2 emissions (~80% of the total). Fossil fuel

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sources made large contributions to PM2.5 (60%–70%) and CO emissions (50%–55%),

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although OTHR (domestic sector and soil-NOx for East Asia Domain) and BIOB also

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made important contributions to these emissions. In particular, domestic emissions

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made important contributions to PM2.5; these contributions were highest in the winter,

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when fuel consumption for residential heating was estimated to be high.30


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Non-combustion sources made major contributions to NMVOC and NH3. The largest

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contributor to NMVOC was BIOG, followed by OTHR (domestic) and TRAN. Over

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Asia, fertilizer application and manure management contributed 55% and 19%,

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respectively, to emissions of NH3.35

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Several characteristics of emission sources differed between Japan and East Asia. First,

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VOLC made larger contributions to SO2 emissions (~80%) than anthropogenic

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combustion sources in Japan, and 13%–16% of the VOLC emissions came from the

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Miyake-jima volcano, which erupted in 2000. Second, the contributions of the domestic

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sector (OTHR) to emissions of NOx, CO, and PM2.5 were much smaller in Japan. Within

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the OTHR category, the commercial sector and waste burning made higher

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contributions to NOx and PM2.5 emissions than the domestic sector made. Third, EVAP

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made important contributions to emissions of total NMVOCs in Japan, and EVAP was

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the largest emitter of NMVOCs in winter. For NH3 emissions in Japan, manure

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management made higher contributions (52%) than fertilizer application (26%).36

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As shown in Figure S2 of SI, seasonal variations of emissions were especially large for

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NMVOCs because their major sources (EVAP and BIOG) underwent large seasonal

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variations. Seasonal variations of biogenic NMVOC emissions were more significant in

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mid-latitude regions than in tropical regions.37 Seasonal variations were therefore larger

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in Japan than in Asia. Emissions of NOx and SO2 were dominated by fossil-fuel

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combustion, and their seasonal variations were therefore relatively small. Emission rates

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of PM2.5 showed distinct seasonal variations in both East Asia and Japan. Emissions

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from domestic burning in East Asia were larger in winter, and those from biomass

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burning in Japan were higher in autumn (the harvest season).

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Sector-based source contributions to PM2.5 concentrations.

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Figure 3 summarizes the contributions of emission sources from both Japan and

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foreign countries Source contributions to simulated PM2.5 concentrations over nine

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regions of Japan. Both models showed that PM2.5 concentrations generally decreased

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from west to east in winter and spring, with the exception of the Kanto region. In

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contrast, during the summer, the PM2.5 concentration was highest over the Kanto region

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and lower in western and northern Japan.

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Both models simulated similar source contributions in all seasons, though contributions of ONH3, BIOG and EVAP differed largely between the two models. In winter, both model simulations indicated that ONH3 emissions made the highest

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contributions to PM2.5 over Japan. However, the resolution of the NO3– overestimation

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in the AERO6VBS model showed that the contributions of ONH3 from foreign

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countries decreased greatly. The OTHR (mostly domestic sources), TRAN, INDS, and

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POWP sectors also made important contributions.

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In spring, the contributions of ONH3 decreased, and the contributions of BIOG and

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EVAP slightly increased in the AERO6VBS model than in the AERO6 model. Overall,

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the INDS and POWP sectors from foreign countries made the highest contributions

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(28%–39% in total). The VOLC, BIOB, ONH3, and TRAN sectors also made nontrivial

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contributions.

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In summer, VOLC made the highest contributions (14%–25%); TRAN, INDS,

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POWP, and BIOG also made important contributions. Contributions of BIOG and

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EVAP in Japan were significantly increased by improvement of the OA model,

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particularly in and around the Kanto region.


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Contributions of domestic emissions were apparently highest in the Kanto region

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during all seasons (40%–60%). In particular, ONH3, TRAN, and INDS sources were

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the dominant sources in Japan. Emission rates of pollutants were particularly high over

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the Kanto region (e.g., Figure S3 of SI), and these high emission rates caused the

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contribution of Japanese emissions to PM2.5 to be particularly high. In contrast, the

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contributions of domestic emissions decreased in southwestern and northern Japan.

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Here we further discuss the factors controlling the simulated PM2.5 source

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contributions in two characteristic and contrasting regions, the Kyushu and Kanto

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regions (Figure 1). The Kyushu region is located in the southwestern edge of Japan and

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is affected mostly by transboundary pollution. In 2012, 34% of the Japanese population

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resided in the Kanto region, and emissions of anthropogenic pollutants there were the

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highest of all Japanese regions.

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Figure 4, Tables S5a-S5c and S6a-S6c of SI shows the source contributions to the

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chemical composition of PM2.5. Contributions of domestic sources were larger in the

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Kanto region than in the Kyushu region for all species during all seasons. In the Kyushu

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region, SO42– and OA concentrations were attributable mainly to foreign sources. Even

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in the cases of NO3– and EC, foreign sources made contributions that were higher or

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comparable to domestic sources in the winter and spring. In contrast, in the Kanto

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region, EC and NO3– were dominated by domestic sources. The OA was also dominated

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by domestic sources, particularly in the summer.

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The dominant sources of SO42– were INDS and POWP from foreign countries;

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VOLC also made important contributions in the Kanto and Kyushu regions. Although

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the source of NO3– appeared to be ONH3, the direct source of NOx was surely not

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ONH3 (Figure 2). However, because atmospheric NH3 reacts with gas-phase HNO3 to



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produce particulate NH4NO3, NH3 emissions are a critical factor controlling

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atmospheric NH4NO3 concentrations. In the improved model, the contribution of

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foreign ONH3 sources decreased significantly. Major sources of EC were TRAN and

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INDS in the Kanto region. INDS and OTHR sources from foreign countries also made

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important contributions in the Kyushu region.

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The source contributions to OA were generally rather complicated, a reflection of the

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fact that OA is composed of primary organic aerosol (POA) and SOA and that OA is

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composed of thousands of organic compounds.38 In the Kanto region, contributions of

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domestic sources to OA concentrations were larger than those of foreign sources in the

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winter and summer. TRAN in Japan made important contributions to OA in the winter

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because a transport sector is an important source of POA. In contrast, during spring and

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summer, BIOG and EVAP (major sources of NMVOCs) contributed much of the SOA

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in the Kanto region.

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The differences of OA concentrations between the two models were larger (smaller)

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in the Kanto region than in the Kyushu region during summer (spring) (Tables S5b and

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S5c). In summer, the contributions of BIOG and EVAP from Japan were higher by 0.52

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µg m−3 and 0.28 µg m−3, respectively, in the AERO6VBS model than in the AERO6

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model. Simulated OA concentration were therefore significantly higher in the Kanto

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region. In contrast, during the spring, simulated OA concentrations in the Kyushu

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region were higher in the AERO6VBS model than in the AERO6 model because

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contributions of EVAP (0.35 µg m−3), TRAN (0.21 µg m−3), and BIOB (0.12 µg m−3)

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from foreign counties were higher in the AERO6VBS model. The higher OA

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concentrations simulated by the AERO6VBS model were mostly associated with higher

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SOA concentrations that resulted from consideration of SOA aging reactions in the


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VBS module.4 Emission sources of VOCs, which are SOA precursors, therefore made

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larger contributions to OA in the AERO6VBS model. In Japan, regulation of VOC

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emissions began in 2005 to reduce air pollution associated with ozone and particulate

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matter. Assessment of the effectiveness of this regulation requires that the processes

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involved in formation of SOA be appropriately modeled. Thus, for assessment of

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compliance with this regulation, simulations with the AERO6VBS model are

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recommended.

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Between these two regions, the major sources of PM2.5 during high–PM2.5 events

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differed (Figure S4 of SI). In winter and spring, contributions of Japanese emissions

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decreased when PM2.5 concentrations were relatively high in the Kyusyu region.

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Contributions of ONH3 and OTHR from foreign countries increased in winter, and

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those of INDS and POWP from foreign countries increased in spring. In contrast,

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contributions of Japanese emissions increased when PM2.5 concentrations were

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relatively high in the Kanto region, where contributions of TRAN and INDS increased

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in winter and those of TRAN and ONH3 increased in spring. These results highlight the

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fact that high PM2.5 events were associated with transboundary pollution in the Kyushu

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region, whereas high PM2.5 concentrations were associated with local pollution in the

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Kanto region. This pattern was not as clear in summer.

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The factors that control and the processes that transport these PM2.5 chemical

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compounds have been discussed in previous studies. It has often been reported that

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SO42– is transported to Japan in spring, when the winds blow primarily from the

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west.10,39 In spring, emissions in central and northern China make the greatest

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contributions to SO42– aerosols and PM2.5 in Japan. Winds transport SO42– from the

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Asian continent to Japan even in summer, when the North Pacific anticyclone is located



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around Japan and southerly winds prevail. An analysis by Shimadera et al.6 has shown

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that when a high-pressure system prevails over the East China Sea and a low-pressure

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system passes north of Japan, synoptic westerly winds associated with this pressure

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pattern transport a large amount of SO42– from the Asian continent to Japan. Because

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local sources had dominant contributions to primary-emitted gaseous-NH3, NH4NO3

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was mostly from domestic sources in the Kanto Area. However, in the Kyushu Area,

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transboundary transport is the major contributor of NH4NO3 in the winter. NH4NO3

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produced in the Asian continent has been shown to be effectively transported to Japan,

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particularly when the rate of SO42– production is low.40 Local sources generally make

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larger contributions to primary particles, such as EC and POA, than to secondary

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particles. In the Kanto Area, the dominant source of EC and POA is local burning of

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fossil fuels.17 However, our model estimate is consistent with observational data and

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trajectory analyses, which suggest that most EC is transported from the Asian continent

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to western Japan from autumn to spring.41

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There have been several estimates of sources of PM2.5 over Japan. Ikeda et al.7,10

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analyzed the area-specific source contributions to PM2.5 over Fukue Island, a remote

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western Japanese island, and over nine regions of Japan. Transboundary pollution made

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the major contributions to PM2.5 over Fukue Island, and domestic sources accounted for

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only 7% (spring) and 19% (summer). Contributions of domestic sources were 20%–

347

30% and 50% over western Japan and the Kanto area, respectively, roughly in accord

348

with our source estimates. Li et al. 42 analyzed the source–receptor relationships of

349

PM10 in East Asian countries. They found that domestic sources contributed 40% of

350

PM10 over Japan. These modeling results revealed that, on average, transboundary

351

pollution made higher contributions to PM2.5 and PM10 than domestic sources in Japan.


Page 16 of 33

Page 17 of 33


352

We did not explicitly evaluate contributions from outside Asia in this study. Estimates

353

from multiple global models have indicated that contributions from sources of

354

emissions in other areas (North America, Europe, and South Asia) to area-averaged and

355

population-weighted PM2.5 concentrations over East Asia are 3% and 8%, respectively.34

356

Given the location of Japan in East Asia, it seems likely that contributions from sources

357

of emissions in areas other than East Asia to PM2.5 concentrations in Japan would be

358

smaller than these percentages. We therefore feel that contributions from other areas are

359

not important.

360 361

Mortality associated with PM2.5 estimated using simulation data.

362

We estimated annual premature mortality associated with PM2.5 over Japan based on

363

eq. 1 and simulation results of the AERO6VBS (Figure S5 of SI) and AERO6 (Figure

364

S6 of SI). Results of the AERO6VBS model indicated that long-term PM2.5-related

365

mortality was 21,600 (5527–41,924) and 79,207 (36,956–120,413) deaths per year

366

based on the βPM2.5 of Pope et al.23 and Laden et al.24, respectively, in the absence of a

367

threshold PM2.5 concentration (Table 1 of SI). These values were somewhat lower than

368

the estimates based on the AERO6 model (33,116 (8513–63,891) and 119,361 (56,403–

369

179,241), respectively). These differences were mostly explained by the decrease of

370

simulated NH4NO3 concentrations. The PM2.5-related mortality estimated in this study

371

is higher than the previous estimate by Nawahda et al.9 (6662 deaths per year), and

372

similar to that by Lelieveld et al.43 (25,516 deaths per year). We should note that the

373

estimated mortality was very sensitive to the threshold concentration, particularly with

374

relatively low PM2.5 concentrations in the case of the AERO6VBS simulation.



375

We also estimated short-term PM2.5-related mortality using the βPM2.5 of Ueda et al.28

376

Based on the AERO6 and AERO6VBS models, PM2.5-related mortality was estimated

377

to be 4530 ± 1617 and 2939 ± 1050 deaths per year. Source contributions to

378

PM2.5-related mortality were estimated during three seasons (Figure 5). Because the

379

contribution of domestic sources to PM2.5 concentration was relatively high in

380

high-population areas, percentage contributions of domestic sources to PM2.5-related

381

mortality (48%, 35%, and 43% in winter, spring, and summer, respectively, Figure 5)

382

were higher than the percentage contributions to PM2.5 concentrations (30%, 26%, and

383

33%, respectively, Figure S7 of SI). Because the population and mortality were highest

384

in the Kanto region (Figure S4 of SI), PM2.5-related mortality was highest in the Kanto

385

region over Japan, followed by the Kansai and Kyushu regions (Figures S8 and S9 of

386

SI).

387

Based on three year-long sensitivity simulations, we here briefly discuss the seasonal

388

variations of PM2.5-related mortality (Figure 6). Contributions from domestic sources

389

were obviously higher in summer and winter than in spring and autumn. In summer,

390

SO42– and OA from domestic sources made important contributions, whereas in winter,

391

NO3– made the highest contributions to domestic PM2.5. In spring and autumn, SO42–

392

and OA were the largest contributors from foreign sources.

393

Recently, contributions of sectoral and regional sources of emissions to PM2.5-related

394

mortality have been evaluated using global models.43,44 Lelieveld et al.43 have estimated

395

that agricultural sources make the largest contributions to PM2.5-related mortality over

396

Japan, followed by the industrial, energy, residential, and transport sectors; our

397

estimates are similar, though natural emission sources (volcanoes and biogenic

398

NMVOC) made larger contributions in our study. Zhang et al. 44 have examined


Page 18 of 33

Page 19 of 33


399

comprehensive source-receptor relationships with PM2.5 in 13 regions globally:

400

although they did not explicitly estimate contributions to and from Japan, they indicated

401

that contributions of Chinese emissions to other East Asian countries were comparable

402

to those from other parts of East Asia, the suggestion being that transboundary pollution

403

makes important contributions in East Asia.

404

We should note that there were several sources of uncertainty in these estimates. One

405

is in the simulation of PM2.5 concentrations. NO3– made a large contribution to PM2.5 in

406

the AERO6 model, and the degree of its overestimation was reduced by enhancing the

407

dry-deposition velocities of HNO3 and NH3. Based on aircraft measurements of power

408

plant plumes, Neuman et al.45 have suggested that dry-deposition velocities of HNO3

409

should be faster than traditional estimates, but the reasons for this difference are still

410

unresolved. Sources of OA are also uncertain because there are large uncertainties in the

411

aging reaction rates and emission profiles of SVOC and intermediate-volatility organic

412

compounds (IVOC) assumed in the VBS model46. Recently, measurements of organic

413

tracers or radiocarbons have been used to directly validate the simulated source

414

contributions of OA, 17,47,48 but few of these observational data are available currently.

415

Radiocarbon measurements have indicated that anthropogenic SOA and biogenic SOA

416

make comparable contributions in the northern TMA in the summer,17 and this behavior

417

was reproduced by the current AERO6VBS model (Figure 4). OA models should be

418

evaluated in detail with organic tracer measurements in the future. In addition,

419

characterization of unspeciated PM2.5 will be necessary to simulate total PM2.5 using

420

numerical models.

421

Another source of uncertainty is the C–R function. To our knowledge, there have

422

been no estimates of mortality due to long-term PM2.5 exposure over Asia. Because the



423

C–R function might differ between continents or countries, a C–R function estimated in

424

the United States could lead to errors if it were applied in Japan. In addition,

425

PM2.5-related mortality might depend on the chemical composition of the particles.43,49

426

There have been several studies of the relationship between mortality and the chemical

427

composition of PM2.5 (e.g., Ueda et al.27), although there has been no consensus

428

regarding the differences of toxicity among PM2.5 compounds. There have been several

429

analyses of the association between source contributions to PM2.5 and its health

430

effects.50-51 These analyses likely will provide insights concerning the toxicity of

431

individual PM2.5 compounds. As already noted, treatment of the threshold PM2.5

432

concentration in the C–R function is also a source of uncertainty.

433

Spatial resolution is another concern. Because suburban cities are often smaller than a

434

grid size, primary particles such as EC cannot be well reproduced by a CTM.4 However,

435

Thompson et al.22 have shown that simulated PM2.5-related mortality in the United

436

States is not very sensitive to the horizontal resolutions of the CTM (36 km, 12 km, and

437

4 km). In Japan, secondary production makes a large contribution to PM2.5, and thus

438

simulated PM2.5-related mortality likely will not change much, even in simulations with

439

finer horizontal resolution.

440

Overall, the PM2.5-related mortalities simulated by the two models were within a

441

factor of 2–3 of each other, but estimated C–R functions differed by more than a factor

442

of 10. Nonetheless, to establish effective control strategies for air pollution, accurate

443

estimates of absolute and relative source contributions are necessary. We found that

444

both domestic and foreign sources made important contributions to PM2.5

445

concentrations and PM2.5-related mortality in Japan. Control of both domestic and


Page 20 of 33

Page 21 of 33


446

foreign emissions will therefore be necessary to reduce health impacts due to PM2.5 in

447

Japan.

448 449

AUTHOR INFORMATION

450

Corresponding author

451

*

Phone: +81-29-850-2544; fax: +81-29-850-2480; e-mail: [email protected]

452 453

ACKNOWLEDGEMENTS

454

This research was supported by the Environment Research and Technology

455

Development Fund (5-1408 and S12-1) of the Ministry of the Environment, Japan, and

456

by a Grant-in-Aid for Scientific Research (24310024) from the Ministry of Education,

457

Culture, Sports, Science, and Technology.

458

Supporting Information Available

459

Additional details of our analysis. This material is available free of charge via the

460

Internet at http://pubs.acs.org.e

461 462

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Table 1. All-cause mortality associated with long-term and short-term PM2.5 exposure. Relative risk per 10 μg m–3 increase in PM2.5 (RRPM2.5) RRPM2.5

Long-term23

Long-term24

Short-term28

1.04 (1.01 – 1.08)

1.16 (1.07 – 1.26)

1.0053 ± 0.0019

Mortality (deaths y–1, AERO6VBS) PM2.5

21,600 (5527 – 41,924)

79,207 (36,956 – 120,413)

PM2.5#1

1104 (280 – 2162)

4156 (1901 – 6448)

PM2.5#2

12,553 (3201 – 24,471)

46,609 (21,548 – 71,546)

SO42–

8384 (2134 – 16,384)

31,348 (14,418 – 48,371)

1134 ± 405

1983 (504 – 3886)

7477 (3417 – 11,611)

268 ± 96

EC

1712 (435 – 3355)

6457 (2951 – 10,030)

231 ± 82

OA

6027 (1533 – 11,790)

22,606 (10,372 – 34,968)

814 ± 291

NO3

–

2939 ± 1050

Mortality (deaths y–1, AERO6) PM2.5

33,116 (8513 – 63,891)

119,361 (56,403 – 179,241)

PM2.5#1

11,431 (2916 – 22,272)

42,383 (19,614 – 64,994)

PM2.5#2

24,178 (6195 – 46,847)

88,220 (41,313 – 133,677)

10,983 (2798 – 21,433)

40,908 (18,868 – 62,946)

1487 ± 532

NO3–

6287 (1599 – 12,295)

23,561 (10,818 – 36,419)

850 ± 304

EC

1913 (486 – 3749)

7213 (3296 – 11,203)

258 ± 92

OA

3708 (942 – 7262)

13,956 (6387 – 21,642)

500 ± 179

SO4

2–

4530 ± 1617

#1: Threshold PM2.5 concentration of 5.8 μg m–3, #2: Threshold PM2.5 concentration of 2.4 μg m–3.



Figure 1. Model domain which covers Asia (Domain 1) with annual mean NOx emission rates (left). Model domain which covers Japan (Domain 2) with nine regions of Japan (right).


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TRAN BIOB

INDS ONH3

POWP OTHR

EVAP VOLC

BIOG NAVI

Asia Fraction of emission rates

1.0 0.8 0.6 0.4 0.2

winter spring summer






0.0

NOx

SO2

CO

NMVOC

NH3

PM2.5

Japan Fraction of emission rates

1.0 0.8 0.6 0.4 0.2







0.0

NOx

SO2

CO

NMVOC

NH3

PM2.5

Figure 2. Sector-based emission rates of atmospheric pollutants (NOx, SO2, CO, NMVOC, NH3, and PM2.5) over East Asia (Domain 1) and Japan. Grouping of sectors are summarized in Table S2 of the Supporting Information (Abbreviations: TRAN, transport; INDS, industry; POWP, power plant; EVAP, stationary evaporative sources; BIOG, biogenic sources; BIOB, biomass burning; ONH3, other NH3; others, OTHR; VOLC, volcano; and NAVI, navigation).



Figure 3. Sector-based source contributions to PM2.5 concentrations estimated by the AERO6 (left) and AERO6VBS (right) models. Bottom axis indicate nine regions of Japan (Figure 1). Abbreviations of sectors are the same with Figure 2.


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AERO6VBS

AERO6

1.0

2

1.5

1

2

1.5

-3

1

Winter Spring Summer









2.0


0

Kanto

Kyushu

Japan

Kanto

Kyushu

Japan

Kanto

Kyushu

Japan

Kanto

Kyushu

AERO6VBS

AERO6

0.5 2.0 1.5

1.0

1.0 1.5 0.5

0.8

0.5

0.6 1.0 0.4 1.5

0.2










2.0


0.0


2.0

0.0


0.0

1.0

-3

2.5

AERO6VBS

Japan

Kanto

Kyushu

Japan

Kanto

Kyushu

Japan

Kanto

Kyushu

Japan

Kanto

Kyushu

Contribution of domestic emissions (-)

0.0


3.0

-3

1.0

3

Japan

AERO6

OA (g m )

4


2.0

0.5

5


0

0.0 6

EC (g m )

-3

SO42- (g m )

0.5 3

AERO6VBS Contribution of domestic emissions (-)

4


0.0

NO3- (g m )

AERO6

Japan/Asia OTHR ONH3 BIOG BIOB EVAP POWP INDS TRAN Asia+Japan VOLC NAVI Contribution

Figure 4. Sector-based source contributions to PM2.5 chemical composition (SO42–, NO3–, OA, and EC) over whole Japan, Kanto region, and Kyushu region estimated by the AERO6 (left) and AERO6VBS (right) models. Contributions of emissions from Japan are also shown.



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Japan 1.0

20

15

0.6 10 0.4

-1

5

0.2

Mortality (death day )

Contributions (-)

0.8

Japan/Asia OTHR ONH3 BIOG BIOB EVAP POWP INDS TRAN Asia+Japan VOLC NAVI Mortality

AERO6VBS

Spring

AERO6

AERO6VBS

Winter

AERO6

AERO6VBS

0 AERO6

0.0

Summer

Figure 5. Sector-based source contributions to short-term PM2.5-related mortality estimated by the AERO6 (left) and AERO6VBS (right) models over Japan.


Page 33 of 33


1.0

8

6

0.6 4

-3

0.4

Conc (g m )

Contributions (-)

0.8

Asia+Japan vol + navi

2

0.2

Japan/Asia Others OA EC NH4 NO3 SO4

PM2.5 Conc. Dec

Nov

Oct

Sep

Aug

Jul

Jun

May

Apr

Mar

Feb

0 Jan

0.0

1.0

12 10 8

0.6 6 0.4 4 0.2

2

Japan/Asia Others OA EC NH4 NO3 SO4 Asia+Japan vol + navi mortality

Dec

Nov

Oct

Sep

Aug

Jul

Jun

May

Apr

Mar

Feb

0 Jan

0.0

Mortality (death/day)

Contributions (-)

0.8

2012

Figure 6. Seasonal variations of source contributions to PM2.5 concentrations and short-term PM2.5-related mortality estimated by the AERO6VBS models over Japan.


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