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Environmental Modeling

Contributions of condensable particulate matter to atmospheric organic aerosol over Japan Yu Morino, Satoru Chatani, Kiyoshi Tanabe, Yuji Fujitani, Tazuko Morikawa, Katsuyuki Takahashi, Kei Sato, and Seiji Sugata Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01285 • Publication Date (Web): 05 Jul 2018 Downloaded from http://pubs.acs.org on July 6, 2018

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Contributions of condensable particulate matter to atmospheric organic aerosol

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over Japan

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Yu Morino,*,† Satoru Chatani,† Kiyoshi Tanabe,† Yuji Fujitani,† Tazuko Morikawa,‡

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Katsuyuki Takahashi,|| Kei Sato,† and Seiji Sugata†

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305-8506, Japan

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10

||

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210-0828, Japan

National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki,

Japan Automobile Research Institute, 2530 Karima, Tsukuba, Ibaraki 305-0822 Japan

Japan Environmental Sanitation Center, 10-6 Yotsuyakami-Cho, Kawasaki, Kanagawa,

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To be submitted to Environmental Science and Technology

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Last revised on May 30, 2018.

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ABSTRACT:

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Because emission rates of particulate matter (PM) from stationary combustion

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sources have been measured without dilution or cooling in Japan, condensable PM has

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not been included in Japanese emission inventories. In this study, we modified an

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emission inventory to include condensable PM from stationary combustion sources

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based on the recent emission surveys using a dilution method. As a result, emission

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rates of organic aerosol (OA) increased by a factor of seven over Japan. Stationary

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combustion sources in the industrial and energy sectors became the largest contributors

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to OA emissions over Japan in the revised estimates (filterable-plus-condensable PM),

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while road transport and biomass burning were the dominant OA sources in the previous

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estimate (filterable PM). These results indicate that condensable PM from large

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combustion sources makes critical contributions to total PM2.5 emissions. Simulated

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contributions of condensable PM from combustion sources to atmospheric OA

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drastically increased around urban and industrial areas, including the Kanto region,

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where OA concentrations increased by factors of 2.5–6.1. Consideration of condensable

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PM from stationary combustion sources improved model estimates of OA in winter but

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caused overestimation of OA concentrations in summer. Contributions of primary and

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secondary OA should be further evaluated by comparing with organic tracer

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measurements.

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TOC Art

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INTRODUCTION

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Regulation of air pollutants has been successfully implemented in developed

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countries over the last several decades, and emissions from combustion sources, such as

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power plants, industrial factories, and motor vehicles, have decreased over the last two

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decades1, 2. The relative contribution of non-combustion sources to air pollutants is

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therefore thought to have increased during this period3. However, contributions of

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combustion sources to air pollutants are still significant4, 5.

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To evaluate the contributions of combustion sources to the ambient PM2.5 (particulate

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matter with diameter less than 2.5 µm), understanding of PM2.5 produced by both

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primary-emitted and secondary-produced PM2.5 is needed. Primary-emitted PM2.5 is

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composed of compounds that range from non-volatile to semi-volatile6-9. Conditions,

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such as temperature and dilution ratios, under which PM2.5 is sampled from combustion

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sources should therefore be carefully selected to provide an accurate estimation of the

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contributions of combustion sources to PM2.5. However, only filterable particulate

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matter (PM), which consists of particles directly emitted under stack conditions, has

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been measured from stationary combustion sources in many countries. Condensable PM,

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which is in the gas phase under stack conditions and condenses into PM immediately

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after discharge from the stack, should also be considered.

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Condensable PM from emission sources has been measured by the cooling method or

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dilution method. In the United States, the cooling method with dry impingers (EPA

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method 202/201a) is employed, although this method is known to have a positive bias

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associated with gas adsorption. Efforts to reduce the bias have been made, but this bias

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still remains10. The dilution method has been developed and used for PM measurements

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at stationary combustion sources for a long time11-13, and International Organization for

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Standardization (ISO) has promulgated ISO 25597 for use of the dilution method to

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measure condensable PM.

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Some recent emission inventories in the United States14 and Europe15 have included

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both filterable and condensable PM. However, filterable and condensable PM have not

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been completely separated even in the National Emission Inventory (NEI) of the United

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States14. Moreover, condensable PM is not measured in the emission surveys of many

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countries, including Japan; and condensable PM is therefore not included in most

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emission inventories4, 16, 17.

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Previous studies have indicated that inorganic compounds (mostly sulfate) have been

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the dominant contributors to condensable PM18-20, although recent studies based on

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emission surveys of new power plant facilities have indicated that organic compounds

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make the largest contributions to condensable PM21. In addition, van der Gon et al.22

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have revised the emission inventory of Europe by considering condensable PM from

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residential wood combustion23 and have shown that the revised simulation better

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reproduces the observed OA concentrations. It has been shown that large amounts of

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atmospheric organic materials are semi volatile (i.e., semi volatile organic compounds,

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SVOC), and gas–particle partitioning of organic compounds is thus sensitive to

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sampling conditions24, 25. Emission characteristics of SVOCs from motor vehicles and

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biomass burning have been examined extensively8, 26, 27. However, to the best of our

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knowledge, few studies have involved measurements of the volatility distributions of

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SVOC from stationary combustion sources.

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For atmospheric simulations of condensable PM, traditional models28,

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are

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unsuitable because they assume primary organic compounds to be non-volatile. The

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volatility basis-set (VBS) framework is certainly one of the dominant methods for

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calculating gas–particle partitioning of organic compounds, including their responses to

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changes of temperature or OA concentrations (dilution ratio)30, 31. However, the VBS

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model includes large uncertainties, for example in multi-generational reaction rates and

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emission profiles of SVOCs32, 33. Emission characteristics of SVOC are one of the key

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uncertainties in current PM2.5 modeling.

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In this study, we constructed an emission inventory of condensable PM in Japan, with

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particular focus on OA. The standard methods in Japan for measuring PM from

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combustion sources have been promulgated in Japan Industrial Standard (JIS) Z8808 or

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Z7152, but these methods involve measurement of only filterable PM. Emissions of

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condensable PM are not measured in Japan, and thus, condensable PM is not included

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in the Japanese emission inventory4, 17. Recently, emission surveys of both filterable and

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condensable PM from stationary combustion sources have been conducted in Japan34-36.

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These data indicate that condensable PM makes important contributions to total PM2.5

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and OA. The implication is that large amounts of SVOC are emitted from stationary

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combustion sources. We modified the Japanese emission inventory by including

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condensable PM from stationary combustion sources. We also briefly assessed

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uncertainties of condensable PM emissions associated with uncertainties in our

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estimation methods. With these emission data, we conducted a simulation with the VBS

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model and estimated emission data to assess the contributions of SVOC (condensable

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PM) to ambient OA concentrations.

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METHODOLOGY

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Estimation of emissions of condensable PM.

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As already noted, only filterable PM is considered in surveys of emissions from

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stationary combustion sources in current emission inventories of Japan17. To estimate

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rates of emission of condensable PM, we used emission survey data currently available

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in Japan. From 2008 to 2013, the Tokyo metropolis34, 35 conducted emission surveys to

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measure filterable and condensable PM at 53 stationary combustion sources, including

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industrial gas boilers, heavy oil boilers, incinerators, marine ships, biomass burning,

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ceramic furnaces, and electric furnaces. We should note that what is defined to be

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filterable and condensable PM depends on the measurement methodology. Based on the

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results of the emission surveys, we considered PM2.5 in the stacks to be filterable PM

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and PM2.5 after dilution by a factor of 20 to be filterable PM plus condensable PM. The

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Supporting Information (SI) provides details of the measurement methodology used in

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the emission surveys. We used measurement data from 25 representative sources for the

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analysis (Table 1). Emission rates of filterable and condensable PM from a

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coal-combustion source were also measured with the same methodology by the Ministry

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of Environment (MOE), Japan36, and these data were also used for this analysis.

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Emission rates of OA in filterable and condensable PM were estimated with the

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following equations:

127 128

129



 FCPM =  . FPM ×  



. 

=  . FPM ×

  ∗ . 

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  ! FCPM =  FCPM ×

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=  FCPM ×

(1)

 ! 

 

 ! 

 

,

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where EX(FPM) is the emission rate of species X in filterable PM, EX(FCPM) is the

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emission rate of species X in both filterable and condensable PM, CX(FPM) is the

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concentration of species X measured without dilution (filterable PM), and CX(FCPM)* is

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the concentration of species X measured after dilution (filterable-plus-condensable PM)

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with a correction for the dilution ratio (i.e., CX(FCPM)* = CX(FCPM) × [dilution ratio]).

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OMlsi is organic material that has low volatility, is semi-volatile, or has intermediate

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volatility (C* ≤ 106 µg m–3) in both the gaseous and the particulate phases.

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In this estimate, we assumed that condensable PM was omitted from the Japanese

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emission inventory17 of PM2.5. This assumption is consistent with the fact that the

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methodologies used in the emission surveys of stationary combustion sources were

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based on JIS Z8808 or Z7152 (methodology to measure filterable PM). Thus rates of

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PM2.5 emissions taken from the Japan Auto-Oil Program17 were equated to EPM2.5 (FPM)

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in eq 1.

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Data from the emission surveys conducted by the Tokyo metropolis34, 35 and the MOE

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(2015) were used to estimate ratios of COA(FCPM)* to CPM2.5(FPM). Ratios of emission

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rates (or emission factors) of compounds should be proportional to the ratios of the

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concentrations of the compounds in the emission surveys. Considering that COA(FPM)

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(i.e., OA concentrations in stacks) were sometimes very low, we chose to use

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EPM2.5(FPM) rather than EOA(FPM) in eq 1 to reduce the uncertainty of the estimate. We

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estimated the ratios of COA(FCPM) to CPM2.5(FPM) from eight emission sectors (Table

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1). In both emission surveys, only concentrations of organic carbon (OC) were reported,

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and we assumed the OA/OC ratio to be 1.237.

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We should note that exhausts from on-road and off-road vehicles were measured after

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a dilution tunnel. However, because sampling temperatures for both on-road and

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off-road vehicle exhausts were 47 °C38, some organic compounds that would have been

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partitioned into the particulate phase at the reference temperature of 30 °C should have

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evaporated during the sampling6, 39. To the best of our knowledge, the impact of the

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sampling temperature on the estimates of total OA emissions from motor vehicles (in

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emission inventories) has not been systematically evaluated. In this study, we simply

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conducted sensitivity simulations to evaluate the contributions of condensable PM from

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motor vehicles. May et al.39 conducted thermodenuder measurements of diesel vehicle

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exhausts and found that about 30% of OA evaporated at around 47 °C. We therefore

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conducted sensitivity simulations by enhancing OA emissions from motor vehicles by

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30%, as detailed in a section of Results and Discussion.

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Emissions of SVOCs were estimated with eq 2: &

 ! 

 

 ! 

(

 

) can be

, where Ci* is the saturation concentration (µg m-3) and fi,

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calculated as 1)∑

%$169

the fraction of compounds with a saturation concentration equal to Ci*, is a metric of the

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distribution of the volatility of the organic compounds. Information on fi was taken from

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previous estimates24, 40.

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The ratio

&'!∗ /

 ! 

 

depends strongly on fi and COA under stack conditions. Given  ! 

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the fi of Grieshop et al.40, we estimated

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concentrations of 8800, 3000, 220, and 30 µg m–3, respectively. OA concentrations

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under stack conditions during the emission survey used for the Japanese emission

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inventory32 were not reported, and we therefore could not accurately calculate

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 ! 

 

 

to be 2.5, 3.0, 5.0, and 7.5 with OA

from emission survey data. In previous studies24, 40,

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 ! 

 

ratios

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 ! 

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were set to 7.5 or 2.5. Based on these values, we chose

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the standard simulation and sensitivity simulation, respectively. The sensitivity to these

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assumptions is assessed in the Results and Discussion section.

 

= 7.5 and 3.0 for

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Chemical transport model.

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

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three-dimensional chemical transport model, the Models-3 Community Multiscale Air

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Quality (CMAQ, v5.0.2) modeling system developed by the United States

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Environmental Protection Agency41. The model setups were the same as those used in

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our previous studies:5,

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Mechanism 05 (CB05) model of Yarwood et al.43, and the aerosol module was a

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sixth-generation aerosol module of CMAQ (AERO6) coupled with a VBS module

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(AERO6VBS). Originally, the AERO6VBS considered five classes of organic

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compounds: one class for non-volatile compounds and four classes for semi-volatile

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compounds with C* = 1, 10, 100, and 1000 µg m–3. For the simulation of this study, we

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changed the framework of the AERO6VBS model so that nine classes of organic

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compounds with C* ranging from 10–2 to 106 µg m–3 could be taken into consideration.

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As already noted in our previous study42, we considered aging reactions for both

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primary and secondary SVOC. We should note that we did not consider fragmentation

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in this simulation.

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the chemical mechanism was based on the Carbon Bond

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Vaporization enthalpy (∆Hvap) is one of the key uncertainty factors in the atmospheric

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simulations of OA44, 45, particularly in the representation of the temperature dependence

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of the volatility distributions of OA. In general, measurement of the ∆Hvap of

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low-volatility compounds is difficult because their concentrations in the gas phase are

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low. The ∆Hvap of OA has been estimated from measurements of the temperature

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dependence of the yields of secondary organic aerosol (SOA)

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rates after heating or introduction into a vacuum49,

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variability of the measurement data is the fact that the ∆Hvap values used in chemical

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transport models are highly variable: Schell et al.28 set ∆Hvap equal to 156 kJ/mol,

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whereas Carlton et al.29 used a ∆Hvap of about 40 kJ/mol. Epstein et al.49 have recently

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derived an equation to calculate ∆Hvap as a function of C*: ∆Hvap = – 11 log10 C* + 129.

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In contrast, Tsimpidi et al.51 set ∆Hvap = – 6 log10 C* + 100. Because ∆Hvap cannot be

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uniquely determined from heating experiments52, its value is highly uncertain.

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Uncertainties associated with ∆Hvap are further discussed in the next section.

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or OA evaporation

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. An indication of the large

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The two simulation domains of this study are shown in Fig. 1. Domain 1 covered East

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Asia with a horizontal resolution of 60 km, and Domain 2 covered Japan with a

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horizontal resolution of 15 km. We conducted simulations for January 1 to February 29

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2012 (winter), April 1 to May 31 2012 (spring), and July 1 to August 31 2012 (summer).

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The spin-up time for the calculations was 10 days.

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We used the observed OC concentrations at 14 sites (Fig. 1) to evaluate the OA

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simulations. Filter samples with a sampling duration of 6 or 12 hours were analyzed

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with a thermal/optical carbon analyzer (DRI model 2001A; Atmoslytic Inc., CA, USA

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or Sunset Laboratory, Inc., OR, USA) on the basis of the IMPROVE protocol.53 Details

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of this measurement are described in our previous study.42

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RESULTS AND DISCUSSION

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Emission estimates.

Figure 2 summarizes the emission rates of OA in filterable PM and condensable PM.

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Before the correction for condensable PM, the total emission rate of OA equaled about

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20% of the emission rate of PM2.5. However, after correction for condensable PM, the

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emission rates of OA increased by a factor of seven and were even higher than those of

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total PM2.5 in filterable PM. These results suggest that use of emission factors and

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speciation of filterable PM has led to serious underestimation of OA emission rates from

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stationary combustion sources in the current Japanese emission inventory. OA is a major

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component of PM2.5 in the revised emission inventory.

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Major sources of OA emissions in filterable PM were a transport sector and biomass

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burning. However, after correction for condensable PM, large stationary combustion

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sources in the industrial and energy sectors and incinerators accounted for the majority

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of OA emissions. OA emission rates from stationary combustion sources increased by a

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factor of 24. The percentages of OA among filterable PM2.5 (i.e.,

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50%, whereas EOA(FCPM) values were comparable to or even larger than EPM2.5(FPM)

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values for most combustion sources (Table 1). Because of these large differences,

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consideration of condensable PM increased OA emissions substantially.

241

 

) were 1–

. 

It should be noted that this estimate includes uncertainties associated with (1) the

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representativeness of tested emission sources and (2) the thermodynamic properties of

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OA emitted from stationary combustion sources.

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(1) As noted above, only a limited number of emission surveys have been conducted,  

. 

estimated from the survey of the Tokyo metropolis34, 35 does

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and thus, the

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not necessarily represent the emission sectors estimated by the Japanese emission

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inventory17. To our knowledge, we have used the best available datasets from the

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emission surveys of condensable PM in Japan, although a comprehensive analysis of the

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uncertainty associated with their representativeness would be difficult. Instead, we

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compared the emission rates of elemental carbon (EC) in filterable PM and

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filterable-plus-condensable PM (EEC(FPM) and EEC(FCPM), respectively) to roughly

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understand the ranges of uncertainty associated with the representativeness of the

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emission sources. EC is practically non-volatile, and thus, EEC(FPM) and EEC(FCPM)

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should be very similar if the PM2.5 speciation datasets are similar between the Japanese

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emission inventory of the Japan Auto-Oil Program17 and the emission survey of the

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Tokyo metropolis34, 35. Overall, EEC(FPM) and EEC(FCPM) agreed to within 10% (Fig.

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2). This difference could have been caused by differences in the EC and PM2.5

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speciation, and these differences could be a rough measure of the uncertainty associated

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with the representativeness of the tested emission sources. The differences between

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EEC(FPM) and EEC(FCPM) were relatively large in some sectors. The PM2.5 speciation

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for each sector could therefore be one of key uncertainties in this estimate.

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Uncertainties of total EOA(FCPM) and EEC(FCPM) associated with variabilities in  

. 

and

 

. 

(Table 1) were ±15% and ±7%, respectively. However,

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these percentages do not necessarily indicate the overall uncertainties of these emission

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estimates. Clearly, the

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these sources contributed 40% of the estimated total EOA(FCPM). Thus, accurate

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estimates for gas combustion sources should be a priority goal of future studies.

268 269

 

. 

ratio from gas combustion sources was very large;

(2) Choice of ∆Hvap and volatility distribution is another source of uncertainty. As shown in eq 2, EOMlsi(FCPM) depends on fi (volatility distribution). Air masses emitted

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from combustion sources are diluted by several orders of magnitude from stacks to the

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ambient atmosphere, and during the course of this dilution, variations of OA

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concentrations depend on two factors that have opposite effects: the OA concentration is

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expected to increase because of condensation caused by cooling and then to decrease

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because of evaporation due to dilution. The behavior of OA during dilution is

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determined by ∆Hvap and volatility distribution. We evaluated whether the simulations

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with the selected ∆Hvap and volatility distributions could reproduce the observed ratios

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of OA concentrations before and after dilution from the stacks (i.e.,

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COA(FPM)/COA(FCPM)*)34, 35 (Fig. 3).

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The COA(FPM)/COA(FCPM)* ratios were high (0.5–1.8) from wood- and

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field-burning sources with low stack temperatures (