Contrasting Diurnal Variations in Fossil and Nonfossil Secondary

Oct 1, 2010 - Considering these factors, dead-SOC and modern-SOC were .... that isoprene and sesquiterpene are important precursors for BSOA (8, 43), ...
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Environ. Sci. Technol. 2010, 44, 8581–8586

Contrasting Diurnal Variations in Fossil and Nonfossil Secondary Organic Aerosol in Urban Outflow, Japan Y U M O R I N O , * ,† K A T S U Y U K I T A K A H A S H I , ‡ AKIHIRO FUSHIMI,† KIYOSHI TANABE,† TOSHIMASA OHARA,† SHUICHI HASEGAWA,§ MASAO UCHIDA,† AKINORI TAKAMI,† YOKO YOKOUCHI,† AND SHINJI KOBAYASHI† National Institute for Environmental Studies, Ibaraki, 305-0081, Japan, Japan Environmental Sanitation Center, Kanagawa, 210-0828, Japan, and Center for Environmental Science in Saitama, Saitama, 347-0115, Japan

Received July 14, 2010. Revised manuscript received September 14, 2010. Accepted September 16, 2010.

Diurnal variations of fossil secondary organic carbon (SOC) and nonfossil SOC were determined for the first time using a combination of several carbonaceous aerosol measurement techniques, including radiocarbon (14C) determinations by accelerator mass spectrometry, and a receptor model (chemical mass balance, CMB) at a site downwind of Tokyo during the summer of 2007. Fossil SOC showed distinct diurnal variation with a maximum during daytime, whereas diurnal variation of nonfossil SOC was relatively small. This behavior was reproduced by a chemical transport model (CTM). However, the CTM underestimated the concentration of anthropogenic secondary organic aerosol (ASOA) by a factor of 4-7, suggesting that ASOA enhancement during daytime is not explained by production from volatile organic compounds that are traditionally considered major ASOA precursors. This result suggests that unidentified semivolatile organic compounds or multiphase chemistry may contribute largely to ASOA production. As our knowledge of production pathways of secondary organic aerosol (SOA) is still limited, diurnal variations of fossil and nonfossil SOC in our estimate give an important experimental constraint for future development of SOA models.

1. Introduction Submicrometer aerosols have large impacts on human health (1) and the radiation budget in the atmosphere (2, 3). Organic aerosol (OA), which represents a large fraction (20-90%) of submicrometer aerosols (4), comprises primary OA (POA, particles directly emitted from sources) and secondary OA (SOA, particles produced in the atmosphere by conversion from gaseous phases). Zhang et al. (4) have shown that SOA makes up the dominant fraction (60-90%) of OA in various environments, including urban, rural, and remote areas. Recent field measurements indicate that observed SOA concentrations cannot be fully explained by the oxidation of * Corresponding author phone: +81-29-850-2544; fax: +81-29850-2580; e-mail: [email protected]. † National Institute for Environmental Studies. ‡ Japan Environmental Sanitation Center. § Center for Environmental Science in Saitama. 10.1021/es102392r

 2010 American Chemical Society

Published on Web 10/01/2010

known SOA precursors around urban areas (5), although observed concentrations of biogenic SOA (BSOA) are relatively well explained by production from biogenic volatile organic compounds (BVOCs) (6). Characterization of SOA is required to help resolve this problem. Recently, three methods have been developed for SOA characterization (7). In the first method, organic tracer species identified in chamber studies are used to directly estimate SOA from each parent volatile organic compound (VOC) (8). This method assumes that the ratios of tracers to organic mass are constant in various environments. Also, the contributions of parent VOCs to SOA are estimated by extrapolation from concentrations of organic tracers, which account for only a few percent of total SOA. Thus, this method includes large uncertainties. In the second method, measurement of mass spectra by Aerosol Mass Spectrometer (AMS), combined with positive matrix factorization, provides detailed information of POA and SOA components (9). This method deals with the whole organic mass, and thus, the possible errors due to extrapolation from organic tracers can be reduced. However, the statistical analysis of the mass spectrometry data assumes a constant mass spectrum for each source over a given time (7). In the third method, measurement of 14C enables direct separation between carbon of fossil fuel origin (∼dead carbon) and carbon of nonfossil origin (∼modern carbon) (10, 11). Results of 14C measurement have shown that nonfossil SOA makes up the dominant fraction (70-80%) of SOA even in urban areas in the summer (12, 13). Also, Weber et al. (13) showed that water-soluble organic carbon (WSOC), which is known as a tracer of secondary organic carbon (SOC), is highly correlated with anthropogenic tracers (carbon monoxide). This result appears to be in contradiction to the high nonfossil carbon contents in WSOC (70-80%) in the same study. Because 14C measurement has been conducted with low time resolution (g12 h), knowledge of the behaviors of anthropogenic SOA (ASOA) and BSOA is limited. In this study, 14C contents of total carbon (TC) in PM2.0 (particulate matter with diameter below 2.0 µm) aerosol samples were measured three times a day (0900-1500, 1500-2100, and 2100-0900 Japan standard time) at a site downwind of Tokyo (14). Diurnal variations of fossil and nonfossil SOC were separately determined from these measurements combined with chemical mass balance (CMB) calculations. Comparison of these observed data with a chemical transport model (CTM) has yielded new insights into the individual behaviors and controlling factors of ASOA and BSOA.

2. Methodology Field Measurement. In situ measurement of gaseous species and aerosols were intensively conducted at the Maebashi site (MS3 in Figure 1), located ∼80 km north-northwest of the urban center of Tokyo, on July 31-August 3 and August 6-10, 2007. This site, at the Gunma Prefectural Institute of Public Health and Environmental Sciences, is surrounded by agricultural and residential land and is ∼2 km from the nearest arterial road. We conducted filter sampling of PM2.0 using a high-volume Andersen air sampler (HVC-1000A, Sibata, Tokyo, Japan). To collect PM2.0 samples, the lowest impaction plate (cutoff diameter 1.1 µm) was replaced with a spacer, and 14C contents of PM2.0 samples collected every 6 h (12 h in nighttime) onto the backup filters (203 × 254 mm quartz fiber filters, Pallflex 2500QAT-UP, Pall, NY, USA) were measured using the accelerator mass spectrometer facility (NIES-TERRA) in the National Institute for Environmental VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Emission rates of aromatics and monoterpenes estimated by EAGrid 2000 over the Tokyo Metropolitan Area (20). Measurement sites are shown in numbered circles (MS1: Komae, MS2: Kisai, MS3: Maebashi, MS4: Tsukuba, MS5: Saitama). MS1 is near the urban center of Tokyo. MS5, MS2, and MS3 are downwind from Tokyo when southerly wind dominates. MS4 is in a suburban area and less affected by urban air masses during the analytical period than MS5, MS2, and MS3. Studies (14-16). As the sampling duration was relatively short in this study, the samples were too small (∼300 µgC of TC) for separate measurements of modern carbon in EC and OC; thus, we measured only modern carbon of TC. The percent modern carbon (pMC) values of the sample were calculated by the following equation: pMC ) {(14C/12C)sample /(0.749 × (14C/12C)HOx II)} × 100 (1) where HOx II is the standard material (SRM 4990C, National Institute of Standard and Technology) with the known 14C/ 12 C ratio (14). Modern and dead carbon amounts in TC are determined as [TC × pMC(TC)/100] and [TC × (1 - pMC(TC)/ 100)], respectively. Also, we conducted filter sampling of PM2.5 using two low-volume samplers (FRM2025 and FRM2000, Thermo Electron Corporation, MA, USA). Elemental carbon (EC) and organic carbon (OC) in the PM2.5 samples collected by the FRM2025 sampler were analyzed using a thermal/ optical carbon analyzer (DRI Model 2001 Carbon Analyzer; Desert Research Institute, NV, USA) by means of the IMPROVE protocol (17). Elemental species in the PM2.5 samples collected by the FRM2000 sampler were analyzed using an inductively coupled plasma mass spectrometer (Agilent 7500cx, Agilent Technologies Inc., Tokyo, Japan) after acid digestion (18). We also measured concentrations of O3, NO, and NO2 with a time resolution of 1 h and nonrefractory PM1 concentrations by AMS as detailed in the Supporting Information. The emission inventory developed by Kannari et al. (19) indicated that emission rates of aromatics, mostly emitted from stationary evaporative sources (20), were high around the urban center of Tokyo (Figure 1). In contrast, emission rates of monoterpenes, mostly emitted from vegetation (20), were high in the northern and western part of the Tokyo Metropolitan Area and low around the urban area (Figure 1). This means that aromatics are strongly emitted to the south of the Maebashi site and that monoterpenes are strongly emitted to the north and west. Chemical Transport Model and Receptor Model. For the sampling period, we simulated distributions of gaseous and particulate species using a three-dimensional CTM, the Models-3 Community Multiscale Air Quality (CMAQ) with a recently developed aerosol module (Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution, MADRID) 8582

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(21), to evaluate the validity of the current state of knowledge on SOA production pathways. An updated version of the Caltech Atmospheric Chemistry Mechanism (CACM) (22, 23) was used to model gas-phase chemical mechanisms. CACM includes detailed oxidation processes of VOCs to calculate explicitly the concentration of secondary and tertiary semivolatile organic compound (SVOC) products that have the potential to act as constituents of SOA. Gas-particle partitioning was calculated using saturated vapor pressures of lumped SVOCs as described by Pun et al. (24). The SOA yield from aromatics and monoterpenes calculated by CACM (23) corresponded well with experimental results of Odum et al. (25) and Griffin et al. (26), suggesting that CACM accurately captures the SOA production pathways from aromatics and monoterpenes. We incorporated the updated version of CACM in CMAQ-MADRID. Other detailed settings of our CTM are given in the Supporting Information. We conducted a CMB calculation to estimate the source contributions to primary carbonaceous aerosols (i.e., EC and POC) (27). We used CMB8J (28), a Microsoft Excel macro transcribed from the algorithm of CMB8 released by the U.S. Environmental Protection Agency (29). Input parameters were observed concentrations of 15 metallic elements (Na, K, Ca, Mg, Al, Fe, V, Cr, Mn, Ni, Cu, Zn, As, Sb, and Pb) and EC. Integration time for the CMB calculation was the same as with the 14C measurement (three times a day). Ten emission sectors were considered in this calculation: motor vehicle exhaust, tire wear, brake wear, soil dust, road dust, field burning, sea salt, electrical furnace for steel, incineration, and heavy oil combustion. Emission profiles of these sectors were supplied by the Ministry of the Environment and many local governments in Japan (27). The analysis focused on August 7-10, 2007, when sealand breezes developed and photochemical activity was high. O3 concentrations exceeded 100 ppbv during daytime at the downwind sites (MS2 and MS3) and were mostly lower than 100 ppbv at an urban site (MS1) and a suburban site (MS4) that were not downwind of Tokyo (Figure S2, Supporting Information). The results suggest that outflow from the urban area largely contributed to high concentrations of secondary pollutants at the downwind sites. Similar behavior has been reported previously (30, 31), and this behavior was well predicted by CMAQ (Figure S2, Supporting Information). In addition, concentrations of NO, NO2, and EC were well

TABLE 1. Source Contributions to EC and POC Concentrations Estimated by CMB and CMAQ at the Maebashi Site on August 7-10, 2007a EC (µg m-3) POC (µg m-3) sectors

pMC

industrial combustion incineration field burning vehicle: exhaust vehicle: tire and brake

2.3% 60% 106% 3.4% 17% (tire); 61% (brake) 3.4%

other transport

CMB CMAQ CMB 0.18 0.02 0.11 1.60 0.23

0.15 0.00 0.12 1.18 0.16

0.03 0.01 0.35 0.72 0.15

0.11

CMAQ 0.02 0.00 0.40 0.53 0.04 0.01

total

2.15

1.73

1.26

1.00

pMC

10%

12%

34%

45%

a

pMC of EC and POC were estimated by applying the reference pMC value from each emission source (14).

predicted at the Maebashi site (MS3), suggesting that CMAQ simulation accurately captures the behavior of primary pollutants there.

3. Results and Discussion Source Apportionment of SOC by 14C Measurement and CMB. Concentrations of dead SOC and modern SOC can be estimated using [dead-SOC] ) [dead-TC] - ([dead-EC] + [dead-POC]) (2) [modern-SOC] ) [modern-TC] - ([modern-EC] + [modern-POC]) where the bracketed terms indicate mass concentrations of the species and POC indicates primary organic carbon. Two previous studies derived concentrations of dead-SOC and modern-SOC by similar calculations. Gelencse´r et al. (32) measured pMC of TC and estimated dead-EC, dead-POC, modern-EC, and modern-POC by the EC-tracer method and organic tracer measurements. Szidat et al. (12) measured pMC(OC) and pMC(EC) separately and estimated dead-POC and modern-POC using the EC-tracer method and organic tracer measurements. In this study, we estimated concentrations of dead-EC, dead-POC, modern-EC, and modern-POC using CMB as follows: [dead-EC] ) [EC]obs × (1 - pMC(EC)CMB /100)

(3)

[dead-POC] ) [POC]CMB × (1 - pMC(POC)CMB /100) [modern-EC] ) [EC]obs × pMC(EC)CMB /100 [modern-POC] ) [POC]CMB × pMC(POC)CMB /100 The source contribution estimated by CMB at Maebashi was compared with the CMAQ result (Table 1). Emissions from vehicle exhaust and other transportation (ships, airplanes, and off-road vehicles) were the predominant contributors (75%) of EC in both estimations. Vehicle exhaust also accounted for the largest contribution to POC (57% by CMB and 53% by CMAQ), and field burning accounted for the second largest contribution (28% by CMB and 40% by CMAQ). The CMB result was consistent with the average source contribution of EC and POC from known emission sources (19). pMC values for EC and POC were calculated by both models to be 10-12% and 33-45%, respectively, with the higher numbers for POC reflecting greater contributions from field burning. For most cases, pMC of EC agreed between

FIGURE 2. (a) Observed concentrations of dead-TC and modern-TC at the Maebashi site (MS3) on August 7-10, 2007. (b) Dead-SOC concentrations, estimated using eqs 2 and 3 in the text, and ASOA concentrations predicted by CMAQ. (c) Modern-SOC concentrations, estimated using eqs 2 and 3 in the text, and BSOA concentrations predicted by CMAQ. ε(pMC(EC + POC)) indicates the range of uncertainties in pMC(EC + POC) estimated in this study. CMB and CMAQ within 20%age points, and pMC of POC agreed between CMB and CMAQ within 40%age points (Figure S4, Supporting Information). The overall uncertainty in pMC(EC + POC) was estimated to be 46% of pMC as detailed in the Supporting Information. The concentrations of dead-TC and modern-TC in eq 2 were directly observed as shown in Figure 2 (14). The deadTC concentration was higher than that of modern-TC during daytime, and dead-TC showed larger diurnal variation (3-4 µg m-3) than did modern-TC (1-2 µg m-3) on August 7-9, 2007. Using eqs 2 and 3, dead-SOC and modern-SOC concentrations were estimated as shown in Figure 2. Concentrations of dead-SOC showed large diurnal variation, and concentrations of modern-SOC were relatively uniform. We note that pMC of anthropogenic and biogenic SOC were 9.5% and 113%, respectively (33), which means that modernSOC and dead-SOC do not exactly correspond to SOC from nonfossil and fossil fuel sources, respectively. Fossil- and nonfossil-SOC are more readily compared with ASOA and BSOA calculated using a CTM than are dead- and modernSOC. However, the pMC value of fossil SOC might not be zero; thus, derivation of fossil and nonfossil SOC from dead and modern carbon may introduce error. The separation between pMC values of modern and dead SOC differed from the separation between fossil and nonfossil SOA only by 6-10%age points, a difference that is much smaller than other uncertainties in this estimate as shown above. Considering these factors, dead-SOC and modern-SOC were analyzed for this study. The behavior of SOC was analyzed using odd oxygen (Ox ) O3 + NO2) as an approximately conserved tracer of the extent of photochemical processing in the urban atmosphere. The values of the three ratios ∆[SOC]/∆[Ox], ∆[dead-SOC]/ ∆[Ox], and ∆[modern-SOC]/∆[Ox], determined by the linear VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Distribution of ASOA concentrations, ASOA production rates, BSOA concentrations, and BSOA production rates in the bottom model layer predicted by CMAQ over the Tokyo Metropolitan Area on August 7-10, 2007. Absolute values were corrected using the predicted/observed ratio of ASOA and BSOA at the Maebashi site. Data were averaged for 6 h periods starting at midnight Japan standard time in each successive row. Predicted wind fields are also shown. Numbered circles indicate measurement sites (Figure 1). regression analysis, were 0.051, 0.032, and 0.019 µgC m-3/ ppbv, respectively (r ) 0.62-0.75, p ) 0.005-0.03). The result indicates that dead carbon accounted for ∼63% of the increase in SOC during daytime at Maebashi. We also analyzed the behavior of oxygenated organic aerosol (OOA) and hydrocarbon-like organic aerosol (HOA) estimated from AMS mass spectra (34) with a time resolution of 1 h. OOA was closely correlated with Ox (slope ) 0.10 µg m-3/ppbv, r ) 0.83, p < 10-5), whereas HOA and Ox were poorly correlated (slope ) 0.00 µg m-3/ppbv, r ) 0.002, p ) 0.73). This result suggests that the daytime increase of organic aerosol was mostly associated with OOA. Also, the result indicates that the ratio ∆[OOA]/∆[SOC] is ∼2, a value consistent with the ratio of organic mass to organic carbon obtained in previous studies (35). The ∆[OOA]/∆[Ox] ratios were similar to those derived in the urban center of Tokyo in the summer of 2004 (0.14 µg m-3/ppbv (35)) and in outflow from Mexico City (0.08-0.12 µg m-3/ppbv (36)) and were higher than those derived from Houston data (0.03 µg m-3/ppbv (37)). In the urban area, the Ox production rate was controlled by reactions of VOCs and hydroxyl radical (OH) (38) and so was ASOA 8584

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production, as detailed later. In addition, OOA and Ox are well correlated in air masses where both species form on similarly short time scales (less than 8 h) (37). Thus, the ∆[OOA]/∆[Ox] ratio is a proxy for the ratio between the SOA yield of VOCs and the OH reactivities of VOCs in urban areas, and the differences in ∆[OOA]/∆[Ox] ratio in each city indicate the differences in VOC components, as discussed in detail by Wood et al. (37). SOC Prediction by a Chemical Transport Model. The diurnal variations of dead-SOC and modern-SOC were roughly reproduced by CMAQ, although daily averaged ASOA and BSOA concentrations were underestimated by factors of 5.6 ( 1.7 and 1.7 ( 0.7, respectively (Figure 2). In the CMAQ simulation, aromatics and long-chain alkanes were the dominant contributors (56% and 41%, respectively) to ASOA over the Tokyo Metropolitan Area. Predicted ASOA was predominantly produced by the reaction of anthropogenic VOCs (AVOCs) and OH; thus, ASOA production occurred during the daytime (Figure 3). The agreement of diurnal variation between observed dead-SOC and predicted ASOA indicates that photochemical reactions between AVOCs and

OH were the dominant initial reaction for ASOA production. ASOA was not produced during nighttime in the CMAQ simulation; thus, predicted ASOA concentrations decreased during nighttime over the Tokyo Metropolitan Area (Figure 3). Concentrations of aromatics measured at the Saitama site (MS5 in Figure 1) were compared with CMAQ results. Saitama is ∼20 km north of the urban center of Tokyo, along the line between Tokyo and Maebashi and ∼60 km upwind from Maebashi under the southerly flow. The predicted-toobserved ratios were 0.84 for AROH concentrations (aggregated from toluene, benzene, and ethyl benzene) and 1.78 for AROL concentrations (aggregated from m-xylenes, pxylenes, and o-xylenes). These predicted-to-observed ratios would be a measure of model performance for aromatics. Even considering the uncertainties in precursor concentrations in the CMAQ simulation, ASOA produced from aromatics cannot account for the observed increase in deadSOC during daytime. Previous studies have shown the possible ASOA production pathways to fill in large gaps between observed and predicted SOA. Volkamer et al. (39) showed that SOA produced from multiphase reactions of glyoxal could account for 15-25% of observed SOA at an urban site in Mexico City. Johnson et al. (40) showed that a large decrease in the gasparticle partition coefficient could increase SOA concentrations by a factor of 5 in the London plume, and these investigators proposed that association and/or accretion reactions in the condensed organic phase may play an important role in SOA formation. Matsui et al. (20) showed that unidentified, high-molecular-weight VOCs could contribute to an increase in SOA concentration by a factor of 5 at the urban site of Tokyo. Dzepina et al. (41) showed that SVOC and intermediate VOC, which were not considered in current SOA models, could account for 60% of observed OOA in Mexico City. As complete chemical characterization of SOA is currently unrealistic, alternative approaches to deal with unidentified VOCs, such as a “volatility basis set” (41, 42), may be needed to resolve the ASOA underestimation. Most processes mentioned above are initiated by photochemical reactions and, thus, may account for the dead-SOC estimated in this study from 14C measurement and CMB calculation. BSOA predicted by CMAQ showed small diurnal variation. This version of CMAQ considers monoterpenes as the exclusive precursor of BSOA. OH, O3, and NO3 contributed to oxidation of monoterpenes by 61%, 20%, and 19%, respectively, over the Tokyo Metropolitan Area and by 26%, 13%, and 60%, respectively, at Maebashi in the CMAQ simulation. Because of the large contribution of NO3 as an oxidant of monoterpenes, BSOA was predicted to be produced even during nighttime (Figure 3). Also, BSOA was predicted to be distributed more homogeneously than ASOA. BSOA production was predicted to occur in the area north of Maebashi, and thus, was predicted to be transported to Maebashi during nighttime by the northerly mountain breeze. The CMAQ simulation better reproduced BSOA concentrations as compared to ASOA concentrations, and this result is consistent with previous studies (6). However, there are many uncertain factors in BSOA prediction. Modern-SOC estimated from observations and CMB showed a small increase during daytime, while BSOA in the CMAQ simulation showed a small decrease. This discrepancy implies some problems in the CMAQ simulation, including diurnal variation in BVOCs emission rates, the partitioning coefficient of biogenic SVOC and BSOA, and/or reaction rates of BVOCs and oxidants. Also, recent studies have suggested that isoprene and sesquiterpene are important precursors for BSOA (8, 43), and adding these species may alter the temporal variation of BSOA. A previous study comparing fossil and nonfossil OA as determined by 14C measurement and a CTM (44) has showed

that nonfossil OA had large contributions to OA in Mexico City. In this study, we estimated fossil and nonfossil SOA and, thus, more directly estimated the model performance of SOA prediction. However, our study did not consider aerosols from cooking and biogenic POC (e.g., plant debris), and thus, nonfossil-SOC concentration might be overestimated. To improve the accuracy of this estimate, measurement of organic tracers (8) is useful. Also, pMC measurement of EC and OC is useful to reduce uncertainties in this estimate (10). Despite these limitations, the diurnal variations of ASOA and BSOA in our estimate give an important experimental constraint for future development of SOA models.

Acknowledgments The authors thank all participants in the measurement campaign.

Supporting Information Available Additional details of our analysis. This material is available free of charge via the Internet at http://pubs.acs.org.

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