Environ. Sci. Technol. 2008, 42, 7287–7293
Organic Aerosol Spatial/Temporal Patterns: Perspectives of Measurements and Model BETTY K. PUN* AND CHRISTIAN SEIGNEUR Atmospheric & Environmental Research, Inc., San Ramon, California
Received February 18, 2008. Revised manuscript received June 10, 2008. Accepted June 26, 2008.
Ambient measurements from SEARCH and model results from CMAQ-MADRID are analyzed side by side for the southeastern United States to understand the strengths and weaknesses of an air quality model in reproducing key spatial and temporal patterns related to organic aerosol (OA), with inferences regarding secondary organic aerosol (SOA). The model predicts a larger difference in OA concentrations between an urban (JST) and a rural site (YRK) than indicated by measurements. Modeled OA concentrations at JST and YRK are more strongly correlated than measurements. On average, models may understate urban OA emissions, while overstating urban SOA production; measurements indicate that SOA production takes place on the regional scale. Modeled diurnal fluctuations for OA are stronger than measured, due partially to overestimations of the temperature dependence parameters (∆Hvap) for SOA in the model. Urban-rural differences in the composition of SOA, inferred from the variations of estimated ∆Hvap, are not properly captured by the model, which does not represent multiple generations of SOA or varied reaction pathways as a function of chemical regimes. Model results are hampered by day-of-the-week and diurnal allocation issues related to EC and OA emissions. Top quintile (20%) afternoon OA concentrations are observed in both warm and cold seasons at the urban site. The frequency of high OA in the cold season is overstated in the model. The model predicts the warm vs cold season frequency of elevated OA episodes better at YRK than at JST, suggesting that regional emissions, chemistry, and transport are better simulated than urban processes.
Introduction Organic aerosols (OA) represent a significant component of atmospheric fine particulate matter (PM) in the United States and around the globe. Unlike other major components, such as sulfate and nitrate, OA comprise a complex mixture of primary and secondary compounds of anthropogenic and biogenic origins. In the United States, concentrations of inorganic components of fine PM are likely to decrease in the future due to controls of anthropogenic NOx and SO2 emissions, and the relative importance of OA will, therefore, increase. Despite decades of research, uncertainties in the formation of OA continue to hinder the ability of air quality and global chemistry models to simulate accurately the * Corresponding author telephone: (925) 244 7125; fax: (925) 244 7129; e-mail:
[email protected]. 10.1021/es800500j CCC: $40.75
Published on Web 08/22/2008
2008 American Chemical Society
distribution and severity of PM air pollution and haze (e.g., refs 1–3). Improvements in model predictions will translate into higher confidence in control strategies designed based on the modeled response to emission changes. Current models represent the myriad of OA using a limited number of model compounds. Models typically underestimate OA (4–7). Research focus has been placed on model formulation for secondary organic aerosol (SOA). Pun and Seigneur (8) used a box model to highlight major areas still in need of future research. Simplifying assumptions are necessary when incorporating these new processes into operational air quality models due to a lack of qualitative and quantitative information (9, 10). Measurements are typically analyzed to understand specific phenomena (11–13). However, in most routine applications of three-dimensional air quality models, measurements are used in operational evaluation ((14); Special issue of Atmospheric Environment, Volume 40 (Issue 26), 2006). Quantified statistically, good model performance provides confidence to use the model in scenarios (15, 16). However, statistical evaluations provide little insight into reasons for the model’s poor performance and no information on the model’s ability to reproduce the underlying processes that govern the state variables. Diagnostic model evaluations attempt to identify the sources of the errors in the model predictions (17). Model performance can be improved by assimilating measurements (18–20). In all these studies, measurements are used to evaluate or optimize the model output (the state variables), either ambient concentrations or deposition fluxes. It is useful to mine the ambient data for insights and judge whether the model can reproduce some of the patterns that are reflected in the measurements. Areas of strengths and weaknesses in the model’s ability to represent atmospheric processes can be inferred this way. Recent intensive field studies have yielded rich measurement data sets of OA that can be analyzed for spatial variability (urban versus rural concentrations) and temporal variability (seasonal, day-of-the-week, and diurnal variability). Model results are then analyzed in a similar manner. The inability of the model to reproduce the variability in the measurements points to deficiencies in the model formulation or input data.
Ambient Measurements Measurements from the Atlanta (JST) and Yorkville (YRK) sites of the Southeastern Aerosol Research and Characterization Study (SEARCH) were used; JST represents an urban site and YRK is typical of a southeastern rural site (21). Raw hourly data for 2003 and 2004 were obtained from http:// www.atmospheric-research.com/public/index.html for PM2.5 and components, gases, and meteorology. (Data for carbonaceous aerosols from 2002 have larger gaps than data from 2003 and 2004.) From observations, organic carbon (OC) is calculated as the difference between total carbon (TC) and elemental carbon (EC) (22). OCobs)TCobs - ECobs
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The EC tracer method is a well-established technique for estimating SOA (23). In its original concept, a constant ratio of primary OC (POC) and EC is assumed, where both PM components originate from combustion sources. The presence of SOA increases the ambient OC/EC ratio. Saylor et al. (24) estimated secondary OC (OCsec) using a modified version of the EC tracer method, which accounts for combustion and noncombustion sources of POC. VOL. 42, NO. 19, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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OCsec)OCobs- (OCprim,other+ OCprim,comb)
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Methods
OCprim,comb)(OC/EC)comb × ECobs
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For the spatial analysis, average values are calculated using all valid hourly data in a given year at JST and YRK. Average OCsec concentrations at JST and YRK are estimated using Saylor et al. (24). OC-to-OM conversion factors of 1.2 for POA, 1.6 for urban SOA, and 2.1 for rural SOA (31) are used to account for oxygen and hydrogen mass associated with the measured OC. A data record of one year is used to generate representative diurnal profiles at JST and YRK. OC and estimated OCsec data are grouped by the hour of the day (up to 365 data points for each hour) and the median value of each group is plotted in Figure 1. A late afternoon period is selected when chemical reactions are expected to cease and the partitioning of a constant amount of condensable compounds (denoted as SC for sum of condensable products originating from the atmospheric oxidation of precursor VOC) between the gas and particulate phases changes as temperature changes. SC is not measured, but is constrained to be greater than OCsec. In this case, the partitioning coefficient (Kp) is estimated as OCsec/OC/GC, where GC is the gas-phase concentration of the condensable product (GC ) SC - OCsec). Based on the Clausius-Clapeyron equation, the partitioning coefficient at a given temperature is expressed as follows:
where primary ECobs and OCprim,comb are EC and POC, respectively, emitted from the same combustion sources. The combustion source mixture is characterized by a single ratio (OC/EC)comb. OCprim,other accounts for noncombustion sources of OC, e.g., vegetative detritus. Using regression analyses on the SEARCH data, Saylor et al. (24) determined OCprim,other to be 0.30 and 1.02 µgC/m3, respectively, at JST and YRK. JST is associated with a higher (OC/EC)comb ratio (2.3) than YRK (0.49). Although these estimated parameters are subject to temporal variability (25), they reflect different mixtures of sources of carbonaceous PM affecting these two locations on average.
Model Simulation Results Because the goal of this work is to determine the model’s ability to reproduce the overall behavior of OA and SOA rather than to compare concentrations at a specific time, it is not necessary to match the model simulation and measurement periods. The model simulation output is obtained from an existing annual 2002 simulation of the southeastern United States. The simulation was conducted using the Community Multiscale Air Quality (CMAQ) model with the Model for Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (26). Advanced Plume Treatment (APT) was used to simulate the subgrid scale mixing and reactions occurring inside plumes from 40 major point sources (27). Emissions and meteorology inputs were obtained from earlier simulations conducted by the regional planning organization VISTAS (5). In the model results, organic mass (OM) is the sum of primary organic aerosols (POA) emissions and SOA. Condensable products are formed from the oxidation of 2 anthropogenic volatile organic compounds (VOC) and 7 biogenic VOC, including 5 monoterpenes, 1 sesquiterpene (28), and isoprene (29). The gas/particle partitioning of condensable products is modeled using laboratory-based partitioning coefficients that are corrected for temperature effects (30).
(
)
∆Hvap T exp (1/T - 1/Tref) Tref R Kp(T) Kp(Tref) ∆Hvap ln ) ln + (1/T - 1/Tref) (4) T Tref R
Kp(T) ) Kp(Tref) ×
( ) (
)(
)
where ∆Hvap is the enthalpy of vaporization and R is the universal gas constant (8.314 J/mol/K). Although ∆Hvap is technically defined for a single compound, it is used here to represent the temperature sensitivity of the partitioning of a mixture of SOA between the gas and particle phases, and assumed to be constant. A straight line regression on the plot of ln(Kp/T) vs 1/T provides a slope that can be used to estimate ∆Hvap of the condensable products as if they behave like a single compound (∆Hvap ) slope × R). An iterative procedure is used: (1) a value for SC is selected, (2) GC and
FIGURE 1. Median concentrations of OC and OCsec from SEARCH data in 2004 and OM and SOA from model results (top) and ln(K/T) vs 1/T during hours 16:00-22:00 (bottom) (measurements-derived vs model data) for Yorkville (YRK). 7288
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FIGURE 2. Diurnal profile for each day of the week at JST for (a) SEARCH OC or modeled OM, (b) SEARCH or modeled EC, and (c) SEARCH or modeled OC/EC. (Key: M in red, T in orange, W in yellow, R in green, F in turquoise, Sa in blue, and Su in purple). Kp are calculated, (3) a linear fit between ln(Kp/T) and 1/T and the coefficient of determination (r2) are determined. SC is adjusted to provide the maximum r2, and ∆Hvap is calculated from the slope of the best fit line. Measurements from 2003 and 2004 were used together to construct representative diurnal profiles for each day of the week. Concentration data for EC and OC are grouped by hour and day of the week, and the median concentrations are plotted in Figure 2. Diurnal profiles of the OC/EC ratio were generated by first calculating the ratio for each hour of each day, followed by the selection of the median ratio. The corresponding day-of-the-week diurnal profiles are also plotted for the model results to test the model’s representation of changes in ambient concentrations due to emission changes. A seasonal analysis is performed using OC data that are less susceptible to temporal variability. Large day-to-day fluctuations during 8:00-10:00 and 18:00-21:00 reflect the effects of sources at JST, especially when mixing is limited. Therefore, data from the period between noon and 5 p.m. (mean concentration comparable to standard deviation of daily values) are used. The same afternoon period is used at YRK for consistency. To analyze the conditions associated with high OC in the cold (November, December, January through April) and warm (May through October) seasons based on measurements, samples with OC concentrations in the top quintile (20th percentile) are compared with the entire data set. Using available hourly data from the two SEARCH sites, including PM, gas, and meteorology mea-
surements, variables are identified that differ between the high OC subset and the entire data set. The 2004 SEARCH data are presented. (Similar conclusions are drawn using the 2003 data.) The same approach is then applied to test the associations among model variables.
Spatial Variability Table 1 shows the estimated average OM and OCsec at JST and YRK. Similar results are obtained for 2003 and 2004, with SOA accounting for 38-40% of OM at JST and 71-72% of OM at YRK. OCsec (and SOA) is, therefore, more abundant at YRK than JST, both in absolute concentration and as a fraction of OC (or OM). On an annual average basis, the modeled SOA concentration is higher than that derived from measurements at JST despite a good agreement with the OM measurement. At YRK, both SOA and OM are lower in the model simulation compared to the measurements, but the fraction of SOA is well reproduced by the model. Over the course of the annual simulation, hourly OM concentrations at JST correlate with those at YRK with an r2 of 0.57, due to a regional distribution of SOA concentrations, which track with an r2 of 0.73. On the other hand, the measured hourly OC at YRK and at JST do not track each other well. The difference in correlation is consistent with the larger fraction of regionally distributed SOA estimated by the model at JST compared to that inferred from the measurements. Therefore, the model simulation likely suffers VOL. 42, NO. 19, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Conditions in Atlanta (JST) and Yorkville (YRK) Based on SEARCH Data and CMAQ-MADRID Simulation SEARCH (2004)
average average average average average average average average average
a
EC OCa OMa OCseca (% of OC) SOAa (% of OM) O3 (ppb) NOx (ppb) NOy (ppb) temperature (°C)
SEARCH (2003)
CMAQ (2002)
JST
YRK
JST
YRK
JST
YRK
1.35 4.28 5.70 1.42 (33) 2.27 (40) 22.3 37.7 39.1 16.7
0.54 2.93 5.07 1.73 (59) 3.63 (72) 35.6 4.52 6.02 16.6
1.47 4.50 5.97 1.42 (32) 2.27 (38) 22.1 43.4 43.8 16.8
0.67 3.14 5.43 1.84 (59) 3.87 (71) 33.8 5.43 7.29 15.8
1.78 s 5.84 s 3.10 (53) 19.6 42.7 45.0 16.7
0.43 s 3.83 s 2.73 (71) 34.4 6.92 9.09 16.3
a Units of EC, OM, and SOA are µg/m3; units for OC and OCsec are µgC/m3. Negative OCsec values are zeroed in the measurements. Factors of 1.2, 1.6, and 2.1 (31) are used to convert primary OC, urban OCsec, and rural OCsec to OM and SOA, respectively.
from a compensation of errors in the urban area with an overestimation of SOA and an underestimation of POA emissions. A significant spatial gradient is modeled for OM from JST to YRK due to a large decrease in POA (60%) and a smaller decrease in SOA (12%). Using the EC tracer method, the estimated OCprim decreased by 58% from JST to YRK, which is consistent with the modeled emission and dispersion processes. However, the smaller decrease in OM in the observations compared to the model would be consistent with OCsec increasing from JST to YRK. The differences between modeled and measured data in the average concentrations of ozone (O3), nitrogen oxides (NOx), and total oxidized nitrogen (NOy) are less than 15% at JST. The measured and modeled average O3 concentrations are similar at YRK, and NOx and NOy values no more different than at JST (Table 1). Therefore, the differences in the spatial (urban/ rural) variability for SOA between the SEARCH data and the model are unlikely to be due to differences in the oxidants and NOx and likely attributable to inaccuracies in the treatment of SOA in the model. SOA may be formed too quickly and too close to the source area in the models; whereas measurements are consistent with a more sustained, regional-scale process. CMAQ-MADRID, like many operational grade models, now treats SOA as first-generation products of VOC oxidation. Moreover, CMAQ-MADRID does not account for different NOx regimes in SOA formation and, therefore, cannot reproduce the change from a high-NOx regime in the urban area to a low-NOx regime in the rural area. This treatment may need revising to account for a more sustained production of SOA between urban and rural areas.
Diurnal Profiles Current air quality models fail to reproduce the measured diurnal profiles of OC. For JST, OCsec concentrations derived from measurements are in the range of 0.9-1.3 µg/m3 for the late afternoon/early evening hours (16:00-22:00). Assuming that during this time, OCsec formation is small and OCsec concentrations change mostly as a function of temperature, the slope of the fit of ln(Kp/T) vs 1/T for the JST data corresponds to a ∆Hvap value of 67 kJ/mol (Figure S1b in the Supporting Information). The optimal value for SC is 1.3 µg/m3. Therefore, a majority of the condensables resides in the particle phase (i.e., a low vapor pressure product). The median modeled OM and SOA diurnal profiles at JST are different from the median measured profiles (Figure S1a). A much larger diurnal range is apparent. Between 16:00 and 22:00, the model shows a marked increase of SOA concentrations, in contrast to a flat profile in the measurementderived OCsec profile. This increase is consistent with a temperature-sensitive Kp with a ∆Hvap of 115 kJ/mol (Figure 7290
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S1b). The value 115 kJ/mol for the mixture falls within the range of the ∆Hvap parameters of individual modeled compounds, which are between 72 and 175 kJ/mol. Therefore, the modeled ∆Hvap values may need to be reduced to better represent the measured diurnal profiles at JST. Figure 1 shows the median diurnal profile of OC measurements and derived OCsec (24) and OM and SOA for the model at YRK. A daytime OCsec minimum (at 12:00 or 13:00) is derived at YRK (but not at JST). Morning decrease in OCsec can be related to increasing temperature. If OCsec production takes place to generate a higher total condensable concentration, lower OCsec later in the morning implies that the new products may be quite volatile. For the evening data, the plots of ln (K/T) vs 1/T are subject to less scatter at YRK (Figure 1) than at JST. Linear fits are very good, and ∆Hvap is estimated to be 24 kJ/mol. Estimates of SC are larger at YRK than at JST, implying a more volatile product at YRK, with a smaller percentage of OCsec in the particle phase. Low values of ∆Hvap may be consistent with a mixture of compounds with a range of saturation vapor pressures (32). In CMAQ-MADRID, the same analysis results in a ∆Hvap of 81 kJ/mol for the modeled mixture of SOA at YRK. The measurements are consistent with a larger fraction of the condensables residing in the PM phase at JST than at YRK (more volatile SOA at YRK). In addition, ∆Hvap differs by a factor of more than 2.5 between JST and YRK (67 kJ/mol vs 24 kJ/mol), whereas the model predicts higher values for both locations, and the ∆Hvap differs between the two locations by only a factor of 1.3 (106 vs 81 kJ/mol). The smaller difference of the ∆Hvap between the two locations is consistent with the model predicting a more spatially homogeneous SOA composition than inferred from the ambient observations. A more diverse representation of SOA with a variety of precursor VOCs, multiple pathways, and generations of SOA compounds appears needed if the models are to correctly predict the response of SOA to temperature variations.
Day-of-the-Week Variability At JST, observed EC diurnal profiles are noticeably lower on weekends than on weekdays; however, observed OC diurnal profiles do not show a statistically significant dependence on the day of the week (Figure 2). There are three scenarios that can be consistent with a lack of change in OC concentrations. First, despite a significant decrease in EC, there is no corresponding change in OC emissions, and there is no change in OCsec formation. Second, there is a decrease in OCprim on weekends, and an associated increase in OCsec. Third, there is an increase in OCprim and a decrease in OCsec. The source mixtures (and hence the (OC/EC)comb ratios) are known to differ on weekdays vs weekends, resulting in a bias if the EC tracer method is used to estimate day-of-the-week-
specific OCsec concentrations (25). Ambient OC/EC ratios in the morning can reflect changes of the emission characteristics on weekdays vs weekends at an urban location, with the effects of OCsec minimized (but not completely eliminated) when the ambient OC/EC ratio is at its lowest in the diurnal cycle and dominated by emissions. The diurnal profiles of the median ratios are shown in Figure 2c. OC/EC ratios are significantly elevated at JST on Sundays and Saturdays compared to weekdays. For the lowest percentiles of OC/EC ratios on weekends and weekday mornings, an increase of 95 to 129% is observed on weekends. Consider the period between noon and 5 p.m. when both OC and EC concentrations are quite stable. Median EC concentration in JST is 0.93 µgC/m3 on weekdays and 0.52 µgC/m3 on weekends, and median OC is essentially unchanged between 3.09 µgC/ m3 and 3.12 µgC/m3. To a first order approximation, afternoon OCprim on weekends (1.27 µgC/m3) is slightly (∼10%) higher than the weekday values (1.12 µgC/m3) based on the increased (OC/EC)prim ratio, and the corresponding OCsec is slightly lower. The measurement data do not support a statistically significant increase in OCsec on weekends. However, the weekday-weekend signal is too small to conclude definitively if OCsec is lower or the same on weekends compared to weekdays. Conversely, the notion that OCprim does not change or is higher on weekends seems to be inconsistent with conventional wisdom that both OC and EC emissions decrease in urban areas on weekends due to anthropogenic activity patterns. For example, as represented in the current simulation, POC emissions are about 20% lower in the vicinity of Atlanta on weekends. The model results show that EC decreases on weekends compared to weekdays at JST, but the decreases from Saturdays to Sundays are smaller than observed (Figure 2b). Therefore, a previously reported day-of-the-week temporal allocation problem (33, 34) persists here. Compared to the measurements, the model shows a much stronger buildup of EC concentrations in the evening, with peak concentrations at 18:00 or 19:00. The erroneous evening peak may be due to misallocation of emissions or misrepresentation of mixing conditions. The simulated median afternoon concentrations of OM decrease by 20% on weekends relative to weekdays (Figure 2a) due to reductions in the emissions inputs. SOA concentrations are modeled to be low on Saturdays, and Sunday concentrations are comparable to weekdays. The modeled weekly behavior of SOA and POA may be related because POA enhances the absorption of SOA. In addition, weekday-weekend changes in NOx-VOC chemistry may also affect the production of SOA. As represented in the CMAQ simulation, local emissions near Atlanta are associated with a 50%-60% increase of the OC/EC ratio in emissions on weekends compared to weekdays. This is smaller than the ∼100% increase estimated using minimum morning ambient ratios. Although modeled ambient OC/EC concentration ratios are higher on Saturdays and Sundays than weekdays, the increase on weekends is smaller than the observations (Figure 2c). The reduced weekday-weekend variability in the model compared to the observations may be due to insufficient reductions in EC emissions on weekends or overstatement of OC emission reductions, or both. Measured EC in YRK is lowest on Sundays and Mondays, with no obvious commute peak in the morning. OC concentrations seem to be greatest on both Friday night/Saturday morning and Saturday night/Sunday morning. The morning ambient OC/EC ratio tends to be higher on Sundays than midweek, but the increase is much less than that observed at JST. Monday morning has the second highest OC/EC ratio, possibly as a delayed weekend signal attributed to transport from the urban area. Although the high Monday ratio seems to be driven primarily by low EC, the high Sunday ratio seems
to be result from relatively high OC and moderate EC concentrations (Figure S2b). Modeled EC shows a morning commute signal on weekdays at YRK and is lowest on Saturdays and Sundays. This may be another piece of evidence of the temporal allocation problem associated with both the day-of-the-week and diurnal profiles on rural highways. The model does not simulate an increase of OM on weekend during the predawn hours, but simulates correctly the relatively high OC/EC ratio on Sundays and early Monday mornings.
Seasonal Distribution of OC On average, afternoon OC is observed to be 20% higher in the warm season than the cold season (Table S1a). The top quintile afternoon OC at JST occurs throughout the year. Forty-four percent of the 382 data points occur in the cold months. The probability of a high OC data point (Nhigh/N) is 0.23 and 0.17 in the warm and cold season, respectively. The model predicts afternoon OM to be 40% higher in the cold season than the warm season at JST. As a result, 64% of the afternoon OM concentrations in the top quintile occur in the colder months, despite ∼70% of the high SOA periods occurring in the warm season. The probability of high OM in the warm season is 0.11 and close to 0.30 in the cold season. In comparison, the probability of a high SOA data point is 0.28 during the warm months and 0.12 during the cold months. Therefore, high afternoon OM concentrations in the model are not driven by high SOA concentrations, despite an annual average concentration of OM that consists of 53% SOA. The model is likely to be overestimating primary OM concentrations during afternoons in the cold season. A measurement gap at YRK results in lower data capture during the warm season than the cold season. Nonetheless, 52% of the periods with high OC occur during warm months, corresponding to a probability of 0.26. The probability of high OC periods in cold months is 0.16. The model results at YRK, with complete data capture, indicate that ∼66% of periods with the top quintile OC concentrations occur in the warm months. This is a much higher frequency than during the cold months. The probability of a high OM period is 0.26 in the warm season versus 0.14 in the cold season, which agrees very well with the observations. Based on SEARCH data, in both warm and cold seasons, high OC at JST is associated with higher PM, nitrate, ammonium, sulfate, EC, and TC, temperature, pressure, O3, CO, HNO3, NOy, total nitrate, and lower wind speed (and associated change in wind direction) (see Table S1). Therefore, conditions that are generally conducive to the accumulation of PM lead to high OC, and high POA is inferred based on high EC concentrations in the subset. In the warm season, high OC is associated with an extra 10 ppb of ozone, as opposed to 2 ppb in the cold season. High OC is also associated with lower NO and a statistically insignificant increase in NO2 in the warm season (as opposed to an increase in the cold season). Oxidant and NOx may play a role in the formation of SOA during periods with high OC in the warm season (35–37). For the model, high OM in the warm and cold seasons is associated with high pressure and pollutant concentrations as indicated by the measurements. In addition, modeled SO2 and NOx are also high, but O3 and temperature tend to be lower when OM is high in the model. Although SOA is high when OM concentrations are in the top quintile, low O3 and temperature support a scenario with reduced chemistry but partitioning toward the PM phase due to availability of OA. As discussed above, the model overestimates the effect of temperature on OM gas/particle partitioning. Also, the model may be missing a process that generates SOA under relatively VOL. 42, NO. 19, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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polluted conditions with higher temperature and oxidants (lower NO) at the urban site. For example, the potential effect of semivolatile organic emissions from mobile sources on urban SOA formation (10), which is not currently treated in most models, should be considered as model formulations are revised. When OC is high at YRK, the higher concentrations of gaseous pollutants seem to indicate a generally polluted air mass, and meteorological conditions (higher pressure, lower winds) suggest poor mixing conditions, possibly combined with upwind photochemical activity (no change in local solar radiation) as the culprit for the accumulation of pollutants. In particular, high CO and NOy suggest the influence of anthropogenic sources in the air mass containing high OCsec. During the cold months, the wind direction data suggest that high SOA at YRK corresponds to Atlanta being upwind. The model seems to reproduce the characteristic conditions of high SOA conditions better at YRK than at JST, resulting in a more accurate prediction of the seasonal distribution of high OC concentrations at the rural site (see Table S2).
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Implications This comparative analysis of ambient measurements and model results tests the model’s ability to reproduce key spatial and temporal patterns of OA and SOA and suggests that the following model improvements are needed. On average, the model understates urban OA emissions, and the weekdayweekend difference in the emissions of POA appears overstated; thus, better emission inventories of carbonaceous PM must be developed. Regional emissions, chemistry, and transport are well simulated but urban SOA processes are misrepresented in the model and must be improved; in particular, an urban warm-temperature process may be missing in the model representation of SOA. The ∆Hvap values for SOA in the model appear to be overestimated and more representative values need to be measured or estimated to improve the model’s ability to reproduce diurnal OA profiles. Finally, the model should represent multiple generations of SOA and varied reaction pathways as a function of chemical regimes in order to better reproduce urban-rural differences in SOA composition.
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Acknowledgments This work was sponsored by CRC under project A-60. We thank Mr. Brent Bailey and the Atmospheric Impacts Committee for their continuous support. Ambient data used in this work were obtained from the SEARCH database (http://www.atmospheric-research.com). Mr. Eric Edgerton and Mr. Rick Saylor provided helpful discussions regarding the data. The CMAQ-MADRID simulation was sponsored by Southern Company (J. Jansen, project manager) and conducted at AER by Mr. Krish Vijayaraghavan and Ms. Shu-Yun Chen.
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Supporting Information Available Figure S1 is similar to Figure 1 for both JST and YRK. Figure S2 is similar to Figure 2 for JST and YRK. Table S1 provides average conditions and conditions when OC is high at JST based on measurements and model data. Table S2 contains similar information for YRK. This material is available free of charge via the Internet at http://pubs.acs.org.
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Literature Cited (1) Seigneur, C.; Moran, M. Chapter 8: Chemical transport models. In Particulate Matter Science for Policy Makers: A NARSTO Assessment; McMurry, P. H., Shepherd, M. , Vickery, J., Eds.; Cambridge University Press: Cambridge, United Kingdom, 2004; pp 283-323. (2) Heald, C. L.; Jacob, D. J.; Park, R. J.; Russell, L.M.; Huebert, B. J. A large organic aerosol source in the free troposphere missing 7292
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