Impacts of Stabilized Criegee Intermediates, surface uptake processes and higher aromatic secondary organic aerosol yields on predicted PM2.5 concentrations in the Mexico City Metropolitan Zone

Atmospheric Environment 94 (2014) 438e447

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Impacts of Stabilized Criegee Intermediates, surface uptake processes and higher aromatic secondary organic aerosol yields on predicted PM2.5 concentrations in the Mexico City Metropolitan Zone ~ o b, Gang Chen a, Sajjad Ali a, Hongliang Zhang a, 1, Qi Ying a, *, Iris V. Curen c Meagan Malloy , Humberto A. Bravo b, Rodolfo Sosa b a

Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, 77843-3136, USA sfera, Seccio n de Contaminacio n Ambiental, Universidad Nacional Auto noma de M Centro de Ciencias de la Atmo exico, Ciudad Universitaria, Mexico DF, CP 04510, Mexico c Department of Civil Engineering, University of Missouri-Kansas City, Kansas City, MO, 64110, USA b

h i g h l i g h t s
The CMAQ model is applied to study PM2.5 concentration.
Stabilized Criegee Intermediates do not contribute significantly to near surface sulfate.
SO2 surface uptake process can be important pathway of sulfate formation.
Glyoxal, methylglyoxal and aromatics all significantly contribute to SOA formation in MCMZ.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 January 2014 Received in revised form 15 May 2014 Accepted 19 May 2014 Available online 20 May 2014

The Community Multiscale Air Quality Model (CMAQ) with the SAPRC-99 gas phase photochemical mechanism and the AERO5 aerosol module was applied to model gases and particulate matter (PM) concentrations in the Mexico City Metropolitan Zone (MCMZ) and the surrounding regions for March 2006 using the official 2006 emission inventories, along with emissions from biogenic sources, petl volcano. The base biomass burning, windblown dust, the Tula Industrial Complex and the Popocate case model was capable of reproducing the observed hourly concentrations of O3 and attaining CO, NO2 and NOx performance similar to previous modeling studies. Although the base case model performance of hourly PM2.5 and PM10 meets the model performance criteria, under-prediction of high PM2.5 concentrations in late morning indicates that secondary PM, such as sulfate and secondary organic aerosol (SOA), might be under-predicted. Several potential pathways to increase SOA and secondary sulfate were investigated, including Stabilized Criegee Intermediates (SCIs) from ozonolysis reactions of unsaturated hydrocarbons and their reactions with SO2, the reactive uptake processes of SO2, glyoxal and methylglyoxal on particle surface and higher SOA formation due to higher mass yields of aromatic SOA precursors. Averaging over the entire episode, the glyoxal and methylglyoxal reactive uptake and higher aromatics SOA yields contribute to ~0.9 mg m3 and ~1.25 mg m3 of SOA, respectively. Episode average SOA in the MCMZ reaches ~3 mg m3. The SCI pathway increases PM2.5 sulfate by 0.2e0.4 mg m3 or approximately 10e15%. The relative amount of sulfate increase due to SCI agrees with previous studies in summer eastern US. Surface SO2 uptake significantly increases sulfate concentration in MCMZ by 1e3 mg m3 or approximately 50e60%. The higher SOA and sulfate leads to improved PM2.5 and PM10 model performance. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Stabilized Criegee Intermediates Secondary sulfate Secondary organic aerosol Aromatic hydrocarbon Sulfur dioxide Mexico City

1. Introduction * Corresponding author. E-mail addresses: [email protected], [email protected] (Q. Ying). 1 Currently at the Department of Civil and Environmental Engineering, University of California, Davis, USA. http://dx.doi.org/10.1016/j.atmosenv.2014.05.056 1352-2310/© 2014 Elsevier Ltd. All rights reserved.

The Mexico City Metropolitan Zone (MCMZ) is situated on an elevated basin 2240 m above mean sea level. It has a population of around 20 million, around 4 million vehicles, and over 40,000

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industries that contribute to atmospheric pollution (Molina and Molina, 2002, 2004). The basin covers an area of approximately 5000 km2 that is constrained by mountain ridges on the east, west and south sides, which leads to unique meteorological conditions that affect formation and distribution of pollutants within the MCMZ basin. Large amounts of anthropogenic emissions often lead to elevated concentrations of ozone (O3) and airborne particulate matter (PM) under unfavorable meteorology conditions. To further improve the understanding of the emission, formation and physical and chemical properties of air pollutants in MCMZ and their long range transport downwind, an extensive field campaign MILAGRO 2006 has been carried out during March 2006 (Molina et al., 2010). A number of regional air quality models have been applied to study this heavily instrumented episode in recent years, including the offline CAMx (Karydis et al., 2011; Song et al., 2010; Tsimpidi et al., 2011) and the fully coupled WRF-Chem (Li et al., 2011a, 2011b; Ying et al., 2011; Zhang et al., 2009). Many aspects of the emission, chemical and physical properties, formation, chemical transformation and long range transport issues have been addressed in these modeling studies. Li et al. (2011b) used WRFChem and an official 2006 inventory to simulate secondary organic aerosol (SOAs) formation in MCMZ during March 24e29, 2006 using a traditional 2-product approach and a volatility basis set (VBS) approach. They found that the traditional 2-product approach could only predict approximately 20e25% of the observed SOA at urban and rural sites. The VBS approach led to approximately 2 times higher SOA predictions but still underpredicts SOA concentrations, especially during daytime. Tsimpidi et al. (2011) applied a CAMx model with a VBS coupled to the SAPRC-99 photochemical mechanism and found that average model predictions during March 4e30, 2006 at the urban, suburban and rural MILAGRO supersites (T0, T1 and T2; see Molina et al. (2010)) agree well with measurements. Higher SOA yields for aromatics under high-NOx conditions and increased organic aerosol from boundary conditions lead to higher predicted organic aerosol concentrations. Karydis et al. (2011) studied semi-volatile inorganic aerosol formation with CAMx during the same modeling period and found that high SO2 emissions from the Tula Industrial Complex were responsible for high sulfate concentrations in the MCMZ. Ying et al. (2011) found that including dust emissions improved PM2.5 and PM10 simulations during March 16e20, 2006 in MCMZ. Lei et al. (2013) studied the impact of biomass burning on March 10e14, and March 17e21, 2006 using WRF-Chem. Changes of ozone was found to be negligible but fraction of primary organic aerosol increased at the urban and suburban supersites and agreed better with aerosol mass spectrometer (AMS) measurements. In this study, we applied the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) system to study the formation of gaseous and particulate pollutants in MCMZ, with a particular focus on assessing the capability of the modeling system in predicting observed hourly concentrations at the RAMA surface monitoring sites. While previous studies mostly focused on comparing model predictions at the MILAGRO supersites, we compared model predictions with observations of hourly CO, SO2, NO2, NOx, O3, PM2.5, PM10 and PM2.5e10 concentrations at all surface RAMA stations with valid measurements. The detailed model performance analyses provided a more thorough evaluation of the emission inventories, and the capability of the model in reproducing the observed concentrations in MCMZ. In addition, sensitivity of PM2.5 sulfate, organic aerosol and mass due to new potential secondary aerosol formation pathways: Stabilized Criegee Intermediates (SCIs) with SO2 and surface reactive uptake of glyoxal, methylglyoxal and SO2 were studied. The impacts of higher SOA yields from aromatic precursors on SOA predictions using the traditional 2-product method in the CMAQ model were

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also evaluated. Although the CMAQ model has been applied in modeling air quality in northern Mexico near the US/Mexico border (Shi et al., 2009; Sierra et al., 2013), to the best of the authors' knowledge, this is the first time that the popular WRF/CMAQ modeling system was used to simulate this extensively studied period in MCMZ. 2. Model description The Community Multiscale Air Quality (CMAQ) model v5.01 with the SAPRC-99 photochemical mechanism (Carter, 2000) and the AERO5 aerosol module (Byun and Schere, 2006; Foley et al., 2010) was modified to include formation of secondary sulfate from SCIs and heterogeneous reactions of SO2 on particle surface, as well as formation of SOA from heterogeneous reactions of glyoxal and methylglyoxal. Gas phase reactions of O3 with unsaturated alkenes generate SCIs, which subsequently react with SO2 to generate gas phase H2SO4 that can partition into the existing particles or form new particles through nucleation. The ozonolysis reactions in SAPRC-99 were modified to include a single lumped SCI species. Other reaction products in these reactions were not modified. The SCIs yields of ethene (0.37), other alkenes (0.319), terpenes (0.21) and isoprene (0.22) were taken from the modified CB05 with SCIs as reported by Sarwar et al. (2013). Reaction rates of SCIs with SO2 (k ¼ 3.91  1011 cm3 molecule1 s1), H2O (k ¼ 1.97  1018) and NO2 (k ¼ 7  1012) were also taken from Sarwar et al. (2013). The importance of SCIs to sulfate formation in regional simulations is significantly affected by the choice of the SCIs þ H2O reaction rate constant (Li et al., 2013; Sarwar et al., 2013) as the water vapor competes with SO2 for the SCIs. The value used for this study represents a lower limit of the rate constant with water vapor and thus an upper limit of sulfate from SCIs. SO2 heterogeneous reaction on particle surface was modeled as a surface-controlled uptake process, following the treatment of Nopmongcol and Allen (2006) and Buzcu et al. (2006) as shown in Equation (1):

  ⅆCSO2 1 gSO2 vSO2 A CSO2 ¼ 4 ⅆt

(1)

where CSO2 is the gas phase SO2 concentration, A is the total aerosol surface area in the Aitken and accumulation mode (m2), gSO2 is the reactive uptake coefficient and vSO2 is the thermal velocity (m s1). Buzcu et al. (2006) recommend gSO2 on the order of 0.01 for heterogeneous reactions occur on wood smoke particles. For this study, as a conservative estimate, 5  103 was used. As the current CMAQ model application does not explicitly simulate particles from different sources, the overall gSO2 is scaled by the faction of elemental carbon (EC) (fEC) in PM2.5, gSO2 ¼ fEC0.05  103. The increase of aerosol sulfate concentration is calculated based on conservation of sulfur in the process. SOA from glyoxal and methylglyoxal during daytime was modeled as a surface-controlled uptake process using an equation similar to that shown in Equation (1), following the treatment in Fu et al. (2008). The uptake coefficients for glyoxal and methylglyoxal were taken as 2.0  102 in this study, as suggested by Ervens and Volkamer (2010). Ervens and Volkamer also suggested that experimental data for SOA formation from glyoxal and methylglyoxal under dark condition can be better fitted with a first-order, surface area independent reaction, as shown in Equation (2):

ⅆCglyoxal ¼ keff;glyoxal Cglyoxal ⅆt

(2)

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In this study, the effective rate constant (keff) for glyoxal and methylglyoxal at nighttime was taken as 3.33  104 s1, as reported by Ervens and Volkamer (2010). In the original CMAQ model, toluene SOA mass yields under high-NOx conditions are parameterized based on the yields determined by Ng et al. (2007) (see Table 1). However, a recent study showed that toluene SOA mass yields were significantly higher than previously thought (Hildebrandt et al., 2009). We fitted the two-product model parameters for toluene SOA under highNOx conditions using the four-bin volatility basis set data in Table 2 of Hildebrandt et al. (2009). The original and revised toluene highNOx two-product parameters are listed in Table 1. Fig. 1 shows the aerosol yield curves for the original parameterization and the new parameterization. The new toluene yield is approximately 1.4e3.4, and 1.9e5.3 times higher than the original toluene and xylene parameterizations, respectively.

Table 2 Performance statistics of WRF meteorological predictions.

Mean Obs. Mean Pred. MB GE RMSE

T2 ( C)

WSPD10 (m s1)

WDIR10 (deg)

RH2 (%)

18.35 18.53 0.18 (±0.5) 1.38 (2.0) 1.80

2.69 3.59 0.90 (±0.5) 1.48 1.93 (2.0)

202.17 187.45 8.41 (±10) 66.51 (30) 82.68

36.14 36.33 0.19 9.65 13.29

Benchmarks suggested by Emery et al. (2001) are listed in the parenthesis. T2: temperature at 2 m; WSPD10: wind speed at 10 m; WDIR10: wind direction at 10 m; RH2: relative humidity at 2 m.

3. Model application The modified CMAQ model was applied to a one-month episode during March 2e31, 2006. The MCMZ and surrounding regions are covered in a domain with 70  70 3-km spatial resolution grid cells. Eighteen stretching vertical layers with a first layer height of 35 m reach to a model top of approximately 20 km above ground level. Fig. 2 shows the surface elevation of the domain and the locations of the surface PM2.5 and PM10 monitoring stations in MCMZ. The hourly boundary conditions and initial condition fields for the CMAQ simulation are generated from the GEOS-Chem model for the nested North America domain with 0.5  0.666 horizontal grid resolution (http://acmg.seas.harvard.edu/geos/). The boundary conditions for organic aerosol were set to 8, 11.5, 7 and 5 mg m3 in the west, east, south and north boundaries, respectively, based on the values used by Tsimpidi et al. (2011). These were injected into the model as primary organic aerosol (POA) but they were more likely a mixture of aged non-volatile and low volatile oxygenated organic aerosols (OOA). The meteorology conditions during this episode were generated using the Weather Research and Forecasting (WRF) model v3.1. A 2-level nested domains with Lambert conformal projection and horizontal grid sizes of 9-km and 3-km were chosen to run the WRF model. The initial and boundary conditions for the WRF simulations were prepared using the 32-km resolution North American Regional Reanalysis (NARR) data set (available from: http://rda.ucar.edu/datasets/ds608.0/). Hourly emissions of gaseous and particulate matter for the MCMZ in a representative weekday in March were provided by Mexico City's Secretary of Environment based on the most recent updates of the 2006 emission inventory. The emissions reported in the 2006 emission inventory for point, mobile and area sources were generated by the Processing System of Atmospheric Emissions program (SPEA) version 1.0.0. (Ortiz, 2005), which produces temporally and spatially allocated emissions for all the criteria pollutants. Due to the changes of human and industrial activities in weekends and holidays, adjustment of emissions was needed when running a one-month long episode. The emissions data for

Table 1 Original and revised condensation concentration (C*, mg m3) and aerosol mass yield (a) of toluene under high-NOx conditions for use with the classical two-product model. Product #

C* (original)

C* (revised)

a (original)

a (revised)

1 2

2.326 21.277

8.024 119.3116

0.058 0.113

0.2252 0.6746

Fig. 1. Aerosol mass yield based on the original toluene and xylene parameterization in CMAQ and the new higher toluene yield based on Hildebrandt et al. (2009).

weekends and holidays were modified based on the typical weekday emissions using a uniform scaling factor while the same spatial and temporal distributions were kept unchanged. Based on the information from various publications in MCMZ, anthropogenic emissions are scaled by 0.85 for Saturday, 0.75 for Sunday, and 0.9 for Holiday (March 21 in this study) (de Foy et al., 2007; Lei et al., 2008; Zhang et al., 2009). Emissions of SO2 were scaled up by a factor of 5 to improve model performance of SO2 on low SO2 days. NH3 emission was not modified because it was mainly related with population density instead of human activities. The day-specific rez Ríos theremission rates of NOx and SO2 from the Francisco Pe moelectric power plant, located in the Tula Industrial Complex, Hidalgo State, were based on the actual fuel oil consumptions records and sulfur content of the fuel oil used by the power plant acquired directly from the plant along with other stack parameters such as stack height and stack diameter. Emissions from the point sources outside of the MCMZ were included from the 1999 Mexico National Emissions Inventory (MNEI). Area and mobile sources outside the MCMZ were not included in the current model emission inventory. Windblown dust emissions in the entire domain were predicted using an in-house offline model based on parameterizations in Choi and Fernando (2008) and Shaw et al. (2008). The in-house model has been applied to generate windblown dust emissions for an air quality modeling study in China (Zhang et al., 2012). Biogenic emissions were generated using the Biogenic Emissions Inventory System, Version 3 (BEIS3) included in the SMOKE distribution using the 1-km resolution BELD3 vegetation data. SO2 emissions from the petl volcano were determined to be significant, active Popocate with an average emission rate of 2.45 Gg day1, based on optical remote sensing during the study period (Grutter et al., 2008). In this study, day-specific SO2 emissions reported by Grutter et al. (2008) were used. Open biomass burning emissions were based on the satellite-based Fire INventory from NCAR (FINN) (Wiedinmyer et al., 2011).

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Fig. 2. (a) Relative location of MCMZ in Mexico, (b) Surface elevation (m) of the modeling domain and (c) locations of PM10 (C) and PM2.5 (O) monitoring sites.

4. Results and discussion 4.1. Base case results Observations from 12 surface monitoring stations were compared with WRF predictions extracted from the grid cells where they were located. The mean observations and predictions, and model performance statistics measure of mean bias (MB), gross error (GE) and the root mean square error (RMSE) were calculated and shown in Table 2. Definitions of these statistical measures are listed in Supplementary Materials. Benchmarks suggested by Emery et al. (2001) based on performance statistics of MM5 form a number of studies over the continental United States are listed in Table 2. WRF predicted temperatures agree well with observations. Over-prediction of wind speed is obvious with a MB value of 0.9, which is consistent with another study that reported similar amount of over-predictions (Zhang et al., 2009). The relatively small GE and RMSE for the wind speed suggest that the wind speed is consistently over-predicted throughout the episode. For wind direction, MB is within the suggested performance criteria but GE is higher, indicating compensating errors in wind direction predictions. In this study, predictions with wind speed greater than 0.5 m s1 were included in the model performance analysis. As the wind direction is poorly defined for slow wind speed, Zhang et al. (2009) reported that model performance statistics for wind direction improved after applying a cutoff wind speed of 2 m s1. Observed and relative humidity are both low (~40%). In general, the WRF model predicted meteorology conditions agree reasonably well with observations during the March 2006 period. Table 3 lists the base case model performance statistics for O3, NO2, NOx, CO, SO2, PM2.5, PM10 and PM2.5e10 based on all available 1-h average surface observation data. The base case simulation used the original CMAQ model without any modifications. It was intended to provide a baseline model performance for comparison with additional simulations with the aforementioned modifications. The definitions of these statistical measures are listed in the Supplementary Materials. The mean normalized bias (MNB) and normalized gross error (NGE) values for O3 are 0.9% and 26.8%, within the US EPA recommended model performance criteria of MNB  ±15% and NGE  30%. Fig. S1 shows the complete time series of O3 at all stations. The day-to-day variation of the O3 concentration is well captured. Averaging over all stations, the accuracy of paired peak (APP) and accuracy of unpaired peak (AUP) are 11% and 4%, indicating that the peak values are generally well captured. For reference, the US EPA suggests AUP be ±20%. For other gas phase species, EPA does not have recommended performance criteria, and the performance statistics are comparable with

Table 3 Base case CMAQ model performance. Avg. Avg. MNB Obs.a Pred.

MNE

NMB

88 87 0.9% 26.8% 1.5% O3 NOx 63 60 8.7% 72.2% 5.0% NO2 34 31 1.2% 53.2% 7.5% CO 1287 1316 25.0% 82.0% 2.2% SO2 13 12 17.3% 83.2% 10.8% PM2.5 34 28 3.1% 42.8% 17.6% PM10 76 66 16.3% 59.8% 13.4% PM2.5e10 55 42 14.1% 70.0% 24.5%

NME

MFB

MFE

np

26.0% 64.4% 48.6% 68.5% 76.2% 42.5% 52.7% 63.1%

5.1% 27.2% 23.0% 17.4% 26.9% 17.1% 8.7% 18.2%

26.3% 1792 67.6% 8380 56.9% 8327 64.9% 12,868 76.8% 3469 44.8% 4302 50.8% 9060 63.5% 1455

a For gas phase species, observed and predicted concentrations are in ppb; for PM2.5, PM10 and PM2.5e10, concentrations are in units of mg m3. Observed PM2.5e10 concentrations are calculated by subtracting PM2.5 from PM10 observations. np: number of data points. Cut-off concentration for O3 is 60 pp; for NOx, NO2 and SO2 is 5 ppb; for CO is 100 ppb; for PM2.5, PM10 and PM2.5e10 is 10 mg m3.

Fig. 3. Predicted and observed (dots) hourly PM2.5 concentrations, and predicted PM2.5 components based on the base case results. Both predictions and observations are averaged concentrations during March 3e30, 2006 at seven PM2.5 observation stations. Units are mg m3. Error bars represent one standard deviation around the mean.

reported values in the literature. The overall average concentrations for CO, NO2, NOx and SO2 all agree well with observations. The MNB values for CO, NOx and SO2 are 25%, 9% and 17%, which are similar to CMAQ model performance of these three species in the US (Zhang et al., 2014). The normalized mean bias (NMB) values for CO, NO2 and NOx are 2%, 8% and 5%, which are similar to the values 8%, 2% and 10% reported in Zhang et al. (2009) for these three species. Complete time series of CO, NO2, NOx and SO2 are included in the Supplementary Materials as Figs. S2eS5. The mean fractional bias (MFB) for PM2.5, PM10 and PM2.5e10 are 17%, 9% and 18%, respectively. This is well within the ±60%

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Fig. 4. Predicted hourly base case (a,c) and modified case (b,d) SOA concentrations at surface PM2.5 monitoring sites (a,b) and at the urban supersite T0 (c,d). Units are mg m3. The modified cases includes reactive surface uptake of glyoxal and methylglyoxal and uses higher SOA mass yields for toluene and xylene. Dots represent hourly HOA þ OOA concentrations measured by AMS. Error bars are one standard deviation around the mean.

performance criteria suggested by Boylan and Russell (2006), although both PM2.5 and PM2.5e10 are under-predicted. Mean fractional error (MFE) for PM2.5, PM10 and PM2.5e10 are 45%, 51% and 64%, respectively, which are all within the proposed model performance criteria of 75% suggested by Boylan and Russell (2006). Complete time series of PM2.5, PM10 and PM2.5e10 can be found in Figs. S6eS8 in the Supplementary Materials. Fig. 3 shows the episode-averaged diurnal variation of observed and predicted PM2.5 concentrations averaged for the seven PM2.5 monitoring stations as well as the predicted PM2.5 chemical compositions. While the predicted concentrations agree well with observations at night and in early morning hours, PM2.5 concentrations are significantly under-predicted with largest difference occur at noon and early afternoon. This suggests that the missing PM2.5 is likely photochemically produced secondary aerosol such as ammonium sulfate, nitrate and SOA. This analysis led us to investigate additional model enhancements to improve the predictions of sulfate and SOA in the following section. As explained in Section 3, high POA concentrations of ~10 mg m3 is mostly due to low volatile oxygenated organic aerosol enters the domain through boundary conditions (Tsimpidi et al., 2011). Model performance for coarse PM (PM2.5e10) has not been reported often and the cause of the under-prediction is less studied. Underprediction of dust emissions is likely due to uncertainties in the predicted meteorology, land use/land cover and dust emission factors. Errors due to gas-to-particle conversion to coarse particles may also need more studies in the future. 4.2. Updated model results 4.2.1. Impacts on SOA In addition to the base case simulation, another simulation (high SOA yield case) was conducted using the revised model with glyoxal and methylglyoxal surface uptake process and higher mass yields of toluene and xylene, as discussed in Section 2. Fig. 4 shows

the predicted hourly POA þ SOA concentrations for the base case and high SOA yield case averaged at the seven PM2.5 monitoring sites and at the urban supersite T0 (19.489N, 99.146W). The base case model predicted an average concentration of approximately 1 mg m3 of SOA during daytime hours (Fig. 4(a)). In addition to contributions from toluene and xylene, SOA from long chain alkanes (ALK5) accounts for ~50% of the predicted SOA. Concentrations at night were low and mostly due to isoprene, terpenes, and oligomers. Predicted SOA concentrations are higher from the high SOA yield case (Fig. 4(b)) with a daytime concentration of approximately 4 mg m3 and a rapid increase of SOA in the early morning hours between 0600 and 0900 local time. Half of the SOA is due to toluene and xylene combined and about 40e50% of SOA is due to glyoxal and methylglyoxal. The predicted SOA remains high during the day and gradually decreases after 1600 local time. The predicted SOA concentrations at the supersite T0 are approximately 0.5e1 mg m3 higher than the corresponding sevencity average concentrations and show similar diurnal variations. The predicted POA þ SOA concentrations from the high SOA yield case (Fig. 4(d)) agree much better with the AMS measurements of hydrocarbon-like organic aerosol (HOA)þoxygenated organic aerosol (OOA) than the base case results. Direct comparison of OOA with SOA, and POA with HOA is not carried out as the simulated POA also includes contributions from low volatile oxygenated organic aerosol that enters the domain through boundary conditions (Tsimpidi et al., 2011), as discussed in the previous section. The higher SOA yield case leads to improved model performance of POA þ SOA at T0. The MFB and MFE based on episode-averaged hourly concentrations are 0.07 and 0.13, respectively. This is better than the base case model MFB and MFE, which are 0.17 and 0.23, respectively. Fig. 5 shows the regional distribution of episode average and peak hour SOA and major component concentrations. Episodeaverage SOA concentration is highest (~3 mg m3) in the urban Mexico City area. Outside the urban area, episode average decreases

Q. Ying et al. / Atmospheric Environment 94 (2014) 438e447

to ~1 mg m3. Toluene, xylene, long chain alkane and glyoxal and methylglyoxal SOA all have similar spatial distribution with highest concentrations (0.35, 0.9, 0.2 and 0.9 mg m3, respectively) in the urban area. Isoprene and terpenes (including monoterpenes and sesquiterpenes) SOA concentration is low and has a relatively uniform regional distribution of 0.05e0.1 mg m3. Slightly higher concentrations are predicted in urban and industrial areas, due to interactions of biogenic emissions with urban emissions. Predicted average SOA concentrations at peak hour (1000e1100 local time) have similar spatial distributions as the episode average concentrations. Overall SOA concentration in the urban area is approximately 7 mg m3, with ~0.9 mg m3 from toluene, ~2.5 mg m3 from xylene, 0.45 mg m3 from long chain alkane, 3 mg m3 from glyoxal and methylglyoxal and 0.3 mg m3 from isoprene and terpenes. The concentration of glyoxal in the domain (peak concentration ~0.8 ppb; average concentrations ~0.2 ppb) and its contribution to

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modeled SOA (~17%) are in general agreement with measurements and box model analysis of SOA formation for MCMZ on April 9, 2003 (Volkamer et al., 2006, 2007). Concentration gradient of SOA is quite sharp during peak hour. Overall SOA concentration drops to ~2 mg m3 only 21e24 km away from the highest concentration location. This implies that direct comparison of observed SOA concentrations in the urban Mexico City area with predictions at the grid cells where the monitors are located might not really reflect the capability of the SOA module, as the spatial distribution can be affected by imperfect meteorology fields. 4.2.2. Impacts on secondary inorganic aerosol Two additional simulations were conducted in addition to the base case, and both simulations include SOA updates discussed in the previous section. In the first case, only SO2 surface uptake was

Fig. 5. Predicted regional distribution of (aef) episode average and (gel) peak time (1000e1100 local time) SOA and major SOA components: total SOA (a,g); toluene SOA (b,h); xylene SOA (c,i); long chain alkane (d,j); glyoxal and methylglyoxal SOA (e,k); and isoprene and terpene SOA (f,l).

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enabled. In the second simulation (the updated case), both surface uptake and SCI reactions were enabled. Fig. 6 shows the predicted PM2.5 sulfate concentrations for the base case and the two additional simulations averaged at the seven urban sites. The base case sulfate peaks at 0800e0900 local time with a concentration of ~4 mg m3 and show relatively small diurnal variations with a daily average concentration of 2.9 mg m3. Adding SO2 surface reactions significantly increases sulfate concentrations at the sites in early morning hours by as much as 6.5 mg m3. The effect of surface uptake on sulfate diminishes in the afternoon and evening due to a change in the predominant wind direction, leading to a daily average increase of ~1.9 mg m3. Including SCI reactions lead to an additional increase in sulfate concentration by as much as 0.7 mg m3. Averaging over the entire day, SCI reactions leads to an increase of sulfate by 0.29 mg m3, which is approximately 10% of the base case sulfate concentrations. Predicted PM2.5 sulfate at the T0 supersite, along with observed sulfate from AMS are also shown in Fig. 6. The predicted concentrations are very similar to the seven-city average. It has been reported that AMS only accounts for 75% of the sulfate comparing with the filter-based measurements (Bae et al., 2007; Drewnick et al., 2003). Thus, the measured PM1 sulfate by AMS at the urban supersite T0 are increased by a factor of 1.33 for a more reasonable comparison with the model predicted PM2.5 sulfate concentrations. The predicted episode average sulfate at T0 from the updated case is 5.3 mg m3, which is closer to the adjusted observation of 4.9 mg m3, than the base case concentration of 2.9 mg m3. The performance statistics shows better overall performance of the updated case (MFB ¼ 0.2%, MFE ¼ 24.7%) than the base case (MFB ¼ 49.6%, MFE ¼ 49.6%). However, the updated case seems to over-predict daytime sulfate but under-predict nighttime sulfate concentrations. This suggests

Fig. 6. Predicted hourly PM2.5 sulfate concentrations (a) averaged at seven urban sites and (b) at the T0 supersite based on three simulations: 1) base case, 2) base case with surface uptake (surface uptake) and 3) based case with surface uptake and SCI reactions (SCI). Dots represent hourly sulfate concentrations measured by AMS. Error bars are one standard deviation around the mean. See text for details of the adjustment of AMS sulfate concentrations. Units are mg m3.

that additional nighttime sulfate formation pathways might be missing and the uptake coefficient of SO2 used in this study might be overestimated. Fig. 7(a) and (b) show the episode average increase of regional PM2.5 sulfate due to surface uptake and SCI reactions, respectively. The largest change in the PM2.5 sulfate due to surface uptake occurs near the SO2 source region in Tula. In the urban Mexico City area, highest increase due to surface uptake is approximately 3 mg m3 which occurs in a few grid cells. Most of the MCMZ has an increase of 1e2 mg m3. The SCI reactions lead to an episode average increase of sulfate by 0.2e0.4 mg m3, which corresponds to a relative increase of 10e15%. This relative amount of increase due to SCI agrees with recent studies of SCI for the summer eastern United States (Li et al., 2013; Sarwar et al., 2013). The unsaturated hydrocarbons responsible for SCI generation in the studies in the eastern US are dominantly biogenic but the dominant sources of unsaturated hydrocarbons in the MCMZ are anthropogenic alkenes. This study suggests that the impacts of SCI on sulfate is likely limited in magnitude regardless of whether it is in polluted urban settings or in areas with influence from both biogenic and anthropogenic sources. Fig. 8 shows the regional distribution PM2.5 sulfate, nitrate and ammonium ion for the updated case with both surface uptake and SCI reactions of SO2. Sulfate concentration is highest near the SO2 source region of Tula. A transport pattern of sulfate between the MCMZ and the Tula Industrial Complex can be clearly seen. Sulfate petl volcano but it concentration is also high near the Popocate seems to less affect the MCMZ region. Sulfate concentrations in the urban area of MCMZ are approximately 3e6 mg m3. Nitrate is highest in the urban MCMZ area with a concentration of 4 mg m3. Ammonium ion has similar spatial patterns as nitrate, with a highest concentration of 3.5 mg m3. 4.2.3. Overall performance of the revised model The higher SOA predictions due to increased yields of aromatic compounds and higher sulfate, mostly due to reactive surface uptake leads to improvements in PM2.5 and PM10 predictions. The MNB of PM2.5 and PM10 in the updated case decrease to 7.5% and 3.8%, respectively, from the base case values of 17.1% and 8.1% for PM2.5 and PM10. The MNE of PM2.5 and PM10 are 45.4% and 49.8%, which are similar to the base case values. Fig. 9 shows the episode-averaged diurnal variation of observed and predicted PM2.5 concentrations averaged for the seven PM2.5 monitoring stations as well as the predicted PM2.5 chemical compositions based on the updated case. The predicted peak concentration of 51 mg m3 is close to the observed peak concentration of 53 mg m3 and show similar diurnal variations. However, there appears to be a two-hour difference in the timing of the predicted and observed peak concentrations, which occurred at 0700e0900 and 1000e1100 local time, respectively. As discussed in the previous section, over-prediction of the wind speed (as shown in Table 2) might the cause Tula emissions to reach MCMZ at an earlier time on average. An improved WRF simulation specific to the MCMZ region that better predicts the wind speed may lead to improved predictions of PM2.5. Fig. 10 shows the complete time series of PM2.5 during March 3e31, 2006 at seven stations for the updated case. The predicted PM2.5 time series generally reproduce the observed PM2.5 time series in day-to-day variations, and as well as most of the peak concentrations. The base case simulation is capable of reproduce the general trend of PM2.5 but misses some of the peaks in the observations (Fig. S6). However, the updated case does over predict sulfate concentration, especially during March 5e7, 2006 in MER, COY and CAM. Analyses of the model results on these days indicate that there were significant amount of sulfate transported from the

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Fig. 7. Predicted regional episode average (a) increase of PM2.5 sulfate due to reactive surface uptake, (b) increase of PM2.5 sulfate due to Stabilized Criegee Intermediates reactions, (c) decrease of PM2.5 nitrate due to increase of PM2.5 sulfate, and (d) increase of PM2.5 ammonium ion due to increase of PM2.5 sulfate. Units are mg m3.

Fig. 8. Predicted regional distribution of episode average PM2.5 (a) sulfate, (b) nitrate and (c) ammonium ion concentrations based on the simulation that includes both SO2 surface uptake and Stabilized Criegee Intermediates reactions. The palette used in panel (a) is different from others to better illustrate the influence the influence of Tule emissions on MCMZ.

Tula Industrial Complex, indicating that SO2 emissions from Tula on these days might be over-estimated in this study. SOA productions are likely underestimated in this study as well. However, based on the AMS-OOA measurement at T0, the episode average OOA concentrations were relatively constant during the day, which could not fully explain the late morning peak in the observed average PM2.5 concentrations. It can be concluded from this study that a combination of SOA and secondary inorganics, such as nitrate, would be needed to explain the observed peak. 5. Conclusions

Fig. 9. Predicted and observed (dots) hourly PM2.5 concentrations, and predicted PM2.5 components based on the simulation that includes all updates in the SOA and sulfate formation pathways. Both predictions and observations are averaged concentrations during March 3e30, 2006 at seven PM2.5 observation stations. Units are mg m3. Error bars represent one standard deviation around the mean.

Hourly concentrations of O3 (MNB ¼ 3.5%, NGE ¼ 25.6 and AUP ¼ 3.8%), PM2.5 (MFB ¼ 30% and MFE ¼ 55%) and PM10 (MFB ¼ 23% and MFE ¼ 58.4%) measured at the surface monitoring sites in MCMZ were successfully reproduced by the based case WRF/CMAQ simulation using the official 2006 MCMZ emission inventory along with gaseous and particulate emissions from the

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Fig. 10. Predicted and observed PM2.5 time series based on the simulation that includes all updates in the SOA and sulfate formation pathways. Shaded area shows the range (minimum and maximum) of predictions based on 9 grid cells centered on the monitor location. Units are mg m3.

Tula Industrial Complex, windblown dust and SO2 emissions from petl volcano. Although PM2.5 met the model perforthe Popocate mance criteria, significant under-predictions of hourly PM2.5 concentrations as indicated by large MFB values were most likely caused by under estimation of secondary PM when observed PM2.5 concentrations increased rapidly within a couple of hours during late morning. The base case CMAQ model was modified to include several potential pathways to increase SOA and secondary sulfate, including SCIs from ozonolysis reactions of unsaturated hydrocarbons and their reactions with SO2, the surface reactive uptake processes of SO2, glyoxal and methylglyoxal and higher mass yields of aromatic SOA precursors. The updated model improved the PM2.5 and PM10 model performance significantly (MFB ¼ 8% for PM2.5 and MFB ¼ 4% for PM10) although the peak concentration predicted at the monitoring sites occurred ~2 h earlier, which is likely due to overestimation of wind speed in urban areas

by the WRF model. Averaging over the entire episode, the glyoxal and methylglyoxal reactive uptake contributes to ~0.9 mg m3 and higher aromatics yields contributions to ~1.25 mg m3 of SOA, respectively. Episode average SOA in the MCMZ reaches ~3 mg m3. The SCI pathway increases PM2.5 sulfate by 0.2e0.4 mg m3 or approximately 10e15%. The relative amount of sulfate increase due to SCI agrees with previous studies in summer eastern US. Surface SO2 uptake significantly increases sulfate concentration in MCMZ by ~1e3 mg m3 or approximately 50e60%. The results from this study indicate that surface reactive uptake of SO2 could be important source of aerosol sulfate in urban areas. While the newly included processes appear promising in improving PM predictions, the uncertainties of the needed parameters such as the uptake coefficients have not been explored in detail in this study. In addition, more applications of these processes in other studies are needed to test whether they are universally applicable or not.

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