1

Regional sources of atmospheric formaldehyde and acetaldehyde, and implications for 1 atmospheric modeling 2 3 D.J. Luecken*, W.T. Hutzell, M.Strum, and G.Pouliot 4 5 U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC, 6 27709 7

* corresponding author: Mail Drop E243-03, Tel: +1-919-541-02544, Fax: +1-919-685-3236 8 Email address: luecken.deborah@epa.gov 9 10 Abstract 11 Formaldehyde and acetaldehyde concentrations over the Eastern half of the United States are 12 simulated with a 3-D air quality model to identify the most important chemical precursors under 13 January and July conditions. We find that both aldehydes primarily result from photochemical 14 production, although 25% or more result from direct emissions in urban areas during winter. 15 Isoprene is the major precursor of formaldehyde in most areas during summer, contributing 20- 16 60% of total production, with the magnitude being spatially variable. Other alkenes from 17 anthropogenic and/or biogenic emissions dominate formaldehyde production in winter, 18 contributing 60-85% of total formation, and are prominent contributors in summer. Alkenes, 19 including biogenic alkenes, dominate acetaldehyde production during both seasons. These 20 conclusions are based on the degradation of emitted VOCs described by the SAPRC07TB 21 chemical mechanism, but even this detailed model has difficulty reproducing observed values 22 better than a factor of 2. The substantial role of isoprene and other alkenes in aldehyde 23 formation emphasizes that we examine and improve emission estimates of these compounds. 24 Until we can estimate the emissions and understand the chemistry of VOC precursors to 25 aldehyde formation with greater certainty, it will be difficult to accurately predict atmospheric 26 concentrations of aldehydes and develop strategies to reduce their concentrations. 27
28 Keywords: Formaldehyde, acetaldehyde, HAPs, photochemical production, isoprene 29 30 1.0 Introduction 31 32 Formaldehyde (HCHO) and acetaldehyde (CH 3 CHO) play important roles in atmospheric 33 photochemistry and air quality. They are a large component of the total VOC reactivity of the 34 atmosphere, providing sources of new radicals that drive ozone formation (Jeffries, 1995; Steiner 35 Manuscript Click here to download Manuscript: SAPRC07_aldehydes_930_noen.docx Click here to view linked References2

et al., 2008; Xie et al., 2011a). They are hazardous air pollutants (HAPs) known or suspected as 36 carcinogens posing health risks (HEI, 2007; Tilgner and Herrmann, 2010; US EPA, 2011). They 37 participate in aqueous chemistry due to their high solubility and interactions with other dissolved 38 species in clouds (Adewuyi et al., 1984; Tilgner and Herrmann, 2010; Warneck, 1989), and 39 contribute to secondary organic aerosols by producing radicals that oxidize hydrocarbons. The 40 chemistry of these two compounds is an indicator of our overall understanding of atmospheric 41 photochemistry. A better understanding of aldehyde production and decay will enable us to 42 develop and analyze policies that consider the effects of air quality measures on a number of 43 different atmospheric pollutants. 44

Both compounds are emitted directly into the atmosphere from a variety of sources. 45 Anthropogenic emissions of HCHO and CH 3 CHO are ubiquitous, concentrated in urban areas, 46 and are among the 25 highest-emitted anthropogenic VOCs in the US (Simon et al., 2010). 47 Mobile sources and stationary combustion dominate anthropogenic sources. Biogenic sources 48 include live and decaying plants, biomass burning, and seawater. 49

Another source of HCHO and CH 3 CHO is secondary formation from degradation of 50 VOCs (Atkinson, 1997; Seinfeld and Pandis, 1998), produced in multiple steps during 51 hydrocarbon oxidation. Equations 1a through 1c show a simple example of HCHO formation 52 from methane (CH 4 ). Hydrogen abstraction from CH 4 , followed by fast addition of oxygen to the 53 alkyl radical (1a) creates a methyl peroxy radical which can oxidize NO to NO 2 (1b), creating a 54 methyl alkoxy radical and eventually leading to ozone formation. A hydrogen atom from the 55 alkoxy radical is abstracted during the reaction with oxygen (1c) along with elimination of HO 2 56 to form formaldehyde: 57

CH 4 + OH + O 2 à CH 3 O 2 l + H 2 O (1a) 58

CH 3 O 2 l + NO à CH 3 O l + NO 2 (1b) 59

CH 3 O l + O 2 à HCHO + HO 2 (1c) 60

Aldehydes can also be formed from alkoxy radicals after scission of a C-C bond, through 61 photolysis, and through alkoxy radical formation in the absence of nitrogen oxides (NO x ), when 62 reactions of the peroxy radical with HO 2 and other peroxy radicals dominate. 633

Because HCHO and CH 3 CHO sources include direct emissions and photochemical 64 production, both sources must be correctly characterized when predicting atmospheric 65 concentrations. The importance of emissions versus photochemistry depends on location, time of 66 day and season, and affects any conclusions about how emission changes might be expected to 67 change aldehydes. An earlier modeling study (Luecken et al., 2006) determined that over 80% of 68 formaldehyde and acetaldehyde was due to secondary production. In urban areas in winter, 69 emissions contributed up to 50%. Statistical techniques have been used with measurements to 70 separate out the two components, concluding that 42% of HCHO is due to primary emissions in 71 Mexico City (Garcia et al., 2006), 44-95% in Los Angeles (Grosjean et al., 1983), and 72 approximately 33%-47% in Houston (Friedfeld et al., 2002; Rappenglück et al., 2010). These 73 measurements represent a “true” value that models must attempt to match, but they are made in 74 cities with large emission sources, and include uncertainties such as unspeciated background 75 contributions. Measurements from a few sites may not represent atmospheric processes at other 76 places and times. 77

Both aldehydes are included in atmospheric chemical mechanisms although their 78 representation varies among mechanisms. A large number of organic compounds participate in 79 atmospheric chemistry and each cannot be explicitly represented in the sequence of secondary, 80 tertiary, quaternary, etc. reactions that result in the production of aldehydes, especially at 81 regional-to-global scales. The Master Chemical Mechanism (MCM) (Saunders et al., 2003) 82 contains over 12,600 reactions for 4500 species, but its size makes it unsuitable for repeated 83 simulations across a large area. Chemical mechanisms in 3-D air quality models (AQMs) are 84 significantly condensed, invoking various assumptions and simplifications in the chemistry. 85

In this study, we use the Community Multiscale Air Quality (CMAQ) model (Byun and 86 Schere, 2006) with the SAPRC07TB chemical mechanism (Hutzell et al., 2011) to track sources 87
of ambient HCHO and CH 3 CHO. A recent evaluation of CMAQ with SAPRC07TB has been 88 made for ozone and fine particulate matter (PM 2.5 ) components (Hutzell et al., 2011). With this 89 system, we simulate detailed atmospheric photochemical formation and decay of HCHO and 90
CH 3 CHO in both low- and high-NOx conditions, within a realistic environment that is affected 91 by atmospheric transport, deposition, precipitation, actinic flux, temperature and pressure 92 gradients, and spatially- and temporally-varying emissions. An increasingly important use of 934

CMAQ is to assess proposed emission changes on a multipollutant basis, where effects of 94 emission changes on ambient concentrations of ozone, PM 2.5 , and hazardous air pollutants are 95 predicted simultaneously. 96

The goal of this work is to characterize regional-scale concentrations and chemical 97 sources of atmospheric HCHO and CH 3 CHO, and identify which VOCs are the largest producers 98 of these aldehydes. Our analysis differs from previous studies by 1.) explicitly tracking direct 99 emissions and photochemical production, 2.) employing a smaller grid resolution than previous 100 aldehyde modeling studies, 3.) allocating production to specific, emitted compounds, and 4.) 101 examining spatial and temporal differences in contributions. The ability to identify direct and 102 indirect contributions to atmospheric HCHO and CH 3 CHO will help us predict how they will 103 respond to emission changes, and guide development of policies that reduce ozone and PM 2.5 104 without increasing HAPs. This can also assist in prioritizing research efforts to improve aldehyde 105 predictions, by focusing on those processes likely to have the largest impact on concentrations. 106
107 2.0 Methods 108 109 The model and methods have been previously described (Hutzell et al., 2011) and are 110 briefly summarized here. 111
112 2.1 CMAQ model, domain, boundary conditions and meteorology 113 114 We applied CMAQ version 4.7.1 (Foley et al., 2009) with a 36x36 km 2 horizontal grid 115 covering the continental US and a nested 12x12 km 2 grid over the Eastern half of the US (Figure 116 1). There are 14 vertical layers, extending from the surface up to about 16 km. Boundary 117
conditions for the continental simulations were extracted from the GEOS-CHEM model with 118 those for the nested domain provided by continental CMAQ simulations. Meteorological inputs 119 were obtained from the MM5 meteorological model (http://box.mmm.ucar.edu/mm5/) version 120 3.6.1. Simulations were performed for two months, January and July, 2002, after a10-day model 121 spin up period. 122 We created emissions using the 2002 National Emissions Inventory (NEI) 123 ( http://www.epa.gov/ttn/chief/net/2002inventory.html ). The Sparse Matrix Operator Kernel 124 Emissions (SMOKE) emission processor version 2.4 ( http://www.smoke-model.org ) was used to 1255

create model ready emissions, with speciation rules ( http://www.cert.ucr.edu/~carter/emitdb/ ) for 126 allocating explicit species to lumped model species. Biogenic emissions were calculated with 127 Biogenic Emissions Inventory System (BEIS) version 3.13. The 2002 HAP inventory was used 128 to estimate HAP emissions and other VOCs were derived from speciation of total VOC 129 emissions (Simon et al., 2010). 130

131 2.2 Chemical Mechanism 132 The SAPRC07 mechanism has been extensively evaluated and described in detail 133 elsewhere (Cai et al., 2010; Carter, 2010). This study uses the SAPRC07TB version, which 134 includes additional explicit high emission and HAP species, including propene, ethanol, 1,2,4- 135 trimethylbenzene, acrolein, 1,3-butadiene, toluene, three isomers of xylene, and a -pinene. The 136 CMAQ implementation of SAPRC07TB (Hutzell et al., 2011) includes temperature and pressure 137 dependencies of the photolysis parameters and extensions for aerosol formation. 138
139 2.3 Process and Integrated Reaction Rate Analyses 140 To quantify contributors to aldehyde formation, we implement the Integrated Reaction 141 Rate (IRR) and Process Analysis (PA) capabilities within CMAQ (Gipson, 1999). This 142 technique has been useful for understanding ozone production and cycling of radicals and 143 nitrogen oxides (Jang et al., 1995; Jeffries and Tonnesen, 1994). We use IRR to track radical 144
and oxidized intermediates which form HCHO and CH 3 CHO, such as peroxy and acyl peroxy 145 radicals, methyl vinyl ketone and methyl ethyl ketone and allocate these intermediates to stable 146 species which are emitted. Products from all VOC reactions are tracked if they eventually react 147
to form either HCHO or CH 3 CHO. The entire chemical mechanism is utilized for the CMAQ 148 simulation (Hutzell et al., 2011), and we use IRR within the simulation to quantify the HCHO 149
and/or CH 3 CHO produced through 251 organic reactions (Supplementary Information, Table S150 1). This information is post-processed to quantify HCHO and/or CH 3 CHO produced from 151 emitted organic species. 152

Since some VOCs are both emitted and produced photochemically, we separate the 153 emitted portion from that formed photochemically, which we track to its precursors. For 154 example, the model species representing methyl ethyl ketone and similar ketones (MEK), an 1556

emitted VOC, produces HCHO via acetyl and methyl peroxy radicals. But MEK can itself be 156 produced by isoprene as a second generation product. We calculate the fraction of MEK 157 concentration at each hour due to emissions, which ranges from 10-95%, depending on location, 158 and use that to apportion HCHO to either MEK emissions or its precursors (in this example, 159 isoprene). Similarly, we separate the formation of HCHO from CH 3 CHO into that fraction due 160 to emissions and to chemistry. We assume that model species transported into the grid cell have 161 the same ratio of formation versus emission as the current grid cell. We list results for four 162 explicit species (isoprene, methane, acetaldehyde and ethanol) and 9 lumped chemical 163 categories: 164

· alkanes: non-methane alkanes lumped in model species ALK1 through ALK5 165

· ethene and propene 166

· lumped alkenes: model species OLE1 and OLE2 167

· aromatics: benzene, m-,p- and o-xylene, toluene, 1,2,4-trimethylbenzene, and 168 model species ARO1 and ARO2 169

· terpenes and pinenes: a -pinene and model species SESQ and TERP, representing 170 sesquiterpenes and other terpenes 171

· PANs: peroxyacetyl nitrate, peroxymethacryloyl nitrate, and model species PAN2 172 (peroxypriopionyl nitrate and higher PANs) and PBZN (from aromatic aldehydes) 173

· C3+ aldehydes: model species RCHO representing aldehydes with 3 or more 174
carbons 175

· lumped oxygenates: methylglyoxal, acetone, and model species MEK, lumped 176 ketones (PRD2 and IPRD) and isoprene products (IPRD) 177

· other: species that individually produce <1% of total formation. 178
179 We selected 10 locations throughout the domain, shown in Figure 1, for performing IRR 180 analyses, to examine how different chemical regimes and emissions mixes affect aldehyde 181 precursors. These locations include five grid cells examined previously (Hutzell et al., 2011) for 182 their oxidant chemistry (Louisville, Baltimore, Atlanta, Alabama, N. Georgia), three rural 183 locations (Alabama, E. Pennsylvania, N. Georgia), and five urban locations where measurements 184 are available in 2002 for comparison (Chicago, Baltimore, New York, Houston, Minneapolis). 1857

These locations, defined by processes within a 12x12 km 2 grid cell at the given location, are not 186 meant to fully characterize specific cities or regions, but do display the range of variability 187 expected in contributors to aldehyde formation. 188
189
3. Results and Discussion 190 191 3.1 Regional concentrations and impact of direct emissions versus photochemical production 192

We focus on results over the Eastern nested domain because the finer scale more 193 accurately characterizes spatial variability in meteorological and photochemical processes. 194 Figure 2 shows monthly-averaged concentrations of HCHO in January and July, and fraction of 195 overall concentration due to photochemistry. The remainder is attributed to direct HCHO 196
emissions. 197

In Figure 2, modeled HCHO concentrations are higher in July than January, by over a 198 factor of 4, due to higher actinic flux, radical concentrations and precursor emissions of biogenic 199 VOCs in summer. During winter, concentrations are highest in urban areas where emissions of 200 HCHO are largest; in these areas emissions account for 25-50% of total HCHO. During summer, 201 HCHO emissions contribute much less: 6-25% . The photochemical contribution and 202 concentration of HCHO is highest in the Southeast, consistent with satellite observations (Millet 203 et al., 2008; Palmer et al., 2006) and earlier modeling results (Luecken et al., 2006). 204

Figure 3 shows concentrations and emission fractions for CH 3 CHO. The seasonal 205 patterns are similar to HCHO, but concentrations are lower by half and the contribution of 206 photochemistry is greater, accounting for over 80% of total CH 3 CHO in winter and 95% in 207

summer. 208

Figure 4 displays seasonal differences in the role of emissions versus production at the 10 209 locations, and the average over the entire domain. For this sampling of grid cells, primary 210 emissions contribute at most 55% of the HCHO in January (Louisville). In summer, emissions 211 contribute less than 20% at all locations. 212
213 3.2 Chemical precursors of aldehydes 214 2158

Relative contributions of different VOCs to aldehyde production depend on the 216 magnitude of emissions and yield of aldehydes per molecule of reacting VOC. We use IRR and 217 PA analyses to quantify the role of different VOCs by summing reaction rates and yields over 218 two periods: January 14-16, 2002 and July 15-17, 2002, focusing on 10 locations. 219

Fractional HCHO formation attributed to individual VOCs and lumped classes is shown 220 in Figure 5. In general, isoprene is the dominant precursor of HCHO during July, although the 221 contribution varies spatially. In the Southeast, such as the location near urban Atlanta, isoprene 222 contributes approximately 50-65% of total formation. In the Mid Atlantic location near 223 Baltimore, isoprene contributes approximately 20% of HCHO formation. The reaction of 224 isoprene with OH dominates HCHO formation when isoprene is present, accounting for 75-95% 225 of HCHO attributed to isoprene, largely through an operator ( xHCHO) which yields 80-99% 226 HCHO in later sequences. Another operator produced by isoprene (xIPRD), produces reactive 227 oxygenated products (IPRD), providing an additional 3-8% HCHO formation. During January, 228 when emissions of isoprene are reduced, production is dominated by lumped and explicit 229 alkenes. In the locations near New York and Louisville, explicit and lumped alkenes are larger 230 contributors than isoprene, even in July. Methane and other alkanes also contribute to HCHO 231
formation. 232

Figure 6 displays VOC contributors to CH 3 CHO production. Isoprene plays a minor role 233
in CH 3 CHO production, and alkenes contribute the largest amount. Terpenes and cyclic terpenes 234 are major contributors in Southeastern locations. Alkanes and PANs, particularly 235 peroxyproprional nitrate, contribute in some locations. Ethanol contributes up to 5% of 236
CH 3 CHO production, and indirectly produces HCHO through CH 3 CHO decay. 237

While lumped alkenes are the largest relative contributors in January, the absolute 238 contribution of these compounds to HCHO and CH 3 CHO formation is smaller than in July. Both 239 compounds are formed in larger amounts in summer (20-50 times higher for HCHO and 7-40 240 times higher for CH 3 CHO) because the major reaction involves the hydroxyl radical (OH), which 241 can be 5 times or more higher in summer than winter (Ren et al., 2006). Supplementary Figure 242 S-1 shows a comparison of the total production of HCHO and CH 3 CHO from lumped alkenes for 243 both periods. Additional factors, such as warmer temperatures, higher actinic fluxes and greater 2449

biogenic emissions increase formation in summer. The same seasonal factors that cause higher 245 production rates via OH also cause higher destruction through OH, so concentration differences 246 between summer and winter are not as large as suggested by formation rate differences (Figures 247 2 and 3). 248
249 3.3 Comparisons of model results with observations 250 Figure 7 compares modeled HCHO and CH 3 CHO with 1, 3 and 24 hr-averaged 251 observations from the PAMs network ( www.epa.gov/air/oaqps/pams ), where the sites are 252 differentiated by state. The observations are limited and insufficient for fully evaluating the 253 model, but do serve as a touchstone to determine whether model predictions are realistic. One 254 limitation of these observations is that sampling frequency varies: some sites report every day, 255 others report every 3-12 days or longer, sometimes only one measurement is reported during the 256 month. In addition, the monitoring sites are sparse and unevenly distributed spatially, making it 257 difficult to evaluate predictions from a regional model. In July, 1299 observations from 96 sites 258 were available, versus over 41 million model surface predictions, so we are evaluating only a 259 small fraction of predicted values. In January, only 79 sites and 331 observations were available. 260 Observations are particularly lacking in rural areas and the Southeast where high isoprene 261 emissions and HCHO concentrations are predicted. 262

In comparison with these limited observations, both compounds are underestimated by 263 the CMAQ simulations, especially at higher concentrations. In July, the mean bias (MB) is -4 264 ppb, and normalized mean bias (NMB) is -0.59. When the highest 5% of observed values are 265 removed, some of which are extremely high, the MB is reduced to -2.43 and NMB to -0.47. In 266 January, the MB is -1.5 and NMB is -0.69, while with the top 5% of values removed, the MB is - 267 0.83 and NMB is -0.55. In many states the bias is much less. It is unclear why some states are 268 modeled well but others underpredicted. At some sites, such as those located around Chicago 269 (IL), and Minneapolis (MN), the model matches predictions well, while others, including New 270 York (NY) and RI are modeled 50% too low. Others, including the Baltimore site (MD) fall in 271
between. 272

Previous attempts to model HCHO had limited success; sometimes models overpredict, 273 but more often underpredict measured values at the surface. Previous comparisons of CMAQ 27410

predictions for HAPs (Luecken et al., 2006) showed that observations could generally be 275 predicted within a factor or two. Comparisons of a box model and GEOS-CHEM predictions 276 against data from the 2004 INTEX campaign (Fried et al., 2008) similarly showed 50% 277 underpredictions. 278

Several possible causes of differences between models and measurements have been 279 noted previously. One cause is error in the measurements. Numerous studies have identified 280
interferences in measurements of HCHO and CH 3 CHO (Arnts and Tejada, 1989; Herrington et 281 al., 2006; Karst et al., 1993; Sirju and Shepson, 1995), which can cause positive or negative 282 biases. Another possible cause is mismatch between point measurements and volume average 283 concentrations predicted by models. The presence of large, localized sources may explain the 284 inability of a 12 x 12 km 2 grid cell to capture the highest values. In a few cases, these values are 285 abnormally high: one site in Michigan reports a 24-hour average HCHO concentration on July 286 13, 2002 of 71.1 m g m -3 , while the next measurement on July 25 is 4.81 m g m -3 . This monitor is 287 south of downtown Detroit, in an area with industrial units, residential housing and highway 288 traffic, so it could be influenced by intermittent high point emissions. A nearby monitor reported 289
5.79 m g m -3 on July 13. 290

Other errors could occur in specification of emissions or chemistry. To identify the 291 largest potential sources of error causing underpredictions, we use information from Section 3 to 292 focus on those chemicals with the largest influence on aldehyde predictions. We discuss the 293 potential magnitude of uncertainties in emissions and chemistry of these VOCs, to prioritize 294 areas for improving aldehyde predictions. 295
296 4.0 Implications for modeling the atmospheric chemistry of aldehydes 297 298 Our ability to accurately predict ambient concentrations of formaldehyde and 299 acetaldehyde depends on our ability to predict concentrations of all VOCs. In this section, we 300 examine factors influencing the concentrations of the most important VOC precursors identified 301 in Section 3.2. We first discuss uncertainties involved in modeling emissions, and then 302 uncertainty in modeling the chemistry of these compounds. 303

4.1 Aldehyde emissions and decay 30411

Emissions of HCHO and CH 3 CHO can be important sources of ambient concentrations, 305 but atmospheric production is generally larger (Figures 2 and 3). For both aldehydes, emissions 306 derived from the NEI are less than emissions derived from total VOC emissions applying 307 speciation profiles (Simon et al., 2010). Differences between the two values may be caused by 308 lack of reporting requirements, different emission factors and speciation profiles. In the most 309 extreme case (Louisville in January), using speciation profiles instead of the NEI could increase 310 our modeled HCHO concentrations by a maximum of 50% and CH 3 CHO by 30% in winter, and 311 HCHO up to 20% in summer, reducing some underprediction. In other areas, especially in 312 summer, the change would only be a few percent. This suggests that direct emissions of 313 formaldehyde and acetaldehyde are not likely to be the only cause of model underprediction. 314 Emissions of larger aldehydes and subsequent photochemical decay are also a source of smaller 315 aldehydes, and errors in these emissions could also increase the HCHO up to 24% and CH 3 CHO 316 up to 29%. 317

The photochemical loss reactions of HCHO and CH 3 CHO have been well studied and are 318 not likely to have major errors. Slightly more than half of the photochemical loss is through 319 photolysis, with most of the remainder from reaction with OH. 320

321 4.2 Isoprene 322 Isoprene has been identified as a major contributor to HCHO in summer (Carslaw et al., 323 2000; Millet et al., 2006; Sumner et al., 2001), consistent with our study. Our results also show a 324 substantial degree of spatial variability across the region, with contributions less than 20% in 325 some areas. In rural locations, isoprene contributes about half of the HCHO production in 326 summer. The ability to accurately predict HCHO concentrations in summer strongly depends on 327 accurately predicting isoprene concentrations, which depends on accurately representing both 328 isoprene emissions and chemistry. 329

Isoprene is almost entirely emitted by biogenic sources and estimates of the source strength are 330 uncertain over regional scales. Several models of isoprene emissions are available, but they give 331 different estimates. A comparison of four models showed a factor of two differences among 332 models (Warneke et al., 2010). In modeling studies using BEIS, isoprene concentrations are 33312

underestimated (Doraiswamy et al., 2009), overestimated (Wiedinmyer et al., 2005) and 334 reasonably well estimated (Hogrefe et al., 2011). Table 2 shows examples of differences that 335 might be seen between inventories developed using two different models for biogenic emissions; 336 the BEIS and MEGAN (Guenther et al., 2006) models, at each of the 10 locations. Isoprene 337 emissions estimated by these two models can differ by 75% or more, with emissions from 338 MEGAN generally larger than BEIS. A factor of 2 error in isoprene emissions could increase 339 modeled formaldehyde by up to 80%. The large influence of isoprene on HCHO combined with 340 large uncertainties in the emissions of isoprene introduces significant uncertainties in HCHO 341 predictions. 342

The atmospheric chemistry of isoprene is currently being re-examined, and it is unclear 343 how updated isoprene chemistry will affect predictions of HCHO. Inclusion of the 1,5-H 344
isomerization reactions of b -hydroxyalkenyl peroxy radicals increased OH, and this channel 345 would increase the HCHO production proportionally (Archibald et al., 2010a). Similarly, 346 consideration of the 1,6-H shift leads to hydroperoxy aldehydes, yielding additional HCHO after 347 rapid photolysis, along with OH and HO 2 (Peeters, 2010; Stavrakou et al., 2010). Increased OH 348 radical regeneration in nonurban environments could increase production of HCHO through 349 other VOCs. Updated isoprene chemical reactions can have significantly higher HCHO yields 350
from d -hydroxy isoprene nitrates (Paulot et al., 2009), and using this chemistry in CMAQ can 351 potentially increase modeled HCHO less than 10% overall (Xie et al., 2011b). A critical 352 evaluation of reaction products must be performed for any new isoprene mechanism. Because 353 no consensus has yet been reached on the best way to represent isoprene chemistry, we offer no 354 recommendation here on improvements, but do expect it will affect HCHO. 355
356 4.3 a -pinene, terpenes and sesquiterpenes 357 The terpenes and cyclic terpenes are known contributors to SOA formation, and this 358 analysis shows that they also contribute to aldehyde formation. Acetaldehyde (up to 25%) and 359 formaldehyde (6%) production depends on correctly estimating emissions of terpenes. These 360 results agree with a previous analysis over Europe (Dufour et al., 2009) in which terpene 361 emissions accounted for 8% of average CH 3 CHO in Western Europe, and up to 25% locally. 362 Like isoprene, these chemicals are almost entirely biogenic but their emissions have not been 36313

studied in as much detail. We conclude that a similar uncertainty as isoprene is likely; 364 uncertainty of a factor of 2 could increase acetaldehyde concentrations by 25%. 365

The chemistry of terpenes beyond first generation products is also not well known, and 366 many assumptions were made in its condensed representation. A comparison of SAPRC07 with 367 the MCM, which has a more complete description of intermediate chemistry, found that MCM 368 predicted 2.3 times the photochemical ozone creation potential (POCP) for a -pinene than 369 SAPRC07, almost twice as much for b -pinene, and 2.8 times more than limonene. This was 370 attributed to different treatments of degradation chemistry beyond first generation products 371 (Derwent et al., 2010). While ozone reactivity is not the same as aldehyde formation, they both 372 occur through similar pathways and it is possible that aldehyde formation would be similarly 373 lower in SAPRC07TB. If the mechanistic reactivity of the terpenes is underrepresented in 374 SAPRC07TB by a factor of 2, the CH 3 CHO might be expected to increase up to 25%. 375
376 4.4 Other alkenes 377 378 Other alkenes, including ethene, propene, and larger alkenes and dienes, are major 379
contributors to both HCHO and CH 3 CHO formation, especially in the January simulation. In a 380 previous allocation of acetaldehyde sources (Millet et al., 2010), alkenes were about half as 381 important as alkanes and slightly larger than ethanol. This contrasts with our findings, where 382 alkenes contribute the majority of acetaldehyde production, perhaps due to inclusion of biogenic 383 emissions. Many biogenic alkenes that are not isoprene or terpenes, such as 2-methyl-3-butene384 2-ol, are lumped in OLE1 and OLE2. Table 2 lists the fraction of lumped alkenes from biogenic 385 sources at each of the 10 locations (Supplementary Figure S-2 shows the fraction across the 386 Eastern US). In contrast to isoprene, alkenes from MEGAN are generally lower than BEIS for 387 most locations, so using MEGAN would lower aldehyde production through alkenes. Are there 388 reactive VOCs not predicted by MEGAN or BEIS that might contribute to HCHO formation? 389 Photochemical modeling at Blodgett Forest (Choi et al., 2010) could not reconcile measured and 390 modeled HCHO concentrations, postulating a missing reactive biogenic VOC source. Other 391 analyses (Kim et al., 2011) found no evidence for significant missing biogenic VOCs in a branch 392 enclosure measuring OH reactivity, and attribute discrepancies to unmeasured isoprene oxidation 39314

products. It is unclear whether errors in biogenic alkene emissions are a major source of 394 aldehyde model underpredictions. 395

Anthropogenic sources of lumped alkenes have equal or greater importance for aldehyde 396 formation at all locations in winter, and urban locations in summer. Studies in Houston, Texas 397 (Wert et al., 2003) agree with our study by pointing to anthropogenic emissions of propene and 398 ethene as major HCHO precursors. Figure 8 shows fifteen alkenes with the highest emissions 399 and general source categories from which they are emitted (Simon et al., 2010). Mobile sources 400 are the largest source of alkenes, along with emissions from general industrial sources, stationary 401 combustion, and waste disposal, treatment and recovery. Mobile source estimation methods rely 402 on models that account for emission differences due to fuel type, vehicle technology, speed, 403 temperature, and modes of operation. There are uncertainties in the variability of vehicle fleets 404 and fuels across the country, and in parameters used to convert monthly modeled emissions to 405 hourly gridded emissions needed by models. For mobile sources, VOC speciation is generally 406 well-characterized, and comparisons for HAP species such as HCHO and CH 3 CHO, which are 407 reported both in the NEI inventory and computed from VOC speciation, are generally within 408 25% (Simon et al., 2010). Comparisons between emissions from the current MOBILE6 409
emissions model and the newer MOVES model show that mobile source VOC emissions from 410 MOVES are similar to those from MOBILE6, sometimes slightly lower (Cook et al., 2010; 411 Houyoux et al., 2009), or slightly higher (Kite, 2011). 412

It is difficult to evaluate integrated emission inventories using ambient observations 413 because measured concentrations include effects of meteorology, deposition, chemistry and 414 emissions. In a comparison of measurements in the Pacific Northwest with CMAQ modeling, 415 the VOC reactivity of propene was overpredicted; reduction of the VOC inventory by 30% was 416 needed for the model to match afternoon observations of ozone production efficiency (Xie et al., 417 2011a). Similarly, a modeling study using CMAQ in the Northeastern US found that modeled 418 ethene concentrations generally overpredicted morning measurements by 20-100% (Doraiswamy 419 et al., 2009). In contrast, an analysis using emission ratios found that inventories of alkenes in 420 the NEI were underpredicted (Warneke et al., 2007). It is unclear whether anthropogenic alkene 421 emissions in the NEI are too high or low; while errors in the inventory might increase HCHO 422
and CH 3 CHO by 22 and 24% in the winter, they might also decrease predictions. 42315

Beyond the simplest alkenes, chemistry becomes more complex, and reactions cannot be 424 easily generalized in a condensed mechanism. Numerous unsaturated compounds are emitted but 425 SAPRC07TB only represents ethene and propene explicitly; the rest are grouped into two model 426 species, OLE1 and OLE2, depending on reaction with OH. On a regional basis, ethene, propene 427 and methyl-substituted butenes make up a majority of alkene emissions (Figure 8). Reactions of 428 these alkenes with OH - the most important decay pathway - have estimated uncertainties of 20- 429 30% (Calvert et al., 2000). In these reactions, the description of b -hydroxy peroxy and alkoxy 430 radical reactions that follow the initial reactions have either been measured or estimated, and 431 explicitly representing glycoaldehyde in SAPRC07TB allows for accurate representation of the 432
fate of b -hydroxyalkoxy radicals from ethene. For larger alkenes, isomerization and reaction 433 with oxygen play a larger role. Reactions of alkenes with ozone are less straightforward - 434 reactions of Criegee intermediates and OH radical generation is an area requiring additional 435 laboratory and modeling studies (Calvert et al., 2000). A 30% uncertainty in chemistry could 436 change aldehyde predictions by 3-24%, although concentrations could increase or decrease. 437
438 4.5 Alkanes 439

Methane and other alkanes contribute to HCHO and CH 3 CHO formation. Simulations 440 using the CHIMERE model (Dufour et al., 2009) also identified methane as the dominant source 441 of HCHO in Western Europe, with isoprene playing a lesser role. Despite the low reactivity of 442 methane (maximum incremental reactivity for ozone 0.001 times that of isoprene) and exclusion 443 from the definition of VOC in the United States (US GPO, 2011), large emissions make methane 444 an important hemispheric driver of atmospheric photochemistry. Methane contributes to ozone 445 formation (Fiore et al., 2008; Jeffries, 1995); our results finds methane is also an important 446 contributor to HCHO. Larger alkanes can contribute up to 20% of CH 3 CHO formation. 447

Anthropogenic sources of alkanes generally dominate biogenic sources in our 448 simulations. Figure 9 shows annual emissions of methane and the fifteen largest VOC alkanes. 449 Mobile sources are important, along with other industrial, waste and transport emissions. 450 Landfills are a major source of methane, while storage and transport of petroleum contributes to 451 n-butane emissions. Anthropogenic emissions from the NEI have been reported to differ from 452 emission ratios in the Northeastern US by up to an order of magnitude (Warneke et al., 2007) 45316

based on measurements and calculated photochemical age. These discrepancies with the NEI 454 must be further examined to quantify uncertainty in alkane emissions. 455

Alkane chemistry is generally better understood than other species, although the 456 representation of larger and multifunctional alkanes in air quality models is highly condensed. 457 Despite uncertainties inherent in representing a large number of species by a small subset, 458 Derwent et al. (2010) found that the POCP of alkanes modeled with SAPRC07 correlated well 459 with those using MCM, a more detailed representation. This gives confidence that SAPRC07 is 460 representing our knowledge of alkane degradation similarly to MCM. 461
4.6 Ethanol 462 Potential changes in acetaldehyde concentrations from fuel reformulation has been 463 recently studied (Cook et al., 2011; Jacobson, 2007). Our simulations predict ethanol contributes 464 only 3-6% of photochemically-produced acetaldehyde. One recent study (Millet et al., 2010) 465 calculated that converting the US vehicle fleet to 85% ethanol fuel would increase acetaldehyde 466 emissions by 21% and double ethanol emissions. We estimate that such a change would increase 467 acetaldehyde by an average of 6%, possibly more near roadways. Using ethanol fuels will affect 468 concentrations of other VOCs (Cook et al., 2011; Graham et al., 2008; Jacobson, 2007; US EPA, 469 2010), which could be as important as changes in CH 3 CHO or ethanol. Changes in alkene 470 emissions, in particular, would impact acetaldehyde. Effects of fuel reformulations on these 471 other VOCs must be accurately characterized to predict their impact on acetaldehyde. 472
4.7 Other chemicals 473 Our analysis does not find a major role for aromatic compounds in producing HCHO or 474
CH 3 CHO. While aromatic chemistry is still uncertain, even a major increase in HCHO formation 475 from aromatics would not make them significant compared to other chemicals. 476

Emissions of methanol and acetone (part of the lumped oxygenates category) contribute 477 less than 2% of total HCHO. Recent analyses of aircraft data in the Pacific Northwest (Xie et al., 478 2011a) postulate that emissions of methanol and acetone would have to increase by factors of 30 479 and 25, respectively, to account for observed values of these compounds. If acetone emissions 480 were 25 times too low in the domain modeled here, HCHO could increase by nearly 20%, 481 bringing the modeled values closer to observations. Increasing methanol would increase its role 48217

in HCHO formation similarly, but isoprene would continue to remain the most important 483 precursor in most areas in summer. Neither methanol nor acetone is considered a VOC (US 484 GPO, 2011) and must be derived from inventories of total organic gas. 485

Lastly, is it possible that our simulations underestimate OH radical concentrations, thus 486 underestimating a critical first step in degradation of stable VOCs? Studies comparing box 487 model predictions to measurements find both underestimates (Chen et al., 2010; Hofzumahaus et 488 al., 2009) and some overestimates (Emmerson et al., 2007). Measurements of OH and HO 2 are 489 difficult so mechanisms have not been thoroughly evaluated for predictions of these radicals. 490 OH participates in both HCHO production and decay, so errors in OH will not change HCHO in 491 a linear fashion. If OH is larger than predicted by SAPRC07TB, this would explain some of the 492
HCHO and CH 3 CHO underpredictions. 493

494 5.0 Summary 495 Because the chemistry of HCHO and CH 3 CHO is integral to almost all VOC decay and 496 production pathways, it reflects our current understanding of VOC photochemistry and tests our 497 entire mechanistic understanding of atmospheric chemistry. To accurately model their 498 concentrations, we must correctly account for emissions, transport, deposition, and the 499 degradation chemistry of not only aldehydes, but also VOC precursors to aldehydes. In this 500 study we conclude that: 501

1. Atmospheric photochemical production accounts for most of the HCHO and 502
CH 3 CHO concentrations in the ambient atmosphere. Aside from dense 503 urban/industrial areas, reduction of HCHO and CH 3 CHO emissions is expected to 504 have a small effect on reducing concentrations. In a few areas, especially in winter, 505 emissions are important and a large source of uncertainty is differences between 506 emissions from the NEI and emissions based on speciated VOC. 507
2. Substantial spatial and seasonal variation occurs in how much various VOCs 508 contribute to photochemical production of aldehydes. For example, isoprene is not 509 always the dominant VOC producing HCHO, even during summer. 51018

3. Biogenic emissions of compounds that photochemically react to produce HCHO and 511
CH 3 CHO are highly uncertain and a potential source of error in aldehyde predictions. 512
4. Resolving uncertainties in the chemistry of isoprene and other alkenes is critical for 513 chemical mechanisms to accurately predict concentrations of aldehydes. Newly 514 proposed isoprene mechanisms must be evaluated for a wide range of species, 515 including HCHO. 516
5. Anthropogenic alkenes, largely from mobile sources, play an important role in 517 aldehyde formation. While mobile sources are perhaps better represented in the NEI 518 than other categories, new uncertainties are constantly being introduced by changes in 519
vehicles and fuels. 520
6. The CMAQ air quality model tends to underpredict aldehyde concentrations. At 521 many sites this underprediction is close to 50%. 522

While much effort has spent developing condensed chemical mechanisms that are as 523 representative of the underlying chemicals as possible, there will necessarily be compromises. 524 Even the most explicit mechanisms, such as the MCM, rely on some degree of condensation. It 525 is encouraging that comparisons between explicit mechanisms and condensed mechanisms are 526 generally consistent (Archibald et al., 2010b; Derwent et al., 2010), although carbonyls tend to 527 be one class of compounds which are often predicted differently among mechanisms. 528

The general underprediction of air quality models for aldehydes is an area which requires 529 more investigation. Because aldehyde decay is a large source of new radicals which drive 530 photochemistry, this raises questions about the robustness of predictions for other photochemical 531 pollutants, such as ozone and PM 2.5 ; species which air quality models predict well. HCHO, in 532 particular, is an important ozone precursor. 533

Our findings on the most significant chemical sources of HCHO and CH 3 CHO are based 534 on our best assessment of emissions and chemistry on a regional scale. The existence of many 535 uncertainties implies that the contributions of different VOCs to aldehyde formation may change 536 somewhat as our knowledge of chemistry and emissions improves. Refining our understanding 537 of aldehyde behavior in the atmosphere is an iterative and imperfect process involving 538 measurements, modeling, and collecting improved data on emission sources. New information 53919

will help to more accurately bound our understanding of how air quality might change under 540 future emissions scenarios, and build confidence in using regional models for air quality 541 management and research. 542
543 6.0 Acknowledgments 544
The authors acknowledge helpful discussions with our US EPA colleagues Ted Palma and Ying Xie, and 545 with William Carter, who developed this SAPRC07TB mechanism. We greatly appreciate the numerous 546 helpful suggestions from our internal and external reviewers, all of whom have helped focused the 547 purpose of this manuscript and clarified the presentation. The United States Environmental Protection 548 Agency, through its Office of Research and Development, funded and managed the research 549 described here. It has been subjected to Agency administrative review and approve d for publication. 550

7.0 References 551 Adewuyi, Y.G., Cho, S.-Y., Tsay, R.-P., Carmichael, G.R., 1984. Importance of formaldehyde in 552 cloud chemistry. Atmospheric Environment 18, 2413-2420. 553
Archibald, A.T., Cooke, M.C., Utembe, S.R., Shallcross, D.E., Derwent, R.G., Jenkin, M.E., 554 2010a. Impacts of mechanistic changes on HOx formation and recycling in the oxidation of 555 isoprene. Atmospheric Chemistry Physics 10, 8097-8118. 556 Archibald, A.T., Jenkin, M.E., Shallcross, D.E., 2010b. An isoprene mechanism 557 intercomparison. Atmospheric Environment 44, 5356-5364. 558 Arnts, R.R., Tejada, S.B., 1989. 2,4-Dinitrophenylhydrazine-coated silica gel cartridge method 559 for determination of formaldehyde in air: identification of an ozone interference. Environmental 560 Science & Technology 23, 1428-1430. 561 Atkinson, R., 1997. Gas-phase tropospheric chemistry of volatile organic compounds: 1. alkanes 562 and alkenes. Journal of Physical and Chemical Reference Data 26, 215-290. 563 Byun, D., Schere, K.L., 2006. Review of the Governing Equations, Computational Algorithms, 564 and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling 565 System. Applied Mechanics Reviews 59, 51-77. 566 Cai, C., Kelly, J.T., Avise, J.C., Kaduwela, A.P., Stockwell, W.R., 2010. Implementation of the 567 SAPRC07C chemical mechanism in the Community Multiscale Air Quality (CMAQ) model for 568 photochemical modeling in California: Comparisons with SAPRC99. Journal of the Air & 569 Waste Management Association submitted. 570 Calvert, J.G., Atkinson, R., Kerr, J.A., Madronich, S., Moortgat, G.K., Wallington, T.J., 571 Yarwood, G., 2000. The mechanisms of atmospheric oxidation of the alkenes. Oxford University 572 Press, New York/Oxford. 57320

Carslaw, N., Bell, N., Lewis, A.C., McQuaid, J.B., Pilling, M.J., 2000. A detailed case study of 574 isoprene chemistry during the EASE96 Mace Head campaign. Atmospheric Environment 34, 575 2827-2836. 576 Carter, W.P.L., 2010. Development of the SAPRC-07 chemical mechanism. Atmospheric 577 Environment 44, 5324-5335. 578 Chen, S., Ren, X., Mao, J., Chen, Z., Brune, W.H., Lefer, B., Rappenglück, B., Flynn, J., Olson, 579 J., Crawford, J.H., 2010. A comparison of chemical mechanisms based on TRAMP-2006 field 580 data. Atmospheric Environment 44, 4116-4125. 581 Choi, W., Faloona, I.C., Bouvier-Brown, N.C., McKay, M., Goldstein, A.H., Mao, J., Brune, 582 W.H., LaFranchi, B.W., Cohen, R.C., Wolfe, G.M., Thornton, J.A., Sonnenfroh, D.M., Millet, 583 D.B., 2010. Observations of elevated formaldehyde over a forest canopy suggest missing sources 584 from rapid oxidation of arboreal hydrocarbons. Atmospheric Chemistry Physics 10, 8761-8781. 585 Cook, R., Glover, E., Yanca, C., Warila, J., Sales, A., 2010. Modeling air toxics in EPA's 586 MOVES emissions model, 2010 Coordinating Research Council Air Toxics Workshop. 587 Coordinating Research Council, Sacramento, CA. 588 Cook, R., Phillips, S., Houyoux, M., Dolwick, P., Mason, R., Yanca, C., Zawacki, M., Davidson, 589 K., Michaels, H., Harvey, C., Somers, J., Luecken, D., 2011. Air quality impacts of increased use 590 of ethanol under the United States' Energy Independence and Security Act. Atmospheric 591 Environment, in Press. 592 Derwent, R.G., Jenkin, M.E., Pilling, M.J., Carter, W.P.L., Kaduwela, A., 2010. Reactivity 593 Scales as Comparative Tools for Chemical Mechanisms Journal of the Air & Waste 594 Management Association 60, 914-924. 595 Doraiswamy, P., Hogrefe, C., Hao, W., Henry, R.F., Civerolo, K., Ku, J.-Y., Sistla, G., Schwab, 596 J.J., Demerjian, K.L., 2009. A diagnostic comparison of measured and model-predicted speciated 597 VOC concentrations. Atmospheric Environment 43, 5759-5770. 598
Dufour, G., Wittrock, F., Camredon, M., Beekmann, M., Richter, A., Aumont, B., Burrows, J.P., 599 2009. SCIAMACHY formaldehyde observations: constraint for isoprene emission estimates over 600 Europe? Atmospheric Chemistry Physics 9, 1647-1664. 601 Emmerson, K.M., Carslaw, N., Carslaw, D.C., Lee, J.D., McFiggans, G., Bloss, W.J., 602 Gravestock, T., Heard, D.E., Hopkins, J., Ingham, T., Pilling, M.J., Smith, S.C., Jacob, M., 603 Monks, P.S., 2007. Free radical modelling studies during the UK TORCH Campaign in Summer 604 2003. Atmospheric Chemistry Physics 7, 167-181. 605 Fiore, A.M., West, J.J., Horowitz, L.W., Naik, V., Schwarzkopf, M.D., 2008. Characterizing the 606 tropospheric ozone response to methane emission controls and the benefits to climate and air 607 quality. Journal of Geophysical Research 113, D08307. 608 Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, 609 G., Young, J.O., Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., Bash, J.O., 2009. 61021

Incremental testing of the community multiscale air quality (CMAQ) modeling system version 611 4.7. Geoscientific Model Development Discussions 2, 1245-1297. 612 Fried, A., Walega, J.G., Olson, J.R., Crawford, J.H., Chen, G., Weibring, P., Richter, D., Roller, 613 C., Tittel, F.K., Heikes, B.G., Snow, J.A., Shen, H., O'Sullivan, D.W., Porter, M., Fuelberg, H., 614 Halland, J., Millet, D.B., 2008. Formaldehyde over North America and the North Atlantic during 615 the summer 2004 INTEX campaign: Methods, observed distributions, and measurement-model 616 comparisons. Journal of Geophysical Research 113, D10302. 617
Friedfeld, S., Fraser, M., Ensor, K., Tribble, S., Rehle, D., Leleux, D., Tittel, F., 2002. Statistical 618 analysis of primary and secondary atmospheric formaldehyde. Atmospheric Environment 36, 619 4767-4775. 620 Garcia, A.R., Volkamer, R., Molina, L.T., Molina, M.J., Samuelson, J., Mellqvist, J., Galle, B., 621 Herndon, S.C., Kolb, C.E., 2006. Separation of emitted and photochemical formaldehyde in 622 Mexico City using a statistical analysis and a new pair of gas-phase tracers. Atmos. Chem. Phys. 623 6, 4545-4557. 624 Gipson, G.L., 1999. Process Analysis, in: D.W., B., Ching, J.K.S. (Eds.), Science Algorithms of 625 the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. U.S. EPA, 626 Washington, DC, pp. 16-11 to 16-39. 627 Graham, L.A., Belisle, S.L., Baas, C.-L., 2008. Emissions from light duty gasoline vehicles 628 operating on low blend ethanol gasoline and E85. Atmospheric Environment 42, 4498-4516. 629 Grosjean, D., Swanson, R.D., Ellis, C., 1983. Carbonyls in Los Angeles air: Contribution of 630 direct emissions and photochemistry. The Science of The Total Environment 29, 65-85. 631 Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C., 2006. Estimates of 632 global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols 633 from Nature). Atmospheric Chemistry Physics 6, 3181-3210. 634 HEI, 2007. Mobile-Source Air Toxics: A Critical Review of the Literature on Exposure and 635 Health Effects, in: Panel, H.A.T.R. (Ed.). Health Effects Institute, Boston, Massachusetts. 636 Herrington, J.S., Fan, Z.-H., Lioy, P.J., Zhang, J., 2006. Low Acetaldehyde Collection 637 Efficiencies for 24-Hour Sampling with 2,4-Dinitrophenylhydrazine (DNPH)-Coated Solid 638 Sorbents. Environmental Science & Technology 41, 580-585. 639 Hofzumahaus, A., Rohrer, F., Lu, K., Bohn, B., Brauers, T., Chang, C.-C., Fuchs, H., Holland, 640 F., Kita, K., Kondo, Y., Li, X., Lou, S., Shao, M., Zeng, L., Wahner, A., Zhang, Y., 2009. 641 Amplified Trace Gas Removal in the Troposphere. Science 324, 1702-1704. 642 Hogrefe, C., Isukapalli, S.S., Tang, X., Georgopoulos, P., He, S., Zalewsky, E.E., Hao, W., Ku, 643 J.-Y., Key, T., Sistla, G., 2011. Impact of biogenic emission uncertainties on the simulated 644 response of ozone and fine particulate matter to anthropogenic emission reductions. Journal of 645 the Air & Waste Management Association 61, 92-108. 64622

Houyoux, M., Strum, M., Mason, R., Dolwick, P., Michaels, H., Huang, A., Allen, C.T., Beidler, 647 J., 2009. Implementation of MOVES-based PM2.5 emissions approach for onroad gasoline 648 sources using hourly, gridded temperatures. 18th Annual International Emission Inventory 649 Conference, Baltimore, MD. 650 Hutzell, W.T., Luecken, D.J., Appel, K.W., Carter, W.P.L., 2011. Interpreting predictions from 651 the SAPRC07 mechanism based on regional and continental simulations. Atmospheric 652 Environment, in press. 653 Jacobson, M.Z., 2007. Effects of Ethanol (E85) versus Gasoline Vehicles on Cancer and 654 Mortality in the United States. Environmental Science & Technology 41, 4150-4157. 655 Jang, J.-C.C., Jeffries, H.E., Tonnesen, S., 1995. Sensitivity of ozone to model grid resolution -- 656 II. Detailed process analysis for ozone chemistry. Atmospheric Environment 29, 3101-3114. 657 Jeffries, H.E., 1995. Photochemical Air Pollution, in: Singh, H.B. (Ed.), Composition, 658 Chemistry, and Climate of the Atmosphere. Van Nostrand Reinhold, New York. 659 Jeffries, H.E., Tonnesen, S., 1994. A comparison of two photochemical reaction mechanisms 660 using mass balance and process analysis. Atmospheric Environment 28, 2991-3003. 661 Karst, U., Binding, N., Cammann, K., Witting, U., 1993. Interferences of nitrogen dioxide in the 662 determination of aldehydes and ketones by sampling on 2,4-dinitrophenylhydrazine-coated solid 663 sorbent. Fresenius' Journal of Analytical Chemistry 345, 48-52. 664 Kim, S., Guenther, A., Karl, T., Greenberg, J., 2011. Branch-level measurement of total OH 665 reactivity for constraining unknown BVOC emission during the CABINEX (Community 666 Atmosphere-Biosphere INteractions Experiments)-09 Field Campaign. Atmospheric Chemistry 667 Physics Discussions 11, 7781-7809. 668 Kite, C., 2011. Preliminary comparison between MOVES and MOBILE6 669 Houston/Galveston/Brazoria (HGB) on-road emission inventories for 2006 and 2018. Presented 670 to the Regional Air Quality Planning Committee, Houston, TX. 671 Luecken, D.J., Hutzell, W.T., Gipson, G.L., 2006. Development and analysis of air quality 672 modeling simulations for hazardous air pollutants. Atmospheric Environment 40, 5087-5096. 673 Millet, D.B., Guenther, A., Siegel, D.A., Nelson, N.B., Singh, H.B., de Gouw, J.A., Warneke, C., 674 Williams, J., Eerdekens, G., Sinha, V., Karl, T., Flocke, F., Apel, E., Riemer, D.D., Palmer, P.I., 675 Barkley, M., 2010. Global atmospheric budget of acetaldehyde: 3-D model analysis and 676 constraints from in-situ and satellite observations. Atmospheric Chemistry Physics 10, 3405- 677 3425. 678
Millet, D.B., Jacob, D.J., Boersma, K.F., Fu, T.-M., Kurosu, T.P., Chance, K., Heald, C.L., 679 Guenther, A., 2008. Spatial distribution of isoprene emissions from North America derived from 680 formaldehyde column measurements by the OMI satellite sensor. J. Geophys. Res. 113, D02307. 68123

Millet, D.B., Jacob, D.J., Turquety, S., Hudman, R.C., Wu, S., Fried, A., Walega, J., Heikes, 682 B.G., Blake, D.R., Singh, H.B., Anderson, B.E., Clarke, A.D., 2006. Formaldehyde distribution 683 over North America: Implications for satellite retrievals of formaldehyde columns and isoprene 684 emission. Journal of Geophysical Research 111, D24S02. 685
Palmer, P.I., Abbot, D.S., Fu, T.-M., Jacob, D.J., Chance, K., Kurosu, T.P., Guenther, A., 686 Wiedinmyer, C., Stanton, J.C., Pilling, M.J., Pressley, S.N., Lamb, B., Sumner, A.L., 2006. 687 Quantifying the seasonal and interannual variability of North American isoprene emissions using 688 satellite observations of the formaldehyde column. Journal of Geophysical Research 111, 689 D12315. 690 Paulot, F., Crounse, J.D., Kjaergaard, H.G., Kroll, J.H., Seinfeld, J.H., Wennberg, P.O., 2009. 691 Isoprene photooxidation: new insights into the production of acids and organic nitrates. Atmos. 692 Chemistry Physics 9, 1479-1501. 693 Peeters, J., 2010. HOx radical regeneration in isoprene oxidation via peroxy radical 694 isomerisations. II: experimental evidence and global impact. Physical Chemistry Chemical 695 Physics: PCCP 12, 14227. 696 Rappenglück, B., Dasgupta, P.K., Leuchner, M., Li, Q., Luke, W., 2010. Formaldehyde and its 697 relation to CO, PAN, and SO2 in the Houston-Galveston airshed. Atmospheric Chemistry 698 Physics 10, 2413-2424. 699 Ren, X., Brune, W.H., Mao, J., Mitchell, M.J., Lesher, R.L., Simpas, J.B., Metcalf, A.R., 700 Schwab, J.J., Cai, C., Li, Y., 2006. Behavior of OH and HO2 in the winter atmosphere in New 701 York City. Atmospheric Environment 40, 252-263. 702 Saunders, S.M., Jenkin, M.E., Derwent, R.G., Pilling, M.J., 2003. Protocol for the development 703 of the Master Chemical Mechanism, MCM v3 (Part A): tropospheric degradation of non704 aromatic volatile organic compounds. Atmospheric Chemistry Physics 3, 161-180. 705 Seinfeld, J.H., Pandis, S.N., 1998. Atmospheric Chemistry and Physics. John Wiley & Sons, 706 Inc., New York. 707 Simon, H., Beck, L., Bhave, P., Divita, F., Hsu, Y., Luecken, D., Mobley, D., Pouliot, G., Reff, 708 A., Sarwar, G., Strum, M., 2010. The development and uses of EPA's SPECIATE database. 709 Atmospheric Pollution Research 1, 196-206 710 Sirju, A.-P., Shepson, P.B., 1995. Laboratory and Field Investigation of the DNPH Cartridge 711 Technique for the Measurement of Atmospheric Carbonyl Compounds. Environmental Science 712 & Technology 29, 384-392. 713 Stavrakou, T., Peeters, J., Müller, J.F., 2010. Improved global modelling of HOx recycling in 714 isoprene oxidation: evaluation against the GABRIEL and INTEX-A aircraft campaign 715 measurements. Atmospheric Chemistry Physics 10, 9863-9878. 71624

Steiner, A.L., Cohen, R.C., Harley, R.A., Tonse, S., Millet, D.B., Schade, G.W., Goldstein, A.H., 717 2008. VOC reactivity in central California: comparing an air quality model to ground-based 718 measurements. Atmosmospheric Chemistry Physics 8, 351-368. 719 Sumner, A.L., Shepson, P.B., Couch, T.L., Thornberry, T., Carroll, M.A., Sillman, S., Pippin, 720 M., Bertman, S., Tan, D., Faloona, I., Brune, W., Young, V., Cooper, O., Moody, J., Stockwell, 721 W., 2001. A study of formaldehyde chemistry above a forest canopy. Journal of Geophysical 722 Research 106, 24387-24405. 723 Tilgner, A., Herrmann, H., 2010. Radical-driven carbonyl-to-acid conversion and acid 724 degradation in tropospheric aqueous systems studied by CAPRAM. Atmospheric Environment 725 44, 5415-5422. 726 US EPA, 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis, 727 February, 2010 ed. U.S Environmental Protection Agency. 728
US EPA, 2011. 2005 National-Scale Air Toxics Assessment. U.S. Environmental Protection 729 Agency. 730 US GPO, 2011. Code of Federal Requirements, Title 40: Protection of the Environment. US 731 Government Printing Office. 732 Warneck, P., 1989. Sulfur dioxide in rain clouds: Gas-liquid scavenging efficiencies and wet 733 deposition rates in the presence of formaldehyde. Journal of Atmospheric Chemistry 8, 99-117. 734 Warneke, C., de Gouw, J.A., Del Negro, L., Brioude, J., McKeen, S., Stark, H., Kuster, W.C., 735 Goldan, P.D., Trainer, M., Fehsenfeld, F.C., Wiedinmyer, C., Guenther, A.B., Hansel, A., 736 Wisthaler, A., Atlas, E., Holloway, J.S., Ryerson, T.B., Peischl, J., Huey, L.G., Hanks, A.T.C., 737 2010. Biogenic emission measurement and inventories determination of biogenic emissions in 738 the eastern United States and Texas and comparison with biogenic emission inventories. Journal 739 of Geophysical Research 115, D00F18. 740 Warneke, C., McKeen, S.A., de Gouw, J.A., Goldan, P.D., Kuster, W.C., Holloway, J.S., 741 Williams, E.J., Lerner, B.M., Parrish, D.D., Trainer, M., Fehsenfeld, F.C., Kato, S., Atlas, E.L., 742 Baker, A., Blake, D.R., 2007. Determination of urban volatile organic compound emission ratios 743 and comparison with an emissions database. Journal of Geophysical Research 112, D10S47. 744 Wert, B.P., Trainer, M., Fried, A., Ryerson, T.B., Henry, B., Potter, W., Angevine, W.M., Atlas, 745 E., Donnelly, S.G., Fehsenfeld, F.C., Frost, G.J., Goldan, P.D., Hansel, A., Holloway, J.S., 746 Hubler, G., Kuster, W.C., Nicks, D.K., Jr., Neuman, J.A., Parrish, D.D., Schauffler, S., Stutz, J., 747 Sueper, D.T., Wiedinmyer, C., Wisthaler, A., 2003. Signatures of terminal alkene oxidation in 748 airborne formaldehyde measurements during TexAQS 2000. Journal of Geophysical Research 749 108, 4104. 750 Wiedinmyer, C., Greenberg, J., Guenther, A., Hopkins, B., Baker, K., Geron, C., Palmer, P.I., 751 Long, B.P., Turner, J.R., Pétron, G., Harley, P., Pierce, T.E., Lamb, B., Westberg, H., Baugh, 752 W., Koerber, M., Janssen, M., 2005. Ozarks Isoprene Experiment (OZIE): Measurements and 753 modeling of the "isoprene volcano". Journal of Geophysical Research 110, D18307. 75425

Xie, Y., Elleman, R., Jobson, T., Lamb, B., 2011a. Evaluation of O3-NOx-VOC sensitivities 755 predicted with the CMAQ photochemical model using Pacific Northwest 2001 field 756 observations. Journal of Geophysical Research 116, in press. 757
Xie, Y., Paulot, F., Pinder, R.W., Carter, W.P.L., Nolte, C.G., Luecken, D.J., Hutzell, W.T., 758 Wennberg, P.O., Cohen, R., 2011b. Understanding the impact of isoprene nitrates and OH 759 reformation on regional air quality using recent advances in isoprene photooxidation chemistry, 760 9th Annual CMAS Conference, Chapel Hill, NC. 761

762

763

764Listing of Tables
Table 1: Example of differences in emissions for alkene species predicted by two different models, BEIS and MEGAN. The emissions are in moles/sec and are the sum over 1 day on July 16, 2002.
Table 2. Fraction of total emissions of alkenes from biogenic sources for January and July simulations, based on the sum over one day on January 15, 2006 and one day on July 16, 2002.
Listing of Figures
Figure 1: Modeled 36km and 12km domains. The locations of ten sites that are used in additional analyses are noted. Sites with an urban composition are shown by x and sites with large biogenic influences by ­

. Figure 2: Average HCHO concentration (left) and fraction of total HCHO due to photochemical production (right) in January (top) and July (bottom). Concentrations are in m g/m 3 and are averaged over the entire month
Figure 3: Average CH 3 CHO concentration (left) and fraction of total due to photochemical production (right) in January (top) and July (bottom). Concentrations are in m g/m 3 and are averaged over the entire month
Figure 4: Average fraction of HCHO and CH 3 CHO due to emissions at each of the 10 locations and the mean over the entire domain. Values are averaged over 3 days in January (solid bars) and July (hatched bars) at the 12 km grid cells sited at each of the locations .
Figure 5: Fraction of total formaldehyde attributed to primary VOC classes in January (top) and July (bottom) at 10 locations. Values are calculated from the sum over 72 hours at the 12 km grid cells sited at each location.
Figure 6: Fraction of total acetaldehyde attributed to primary VOC classes in January (top) and July (bottom) at 10 locations. Values are calculated from the sum over 72 hours at the 12 km grid cells sited at each location.
Figure 7: Comparison of observed and modeled HCHO in January (top, left) and July (top, right) and the same comparison for CH 3 CHO in January (left) and July (right). Different colors represent different States, which may have multiple monitors, and symbols represent whether sampling is 1 or 3 hour intervals (o) or 24-h intervals (+).
Figure 8: Annual anthropogenic emissions of 15 alkenes with the largest emission rates, and their allocation by source categories in the NEI. The allocation of explicit species to SAPRC07 model species (OLE1 and OLE2) is also shown.
Figure 9: Annual anthropogenic emissions of methane and the highest 15 non-methane alkanes and their allocation by source categories in the NEI. The allocation of explicit species to SAPRC07 model species (ALK1 through ALK5) is also shown. FigureTable 1.

Location Isoprene Ethene + Propene OLE1+OLE2

BEIS MEGAN BEIS MEGAN BEIS MEGAN New York 4.4 33.6 1 2.8 1.2 .4 Baltimore 3.5 57.5 .6 5.1 1 .9 Louisville 4.5 12.8 1.2 4.4 1.5 .7 Chicago 19.4 50.3 1 5.2 1.6 .8 Houston 2.9 22.0 1.3 2.9 1.6 .8 Minn 15.9 16.1 2 3.6 2.2 .6 Atlanta 19.1 26.8 2.3 3.7 2.0 .9 Alabama 15.8 53.8 2.3 3.9 3.9 1.4 E. Penn 15.5 41.1 1.5 3.4 1.5 .6 N. Georgia 20.5 34.2 1.5 3.1 1.2 .6

Table 2.

Location Ethene + Propene OLE1+OLE2 Pinenes and terpenes January July January July January July New York .00 .08 .00 .08 .02 .52 Baltimore .00 .06 .00 .14 .02 .40 Louisville .00 .16 .01 .24 .04 .65 Chicago .00 .08 .00 .23 .01 .34 Houston .12 .35 .10 .31 .55 .82 Minn .03 .70 .03 .69 .33 .93 Atlanta .15 .59 .14 .53 .86 .98 Alabama .07 .82 .45 .91 .99 1.00 E. Penn .07 .79 .09 .83 .29 .94 N. Georgia .20 .83 .21 .80 .98 1.00Figure 1. 

### -120 -110 -100 -90 -80 -70 **25 30**  **35 40**  **45 50** 



# N. Georgia
Alabama 

# Chicago

Atlanta* Louisville Baltimore



# Houston New York Minneapolis



# E. PennHCHO concentration Fraction of HCHO due to photochemistry

Figure 2JanuaryJulyCH 3 CHO concentration Fraction of CH 3 CHO from photochemistry

Figure 3JanuaryJulyFigure 4 **A t**  **l a**  **n t**  **a N** 

**.**

**G e**  **o r**  **g i**  **a A**  **l a**  **b a**  **m a**  **C h**  **i c**  **a g**  **o L**  **o u**  **i s**  **v i**  **l l**  **e B**  **a l**  **t i**  **m o**  **r e**  **H**

**o u**  **s t**  **o n**  **E**

**. P**  **e n**  **n M**  **i n**  **n N**  **e w**  **Y o**  **r k**  **m e**  **a n**  

### Sample grid cell **0 0.2**  **0.4 0.6**  **0.8 1** **Fractional contribution from emissions**



### 0 **0.2 0.4**  **0.6 0.8**  **1 HCHO, Jan**  **HCHO, July CH**  **3 CHO, Jan**  **CH 3**  **CHO, July**Figure 5 **A l**  **a b**  **a m**  **a C**  **h i**  **c a**  **g o**  **L o**  **u i**  **s v**  **i l**  **l e**  **B a**  **l t**  **i m**  **o r**  **e H** 

**o u**  **s t**  **o n**  **E**

**. P**  **e n**  **n M**  **i n**  **n N**  **e w**  **Y o**  **r k**  **A t**  **l a**  **n t**  **a N** 

**.**

**G e**  **o r**  **g i**  **a 0**  **0.1 0.2**  **0.3 0.4**  **0.5 0.6**  **0.7 Fraction of total HCHO formation** 

**0 0.1**  **0.2 0.3**  **0.4 0.5**  **0.6 0.7**  

## Jan

**A l**  **a b**  **a m**  **a C**  **h i**  **c a**  **g o**  **L o**  **u i**  **s v**  **i l**  **l e**  **B a**  **l ti**  **m o**  **r e**  **H**

**o u**  **s t**  **o n**  **E**

**. P**  **e n**  **n M**  **i nn**  **N e**  **w Y**  **o r**  **k A**  **tl a**  **n t**  **a N** 

**.**

**G e**  **o r**  **g i**  **a 0**  **0.1 0.2**  **0.3 0.4**  **0.5 0.6**  **0.7 Fraction of total HCHO formation** 

**0 0.1**  **0.2 0.3**  **0.4 0.5**  **0.6 0.7** 

**isoprene lumped alkenes (OLE1/2)**  **ethene and propene methane**  **acetaldehyde (emis) ethanol**  **lumped oxygenates other**  **terpenes and pinenes PANs**  **alkanes aromatics**  

## JulyFigure 6 **A l**  **a b**  **a m**  **a C**  **h i**  **c a**  **g o**  **L o**  **u i**  **s v**  **i l**  **l e**  **B a**  **l t**  **i m**  **o r**  **e H** 

**o u**  **s t**  **o n**  **E**

**. P**  **e n**  **n M**  **i n**  **n N**  **e w**  **Y o**  **r k**  **A t**  **l a**  **n t**  **a N** 

**.**

**G e**  **o r**  **g i**  **a 0**  **0.1 0.2**  **0.3 0.4**  **0.5 0.6**  **0.7 Fraction of total CH** **3 CHO formation** 

**0 0.1**  **0.2 0.3**  **0.4 0.5**  **0.6 0.7**  

## Jan

**A l**  **a b**  **a m**  **a C**  **h i**  **c a**  **g o**  **L o**  **u i**  **s v**  **i l**  **l e**  **B a**  **l t**  **i m**  **o r**  **e H** 

**o u**  **s t**  **o n**  **E**

**. P**  **e nn**  **M i**  **n n**  **N e**  **w Y**  **o r**  **k A**  **t l**  **a nt**  **a N** 

**.**

**G e**  **o r**  **g i**  **a 0**  **0.1 0.2**  **0.3 0.4**  **0.5 0.6**  **0.7 Fraction of total CH** **3 CHO formation** 

**0 0.1**  **0.2 0.3**  **0.4 0.5**  **0.6 0.7** 

**isoprene lumped alkenes (OLE1/2)**  **ethene and propene terpenes and pinenes**  **PANs alkanes**  **aromatics C3+ aldehyde (emis)**  **ethanol lumped oxygenates**  **other** 

## JulyFigure 7

**0 2 4 6 8 10**

**Observed HCHO, ppb 0**  **2 4**  **6 8**  **10 Modeled HCHO, ppb**  **1:1 line**

**2:1 line**

**0 2 4 6 8 10 Observed HCHO, ppb**  **0 2**  **4 6**  **8 10**  **CT FL**  **GA IL**  **IN IA**  **LA ME**  **MD MA**  **MI MN**  **MS MO**  **NE NH**  **NJ NY**  **OH PA**  **RI SC**  **TN TX**  **VA VT**  **WI 1:1 line** 

**2:1 line**

**0 2 4 6 8 10**

**Observed CH 3**  **CHO, ppb 0**  **2 4**  **6 8**  **10 Modeled CH** **3 CHO, ppb**  **1:1 line**

**2:1 line**

**0 2 4 6 8 10 Observed CH**  **3 CHO, ppb**  **0 2**  **4 6**  **8 10**  **FL GA**  **IL IN**  **IA LA**  **ME MD**  **MA MI**  **MN MS**  **MO NE**  **NH NJ**  **NY OH**  **PA RI**  **SC TN**  **TX VA**  **VT WI**  **1:1 line**

**2:1 line**Figure 8.**ethene propene** **3-methyl-1-butene 1-butene & isobutene** **1-pentene 1-butene** **2-methyl-1-butene 2-methyl-2-butene** **trans-2-pentene 1,3-butadiene** **styrene cis-2-pentene** **trans-2-butene isomers of pentene** **cis-2-butene**

**0 100000**  **200000 300000**  **400000 500000**  **600000 Emissions, metric tons per year**  **External combustion Internal combustion**  **Industrial Processes Petroleum and Solvent**  **Waste Disposal MACT categories**  **Stationary Fuel Combustion Mobile Sources**  **Solvent Utilization Storage and Transport**  **Waste DTR Miscellaneous** 

**represented by OLE2 represented by OLE1** Figure 9: Annual anthropogenic emissions of methane and the highest 15 non-m ethane alkanes and their allocation by source categories in the NEI. The allocation of explicit species to SAPRC07 model species ( ALK1 through ALK5) is also shown.**methane ethane** **propane n-butane** **isobutane 2,2,4-trimethylpentane** **2,3-dimethylpentane 2-methylbutane** **2-methylpentane N-pentane** **N-hexane 3-methylpentane** **N-heptane methylcyclopentane** **2,3-dimethylbutane 2-methylhexane** 

**0 1000000**  **2000000 3000000**  **4000000 5000000**  **6000000 7000000** **Emissions, metric tons per year External combustion**  **Internal combustion Industrial Processes**  **Petroleum and Solvent Waste Disposal**  **MACT categories Stationary Fuel Combustion**  **Mobile Sources Solvent Utilization**  **Storage and Transport Waste DTR**  **Miscellaneous**

**ALK4 ALK3** 

**ALK1**

**ALK2 ALK5**