1 

1 Regional sources of atmospheric formaldehyde and acetaldehyde, and implications for 

2 atmospheric modeling 

3 

4 D.J. Luecken*, W.T. Hutzell, M.Strum, and G.Pouliot 

5 

6 U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC, 

7 27709 

*

8 corresponding author: Mail Drop E243-03, Tel: +1-919-541-02544, Fax: +1-919-685-3236 

9 Email address: luecken.deborah@epa.gov

10 

11 Abstract 

12 Formaldehyde and acetaldehyde concentrations over the Eastern half of the United States are 

13 simulated with a 3-D air quality model to identify the most important chemical precursors under 

14 January and July conditions. We find that both aldehydes primarily result from photochemical 

15 production, although 25% or more result from direct emissions in urban areas during winter. 

16 Isoprene is the major precursor of formaldehyde in most areas during summer, contributing 20-

17 60% of total production, with the magnitude being spatially variable. Other alkenes from 

18 anthropogenic and/or biogenic emissions dominate formaldehyde production in winter, 

19 contributing 60-85% of total formation, and are prominent contributors in summer. Alkenes, 

20 including biogenic alkenes, dominate acetaldehyde production during both seasons. These 

21 conclusions are based on the degradation of emitted VOCs described by the SAPRC07TB 

22 chemical mechanism, but even this detailed model has difficulty reproducing observed values 

23 better than a factor of 2. The substantial role of isoprene and other alkenes in aldehyde 

24 formation emphasizes that we examine and improve emission estimates of these compounds. 

25 Until we can estimate the emissions and understand the chemistry of VOC precursors to 

26 aldehyde formation with greater certainty, it will be difficult to accurately predict atmospheric 

27 concentrations of aldehydes and develop strategies to reduce their concentrations. 

28 

29 Keywords: Formaldehyde, acetaldehyde, HAPs, photochemical production, isoprene 

30 

31 1.0 Introduction 

32 

33 Formaldehyde (HCHO) and acetaldehyde (CH3CHO) play important roles in atmospheric 

34 photochemistry and air quality. They are a large component of the total VOC reactivity of the 

35 atmosphere, providing sources of new radicals that drive ozone formation (Jeffries, 1995; Steiner 

Manuscript

Click here to download Manuscript: SAPRC07_aldehydes_930_noen.docx Click here to view linked References
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36 et al., 2008; Xie et al., 2011a). They are hazardous air pollutants (HAPs) known or suspected as 

37 carcinogens posing health risks (HEI, 2007; Tilgner and Herrmann, 2010; US EPA, 2011). They 

38 participate in aqueous chemistry due to their high solubility and interactions with other dissolved 

39 species in clouds (Adewuyi et al., 1984; Tilgner and Herrmann, 2010; Warneck, 1989), and 

40 contribute to secondary organic aerosols by producing radicals that oxidize hydrocarbons. The 

41 chemistry of these two compounds is an indicator of our overall understanding of atmospheric 

42 photochemistry. A better understanding of aldehyde production and decay will enable us to 

43 develop and analyze policies that consider the effects of air quality measures on a number of 

44 different atmospheric pollutants. 

45 Both compounds are emitted directly into the atmosphere from a variety of sources. 

46 Anthropogenic emissions of HCHO and CH3CHO are ubiquitous, concentrated in urban areas, 

47 and are among the 25 highest-emitted anthropogenic VOCs in the US (Simon et al., 2010). 

48 Mobile sources and stationary combustion dominate anthropogenic sources. Biogenic sources 

49 include live and decaying plants, biomass burning, and seawater. 

50 Another source of HCHO and CH3CHO is secondary formation from degradation of 

51 VOCs (Atkinson, 1997; Seinfeld and Pandis, 1998), produced in multiple steps during 

52 hydrocarbon oxidation. Equations 1a through 1c show a simple example of HCHO formation 

53 from methane (CH4). Hydrogen abstraction from CH4, followed by fast addition of oxygen to the 

54 alkyl radical (1a) creates a methyl peroxy radical which can oxidize NO to NO2 (1b), creating a 

55 methyl alkoxy radical and eventually leading to ozone formation. A hydrogen atom from the 

56 alkoxy radical is abstracted during the reaction with oxygen (1c) along with elimination of HO2

57 to form formaldehyde: 

58 CH4 + OH + O2 à CH3O2l + H2O (1a) 

59 CH3O2l+ NO à CH3Ol + NO2 (1b) 

60 CH3Ol + O2 à HCHO + HO2 (1c) 

61 Aldehydes can also be formed from alkoxy radicals after scission of a C-C bond, through 

62 photolysis, and through alkoxy radical formation in the absence of nitrogen oxides (NOx), when 

63 reactions of the peroxy radical with HO2 and other peroxy radicals dominate. 
3 

64 Because HCHO and CH3CHO sources include direct emissions and photochemical 

65 production, both sources must be correctly characterized when predicting atmospheric 

66 concentrations. The importance of emissions versus photochemistry depends on location, time of 

67 day and season, and affects any conclusions about how emission changes might be expected to 

68 change aldehydes. An earlier modeling study (Luecken et al., 2006) determined that over 80% of 

69 formaldehyde and acetaldehyde was due to secondary production. In urban areas in winter, 

70 emissions contributed up to 50%. Statistical techniques have been used with measurements to 

71 separate out the two components, concluding that 42% of HCHO is due to primary emissions in 

72 Mexico City (Garcia et al., 2006), 44-95% in Los Angeles (Grosjean et al., 1983), and 

73 approximately 33%-47% in Houston (Friedfeld et al., 2002; Rappenglück et al., 2010). These 

74 measurements represent a “true” value that models must attempt to match, but they are made in 

75 cities with large emission sources, and include uncertainties such as unspeciated background 

76 contributions. Measurements from a few sites may not represent atmospheric processes at other 

77 places and times. 

78 Both aldehydes are included in atmospheric chemical mechanisms although their 

79 representation varies among mechanisms. A large number of organic compounds participate in 

80 atmospheric chemistry and each cannot be explicitly represented in the sequence of secondary, 

81 tertiary, quaternary, etc. reactions that result in the production of aldehydes, especially at 

82 regional-to-global scales. The Master Chemical Mechanism (MCM) (Saunders et al., 2003) 

83 contains over 12,600 reactions for 4500 species, but its size makes it unsuitable for repeated 

84 simulations across a large area. Chemical mechanisms in 3-D air quality models (AQMs) are 

85 significantly condensed, invoking various assumptions and simplifications in the chemistry. 

86 In this study, we use the Community Multiscale Air Quality (CMAQ) model (Byun and 

87 Schere, 2006) with the SAPRC07TB chemical mechanism (Hutzell et al., 2011) to track sources 

88 of ambient HCHO and CH3CHO. A recent evaluation of CMAQ with SAPRC07TB has been 

89 made for ozone and fine particulate matter (PM2.5) components (Hutzell et al., 2011). With this 

90 system, we simulate detailed atmospheric photochemical formation and decay of HCHO and 

91 CH3CHO in both low- and high-NOx conditions, within a realistic environment that is affected 

92 by atmospheric transport, deposition, precipitation, actinic flux, temperature and pressure 

93 gradients, and spatially- and temporally-varying emissions. An increasingly important use of 
4 

94 CMAQ is to assess proposed emission changes on a multipollutant basis, where effects of 

95 emission changes on ambient concentrations of ozone, PM2.5, and hazardous air pollutants are 

96 predicted simultaneously. 

97 The goal of this work is to characterize regional-scale concentrations and chemical 

98 sources of atmospheric HCHO and CH3CHO, and identify which VOCs are the largest producers 

99 of these aldehydes. Our analysis differs from previous studies by 1.) explicitly tracking direct 

100 emissions and photochemical production, 2.) employing a smaller grid resolution than previous 

101 aldehyde modeling studies, 3.) allocating production to specific, emitted compounds, and 4.) 

102 examining spatial and temporal differences in contributions. The ability to identify direct and 

103 indirect contributions to atmospheric HCHO and CH3CHO will help us predict how they will 

104 respond to emission changes, and guide development of policies that reduce ozone and PM2.5 

105 without increasing HAPs. This can also assist in prioritizing research efforts to improve aldehyde 

106 predictions, by focusing on those processes likely to have the largest impact on concentrations. 

107 

108 2.0 Methods 

109 

110 The model and methods have been previously described (Hutzell et al., 2011) and are 

111 briefly summarized here. 

112 

113 2.1 CMAQ model, domain, boundary conditions and meteorology

114 

We applied CMAQ version 4.7.1 (Foley et al., 2009) with a 36x36 km2115 horizontal grid 

covering the continental US and a nested 12x12 km2116 grid over the Eastern half of the US (Figure 

117 1). There are 14 vertical layers, extending from the surface up to about 16 km. Boundary 

118 conditions for the continental simulations were extracted from the GEOS-CHEM model with 

119 those for the nested domain provided by continental CMAQ simulations. Meteorological inputs 

120 were obtained from the MM5 meteorological model (http://box.mmm.ucar.edu/mm5/) version 

121 3.6.1. Simulations were performed for two months, January and July, 2002, after a10-day model 

122 spin up period. 

123 We created emissions using the 2002 National Emissions Inventory (NEI) 

124 (http://www.epa.gov/ttn/chief/net/2002inventory.html). The Sparse Matrix Operator Kernel 

125 Emissions (SMOKE) emission processor version 2.4 (http://www.smoke-model.org) was used to 
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126 create model ready emissions, with speciation rules (http://www.cert.ucr.edu/~carter/emitdb/) for 

127 allocating explicit species to lumped model species. Biogenic emissions were calculated with 

128 Biogenic Emissions Inventory System (BEIS) version 3.13. The 2002 HAP inventory was used 

129 to estimate HAP emissions and other VOCs were derived from speciation of total VOC 

130 emissions (Simon et al., 2010). 

131 

132 2.2 Chemical Mechanism

133 The SAPRC07 mechanism has been extensively evaluated and described in detail 

134 elsewhere (Cai et al., 2010; Carter, 2010). This study uses the SAPRC07TB version, which 

135 includes additional explicit high emission and HAP species, including propene, ethanol, 1,2,4-

136 trimethylbenzene, acrolein, 1,3-butadiene, toluene, three isomers of xylene, and a-pinene. The 

137 CMAQ implementation of SAPRC07TB (Hutzell et al., 2011) includes temperature and pressure 

138 dependencies of the photolysis parameters and extensions for aerosol formation. 

139 

140 2.3 Process and Integrated Reaction Rate Analyses

141 To quantify contributors to aldehyde formation, we implement the Integrated Reaction 

142 Rate (IRR) and Process Analysis (PA) capabilities within CMAQ (Gipson, 1999). This 

143 technique has been useful for understanding ozone production and cycling of radicals and 

144 nitrogen oxides (Jang et al., 1995; Jeffries and Tonnesen, 1994). We use IRR to track radical 

145 and oxidized intermediates which form HCHO and CH3CHO, such as peroxy and acyl peroxy 

146 radicals, methyl vinyl ketone and methyl ethyl ketone and allocate these intermediates to stable 

147 species which are emitted. Products from all VOC reactions are tracked if they eventually react 

148 to form either HCHO or CH3CHO. The entire chemical mechanism is utilized for the CMAQ 

149 simulation (Hutzell et al., 2011), and we use IRR within the simulation to quantify the HCHO 

150 and/or CH3CHO produced through 251 organic reactions (Supplementary Information, Table S151 1). This information is post-processed to quantify HCHO and/or CH3CHO produced from 

152 emitted organic species. 

153 Since some VOCs are both emitted and produced photochemically, we separate the 

154 emitted portion from that formed photochemically, which we track to its precursors. For 

155 example, the model species representing methyl ethyl ketone and similar ketones (MEK), an 
6 

156 emitted VOC, produces HCHO via acetyl and methyl peroxy radicals. But MEK can itself be 

157 produced by isoprene as a second generation product. We calculate the fraction of MEK 

158 concentration at each hour due to emissions, which ranges from 10-95%, depending on location, 

159 and use that to apportion HCHO to either MEK emissions or its precursors (in this example, 

160 isoprene). Similarly, we separate the formation of HCHO from CH3CHO into that fraction due 

161 to emissions and to chemistry. We assume that model species transported into the grid cell have 

162 the same ratio of formation versus emission as the current grid cell. We list results for four 

163 explicit species (isoprene, methane, acetaldehyde and ethanol) and 9 lumped chemical 

164 categories: 

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

166 · ethene and propene 

167 · lumped alkenes: model species OLE1 and OLE2 

168 · aromatics: benzene, m-,p- and o-xylene, toluene, 1,2,4-trimethylbenzene, and 

169 model species ARO1 and ARO2 

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

171 sesquiterpenes and other terpenes 

172 · PANs: peroxyacetyl nitrate, peroxymethacryloyl nitrate, and model species PAN2 

173 (peroxypriopionyl nitrate and higher PANs) and PBZN (from aromatic aldehydes) 

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

175 carbons 

176 · lumped oxygenates: methylglyoxal, acetone, and model species MEK, lumped 

177 ketones (PRD2 and IPRD) and isoprene products (IPRD) 

178 · other: species that individually produce <1% of total formation. 

179 

180 We selected 10 locations throughout the domain, shown in Figure 1, for performing IRR 

181 analyses, to examine how different chemical regimes and emissions mixes affect aldehyde 

182 precursors. These locations include five grid cells examined previously (Hutzell et al., 2011) for 

183 their oxidant chemistry (Louisville, Baltimore, Atlanta, Alabama, N. Georgia), three rural 

184 locations (Alabama, E. Pennsylvania, N. Georgia), and five urban locations where measurements 

185 are available in 2002 for comparison (Chicago, Baltimore, New York, Houston, Minneapolis). 
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These locations, defined by processes within a 12x12 km2186 grid cell at the given location, are not 

187 meant to fully characterize specific cities or regions, but do display the range of variability 

188 expected in contributors to aldehyde formation. 

189 

190 3. Results and Discussion 

191 

192 3.1 Regional concentrations and impact of direct emissions versus photochemical production

193 We focus on results over the Eastern nested domain because the finer scale more 

194 accurately characterizes spatial variability in meteorological and photochemical processes. 

195 Figure 2 shows monthly-averaged concentrations of HCHO in January and July, and fraction of 

196 overall concentration due to photochemistry. The remainder is attributed to direct HCHO 

197 emissions. 

198 In Figure 2, modeled HCHO concentrations are higher in July than January, by over a 

199 factor of 4, due to higher actinic flux, radical concentrations and precursor emissions of biogenic 

200 VOCs in summer. During winter, concentrations are highest in urban areas where emissions of 

201 HCHO are largest; in these areas emissions account for 25-50% of total HCHO. During summer, 

202 HCHO emissions contribute much less: 6-25% . The photochemical contribution and 

203 concentration of HCHO is highest in the Southeast, consistent with satellite observations (Millet 

204 et al., 2008; Palmer et al., 2006) and earlier modeling results (Luecken et al., 2006). 

205 Figure 3 shows concentrations and emission fractions for CH3CHO. The seasonal 

206 patterns are similar to HCHO, but concentrations are lower by half and the contribution of 

207 photochemistry is greater, accounting for over 80% of total CH3CHO in winter and 95% in 

208 summer. 

209 Figure 4 displays seasonal differences in the role of emissions versus production at the 10 

210 locations, and the average over the entire domain. For this sampling of grid cells, primary 

211 emissions contribute at most 55% of the HCHO in January (Louisville). In summer, emissions 

212 contribute less than 20% at all locations. 

213 

214 3.2 Chemical precursors of aldehydes

215 
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216 Relative contributions of different VOCs to aldehyde production depend on the 

217 magnitude of emissions and yield of aldehydes per molecule of reacting VOC. We use IRR and 

218 PA analyses to quantify the role of different VOCs by summing reaction rates and yields over 

219 two periods: January 14-16, 2002 and July 15-17, 2002, focusing on 10 locations. 

220 Fractional HCHO formation attributed to individual VOCs and lumped classes is shown 

221 in Figure 5. In general, isoprene is the dominant precursor of HCHO during July, although the 

222 contribution varies spatially. In the Southeast, such as the location near urban Atlanta, isoprene 

223 contributes approximately 50-65% of total formation. In the Mid Atlantic location near 

224 Baltimore, isoprene contributes approximately 20% of HCHO formation. The reaction of 

225 isoprene with OH dominates HCHO formation when isoprene is present, accounting for 75-95% 

226 of HCHO attributed to isoprene, largely through an operator ( xHCHO) which yields 80-99% 

227 HCHO in later sequences. Another operator produced by isoprene (xIPRD), produces reactive 

228 oxygenated products (IPRD), providing an additional 3-8% HCHO formation. During January, 

229 when emissions of isoprene are reduced, production is dominated by lumped and explicit 

230 alkenes. In the locations near New York and Louisville, explicit and lumped alkenes are larger 

231 contributors than isoprene, even in July. Methane and other alkanes also contribute to HCHO 

232 formation. 

233 Figure 6 displays VOC contributors to CH3CHO production. Isoprene plays a minor role 

234 in CH3CHO production, and alkenes contribute the largest amount. Terpenes and cyclic terpenes 

235 are major contributors in Southeastern locations. Alkanes and PANs, particularly 

236 peroxyproprional nitrate, contribute in some locations. Ethanol contributes up to 5% of 

237 CH3CHO production, and indirectly produces HCHO through CH3CHO decay. 

238 While lumped alkenes are the largest relative contributors in January, the absolute 

239 contribution of these compounds to HCHO and CH3CHO formation is smaller than in July. Both 

240 compounds are formed in larger amounts in summer (20-50 times higher for HCHO and 7-40 

241 times higher for CH3CHO) because the major reaction involves the hydroxyl radical (OH), which 

242 can be 5 times or more higher in summer than winter (Ren et al., 2006). Supplementary Figure 

243 S-1 shows a comparison of the total production of HCHO and CH3CHO from lumped alkenes for 

244 both periods. Additional factors, such as warmer temperatures, higher actinic fluxes and greater 
9 

245 biogenic emissions increase formation in summer. The same seasonal factors that cause higher 

246 production rates via OH also cause higher destruction through OH, so concentration differences 

247 between summer and winter are not as large as suggested by formation rate differences (Figures 

248 2 and 3). 

249 

250 3.3 Comparisons of model results with observations

251 Figure 7 compares modeled HCHO and CH3CHO with 1, 3 and 24 hr-averaged 

252 observations from the PAMs network (www.epa.gov/air/oaqps/pams), where the sites are 

253 differentiated by state. The observations are limited and insufficient for fully evaluating the 

254 model, but do serve as a touchstone to determine whether model predictions are realistic. One 

255 limitation of these observations is that sampling frequency varies: some sites report every day, 

256 others report every 3-12 days or longer, sometimes only one measurement is reported during the 

257 month. In addition, the monitoring sites are sparse and unevenly distributed spatially, making it 

258 difficult to evaluate predictions from a regional model. In July, 1299 observations from 96 sites 

259 were available, versus over 41 million model surface predictions, so we are evaluating only a 

260 small fraction of predicted values. In January, only 79 sites and 331 observations were available. 

261 Observations are particularly lacking in rural areas and the Southeast where high isoprene 

262 emissions and HCHO concentrations are predicted. 

263 In comparison with these limited observations, both compounds are underestimated by 

264 the CMAQ simulations, especially at higher concentrations. In July, the mean bias (MB) is -4 

265 ppb, and normalized mean bias (NMB) is -0.59. When the highest 5% of observed values are 

266 removed, some of which are extremely high, the MB is reduced to -2.43 and NMB to -0.47. In 

267 January, the MB is -1.5 and NMB is -0.69, while with the top 5% of values removed, the MB is -

268 0.83 and NMB is -0.55. In many states the bias is much less. It is unclear why some states are 

269 modeled well but others underpredicted. At some sites, such as those located around Chicago 

270 (IL), and Minneapolis (MN), the model matches predictions well, while others, including New 

271 York (NY) and RI are modeled 50% too low. Others, including the Baltimore site (MD) fall in 

272 between. 

273 Previous attempts to model HCHO had limited success; sometimes models overpredict, 

274 but more often underpredict measured values at the surface. Previous comparisons of CMAQ 
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275 predictions for HAPs (Luecken et al., 2006) showed that observations could generally be 

276 predicted within a factor or two. Comparisons of a box model and GEOS-CHEM predictions 

277 against data from the 2004 INTEX campaign (Fried et al., 2008) similarly showed 50% 

278 underpredictions. 

279 Several possible causes of differences between models and measurements have been 

280 noted previously. One cause is error in the measurements. Numerous studies have identified 

281 interferences in measurements of HCHO and CH3CHO (Arnts and Tejada, 1989; Herrington et 

282 al., 2006; Karst et al., 1993; Sirju and Shepson, 1995), which can cause positive or negative 

283 biases. Another possible cause is mismatch between point measurements and volume average 

284 concentrations predicted by models. The presence of large, localized sources may explain the 

inability of a 12 x 12 km2285 grid cell to capture the highest values. In a few cases, these values are 

286 abnormally high: one site in Michigan reports a 24-hour average HCHO concentration on July 

13, 2002 of 71.1 mg m-3, while the next measurement on July 25 is 4.81 mg m-3 287 . This monitor is 

288 south of downtown Detroit, in an area with industrial units, residential housing and highway 

289 traffic, so it could be influenced by intermittent high point emissions. A nearby monitor reported 

5.79 mg m-3 290 on July 13. 

291 Other errors could occur in specification of emissions or chemistry. To identify the 

292 largest potential sources of error causing underpredictions, we use information from Section 3 to 

293 focus on those chemicals with the largest influence on aldehyde predictions. We discuss the 

294 potential magnitude of uncertainties in emissions and chemistry of these VOCs, to prioritize 

295 areas for improving aldehyde predictions. 

296 

297 4.0 Implications for modeling the atmospheric chemistry of aldehydes 

298 

299 Our ability to accurately predict ambient concentrations of formaldehyde and 

300 acetaldehyde depends on our ability to predict concentrations of all VOCs. In this section, we 

301 examine factors influencing the concentrations of the most important VOC precursors identified 

302 in Section 3.2. We first discuss uncertainties involved in modeling emissions, and then 

303 uncertainty in modeling the chemistry of these compounds. 

304 4.1 Aldehyde emissions and decay
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305 Emissions of HCHO and CH3CHO can be important sources of ambient concentrations, 

306 but atmospheric production is generally larger (Figures 2 and 3). For both aldehydes, emissions 

307 derived from the NEI are less than emissions derived from total VOC emissions applying 

308 speciation profiles (Simon et al., 2010). Differences between the two values may be caused by 

309 lack of reporting requirements, different emission factors and speciation profiles. In the most 

310 extreme case (Louisville in January), using speciation profiles instead of the NEI could increase 

311 our modeled HCHO concentrations by a maximum of 50% and CH3CHO by 30% in winter, and 

312 HCHO up to 20% in summer, reducing some underprediction. In other areas, especially in 

313 summer, the change would only be a few percent. This suggests that direct emissions of 

314 formaldehyde and acetaldehyde are not likely to be the only cause of model underprediction. 

315 Emissions of larger aldehydes and subsequent photochemical decay are also a source of smaller 

316 aldehydes, and errors in these emissions could also increase the HCHO up to 24% and CH3CHO 

317 up to 29%. 

318 The photochemical loss reactions of HCHO and CH3CHO have been well studied and are 

319 not likely to have major errors. Slightly more than half of the photochemical loss is through 

320 photolysis, with most of the remainder from reaction with OH. 

321 

322 4.2 Isoprene

323 Isoprene has been identified as a major contributor to HCHO in summer (Carslaw et al., 

324 2000; Millet et al., 2006; Sumner et al., 2001), consistent with our study. Our results also show a 

325 substantial degree of spatial variability across the region, with contributions less than 20% in 

326 some areas. In rural locations, isoprene contributes about half of the HCHO production in 

327 summer. The ability to accurately predict HCHO concentrations in summer strongly depends on 

328 accurately predicting isoprene concentrations, which depends on accurately representing both 

329 isoprene emissions and chemistry. 

330 Isoprene is almost entirely emitted by biogenic sources and estimates of the source strength are 

331 uncertain over regional scales. Several models of isoprene emissions are available, but they give 

332 different estimates. A comparison of four models showed a factor of two differences among 

333 models (Warneke et al., 2010). In modeling studies using BEIS, isoprene concentrations are 
12 

334 underestimated (Doraiswamy et al., 2009), overestimated (Wiedinmyer et al., 2005) and 

335 reasonably well estimated (Hogrefe et al., 2011). Table 2 shows examples of differences that 

336 might be seen between inventories developed using two different models for biogenic emissions; 

337 the BEIS and MEGAN (Guenther et al., 2006) models, at each of the 10 locations. Isoprene 

338 emissions estimated by these two models can differ by 75% or more, with emissions from 

339 MEGAN generally larger than BEIS. A factor of 2 error in isoprene emissions could increase 

340 modeled formaldehyde by up to 80%. The large influence of isoprene on HCHO combined with 

341 large uncertainties in the emissions of isoprene introduces significant uncertainties in HCHO 

342 predictions. 

343 The atmospheric chemistry of isoprene is currently being re-examined, and it is unclear 

344 how updated isoprene chemistry will affect predictions of HCHO. Inclusion of the 1,5-H 

345 isomerization reactions of b-hydroxyalkenyl peroxy radicals increased OH, and this channel 

346 would increase the HCHO production proportionally (Archibald et al., 2010a). Similarly, 

347 consideration of the 1,6-H shift leads to hydroperoxy aldehydes, yielding additional HCHO after 

348 rapid photolysis, along with OH and HO2 (Peeters, 2010; Stavrakou et al., 2010). Increased OH 

349 radical regeneration in nonurban environments could increase production of HCHO through 

350 other VOCs. Updated isoprene chemical reactions can have significantly higher HCHO yields 

351 from d-hydroxy isoprene nitrates (Paulot et al., 2009), and using this chemistry in CMAQ can 

352 potentially increase modeled HCHO less than 10% overall (Xie et al., 2011b). A critical 

353 evaluation of reaction products must be performed for any new isoprene mechanism. Because 

354 no consensus has yet been reached on the best way to represent isoprene chemistry, we offer no 

355 recommendation here on improvements, but do expect it will affect HCHO. 

356 

357 4.3 a-pinene, terpenes and sesquiterpenes

358 The terpenes and cyclic terpenes are known contributors to SOA formation, and this 

359 analysis shows that they also contribute to aldehyde formation. Acetaldehyde (up to 25%) and 

360 formaldehyde (6%) production depends on correctly estimating emissions of terpenes. These 

361 results agree with a previous analysis over Europe (Dufour et al., 2009) in which terpene 

362 emissions accounted for 8% of average CH3CHO in Western Europe, and up to 25% locally. 

363 Like isoprene, these chemicals are almost entirely biogenic but their emissions have not been 
13 

364 studied in as much detail. We conclude that a similar uncertainty as isoprene is likely; 

365 uncertainty of a factor of 2 could increase acetaldehyde concentrations by 25%. 

366 The chemistry of terpenes beyond first generation products is also not well known, and 

367 many assumptions were made in its condensed representation. A comparison of SAPRC07 with 

368 the MCM, which has a more complete description of intermediate chemistry, found that MCM 

369 predicted 2.3 times the photochemical ozone creation potential (POCP) for a-pinene than 

370 SAPRC07, almost twice as much for b-pinene, and 2.8 times more than limonene. This was 

371 attributed to different treatments of degradation chemistry beyond first generation products 

372 (Derwent et al., 2010). While ozone reactivity is not the same as aldehyde formation, they both 

373 occur through similar pathways and it is possible that aldehyde formation would be similarly 

374 lower in SAPRC07TB. If the mechanistic reactivity of the terpenes is underrepresented in 

375 SAPRC07TB by a factor of 2, the CH3CHO might be expected to increase up to 25%. 

376 

377 4.4 Other alkenes

378 

379 Other alkenes, including ethene, propene, and larger alkenes and dienes, are major 

380 contributors to both HCHO and CH3CHO formation, especially in the January simulation. In a 

381 previous allocation of acetaldehyde sources (Millet et al., 2010), alkenes were about half as 

382 important as alkanes and slightly larger than ethanol. This contrasts with our findings, where 

383 alkenes contribute the majority of acetaldehyde production, perhaps due to inclusion of biogenic 

384 emissions. Many biogenic alkenes that are not isoprene or terpenes, such as 2-methyl-3-butene385 2-ol, are lumped in OLE1 and OLE2. Table 2 lists the fraction of lumped alkenes from biogenic 

386 sources at each of the 10 locations (Supplementary Figure S-2 shows the fraction across the 

387 Eastern US). In contrast to isoprene, alkenes from MEGAN are generally lower than BEIS for 

388 most locations, so using MEGAN would lower aldehyde production through alkenes. Are there 

389 reactive VOCs not predicted by MEGAN or BEIS that might contribute to HCHO formation? 

390 Photochemical modeling at Blodgett Forest (Choi et al., 2010) could not reconcile measured and 

391 modeled HCHO concentrations, postulating a missing reactive biogenic VOC source. Other 

392 analyses (Kim et al., 2011) found no evidence for significant missing biogenic VOCs in a branch 

393 enclosure measuring OH reactivity, and attribute discrepancies to unmeasured isoprene oxidation 
14 

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

395 aldehyde model underpredictions. 

396 Anthropogenic sources of lumped alkenes have equal or greater importance for aldehyde 

397 formation at all locations in winter, and urban locations in summer. Studies in Houston, Texas 

398 (Wert et al., 2003) agree with our study by pointing to anthropogenic emissions of propene and 

399 ethene as major HCHO precursors. Figure 8 shows fifteen alkenes with the highest emissions 

400 and general source categories from which they are emitted (Simon et al., 2010). Mobile sources 

401 are the largest source of alkenes, along with emissions from general industrial sources, stationary 

402 combustion, and waste disposal, treatment and recovery. Mobile source estimation methods rely 

403 on models that account for emission differences due to fuel type, vehicle technology, speed, 

404 temperature, and modes of operation. There are uncertainties in the variability of vehicle fleets 

405 and fuels across the country, and in parameters used to convert monthly modeled emissions to 

406 hourly gridded emissions needed by models. For mobile sources, VOC speciation is generally 

407 well-characterized, and comparisons for HAP species such as HCHO and CH3CHO, which are 

408 reported both in the NEI inventory and computed from VOC speciation, are generally within 

409 25% (Simon et al., 2010). Comparisons between emissions from the current MOBILE6 

410 emissions model and the newer MOVES model show that mobile source VOC emissions from 

411 MOVES are similar to those from MOBILE6, sometimes slightly lower (Cook et al., 2010; 

412 Houyoux et al., 2009), or slightly higher (Kite, 2011). 

413 It is difficult to evaluate integrated emission inventories using ambient observations 

414 because measured concentrations include effects of meteorology, deposition, chemistry and 

415 emissions. In a comparison of measurements in the Pacific Northwest with CMAQ modeling, 

416 the VOC reactivity of propene was overpredicted; reduction of the VOC inventory by 30% was 

417 needed for the model to match afternoon observations of ozone production efficiency (Xie et al., 

418 2011a). Similarly, a modeling study using CMAQ in the Northeastern US found that modeled 

419 ethene concentrations generally overpredicted morning measurements by 20-100% (Doraiswamy 

420 et al., 2009). In contrast, an analysis using emission ratios found that inventories of alkenes in 

421 the NEI were underpredicted (Warneke et al., 2007). It is unclear whether anthropogenic alkene 

422 emissions in the NEI are too high or low; while errors in the inventory might increase HCHO 

423 and CH3CHO by 22 and 24% in the winter, they might also decrease predictions. 
15 

424 Beyond the simplest alkenes, chemistry becomes more complex, and reactions cannot be 

425 easily generalized in a condensed mechanism. Numerous unsaturated compounds are emitted but 

426 SAPRC07TB only represents ethene and propene explicitly; the rest are grouped into two model 

427 species, OLE1 and OLE2, depending on reaction with OH. On a regional basis, ethene, propene 

428 and methyl-substituted butenes make up a majority of alkene emissions (Figure 8). Reactions of 

429 these alkenes with OH - the most important decay pathway - have estimated uncertainties of 20-

430 30% (Calvert et al., 2000). In these reactions, the description of b-hydroxy peroxy and alkoxy 

431 radical reactions that follow the initial reactions have either been measured or estimated, and 

432 explicitly representing glycoaldehyde in SAPRC07TB allows for accurate representation of the 

433 fate of b-hydroxyalkoxy radicals from ethene. For larger alkenes, isomerization and reaction 

434 with oxygen play a larger role. Reactions of alkenes with ozone are less straightforward - 

435 reactions of Criegee intermediates and OH radical generation is an area requiring additional 

436 laboratory and modeling studies (Calvert et al., 2000). A 30% uncertainty in chemistry could 

437 change aldehyde predictions by 3-24%, although concentrations could increase or decrease. 

438 

439 4.5 Alkanes

440 Methane and other alkanes contribute to HCHO and CH3CHO formation. Simulations 

441 using the CHIMERE model (Dufour et al., 2009) also identified methane as the dominant source 

442 of HCHO in Western Europe, with isoprene playing a lesser role. Despite the low reactivity of 

443 methane (maximum incremental reactivity for ozone 0.001 times that of isoprene) and exclusion 

444 from the definition of VOC in the United States (US GPO, 2011), large emissions make methane 

445 an important hemispheric driver of atmospheric photochemistry. Methane contributes to ozone 

446 formation (Fiore et al., 2008; Jeffries, 1995); our results finds methane is also an important 

447 contributor to HCHO. Larger alkanes can contribute up to 20% of CH3CHO formation. 

448 Anthropogenic sources of alkanes generally dominate biogenic sources in our 

449 simulations. Figure 9 shows annual emissions of methane and the fifteen largest VOC alkanes. 

450 Mobile sources are important, along with other industrial, waste and transport emissions. 

451 Landfills are a major source of methane, while storage and transport of petroleum contributes to 

452 n-butane emissions. Anthropogenic emissions from the NEI have been reported to differ from 

453 emission ratios in the Northeastern US by up to an order of magnitude (Warneke et al., 2007) 
16 

454 based on measurements and calculated photochemical age. These discrepancies with the NEI 

455 must be further examined to quantify uncertainty in alkane emissions. 

456 Alkane chemistry is generally better understood than other species, although the 

457 representation of larger and multifunctional alkanes in air quality models is highly condensed. 

458 Despite uncertainties inherent in representing a large number of species by a small subset, 

459 Derwent et al. (2010) found that the POCP of alkanes modeled with SAPRC07 correlated well 

460 with those using MCM, a more detailed representation. This gives confidence that SAPRC07 is 

461 representing our knowledge of alkane degradation similarly to MCM. 

462 4.6 Ethanol

463 Potential changes in acetaldehyde concentrations from fuel reformulation has been 

464 recently studied (Cook et al., 2011; Jacobson, 2007). Our simulations predict ethanol contributes 

465 only 3-6% of photochemically-produced acetaldehyde. One recent study (Millet et al., 2010) 

466 calculated that converting the US vehicle fleet to 85% ethanol fuel would increase acetaldehyde 

467 emissions by 21% and double ethanol emissions. We estimate that such a change would increase 

468 acetaldehyde by an average of 6%, possibly more near roadways. Using ethanol fuels will affect 

469 concentrations of other VOCs (Cook et al., 2011; Graham et al., 2008; Jacobson, 2007; US EPA, 

470 2010), which could be as important as changes in CH3CHO or ethanol. Changes in alkene 

471 emissions, in particular, would impact acetaldehyde. Effects of fuel reformulations on these 

472 other VOCs must be accurately characterized to predict their impact on acetaldehyde. 

473 4.7 Other chemicals

474 Our analysis does not find a major role for aromatic compounds in producing HCHO or 

475 CH3CHO. While aromatic chemistry is still uncertain, even a major increase in HCHO formation 

476 from aromatics would not make them significant compared to other chemicals. 

477 Emissions of methanol and acetone (part of the lumped oxygenates category) contribute 

478 less than 2% of total HCHO. Recent analyses of aircraft data in the Pacific Northwest (Xie et al., 

479 2011a) postulate that emissions of methanol and acetone would have to increase by factors of 30 

480 and 25, respectively, to account for observed values of these compounds. If acetone emissions 

481 were 25 times too low in the domain modeled here, HCHO could increase by nearly 20%, 

482 bringing the modeled values closer to observations. Increasing methanol would increase its role 
17 

483 in HCHO formation similarly, but isoprene would continue to remain the most important 

484 precursor in most areas in summer. Neither methanol nor acetone is considered a VOC (US 

485 GPO, 2011) and must be derived from inventories of total organic gas. 

486 Lastly, is it possible that our simulations underestimate OH radical concentrations, thus 

487 underestimating a critical first step in degradation of stable VOCs? Studies comparing box 

488 model predictions to measurements find both underestimates (Chen et al., 2010; Hofzumahaus et 

489 al., 2009) and some overestimates (Emmerson et al., 2007). Measurements of OH and HO2 are 

490 difficult so mechanisms have not been thoroughly evaluated for predictions of these radicals. 

491 OH participates in both HCHO production and decay, so errors in OH will not change HCHO in 

492 a linear fashion. If OH is larger than predicted by SAPRC07TB, this would explain some of the 

493 HCHO and CH3CHO underpredictions. 

494 

495 5.0 Summary 

496 Because the chemistry of HCHO and CH3CHO is integral to almost all VOC decay and 

497 production pathways, it reflects our current understanding of VOC photochemistry and tests our 

498 entire mechanistic understanding of atmospheric chemistry. To accurately model their 

499 concentrations, we must correctly account for emissions, transport, deposition, and the 

500 degradation chemistry of not only aldehydes, but also VOC precursors to aldehydes. In this 

501 study we conclude that: 

502 1. Atmospheric photochemical production accounts for most of the HCHO and 

503 CH3CHO concentrations in the ambient atmosphere. Aside from dense 

504 urban/industrial areas, reduction of HCHO and CH3CHO emissions is expected to 

505 have a small effect on reducing concentrations. In a few areas, especially in winter, 

506 emissions are important and a large source of uncertainty is differences between 

507 emissions from the NEI and emissions based on speciated VOC. 

508 2. Substantial spatial and seasonal variation occurs in how much various VOCs 

509 contribute to photochemical production of aldehydes. For example, isoprene is not 

510 always the dominant VOC producing HCHO, even during summer. 
18 

511 3. Biogenic emissions of compounds that photochemically react to produce HCHO and 

512 CH3CHO are highly uncertain and a potential source of error in aldehyde predictions. 

513 4. Resolving uncertainties in the chemistry of isoprene and other alkenes is critical for 

514 chemical mechanisms to accurately predict concentrations of aldehydes. Newly 

515 proposed isoprene mechanisms must be evaluated for a wide range of species, 

516 including HCHO. 

517 5. Anthropogenic alkenes, largely from mobile sources, play an important role in 

518 aldehyde formation. While mobile sources are perhaps better represented in the NEI 

519 than other categories, new uncertainties are constantly being introduced by changes in 

520 vehicles and fuels. 

521 6. The CMAQ air quality model tends to underpredict aldehyde concentrations. At 

522 many sites this underprediction is close to 50%. 

523 While much effort has spent developing condensed chemical mechanisms that are as 

524 representative of the underlying chemicals as possible, there will necessarily be compromises. 

525 Even the most explicit mechanisms, such as the MCM, rely on some degree of condensation. It 

526 is encouraging that comparisons between explicit mechanisms and condensed mechanisms are 

527 generally consistent (Archibald et al., 2010b; Derwent et al., 2010), although carbonyls tend to 

528 be one class of compounds which are often predicted differently among mechanisms. 

529 The general underprediction of air quality models for aldehydes is an area which requires 

530 more investigation. Because aldehyde decay is a large source of new radicals which drive 

531 photochemistry, this raises questions about the robustness of predictions for other photochemical 

532 pollutants, such as ozone and PM2.5; species which air quality models predict well. HCHO, in 

533 particular, is an important ozone precursor. 

534 Our findings on the most significant chemical sources of HCHO and CH3CHO are based 

535 on our best assessment of emissions and chemistry on a regional scale. The existence of many 

536 uncertainties implies that the contributions of different VOCs to aldehyde formation may change 

537 somewhat as our knowledge of chemistry and emissions improves. Refining our understanding 

538 of aldehyde behavior in the atmosphere is an iterative and imperfect process involving 

539 measurements, modeling, and collecting improved data on emission sources. New information 
19 

540 will help to more accurately bound our understanding of how air quality might change under 

541 future emissions scenarios, and build confidence in using regional models for air quality 

542 management and research. 

543 

544 6.0 Acknowledgments 

545 The authors acknowledge helpful discussions with our US EPA colleagues Ted Palma and Ying Xie, and 

546 with William Carter, who developed this SAPRC07TB mechanism. We greatly appreciate the numerous 

547 helpful suggestions from our internal and external reviewers, all of whom have helped focused the 

548 purpose of this manuscript and clarified the presentation. The United States Environmental Protection 

549 Agency, through its Office of Research and Development, funded and managed the research 

550 described here. It has been subjected to Agency administrative review and approved for publication.

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753 W., Koerber, M., Janssen, M., 2005. Ozarks Isoprene Experiment (OZIE): Measurements and 

754 modeling of the "isoprene volcano". Journal of Geophysical Research 110, D18307. 
25 

755 Xie, Y., Elleman, R., Jobson, T., Lamb, B., 2011a. Evaluation of O3-NOx-VOC sensitivities 

756 predicted with the CMAQ photochemical model using Pacific Northwest 2001 field 

757 observations. Journal of Geophysical Research 116, in press. 

758 Xie, Y., Paulot, F., Pinder, R.W., Carter, W.P.L., Nolte, C.G., Luecken, D.J., Hutzell, W.T., 

759 Wennberg, P.O., Cohen, R., 2011b. Understanding the impact of isoprene nitrates and OH 

760 reformation on regional air quality using recent advances in isoprene photooxidation chemistry, 

761 9th Annual CMAS Conference, Chapel Hill, NC. 

762 

763 

764 
 

Listing 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 mg/m3 and are averaged over the entire 

month 

Figure 3: Average CH3CHO concentration (left) and fraction of total due to photochemical production 

(right) in January (top) and July (bottom). Concentrations are in mg/m3 and are averaged over the entire 

month 

Figure 4: Average fraction of HCHO and CH3CHO 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 CH3CHO 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. 

Figure
Table 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 1.00 

 
Figure 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. Penn
 HCHO concentration Fraction of HCHO due to photochemistry 

 

 

Figure 2 

 

January July
 CH3CHO concentration Fraction of CH3CHO from photochemistry 

 

 

Figure 3 

 

January July 
Figure 4 

 

Atlanta

N.Georgia

Alabama

Chicago

Louisville

Baltimore

Houston

E.Penn

Minn

New York

mean

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

CH3CHO, Jan

CH3CHO, July
Figure 5 

Alabama

Chicago

Louisville

Baltimore

Houston

E.Penn

Minn

New York

Atlanta

N.Georgia

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

Alabama

Chicago

Louisville

Baltimore

Houston

E.Penn

Minn

New York

Atlanta

N.Georgia

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

July
Figure 6 

Alabama

Chicago

Louisville

Baltimore

Houston

E.Penn

Minn

New York

Atlanta

N.Georgia

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Fraction of total CH CHO formation 3

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7 Jan

Alabama

Chicago

Louisville

Baltimore

Houston

Minn

E.Penn

New York

Atlanta

N.Georgia

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Fraction of total CH CHO formation 3

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

July
 

 

Figure 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

CT 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

0 2 4 6 8 10

Observed CH3CHO, ppb

0

2

4

6

8

10

Modeled CH CHO, ppb 3

1:1 line

2:1 line

0 2 4 6 8 10

Observed CH3CHO, ppb

0

2

4

6

8

FL 10

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-butene2-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-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. 

 

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