Modelling the ambient distribution of organic compounds during

the August 2003 ozone episode in the southern UK

Steven R. Utembe,a Michael E. Jenkin,*a Richard G. Derwent,a Alastair C. Lewis,b

James R. Hopkinsb and Jacqueline F. Hamiltonb

aDepartment of Environmental Science and Technology, Imperial College London,

Silwood Park, Ascot, Berkshire, UK, SL5 7PY. E-mail: m.jenkin@imperial.ac.ukbDepartment of Chemistry, University of York, Heslington, York, UK, YO10 5DD

Received 16th November 2004, Accepted 1st February 2005First published as an Advance Article on the web 20th April 2005

A photochemical trajectory model containing speciated emissions of 124 non-methane

volatile organic compounds (VOC), and a comprehensive description of the chemistry of

VOC degradation, has been used to simulate the chemical evolution of boundary layer air

masses arriving at a field campaign site in the southern UK during a widespread and

prolonged photochemical pollution event in August 2003. The simulated concentrations

and distributions of organic compounds at the arrival location are compared with

observations of a series of hydrocarbons and carbonyl compounds, which were measured

using GC-FID and multidimensional GC methods. The comparison of the simulated and

observed distributions of 34 emitted hydrocarbons provides some support for the

magnitude and applied emissions speciation of anthropogenic hydrocarbons, but is

indicative of an under representation of the input of biogenic hydrocarbons, particularly at

elevated temperatures. Simulations of the detailed distribution of ca. 1250 carbonyl

compounds, formed primarily from the degradation of the 124 emitted VOC, focus on 61

aldehydes, ketones, dicarbonyls, hydroxycarbonyls and aromatic aldehydes which

collectively account for ca. 90% of the simulated total molar concentration of carbonyls.

The simulated distributions indicate that the photolysis of formaldehyde and a-dicarbonylsmake major contributions to free radical production for the arrival conditions of five case

study trajectories. The simulated concentrations of hydroxycarbonyls demonstrate

preferential formation of the 1,4-substituted isomers (compared with 1,2- and 1,3-isomers

of the same carbon number), which are formed during the initial oxidation sequence of

longer chain alkanes.

1. Introduction

Volatile organic compounds (VOC) are emitted from both biogenic and anthropogenic sources,1,2

and have a major influence on the chemistry of the lower atmosphere. It is well documented that thedegradation of VOC plays a central role in the generation of a variety of secondary pollutants3–6

(e.g., ozone and secondary organic aerosol, SOA), which have harmful impacts on human healthand on the environment. Emissions speciation data indicate that many hundreds of VOC are

DOI: 10.1039/b417403h Faraday Discuss., 2005, 130, 311–326 311

This journal is r The Royal Society of Chemistry 2005

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emitted,7 which possess a variety of physico-chemical properties, by virtue of differences in structureand functional group content. Consequently, the mechanism by which each emitted VOC isdegraded is essentially unique, and the atmospheric oxidation of all emitted VOC (ultimately toCO2 and H2O) generates thousands of partially oxidised intermediate organic products. Theseproducts possess a wide variety of properties (e.g., reactivity; volatility), which can influence theeffect that precursor VOC emissions have on the formation of ozone or SOA. For example, theformation of photolabile carbonyl products (e.g. formaldehyde) can lead to additional radicalproduction, thereby influencing oxidation rates and secondary pollutant formation in evolvingphotochemical plumes. Alternatively, unreactive carbonyl products (e.g. acetone) have little impacton local oxidation processes, but can undergo long range transport and contribute to free radicalproduction in the upper troposphere.8 The possible formation of bi- and multifunctional organicproducts through sequential oxidation steps is also of significance, because such products aregenerally of lower volatility and/or higher aqueous solubility and are thus more likely to contributeto SOA formation.Modelling studies using detailed emitted VOC speciation and comprehensive descriptions of the

chemistry of VOC degradation are able to quantify the roles played by individual VOC inatmospheric chemistry generally, and in ozone formation in particular. Furthermore, they allowthe prediction of the patterns and structures of oxidised products, such as simple and multi-functional carbonyls, thereby allowing a much more detailed examination of atmospheric chemicalmechanisms.In the present paper, the results of detailed simulations of the ambient distributions of organic

compounds are described. A photochemical trajectory model containing speciated emissions of 124non-methane VOC, and a comprehensive description of the chemistry of VOC degradation(provided by the Master Chemical Mechanism and the Common Representative IntermediatesMechanism), has been used to simulate the chemical evolution of boundary layer air masses arrivingat a field campaign site in the southern UK, during the August 2003 photochemical pollutionepisode. The simulated concentrations and distributions of organic compounds at the arrivallocation are compared with observations of a series of hydrocarbons and selected carbonylcompounds, which were measured as part of the first campaign of the Tropospheric OrganicChemistry Experiment (TORCH). The results are discussed in terms of the magnitude andspeciation of emitted VOC, the distribution of simple and multifunctional carbonyls formed asdegradation products, and the impacts of these carbonyls on free radical formation.

2. The TORCH-1 campaign

2.1. Campaign overview

In late July and August 2003, six research institutes in the UK took part in a field measurementcampaign as part of the Tropospheric Organic Chemistry Experiment (TORCH) consortiumproject. Measurements took place at Writtle College, Writtle, Essex, UK (511 440 1200 N; 01 250

2800 E), a site approximately 25 miles to the north-east of central London. The site itself was a fouracre grass field situated at the south east edge of the main college buildings, which was unimpactedby any significant local vehicular, domestic or industrial sources. It was situated two miles from thenearest major ‘A’ road (to the south), with the prevailing south-westerly fetch passing over an areaof flat arable farmland with scattered buildings for a distance of approximately 10 miles beforereaching the eastern edge of the major M25 motorway. All instrumentation at the site was housed inmobile laboratories, with a common 14 m sampling tower and low residence time manifold. Powerfor instruments was provided by a 120 kVa diesel powered generator, situated ca. 200 m to the eastof the sampling location. All data from this sector were removed accordingly.The main feature of the campaign was a persistent period of high pressure, which brought stable

conditions for the first two weeks of August. This was accompanied by successive days of long hoursof sunshine (i.e. cloudless skies) and high temperatures. The period was characterised by low windspeeds (2–4 m s�1), with broad air flow from the European continent and recirculation over the UK.The highest temperatures (i.e., in excess of 30 1C) were observed during the period 6–11 August.

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2.2. Measurements of hydrocarbons and organic oxygenates

A series of volatile organic compounds were measured during the experiment using two in situ gaschromatography systems. A two-column Perkin Elmer AutoXL GC with two FID detectors wasused in combination with a Peltier cooled adsorbent sampling system to determine C2–C8 non-methane hydrocarbons (NMHC, including alkanes, alkenes, alkynes and aromatics), and smalloxygenated molecules. The instrument made one measurement per hour, with 10 min samplingintegration collecting a 1 l volume of air followed by 50 min for analysis and instrument re-equilibration. Separation of hydrocarbon species was made using a Al2O3 porous layer opentubular (PLOT) capillary column, and separation of oxygenated species made with a mixed phaseLowox PLOT column. The sample was split approximately 50:50 between columns, the exact splitmonitored via components which eluted on both separation systems.Calibration of NMHCs was made with regular reference to a 27 component ppb level hydro-

carbon standard for C2–C7 NMHCs (National Physical Laboratory, Teddington), and a 74component ppb standard for C2–C10 NMHCs (Apel Riemer Associates, Denver, USA). Primarycalibration of oxygenated hydrocarbons was via permeation tube methods and regularly on-line byreference to a NMHC surrogate species. Regular instrument zeros were performed using ultra-purenitrogen (cleaned viamolecular sieve and heated metal getter) which was subsequently humidified toambient levels using a bubbler with distilled and deionised water. The detection limit for individualcompounds was between 1–5 ppt dependent on functional group. Run to run reproducibility wasbetter than 2% for hydrocarbon species and around 10% for oxygenated species. Accuracy in bothcases was dependent on the gravimetric preparation of standards giving an uncertainty in the region10–15%. Further details of species coverage and experimental methods can be found in Hopkinset al. 2003.9

Higher molecular weight species were determined using a multidimensional GC method (GC �GC), with modulation between columns enabled via a high speed switching valve. Primaryseparation was based on volatility and secondary separation on polarity. An Aglient 6890 gaschromatograph with FID was used in conjunction with sampling apparatus similar to thatdescribed above for the light hydrocarbons, but with a mixed adsorbent bed of both carbon andTenax based materials. 3 l air samples were collected, thermally desorbed and separated usingmultidimensional analysis. The measurement resolution of this instrument was lower than forNMHCs, with one 2 h integrated sample (25 ml min�1 sample flow rate) collected every 3 h.Calibration was via direct gaseous sampling of the 74 component Apel Riemer standard, liquidinjections of polar species and on-line calibration made by reference to hydrocarbon surrogates.Uncertainty with this type of measurement is significantly higher than the dual channel singlecolumn approach. Direct gas phase calibrations are often impossible to perform, and rely heavily onliquid equivalents. The GC � GC approach adds additional uncertainty since there is a degree ofpeak reconstruction from the individual secondary chromatograms, where the primary peak is sub-sampled at a level that is not optimal. Finally concentrations of the larger oxygenated organicspecies are often near the detection limit and integration errors become significant. Run to runreproducibility may be considered good at around 10%, however absolute uncertainty in largeroxygenate concentration must be placed at the 40–60% level (e.g. for a range 200 ppt to 20 ppt).Further details can be found elsewhere.10,11

3. The photochemical trajectory model

3.1. Model overview

The photochemical trajectory model (PTM) used in the present study was based on that which hasbeen widely applied to the simulation of photochemical ozone formation in north-west Europe.12–14

The PTM simulates the chemical development in a well-mixed boundary layer air parcel beingadvected along multi-day trajectories over Europe. The air parcel picks up emissions of NOx, CO,SO2, methane, anthropogenic VOC and biogenic VOC (based on available emissions inventories),which are processed using an appropriate description of the chemical and photochemical transfor-mations leading to the formation of ozone and other secondary pollutants. The model wasspecifically formulated to represent the broad meteorological conditions associated with photo-chemical pollution episodes in the UK, and diurnal variations in atmospheric boundary layer depth,

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temperature and humidity are therefore represented as climatological means over a number ofphotochemical episodes.12 The maximum boundary layer depth is 1300 m. To allow the diurnalvariation to be represented, the model is divided into two vertical layers at nightfall, through theestablishment of a shallow inversion layer (300 m deep), which persists throughout the night. Atsunrise, this lower layer expands and gradually entrains the air trapped aloft overnight, until the fullboundary layer height is achieved at 1400 h. As described in detail previously,12 removal of speciesfrom the lower layer also occurs by dry deposition to the ground or sea surface.

3.1. Emissions

As in the study of Jenkin et al.,14 the UK emissions maps used for anthropogenic VOC, NOx, COand SO2 were the 1998 data defined by the National Atmospheric Emissions Inventory (NAEI)(http://www.aeat.co.uk/netcen/airqual). For other European countries, the mapped emissions fromthe original study of Derwent et al.12 (based on EC CORINAIR and EMEP), were scaled so thatthe country totals were consistent with the corresponding figures available from EMEP (http://www.emep.int/). Biogenic VOC were based on the mapped annual totals reported by Simpsonet al.15

The speciation of the emitted anthropogenic VOC was also based on the NAEI, which identifiesca. 650 individual species.7 To allow coupling with the chemical mechanisms (see below), thespeciation was represented by the 124 species identified in Fig. 1, which collectively account forca. 70% of the NAEI emissions, by mass. The outstanding4500 species, which each makes a smallcontribution to the remaining 30%, were emitted in the form of appropriate surrogates, which were

Fig. 1 Speciation of anthropogenic non-methane VOC emissions used in PTM (see text).

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assigned on the basis of chemical class and reactivity. For example, the longer chain n-alkanes ZC8

were generally used to represent alkanes isomers of the same carbon number, dodecane was used torepresent all alkanes ZC12, and cyclohexane was used to represent all cycloalkanes. Biogenic VOCwere emitted as isoprene (50%) and the monoterpenes, a-pinene (30%) and b-pinene (20%).The temporal variations applied to the emissions of VOC and NOx were based on those estimated

for the UK by Jenkin et al.16 In that study, single representative profiles describing the seasonal,day-of-week and hour-of-day variations in emissions were assigned to each of over 180 sourcecategories included in the NAEI. Where possible, the profiles were based on available temporallyresolved data (e.g., fuel consumption, electricity generation, traffic volume statistics), which can berelated to emissions. The factors presented in Fig. 2 for NOx and anthropogenic VOC represent thecomposite temporal variation of all the source categories combined, with the contribution of eachcategory to the total based on the 1998 NAEI figures. The factors for biogenic VOC take account oftypical seasonal and diurnal variations in photosynthetically active radiation (PAR) and tempera-ture, and were determined using the methodology of Guenther et al.17 as also discussed for Europeby Simpson et al.15

3.2. Chemical processing

The chemical processing was represented by either the Master Chemical Mechanism, version 3.1(MCM v3.1) or by the Common Representative Intermediates (CRI) mechanism. MCM v3.1 is anear-explicit mechanism, which describes the complete degradation of methane and 124 emittednon-methane VOC and the associated formation of ozone and other secondary pollutants, asdescribed in detail elsewhere.18–20 The mechanism contains ca. 14 000 reactions, which represent thechemistry of ca. 5000 molecular and free radical species. It therefore represents the formation ofintermediate oxygenated compounds containing carbonyl, nitrate, peroxy nitrate, hydroxyl,

Fig. 2 Seasonal, hebdomadal and diurnal scaling factors applied to emissions of NOx and VOC in thephotochemical trajectory model, based on Jenkin et al.16

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hydroperoxy, acid and peracid groups, and multifunctional species containing two or more of thesesubstitutent groups. In the present study, MCM v3.1 was used to simulate the development of thedetailed chemical composition of air masses along five case study trajectories.The CRI mechanism treats the degradation of the emitted VOC using about 570 reactions of 250

species, as described by Jenkin et al.21 It therefore provides an economical alternative to the near-explicit representation in the MCM, thereby allowing a much larger number of trajectorycalculations to be carried out comparatively efficiently. As the name suggests, the CRI mechanismcontains a series of generic intermediate radicals and products, which mediate the breakdown oflarger emitted VOC into smaller fragments (e.g., formaldehyde), the chemistry of which is treatedexplicitly. The mechanism was optimised in terms of its ability to generate ozone, by comparisonwith the MCM, using the PTM operating on a standard five day trajectory. 21 In the present study,the CRI mechanism was used for simulations of the chemical processing along 156 trajectoriesthroughout the TORCH-1 campaign.

3.3. Trajectories and initialisation

The model was used to simulate the chemical development over a 96 h period along trajectoriesarriving at the Writtle site. The trajectories were obtained from the NOAA on-line trajectory service(http://www.arl.noaa.gov/ready/hysplit4.html) for arrival times of 0000, 0600, 1200 and 1800 h forthe period 26 July–2 September 2003.The initial concentrations for the majority of species in the chemical mechanisms were set to zero.

As described by Hayman and Jenkin,22 the initial concentrations for a limited number of inorganic,carbonyl and light hydrocarbon species were set at realistic tropospheric background concentra-tions for a relatively clean boundary layer: ozone (34 ppb); NO (20 ppt); NO2 (100 ppt); SO2 (100ppt); H2 (550 ppb); CO (120 ppb); methane (1.7 ppm); ethane (1.2 ppb); propane (180 ppt); butane(70 ppt); methylpropane (86 ppt); pentane (24 ppt); 2-methylbutane (27 ppt); hexane (11 ppt);heptane (28 ppt); octane (2 ppt); ethene (96 ppt); propene (32 ppt); but-1-ene (10 ppt); methylpro-pene (0.4 ppt); trans-but-2-ene (11 ppt); cis-but-2-ene (2 ppt); pent-1-ene (10 ppt); benzene (46 ppt);toluene (42 ppt); o-xylene (23 ppt); m-xylene (8 ppt); p-xylene (8 ppt); formaldehyde (300 ppt);acetaldehyde (150 ppt); acetone (600 ppt). The initial concentrations of the hydrocarbons werebased on the summertime measurements of Penkett et al.23 over the North Atlantic.

4. Results and discussion

4.1. Simulations with the CRI mechanism

The PTM-CRI was used to simulate the chemical development of air parcels arriving at the Writtlesite at six-hourly resolution for the entire TORCH-1 campaign. Comparisons of the simulatedand observed concentrations of ozone and a series of 34 emitted hydrocarbons are presented inFigs. 3–6.

4.1.1. Ozone. Fig. 3 shows a comparison of the simulated ozone concentrations at the arrivalpoint with those observed. The model is able to recreate the general variations of ozone throughoutthe campaign, suggesting that the combination of boundary layer trajectories, precursor emissionsand chemical processing broadly describes the observations for the stable, anticyclonic conditionswhich were generally observed during the campaign. Some underestimation of the simulated peakconcentrations is apparent during the latter part of most severe period of the photochemicalpollution episode (6–11 August). This is partially due to the resolution of the simulations, i.e. thepeak simulated ozone at 1800 h may tend to underestimate the observed maxima, which typicallyoccur during the period 1500–1700 h. It is also probable that the input of some VOC isunderestimated, as discussed further below.

4.1.2. Emitted hydrocarbons. Comparisons of the simulated concentrations of emitted hydro-carbons at the arrival point are compared with those observed in Figs. 4–6. This provides a test of acombination of aspects of the model, i.e. the emissions speciation of the hydrocarbons, theirlifetimes under the prevailing conditions and the quantity collected along the applied trajectory

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paths. Once again, the simulated concentrations generally recreate the overall features of thecampaign (Figs. 4 and 5). The correlation of the campaign mean concentrations (Fig. 6) alsodemonstrates that the simulated concentrations of the majority of hydrocarbons were within afactor of ca. 2 of those observed. The model generally provides a good description of the observedconcentrations of anthropogenic hydrocarbons. As shown in Figs. 4 and 6, the concentrationvariation and mean concentrations of aromatic hydrocarbons, buta-1,3-diene and acetylene are invery good agreement with observations. The concentrations of C4 alkanes (butane and methylpro-pane) are also reasonably well described, although the smaller alkanes (ethane and propane) tend tobe slightly underestimated by the model. The comparison for C5–C7 aliphatic alkanes tends to showscatter around the 1:1 line, although the simulated and observed total concentrations for a givencarbon number are generally in reasonable agreement. For example, Fig. 6 shows that the meanconcentration of hexane is overestimated by the model, whereas the concentrations of themethylpentane and dimethylbutane isomers are underestimated. As shown in Fig. 5, however,

Fig. 3 Comparison of observed and simulated ozone concentrations. The heavy black line represents theobserved concentrations (based on hourly mean values). The points are the simulated concentrations at six-hourly intervals. The grey line joining the points is to guide the eye, and is not intended to infer intermediatesimulated concentrations.

Fig. 4 Comparison of observed and simulated concentrations of selected aromatic hydrocarbons and acetylene.The heavy black lines represent the observed concentrations. The points are the simulated concentrations at 6 hintervals. The grey line joining the points is to guide the eye, and is not intended to imply intermediate simulatedconcentrations.

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the concentration sum of these C6 alkanes agrees reasonably well with the observations. It is likelythat the inventory speciation within a given carbon number tends to overestimate the contributionof the n-alkanes, since information supplied to inventory compilers often includes nominal

Fig. 5 Comparison of observed and simulated concentrations of selected alkanes. C6 alkanes are hexane,methylpentane isomers and dimethylbutane isomers. The heavy black lines represent the observed concentra-tions. The points are the simulated concentrations at 6 h intervals. The grey line joining the points is to guide theeye, and is not intended to imply intermediate simulated concentrations. The smaller points on the cyclohexaneplot are 10% of the simulated concentrations (see text).

Fig. 6 Comparison of observed and simulated campaign mean concentrations of 34 emitted hydrocarbons.

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components such as ‘hexane’, when it is probable that a mixture of hexane isomers is actuallyemitted.The simulated concentrations of longer-chain alkanes (e.g. octane) and cyclohexane are typically

overestimated by the model, mainly due to these species being used as surrogates for other long-chain alkanes which are not included in the chemical mechanisms. This is demonstrated forcyclohexane (see Fig. 5) which, as indicated above, is used as a surrogate for all cycloalkanes in theNAEI inventory, even though it only accounts for ca. 10% of cycloalkane emissions. Consequently,the simulated concentrations are about an order of magnitude greater than those observed.The simulated concentrations of the alkenes are typically lower than those observed, particularly

for some of the larger species (e.g., cis-2-pentene and 2-methylbut-2-ene), with a similar effect alsoobserved for isoprene (see Fig. 6). This may be indicative of under representation in the emissionsspeciation. However, the larger alkenes and isoprene are generally the most reactive hydrocarbons,and are therefore most prone to underestimation if there are sources comparatively local to themeasurement site. The local representation of emissions in the model is based on the average overthe 10 km square containing the measurement site, and the simulated concentrations also assumerapid vertical mixing within the boundary layer. Because the lifetimes of the larger alkenes andisoprene are typically of the order of 10–30 min under the prevailing daytime conditions, theambient concentrations would be expected to show horizontal and vertical variations which maynot be captured by the model. This would lead to a bias towards underestimation by the model iflocal sources are greater than the 10 � 10 km average, and a bias toward overestimation if localsources are below the average.The extent of under simulation for isoprene was found to be greatest (i.e., typically a factor of

ca. 10) during the most severe period of the photochemical episode, when the highest temperatureswere also observed. This suggests that the representation of biogenic emissions, based on meanmonthly and diurnal temperatures (see Section 3), is inadequate, and work is currently in progressto implement biogenic emissions based on trajectory-specific meteorological conditions. Thediscrepancies between simulated and observed concentrations of the larger alkenes also tend tobe greater during the high temperature period, suggesting that there could be a biogenic input ofthese hydrocarbons. Support for this is provided by the measurements of Klemp et al.24 atSchauinsland, Germany. They observed evidence for biogenic sources of larger alkenes (specificallythe butene isomers in their study), which displayed a clear dependence of temperature. Sensitivitytests with the current methodology suggest that the under-representation of biogenic emissions islikely to make a notable contribution to the shortfall in the simulated ozone concentrations duringthe period 6–11 August.

4.1.3. Age of air masses. The differential OH reactivity of the series of aromatic hydrocarbons,in conjunction with their relative emissions and simulated concentrations, was used to inferchemical ages for the air masses at the arrival point, relative to the last significant injection ofpollution. This type of analysis yields an estimate of the product of processing time and OHconcentration, [OH]t, which may be regarded as a chemical processing parameter. This quantitywas found to vary over the approximate range (0.7–5) � 1010 molecule cm�3 s, corresponding to

Table 1 Details of case study trajectories considered with PTM-MCM v3.1 (see text)

Trajectory number

1 2 3 4 5

Arrival day 2 August 4 August 6 August 7 August 31 August

Arrival time 1800 1800 1800 1800 1800

Arrival direction W SE SW NW N

Integrated NOx emission

(relative to trajectory 1)

1.00 0.77 2.11 2.06 0.70

Integrated VOC emission

(relative to trajectory 1)

1.00 0.67 2.10 1.52 1.05

[OH]t/molecule cm�3 s 5.2 � 1010 6.9 � 109 2.5 � 1010 2.6 � 1010 3.0 � 1010

t/h 7.8 0.5 4.0 5.0 6.8

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processing times typically lying between ca. 0.5 and 8 h for the prevailing simulated (i.e. variable)OH concentrations. On this basis, five trajectories were selected as case studies for simulations usingthe PTM-MCM v3.1 (as summarised in Table 1), which include cases at the low end of the range ofchemical processing (Trajectory 2: [OH]t ¼ 6.9 � 109 molecule cm�3 s) and at the high end(Trajectory 1: [OH]t¼ 5.2� 1010 molecule cm�3 s). The processing times are all short in comparisonwith the timescale of the trajectories, indicating that the final composition of the air mass is alsolikely to be influenced by its history prior to the last significant injection of pollution. The relativeintegrated emissions of NOx and anthropogenic VOC emitted over the entire length of eachtrajectory are also given in the table, the latter providing an indication of the variation in the totalburden of organic material processed prior to arrival.

4.2. Simulations of carbonyl distributions with the MCM

The PTM-MCM v3.1 was used to simulate the detailed chemical composition of the air parcelstravelling along the five case study trajectories, allowing calculated concentration distributions oforganic species which are produced by chemical processing of the emitted VOC. The resultantinformation for carbonyl compounds at the arrival point is shown in Table 2.Simulated concentration data for ca. 1250 compounds containing the carbonyl functionality were

obtained. This includes both simple carbonyls (i.e. aldehydes and ketones), and carbonyls contain-ing one or more additional functional groups (e.g., carbonyl; hydroxyl). The simulated concentra-tions of 61 selected aldehydes, ketones and bifunctional carbonyls for each of the five case studytrajectories are given in Table 2. These species collectively account for between 88 and 91% of thesimulated total molecular concentrations of carbonyl species. Additional small (and probablyunderestimated) contributions were made by species such methacrolein, methylvinyl ketone,pinonaldehyde and nopinone, which are derived from the biogenic precursors isoprene, a-pineneand b-pinene. The balance was made up of almost 1200 carbonyl species individually making verysmall contributions, but collectively accounting for up to ca. 10% of the total carbonyl concentra-tion.The simulated concentrations of the carbonyl compounds are dominated by their secondary

formation from the degradation of emitted VOC, with primary emissions of the limited number ofcarbonyls (Fig. 1) making only very small contributions to their concentrations at the arrival point.Accordingly, the total carbonyl concentrations given in Table 2 broadly correlate with theintegrated VOC emissions along the corresponding trajectory, with the chemical processingparameter also having a secondary influence (particularly apparent for the extreme cases,trajectories 1 and 2). The simulated distributions for each class of carbonyl compound shows ageneral decrease in concentration as carbon number increases, consistent with a progressivelysmaller number of precursor VOC being available: and also because larger carbonyls are generallydegraded to form smaller ones, as illustrated for aldehydes by Derwent et al.25 The results of thesimulations are therefore in broad agreement with reported trends in ambient distributions ofcarbonyl compounds with size.26–32

Fig. 7 shows a comparison of the simulated concentrations for seven aldehyde and ketone specieswith observed concentrations measured using the GC � GC technique, for the arrival hour of fourof the case study trajectories. It is apparent that the relative concentration variation for aldehydesand ketones shows a good degree of consistency. However, there are also clear systematicdiscrepancies, with the simulated aldehyde concentrations being consistently lower than theobservations, and the simulated concentrations of the ketones (with the exception of acetone)being consistently greater than the observations. It is recognised that there are uncertaintiesassociated with both the measurements (as described in Section 2), and with aspects of the model.For example, the likely under-representation of the input of higher alkenes in the model (see Section4.1.2) might be expected to have an impact on simulated aldehyde concentrations, because linearalkenes are rapidly degraded to form aldehydes (e.g., the major products of cis-pent-2-ene oxidationare acetaldehyde and propanal). However, it is generally more difficult to reconcile the opposite-sense discrepancy between measurement and model for the ketones, because additional inputs ofhydrocarbons will also tend to increase simulated ketone formation. In the specific case ofbutanone, the major source is the degradation of butane, and its formation in the model wouldbe expected to be well represented. This is because the reactivities of butane and butanone, and the

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Table 2 Simulated concentrations (in ppt by volume)a of selected carbonyls at arrival point for the five case

study trajectories summarised in Table 1

Trajectory number

1 2 3 4 5

Total carbonyl speciesb 11 321 3788 13 127 9432 6573

Simple aldehydes (rC6) 4072 (36.0%) 1540 (40.7%) 6150 (46.9%) 3347 (33.9%) 3116 (47.4%)

Formaldehyde 2988 (26.4%) 1283 (33.9%) 3852 (29.3%) 2480 (26.3%) 2205 (34.6%)

Acetaldehyde 901 (7.96%) 216 (5.71%) 1773 (13.5%) 737 (7.81%) 729 (11.1%)

Propanal 138 (1.22%) 28 (0.74%) 413 (3.15%) 90 (0.95%) 143 (2.18%)

Butanal 20 (0.17%) 6 (0.15%) 50 (0.38%) 17 (0.18%) 17 (0.27%)

2-Methylpropanal 9 (0.08%) 2 (0.06%) 21 (0.16%) 7 (0.08%) 8 (0.12%)

Pentanal 4 (0.03%) 2 (0.05%) 5 (0.04%) 5 (0.05%) 2 (0.03%)

2-Methylbutanal 1 (0.01%) — 4 (0.03%) 2 (0.02%) 2 (0.03%)

Hexanal 6 (0.05%) 2 (0.05%) 7 (0.06%) 6 (0.06%) 3 (0.05%)

Acrolein 5 (0.05%) 1 (0.03%) 25 (0.19%) 3 (0.04%) 7 (0.11%)

Simple ketones (rC6) 5139 (45.4%) 1728 (45.6%) 4467 (34.0%) 4544 (48.2%) 2211 (33.6%)

Acetone 4371 (38.6%) 1340 (35.4%) 3118 (23.8%) 3374 (35.8%) 1717 (26.2%)

Butanone 436 (3.85%) 278 (7.35%) 793 (6.04%) 745 (7.89%) 273 (4.16%)

Pentan-2-one 21 (0.19%) 11 (0.28%) 33 (0.25%) 30 (0.32%) 13 (0.20%)

Pentan-3-one 54 (0.48%) 28 (0.73%) 89 (0.68%) 84 (0.89%) 33 (0.50%)

3-Methylbutan-2-one 44 (0.39%) 19 (0.51%) 71 (0.54%) 65 (0.69%) 26 (0.40%)

Hexan-2-one 4 (0.03%) 2 (0.06%) 7 (0.05%) 6 (0.07%) 2 (0.04%)

Hexan-3-one 12 (0.10%) 5 (0.13%) 18 (0.14%) 15 (0.16%) 7 (0.11%)

4-Methylpentan-2-one 8 (0.07%) 3 (0.08%) 35 (0.27%) 65 (0.69%) 11 (0.17%)

3-Methylpentan-2-one 3 (0.02%) 1 (0.03%) 4 (0.03%) 3 (0.03%) 2 (0.03%)

2-Methylpentan-3-one 6 (0.05%) 2 (0.06%) 9 (0.07%) 7 (0.08%) 4 (0.06%)

Cyclohexanonec 180 (1.59%) 39 (1.03%) 290 (2.21%) 150 (1.58%) 123 (1.88%)

Dicarbonyls (rC6) 394 (3.48%) 87 (2.30%) 599 (4.56%) 277 (2.94%) 246 (3.74%)

Glyoxal 183 (1.62%) 31 (0.82%) 219 (1.66%) 110 (1.17%) 99 (1.51%)

Methylglyoxal 76 (0.67%) 14 (0.38%) 197 (1.50%) 44 (0.46%) 65 (0.99%)

Malonaldehyde 34 (0.30%) 3 (0.08%) 10 (0.08%) 11 (0.12%) 17 (0.26%)

Ethylglyoxal 14 (0.12%) 5 (0.12%) 36 (0.27%) 11 (0.12%) 11 (0.16%)

3-Oxobutanal 14 (0.13%) 4 (0.10%) 22 (0.16%) 14 (0.15%) 9 (0.14%)

Propylglyoxal 1 (0.01%) — 1 (0.01%) 1 (0.01%) —

i-Propylglyoxal — — 1 (0.01%) — —

3-Oxopentanal 18 (0.16%) 3 (0.07%) 27 (0.21%) 12 (0.12%) 13 (0.19%)

4-Oxopentanal 10 (0.09%) 2 (0.06%) 13 (0.10%) 9 (0.10%) 6 (0.09%)

4-Oxohexanal 7 (0.06%) 1 (0.04%) 9 (0.07%) 6 (0.06%) 5 (0.07%)

Buta-2,3-dione 4 (0.04%) 2 (0.05%) 13 (0.10%) 4 (0.04%) 4 (0.05%)

Penta-2,3-dione — — 1 (0.01%) — —

Penta-2,4-dione 14 (0.12%) 13 (0.35%) 23 (0.18%) 31 (0.33%) 7 (0.11%)

Hexa-2,4-dione — 3 (0.07%) 1 (0.01%) — —

Hexa-2,5-dione 19 (0.17%) 6 (0.16%) 26 (0.20%) 24 (0.26%) 10 (0.16%)

Hydroxycarbonyls (rC6) 305 (2.69%) 77 (2.03%) 483 (3.68%) 254 (2.69%) 209 (3.18%)

Glycolaldehyde 92 (0.81%) 24 (0.64%) 133 (1.01%) 78 (0.82%) 61 (0.93%)

2-Hydroxypropanal — — 1 (0.01%) 1 (0.01%) —

3-Hydroxypropanal 21 (0.18%) 5 (0.13%) 31 (0.24%) 18 (0.19%) 15 (0.23%)

2-Hydroxybutanal 1 (0.01%) 1 (0.02%) 1 (0.01%) 2 (0.02%) —

3-Hydroxybutanal 2 (0.02%) 1 (0.02%) 3 (0.03%) 3 (0.03%) 1 (0.02%)

4-Hydroxybutanald 14 (0.13%) 3 (0.09%) 25 (0.19%) 13 (0.14%) 10 (0.16%)

2-Hydroxypentanal 1 (0.01%) 1 (0.02%) 1 (0.01%) 2 (0.02%) —

3-Hydroxypentanal 1 (0.01%) — 1 (0.01%) 1 (0.01%) —

4-Hydroxypentanald 10 (0.08%) 2 (0.06%) 15 (0.11%) 8 (0.08%) 7 (0.11%)

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yield of butanone from butane oxidation are well determined and represented in MCM v3.1,33 andthe input of butane appears to be consistent with observations (Section 4.1.2). It is therefore notpossible to reconcile fully the discrepancy between the simulated and observed concentrations of thecarbonyls in Fig. 7, although comparisons on a campaign mean basis (if MCM simulations for theentire campaign were available) might improve the agreement.

Table 2 (continued )

Trajectory number

1 2 3 4 5

4-hydroxyhexanald 7 (0.06%) 1 (0.04%) 10 (0.07%) 5 (0.05%) 5 (0.08%)

4-Hydroxy-4-methylpentanald 3 (0.03%) 1 (0.01%) 7 (0.05%) 2 (0.02%) 3 (0.04%)

Hydroxyacetone 11 (0.10%) 7 (0.19%) 18 (0.13%) 13 (0.14%) 8 (0.12%)

1-Hydroxybutan-2-one 4 (0.04%) 2 (0.06%) 8 (0.06%) 7 (0.07%) 3 (0.05%)

3-Hydroxybutan-2-one 2 (0.02%) 1 (0.03%) 4 (0.03%) 3 (0.03%) 1 (0.02%)

4-Hydroxybutan-2-one 13 (0.11%) 5 (0.12%) 20 (0.15%) 9 (0.10%) 9 (0.14%)

1-Hydroxypentan-2-one 1 (0.01%) — 1 (0.01%) 1 (0.01%)

3-Hydroxypentan-2-one — — 1 (0.01%) — —

5-Hydroxypentan-2-oned 62 (0.55%) 12 (0.32%) 99 (0.76%) 52 (0.55%) 42 (0.65%)

5-Hydroxyhexan-2-oned 22 (0.19%) 3 (0.08%) 37 (0.28%) 14 (0.14%) 16 (0.25%)

6-Hydroxyhexan-3-oned 27 (0.24%) 5 (0.12%) 44 (0.34%) 19 (0.20%) 19 (0.29%)

4-Hydroxy-4-methylpentan-2-oned 7 (0.06%) 2 (0.05%) 16 (0.12%) — 6 (0.09%)

5-Hydroxy-3-methylpentan-2-one 4 (0.04%) 1 (0.02%) 7 (0.06%) 3 (0.03%) 3 (0.05%)

Aromatic aldehydes (rC8) 21 (0.19%) 26 (0.68%) 53 (0.40%) 12 (0.13%) 20 (0.30%)

Benzaldehyde 14 (0.12%) 26 (0.68%) 36 (0.28%) 8 (0.09%) 13 (0.20%)

2-Methylbenzaldehyde 1 (0.01%) — 3 (0.02%) 1 (0.01%) 1 (0.02%)

3-Methylbenzaldehyde 2 (0.02%) — 5 (0.04%) 1 (0.01%) 2 (0.03%)

4-methylbenzaldehyde 4 (0.04%) — 9 (0.07%) 2 (0.02%) 4 (0.05%)

a Concentrations are rounded to the nearest ppt. A missing entry indicates a sub-ppt concentration. Percentage

contribution to total (rounded to nearest 0.01% for less abundant species), is given in brackets. b Concentration

sum of species containing one or more carbonyl groups. c Concentrations for cyclohexanone should be regarded as

indicative of total anthropogenically-derived cycloalkanone concentrations. d Concentrations for 1,4-hydroxy-

carbonyls notionally include contributions from isomeric cyclic hemiacetals (see text).

Fig. 7 Comparison of observed and simulated concentrations of seven carbonyls for the arrival hour oftrajectories 1–4 in Table 1. Open symbols are aldehydes; filled symbols are ketones.

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It is well established that the photolysis of carbonyl compounds makes a potentially importantcontribution to ambient free radical formation. In particular, the important role of formaldehyde asa free radical precursor at observed ambient concentrations is well documented,6,34,35 which resultsfrom a combination of its large contribution to total carbonyl concentrations and its relativelyefficient photolysis to generate free radicals. The present simulations estimate that formaldehydeaccounts for between 26% and 35% of the total carbonyl concentration at the arrival point,consistently being the dominant aldehyde species. Fig. 8 also demonstrates that its photolysis togenerate free radicals is of comparable importance to ozone photolysis (i.e., OH formation from thereaction of O(1D) with H2O) for the arrival hour of the five case study trajectories. Because carbonylcompounds possess varying abilities to generate free radicals upon photolysis, their contributions tofree radical formation under atmospheric conditions do not scale with their ambient concentration(see Figs. 8 and 9). Consequently, formaldehyde accounts for 52–76% (mean ¼ 64%) of free radicalformation from carbonyl photolysis sources. Despite their generally high concentrations, acetoneand the higher ketones are inefficient free radical sources, by virtue of their slow photolysis in thelower atmosphere. Conversely, some species which make only small contributions to the total

Fig. 8 Comparison of simulated radical production rates from the photolysis of carbonyls and ozone (i.e., OHformation from the reaction of O(1D) with H2O) at the arrival point for the five case study trajectories in Table 1.

Fig. 9 Simulated relative contributions to radical production made by the photolysis of formaldehyde andother carbonyl classes in Table 2 at the arrival point for the five case study trajectories.

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carbonyl concentration make very significant contributions to free radical generation. In particular,the a-dicarbonyls (glyoxal, alkylgyoxals, butan-2,3-dione and pentan-2,3-dione) are present at acollective concentration of between 1.4% and 3.6% of the total carbonyl concentration (see Table2). However, they are particularly photolabile,36–38 such that their contribution to radical formationis calculated to be between 15% and 36% (mean ¼ 26%) of free radical formation from carbonylphotolysis sources, the mean contribution being ca. 40% of that due to formaldehyde photolysis(see Fig. 9). This emphasises the need for detailed characterisation of ambient air masses.Other carbonyl species of interest are the 1,4-hydroxycarbonyls (4-hydroxybutanal, 4-hydroxy-

pentanal, 4-hydroxyhexanal, 4-hydroxy-4-methylpentanal, 5-hydroxypentan-2-one, 5-hydroxyhex-an-2-one, 6-hydroxyhexan-3-one and 5-hydroxy-3-methylpentan-2-one), the simulated concentra-tions of which are consistently much greater than those of isomeric 1,2- and 1,3-substituted species.These 1,4-hydroxycarbonyls are formed mainly from the degradation of butane, pentane, hexane, 2-methylpentane and 3-methylpentane, as a result of alkoxy radical isomerisation reactions in theinitial oxidation sequence. They account for about 1–2% of the total carbonyl (molar) concentra-tion, although analogous species formed from the degradation of ZC7 alkanes (not shown in thetable) contribute a further 0.5–1% (it should also be noted that the fractional contribution of the1,4-hydroxycarbonyls on a mass concentration basis is considerably higher). The formation of 1,4-hydroxycarbonyls has been observed in a number of laboratory studies.39–41 More recently,evidence has been obtained for their isomerisation to form cyclic hemiacetals, with possiblesubsequent dehydration to generate highly reactive dihydrofurans.42 These cyclic species are notcurrently represented in MCM v3.1. Because the further oxidation of cyclic species tends to lead toinclusion of additional polar substituents groups without reduction in carbon number (i.e.fragmentation processes are ring-opening), such species may represent precursors to involatileproducts which can contribute to SOA. It is noted that significant uncertainties remain in the detailsof some aspects of the oxidation of longer chain alkanes (i.e. ZC5), which account for ca. 25% ofthe mass emissions of anthropogenic non-methane VOC (see Fig. 1).

5. Summary and conclusions

A photochemical trajectory model (PTM) containing speciated emissions of 124 non-methanevolatile organic compounds (VOC), and a comprehensive description of the chemistry of VOCdegradation, has been used to simulate the chemical evolution of boundary layer air masses arrivingat a field campaign site in the southern UK during August 2003. The period considered wasdominated by stable anticyclonic conditions, and included a widespread and prolonged photo-chemical pollution event.The simulated concentrations and distributions of organic compounds at the arrival location

have been compared with measurements of a series of hydrocarbons and carbonyl compounds,which were measured using GC-FID and multidimensional GC methods. The distribution of 34emitted hydrocarbons (simulated with PTM-CRI), was found to correlate reasonably well with theobservations, providing some support for the applied anthropogenic VOC speciation, based on theUK NAEI. The simulated concentrations of six aromatic hydrocarbons, acetylene, buta-1,3-dieneand the C4 alkanes were in general good agreement with observations, both in terms of theconcentration variation throughout the campaign and the campaign mean concentrations. The totalisomer concentrations of C5–C7 alkanes also appeared to agree well with observations, although thesimulated distribution for a given carbon number was distorted in favour of the n-alkanes. Thesimulated concentrations of the smaller alkanes (ethane and propane) were found to be lower thanthose observed, whereas those of the larger alkanes ZC8 and cyclohexane significantly exceededthe observed concentrations. This overestimation results mainly from the use of the larger alkanesand cyclohexane as surrogates for a large number of emitted species which are not explicitly treatedin the chemical mechanisms.The simulated concentrations of alkenes and isoprene were generally lower than those observed,

with the greatest discrepancies for isoprene and larger alkenes occurring during the most severeperiod of the photochemical episode when temperatures were elevated. The disagreement forisoprene points clearly to an underestimation in the biogenic source term in the representationcurrently applied in the model, and it is probable that unrepresented biogenic sources of other largealkenes also contribute to the their simulated shortfall at elevated temperatures.

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Detailed distributions of ca. 1250 carbonyl compounds were simulated using the PTM-MCMv3.1, for five case study trajectories. Data are presented for 61 aldehydes, ketones, dicarbonyls,hydroxycarbonyls and aromatic aldehydes which collectively account for ca. 90% of the simulatedtotal molar concentration of carbonyls. The simulated distributions of four aldehydes and threeketones correlated well with observations for each class of compound. However, the simulatedconcentrations of the aldehydes were systematically lower than those observed, whereas those of thehigher ketones were systematically higher. Possible reasons for these differences are explored,although it is not possible to reconcile fully the discrepancies.The simulated carbonyl distributions indicate that the photolysis of formaldehyde and

a-dicarbonyls make major contributions to free radical production for the arrival conditions ofthe case study trajectories, accounting on average for 64% and 26%, respectively, of free radicalproduction from the photolysis of the 61 considered carbonyl compounds. The preferentialformation of 1,4-hydroxycarbonyls (compared with 1,2- and 1,3-isomers of the same carbonnumber) is also noted. The 1,4-hydroxycarbonyls are formed in the initial oxidation sequence oflonger chain alkanes, and their further oxidation potentially generates low volatility multifunctionalspecies.

Acknowledgements

The work described in this paper was funded by the Natural Environment Research Council,(NERC), as part of the Tropospheric Organic Chemistry Experiment (TORCH) under contractNER/T/S2002/00498 at York and NER/T/S2002/00495 at Imperial College London, and by theDepartment for Environment, Food and Rural Affairs (DEFRA) under contract EPG 1/3/200.MEJ also acknowledges NERC for support via a Senior Research Fellowship (NER/K/S/2000/00870). JRH acknowledges support from the UK Universities’ Facility for Atmospheric Measure-ments. We are grateful to Dr James Lee (University of York) for the provision of the ozone datapresented in this study, and to Dr Sam Saunders (University of Western Australia) for helpfuldiscussions.

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