Improvement of the Chemical Mass Balance model for apportioning—sources of non-methane hydrocarbons using composite aged source profiles

ARTICLE IN PRESS

Atmospheric Environment 42 (2008) 1319–1337 www.elsevier.com/locate/atmosenv

Improvement of the Chemical Mass Balance model for apportioning—sources of non-methane hydrocarbons using composite aged source profiles Ann E. (Beth) Wittiga,, David T. Allenb a

CUNY City College of New York, Department of Civil Engineering, Convent Avenue at 140th Street, Steinman Hall T-104, New York, NY 10031, USA b Department of Chemical Engineering, University of Texas at Austin, 1 University Station C0400, Austin, TX 78712, USA Received 7 May 2007; received in revised form 18 October 2007; accepted 19 October 2007

Abstract The Chemical Mass Balance (CMB) receptor model is commonly used to evaluate the relationship between emissions of air pollutants and their concentration in the ambient air. However, it is not clear that it can accurately achieve this goal when evaluating sources of reactive air pollutants such as non-methane hydrocarbons (NMHC). This work examines the ability of CMB Version 8.2 to accurately resolve sources of NMHC simulated for Houston, Texas, and assesses whether model performance can be improved by providing the model with composite aged profiles. The NMHC are simulated using an unsteady multi-stage photochemical transport model which uses reported emission rates from mobile and industrial sources and meteorological measurements as inputs. Composite aged profiles are also developed using the photochemical transport model, and account for the distribution and strength of sources within the common trajectories to the receptor. The ability of the model is examined through 128 cases and 4 case studies, which challenge CMB to resolve the sources of simulated NMHC when reasonable levels of uncertainty are introduced into the fitting species, source profiles, and/or sources provided to the model, or when composite aged source profiles are also made available to the model. CMB performs best when all contributing sources are known and perfect information is available to describe these sources. When reasonable uncertainties are introduced into any of its inputs, performance is poorer. Many cases do not result in a solution. In many of the cases that result in a solution, CMB fails to identify or attribute the mass of a relevant source to within 730% of the true contribution, or identifies irrelevant sources and attributes to them more than 5% of the total mass. When composite aged profiles are made available to the model, inaccuracy is eliminated but many cases still do not result in a solution. r 2007 Elsevier Ltd. All rights reserved. Keywords: Receptor model; Source apportionment; CMB; NMHC; Reactivity; Houston; Simulated data

1. Introduction Corresponding author. Tel.: +1 212 650 8397;

fax: +1 212 650 6965. E-mail addresses: [email protected] (A.E. (Beth) Wittig), [email protected] (D.T. Allen). 1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.10.072

There are a variety of models used to evaluate the relationship between air pollutant emissions and their resulting concentration in the ambient air.

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All of these models have uncertainties associated with them and the most effective model or combination of models depends on the application. Chemical transport models, such as Gaussian plume models and gridded photochemical models, begin with pollutant emissions estimates and meteorological observations and use chemical and physical principles to predict ambient pollutant concentrations. Since these models require temporally and spatially resolved data and can be computationally intensive, they can only be used for well-characterized regions and over select time periods. Receptor models, such as Positive Matrix Factorization and Chemical Mass Balance (CMB), begin with ambient pollutant observations and use statistical or mass balance approaches to estimate emission rates from individual or composited upwind sources. Knowledge of meteorological variables is not required but may be used to refine the analysis. Knowledge of emission sources is useful for the interpretation of results from statistical-based receptor models and is required by receptor models that use a mass balance approach. Receptor models can be a more accessible means of evaluating the relationship between pollutant emission inventories and their ambient observations since they require less data and computational resources than chemical transport models do. However, it is uncertain whether they can achieve this goal for air pollutants that are reactive. This work examines the ability of CMB Version 8.2 receptor model to accurately resolve sources of simulated reactive air pollutants and assesses whether model performance can be improved, even when reasonable uncertainties exist in the model inputs, by supplementing fresh emission profiles with composite aged source profiles. The reactive pollutants addressed in this study are non-methane hydrocarbons (NMHC), a set of 54 C2 through C9 hydrocarbons of varying reactivity, excluding carbonyls and halocarbons, measured using EPA method TO-14A (U.S.E.P.A., 1997). NMHC concentrations are simulated for Houston, Texas, during August 1993 using a photochemical transport model. This modeling domain was selected because recent field campaigns indicate that industrial NMHC emissions to the area may be substantially underestimated in the official emission inventory (Daum et al., 2004; Murphy and Allen, 2005). Further, the resolution of NMHC sources in a city like Houston will challenge any receptor model because Houston’s air quality is affected by

both local and distant sources of NMHC, many of which are both collinear and highly reactive. 2. Methods This section details the mechanics of CMB, the development of and resultant simulated NMHC data sets and composite aged profiles, and the plan to evaluate CMB accuracy. 2.1. CMB modeling CMB was originally developed to resolve the sources of ambient aerosols observed at a downwind receptor location (Miller et al., 1972) and has been widely used for this purpose. In the last 30 years, it has also been used to resolve sources of ambient hydrocarbons at more than 20 urban areas, mostly in the US (Watson et al., 2001). When used to resolve sources of NMHC, the model solves 54 independent mass balances each of which relate the concentration of the NMHC observed at a receptor site to the linear sum of the contributions of the NMHC from its upwind sources: Ci ¼

J X j¼1

F ij S j

for i ¼ 1; . . . ; I

where

I X

F ij ¼ 1,

i¼1

(1) where Ci is the measured concentration of species i at the receptor site in g m3, Fij is the measured fraction of species i in the source profile of emissions from source j in g g1, and Sj is the source contribution of NMHC estimated from source j in g m3. When all NMHC sources are accounted for, the sum of the I Ci values equals the sum of the J Sj values. CMB Version 8.2 iteratively solves the 54 mass balances using an effective variance weighted least squares algorithm to determine the Sj values for a subset of the NMHC called ‘fitting species’. This particular algorithm minimizes the weighted sum of the squares of the differences between measured and calculated Ci for the full set of NMHC. It is favored over methods such as the tracer method or ordinary weighted least squares because it accounts for all of the NMHC and uncertainties in both Ci and Fij values (Watson et al., 1984, 1998). The results of a CMB analysis include the magnitude and standard error of the source contributions that explain the observed Ci concentrations

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and three diagnostic parameters. The standard error (sj) indicates the precision of the Sj value and depends on the Ci and Fij uncertainties and the degree of source collinearity. The diagnostic parameters are intended to indicate the goodness of fit. The chi-squared (w2) statistic should range between 0 and 1, but values up to 2 are considered acceptable. The R-squared (R2) statistic is a measure of the variance in the receptor concentrations that is explained by the calculated NMHC concentrations and should range between 0.8 and 1. The total percent mass (TPM) accounted for indicates the how much of the ambient NMHC is explained by the Sj values computed by CMB. While its target value is 100%, values between 80% and 120% are considered to be acceptable. When all of the diagnostic parameters are within bounds, computed Sj values are assumed to be accurate within 730%. When any of the diagnostic parameters are out of bounds, the solution is considered to be statistically unreasonable and is disregarded. There are several assumptions inherent in the model formulation described by Eq. (1). Reasonable uncertainties in these assumptions are the foundation of the case studies investigated in this work. First, the number of identified sources is less than or equal to the number of fitting species. When this assumption is not met, the model cannot solve the mass balances. Second, all contributing sources are identified and emit NMHC in ratios that are reasonably well characterized, constant over the period of time of the Ci measurement, and linearly independent from those of other sources. Third, the measurement uncertainties associated with the Ci or Fij values are random, uncorrelated and normally distributed. The effect of uncertainties in the second and third assumptions on CMB performance has been examined in some detail (Henry, 1982, 1992; Dzubay et al., 1984; Lowenthal et al., 1987; Javitz et al., 1988; Kim and Henry, 1989; Christensen, 2004; Christensen and Gunst, 2004). Uncertainties due to variable source composition, missing sources and measurement uncertainties can be large, but have not been found to limit the utility of CMB any more than they do other receptor models. Uncertainties due to collinear sources can be decreased by estimating their contributions as a lump sum, an approach which greatly limits the utility of the model if the collinear sources are the result of different activities (e.g., fugitive emissions from refineries and evaporative losses from cars). Fourth, the NMHC included in the analysis are unreactive,

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so that the loss of ambient NMHC during transport is systematic and does not change the relative amount of the i NMHC species in the Fij source profile. Since all NMHC are somewhat reactive, the fourth assumption cannot actually be met in practice. Typically, uncertainties in this assumption are minimized by using less reactive or similarly reactive fitting species to solve the set of mass balances (Mayrsohn and Crabtree, 1976; Wadden et al., 1986; O’Shea and Scheff, 1988; Scheff and Wadden, 1993; Harley et al., 1992; Lewis et al., 1993; Fujita et al., 1995), or by only resolving sources of ambient concentrations measured in early morning (Nelson et al., 1983; Scheff and Klevs, 1987). These restrictions exclude potential fitting species or reduce the number of data sets available for analysis. In a few instances and with unclear success, pollutant reactivity has been accounted for by providing CMB with fresh emission source profiles adjusted using speciesspecific decay factors. Venkataraman and Friedlander (1994) developed decay factors for polyaromatic hydrocarbons (PAHs) in particulate matter by modeling the degradation of PAHs using firstorder kinetics and a continuous stirred tank reactor (CSTR) scheme ai ¼ ð1 þ ki tÞ1

for i ¼ 1; . . . ; I,

(2)

where ai is the dimensionless decay factor for species i, ki is the first-order rate constant and t is the average residence time in the CSTR. Lin and Milford (1994) used a similar approach to develop decay factors for NMHC. Their factors were computed by assuming the NMHC degrade by first-order reaction with the hydroxyl radical (OH) and using a batch scheme ai ¼ expðki xÞ for i ¼ 1; . . . ; I,

(3)

where x is an average aging coefficient empirically estimated from ambient NMHC samples. 2.2. Simulation of ambient NMHC concentrations Simulated, but realistic, ambient concentrations of NMHC provide a means to rigorously evaluate the accuracy of CMB since the actual contributions of the NMHC sources are known. When derived from the same emission source profiles provided to CMB, simulated data also possess similar measurement uncertainties as the profiles, removing this potentially confounding variable from the analysis. Realistic simulated ambient pollutant

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concentrations have been used to evaluate the ability of receptor models including CMB to apportion sources of particulate matter (e.g., DeCesar and Cooper, 1982; Gerlach et al., 1983; Javitz et al., 1988). However, the present study is the first to use this caliber of simulated data to evaluate the ability of CMB to resolve NMHC sources. The NMHC data simulated by Lin and Milford (1994) accounted for few sources and did not consider the spatial arrangement of sources or the effects of dilution and dispersion. The NMHC data simulated by Miller et al. (2002) used a realistic mixture of sources but did not account for any physical or chemical transformation. In this study, concentrations of NMHC are simulated using a simply parameterized photochemical transport model. This approach is favored over simple additive mixtures of sources since it allows for the enrichment of less reactive NMHC over time. It is also favored over the complex Comprehensive Air Quality Model with Extensions (CAMx) since CAMx describes the 54 NMHC species using 10 carbon-bond groupings, effectively reducing the number of species that are potentially unique and increasing collinearity among emissions. The photochemical transport model used in this study accounts for the dispersion, dilution and reaction of NMHC emitted in various strengths and from different locations to an air mass as it convects along an 8 h trajectory to the Galleria site (GLRC, AIRS #481130045) in Houston, TX. This receptor site and its upwind domain are illustrated in Fig. 1.

The ambient air quality along an 8 h trajectory is simulated using eight unsteady batch reactors in series. Fig. 2 illustrates the relationship between a trajectory and the model reactors. The simulation begins at 8 am in reactor one. Initial concentration is computed as ( Ak ½C i;k;t¼0  ¼ ½C i;k1;t¼1 h V k1 Ak1 ) J X qj;k f i;j =MWi N A V 1 þ k j¼1

for k ¼ 1; . . . ; 8 and i ¼ 1; . . . ; I,

ð4Þ

where i is the NMHC species, j is the source of the NMHC, k is the reactor number, Ci,k,t ¼ 0 is the initial concentration of species i in reactor k in molec cm3, Ci,k1,t ¼ 1 h is the concentration of species i in the prior reactor at the end of 1 h of reaction in molec cm3, Vk is the volume of reactor k in cc, Ak/Ak1 is the relative surface area of reactors k and k1 and accounts for dispersion, qj,k is the emission rate of NMHC from source j within the footprint of reactor k for 1 h in g, fi,j is the fraction of species i in the emissions from source j in g g1, MWi is the molecular weight of species i in g mol1 and NA is Avogadro’s number. For reactor one, the value of the Ci,0,t ¼ 1 h upwind concentration is zero. Changing NMHC concentration is assumed to be limited by reaction with the OH radical and is modeled using a batch scheme. Changing concentration of NMHC is computed in

GLRC site Northeasterly 2.5 km h-1 trajectory Houston

Easterly 5 km h-1 trajectory Baytown

Seabrook

Gulf of Mexico

Trade Store Chem1 Chem2 LPG Petrol Metal Other < 249 ton yr-1 250 – 499 500 – 999 > 1000

Fig. 1. The location of GLRC receptor site and the sources of mobile and industrial NMHC emission sources in the region upwind of the GLRC site.

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Wind direction

Dispersion1

Dispersion2 Emission2

Emission1 Ci,1

Reactor7 Reactor8

Reactor5

Reactor6

Reactor4

Reactor2

Reactor3

Reactor1

Ci,0

Receptor location

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Dispersion8 Emission8 Ci,2

…

Ci,8

Ci,7

C•OH,1

C•OH,2

C•OH,8

Volume1

Volume2

Volume8

Reactor1

Reactor2

Reactor8

Fig. 2. The structure of the photochemical transport model and the relationship between the trajectory sections and the eight batch reactors in series employed in the model.

Table 1 Select inputs to the photochemical transport model for the 2.5 and 5 km h1 trajectories to the GLRC site Reactor (k)

1 2 3 4 5 6 7 8

Dispersion factor (Ak/Ak1)

1.00 0.87 0.85 0.82 0.78 0.71 0.60 0.33

OH concentration (COH in mol cm3)

500,000 628,000 777,000 872,000 947,000 973,000 1,000,000 915,000

700 910 1120 1440 1750 1970 1940 1750

0.01 h timesteps for 1 h as d½C i;k;t  ¼ ki ½C i;k;t ½C OH;k  dt

Mixing height (hk in m)

for k ¼ 1; . . . ; 8 and i ¼ 1; . . . ; I,

(5) where t is time in s, ki is the rate constant for the reaction of OH with a specific NMHC at 298 K in cc molec1 s1 and COH is the OH concentration in mol cm3. The second hour of the simulation (i.e., starting at 9 am) begins in the second reactor. Ci,2 initial concentration is computed using Eq. (4) and is now dependent upon the A2/A1 dispersion factor, the final Ci,1 upwind concentrations in reactor one, the qj,2 fresh emissions to reactor two, and the dilution of all sources of NMHC into the new reactor volume. Changing NMHC concentration is again computed in 0.01 h timesteps for 1 h using Eq. (5). This sequential process is repeated for all eight trajectory sections. The NMHC concentrations computed at the end of the eighth hour in the eighth reactor approximate the air quality at GLRC at 4 pm after 8 h of dilution, dispersion and reaction

Surface area (Ak in m2) 2.5 km h1 trajectories

5 km h1 trajectories

25.11  106 21.77  106 18.43  106 15.07  106 11.72  106 8.73  106 5.02  106 6.7  106

100.5  106 87.1  106 73.7  106 60.3  106 46.9  106 33.5  106 21.0  106 6.7  106

of emissions, and are converted to mass concentration units before being input to CMB. The specific inputs to the model are listed in Tables 1 and 2 and include spatially and temporally variable inputs such as reactor volume and NMHC emission rates, temporally variable OH concentrations, and rate constants for the reaction of NMHC and OH. Spatially variable inputs depend on the dimension and orientation of the trajectories to GLRC, which were observed during the study period to be approximately northeasterly, easterly, and southeasterly at 2.5 and 5 km h1. The width of each trajectory is assumed to be 7151 from either side of the centerline and its overall length allows for 8 h of convection at the mean wind speed to the GLRC site. The depth of each trajectory section is estimated from mixing heights reported for the area during the study period (Dye et al., 1995). Mobile and industrial NMHC emissions to the trajectories are based on official emission inventories compiled by the Texas Commission on Environmental Quality (TCEQ). Mobile source emission rates are

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Table 2 Emissions input to the photochemical transport model for all six trajectories to the GLRC site 5 km h1 trajectory emissions (qjk in kg)

2.5 km h1 trajectory emissions (qjk in kg)

EXH

IND type

EXH

GAS

VAP

None CHEM2 CHEM2 TRADE None None None None

64.0 54.9 55.6 62.2 118.1 16.0 7.3 6.4

13.6 13.2 14.8 15.8 33.6 2.7 1.2 1.0

9.0 30.8 32.3 26.6 48.6 0.9 0.5 0.4

0 2.3 0 0 5.8 14.0 0 0

None CHEM2, LPG None None CHEM2 CHEM2, TRADE None None

CHEM1, CHEM2, METAL, PETROL, STORE CHEM1, CHEM2, STORE, TRADE

37.6

7.6

4.9

338.8

CHEM1, STORE

66.2

15.2

35.6

1220.3

GAS

VAP

Northeasterly reactor (k) 1 35.6 7.0 4.7 2 55.9 12.7 29.6 3 99.0 25.7 55.6 4 90.5 25.4 37.2 5 73.2 21.3 31.0 6 3.8 0.6 0.2 7 1.1 0.2 0.1 8 4.5 0.7 0.3

IND

0 5.8 4.8 9.2 0 0 0 0

IND

IND type

Easterly reactor (k) 1 86.1 18.5

11.6

795.1

2

68.5

15.7

36.0

21.2

3

71.9

17.9

38.8

1.3

CHEM2

51.3

12.1

28.1

242.8

4

103.3

28.6

41.2

2.8

METAL

38.6

8.8

15.3

450.9

5

78.2

23.2

32.4

0

None

191.5

57.7

81.3

816.3

6 7 8

2.0 1.8 3.2

0.3 0.3 0.5

0.2 0.1 0.2

0 0 0

None None None

16.5 6.5 5.9

27.6 1.0 0.9

0.8 0.4 0.4

4.1 0 0

7.6

810.4

28.9

5.3

4.4

453.0

CHEM1, CHEM2

57.0

12.8

30.6

400.3

CHEM1, CHEM2, TRADE, LPG PETROL, TRADE, LPG PETROL, STORE, TRADE CHEM1, CHEM2, PETROL, METAL, STORE, TRADE None METAL None

Southeasterly reactor (k) 1 56.7 12.3

2

53.5

12.6

28.9

3.9

CHEM1, CHEM2, PETROL, STORE, METAL CHEM1, TRADE

3

82.1

20.8

44.9

0

None

81.3

19.9

45.4

624.2

4

79.2

22.4

32.5

0

None

99.0

24.8

37.4

162.6

5

86.5

25.0

34.3

1.6

METAL

164.7

50.2

69.7

814.3

6 7 8

2.4 1.6 3.8

0.4 0.2 0.6

0.2 0.1 0.2

0 0 0

None None None

15.1 8.0 7.0

2.6 1.2 1.1

0.8 0.4 0.4

0 1.6 0

extracted from a 4 km2 resolution emission inventory of three 22 km  22 km domains in Harris County on 19 August 1993. Emission rates to each 4 km2 cell in the inventories are scaled by the fraction of the cell area present in the trajectory. Emission rates to cells not included in the resolved inventory are estimated from cells in the inventory

CHEM1, CHEM2, METAL, PETROL, STORE, TRADE CHEM1, CHEM2, LPG, TRADE CHEM1, CHEM2, LPG, PETROL, STORE, TRADE CHEM1, CHEM2, METAL, PETROL, STORE, TRADE CHEM2, METAL None None

with similar road and highway density. The mobile source types accounted for in this investigation include exhaust from gasoline combustion (i.e., EXH); spillage of raw gasoline during refueling (i.e., GAS); and evaporative hot soak, diurnal, resting, and running emissions of gasoline (i.e., VAP). For each of these mobile source types, two

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different profiles are used to speciate the emissions: one that describes the sources using a composite of sources located around the country (e.g., US-EXH) and one describes the sources using measurements of representative sources located in the study domain (e.g., TX-EXH). Industrial source emission rates are determined from the emission inventory of facilities that released greater than 10 t yr1 of NMHC in 1993. Industrial source types accounted for in this investigation include industries manufacturing petroleum and coal products (i.e., PETROL), industries producing feedstock chemicals (i.e., CHEM1) or rubber, plastic and end-product chemicals (i.e., CHEM2), industries involved in the transport (i.e., TRADE) or warehousing (i.e., STORE) of non-durable goods, industries manufacturing metal products (i.e., METAL), and liquid petroleum gas production, transport and use (i.e., LPG). Two sets of source profiles are also used to speciate these emissions: one that describes the myriad source types using a single composite profile (i.e., US-IND), and several that describe each type individually using local measurements of representative facilities (e.g., TX-PETROL). The emission rates of the mobile and industrial sources to each trajectory section are summarized in Table 2 and the source profiles used to describe them are presented in Fig. 3. Area sources such as biogenic emissions (i.e., BIO), commercial natural gas production, transport and use (i.e., CNG) and consumer solvent use (i.e., SOLV) are not considered in the model, but their source profiles are presented in Fig. 3 for comparison purposes. The OH concentrations within each reactor are assumed to vary diurnally between 5 and 10 million mol cm3, consistent with typical tropospheric levels (Finlayson-Pitts and Pitts, 1986) and the OH concentration of 5 million molec cm3 reported in Houston during August 1993 (Fujita et al., 1995). Reaction rate constants for the reaction of NMHC and OH are taken from Fujita et al. (1995). Unknown rate constants are estimated from those of structurally similar compounds (e.g., P1E2ME and P1E4ME as B1E3ME). Photochemical reaction rates for all 54 NMHC species are given in Table 3. The photochemical transport model is used to develop four sets of simulated ambient NMHC data for the northeasterly 2.5 km h1 (‘northeasterly’ for short) trajectory or the easterly 5 km h1 (‘easterly’ for short) trajectory using US or TX source profiles. These two trajectories were selected since the northeasterly trajectory possesses the most uni-

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formly distributed mobile sources and few weak industrial sources and the easterly trajectory possesses the most non-uniformly distributed sources of various type and strength. The simulated NMHC concentrations are shown in Fig. 4. The prominent species in both simulated data sets are similar because of the low reactivity of some species which allows them to become enriched over time (e.g., NBUTA, IPENTA and NPENT) and because of the proximity of their sources to the GLRC receptor site (e.g., EXH contains ETHENE, PROPE and MPXYL). Despite the simple parameterization of phenomena in the photochemical transport model, the simulated NMHC concentrations are in reasonable agreement with ambient NMHC concentrations measured on an hourly basis between 17th August and 11th September 1993 at GLRC. The underpredicted concentrations of NMHC may be explained by uncertainties in the emission inventory of mobile and industrial source types. However, perfect agreement is not expected. The box whiskers account for more than 200 different 1-h measurements collected over a much broader time period than accounted for by the simulated NMHC and for a potentially broader set of conditions than assumed for the simulated data set. In addition, the simulated data do not include BIO or CNG sources, which emit much of the ETHANE and all of the I_PREN to the ambient air. 2.3. Development of composite aged source profiles Composite aged profiles are also developed using the photochemical transport model. Twenty four different data sets (i.e., six trajectories  2 source types  2 source profile types) are generated using the model, and data sets with a common source type and source profile type are normalized and then averaged. As such, two A-MOB composite aged mobile source are developed, one using US profiles and one using TX profiles, both of which account for EXH, GAS and VAP source types, their spatial and temporal distribution and various strengths, and their convection, dispersion and reaction along six different 8 h paths to GLRC. Similarly, the two A-IND composite aged industrial source profiles account for the various industrial sources, their distribution and strengths, and their convection, dispersion and reaction along the same six paths to GLRC. Although composite aged profiles do not exactly match the simulated NMHC concentrations in any one path, they are preferred

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0.3 0.2

US-EXH a EXHComp2

0.1 0.0 0.3 0.2

TX-EXH b COMVComp

0.1 0.0 0.3 0.2

US-GAS a GASComp

0.1 0.3 0.0 0.2

TX-GAS b HSkAD_DC

0.1

Fraction (g g-1)

0.3 0.0 0.2

US-VAP a VAPComp

0.1 0.0 0.3 0.2

TX-VAP b HsvapGC

0.1 0.0 0.3 0.2

BIO a Biogenic

0.1 0.0 0.3 0.2

SOLV a Acoat196

0.1 0.0 0.3 0.2

CNG c CNG

0.1

ETHANE ETHENE ACETYL N_PROP PROPE I_BUTA BEABYL N_BUTA T2BUTE C2BUTE B1E3ME IPENTA PENTE1 N_PENT I_PREN T2PENE C2PENE B2E2M BU22DM CPENTE P1E4ME CPENTA PENA3M PENA2M BU23DM P1E2ME N_HEX T2HEXE C2HEXE MCYPNA PEN24M BENZE CYHEXA HEXA2M PEN23M HEXA3M PA224M N_HEPT MECYHX PA234M TOLUE HEP2ME HEP3ME N_OCT ETBZ MP_XYL STYR O_XYL N_NON IPRBZ N_PRBZ BZ135M BZ124M UNID

0.0

Fig. 3. Composition of sources considered in this investigation, along with the official name of the source profile as given in Fujita et al. (1995) (see C.A.R.B., 1992).

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0.4 0.3

US-IND a PEin_fug

0.2 0.1 0.3 0.0 0.2

TX-PETROL b IndSL_DC

0.1 0.0 0.3 0.2

TX-CHEM1 b HG0562P

0.1

0.2

TX-CHEM2 b HG0566H

0.1 0.3 0.0 0.2

TX-STORE b HG0262O

0.1 0.0 0.3 0.2

TX-TRADE b HG0786O

0.1 0.3 0.0 0.2

TX-METAL b HG0076G

0.1 0.3 0.0 0.2

TX-LPG c LPG

0.1 0.0 ETHANE ETHENE ACETYL N_PROP PROPE I_BUTA BEABYL N_BUTA T2BUTE C2BUTE B1E3ME IPENTA PENTE1 N_PENT I_PREN T2PENE C2PENE B2E2M BU22DM CPENTE P1E4ME CPENTA PENA3M PENA2M BU23DM P1E2ME N_HEX T2HEXE C2HEXE MCYPNA PEN24M BENZE CYHEXA HEXA2M PEN23M HEXA3M PA224M N_HEPT MECYHX PA234M TOLUE HEP2ME HEP3ME N_OCT ETBZ MP_XYL STYR O_XYL N_NON IPRBZ N_PRBZ BZ135M BZ124M UNID

Fraction (g g-1)

0.0 0.3

References: a C.A.R.B., 1992, b Fujita et al., 1995, c Mayrsohn et al., 1977 Fig. 3. (Continued)

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Table 3 The NMHC species considered in this work, their reactivity, and their use by CMB as a fitting species Non-methane hydrocarbon

kOH  1012 at 298 Ka

Fitting species set A

ETHANE ETHENE ACETYL N_PROP PROPE I_BUTA BEABYL N_BUTA T2BUTE C2BUTE

Ethane Ethene Acetylene N-Propane Propene Isobutene 1-Butene, i-butylene N-Butane t-2-Butene c-2-Butene

0.27 8.52 0.9 1.15 26.3 2.34 31.4 2.54 64.0 56.4

B1E3ME IPENTA PENTE1

3-Methyl-1-butene Isopentane 1-Pentene

31.8 3.9 31.4

N_PENT I_PREN T2PENE C2PENE B2E2M BU22DM CPENTE P1E4ME CPENTA PENA3M PENA2M BU23DM

N-Pentane Isoprene t-2-Pentene c-2-Pentene 2-Methyl-2-butene 2,2-Dimethylbutane Cyclopentene 4-Methyl-1-pentene Cyclopentane 3-Methylpentane 2-Methylpentane 2,3-Dimethylbutane

3.94 101.0 67.0 65.0 86.9 2.32 67.0 31.8b 5.16 5.7 5.6 6.2

P1E2ME

2-Methyl-1-pentene

31.8b

N_HEX

n-Hexane

5.61

   

A0

      

    

    



  

  

  

 

   



Fitting species set A

T2HEXE C2HEXE MCYPNA PEN24M BENZE CYHEXA HEXA2M PEN23M HEXA3M PA224M N_HEPT MECYHX PA234M TOLUE HEP2ME HEP3ME N_OCT ETBZ MP_XYL STYR O_XYL N_NON IPRBZ N_PRBZ BZ135M BZ124M



kOH  1012 at 298 Ka

B



 

Non-methane hydrocarbon

UNID

t-2-Hexene c-2-Hexene Methylcyclopentane 2,4-Dimethylpentane Benzene Cyclohexane 2-Methylhexane 2,3-Dimethylpentane 3-Methylhexane 2,2,4Trimethylpentane n-Heptane Methylcyclohexane 2,3,4Trimethylpentane Toluene 2-Methylheptane 3-Methylheptane n-Octane Ethylbenzene m,p-Xylene Styrene o-Xylene n-Nonane Isopropylbenzene n-Propylbenzene 1,3,5Trimethylbenzene 1,2,4Trimethylbenzene Unidentified

b

67.0 65.0b 8.81c 5.1 1.23 7.49 6.79c 4.87 7.16b 3.68

B

  

 

  

7.15 10.4 7.0 5.96 8.18c 8.56c 8.68 7.1 18.95c 58.0 13.7 10.2 6.5 6.0 57.5

              

    

32.5 25.62

A0

d



a

kOH rate constant at 298 K in units of mol cm3 s1 (Fujita et al., 1995). Values for P1E2ME and P1E4ME estimated from B1E3ME, for T2HEXE from T2PENE and for C2HEXE from C2PENE. c Estimated value (Fujita et al., 1995). d Value estimated as average of values for IPRBX, N_PRBZ, BZ135M and BZ124M C9 compounds. b

0.20

Fraction (g g-1)

NE 2.5 km h-1 trajectory (19.8 μg m-3 total) E 5 km h-1 trajectory (25.8 μg m-3 total)

0.15

Observations (135.5 μg m-3 on average) 0.10 0.05

ETHANE ETHENE ACETYL NPROP PROPE IBUTA BEABYL NBUTA T2BUTE C2BUTE B1E3ME IPENTA PENTE1 NPENT IPREN T2PENE C2PENE B2E2M BU22DM CPENTE P1E4ME CPENTA PENA3M PENA2M BU23DM P1E2ME NHEX T2HEXE CEHEXE MCYPNA PEN24M BENZE CYHEXA HEXA2M PEN23M HEXA3M PA224M NHEPT MECYHX PA234M TOLUE HEP2ME HEP3ME NOCT ETBZ MPXYL STYR OXYL NNON IPRBZ NPRBZ BZ135M BZ124M NHNE2R8

0.00

Fig. 4. Comparison of mass fractions of simulated NMHC (lines) and observed NMHC (box whisker, n ¼ 231) at GLRC.

to trajectory-specific ones since they do not require a priori knowledge of the wind speed and direction during sample collection. Since they account for

emissions from different sources types, the composite profiles are not collinear with the fresh source emission profiles.

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0.25 A-IND

Fraction (g g-1)

0.20 0.15 0.10 0.05 0.00 0.25

A-MOB

Fraction (g g-1)

0.20 0.15 0.10 0.05

ETHANE ETHENE ACETYL NPROP PROPE IBUTA BEABYL NBUTA T2BUTE C2BUTE B1E3ME IPENTA PENTE1 NPENT IPREN T2PENE C2PENE B2E2M BU22DM CPENTE P1E4ME CPENTA PENA3M PENA2M BU23DM P1E2ME NHEX T2HEXE CEHEXE MCYPNA PEN24M BENZE CYHEXA HEXA2M PEN23M HEXA3M PA224M NHEPT MECYHX PA234M TOLUE HEP2ME HEP3ME NOCT ETBZ MPXYL STYR OXYL NNON IPRBZ NPRBZ BZ135M BZ124M UNID

0.00

Fig. 5. Composition of industrial (i.e., A-IND) and mobile (i.e., A-MOB) aged profiles developed using the regionally specific TX source profiles for the six trajectories considered in the analysis. The line indicates the composite aged profile composition. Box whiskers indicate the range of composition for each of the six trajectory paths.

The resulting composite aged profiles are shown in Fig. 5. The composite aged mobile profiles represent the trajectory-specific profiles well because of the similarities in highway networks in all six trajectories. The composite aged industrial profiles are less reflective of any one trajectory path since the location and strength of these sources vary considerably. Although the composite aged profiles are developed using the photochemical transport model, they differ from the simulated ambient data sets in two distinct ways. First, the aged profiles account for either mobile or industrial sources while the ambient data sets account for both. Second, the aged profiles account for all six common trajectories to the GLRC receptor site during the study period, while an ambient data set only accounts for one. The composite aged profiles differ from those developed by Venkataraman and Friedlander (1994) and Lin and Milford (1994) since they account for various source types at various locations and since they are intended to supplement, and not replace, the fresh emission profiles used by CMB.

2.4. Case study evaluations CMB performance is assessed by repetitively challenging the model to apportion the mass of a simulated data set when reasonable uncertainties are systematically introduced into its inputs. In total 64 different permutations of the inputs to CMB (i.e., cases) are investigated. Each case is described in Table 4. In only two of these cases, is CMB provided relevant fitting species and accurate source profiles for only relevant sources: cases 1a and 3a. All other cases feature uncertainty in at least one input and most cases feature uncertainty in more than one input, representing a more realistic use of the model. The four case studies that are conducted each evaluate the effect of uncertainty in a different input by comparing the cases possessing the uncertainty to the cases not possessing it. Fitting species case study: compares model performance in cases that use the A or A0 fitting species selected for the specific sources in Houston (n ¼ 16; all 1, 3, 5 and 7 cases) to those that use the B set of generic fitting species recommended for use in CMB studies

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Table 4 The permutation of inputs for each case that does not use composite aged source profiles Fitting species set:

A

B

A

B

A

B

A

B

Source profile: Profile used in simulation Profile used by CMB Sources: Relevant only Sources: Irrelevant added Sources: Relevant omitted Sources: Elimination used

Accurate US US Case 1a Case 1b Case 1c Case 1d

US US Case Case Case Case

TX TX Case Case Case Case

TX TX Case Case Case Case

Inaccurate US TX Case 5a Case 5b Case 5c Case 5d

US TX Case Case Case Case

TX US Case Case Case Case

TX US Case Case Case Case

2a 2b 2c 2d

3a 3b 3c 3d

4a 4b 4c 4d

6a 6b 6c 6d

7a 7b 7c 7d

8a 8b 8c 8d

Cases that use composite aged profiles are otherwise identical to those listed in the table and are noted with an accent in the text. For example, case 1a provides CMB with the A set of fitting species, accurate source profiles (i.e., identical US profiles were used to simulate the data set and by CMB in its analysis), and profiles for only relevant sources.

around the country (Fujita et al., 1995) (n ¼ 16; all 2, 4, 6 and 8 cases). Source profile case study: compares model performance in cases that use accurate fresh emission profiles (n ¼ 16; all 1–4 cases) to those that use inaccurate ones (n ¼ 16; all 5–8 cases). Source case study: compares performance in cases that only provide the model with information about relevant sources (n ¼ 8; all a cases), to those that also provide the model with information about irrelevant sources (n ¼ 8; all b cases) or omit information about a key relevant source (n ¼ 8; all c cases), to those that use source elimination (n ¼ 8; all d cases). Reactivity case study: compares model performance in cases that do not provide CMB with composite aged profiles (n ¼ 32; all 1–8 cases) to those that do (n ¼ 32; all cases with accent). All cases and case studies challenge CMB to resolve the known Sj source emission rates to the simulated Ci ambient NMHC concentrations (i.e., Ci in Eq. (1)). Separate assessments are conducted using the northeasterly and easterly simulated data sets. The value of each Ci uncertainty is assumed to equal the maximum uncertainty of the EXH, GAS, and VAP mobile sources plus the maximum uncertainty of all the industrial sources. Each model result is evaluated to reveal its relative goodness and absolute accuracy. The relative goodness of each case and case study are determined as a function of the absolute error. The total absolute error (TAE) of each case is computed as the sum of the absolute error of all the contribution estimates identified by CMB in the case. The average absolute error (AAE) of each case study computed as the linear average of all of the TAE of the cases in the case study. Although absolute error is conventionally used to evaluate

receptor model results, it does not convey whether CMB has failed to identify a relevant source or has allocated mass to an irrelevant source. As a result, the absolute accuracy of each case is also assessed by determining whether the solution is statistically reasonable and if so, whether it is also accurate. Three outcomes are possible. No solution: CMB is unable to converge to a solution or the reported diagnostic parameters are out of bounds (i.e., not within 0.8oR2o1, 0ow2o2 and 80o TPMo120). Accurate solution: diagnostic parameters are within bounds and all relevant sources are identified and assigned Sj values within 730% of the actual contributions to the simulated data sets. Irrelevant sources such as CNG, BIO, SOLV, and particular industrial sources are assigned low Sj values between 0% and 5%. The actual contributions for the northeasterly cases are 59.1% EXH, 14.9% GAS, 23.3% VAP and 2.7% IND (1.2% CHEM2 and 1.5% TRADE) or 6.5% EXH, 1% GAS, 0.4% VAP, 89.5% A-MOB, 0% IND, and 2.7% A-IND if one considers the aged NMHC separately. The actual contributions for the easterly cases are 13.8% EXH, 4.7% GAS, 4.9% VAP and 76.6% IND (39% PETROL, 17.1% CHEM1, 5.6% CHEM2, 1.8% TRADE, 11.8% STORE, 0.7% METAL and 0.6% LPG) or 1.5% EXH, 0.2% GAS, 0.1% VAP, 21.6% A-MOB, 0% IND, and 76.6% A-IND. Since these contributions account for the dilution, dispersion and reaction of the NMHC, they are not simply normalized sums of the emissions presented in Table 2. Inaccurate solution: diagnostic parameters are within bounds but CMB fails to identify or allocate mass to a relevant source within 730% of the actual contribution, or allocates more than 5% of the total mass to an irrelevant source.

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3. Results and discussion This section details the results of the case study evaluations that introduce reasonable uncertainties into the fitting species, source profiles or sources the model uses as inputs and discusses potential explanations for model behavior. The absolute accuracy,

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relative accuracy, diagnostic parameters and source contribution estimates reported by CMB for each of the 64 northeasterly and easterly cases are presented in Figs. 6 and 7, respectively. The absolute accuracy and relative goodness of each case study are presented in Fig. 8. The results of the two data sets are presented separately to allow the effects of

χ

Fig. 6. Diagnostic parameters and Sj estimates for the northeasterly 2.5 km h1 trajectory cases. Actual contributions of sources to the simulated NMHC are labeled ‘Actual’. Cases that did not converge to a solution or had performance parameters out of bounds are labeled ‘NS’. Cases that are accurate or inaccurate are labeled ‘Acc’ or ‘Inac’, respectively. AE values for each case are determined as the sum of the absolute errors for all Sj estimates.

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NS NS NS NS

Inac Inac

Inac NS NS

NS NS

NS

NS NS

NS

NS

NS

Acc

NS

Acc Acc

Inac Acc

Acc

Acc

+

NS

Acc Acc

Acc NS

Acc

+

NS

Parameter (%)

Aged profiles provided

a bcd 4

ab cd 6

ab cd 7

NS

NS

NS

NS NS

NS NS

NS ab cd 5

NS

Acc Acc

NS

a bc d 3

NS NS

abc d 2

NS NS

abc d 1

Acc

Acc Acc Acc Acc

NS

+

NS NS

Acc Acc Acc Acc

175 150 125 100 75 50 25 0

+

Acc Acc Acc Acc

125 100 75 50 25 0

Actual

Source contribution EXH GAS VAP AMOB IND AIND BIO CNG SOLV

150 125 100 75 50 25 0

Parameter (%)

R2 χ2 AE + AE off-scale

Contribution (%)

Diagnostic parameter

125 100 75 50 25 0

Contribution (%)

Aged profiles not provided

ab cd 8

Case number

Fig. 7. Diagnostic parameters and Sj estimates for the easterly 5 km h1 trajectory cases. Actual contributions of sources to the simulated NMHC are labeled ‘Actual’. Cases that did not converge to a solution or had performance parameters out of bounds are labeled ‘NS’. Cases that are accurate or inaccurate are labeled ‘Acc’ or ‘Inac’, respectively. AE values for each case are determined as the sum of the absolute errors for all Sj estimates.

source distribution to be considered in the analysis. Only one modification is made to the results reported by CMB: contributions smaller than 20% or greater than 120% of the total simulated NMHC mass are omitted since they are individually outside of the acceptable range of TPM. For the most part, this modification resulted in inaccurate cases becoming those with no solution. 3.1. Overall results In the 64 cases that do not provide CMB with aged profiles, the introduction of uncertainty into

the model inputs increases the likelihood that a solution will not be reached or contribution estimates will be less accurate. Model performance is better for the perfect cases 1a and 3a than for any case study that does not use aged profiles. The relative goodness of the cases is high since the AAE values are less than 30%. Even the absolute accuracy is good. Only one of these cases do not result in a solution and none of the cases have inaccurate solutions. In these cases, CMB has access to perfect information to describe the fresh emission sources. Only relevant sources are made available to CMB and their source profiles are accurate since the

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Northeasterly 2.5 km h-1 trajectory cases

Values above bars: Average absolute error of the study cases

100

21% 29%

31% 27%

25% 32%

27% 28% 31% 28%

24% 76%

26% 29% 43% 39%

80 60 40 20 0

Number of cases (%)

Bars: Number of cases in study with the following classifications No Solution Accurate Inaccurate

Number of cases (%)

Aged profiles not provided

Aged profiles provided 100

28% 38%

39% 34%

80 60 40 20 0 Easterly 5 km h-1 trajectory cases

Number of cases (%)

Aged profiles not provided 100

26% 52%

73% 15%

34% 101%

34% 15% 177% 25%

21% 63%

11% 17% 31% 41%

80 60 40 20

100

9%

29%

Any uncertainty (n=30; all other cases)

Aged profiles provided

Perfect information (n=2; cases 1a, 3a)

Number of cases (%)

0 34% 12%

80 60 40 20

Overall study

Species study

Profile study

Source elimination used (n=8; all d cases)

Relevant source missing (n=8; all c cases)

Extra sources included (n=8; all b cases)

Relevant sources only (n=8; all a cases)

Inaccurate profiles (n=16; all 5-8 cases)

Accurate profiles (n=16; all 1-4 cases)

Generic species (n=16; all 2,4,6,8 cases)

Specific species (n=16; all 1,3,5,7 cases)

0

Sources study

Fig. 8. Summary of CMB performance for the northeasterly 2.5 km h1 trajectory cases and for the easterly 5 km h1 trajectory cases.

same profiles are also used by the photochemical transport model to simulate the ambient NMHC. While this level of accuracy in model inputs is

unlikely, these cases provide a baseline against which cases that possess a more reasonable level of uncertainty can be compared.

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The remaining 60 cases feature uncertainty in at least one input and represent a more realistic use of the model. The relative goodness of the cases is affected by the uncertainty in the CMB inputs, since higher AAE values for these cases are observed. Although these AAE values would probably be considered acceptable, the absolute accuracy of these cases is actually much lower. Total 50% (n ¼ 30) of these cases result in diagnostic parameters that are out of bounds (i.e., the cases with no solution), preventing a user from being able to use CMB to resolve NMHC sources at all. In total, 12% (n ¼ 7) of the cases with diagnostic parameters within bounds result in inaccurate solutions. In these instances, a user would be unaware that the CMB result is inaccurate because the diagnostic parameters indicate otherwise. These inaccuracies are not limited to a single case study. Of the inaccurate northeasterly cases, all three feature the use of source elimination (i.e., 3d, 4d and 5d), one features generic fitting species (i.e., 4d) and one features inaccurate source profiles (i.e., 5d). Solutions for these cases are considered inaccurate because CMB identified at least one irrelevant source (e.g., METAL in 4d) or failed to identify a relevant source (e.g., GAS in 4d or 5d and IND in 5d). Of the inaccurate easterly cases, three feature inaccurate source profiles (i.e., 7a, 7c and 7d), two omit a relevant source (i.e., 3c and 3d), and one uses source elimination (i.e., 7d). These cases are inaccurate because CMB overestimated the contribution of a relevant source (e.g., EXH in 3c, 7a and 7c and GAS in 7c) or because CMB failed to identify a relevant source (e.g., GAS in 3c, 7c or 7d). The 64 cases that also provide CMB with composite aged source profiles experience improved model performance, even given the uncertainties in other inputs. Cases using aged profiles are distinguished from those that do not, in the discussion, by an accent. Although the relative goodness of these cases is decreased when there are uncertainties in at least one input, the absolute accuracy of the cases is improved in even the four perfect cases (i.e., 1a0 and 3a0 ) since all of the cases (n ¼ 4) result in an accurate solution. Performance is also improved in the cases that possess some degree of uncertainty, since inaccuracy is completely eliminated. All seven of the cases that previously resulted in inaccurate solutions now result in accurate solutions (i.e., northeasterly case 3d0 , and easterly cases 3c0 , 7c0 and 7d0 ) or in no solution (i.e., northeasterly cases 4d0 and 5d0 , and easterly case 7a0 ). However, some

cases that formerly reached an accurate solution do not reach a solution at all, resulting in a larger number of cases with no solution. Most of the cases that do not reach a solution possess uncertainties in source profiles. Since AAE values are not necessarily large when cases are inaccurate, in the present study the absolute error is considered to be an optimal indicator of performance. As a result, they are presented in Figs. 6–8 but are not discussed further. 3.2. Fitting species case study CMB iteratively solves the mass balances for the fitting species. As a result, one unique fitting species that is also unreactive is needed for each source provided to CMB that is unreactive and unique to the source. Since these constraints are difficult to meet in practice, species that are less reactive than toluene are typically selected (Fujita et al., 1995). This case study compares the 32 cases that use the A or A0 fitting species selected for the specific sources in Houston (i.e., cases 1, 3, 5 and 7) to the 32 cases that use the B generic fitting species (i.e., cases 2, 4, 6 and 8). The A species are used in cases without composite aged profiles and possess reactivities less than that of toluene with one exception: I_PREN, the sole compound in BIO. A0 species are used in cases with composite aged profiles and expands on the A set by also including reactive species specific to sources in Houston. Generic species are likely to impair performance since the species may not be unique to the actual sources in the domain. Contrary to expectation, the use of generic fitting species in the cases that do not use composite aged profiles, reduces inaccuracy over cases that use specific species (i.e., 3.1% versus 18.8%). However, a similar number of cases do not reach a solution (i.e., 50% for both). These trends are observed in both northeasterly and easterly cases, as shown in Figs. 6 and 7, but easterly cases using generic species are less inaccurate than their northeasterly counterparts (13% versus 25%). Although both sets of fitting species lead to performance issues, 100% of the 32 easterly trajectory cases that are inaccurate use specific fitting species. 3.3. Source profiles case study Source profiles describe the distribution of NMHC in fresh emissions, and are represented by

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the Fij term in the mass balances solved by CMB. This case study compares model performance in the 32 cases that use accurate source profiles to describe the fresh emissions (i.e., cases 1–4) to the 32 cases that use profiles that only approximately describe the relevant sources (i.e., cases 5–8). Profiles are considered accurate if they were also used to simulate the ambient NMHC. They are considered to be inaccurate if this is not the case (e.g., US profiles provided to CMB but TX ones used to simulate ambient NMHC). Domains that are not already well characterized may not possess accurate profiles to describe their particular sources. However, inaccurate source profiles are more likely to impair model performance since they do not actually describe the actual fresh emissions. Although CMB should be able to tolerate small differences in source profiles, its performance declines in this case study. On average, a larger number of cases do not reach a solution (i.e., 62.5% versus 34.4%) and a larger number of the cases that do reach a solution are inaccurate (i.e., 12.5% versus 9.4%). These trends are the most clear in the easterly cases since industrial sources are prominent in this trajectory. The use of inaccurate profiles in the easterly versus the northeasterly cases results in fewer solutions (i.e., 81.3% versus 43.8%) and more inaccuracy (i.e., 19% versus 6%). 3.4. Sources case study In a poorly characterized region relevant NMHC sources may be unknown and, as a result, irrelevant sources may be made available to CMB or relevant sources may be omitted. This case study compares the 16 cases that provide CMB with source profiles for only the relevant EXH, GAS, VAP and IND sources (i.e., a cases), to the 16 that provide CMB with additional profiles of irrelevant BIO, CNG and SOLV sources (i.e., b cases), the 16 that omit the profile for the relevant IND source (i.e., c cases), and the 16 that use source elimination to eliminate collinear or potentially irrelevant sources (i.e., d cases). The omission of relevant sources or the inclusion or irrelevant ones is likely to impair model performance. Although the profiles of the irrelevant area sources are not collinear with those of the sources actually present in the simulated NMHC data, they may challenge CMB since SOLV contains N_HEX and CYHEXA which are present in fitting species set B and are present in several of the relevant sources. The source elimination feature is

1335

expected to improve performance since many of the sources in the study region are collinear. When composite aged profiles are not provided to the model, performance across this case study varies substantially. When profiles for only the relevant sources are provided to the model, 43.8% of the cases result in no solution and 6.3% result in inaccurate solutions. More northeastern cases do not result in a solution but those that do are more accurate than their eastern counterparts. When the irrelevant BIO, CNG and SOLV profiles are provided to CMB, model performance is mixed. CMB rarely assigns contributions that are greater than 5% of the total mass to these irrelevant sources and model inaccuracy decreases from 6.3% to 0%. However, the number of eastern cases that result in no solution decreases from 50% to 25% while the number of northeastern cases with no solution increases from 37.5% to 75%. Consistent with expectation, when the relevant IND source is omitted, nearly 75% of the cases result in no solution. More cases result in inaccurate solutions, but only because the number of inaccurate easterly cases increase. Contrary to expectation, use of source elimination does not consistently improve model performance. While its use reduces the number of northeasterly cases that result in no solution to 12.5%, it dramatically increases the inaccuracy of the northeasterly and easterly cases that do reach a solution to 38% and 13%, respectively. Notably, all of the inaccurate northeasterly cases use this feature. 3.5. Reactivity case study The prior case studies demonstrate that even when less reactive NMHC are used as fitting species, uncertainties in any of the model inputs can severely limit the ability of CMB to reach an accurate solution. This poor performance may actually be explained by the nonlinear enrichment of the less reactive NMHC during transport from source to receptor. This final case study compares the 64 cases already discussed in Sections 3.2–3.4 to 64 new cases that supplement the fresh emission source profiles with composite aged ones. CMB generally identifies contributions from both aged and fresh sources with significantly larger contributions from the aged sources, consistent with the actual source contributions provided in Section 2.4. Uncertainties in fitting species have less of an impact on model performance when

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composite aged profiles are provided to CMB. No cases are inaccurate but the use of generic species still impairs model performance since fewer cases reach a solution, regardless of trajectory. Uncertainties in fresh source profile accuracy still negatively impact model performance even when aged profiles are made available to the model. Although none of the cases are inaccurate, many cases do not reach a solution. Uncertainties in sources do not pose a significant challenge to CMB when aged profiles are also provided to the model. None of the cases are inaccurate and with few exceptions, the number of cases that results in no solution are greatly reduced. In summary, when composite aged profiles are made available to the model, CMB performance is far less sensitive to uncertainties in the fitting species or sources input to the model. None of the cases are inaccurate and more cases result in accurate solutions. However, the model is still quite sensitive to the accuracy of source profiles since a great many of these cases still result in no solution. The success of the composite aged profiles may be explained by the composited nature of the profiles which makes them distinct from fresh emission source profiles or by the fact that they reflect the distribution of relevant sources in the domain. 4. Conclusions Although CMB is intended to be used alongside other analysis approaches (Watson et al., 1984), its accessibility relative to many chemical transport models may result in its sole use in the evaluation of the relationship between emissions and ambient observations of NMHC. This study is the first to use of realistic simulated data to rigorously evaluate the ability of CMB to perform this task accurately or to develop aged NMHC source profiles. Four case studies, each with multiple cases, are conducted to evaluate the effect of reasonable uncertainties in the inputs to the model on model performance. The first three case studies evaluate performance when fitting species are not selected for the sources actually in the domain, when inaccurate source profiles are used to describe the relevant sources, and when a relevant source is omitted or irrelevant sources are included in the analysis or CMB uses source elimination to remove collinear sources. The final case study compares performance, even given uncertainties in the three inputs already noted, when composite

aged source profiles are also made available to the model. Performance is evaluated using absolute error since relative goodness does not reflect whether all of the relevant sources are identified or if any irrelevant sources are identified. When composite aged source profiles are not made available to the model, CMB performs consistently well only when all of the contributing sources are known and perfect information is available to describe them. When there are reasonable uncertainties in any of its inputs, the model results are not necessarily accurate even when the diagnostic parameters indicate otherwise. Model performance is most sensitive to the accuracy of the source profiles, but is also influenced by uncertainties in fitting species and sources. In nearly half of the cases, the model is unable to reach any solution, severely limiting its utility. When regionally relevant composite aged profiles are made available to the model, inaccuracy is completely eliminated. The model is insensitive to uncertainties in fitting species and sources, but is still sensitive to uncertainties in the fresh emission source profiles, as indicated in the large number of those cases that do not reach a solution. Acknowledgments This investigation was financially supported by the Texas Council on Environmental Quality (TCEQ) and by the Professional Staff Congress of the City University of New York (PSC CUNY). The authors gratefully thank the TCEQ for the NMHC emission inventories.

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