The use of positive matrix factorization with conditional probability functions in air quality studies: An application to hydrocarbon emissions in Houston, Texas

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Atmospheric Environment 40 (2006) 3070–3091 www.elsevier.com/locate/atmosenv

The use of positive matrix factorization with conditional probability functions in air quality studies: An application to hydrocarbon emissions in Houston, Texas Yulong Xie, Carl M. Berkowitz Pacific Northwest National Laboratory, Richland, WA 99352, USA Received 30 September 2005; received in revised form 13 December 2005; accepted 21 December 2005

Abstract In this paper, we describe two advanced statistical techniques suited to address the following questions: which source categories of emissions affect given areas and where do these source categories come from? A source category is defined as a combination of volatile organic compounds (VOCs) associated with a specific industrial process. A discussion of the positive matrix factorization (PMF) multivariate receptor model is presented, and this PMF technique applied to hourly average concentrations of VOCs measured at five Photochemical Assessment Monitoring Stations (PAMS) located near the emission-rich Houston Ship Channel region in Texas. The observations were made between June and October 2003, and the PMF analysis was limited to nighttime measurements (21:00–06:00 CDT) to remove the complexity of photochemical processing and associated changes in the concentrations of primary and secondary VOCs. Six to eight VOCs source categories were identified for the five Ship Channel sites. Specific geographic areas associated with each source category were identified through the use of conditional probability functions that identify source regions when superimposed on maps of VOC emissions. r 2006 Elsevier Ltd. All rights reserved. Keywords: Receptor modeling; VOC; Positive matrix factorization (PMF); Conditional probability function (CPF); Houston; Air quality

1. Introduction Practical questions to be addressed when implementing any emission control strategy are which emissions are affecting which areas and where do these emissions come from? The answers to such questions require knowledge of the ambient chemistry and local meteorology, with the latter knowledge needed to describe the geographic path followed by air Corresponding author. Tel.: +1 509 372 6183; fax: +1 509 372 6168. E-mail address: [email protected] (C.M. Berkowitz).

1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.12.065

parcels carrying the emissions of concern. In this paper, we describe techniques suited for obtaining such knowledge and present results using an extensive set of hydrocarbon measurements and surface meteorological observations from Houston, Texas. The compounds of interest for our study are the 55 volatile organic compounds (VOCs) measured along and downwind of the Houston Ship Channel, which is a major waterway of Galveston Bay and home to one of the largest petrochemical processing facilities in the world. These VOCs, in conjunction with NOx emissions, have long been known to mix in the presence of sunlight to form ozone. Ozone

ARTICLE IN PRESS Y. Xie, C.M. Berkowitz / Atmospheric Environment 40 (2006) 3070–3091

concentrations in this area are among the highest in North America not only because of the ample ozone-precursors but also because of the abundance of moisture and a coastal circulation pattern that allows an air mass to make multiple passes over key VOC source regions. The basis for our analysis is a sophisticated analytical technique called positive matrix factorization (PMF) (Paatero, 1997; Hopke, 2000, 2003). This technique was used to derive a set of source composition profiles, each identifying a mix of compounds associated with a particular category of emissions (e.g., on-road emissions, solvent manufacturing, etc.). It also gave a measure of the relative importance of each profile at each of the Photochemical Assessment Monitoring Stations (PAMS) sites shown in Fig. 1. The major VOC point sources from a recent Texas Commission on Environmental Quality (TCEQ) emissions inventory also are shown in Fig. 1. We begin this paper by reviewing the theory of PMF and defining the Conditional Probability Functions (CPF) used to identify the wind directions with which each source category, or ‘factor,’ was associated. Next, a short statistical overview is given of all the observations. We then present results of the PMF analysis, limiting the analysis to measurements made at night so as to remove the uncertainty in the interpretation associated with daytime photochemistry. Five major factors are identified, and their roles in the chemical mix at each of the measurement sites are discussed. We concluded that if these techniques work in an area as complex as Houston, they are probably suitable for use in other areas.

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2. A brief review and summary of PMF and CPF While the basic ideas of PMF are straightforward, their implementation can be quite detailed. For this reason, we present a short review of how the PMF calculations are carried out and how the results can be interpreted. 2.1. Positive matrix factorization The PMF method was comprehensively described by Paatero and Tapper (1994) and Paatero (1997) and has been used in many source identification studies involving particulate matter (Hopke, 2003; Polissar et al., 1998; Xie et al., 1999; Willis, 2000) and VOCs (Miller et al., 2002; Roberts et al., 2004; Zhao et al., 2004). The implementation of PMF in the PMF2 program (Paatero, 2000) was used in this study. Additional guidance on the use of PMF for receptor modeling also was published by Hopke (2000). Here, we present a brief overview of the technique, noting modifications needed for its application to the PAMS data set. The two-way formulation of PMF solves the general receptor model, which is formulized as a chemical mass balance problem in terms of contributions from p independent sources to all chemical species measured in a given sample (Miller et al., 1972; Hopke, 1985, 1991): xij ¼

p X

gik f kj þ eij ,

(1)

k¼1

where xij is the jth species concentration measured in the ith sample, gik is the emitted mass concentration

Fig. 1. Map of the Houston area showing the locations of the PAMS auto-gas chromatographs that provided the observations used in this study. Also shown are major point sources of VOC emissions.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

Index from Figs. 4–9

(a)

Ethane Ethylene Propane Propylene Acetylene N-Butane ISO-Butane TRANS-2-Butene CIS-2-Butene 1,3-Butadiene N-Pentane ISO-Pentane 1-Pentene TRANS-2-Pentene CIS-2-Pentene 3-Methylpentane N-Hexane N-Heptane N-Octane N-Nonane N-Decane Cyclopentane Isoprene 2,2-Dimethylbutane 2,4-Dimethylpentane Cyclohexane 3-Methylhexane 2,2,4-Trimethylpenta 2,3,4-Trimethylpenta 3-Methylheptane Methylcyclohexane Methylcyclopentane 2-Methylhexane 1-Butene 2,3-Dimethylbutane 2-Methylpentane 2,3-Dimethylpentane N-Undecane 2-Methylheptane M&P-Xylene Benzene Toluene Ethylbenzene O-Xylene

1034 1034 1034 1034 1033 1034 1034 1034 1033 522 872 1033 855 1013 1001 0 1067 876 1067 1067 1067 1034 1034 875 7 1061 1020 1017 1054 1065 1067 408 1020 1033 0 0 762 221 1065 1067 1067 1067 1066 1067

33 33 33 33 33 33 33 33 33 544 195 33 204 33 33 1067 0 190 0 0 0 33 33 190 651 0 41 41 0 0 0 651 41 33 1067 1067 143 845 0 0 0 0 0 0

0 0 0 0 1 0 0 0 1 1 0 1 8 21 33 0 0 1 0 0 0 0 0 2 409 6 6 9 13 2 0 8 6 1 0 0 162 1 2 0 0 0 1 0

0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 2 0 1 0 0 0 0 0 1 2 0 0 0 0 0 0 1 0 0 2 2 1 2 0 0 0 0 0 0

891 463 867 891 889 891 891 891 857 446 891 891 874 891 882 0 890 884 890 494 494 890 451 891 35 886 879 888 867 881 890 886 628 891 0 0 356 0 882 494 890 890 494 494

0 428 24 0 2 0 0 0 34 445 0 0 16 0 8 891 1 1 1 397 397 0 436 0 1 1 1 1 1 1 1 1 228 0 891 891 228 891 1 397 1 1 397 397

0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 6 0 0 0 1 4 0 855 4 11 2 23 9 0 4 35 0 0 0 307 0 8 0 0 0 0 0

No999b No0c

Noa

PMF coded

Noa

Name

No999b No0c

Clinton (night)

Channel view (night)

VOC

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 1 1 0 1 0 2 0 0 0 0 0 0 0 1 0 2 2 2 2 0 1 0 0 1 1

PMF coded 1134 1134 1135 1135 900 1135 1135 1135 1135 1135 913 913 1130 1115 1104 1124 1146 1146 1145 1146 1146 1129 1118 1128 1067 1145 1146 1139 1143 1145 1146 1146 1146 1135 1126 1130 1125 1146 1146 1146 1146 1146 1146 1146

Noa

12 12 11 11 246 11 11 11 11 11 233 233 11 11 11 15 0 0 0 0 0 17 19 17 0 0 0 7 0 0 0 0 0 11 15 15 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 5 20 31 7 0 0 1 0 0 0 9 1 79 1 0 0 3 1 0 0 0 0 5 1 21 0 0 0 0 0 0 0

No999b No0c

Haden Road (night)

0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

PMF coded 1063 1053 1063 1063 692 1063 1063 1061 1059 1053 820 624 876 872 863 787 1005 1025 1017 993 992 1062 785 913 349 978 972 742 859 949 1037 1003 925 1056 704 794 584 939 954 1047 1041 1011 1018 1019

Noa

5 5 5 5 366 5 5 7 5 14 248 445 147 110 17 272 47 17 17 22 72 5 272 114 17 17 17 225 17 17 17 17 17 7 358 272 17 119 17 17 17 57 33 22

1 11 1 1 11 1 1 1 5 2 1 0 46 87 189 10 17 27 35 54 5 2 12 42 703 74 80 102 193 103 15 49 127 6 7 3 468 11 98 5 11 1 18 28

No999b No0c

Lynchburg Ferry (night)

0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 0 2 0 0 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0

PMF coded 1090 1088 1090 1089 1090 1090 1090 1034 1087 1089 706 706 1035 983 834 1089 1097 1090 1082 1005 1091 1090 1064 1073 600 1079 1089 1084 924 1007 1094 1088 1079 1089 1088 1090 884 1025 1033 1097 1096 1097 1094 1089

Noa

8 8 8 8 8 8 8 64 8 8 392 392 8 8 8 8 1 0 0 0 0 8 9 8 0 1 0 0 0 0 1 0 1 8 8 8 0 0 1 1 2 1 0 0

0 2 0 1 0 0 0 0 3 1 0 0 55 107 256 1 0 8 16 93 7 0 25 17 498 18 9 14 174 91 3 10 18 1 2 0 214 73 64 0 0 0 4 9

No999b No0c

Wallisville (night)

0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

PMF coded

Table 1 Sample size and counting statistics for hydrocarbon concentration (a) and basic statistics of the measured hydrocarbon concentrations (b) in the sites at Houston/Galveston area

3072 Y. Xie, C.M. Berkowitz / Atmospheric Environment 40 (2006) 3070–3091

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Index from Figs. 4–9

891

397 428 397 397 891 891 891 891 397 397

Ethane Ethylene Propane Propylene Acetylene N-Butane ISO-Butane TRANS-2-Butene CIS-2-Butene 1,3-Butadiene N-Pentane ISO-Pentane 1-Pentene TRANS-2-Pentene CIS-2-Pentene 3-Methylpentane N-Hexane N-Heptane N-Octane N-Nonane N-Decane Cyclopentane Isoprene 2,2-Dimethylbutane 2,4-Dimethylpentane Cyclohexane 3-Methylhexane 2,2,4-Trimethylpenta 2,3,4-Trimethylpenta 3-Methylheptane Methylcyclohexane Methylcyclopentane 2-Methylhexane 1-Butene

50.37 12.95 68.98 18.47 2.59 41.15 32.35 1.25 1.04 4.38 16.03 30.25 0.61 0.69 0.43 8.14 2.15 1.09 0.79 1.09 1.98 1.36 0.60 0.76 6.23 1.91 3.30 0.67 0.50 3.10 3.83 1.22 3.97

2.53 0.1 1.8 0.32 0.08 0.72 0.52 0.21 0.09 0.07 0.06 0.52 0.03 0.03 0.01

0.07 0.15 0.1 0.08 0.09 0.14 0.04 0.05 0.37 0.06 0.04 0.05 0.04 0.03 0.14 0.09 0.06 0.05

Meanf

57.95 15.13 7.48 11.58 13.77 18.16 29.14 26.9 2.05 287.82 18.27 47.07 9.47 3.5 30.16 31.9 12.11 100.87

528.82 128.84 789.24 522.69 80.64 656.93 370.4 26.36 19.78 122.12 127.48 510.84 8.67 16.27 9.4 8.66 2.14 0.95 0.74 1.02 2.10 1.83 1.14 0.59 15.61 2.08 3.93 0.80 0.43 3.18 4.49 1.42 8.96

53.10 16.67 85.86 35.78 5.54 59.16 39.63 1.73 1.49 10.16 17.82 36.65 0.84 1.23 0.72 0.31 0.06 0.11 0.08 0.09 0.09 0.01 0.01 0.34 0.05 0.07 0.05 0.08 0.03 0.14 0.19 0.05 0.06

2.6 0.28 1.16 0.04 0.16 1.07 0.83 0.07 0.04 0.02 0.82 1.7 0.02 0.06 0.02 7.96 2.76 1.29 0.88 0.96 1.79 0.87 0.83 3.70 2.59 2.89 3.60 1.06 0.91 2.80 4.50 2.11 1.80

29.27 9.14 25.40 6.78 2.66 23.55 15.26 2.72 2.05 2.08 13.31 26.74 1.14 1.48 0.72

Meanf

Mine

0

488 463 476 469 0 0 0 0 396 494

Mine St. dev.h

2

0 0 0 0 2 2 2 2 0 0

Name

Maxg

0

2 0 3 24 2 0 0 0 1 0

Clinton (night)

1067

0 0 0 0 845 1067 1067 1067 0 0

Channel view (night)

0

1065 1067 1064 1043 220 0 0 0 1066 1067

VOC

M-Diethylbenzene

55

(b)

1,3,5-TRI-M-Benzene 1,2,4-TRI-M-Benzene N-Propylbenzene ISO-Propylbenzene O-Ethyltoluene M-Ethyltoluene P-Ethyltoluene P-Diethylbenzene Styrene 1,2,3-TRI-M-Benzene

45 46 47 48 49 50 51 52 53 54

203.63 41.3 24.6 16.47 10.02 478.23 17.2 15.68 15.03 55.97 55.17 70.34 23.11 13.08 24.63 119.88 38.46 59.23

311.59 92 236.61 84.89 33.04 529.38 540.41 796.43 555.43 57.05 312.13 771.13 44.96 53.44 27.31

Maxg

0

6 0 18 25 0 0 0 0 98 0

17.27 4.06 1.81 1.17 0.82 16.10 1.42 1.61 3.10 4.78 4.98 5.12 1.55 1.15 2.88 10.00 3.94 3.70

25.11 8.05 23.62 8.23 2.81 43.89 31.59 29.58 21.08 5.05 25.62 51.67 3.11 3.30 1.64

St. dev.h

2

1 1 1 1 2 2 2 2 1 1 696

3 0 0 0 1 28 33 0 0 0 0

2 0 11 43 10 1 78 21 5 0

1.6 0.05 1 0.17 0.01 0.55 0.4 0.27 0.05 0.01 0.32 0.65 0.01 0.02 0.01 0.08 0.18 0.04 0.04 0.05 0.16 0.02 0.01 0.01 0.01 0.06 0.09 0.01 0.01 0.04 0.11 0.12 0.02 0.04

Mine

23.68 10.93 26.43 9.88 1.25 37.12 16.71 1.59 1.37 2.53 16.64 37.74 1.15 2.32 1.12 5.02 8.56 2.79 2.11 0.76 1.02 1.68 1.00 1.11 1.14 4.41 2.40 4.04 1.19 0.91 2.84 4.03 2.07 4.35

Meanf St. dev.h

1

0 0 0 0 0 0 0 0 0 0

176.63 17.29 201.9 18.13 267.92 23.02 307.08 19.30 10.29 1.27 2263.75 142.61 523.59 27.04 43.6 3.18 39.56 2.75 97.96 6.15 318 29.23 1337.55 78.49 33.92 2.77 108.29 6.12 46.45 2.93 62.66 7.99 79.43 9.90 24.95 3.40 29.24 3.19 6.8 0.80 9.23 0.75 103.66 3.89 30.88 1.76 71.15 4.15 15.44 1.60 133.77 9.32 26.64 2.86 52.41 5.87 13.03 1.65 8.2 0.97 24.74 2.83 49.8 5.09 29.36 2.58 388.82 15.81

Maxg

Haden Road (night)

450

1141 1146 1135 1103 1135 1117 1035 1125 1141 1146 855

17 129 56 30 17 17 17 285 57 72 17

73 8 146 278 168 33 401 42 72 36

1.35 0.04 0.54 0.18 0.03 0.22 0.27 0.12 0.04 0.03 0.35 0.06 0.01 0.02 0.01 0.02 0.05 0.04 0.06 0.05 0.06 0.09 0.01 0.01 0.02 0.02 0.01 0.02 0.03 0.02 0.09 0.04 0.02 0.03

Mine

32.38 18.05 45.14 46.53 1.48 32.78 28.56 1.77 1.40 3.94 18.97 26.32 0.77 1.15 0.46 4.62 12.83 2.99 1.74 0.86 1.08 3.63 1.34 0.68 1.43 10.18 2.37 3.08 1.05 0.90 3.02 4.85 2.06 4.67

Meanf

St. dev.h

2

0 0 1 1 1 0 1 1 0 0

221.66 28.04 454.8 31.21 377.94 52.33 1281.88 106.51 50.48 3.66 812.84 55.97 616.17 45.58 117.87 5.05 74.06 4.08 87.98 7.57 1164.65 52.10 564.33 44.92 17.6 1.42 289.79 9.95 11.04 0.90 249.87 13.02 302.62 22.40 73.45 5.72 36.79 2.98 25.98 1.69 33.17 1.87 1320.42 45.42 13.6 1.87 29.37 1.91 22.56 2.09 361.85 23.42 94.96 5.77 86.29 6.24 42.62 2.56 44.52 2.26 86.24 4.61 332.81 12.49 76.39 4.88 247.87 14.25

Maxg

Lynchburg Ferry (night)

197

979 932 867 761 884 1019 651 742 940 961 384

0 1 1 0 0 72 72 1 0 0

1.53 0.03 0.73 0.08 0.06 0.61 0.37 0.5 0.16 0.01 0.35 0.48 0.01 0.01 0.01 0.06 0.06 0.01 0.01 0.01 0.01 0.1 0.01 0.01 0.02 0.07 0.05 0.06 0.01 0.01 0.06 0.11 0.02 0.05

Mine

24.84 10.15 29.68 26.38 0.91 16.39 15.82 1.58 0.55 1.01 7.13 12.40 0.20 0.22 0.13 1.91 4.69 0.90 0.52 0.20 0.32 0.67 0.82 0.28 0.37 3.08 0.84 1.13 0.31 0.21 1.58 1.86 0.71 1.90

Meanf

Wallisville (night)

574

947 1085 919 703 831 862 371 825 1019 1071

241.54 109.56 180.82 528.58 8.65 329.83 289.45 8.23 3.62 31.79 49.25 161.8 2.1 3.94 1.72 13.75 94.32 10.01 7.69 2.59 2.97 4.7 40.69 5.34 2.8 155.21 5.41 15.89 4.8 2.63 25.67 14.14 5.05 45.43

Maxg

140

151 12 178 395 267 164 655 272 79 27

21.22 13.59 27.52 56.03 0.87 22.57 22.30 0.58 0.35 2.26 7.25 13.89 0.26 0.35 0.18 1.94 6.84 0.92 0.62 0.21 0.27 0.65 1.92 0.34 0.31 7.28 0.75 1.14 0.34 0.23 1.75 1.81 0.64 4.18

St. dev.h

1

1 0 1 1 1 1 2 1 0 0

Y. Xie, C.M. Berkowitz / Atmospheric Environment 40 (2006) 3070–3091

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35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

St. dev.h

2,3-Dimethylbutane 2-Methylpentane 2,3-Dimethylpentane N-Undecane 2-Methylheptane M&P-Xylene Benzene Toluene Ethylbenzene O-Xylene 1,3,5-TRI-M-Benzene 1,2,4-TRI-M-Benzene N-Propylbenzene ISO-Propylbenzene O-Ethyltoluene M-Ethyltoluene P-Ethyltoluene P-Diethylbenzene Styrene 1,2,3-TRI-M-Benzene M-Diethylbenzene 1.30 0.65 0.42 3.24 8.26 8.60 1.50 1.27 0.86 1.94 0.37 0.66 0.34

1.47 1.67

0.01 0.06 0.04 0.14 0.09 0.39 0.08 0.09 0.03 0.2 0.03 0.02 0.03

0.05 0.12

37.04 8.13

14.55 6.16 4.76 58.35 425.73 113.31 23.97 14.22 10.04 27.62 3.37 19.99 1.34

2.49 1.36

1.42 0.62 0.52 3.32 16.07 8.78 2.13 1.19 0.90 2.05 0.32 1.36 0.27

1.01 1.75

0.97 4.57 4.54 8.25 1.41 1.67 0.68 2.50 0.45 0.29

0.03 0.27 0.06 0.62 0.05 0.1 0.04 0.2 0.03 0.02

0.06 0.14

2.59

0.02

Meanf

Mine

Maxg

Mine

Name

Meanf

Clinton (night)

Channel view (night)

VOC

15.25 11.07

19.01 24.4 165.14 60.03 6.93 9.17 4.36 9.11 2.04 2.9

26.99

Maxg

1.74 1.63

1.32 3.93 9.66 8.11 1.18 1.39 0.59 1.84 0.34 0.29

3.49

St. dev.h 0.02 0.1 0.01 0.04 0.04 0.22 0.24 0.54 0.05 0.1 0.03 0.04 0.02 0.01 0.01 0.07 0.03 0.01 0.02 0.08 0.02

Mine

1.49 5.58 1.16 0.61 0.77 5.04 5.38 11.03 1.56 1.69 0.64 2.02 0.35 0.51 0.43 1.34 0.59 0.47 1.90 1.31 0.34

Meanf

28.32 81.52 12.62 8.8 8.16 71.11 152.21 98.91 17.54 15.58 3.75 24.64 2.34 26.47 2.95 7.04 3.27 3.07 90.87 9.74 1.65

Maxg

Haden Road (night)

2.97 9.92 1.44 0.61 0.93 5.35 7.11 11.09 1.53 1.59 0.53 2.06 0.30 1.71 0.37 1.05 0.48 0.35 4.07 1.12 0.25

St. dev.h

b

Number of non-zero VOCs concentration. Number of missing values. c Number of zero concentrations. d Code for the inclusion of species in PMF analysis: 0, normal; 1, weak with further downweighting; 2, exclusion. e Minimum non-zero concentration. f Average non-zero concentration. g Maximum of non-zero concentrations. h Standard deviation of non-zero concentrations.

a

Index from Figs. 4–9

(b)

Table 1 (continued )

0.02 0.03 0.02 0.02 0.05 0.08 0.06 0.1 0.06 0.06 0.03 0.04 0.03 0.02 0.03 0.04 0.05 0.06 0.03 0.04 0.05

Mine

1.07 4.46 1.44 0.76 0.85 5.02 29.57 13.50 3.08 3.82 0.64 1.90 0.48 2.22 0.55 1.20 0.82 0.60 20.16 0.98 0.57

Meanf St. dev.h

45.84 2.84 189.52 10.85 34.93 2.68 42.23 1.86 30.5 1.78 114.33 7.78 2828.57 160.11 370.77 22.13 452.97 16.70 716.63 23.71 21.55 1.08 43.96 2.79 25.63 1.10 568.02 20.91 17.1 1.05 64.21 2.61 37.24 1.73 5.84 0.65 7897.54 276.30 22.06 1.20 3.9 0.48

Maxg

Lynchburg Ferry (night)

0.04 0.08 0.03 0.01 0.01 0.11 0.08 0.11 0.01 0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.01 0.01

Mine

0.51 2.23 0.45 0.18 0.21 1.45 2.61 3.50 0.46 0.51 0.20 0.57 0.16 0.24 0.15 0.56 0.40 0.23 0.39 0.62 0.19

Meanf

Wallisville (night)

5.26 19.69 2.48 2.69 2.89 22.99 30.43 49.95 5.97 9.34 2.1 5.55 1.96 2.8 1.88 5.09 2.66 2.1 33.54 6.56 1.08

Maxg

0.59 2.44 0.35 0.21 0.22 1.69 2.81 3.87 0.52 0.61 0.21 0.57 0.16 0.31 0.17 0.52 0.35 0.21 1.41 0.59 0.17

St. dev.h

3074 Y. Xie, C.M. Berkowitz / Atmospheric Environment 40 (2006) 3070–3091

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ARTICLE IN PRESS Y. Xie, C.M. Berkowitz / Atmospheric Environment 40 (2006) 3070–3091

from the kth source to the ith sample, fkj is the jth species mass fraction from the kth source, p is the number of independent sources, and eij is the residual associated with the concentration of the jth species in the ith sample. PMF solves the receptor modeling through the minimization of the sum of squared residual, Q, given as P 2 n X m
2 n X m
X X xij
pk¼1 gik f kj eij QðEÞ ¼ ¼ , sij sij i¼1 j¼1 i¼1 j¼1 gik X0; f kj X0.

ð2Þ

Use of individual data uncertainty, sij for the jth species in the ith sample, for optimal data scaling is one of the strengths of the PMF method. The uncertainties of the data values are usually estimated empirically through various strategies (Hopke, 2000; Hopke et al., 2003; Huang et al., 1999; Kim et al., 2003a, b; Paatero and Hopke, 2003; Polissar et al., 1998, 2001; Qin et al., 2002). Letting MDL stand for minimum detection limit, the following error estimate strategy was used in our study (Hopke, 2000): xij ¼ nij ;

sij ¼ MDLij =3 þ C2  xij

xij ¼ MDLij =2;

sij ¼ MDLij =3 þ MDLij =2 for data below MDLij ;

xij ¼ n¯ ij ;

sij ¼ 4  n¯ ij for missing values;

for determined value nij ; (3)

where u¯ ij is the mean: A value of 0.001 ppb, a constant smaller than any non-zero measured concentrations, was assigned to the MDL. The optimal values of the percentage parameters C2 were determined through trial and error. In any set of field measurements, some VOCs concentrations will be below the instrument detection limit values or simply be missing (because of instrument problems or other unavoidable field issues). It has been noted by other researchers that species with many missing values be deleted from the analysis (Paatero and Hopke, 2003) and that the importance of species having a very high degree of variability be reduced if included in the analysis (Qin et al., 2002). In our study, if the percentage of the number of missing and zero values of a species exceeded 63%, the species was excluded from the analysis (coded as 2 in Table 1a). If the percentage of the missing and zero values of a species at a site was between 50% and 63%, the influence of the

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species in the PMF analysis was further reduced by doubling the associated error estimates (coded as 1 in Table 1a). The PMF2 program was run in the robust mode so the weights for outlying points could be reduced during the iterations (Paatero, 2000). The free rotation of the PMF factors was investigated by adjusting the non-zero Fpeak parameter in the PMF2 program (Paatero, 2000). It was found that the degree of rotation freedom was limited and only small differences were found in the PMF factors with the enforced rotations. The results presented in this paper were thus the non-rotated PMF factors. The resolved f factors from the PMF do not give the absolute source composition profile because they have a degree of freedom corresponding to a scaling factor, sk (Hopke, 2000):  p p  p X X X gik xij ¼ gik f kj ¼ g0ik f 0kj . (4) ðsk f kj Þ ¼ s k k¼1 k¼1 k¼1 The scaling factor, sk, was estimated by regressing the measured total mass concentration of VOC against the source contribution factors from PMF subject to the constraint that the scaling constants had to be non-negative. A regression that yields a negative scaling constant implies that the wrong number of factors had been used in the PMF analysis. An important parameter to come from the PMF analysis is the explained variation (EV) and the EV value of chemical species i in the kth factor. These explained variations are defined by Paatero (2000) as Pn jgik f kj j=sij EVkj ¼ Pn Pp i¼1 i¼1 ð h¼1 jgih f hj j þ jeij jÞ=sij for k ¼ 1; . . . ; p and

Pn je j=s Pp i¼1 ij ij ð jg ih f hj j þ jeij jÞ=sij i¼1 h¼1

EVkj ¼ Pn

for k ¼ p þ 1,

ð5Þ

where the (p+1) factor is the unexplained residual. The dimensionless quantity EV is a measure of the contribution of each chemical species in each source. It can be used for qualitative identification of the sources (Lee et al., 1999; Paatero, 2000). Illustrating by way of example, a factor that explains a great proportion (i.e., high EV values) of alkenes would be identified as a petrochemical emission source. Although the EV plot helps

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Y. Xie, C.M. Berkowitz / Atmospheric Environment 40 (2006) 3070–3091

identify source profiles, the estimate of source profiles are given by the original f factors. A key assumption for the use of PMF in receptor modeling is that the chemical profiles from all contributing sources remain constant as they move from source locations to the receptor sites. Of course, photochemical reactions of the VOCs and variations in the mixing layer height during daytime will change the concentrations of emitted species. For this reason, only nighttime observations were included in our PMF analysis. There is no generally accepted way to determine the correct number of factors in the PMF analysis. We therefore followed the guidance of Hopke (2000) and determined the number of factors such that (1) additional factors did not result in further significant improvement on the Q values, (2) the desirable residual had symmetric distributions and the scaled residuals were within 73 standard deviations, (3) scaling factors from regression were all positive, and (4) the factors correspond to realistic physical phenomena. 2.2. Conditional probability function Having identified groups of VOCs associated with different source categories, we next wanted to identify the physical locations associated with such groups. This was accomplished using CPF (Ashbaugh et al., 1985; Begum et al., 2004; Kim et al., 2003a, b), which in turn were derived from surface wind directions and the results of the PMF analysis at each of the five sites. The CPF is defined as CPF ¼ my =ny where my is the number of samples in the wind sector y with mixing ratios greater than some ‘high’ concentration and ny is the total number of samples in the same wind sector. We have defined ‘high’ as measurements greater than the 75th percentile of all the observations from a given station. Recall that two matrices come from the PMF analysis: the source contribution time series and the species compositions of the PMF factors. For the CPF analysis of a PMF factor, the concentration refers to the value in the source contribution time series, and the high values also refer to the high values in that time series. A plot in polar coordinates, with the radial distance defined by the magnitude of CPF and the angle defined by the associated wind direction, visually illustrates the fraction of samples coming from a given direction that have ‘high’ values, and point to regions associated with these

high values. Wind directions for this part of the analysis were binned into 36 sectors of 101 per sector. These figures, when drawn upon an emission inventory, such as that shown in Fig. 1, point to the source region for the PMF factor. Calm winds with speeds of less than 1 ms
1 were excluded from the calculations because of the uncertainty in defining directions for low wind speeds. Although the CPF analysis does not itself use back trajectories, the paths of the air parcels arriving at the PAMS sites were examined to distinguish paths that were straight from those that were circuitous. Only ‘straight’ paths, as will be defined later, were employed in this analysis. The back trajectories used in these calculations are based on 5-min wind fields derived from a network of surface anemometers located throughout the Houston/Galveston area. The model has been documented and extensively used in past studies of TexAQS 2000 (Berkowitz et al., 2004, 2005; Daum et al., 2003, 2004; Jobson et al., 2004). The trajectory model starts its computation at a user-specified point (e.g., the individual PAMS stations) and computes the movement of an air parcel relative to that point for progressive time steps. The trajectories are defined as the locations of the air parcel computed at each time step. An inverse distance-squared algorithm is used to interpolate the 5-min surface winds to the most recent geographical coordinates (i.e., latitude and longitude) of a parcel. These interpolated values are then used to calculate an updated location and advance the position of the parcel. The formulations for converting latitude and longitude to a linear coordinate system are taken from Haltiner (1971). The implicit assumption of CPF analysis is that the air arriving at a receptor site has traveled a relatively straight path from the source. While this assumption is probably true for receptors and sources that are quite close to each other, it is not generally the case. Illustrating this point by way of example, Fig. 2 shows the 6-h wind trajectories associated with the highest measured iso-butane concentrations made at night from each of the five auto-gas chromatograph sites. The trajectory followed by air arriving at Lynchburg Ferry was relatively straight over 6 h, implying that a single direction could be associated with this measurement. The corresponding trajectory associated with measurement at Haden Road circulated around the site before actually encountering the auto-gas

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30.2

30

Latitude

29.8

Channelview Wallisville (0.58) (0.97) Haden Road (0.35) Lynchburg Ferry (0.99) Clinton (0.96)

29.6

29.4

29.2 -95.4

-95.2

-95

-94.8

-94.6

Longitude Fig. 2. Trajectories corresponding to seasonal maxima of iso-butane at the sites. The number adjacent to the site is the corresponding curvature measure of the trajectory (see text).

chromatograph, implying that a single direction could not be associated with this measurement. Zhou et al. (2004) previously noted that the CPF technique does not produce the desired results if an air mass follows a circuitous pathway before arriving at the receptor site being represented by the CPF. To eliminate this deficiency from our analysis, only PAMS measurements associated with relatively straight trajectories, were employed in our analysis. We illustrate this in Fig. 2, using trajectories calculated for the Lynchburg Ferry site. These trajectories were selected by defining a measure of trajectory curvature, S, such that trajectories with a value greater than some S* were retained in the analysis and those with SoS* were excluded. We found a suitable definition of S to be S ¼ Dstraight =Dactual ,

(6)

where Dstraight is the straight line distance between the last endpoint of the 6-h back trajectory to the receptor site and Dactual is the distance along the trajectory path, calculated as the sum of the distances for the individual segments over 5-min intervals. Small values of S reflect trajectories with curved approach patterns, in contrast to straight trajectories having S ¼ 1. Using a cut-off value, S*,

of 0.95, we retained about half of all observations (at night) during the sampling interval.

3. Results and discussions 3.1. Overview of the statistics of the data set Our analysis is based on measurements of 55 individual VOCs (Table 1), total non-methane organic carbon species, surface wind direction, and wind speed measured between 1 June and 31 October 2003, at five PAMS located near the Houston Ship Channel (see Fig. 1). The sampling frequency of VOCs was 1 h while the associated surface meteorological observations were collected every 5 min. A statistical summary of the nighttime observations of all the VOCs is given in Table 1 for each of the five sites. As might be expected, given the reactivity of many VOCs, we found that mean nighttime concentrations of both individual species and total VOC were higher than those measured during the daytime (not shown here). The mean total VOC mixing ratios ranged from 107 to 270 ppb during the daytime and 226 to 513 ppb at night. The most

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greater concentration of the alkanes is likely a result of both high emission rates and their longer lifetime, which results from their lower reactivities. The abundance of the highly reactive alkenes and

600

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TNMHC (ppbC)

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TNMHC (ppbC)

TNMHC (ppbC)

abundant VOCs included the alkanes ethane, propane, butanes, pentanes, n-hexane, and cyclohexane followed by the alkenes and aromatics such as ethene, propylene, benzene, and toluene. The

400 300 200 100

300 200 100

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TNMHC (ppbC)

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HOUR (Lynchburg Ferry)

2.5

6

4

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5

3 2 1

isoprene (ppbC)

5 isoprene (ppbC)

isoprene (ppbC)

(a)

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HOUR (Clinton)

2

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 HOUR (HadenRoad)

2.5

1.5

isoprene (ppbC)

isoprene (ppbC)

2.0

1.0 0.5

2.0 1.5 1.0 0.5

0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

(b)

3

1

0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

HOUR (ChannelView)

4

HOUR (Wallisville)

0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 HOUR (Lynchburg Ferry)

Fig. 3. (a) Diurnal patterns of measured total VOC (solid line with solid circle: weekday mean; solid line with solid up-triangle: weekday median; dash line with open circle: weekend mean; dash line with open up-triangle: weekend median). (b) Diurnal patterns of isoprene (solid line with solid circle: weekday mean; solid line with solid up-triangle: weekday median; dash line with open circle: weekend mean; dash line with open up-triangle: weekend median).

ARTICLE IN PRESS Y. Xie, C.M. Berkowitz / Atmospheric Environment 40 (2006) 3070–3091

aromatics is consistent with the high emission rates from local sources. Very high concentrations of benzene, toluene, and styrene were observed at Lynchburg Ferry site relative to the other auto-gas chromatograph locations. Histograms of the VOC species (not shown here) had a strong positive skew. The diurnal concentration patterns for most of the compounds were similar to that shown in Fig. 3a (for total VOCs). The average concentrations of most species had minima around noon, gradually increasing to their maxima in early morning (5:00–8:00 a.m.), and then decreasing to their minima around noon again. This pattern is consistent with the expected accumulation of fresh emissions during the nighttime in the absence of photochemical loss processes. Additionally, nighttime emissions would be released in a relatively shallow nocturnal stable layer, relative to the greater depth of the mixing layer encountered by emissions released during the daytime. The exception to the pattern characterized in Fig. 3a was for isoprene, which is shown in Fig. 3b. The concentrations of isoprene were lowest during the nighttime and increased from midnight, reaching their maxima in the morning. The broad peaks remained throughout most of the day and then decreased to a minimum close to midnight. This diurnal pattern is indicative of the importance of biogenic emissions within the data. Biogenic emissions are a significant fraction of the total VOC inventory in eastern Texas because of the dense hardwood and coniferous forests (Wiedinmyer et al., 2000). Even in urban ozone non-attainment areas such as Houston/Galveston, 40–50% of the total VOC emission inventories are biogenic in origin (Texas Natural Resource Conservation Commissions, 2000; Vizuete et al., 2002). Isoprene was found to make a significant contribution (frequently 40% or more) to the reactivity of the VOC mixture in western Houston (Berkowitz et al., 2005). Isoprene is recognized as a tracer species of biogenic emissions mainly from vegetation, soil microorganism, and forest leaves, and it is primarily associated (35%) with natural VOC emissions (Guenther et al., 1996a, b, 2000; Wiedinmyer et al., 2000, 2001a, b). However, it is somewhat surprising that this highly reactive species was transported from these relatively remote areas. While the gradual increase of isoprene at the Haden Road site to a maximum at noon can be explained by natural emissions, no such increase was observed at the Clinton and Wallisville sites,

3079

suggesting that non-biogenic sources also contribute to the ambient isoprene at these locations. No noon maximum was observed at the Lynchburg Ferry site, which, in addition to the observation that there was a high degree of variability during the day, also suggests anthropogenic sources for isoprene for this site. Anthropogenic sources of isoprene have been reported in motor vehicle exhaust (Wiedinmyer et al., 2001a) and in production related to the rubber industry (Zhao et al., 2004). Several studies have suggested that biogenic hydrocarbon emissions play a significant role and are important contributors to the total VOC reactivity in ozone formation in many urban and sub-urban locations (Berkowitz et al., 2005; Cardeline and Chameides, 2000; Chameides et al., 1988, 1992; Choi and Ehrman, 2004; Ryerson et al., 2003; Wert et al., 2003). If biogenic emissions do play significant role, their presence would certainly complicate the implementation of an effective ozone reduction strategy (Choi and Ehrman, 2004). 3.2. Results and discussion from the PMF analysis Ideally, each pair of the resolved PMF factors (f and g in Eq. (1)) represents an identified source category. The f factor defines the source composition profile, and the g factor defines the source contribution time series. For complicated systems, a PMF factor may consist of features from several sources. Six to eight factors were retained for the PMF models for each of the five sites as identified in Table 2. 1. Seven PMF factors were retained for the Channelview site. Very good total mass reproduction was obtained with this set of factors. The multiple determination R2 was 0.976, indicating about 98% of data variation was expressed by the PMF model. Over 92% of the data points had scaled residual within 72.25. A C2 value of 0.20 was used. 2. Seven factors were retained for the Clinton site, with good mass reproduction (indicated by a multiple determination R2 of 0.883). Over 98.7% of the data points had scaled residual within 72.25. A C2 value of 0.25 was used. 3. Eight PMF factors were retained for the Haden Road site, giving 88% of mass reproduction indicated by the multiple determination R2 of 0.881. Over 85% of the data points had scaled residual between 72.25. A C2 value of 0.15 was used.

Accumulations/ natural gas; evaporative Solvent, painting industry

Vehicle exhaust Fuel evaporative

Solvent industry Petrochemical/ accumulation/natural gas/evaporation Petrochemical plant Industrial Evaporation

Industry Biogenic

2

4 5

6 7

8 9 10

11 12

3

Petrochemical

1

Possible source

n-Hexane, n-Octane, nNonane, n-decane, 2methylheptane, 3methylheptane, aromatics Acetylene, aromatics n-Butane; pentanes ; C4/C5 Olefins; n-Hexane, pentanes, butanes Ethane, propane, butanes, pentanes, c2-/t2-butene, 1butene Styrene, ethylbenzene 1,3-Butadiene Pentane and branched pentanes Aromatics Isoprene

Ethene, propylene, 1-butene, 1,3-butadiene, benzene and C4/C5 olefins Ethane, propane, butanes, pentanes, C5/C6 alkanes

Feature species

6.16

2.65

7.46 9.3

6.81

50.43

21.06

9.05

25.52 31.8

23.3

16.23 11.8

9.77 14.29

9.38

17.62

20.92

58.87

172.51

Mean (%)

Mean conc. (ppbC)

Mean (%)

17.21

Clinton

Channel view

Table 2 Source identification and apportionment for the five sites near the Houston Ship Channel

36.48 26.53

21.95 32.11

21.07

39.6

47.02

Mean conc. (ppbC)

10.69

11.19 4.56

1.22 19.48

23.29

21.04

8.52

Mean (%)

33.81

35.38 14.41

3.85 61.62

73.67

66.55

26.96

Mean conc. (ppbC)

Haden Road

25.22 14.44

6.96

8.66

22.96

21.76

Mean (%)

111.28 63.69

30.72

38.21

101.31

95.98

Mean conc. (ppbC)

Lynchburg Ferry

3.87

10.09

10.04 2.18

6.2

35.7

31.92

Mean (%)

Wallisville

7.8

20.33

20.25 4.39

12.5

71.96

64.35

Mean conc. (ppbC)

3080 Y. Xie, C.M. Berkowitz / Atmospheric Environment 40 (2006) 3070–3091

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4. A six-factor PMF solution was found to be optimal for Lynchburg Ferry site. The mass reproduction at Lynchburg Ferry site was the poorest with a multiple determination R2 of 0.77. The scaled residuals over 92.4% of the data points were within 72.25. The poorer performance at the Lynchburg Ferry site might be

10

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partially caused by the fact that about half of the samples collected at this site did not have the corresponding total VOC concentrations. A C2 value of 0.25 was used. 5. At the Wallisville site, a seven-factor solution from PMF gave the most reliable and interpretable results with a multiple determination R2 of

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Fig. 4. (Left column) Source composition profiles. (Right column) Percentage of VOC species variation expressed by the PMF factors. (Bottom) CPF plots of the PMF factors corresponding to petrochemical sources at the five sites. The names of the species referred to the index on the horizontal axis are given by the corresponding entries in Table 1.

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0.944. Over 92.5% of the data points had scaled residuals within 72.25. A C2 value of 0.20 was used. As shown in Table 2, some source categories identified from the PMF factors were common to all the auto-gas chromatograph sites while other factors were more site-specific. Five common source categories found in all the sites are discussed below.

Figs. 4–8 show the f factors resolved from the PMF analysis at all five sites in both mass concentrations (panels on the left side of each figure) and the explained variation (on the right side of each figure), which corresponds to a common source category. Also shown in each figure are graphical representations of the CPF analysis (bottom panel of each figure) at five sites for each of five common sources with key point sources of total VOCs in emissions

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Fig. 5. As in Fig. 4, for accumulation/natural gas and evaporation sources at the five sites.

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0.50

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Fig. 6. As in Fig. 4, for industrial solvent and painting sources at the five sites.

inventory as background. The profiles of both the explained variations and the mass concentrations were inspected to identify the PMF factor as a potential source category (Lee et al., 1999; Paatero, 2000; Roberts et al., 2004). The results presented in Figs. 4–8 and in Table 2 were included after consideration was given to the quality of the PMF models as determined by multiple criteria including change of the Q values, the ratios of key species in possible source types, the

explained variation of the key species, the frequency distribution of scaled residuals, the closeness of results among multiple runs with different random seeds, and the results of the linear regression calculation in reconstructing the mass concentration with the resolved factors. Using the Clinton site as an example to illustrate how these factors were used, we note that a six-factor PMF analysis produced six of the seven sources shown in Figs. 4–8 (and in Table 2) but did not have the petrochemical factor

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29.78 Clinton

29.76

Lynchburg Ferry

29.74 29.72 29.7 29.68 -95.3

-95.2

-95.1

-95

-94.9

Longitude

Fig. 7. As in Fig. 4, for vehicle sources at the five sites.

dominated by ethene and propylene. When most of the six source profiles were almost identical as those identified from the seven-factor analysis, the composition profiles of sources 4 and 7 of Table 2 were mixed with higher ethene and propylene. When the number of factors was increased to eight, negative coefficients were produced sometimes when the PMF factors were regressed to the VOC mass, thus invalidating the PMF models. In addition, very small contributions (o1%) were found for some factors from those multiple runs where positive coefficients were obtained for all the PMF factors. The Q values of multiple runs when only six factors were used varied from 27,512 to 28,064, while when seven factors were used, the Q values of multiple

runs were in a quite narrow range from 23,992 to 23,995. Further increasing the number of factors to eight, the Q values of multiple runs were not reduced by a significant amount and were in the range from 21,582 to 21,772. In addition, the source profiles generated by the seven-factor model with different random seeds were much more consistent. Considering all criteria and the fact that ethene and propylene are well-known emissions from petrochemical facilities and such a factor was resolved in a previous study (Roberts et al., 2004), a sevenfactor model was taken as the most reliable approach. The same criteria and methodologies were applied to obtain the final results presented in the figures and tables for other receptor sites.

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Fig. 8. As in Fig. 4, for fuel evaporative sources at the five sites.

3.3. Five source categories common to all sites 1. Petrochemical emissions: As illustrated in Fig. 4, the PMF factors from all sites are characterized by the enrichment of mainly ethene and propylene as well as other light olefins like 1,3-butadiene, 1butene, benzene, and cis-2/trans-2-butenes and cis2/trans-2-pentene. Elevated alkenes levels, especially ethene and propylene, have been a feature of the Houston area for an extended period of time. Although olefins could result from incomplete combustion in motor vehicle engines (Choi and

Ehrman, 2004; Henry et al., 1994; McGaughey et al., 2004), tailpipe emissions usually have characteristic ethene/acetylene ratios that range from 1 to 3 and propylene/acetylene ratios that range from 0.5 to 1.5 based on airborne VOC measurements made in downtown Nashville, Tennessee, and Atlanta, Georgia, in 1999, and in Houston and Dallas, Texas, in 2000 (Ryerson et al., 2003). Previous studies have found elevated mixing ratios of propylene and ethene particularly in the vicinity of the Houston Ship Channel for over 20 years (Ryerson et al., 2003). The high ratios of propylene/

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acetylene and ethene/acetylene (e.g., 7.1 and 5 for mean nighttime concentration, respectively, at the Channelview site and 3.44 and 2.55 at the Clinton site) are consistent with past findings in resolved plumes from geographically isolated petrochemical sources (Ryerson et al., 2003). The observed elevated alkene mixing ratio enhancements suggest that the on-road tailpipe emissions were insignificant contributors, leading us to conclude that this factor is associated with petrochemical emissions. Previous studies have shown that light molecular weight alkenes make significant contributions to the ozone formation, and previous estimates of alkene emissions, including ethene and propylene, are substantially less than emissions inferred from measurements (Berkowitz et al., 2004; Jiang and Fast, 2004; Karl et al., 2003; Kleinman et al., 2002; Ryerson et al., 2003; Wert et al., 2003). The mass contribution of this source category to the mean total VOC varies from 8.5% at the Haden Road site to 31.9% at the Wallisville site. The CPF plots, shown in the bottom panel of Fig. 4, are consistent with these conclusions based on strictly chemical arguments, showing that major directions of elevated concentrations are from the southern area encompassed by the Clinton and Wallisville sites on both sides, where most of the petrochemical facilities are located. 2. Accumulation, natural gas, and evaporation sources: The factors shown in Fig. 5 represent a source category abundant with ethane, propane, butanes, and pentanes. Because of their low reactivity, light paraffins like ethane and propane are known to be associated with aged background air. However, they are also prominent in emissions from natural gas use (Brown and Hafner, 2003; Choi and Ehrman, 2004; Roberts et al., 2004). The enrichment of butanes and pentanes is most likely results from evaporative sources like liquid petroleum gas (LPG), gasoline vapor leaks, and evaporated gasoline that usually do not consist of the combustion compounds, or from heavier hydrocarbons that volatilize more slowly (Jorquera and Rappenglu¨ck, 2004; Watson et al., 2001). Therefore, we associate this factor with aged air masses having natural gas emissions and fuel evaporates. At the Lynchburg Ferry site, a large portion of acetylene variation was expressed by this factor, suggesting a contribution from the vehicle exhaust emission category in this factor. As will be described later, a separate vehicle emission category characterized by a large portion of acetylene was identified at all

sites other than the Lynchburg Ferry site. However, a separate vehicle exhaust emission still could not be resolved at the Lynchburg Ferry site by increasing the number of factors used in the PMF analysis, and the vehicle exhaust emission at the Lynchburg Ferry site was just mixed with other emission categories as is shown here. This factor was also abundant with C6–C8 alkanes at the Wallisville site, indicating possible mixture with solvent emission. The mass contribution of this source category was found to vary from 17.6% at the Clinton site to 50.4% at the Channelview site, representing the biggest contributor to the ambient VOCs. As seen in the CPF figures in the bottom panel of Fig. 5, the dominant wind direction for this group of compounds is to the south of the Channelview site; to the northeast at the Clinton site; to the south, southeast, and northeast at the Haden Road site; to the southwest at the Lynchburg Ferry site; and to the northwest at the Wallisville site. 3. Mix of industrial compounds with mobile sources: The factors shown in Fig. 6 are dominated by heavy alkanes like n-octane, n-nonane, n-decane, 2-methylheptane/methylheptane, and n-hexane as well as xylene, toluene, and other substituted aromatics. Highly volatile alkanes (e.g., n-nonane, n-decane, and n-undecane with trimethylbenzene, propylbenzene, ethylbenzene, and xylenes) are produced by combustion process (Edwards et al., 2001), and C10–C11 (heavy) alkanes (e.g., n-decane and n-undecane) are considered as markers of diesel exhaust (Brown and Hafner, 2003; Jorquera and Rappenglu¨ck, 2004; Roberts et al., 2004; Watson et al., 2001). The diesel-powered ships that use the Houston Ship Channel may be contributors, although their signal would be expected to be much less than those from the fixed-based facilities in this area. However, it also has been observed that ndecane is abundant in solvents used in paints and for other industrial applications, n-nonane shows enrichment in printing ink solvent from offset printing and rotogravure processes, and toluene is used for thinning rotogravure inks (Watson et al., 2001). Although benzene, toluene, ethylbenzene, and xylenes (BTEX) were considered to be associated with mobile sources and the ratios of BTEX were calculated to use in comparisons of mobile sources in different regions (Chiang et al., 1996; Ho et al., 2002; Lee et al., 2002), the enrichment of aromatics suggests industrial sources (Brown and Hafner, 2003; Roberts et al., 2004) such as chemical and refinery industries (Watson et al., 2001). For

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these reasons, we have labeled this factor as a mix of industrial compounds with mobile sources. The similarity in source composition profiles of this factor among sites is not as strong as the previous two source categories. The C6–C10 alkanes were more enriched at the Clinton and Haden Road sites, and the aromatics compounds were more enriched at the Channel View and Lynchburg sites. The mass contributions of this factor to the total VOCs are ranged from 6.2% at Wallisville to 23.3% at Haden Road site and elevated concentration were always associated with locations to the southeast or southwest. 4. Vehicle emission sources: The factors shown in Fig. 7 suggest a vehicle exhaust emission category because of the abundance of acetylene and xylenes, benzene, toluene, and substituted benzene (Roberts et al., 2004; Watson et al., 2001). The acetylene is typically regarded to be combustion derived, and the predominant source of urban acetylene is motor vehicle exhaust (Henry et al., 1994; McGaughey et al., 2004). Although earlier work also indicated that ethene and acetylene were evidence of flare emissions (Brown and Main, 2002), this factor was identified as a vehicle exhaust source according to the source composition profile. We had previously noted that there was no such factor for the Lynchburg Ferry site and most of the acetylene at that site was split into other factors. The absence of a vehicle-related factor for the Lynchburg Ferry site may be explained by the high number of missing acetylene measurements. The contributions of this source category are about or less than 10% to the total VOC mass. The source region identified from the CPF plots points to nearby highways. 5. Fuel evaporative and industrial sources: The source category represented by the factors shown in Fig. 8 mainly consists of n-butane, n-pentane, and iso-pentane, which are major components of gas vapor. Emissions from oil refineries were enriched with propane, n-butane, and n- and iso-pentanes, but propane was not so enriched in the factors shown in Fig. 8. We noted that this factor also accounted for the majority of pentene and geometric isomers of pentene (1-pentene, trans-2-/cis-2pentene), geometric isomers of butenes and substantial butene (trans-2-/cis-2-butene, 1,3-butadiene) as well as some C4/C5 paraffins. The group of compounds identified as trans-/cis-2-pentenes have been associated with traffic emissions (Jorquera and Rappenglu¨ck, 2004). In a recent study of observations collected in La Porte, Texas, on the east side of

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Houston, the ratios of trans-2-pentene versus cis-2pentene and trans-2-butene versus cis-2-butene were compared with similar observations from tunnel data, with the conclusion that the major source of these species was vehicle exhaust (Jobson et al., 2004). However, there exist many point sources of butenes and pentenes in the Houston Ship Channel. These species could also come from industrial sources (Roberts et al., 2004), a hypothesis supported by the observed absence of most of the aromatics normally associated with vehicle exhaust emission such as benzene, toluene, ethylbenzene, xylenes, and substituted benzene. For these reasons, we associate this factor with a mixture of fuel evaporative and industrial sources. At the Lynchburg Ferry site, this factor was complicated by the existence of C6–C9 mid-weight alkanes, known to be markers of solvent chemical and petrochemical sources. At the Wallisville site, the co-existence of heavy alkanes (n-decane and n-undecane) and aromatics also suggests contributions from diesel exhaust and/or other chemical industrial sources. The mass contribution of this factor was found to range from 2.2% at the Wallisville site to 19.5% at the Haden Road site, and the CPF plots show a source region to the south of the sites with the exception of the Clinton site, which showed a peak coming from the northeast. Biogenic sources: a special category: The source categories identified from the resolved PMF factors described above were common to many of the sites. Other factors resolved from different sites were less common and more site-specific. Such a factor was found at the Wallisville site, which was abundant in isoprene (Fig. 9). It was previously noted (Fig. 3b) that the diurnal pattern of isoprene indicates a biogenic source for most of the five sites under investigation. However, no separate isoprene factor was detected for the other four sites probably because we limited the PMF analyses to nighttime observations when isoprene emissions were not significant. The greater distance of the Wallisville site from the Houston Ship Channel and its proximity to the hardwood forest is suspected to be the reason for finding an isoprene factor at only this location. Other investigators have found evidence that isoprene originating from the forested regions to the northeast of Houston is an important VOC source in the Houston urban area (Kleinman et al., 2002; Vizuete et al., 2002), and these conclusions are supported by the CPF analysis, which shows a preferential direction from the

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2 0

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Fig. 9. (Left top) Source composition profile. (Left bottom) Percentage of VOC species variation expressed by the PMF factors. (Right) CPF plot of the biogenic factor resolved for Wallisville site.

northeast for high isoprene at Wallisville (Fig. 9, right panel). The mass contribution of this biogenic source to the total mean VOC concentration is 3.9%. It has been suggested (Choi and Ehrman, 2004; Chameides et al., 1988, 1992; Cardeline and Chameides, 2000) that emissions from biogenic sources have been underestimated and that these sources make a significant contribution to the biogenic hydrocarbons, and hence production of ozone, in many urban and suburban areas. Evidence of the existence of a biogenic source contribution was presented in Fig. 3b, but may be missing from the PMF analysis as a consequence of using only nighttime observations for this part of the study. 4. Conclusions We have presented the results of an analysis using PMF to identify groups of compounds measured at night at five monitoring stations near the Houston Ship Channel. In applying PMF to VOC emissions, we found that the degree of rotational freedom was limited, producing only small differences in the resolved PMF factors with enforced rotations. We determined the number of factors so that additional factors did not result in further improvements to the Q values, and scaling factors from the regression were all positive. Only observations associated with ‘straight’ trajectories were used in the CPF analysis, as defined by the ratio of straight-line travel distance by a parcel over 6 h and the actual path distance. Finally, only nighttime observations were used to remove the high degree of uncertainty associated with daytime photochemical processes. A number of rigorous conditions regarding missing information and low values were also imposed on the data.

Between six and eight source categories were identified. Focusing on the source categories common to all sites, we found that although the mass contributions of the common source categories differed from site to site, the most significant contribution, in terms of mass, always came from the source category associated with aged natural gas and/or evaporative emissions. The next most abundant category included petrochemical emissions. Solvent/paint production and fuel evaporation contributed the next highest levels, while onroad vehicle exhaust generally contributed less than 10% of the total ambient VOCs. A biogenic source category characterized by the enrichment of isoprene was resolved only at the Wallisville auto-gas chromatograph site, although a diurnal pattern (Fig. 3b) suggests the existence of biogenic sources for almost all the sites. This may have been a consequence of the fact that only nighttime observations were included in the PMF analysis when contributions from biogenic sources were low (see Fig. 3b). The CPF analysis showed that most of the elevated concentrations originated to the south of the auto-GC sites shown in Fig. 1. However, the predominant source direction for the Wallisville aged air/natural gas factor (illustrated in Fig. 5) and the biogenic source (Fig. 9) is to the northeast, in a direction away from the Houston Ship Channel. While that direction is in accord with the biogenic source emanating from the forest areas in this region, it is somewhat surprising that such a reactive species could be transported any significant distance. We have shown how PMF analysis can identify groups of compounds associated with specific

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categories of emissions and that these results, through the use of relatively simple meteorological measurements (wind direction and speed), can, in turn, be used with CPF analysis to identify the direction from which these categories arise. That these techniques can work in an area with the complex emission and wind patterns of Houston certainly suggests that they could be used successfully in other areas with similar pollution problems and with simpler patterns of emissions and meteorology.

Acknowledgments Grateful acknowledgement is made to Jim Droppo, Pacific Northwest National Laboratory, for running the trajectory code used to calculate values of S (Eq. (5)). We also gratefully acknowledge the patience and support of Mark Estes and Zarena Post of the Technical Analysis Division of the Texas Commission on Environment Quality (TCEQ) as they answered our many questions as we worked through the auto-gas chromatograph data. This work was supported by TCEQ. Although the research described in this article has been funded wholly or in part by TCEQ, it has not been subjected to the Commission’s peer and policy view and, therefore, does not necessarily reflect the views of the Commission and no official endorsement should be inferred. Pacific Northwest National Laboratory is operated for the US Department of Energy by Battelle.

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