Source Identification of Volatile Organic Compounds in Houston, Texas WEIXIANG ZHAO, † PHILIP K. HOPKE,* ,† AND THOMAS KARL ‡ Department of Chemical Engineering, Clarkson University, Box 5708, Potsdam, New York 13699-5708, and The National Center for Atmospheric Research, Atmospheric Chemistry Division, P.O. Box 3000, Boulder, Colorado 80307 The complexity of the volatile organic compound (VOC) mixture in the Houston area makes studies of the air quality in that area very challenging. In this paper, a novel factor analysis model, where the normal chemical mass balance model was augmented by a parallel equation that accounted for wind speed and direction, temperature, and weekend/weekday effects, was fitted with a multilinear engine (ME) to provide identification and apportionment of the VOC sources at the La Porte Municipal Airport site in Houston during the Texas Air Quality Study (TexAQS) 2000. The analysis determined the profiles and contributions of nine sources and the corresponding wind speed, wind direction, temperature, and weekend factors. The reasonableness of these results not only suggests the high resolving power of the expanded factor analysis model for source apportionment but also provides the novel and effective auxiliary information for more specific source identification. In addition, a new approach to estimate the measurement uncertainty and the details of determining the source number and dealing with missing values are also presented as important parts of the data analysis process. This study demonstrates the feasibility of the expanded model to identify sources in complex VOC systems and extract useful information for locating VOC emitters and controlling their emissions in the Houston area. Introduction Volatile organic compounds (VOC) are organic chemicals that easily vaporize at room temperature. Many VOCs have been found to have adverse effects on air quality and human health (1). For example, long time exposure to benzene will increase the risk of leukemia, and reactive VOCs such as primary olefinsare importantin theformation oftropospheric ozone. However, motor vehicle exhaust, chemical manu- facturing, paints, solvents, biogenic emissions, and many other sourcescreate exposureto VOCs.Because identification of the potential sources of VOCs is a prerequisite for controlling VOCs’ emissions and protecting air quality and public health, it has been paid more and more attention (2). To identify the number of sources and their profiles, receptor modelsare widelyused (3, 4).There aretwo principal approaches to receptor modeling. If the number and the profiles of sources are known, chemical mass balance (CMB) can be used to estimate the contribution of each source to the pollution (5) where regression methods are used to provide quantitative results. However, in manycases, source information is unknown a priori, so factor analysis (multi- variate analysis) needs to be used to extract the sources information. Hopke and co-workers (6), Heidam (7), Henry (8), and Barrie and Barrie (9) applied principal component analysis (PCA) to source identification, but Paatero and Tapper (10, 11) showed that PCAcannot provide a true minimal variance solution since they are based on an incorrect weighting. In view of the limitations of PCA, a new technique, positive matrix factorization (PMF), was developed for sources identification and apportionment (12). The distinct advan- tages of PMF over PCA are that non-negative constraints are built in PMF models and PMF does not rely on the information from the correlation matrix but utilizes a point- by-point least-squaresminimization scheme (12). It has been reported (13) that the source profiles produced by PMF are better andmore reasonable at describing thesource structure than those by PCA. Over the past few years, PMF has been applied to a number of particle composition data sets (e.g., 14, 15). Recently, the PMF analysis can be expanded by using a more general model (16), and a new analysis tool called the multilinear engine (ME) was developed (17) to solve such problems. ME is very flexible and provides a general framework for fitting any of the multilinear model (18, 19), so it becomes possible to obtain not only the sourcesprofiles but also other interesting parametric factors that may be important forsource identification andpollution control and planning. Forexample, winddirectional information can help locate the potential sources. It was reported (16, 19) that in some cases the expanded factor models could determine more sources than PMF. The coexistingsystem of VOCs is complex. A small change in environmental conditions (e.g., temperature) may result in changes on VOC concentrations, and also some VOCs may be involved in chemical reactions during the trans- portation. Meanwhile, as a consequence of high density of petroleum refineries, synthetic organic chemicalplants, and various mobilesources, the formation rate andconcentration of ozonein the Houston area areextremely high, and propene becomes a dominant reactive hydrocarbon (20). The specific VOC mixture in the Houston area represents a specific air quality problem compared with other metropolitans (21), which makes studies of the air quality in Houston very challenging. Henry et al. (22, 23) made some studies on VOC source identification, one of which was also for the Houston area with the data for the period June through November 1993, butthese studies did not provideany information about the influencesof environmentalparametric factors (e.g., wind speed, wind direction, and temperature) on the observed pollutant concentrations, and only three sources in the Houston areawere identified. Therefore, in thepresent study, an expanded factor analysis model will be used to identify the VOCs sources in the Houston area with the goals of (1) checking the feasibility of the expanded modeling to VOC sources, (2) observing the influences of environmental parametric factors on the observed concentrations, and (3) supplying convincing information that will be useful for air pollution control in the Houston area. ME will be used as an optimization tool for data fitting in this study since it has proved to be effective in model fitting (16, 18, 19). * Corresponding author phone: (315)2683861; fax: (315)2684410; e-mail: hopkepk@clarkson.edu. † Clarkson University. ‡ The National Center for Atmospheric Research. Environ. Sci. Technol. 2004, 38, 1338-1347 1338 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 38, NO. 5, 2004 10.1021/es034999c CCC: $27.50  2004 American Chemical Society Published on Web 01/28/2004 Expanded Factor Analysis Model In general,the ordinarybilinear receptormodel canbe written as where X is the matrix ofVOCs’ concentrations, F isthe matrix of source profiles, G is the matrix of source contributions, and E is the residuals matrix. Their elements x ij , f jp , and g ip can be respectively understood as the concentration of compound j measured in sample i, the concentration of compound j in the emission of source p, and the strength of source p on sample i (16, 19). In this section, wind direction and wind speed will be used to illustrate the construction of the expanded factor analysis model(16,19). Inthe bilinear model, the contribution of source p to the concentration of compound j in sample i, u ijp , is represented by u ijp ) g ip f jp . In the expanded model, a parallel equation is developed where the contribution u ijp can be represented by another form where d i and s i are the values of wind direction and wind speed of sample i. The ranges of wind direction and wind speed are divided into a series of subranges having similar numbers of samples in each. Then each wind direction/ speed belongs to a specific range. Thus, D(d i ,p), an element of D, represents the action of source p on pollution in the wind direction range of d i . For example, if source 3 strongly affects the observed concentration at the wind direction of 80°, D(4,3) (the first index, 4, corresponds to 80° if the wind direction range 0-360° are evenly divided into 18subranges) should be a relatively larger value. S(s i ,p) has a similar definition for wind speed. Thus, z ip can be considered as a multiplier that represents the comprehensive action of wind direction and speed on the observed pollution. Obviously, in different physical models, z ip can correspond to different expressions. In this study, z ip corresponds to the factors for wind speed, wind direction, temperature, and weekday/ weekend. The expanded receptor model can then be expressed by where W(w i ,p) denotes the action of weekdays or weekends by source p on the observed concentration, I is the number of samples,and Jis the number ofmeasured chemicalspecies. By fixing the weekday coefficient at unity, W(w i ,p) is a vector with n p (the number of sources) elements. T(t i ,p) represents the action of temperature t i for source p. The task of solving this expanded PMF model is to determine the values of F, G, D, S, W, and T to fit the data as well as possible. The optimization problem can be defined as in which e ij and e′ ij are determined by eqs 3a and 3b and σ ij and σ′ ij are the error estimates, which can be considered as special weights. Clearly, z ip is the combination of all influencing factors such as wind speed, temperature, and weekday/weekend factors, so with ME an expanded factor analysis model can provide us not only the source profiles and contributions but also the strength of other factors affecting the observed concentrations. A prerequisite to applying this model is that the action (contribution) of considered parametric factors can be expressed in linear terms. As an optimization method for factor analysis, ME has two problems to be solved, that is, how to determine the number of factors and how to avoid local optimal. In the section of Results and Discussion, the methods for solving these two problems for this case will be described in detail. Because eq 3b will generate a poorer fit to the data than eq 3a, the error estimate for eq 3b, σ′ ij , must be (much) larger than that for eq 3a, σ ij (16, 19). In this study σ′ ij is 8 times of σ ij and σ ij is represented as where c 1 denotes the uncertainty of measurement and c 3 is a constant. Here c 3 is valued at 0.2. Because of the complex VOC mixture in the ship channel area and the potential interference at low concentration, the experimental uncer- tainties obtained by the measurement technique used here were hard to access. An approach using the fast Fourier transformation (FTT) was applied to solve this problem. The procedure can be briefly described as follows. Xylene will be used as an example from the species being studied in this paper. Let c be the concentration series of xylene, which has 7292 measurements. Thus, the key steps to estimate the measurement uncertainty from the measurement series are as follows: (a) Generate a random series r with the same length as c (7292 elements) and variance ν r 2 ) 1. (b) Perform FFT on c and r, and calculate theirmagnitude spectra and call them mfc and mfr. (c) Plot mfc and mfr, respectively. It can be seen from Figure 1 that mfc consists of two parts; one with low frequencies representsthe useful information while theother with high frequencies represents the noise, and mfr does not show two different parts because it is generated by a random series. (d) Selectan interval ofnoise in mfc.Although it isdifficult to determinethe exact startingand ending pointsof the noise interval, the selected noise range should be sufficiently long to reflect thenoise information. In thisexample, the selected interval was from mfc(1000) to mfc(6000). Then, calculate the mean value of the selected interval and name it m_mfc. X ) GF T + E (1) u ijp ) z ip f jp ) D(d i ,p)S(s i ,p)f jp (2) x ij ) ∑ p)1 N g ip f jp + e ij (3a) x ij ) ∑ p)1 N z ip f jp + e′ ij ) ∑ p)1 N D(d i ,p)S(s i ,p)W(w i ,p)T(t i ,p)f jp + e′ ij (3b) min Q ) ∑ i)1 I ∑ j)1 J (e ij /σ ij ) 2 + ∑ i)1 I ∑ j)1 J (e′ ij /σ′ ij ) 2 (4) FIGURE 1. Illustration of FFT-based uncertainty estimation. (a) The concentration series of xylene, c; (b) the magnitude spectrum of concentration series, mfc; (c) the random data series, r; (d) the magnitude spectrum of random data series, mfr. σ ij ) c 1 + c 3 x ij (5) VOL. 38, NO. 5, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 1339 (e) Select the same range in mfr, and calculate the mean value of this range and name it m_mfr. Actually, it is also feasible to use the whole range of mfr since there is only noise in mfr. (f) Calculate ν c according to and consider it as the estimation of the uncertainty of the xylene concentration series. Although this estimation could not be guaranteed to be fully accurate because, strictly speaking, each measurement should have its own uncertainty, it is practical since it produces satisfactory analysis results. Finally, the estimated uncertainties for the compounds in this study, acrylonitrile, isoprene, benzene, toluene, styrene, c8-benzenes (with the dominant component: xylene), c7-ketone, c9-benzenes,c10- benzenes, c13-benzenes,M43, M61,and M87,are 0.074,0.102, 0.114, 0.147, 0.033, 0.114, 0.070, 0.082, 0.047, 0.016, 0.835, 0.360, and 0.089, respectively. Here M43, M61, and M87 denote theclasses ofcompounds withthe mass/chargevalues of 43, 61, and 87, respectively. In this study, the dominant components for them are propene, acetic acid, and vinyl acetate, respectively. Data Preprocessing As part of the TexAQS 2000, a proton transfer reaction mass spectrometer (PTR-MS) from the University of Innsbruck was placed in an air-conditioned trailer situated next to a 10-m sampling tower at the southwest side of the municipal airport at La Porte, TX, to identify and quantify the VOC mixture in that area. A map showing the sampling site is presented in Figure 2. The PTR-MS technique has been previously described in detail (24), soonly a brief description is given here. The principle of the PTR-MS is the reaction of organic species in ambient air with H 3 O + ions, generated from thehollow cathode discharge of watervapor, to produce the protonated organic species (RH + ). The concentration of the product ions can be calculated from a reaction dynamic equation (24). Only organic species with a proton affinity greater than that of water can be detected by the mass spectrometer. More details about the sampling procedure can be found in ref 20. The sampling period for the data in this study was from 08/20/00 to 09/08/00, and the most sampling frequencies were about1/4-6 min -1 , butthe frequenciesfor someperiods were 1min -1 . Allthe samples wereused for analysisto ensure a sufficiency of samples. The concentrations of 14 VOCs (methanol, acrylonitrile,isoprene, benzene, toluene,styrene, c8-benzenes (xylenes), c7-ketone, c9-benzenes, c10-ben- zenes, c13-benzenes, M43 (propene), M61 (acetic acid), and M87 (vinyl acetate)) were selected for this study, with the detection limits being 100 pptv, 60 pptv, 20 pptv, 70 pptv, 70 pptv, 30 pptv, 70 pptv, 60 pptv, 30 pptv, 30 pptv, 30 pptv, 1 ppbv, 1ppbv, and 200pptv, respectively. Someof the reasons for this selection are benzene, toluene, and xylene (BTX) compounds are usually considered of high importance for urban VOC reactivity/air quality, propene is one of the dominant reactive hydrocarbons in Houston, which makes Houston a special case when compared to other U.S. cities, and the toxicity of acrylonitrile directly affects the air quality in the vicinity of an emitter. The meteorological data like temperatures were from the NOAA AeronomyLab and winds were measured next to the VOC inlet at 10 m above ground. Because there were some missing andbelow detection limits values in the concentration measurements and meteorologi- cal data, 7292 samples were retained for analysis following the pretreatment below. Consecutive missing values (for example, c7-ketone has 2240 consecutive missing values) were deleted and the values below detection limits were replaced with half of the detection limits (25). Additionally, the data for wind direction,wind speed, and temperature were divided approximately evenly into 31, 5, and 5 levels, respectively, so in each factor, the effect of any level will not be overwhelmed by any of the others. Results and Discussion Due to the long lifetime and multiple sources, methanol proves to be a ubiquitous compound. The initial trials including methanol showed that methanol had a significant contribution in each source profile, suppressing other compounds such as vinyl acetate and c13-benzenes. Such behavior typically suggested that there was a high variability in the amounts of methanol associated with its sources. In this case, an increase of the uncertainty of methanol could not resolve this problem. Thus, methanol was excluded from the final analysis. Figure 3 shows the concentration time series for all compounds except methanol. The determination of the number of sources is one of the major problems in any factor analysis. In this study, three rules were applied to decide the proper source number that (1) the resolved source profiles should be explainable, (2) Q value defined in eq 4 is expected to show a change in slope with the number of sources from rapid to slow at the point of the decidednumber, and (3) there should bea satisfactory fit between the predicted concentrations and the measured values. In detail, when the source number increased from 6 to 7, 7 to 8, 8 to 9, and 9 to 10, the decreases in Q were 6907, 8325, 5723, and 4915, respectively. Clearly, there is a change in the slope at 9 sources. In addition, there was a better fit between the predicted concentrations and the measured values at that source number. The fit between the predicted c13-benzenes con- centration and the measured values increased exceptionally quickly when the source number changed from 9 to 10. (The correlation coefficient for 10 sources was 0.932 while that for 9 sources was 0.58.) However, actually more than 75% of the c13-benzenes concentration measurements were below the detection limit and replaced by half of the detection limit, so the exceptionally good fit might suggest that there was overfitting of c13-benzene for the case of 10 sources. Neither poor fit nor over fit is acceptable. So the number of sources for this analysis was chosen to be 9. During the experiments, FIGURE 2. Sampling site (La Porte Municipal Airport) in Houston, Texas. v c v r ) m_mfc m_mfr (6) 1340 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 38, NO. 5, 2004 the candidate cases had at least three runs to avoid local optima, andfinally thecase of 9sources (excludingmethanol) was selected as the best solution. The results are discussed below. The profiles and time-resolved contributions of nine sources are shown in Figures 4 and 5. To describe the contribution variation of source i between weekdays and weekends in quantity, a ratio called KD is defined as eq 7: The plots of the wind direction factor, wind speed factor, temperature factor, and weekend factor of 9 sources are shown in Figures 6, 7, 8, and 9, respectively. In the wind directional plots, each column of matrix D is displayed in a polar plot to represent the factorvalues for thedifferent wind directions (i.e., the longer the radius is, the bigger the contribution at that direction). In addition, the emitter location plots of acrylonitrile, toluene-xylene, benzene, styrene, and propene are presented in Figures 10-14. The plot showing emitter locations was produced by superim- posing the wind directional plot (blue area) onto the map where the corresponding emitters in the observed area were displayed as circles, squares, or triangles. Emission rates for 2000 that were obtained from the Toxic Release Inventory (26) were shown on the plots with the corresponding colors, if available. The size of blue area in each plot does not represent the distance between receptor site and emitter but denotes the strengths of the identified source on the pollution at different wind directions. Activity can change considerably from weekdays to weekends. Some production factories do not operate on weekends, sothe emissions of these sourcesvary accordingly. In addition, the pattern of motor vehicle use also changes as fewerpeople commuteto workand fewerheavy-duty diesel trucks will be operated on weekends. Thus, the weekend factor should reflect changes in the human activities. However, inthis study,the numberof weekendsamples (there are only 3 weekends in this study and moreover they contain many missingvalues) mightnot besufficient enoughto obtain FIGURE 3. Concentration series of each compound. FIGURE 4. Profiles ofthe identifiedVOC sources inLa Porte,Houston. FIGURE 5. Time-resolved contribution ofeach identified VOC source in La Porte, Houston. KD i ) mean{g i,j |j ∈ weekends} mean{g i,j |j ∈ weekdays} (7) VOL. 38, NO. 5, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 1341 a general conclusion, but they can provide us an initial estimate of the influence of weekend factors on pollution. It can be seen from Figure 9 (the value for the source without weekend effect should be around 1) that sources 3 and 9 have significant weekend influence while sources 1, 2, 4, and 8 show only a weak weekend influence. The possible reasons for weak weekend effect might be(1) although relatively little isoprene in source 2 is biogenerated, its emission should be independent of weekend/weekday and (2) the c9-benzene, c10-benzene, toluene,xylene, and othercompounds in these 4 sources are from refineries that are usually operated in continuous mode,and their emissionrates on weekends may overwhelm the negative effect caused by the decreased number of motor vehicles. Source 1 contains mainly acrylonitrile. Figure 10 shows that most acrylonitrile emitters are located to the northwest and south of the sampling site. Likely, these emitters include the boilers, dryer stacks, aeration tanks, ponds, and waste gas processingequipments of chemical or rubber plants (27). The wind directional plot for this source agrees with the emitter locations as it shows a large contribution from the northwest and a peak at about 150°. The high peaks of the contribution plot for this source correspondto the nighttime period when southerly winds dominate. No information is available on the diurnal patterns of the source. In addition, the KD value of this source in Table 1 is 0.404, so this source is expectedto have asignificant weekend influence.However such an influence does not agree with the result in Figure 9. As mentioned above, the limited number of weekend measurements for analysis may notbe sufficient, which may be the cause of this disagreement. Better results for the weekend factor might be obtained if a larger data set were available. Source 2shows isopreneand M87 (vinylacetate). Isoprene is atypical biogenicVOC (28),but the contribution of biogenic isoprene is small in the immediate proximity of the La Porte site (29).A numberof anthropogenic isoprene emitters (likely, rubber industry) are located to the north and south of the sampling site (20, 27). For M87, there are a number of vinyl acetate emitters to the north and south of the sampling site, and especially several large vinyl acetate emitters are located to the north (27). These emitters are most likely the storage tank and other equipment of chemical plants. The wind directional plot for this source in Figure 6 largely confirms the location of these emitters, as it shows some convexes in FIGURE 6. Wind direction factor plots. TABLE 1. Ratio of Mean Contribution of Weekend Samples to That of Weekday Samples a no. 1 2 (M) 3 (M) 4 (M) 5 (M) 6 (M) 7 (M) 8 9 (M) KD 0.404 0.964 0.599 1.039 0.579 0.662 0.738 1.410 0.493 a The (M) after the number denotes the KD value of this source is identical to the corresponding weekend factor result. 1342 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 38, NO. 5, 2004 FIGURE 7. Wind speed factor plots. FIGURE 8. Temperature factor plots. VOL. 38, NO. 5, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 1343 the south and a broad contribution from the north. The contribution of biogenic isoprene is small, but a number of anthropogenic isoprene emitters are located at similar directions as the vinyl acetate emitters. This might be one of the reasons why isoprene and M87 occur in the same source. The KD value of this source is 0.964, which agrees with the result of weekend factor that there is a very weak weekend effect for this source. Source 3 is characterized by c7-ketone. There are some possible emitters (synthetic organic manufacturing plants) to the southeast of the sampling site (20), so the wind FIGURE 9. Weekend factor plot. FIGURE 10. Locations of acrylonitrile emitters. The circles denote the acrylonitrile emitters. The blue area corresponds to the wind direction plot for this source. The red × at the center of the blue area is the sampling site. FIGURE 11. Location of toluene-xylene emitters. The red squares and blue circles denote xylene and toluene emitters, respectively. The blue area correspondsto the winddirection plot for thissource. The red × at the center of the blue area is the sampling site. FIGURE 12. Location ofbenzene emitters. Thegreen trianglesdenote benzene emitters. The blue area corresponds to the wind direction plot for this source. The red × at the center of the blue area is the sampling site. FIGURE 13. Location of styrene emitters. The black circles denote styrene emitters. The blue area corresponds to the wind direction plot for this source. The red × at the center of the blue area is the sampling site. FIGURE 14. Location of propene. The circles denote the propene emitters. The blue area corresponds to the wind direction plot for this source. The red × at the center of the blue area is the sampling site. 1344 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 38, NO. 5, 2004 directional plot shows a broad contribution from that direction. There are a number of peaks in the corresponding contribution plot, but none of these peaks were on the weekend. In addition, the KD value of this source is 0.599 and identical to the result of weekend factor. Source 4 contains toluene and xylene. These emitters are operation units and equipment of the chemical and refining industry (e.g.,tanks, boilers, reactors, pyrolysis furnaces)(27). Figure 11 shows the emitters are mainly located to the northwest and south of the sampling. The wind directional plot shows a large contribution from the north and a sharp spike in the south, in agreement with the locations of the major emitters. In addition, toluene and xylene can be generated by motorvehicles (highway 225 isjust to the north and highway 146 to the east and southeast of the sampling site). This can be another cause of the shape of the wind directional plot. There are many peaks almost evenly distributed in the contribution plot and the KD value of this source is 1.039, which agrees with the weak weekend effect in Figure 9. In addition, the time corresponding to the peaks was mainly in the morning (6:00-8:00) and night (22:00- 24:00). Source 5 is characterized by benzene mostly from chemical plants or refineries. Figure 12 shows that most benzene emitters are distributed to the north and south of the sampling site (27). Particularly, one benzene source is located on Bay Area Blvd (in the direction of 150°)(20). In addition, the motor vehicles on highways 225 and 146 may increase the concentration of benzene. The location infor- mation of the emitters is supported by the wind directional plot that shows a broad contribution from the north and a spike in the direction at about 150°. The contribution plot for this source shows a number of peaks, noneof whichwere on the weekends, and the KD value of this source is 0.579. This agrees with the result of weekend factor. The significant variation between weekends and weekdayssuggests that the contribution of mobile sources on weekdays might have a greater impact on benzene emission. Source 6 is characterized by styrene. Figure 13 shows the location of the styrene emitters, most of which are likely to be the units of petrochemical plants. There are many styrene emitters around the sampling site and most of them are located to the northwest and south of the sampling site (27). Particularly there is a large styrene emitter at about 210°. The corresponding wind directional plot in Figure 6 agrees with thelocation informationas itshows a broad contribution from the northand also a sharpspike at the directionof 210°. Most of the high peaks in the contribution plot were at nighttime when the southerly wind was dominant. The KD FIGURE 15. Distribution plot for scaled residual errors. VOL. 38, NO. 5, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 1345 value of this source is 0.662, which agrees with the weekend factor result that this source has a weekend effect. Source 7 is represented by M61 whose kernel component is acetic acid. Acetic acid is a typical photochemical reaction product (30), so the wind direction plot shows a relatively smooth shape.Meanwhile, a number of anthropogenicacetic acid emitters (e.g., acetic acid storage tanks, boilers, and exhausted liquid tanks) are located to the north and south of the sampling site (27). The KD value of this source is 0.738 and identicalto theweekend factorresult. The photochemical sources should not have weekend effect, so the variation between weekday and weekend may be due to the changes of theemission ratesof the anthropogenic acetic acidsources. Source 8 is characterized by c9-benzenes and c10- benzenes. A number of c9- and c10-benzenes emitters (e.g., the operation units of chemical or petrochemical plants) are located to the north of the sampling site (27). The wind directional plot for this source shows a broad contribution from the northwest and a spike in the south. The motor vehicles on highways 225 and 146 and Spencer Hwy may increase the concentrations of c9- and c10-benzenes and may be a reason for the spike in the south. The peaks in the contribution plot are distributed over both weekdays and weekends (August 27 and September 2), but the KD value of this source is 1.410. Therefore, a weekend effect is expected. However, the weekend factor result in Figure 9 shows only a weak weekend influence. As before, this discrepancy may arise from the limited weekend data. Source 9 is represented by M43 (propene). Propene is most likely emitted by the refineries along the ship channel (20). Figure 14 shows a number of large emitters are located to the north and northeast of the sampling site, which is supported by the wind directional plot with a large contri- bution from the northeast. Most high peaks in the contribu- tion plot correspond to daytime periods when the dominant wind is a northerly wind. The KD value of thissource is 0.493, so this source seems to have a significant weekend effect, which agrees with the weekend factor result. The 13 VOCs, except c13-benzene which only appears in small amounts, are distributed into reasonable source profiles, and the corresponding contribution anddirectional patterns are in general agreement with known source information. One reason for the absence of c13-benzenes can be that almost 75% of this compound’s measurements were below the detection limit and were replaced with half of the detection limit. Therefore, it may not be possible to make any quantitative attributions for this compound. Because of the same reason, the scaled residual errors for c13-benzenes inFigure 15are notsatisfactory while the others have a reasonable distribution. Although the weekend data are not sufficient enough to make a correct conclusion on weekend effect for each source, the weekend factors of most sources (7 out of 9) are identical with the defined KD values. These results suggest the feasibility ofincluding the weekend effect analysis. Wind speed and temperature are two potentially impor- tant meteorological factors that can help interpret the observed VOCconcentrations. Figure 7 shows thewind speed factor. Formost factors,the wind speed factor valuesdecrease with increasing wind speed. This trend suggests a dilution effect that the same emitted mass is released into a larger volume of air as wind speed increases; the concentration therefore decreases (16). However, the factors of sources 2 and 9increase withincreasing wind speed and source3 shows an almost flat curve. The possible reasons for these phe- nomena mightbe (1) forthese sources that may becomposed of pointemitters (e.g.,high-concentration storagetank), there may be more coherent plume effect at higher wind speed (higher wind speed makes these emitted VOCs gathered together ratherthan dispersed) and (2) high-speed wind may enhance the evaporations of some VOCs. The influence of temperature on pollutants is more complex than that of wind speed because increasing tem- perature will not only speed up the vaporization of VOCs but also change the chemical properties of VOCs and enhance the reactions between VOCs and oxidants in the air. It is relatively difficult to summarize the action of temperature on the observed concentration. For some sources (e.g., Nos. 2, 3,and 7), the temperature factor values in Figure 8 increase with temperature. This trend might bethe result ofincreased vaporization. Another explanation for source 7 is likely that the increase in temperature enhances the rates of the photochemical reactions. However, the temperature factors for other sources do not show increasing trends. The source identification of VOCs in the La Porte Airport has been successfully performed using an expanded factor analysis modeland the correspondingoptimization tool, ME. The profiles and contributions of the 9 identified sources proved tobe reasonable. Besides, wind direction,wind speed, temperature, and weekend factors were also determined. The information on wind directions appears to agree with the known emission inventories and the wind speed factors for most sources suggest a dilution effect. For many sources, weekend and temperature factors help in interpreting their influences on the observed concentrations. It is not clear that this model is the best representation of the physical and chemical influences of such factors on the observed con- centrations. However, the results do suggest this is a feasible direction for such a study. In addition, the results suggest that the error estimation obtained through the FFT is reasonable in term of finding an interpretable solution. It appears that expanded modeling is feasible for not only identifying VOC sources in complex systems such as the air system in Houston but also revealing the various important features of these sources. Acknowledgments This workwas supported bythe United States Environmental Protection Agency through cooperative agreement number R-82806201 under a subcontract to Clarkson University by The Universityof Texas atAustin (UT). Althoughthe research described in this article has been funded wholly or in part by the United States Environmental Protection Agency, it has not been subjected to the Agency’s required peer and policy review and, therefore, does not necessarily reflect the views of the Agency and no official endorsement should be inferred. 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The ranges of wind direction and wind speed are divided into a series of subranges having similar numbers