PERCH Air Quality Study. Quarterly Report. February 7, 2004

Transcription

1 PERCH Air Quality Study Quarterly Report February 7, 2004 Submitted to: Professor Ranga Rao Center for Environmental Diagnostics and Bioremediation University of West Florida University Parkway Pensacola, FL Sangil Lee 2, Mohan Turaga 3, Rick Peltier 1, Michael E. Chang 1, Karsten Baumann 1, Ann Bostrom 3, Armistead Russell 2 1 School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA School of Public Policy, Georgia Institute of Technology, Atlanta, GA GTRC Project #: 3506B35

2 PERCH Air Quality Study (PAQS) Summary The primary focus of the PAQS during the October-December 2003 quarter was analysis of data collected during the summer 2003 pilot field study of particulate matter (PM), ozone (O 3 ), air toxics, and meteorology in the Pensacola, FL area. In this report we present progress and, if available, findings from three independent efforts. In the first analysis, the relative source contributions to concentrations of volatile organic compounds observed during the pilot study are estimated using a Chemical Mass Balance (CMB) approach. This approach suggested that gasoline related sources were the dominant contributors (about 65 %) during the study at the OJ Semmes Elementary School site. Other significant contributions were associated with primers and enamel (18%), refinery fugitives (10%), biogenics (5%), and diesel exhaust (2%). The gasoline contribution is relatively higher at morning and at night probably due to the relatively lower mixing heights during these periods. The biogenic contribution however, is relatively higher in the afternoon (7%) compared to the morning (3%) and nighttime (0.4%). This is likely due to the fact that isoprene is only emitted during the day with peak emissions during the afternoon. In the second analysis presented here, work is proceeding to identify, among the 83 VOC compounds monitored, the air pollutants that are defined as toxic pollutants by the 1990 Clean Air Act (CAA) Amendment, and those that were part of the National Air Toxics Assessment (NATA) Of those common pollutants identified, variations in the observed concentrations were assessed across all the samples collected, and compared with the NATA-modeled concentrations. Relative to the NATA-modeled concentrations, the comparison shows higher observed concentrations of perchloroethylene, methylene chloride, and chloroform, and lower observed concentrations of trichloroethylene and benzene. It is not yet understood what might be the significance or implications of these findings. Lastly, instrument revalidation and recalibrations were completed for the fast-response particle composition measurements to ensure data quality, per the submitted QAPP. The data are now certified, and work continues to examine the variability of PM in space, time, and chemical composition. 1. VOC Chemical Mass Balance (Sangil Lee) 1.1. Introduction Volatile Organic Compounds (VOCs) are important in urban areas in terms of atmospheric chemistry and public health concerns. Seinfeld and Pandis (1998) show that the photostationary reaction itself can not explain the actual observed ozone concentration in the atmosphere. Adding VOCs in the photostationary reaction increases ozone accumulation since organic radicals from oxidizing VOCs convert NO to NO 2 without consuming ozone. Oxidation of VOCs that have higher carbon numbers (=C 7 ) produces low carbon number VOCs which can increase particulate matter mass by forming secondary organic aerosols (Grojean et al., 1989, 1992). However, the exact pathways, precursors, and composition of secondary organic aerosols still remain unknown due to its complexity. US EPA regulates VOCs (i.e., benzene, toluene) concerning public health since many of them are identified as toxics. Due to the important role of VOCs in the atmosphere, it is equally important to understand their emission origins, and if necessary, options for controlling them. It is not an easy task however to control ambient VOC concentrations since there are many VOCs and also since there different sources may emit the

3 same VOCs making accountability difficult. Meng et al. (1997) further showed that reduction in VOC emissions, while leading to a decrease in ozone concentrations, could also increase particulate matter mass, and vice versa. The chemical mass balance (CMB) can be used to help since this receptor model can identify the source of VOCs and even quantify the source contribution to the receptor. Here we try to identify the sources and their contributions to the receptor using a CMB model together with ambient VOC measurements taken in Pensacola, Florida during the summer of Theory The CMB model can relate the measured VOC concentration at the receptor with the potential sources doing appropriate chemical mass balance in order to identify source contributions. The CMB model is based on the following equation: m C ik = j= 1 f ij S jk i = 1 m, j = 1 n. where C ik is the observed concentration of species i in sample k, S jk is the total concentration of material (particles, VOCs, etc) from source j in sample k and f ij is the mass fraction of species i in source j (i= 1 m; J= 1...n; k= 1 l). The concentration of each chemical species at the receptor becomes a linear combination of the contributions of each source to the total concentration of each species at the site. Given the chemical composition of the ambient sample, C ik, and the source profiles, f ik, the equation can be solved to provide the source contribution S ij by doing multiple regressions. The CMB model works under some assumptions: 1) compositions of source emissions are constant over the period of ambient and source sampling; 2) chemical species do not react with each other; 3) all sources with a potential for contributing to the receptor have been identified and their emissions have been characterized; 4) the number of sources or source categories is less than or equal to the number of chemical species; 5) the source profiles are linearly independent of each other; 6) measurement uncertainties are random, uncorrelated, and normally distributed (Watson et al., 1998) Methods Collection of ambient VOCs Figure 1.1 shows the location of the monitoring site for this study. This site is located in a residential area about 2 miles north of downtown Pensacola, Florida (30.45 North, West). Surface VOCs were collected for 23 days at the monitoring site from July 18 to August 13, On most days, VOCs were sampled four times per day (07:00, 12:00, 17:00, 23:00). A total of 86 samples were collected using air canisters. All samples were then analyzed by gas chromatography/flame ionization detection (GS/FID) at the University of California, Irvine Source profiles It would have been ideal if samples from possible sources of VOCs around the monitoring site could have been collected to make source profiles during this study. Unfortunately, this could not be done given the scope of the pilot study. Instead, general source profiles derived from other studies involving Photochemical Assessment Monitoring Stations (PAMS) in the United States were used for chemical mass balance. While not exact, these generic source profiles are believed to be somewhat representative source profiles since they are derived from other ozone non-attainment urban areas in the United States. Table 1.1 shows source profiles that include diesel exhaust, gasoline exhaust, liquid gasoline, evaporated gasoline,

4 refinery fugitives, industrial coating, primers and enamel, and printing. 55 compounds are identified for each source profile, and the concentration of each compound is normalized to the sum of 55 compounds to give a fractional source profile. Here, it is assumed that isoprene is the only chemical species for the biogenic source profile (even though there are other emissions such as α and β pinene). Measurement Site Figure 1.1. Measurement site location: O.J. Semmes Elementary School, Pensacola, FL.

5 Table 1.1. fractional source profiles of VOCs from photochemical assessment monitoring station (Watson et al., 2001) PAMS compound Diesel exhaust Gasoline exhaust Liquid gasoline Evaporated gasoline Refinery fugitives Industrial coating Primers & enamel Printing Fraction Std Fraction Std Fraction Std Fraction Std Fraction Std Fraction Std Fraction Std Fraction Std Ethene Acetylene Ethane Propene n-propane Isobutane Butene n-butane t-2-butene c-2-butene Isopentane Pentene n-pentane Isoprene t-2-pentene c-2-pentene ,2-Dimethylbutane Cyclopentane ,3-Dimethylbutane Methylpentane Methylpentane Methyl-1-pentene n-hexane Methylcyclopentane ,4-Dimethylpentane Benzene Cyclohexane Methylhexane ,3-Dimethylpentane Methylhexane ,2,4-Trimethylpentane n-heptane Methylcyclohexane ,3,4-Trimethylpentane Toluene Methylheptane Methlyheptane n-octane Ethylbenzene Mp-Xylene

6 Styrene o-xylene n-nonane Isopropylbenzene n-propylbenzene m-ethyltoluene p-ethyltoluene ,3,5-Trimethylbenzene o-ethyltoluene ,2,4-Trimethylbenzene n-decane ,2,3-Trimethylbenzene m-diethylbenzene p-diethylbenzene n-undecane

7 Source apportionment In this study, the Chemical Mass Balance Model Version 8 (CMB8) of the US EPA/Desert Research Institute was applied. Input data files were created by using ambient measurements at the monitoring site and source profiles from the previous literature (Watson et al., 2001). The PAMS source apportionment profiles for 32 compounds, which have a life time larger than toluene (8 hours) except isoprene, were used as fitting species for CMB model. Even though Isoprene has very short life time, it was included as fitting species since it is a good tracer for biogenic emissions. In this study, ethane and benzene were not used, but ethene was included as a fitting species since the CMB calculation was not converged without giving any results. Source apportionment was conducted with a total of 31 compounds as fitting species Results and Discussion CMB results are shown in Table 1.2. The CMB model estimates not only the contribution of each source but also produces some parameters. The model performance can be verified by those parameters such as R-square (R 2 ), Chi-square (χ 2 ), and calculated percent concentration (%). The R-square value is the variance in ambient species concentration explained by the calculated species concentrations. The Chi-square represents the uncertainties of the calculated species concentrations. A large value (>4.0) means that one or more of the calculated species differs from the measured concentrations by several uncertainty intervals. To get reasonable results from the CMB model, the R-square should be larger than 0.80, and Chi-square should be less than 4.0. In this study, R-square values are from 0.78 to 1.0 ( ), and Chi-square values are from 0.37 to 1.83 ( ). Therefore, the performance parameters of this CMB application are in a reasonably good range, which makes the estimated results acceptable for the further analysis. Figure 1.2 shows that there is a good agreement between the measured and calculated concentrations of VOCs. The linear regression in Figure 1.3 indicates that the CMB overestimated VOC concentration by about 11 % compared to the measured concentrations. The CMB also provides fairly good estimations for the individual chemical species compared to their measured concentrations (Figure 1.4). The source apportionment results indicate that 6 sources such as diesel exhaust, gasoline exhaust, evaporative gasoline, refinery fugitive, primers and enamel, and biogenic, are contributing to measured VOCs at the site. Figure 1.5 shows a time series plot of source contribution to measured VOCs. It appears that there are several peaks of VOCs at the morning (07:00) and night time (23:00). In general, the concentrations at noon and late afternoon are lower than those at the morning and night (Figure 1.6). Figure 1.7 shows the diurnal pattern of the contribution from each source in terms of concentration. All except the biogenic source have a similar trend with that of the measured VOC concentration. The concentrations increased when the atmosphere had less mixing height leading to less dilution. However, this is not the case for the biogenic source. Isoprene, which is a good tracer of the biogenic source, is emitted by vegetation during daytime when the solar radiation is available. Isoprene has a very short lifetime about 1.7 hours with respect to OH in the atmosphere. There is little isoprene emission due to vegetation activity at night time. Therefore, the diurnal pattern of the biogenic source is opposite of that of other sources. The main sources are the gasoline related sources such as gasoline exhaust and evaporative gasoline at the site (Figure 1.8). They are followed by primers and enamel, and refinery fugitive. The contributions of diesel exhaust and biogenic are much less compared to other sources.

8 Table 1.2. Result summary from chemical mass balance model for ambient measurement. Time R square CHI square Percent Conc. (%) Measured Conc.(ppbv) Calculated Conc.(ppbv) HDDE (%) LDGE (%) LGAS (%) EGAS (%) REFG (%) INCO (%) PMEN (%) PRNT (%) BIOG (%) 7/18/03 7: /19/03 7: /19/03 12: /19/03 17: /19/03 23: /20/03 7: /20/03 12: /20/03 17: /20/03 23: /21/03 7: /21/03 11: /21/03 16: /21/03 23: /22/03 7: /22/03 12: /22/03 17: /22/03 23: /23/03 7: /23/03 12: /23/03 17: /23/03 23: /24/03 7: /24/03 12: /24/03 17: /24/03 22: /25/03 7: /25/03 12: /25/03 16: /25/03 23: /26/03 7: /26/03 12: /26/03 17: /26/03 23: /27/03 7: /27/03 12: /27/03 17: /27/03 23:

9 7/28/03 7: /28/03 12: /28/03 17: /28/03 23: /29/03 7: /29/03 12: /29/03 17: /29/03 23: /30/03 7: /30/03 11: /30/03 17: /30/03 23: /31/03 7: /31/03 12: /31/03 17: /31/03 23: /1/03 7: /1/03 12: /1/03 17: /1/03 23: /2/03 7: /2/03 12: /2/03 17: /2/03 22: /3/03 7: /3/03 12: /3/03 17: /3/03 23: /4/03 7: /4/03 12: /4/03 17: /4/03 23: /5/03 7: /5/03 12: /5/03 17: /5/03 23: /6/03 7: /6/03 12: /8/03 7: /8/03 12: /11/03 12: /11/03 17: /11/03 23:

10 8/12/03 7: /12/03 12: /12/03 17: /12/03 23: /13/03 7:

11 70 60 Measured VOCs Calculated VOCs 50 VOCs, ppbv /18/03 7:00 7/19/03 12:00 7/19/03 23:00 7/20/03 12:00 7/20/03 23:00 7/21/03 11:00 7/21/03 23:00 7/22/03 12:00 7/22/03 23:00 7/23/03 12:00 7/23/03 23:00 7/24/03 12:00 7/24/03 22:00 7/25/03 12:00 7/25/03 23:00 7/26/03 12:00 7/26/03 23:00 7/27/03 12:00 7/27/03 23:00 7/28/03 12:00 7/28/03 23:00 7/29/03 12:00 7/29/03 23:00 7/30/03 11:00 7/30/03 23:00 7/31/03 12:00 7/31/03 23:00 8/1/03 12:22 8/1/03 23:19 8/2/03 12:08 8/2/03 22:57 8/3/03 12:17 8/3/03 23:20 8/4/03 12:05 8/4/03 23:15 8/5/03 12:18 8/5/03 23:29 8/6/03 12:32 8/8/03 12:22 8/11/03 12:30 8/11/03 23:16 8/12/03 12:04 8/12/03 23:10 Figure 1.2. Time series plot of comparison between measured and calculated VOC concentration.

12 70 Calculated VOC concentration (ppbv) y = 1.11x R 2 = Measured VOC concentration (ppbv) Figure 1.3. Scatter plot of measured vs calculated VOC concentration.

13 100 Measured Calculated 10 (ppbv) Ethene Acetylene n-propane Isobutane n-butane Isopentane n-pentane Isoprene 2,2-Dimethylbutane 2-Methylpentane 3-Methylpentane n-hexane 2,4-Dimethylpentane 3-Methylhexane 2,3-Dimethylpentane 3-Methylhexane 2,2,4-Trimethylpentane n-heptane Toluene 2-Methylheptane 3-Methylheptane n-octane n-nonane Figure 1.4. Comparison between the measured and calculated concentrations of each species for 7/30/03 07AM sample.

14 70 60 Diesel Exh. Gasoline Exh. Evap. Gasoline Refinery Fug. Primers & Enamel Biogenic Measured Mass (ppbv) /18/03 7:00 7/19/03 12:00 7/19/03 23:00 7/20/03 12:00 7/20/03 23:00 7/21/03 11:00 7/21/03 23:00 7/22/03 12:00 7/22/03 23:00 7/23/03 12:00 7/23/03 23:00 7/24/03 12:00 7/24/03 22:00 7/25/03 12:00 7/25/03 23:00 7/26/03 12:00 7/26/03 23:00 7/27/03 12:00 7/27/03 23:00 7/28/03 12:00 7/28/03 23:00 7/29/03 12:00 7/29/03 23:00 7/30/03 11:00 7/30/03 23:00 7/31/03 12:00 7/31/03 23:00 8/1/03 12:22 8/1/03 23:19 8/2/03 12:08 8/2/03 22:57 8/3/03 12:17 8/3/03 23:20 8/4/03 12:05 8/4/03 23:15 8/5/03 12:18 8/5/03 23:29 8/6/03 12:32 8/8/03 12:22 8/11/03 12:30 8/11/03 23:16 8/12/03 12:04 8/12/03 23: Figure 1.5. Source contribution of VOCs

15 40 30 (ppbv) :00 12:00 17:00 23:00 Time (hour) Figure 1.6. Diurnal pattern of the measured VOC concentration.

16 1.2 a. 15 b :00 12:00 17:00 23:00 0 7:00 12:00 17:00 23:00 20 c. 4 d :00 12:00 17:00 23:00 0 7:00 12:00 17:00 23:00 10 e. 2.0 f :00 12:00 17:00 23: :00 12:00 17:00 23:00 Figure 1.7. Diurnal pattern of source contribution. a) Diesel exhaust, b) Gasoline exhaust, c) Evaporative Gasoline, d) Refinery fugitives, e) Primers and enamel, f) Biogenic.

17 /18/03 7:00 7/19/03 12:00 7/19/03 23:00 7/20/03 12:00 7/20/03 23:00 7/21/03 11:00 7/21/03 23:00 7/22/03 12:00 7/22/03 23:00 7/23/03 12:00 7/23/03 23:00 7/24/03 12:00 7/24/03 22:00 7/25/03 12:00 7/25/03 23:00 7/26/03 12:00 7/26/03 23:00 7/27/03 12:00 7/27/03 23:00 7/28/03 12:00 7/28/03 23:00 7/29/03 12:00 7/29/03 23:00 7/30/03 11:00 7/30/03 23:00 7/31/03 12:00 7/31/03 23:00 8/1/03 12:22 8/1/03 23:19 8/2/03 12:08 8/2/03 22:57 8/3/03 12:17 8/3/03 23:20 8/4/03 12:05 8/4/03 23:15 8/5/03 12:18 8/5/03 23:29 8/6/03 12:32 8/8/03 12:22 8/11/03 12:30 8/11/03 23:16 8/12/03 12:04 8/12/03 23:10 (%) Diesel Exh. Gasoline Exh. Evap. Gasoline Refinery Fug. Primers & Enamel Biogenic Figure 1.8. Fractional contribution of each source.

18 1.5. Summary This approach suggested that gasoline related sources were the dominant contributors (about 65 %) during the study at the OJ Semmes Elementary School site. Other significant contributions were associated with primers and enamel (18%), refinery fugitives (10%), biogenics (5%), and diesel exhaust (2%). The gasoline contribution is relatively higher at morning and at night probably due to the relatively lower mixing heights during these periods. The biogenic contribution however, is relatively higher in the afternoon (7%) compared to the morning (3%) and nighttime (0.4%). This is likely due to the fact that isoprene is only emitted during the day with peak emissions during the afternoon References Seinfeld, J.H., Pandis, S.N., Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. Wiley, New York, NY. Watson, J.G., Chow, J.C., Fujita, E.M., Review of volatile organic compound source apportionment by chemical mass balance, Atmospheric Environment 35, Meng, Z., Dabdub, D., Seinfeld, J.H., Chemical Coupling Between Atmospheric Ozone and Particulate Matter, Science 277, Watson, J.G., Robinson, N.F., Fujita, E.M., Chow, J.C., Pace, T.G., Lewis, C., Coulter, T., CMB8 applications and validation protocol for PM2.5 and VOCs. Report No D1. Prepared for U.S. Environmental Protection Agency, Research Triangle Park, NC, Desert Research Institute, Reno, NV. Grosjean, D., In-situ organic aerosol formation during a smog episode: estimated production and chemical functionality. Atmospheric Environment 26A, Grosjean, D., Seinfeld, J.H., Parameterization of the formation potential of secondary organic aerosols. Atmospheric Environment 23,

19 2. Analysis of Volatile Organic Carbon (VOC) Data Collected in Pensacola during Summer 2003 (Mohan Turaga) The mobile air quality laboratory of Georgia Institute of Technology collected primary data in Pensacola during July-August 2003 to measure some criteria air pollutants as well as volatile organic carbons (VOC). This document presents a brief summary of the analysis conducted so far and the future plan for analysis on the VOC data collected. 2.1 Objectives of Analysis As part of the VOC monitoring in Pensacola, we collected canister data for 83 VOC compounds including some toxic pollutants. A grab sample was collected every four hours for 30 days during July 18 August The following are the objectives of the VOC analysis presented in this report: Identify, among the 83 VOC compounds monitored, the air pollutants that are defined as toxic pollutants by the 1990 Clean Air Act (CAA) Amendment Identify the air toxics that were part of the National Air Toxics Assessment (NATA) 1996 Assess the variations in the pollutant concentrations across the samples Compare the NATA-modeled concentrations of the toxic pollutants in 1996 in Escambia with the monitored concentrations in Analysis Results The Clean Air Act Amendment of 1990 identified 188 air pollutants as toxic pollutants. The 1996 NATA assessment identified 32 toxic air pollutants as the most important pollutants for its assessment. Among the 83 VOC compounds we monitored, seven pollutants were part of the 32 NATA air toxics. They include benzene, chloroform, carbon tetrachloride, methylene chloride, trichloroethylene, perchloroethylene, and 1,3-butadiene. We also monitored toluene, which is a toxic air pollutant according CAA definition but was not considered in NATA assessment. The third and fourth columns of Table 2.1 show the mean and standard deviation for the seven toxic pollutants (based on 89 samples). Figures 2.1 through 2.7 show the variation in the seven identified toxic air pollutants across the samples collected during the monitoring period. In general, carbon tetrachloride showed least variation across samples. Benzene concentrations had peak values generally in the samples collected at 11 pm, as did carbon tetrachloride concentrations. Trichloroethylene concentrations showed little variation for most part of the monitoring period, but had two large peaks (0.6 and 0.44 µg/m 3 in comparison to its overall mean concentration of µg/m 3 ). In case of the other three pollutants, there are no consistent patterns of peaks and troughs even though there are clear variations across the samples. Table 2.1 also shows the comparison of the concentrations modeled under the NATA assessment in 1996 with the concentrations monitored in Figure 2.8 shows the same comparison in a graphical format. The observed concentrations of perchloroethylene, methylene chloride, and chloroform were greater than in the NATA study, the increase being highest for perchloroethylene. In case of trichloroethylene and benzene the mean observed concentrations 1 Although it is not entirely appropriate to compare data estimated by models (which was the case in NATA in 1996) with field monitored data, we made this comparison due to lack of monitored data for 1996

20 are lower than in the NATA study. Subsequent analysis will investigate the possible sources of variation across samples in 2003 as well as possible explanations for the differences in the observed and NATA concentrations. Future Analysis Plan Investigate the source of variation across samples in the monitored data for all seven toxic pollutants. For example why certain pollutants seem to have peak concentrations consistently during a particular time of the day. Collect the emissions data for the seven pollutants from National Emission Inventory (NEI) of US EPA and also from toxic release inventory (TRI) data, if available. Use this emission data to assess if the differences in concentrations between 1996 NATA results and the 2003 observations can be explained by changes in emissions. Identify the toxics that were monitored in summer which were not part of NATA s 32 toxics and investigate if risks from those toxics contribute significantly to the overall estimated risk in Escambia (estimated based on 32 pollutants of NATA). Compare the monitored concentrations in Pensacola with those in the state of Georgia to assess if there are any unexpectedly higher concentrations in Pensacola. Table 2.1. Comparison of 1996 modeled NATA Data and 2003 Monitored Data Pollutant 1996 (NATA) 2003* (Monitored) Mean Mean SD Chloroform Carbon Tetra Chloride Methylene chloride Trichloroethylene Perchloroethylene , 3 Butadiene Benzene All concentration in µg/m 3 * Mean and SD are based on 89 samples

21 Fig 2.1. Variation in Chloroform concentrations in Pensacola during July-Aug Conc. ( g/m3) /18/03 0:00 7/23/03 0:00 7/28/03 0:00 8/2/03 0:00 8/7/03 0:00 8/12/03 0:00 8/17/03 0:00 Time Fig 2.2. Variation in Carbon Tetrachloride concentrations in Pensacola during July-Aug Conc. ( g/m 3 ) /18/03 0:00 7/23/03 0:00 7/28/03 0:00 8/2/03 0:00 8/7/03 0:00 8/12/03 0:00 8/17/03 0:00 Time

22