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PAMS Analysis for Southern California: Characteristics of VOC Data 1994-1997

PAMS Analysis for Southern California: Characteristics of VOC Data 1994-1997. Hilary H. Main Sonoma Technology, Inc. Petaluma, CA. Presented at SCOS97-NARSTO Data Analysis Conference February 13, 2001 Los Angeles, CA. STI-2054. Acknowledgements.

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PAMS Analysis for Southern California: Characteristics of VOC Data 1994-1997

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  1. PAMS Analysis for Southern California: Characteristics of VOC Data 1994-1997 Hilary H. Main Sonoma Technology, Inc. Petaluma, CA Presented at SCOS97-NARSTO Data Analysis Conference February 13, 2001 Los Angeles, CA STI-2054

  2. Acknowledgements • STI Coauthors: Lyle Chinkin, Nicole Hyslop • SCAQMD for funding the analysis • Xinqiu Zhang, SCAQMD, for directing this project • Randy Pasek and Eileen McCauley, ARB, for providing data and reports • Steve Barbosa and Cari Eldred, SCAQMD, for helpful discussions regarding the data

  3. Data Analysis Objectives • Organize and validate the data; make recommendations relative to PAMS operations and data management • Investigate spatial and temporal distributions of the PAMS measurements

  4. Monitoring Stations

  5. Data Validation: Pico Rivera

  6. Data Management: Non-PAMS “Tracers” Unidentified, ppbC • Consider reporting high concentrations of non-PAMS VOCs (e.g., identified using GC-MS)

  7. Comparing PAMS and Canisters (1 of 2) Median concentrations (11 matching samples) 0500-0700 PST • PAMS acetylene concentrations were an average of 34% lower than the collocated canisters Azusa Acetylene

  8. Comparing PAMS and Canisters (2 of 2) • PAMS TNMOC concentrations were an average of 20% lower than collocated canister concentrations • PAMS unidentified mass was about half the canister unidentified

  9. TNMOC Varies Across the Basin Hawthorne Burbank Pico Rivera Banning Azusa Upland

  10. Urban VOC composition changes little with time of day COMPLETE LIST OF SPECIES ABBREVIATIONS PST

  11. Downwind VOC composition shows more change with time of day

  12. VOC composition has changed over time (1 of 2)

  13. VOC composition has changed over time (2 of 2)

  14. RFG resulted in ambient benzene reductions Los Angeles Benzene, wt% • End 1992: California’s Phase 1 gasoline (target = lower RVP) • Early 1995: federal RFG (EPA Phase I) in Los Angeles, Ventura, and San Diego counties. Target = lower RVP, add oxygenates, and limit benzene content. • Early 1996: California’s Phase 2 RFG (entire state). Target = limit benzene and lower RVP.

  15. Formaldehyde has changed over time Los Angeles 24-hr data Formaldehyde (ppb)

  16. Is the TNMOC “less reactive”? • Ambient benzene reductions have been observed, but TNMOC concentrations have not declined significantly. • A statistically significant decline in total reactivity has occurred between 1995 and 1997. Sum MIR*[species]

  17. Upwind/Downwind sites show more aging Morning Median Ratios (PAMS and SCOS97)

  18. Is there a day-of-week difference in precursor concentrations? Pico Rivera (1997-1998) Morning TNMOC Concentrations (ppbC) • Median TNMOC concentrations are lower on weekends at Pico Rivera, but the difference is not significant. • Further investigation is on-going (e.g., more species, more years of data). Sunday Monday

  19. Some non-PAMS VOCs are important

  20. Conclusions/Recommendations (1 of 2) • Data validation is important. • QA efforts should be continued. • The unidentified mass can be significant. • Reductions can be made by reporting more species. • Spatial and temporal investigations showed that data were generally consistent with current understanding (e.g., afternoon aging at more downwind sites; increased evaporative emissions). • The PAMS designation for Banning in AIRS should be changed (data are characteristic of Type 4).

  21. Conclusions/Recommendations (2 of 2) • Changes in ambient benzene since 1995 were consistent with emissions changes due to the introduction of reformulated gasoline. • A sufficient number of speciated samples are required to assess this. • Speciated data are critical to explorations of ozone formation potential, day-of-week differences, year-to-year changes in composition, and air mass age (transport) estimates. • PAMS data are useful!

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