Intercomparison of automated methodologies for determination of ambient isoprene during the PROPHET 1998 summer campaign

TitleIntercomparison of automated methodologies for determination of ambient isoprene during the PROPHET 1998 summer campaign
Publication TypeJournal Article
Year of Publication2001
AuthorsJr. DJBarket, Hurst JM, Couch TL, Colorado A, Shepson PB, Riemer DD, Hills AJ, Apel EC, Hafer R, Lamb BK, Westberg HH, Farmer CT, Stabenau ER, Zika RG
JournalJournal of Geophysical Research
Volume106
IssueD20
Pagination24,301-24,313
KeywordsVOLATILE ORGANIC COMPOUNDS
Abstract

The Program for Research on Oxidants: PHotochemistry, Emissions, and Transport (PROPHET) 1998 summer campaign, conducted at the UMBS, provided a unique opportunity to compare isoprene measurement techniques that were automated, sampled and analyzed on-line, and provided relatively fast time resolution. Assessment of the data quality for fast isoprene measurements is important because isoprene dominates the surface chemistry at many rural sites and even some urban environments. An informal intercomparison was conducted by evaluating ambient isoprene mixing ratio data generated by five different instruments: quadrupole ion trap (QIT) MS, the chemiluminescent-based fast isoprene sensor (FIS), and three gas chromatograph/mass spectrometry (GC/MS) techniques. The GC/MS methods were deployed and maintained by Purdue University (GC/MS-P), the National Center for Atmospheric Research (GC/MS-NCAR), and the Rosenstiel School of Marine and Atmospheric Science (GC/MS_RSMAS). The FIS was deployed and maintained by NCAR, Hills-Scientific.com and Washington State University, while the QITA was implemented by Purdue University. The GC/MS-P was chosen as the reference method to evaluate the agreement of the data set. The data were evaluated for time-matched samples through regression analysis, ratio analysis, and percent difference analysis relative to GC/MS-P. For measurement data in the central 90th percentile relative to the median, the mean percent difference was 21% for GC/MS-NCAR, 41% for QIT, 42% for GC/MS-RSMAS, and 88% for the FIS. Potential sources of disagreement, especially for low-concentration data, such as variations in sampling time, interferences, method precision and accuracy, and limited cross-calibration, are discussed.