Bias of Organochlorine Pesticide Data: A Comparison of Analyses by GC/ECD vs. HRGC/MS/MS

Oral Presentation

Prepared by J. McAteer1, E. Carroll Hughes2
1 - QA/QC Solutions, LLC, 7532 Champion Hill Rd. SE, Salem , OR, 97306, United States
2 - GSI Water Solutions, Inc., 55 SW Yamhill Street, Suite 400, Portland, OR, 97204, United States


Contact Information: jjmcateer@msn.com; 503-763-6948


ABSTRACT

Analyses for organochlorine pesticides is most commonly performed by GC/ECD using SW-846 Method 8081A. However, when contaminated sediments, significant matrix effects are often encountered which often results in the reporting of concentrations that may be biased high, reported as false positives, or reported as false negatives.

The determination of organochlorine pesticides by GC/ECD has procedures for confirming the presence of a target compound (i.e., dual column confirmation) and a variety of sample extract cleanup steps to help minimize matrix interferences. However, the presence of non-target halogenated compounds (e.g., PCBs and PCNs), as well as other constituents that may not be removed during extract cleanup directly affects the reliability of compound identification and quantification. When this situation is encountered the interpretation of the data and the subsequent decision-making process will likely be biased.

To illustrate the potential bias of organochlorine pesticide results in complex sediment matrices obtained using the standard GC/ECD, a comparison of split sample analyses completed using high performance gas chromatography/mass spectrometry/mass spectrometry (HPGC/MS/MS) using a modified EPA Method 1699 will be presented. Results from the GC/ECD and HPGC/MS/MS were generally comparable for most analytes, as has been shown by similar studies on biotic samples. However, data obtained using HPGC/MS/MS in this data set showed there was a very high positive bias most often associated with 2.4’- and 4,4’-DDT and associated breakdown compounds and total chlordanes when compared to the GC/ECD analysis. A discussion on sample collection, processing, and analytical procedures will be summarized. In addition, the impacts that this biased data may have on data interpretation, the decision-making process, and suggestions on improving project planning and data usability when such circumstances may be encountered will be presented.