Air Data Quality Control: A Case Study

• • Issues surrounding detectable levels of contamination that can adversely affect data quality due to SUMMA canisters
  • A vapor intrusion mitigation system (VIMS) case study where inexplicable anomalies were observed in the monitoring data set
  • A numerical approach presented identifying anomalous data developed by Langan to address this problem

In recent years, both anecdotal evidence and rigorous research have indicated that even individually certified SUMMA canisters may arrive at a project site with detectable levels of contamination that can adversely affect data quality. Research (by others) indicates that while approximately three percent of duplicate groundwater samples have laboratory analytical results that differ significantly from those of the primary sample, with whole air samples this inconsistency increases ten-fold. This presentation will focus on one site in the San Francisco Bay Area at which a passive sub-slab vapor intrusion mitigation system (VIMS) was installed during construction and whole air sampling, for the purposes of VIMS performance monitoring, began in the fall of 2016. After several years of regular monitoring, inexplicable anomalies were observed in the data set, and the frequency of anomalies increased over subsequent monitoring events. In response, Langan developed a numerical approach to identify anomalous data that was subsequently applied to the data sets. This presentation will provide a brief project background, describe the QC approach, discuss the ramifications to the project of the data anomalies and their identification, and describe the changes to the sampling program that will be implemented in the immediate future.