Rewriting the Book on Supercritical Fluid Chromatography

In this article, the results of a DIMS air monitoring campaign in Auckland, New Zealand, are described. Real-time SIFT–MS measurements were obtained at three sites over a period of several weeks. At each site, SIFT–MS provided effective on-site analysis to sub-ppbV levels with better than 1-min time resolution.

This application note describes the determination of method detection limits (MDLs) for the newly regulated HON (Hazardous Organic NESHAP) compounds, which validate SIFT-MS as an effective solution for measuring these toxic VOCs and other environmental pollutants in ambient air, whether at the fence line or in a mobile setting.

This study demonstrates high-throughput analysis of BTEX compounds from several matrices (air, water and soil). Detection limits in the single-digit part-per-billion concentration range (by volume) are readily achievable within seconds using SIFT-MS, because sample analysis is achieved without chromatography, pre-concentration, or drying. We also present a calibration approach that enables speciation of ethylbenzene from the xylenes in real-time.

This application note summarizes key SIFT-MS results presented in a peer reviewed article entitled “Unreported VOC Emissions from Road Transport Including from Electric Vehicles.” Learn how the Wolfson Atmospheric Chemistry Laboratory at the University of York used SIFT-MS for VOC analysis in its platform to experimentally verify that motor vehicle screen wash is a significant unreported source of VOC emissions (especially for ethanol and methanol).

This publication reviews VOC pollutant monitoring applications of SIFT-MS in South Korea. SIFT-MS has been applied to emission source characterization, fenceline monitoring, ambient monitoring, pollution mapping, and incident response (including the use of drone-based sampling) for hazardous air pollutants (HAPs), odor nuisance species, and compounds that have high ozone formation potential (OFP) and/or contribute to secondary aerosol (SOA) formation.