Evaluation of organic carbon measurements in the IMPROVE and CSN networks

The gain and loss of gaseous carbonaceous material from quartz filters can bias the measurement of organic particulate matter. The Air Quality Research Lab scientists have worked with others in the IMPROVE and Chemical Speciation Network (CSN) communities to investigate organic carbon measurement uncertainty related to determining a consistent method for estimating sampling artifacts and evaluating post-sampling losses during field latency and transport.

FT-IR analysis of Teflon (PTFE) filter samples

FT-IR is a non-destructive which allows the filter samples to be analyzed by other non-destructive methods such as gravimetric for mass, XRF for elements, HIPS for light absorption, or destructive methods such as ion chromatography for ions or GC/MS for organic compounds after FT-IR analysis. FT-IR analysis and multivariate calibrations measure TOR-equivalent OC and EC, Functional groups such as aliphatic CH, carboxylic acids, carboxylates, non-acid carbonyl and alcohol OH (carbohydrates), Inorganic ions including sulfate, nitrate and ammonium, Soil elements and PM mass.

For questions on ordering services contact Ann Dillner.

FT-IR analysis of Teflon (PTFE) filter samples

FT-IR is a non-destructive which allows the filter samples to be analyzed by other non-destructive methods such as gravimetric for mass, XRF for elements, HIPS for light absorption, or destructive methods such as ion chromatography for ions or GC/MS for organic compounds after FT-IR analysis. FT-IR analysis and multivariate calibrations measure TOR-equivalent OC and EC, Functional groups such as aliphatic CH, carboxylic acids, carboxylates, non-acid carbonyl and alcohol OH (carbohydrates), Inorganic ions including sulfate, nitrate and ammonium, Soil elements and PM mass.

For questions on ordering services contact Ann Dillner.

Characterization of IMPROVE data quality

Each year the Air Quality Research Center delivers nearly one million new measurement values for the IMPROVE network. A large community of researchers and analysts use these data to address a wide range of scientific and regulatory issues. All data pass through multiple layers of review and quality assurance before publication, but all carry some uncertainty from the irreducible error that is present in any measurement.