Journal
ATMOSPHERIC ENVIRONMENT
Volume 149, Issue -, Pages 84-94Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2016.11.002
Keywords
Organic carbon; Elemental carbon; CIE-Lab optical color space system; Model uncertainty; Teflon filters
Funding
- TWAS-CAS
- Key Research Program of the Chinese Academy of Sciences [KJZD-EW-TZ-G06-01-0]
- National Natural Science Foundation of China (NSFC) [91543122]
- China Ministry of Environmental Protection [201509062]
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Optical color space sensing proved to be a valuable method in particle characterization since it provides a non-destructive, fast, and cost-efficient way to estimate organic and elemental carbon (OC, EC) in atmospheric particulate matter. Here we expand and validate the previous work with the measurement of the CIE-Lab optical color space system and development of a multi-parameter reference calibration model to estimate EC/OC collected on Teflon and quartz filters. Analysis of approximately 500 Teflon and quartz filter samples demonstrated that the described method could be a robust way to estimate EC and OC loadings on the filters. The results showed that the correlation coefficients between predicted and measured loadings for the model-estimated EC and OC were 0.951 (CV (RMSE) = 15.1%) and 0.908 (CV (RMSE) = 20.7%) on Teflon filters and 0.948 (CV (RMSE) = 17.4%) and 0.895 (CV (RMSE) = 21.1%) on quartz filters respectively. The model cross-validation also provided good agreement between the predicted EC and OC loadings on Teflon and quartz filters and the NIOSH thermal-optical method. It was also found that the mixing state of the chemical composition and sources of OC could interfere the absorption and scattering of light, which results in a model estimation uncertainty that should be evaluated within the model estimations. The non-destructive and low analytical cost of the method, when applied to Teflon filters that are used for mass measurement are a cost effective means for estimating EC and OC exposures and concentrations in health studies and large-scale monitoring campaigns. (C) 2016 Elsevier Ltd. All rights reserved.
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