Quantification of elemental and organic carbon in atmospheric particulate matter using color space sensing-hue, saturation, and value (HSV) coordinates

- PM composition data is needed to address global health and climate challenges.
- HSV color space was measured from a large and diverse set of PM loaded filters.
- A predictive model for EC and OC was developed using HSV color coordinates.
- Both EC and OC could be estimated effectively using the model.
- A cost effective EC/OC method can be applied to previously cost prohibitive research.

A fast and cost effective application of color sensing was used to quantify color coordinates of atmospheric particulate matter collected on filters to quantify elemental and organic carbon (EC/OC) loading. This is a unique and novel approach for estimating OC composition. The method used a colorimeter and digital photography to obtain XYZ color space values and mathematically transformed them to HSV cylindrical-coordinates; a quantification method was applied to estimate the NIOSH and IMPROVE (TOR) EC/OC loadings from a set of globally diverse PM samples. When applied to 315 samples collected at three US EPA Chemical Speciation Network (CSN) sampling sites, the HSV model proved to be a robust method for EC measurement with an R2 = 0.917 for predicted versus measured loading results and a CV(RMSE) = 16.1%. The OC quantified from the same sample filters had an R2 = 0.671 and a CV(RMSE) = 24.8% between the predicted and measured results. The method was applied to NIOSH EC/OC results from a set of samples from rural China, Bagdad, and the San Joaquin Valley, CA, and the EC and OC CV(RMSE) were 30.8% and 49.3%, respectively. Additionally, the method was applied to samples with color quantified by a digital photographic image (DPI) with EC results showing good agreement with a CV(RMSE) of 22.6%. OC concentrations were not captured as accurately with the DPI method, with a CV(RMSE) of 77.5%. The method's low analytical cost makes it a valuable tool for estimating EC/OC exposure in developing regions and for large scale monitoring campaigns.

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