Quantifying residual elemental carbon by thermal-optical analysis using an extended IMPROVE_A protocol with higher maximum temperature

ABSTRACT

Thermal-optical analysis (TOA) has long been used to quantify organic carbon (OC) and elemental carbon (EC) on quartz-fiber filter samples collected in national ambient air monitoring networks. In the routine analysis of samples from the Chemical Speciation Network (CSN), we observed a considerable fraction of filter punches that remain gray or black in color after TOA was completed, suggesting the presence of EC that was not fully evolved at the highest temperature specified by the IMPROVE_A protocol (840°C). In this work, we explored the operational conditions necessary to evolve and quantify such residual EC. First, four heavily loaded CSN samples were analyzed to evaluate modifications to the IMPROVE_A protocol. We found that adding a higher temperature step at 930°C more effectively evolved the residual EC than did lengthening the duration of the 840°C step. Compared with the standard IMPROVE_A results, the modified protocol evolved additional EC of 1.08 to 4.45 µg cm⁻² in mass, or 0.12 to 0.50 µg m⁻³ in concentration. This excess EC accounts for 27.1% to 45.3% of the total EC and 7.6% to 25.1% of the total carbon by standard IMPROVE_A. We then analyzed over 2600 samples from CSN using the extended IMPROVE_A protocol with higher maximum temperature (930°C). A total of 168 samples (6.4% of the total samples analyzed) contained measurable EC at the 930°C step. The average fraction of the evolvable residual EC mass in total EC is 5.7%, and up to 28% for samples with high total EC mass loading (i.e., 95th percentile and above).

Implications: Our results suggest that CSN EC measured by the standard IMPROVE_A protocol should be considered a lower limit, and that a higher maximum heating temperature can be used to better quantify EC from CSN sites impacted by fresh urban emissions.  

Acknowledgment

This work was supported in part by the Environmental Protection Agency (EPA) (contract number EP-D-15-020). The conclusions are those of the authors and do not necessarily reflect the views of the sponsoring agency.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, XZ, upon request.

Additional information

Funding

This work was supported in part by the Environmental Protection Agency (EPA) (contract number EP-D-15-020).

Notes on contributors

Xiaolu Zhang

Xiaolu Zhang is an associate project scientist in the Air Quality Research Center at the University of California Davis. Her current research focuses on characterizing carbon measurement techniques for PM2.5 filter samples to ensure data quality and consistency for long-term ambient monitoring network. Prior to joining AQRC, Xiaolu worked as a post-doctoral researcher in Department of Civil and Environmental Engineering at UC Davis and earned her PhD degree in Earth and Atmospheric school at Georgia Institute of Technology.

Krystyna Trzepla

Krystyna Trzepla was the Laboratory Manager for the Air Quality Research Center at the University of California Davis until her retirement in 2020. Ms. Trzepla provided support for all research studies involving monitoring particles in the atmosphere, with special emphasis on the application of elastic lidar system for monitoring spatial distribution and elemental analyses by X-Ray Fluorescence and Proton Elastic Scattering.

Warren White

Warren White is a mathematician with the Air Quality Monitoring Team at the University of California at Davis, with broad air quality interests and experience. He currently focuses on carbon and light absorption measurements by particle speciation networks.

Nicole Pauly Hyslop

Nicole Pauly Hyslop is the Associate Director for Quality Research in the Air Quality Research Center at the University of California Davis. Dr. Hyslop conducts research to characterize data quality to gain a better understanding of the sources of error in the measurements and improve quality assurance protocols to identify and reduce errors.