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Original Articles

A semi-automated multi-endpoint reactive oxygen species activity analyzer (SAMERA) for measuring the oxidative potential of ambient PM2.5 aqueous extracts

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Pages 304-320 | Received 13 Aug 2019, Accepted 06 Nov 2019, Published online: 06 Dec 2019
 

Abstract

Many acellular assays have been developed for assessing the oxidative potential (OP) of ambient PM2.5, yet no consensus has been reached on the most appropriate method. Most of these methods are highly time- and labor-intensive, making it difficult to analyze a large sample-set. Here, we have developed a semi-automated multi-endpoint ROS-activity analyzer (SAMERA) for measuring the five most commonly used endpoints of OP: consumption rate of dithiothreitol (OPDTT), ascorbic acid (OPAA-SLF) and glutathione (OPGSH-SLF), and the generation rate of •OH in DTT (OPOH-DTT) and in surrogate lung fluid (OPOH-SLF). A high analytical precision (coefficient of variation = 5–8% for all endpoints using positive controls such as Cu(II), Fe(II), phenanthrenequinone (PQ) and 5-hydroxy-1,4-naphthoquinone (5-H-1,4-NQ), and 8–13% using PM2.5 samples) was obtained for SAMERA. The results generated from SAMERA were in good agreement with those obtained from the manual operation using both positive controls (slope = 0.95–1.15 for automated vs. manual, R2 = 0.99) and ambient samples (slope = 0.89–1.09, R2 = 0.86–0.97). SAMERA takes 3 h to analyze one sample for all these OP endpoints, which is a substantial improvement over the manual analysis protocol. SAMERA was employed to analyze a subset (N = 44) of ambient PM2.5 samples collected from the Midwest US. Elevated OP activities in the week of Independence Day (3–5 July, 2018) were observed for most endpoints measured by SAMERA at all the sites. Preliminary results demonstrate the stability and capability of SAMERA for providing a comprehensive OP dataset, which can be integrated into the epidemiological models in future studies.

Copyright © 2019 American Association for Aerosol Research

Additional information

Funding

This material is based upon work supported by the National Science Foundation under Grant No. CBET-1847237.

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