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Personal Exposure to PM₂.₅ Black Carbon and Aerosol Oxidative Potential using an Automated Microenvironmental Aerosol Sampler (AMAS)
- Quinn, Casey, Miller-Lionberg, Daniel D., Klunder, Kevin J., Kwon, Jaymin, Noth, Elizabeth M., Mehaffy, John, Leith, David, Magzamen, Sheryl, Hammond, S. Katharine, Henry, Charles S., Volckens, John
- Environmental science & technology 2018 v.52 no.19 pp. 11267-11275
- aerosols, algorithms, automation, carbon, dithiothreitol, filters, global positioning systems, particulates, risk, California
- Traditional methods for measuring personal exposure to fine particulate matter (PM₂.₅) are cumbersome and lack spatiotemporal resolution; methods that are time-resolved are limited to a single species/component of PM. To address these limitations, we developed an automated microenvironmental aerosol sampler (AMAS), capable of resolving personal exposure by microenvironment. The AMAS is a wearable device that uses a GPS sensor algorithm in conjunction with a custom valve manifold to sample PM₂.₅ onto distinct filter channels to evaluate home, school, and other (e.g., outdoors, in transit, etc.) exposures. Pilot testing was conducted in Fresno, CA where 25 high-school participants (n = 37 sampling events) wore an AMAS for 48-h periods in November 2016. Data from 20 (54%) of the 48-h samples collected by participants were deemed valid and the filters were analyzed for PM₂.₅ black carbon (BC) using light transmissometry and aerosol oxidative potential (OP) using the dithiothreitol (DTT) assay. The amount of inhaled PM₂.₅ was calculated for each microenvironment to evaluate the health risks associated with exposure. On average, the estimated amount of inhaled PM₂.₅ BC (μg day–¹) and OP [(μM min–¹) day–¹] was greatest at home, owing to the proportion of time spent within that microenvironment. Validation of the AMAS demonstrated good relative precision (8.7% among collocated instruments) and a mean absolute error of 22% for BC and 33% for OP when compared to a traditional personal sampling instrument. This work demonstrates the feasibility of new technology designed to quantify personal exposure to PM₂.₅ species within distinct microenvironments.