Abstract View
Identifying Patterns and Sources of Urban Ultrafine Particulate Matter Using Mobile Measurements of Lung-Deposited Surface Area
RISHABH SHAH, Lauren Padilla, Daniel Peters, Megan Dupuy-Todd, Elizabeth Fonseca, Geoff Ma, Rod Jones, Jim Mills, Nick Martin, Ramon Alvarez, Environmental Defense Fund
Abstract Number: 255
Working Group: Urban Aerosols
Abstract
Scientific literature increasingly suggests that among PM2.5 components, health effects of ultrafine particles (UFP; diameter < 100 nm) are concerning because of deeper penetration in the respiratory tract and blood-borne translocation to vital organs. Further, UFP surface area has important implications for adsorptive chemistry in the lung tissue.
Within 500 m of sources (e.g., highways), UFP concentrations decline rapidly due to dilution and coagulation. These sharp spatial gradients are difficult to detect with PM2.5 mass measurements due to the small mass contribution from UFP and, in case of optical sensors, size cut-off. However, number and surface area concentrations within the UFP size range can capture near-source gradients and aid in identifying hyperlocal UFP sources.
We present mobile, high spatio-temporal resolution measurements of lung-deposited surface area (LDSA) concentrations with a medium-cost aerosol dosimeter (Naneos Partector, measuring surface area of 20-400 nm particles via diffusion charging). We equipped two Google Street View vehicles in London to perform 1 Hz measurements of LDSA, black carbon (BC), optical PM2.5 mass, and NO2 from September 2018 to October 2019. Our findings show that variations in mobile time series of PM2.5 mass in London are small (3-5× background; often confounded with temporal variability), compared to those in LDSA and BC (10-20×), which we associate with sources e.g., higher concentrations on high-volume roadways during morning and afternoon traffic. Further, we examine the applicability of ratios of LDSA to BC, NO2, and CO2 to distinguish source types (e.g., low LDSA:BC may indicate diesel exhaust, while high LDSA:BC may indicate other sources such as gasoline exhaust and/or cooking). Our rich dataset enables us to perform high spatio-temporal resolution mapping of LDSA, assessing patterns that may go unnoticed with PM2.5 mass measured by optical particle counters.