ABSTRACT
Traffic related non-tailpipe particulate matter emissions can rival the continuously decreasing tailpipe emissions in modern fleets. Non-tailpipe emissions have become the dominating source of traffic emissions in California already. This study measured ambient PM2.5 and PM10 concentrations at near road environments for two major highways in California, I-5 in Anaheim and I-710 in Long Beach. A total of 51 elements were measured from filter samples collected over four-hour intervals for a two-week period in the winter of 2020 before the statewide lockdown by the COVID-19 pandemic. Iron was the most abundant element in ΔPM10 (differences between downwind and upwind sites), contributing to 30 % and 24 % of total measured elements in ΔPM10 at the I-5 and I-710 locations, respectively. Iron correlated highly with other brake wear markers (e.g., titanium, copper, barium, manganese, and zirconium) with coefficient of determination (r2) ranging from 0.67 to 0.90 in both PM2.5 and PM10. Silicon was the second most abundant element, contributing to 21 % of total measured elements in ΔPM2.5 and ΔPM10. Silicon showed strong correlations with crustal elements such as calcium (r2 = 0.90), aluminum (r2 = 0.96), and potassium (r2 = 0.72) in ΔPM2.5, and the correlations were even higher in ΔPM10. Barium had a weak correlation with zinc, a commonly used maker for tire wear, with r2 = 0.63 and r2 = 0.11 for ΔPM10 at the I-5 and I-710 locations respectively. Barium showed a positive correlation with crosswind speed and could serve as a good brake wear PM marker. Hourly PM2.5 concentrations of iron and zinc showed cyclic peaks from 0800 to 1000 h at I-5 during weekdays. Particle mass distributions showed peaks near ~7 µm, while particle number distributions showed peaks near 2.1 µm and 6.5 µm, respectively. This is consistent with brake wear and road dust size ranges previously reported.
ABSTRACT
Rationale: Evidence linking outdoor air pollution with coronavirus disease (COVID-19) incidence and mortality is largely based on ecological comparisons between regions that may differ in factors such as access to testing and control measures that may not be independent of air pollution concentrations. Moreover, studies have yet to focus on key mechanisms of air pollution toxicity such as oxidative stress. Objectives: To conduct a within-city analysis of spatial variations in COVID-19 incidence and the estimated generation of reactive oxygen species (ROS) in lung lining fluid attributable to fine particulate matter (particulate matter with an aerodynamic diameter ⩽2.5 µm [PM2.5]). Methods: Sporadic and outbreak-related COVID-19 case counts, testing data, population data, and sociodemographic data for 140 neighborhoods were obtained from the City of Toronto. ROS estimates were based on a mathematical model of ROS generation in lung lining fluid in response to iron and copper in PM2.5. Spatial variations in long-term average ROS were predicted using a land-use regression model derived from measurements of iron and copper in PM2.5. Data were analyzed using negative binomial regression models adjusting for covariates identified using a directed acyclic graph and accounting for spatial autocorrelation. Measurements and Main Results: A significant positive association was observed between neighborhood-level ROS and COVID-19 incidence (incidence rate ratio = 1.07; 95% confidence interval, 1.01-1.15 per interquartile range ROS). Effect modification by neighborhood-level measures of racialized group membership and socioeconomic status was also identified. Conclusions: Examination of neighborhood characteristics associated with COVID-19 incidence can identify inequalities and generate hypotheses for future studies.