Reductions in Black Carbon and Ultrafine Particle Concentrations in an Urban Area Near Boston (MA, USA) During the COVID-19 Pandemic

Abstract:

To quantify the effects from changes in traffic volume and composition during the COVID-19 pandemic on neighborhood-scale air pollution, we measured traffic-related black carbon (BC) and particle number concentrations (PNC; a proxy for ultrafine particles, defined as <100 nanometers diameter) in Somerville, Massachusetts (USA) before and during the pandemic. Investigations of air quality impacts during COVID-19 lockdown at regional and national scales (100-1000s of km²), particularly with regulated pollutants, have been widely reported, but investigation of air pollution changes at neighborhood scales (<1 km²) has been lacking. Measurements were recorded at 1/sec (PNC) and 1/min (BC) sampling rates using rapid-response instruments housed in a mobile laboratory. The laboratory was driven repeatedly on fixed routes that included highways and major and minor roadways, between 0500-2200 hours. We compared air quality and traffic measurements during the COVID-19 lockdown (27 March – 14 May 2020) to historical (spring 2012 and 2013, fall 2018, and winter 2019) on-road measurements in the same study area. Median PNC and BC were reduced by 60-69% and 22-46%, respectively, during the lockdown compared to 2018-2019 measurements across all road types, coinciding with a 71% decrease in passenger-car and a 46% decrease in heavy-duty-vehicle traffic. Controlling for changes in background concentrations (daily to seasonal), we found that the contribution of local traffic to median PNC and BC decreased by 45-69% and 22-56%, respectively. Interestingly, the BC-to-PNC ratio increased from the pre-pandemic period to the lockdown, likely due to the higher fraction of diesel engines in the fleet operating during the lockdown. These findings provide a lens by which we can better estimate the effects of possible future changes to the transportation sector (e.g., shifts in mode or traffic composition) on local air pollution, as opposed to relying on sparse regulatory monitors or model predictions. Our results underscore the importance of in-situ data at scales relevant to the temporal and spatial variability of the pollutants being measured and could be useful to inform air-quality management for urban localities.