Seoul, the most populous city in South Korea, has been practicing social distancing to slow down the spread of coronavirus disease 2019 (COVID-19). Fine particulate matter (PM_2.5) and other air pollutants measured in Seoul over the two 30 day periods before and after the start of social distancing are analyzed to assess the change in air quality during the period of social distancing. The 30 day mean PM_2.5 concentration decreased by 10.4% in 2020, which is contrasted with an average increase of 23.7% over the corresponding periods in the previous 5 years. The PM_2.5 concentration decrease was city-wide and more prominent during daytime than at nighttime. The concentrations of carbon monoxide (CO) and nitrogen dioxide (NO₂) decreased by 16.9% and 16.4%, respectively. These results show that social distancing, a weaker forcing toward reduced human activity than a strict lockdown, can help lower pollutant emissions. At the same time, synoptic conditions and the decrease in aerosol optical depth over the regions to the west of Seoul support that the change in Seoul’s air quality during the COVID-19 social distancing can be interpreted as having been affected by reductions in the long-range transport of air pollutants as well as local emission reductions.

Mobile monitoring and computational fluid dynamics (CFD) modeling are complementary methods to examine spatio-temporal variations of air pollutant concentrations at high resolutions in urban areas. We measured nitrogen oxides (NO_x), black carbon (BC), particle-bound polycyclic aromatic hydrocarbons (pPAH), and particle number (PN) concentrations in a central business district using a mobile laboratory. The analysis of correlations between the measured concentrations and traffic volumes demonstrate that high emitting vehicles (HEVs) are deterministically responsible for poor air quality in the street canyon. The determination coefficient (R²) with the HEV traffic volume is the largest for the pPAH concentration (0.79). The measured NO_x and pPAH concentrations at a signalized intersection are higher than those on a road between two intersections by 24% and 25%, respectively. The CFD modeling results reveal that the signalized intersection plays a role in increasing on-road concentrations due to accelerating and idling vehicles (i.e., emission process), but also plays a countervailing role in decreasing on-road concentrations due to lateral ventilation of emitted pollutants (i.e., dispersion process). It is suggested that the number of HEVs and street-canyon ventilation, especially near a signalized intersection, need to be controlled to mitigate poor air quality in a central business district of a megacity.

In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NO_x), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM_2.5) concentration below the apartment building height. Atmospheric NO_x dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NO_x concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods.