ABSTRACT
Hanoi, Vietnam, is usually ranked as one of the most polluted capital cities in terms of air quality, particularly PM2.5. However, there has not been enough data to determine the main source of this pollution. In this study, we utilized the rare opportunity of the COVID-19 social distancing to assess the contribution of traffic emission to PM2.5 and CO levels when traffic volume was reduced significantly in Hanoi. Hourly PM2.5 and CO concentrations were measured from nine urban and traffic monitoring stations during pre-, soft, hard, and post-social distancing periods. As a result, we observed large reductions in both PM2.5 and CO levels during social distancing periods. PM2.5 concentrations were 14–18% lower during the social distancing than before this period, while CO concentrations had a more considerable drop by 28–41%. It is known that meteorological conditions can have significant effects on the ambient levels of air pollutants. To overcome this challenge, weather normalized concentrations of those pollutants were estimated using the random forest model, a machine learning technique. The normalized weather concentrations showed smaller reductions by 7–10% for PM2.5 and 5–11% for CO, indicating the presence of favorable weather conditions for better air quality during the social distancing period. In further analysis, the apparent improvement of air quality in Hanoi during the social distancing period was in line with reducing traffic emissions while emissions from coal-fired power plants remained relatively stable.