ABSTRACT
In this study, we performed a comprehensive assessment of the vertical CO2 concentration in the urban atmosphere using measurements at two different heights (113 m and 420 m) in Seoul, South Korea. The difference in CO2 concentration between the two altitudes (△CO2 = CO2 at 113 m minus CO2 at 420 m) showed a significant diurnal variation, with the highest at 07:00 (19.9 ppm) and the lowest at 16:00 (3.9 ppm). When the planetary boundary layer (PBL) rose above the two sites (daytime), the CO2 concentrations at the two altitudes were highly correlated (r = 0.87) with low △CO2. In contrast, when the PBL was located between the two sites (night time), the correlation coefficient of the CO2 concentration between the two altitudes decreased by 0.55 with a high △CO2. To explain the cause of this variation in △CO2 according to PBL, we performed Weather Research and Forecasting-stochastic time-inverted Lagrangian transport (WRF-STILT) simulations. Simulations showed that CO2 measurements at two different heights were influenced by the same nearby urban areas during the daytime. However, the site above the PBL only measured the CO2 of air transported from the outside downtown area during the night time. Consequently, the observed night time △CO2 is explained by the difference in air mass between the two measurements owing to PBL variations. The night time △CO2 further implicates the local attribution of observed CO2 below the PBL by removing the effect from the remote area. Because of this unique night time characteristic of △CO2, we evaluated the changes in CO2 concentration in Seoul during the COVID-19 period. Compared to the pre-COVID-19 period, △CO2 clearly decreased from 26.5 ppm to 6.2 ppm with the implementation of social distancing, thus confirming the decreasing local influence of CO2 concentrations. Our findings highlight the potential of atmospheric CO2 monitoring at high altitudes as an observation-based method to assess the effectiveness of local carbon management. © 2022 Elsevier B.V.
ABSTRACT
BACKGROUND: During the initial phase of the global COVID-19 outbreak, most countries responded with non-pharmaceutical interventions (NPIs). In this study we investigate the general effectiveness of these NPIs, how long different NPIs need to be in place to take effect, and how long they should be in place for their maximum effect to unfold. METHODS: We used global data and a non-parametric machine learning model to estimate the effects of NPIs in relation to how long they have been in place. We applied a random forest model and used accumulated local effect (ALE) plots to derive estimates of the effectiveness of single NPIs in relation to their implementation date. In addition, we used bootstrap samples to investigate the variability in these ALE plots. RESULTS: Our results show that closure and regulation of schools was the most important NPI, associated with a pronounced effect about 10 days after implementation. Restrictions of mass gatherings and restrictions and regulations of businesses were found to have a more gradual effect, and social distancing was associated with a delayed effect starting about 18 days after implementation. CONCLUSIONS: Our results can inform political decisions regarding the choice of NPIs and how long they need to be in place to take effect.