Exploring the driving factors of haze events in Beijing during Chinese New Year holidays in 2020 and 2021 under the influence of COVID-19 pandemic.

Unexpected outbreak of the 2019 novel coronavirus (COVID-19) has profoundly altered the way of human life and production activity, which posed visible impacts on PM2.5 and its chemical species. The abruptly emergency reduction in human activities provided an opportunity to explore the synergetic impacts of multi-factors on shaping PM2.5 pollution. Here, we conducted two comprehensive observation measurements of PM2.5 and its chemical species from 1 January to 16 February in Beijing 2020 and the same lunar date in 2021, to investigate temporal variations and reveal the driving factors of haze before and after Chinese New Year (CNY). Results show that mean PM2.5 concentrations during the whole observation were 63.83 and 66.86 µg/m3 in 2020 and 2021, respectively. Higher secondary inorganic species were observed after CNY, and K+, Cl- showed three prominent peaks which associated closely with fireworks burnings from suburb Beijing and surroundings, verifying that they could be used as two representative tracers of fireworks. Further, we explored the impacts of meteorological conditions, regional transportation as well as chemical reactions on PM2.5. We found that unfavorable meteorological conditions accounted for 11.0 % and 16.9 % of PM2.5 during CNY holidays in 2020 and 2021, respectively. Regional transport from southwest and southeast (south) played an important role on PM2.5 during the two observation periods. Higher ratio of NO3-/SO42- were observed under high OX and low RH conditions, suggesting the major pathway of NO3- and SO42- formation could be photochemical process and aqueous-phase reaction. Additionally, nocturnal chemistry facilitated the formation of secondary components of both inorganic and organic. This study promotes understandings of PM2.5 pollution in winter under the influence of COVID-19 pandemic and provides a well reference for haze and PM2.5 control in future.

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning.

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil-Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015-2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO2, PM2.5, and O3 shown variety change trend, and at -30.8 %, -27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

Meteorology-normalized variations of air quality during the COVID-19 lockdown in three Chinese megacities.

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in air quality. Here, we applied a machine learning algorithm (random forest model) to eliminate meteorological effects and characterize the high-resolution variation characteristics of air quality induced by COVID-19 in Beijing, Wuhan, and Urumqi. Our RF model estimates showed that the highest decrease in deweathered PM2.5 in Wuhan (-43.6%) and Beijing (-14.0%) was at traffic stations during lockdown period (February 1- March 15, 2020), while it was at industry stations in Urumqi (-54.2%). Deweathered NO2 decreased significantly in each city (∼30%-50%), whereas accompanied by a notable increase in O3. The diurnal patterns show that the morning peaks of traffic-related NO2 and CO almost disappeared. Additionally, our results suggested that meteorological effects offset some of the reduction in pollutant concentrations. Adverse meteorological conditions played a leading role in the variation in PM2.5 concentration in Beijing, which contributed to +33.5%. The true effect of lockdown reduced the PM2.5 concentrations in Wuhan, Beijing, and Urumqi by approximately 14.6%, 17.0%, and 34.0%, respectively. In summary, lockdown is the most important driver of the decline in pollutant concentrations, but the reduction of SO2 and CO is limited and they are mainly influenced by changing trends. This study provides insights into quantifying variations in air quality due to the lockdown by considering meteorological variability, which varies greatly from city to city, and provides a reference for changes in city scale pollutant concentrations during the lockdown.