Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO₂ and PM_2.5 over the...

Unprecedented travel restrictions due to the COVID-19 pandemic caused remarkable reductions in anthropogenic emissions, however, the Beijing area still experienced extreme haze pollution even under the strict COVID-19 controls. Generalized Additive Models (GAM) were developed with respect to inter-annual variations, seasonal cycles, holiday effects, diurnal profile, and the non-linear influences of meteorological factors to quantitatively differentiate the lockdown effects and meteorology impacts on concentrations of nitrogen dioxide (NO₂) and fine particulate matters (PM_2.5) at 34 sites in the Beijing area. The results revealed that lockdown measures caused large reductions while meteorology offset a large fraction of the decrease in surface concentrations. GAM estimates showed that in February, the control measures led to average NO₂ reductions of 19 μg/m³ and average PM_2.5 reductions of 12 μg/m³. At the same time, meteorology was estimated to contribute about 12 μg/m³ increase in NO₂, thereby offsetting most of the reductions as well as an increase of 30 μg/m³ in PM_2.5, thereby resulting in concentrations higher than the average PM_2.5 concentrations during the lockdown. At the beginning of the lockdown period, the boundary layer height was the dominant factor contributing to a 17% increase in NO₂ while humid condition was the dominant factor for PM_2.5 concentrations leading to an increase of 65% relative to the baseline level. Estimated NO₂ emissions declined by 42% at the start of the lockdown, after which the emissions gradually increased with the increase of traffic volumes. The diurnal patterns from the models showed that the peak of vehicular traffic occurred from about 12pm to 5pm daily during the strictest control periods. This study provides insights for quantifying the changes in air quality due to the lockdowns by accounting for meteorological variability and providing a reference in evaluating the effectiveness of control measures, thereby contributing to air quality mitigation policies.