Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO2 and PM2.5 over the Beijing area using Generalized Additive Models (GAM).

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 (PM2.5) 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 PM2.5 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 PM2.5, thereby resulting in concentrations higher than the average PM2.5 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 PM2.5 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.

Source attribution of air pollution using a generalized additive model and particle trajectory clusters.

Speciated hourly measurements of fine aerosols were made for more than two years at an urban, an industrial and a port site in Busan, Korea. A Generalized Additive Model (GAM) was designed to deconvolve factors contributing to the pollutant concentrations at multiple scales. The model yields estimates of source contributions to pollution by separately identifying the signals in the time series due to meteorology, vertical mixing, horizontal wind transport and temporal variations such as diurnal, weekly, seasonal and annual trends. The GAM model was expanded to include FLEXPART back trajectory clusters generated using fuzzy c-means clustering. This made it possible to quantify the impact of long-range transport using the Trajectory Cluster Contribution Function (TCCF). TCCF provides a development of methods such as Concentration Field Analysis and Potential Source Contribution Function by providing numerical estimates of concentration changes associated with different air mass transport patterns while accounting for possible confounding factors from meteorology. The GAM simulations identified the importance of local transport for primary pollutants and long-range transport from China for secondary pollutants. Local factors accounted for up to 72% of the variance in concentrations of NO2 and elemental carbon whereas large-scale/seasonal factors accounted for up to 56% of PM2.5 and 80% of inorganic species. The algorithm further identified the importance of the weekend effect and the holiday effect at the different sites in Busan. The residual from the analysis was used to estimate the impact of the COVID-19 pandemic. The signature of the pandemic was different between the pollutants as well as from site to site. The model was able to distinguish small impacts from local pollutants at the residential site; short-lived acute impacts from industrial changes; and longer-term changes due to the early pandemic response in China.