Characteristics, Secondary Formation and Regional Contributions of PM2.5 Pollution in Jinan during Winter

Variations in characteristics and transport pathways of PM2.5 during heavy pollution episodes in 2013-2019 in Jinan, a central city in the north China Plain

The characteristics and transport pathways of air masses vary during heavy pollution episodes (HPEs). Three categories of HPEs have been defined: HPE Ι, II, and III, corresponding to HPE durations of 1, 2, and at least 3 days, respectively. Sixty HPEs were investigated in this study. The number of HPEs decreased from 2013 to 2017 and then increased from 2017 to 2019, dominated by emission reductions and meteorological conditions. The average and maximum PM_2.5 (i.e., aerodynamic diameter of <2.5 μm) concentrations during those HPEs in 2019 decreased by 5.6%-11.8% and 11.9%-38.5%, respectively, compared with those in 2013. The longer the duration of an HPE, the higher the PM_2.5 concentration. Secondary inorganic aerosol concentrations and their contents in PM_2.5 during HPE Ⅲ were found to be higher than those during HPEs Ι and Ⅱ, as secondary transformations of precursor gases are more intense during long-term HPEs. The dominant trajectories of airflow arriving in Jinan originated from the southern and southeastern regions during HPEs, realized using the Hybrid Single Particle Lagrangian Integrated Trajectory. The trajectories from the north and west of Jinan contained the highest PM_2.5 concentrations of 323.3-432.1 μg/m³ during HPE Ⅲ, although these trajectories only contributed 5.6%-11.1% of the total dominant transport pathways, while those in trajectories from the northwest were highest during HPEs Ι and Ⅱ. The highest contributions of air masses from short distances were found during HPE Ⅲ, of 77.8%, while they were only 65.6% and 47.8% during HPEs Ι and II, respectively. More attention should be given to transport pathways within the short distance from Jinan. Therefore, enhancing regional cooperation in Jinan and surrounding regions (particularly in the south, southeast, northwest, west, and north) is critical for improving air quality in the North China Plain.

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model

Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions.