Source Apportionment of PM2.5 using Dispersion Normalized Positive Matrix Factorization (DN-PMF) at Beijing and Baoding in China

ILHAN RYOO, Jieun Park, Taeyeon Kim, Jiwon Ryu, Yeonseung Cheong, Hyewon Park, Kwon Ho Jeon, Jae-Hyun Lim, Seung-Muk Yi, Sang-Rin Lee, Seoul National University

     Abstract Number: 252
     Working Group: Source Apportionment

Abstract
PM2.5 refers to particulate matter with an aerodynamic diameter of 2.5 µm or less, and is an air pollutant designated as a carcinogen by the WHO in 2013. Recently, economy in China has been rapidly growing with expanding industries which led to the poor air quality, especially the PM2.5 mass concentrations.

In China, the Action Plan for Air Pollution Control was implemented from 2013 to 2017 to reduce air pollutants and achieved its goal. However, PM2.5 high concentrations events are still occurring. Simultaneous ground-based monitoring of PM2.5 in multiple cities in Northeastern China is needed in order to confirm air quality after air pollution reduction efforts. Therefore, in this study, we conducted PM2.5 sampling in Beijing and Baoding for source apportionment and to derive potential contaminated areas.

Sampling was conducted using a three-channel low-volume air sampler in Beijing (2017.06.21 ~ 2019.12.17) and Baoding (2018.04.09 ~ 2019.08.24).

The samples were analyzed for trace elements (21 species, Al, Si, S, Cl, etc.), carbonaceous species (Organic Carbon, Elemental Carbon), and ionic species (6 species, SO42-, NO3-, etc.). PM2.5 mass concentration and its chemical composition were applied to DN-PMF (Dispersion Normalized Positive Matrix Factorization) for source apportionment. DN-PMF calibrate the weather effect with the ventilation coefficient calculated by Mixing Layer Height by the wind speed. Subsequently, directional analysis of PM2.5 sources was performed by CBPF (Conditional Bivariate Probability Function). And a back trajectory analysis was performed using the HYSPLIT4 (Hybrid Single-Particle Lagrangian Integrated Trajectory 4), and the PSCF (Potential Source Contribution Function) was performed to analyze potential pollutant regions using back trajectory results. Furthermore, the Joint-PSCF was carried out by combining the results of the PSCF to create a commonly contaminated area in the two cities.

Our results can be used as basic data to prepare future PM2.5 reduction measures and management strategies.