ABSTRACT
Particulate matter (PM) is a major air pollutant in Northeast Asia, with frequent high PM episodes. To investigate the nationwide spatial distribution maps of PM2.5 and secondary inorganic aerosols in South Korea, prediction models for mapping SO42- and NO3- concentrations in PM2.5 were developed using machine learning with ground-based observation data. Specifically, the random forest algorithm was used in this study to predict the SO42- and NO3- concentrations at 548 air quality monitoring stations located within the representative radii of eight intensive air quality monitoring stations. The average concentrations of PM2.5, SO42-, and NO3- across the entire nation were 17.2 ± 2.8, 3.0 ± 0.6, and 3.4 ± 1.2 µg/m3, respectively. The spatial distributions of SO42- and NO3- concentrations in 2021 revealed elevated concentrations in both the western and central regions of South Korea. This result suggests that SO42- concentrations were primarily influenced by industrial activities rather than vehicle emissions, whereas NO3- concentrations were more associated with vehicle emissions. During a high PM2.5 event (November 19-21, 2021), the concentration of SO42- was primarily influenced by SOX emissions from China, while the concentration of NO3- was affected by NOX emissions from both China and Korea. The methodology developed in this study can be used to explore the chemical characteristics of PM2.5 with high spatiotemporal resolution. It can also provide valuable insights for the nationwide mitigation of secondary PM2.5 pollution.
ABSTRACT
In the context of air quality research, the collection and analysis of fine particulate matter (PM2.5, with a diameter less than 2.5 µm) and volatile organic compound (VOCs) play a pivotal role in understanding and addressing environmental issues across the Korean Peninsula. PM2.5 and VOCs were collected over 4-hr intervals from October 17 to November 26, 2021 during the 2021 Satellite Integrated Joint Monitoring of Air Quality campaign at Olympic Park in the Republic of Korea to understand the factors controlling air quality over the Seoul Metropolitan Area. Source apportionment was performed using the positive matrix factorization (PMF) model incorporating PM2.5 and VOCs. The factor identified by chlorinated VOCs as a major component was presumed to be due to transboundary influx and was referred to as the long-range transport factor. The long-range transport factor of PM2.5 was composed of NO3-, SO42-, NH4+, and di-carboxylic acids. Back trajectory analysis showed that the airflows originated from China and passed through the west coast of Korea to the Korean Peninsula. In the PMF results using PM2.5 and VOCs, long-range transport factors were identified in both analyses, and the high correlation observed between these factors confirms that they were transported from abroad. The dithiothreitol oxidation potential normalized to quinine showed the highest oxidation potential during the same period as the long-range transport factors increased. In conclusion, PM2.5 from external sources significantly contribute to elevated levels of dithiothreitol assay-oxidative potential (DTT-OP) in Korea. The toxic concentration, expressed as the mean ± standard deviation, was determined to be 0.29 ± 0.05 µM/m³, peaking at 0.39 µM/m³. This level is 1.8 times higher than that observed outside the event period. A notable increase in secondary pollutants was observed during these periods. These pollutants are known to enhance oxidative potential, thereby potentially impacting human health.
