Cite this article: Liang, C.S., Yu, T.Y. and Lin, W.Y. (2015). Source Apportionment of Submicron Particle Size Distribution and PM2.5 Composition during an Asian Dust Storm Period in Two Urban Atmospheres.
Aerosol Air Qual. Res.
15: 2609-2624. https://doi.org/10.4209/aaqr.2015.08.0505
By using the PCA, the potential sources can be successful identified.
The nucleation mode arrived Southern Taiwan 12 hours ahead of ADS.
The nucleation component could be used as predictors of arrival time for an ADS.
Asian dust storms (ADS), coming from deserts of China and Mongolia, have serious environmental impact on particulate matter (PM) and other pollutants in Taiwan. This study selected two urban sites, Taipei and Kaohsiung, to evaluate the influence of ADS on air quality. During the ADS periods, the hourly PM10 mass concentrations were 800 µg m–3 in Taipei and the 400 µg m–3 in Kaohsiung, which was three to five times higher than PM episodes during the non-ADS periods. By using the principal component analysis (PCA) manner, the potential sources, the dust storm contained, can be successfully identified during ADS periods. The other potential sources can be identified as vehicular emission and secondary organic aerosols from local area. There have been many studies conducted on the impact of ADS on airborne coarse particle concentration, but very few on fine particle concentration. This study focused on, using PCA for analysis and discussion, the impact of ADS on submicron particle size distribution. The results showed that there was no close relationship between the ADS and Aitken mode (30–100 nm or D30–100nm) or accumulation mode (from 0.1–1 µm). However, it was found that strong correlation existed between the ADS and nucleation mode (10–30 nm or D10–30nm). In addition, it was found that nucleation mode appeared first, followed by an air plume of dust particles twelve (12) hours later. The nucleation component from the PCA could be used as predictors of arrival time for an ADS. Taking into account the effects of meteorological conditions and employing technique of backward trajectories, PCA can be utilized as a powerful tool to better identify the source of dust storms and provide accurate results.
Keywords: Dust storm; Principal component analysis; Aerosol size distribution