Ho-Wen Chen , Wei-Yea Chen, Cheng-Nan Chang, Yen Hsun Chuang

  • Department of Environmental Science and Engineering, Tunghai University, No. 181, Sec. 3, Taichung Port Rd., Xitun Dist., Taichung City 407, Taiwan

Received: June 21, 2012
Revised: October 28, 2012
Accepted: October 28, 2012
Download Citation: ||https://doi.org/10.4209/aaqr.2012.06.0155 

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Cite this article:
Chen, H.W., Chen, W.Y., Chang, C.N. and Chuang, Y.H. (2013). Characterization of Particles in the Ambience of the High-Tech Industrial Park of Central Taiwan. Aerosol Air Qual. Res. 13: 699-708. https://doi.org/10.4209/aaqr.2012.06.0155


 

ABSTRACT


Medical scholars have confirmed that prolonged exposure to high concentrations of suspended particles may result in respiratory and heart diseases. To identify the characteristics of particles around high-tech industrial parks, engaged in the manufacturing of semiconductors, electronics and electrical peripherals, this study performed a two-year monitoring program to measure the heavy metal composition in airborne particles in order to find the potential sources of pollution as well as the causal relationships between meteorological conditions and pollutant concentrations. Due to the complexity arising from the highly inter-related meteorological and topographical factors in local and regional wind fields, a comprehensive data-mining algorithm based on clustering analysis is proposed to explore the useful information obtained from the chaotic monitoring data sets, as well as to profile the pollution sources. Hierarchical cluster analysis confirms that the concentration of arsenic in fine particles significantly increased at the end of 2010, and increased in conditions of high relative humidity (50%–80%). During the periods with high wind speed, the amount of arsenic in fine particles increases and comes from the northeast. In contrast, dispersion controls the regional air quality at low wind speeds, and during such times the arsenic comes from the northwest.


Keywords: High-tech industrial park; PM10 and PM2.5; Meteorological factor; Data mining


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