Identification of redundant air quality measurements through the use of principal component analysis |
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| Authors: | JCM Pires MC Pereira MCM Alvim-Ferraz FG Martins |
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| Institution: | 1. Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan, ROC;2. Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City, 25137, Taiwan, ROC;1. School of Energy and Power Engineering, Jiangsu University, PO Box 28, Zhenjiang, Jiangsu, 212013, China;2. School of Engineering- Mechanical &Automotive Engineering, RMIT University, PO Box 71, Bundoora, VIC, 3083, Australia;1. School of Economics and Management, Shaanxi University of Science and Technology, Xi''an, 710021, China;2. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, 1954 Huashan Rd., Shanghai, 200030, China;3. Antai College of Economics and Management, Shanghai Jiao Tong University, 1954 Huashan Rd., Shanghai, 200030, China;4. China Institute of Regulation Research, Zhejiang University of Finance and Economics, Hangzhou, 310018, China |
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| Abstract: | This study aims to show how principal component analysis (PCA) can be used to identify redundant measurements in air quality monitoring networks. The minimum number of air quality monitoring sites in Oporto Metropolitan Area (Oporto-MA) was evaluated using PCA and then compared to the one settled by the legislation. Nine sites, monitoring NO2, O3 and PM10, were selected and the air pollutant concentrations were analysed from January 2003 to December 2005. PCA was applied to the data corresponding to the first two years that were divided into annual quarters to verify the persistence of the PCA results. The number of principal components (PCs) was selected by applying two criteria: Kaiser (PCs with eigenvalues greater than 1) and ODV90 (PCs representing at least 90% of the original data variance). Each pollutant was analysed separately. The two criteria led to different results. Using Kaiser criterion for the eight analysed periods, two PCs were selected in: (i) five periods for O3 and PM10; and (ii) six periods for NO2. These PCs had important contributions of the same groups of monitoring sites. The percentage of the original data variance contained in the selected PCs using this criterion was always below 90%. Thus, the results obtained using ODV90 were considered with more confidence. Using this criterion, only five monitoring sites for NO2, three for O3 and seven for PM10 were needed to characterize the region. The number of monitoring sites for NO2 and O3 was in agreement with what was established by the legislation. However, for PM10, Oporto-MA needed two more monitoring sites. To validate PCA results, statistical models were determined to estimate air pollutant concentrations at removed monitoring sites using the concentrations measured at the remaining monitoring sites. These models were applied to a year's data. The good performance obtained by the models showed that the monitoring sites selected by the procedure presented in this study were enough to infer the air pollutant concentrations in the region defined by the initial monitoring sites. Additionally, the air pollutant analysers corresponding to the redundant measurements can be installed in non-monitored regions, allowing the enlargement of the air quality monitoring network. |
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