Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models

Seunghon Ham, Sunju Kim, Naroo Lee, Pilje Kim, Igchun Eom, Byoungcheun Lee, Perng Jy Tsai, Kiyoung Lee, Chungsik Yoon

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

Real-time monitoring is necessary for nanoparticle exposure assessment to characterize the exposure profile, but the data produced are autocorrelated. This study was conducted to compare three statistical methods used to analyze data, which constitute autocorrelated time series, and to investigate the effect of averaging time on the reduction of the autocorrelation using field data. First-order autoregressive (AR(1)) and autoregressive-integrated moving average (ARIMA) models are alternative methods that remove autocorrelation. The classical regression method was compared with AR(1) and ARIMA. Three data sets were used. Scanning mobility particle sizer data were used. We compared the results of regression, AR(1), and ARIMA with averaging times of 1, 5, and 10 min. AR(1) and ARIMA models had similar capacities to adjust autocorrelation of real-time data. Because of the non-stationary of real-time monitoring data, the ARIMA was more appropriate. When using the AR(1), transformation into stationary data was necessary. There was no difference with a longer averaging time. This study suggests that the ARIMA model could be used to process real-time monitoring data especially for non-stationary data, and averaging time setting is flexible depending on the data interval required to capture the effects of processes for occupational and environmental nano measurements.

原文English
頁(從 - 到)685-699
頁數15
期刊Journal of Applied Statistics
44
發行號4
DOIs
出版狀態Published - 2017 3月 12

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 統計、概率和不確定性

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