The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data | Zendy

Yadpirun Supharakonsakun | Zendy; Yupaporn Areepong | Zendy; Saowanit Sukparungsee | Zendy

Open Access

The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data

Author(s) -

Yadpirun Supharakonsakun,

Yupaporn Areepong,

Saowanit Sukparungsee

Publication year - 2020

Publication title -

peerj

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.927

H-Index - 70

ISSN - 2167-8359

DOI - 10.7717/peerj.10467

Subject(s) - ewma chart , control chart , environmental science , chart , statistics , carbon monoxide , mathematics , meteorology , computer science , chemistry , process (computing) , geography , biochemistry , catalysis , operating system

PM2.5 (particulate matter less than or equal to 2.5 micron) is found in the air and comprises dust, dirt, soot, smoke, and liquid droplets. PM2.5 and carbon monoxide emissions can have a negative impact on humans and animals throughout the world. In this paper, we present the performance of a modified exponentially weighted moving average (modified EWMA) control chart to detect small changes when the observations are autocorrelated with exponential white noise through the average run length evaluated (ARLs) by explicit formulas. The accuracy of the solution was verified with a numerical integral equation method. The efficacy of the modified EWMA control chart to monitor PM2.5 and carbon monoxide air pollution data and compare its performance with the standard EWMA control chart. The results suggest that the modified EWMA control chart is far superior to the standard one.

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