Supharakonsakun Yadpirun, Areepong Yupaporn, Sukparungsee Saowanit
Department of Applied Statistics, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
PeerJ. 2020 Dec 15;8:e10467. doi: 10.7717/peerj.10467. eCollection 2020.
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.
细颗粒物（PM2.5，即直径小于或等于2.5微米的颗粒物）存在于空气中，包括灰尘、污垢、煤烟、烟雾和液滴。PM2.5和一氧化碳排放会对全球人类和动物产生负面影响。在本文中，我们通过显式公式评估的平均运行长度（ARLs），展示了一种改进的指数加权移动平均（modified EWMA）控制图在观测值与指数白噪声自相关时检测微小变化的性能。用数值积分方程法验证了该解决方案的准确性。研究了改进的EWMA控制图监测PM2.5和一氧化碳空气污染数据的功效，并将其性能与标准EWMA控制图进行比较。结果表明，改进的EWMA控制图远优于标准控制图。
