Bias detection of PM_(2.5) monitor readings using hidden dynamic geostatistical calibration model

Accurate and reliable data stream plays an important role in air quality assessment. Air pollution data collected from monitoring networks, however, could be biased due to instrumental error or other interventions, which covers up the real pollution status and misleads policy making. In this study, motivated by the needs for objective bias detection, we propose a hidden dynamic geostatistical calibration (HDGC) model to automatically identify monitoring stations with constantly biased readings. The HDGC model is a two-level hierarchical model whose parameters are estimated through an efficient Expectation-Maximization algorithm. Effectiveness of the proposed model is demonstrated by a simple numerical study. Our method is applied to hourly $PM_{2.5}$ data from 36 stations in Hebei province, China, over the period from March 2014 to February 2017. Significantly abnormal readings are detected from stations in two cities.