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

Bias detection of $PM_{2.5}$ monitor readings using hidden dynamic geostatistical calibration model