  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.
