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

## TL;DR

This paper introduces a hidden dynamic geostatistical calibration model to automatically detect biased $PM_{2.5}$ monitor readings, improving air quality data reliability for better policy decisions.

## Contribution

The paper proposes a novel hierarchical model with an EM algorithm for bias detection in air quality monitoring stations, demonstrated through real-world data application.

## Key findings

- Detected abnormal readings in two cities' stations.
- Validated model effectiveness with numerical study.
- Improved bias detection accuracy in air quality data.

## Abstract

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.