Performance evaluation of air quality sensors for environmental epidemiology

Abstract. Over the past few decades, the study and the use of air quality sensors have significantly increased, leading to a wealth of experience and a deeper understanding of their strengths and limitations. This study aimed to transcend the limitations by developing and evaluating a methodology for PM_2.5 and NO₂ sensors to enhance sensor accuracy to a level suitable for epidemiological studies, where ensuring data quality is paramount. The performance evaluation of indoor and outdoor sensors was carried out during the co-location phase with reference instruments (RIs), by calculating common error metrics, target diagrams and the relative expanded uncertainties (REUs) stated in the EU Air Quality Directive 2008/50/EC and the recently published EU Directive 2024/2881, before the deployment of the air quality sensor systems (AQSSs) in the houses of patients suffering from chronic obstructive pulmonary disease (COPD) or asthma in Stuttgart (Germany). Regression and machine learning models for sensor calibration were tested during the co-location. Moreover, an original methodology was designed and evaluated to validate the sensor data during the epidemiological study. The study found that indoor sensor calibration using artificially generated NO₂ and aerosols does not ensure model transferability, emphasizing the need for training data that matches the intended deployment environment in terms of real patterns of concentration, particle composition and environmental conditions. Integrating metadata such as activity logs, window status, and data from official monitoring stations, proved essential for data validation and interpretation during the sensor deployment in the houses of the participants. Despite limitations at low pollutant levels, calibrated AQSSs are a promising tool to increase the ubiquity of epidemiological studies for low- and middle-income countries or regions where higher air pollutant concentrations are expected.