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This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer's calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most important problems in big cities, resulting in adverse health effects. The aim of the present study was to characterize the personal exposure to indoor and outdoor pollution in the Greater Athens Area in Greece by taking measurements during a journey from suburban to mixed industrial–urban areas, encompassing walking, waiting, bus travel, and metro travel at various depths. For this reason, low-cost (LC) sensors were used, and the inhaled dose of particulate matter with an aerodynamic diameter of less than or equal to 2.5 μm (PM2.5) in different age groups of passengers was calculated. Specific bus routes and the Athens metro network were monitored throughout different hours of the day. Then, the average particulate matter (PM2.5) exposure for a metro passenger was calculated and evaluated. By considering the ventilation rate of a passenger, an estimation of the total PM2.5 inhaled dose for males and females as well as for different age groups was made. The results showed that the highest PM2.5 concentrations were observed inside the wagons with significant increases during rush hours or after rush hours. Furthermore, there should be a concern regarding older individuals using the subway network in Athens during rush hours and in general for sensitive groups (people with asthma, respiratory and cardiovascular problems, etc.). [ABSTRACT FROM AUTHOR]