Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, many low-cost and low-accuracy sensors have been deployed. Calibrating low-cost sensors is essential to fill the geographical gap in sensor coverage. We systematically examined how different machine learning (ML) models and open-source packages could help improve the accuracy of particulate matter (PM) 2.5 data collected by Purple Air sensors. Eleven ML models and five packages were examined. This systematic study found that both models and packages impacted accuracy, while the random training/testing split ratio (e.g., 80/20 vs. 70/30) had minimal impact (0.745% difference for R²). Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R² scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m³ and 4.26 µg/m³, respectively. However, LSTM models may be too slow (1.5 h) or computation-intensive for applications with fast response requirements. Tree-boosted models including XGBoost (0.7612, 5.377 µg/m³) in RStudio and Random Forest (RF) (0.7632, 5.366 µg/m³) in TensorFlow offered good performance with shorter training times (<1 min) and may be suitable for such applications. These findings suggest that AI/ML models, particularly LSTM models, can effectively calibrate low-cost sensors to produce precise, localized air quality data. This research is among the most comprehensive studies on AI/ML for air pollutant calibration. We also discussed limitations, applicability to other sensors, and the explanations for good model performances. This research can be adapted to enhance air quality monitoring for public health risk assessments, support broader environmental health initiatives, and inform policy decisions.

Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollu-tion to human health and ecosystems. Due to the limited number and geographical coverage of advanced high accurate sensors to monitor air pollutants, many low-cost but low accuracy sensors have been deployed. Calibrating the low-cost sensors is essential to leverage them for filling this geographical gap in sensor coverage. We examine systematically how different Machine Learning (ML) models and open-source packages could help improve the accuracy of particulate matter (PM) 2.5 data collected by Purple Air sensors. Eleven ML models and five packages are examined. This systematic study found that both models and packages impact the accuracy while the choice of random training/testing split ration (e.g., 80/20 vs 70/30) has minimal impact (0.745% difference for R2). Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R2 scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m3 and 4.26 µg/m3, respectively. However, LSTM may be too slow or computation intensive (1.5 hours) in applications with fast response requirements. Tree-boosted models including XGBoost (0.7612, 5.377 µg/m3) in RStudio and Random Forest (RF) (0.7632, 5.366 µg/m3) in TensorFlow, offer good performance at shorter training times (< 1 minute), and may be suitable for such ap-plications. These findings suggest that AI/ML models, particularly LSTM, can effectively calibrate low-cost sensors to produce precise, localized air quality data. This research is the most com-prehensive in comparison to existing literature on AI/ML for air pollutants calibration. We also discussed the limitation, applicability to other sensors and the reason for well performed models. The research can be adapted to enhance air quality monitoring for public health risk assessments and support broader environmental health initiatives and inform policy decisions.