Low-Cost Particulate Matter Mass Sensors: Review of the Status, Challenges, and Opportunities for Single-Instrument and Network Calibration.

As an emerging atmospheric monitoring technology, low-cost sensors for particulate matter of diameters below 2.5 μm (PM2.5LCSs) supplement traditional air quality monitoring instruments. Because their stability and accuracy are typically low, they require adequate calibration to meet operational requirements. Numerous studies have now been published on single-sensor PM2.5LCS calibration models, and research on monitoring networks, designed to measure pollutant concentration with high spatiotemporal resolution, is gradually starting. However, there is no established standard procedure for sensor calibration. Here we comprehensively reviewed published studies on PM2.5LCS calibration to evaluate the current research status, identify major challenges, and provide support for atmospheric monitoring applications of PM2.5LCS networks. Regression and machine learning were the most common calibration methods for single PM2.5LCSs. Environmental factors and the duration of the calibration period influenced the calibration model accuracy, especially for machine learning (data-driven) algorithms. For PM2.5LCS networks, common methods included early evaluation and homogeneous or colocated calibration. Method selection depended on regional environmental conditions, pollutant concentration, and the presence or absence of reference instruments. Quality control is crucial to the operation of the network, and common methods included online drift detection and management measures for routine quality assurance and control. In conclusion, sensor calibration is crucial for PM2.5LCS operational use, and intensive research on machine-learning-based calibration methods must be conducted for practical application of large-scale PM2.5LCS networks.