Low-Cost Particulate Matter Mass Sensors: Review of the Status, Challenges, and Opportunities for Single-Instrument and Network Calibration.
Journal:
ACS sensors
Published Date:
May 7, 2025
Abstract
As an emerging atmospheric monitoring technology, low-cost sensors for particulate matter of diameters below 2.5 μm (PMLCSs) 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 PMLCS 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 PMLCS calibration to evaluate the current research status, identify major challenges, and provide support for atmospheric monitoring applications of PMLCS networks. Regression and machine learning were the most common calibration methods for single PMLCSs. Environmental factors and the duration of the calibration period influenced the calibration model accuracy, especially for machine learning (data-driven) algorithms. For PMLCS 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 PMLCS operational use, and intensive research on machine-learning-based calibration methods must be conducted for practical application of large-scale PMLCS networks.
