A Network Calibration Approach Improves the Accuracy and Long-Term Stability of a Low-Cost Air Quality Mesonet in New York City.

A new calibration approach, the Network Calibration Algorithm (NCA), was developed and applied to low-cost sensors measuring PM2.5, O3, NO2, NO, and CO at 38 New York State Mesonet sites in the New York City Metropolitan Area. A single low-cost sensor package (the "keystone" package) was colocated alongside regulatory-grade (reference) instruments at the New York State Department of Environmental Conservation Queens College monitoring site for 16 months. For each pollutant, hourly data from the keystone package and reference instruments were used to train a single calibration model that was subsequently applied to all packages at field sites across the network. The calibration models included multiple linear regression (MLR) for CO and a hybrid approach that combined MLR with a Random Forest model for PM2.5, O3, NO2, and NO. The performance of the NCA-calibrated low-cost sensors was quantified using multiple evaluation data sets, with a focus on accuracy and long-term stability over the 16-month period. The performance statistics were consistent with or better than previous reports for similar low-cost sensors, and the NCA was able to compensate for sensor degradation and drift. Empirical estimates of the field limit of detection for each of the low-cost sensors are presented.