How Accurate Are Low-Cost Air Quality Sensors? A 10-Month Study of Three-Tier Comparison with Three Calibration...

Low-cost sensors could increase the spatial granularity of air-quality monitoring at 700-1000 times less cost than reference stations (2026 prices), but few field studies evaluate them for gases. This study evaluates a three-tier architecture (low-cost metal oxide and optical, mid-cost electrochemical, high-cost government reference) over 10 months at two London sites, 1.6 km apart. The low-cost sensors and mid-cost NO2 sensor were calibrated with manufacturer curves, linear regression on temperature and relative humidity, and machine learning (ML). After field calibration, the low-cost PM2.5 sensor explains over half the reference variance at both sites, and linear calibration equals ML. The low-cost NO2 sensor agrees less with its reference than the PM2.5 sensor. The low-cost sensors explain over half the variance of a factory-calibrated mid-cost electrochemical CO sensor at both sites only with ML. The mid-cost NO2 and low-cost O₃ sensors fail under all three methods. Calibrations trained on April-October 2025 stop describing November 2025-February 2026 data for NO2 at both sites and CO at one, whereas PM2.5 stays stable. Transferred between sites, PM2.5 calibrations reach R2≥0.56 and gas calibrations R2≤0.28. Low-cost PM2.5 sensors calibrated at a reference site could serve neighborhood-scale monitoring where reference stations are absent, whereas NO2 needs co-location.