Quantifying calibration drift in low-cost PM₁₀ and sound sensors across contrasting indoor environments

Abstract. Low-cost sensor networks offer an affordable option for long-term air quality and noise monitoring, but their performance can be limited by calibration drift. This study quantified drift in low-cost particulate matter (PM) and sound level sensors using measurements from three co-located units and reference instruments deployed in two contrasting indoor environments: a lower-concentration painting environment and a higher-concentration swine environment. Drift varied substantially by environment. In the painting environment, drift in both PM₁₀ and sound sensors was small (PM₁₀: 2.1 μg m^-3 per year; sound: 0.78 dBA per year) and either within or near expected performance ranges based on manufacturer specifications and sound level tolerance criteria. In the swine environment, PM₁₀ sensors exhibited rapid degradation (−46.2 μg m^-3 per year), indicating substantial time-dependent drift under elevated particulate concentrations. Incorporating a drift term in the calibration model reduced RMSE and MAE by approximately 11 % in the swine environment, with negligible improvements in the painting environment. Sound sensors showed minimal drift and strong inter-sensor agreement across both deployments. These findings highlight the need for environment-specific recalibration strategies for low-cost PM sensors and demonstrate that drift-aware modelling can improve measurement reliability and support maintenance and lifecycle planning in high-concentration settings, with future work investigating how particulate characteristics and operating conditions influence drift.