Enhancing Low-Cost PM_2.5 Sensor Reliability Through Multi-Model Calibration Against a Beta Attenuation Monitor

Abstract. Accurate particulate matter (PM_2.5) monitoring using low-cost sensors requires careful consideration of meteorological influences and calibration against reference instruments. This study evaluates the performance of a low-cost optical sensor through an outdoor co-location experiment with a Beta Attenuation Monitor (BAM 1022). Raw measurements showed strong temporal agreement but substantial overestimation, particularly under high relative humidity, which induced hygroscopic particle growth and amplified light-scattering responses. Correlation and regression analyses confirmed humidity as the dominant environmental factor affecting low-cost sensor bias, while temperature exhibited only minor influence. To address these limitations, multiple calibration models (including Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, and an Adaptive-blend ensemble) were developed and assessed. Nonlinear and ensemble-based models significantly improved accuracy, reducing MAE from 17.40 μg/m³ (uncalibrated) to 5.85 μg/m³ after calibration. These findings demonstrate the necessity of environmental compensation and model-based correction for reliable low-cost PM_2.5 monitoring and support their integration into high-resolution air quality networks.