Analytical performance and calibration strategies of low-cost particulate matter sensors for indoor and workplace monitoring—a review

Abstract

This review summarizes current evidence on low-cost particulate matter (PM) sensors for indoor and occupational environments and proposes a framework that links performance evaluation, calibration, and uncertainty to decision-making. Results from laboratory and field co-locations are synthesized to define reporting standards—accuracy, precision, dynamic range, detection capability, and temporal response—and to compare calibration strategies. Optical sensors consistently capture temporal dynamics of indoor sources but show mass bias that depends on concentration range, aerosol composition, and humidity. Context-specific reporting, with conditioning on environmental state and source regime, is therefore essential. Calibration practices range from simple linear corrections, often adequate at low to moderate concentrations, to multivariate or nonlinear models incorporating humidity, temperature, or volatile organic compounds, which reduce residual bias under high or mixed-source loadings. A staged quality assurance and quality control workflow—including procurement screening, bench checks, co-location with blocked validation, external validation for transportability, and rotating “gold-node” drift checks—ensures reproducibility and decision-relevant uncertainty. Deployment studies demonstrate that event-aware sensor networks can support targeted ventilation and filtration, reducing exceedance time and cumulative exposure without unnecessary energy use. Standardized reporting tables, model versioning, applicability limits, and anomaly-handling rules further enhance reliability and governance. Overall, low-cost PM sensors can provide decision-relevant data when embedded in calibrated, uncertainty-aware pipelines with explicit scope statements. While reference-grade methods remain necessary for compliance, calibrated networks are well suited to hotspot detection, intervention design, and operational optimization in buildings and workplaces.