Calibration strategies and measurement frameworks for low-cost particulate matter sensors: a comprehensive review

Abstract Low-cost particulate matter (PM) sensors have transformed air-quality monitoring by enabling high-resolution, real-time measurements across ambient, mobile, indoor, and workplace environments. However, their affordability and portability come at the cost of reduced precision and susceptibility to environmental factors such as humidity, temperature, and sensor drift. This review synthesizes recent advances in machine learning-based and physics-informed calibration methods that correct low-cost sensor biases and extend their applicability to regulatory and occupational hygiene contexts. We first examine real-world deployments—ranging from community networks and vehicle-mounted platforms to smart-manufacturing floors and mining operations—highlighting how data-driven corrections improve spatial coverage and temporal responsiveness. Next, we critically evaluate calibration strategies: purely data-driven algorithms, purely physics-based models, and hybrid physics-informed machine learning frameworks. We compare their calibration accuracy, transferability, computational requirements, and interpretability, noting that while non-linear models often achieve sub-10 µg m −3 errors, they face challenges in embedded deployment, dependence on representative training data, and region-specific performance. Overall, no single calibration strategy is universally optimal, with machine-learning-based methods excelling in predictive performance, physics-based methods in interpretability and physical consistency, and hybrid approaches in balancing accuracy, robustness, and transferability. Finally, we discuss persistent barriers—sensor fouling, data governance, model generalization, and hardware constraints—and propose emerging solutions such as self-cleaning optics, federated learning, and knowledge-distilled on-device calibration. By mapping the current state of the art and outlining future research directions, this review aims to guide the integration of low-cost PM sensors into robust, scalable air-quality management and occupational health systems.