Mass concentration is a commonly used but insufficient metric to evaluate the particulate matter (PM) exposure hazard. Recent studies have declared that small particles have more serious impacts on human health than big particles given the same mass concentration. However, state-of-the-art PM sensors cannot provide explicit information of the particle size for further analysis. In this work, we adopt Sauter mean diameter (SMD) as a key metric to reflect the particle size besides the mass concentration. To measure SMD, an effective optical sensing method and a proof-of-concept prototype sensor are proposed by using dual wavelengths technology. In the proposed method, a non-linear conversion model is developed to improve the SMD measurement accuracy for aerosol samples of different particle size distributions and reflective indices based on multiple scattering channels. In the experiment of Di-Ethyl-Hexyl-Sebacate (DEHS) aerosols, the outputs of our prototype sensor demonstrated a good agreement with existing laboratory reference instruments with maximum SMD measurement error down to 7.04%. Furthermore, the simplicity, feasibility and low-cost features of this new method present great potential for distributed PM monitoring, to support sophisticated human exposure hazard assessment.

In fire smoke detection, interference aerosols frequently cause false alarms for photoelectric smoke detectors. Conventional closed-set classification models trained on limited samples cannot reliably handle unknown aerosol types, which further leads to false and missed alarms. To address this issue, we propose FireOSR-Net, an open-set recognition network for fire smoke identification using the signals of a four-channel optical scattering detector, which integrates three modules. First, Conv1d-LSTM is used to extract features from dual-wavelength, dual-angle scattered signals. Next, the out-of-distribution detection module constructs an open boundary between known and unknown aerosols and generates pseudo-unknown features to enhance the detection capability for out-of-domain samples. Finally, the distance of all scattered intensity signals to both the fire and the non-fire domain is calculated, and fire and non-fire proximity scores are output to assess their risk propensity. Experiments are performed on eight-class aerosol data acquired with our self-developed four-channel detector, including six known and two unknown test categories. It achieves a peak F1-score of 0.95 on known samples, while unknown smoldering-wire smoke and oil fumes reach 96.29% fire proximity and 89.68% non-fire proximity correspondingly. Such results demonstrate FireOSR-Net’s capacity for aerosol classification, out-of-domain detection and risk evaluation towards interpretable low-false-alarm fire sensing.