Holographic Air-quality Monitor (HAM)

TL;DR

The HAM system leverages lensless holography and deep learning to detect and analyze large particulate matter in real-time, offering a high-throughput, accurate, and comprehensive indoor air quality monitoring solution.

Contribution

This paper introduces the first holographic air-quality monitor capable of real-time detection and analysis of large PMs over 10 um, outperforming traditional sensors in speed and accuracy.

Findings

Achieves >97% true positive rate with <0.6% false positives.

Samples air at 26 LPM, detecting up to 4000 particles per liter.

High concordance with microscopy in size measurement.

Abstract

We introduce the holographic air-quality monitor (HAM) system, uniquely tailored for monitoring large particulate matter (PM) over 10 um in diameter, i.e., particles critical for disease transmission and public health but overlooked by most commercial PM sensors. The HAM system utilizes a lensless digital inline holography (DIH) sensor combined with a deep learning model, enabling real-time detection of PMs, with greater than 97% true positive rate at less than 0.6% false positive rate, and analysis of PMs by size and morphology at a sampling rate of 26 liters per minute (LPM), for a wide range of particle concentrations up to 4000 particles/L. Such throughput not only significantly outperforms traditional imaging-based sensors but also rivals some lower-fidelity, non-imaging sensors. Additionally, the HAM system is equipped with additional sensors for smaller PMs and various air quality conditions, ensuring a comprehensive assessment of indoor air quality. The performance of the DIH sensor within the HAM system was evaluated through comparison with brightfield microscopy, showing high concordance in size measurements. The efficacy of the DIH sensor was also demonstrated in two two-hour experiments under different environments simulating practical conditions with one involving distinct PM-generating events. These tests highlighted the HAM system's advanced capability to differentiate PM events from background noise and its exceptional sensitivity to irregular, large-sized PMs of low concentration.