Intelligent PM 2.5 mass concentration analyzer using deep learning algorithm and improved density measurement chip for high-accuracy airborne particle sensor network

Seung Soo Lee, Woo Young Song, Yong Jun Kim

Research output: Contribution to journalArticlepeer-review

Abstract

The concentration of ultrafine airborne particles is continuously increasing, and research has shown that it has adverse effects when inhaled into the human body. Accordingly, there is a growing demand for a measurement network utilizing sensors to evaluate an individual's exposure to these airborne particles. However, the current low-cost sensors have the limitation of low accuracy. To solve this, we devised a method to calibrate a low-cost mass concentration sensor accurately in real time. In a previous study, we developed an analyzer that could measure the effective density and nanoparticles that cause the low accuracy of the mass concentration sensor. However, it had a hardware stability problem when used to monitor the outside air for a long period. This has been improved by modifying the shape of the Micro-electromechanical system (MEMS)-based chip integrated with the electrical/inertial analysis technology. Hence, the device can now operate with sufficient stability in the outdoor air. In addition, the retrieval algorithm used to convert the measured current values into the effective density and nanoparticle size distribution was prone to errors. It was modified to a deep learning-based physical parameter conversion algorithm to minimize the errors. Thus, we developed a standalone analyzer that integrates the improved nanoparticle and effective density analyzer, temperature and humidity sensor, and low-cost mass concentration sensor. In addition, we developed a technique to calibrate the mass concentration sensor data accurately in real time based on the data measured by each sensor and analyzer and a deep learning-based mass concentration calibration algorithm. The calibration precision was confirmed through comparative evaluation of the analyzer with the results of a beta attenuation mass monitor. In the future, this analyzer can be utilized to build a sensor network that precisely monitors the mass concentration in a large area through multi-point deployment. Alternatively, it can be used for monitoring the mass concentration in an indoor private space, where it is difficult to place expensive and large equipment.

Original languageEnglish
Article number106097
JournalJournal of Aerosol Science
Volume167
DOIs
Publication statusPublished - 2023 Jan

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Pollution
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes
  • Atmospheric Science

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