Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Assessment and Calibration on Low-cost PM2.5 Sensor using Machin Learning (Hybrid-LSTM Neural Network): Feasibility Study to Build Air Quality Monitoring System

Version 1 : Received: 7 September 2021 / Approved: 7 September 2021 / Online: 7 September 2021 (14:24:56 CEST)

How to cite: Park, D.; Yoo, G.; Park, S.; Lee, J. Assessment and Calibration on Low-cost PM2.5 Sensor using Machin Learning (Hybrid-LSTM Neural Network): Feasibility Study to Build Air Quality Monitoring System. Preprints 2021, 2021090130 (doi: 10.20944/preprints202109.0130.v1). Park, D.; Yoo, G.; Park, S.; Lee, J. Assessment and Calibration on Low-cost PM2.5 Sensor using Machin Learning (Hybrid-LSTM Neural Network): Feasibility Study to Build Air Quality Monitoring System. Preprints 2021, 2021090130 (doi: 10.20944/preprints202109.0130.v1).

Abstract

Although commercially-available low-cost air quality sensors have low accuracy, the sensor system are being used to collect the data for the regulation of PM2.5 emission caused by industrial activities or to estimate the personal exposure for PM2.5. In this work, to solve the accuracy problem of low-cost PM sensor, we developed a new PM2.5 calibration model by combining the deep neural network (DNN) optimized in calibration problem and a LSTM optimized in time-dependent characteristics. First, two datasets were generated to test the accuracy performance and generalization performance of the PM2.5 calibration machine learning (ML) model. The PM2.5 concentrations, temperature and humidity by low-cost sensor and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmark (multiple linear regression model) and low-cost sensor results. For root mean square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41-60% of error compared to the raw data of low-cost sensor, and reduced 30-51% of error compared to the benchmark model. R2 of ML model, MLR and raw data were 93, 80 and 59 %. Also, the developed model still showed consistent calibration performance when calibrated with new sensors in different locations. Low-cost sensors combined with ML model not only can improve the calibration performance of benchmark, but also can be applied to the sensor monitoring systems for various epidemiologic investigations and regulatory decisions.

Keywords

machine learning; deep learning; calibration; air quality; low-cost sensors; exposure assessment

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