Abstract
Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms.
Highlights
Air pollution caused by industrialization and urbanization is causing serious environmental and health problems
We propose a state-of-the-art PM2.5 calibration model (HybridLSTM) by combining the deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms (DNN, multiple linear regression (MLR)) of a low-cost
A new PM2.5 machine learning calibration model (HybridLSTM) was developed, and the calibration performance was compared with the raw data, MLR model and DNN model, which has shown a superior calibration performance
Summary
Air pollution caused by industrialization and urbanization is causing serious environmental and health problems. Fine particulate matter (PM) is generated from various emission sources of industrial activities such as industry, transportation and combustion. It is very important to obtain the data for regulation on industrial emission by monitoring the PM2.5 concentration generated by the emission activity [1]. In South Korea, a gravimetric-based PM2.5 measuring instrument has been used as a national reference method (NRM) to monitor the PM2.5 concentrations. It is expensive to install NRM equipment at the sampling location for each close distance (>USD 10,000) [2]. This limits obtaining PM2.5 information from the NRM method at the community level
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