Abstract
The traditional photoacoustic cavity has the advantages of simple structure, low cost, and easy integration with optical cavity technology, so it has significant advantages in the measurement of the optical characteristics of respirable dust. In order to meet the demand of high-precision respirable dust measurements in practical applications, it is necessary to improve the measurement accuracy of respirable dust by traditional photoacoustic spectroscopy technology. Therefore, the structure size of the photoacoustic cavity was determined by theoretical and simulation analysis. A system for measuring respirable dust by photoacoustic spectroscopy was designed, which was applied to the atmospheric respirable dust detection simultaneously with the cavity ring-down spectroscopy system. The results showed that the correlation between the two systems was poor. Therefore, the three-layer back propagation neural network algorithm was used to correct the photoacoustic response values, and the measured value of the cavity ring-down spectroscopy system was used as the reference truth value. The calibration results showed that the output value of the neural network model was in good agreement with the reference true value: the slope was above 0.96. The results showed that the neural network algorithm could effectively improve the measurement accuracy of the photoacoustic spectroscopy system to respirable dust, improve the linearity, and reduce the detection error.
Highlights
With the rapid development of industrialization and urbanization in China, the problem of environmental pollution has become increasingly prominent
The results showed that the neural network algorithm could effectively improve the measurement accuracy of the photoacoustic spectroscopy system to respirable dust, improve the linearity, and reduce the detection error
This paper aims to improve the measurement accuracy of respirable dust by a photoacoustic spectroscopy system
Summary
With the rapid development of industrialization and urbanization in China, the problem of environmental pollution has become increasingly prominent. The problem of high-precision dust concentration detection needs to be solved. Zhang et al. conducted a study on dust dynamic concentration distribution based on the “ultrasoundelectricity” hybrid detection system and fusion model, providing a fast and accurate method for detecting dynamic and complex dust concentration. She et al. developed the dust detector and its scitation.org/journal/adv concentration estimation method by using an advanced imager, and the measured data were in good agreement with the high-precision measuring instrument. The above measurement methods and research play an important role in the detection of dust. Photoacoustic spectroscopy technology has been widely used in environmental atmospheric trace gas detection, medical respiratory gas analysis, optical absorption characteristics of agricultural grains, and molecular spectroscopy.
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