Online detection and source tracing of crop straw burning

Abstract

The carbon dioxide, sulfur dioxide, and metal ions produced by straw burning can severely pollute the atmosphere; thus, online detection and traceability for straw burning is very important. However, to our best knowledge, there is no comprehensive system that can satisfy online detection, classification, and traceability due to the challenging online detection and traceability of straw burning. In this paper, a new system based on laser-induced breakdown spectroscopy (LIBS) and machine learning is developed, and this developed system is employed for the first time in online detection and traceability of straw combustion. Four different types of straw are selected and the straw burning smoke is monitored online using this developed system. The analysis of straw smoke spectra shows that there are Fe, Mn, and Ba heavy metal spectra in the smoke spectra. By comparing the smoke spectra of different types of straw, the characteristic spectral lines with large differences are selected and dimensionality reduction is performed by linear discriminant analysis algorithm. Then, combined with random forest to achieve classification, the final smoke recognition accuracy reaches 87.0%. Straw ash is then used as a reference analysis and the same operation is performed on it. Mn, Ba, and Li heavy metal spectral lines are found in the spectra of ash, and the final recognition accuracy is 92.6%. The innovative and developed system based on LIBS and machine learning is fast, online, and in situ and has far-reaching application prospects in the environment.