Study on the Law of Harmful Gas Release from <i>Limnoperna fortunei</i> (Dunker 1857) during Maintenance Period of Water Tunnel Based on K-Means Outlier Treatment

Abstract

It is of great significance for air pollution control and personnel safety guarantee to master the release characteristics of harmful gases in the process of Limnoperna fortunei corruption. In view of the lack of research on the environmental pollution caused by the corruption of Limnoperna fortunei, a model experiment was designed to study the three harmful gases of NH3, H2S, and CH4 in the putrid process of Limnoperna fortunei by considering the density of Limnoperna fortunei and the time of leaving water. The results show that: (1) The recognition and processing of outliers based on wavelet decomposition and K-means algorithm can effectively reduce the standard deviation and coefficient of variation of the data set and improve the accuracy of the data set. (2) The variation of NH3 and H2S gas concentrations with the time of water separation satisfies polynomial linear regression (R2 > 99%). (3) At a density of 0.5–7.0 × 104 mussels/m2, the highest concentration of NH3 reached 47.9777–307.9454 mg/m3 with the increase in the density of Limnoperna fortunei and the extension of the time away from water, far exceeding the occupational exposure limit of NH3 of 30 mg/m3, potentially threatening human health and safety. The highest detection value of H2S concentration is 0.1909–5.0946 mg/m3, and the highest detection concentration of CH4 is 0.02%, both of which can be ignored.

Keywords

• <i>Limnoperna fortunei</i>
• corruption laws
• the harmful gas
• wavelet decomposition
• K-means algorithm
• outlier identification