Multivariate Characterization of a Continuous Soot Monitoring System Based on Raman Spectroscopy

Abstract

The demand for precise and continuous monitoring of air quality has increased. An important descriptor of air quality is the concentration of problematic carbonaceous particles responsible for diseases and climate change. The specific measurement of carbonaceous components in the air is still a topic in research and development. Here, we introduce an integrated and continuous soot monitoring system based on Raman spectroscopy. In comparison to the often utilized light absorption, Raman spectroscopy is capable of determining the graphitic microstructure found in carbonaceous particles. We present first measurements taken in a controlled environment contaminated with varying concentrations of diesel soot. The Raman bands of soot turn out to be tightly mixed up with signals from secondary physical factors. In order to evaluate the data, multivariate methods are applied. After determination of the latent variables using principal component analysis (PCA), the system is further rotated using a linear discriminant analysis (LDA)-criterion and a subsequent nonlinear iterative partial least squares (NIPALS)-like step. One of the variables obtained by this methodology can be shown to exclusively describe the optical filter loading while the orthogonal factor space allows for conclusions on the secondary factors.Copyright 2015 American Association for Aerosol Research © 2015