Non-destructive method based on infrared spectroscopy and partial least square regression for the quantification of the ionic component of atmospheric particulate matter

Atmospheric aerosols influence radiative forcing through interaction with solar radiation and indirectly by acting as cloud condensation nuclei and have a negative impact on air quality especially in urban scenarios. With socio-economic models suggesting that in a growing global population, 70% of the humans will live in urban areas by 2050, the adverse impact on urban air quality is a prominent societal and health issue, expected to become more and more severe in the future. In order to introduce effective mitigation strategies and monitor their effect, the state and characteristics of pollution need to be characterized and main sources identified. Offline-analysis of particulate matter (PM) collected on filter samples offers such insight. However, PM chemical composition is highly complex, and its comprehensive characterization and quantification requires advanced instrumentation and data analysis techniques and strategies.

Here, we present the development and application of a novel analytical nondestructive method. We acquired Fourier-transform infrared spectroscopy (FTIR) spectra of ambient PM collected on Teflon filters at various locations in Italy. FTIR allows to obtain high-resolution spectral data non-destructively and therefore to detect and quantify functional groups of organic and inorganic species present in the aerosol PM. The spectral dataset was analyzed by applying partial least squares regression (PLS regression) methods in order to allow quantification of ammonium, sulphate and nitrate ionic PM components. This statistical method allowed to disentangle the inner complexity of the PM sample and to train a statistical model for each of the three ionic species. In our conference contribution, the so developed models are discussed and compared with the more traditional analytical method, ionic chromatography (IC).

 

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