Quantitative Analysis of Eutrophication Marker Molecules in Atmospheric Air Based on Model IR Spectra and the SIMCA Method

Abstract

Monitoring the state of water bodies requires reliable detection of gaseous products of anaerobic decomposition. This work evaluates the SIMCA (Soft Independent Modeling of Class Analogy) algorithm for the qualitative and quantitative analysis of infrared (IR) absorption spectra of multicomponent gas mixtures containing eutrophication marker molecules (CO2, H2S, CH4, N2O, PH3) in near-surface atmospheric air. Model absorption spectra were computed via the HITRAN2020 database over the 2000–7000 cm–1 range for temperatures of 278, 293 K and relative air humidity of 10%, 60%, respectively. The predictive model combines Savitzky–Golay preprocessing, principal component analysis (PCA), SIMCA classification with Mahalanobis distance, and k-fold cross-validation, together with an original visualization of classes in two-dimensional PCA cross-sections. Within the informative interval, 4250–4755 cm–1, at the temperature of 273 K and relative air humidity of 10%, the model determines the concentrations of CH4 (0,00017–0,01783% with step 0,001766%) and N2O (3,2·10–5–1,117·10–3% with step 1,09·10–4%) with an average accuracy of about 100% and 99,7%, respectively. The results were obtained at the noise level on the order of 10–6 cm–1, which is attainable using photoacoustic spectroscopy. For the listed molecules, the average accuracy of classification decreases by less than 0.1% when temperature and humidity increase. Using the predictive model based on SIMCA, the PCA two-dimensional cross-sections form a universal "concentration space," providing an interpretable tool for practical assessment of water-body eutrophication process.

The work was performed according to the Government research assignment for TSU, project FSWM-2025-0038.

Speaker

Tretyakov Akim Konstantinovich
Tomsk State University
Russia

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