Fast assesment of odor constituents in wastewater treatment plants using ion mobility spectrometry combined with blind source separation techniques.

Introduction: This study explores the use of Ion Mobility Spectrometers (IMS) for odor monitoring in Wastewater Treatment Plants (WWTP). IMS offers advantages like portability, rapid response, and high sensitivity for detecting volatile organic compounds (VOCs). However, the complexity of multiple odor sources in WWTPs complicates odor evaluation. Blind source separation techniques can enhance the extraction of chemical information from IMS data. Materials & Methods: We used the GDA2 IMS (AIRSENSE Analytics) to detect VOCs and generate mobility spectra, acting as unique gas fingerprints. Data was collected at 46 locations in the Pinedo WWTP in Spain, categorized as 'water' and 'sludge'. Our aim was to analyze these molecular fingerprints and extract VOC concentration profiles using Multivariate Curve Resolution - Least Absolute Shrinkage and Selection Operator (MCR-LASSO). Results: The MCR-LASSO method decomposed each sample into time concentration profiles and multi-modal mobility spectral profiles due to ion correlation. To obtain a single spectrum per ion, Gaussian unimodal spectra were generated. Final concentration profiles were then derived by applying a Least Squares approach to the original data and unimodal spectra, revealing differences between the two sample classes. Conclusion: Our methodoly succesfully extracted concentration profile information for ions from IMS data collected from WWTP samples.