Sustainable Soil Volatilome: Discrimination of Land Uses Through GC-MS-Identified Volatile Organic Compounds

This study investigates soil volatilomics as an innovative approach to assessing the impact of land use on soil quality. This research addresses the critical need for sensitive diagnostic tools to distinguish subtle biochemical variations in soils influenced by different land use management practices. Soil samples were collected along a land use transect in Cluj County. Their volatile organic compounds were extracted by headspace solid-phase microextraction (HS–SPME) followed by a gas chromatography–mass spectrometry (GC–MS) analysis. A multivariate statistical method was used to differentiate the volatilome profile. Among the 106 detected compounds, oxygenated species dominated across all land uses, with the highest concentrations in forest soils (77%), followed by grasslands (71%) and agricultural soils (65%). Principal component analysis revealed distinct clustering patterns, with the first two components explaining 72.8% of the total variance (PC1: 41.7%, PC2: 31.1%). Supervised PLS-DA modeling demonstrated robust land use discrimination, achieving AUC values of 0.868 for agricultural versus forest comparisons and 0.810 for both forest versus grassland and grassland versus agricultural comparisons. The volatilome diversity analysis indicated that grasslands contained the highest number of distinct compounds (64), closely followed by forest soils (63), while agricultural soils showed reduced diversity (51). These key findings revealed distinct volatile signatures, with forest soils exhibiting the highest complexity and agricultural soils demonstrating a more homogeneous profile, whereas grassland soils presented high internal variability. These results underscore the potential of soil volatilome profiling as a sensitive indicator of the effects of land use on soil biochemical processes and support the utility of soil volatilomics in sustainable land management and ecosystem monitoring.