###### Abstract
This richness in volatilome profile is increasingly recognized as a proxy for soil resilience and bioactivity [9]. In intensively managed agricultural soils, for example, the volatilome profile is often simplified because of reduced microbial complexity and altered metabolic processes [16]. In contrast, less disturbed systems such as natural grasslands [17] and forest soils [18] typically exhibit more complex volatilome profiles, reflecting stable microbial ecosystems and efficient biogeochemical cycles. These observations highlight the potential of volatile organic compound-based analyses to provide nuanced insights into soil health and ecosystem functions. A significant challenge in soil volatilomics lies in the complexity of volatile profiles, which can include hundreds of distinct compounds spanning multiple chemical classes and concentration ranges [13]. This complexity is further amplified by the fact that soils from diverse land uses often have common volatile compounds but exhibit significantly different relative abundances or intensity patterns [14]. Such quantitative variations, rather than simple qualitative differences [19; 20; 21], may be crucial indicators of specific ecological processes or disturbances [14; 22]. In addition, the multifunctional roles of various volatile organic compounds require a multidisciplinary approach to correlate soil volatilome profiles with traditional soil quality parameters. The complex nature of these profiles, characterized by both compositional diversity and intensity variations, requires sophisticated analytical strategies for their meaningful interpretation. State-of-the-art multivariate statistical techniques [23] and machine learning algorithms [24] have shown promise in analyzing these complex patterns, but rigorous validation remains essential to ensure that these models maintain biological interpretability. Furthermore, integrating volatilomics with other omics platforms complicates analytical strategies but is essential for delineating the mechanistic relationships between volatile emissions and key soil processes [25; 26]. Establishing consensus protocols for data preprocessing, normalization, and quality control, especially for managing the qualitative and quantitative aspects of volatile profiles, is an indispensable step to strengthening volatilomics as a reliable tool for diagnosing soil health. The present study aims to advance soil volatilomics as a diagnostic tool by leveraging high-dimensional analytical techniques and robust data integration methodologies. Specifically, our research objectives are twofold. First, the soil volatilome profiles along a land use transect are characterized and compared. We used agricultural, grassland, and forest ecosystems that are next to each other, thereby revealing unique volatile signatures indicative of distinct land use practices, with a focus on the presence and relative abundance of key volatile compounds. Second, the current challenges in volatilomics data interpretation can be overcome by developing and rigorously validating a multivariate statistical framework suitable for large datasets that can effectively capture qualitative and quantitative variations in volatilome profiles. This study elucidates the mechanistic links between land use and soil biochemical dynamics by integrating targeted analytical techniques with state-of-the-art data interpretation approaches. Ultimately, our findings are able to enhance the utility of volatilomics in sustainable land management and ecological monitoring, providing novel insights into the volatile-mediated interactions that govern soil functionality in the context of global environmental change.