Rapid Assessment of Soil Headspace Dynamics Under Denitrification-Promoting Conditions via an IoT-MOX Sensor Array

Real-time monitoring of soil gaseous emissions is important for understanding nitrogen transformations and associated emissions of reactive and greenhouse gases. In this study, controlled soil microcosms were used to investigate changes in gas emissions, volatile profiles, and microbial functional markers under control and denitrification-promoting conditions, and to explore the ability of an IoT-enabled metal oxide (MOX) gas sensor array to monitor dynamic changes in the overall soil headspace through multivariate resistance-response patterns. The experimental workflow included preliminary Fourier transform infrared (FTIR) gas analysis on dedicated microcosms to verify the response induced by the BOOST treatment and identify relevant monitoring windows. Separate microcosms were subsequently investigated by solid-phase microextraction gas chromatography–mass spectrometry (SPME–GC–MS), qPCR targeting key nitrogen-cycling functional genes, and MOX-based headspace monitoring. BOOST microcosms, amended with mineral nitrogen and glucose and maintained at 100% field capacity, exhibited an early increase in N2O emissions, with the highest measured concentration observed at 24 h, together with changes in bacterial abundance and nitrogen-cycling functional gene profiles. Volatilome analysis revealed a broader distribution of detected VOCs among chemical classes under BOOST conditions, with marked temporal changes and the emergence or enrichment of specific VOC classes, particularly at intermediate sampling times. MOX measurements captured concurrent changes in the overall headspace response and generated distinct resistance-based response patterns across treatments and sampling times. Exploratory multivariate analysis showed separated response clusters, which were interpreted alongside the temporal changes independently observed by FTIR and GC–MS. Overall, the findings indicate that MOX sensor arrays can rapidly capture complex and time-dependent response patterns associated with changes in the overall soil headspace under controlled conditions.