Process-Aware Deep Learning for Low-Cost Greenhouse Gas Sensing: Insights from Composting toward Scalable Anthropogenic Activities Applications

= 0.9310) by embedding causal emission drivers rather than relying on intergas correlations only. Model interpretability analysis based on SHapley Additive exPlanations-Partial Dependence Plot (SHAP-PDP), together with 16S rRNA microbial evidence, demonstrated that the identified drivers are consistent with established biogeochemical pathways governing methanogenesis and nitrification-denitrification. Independent validation using large-scale composting data confirmed strong generalization, while cross-domain tests of TGNS highlighted the necessity of incorporating process-aware information. Overall, GHGsNet provides a robust and interpretable framework for low-cost GHGs monitoring in high-temperature and high-humidity composting systems, with potential for future extension toward other process-driven anthropogenic activities.