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

作者：Zhonghao He, Haihong Jiang, Jing He, Ci Song, Hongfu Deng, Leyuan Fang, Haoran Dong, Qiqi Fu, Lin Tang, Jing Tang · 发表于：Analytical Chemistry · 年份：2026 · DOI：10.1021/acs.analchem.6c00490 · 研究领域：Composting and Vermicomposting Techniques、Wastewater Treatment and Nitrogen Removal、Microbial Fuel Cells and Bioremediation

Abstract Greenhouse gases (GHGs) monitoring is essential for mitigating emissions from anthropogenic activities, yet the deployment of high-precision analyzers remains constrained by cost and operational complexity. Low-cost GHGs sensors often suffer from signal drift, cross-sensitivity, and unstable responses under extreme temperature and humidity conditions. To address these challenges, GHGsNet was developed as a low-cost, process-aware deep learning framework for predicting CO2, CH4, and N2O emissions during composting, a representative high temperature and high humidity anthropogenic activity. GHGsNet integrates low-cost gas sensor signals with gas-state variables and key process parameters using gas-specific deep learning architectures. Compared with a sensor-only baseline model (TriGasNetSensor, TGNS), GHGsNet significantly improved prediction accuracy for CH4 (R2 = 0.9268) and N2O (R2 = 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 informatio...