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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\u2013Partial Dependence Plot (SHAP\u2013PDP), together with 16S rRNA microbial evidence, demonstrated that the identified drivers are consistent with established biogeochemical pathways governing methanogenesis and nitrification\u2013denitrification. 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.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c00490","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T05:06:57Z","timestamp":1785128817000},"page":"22687-22701","source":"Crossref","is-referenced-by-count":0,"title":["Process-Aware\nDeep Learning for Low-Cost Greenhouse\nGas Sensing: Insights from Composting toward Scalable Anthropogenic\nActivities Applications"],"prefix":"10.1021","volume":"98","author":[{"given":"Zhonghao","family":"He","sequence":"first","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haihong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University , , ,","place":["Guangzhou, China, 510006"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"He","sequence":"additional","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ci","family":"Song","sequence":"additional","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfu","family":"Deng","sequence":"additional","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leyuan","family":"Fang","sequence":"additional","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4437-2060","authenticated-orcid":true,"given":"Haoran","family":"Dong","sequence":"additional","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiqi","family":"Fu","sequence":"additional","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6996-7955","authenticated-orcid":true,"given":"Lin","family":"Tang","sequence":"additional","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8745-9177","authenticated-orcid":true,"given":"Jing","family":"Tang","sequence":"additional","affiliation":[{"name":"Hunan University , , ,","place":["Changsha, China, 410082"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,25]]},"reference":[{"issue":"1","key":"2026081109093719800_cit1","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.1038\/s41467-024-46991-5","article-title":"Meta-analysis\nshows the impacts of ecological restoration on greenhouse gas emissions","volume":"15","author":"He","year":"2024","journal-title":"Nat. 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