[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4551":3,"related-4551":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":38,"slug":41,"view_count":42,"doi":43,"paper":44,"created_at":72},4551,"Exploring the analytical limits of the indirect CO2 balancing method: A round-robin study for quantifying CH4 and NH3 emissions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104608","Exploring the analytical limits of the indirect CO2 balancing method: A round-robin study for quantifying CH4 and NH3 emissions。Biosystems Engineering",null,"Biosystems Engineering","2026-10-10T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},8,20,17,14,1,"核心期刊方法学论文，提出间接CO2平衡法的分析极限并开展环试，对农业温室气体与氨排放监测有参考价值，但属细分方法学研究，公共影响有限。",[23],{"name":9,"url":6},[25,26,27,28,29],"甲烷排放","温室气体排放","氨排放","农业环境监测","间接CO2平衡法",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":13,"reason":37},[],"农业相关性需要复核",[39,40],"农业环境监测 温室气体排放 间接CO2平衡法 甲烷排放","农业环境监测 温室气体排放","农业环境监测温室气体排放间接CO2平衡法甲烷排放-4551",0,"10.1016\u002Fj.biosystemseng.2026.104608",{"doi":43,"openalex_id":45,"authors":46,"venue":9,"cited_by_count":42,"oa_url":6,"card":8,"direction":8,"ingested_from":71},"W7221170641",[47,50,53,56,59,61,63,65,68],{"name":48,"orcid":49},"Harsh Sahu","https:\u002F\u002Forcid.org\u002F0009-0008-5795-4098",{"name":51,"orcid":52},"Thomas Amon","https:\u002F\u002Forcid.org\u002F0000-0003-2468-3160",{"name":54,"orcid":55},"Tariq Mehmood","https:\u002F\u002Forcid.org\u002F0000-0003-3549-2300",{"name":57,"orcid":58},"Manfred Trimborn","https:\u002F\u002Forcid.org\u002F0000-0003-4040-2082",{"name":60,"orcid":8},"Lars Broer",{"name":62,"orcid":8},"Diana Nett",{"name":64,"orcid":8},"Hergen Haase",{"name":66,"orcid":67},"Aditya Rawat","https:\u002F\u002Forcid.org\u002F0009-0005-2112-6408",{"name":69,"orcid":70},"David Janke","https:\u002F\u002Forcid.org\u002F0000-0002-4211-3404","openalex","2026-10-10T23:30:04.622336Z",{"total":74,"page":20,"page_size":74,"items":75},4,[76,139,241,317],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":8,"content":8,"source_name":9,"source_url":79,"published_at":81,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":82,"score_detail":83,"sources":88,"tags":90,"primary_topic":8,"secondary_topics":95,"classification":96,"search_phrases":104,"slug":107,"view_count":42,"doi":108,"paper":109,"created_at":138},5138,"Housing-level demonstration of the mitigating effects of tannin-rich diets fed to dairy cows on ammonia and methane emissions in naturally ventilated conditions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104616","Housing-level demonstration of the mitigating effects of tannin-rich diets fed to dairy cows on ammonia and methane emissions in naturally ventilated conditions。Biosystems Engineering","2026-10-16T00:00:00Z",81,{"impact":84,"substance":85,"depth":84,"authority":19,"freshness":86,"relevant":20,"comment":87},18,22,9,"核心期刊论文，在牛舍尺度验证单宁日粮对氨与甲烷减排的效果，对畜牧环境信息化与绿色养殖有参考价值，但属细分领域研究，未达重大突破层级。",[89],{"name":9,"url":79},[91,92,27,93,94],"甲烷减排","奶牛养殖","畜牧减排","单宁饲料",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":97},{"candidates":98,"agricultural_evidence":13,"reason":37},[99],{"code":100,"score":20,"supported":13,"title":101,"abstract":102,"tags":103},"C2",[],[],[92],[105,106],"奶牛 单宁饲料 甲烷排放","Biosystems Engineering 奶牛 氨排放","奶牛单宁饲料甲烷排放-5138","10.1016\u002Fj.biosystemseng.2026.104616",{"doi":108,"openalex_id":110,"authors":111,"venue":9,"cited_by_count":42,"oa_url":79,"card":8,"direction":8,"ingested_from":71},"W7222630876",[112,115,118,121,124,126,129,132,135],{"name":113,"orcid":114},"Giovanni Lazzari","https:\u002F\u002Forcid.org\u002F0000-0002-7820-7680",{"name":116,"orcid":117},"Michael Zähner","https:\u002F\u002Forcid.org\u002F0000-0002-2750-1433",{"name":119,"orcid":120},"Christian Ammon","https:\u002F\u002Forcid.org\u002F0000-0001-6852-4992",{"name":122,"orcid":123},"A. Münger","https:\u002F\u002Forcid.org\u002F0000-0003-0360-366X",{"name":125,"orcid":8},"David Steger",{"name":127,"orcid":128},"Joachim Mohn","https:\u002F\u002Forcid.org\u002F0000-0002-9799-1001",{"name":130,"orcid":131},"Michael Kreuzer","https:\u002F\u002Forcid.org\u002F0000-0002-9978-1171",{"name":133,"orcid":134},"Frigga Dohme‐Meier","https:\u002F\u002Forcid.org\u002F0000-0002-1693-2246",{"name":136,"orcid":137},"Sabine Schrade","https:\u002F\u002Forcid.org\u002F0000-0002-1846-3498","2026-10-17T23:30:05.389670Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":8,"source_name":145,"source_url":142,"published_at":146,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":147,"score_detail":148,"sources":152,"tags":154,"primary_topic":158,"secondary_topics":163,"classification":164,"search_phrases":215,"slug":218,"view_count":42,"doi":219,"paper":220,"created_at":240},4558,"From measurement to mitigation: a historical review of livestock emission research in the U.S. Southern and Central Great Plains","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1951531","As airborne emissions from agricultural operations began to receive more concentrated scrutiny, the federal Task Force on Agricultural Air Quality was established in the 1996 Farm Bill and began operations in 1997 to advance research and provide solutions for sustainable agricultural operations with primary focus on particulate matter, ozone, and odors. This historical review synthesizes research conducted by institutions serving the southern and central Great Plains from the early 2000s to mid-2010s across cattle, dairy, swine, and poultry production systems, identifying three interdependent research pillars: measurement technology, emission quantification, and abatement solutions. Research priorities varied by species, with cattle and dairy operations focusing primarily on particulate matter (PM), ammonia (NH 3 ), and greenhouse gas (GHG) emissions, while swine and poultry studies concentrated on odor and volatile organic compounds (VOCs). Measurement technology innovations proved foundational to reliable emission science. Quantification studies established baseline emission factors essential for regulatory compliance while revealing critical temporal dynamics and environmental drivers. However, persistent knowledge gaps emerged, particularly in transferring abatement solution effectiveness from controlled laboratory conditions to commercial settings, where performance declined substantially due to extreme spatial and environmental variability. Most importantly, the research field shifted from pursuing simple pollution reduction toward systematic approaches that monitor and manage multiple pollutants. We recommend that future research must develop frameworks that systematically assess pollutant trade-offs alongside economic feasibility to enable science-based mitigation strategies balancing environmental protection with agricultural sustainability.","随着农业活动的空气排放开始受到更集中的审视，联邦农业空气质量特别工作组在1996年《农业法案》中成立，并于1997年开始运作，旨在推进研究并为可持续农业经营提供解决方案，重点关注颗粒物、臭氧和气味。本历史综述综合了服务于大平原南部和中部地区的机构在2000年代初至2010年代中期针对肉牛、奶牛、猪和家禽生产系统所开展的研究，识别出三个相互依存的研究支柱：测量技术、排放量化和减排解决方案。研究重点因畜种而异，肉牛和奶牛养殖主要关注颗粒物（PM）、氨（NH₃）和温室气体（GHG）排放，而猪和家禽研究则集中于气味和挥发性有机化合物（VOCs）。测量技术创新被证明是可靠排放科学的基础。量化研究建立了合规监管所必需的基准排放因子，同时揭示了关键的时间动态和环境驱动因素。然而，持续存在的知识空白也随之显现，特别是在将减排解决方案的有效性从受控实验室条件转移到商业环境方面，由于极大的空间和环境变异性，其性能显著下降。最重要的是，该研究领域已从追求简单的污染削减转向监测和管理多种污染物的系统性方法。我们建议，未来研究必须开发能够系统评估污染物权衡与经济可行性的框架，以促成基于科学的减排策略，在环境保护与农业可持续性之间取得平衡。","Frontiers in Sustainable Food Systems","2026-10-09T00:00:00Z",78,{"impact":84,"substance":149,"depth":18,"authority":150,"freshness":86,"relevant":20,"comment":151},21,13,"系统梳理美国大平原畜牧排放研究三大支柱与减排落地瓶颈，对农业环境信息化与绿色养殖有参考价值，但属综述性论文、地域性强，未达重大突破层级。",[153],{"name":145,"url":142},[155,27,156,93,157],"农业温室气体","可持续养殖","空气质量",{"code":100,"slug":159,"name":160,"path":161,"parent_code":162},"ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[],{"status":165,"locked":13,"version":33,"method":34,"evidence":166},"classified",{"candidates":167,"agricultural_evidence":169},[168,176,181,189,196,203,209],{"code":100,"score":86,"supported":169,"title":170,"abstract":171,"tags":175},true,[],[172,173,174],"奶牛","肉牛","cattle",[],{"code":162,"score":16,"supported":169,"title":177,"abstract":179,"tags":180},[178],"livestock",[],[],{"code":182,"score":183,"supported":169,"title":184,"abstract":185,"tags":188},"B7",6,[],[186,187],"温室","greenhouse",[],{"code":190,"score":183,"supported":169,"title":191,"abstract":192,"tags":195},"C3",[],[193,194],"家禽","poultry",[],{"code":197,"score":198,"supported":169,"title":199,"abstract":200,"tags":202},"C1",3,[],[201],"swine",[],{"code":204,"score":198,"supported":169,"title":205,"abstract":206,"tags":208},"H8",[],[207],"可持续农业",[],{"code":210,"score":211,"supported":13,"title":212,"abstract":213,"tags":214},"H4",2,[],[],[155,27],[216,217],"美国大平原 畜牧 排放","畜牧 氨排放 温室气体","美国大平原畜牧排放-4558","10.3389\u002Ffsufs.2026.1951531",{"doi":219,"openalex_id":221,"authors":222,"venue":145,"cited_by_count":42,"oa_url":142,"card":234,"direction":238,"ingested_from":71},"W7221195799",[223,225,228,231],{"name":224,"orcid":8},"Sirapoom Peanusaha",{"name":226,"orcid":227},"Guillermo S Marcillo","https:\u002F\u002Forcid.org\u002F0000-0002-5343-1088",{"name":229,"orcid":230},"Jacek A. Koziel","https:\u002F\u002Forcid.org\u002F0000-0002-2387-0354",{"name":232,"orcid":233},"Brent W. Auvermann","https:\u002F\u002Forcid.org\u002F0000-0001-6506-2921",{"tldr":235,"method":236,"finding":237,"direction":238,"opportunity":239},"综述美国南部大平原畜牧排放研究，梳理测量、量化与减排三大支柱及知识缺口。","历史文献综述，覆盖2000年代初至2010年代中期牛、奶牛、猪、禽研究。","减排技术从实验室到商业场景效果显著下降，研究转向多污染物系统管理。","农业绿色发展与碳","可构建多污染物权衡与经济可行性耦合的评估框架，填补商业场景减排验证空白。","2026-10-10T23:30:06.314794Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":8,"source_name":247,"source_url":244,"published_at":248,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":42,"score_detail":249,"sources":251,"tags":253,"primary_topic":256,"secondary_topics":262,"classification":263,"search_phrases":277,"slug":280,"view_count":20,"doi":281,"paper":282,"created_at":316},4357,"Evaluating When and Where TROPOMI XCH4 Uncertainties Yield Consistent Flux Estimation over East, South, and Central Asia","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-4468","Abstract. Satellite remote sensing of methane (CH4) offers the potential to monitor emissions across large scales, but skepticism remains among regulators, policymakers, and the scientific community of both the precision and accuracy of such estimations due to unquantified observational uncertainties. Using 500 perturbation experiments with four error distributions applied to TROPOMI XCH4 over East, South, and Central Asia, we systematically assess where and when divergence‑based emission estimates are reliable. We show that the shape of the uncertainty distribution – not just its magnitude – governs error propagation and thus effects the stability of derived fluxes. Furthermore we show that uncertainty propogates over both space and time, indicating that purely local accounting may lead to an underestimate of uncertainty. Coal‑dominated industrial grids retain trustworthy signals (30% retention of valid perturbations), whereas agricultural and mixed‑use grids retain only 16%, indicating that current filtering practices are insufficient for these land types. These results provide a practical, uncertainty‑aware filter for satellite‑derived methane emissions, directly supporting the Global Methane Pledge and national greenhouse gas inventories by identifying where satellite data can be trusted (coal‑dominated regions) and where additional caution or alternative approaches are needed. The process is data-neutral and can be extended to other satellite platforms and adopted by other regions around the globe.","摘要：卫星遥感甲烷（CH₄）具有在大尺度上监测排放的潜力，但由于观测不确定性尚未量化，监管机构、政策制定者和科学界对此类估算的精度和准确度仍存疑虑。本研究利用500组扰动实验，将四种误差分布应用于东亚、南亚和中亚地区的TROPOMI XCH₄数据，系统评估了基于散度（divergence）的排放估算在何时何地是可靠的。研究表明，不确定性分布的形状——而不仅仅是其量级——决定了误差传播，进而影响所推导通量的稳定性。此外，不确定性在空间和时间上均会传播，这表明纯粹的局地核算可能导致对不确定性的低估。以煤炭为主的工业网格保留了可信信号（有效扰动的保留率为30%），而农业和混合用途网格仅保留16%，表明当前的滤波方法对这些土地类型而言尚不充分。这些结果为卫星反演的甲烷排放提供了一种实用的、考虑不确定性的滤波方案，通过识别卫星数据可信的区域（以煤炭为主的地区）以及需要额外谨慎或替代方法的区域，直接支持全球甲烷承诺（Global Methane Pledge）和国家温室气体清单的编制。该方法对数据具有中立性，可推广至其他卫星平台并被全球其他地区采用。","OpenAlex","2026-10-06T00:00:00Z",{"impact":42,"substance":42,"depth":42,"authority":42,"freshness":42,"relevant":42,"comment":250},"该论文聚焦卫星甲烷排放通量估算不确定性，属环境遥感领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[252],{"name":247,"url":244},[254,25,255],"遥感监测","卫星数据",{"code":257,"slug":258,"name":259,"path":260,"parent_code":261},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":165,"locked":13,"version":33,"method":34,"evidence":264},{"candidates":265,"agricultural_evidence":169},[266,273],{"code":257,"score":267,"supported":169,"title":268,"abstract":269,"tags":272},7,[],[270,271],"遥感","remote sensing",[254],{"code":182,"score":183,"supported":169,"title":274,"abstract":275,"tags":276},[],[186,187],[],[278,279],"TROPOMI XCH4 甲烷","卫星遥感 甲烷排放","TROPOMIXCH4甲烷-4357","10.5194\u002Fegusphere-2026-4468",{"doi":281,"openalex_id":283,"authors":284,"venue":8,"cited_by_count":42,"oa_url":244,"card":310,"direction":314,"ingested_from":71},"W7220708326",[285,288,291,294,296,299,302,305,307],{"name":286,"orcid":287},"Wei Xin Hu","https:\u002F\u002Forcid.org\u002F0000-0002-8780-7362",{"name":289,"orcid":290},"Jason Blake Cohen","https:\u002F\u002Forcid.org\u002F0000-0002-9889-8175",{"name":292,"orcid":293},"Lingxiao Lu","https:\u002F\u002Forcid.org\u002F0000-0002-3839-9083",{"name":295,"orcid":8},"Bo Zheng",{"name":297,"orcid":298},"Pravash Tiwari","https:\u002F\u002Forcid.org\u002F0000-0003-4770-4526",{"name":300,"orcid":301},"Simone Lolli","https:\u002F\u002Forcid.org\u002F0000-0001-6111-152X",{"name":303,"orcid":304},"Andrea Garzelli","https:\u002F\u002Forcid.org\u002F0000-0003-2332-780X",{"name":306,"orcid":8},"Jun Li",{"name":308,"orcid":309},"Kai Qin","https:\u002F\u002Forcid.org\u002F0000-0002-1280-6330",{"tldr":311,"method":312,"finding":313,"direction":314,"opportunity":315},"用500组扰动实验评估TROPOMI XCH4在亚洲三区甲烷通量估算的可靠性。","对TROPOMI XCH4施加四种误差分布的500次扰动实验，分析通量反演稳定性","不确定性分布形状而非大小主导误差传播，且跨时空传播；煤炭区信号可信度30%，农业混合区仅16%。","农业遥感与作物表型","可发展面向农业混合区的误差感知滤波与替代反演方法，并推广至其他卫星平台与区域。","2026-10-07T23:30:26.011458Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":8,"source_name":323,"source_url":320,"published_at":324,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":82,"score_detail":325,"sources":327,"tags":329,"primary_topic":333,"secondary_topics":337,"classification":338,"search_phrases":358,"slug":361,"view_count":362,"doi":363,"paper":364,"created_at":380},2662,"Annual Gridded Anthropogenic CH4 Emissions Estimation in China (2019–2025) Integrating Multisource Data: SHAP-Based Driver Attribution and Spatio-Temporal Patterns","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183168","Accurately quantifying the spatiotemporal dynamics and driving mechanisms of anthropogenic methane (CH4) emissions (MEs) is of great significance for achieving regional “dual-carbon” goals and global climate collaborative governance. However, existing ME inventories and macro-inversion models generally face bottlenecks such as coarse spatial resolution, lack of data update timeliness, and the inability of traditional static emission factors to capture non-linear responses. To address these issues, this study proposes an annual ME inventory enhancement framework integrating multi-source geographic and remote sensing data. This framework evaluates four advanced machine learning (ML) algorithms, including Random Forest (RF), Categorical Boosting (CB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), to construct a 0.1° high-resolution spatial grid of anthropogenic ME in China from 2019 to 2025. Furthermore, it introduces the SHapley Additive exPlanations (SHAP) framework and multi-scale spatial autocorrelation analysis to parse the driving mechanisms and clustering patterns. The results show the following: (1) LGBM exhibits the optimal comprehensive estimation accuracy (R2 = 0.938, RMSE = 3.707 Kt) and robust capability in capturing extreme ME sources (RTop2 = 0.929). (2) SHAP attribution reveals that coal mining and nighttime light (NTL) represent the primary contributing features to ME predictions (with a cumulative contribution of 65.60%), followed by agricultural and pastoral activities (24.98%), and all factors exhibit significant non-linear threshold and step-response characteristics. (3) Regarding spatiotemporal evolution, China’s total anthropogenic ME shows a trend of initial slow increase followed by high-level stabilization; spatially, it presents a “hot in the north, cold in the south” pattern, with extreme high values highly clustered in the Shanxi–Shaanxi–Inner Mongolia energy triangle and its peripheral expansion nodes. This study provides scientific references for formulating tailored, multi-scale, and refined CH4 mitigation strategies.","准确量化人为甲烷（CH₄）排放（MEs）的时空动态与驱动机制，对实现区域“双碳”目标及全球气候协同治理具有重要意义。然而，现有ME清单与宏观反演模型普遍面临空间分辨率粗、数据更新时效性不足、传统静态排放因子难以刻画非线性响应等瓶颈。针对上述问题，本研究提出了一种融合多源地理与遥感数据的年度ME清单增强框架。该框架评估了四种先进机器学习（ML）算法，包括随机森林（RF）、类别提升（CB）、极端梯度提升（XGB）和轻量梯度提升机（LGBM），构建了2019—2025年中国0.1°高分辨率人为ME空间网格。此外，引入SHapley加性解释（SHAP）框架与多尺度空间自相关分析，解析驱动机制与集聚模式。结果表明：（1）LGBM具有最优的综合估算精度（R² = 0.938，RMSE = 3.707 Kt），且在捕捉极端ME源方面表现稳健（RTop2 = 0.929）。（2）SHAP归因揭示，煤炭开采与夜间灯光（NTL）是ME预测的主要贡献特征（累计贡献率65.60%），其次为农牧活动（24.98%），且所有因子均呈现显著的非线性阈值与阶跃响应特征。（3）在时空演变方面，中国人为ME总量呈先缓慢增长后高位稳定的趋势；空间上表现为“北热南冷”格局，极端高值高度集聚于晋陕蒙能源金三角及其外围扩展节点。本研究为制定因地制宜、多尺度、精细化CH₄减排策略提供了科学参考。","Remote Sensing","2026-09-15T00:00:00Z",{"impact":84,"substance":85,"depth":84,"authority":19,"freshness":86,"relevant":20,"comment":326},"基于多源遥感与机器学习构建中国0.1°人为甲烷排放网格清单，方法新颖、数据规模大，对农业源排放核算与双碳治理有参考价值，但偏学术、非直接农业应用。",[328],{"name":323,"url":320},[330,331,270,25,332],"农业信息化","机器学习","双碳目标",{"code":261,"slug":334,"name":335,"path":336,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":339,"locked":13,"version":33,"method":34,"evidence":340},"partial",{"candidates":341,"agricultural_evidence":169,"reason":357},[342,348,352],{"code":343,"score":267,"supported":169,"title":344,"abstract":345,"tags":347},"I2",[],[331,346],"machine learning",[331],{"code":257,"score":267,"supported":169,"title":349,"abstract":350,"tags":351},[],[270,271],[270],{"code":353,"score":20,"supported":13,"title":354,"abstract":355,"tags":356},"I1",[],[],[330],"一级领域明确，二级方向需要细分",[359,360],"农业信息化 双碳目标 机器学习 甲烷排放","农业信息化 双碳目标","农业信息化双碳目标机器学习甲烷排放-2662",5,"10.3390\u002Frs18183168",{"doi":363,"openalex_id":365,"authors":366,"venue":323,"cited_by_count":42,"oa_url":320,"card":375,"direction":314,"ingested_from":71},"W7213229152",[367,370,373],{"name":368,"orcid":369},"Chaokang He","https:\u002F\u002Forcid.org\u002F0009-0005-8014-434X",{"name":371,"orcid":372},"Qinjun Wang","https:\u002F\u002Forcid.org\u002F0000-0001-6084-1889",{"name":374,"orcid":8},"Wenyue Xie",{"tldr":376,"method":377,"finding":378,"direction":238,"opportunity":379},"融合多源数据与机器学习构建中国2019-2025年0.1°人为甲烷排放网格，并用SHAP解析驱动机制","随机森林、CatBoost、XGBoost、LightGBM对比，结合SHAP归","LGBM精度最优（R²=0.938），煤炭开采与夜间灯光贡献65.6%，排放呈北热南冷格局。","可延伸至农业源甲烷排放的精细网格化与非线性驱动阈值研究，支撑分区减排。","2026-09-16T23:30:28.864720Z"]