[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2531":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":49},2531,"Spatial Coupling of Greenhouse Gas Emissions, Vegetation Activity, Groundwater Nitrate Occurrence, and Soil Environments Across Denmark: A 5-km Spatial Association and Soil-Stratified Analysis","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5rj7d","Integrated environmental assessment increasingly requires the joint analysis of atmospheric emissions, vegetation dynamics, groundwater quality, and soil context rather than isolated thematic mapping. We developed a Denmark-wide 5-km analytical grid in EPSG:25832 containing 1,803 cells and harmonized four continuous environmental indicators: CO2 and CH4 emissions from EDGAR, vegetation activity represented by 2024 MODIS NDVI, and groundwater nitrate occurrence derived from national GEUS observations. Soil information from the GEUS Digital Soil Map was used to stratify environmental associations into five broad environmental groups. Global Pearson, Spearman, and Kendall associations were complemented by global Moran’s I, directional bivariate Moran’s I, local bivariate spatial association, an upper-1% CO2 sensitivity analysis, and soil-stratified statistics. CO2 and NDVI showed the strongest national association (Pearson r = −0.2621), while CH4 was positively associated with CO2 (r = 0.0898) and nitrate occurrence (r = 0.0792). All four continuous variables exhibited significant positive spatial autocorrelation under both Queen and Rook contiguity, with the strongest structure for CH4 and CO2 and the weakest for nitrate. Bivariate Moran analysis supported a negative CO2–NDVI cross-association and positive CH4–CO2 and CH4–nitrate cross-associations. Soil-stratified results showed substantial heterogeneity; the strongest Pearson CO2–NDVI association occurred in Human-affected & Technological environments (r = −0.6208), whereas Sandy & Poor and Sedimentary & Clay-rich environments displayed the clearest positive CH4–CO2 associations. The study is explicitly associative rather than causal. Its principal contribution is a spatially explicit national screening framework that reveals where environmental indicators co-occur, where relationships are spatially organized, and where targeted process-based investigation and monitoring may be most informative.","综合环境评估日益需要对大气排放、植被动态、地下水质量和土壤背景进行联合分析，而非孤立的专题制图。我们构建了一个覆盖丹麦全境、分辨率为5 km的EPSG:25832分析网格，包含1,803个单元，并统一整合了四项连续环境指标：来自EDGAR的CO2和CH4排放、以2024年MODIS NDVI表示的植被活动，以及源自GEUS国家观测数据的地下水硝酸盐赋存情况。来自GEUS数字土壤图的土壤信息被用于将环境关联分层为五个广泛的环境组。在全局Pearson、Spearman和Kendall关联分析的基础上，辅以全局Moran's I、方向性双变量Moran's I、局部双变量空间关联、前1% CO2敏感性分析以及土壤分层统计。CO2与NDVI在全国尺度上表现出最强的关联（Pearson r = −0.2621），而CH4与CO2（r = 0.0898）及硝酸盐赋存（r = 0.0792）呈正相关。四个连续变量在Queen和Rook邻接下均表现出显著的正空间自相关，其中CH4和CO2的空间结构最强，硝酸盐最弱。双变量Moran分析支持CO2–NDVI的负向交叉关联以及CH4–CO2和CH4–硝酸盐的正向交叉关联。土壤分层结果显示出显著异质性；最强的Pearson CO2–NDVI关联出现在人类影响与技术环境中（r = −0.6208），而砂质贫瘠环境和沉积物–黏土丰富环境则表现出最明显的CH4–CO2正相关。本研究明确为关联性而非因果性研究。其主要贡献在于提供了一个空间显式的全国筛查框架，揭示环境指标在何处共现、关系在何处呈现空间组织，以及有针对性的过程性研究与监测在何处可能最具信息价值。",null,"OpenAlex","2026-09-14T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,9,1,"基于全国5公里网格整合温室气体、植被、地下水硝酸盐与土壤环境的空间关联研究，方法系统、数据规模大，对农业环境监测与空间筛查有参考价值，但属关联性分析而非因果结论。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业遥感","温室气体","空间分析","地下水质量","土壤环境",0,"10.31223\u002Fx5rj7d",{"doi":33,"openalex_id":35,"authors":36,"venue":9,"cited_by_count":32,"oa_url":40,"card":41,"direction":47,"ingested_from":48},"W7213077215",[37],{"name":38,"orcid":39},"Avideh Asadollahi","https:\u002F\u002Forcid.org\u002F0009-0000-9847-2177","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F14981\u002Fdownload\u002F26031\u002F",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"构建丹麦5公里网格，分析温室气体、植被、地下水硝酸盐与土壤的空间耦合关系。","EDGAR排放、MODIS NDVI、GEUS硝酸盐与土壤图，用Moran's ","CO2与NDVI负相关最强，CH4与CO2、硝酸盐正相关，土壤类型显著调节这些关联。","农业绿色发展与碳","可基于该空间筛查框架，在热点区域开展过程模型与监测，探究土壤调节机制及因果路径。","农业遥感与作物表型","openalex","2026-09-15T23:30:20.707747Z"]