[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2870":3,"related-2870":60},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":29,"slug":30,"view_count":31,"doi":32,"paper":33,"created_at":59},2870,"Urban green spaces and air quality in the State of São Paulo: a spatiotemporal analysis of the metropolitan regions of Campinas and Piracicaba","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10098-026-03604-7","Abstract Air pollution remains a major environmental and public-health concern, especially in urban and peri-urban regions where emissions, land use, and seasonal meteorology interact. Although GI is often proposed as a strategy to improve air quality, its effects are highly context-dependent and difficult to isolate. This study examined the association between vegetation greenness, represented by NDVI, and monthly concentrations of particulate matter (PM 10 and PM 2.5 ), and NO 2 in the MRC and MRP, São Paulo State, Brazil, from 2019 to 2025. Pollutant and meteorological data were obtained from CETESB AQMS, and NDVI was derived from Sentinel-2 imagery in Google Earth Engine within 100, 250, 500, and 1000 m buffers around each AQMS. Associations were assessed using Spearman correlations, LOWESS smoothing, Mann–Whitney U tests, and multiple linear regression models adjusted for meteorological variables and temporal fixed effects. NDVI was predominantly negatively correlated with pollutant concentrations, with coefficients ranging from − 0.24 to − 0.83 for PM 10 , − 0.17 to − 0.82 for PM 2.5 , and − 0.31 to − 0.75 for NO 2 . Seasonal patterns were evident, with higher pollutant concentrations and lower NDVI values during the dry season. In adjusted regression models, significant negative NDVI associations were mainly observed in MRP, particularly for PM 10 at 100–250 m, PM 2.5 at 100–500 m, and NO 2 at 100–500 m, whereas most associations in MRC were non-significant. PM 10 and NO 2 concentrations were significantly higher in MRP than in MRC, whereas PM 2.5 showed no significant regional difference. These findings suggest that urban greenness is associated with air quality patterns in a context-dependent manner, reinforcing the need to account for spatial scale, seasonality, and local land use conditions in GI planning. Graphical abstract","摘要 空气污染仍然是一个主要的环境和公共卫生问题，尤其是在排放、土地利用和季节性气象相互作用强烈的城市和城郊地区。尽管绿色基础设施（GI）常被提出作为改善空气质量的策略，但其效果高度依赖具体情境，且难以单独分离。本研究考察了2019年至2025年巴西圣保罗州MRC和MRP地区以NDVI表示的植被绿度与颗粒物（PM10和PM2.5）及NO2月浓度之间的关联。污染物和气象数据来自CETESB空气质量监测站（AQMS），NDVI则通过Google Earth Engine中的Sentinel-2影像获取，并分别以每个AQMS周围100、250、500和1000 m缓冲区进行计算。关联分析采用Spearman相关、LOWESS平滑、Mann–Whitney U检验以及经气象变量和时间固定效应调整的多元线性回归模型。NDVI与污染物浓度主要呈负相关，PM10的相关系数为−0.24至−0.83，PM2.5为−0.17至−0.82，NO2为−0.31至−0.75。季节性模式明显，旱季污染物浓度较高而NDVI值较低。在调整后的回归模型中，显著的NDVI负向关联主要出现在MRP，尤其是100–250 m处的PM10、100–500 m处的PM2.5以及100–500 m处的NO2，而MRC中的大多数关联不显著。MRP的PM10和NO2浓度显著高于MRC，而PM2.5未表现出显著的区域差异。这些发现表明，城市绿度与空气质量格局的关联具有情境依赖性，进一步说明在GI规划中有必要考虑空间尺度、季节性和当地土地利用条件。图形摘要",null,"Clean Technologies and Environmental Policy","2026-09-17T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦巴西圣保罗州城市绿地与空气质量，属城市环境与遥感领域，与三农、农业信息化、智慧农业等主题无关。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":28},[],"农业相关性需要复核",[],"UrbangreenspacesandairqualityintheStateo-2870",1,"10.1007\u002Fs10098-026-03604-7",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":15,"oa_url":51,"card":52,"direction":56,"ingested_from":58},"W7213461135",[36,39,42,45,48],{"name":37,"orcid":38},"Ana Laura Fragoso Favoreti","https:\u002F\u002Forcid.org\u002F0009-0002-1869-6299",{"name":40,"orcid":41},"Bruno Noronha Rodrigues","https:\u002F\u002Forcid.org\u002F0000-0002-8651-5165",{"name":43,"orcid":44},"William Machado Emiliano","https:\u002F\u002Forcid.org\u002F0000-0002-6743-7341",{"name":46,"orcid":47},"Felippe Benavente Canteras","https:\u002F\u002Forcid.org\u002F0000-0002-1979-0765",{"name":49,"orcid":50},"Vitor Eduardo Molina","https:\u002F\u002Forcid.org\u002F0000-0002-6205-961X","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10098-026-03604-7.pdf",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"分析巴西圣保罗两都市区NDVI与PM10、PM2.5、NO2的时空关联。","Sentinel-2 NDVI多缓冲半径，结合CETESB监测数据与回归模型。","NDVI与污染物多呈负相关，效应随区域、粒径和缓冲尺度而异，旱季更明显。","农业遥感与作物表型","可探索不同城市绿地类型与配置对空气质量的尺度依赖效应及因果机制。","openalex","2026-09-18T23:30:29.100327Z",{"total":15,"page":31,"page_size":31,"items":61},[]]