[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1315":3,"related-1315":70},{"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":22,"classification":23,"search_phrases":38,"slug":39,"view_count":15,"doi":40,"paper":41,"created_at":69},1315,"Spatiotemporal assessment of urban PM₁₀ and population exposure using integrated satellite, meteorological, and socio-spatial data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpubh.2026.1940025","Introduction The pollution with particulate matter is a major risk factor for population health, especially in urban environments, where the interaction between anthropogenic sources, meteorological conditions and built environment characteristics generates complex spatial patterns of exposure. In this context, the present study aims to estimate the spatial distribution of PM₁₀ concentrations and assess population exposure in a medium-sized city in Romania by integrating satellite data, in situ measurements and socio-spatial indicators. Methods The methodology is based on MODIS-MAIAC satellite products to retrieve Aerosol Optical Depth (AOD) values, which are correlated with PM₁₀ data and meteorological variables from four ground-based air quality monitoring stations. Based on these data, a multiple regression model was developed to estimate PM₁₀ concentrations, and subsequently, a composite exposure index was constructed by integrating relevant socio-spatial indicators, such as population density, built environment characteristics, the Normalized Difference Vegetation Index (NDVI), and terrain elevation. Results and discussion The results highlight pronounced seasonal variability in PM₁₀ concentrations, with maximum values in the cold season (up to 41.8 μg\u002Fm 3 ) and an annual average of approximately 20.5 μg\u002Fm 3 , exceeding the thresholds recommended by the World Health Organisation but falling within the limits set by the European Union. The direct relationship between AOD and PM₁₀ is relatively weak ( R = 0.28), but integrating meteorological variables significantly improves the model’s performance. The analysis of the composite index indicates that approximately 20.1% of the population of the Oradea Metropolitan Area lives in areas with a high predisposition to exposure, particularly near industrial areas and in densely built urban environments. The results confirm that population exposure to PM₁₀ is determined not exclusively by concentration levels but by the complex interactions among pollution, population distribution, and the characteristics of the built environment. The proposed integrated approach provides a robust tool for identifying vulnerable areas and supporting urban planning and air quality management, contributing to the development of strategies to reduce risks to human health.","引言 颗粒物污染是影响人群健康的主要风险因素，尤其是在城市环境中，人为源、气象条件与建成环境特征之间的相互作用形成了复杂的空间暴露格局。在此背景下，本研究旨在通过整合卫星数据、地面监测数据和社会空间指标，估算罗马尼亚一座中等城市PM₁₀浓度的空间分布并评估人群暴露水平。方法 研究方法基于MODIS-MAIAC卫星产品获取气溶胶光学厚度（Aerosol Optical Depth, AOD）值，并将其与来自四个地面空气质量监测站的PM₁₀数据及气象变量进行相关性分析。基于这些数据，构建了多元回归模型以估算PM₁₀浓度，随后通过整合相关社会空间指标（如人口密度、建成环境特征、归一化植被指数（Normalized Difference Vegetation Index, NDVI）和地形海拔）构建了综合暴露指数。结果与讨论 研究结果凸显了PM₁₀浓度的显著季节性变化，冷季达到最大值（最高达41.8 μg\u002Fm³），年均值约为20.5 μg\u002Fm³，超过了世界卫生组织建议的阈值，但在欧盟设定的限值范围内。AOD与PM₁₀之间的直接关系相对较弱（R = 0.28），但整合气象变量后模型性能显著提升。综合指数分析表明，奥拉迪亚都市区约20.1%的人口居住在高暴露倾向区域，尤其是靠近工业区和建筑密集的城市环境。研究结果证实，人群对PM₁₀的暴露不仅取决于浓度水平，还取决于污染、人口分布和建成环境特征之间的复杂相互作用。所提出的综合方法为识别脆弱区域、支持城市规划和空气质量管理提供了有力工具，有助于制定降低人类健康风险的战略。",null,"Frontiers in Public Health","2026-08-31T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦城市PM10暴露评估，与三农、农业信息化等主题无关。",[19],{"name":10,"url":6},[21],"城市环境",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":14,"reason":37},[29],{"code":30,"score":31,"supported":32,"title":33,"abstract":34,"tags":36},"I7",3,true,[],[35],"研究方法",[],"农业相关性需要复核",[],"SpatiotemporalassessmentofurbanPM₁₀andpo-1315","10.3389\u002Ffpubh.2026.1940025",{"doi":40,"openalex_id":42,"authors":43,"venue":10,"cited_by_count":15,"oa_url":60,"card":61,"direction":67,"ingested_from":68},"W7204845815",[44,46,49,51,54,57],{"name":45,"orcid":9},"Tudor Caciora",{"name":47,"orcid":48},"Grigore Vasile Herman","https:\u002F\u002Forcid.org\u002F0000-0002-7586-6226",{"name":50,"orcid":9},"Monica Costea",{"name":52,"orcid":53},"Vasile Grama","https:\u002F\u002Forcid.org\u002F0000-0003-0600-1138",{"name":55,"orcid":56},"Thowayeb H. Hassan","https:\u002F\u002Forcid.org\u002F0000-0003-0510-3730",{"name":58,"orcid":59},"Tomasz Oberski","https:\u002F\u002Forcid.org\u002F0000-0002-0985-3363","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fpublic-health\u002Farticles\u002F10.3389\u002Ffpubh.2026.1940025\u002Fpdf",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"结合卫星、气象和社会空间数据评估城市PM10浓度及人口暴露，构建综合暴露指数。","MODIS-MAIAC AOD与地面PM10、气象数据回归建模，结合人口密度、N","PM10冬季最高，年均20.5 μg\u002Fm³，约20.1%人口高暴露风险，暴露受多因素交互影响。","其他","可延伸至农业区域PM暴露与作物影响，或结合高分辨率遥感与机器学习提升模型精度。","农业遥感与作物表型","openalex","2026-09-01T23:30:20.428818Z",{"total":71,"page":71,"page_size":71,"items":72},1,[73],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":78,"content":9,"source_name":79,"source_url":76,"published_at":80,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":81,"sources":83,"tags":85,"primary_topic":9,"secondary_topics":89,"classification":90,"search_phrases":104,"slug":107,"view_count":108,"doi":109,"paper":110,"created_at":131},4754,"Development of a city level nitrogen dioxide exposure (CLeNE) intelligence platform for West Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01108-1","Abstract Nitrogen dioxide (NO 2 ) is a major urban air pollutant, yet assessment of population exposure remains challenging across West Africa due to lack of uncoordinated sparse monitoring infrastructure. We present the City-Level Nitrogen Dioxide Exposure (CLeNE) platform, an interactive exposure intelligence system that integrates Sentinel-5P TROPOMI NO 2 observations, population data, and city boundaries to quantify and visualize urban exposure burden. Population-weighted exposure (PWE) and a Normalized Population-Weighted Exposure Index (NPWEI) were developed to enable standardized comparison among cities. Analysis of 2019–2024 observations identified persistent exposure hotspots along the Gulf of Guinea corridor, particularly in southern Nigeria, Ghana, Benin, and Côte d’Ivoire. Nigeria recorded the highest annual country-level PWE (1.59 × 10 15 molecules cm⁻ 2 ), while Abuja had the highest relative exposure ranking among capital cities (NPWEI = 100). The platform transforms satellite-derived observations into accessible exposure intelligence products, providing a scalable framework for sustainable urban planning, air-quality assessment, hotspot identification, and environmental decision support in data-sparse regions.","摘要 二氧化氮（NO₂）是城市主要空气污染物，然而由于西非地区缺乏协调且稀疏的监测基础设施，人口暴露评估仍面临挑战。我们提出了城市级二氧化氮暴露（CLeNE）平台，这是一个交互式暴露情报系统，整合了Sentinel-5P TROPOMI NO₂观测数据、人口数据和城市边界，以量化和可视化城市暴露负担。我们开发了人口加权暴露（PWE）和归一化人口加权暴露指数（NPWEI），以实现城市间的标准化比较。对2019—2024年观测数据的分析发现，几内亚湾走廊沿线持续存在暴露热点，尤其是尼日利亚南部、加纳、贝宁和科特迪瓦。尼日利亚录得最高的国家层面年度PWE（1.59 × 10¹⁵ 分子 cm⁻²），而阿布贾在首都城市中相对暴露排名最高（NPWEI = 100）。该平台将卫星衍生观测数据转化为可获取的暴露情报产品，为数据稀疏地区的可持续城市规划、空气质量评估、热点识别和环境决策支持提供了一个可扩展框架。","Discover Environment","2026-10-10T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":82},"该研究聚焦西非城市二氧化氮暴露评估，属城市环境遥感领域，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。",[84],{"name":79,"url":76},[86,21,87,88],"遥感监测","卫星数据","空气质量",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":91},{"candidates":92,"agricultural_evidence":14,"reason":37},[93,99],{"code":94,"score":31,"supported":32,"title":95,"abstract":96,"tags":98},"I2",[],[97],"decision support",[],{"code":100,"score":71,"supported":14,"title":101,"abstract":102,"tags":103},"I3",[],[],[86],[105,106],"CLeNE 平台 西非","Sentinel-5P 二氧化氮 暴露","CLeNE平台西非-4754",2,"10.1007\u002Fs44274-026-01108-1",{"doi":109,"openalex_id":111,"authors":112,"venue":79,"cited_by_count":15,"oa_url":125,"card":126,"direction":67,"ingested_from":68},"W7221364661",[113,116,119,122],{"name":114,"orcid":115},"Prince Junior Asilevi","https:\u002F\u002Forcid.org\u002F0000-0001-9162-326X",{"name":117,"orcid":118},"Alex Kwao Ablerdu","https:\u002F\u002Forcid.org\u002F0009-0002-2332-1432",{"name":120,"orcid":121},"Stephen Amankwah","https:\u002F\u002Forcid.org\u002F0000-0002-0397-5810",{"name":123,"orcid":124},"Emmanuel Quansah","https:\u002F\u002Forcid.org\u002F0000-0002-3382-1775","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01108-1.pdf",{"tldr":127,"method":128,"finding":129,"direction":67,"opportunity":130},"开发了西非城市级二氧化氮暴露智能平台，整合卫星与人口数据量化城市暴露负担。","利用Sentinel-5P TROPOMI NO2、人口和城市边界数据，构建人口","几内亚湾沿岸持续为暴露热点，尼日利亚年PWE最高，阿布贾相对暴露排名第一。","可借鉴该暴露评估框架，研究农业区大气污染对作物表型与产量的遥感影响。","2026-10-12T23:30:25.925235Z"]