Urban and socioeconomic disparities in PM2.5 exposure across 340 Latin American cities.

IF 5.6 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Environmental Research Letters Pub Date : 2025-12-01 Epub Date: 2025-12-02 DOI:10.1088/1748-9326/ae20a4
Edson J Ascencio, Antony Barja, Jose Montes-Alvis, Josiah L Kephart, Nelson Gouveia, Daniel A Rodriguez, Tarik Benmarhnia, Ana V Diez Roux, Usama Bilal, J Jaime Miranda, Gabriel Carrasco-Escobar
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引用次数: 0

Abstract

Background. Fine particulate matter (PM2.5) is a leading global health risk. Latin American cities exhibit some of the world's highest urban PM2.5 levels, yet studies of neighborhood-level PM2.5 exposure and associated disparities in the region are limited. Methods. We conducted a cross-sectional ecological analysis of 53 041 neighborhoods across 340 cities in eight Latin American countries, leveraging the Salud Urbana en America Latina study dataset. Annual PM2.5 concentrations were derived from satellite data and linked to socioeconomic and urban characteristics. A multilevel model analyzed associations between neighborhood PM2.5 levels and neighborhood- and city-level characteristics. Results. The median annual neighborhood PM2.5 concentration was 18.49 µg m-3. Of the 256 million residents, all lived in neighborhoods with ambient PM2.5 concentrations that exceeded the 2021 World Health Organization guidelines (5 µg m-3). Variability was greatest between cities (54.3% of total variance), but substantial within-city variation (26% of variance) was observed. Higher neighborhood PM2.5 levels were associated with higher neighborhood educational attainment (mean difference [MD] comparing top to bottom tertile = 0.17 µg m-3), higher neighborhood intersection density (MD comparing top to bottom tertile = 0.17 µg m-3), and older cities (MD comparing top to bottom tertile = 1.45 µg m-3). Lower neighborhood PM2.5 levels were related to higher neighborhood population density (MD comparing top to bottom tertile = - 0.55 µg m-3), more greenness (MD comparing top to bottom tertile = - 0.76 µg m-3), and larger distance from city centers (MD comparing top to bottom tertile = - 0.86 µg m-3). Conclusions. Neighborhoods with higher PM2.5 concentrations tended to have higher educational attainment, more intersections, and be located in older cities, while lower concentrations were associated with denser populations, more green space, and greater distance from city centers. Our findings reveal important within-city heterogeneity in PM2.5 and the factors associated with it, suggesting strategies to mitigate air pollution within cities.

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340个拉丁美洲城市PM2.5暴露的城市和社会经济差异。
背景。细颗粒物(PM2.5)是全球主要的健康风险。拉丁美洲的一些城市是世界上PM2.5水平最高的城市之一,但对该地区社区PM2.5暴露和相关差异的研究有限。方法。我们利用Salud Urbana en America Latina研究数据集,对8个拉丁美洲国家340个城市的53041个社区进行了横断面生态分析。PM2.5的年浓度来源于卫星数据,并与社会经济和城市特征有关。一个多层次模型分析了社区PM2.5水平与社区和城市水平特征之间的关系。结果。邻里PM2.5年均浓度中位数为18.49µg -3。在2.56亿居民中,所有居民居住的社区的环境PM2.5浓度都超过了2021年世界卫生组织的指导方针(5微克-3)。城市之间的差异最大(占总方差的54.3%),但城市内部的差异很大(占方差的26%)。较高的邻里PM2.5水平与较高的邻里受教育程度(顶层与底层的平均差值[MD]比较= 0.17µg -3)、较高的邻里交叉密度(顶层与底层的MD比较= 0.17µg -3)以及较老的城市(顶层与底层的MD比较= 1.45µg -3)相关。较低的邻里PM2.5水平与较高的邻里人口密度(上层与下层的MD比较= - 0.55µg m-3)、更多的绿化(上层与下层的MD比较= - 0.76µg m-3)以及距离城市中心较远(上层与下层的MD比较= - 0.86µg m-3)有关。结论。PM2.5浓度较高的社区往往受教育程度更高、十字路口更多、位于老城市,而浓度较低的社区则与人口密度更大、绿地更多、距离市中心更远有关。我们的研究结果揭示了PM2.5在城市内部的重要异质性及其相关因素,为缓解城市空气污染提出了策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Research Letters
Environmental Research Letters 环境科学-环境科学
CiteScore
11.90
自引率
4.50%
发文量
763
审稿时长
4.3 months
期刊介绍: Environmental Research Letters (ERL) is a high-impact, open-access journal intended to be the meeting place of the research and policy communities concerned with environmental change and management. The journal''s coverage reflects the increasingly interdisciplinary nature of environmental science, recognizing the wide-ranging contributions to the development of methods, tools and evaluation strategies relevant to the field. Submissions from across all components of the Earth system, i.e. land, atmosphere, cryosphere, biosphere and hydrosphere, and exchanges between these components are welcome.
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