Jacobson Ludmilla Viana, Hacon Sandra, Schumacher Vanúcia, Santos Clarcson Plácido Conceição Dos, Vianna Nelzair
Universidade Federal Fluminense, Department of Statistics - Niterói (RJ), Brazil.
Fundação Oswaldo Cruz, Sérgio Arouca National School of Public Health - Rio de Janeiro (RJ), Brazil.
Rev Bras Epidemiol. 2024 Dec 16;27:e240068. doi: 10.1590/1980-549720240068. eCollection 2024.
To evaluate the performance of satellite-derived PM2.5 concentrations against ground-based measurements in the municipality of Salvador (state of Bahia, Brazil) and the implications of these estimations for the associations of PM2.5 with daily non-accidental mortality.
This is a daily time series study covering the period from 2011 to 2016. A correction factor to improve the alignment between the two data sources was proposed. Effects of PM2.5 were estimated in Poisson generalized additive models, combined with a distributed lag approach.
According to the results, satellite data underestimated the PM2.5 levels compared to ground measurements. However, the application of a correction factor improved the alignment between satellite and ground-based data. We found no significant differences between the estimated relative risks based on the corrected satellite data and those based on ground measurements.
In this study we highlight the importance of validating satellite-modeled PM2.5 data to assess and understand health impacts. The development of models using remote sensing to estimate PM2.5 allows the quantification of health risks arising from the exposure.
评估巴西巴伊亚州萨尔瓦多市基于卫星反演的PM2.5浓度与地面测量值的匹配情况，以及这些估算值对PM2.5与每日非意外死亡率关联的影响。
这是一项涵盖2011年至2016年的每日时间序列研究。提出了一个校正因子以改善两个数据源之间的一致性。在泊松广义相加模型中结合分布滞后方法估计PM2.5的影响。
结果显示，与地面测量相比，卫星数据低估了PM2.5水平。然而，校正因子的应用改善了卫星数据与地面数据之间的一致性。我们发现，基于校正后的卫星数据估算的相对风险与基于地面测量估算的相对风险之间没有显著差异。
在本研究中，我们强调了验证卫星模型PM2.5数据以评估和理解健康影响的重要性。利用遥感技术开发估算PM2.5的模型能够量化暴露所产生的健康风险。
