Performance of fine particulate matter data on air quality in an epidemiological study in Salvador, Brazil

Abstract

ABSTRACT Objective: 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. Methods: 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. Results: 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. Conclusion: 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.

Keywords

• Air pollution
• Public health
• Time series
• Information systems