Avoidable mortality due to long-term exposure to PM2.5 in Colombia 2014–2019

Abstract

Objective To compare estimates of spatiotemporal variations of surface PM2.5 concentrations in Colombia from 2014 to 2019 derived from two global air quality models, as well as to quantify the avoidable deaths attributable to the long-term exposure to concentrations above the current and projected Colombian standard for PM2.5 annual mean at municipality level. Methods We retrieved PM2.5 concentrations at the surface level from the ACAG and CAMSRA global air quality models for all 1,122 municipalities, and compare 28 of them with available concentrations from monitor stations. Annual mortality data 2014–2019 by municipality of residence and pooled effect measures for total, natural and specific causes of mortality were used to calculate the number of annual avoidable deaths and years of potential life lost (YPLL) related to the excess of PM2.5 concentration over the current mean annual national standard of 25 µg/m3 and projected standard of 15 µg/m3. Results Compared to surface data from 28 municipalities with monitoring stations in 2019, ACAG and CAMSRA models under or overestimated annual mean PM2.5 concentrations. Estimations from ACAG model had a mean bias 1,7 µg/m3 compared to a mean bias of 4,7 µg/m3 from CAMSRA model. Using ACAG model, estimations of total nationally attributable deaths to PM2.5 exposure over 25 and 15 µg/m3 were 142 and 34,341, respectively. Cardiopulmonary diseases accounted for most of the attributable deaths due to PM2.5 excess of exposure (38%). Estimates of YPLL due to all-cause mortality for exceeding the national standard of 25 µg/m3 were 2,381 years. Conclusion Comparison of two global air quality models for estimating surface PM2.5 concentrations during 2014–2019 at municipality scale in Colombia showed important differences. Avoidable deaths estimations represent the total number of deaths that could be avoided if the current and projected national standard for PM2.5 annual mean have been met, and show the health-benefit of the implementation of more restrictive air quality standards.

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

“…It showed that the Copernicus Atmospheric Monitoring Service Reanalysis (CAMRA) and the Atmospheric Composition Analysis Group (ACAG) models had a low correlation and tended to overestimated surface concentrations when both models were compared to surface data from 28 cities in 2019. However, ACAG outperformed CAMSRA in terms of mean bias of the model and the spatial representation of the highest concentrations (Rodriguez-Villamizar et al 2022 ). Using a mobile monitoring campaign in the city of Bucaramanga in 2019, estimations of within-city spatial variations in ultrafine particle and black carbon concentrations were predicted using a combination of LUR and convolutional neural networks trained using satellite and street-level images, showing the improvement of prediction when using a hybrid approach (Lloyd et al 2021 ).…”