Abstract
PM2.5 severely affects human health. Remotely sensed (RS) data can be used to estimate PM2.5 concentrations and population exposure, and therefore to explain acute respiratory disorders. However, available global PM2.5 concentration forecast products derived from models assimilating RS data have not yet been exploited to generate early alerts for respiratory problems in Brazil. We investigated the feasibility of building such an early warning system. For this, PM2.5 concentrations on a 4-day horizon forecast were provided by the Copernicus Atmosphere Monitoring Service (CAMS) and compared with the number of severe acute respiratory disease (SARD) cases. Confounding effects of the meteorological conditions were considered by selecting the best linear regression models in terms of Akaike Information Criterion (AIC), with meteorological features and their two-way interactions as explanatory variables and PM2.5 concentrations and SARD cases, taken separately, as response variables. Pearson and Spearman correlation coefficients were then computed between the residuals of the models for PM2.5 concentration and SARD cases. The results show a clear tendency to positive correlations between PM2.5 and SARD in all regions of Brazil but the South one, with Spearman’s correlation coefficient reaching 0.52 (p < 0.01). Positive significant correlations were also found in the South region by previously correcting the effects of viral infections on the SARD case dynamics. The possibility of using CAMS global PM2.5 concentration forecast products to build an early warning system for pollution-related effects on human health in Brazil was therefore established. Further investigations should be performed to determine alert threshold(s) and possibly build combined risk indicators involving other risk factors for human respiratory diseases. This is of particular interest in Brazil, where the COVID-19 pandemic and biomass burning are occurring concomitantly, to help minimize the effects of PM emissions and implement mitigation actions within populations.
Highlights
Solid and liquid particles in the air come from a variety of sources: transport, residential heating, agriculture, biomass burning, etc
By considering the archived Copernicus Atmosphere Monitoring Service (CAMS) near real-time (NRT) forecasts on a four-day horizon from 10 September 2019 to 9 September 2020, i.e., for an entire year associated with the latest Integrated Forecasting System (IFS) version (46R1), we found that the daily mean PM2.5 concentrations per municipality exceeded the World Health Organization (WHO) Air Quality Guidelines (AQGs) of 25 μg m−3 at least one day over the entire year for 2071 municipalities (37%; Figure 11)
We demonstrated the possibility of using PM2.5 concentration forecasts provided by CAMS to predict severe acute respiratory disease (SARD) cases in Brazil up to four days in advance and, to be part of an early warning system of the health-related impacts of particulate matter (PM)
Summary
Solid and liquid particles in the air come from a variety of sources: transport, residential heating, agriculture, biomass burning (including forest fires), etc. In 2017, long-term exposure to ambient PM2.5 was estimated to contribute to 2.9 million deaths (deaths that likely occurred earlier than would be expected in the absence of PM2.5 pollution) and to a loss of 83 million disability-adjusted life-years (DALYs, defined as the sum of the years of life lost from early deaths plus the years lived with a disability) worldwide, making PM2.5 responsible for 5.2% of all deaths and 3.3% of all DALYs in the world [2] For this estimation, the study considered ischemic heart disease, cerebrovascular disease (ischemic stroke and hemorrhagic stroke), lung cancer, chronic obstructive pulmonary disease (COPD), and lower-respiratory infections (in particular, pneumonia) and included type 2 diabetes for the first time [3]
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