Drought impacts on children's respiratory health in the Brazilian Amazon

Abstract

Drought conditions in Amazonia are associated with increased fire incidence, enhancing aerosol emissions with degradation in air quality. Quantifying the synergic influence of climate and human-driven environmental changes on human health is, therefore, critical for identifying climate change adaptation pathways for this vulnerable region. Here we show a significant increase (1.2%-267%) in hospitalisations for respiratory diseases in children under-five in municipalities highly exposed to drought. Aerosol was the primary driver of hospitalisations in drought affected municipalities during 2005, while human development conditions mitigated the impacts in 2010. Our results demonstrated that drought events deteriorated children's respiratory health particularly during 2005 when the drought was more geographically concentrated. This indicates that if governments act on curbing fire usage and effectively plan public health provision, as a climate change adaptation procedure, health quality would improve and public expenditure for treatment would decrease in the region during future drought events.

Figure 1. Standardised JAS anomalies as a departure from the 2001–2010 mean.

(a) mean rainfall values (mm) (2001–2010), (b) mean active fire count (2001–2010), (c) mean aerosol levels (2001–2010), (d) standardised rainfall anomalies for 2005, (e) standardised active fire anomalies for 2005, (f) standardised aerosol anomalies for 2005, (g) standardised rainfall anomalies 2010, (h) standardised active fire anomalies 2010, (i) standardised aerosol anomalies for 2010. The anomalies are normalised by the standard deviation of the time-series (2001–2010) for each grid-cell. Anomalies were generated using ENVI 4.8 and ArcGIS 10.

Figure 2. Temporal trends in Acre 2005.

The right column shows average monthly inter-annual variability (2001–2010) for Acre State, while the left column represents the difference between 2005 values for Acre State compared to the mean values. (a) average rainfall, (b) cumulative active fires, (c) average aerosol values, (d) number of hospitalisations, (e) difference in average rainfall, (f) difference in cumulative active fires, (g) difference in average aerosol, (h) difference in the number of hospitalisations.

Figure 3. Percent of deviance explained.

(a) goodness-of-fit for the ENV model 2005, (b) goodness-of-fit for the ENV model 2010 and 2010, (c) goodness-of-fit for the SOCIO model 2005, (d) goodness-of-fit for the SOCIO model 2010. Red shades show an increase in the goodness-of-fit of the model. Percent of deviance explained was generated in GWR4 and mapped using ArcGIS 10.

Figure 4. Significant values with a confidence level greater than 95% for the SOCIO model in drought affected municipalities.

Non-significant municipalities and municipalities that were not affected by the droughts are masked out. (a) 2005 rainfall, (b) 2005 deforestation, (c) 2005 fire, (d) 2005 aerosol, (e) 2005 population density, (f) 2005 HDI, (g) 2010 rainfall, (h) 2010 deforestation, (i) 2010 fire, (j) 2010 aerosol, (k) 2010 population density, (l) 2010 HDI. Red shades show positive z values of local coefficients, while blue shades show negative estimates of local coefficients - the darker the shade the stronger the relationship. Z values of local coefficients were generated in GWR4 and mapped using ArcGIS 10.