Atmospheric pollutants affect physical performance: a natural experiment in horse racing studied by principal component analysis

Abstract

Simple Summary Thoroughbred horse racing is a natural experiment to study the effect of air pollutants on animal performance. In this activity, the animals are exposed to multiple mixtures of pollutants in the air and varying conditions of humidity and ambient temperature. Thus, in this work, in a homogeneous group of races, we used principal component analysis, which allowed us to gather information from all the environmental parameters measured, forming new variables called principal components. We found that the principal component is mainly determined as nitrogen oxides and carbon monoxide, while secondarily as particulate matter and sulfur oxides. Furthermore, this component is negatively related to the speed of the analyzed races. Thus, it is shown that air pollutants affect animal performance.

The impact of some atmospheric pollutants (PM10, PM2.5, O-3, NO2, NO, SO2, CO), humidity and temperature were studied on the performance of thoroughbred racehorses. The study included 162 official handicap races held in 2012 in Santiago de Chile, at distances of 1000, 1100 and 1200 m, on a track in good condition, with a layout that included a bend, during the summer and winter months. The environmental variables were measured at the time of the race and were obtained from a monitoring station located 470 m from the equestrian center. The environmental variables showed an autocorrelation of variables, so they were reduced using principal component analysis. Subsequently, the principal components were correlated with running speed using Pearson's method. Totals of 60.17 and 23.29% of the total variability of the data was explained by principal components 1 and 2 (PC1 and PC2), respectively. PC1 was mainly determined by NO, NO2, and CO (loadings similar to 0.90) and secondarily by PM10, PM2.5, and SO2 (loadings similar to 0.6), with which the data showed inverse associations, while with temperature and O-3 it showed direct associations (loadings similar to 0.7). In addition, this component correlated negatively with running speed (r = -0.50), while PC2 was not associated with this variable. In conclusion, using the principal component analysis strategy, it was determined that running speed is affected by air pollutants.