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. 2025 May;57(3):712-722.
doi: 10.1111/evj.14415. Epub 2024 Sep 3.

The effects of ambient air pollution exposure on Thoroughbred racehorse performance

Linda D Kim  1 , Kimberly Kreitner  1 , Danielle M Scott  2 , Katie Seabaugh  2 , Colleen G Duncan  2 , Sheryl Magzamen  1
Affiliations

The effects of ambient air pollution exposure on Thoroughbred racehorse performance

Linda D Kim et al. Equine Vet J. 2025 May.

Abstract

Background: Limited research exists on impacts of air pollution on non-human mammals, particularly animal athletes such as Thoroughbred racehorses. Athletes have a greater risk of exposure as heightened exertion and increased airflow carry more pollutants deeper into the respiratory tract.

Objectives: To provide insights into the impact of ambient air pollution, particularly fine particulate matter (PM2.5), on race speed.

Study design: Retrospective observational study.

Methods: Data were obtained from The Jockey Club Information Systems, covering 31 407 winning races by Thoroughbred horses in California spanning 10 years (2011-2020) and evaluated the association between air pollution and winning race speeds. For race days, we collected PM2.5 data from the nearest U.S. Environmental Protection Agency (EPA) monitoring site within 100 km of each racetrack (n = 12). We assessed the associations between daily average PM2.5 concentrations and speed of winning horses with linear mixed effects regression. We adjusted for horse characteristics, race-related covariates, temporal indicators (e.g., year), other air pollutants and temperature. We conducted sensitivity analyses by adjusting extreme air pollution days by reassigning values to the 95th percentile value and conducting linear mixed effects regression on series of datasets with incremental cutpoints of PM2.5.

Results: In the cutpoint analysis, we found that for PM2.5 between 4 and 23.6 μg/m3, speed decreased 0.0008 m/s (95% CI: -0.0014562 to -0.00018) for every 1 μg/m3 increase of PM2.5.

Main limitations: Limitations include the use of offsite monitors leading to imprecise exposure measurements, not using training practice data, and generalisability as the study focuses on California racetracks.

Conclusion: This study highlights the need to create advisories to safeguard the performance of horses during periods of poor air quality. Further research is recommended to explore additional factors influencing the relationship between air pollution and equine welfare.

Keywords: air pollution; epidemiology; particulate matter; performance; racehorses; speed.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Map of California showing all racetracks in the study, and a closer look at the Golden Gate Fields racetrack. Coloured circles show the location of monitors and racetracks in relation to major roads.
FIGURE 2
FIGURE 2
Using the truncated dataset, the full sample (n = 31 407) was divided into two subsets repeatedly based on the specified daily average PM2.5 cutpoint levels as shown on the x‐axis. We applied the full regression model to these series of data subsets for different ranges of PM2.5 showing the complex relationship between speed and PM2.5. Each point (yellow circle or blue triangles) with 95% confidence intervals (bars) shows the change in speed for every increase of PM2.5 for each data subset. The estimates from the yellow circles came from data subsets that included PM2.5 ranging between 0 μg/m3 to the indicated cutpoint level. The estimates from the blue triangles came from data subsets that included PM2.5 between at cutpoint level to 23.6 μg/m3 of PM2.5. Overall, changes in speed vary for different ranges of PM2.5 concentrations. For the low ranges of PM2.5 (values starting at 0 μg/m3 or yellow circles), the estimates show a gradual decrease in speed. For the higher ranges of PM2.5 (values starting at specified cutpoint or blue circles) especially cutpoints 4–11 μg/m3 of PM2.5, racehorses are running significantly slower for every increase of PM2.5 as shown by the confidence intervals not crossing the red null line (p < 0.05).
FIGURE 3
FIGURE 3
After including racetrack as an interaction term in the full regression model for truncated data, we found racetracks modify the relationship between speed and PM2.5. Each racetrack has a different change in speed estimate for every increase of PM2.5. All racetracks ran slower than reference racetrack SR, a track with low average pollutant concentrations across all air pollutants in the study. Eight racetracks have statistically significantly different changes in speed, demonstrated by non‐overlapping confidence intervals, compared with the reference racetrack SR. Racetracks DMR, FER, FPX and GG showed racehorses ran slower as PM2.5 concentrations increased.
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