Models for Predicting Surface Temperatures on Synthetic Turf Playing Surfaces

Abstract

Despite widespread use on athletic fields, synthetic turf surface temperatures are considerably higher than those measured on natural turfgrass when exposed to solar radiation. Elevated temperatures may pose health risks to athletes competing on synthetic turf. Research was conducted at the University of Tennessee Centre for Athletic Field Safety to create a model for predicting synthetic turf surface temperature using atmospheric data. Synthetic turf surface temperature was measured on ten different synthetic turf plots (42 m2) varying in fibre type and infill characteristics. Plots were arranged in a randomized complete block design with three replications. Two temperature sensors placed in the centre of each plot measured surface temperature on 10-minute intervals for three 8-week periods over the course of two years. Atmospheric data including air temperature (°C), relative humidity (%), precipitation (mm), and solar radiation (W m-2) were collected on the same interval. Synthetic turf surface temperature varied due to both air temperature and solar radiation. Predictive models using these data accounted for 86, 95, and 94% of the variation in daily maximum, minimum, and mean synthetic turf surface temperature. Accuracy of these models for predicting daily mean and minimum synthetic turf surface temperature using 48 and 72-hour forecasted air temperature data was excellent (+/- 1°C). Models using 48 and 72-hour forecasted were less accurate in predicting daily maximum synthetic turf surface temperature (+/- 4.75 to 5.33°C). Our findings indicate that 72 hour forecasted air temperature data can be used to predict daily minimum and mean surface temperature of synthetic turf. Such models could be used to schedule athletic events around periods of potentially hazardous surface temperatures.