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Articles

Modeling the size of small spills of pure volatile liquids for use in evaporation rate and air concentration modeling

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Abstract

Exposure modeling is a valuable tool for assessing chemical vapor exposures that occur during transient events such as small spills of volatile liquids. Models are available to estimate liquid evaporation rates and resulting air concentrations. However, liquid evaporation rate models require the surface area of the puddle in order to provide vapor generation rates in terms of mass per time. This study developed an approach to model the surface area of small spills of pure liquids. A theoretical equation exists relating puddle depth to a liquid’s surface tension, density, and contact angle. A contact angle is a characteristic of liquid-solid interactions at the edge of a puddle. If the depth of a puddle can be calculated and the volume of the liquid spilled is known, the surface area of the puddle can be determined. Values for density and surface tension are published. Contact angles, however, are not readily available. Five hundred and eighty experimental spills were conducted using acetone, ethanol and water. The effective contact angle for each spill was determined. Spill volumes varied from 1.0–30.0 mL. The height of the liquid release varied from 0–15 cm onto a variety of surfaces. The effective contact angle of a puddle was most strongly associated with the liquid’s polarity. The height of the liquid release and type of surface had significant, but smaller effects on the puddle size. The effective contact angle of a puddle from a spill can be estimated as ln(ϴeff) = 3.73 – 1.17 · 1χυ/f – 0.06 · h + S. In this equation, 1χυ/f is the polarity index of the liquid, h is the height of liquid release (cm), and S is a surface constant. ϴeff can be used with the liquid density, surface tension and volume to calculate the surface area of the puddle. The surface area of the puddle can then be used in evaporation rate models to determine a vapor generation rate for input to vapor concentration models.

Acknowledgments

The authors would like to thank Cindy Hu, Danning Lu, and Miriam Ritchie for their help with data collection.

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