A Study of the Mass Emission Rates of Small Spills of Chlorinated. Hydrocarbons Based on the Vapor Pressure and Surface Area to

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2 A Study of the Mass Emission Rates of Small Spills of Chlorinated Hydrocarbons Based on the Vapor Pressure and Surface Area to Volume Ratio of the Spill Chad Joseph Positano Medical College of Ohio 2004

3 DEDICATION There are so many individuals that must be acknowledged for their support, encouragement, and resources that helped to make this project possible. I must extend my deepest gratitude to my advisory committee of Dr. Bisesi, Dr. Akbar, and my Major Advisor Dr. Keil, for their support, patience and advisement during the entire research project. Dr. Keil spent many hours reviewing and analyzing the data and assisting in refining the experimental design. Dr. Bisesi has always been a source of inspiration and guidance through my entire experience at the Medical College of Ohio. To all of you, my sincerest thanks. Without the support of my family, this project would not have been possible. Thank you all for your help through this experience. My wife, Jessica, has been the strongest source of encouragement and inspiration for nearly 6 years to you my warmest heartfelt thanks and appreciation. With you, I feel anything is possible. Thank you, always. ii

4 TABLE OF CONTENTS Introduction...1 Literature Review...9 Materials...19 Methods...22 Results...30 Discussion...37 Conclusion...42 References...43 Appendices...46 Abstract...64 iii

5 INTRODUCTION Overview Modeling the airborne concentrations of chemical contaminants due to small spills is essential in the study of prospective and retrospective exposures of workers in the spill area. The rapid deployment of clean-up personnel into a spill area often inhibits integrated or instantaneous air monitoring of breathing zone exposure in such situations. Therefore, mathematical models are often the best source of assessment of the exposure involved with the spill. It has been hypothesized based on the results of recent experimental data, that the mass emission rates of small spills of chlorinated hydrocarbons differ from other classes of volatile organic hydrocarbons (Keil and Nicas, 2003). Many studies have been conducted to determine the effectiveness and practical application of various models on characterizing occupational exposure to airborne contaminants evaporating from solvent spills (Lennert et al., 1997; Hummell et al., 1996; Fehrenbacher and Hummell, 1996; Keil and Nicas, 2000; Flynn and George, 1996; Wu and Schroy, 1979; and Reinke and Brosseau, 1997). These models have all looked at variables that can affect the evaporation of the solvents into the airborne environment. Few researchers, save Keil and Nicas (2003), have spent time studying the mass emission rates of spilled solvents as the surface area to volume ratio of a small spill decreases. The research that has examined this factor has been conducted on a variety of chemical classes. Due to the extensive use of chlorinated hydrocarbons in industry, research is needed to more accurately assess the potentially adverse health effects of emissions of chlorinated hydrocarbons from spills with diminishing surface areas. 1

6 Statement of the Problem Much research has been conducted on factors such as vapor pressure (Popendorf, 1984), airflow into a contaminated room (Hummell et al., 1996), and generation rates of a spilled material (Lennert et al., 1997; Hummell et al., 1996; Fehrenbacher and Hummell, 1996; Flynn and George, 1996). Little research however, has taken into consideration the effects of evaporative cooling on the reduction in the surface area to volume ratio of small spills, and how this phenomena relate to the mass emission rate of the spilled liquid (Keil and Nicas, 2003). It may be necessary in emergency response situations to perform remediation work without fully characterizing the potential health hazards involved. While occupational health professionals attempt to control hazardous environmental conditions to the greatest possible extent, situations arise when personal air sampling cannot easily be conducted to characterize potential health concerns. In these situations, mathematical models can assist in making judgments to characterize, sometimes retrospectively, the exposure to airborne contaminants in a given work environment. In understanding mass emission rates of small spills, which may be found in emergency response situations and daily clean-up activities, several physical and chemical factors of the work environment and the chemical involved in the spill must be recognized and accounted for. Purpose and Significance In order to adequately assess potential occupational and environmental exposure to hazardous chemicals, occupational health professionals must incorporate a variety of methods and approaches. An important component of these risk assessment strategies is 2

7 exposure modeling. Mathematical modeling enables the occupational health professional to estimate worker exposure from a chemical process by incorporating a variety of information relating to the chemical and the process to determine if an unacceptable exposure could occur. These mathematical models can be simple or complex. They allow the occupational health professional to take into consideration environmental factors, ventilation design, and chemical and physical properties of the chemical(s) involved in the process. Modeling occupational and environmental exposures offers the occupational health professional several benefits to other risk assessment strategies, such as personal air sampling for exposure assessment. Using modeling as a tool, environmental and occupational health professionals can conduct hazard assessments to identify and prioritize potential and actual occupational exposures. The objective is to utilize the model that incorporates the environmental conditions necessary to predict the occupational exposure to a degree of accuracy that the occupational and environmental health professional determines appropriate, considering the conditions present in the workplace. As with any risk assessment tool, there is a balance between accuracy and simplicity. The professional should employ the model that best incorporates variables influencing the hazard situation at hand, with the least complex mathematical function, to predict the exposure concentration to a level of accuracy that is acceptable to the professional. The occupational health professional should use the results of exposure modeling, or any other risk assessment strategy as an estimate of the actual exposure. The resulting exposure level and expected error of the risk assessment instrument should be compared to the applicable occupational 3

8 exposure limits to determine if further, more accurate assessment strategies should be utilized to characterize the exposure to the worker. Time Restrictions In industry it is often necessary to characterize possible occupational or environmental exposure to a process in a short amount of time due to the fast pace and changing face of many production activities. Therefore, it is not always feasible to wait the long period of time that it often takes to collect samples and analyze the collected media. Instances may arise where the production process in question has not yet begun, but a risk assessment of occupational exposure is still needed to characterize the potential hazard to the worker. Under these conditions, mathematical modeling can be a useful assessment tool to give health and safety professionals, as well as engineers, management and the process worker, information regarding the potential health risk of the production process. Many of the models can be utilized in a matter of minutes by a trained and competent professional, offering an estimate of the potential hazard associated with the process without the time consuming technique of sampling and analyzing the samples taken. Determine the Need for Personal or Area Monitoring Modeling can also help the occupational health professional assess the specific processes in his/her facility that offer the highest risk for unacceptable exposure. This knowledge can then direct the need and importance of identifying similar exposure groups (SEGs) that require monitoring to characterize their potential exposure to occupational and environmental health hazards. Occupational health professionals can use this information 4

9 to prioritize their supplemental monitoring efforts and target areas that require immediate environmental control. Economical Feasibility Completing a sampling strategy for the risk assessment of chemical exposures can be time consuming. In addition to the time and efforts required to conduct air sampling and the operation of applicable sampling instruments, the analysis of sampling media can be expensive to complete. In contrast, modeling can be done at a relatively minimal cost, with consideration to both time and materials. A competent occupational health professional can use a relatively simple mathematical model to characterize a health hazard in a matter of minutes with a degree of accuracy acceptable to the occupational health professional for the existing conditions. Utilizing more complex models and the research involved in implementing these models may take somewhat more time, but may offer a more accurate representation of the expected exposure. Most information needed to characterize a production process in this manner can be found in industrial hygiene text books on the subject and in the material safety data sheets (MSDSs) of the contaminant for which the model is being constructed. Research Hypothesis The mass emission rate constant, α, is used as a function of time in the equation describing the mass emission of some volatile solvent. In order to fully explain the thermodynamic relationship that the effect of evaporative cooling has on the exponentially decreasing mass equation, mathematical algorithms that are beyond the scope of this research would need to be employed. For model simplicity purposes, a constant α value 5

10 was defined and experimentally determined to explain the changing emission rate of a spill. Previous studies have indicated that the highest potential employee exposure occurs in the first seventy-five percent of mass emission, and for research purposes it will be assumed that the value obtained for α will remain constant over the course of the evaporative process. In reality we know that the value for α increases as the mass of liquid remaining decreases. The surface area to volume ratio of the spill is also being taken into consideration to indirectly account for the surface tension of the liquid under study. Different compounds may offer varying surface areas from one another when spilled, even when the volumes from one to the next remain equal. In this fact, by using the surface area to volume ratio of the spill as part of the predictive behavior of the mass emission rate constant, the researchers are inherently accounting for the effect of surface tension of the liquid on the model for mass emission. The surface tension will indeed influence the resulting initial surface area to volume ratio from a spill of a given volume of liquid, as well as the dynamics of the shrinking surface size. Therefore, the effect of surface tension on the change in the surface area to volume ratio is captured in the experimentally determined α values for the chemicals studied. The researchers recognize that factors affecting the surface area of a spill include surface tension of the liquid involved, the vertical distance between the surface onto which the liquid is spilled and the point at which the spill occurs, the volume and mass of the liquid spilled, and the effects of gravity on the liquid. These influences are the focus of ongoing research that may lead to an incorporation of these factors into future decreasing spill size exposure modeling, but were beyond the scope of this research. The surface areas of the compounds used in this study 6

11 were examined after the spills occurred. The effect of surface tension on the surface area to volume ratio of a spill is more applicable if looking at spills retrospectively, and not prospectively, as this research has done. Compounds classified as chlorinated hydrocarbons may require different mathematical rate constants than those used to describe other classes of volatile organic compounds to characterize the mass emission rates of small sizes of spilled liquids. The recently developed equation by Keil and Nicas (2003), takes into account a mass emission rate constant, α, but does not explain the differences observed between the α of other organic solvents and chlorinated hydrocarbon compounds, which do not appear to follow this predictive equation, as did the other solvents tested. Since currently accepted mathematical models do not adequately predict the mass emission rates of this class of chemicals, rate constants may need to be experimentally determined in order to offer a better prediction of the mass emission rates of small spills. Since it has been found that chlorinated hydrocarbons likely require their own specific predictive equations to determine the rate constants to be used in the newly developed equation based on recent research, then these predictive equation relationships must be experimentally determined through laboratory research. Therefore, I hypothesize that: Mass emission rate constants, vapor pressure and the surface area to volume ratio can be used to describe the difference in mass emission rates of small spills of chlorinated hydrocarbons, as compared to other organic solvents. Mainly, the surface area to volume ratio and the vapor pressure of the solvent can be used to 7

12 predict the mass emission rate constant of the chemical spilled with a reasonable degree of accuracy, significant to the p < 0.05 level. The number of chlorine substitutions in a hydrocarbon compound will not affect the mass emission rate of the compound, as determined through a comparison of the mass emission rate constants obtained. Objectives There has been little research conducted on the mass emission rate models for spills of volatile chlorinated hydrocarbons in small quantities. Most research conducted thus far has examined larger spills, and not specifically for chlorinated hydrocarbons. The purpose of this study was to examine the mass emission rates of small size spills of various chlorinated hydrocarbon compounds in a well-mixed room model. The results of this research can be compared to hypothesized values for mass emission rate constants for the chlorinated hydrocarbons being examined, based on the values determined by using the predictive equations obtained from past research of a broader range of hydrocarbons. 8

13 LITERATURE REVIEW Overview Approximately 2000 new chemical substances are introduced into manufacturing and industrial processes each year (Hummell et al., 1996). These new substances often do not have adequate personal air sampling or toxicological data available for occupational and environmental health professionals to assess the health risks to workers who handle these chemicals on a daily basis. Measuring the concentration of airborne contaminants in the breathing zone of workers can be time consuming and economically infeasible (Lennert et al., 1997). Modeling for these occupational and environmental exposures can help ensure an accurate assessment of the air quality of a given room before workers enter, thereby ensuring that appropriate controls and protection are in place. Occupational and environmental response to cleaning up small spills of various organic solvents can pose higher health risks than other chemical classes due to the oftenvolatile nature of the chemicals, allowing for rapid dispersion into the air, and ultimately the breathing zone of the workers entering or working in the room. Due to the decreasing mass emission rate of small spills of solvents, the highest airborne concentrations occur at the initial time of the spill, and then decrease thereafter. Workers therefore are at highest risk for acute effects of exposure at the onset of the spill. This exposure may induce such health effects as occupational asthma or acute irritation, which the worker may not otherwise have had. Research to develop accurate models pertaining to mass emission rates of small spills of chlorinated hydrocarbons in the occupational environment is needed 9

14 to assist occupational and environmental health professionals in assessing possible exposure scenarios that occur in workplaces everyday. Conceptual Framework It is important to recall when modeling for airborne contaminant concentrations that these concentrations are governed by the source strength and the dispersion of the contaminant in the air (Lennert et al., 1997). In constructing a model on which to form a basis for solvent evaporation, many physical and chemical characteristics must be considered. Vapor pressure of the chemical in question is essential in predicting the rate of emission of a solvent from a spill (Popendorf, 1984). The physical characteristics include those of the surrounding environmental conditions. These environmental factors include airflow through the system or room, ambient temperature of the environment, air velocity over the spill, barometric pressure of the spill environment, and the humidity of the room or system. A factor that has been the subject of little research is the role that evaporative cooling plays on the mass emission rate of particularly small spills. For the purpose of this research, a small spill will be defined as a volume of less than 100 milliliters. This can be considered the size of a typical spill of laboratory chemicals or that of a small industrial spill. Most models are based on evaporation characteristics of relatively larger spills and consider a constant emission rate during the evaporation process. Studying the evaporation of smaller spills and their most influential parameters (i.e., vapor pressure and surface area to volume ratio), a more accurate determination of whether or not there has been an unacceptable exposure can be examined. Research conducted on mass emissions of small spills would be useful in a laboratory or research setting. Clearly large industrial 10

15 spills will not be sufficiently explained by the exponentially decreasing mass emission rate model. Evaporative cooling can slow the evaporation of contaminant into the air, but the absolute effect the phenomena has on the rate of emission is still undecided (Keil and Nicas, 2003). As mass emission progresses, the surface area to volume ratio of the small spill decreases as a function of time. As the surface area offering itself to evaporation decreases, so does the emission of the contaminant into the surrounding air. As the shrinking of the surface area occurs, the liquid cools (evaporative cooling) which further slows the evaporation of the contaminant (Nicas, 2000). Therefore, evaporative cooling appears to be directly related to the decreasing surface area of the spill involved. Rate constants used in modeling the decreasing rate of emission must take this rate change into consideration. A simple equation constructed to explain the exponentially decreasing rate of emission from a small spill, which considers the minimal factors previously mentioned is as follows: Gt = Go e where: G o = initial mass emission rate (mg/min) G t = mass emission rate at some time, t (mg/min) α = mass emission rate constant (min -1 ) t = time of evaporation (min). (Nicas, 2000) Incorporation of this concept into a mass balance equation will result in the following model: -α t 11

16 Q t / v α t o( e e ) α L Ct = α V Q where: C t = vapor concentration at some time, t (min) L o = initial liquid mass (mg) α = mass emission rate constant (min -1 ) t = time of evaporation (min) Q = airflow through the system (m 3 /min) V = room volume (m 3 ). (Nicas, 2000) The mass emission rate constant, α, can be found based on the environmental conditions and the characteristics of the solvent being studied. Experimental examination of the rate constant varies with the surface area to volume ratio and decreasing emission rate of the spill (Nicas, 2000). It is a predictive equation for this solvent emission rate constant that the research of this study will identify for the chlorinated hydrocarbons examined. Review of Prior Research During the past quarter century, the need for and use of mathematical models to predict worker exposure to airborne contaminants has been recognized and developed. Much experimental research has been conducted to further develop models to accurately assess the health risk posed to workers. These models can be either simple, or complex. They can incorporate many environmental conditions and chemical characteristics, or summarize only basic generation rates and concentrations. By comparing the exposure 12

17 assessment to the exposure limits associated with a given chemical hazard, the occupational health professional can determine the health risks produced by the potential exposure (Nicas, 2000). This can be done in advance of a work shift in which a worker may be exposed to hazardous conditions, or it can be done retrospectively to determine the extent of the exposure that occurred. Modeling for contaminant mass emission rates and concentrations has undergone development since the early 1970s beginning with such researchers as Mackay, and Matsugu (1973), Gray (1974), Mackay, Patterson, and Nadeau (1980), as well as models developed by the United States Environmental Protection Agency (USEPA), and the National Institutes of Occupational Safety and Health (NIOSH). Mathematical models are useful aids in predicting concentration for worker exposure, but as with all mathematical explanations of natural phenomena, they have drawbacks. Due to the complex nature of the physical processes occurring, even complex equations have difficulties incorporating all known or unknown variables into a solution (Flynn and George, 1996). Nevertheless, a great deal of experimental research has been conducted to validate the effectiveness and accuracy of the models that have been formulated. Several approaches have been adopted to test for this accuracy. The recent interest in modeling for the potential exposure to small spill evaporation sources has no doubt led to more testing and refining of the dependability of the newly developed models. To date, mathematical models of airborne contaminant exposure have been tested by comparing experimental test results to predictions made by using the model in question (Lennert et al., 1997). Most tests of the reliability of these models take place in closed test environments where environmental conditions are controlled. In a test chamber study 13

18 conducted by Lennert et al.(1997), the researchers examined the effectiveness of several concentration and evaporation models developed in past years. The researchers examined one environmental factor in particular air velocity through the system. The results of the experiments show that most models used to predict the airborne concentrations significantly underestimated the mass emission rates of the contaminant source (Lennert et al., 1997). Air velocity influenced the results of airborne concentration more so than did the vapor pressure of the chemical tested, or the diameter of the evaporation disk (surface area of the spill). Hummel, Braun, and Ferhenbacher (1997) developed a model for an evaporating liquid in a flowing air stream, and used several environmental factors in the equation. These factors included temperature, air velocity, direction of the airflow, pool size, barometric pressure, and turbulence in the system. The researchers measured the mass emission rate of the liquids chosen for the study at several combinations of varied air velocity and air temperature. All other variables mentioned were held constant for experimental purposes. The results of the experiment indicate that the models used underestimated the mass emission rate when the air velocities were at low ranges (Hummell et al., 1996). This is consistent with the results of the study by Lennert et al. (1997); mainly that air velocity is of critical importance in determining the mass emission rate of volatile organic solvents. Through the experimental results the authors were able to develop a model that incorporates all of these parameters (through the substitution of previously developed models), and adjusts for the change in the size of the pool of contaminant. The equation is as follows: 14

19 Evaporation Rate (g/sec) = 5 ( MWA) ( VP) (1/ MWA + 1/ 29) 0.05 T 0.25 Vx xp where: MW A = molecular weight of the evaporating liquid p = overall pressure, atmospheres x = length of pool along airflow, cm T = surface temperature of the pool, Kelvin VP = vapor pressure of substance A, atmospheres V x = velocity of air, cm/sec. Other studies have focused on determining the effectiveness of using the vapor pressure of the chemical evaporating into the ambient air of the room to adequately evaluate the health risk posed to the workers cleaning the spill. In a 1984 study by Popendorf, the author describes the use of a model that determines a Vapor Hazard Rating to describe the potential health risks of an evaporating spill. The process of the Vapor Hazard Rating System incorporates the vapor pressure of the chemical and the Occupational Exposure Limit (OEL) defined for the chemical. The equations used in this hazard rating system are as follows: ( VP, mmhg) VHI = log = log( VHR) ( OEL, ppm) where: VHI = Vapor Hazard Index VHR = Vapor Hazard Rating. Under this rating system, the higher the VHR or VHI, the greater the potential emission rate, and dependent on the toxicological nature of the chemical, the greater the 15

20 health risk to the worker (Popendorf, 1984). Although this system is helpful for a rapid determination of the evaporation rate of the chemical for which response is occurring, it is still not useful in predicting the concentration to which workers may be exposed. Neither does the OEL necessarily offer a true determination of the toxicological nature of a given chemical. As is evident from reviewing already published literature, many factors have been incorporated into mathematical models to more accurately assess the potential health effects that evaporating chemical compounds pose to workers cleaning up spills. One physical factor that has not been the topic of much research is the manner in which the decreasing surface area of a small spill affects the rate at which evaporation occurs. The research conducted to date, save a recently published piece of research by Keil and Nicas (2003), describe the evaporation for a spill with a constant mass emission rate. For small spills with decreasing surface areas, the emission rate does not remain constant (Keil and Nicas, 2000; Nicas, 2000). The rate tends to decrease exponentially with the decrease in the surface area to volume ratio of the spill. Due to the effects of evaporative cooling caused by the decreasing surface area of the spill as it evaporates, more research must be conducted to construct a model that accurately reflects this phenomenon. For the purpose of the current research project in an effort to afford a more simplified mathematical model, the researchers will assume that the value for α remains constant. The mathematics involved in explicitly accounting for a dynamic emission rate constant are beyond the scope of this research. 16

21 The research by Keil and Nicas (2000) indicates that the rate constant influencing the exponential decrease in emission is a function of the vapor pressure of the contaminant and the initial surface area to volume ratio of the spill. As has been noted by previous research by Popendorf (1984), vapor pressure is an important consideration in any mathematical model used to assess the evaporation of volatile organic compounds. Keil and Nicas (2000) conclude through experimental design that the model for exponentially decreasing emission of volatile chemicals from small spills with decreasing surface areas and volumes acts as a more accurate predictor of evaporation than do previously developed models which use constant mass emission rates. Initially, the researchers hypothesized that the rate constant could be accurately predicted based on the vapor pressure of the chemical compound, but experiments with carbon disulfide and two chlorinated hydrocarbons refuted this hypothesis (Keil and Nicas, 2000). It is therefore necessary to experimentally determine a predictive equation for the mass emission rate constant for various classes of volatile organic liquids. It is the purpose of this experimental research to identify, by means of the identical process used by Keil and Nicas, a method for determining the predictive equation for the rate constant for one class of volatile organic compounds - the chlorinated hydrocarbon. Summary Based on recent research conducted by Keil and Nicas (2000), further experimental data is needed to explain the behavior of evaporation of small spills of chlorinated hydrocarbons. It is the proposal of this research to study the mass emission rates of small spills of chlorinated hydrocarbons and report to the community of occupational and 17

22 environmental health professionals, specific rate constants to be used to aid in the modeling of potentially hazardous exposures to this class of chemicals due to small spills of solvents. 18

23 MATERIALS Experimental Solvents As stated in the literature review, the class of chemicals used in this study was the chlorinated hydrocarbon. These chemicals have wide industrial uses in this country. The solvents studied are both aliphatic and aromatic, and have varying molecular weights, vapor pressures, and densities. Those identified as solvents of interest for the purpose of this research were chosen for their use in industry, as well as their similarities in the class of compounds. One purpose of the research was to determine any similarities that may exist in the mass emission rates of hydrocarbons with the same number of chlorine substitutions. The chlorinated hydrocarbons chosen offered a variety in the number of chlorine substitutions ranging from 1 3. Table I lists the chemicals chosen for this research, along with some common physical characteristics and exposure limits. Many of these solvents are used every day in industries such as dry cleaning processes, chemical manufacturing, surface coating, degreasing, washing operations, and pharmaceutical production. Since workers in these industries are given the task of cleaning up small spills of these chemicals after they occur, accurate modeling of possible unacceptable exposure to airborne concentrations of these chemicals is essential. The health effects of chlorinated hydrocarbons in some cases are relatively minor, with symptoms including irritation, and causing a slight intoxication effect. Intentional exposure to many of these solvents can lead to damage to the central and peripheral nervous systems. 19

24 Table I Select Physical Properties, Occupational Exposure Limits and Health Chlorinated Hydrocarbon Dichloromethane Trichloromethane 1,2 Dichloroethane Vapor Pressure (mm Hg) at 20 deg C Effects of Experimental Compounds Molecular Weight (grams/ mole) ACGIH Threshold Limit Value (TLV - TWA-8) Target Organs/ Health Effects ppm CNS; Anoxia ppm Liver; Reproductive ppm Liver 1,1, ppm CNS; Liver Trichloroethane Chlorobenzene ppm Liver o-dichlorobenzene 2- Chlorotoluene ppm Irritation; Liver ppm Irritation; dizziness Source: NIOSH Pocket Guide to Chemical to Chemical Hazards (June 1997), ACGIH-TLVs and BEIs (2001), and product Material Safety Data Sheets. Some chlorinated hydrocarbons, for example trichloromethane can also cause more severe health effects. These effects include irritation to the heart muscle, which in turn can cause a fatal arrhythmia or ventricular fibrillation if the exposure is significant. 20

25 Apparatus/Equipment and Supplies The experimental component of this research required specific equipment and supplies. A complete listing of the equipment and supplies used in this research is as follows: The above mentioned lab-grade solvents in quantities of 100 milliliters each Finn-tip Pipettes Watch Glasses Electronic Gravimetric Balance Personal Computer with Data-Logging and Spreadsheet Software Dry Bulb Thermometer Shield cover for the mass balance Hot-Wire Anemometer (Alnor Model 8575 serial number 11661; Probe Model 275 serial number 5103). 21

26 METHODS Selection of Experimental Solvents The selection of the experimental solvents was based on inclusion in the chemical class of the chlorinated hydrocarbon. As previously noted, this class of chemicals has a variety of uses in industry, hence the need for accurate modeling of potential worker exposure. The criteria for inclusion into the experimental design were selected to provide the most informational data sets possible for analysis. In order to determine trends in emission rates in relation to the number of chlorine substitutes on a hydrocarbon, compounds were chosen within the same aliphatic or aromatic hydrocarbons, with differences being in the number of substituted chlorine atoms. This allowed for regression plots comparing various chlorinated alkanes and aromatics to one another, as well as within each group. The values were taken from the material safety data sheets supplied with the lab-grade chemicals from the chemical manufacturer. Experimental Design This research study should help to explain the mass emission rate of small spills of chlorinated hydrocarbons. To determine a practice for determining the rate constant for each individual or class of chemical compound for which small spills may occur, the mass emission of these solvents must be studied on a small scale, and compared to the time taken for at least seventy-five percent mass evaporation. Previous research has shown that the decreasing rate of emission offers the highest potential exposure during the first seventy- 22

27 five percent of mass emission, after which the rate remains relatively steady until complete evaporation (Keil and Nicas, 2000). The design of this study was identical to that of Keil and Nicas (2000) save that the only solvents used were the chlorinated hydrocarbons listed above. Prior to the start of each test, a record check-sheet (Appendix II) was used to record environmental information. A methodology check-sheet will be used to track the solvent used, volume tested, surface area created, and other information pertinent to the study. To conduct this study, a small amount (1, 2, 4, or 8 milliliters) of each solvent was placed on a watch glass, by means of a Finn pipette. The balance was in-line with a personal computer, which allowed for the direct logging of data, and hence systematic recording, of the mass emission of the solvent versus the time in which emission occurred. For each of the seven chemicals used in the study, 12 tests were run. Three tests were run for each chemical at 1, 2, 4, and 8-milliliter spills. The doors of the control enclosure were kept open to allow for the solvent to evaporate into the ambient room environment. The enclosure however, prevented variable airflow from interfering and influencing the evaporation of the solvent. To ensure accurate results, environmental conditions were monitored as described below. These variables were not controlled, but simply monitored to ensure that they were within acceptable, preset ranges necessary to conduct the research. The initial surface area of the spill was measured by utilizing a standard marked ruler placed on the mass balance beneath the watch glasses used to hold the experimental solvents. The location of the ruler to watch glass allowed for the researcher to directly read 23

28 the initial surface area diameter of the spill, without interfering with the experimental design. Independent Variables The barometric pressure of the laboratory was monitored for a range of +/- 3 % from one trial run to the next. The barometric pressure was measured and recorded from the website Since it is generally accepted that the indoor barometric pressure varies from outdoor barometric pressure by a degree of less than 1/100ths of an inch, the outdoor pressure was recorded and used to monitor the environmental conditions of this research. It has been shown in previous research studies involving mathematical modeling, that air velocity can be a major predictor of evaporation rate, and hence breathing zone contaminant concentration (Lennert et al., 1997; Hummell et al., 1996). A partially enclosed chamber helped to prevent airflow from affecting the evaporation of the solvent. For the purposes of this study, air velocity through the system was measured on the open side of the chamber by a thermo-anemometer, and was monitored for a range of 0.1 < v < 1.0 meters per second. This range of air velocity should accurately represent the velocity of air in a closed room due to industrial ventilation systems. The experimental results obtained under these conditions may underestimate mass emission rates and therefore potential exposure in areas with noticeable crossdrafts. The temperature of the laboratory was kept near normal temperature, and was measured by use of a dry bulb thermometer. This range must have been between 22 º < t < 28 º C in order for research to be conducted. The differences in this range should only 24

29 minimally contribute to the changing evaporation rate of the solvents during experimental study. Collection of Data Collection of data took place with the aid of a personal computer. The mass balance was linked to the computer and the mass remaining on the balance was recorded every 1.5 sec as the evaporation of the solvent progressed. After importing the data collected into a Microsoft Excel spreadsheet, analysis produced a time versus mass relationship of the emission rate of the solvents under study. By logging the data directly into a computer spreadsheet, analysis of the data was much simpler to conduct. Analysis of Data The data was analyzed using linear and bivariate regression to produce graphs of the time versus mass relationship of the evaporating hydrocarbons. By determining how the rate of evaporation varies with the surface area to volume ratio and vapor pressure of the solvents being studied, a predictive equation to explain the relationship was produced. Regardless of whether or not the predictive equation is used in practice from the research conducted, the mass emission rate constants for small spills of the solvents being examined were obtained and could be published for future use in mathematical models by occupational and environmental health professionals. A graph of the natural log of the percent mass remaining versus time was useful in identifying the mass emission rate constant. We already know from a generation rate 25

30 perspective that the equation for an exponentially decreasing emission rate can be expressed with the following equation: Gt = Go e -α t where: G o = initial mass emission rate (mg/min) G t = mass emission rate at some time, t (mg/min) α = the mass emission rate constant (min -1 ) t = time of evaporation (min). (Nicas, 2000) By substituting the mass of the chemical under study for the mass generation rate, we can use the equation to determine the α value for the exponentially decreasing emission rate model under study. The substituted equation is as follows: Mt = Mo e -α t where: M o = initial mass (mg) M t = mass at some time, t (mg) α = solvent s mass emission rate constant (min -1 ) t = time of evaporation (min). We were able to experimentally determine the initial mass (M o ) of the chemical being studied, and systematically data-logged the decreasing mass (M t ) at some time, t, during the evaporation of the chemical. By rearranging the above equation, we can create a linear relationship as follows: 26

31 Mt ln( ) = α t Mo Mt By plotting the above equation (with ln( ) on the Y-axis and time, t, on the X- Mo axis) the slope of the line produced yielded the experimental value of α, the mass emission rate constant, just as could be done for any general linear equation: y = m x; where m = slope of the line. It should be noted that the absolute value of the slope of the best-fit line on the graph is equal to the α value. The division of M t by M o is a fraction (the natural logarithm of any fraction is negative), therefore the slope of the data points is also negative. The above analysis and plotting to determine α values was completed for each of the seven experimental solvents at four different volumes for each. Three test runs for each volume were completed for a total of 84 experimentally determined α values. The final analysis of the data was conducted using linear and bivariate regression. The purpose of the research was to develop a predictive equation so that future researchers and occupational and environmental health professionals can use the information to prospectively or retrospectively model for worker exposure to small spills of chlorinated hydrocarbons. The final equation was developed by running a linear regression, first with the vapor pressure of the chemicals tested as the independent variables and the average α values obtained experimentally through the research as the dependent variable. A bivariate analysis was then conducted with both vapor pressure and the surface area to volume ratios 27

32 of the spills as the independent variables and the experimental α values as the dependent variables. The resulting general equation for the analysis conducted is as follows: α = β o + β 1 Vapor Pressure + β 2 Surface Area to Volume Ratio. The equation can be used for any chlorinated hydrocarbon (if the vapor pressure of the substance and the surface area to volume ratio of the spill are known) to determine the mass emission rate constant to be used in other mathematical models to determine worker exposure. The data also were analyzed to determine if the number of chlorine substitutions can be a predictor of the alpha value of a compound by graphing the observed alpha value (dependent variable) with the number of chlorine substitutions in the compound (independent variable). Analyses were conducted in which the aromatic compounds were analyzed separately from the aliphatic compounds, and again with all compounds included in the same graph. The researchers note that this is a relatively limited sample size to show a trend in this hypothesis. The slope of the line on the graph can be used to determine if the number of chlorine substitutions is an accurate predictor of the magnitude of alpha value of the compound tested. Summary As stated above, the development of a predictive equation to explain the mass emission rate of small spills of chlorinated hydrocarbons is necessary to assist in the determination of proper control and protection of occupational and environmental exposures to these solvents on a small-scale basis. This experimental design allowed for the development of rate constants and equations for the chemicals being studied. This 28

33 research built upon previous research conducted by Keil and Nicas (2000), to assist in determining an accurate model for the evaporation of small spills of chlorinated hydrocarbons. 29

34 RESULTS The environmental conditions recorded during the experiment were used to determine the variability that could be expected based on the parameters of air velocity, atmospheric pressure and temperature at the testing location. Table II displays the environmental conditions of the laboratory during the experimental determinations of the mass emission rate constant. All ranges were within acceptable preset testing limits. Table II Monitored Environmental Conditions Temerature Air Flow Relative Atmospheric ( C) (m/s) Humidity (%) Pressure (mm Hg) Min/Max Average The range of the above environmental conditions were applied to an equation for generation rates to determine the variability between the highest and lowest possible generation rates due to the conditions that existed during the collection of data. The equation used to determine this range is as follows: G i = 8.33x10 D ν V L P R T MW A where: G = evaporation rate of the substance (g/sec) V = air velocity over the surface (m/s) MW = molecular weight of the substance (g/mol) A = surface area of the substance (m 2 ) 30

35 D = molecular diffusity of the substance (m 2 /sec) υ = kinematic velocity of air (m 2 /sec) L = surface length (m) P = atmospheric pressure (mm Hg) R = ideal gas constant (mm Hg m 3 / mol K) T = temperature (K). In order to determine the lowest generation rate possible, the lowest air velocity and atmospheric pressure measured were used in the equation with the largest temperature measured. All other values were held constant, but not necessarily accurate measurements of the environmental conditions. Likewise, to determine the highest possible generation rate based on the environmental parameters monitored, the highest air velocity and atmospheric pressure measured were used in the equation with the smallest temperature measured. This resulted in a maximum variability between generation rates of 20.3 %. This variability assumes extreme environmental conditions occurring simultaneously, which was not likely the case. Actual variability in generation rates should be less. The researchers assume that the environmental conditions still offered a normal distribution of generation rates, indicating that approximately +/-10% of the test runs were not uniformly distributed. The arbitrary values obtained while keeping the other variables in the equation constant offered generation rates of between x 10-3 and x 10-3 g/sec. The final data for all tests of the chemicals at all volumes researched is included in the final tabulated data tables within the text of the paper and in the appendices where applicable. The raw data collected from the data-logging of the mass emission of the 31

36 solvents in the experimental model was first downloaded to an MS Excel spreadsheet to create graphs of the mass of solvent remaining versus time. A best-fit line of the first 75% of mass emission was created to determine the mass emission rate constant, α (see Appendix 3 for a sample of the spreadsheet data). Figure 1 shows the graphical display of the experimental determination for the value of α (example of trichloromethane at an 8 milliliter trial). The individual marks on the graph represent the data points obtained by data-logging the decreasing mass from the mass balance into the computer spreadsheet. The solid line represents the best-fit line over the decreasing mass data points, which was used to determine the slope of the graph, giving the experimental α values. The experimental α values as well as the measured surface area to volume ratios for each chemical at each volume can be seen in Table III for all solvents tested. The values for each of the α values and surface area to volume ratios are the average of the three trial runs for each of the chemicals. The average α values were used to perform the linear and bivariate regression analyses, using as independent variables the vapor pressure and the average surface area to volume ratios for each chemical at each volume tested. The basic equation for the linear and bivariate regression analysis is: α = β 0 + (β 1 Vapor Pressure) + (β 2 Surface Area/Volume) 32

37 Figure 1 Sample Scatterplot for Determination of α Constant Trichloromethane - Run 3-8 ml ln(m(t)/m(o)) Time (m) ln(m(t)/m(o)) Linear (ln(m(t)/m(o))) y = x R 2 = The results of the analysis conducted using SPSS 9.0 software produced coefficients for the above equation, using vapor pressure alone, as follows: α = x 10-4 Vapor Pressure. For this linear regression using vapor pressure alone, the r 2 value produced is This coefficient for the independent variable is significant at a statistical α < level. The F- statistic associated with the linear regression using vapor pressure alone as the independent variable is The resulting equation produced using a bivariate regression analysis with both vapor pressure and the surface area to volume ratio as independent variables is as follows: α = x (3.307 x 10-4 VP) + (1.723 x 10-2 SA/Vol) 33

38 For the bivariate regression using vapor pressure and the surface area to volume ratio of the spill, the r 2 value produced is This equation is significant at the α < level. The F-statistic associated with the linear regression using vapor pressure alone as the independent variable is 3,160. A partial F-test was conducted to determine the value added by the inclusion of the surface area to volume ratio into the predictive equation that contained the vapor pressure of the chemical under study as well. The partial F-statistic was determined by dividing the regression sum of squares by the mean square residual using both vapor pressure and the surface area to volume ratio of the chemical under study. The calculated F-statistic for the regression is 55.6, which is significant at the α < level (1 df in numerator, 4 df in denominator). The complete statistical results of the SPSS output can be seen in Appendix IV. An analysis of the number of chlorine substitutions in the compound tested showed that although there appears to be a trend in the number of chlorine substitutions in the chemicals studied, a regression analysis shows no relationship at the p < 0.05 level. The association between the number of chlorine substitutions and the α values for the chemicals studied is not significant. 34