A COMPARISON OF THREE WASTEWATER COLLECTION SYSTEM VOC EMISSIONS MODELS

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1 ABSTRACT A COMPARISON OF THREE WASTEWATER COLLECTION SYSTEM VOC EMISSIONS MODELS Chris Quigley, Tom Card 1, Hugh Monteith 2, Jay Witherspoon 3, Greg Adams 4, Will Pettit 5, Ed Torres 6, Primary contact Chris Quigley, CH2M HILL Research Boulevard, Building 4 Suite 250 Austin, TX EMC, 2-Hydromantis, 3-CH2M HILL, 4-LACSD, 5-NACWA, 6-OCSD Three models were compared to determine their ability to accurately predict emissions from two wastewater collection system components; gravity flow reaches and drop structures. Models investigated included the Model, TOXCHEM+ and WATER9. Data used to perform the analysis were obtained from published literature. A total of 128 individual runs per model were completed in performing the analysis; 60 runs for the reach evaluation and 68 runs for the drop structure assessment. Compounds and range of operating conditions utilized in the analyses were selected to ensure a broad coverage of compound volatilities and operating conditions. In addition, only data meeting stringent mass closure requirements, i.e., acceptable range between % were deemed acceptable for inclusion in the analyses. Results indicate that on average, WATER9 under-predicted reach emissions by 44% whereas the and TOXCHEM+ models over-predicted emissions by 32 and 36%, respectively. For the drop structure analyses, WATER9 over-predicted by 103 and 108% (depending on how drop structures were configured) vs. and TOXCHEM+ which under-predicted by 6 and 36% respectively. An error analysis was completed to determine if the results were statistically significant. A Duncan s Multiple Range Test, a procedure to determine which treatment means are different than others, was used to perform this assessment. Based on the statistical assessment completed, it was determined that the results generated by WATER9 are significantly different than the field-measured data and the and TOXCHEM+ models. In order to better understand the reasons for the differences between the models, an in-depth analysis of the algorithms, assumptions and compound property databases used by each model was completed. 1154

2 KEYWORDS Sewers, drops, VOC emissions, modeling INTRODUCTION The United States Environmental Protection Agency (USEPA) has developed a computer-based model (WATER9) to simulate volatile organic compound (VOC) and hazardous air pollutant (HAP) emissions from wastewater collection systems (WCS) and treatment plants. Earlier versions of the WATER9 model (WATER7 and WATER8) were used by EPA to estimate HAP emissions from POTW treatment plants during the POTW MACT regulatory development process. These earlier versions significantly overestimated HAP emissions leading EPA to conclude that POTWs 50 MGD or above were major sources of HAPs. As a result, there is concern that the WATER9 model may contain conservative algorithms based on limited field data conducted by academic researchers and by using these conservative assumptions, EPA could conclude that POTWs as small as 5 MGD could be major sources of HAPs. WATER9 is currently under technical review and representatives from the National Association of Clean Water Agencies (NACWA, formerly known as AMSA) Air Quality Committee have been participating in this review process. Once this process is completed, it is possible that the WATER9 model will be used to support new VOC/HAP emissions control regulations for wastewater collection and treatment systems. Potential model uses include development of national level VOC/HAP emission inventories, which could be used in the development of national regulations. Possible regulatory vehicles that EPA could use include the Urban Air Toxics Control Strategy, amending the current POTW MACT to include collection system emissions and listing POTW collection systems as a separate MACT category. The likely timeline for EPA to develop regulations under any of these regulatory vehicles is 5 to 8 years. A more likely scenario is for the WATER9 model to be recommended to State and Local air permitting authorities that are developing Title V permits or New Source Review permits for NACWA member agencies. Since most State and Local air permitting authorities do not have much knowledge concerning air emissions from POTWs, coupled with the general lack of direct source testing data for HAPs from POTW collection systems, it is very likely that EPA s WATER9 model (or the national HAP emission estimated that may be developed from WATER9) would be the primary source of information to the States. In response to this possibility, NACWA has funded an independent evaluation of WATER9 and two other WCS models ( Model and TOXCHEM+) to ensure that VOC/HAP emission estimates developed using these emissions models are accurate. This evaluation has been separated into two phases. Phase I consisted of an evaluation of the ability of the three emissions models to appropriately simulate wastewater collection system emissions. For the WCS emissions comparison, WATER9 model results were compared with two other models currently available for wastewater collection system (WCS) modeling, TOXCHEM+, and the Model. There were three subtasks associated with Phase I. These included: 1155

3 Emissions Dataset Selection Model Setup and Execution Modeling Results Report Based on the results of the Phase I study, NACWA elected to conduct Phase II to examine differences associated with Phase I results, e.g., differences between models and observed data, differences in algorithms/assumptions used during individual model development. PHASE I Emissions Dataset Selection This section documents the selection procedure used to generate the emissions dataset used for the wastewater collection system (WCS) emissions estimation comparison project. The dataset, which contains field-measured compound-specific emissions, has been used to compare the ability of three emissions models selected to accurately estimate WCS emissions. The focus of this evaluation has been limited to two components common to municipal WCS; (1) uniform gravity flow reaches and (2) drop structures. Literature Search A literature search was performed to identify published data available for use in the model comparison. Published data were searched using four databases supported by the University of Texas Library System including: Applied Science and Technology Abstracts Chemical Abstracts Engineering Index Pollution Abstracts In addition to the database search, all CH2M HILL project team members reviewed information maintained within personal files. CH2M HILL staff also met with Dr. Richard Corsi, University of Texas and obtained several sources of data related to WCS emissions. These sources of data are considered published and publicly available, as they were components of individual students' MSc theses or Ph.D. dissertations. The criteria used for selecting various datasets to include in the model comparison are provided below. Preliminary Dataset Screening The first round of data screening was performed by reviewing data available in each data source. Of primary concern was the ability to accurately represent field-measured conditions within each emissions model. Therefore, datasets that were deemed to lack data required to adequately setup each model were dropped from the list. 1156

4 Dataset Selection Reach Data Mass Closure Requirement Reach datasets were screened using mass closure criteria in order to identify high quality data. The requirement for good mass closure should reduce the potential for problems during comparative analyses related to either field sampling or analytical errors. For this assessment, an acceptable mass closure range was considered to be between 75 and 125%. Table 1 contains the results of this assessment. Table 1 - Recommended Reach Dataset Summary Dataset Original No. of Datapoints No. of Datapoints meeting Mass Closure Criteria 1 Corsi 4 1 Whitmore 8 4 Koziel Total Data with mass closure values between % were considered acceptable. As can be seen from the results in Table 1 including the mass closure criteria on the reach datasets reduced the total number of datapoints to be included in the model comparisons from 77 to 60. Drop Structure Data Data contained in the two drop structure datasets selected for review are provided in Table 2. The combination of experiments or operating conditions and compounds analyzed result in a total of 185 potential individual data points capable of being used during model comparison, i.e., field-measured vs. model-predicted value. Table 2 - Initial Drop Structure Dataset Summary Dataset No. of Experiments No. of Compounds Datapoints Koziel Madani Total

5 In order to reduce the number of data to a manageable quantity and yet still retain data of high quality that included compounds of varying volatility and a wide range of operating conditions, selection was based on three criteria. Criteria 1 Mass Closure Mass closure acceptance criteria were similar to reach data in that only data with mass closure values within % were considered acceptable. Criteria 2 Volatility For each individual dataset, i.e., Koziel and Madani, volatilities of compounds included in experiments were reviewed. A subset of each compound list was selected for inclusion in order to represent a broad range of volatilities. The list of compounds selected for inclusion is provided below. Henry s law coefficients were used as the parameter to measure volatility. Table 3 - Recommended Drop Structure Dataset Compound Selection List Dataset Compound Henry s Law Value 1 Recommended for Use Koziel Ethyl acetate Yes Koziel Acetone Yes Koziel Toluene Yes Koziel Ethylbenzene No Koziel Cyclohexane Yes Madani 1,1,2,2 Tetrachloroethane Yes Madani Bromoform No Madani 1,4 Dichlorobenzene No Madani Chloroform Yes Madani Toluene Yes Madani 1,3,5 Trimethylbenzene No Madani p-xylene No Madani Trichlorothene Yes Madani Tetrachlorothene No Madani 1,1,1 Trichloroethane Yes 1. Henry s Law coefficient values expressed in unitless terms (Cg/Cl; m 3 liq/m 3 vapor) 1158

6 Criteria 3 Operating Conditions Operating conditions were assessed in order to ensure a broad range of conditions were captured in the final dataset. Parameters assessed included drop height, tailwater depth, liquid and vapor flowrates. Table 4 provides the results of sequential application of the three criteria described above. A total of 68 datapoints meet all three criteria and are recommended for inclusion in the model comparison dataset. Table 4 - Drop Structure Dataset Refinement No. of Datapoints Koziel Madani Total Original Datasets Criteria Criteria 1 and Criteria 1, 2 and Recommended for Use Criteria 1 refers to mass closure criteria. 2. Application of Criteria 1 and 2. Criteria 2 contains volatility criteria. 3. Application of Criteria 1, 2 and 3. Criteria 3 contains operating conditions assessments. MODELING RESULTS Reach Modeling Results Reach modeling results are presented graphically in Figures 1 and 2. Within each figure, an ideal line representing a 1:1 ratio between model-predicted and field-measured has been added to facilitate analysis of general trends for each model to determine if any of the models are tending to either over-predict or under-predict emissions. Figures 1 and 2 facilitate a direct comparison of the three models in terms of the Corsi/Whitmore and Koziel datasets, respectively. In both figures, WATER9 results are generally farthest away from the ideal line indicating the greatest differences between model-predicted and fieldmeasured results. 1159

7 Figure 1- Corsi/Whitmore Reach Datasets: Model Comparison Results Fraction Emitted: Model Results Ideal TOXCHEM+ WATER Fraction Emitted: Field Results Figure 2 - Koziel Reach Dataset: Model Comparison Results Fraction Emitted: Model Results Ideal TOXCHEM+ WATER Fraction Emitted: Field Results 1160

8 Table 5 contains the average model-predicted/field measured values for the Reach evaluations. As can be seen from this table, the average model-predicted/field measured result for each of the three models for the entire 60 data point data set were 1.32 for Model (on average, over-predicting by 32 %); 1.36 for TOXCHEM+ Model (over-predicting by 36 %), and 0.56 for TOXCHEM+ (on average, under-predicting by 44%). Table 5 - Reach Modeling Results Summary Average Predicted/Measured Value Component Dataset No. Data points TOXCHEM+ WATER9 Reach Corsi / Whitmore Koziel All Drop Structure Modeling Results Drop structure modeling results are presented graphically in Figures 3 and 4. It should be noted that two different methods were proposed by Dr. Clark Allen/RTI (WATER9 developer) for representing drop structures within WATER9. These methods included: Method 1 - Drop structure component Method 2 - Lift station component Results associated with each WATER9 method are presented in order to illustrate the potential variation in results that can obtained, and hence the importance in selecting appropriate model components for accurate system representation. As shown in Figure 3 WATER9 Method 2 appears to significantly over-predict while in Figure 4, both Methods 1 and 2 tend to over-predict emissions. Table 6 contains the average model-predicted/field measured values for the Drop Structure evaluations. As can be seen from this table, the average model-predicted/field measured result for each of the three models for the entire 68 data point data set were 0.94 for Model (on average, under-predicting by 6%); 0.64 for TOXCHEM+ Model (under-predicting by 36%); and either 2.03 or 2.08 for WATER9 (on average, over-predicting by either 103 or 108%). 1161

9 Figure 3 - Koziel Drop Structure Dataset: Model Comparison Results Fraction Emitted: Model Results Fraction Emitted: Field Results Ideal Toxchem+ WATER9 Method 1 WATER9 Method 2 Figure 4- Madani Drop Structure Dataset: Model Comparison Results Fraction Emitted: Model Results Ideal Toxchem+ WATER9 Method 1 WATER9 Method Fraction Emitted: Field Results 1162

10 Table 6 - Drop Structure Modeling Results Summary Average Predicted/Measured Value Component Dataset No. Data points TOXCHEM+ WATER9 METHOD 1 WATER9 METHOD 2 Drop Koziel Madani All DISCUSSION OF RESULTS Reach Modeling Results Reach modeling results presented previously indicate that while both the and TOXCHEM+ models are on average capable of estimating emissions within 34% of actual values, the WATER9 model is off by 44%. To facilitate an understanding of the significance of the different results obtained by each model, an error analysis using Duncan s Multiple Range Test was completed. The intent of the error analysis was to determine if significant differences exist between the observed database fraction emitted values, and those fraction emitted values predicted by the different models. Duncan s Multiple Range Test (MRT) (Duncan, 1955) is a procedure to determine which treatment means are different from others. This test orders the sample means (in this case the fraction emitted values for each model and observed data set) by numerical rank. From the data, the shortest significant range or SSR p is determined. Then, any two means that differ by more than this value are considered significantly different. Table 7 summarizes the results of the statistical analyses and indicates that WATER9 was significantly different than either the Observed or other model results. The error analysis indicated that the Model results were not different than the Observed. Table 7- Summary of Error Analysis for Reaches WATER9 Observed TOXCHEM+ Different than WATER9 TRUE TRUE TRUE Different than Observed FALSE TRUE Different than FALSE 1163

11 Drop Structure Modeling Results Drop structure modeling results presented in Table 6 indicate that on average the Models is capable of estimating emissions within 6% of actual values (on average under-predicting by 6%), while TOXCHEM+ under predicted by 36%. WATER9 Methods 1 and 2 over-predict emissions by 103 and 108%, respectively. For this assessment, two algorithms for estimating emissions from drop structures were used in the WATER9 model. Algorithm 1 was developed by the assessment team, while algorithm 2 was developed principally by Dr. C. Allen of RTI. The drop structure error analysis results are summarized in Table 8. Table 8 - Summary of Error Analysis for Drop Structures WATER9-1 TOXCHEM+ Interceptor Observed WATER9-2 Different than WATER9-1 TRUE TRUE TRUE TRUE Different than TOXCHEM+ FALSE TRUE TRUE Different than Interceptor TRUE TRUE Different than Observed FALSE All the models and the observed data values were statistically different from the WATER9 Algorithm 1 results. TOXCHEM+ and Fe values were not different, however both TOXCHEM+ and were statistically different from the observed Fe data. While the WATER9 Algorithm 2 model Fe values were statistically different from both TOXCHEM+ and model values, the observed Fe values from the dataset and the WATER9 Algorithm 2 Fe values were not statistically different. PHASE II Overview Phase I findings indicated that there were significant differences in results when comparing emissions from reaches and drops using the, TOXCHEM+ and WATER9 models. Based on these findings, further analyses have been completed in order to determine the causes for these differences. These analyses have included: An evaluation of each model s compound property database An investigation and compilation of the algorithms and procedures used by each model 1164

12 Compound Database Evaluation An assessment of the compound property database utilized by each model has been completed. Compound properties evaluated included Henry s Law coefficients, liquid-phase and gas-phase diffusivities and vapor pressure. An assessment of how each model accounted for temperature adjustment to each parameter was also performed. Reference Temperature Database Information All models have compound property databases that utilize a 25 ºC reference temperature. During Task 1, a total of eight compounds were used during analyses of the reach and drop experiments. These compounds were: Acetone Chloroform Cyclohexane Ethylacetate Ethylbenzene 1,1,2,2 Tetrachlorethane Toluene 1,1,1 Trichloroethane Table 9 provides a summary of the values contained within the three model databases. Table 9 - Compound-specific Parameter Values (reference temperature) Henry's Law (unitless), 25 o C Vapor Pressure (atm), 25 o C COMPOUND Water 9 Toxchem Water 9 Toxchem Acetone N/A Chloroform N/A Cyclohexane N/A Ethylacetate N/A Ethylbenzene N/A 1,1,2,2 Tetrachlorethane N/A Toluene N/A 1,1,1 Trichloroethane N/A D l (cm 2 /s), 25 o C D v (cm 2 /s), 25 o C COMPOUND Water 9 Toxchem Water 9 Toxchem Acetone 1.14E E E Chloroform 1.00E E E Cyclohexane 9.10E E E Ethylacetate 9.66E E E Ethylbenzene 7.80E E E ,1,2,2 Tetrachlorethane 7.90E E E Toluene 8.60E E E ,1,1 Trichloroethane 8.80E E E Henry s Law values are relatively similar for all models for each compound with the exception of Ethylacetate which has a value approximately two times the value used by and TOXCHEM+ models. Vapor pressure values for and WATER9 are very similar with the exception of chloroform which differs by almost one order of magnitude. 1165

13 TOCHEM+ does not use vapor pressure so this parameter is not included in the compound database. WATER9 and have virtually identical reference temperature values for liquid and gas diffusivities for all compounds. TOXCHEM+ has higher liquid and gas diffusivity values for all compounds. Temperature Adjustment Impacts on Henry s Law Coefficient Henry s Law coefficient is a parameter that represents compound volatility. As such, and given the importance that volatility has in terms of emissions, a further assessment of the impacts of temperature adjustment on this parameter was completed. Figures provide data collected from each of the three models for the 8 compounds included in Task 1 for 15, 25 and 35 ºC. 1166

14 Figure 5 - Henry s Law Coefficient Comparison for Acetone Acetone Henry's Law (unitless) Water 9 Toxchem 15 C 25 C 35 C Figure 6 - Henry s Law Coefficient Comparison for Chloroform Chloroform Henry's Law (unitless) Water 9 Toxchem 15 C 25 C 35 C 1167

15 Figure 7 - Henry s Law Coefficient Comparison for Cyclohexane Cyclohexane Henry's Law (unitless) Water 9 Toxchem 15 C 25 C 35 C Figure 8 - Henry s Law Coefficient Comparison for Ethylacetate Ethylacetate Henry's Law (unitless) Water 9 Toxchem 15 C 25 C 35 C 1168

16 Figure 9- Henry s Law Coefficient Comparison for Ethylbenzene Ethylbenzene Henry's Law (unitless) Water 9 Toxchem 15 C 25 C 35 C Figure 10 - Henry s Law Coefficient Comparison for 1,1,2,2 Tetrachloroethane 1,1,2,2 Tetrachloroethane Henry's Law (unitless) Water 9 Toxchem 15 C 25 C 35 C 1169

17 Figure 11 - Henry s Law Coefficient Comparison for Toluene Toluene Henry's Law (unitless) Water 9 Toxchem 15 C 25 C 35 C Figure 12 - Henry s Law Coefficient Comparison for 1,1,1 Trichloroethane 1,1,1 Trichloroethane Henry's Law (unitless) Water 9 Toxchem 15 C 25 C 35 C 1170

18 As depicted in these figures, it appears that with the exception of ethylacetate (all temperatures) and 1,1,2,2 tetrachloroethane (35 ºC), the Henry s law values used by the three models are relatively comparable, e.g., generally within + 25% of each other. Therefore, based on the data compiled as part of this assessment, it is not anticipated that differences in modeling results reported during Task 1 are attributable to compound database issues alone. Instead, it is more likely that differences in the algorithms used within the individual models are the source of the differences in model predictions. MODEL ALGORITHM ASSESSMENT Algorithms used by each model during quantification of emissions from reaches and drops were evaluated to determine areas of differences. Table 10 summarizes the main mass transfer algorithms used by each model. For reaches, all models utilize the Parkhurst and Pomeroy liquid-phase mass transfer algorithm. However, both TOXCHEM+ and WATER9 additionally utilize a gas-phase mass transfer component. Additional information on the J-Factor method utilized by WATER9 is required in order to understand it s validity. As is presented below under WATER9 Modeling Options, there are additional functions or algorithms that are available to WATER9 that are not identified or explained in the code. Similarly, for drops, all models initially utilize the Nakasone algorithm. However, differences compound-specific adjustments and inclusion of different methods of accounting for gas-phase mass transfer results in significant differences in modeling results. Table 10 Mass Transfer Algorithm Summary Component Model Algorithm(s) Reach Parkurst and Pomeroy (P&P) TOXCHEM+ P&P, Mackay and Yeun WATER9 Owens, P&P, or Turbulent, J-Factor Drop Nakasone TOXCHEM+ Nakasone WATER9 Nakasone 1171

19 The Parkhurst and Pomeroy equation used by all models in representing mass transfer in reaches is as follows: Kl = where * v 981* Hl 2 ( v *0.01* slope) / 8 v is noted as the gas phase velocity (cm/s), dl DOW Hl is the depth (cm), slope is the conduit slope (decimal fraction), dl is the liquid phase diffusion coefficient (cm2/s), DOW is likely the liquid phase diffusion coefficient of oxygen and is given as , and EP is a constant with a value of 0.7. Stripping of VOCs at drop structures is estimated using empirical algorithms developed by Nakasone (1986), which predict oxygen mass transfer across drops as a function of water surface width, water flow rate, drop height, and tail water depth. The Nakasone algorithm, which expresses oxygen mass transfer in terms of an oxygen deficit ratio, r o, is: r o C = C s s C C top bot = exp n b c ( C h q Y ) a EP where C s is the saturation dissolved oxygen concentration (mg/l); C top is the dissolved oxygen concentration at the top of the drop (mg/l); C bot is the dissolved oxygen concentration at the bottom of the drop (mg/l); Ca, n, b, and c are experimentally determined constants; q is flow rate divided by water surface width (m 2 /hr); h is the drop height (m); and Y is the tail water depth (m). WATER9 Modeling Options During WATER9 model setup for the reach experiments, it was determined that there are three different methods for representing reaches within WATER9. These include: Open trench method Closed Trench method Conduit method 1172

20 In addition, there are also three different options in terms of mass transfer representation. These include: Trench model (denoted as WATER9 default mass transfer option) Municipal model Turbulent model As it was not clear which representation method or mass transfer option was the best to select, a comparison using the same input data was completed to determine the impacts that model setup had on results. For this comparison the following data were used: Compound Toluene Reach length 200 ft Reach relative depth of flow 0.13 Air:water flowrate 4:1 The results of this evaluation are provided in Figure 13. Figure 13 WATER9 Reach Representations Summary Fraction Emitted Trench Model Municipal Model Turbulent Model Measured Open Trench Closed Trench Conduit As can be seen from Figure 13, the fraction of toluene emitted is highly dependent on how WATER9 is setup and can vary from approximately 10% stripped (Closed trench method with Municipal transfer) to 100% (Open trench). As such, it is very important that the user understand what method and mass transfer option is the most appropriate to use. 1173

21 SUMMARY AND RECOMMENDATIONS Phase I and Phase II results indicate that there are significant differences in predicted vs. measured emissions between the three models. For both reaches and drops, WATER9 had the lowest performance in terms of average predicted/measured ratios. During completion of Phase I and II, the evaluation team encountered significant problems in locating sufficient documentation to allow for complete understanding of how to select appropriate WATER9 modeling components, setup and execute the model, and confirm modeling results through hand calculations. The actual reach method and mass transfer option selected in WATER9 has significant impacts on the results with potential differences in the fraction emitted differing by an order of magnitude, e.g. 0.1 to 1.0 fraction emitted. As a result of these problems, the following recommendations for WATER9 are suggested: 1. Improved documentation on model component selection, e.g., how to represent gravity-flow sewers in the model 2. Improved documentation on model setup and execution 3. Improved documentation on algorithms used, thereby allowing for independent evaluation of model results. REFERENCES Duncan, D.B (1955). Biometrics, 11, pp Nakasone, H.R. (1986) Study of aeration at weirs and cascades, Journal of Environmental Engineering, ASCE, 113(1): 64. Nakasone, H. (1987). Study of Aeration at Weirs and Cascades. Journal of Environmental Engineering, ASCE Vol. 113, No. 1, p. 64. Parkhurst, J.D. and R. D. Pomeroy (1972). Oxygen Absorption in Streams. Journal of the Sanitary Engineering Division, ASCE 98,