Assessment of Uncertainty in Benzene Concentration Estimates in the Houston, TX, Area

Transcription

1 Assessment of Uncertainty in Benzene Concentration Estimates in the Houston, TX, Area Abstract Adel Hanna, Jeff Vukovich, Sarav Arunachalam, and Dan Loughlin Carolina Environmental Program University of North Carolina at Chapel Hill H. Christopher Frey North Carolina State University Jawad Touma and John Irwin U. S. Environmental Protection Agency Vlad Isakov California Air Resources Board The National-Scale Air Toxics Assessment (NATA) for 1996 is a nationwide study of the potential inhalation exposures and health risks associated with thirty-two air toxics and diesel particulate matter. Our research objectives are: i) develop a methodology for quantifying the uncertainty in the NATA results, and ii) demonstrate the use of the methodology in a case study for a particular urban area. We present the results of a case study involving benzene emissions in the Houston area. The study method and results can be used to provide insights regarding toxics uncertainties to the nationwide domain and the remaining thirty-one air toxics. Major emissions source categories for benzene from the 1996 Toxics Emissions Inventory are used as the basis for preparing emissions inputs for the case study. The sources of benzene for our study consist of twenty-four major source categories that include point sources, mobile (on-road and off-road) sources, and area/nonpoint sources in the Houston area. Estimated distributions of emission rate uncertainty from these major benzene emission sources are used to prepare inputs to the Industrial Source Complex Short Term (ISCST3) model. We propagate individual emissions uncertainties through the ISCST3 model using the Monte Carlo simulation technique to assess the uncertainty in the annual benzene concentration estimates. Our analysis of the modeling results identifies the input uncertainties that contribute the most to annual concentration uncertainties. Our preliminary results show that the effects of the twenty-four source categories on the concentration uncertainties can be adequately represented by considering just five or six source categories that contribute the most to the uncertainty. Our analysis also shows that the location of individual receptors relative to such selected major source categories generally corresponds to the level of uncertainty in the concentration: Receptors close to sources reveal larger uncertainty rates than those further from them. 1

2 Introduction The National-Scale Air Toxics Assessment (NATA) for 1996 is a nationwide study of the potential inhalation exposures and health risks associated with thirty-two air toxics and diesel particulate matter (PM). The study is a complex, multiphase, multi-component effort in which emissions, air quality, and exposure-response models are being used to simulate pollutant emissions, atmospheric reactions and transport, and intake and response, respectively. While NATA provides valuable information on these phenomena, it is important to consider the uncertainties inherent in the study. For example, emissions data for many of the toxic species are not well known. There is also uncertainty in our knowledge about meteorology and pollutant reactions. Further, both human behavior leading to exposure and the resulting physiological responses are not well characterized. These uncertainties, which affect the inputs and models used within the study, have the potential to yield considerable uncertainties in the study results. Our research objectives are thus: i) to develop a methodology for quantifying the uncertainty in the NATA results, and ii) to demonstrate the use of the methodology in a case study for a particular urban area, estimating uncertainties in the NATA results. The terms variability and uncertainty are used to describe aspects of data in space and time. It is important to distinguish the difference between the two terms (e.g. Frey and Bammi, 2003). Variability refers to real differences when characterizing a specific data set such as multiple sources of emissions at any given time or over time for a single source. Such variability may be attributed to variations in technical or physical conditions. For example emissions may vary as a result of change in ambient temperature, fuel type, or design parameters. Uncertainty on the other hand refers to the lack of knowledge regarding the true values of the data in hand. Sources of uncertainty may include the size of the available data for analysis or errors in measurements In this research, we focus on a case study to quantify uncertainty in modeled benzene concentrations in the Houston area that may result from the propagation of emission input uncertainties. The study method and results can be used to provide insights regarding toxics uncertainties to the nationwide domain and the remaining thirty-one air toxics. Major emissions source categories for benzene from the 1996 Toxics Emissions Inventory are used as the basis for preparing emissions inputs for the case study. The sources of benzene for our study consist of twenty-four major source categories that include point sources, mobile (on-road and off-road) sources, and area/nonpoint sources in the Houston area. Estimated distributions of emission rate uncertainty from these major benzene emission sources are used to prepare inputs to the Industrial Source Complex Short Term (ISCST3) dispersion model. We propagate individual emissions uncertainties through the ISCST3 modeling system using the Monte Carlo simulation technique to assess the uncertainty in the annual benzene concentration estimates. Study Protocol We seek to quantify the uncertainties in Toxics air quality predictions that may be attributed to uncertainties in emissions data used in toxic air quality modeling applications, given the present knowledge of the emissions inventories and processors, 2

3 and also the uncertainty of present knowledge of the ancillary data used in emissions processing. The present 1996 National Toxics Inventory (NTI) contains over 3 million records. The ancillary data used in the present emissions models can also include huge datasets that frequently handle multiple regional-type issues (e.g., surrogates, temporal profiles) over the entire United States. The emissions data used in the toxics air quality models (e.g., the Industrial Source Complex Model, ISCST3) are broken up into four major components: (1) point, (2) area/nonpoint, (3) mobile (on-road) and (4) mobile (nonroad) sources. Moreover, the toxics inventories contain information that allows for further categorizing of emissions sources into subgroups. This further categorization is possible in the inventory datasets because each source has a source category code (SCC) or other identifier (e.g., MACT503: Petroleum Refinery sources are a subgroup of the point-source component). We used the Emissions Modeling System-Hazardous Air Pollutant (EMS-HAP) as the emissions processing system and ISCST3 as the air quality model. Emissions processors perform functions that take the inventory data at the county, facility/stack, or census tract level and spatially and temporally (over an average day) allocate them, and also speciate relevant pollutants. EMS-HAP is written in SAS and consists of many different processors. To study each of these processors to obtain estimates of uncertainty would require extensive resources. Emissions uncertainty studies typically require the production of hundreds of different emissions datasets to better understand the sources of high uncertainty. However, the processing time of EMS-HAP makes producing hundreds of different emissions datasets difficult. Moreover, sources of uncertainty will probably vary by pollutant, by region, and by the data available for a particular region. To better understand the process needed to possibly quantify the uncertainty with these huge datasets, multiple regional-type issues, and at the subgroup emissions source category level, we focused on benzene and in just one region (the Houston area). We also included only the source categories in the highest emitting subgroup. Our approach for addressing the uncertainties in benzene emissions is as follows: Identify the highest emitters in the four major categories that contribute to about 80% of the total benzene emissions in the Houston area. (Initially, we performed sensitivity analyses with the ISCST3 model to identify the top percentile of emissions that gave the best correlations in output concentrations with the base case). Quantify the emissions rate uncertainty for each high-benzene-emitting subgroups in the Houston area. Use the emissions rate uncertainty data to formulate a series of Monte Carlo simulations that are used to generate numerous emissions datasets for input into ISCST3 in order to quantify the uncertainties in the output simulated concentrations of subject pollutants ( in this case benzene). Major Sources of Benzene in the Houston Area Table 1 ranks Houston-area source categories by their benzene emissions based on the annual emissions rate. The emissions sources described in the table were the basis for the benzene emission analyses described in this section 3

4 Case no./ SCC 1-onroad onroad point 00MACT nonroad nonroad nonroad area area onroad area point point point point area point Table 1 Top emissions subgroups ranked by benzene emissions totals (on-road mobile, major-point, nonroad mobile, and black=area/nonpoint) for major a sources Emission Source Description Mobile Sources; Highway Vehicles - Gasoline; Light Duty Gasoline Vehicles (LDGV); Total: All Road Types Mobile Sources; Highway Vehicles - Gasoline; Light Duty Gasoline Trucks 1 & 2 (LDGT); Total: All Road Types Petroleum Refineries - Other Sources Not Distinctly Listed (4yr) Mobile Sources; Off-highway Vehicle Gasoline, 4- Stroke; 4-Stroke Gasoline except Rail and Marine; All Mobile Sources; Off-highway Vehicle Gasoline, 2- Stroke; 2-Stroke Gasoline except Rail and Marine; All Mobile Sources; Off-highway Vehicle Diesel; Compression Ignition Equipment except Rail and Marine; Total Industrial Processes; Oil and Gas Production: SIC 13; All Processes; Total: All Processes Storage and Transport, Natural Gas Transmissions and Marine transport Mobile Sources; Highway Vehicles - Gasoline; Heavy Duty Gasoline Vehicles (HDGV); Total: All Road Types Miscellaneous Area Sources; Other Combustion; Forest Wildfires; Total Waste Disposal; Solid Waste Disposal - Government; Sewage Treatment; Aeration Tank Industrial Processes; Chemical Manufacturing; Acetylene Production; General Industrial Processes; Chemical Manufacturing; Fuel Fired Equipment; Specify in Comments Field Industrial Processes; Chemical Manufacturing; Butylene, Ethylene, Propylene, Olefin Production; Ethylene; general Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Total Industrial Processes; Petroleum Industry; Fugitive Emissions; Specify in Comments Field Emissions (tons/year) b Data status c D S D D/S D/S D/S S S S Data source Bammi (2001); Frey and Zheng (2002) Data of Case No. 1 EPA (1998); CARB (2000) Bammi (2001); Frey and Bammi (2002) Bammi (2001); Frey and Bammi (2002) CRC (2003); Frey and Bammi (2003) Data of Case No D EPA (1998) D EPA (1998) S D EPA (1993) D EPA (1998) D EPA (1998) S 4

5 Case no./ SCC 17-area onroad point nonroad point point point point Emission Source Description Miscellaneous Area Sources; Other Combustion; Managed Burning, Prescribed; Total Mobile Sources; Highway Vehicles - Diesel; Heavy Duty Diesel Vehicles (HDDV); Total: All Road Types Industrial Processes; Chemical Manufacturing; Fugitive Emissions; Specify in Comments Field Mobile Sources; Aircraft; All Aircraft Types and Operations; Total Emissions (tons/year) b Data status c Data source D EPA (1998) D CRC (2003) S Industrial Processes; Petroleum Industry; Fugitive Emissions; Miscellaneous: Sampling/Non-Asphalt, Blowing/Purging/etc S Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Deleted - Do Not Use (See and 4-07); Product Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels, tanker/barge cleaning Industrial Processes; Chemical Manufacturing; General Processes; Fugitive Leaks S TOTAL for major-scc emitters: D/S Bammi (2001) D EPA (1998) D EPA (1998) a Source categories with benzene emissions greater than 20 tons/year are defined as major sources. They account for 89.9% of total emissions. b Point estimates of the emission inventory. c D = directly relevant data; S = surrogate data; D/S = directly relevant data for total hydrocarbon (THC), surrogate data for % of total organic gas (TOG) (or THC) emitted as benzene. 100% of major-scc emissions have directly relevant or surrogate data. d Used uncertainty in methane fugitive emissions from oil and gas industry as surrogate; refer to IPCC (2002). Uncertainties in Benzene Emissions Generally, emissions inventories for an emissions source category i, E i, can be estimated using the following equation: E i = F i * A i where F i is the emissions factor and A i is the activity/production factor for the given emissions source category i. Estimating the uncertainty in A usually involves interviewing many experts to cover many major emitting emissions SCCs. However, that process is beyond the scope of this project. Another alternative for estimating the uncertainty in activity/production rate is to estimate as a group of experts or scientists or based on previous studies. In this project a nominal range of uncertainty is assigned for the activity/production factors as a placeholder until further information becomes available. Based on previous uncertainty studies, we use an uncertainty value of 1.10 for 5

6 the activity/production factor A. It is important to note that the uncertainty associated with A will usually only slightly increase the uncertainty in the emissions rate, E so some error in its uncertainty value should not alter the results drastically. Detailed analyses, results and discussions of emissions uncertainties for benzene used in this study are presented in the companion paper in this conference by Chris Frey and associates Monte Carlo Approach Often the inputs to a model are uncertain. In some instances, it may be appropriate to characterize these inputs using statistical or empirical probability distributions. These distributions can then be propagated through a model as part of an uncertainty analysis. Potential outcomes of the uncertainty analyses include: Output characterization: The output distributions can be used to determine the expected value, variance, and general response (e.g., shape) of each output to uncertainties in the inputs. Risk assessment: An estimation of the likelihood that some environmental threshold will be exceeded can be determined by examining the cumulative distribution function of the model output. Sensitivity analysis: The contribution of each uncertain input to uncertainty in each model output can be determined. This information may be of use in better understanding the problem and in most effectively allocating resources to reduce output uncertainties. Figure 1. Sampling a value from an input distribution using Monte Carlo sampling. Monte Carlo (MC) simulation techniques are the most commonly used numerical techniques for propagating uncertainty through a model. In MC simulation, a realization of model inputs is generated by randomly selecting values from the 6

7 distributions of each uncertain input (The selection process is discussed below). The realization is then tested using the model to provide a corresponding realization of the model outputs. The process of generating and testing realizations is typically repeated fifty to several hundred times (in our applications we use 100 MC runs). In Monte Carlo sampling (sometimes called 'simple' or 'crude' Monte Carlo sampling), a value for each uncertain input parameter is selected at random. This could be accomplished, for example, by generating a random value from 0.0 to 1.0 and mapping to the corresponding input value using a cumulative distribution function, as shown in Figure 1. This mapping can be implemented using numerical integration or through the use of tables or functions that approximate integration. The use of the MC method is granted in this study due to our experience of this approach as well as the availability of software techniques that we have previously developed to implement the uncertainties in emissions into the MC simulations. MC becomes more feasible than before due the availability of faster computers. Further, EPA has guidelines on MC applications which were mainly used for non air applications such as superfund sites where water and soil may be contaminated. Nuclear regulatory commissions use MC approach for many of their applications. A fast and effective way to generate random numbers uniformly distributed on the interval [0,1] was also implemented. The Monte Carlo tools proceed by generating random numbers and fitting them to the distribution that corresponds to each emissions subgroup. This generates an uncertainty factor which is then applied multiplicatively to the original emissions. The general equation looks like this: E bias = E orig * U Where E bias is the perturbed emissions, E orig is the original emissions estimate and U is the uncertainty factor generated by generating the random numbers and applying the appropriate distribution for each emissions subgroup. Figure 2 provides an illustration of the general data flows for this Monte Carlo tool. 7

8 Figure 2. Overview of Monte Carlo random sampling technique applied to benzene emissions to support ISCST3 modeling. The Monte Carlo tool outputs emissions data can be read directly into ISCST3 which was ideal to support the many ISCST3 model results needed to investigate uncertainty. Finally, the Monte Carlo tool also outputs all the uncertainty factors, U, generated by the tool for reference when analyzing the ISCST3 results. Uncertainty Analysis of Benzene Concentrations One hundred MC runs were conducted using the emissions perturbations based on the uncertainty analysis as described above for the top Benzene emitters for source categories: onroad, nonroad, area, and point source. Only uncertainties due to the emissions inputs were propagated into the ISCST3 model to perform annual benzene simulations in the Houston area. No uncertainty was assumed in the ISCST3 model meteorological inputs or calculated parameters. We conducted a series of sensitivity studies to reduce the number of receptors in the Houston area from 700 to 100 receptors. We performed this sensitivity to identify the location of receptors that had the highest concentrations, and to reduce the model runtimes, that could be crucial in the later phases of this project. Figure 3 shows the location of the 100 receptors. Those include 89 census tract centroid receptors and 11 monitors in the Houston domain. We also conducted a sensitivity analysis on the effect of reducing the emissions to the top 20%. The comparisons between control and trimmed receptors and emissions runs revealed very small differences. The r-squared values are more than for both categories. 8

9 Figure 3: The location of the hundred receptors ST DEV ST DEV Figure 4. Standard Deviation of the 100 MC runs (100 years) of the simulated ISC concentrations for the 100 receptors (Benzene all) Median 5 Median (ug/m3) Receptor Figure 5. Median of the 100 MC runs (100 years) of the simulated ISC concentrations for the 100 receptors (Benzene- all) It is noticed that the standard deviation and the median are large for the first few receptors (figures 4 and 5) possibly because of the close location of these receptors to the sources. This may be a result of the proximity of these receptors to low level sources which in general impact nearby receptors by as much as 5 times more than elevated sources of the same emission rate. 9

10 RATIO SD/MEAN RATIO SD/MEAN Receptor Figure 6. Ratio (Standard Deviation/Mean) of the simulated ISC concentrations for 100 MC runs (100 years) at the 100 receptors The ratio (standard deviation / mean) shown in figure 6, can be considered to be a measure of the uncertainty in Benzene concentrations at various sites. While still the first few receptors show a higher ratio, the rest of the receptors show almost consistent ratio of about Benzene Annual Concentrations (Houston-20 Receptors) 15 Range (ug/m 3 ) Figure 7. Percentile of Benzene concentrations based on 100 MC emissions simulations, for the first 20 receptors in the Houston area. Each box contains 90 % of the data. The bottom line of the box (5%), the top line (95%). Dashed lines are the 25 % and 75 % and the continuous line inside the box shows the median 10

11 Figure 7 gives a closer look at the first 20 receptors of the 100 used in the study. The figure shows the percentile of benzene concentrations at each of those receptors using the 100 MC runs. The difference between the 25 % and the 75 % percentiles is larger at the first seven receptors than most of the remaining ones. It is also noticed that in all cases at least 75% of values are less than 6 µg/m 3. Uncertainty Range (Percentage) 95% CI on Mean (%) Receptor Figure 8. Uncertainty Range at the 100 receptors for benzene concentrations The range of uncertainty at the 100 receptors can be calculated by using the Confidence Interval (CI) approach for the 95% Confidence Interval (CI) on the mean of the 100 Monte Carlo runs for each of the 100 receptors. Figure 8 shows the range of the 95% CI on mean percentages. We notice the range varies from -17% to 17 % for the first receptor then gradually decrease through the rest of the receptors to a range between -5% to +5%. Conclusions We performed a study to quantify uncertainties in Benzene concentrations in the Houston area. The study is developed as a benchmark that can be used to quantify uncertainties for other Toxic pollutants in the NATA domain. Using uncertainties of top major source emissions of benzene from point, area, on-road, and non-road mobile sources, we developed Monte Carlo based input emissions scenarios to simulate benzene concentrations at 100 receptors in the Houston area. The emissions uncertainties were propagated into the ISCT3 model for 100 simulation runs of annual benzene concentrations in the Houston area. Analyses of uncertainties of the ISCST3 annual benzene concentrations at the 100 sites reveal a correspondence to the distance between the receptor and the emissions sources. Closer receptors reveal higher uncertainties than receptors at further distances. The range of uncertainties is between -17% to +17%. This range however may change if hourly instead of annual concentrations were analyzed. Disclaimer The contents of this paper and the findings of its authors do not necessarily reflect the opinions, views, and policies of the US Environmental Protection Agency and was not 11

12 reviewed by the agency. Mentioning of names or commercial products do not constitute endorsement or recommendation for their use. References Bammi, S. (2001), Quantitative Analysis of Variability and Uncertainty in On-Road and Non-Road Mobile Source Emission Factors, Master thesis, Department of Civil Engineering, North Carolina State University, Raleigh, NC. CARB (2000), California Air Toxics Emission Factor II Database, Research Division, Air Resources Board, California Environmental Protection Agency. CRC (2003), Heavy-duty Gasoline Vehicle Chassis Dynamometer Testing for Emissions Inventory, Coordinating Research Council. EPA (1993b), Emission Factor Documentation for AP-42 Section 1.3 Fuel Oil Combustion, by Eastern Research Group, for Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA (1998b), Locating and Estimating Air Emissions from Sources of Benzene, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Frey, H.C. and Bammi, S. (2002), Quantification of Variability and Uncertainty in Lawn and Garden Equipment NO x and Total Hydrocarbon Emission Factors, Journal of the Air & Waste Management Association, 52(4): Frey, H.C. and Bammi, S. (2003), Probabilistic Nonroad Mobile Source Emission Factors, Journal of Environmental Engineering, 129(2): Frey, H.C. and Zheng, J. (2002), Probabilistic Analysis of Driving Cycle-Based Highway Vehicle Emission Factors, Environmental Science and Technology, 36(23): IPCC (2000), Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, Intergovernmental Panel on Climate Change (IPCC), (available at nggip.iges.or.jp/public/gp/gpgaum.htm). IPCC (2002), Background Papers: IPCC Expert Meetings on Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, by Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme, Institute for Global Environmental Strategies, , Kamiyamaguchi, Hayama, Kanagawa, Japan. 12