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Evaluating PM and Precursor Emission Inventories

Introduction The Importance of Emission Inventory Evaluation Emission Inventory Development Emission Inventory Evaluation Tools and Methods Using Engineering Judgment. Bottom-up Evaluation Multivariate Techniques Top-down Inventory Evaluation The Process of Evaluation

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Evaluating PM and Precursor Emission Inventories

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  1. Introduction The Importance of Emission Inventory Evaluation Emission Inventory Development Emission Inventory Evaluation Tools and Methods Using Engineering Judgment Bottom-up Evaluation Multivariate Techniques Top-down Inventory Evaluation The Process of Evaluation Available Methods and Data Summary References Evaluating PM and Precursor Emission Inventories “Experience with the 1990 State Implementation Plan (SIP) base year (emissions) inventories brought to light deficiencies and inconsistencies in the inventory process now being used. In addition, the current leeway in selecting these processes has resulted in data sets of unknown quality and varying degrees of completeness.” - Emission Inventory Improvement Program (U.S. EPA,1997) PM Data Analysis Workbook: Emission Inventory Evaluation

  2. Introduction Particulate matter and precursor emission inventory development is critical because the inventory is used • To support air quality modeling (i.e., model input) • To support exposure modeling and health assessments • To analyze emission control costs • To develop regulatory control strategies “The Clean Air Act requires state and local air quality agencies to develop complete and accurate inventories as an integral part of their air quality management responsibilities. These air emission inventories are used to evaluate air quality, track emissions reduction levels, and set policy on a national and regional scale....” - Emission Inventory Improvement Program (U.S. EPA,1997) PM Data Analysis Workbook: Emission Inventory Evaluation

  3. The Importance of Emission Inventory Evaluation (1 of 2) Why bother evaluating emissions data? • Emission inventory development is a complex process that involves estimating and compiling emissions activity data from hundreds of point, area, and mobile sources in a given region. Because of the complexities involved in developing emission inventories and the implications of errors in the inventory on air quality model performance and control strategy assessment, it is important to evaluate the accuracy and representativeness of any inventory that is intended for use in air quality modeling. Furthermore, existing emission factor and activity data for sources of PM2.5 are limited and the quality of the data is questionable. An emission inventory evaluation should be performed before the data are used in photochemical modeling. What tools are available for assessing emissions data? • There are several techniques used to evaluate emissions data including “common sense” review of the data; source-receptor methods such as the chemical mass balance model; bottom-up evaluations that begin with emissions activity data and estimate the corresponding emissions; and top-down evaluations that compare emission estimates to ambient air quality data. Each evaluation method exhibits strengths and limitations. • Based on the results of the emissions evaluation, recommendations can be made on possible improvements to the emission inventory. Local agencies responsible for developing the inventory can then make revisions to the inventory data prior to air quality modeling. PM Data Analysis Workbook: Emission Inventory Evaluation

  4. The Importance of Emission Inventory Evaluation (2 of 2) • Reviewing and evaluating emission inventories is critical because development of effective air pollution control strategies is predicated on the accuracy of the underlying emission inventory. • Uncertainties associated with PM2.5 emission estimates can range from 10% to an order of magnitude. • Emissions data commonly contain errors in pollutant mass. • Quality control and assurance of emission estimates can significantly improve the quality of the data. PM Data Analysis Workbook: Emission Inventory Evaluation

  5. Emission Inventory Development (1 of 4) • Estimating PM2.5 emissions is a complex process involving many data parameters. • Uncertainties in emissions inventory estimates could range from about 10% for well-defined sources (e.g., emissions from power plants) to an order of magnitude for widespread and sporadic sources (e.g., fugitive dust). • The general equation for estimating emissions gets complex when estimating PM: E = A x EF x (1-ER/100) Where: E = emissions EF = emission factor A = activity ER = overall emission reduction efficiency (%) PM Data Analysis Workbook: Emission Inventory Evaluation

  6. Emission Inventory Development (2 of 4) Spatial allocation of emissions activities • Emissions sources are spatially allocated to a region using the actual locations of the emissions sources and/or using spatial surrogate data which are physical parameters that can be associated with emissions activities (e.g., acres of farmland might be the surrogate for emissions from farming operations). Temporal allocation of emissions activities • Emissions sources are temporally allocated by assigning a temporal profile, a distribution of emissions activity over a 24-hr period, to each source category. PM Data Analysis Workbook: Emission Inventory Evaluation

  7. Emission Inventory Development (3 of 4) Chemical speciation of emission sources • In order to disaggregate PM emissions into individual chemical species, each emission source category is assigned a speciation profile which provides a detailed chemical breakdown of the individual chemical species emitted from that source. • Several sources of PM speciation data currently exist including • EPA’s SPECIATE: <http://www.epa.gov/ttn/chief/software.html#speciate> • U.C. Davis [via California Air Resources Board (ARB)]: <http://www.arb.ca.gov/emisinv/speciate/pmucd.pdf> • Desert Research Institute (DRI): <http://www.dri.edu> • OMNI (via California ARB): <http://www.arb.ca.gov/emisinv/speciate/pmomni.pdf> • North American Front Range Air Quality Study: <http://www.nfraqs.colostate.edu/index2.htm> PM Data Analysis Workbook: Emission Inventory Evaluation

  8. Emission Inventory Development (4 of 4) Challenges in developing PM2.5 emission inventories • PM2.5 activity and emission factor data are limited. • The quality of existing PM2.5 data is variable. • There are uncertainties in the techniques used for estimating and in the estimates of secondary PM2.5. PM Data Analysis Workbook: Emission Inventory Evaluation

  9. Emission Inventory Evaluation Tools and Methods Use of mathematical techniques to evaluate emission estimates: • Engineering judgment approach • Bottom-up emissions evaluation Use of ambient air quality data to evaluate and reconcile emission estimates: • Multivariate Techniques - such as Principal Component Analysis (PCA) and Chemical Mass Balance (CMB) receptor modeling. There are more than a dozen currently existing receptor models; however, EPA’s OAQPS has only recognized CMB and PCA as part of their SIP development guidance documents. • Top-down emissions evaluation PM Data Analysis Workbook: Emission Inventory Evaluation

  10. Strengths Limitations Using Engineering Judgment to Evaluate Emission Estimates (1 of 2) • Begin with knowledge of the region for which the emission inventory was developed (i.e., likely emission sources, population, demographic characteristics). • Review major sources of emissions and perform per-capita checks combined with conventional wisdom to evaluate emissions data. • Can identify errors in emissions data; gives no insight as to where errors emanate. • Provides a quick and inexpensive method to quality control emission estimates. • Does not require extensive data. • Can quickly identify gross errors in emissions data. PM Data Analysis Workbook: Emission Inventory Evaluation

  11. Summer emission inventories often report significant emissions from seasonal sources such as residential fireplaces or wood stoves and snowmobiles. Residential fireplace and wood stove emissions in the summer? Case Study: Using Engineering Judgment to Evaluate Emission Estimates (2 of 2) Residential fireplaces and wood stoves are large contributors to PM emissions in the wintertime. Emission contributions from this source should be low in the summer months. Taking the time to review seasonal emission inventory data can catch errors like this. PM Data Analysis Workbook: Emission Inventory Evaluation

  12. Limitations Strengths • The emission estimates generated using this methodology can be very accurate if demographic and activity data are accurate. • Extensive data requirements. • Accuracy of emission factors • Accuracy of activity data • Time consuming. Bottom-up Evaluation of Emissions Activity Data(1 of 2) • Bottom-up evaluations are a method of assessing emissions data using census information and emissions activity data combined with emission factors to generate independent estimates to compare to existing data. • This method is most useful when combined with the top-down evaluation when assessing large data sets. Top-down evaluations identify problem categories; bottom-up evaluations are used to investigate the underlying information used to estimate categories. PM Data Analysis Workbook: Emission Inventory Evaluation

  13. Mobile source emissions activity data for anonymous city: Urban region with a total fleet of 366,699 on-highway motor vehicles. Emissions data reported that 494 of these vehicles are heavy-duty diesel trucks (HDDTs). According to these figures, only 0.1% of the vehicle fleet are HDDTs. Case Study: Bottom-up Evaluation of Emissions Activity Data (2 of 2) In other parts of the country with similar characteristics, HDDTs make up approximately 10 to 20% of highway vehicles. HDDTs are significant contributors to PM; consequently, errors in activity data can lead to errors in emissions estimates. Adapted from Haste et. al., 1998. PM Data Analysis Workbook: Emission Inventory Evaluation

  14. Issues Associated With Emissions EvaluationsUsing Ambient Data Ambient air quality data can be used to evaluate emission estimates and source apportionment; however, the following issues should be considered: • Proper spatial and temporal matching of emission estimates and ambient data. • Ambient levels of background PM2.5. • Meteorological effects on comparison. • Comparisons only valid for primary PM2.5. • Temporal resolution of ambient data (i.e., 24-hr average versus hourly ambient PM data). PM Data Analysis Workbook: Emission Inventory Evaluation

  15. Limitations Strengths • Do not apportion secondary aerosol to sources of precursors. • Analyst must infer how certain statistical species groupings relate to emission sources • Depend on correlation that can be driven by meteorology or collocation. • Relatively straight-forward statistical methods. • Do not require speciation profile data. • Ability to summarize multivariate data set using few components. • Identify unusual ambient samples. Multivariate Techniques • Statistical procedures that can be used to infer the mix of PM sources impacting a receptor location. • Procedures including cluster, factor/principal component, regression, and other multivariate techniques are available in statistical software and stand-alone packages. (See the source apportionment section in this workbook for example applications). American Petroleum Institute, 1998 PM Data Analysis Workbook: Emission Inventory Evaluation

  16. Overview of Receptor Models Receptor models provide empirical relationships between ambient data at a receptor and PM emissions by source category. The fundamental principal of receptor modeling is that mass conversion is assumed and a mass balance analysis is used to identify and apportion sources of PM in the atmosphere. Receptor models are useful for resolving the composition of ambient primary PM into components related to emission sources. Three main types of receptor models: • Models that apportion primary PM using source information. • Models that apportion primary PM without using source information. • Models that apportion primary and secondary PM. American Petroleum Institute, 1998 PM Data Analysis Workbook: Emission Inventory Evaluation

  17. Strengths Limitations • Requires source profile information. • Results of CMB model are only as accurate as the speciation profile input data. • Significant portion of PM2.5 mass is due to secondary compounds which are not apportioned to the precursor sources by CMB. • Can mis-specify emission sources. • Sensitive to collinearity. • CMB (version 8) operates in a Windows-based environment and accepts inputs and creates outputs in a wide variety of formats. • CMB has been accepted by EPA’s OAQPS for SIP development. • Generates errors in source compositions accepted by OAQPS. The Chemical Mass Balance Receptor Model(1 of 2) PM Data Analysis Workbook: Emission Inventory Evaluation

  18. CMB modeling results and emission inventory source apportionment are very different. The results of CMB modeling show that mobile sources are responsible for a much larger percentage of PM2.5 in the ambient air while the emission inventory data shows dust being the main contributor to PM2.5. These types of discrepancies are important to consider prior to control strategy development. The Chemical Mass Balance Receptor Model(2 of 2) CMB PM2.5 Source Apportionment Emission Inventory PM2.5 Source Apportionment Watson et. al., 1998 PM Data Analysis Workbook: Emission Inventory Evaluation

  19. Top-Down Emission Inventory Evaluation(1 of 7) Top-down emissions evaluation is a method of comparing emissions estimates with ambient air quality data. Ambient/emission inventory comparisons are useful for examining the relative composition of emission inventories; they are not useful for verifying absolute pollutant masses unless they are combined with bottom-up evaluations. The top-down method has demonstrated success at reconciling emission estimates of VOC and NOx; however, using the top-down method for PM is currently being explored. Top-down approach for PM: • Compare morning (e.g., 0700-0900) ambient- and emissions-derived primary PM2.5/NOx ratios. Early morning sampling periods are the most appropriate to use in these evaluations because emissions are generally high, mixing depths are low, winds are usually light, and photochemical reactions are minimized. PM Data Analysis Workbook: Emission Inventory Evaluation

  20. Ambient data requirements Select ambient monitoring sites dominated by fresh urban source emissions. Validate and process elemental PM data. Select early morning (e.g., 0700-0900) hourly data. Analyze meteorological data to determine the emission areas and elevated point sources that may influence ambient measurements. Top-Down Emission Inventory Evaluation(2 of 7) Emissions data requirements • Evaluate emissions for same locations as ambient monitor. • Process emissions data to get gridded, hourly PM2.5 data. • Convert emissions data units to be compatible with ambient data units. • Existing emissions processing software: EPS 2.0, SMOKE, EMS-95. PM Data Analysis Workbook: Emission Inventory Evaluation

  21. Top-Down Emission Inventory Evaluation(3 of 7) Analysis objectives • Compare ambient data with emission estimates from different wind quadrants surrounding the monitoring site. • Compare ambient data with emission estimates with and without elevated point source emissions. The inclusion of elevated point sources will depend on the meteorological conditions. • Perform primary PM2.5/NOx ratios for day specific, weekday, and weekend data. • Compare individual chemical species in the ambient air to chemical species in the emission inventory when speciation data are available. PM Data Analysis Workbook: Emission Inventory Evaluation

  22. Top-Down Emission Inventory Evaluation(4 of 7) Wind quadrant definitions used in the top-down evaluation PM Data Analysis Workbook: Emission Inventory Evaluation

  23. Top-Down Emission Inventory Evaluation(5 of 7) • Estimating primary PM2.5 in the ambient air using chemistry data: • [Primary PM2.5] = 1.89*[Al] + 1.57*[Si] + 1.2*[K] + 1.4*[Ca] + 1.43*[Fe] + EC + 0.7*(1.4*OC) • Where: • [Al] = concentration of aluminum [Si] = concentration of silicon • [K] = concentration of potassium [Ca] = concentration of calcium • [Fe] = concentration of iron EC = elemental carbon • OC = organic carbon • The multipliers in the equation account for the extra mass of oxygen in the crustal oxides and for the extra mass of hydrogen and oxygen with the organics. • It is estimated that 70 to 90% of organic carbon is primary. Kumar and Lurmann, 1996 PM Data Analysis Workbook: Emission Inventory Evaluation

  24. Top-Down Emission Inventory Evaluation(6 of 7) Uncertainty issues • Proper temporal and spatial matching of emissions data and ambient data. • Meteorological factors including temperature, wind speed, and inversion height. • Level of ambient background PM and precursor concentrations due to transport and carry-over. • Collocation of emission sources. • Uncertainties associated with primary versus secondary PM. • Underlying assumption that emission inventory NOx estimates are reasonable. PM Data Analysis Workbook: Emission Inventory Evaluation

  25. Strengths Limitations and Uncertainties • Provides a method to assess areas of an emission inventory that appear to be suspect; improvements can be made prior to photochemical modeling. • Can assess detailed chemical species composition between the inventory and ambient air if accurate PM species data is available. • Can greatly improve emission estimates. • Extensive data requirements. • Uncertainties in the emission estimates affect comparisons. • Uncertainties in the ambient data measurements affect comparisons. • Requires ambient speciated PM data collected on a <24-hr basis. • Comparison-related uncertainties • include: • Proper temporal and spatial matching of emissions data and ambient data. • Meteorological factors • Level of ambient background PM concentrations and chemical reactions Top-Down Emission Inventory Evaluation(7 of 7) PM Data Analysis Workbook: Emission Inventory Evaluation

  26. Primary PM10/NOx Ambient Ratio Emission Inventory Ratio Comparison of the ambient- and emissions-derived PM10/NOx ratios in two cities are quite different. It appears as though PM10 is overestimated in the emission inventory by approximately a factor of two. Recommendation: the PM10 portion of the inventory should be investigated from the bottom-up. City #1 City #2 Case Study: Top-Down Emissions Evaluation Top-down comparison of ambient- and emissions-derived primary PM10/NOx in two cities. Note that this example corresponds to PM10; a similar comparison could be made for PM2.5 Haste et. al., 1998 PM Data Analysis Workbook: Emission Inventory Evaluation

  27. The Process of Evaluation Emission inventory evaluations should be conducted as part of an iterative process: Step 1: Prepare the emission inventory. Step 2: Evaluate the emission inventory using a top-down approach. Step 3: Revise the emission inventory using bottom-up improvements (i.e., using new/improved emission factors, activity data, temporal and spatial allocation, speciation profiles). Step 4: Reevaluate the emission inventory using a top-down approach. PM Data Analysis Workbook: Emission Inventory Evaluation

  28. Available Methods and Data (1 of 3) • Emission inventory data sources • State and local air quality management agencies • EPA National Emissions Trends Inventory: <http://www.epa.gov/ttn/chief/ei/> • Emission inventory improvement program guidance documents: <http://www.epa.gov/ttn/chief/eiip/techrep.htm> • Emissions processing software: • EMS95: see <http://www.ladco.org> • SMOKE: see <http://envpro.ncsc.org/products/smoke/> • Ambient data sources: • AIRS Data via public web site: <http://www.epa.gov/airsdata> • AIRS AQS via registered users: register with EPA/NCC (703-487-4630) PM Data Analysis Workbook: Emission Inventory Evaluation

  29. Available Methods and Data (2 of 3) • Source profile sources • EPA SPECIATE <http://www.epa.gov/ttn/chief/software.html#speciate> was recently updated and now contains many measurements conducted as part of recent studies • U.C. Davis (via California Air Resources Board [ARB]): <http://www.arb.ca.gov/emisinv/speciate/pmucd.pdf> • Desert Research Institute (DRI): <http://www.dri.edu> • OMNI (via California ARB): <http://www.arb.ca.gov/emisinv/speciate/pmomni.pdf> • North American Front Range Air Quality Study: <http://www.nfraqs.colostate.edu/index2.htm> • Local, state, and federal agencies; for example, California has information available at <http://arbis.arb.ca.gov/emisinv/emsmain/emsmain.htm> • Analysis of ambient data (e.g., using UNMIX, PMF) PM Data Analysis Workbook: Emission Inventory Evaluation

  30. Available Methods and Data (3 of 3) • Meteorology data sources: • Meteorological parameters from NWS: <http://www.nws.noaa.gov> • Meteorological parameters from PAMS/AIRS AQS: register with EPA/NCC (703-487-4630) • Private meteorological agencies (e.g., forestry service, agricultural monitoring, industrial facilities) • State and local agencies PM Data Analysis Workbook: Emission Inventory Evaluation

  31. Summary • Emission inventory evaluation is an important part of the PM2.5 program. A high quality emission inventory is needed to best support air quality modeling, exposure modeling, and control strategy development. • A variety of evaluation techniques are available including engineering judgment, “bottom-up” evaluations, “top-down” evaluations, and receptor modeling. • PM2.5 mass and speciation data are a useful part of an emission inventory evaluation and improvement process. PM Data Analysis Workbook: Emission Inventory Evaluation

  32. References American Petroleum Institute (1998) Review of air quality models for particulate matter. Technical Summary, Publication Number 4669, March. Haste T.L., Chinkin L.R., Kumar N., Lurmann F.W., and Hurwitt, S.B. (1998) Use of ambient data collected during IMS95 to evaluate a regional emission inventory for the San Joaquin Valley. Final report prepared for the San Joaquin Valleywide Air Pollution Study Agency and the California Air Resources Board, Sacramento, CA by Sonoma Technology, Inc., Petaluma, CA, STI-997211-1800-FR, July. Husar R. (1999) Draft PM2.5 topic summary available at <http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMDefinitions/sld001.htm>. Kumar N. and Lurmann F.W. (1996) User’s guide to the speciated rollback model for particulate matter. Report prepared for San Joaquin Valleywide Air Pollution Study Agency, Sacramento, CA by Sonoma Technology, Inc., Santa Rosa, CA, STI-94250-1576-UG, September. Lurmann F.W., et. al., (1999) Personal communication. Pace F.G. and Kuykendal W.B. (1997) Planning tools for PM2.5 emission factors and inventories. Paper 98-RA90A.05 presented at the AWMA 90th Annual Meeting & Exhibition, June. U.S. Environmental Protection Agency (1996) Air Quality Criteria for Particulate Matter, chapter 1, Executive Summary: EPA 600/P-95/001aF, April. U.S. Environmental Protection Agency (1997) Quality Assurance Committee Emission Inventory Improvement Program Introduction: The Value of QA/QC, Volume VI, Chapter 1: January. Watson J.G., Fujita E.M., Chow J.C., Richards L.W., Neff W., and Dietrich D. (1998) Northern Front Range Air Quality Study. Final report prepared for Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO by Desert Research Institute, Reno, NV. PM Data Analysis Workbook: Emission Inventory Evaluation

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