Particulate Matter Emissions for eGRID2019 S>%-\ eGRID 1 > DRAFT White Paper May 2021 ------- Table of Contents Introduction 3 Methodology 4 Results 5 eGRID2018 Comparison 9 Table of Figures Figure 1. eGRID subregion map 6 Figure 2. eGRID Subregion-level 2019 generation, PM2 5 emissions, and PM2 5 emission rates 7 Figure 3. Comparison of PM2 5 output emission rates between eGRID2018 and NEI2018 9 Table of Tables Table 1. eGRID Subregion-level 2019 generation, PM2 5 emissions, and PM2 5 output emission rates 8 2 ------- Introduction EPA's Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data on air pollution emissions and electricity generation for virtually all electric generating units in the United States. Currently, eGRID includes emissions data on carbon dioxide (CO2), nitrogen oxides (NOx), sulfur dioxide (SO2), methane (CH4), and nitrous oxide (N2O), but does not include information on particulate matter (PM). PM pollution—principally fine particulate matter 2.5 microns in diameter or smaller (PM2 5)—can lead to negative health impacts, including asthma exacerbations, heart attacks, and premature mortality. For example, Lelieveld et al. (2015) estimated that in 2010, 55,000 premature deaths in the United States were attributable to two types of air pollution — PM2 5 and ozone.1 Additionally, EPA's retrospective analysis of the Clean Air Act found that approximately 85 percent of the public health benefits of air quality regulations are due to PM2 5 reductions, rather than ozone (EPA 201 la).2 PM2 5 can also lead to reduced visibility, known as haze, which negatively affects much of the country, including national parks. In an effort to start including PM2 5 emissions in eGRID, EPA released draft PM2 5 emissions and emissions rates for eGRID2018 and requested feedback. This white paper provides an overview of the methodology used to develop the draft PM2 5 emissions rates and the emissions rates for eGRID2019. eGRID uses CAMD's Power Sector Emissions Data reported to EPA's Clean Air Markets Division (CAMD)3 to determine the CO2, NOx, and SO2 emissions at many electric generating units. For electric generating units that do not report to CAMD, eGRID estimates emissions based on fuel use, as reported to the Energy Information Administration (EIA).4 Neither CAMD nor EIA collect data on PM2 5 emissions.5 For this reason, it is not possible to use PM data from either CAMD or EIA to estimate the PM2 5 emissions and PM2 5 emission rates from power plants. EPA's National Emissions Inventory (NEI) is a source of PM2 5 emissions data. The annual emissions of air pollutants, including PM2 5, from most electric generating units get reported to the NEI.6 While EPA has not previously used the NEI data for eGRID, EPA is proposing to use NEI data to determine PM2 5 emissions at electric generating units. The most recent data year for both eGRID and NEI data is 2018. This paper discusses EPA's proposed methods to determine PM2 5 emission rates for each power plant, including steps to estimate emissions for units that may not report to the NEI. First, 2018 PM2 5 emission rates were calculated and then applied to the eGRID2019 data to estimate 2019 PM2 5 emissions. The accompanying Excel data file lists the unit- and plant-level heat input, plant-level generation, and unit-, plant-, and eGRID subregion-level PM2 5 emissions and emission rates for 2018 and 2019. Note that PM can be emitted in two forms—as particles (filterable PM) or as a gas that later condenses 1 Lelieveld, J., J.S. Evans, M. Fnais, D. Giannadaki, and A. Pozzer. 2015. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525: 367-371. 2 EPA. 201 la. The Benefits and Costs of the Clean Air Act from 1990 to 2020. U.S. Environmental Protection Agency Office of Air and Radiation. Final Report - Rev. A. April. Available: https://www.epa.gov/sites/production/files/2015- 07/documents/fullreport_rev_a.pdf. U.S. Environmental Protection Agency. 3 These data are reported to EPA under chapter 40 of the Code of Federal Regulations part 75 (40 CFR part 75) for several Clean Air Act programs, including the Acid Rain Program and Cross-state Air Pollution Rule. 4 These data are reported to EIA through form EIA-923. 5 EIA collects some data on PM emission rates, but it does not specify whether the rates are for PM2.5 or PM10 (particulate matter 10 microns in diameter or smaller). 6 Electric generating units and other point sources of air pollution emissions do not report emissions directly to the NEI. Rather they report to state, local, or tribal agencies, which then report the data to the NEI. 3 ------- into particles when it enters the atmosphere (condensable PM). The eGRID methodology is designed to include both types of PM, also known as primary PM2.5.7 Methodology The most recent year in which both eGRID and NEI data are available is 2018.8 To estimate PM2 5 emissions and emission rates for 2018, unit-level PM2 5 emission rates (lb/MMBtu) were developed using the 2018 NEI PM2 5 mass data and the 2018 EIA heat input data. The unit-level PM2 5 emission rates developed for 2018 were then multiplied by the 2019 unit-level heat input to estimate 2019 unit-level PM2 5 emissions. The 2019 unit-level PM2 5 emissions were summed to the plant-level and eGRID subregion-level to estimate 2019 plant-level PM2 5 emissions and eGRID subregion PM2 5 output emission rates. The following methodology discusses how the 2018 unit-, plant-, and eGRID subregion-level PM2 5 emissions and emission rates were calculated. The NEI contains annual PM2 5 emissions data for electric generating units, but the first step in integrating NEI data is to match the electric generating units to the eGRID data. The NEI uses Emissions Inventory System (EIS) codes to identify facilities and units, while eGRID uses Office of Regulatory Information Systems Plant (ORISPL) codes to identify facilities and units. EPA's Office of Air Quality Planning and Standards (OAQPS), which compiles the NEI, has matched electric generating units between EIS identifiers used in the NEI and the ORISPL identifiers used in eGRID.9 The EIS and ORISPL systems do not always have a one-to-one relationship; in some cases, multiple EIS IDs are used to refer to a single unit in eGRID (or vice versa). In order to use the NEI data in eGRID, the NEI data are mapped to the appropriate ORISPL plant and unit ID. Multiple units in the NEI that are reported as matching to one eGRID unit are grouped and summed to determine the total emissions for each eGRID unit. For units that cannot be matched directly to the NEI, EPA estimated the PM2 5 emissions using a series of emissions factors. In general, the process of determining the PM2 5 emissions for each unit follows a four-step process: 1. Direct match. First the EPA matched operational combustion units with positive heat input directly between the 2018 NEI and 2018 eGRID. Units that could not be matched directly between the NEI and eGRID either do not report to the NEI as point sources or an adequate match between NEI and eGRID could not be determined. 2. Average emissions factors by fuel type, unit firing type, and prime mover. EPA developed average emissions factors by grouping the units from the NEI that can be matched to eGRID by fuel type (e.g., bituminous coal), unit firing type (e.g., wall-fired), and prime mover (e.g., steam turbine). The PM2 5 emissions and heat input, expressed as million British thermal units (MMBtu), for all units in each group were summed. The emissions factor was calculated by dividing the total emissions in each group by the total heat input in each group. This emissions factor was multiplied by the heat input reported by EIA for all units that could not be matched to a unit in the NEI, but which have the same fuel type, firing type, and prime mover. 3. Average emissions factors by fuel type and prime mover. For units that could not be matched directly between eGRID and the NEI or that could not be matched using the emissions factors 7 In addition to primary PM2.5 emitted by electric generating units, secondary PM2.5 can form in the atmosphere based on reactions of gases, such as NOx, SO2, and ammonia. This proposed method only addresses primary PM2.5. 8 The National Emissions Inventory is compiled for all sources every three years, and the most recent release is for 2017 data. However, data on point sources, including electricity generating units, is collected annually, and the most recent data for the point source emissions is 2018. 9 This analysis uses the data from the 2016vl air emissions modeling platform (available at fattps://www.epa.gov/airemissions- modeling/201.6v 1.-platform) to identify PM2.5 emissions from power plants. 4 ------- developed under step 2, EPA next developed more general average emissions factors by grouping the units from the NEI by fuel and prime mover. To capture more units, firing type was not included in this step because not all units have firing type data. 4. Emissions factors from AP-42. For the remaining units, EPA estimated the PM2 5 emissions using an emissions factor reported in EPA's AP-42.10 The emissions factors from AP-42 are specific to the unit's fuel, firing type, and prime mover. For some of these units, EPA was able to match the units to a PM control efficiency reported in the EIA-923. Therefore, for these units, the PM2 5 emissions estimated using the emissions factor were adjusted to account for the control efficiency. Since the NEI emissions included in step 1 and the average emissions factors developed in steps 2 and 3 are based on reported emissions to the NEI, the control efficiency is already accounted for in these emissions factors. The emissions in steps 1 through 3 therefore did not need to be further adjusted for any control efficiencies. There are some fuel types for which there are no emissions factors in AP-42 or another source. For these factors, an emissions factor from a similar fuel type was applied. For example, there is no emission factor for other gas (OG), so the emissions factor for natural gas (NG) was used. For some fuel types, including lignite coal, petroleum coke, and waste oil, the PM2 5 emissions factors depend on the ash content of the fuel. For the units combusting these fuels that could not be directly matched to the NEI, an ash content of the fuel was first estimated. EIA-923 reports ash content at the unit-level for each month. A weighted average ash content was calculated for each unit that uses lignite, petroleum coke, or waste oil, weighted by the amount of heat input for each unit in each month, which were used with equations from AP-42 to determine unit-specific emissions factors for those three fuel types. Unit-level PM2 5 emission rates were developed with the 2018 data and applied to eGRID2019 unit-level heat input to determine the 2019 PM2 5 unit- and plant-level emissions and emission rates. For any new plants in 2019, steps 2-4 (listed above) were repeated to develop emissions factors to estimate PM2 5 emissions. Unit- and plant-level PM2 5 emission rates were then calculated for 2019. Results Figure 1 displays the 27 eGRID subregions, including the new subregion for Puerto Rico (PRMS), added for eGRID2019. The 2019 subregion-level annual net generation, PM2 5 emissions, and PM2 5 output emission rates are shown in Figure 2 and in Table 1. The 2018 and 2019 unit-, plant-, and subregion-level annual net generation, PM2 5 emissions, and PM2 5 output emission rates are included in the Excel data file. The 2019 subregion-level PM25 output emission rates range from 0.017 lbs/MWh in the NYUP subregion to 0.825 lbs/MWh in the AKMS subregion. The highest output emission rates are in the subregions in Alaska and Hawaii, which have a higher percentage of generation from oil and a lower percentage of generation from natural gas when compared to the subregions in the contiguous United States. These higher output emission rates are explained by the fact that oil has a high PM2 5 output emission rate compared to natural gas. 10 United States Environmental Protection Agency, Office of Air Quality Planning and Standards. Compilation of Air Pollutant Emission Factors, AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources 5 ------- «) NWPP MROE RFCM NYCW RFCW CAMX RMPA SRMW SPNO SRTV SRVC FRCC Map of eGRID Subregions I NYLI USEPA, eGRID, February 2021 Crosshatdhing tndlcates thai an area falls within overlapping eGRID subregons due to the preserve of muitipte electric service providers. Visit Power Profiler to definitively determine the «GRID subregion associated with yojr location and electric service provider. http:/ymiv/.epa gov/energy/powei -piofila Alaska AK, AKMS AKGD Hawaii HIOA HIMS Puerto Rico PRMS Figure 1. eGRID subregion map 6 ------- ¦£ 600,000,000 | 500,000,000 o 400,000,000 300,000,000 (3 200,000,000 5 100,000, c ' ; c < 000 o - Q < 1-1 _ _ n 1 n _ 11 m 1 n n (J 00 < LU £ I 9 g Q_ Q_ Z> >- U u < Q_ CC CC C£L O O > CC 00 ° £ 00 I— cc CC 00 ^ _ 70,000 to C r\ rrrs r\r\r\ PM2.5 Emissions ~ OU,UUU +-» 0 50,000 40,000 c "J7i 30.000 £ 20,000 J? 10,000 I I - _ _ 1.11 l.i 1. I ..¦¦ill c AKGD AKMS AZNM CAMX ERCT FRCC HIMS HIOA MROE MROW NEWE NWPP NYCW NYLI NYUP PRMS RFCE RFCM RFCW RMPA SPNO SPSO SRMV SRMW SRSO SRTV (D +J CU C o 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 o l/> < < n n n n 5 X n n n n n H fl n < u u en U 00 < £ I 9 o en u >- 3 > cc Q_ PM2.5 Emission Rates n n n n [ 1 n U u < Q_ o o Or £ cc 00 cc 00 o 00 cc en oo Figure 2. eGRlD Snbregion-level 2019 generation, PM2.5 emissions, and PM2.5 emission rates 1 ------- Table 1. eGRlD Subregion-level 2019 generation, PM2.5 emissions, andPM2.5 output emission rates Suh region Anniiiil (ieneriilion (MWIi) Anniiiil PM;? Kmissions (short (011s) Anniiiil PM:..; Output Kmission K;i(e (Ibs/MWh) AKGD 4,513,906 582 0.2578 AKMS 1,554,337 641 0.8247 AZNM 169,846,256 6,099 0.0718 CAMX 204,484,755 2,957 0.0289 ERCT 418,830,337 8,631 0.0412 FRCC 235,320,760 6,759 0.0574 HIMS 2,758,699 1,095 0.7940 HIOA 6,991,299 2,060 0.5892 MROE 20,844,888 293 0.0281 MROW 232,826,410 14,550 0.1250 NEWE 100,011,791 2,301 0.0460 NWPP 282,811,235 7,934 0.0561 NYCW 41,509,809 1,109 0.0534 NY LI 8,943,357 239 0.0534 NYUP 87,477,873 761 0.0174 PRMS 18,166,188 836 0.0920 RFCE 296,156,271 13,923 0.0940 RFCM 97,428,154 3,032 0.0622 RFCW 514,167,896 57,725 0.2245 RMPA 66,259,360 1,262 0.0381 SPNO 70,052,261 1,924 0.0549 SPSO 157,750,108 3,447 0.0437 SRMV 173,701,305 5,062 0.0583 SRMW 121,427,325 5,347 0.0881 SRSO 260,293,360 4,949 0.0380 SRTV 216,125,641 18,419 0.1704 SRVC 328,960,224 12,223 0.0743 U.S. 3,810,253,579 184,157 0.0890 ------- eGRID2018 Comparison EPA previously estimated eGRID2018 PM2.5 emissions using 2016 NEI data. Now that actual reported 2018 PM2.5 emissions are available from the NEI, EPA can compare the projected eGRID2018 PM2.5 emissions rates to the rates calculated using 2018 PM2.5 emissions reported to the NEI. Figure 3 compares the projected eGRID2018 PM2.5 emissions rates to those calculated using the actual NEI reported 2018 PM2.5 emissions. For most subregions, the projected and actual emission rates are very similar—to within 0.01 lbs./MWh. There are some notable differences, however, between the projected and actual emission rates, such as in the MROW, RFCW, and SRTV regions, where the actual 2018 emission rates were higher than the projected emission rates in eGRID. In these cases, the differences in the rates are driven largely by differences in the actual and projected emissions in a small number of plants. For example, in MROW there were five plants (out of 1,139) that had significantly higher emission rates in 2018 than in 2016. If these five plants are excluded, the overall MROW PM2.5 emission rates would be similar between the projected eGRID2018 values and the calculated values using the actual NEI reported 2018 PM2.5 emissions. Such plant-level differences are difficult to predict year-to-year and can have a noticeable impact on the subregion-level rates. However, for most plants and subregions, the eGRID PM2.5 projection methodology accurately estimates emissions. AKGD AKMS AZNM CAMX ERCT FRCC HIMS HIOA MROE MROW NEWE NWPP NYCW NYLI NYUP RFCE RFCM RFCW RMPA SPNO SPSO SRMV SRMW SRSO SRTV SRVC 0 0.2 0.4 0.6 0.8 1 PM25 Output Emission Rate (Ib/MWh) Figure 3. Comparison ofPMj.s output emission rates between eGRID2018 and NEI2018. 9 ------- |