D-1

Appendix D: Sensitivity of Emissions Inventory 

Synopsis 

As described in Section 2.3.3 of the RIA, the approach for future-year emissions projections for 

non-EGU stationary sources was modified for this analysis. Emission projection methods for all 

other source categories (including mobile sources and electric generating units) remain 

essentially unchanged from recent analyses. The methodology used in this RIA to forecast nonEGU stationary source categories recognizes the disconnection between prior projection 

estimates and the historical record. The methodology is called an ‘interim’ emissions projection 

approach to acknowledge that we will work to develop improved and consistent emissions 

forecasting model(s) for future analyses. Due to the potential significance of this analytical 

assumption, the EPA sought consultation and advice from the Advisory Council on Clean Air 

Compliance Analysis and Air Quality Modeling Subcommittee (Council) of the Science 

Advisory Board on this interim emissions projection approach and requested recommendations 

on long-term methodological improvements that could be made in emissions forecasting for the 

non-EGU stationary source sectors. This appendix includes information presented to the 

Council, the Council’s advice to the EPA, a discussion of the implication of recommendations by 

the Council for three sectors in 2020, and a sensitivity analysis of the emissions and air quality 

impacts of this interim emissions projection approach. The sensitivity analysis included in this 

appendix presents the impact of this analysis change on emissions and air quality predictions in 

2015. 

D.1 Consultation with the Council 

On August 31, 2006, the EPA consulted with the Council by teleconference. In this consultation, 

the EPA requested advice and comments from the Council on its interim forecasting approach 

for emissions from stationary non-EGU sources used in this RIA. Specifically, the EPA 

requested recommendations on caveats and sensitivities that could be provided in the RIA in the 

discussion of this approach and suggestions or data that could be provided to help with a longer 

term approach to emission forecasting for these source categories. A background document was 

prepared for the Council’s consideration and is attached to this appendix as Attachment 1. 

On September 15, 2006, the Council issued a letter to Stephen L. Johnson, Administrator of EPA 

with the findings of its consultation. The Council’s letter is available at 

http://www.epa.gov/sab/pdf/council-con-06-007.pdf. In its response the Council recommends an 

alternative to the ‘interim’ method used by the EPA. This alternative would capture the 

underlying technological change that the Council contends is likely driving the decline in 

emissions, i.e., the efficiency gains in production processes and improvements in air pollution 

control technologies that can be expected over time. The Council suggests using the National 

Emissions Inventory in the 1990s to estimate a declining “emissions intensity” as it relates to the 

level of output by sector. The Council recommends the first step in this process be to factor out 

any decline in emissions that could be attributable to the Clean Air Act. As a default, the EPA 

could assume the residual historical rate of decline (i.e., after removing declines attributable to 

the Clean Air Act) would continue to be constant in future years. The Council did recognize the 

limitations of a court-ordered schedule for the PM NAAQS in the EPA’s ability to implement its 
D-2

recommendations into this RIA. Detailed recommendations of longer term approaches were also 

discussed and included in the meeting minutes. These minutes are available at 

http://www.epa.gov/sab/06minute.htm. 

In response to the Council’s recommendation, the EPA did endeavor to conduct a limited 

analysis using the Council’s recommended approach for three important non-EGU stationary 

source sectors including Pulp and Paper Manufacturing, Petroleum Refining, and Chemicals and 

Allied Products for SO2 emissions only. The court-ordered schedule for the PM NAAQS review 

does not allow for further investigation of the merits of this method for all relevant non-EGU 

stationary source categories or relevant pollutants. We found that the Council’s suggested 

approach resulted in essentially a downward trend in future year SO2 emissions for these source 

categories. Using an approach similar to the Counsel’s suggested approach, emissions would 

decline significant from 2002 to 2020 for these industries. This is because historical emissions 

reductions used in this analysis could not be directly attributed to Clean Air Act mandated 

controls and therefore the entire declining emission trend for these three sectors was assumed to 

continue into the future. We recognize the limitations of this analysis since some historical 

emission reductions may have been due to Clean Air Act mandated controls (e.g., SIPs, NSPS) 

that are applied to individual facilities (rather than mandated controls that would be applicable to 

the entire sector), but given the limited time and quality of the control information in the 

emission inventory an accurate attribution of these historical emission reductions to the Clean 

Air Act was not possible. 

This comparison suggests the interim approach used for this RIA by EPA is conservative with 

respect to the emissions projections (i.e., results in emission projections that are lower than those 

used in previous analyses but higher than those resulting from the Council’s recommended 

approach) relative to the alternative suggested. The EPA does recognize the need to develop a 

long-term more robust and consistent method for forecasting emissions for the non-EGU 

stationary sources sectors. The EPA feels the Council’s advice will be helpful to formulate a 

new and improved emissions forecasting methodology for the stationary non-EGU sources for 

future analyses. 

In addition to the analysis conducted in response to the Council’s recommendation, ongoing 

emission inventory analysis has been conducted for the second 812 Prospective Benefits and 

Costs of the Clean Air Act analysis.1

 The results of a historical inventory analysis for the 812 

study suggest the complexity involved in developing a new and improved emissions projections 

methodology that recognizes key components of historical emissions changes. This study found 

that sector-specific research needs to be done to improve emissions projections. The study 

showed that even within a specific source category the bias in projection methods and historical 

data may differ across pollutants demonstrating the challenges involved in developing a new 

method of emissions forecasting. The EPA recognizes that significant effort will be required to 

design an improved emission forecasting method for the stationary non-EGU sources, and the 

EPA is committed to designing an improved approach in the future. 

 

1

 Memorandum from Jim Neumann, IEc, Jim Wilson and Andy Bollman, EH Pechan and Associates to Jim 

DeMocker, EPA/OAR/OPAR. “Documentation of Analysis of 1990-2002 Emissions for Selected Non-EGU 

Stationary Point Sources,” September 19, 2006. 
D-3

D.2 Comparison of Sensitivity Analysis Emissions 

For this sensitivity, we created two emissions cases for input to the CMAQ model. In Case 1 use 

the interim approach (i.e., removal of the economic growth term from the emissions projections 

equation) for projecting stationary non-EGU sources. Case 2 contains emission using our 

previous growth assumptions for these sources that was used for the Clean Air Interstate Rule 

(CAIR). 

Both cases use most of our revised control assumptions that are described in Section 2.3.2 of the 

main body of the PM NAAQS RIA. Because the sensitivity was performed prior to the final 

version of the 2015 emissions used for the RIA modeling, there are some differences between the 

control assumptions in the 2015 inventories used for this sensitivity and those of the final 2015 

emissions used for the RIA. These differences are relatively localized to a handful of plants 

affected by the changes, so we have concluded that the results of this sensitivity are sufficiently 

applicable for the purpose of characterizing the AQ modeling sensitivity to the revised growth 

approach. 

In this section, we first describe the differences between Case 1 of the sensitivity and the final 

2015 baseline emissions. Second, we describe how we created the Case 2 emissions and 

summarize the differences between Case 1 and Case 2. 

D.1.1 Difference in 2015 Emissions Used in Sensitivity Comparison to Final Analysis 2015 

Baseline Emissions 

For both Case 1 and Case 2, there were two differences in the control assumptions used as 

compared to the final 2015 and 2020 emissions used for the PM NAAQS RIA. These were: 

1. Included SO2 reductions in the non-EGU point sources for the “Industrial, Commercial, 

and Institutional Boilers and Process Heaters Rule”. These were ultimately determined to 

be invalid and therefore removed from the final analysis used for the RIA. 

2. Used original EGU emissions including CAIR, CAMR, and CAVR (used for the Clear 

Skies analyses). These were ultimately revised as described in Section 2.3.3 and Table 2-

8 of the main body of the RIA. 

These changes resulted in emissions differences in selected counties. We compare Case 1 with 

the final RIA emissions in Table 1(a) for EGUs and Table 1(b) for non-EGU point sources. 
D-4

Table D-1(a): EGU Sector Comparison of Case 1 Emissions with Final RIA Baseline 

Emissions* 

2015 NOX 2015 SO2 2015 PM2.5 

State Case 1 RIA 

Baseline

Diff %Diff Case 1 RIA 

Baseline

Diff %Dif

f

Case 1 RIA 

Baseline

Diff %Diff

Alabama 49,144 48,501 -643 -1.3% 260,267 247,538 -12,729 -

4.9%

15,853 15,993 140 0.9%

Arizona 65,858 65,840 -18 0.0% 60,347 60,321 -26 0.0% 10,012 10,010 -2 0.0%

Arkansas 31,908 31,925 17 0.1% 22,801 22,795 -6 0.0% 4,731 4,735 4 0.1%

California 21,968 21,964 -4 0.0% 5,068 5,066 -1 0.0% 4,835 4,833 -2 -0.1%

Colorado 60,440 60,437 -3 0.0% 57,467 57,452 -15 0.0% 3,942 3,943 1 0.0%

Connecticut 6,936 6,901 -34 -0.5% 3,902 3,901 -1 0.0% 676 668 -8 -1.2%

Delaware 9,551 8,198 -1,352 -14.2% 27,646 22,992 -4,653 -

16.8

%

4,623 3,962 -661 -14.3%

District of 

Columbia

53 54 0 0.6% 0 0 0 0.0% 7 7 0 3.3%

Florida 61,483 60,411 -1,072 -1.7% 167,199 163,704 -3,495 -

2.1%

19,847 19,771 -76 -0.4%

Georgia 66,780 66,773 -7 0.0% 240,913 220,749 -20,164 -

8.4%

21,102 20,235 -866 -4.1%

Idaho 587 587 0 0.0% 0 0 0 0.0% 69 69 0 0.0%

Illinois 65,352 65,728 376 0.6% 245,328 230,488 -14,840 -

6.0%

13,786 14,271 484 3.5%

Indiana 81,795 80,329 -1,467 -1.8% 376,779 362,960 -13,819 -

3.7%

32,326 31,181 -1,144 -3.5%

Iowa 51,741 52,456 715 1.4% 163,493 162,891 -602 -

0.4%

8,228 8,100 -128 -1.6%

Kansas 39,816 39,799 -17 0.0% 58,540 58,525 -15 0.0% 6,219 6,217 -2 0.0%

Kentucky 76,860 79,310 2,450 3.2% 264,152 262,778 -1,374 -

0.5%

24,202 24,195 -7 0.0%

Louisiana 32,486 32,475 -11 0.0% 62,050 62,034 -17 0.0% 3,536 3,535 -1 0.0%

Maine 1,797 1,816 19 1.1% 5,335 4,801 -533 -

10.0

%

231 238 6 2.8%

Maryland 12,843 12,815 -27 -0.2% 42,787 34,267 -8,520 -

19.9

%

4,867 4,867 0 0.0%

Massachusetts 19,111 19,179 68 0.4% 17,400 17,741 341 2.0% 2,869 2,874 5 0.2%

Michigan 92,411 92,275 -136 -0.1% 393,060 369,805 -23,255 -

5.9%

22,347 21,622 -725 -3.2%

Minnesota 40,086 40,156 71 0.2% 84,742 84,979 237 0.3% 14,481 14,485 4 0.0%

Mississippi 7,878 7,893 15 0.2% 85,649 57,919 -27,730 -

32.4

%

4,009 3,584 -425 -10.6%

Missouri 69,950 69,921 -29 0.0% 266,422 266,369 -53 0.0% 26,508 26,499 -8 0.0%

Montana 38,431 38,420 -10 0.0% 22,480 22,474 -6 0.0% 4,831 4,830 -1 0.0%

Nebraska 42,854 42,842 -12 0.0% 36,760 36,750 -10 0.0% 2,905 2,904 -1 0.0%

Nevada 30,589 30,596 8 0.0% 27,394 27,424 30 0.1% 4,123 4,126 3 0.1%

New 

Hampshire

2,932 2,968 36 1.2% 7,423 7,426 3 0.0% 928 940 12 1.3%

New Jersey 13,244 12,732 -512 -3.9% 32,490 29,426 -3,065 -

9.4%

5,978 5,870 -108 -1.8%

New Mexico 71,538 71,517 -21 0.0% 52,899 52,884 -14 0.0% 7,916 7,915 -2 0.0%

New York 23,405 23,616 212 0.9% 48,835 48,544 -290 - 8,703 8,652 -50 -0.6%
D-5

0.6%

North Carolina 50,814 50,855 41 0.1% 124,591 124,637 46 0.0% 18,966 19,001 35 0.2%

North Dakota 39,857 39,862 6 0.0% 85,061 85,050 -11 0.0% 6,132 6,132 1 0.0%

Ohio 93,344 90,204 -3,140 -3.4% 271,778 266,292 -5,486 -

2.0%

33,425 32,821 -604 -1.8%

Oklahoma 57,929 57,815 -115 -0.2% 46,670 45,755 -915 -

2.0%

13,354 13,349 -5 0.0%

Oregon 10,607 10,604 -2 0.0% 10,037 10,034 -3 0.0% 807 807 0 0.0%

Pennsylvania 74,277 74,813 536 0.7% 141,443 136,360 -5,084 -

3.6%

23,956 23,718 -238 -1.0%

Rhode Island 481 475 -5 -1.1% 0 0 0 0.0% 111 110 -2 -1.6%

South Carolina 36,391 36,380 -11 0.0% 105,427 104,914 -512 -

0.5%

14,487 14,453 -34 -0.2%

South Dakota 1,749 1,748 0 0.0% 4,149 4,148 -1 0.0% 372 372 0 0.0%

Tennessee 27,310 27,191 -119 -0.4% 191,511 173,081 -18,431 -

9.6%

14,363 13,690 -674 -4.7%

Texas 158,008 158,413 405 0.3% 373,127 363,943 -9,183 -

2.5%

28,995 29,603 608 2.1%

Utah 53,408 53,393 -14 0.0% 53,123 53,109 -14 0.0% 4,361 4,360 -1 0.0%

Vermont 35 41 7 19.6% 0 0 0 0.0% 7 9 2 32.0%

Virginia 39,960 39,739 -221 -0.6% 94,576 87,365 -7,212 -

7.6%

10,296 10,043 -254 -2.5%

Washington 14,996 14,995 -1 0.0% 11,077 11,074 -3 0.0% 2,641 2,641 0 0.0%

West Virginia 39,545 39,534 -11 0.0% 111,001 111,953 952 0.9% 17,690 17,687 -3 0.0%

Wisconsin 40,843 42,412 1,569 3.8% 150,657 148,032 -2,625 -

1.7%

8,942 8,727 -215 -2.4%

Wyoming 53,079 53,065 -14 0.0% 74,265 73,846 -420 -

0.6%

7,246 7,244 -2 0.0%

1,982,455 1,979,977 -2,479 -0.1% 4,988,12

1

4,804,595 -183,526 -

3.7%

490,841 485,895 -4,946 -1.0%

* Differences of 5% are more are shaded. Differences in other pollutants exist, but not shown. 
D-6

Table D-1(b): Non-EGU Comparison of Case 1 Emissions with Final 

RIA Baseline Emissions* 

2015 SO2 

State County Case 1 RIA 

Baseline

Diff

Illinois Macon Co 25,164 29,605 4,441

Illinois Peoria Co 2,890 9,763 6,873

Iowa Clinton Co 3,778 18,879 15,101

Iowa Muscatine Co 4,042 16,115 12,073

Iowa Story Co 2,267 11,336 9,069

Maryland Allegany Co 4,423 19,227 14,804

Ohio Ross Co 6,597 30,735 24,138

Pennsylvania Erie Co 2,286 10,210 7,924

Pennsylvania York Co 6,161 12,363 6,202

Tennessee Davidson Co 2,746 8,554 5,808

Tennessee Sullivan Co 14,600 32,539 17,939

West Virginia Marshall Co 15,159 24,799 9,640

Wisconsin Brown Co 6,200 20,959 14,759

* Only counties with differences are shown 

D.1.2. Difference in 2015 Emissions Due to Revised Growth Assumptions 

To create the Case 2 emissions, we applied the growth factors used for CAIR to the non-EGU 

point, other area and fugitive dust sectors. These CAIR growth factors were applied with our 

revised control assumptions used for PM NAAQS, with the exception of those two revisions 

listed in Section 2.1. The origin of the CAIR growth data is described more fully in Section 4.1 

of the CAIR Emission Inventory Technical Support Document, available at 

http://www.epa.gov/air/interstateairquality/pdfs/finaltech01.pdf. The emissions were the same 

between Case 1 and Case 2 for emissions from EGUs, on-road mobile sources, nonroad mobile 

sources, agricultural livestock and fertilizer application, and fires (wildfires, prescribed burning, 

agricultural burning, and open burning). 

Table 2(a) provides state-total differences for non-EGU point and stationary area sources 

between the Case 1 and Case 2 for VOC, NOx, SO2, NH3, and PM2.5. Table 2(b) provides statetotal differences for the entire state between the two cases for the same pollutants. 
Table D-2(a): Case 1 Compared to Case 2 Emissions by State, Sectors that Changed and Pollutant 

VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Alabama Nonpoint 

fugitive dust

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

Nonpoint 152,290 196,383 29% 10,612 13,175 24% 44,895 31,436 -30% 1,370 1,635 19% Point fugitive dust

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

Non-EGU Point

47,520 63,838 34% 89,158 112,327 26% 113,811 128,959 13% 494 589 19% 

Alabama Total 199,810 260,220 30% 99,770 125,502 26% 158,706 160,395 1% 1,863 2,225 19% Arizona Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 91,612 130,416 42% 53,957 67,996 26% 3,457 3,504 1% 2,699 3,159 17%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

5,706 9,204 61% 29,725 39,710 34% 32,568 48,431 49% 41 78 91% 

Arizona Total 97,318 139,620 43% 83,682 107,706 29% 36,025 51,935 44% 2,740 3,237 18% Arkansas Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 92,027 112,573 22% 35,729 42,949 20% 19,998 30,680 53% 1,069 1,377 29%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

26,495 35,455 34% 54,281 69,560 28% 26,849 33,652 25% 1,235 1,454 18% 

Arkansas Total 118,522 148,028 25% 90,010 112,509 25% 46,846 64,332 37% 2,305 2,832 23% California Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 415,895 506,517 22% 145,151 167,149 15% 10,453 12,196 17% 1,936 2,254 16%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

45,589 62,985 38% 101,665 120,422 18% 37,134 44,628 20% 13,902 13,699 -1% 

California Total 461,484 569,502 23% 246,816 287,570 17% 47,586 56,824 19% 15,837 15,953 1% Colorado Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 84,216 105,390 25% 11,237 15,622 39% 1,991 2,361 19% 72 97 35%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

 Non-EGU 

Point 

33,869 47,126 39% 38,415 48,359 26% 9,191 11,137 21% 242 295 22% 

Colorado Total 118,085 152,516 29% 49,651 63,981 29% 11,183 13,498 21% 314 392 25% Connecticut Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 118,202 127,272 8% 13,721 14,873 8% 12,121 12,148 0% 2,285 2,766 21%  Non-EGU 

Point 

5,615 7,596 35% 3,293 4,266 30% 2,946 4,097 39% 39 52 34% 

Connecticut Total 123,817 134,868 9% 17,014 19,139 12% 15,066 16,244 8% 2,324 2,818 21% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Delaware Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 14,820 18,917 28% 3,857 5,247 36% 10,594 16,086 52% 379 466 23%  Non-EGU 

Point 

3,641 4,610 27% 8,550 10,363 21% 20,096 22,457 12% 671 762 14% 

Delaware Total 18,461 23,527 27% 12,407 15,610 26% 30,690 38,543 26% 1,050 1,228 17% District of Columbia Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 9,561 10,898 14% 2,326 3,061 32% 5,938 7,448 25% 982 1,133 15%  Non-EGU 

Point 

4 5 24% 477 547 15% 792 924 17% 9 12 38% 

District of Columbia Total 9,565 10,903 14% 2,803 3,608 29% 6,730 8,371 24% 990 1,145 16% Florida Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 269,923 359,803 33% 30,248 35,731 18% 39,817 62,248 56% 3,389 4,548 34%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

40,347 52,998 31% 59,586 76,949 29% 87,311 105,004 20% 569 737 29% 

Florida Total 310,270 412,800 33% 89,834 112,680 25% 127,128 167,252 32% 3,958 5,284 34% Georgia Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 167,804 215,134 28% 26,056 32,418 24% 4,407 5,934 35% 2,452 3,180 30%  Non-EGU 

Point 

30,264 40,575 34% 77,356 95,851 24% 84,486 115,947 37% 4,778 6,247 31% 

Georgia Total 198,068 255,710 29% 103,412 128,269 24% 88,893 121,881 37% 7,230 9,427 30% Idaho Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 169,139 261,550 55% 36,323 50,800 40% 1,652 2,040 23% 562 740 32%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

3,942 5,592 42% 11,298 14,393 27% 18,109 23,392 29% 984 1,292 31% 

Idaho Total 173,080 267,142 54% 47,620 65,192 37% 19,762 25,432 29% 1,546 2,032 31% Illinois Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 272,712 341,249 25% 39,045 46,306 19% 41,299 56,996 38% 9,979 12,273 23%  Point fugitive 0 0 0% 0 0 0% 0 0 0% 0 0 0% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

dust 

 Non-EGU 

Point 

82,756 113,170 37% 100,177 127,465 27% 174,790 194,627 11% 9,215 13,490 46% 

Illinois Total 355,468 454,419 28% 139,221 173,771 25% 216,089 251,622 16% 19,194 25,763 34% Indiana Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 190,403 236,149 24% 43,924 46,630 6% 8,922 9,015 1% 2,948 3,754 27%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

63,344 88,865 40% 89,582 112,702 26% 168,608 194,805 16% 3,460 4,802 39% 

Indiana Total 253,747 325,014 28% 133,506 159,331 19% 177,530 203,820 15% 6,409 8,556 34% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Iowa Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 123,428 132,131 7% 29,622 34,596 17% 23,947 24,405 2% 7,234 7,583 5%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

8,295 11,259 36% 28,043 32,049 14% 54,132 56,262 4% 4,145 5,382 30% 

Iowa Total 131,723 143,389 9% 57,665 66,645 16% 78,079 80,667 3% 11,379 12,965 14% Kansas Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 88,932 97,015 9% 14,362 16,382 14% 3,800 4,376 15% 1,637 1,895 16%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

22,742 30,772 35% 85,488 108,635 27% 17,165 23,787 39% 858 1,116 30% 

Kansas Total 111,674 127,787 14% 99,850 125,017 25% 20,965 28,163 34% 2,495 3,011 21% Kentucky Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 106,387 130,612 23% 73,937 84,608 14% 56,977 56,666 -1% 1,242 1,521 23%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

64,477 82,869 29% 35,240 41,784 19% 34,990 39,571 13% 575 664 15% 

Kentucky Total 170,865 213,481 25% 109,177 126,392 16% 91,967 96,236 5% 1,817 2,185 20% Louisiana Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 93,605 110,099 18% 93,604 112,916 21% 90,933 135,352 49% 22,828 23,289 2%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

55,074 67,429 22% 234,799 282,924 20% 163,566 206,605 26% 8,507 10,777 27% 

Louisiana Total 148,679 177,528 19% 328,402 395,840 21% 254,499 341,956 34% 31,334 34,067 9% Maine Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 90,770 115,111 27% 8,218 8,804 7% 15,722 16,897 7% 1,278 1,574 23%  Non-EGU 

Point 

4,230 5,638 33% 18,897 25,403 34% 30,595 43,305 42% 123 170 39% 

Maine Total 95,001 120,749 27% 27,115 34,208 26% 46,317 60,202 30% 1,401 1,744 24% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Maryland Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 80,866 113,550 40% 17,069 20,955 23% 41,581 54,861 32% 1,636 2,166 32%  Non-EGU 

Point 

5,264 6,738 28% 18,529 23,637 28% 22,836 28,755 26% 372 511 37% 

Maryland Total 86,130 120,287 40% 35,598 44,592 25% 64,416 83,615 30% 2,008 2,677 33% Massachusetts Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 146,756 178,720 22% 25,595 28,858 13% 68,235 85,836 26% 5,665 6,917 22%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

9,078 12,524 38% 17,675 22,121 25% 17,904 24,535 37% 64 87 35% 

Massachusetts Total 155,835 191,244 23% 43,271 50,978 18% 86,140 110,371 28% 5,729 7,004 22% Michigan Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 249,950 283,840 14% 48,563 55,971 15% 34,238 38,797 13% 5,489 6,912 26%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

43,667 60,547 39% 90,725 114,013 26% 76,286 91,921 20% 393 492 25% 

Michigan Total 293,617 344,387 17% 139,287 169,983 22% 110,524 130,718 18% 5,883 7,404 26% Minnesota Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 162,881 171,010 5% 21,747 24,126 11% 5,662 6,122 8% 3,776 4,237 12%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

23,284 30,693 32% 55,734 67,831 22% 21,466 25,656 20% 990 1,106 12% 

Minnesota Total 186,165 201,703 8% 77,481 91,958 19% 27,129 31,778 17% 4,766 5,343 12% Mississippi Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 114,534 141,358 23% 4,154 5,243 26% 492 480 -2% 798 1,022 28%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

49,184 72,732 48% 103,232 134,960 31% 69,285 81,194 17% 1,146 1,703 49% 

Mississippi Total 163,718 214,090 31% 107,387 140,203 31% 69,777 81,673 17% 1,944 2,725 40% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Missouri Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 141,792 157,892 11% 35,170 37,137 6% 34,207 37,589 10% 3,806 4,153 9%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

28,479 37,598 32% 31,422 40,255 28% 114,680 142,441 24% 3,968 4,917 24% 

Missouri Total 170,271 195,491 15% 66,593 77,391 16% 148,887 180,029 21% 7,774 9,070 17% Montana Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 41,974 45,221 8% 10,310 13,082 27% 1,233 1,248 1% 269 344 28%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

3,365 4,480 33% 15,350 18,919 23% 19,805 24,536 24% 407 473 16% 

Montana Total 45,339 49,700 10% 25,661 32,001 25% 21,038 25,784 23% 676 816 21% Nebraska Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 70,366 73,827 5% 13,784 16,517 20% 9,850 13,536 37% 598 783 31%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

6,702 10,179 52% 11,537 15,194 32% 7,097 9,426 33% 14 18 24% 

Nebraska Total 77,068 84,007 9% 25,321 31,712 25% 16,948 22,962 35% 612 801 31% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Nevada Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 33,547 50,610 51% 7,220 8,992 25% 3,452 3,463 0% 915 1,302 42%  Non-EGU 

Point 

840 1,378 64% 4,693 6,460 38% 656 867 32% 14 21 42% 

Nevada Total 34,387 51,988 51% 11,912 15,452 30% 4,108 4,330 5% 929 1,323 42% New Hampshire Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 53,387 67,255 26% 5,385 5,947 10% 10,185 11,121 9% 945 1,179 25%  Non-EGU 

Point 

2,229 3,098 39% 2,743 3,648 33% 5,250 7,610 45% 47 69 48% 

New Hampshire Total 55,617 70,353 26% 8,128 9,595 18% 15,435 18,731 21% 992 1,248 26% New Jersey Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 145,975 168,922 16% 37,797 41,146 9% 47,838 52,714 10% 4,051 4,809 19%  Non-EGU 

Point 

19,237 25,132 31% 17,022 20,304 19% 6,553 7,451 14% 186 226 22% 

New Jersey Total 165,212 194,054 17% 54,819 61,449 12% 54,391 60,166 11% 4,237 5,035 19% New Mexico Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 44,554 57,577 29% 25,426 31,398 23% 8,451 5,939 -30% 389 487 25%  Non-EGU 

Point 

12,101 15,477 28% 79,394 100,801 27% 74,580 102,463 37% 42 51 22% 

New Mexico Total 56,656 73,054 29% 104,820 132,199 26% 83,031 108,402 31% 430 538 25% New York Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 598,612 715,327 19% 65,289 67,254 3% 159,191 159,552 0% 13,437 16,040 19%  Non-EGU 

Point 

5,465 7,385 35% 37,583 46,857 25% 71,006 79,401 12% 972 1,095 13% 

New York Total 604,077 722,712 20% 102,873 114,111 11% 230,197 238,953 4% 14,409 17,135 19% North Carolina Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 207,535 260,171 25% 14,412 17,774 23% 31,822 33,669 6% 2,122 2,657 25%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

68,168 93,710 37% 46,995 58,432 24% 66,220 84,852 28% 1,876 2,468 32% 

North Carolina Total 275,703 353,881 28% 61,407 76,206 24% 98,042 118,520 21% 3,998 5,125 28% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

North Dakota Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 60,442 52,882 -13% 18,442 19,719 7% 56,231 52,831 -6% 202 243 21%  Non-EGU 

Point 

661 754 14% 10,627 11,688 10% 21,629 24,007 11% 12 14 16% 

North Dakota Total 61,103 53,636 -12% 29,069 31,407 8% 77,860 76,838 -1% 214 258 20% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Ohio Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 259,823 319,076 23% 60,160 67,867 13% 67,415 75,340 12% 7,196 8,996 25%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

33,261 45,189 36% 69,407 80,740 16% 79,844 83,247 4% 2,505 3,070 23% 

Ohio Total 293,084 364,265 24% 129,567 148,607 15% 147,259 158,587 8% 9,701 12,065 24% Oklahoma Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 122,510 150,164 23% 30,256 35,282 17% 5,277 6,735 28% 7,736 8,029 4%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

19,900 24,599 24% 98,984 116,193 17% 27,498 31,773 16% 3,490 4,268 22% 

Oklahoma Total 142,410 174,764 23% 129,241 151,475 17% 32,774 38,508 17% 11,226 12,297 10% Oregon Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 252,174 305,486 21% 17,460 20,325 16% 22,142 24,124 9% 292 349 20%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

11,890 16,225 36% 15,988 19,685 23% 8,932 11,003 23% 67 75 12% 

Oregon Total 264,064 321,710 22% 33,448 40,010 20% 31,074 35,126 13% 359 424 18% Pennsylvania Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 233,160 277,662 19% 53,241 60,281 13% 94,191 105,063 12% 6,050 7,261 20%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

38,255 51,239 34% 89,806 105,159 17% 82,718 91,483 11% 1,277 1,518 19% 

Pennsylvania Total 271,415 328,901 21% 143,047 165,440 16% 176,909 196,546 11% 7,328 8,779 20% Rhode Island Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 30,425 44,695 47% 4,901 5,981 22% 5,263 5,711 9% 97 107 10%  Non-EGU 

Point 

1,566 2,221 42% 1,650 2,212 34% 2,505 3,493 39% 3 4 47% 

Rhode Island Total 31,991 46,916 47% 6,551 8,193 25% 7,768 9,204 18% 100 111 11% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

South Carolina Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 167,921 217,715 30% 18,945 22,279 18% 14,763 15,286 4% 1,005 1,268 26%  Non-EGU 

Point 

25,434 36,526 44% 35,917 42,427 18% 52,420 61,982 18% 1,111 1,470 32% 

South Carolina Total 193,355 254,241 31% 54,862 64,706 18% 67,183 77,268 15% 2,116 2,737 29% South Dakota Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 40,987 38,866 -5% 6,292 6,657 6% 20,387 20,634 1% 309 386 25%  Non-EGU 

Point 

1,256 1,893 51% 4,503 5,965 32% 1,363 1,867 37% 1 1 54% 

South Dakota Total 42,243 40,758 -4% 10,795 12,622 17% 21,750 22,502 3% 310 387 25% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Tennessee Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 178,994 233,527 30% 23,997 29,385 22% 41,818 46,434 11% 3,377 4,946 46%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

81,141 116,387 43% 62,850 75,238 20% 75,252 88,682 18% 2,246 2,950 31% 

Tennessee Total 260,134 349,914 35% 86,846 104,623 20% 117,069 135,116 15% 5,624 7,897 40% Texas Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 528,746 636,524 20% 43,589 49,983 15% 7,113 8,982 26% 6,917 8,777 27%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

118,284 149,692 27% 423,216 495,841 17% 204,910 238,233 16% 0 0 0% 

Texas Total 647,031 786,216 22% 466,804 545,824 17% 212,022 247,215 17% 6,917 8,777 27% Utah Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 47,699 64,544 35% 18,576 23,020 24% 10,560 9,720 -8% 632 871 38%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

6,751 9,163 36% 24,839 30,025 21% 9,391 11,641 24% 785 932 19% 

Utah Total 54,450 73,707 35% 43,415 53,045 22% 19,951 21,361 7% 1,417 1,803 27% Vermont Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 22,491 25,441 13% 3,999 4,658 16% 6,988 8,426 21% 272 339 25%  Non-EGU 

Point 

1,767 2,442 38% 877 1,432 63% 1,294 1,904 47% 1 1 53% 

Vermont Total 24,257 27,883 15% 4,876 6,091 25% 8,283 10,331 25% 272 340 25% Virginia Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 168,516 216,083 28% 43,689 50,568 16% 15,237 18,193 19% 685 809 18%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

43,536 58,742 35% 68,155 80,635 18% 73,384 87,033 19% 727 788 8% 

Virginia Total 212,052 274,825 30% 111,844 131,203 17% 88,622 105,226 19% 1,413 1,597 13% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

Washington Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 156,929 201,834 29% 17,915 21,626 21% 3,086 3,291 7% 3,715 4,650 25%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

12,290 16,945 38% 31,619 39,609 25% 36,290 44,551 23% 4,206 5,514 31% 

Washington Total 169,219 218,779 29% 49,533 61,235 24% 39,376 47,843 22% 7,921 10,164 28% 
VOC NOX SO2 NH3 

State Sector Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % 

Diff

Case 1 Case 2 % Diff 

West Virginia Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 47,466 55,715 17% 12,988 15,476 19% 13,003 14,599 12% 441 522 18%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

16,531 20,608 25% 44,318 52,440 18% 51,470 59,383 15% 514 587 14% 

West Virginia Total 63,997 76,323 19% 57,306 67,916 19% 64,473 73,983 15% 955 1,109 16% Wisconsin Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 211,413 257,205 22% 29,434 32,713 11% 43,831 57,219 31% 2,596 3,325 28%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

31,347 44,210 41% 41,740 49,030 17% 56,804 58,937 4% 846 1,045 24% 

Wisconsin Total 242,760 301,416 24% 71,175 81,743 15% 100,634 116,156 15% 3,442 4,370 27% Wyoming Nonpoint 

fugitive dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Nonpoint 17,354 20,439 18% 60,241 74,895 24% 14,903 14,276 -4% 292 353 21%  Point fugitive 

dust 

0 0 0% 0 0 0% 0 0 0% 0 0 0% 

 Non-EGU 

Point 

11,418 14,085 23% 36,495 42,874 17% 38,120 39,916 5% 654 752 15% 

Wyoming Total 28,772 34,524 20% 96,735 117,769 22% 53,023 54,192 2% 946 1,105 17% Grand Total 8,467,76

6 

10,532,92

8

24% 4,127,62

7

4,962,70

6

20% 3,770,157 4,429,40

7 

17% 228,83

4

275,32

5

20% 
Table D-2(b): Case 1 Compared to Case 2 Emissions by State and Pollutant (all anthropogenic emission sectors 

VOC NOX SO2 NH3 

State Case 1 Case 2 % 

Diff 

Case 1 Case 2 % 

Diff 

Case 1 Case 2 % 

Diff 

Case 1 Case 2 % 

D

Alabama 286,037 346,447 21% 255,044 280,775 10% 424,702 426,391 0% 85,199 85,561 Arizona 193,129 235,431 22% 263,284 287,307 9% 100,500 116,410 16% 45,348 45,845 Arkansas 170,115 199,621 17% 203,948 226,447 11% 74,130 91,616 24% 146,535 147,062 California 849,540 957,558 13% 825,242 865,997 5% 70,243 79,480 13% 324,066 324,181 Colorado 191,953 226,383 18% 205,042 219,372 7% 71,260 73,575 3% 78,299 78,377 Connecticut 160,769 171,820 7% 81,643 83,768 3% 20,352 21,529 6% 9,806 10,299 Delaware 30,215 35,281 17% 43,139 46,342 7% 59,755 67,608 13% 17,752 17,930 District of Columbia 

12,939 14,277 10% 9,331 10,136 9% 6,780 8,422 24% 1,509 1,664 1

Florida 609,012 711,543 17% 432,336 455,182 5% 310,958 351,082 13% 73,489 74,815 Georgia 355,854 413,495 16% 350,655 375,512 7% 335,024 368,012 10% 124,332 126,529 Idaho 274,187 368,248 34% 98,419 115,991 18% 23,876 29,546 24% 81,136 81,621 Illinois 486,738 585,689 20% 444,125 478,674 8% 471,599 507,132 8% 111,111 117,680 Indiana 344,207 415,474 21% 371,901 397,726 7% 561,507 587,797 5% 100,650 102,797 Iowa 177,629 189,295 7% 201,599 210,579 4% 243,118 245,707 1% 241,350 242,935 Kansas 154,747 170,860 10% 227,556 252,723 11% 80,132 87,330 9% 151,498 152,014 Kentucky 234,191 276,806 18% 310,692 327,907 6% 368,433 372,702 1% 61,098 61,467 Louisiana 234,179 263,028 12% 613,661 681,099 11% 358,309 445,767 24% 66,430 69,162 Maine 126,724 152,472 20% 54,420 61,513 13% 52,086 65,971 27% 9,075 9,418 Maryland 152,199 186,357 22% 157,258 166,252 6% 110,597 129,796 17% 35,107 35,776 Massachusetts 220,112 255,522 16% 175,130 182,838 4% 105,995 130,226 23% 14,659 15,934 Michigan 473,221 523,990 11% 423,462 454,158 7% 513,080 533,273 4% 71,642 73,163 Minnesota 306,630 322,167 5% 270,278 284,755 5% 119,636 124,286 4% 163,719 164,297 Mississippi 222,793 273,165 23% 210,436 243,252 16% 165,720 177,617 7% 76,176 76,956 Missouri 259,237 284,456 10% 295,736 306,535 4% 422,967 454,109 7% 121,992 123,288 Montana 73,469 77,830 6% 127,553 133,893 5% 45,202 49,948 11% 47,456 47,596 Nebraska 104,123 111,061 7% 152,303 158,693 4% 54,225 60,239 11% 142,849 143,038 Nevada 76,173 93,774 23% 91,068 94,607 4% 33,406 33,628 1% 11,802 12,195 New Hampshire 80,159 94,895 18% 38,997 40,464 4% 23,356 26,652 14% 3,926 4,182 New Jersey 249,603 278,446 12% 197,426 204,056 3% 90,234 96,008 6% 18,307 19,105 New Mexico 116,160 132,559 14% 246,040 273,418 11% 139,821 165,191 18% 52,614 52,722 New York 787,657 906,292 15% 427,458 438,696 3% 290,825 299,581 3% 75,399 78,125 
VOC NOX SO2 NH3 

State Case 1 Case 2 % 

Diff 

Case 1 Case 2 % 

Diff 

Case 1 Case 2 % 

Diff 

Case 1 Case 2 % 

D

North Carolina 471,046 549,225 17% 267,441 282,240 6% 225,881 246,359 9% 198,260 199,387 North Dakota 76,760 69,292 -10% 117,103 119,441 2% 163,147 162,126 -1% 57,449 57,492 Ohio 436,266 507,447 16% 451,373 470,413 4% 431,072 442,399 3% 86,803 89,168 Oklahoma 201,035 233,388 16% 272,262 294,496 8% 80,694 86,427 7% 123,609 124,680 Oregon 365,104 422,750 16% 153,020 159,582 4% 50,840 54,892 8% 49,658 49,724 Pennsylvania 414,605 472,091 14% 462,695 485,087 5% 328,200 347,837 6% 92,757 94,208 Rhode Island 41,240 56,165 36% 23,122 24,764 7% 8,431 9,866 17% 1,705 1,716 South Carolina 261,951 322,836 23% 174,160 184,004 6% 175,256 185,341 6% 37,309 37,930 South Dakota 60,161 58,676 -2% 46,961 48,788 4% 26,552 27,303 3% 83,011 83,088 Tennessee 349,379 439,159 26% 250,355 268,131 7% 316,869 334,915 6% 54,005 56,278 Texas 951,427 1,090,613 15% 1,145,445 1,224,465 7% 620,673 655,866 6% 319,202 321,061 Utah 112,574 131,831 17% 153,361 162,991 6% 75,448 76,858 2% 31,154 31,540 Vermont 38,222 41,848 9% 20,185 21,399 6% 8,436 10,484 24% 9,867 9,935 Virginia 310,041 372,814 20% 319,590 338,950 6% 188,262 204,866 9% 59,252 59,436 Washington 252,421 301,981 20% 190,411 202,112 6% 57,377 65,843 15% 62,892 65,134 West Virginia 91,769 104,095 13% 160,080 170,690 7% 183,911 193,420 5% 14,041 14,195 Wisconsin 346,558 405,213 17% 223,676 234,244 5% 254,576 270,098 6% 86,221 87,150 Wyoming 51,472 57,223 11% 184,094 205,128 11% 128,540 129,709 1% 17,725 17,884 Grand Total 12,845,731 14,910,893 16% 12,420,516 13,255,595 7% 9,071,990 9,731,241 7% 3,949,250 3,995,741 
D-23

D.3 Impact of Emissions Changes on Air Quality Model Prediction 

The results of the growth sensitivity model runs (i.e., 2015 Case 1 and Case 2) are 

provided in Table 3. This table contains the county PM2.5 concentrations for those 

counties that are projected to be nonattainment of the current PM2.5 annual NAAQS in 

either of the two cases. The data in Table 3 indicate that all of these counties have higher 

PM2.5 in Case 2 compared to Case 1. The average increase between the two cases is 1.3 

μg/m3

. In over 50 percent of the counties, the increase in PM2.5 is less than 1 μg/m3. 

The largest differences, which are 3 μg/m3

 or more, are predicted for several counties in 

California. Between Case 1 and Case 2, the number of nonattainment counties increases 

from 20 to 29. Of the additional nonattainment counties, 3 are in the West and 6 are in 

the East. 

Table D-3: Comparison of Projected Annual Average PM2.5

Concentrations for 2015 Case 1 and Case 2. 

State County 

2015 

Case 1 

2015 

Case 2 

Difference in 

PM2.5 

(Case 2 - Case 1) 

Alabama Jefferson Co 16.1 17.4 1.2 

California Fresno Co 20.3 21.1 0.8 

California Imperial Co 14.8 15.2 0.4 

California Kern Co 21.6 22.6 0.9 

California Kings Co 17.4 18.0 0.6 

California Los Angeles Co 23.7 27.7 3.9 

California Merced Co 15.8 16.4 0.6 

California Orange Co 20.0 23.0 3.0 

California Riverside Co 27.8 30.8 3.0 

California San Bernardino Co 24.6 27.9 3.3 

California San Diego Co 15.8 16.5 0.7 

California San Joaquin Co 15.3 16.2 0.8 

California Stanislaus Co 16.5 17.3 0.8 

California Tulare Co 21.4 22.3 0.9 

California Ventura Co 14.1 15.3 1.2 

Georgia Bibb Co 13.9 15.1 1.2 

Georgia Clayton Co 14.2 15.3 1.1 

Georgia Floyd Co 14.4 16.2 1.8 

Georgia Fulton Co 15.9 16.7 0.8 

Georgia Wilkinson Co 13.8 15.2 1.4 

Illinois Cook Co 15.5 16.9 1.4 

Illinois Madison Co 15.3 16.6 1.3 

Illinois St. Clair Co 14.7 15.9 1.2 

Michigan Wayne Co 17.6 18.5 0.9 

Montana Lincoln Co 15.0 15.4 0.4 

Ohio Cuyahoga Co 15.6 16.4 0.8 

Ohio Hamilton Co 14.4 15.2 0.8 

Ohio Scioto Co 15.6 16.3 0.6 

Pennsylvania Allegheny Co 16.5 17.1 0.6 

 
D-24

Maps of the increase in emissions associated with the comparison of sensitivity Case 2 

that incorporates growth for the non-EGU stationary sources to the estimates for Case 1 

are shown in Figure 2 for the eastern US and Figure 3 for the western states. Figures 4 

and 5 present the distribution of increases for individual grid cells of this comparison for 

the east and west, respectively. This analysis shows that geographically the largest 

increases in PM 2.5 associated with the growth sensitivity case are predicted in the 

Southeast from Arkansas and Louisiana to Georgia and Tennessee, and western Kentucky 

northward into Illinois, Indiana and Ohio. Figure 2 and 4 indicate PM 2.5 is higher by 

more than a ug/m3 in Birmingham, St. Louis, Chicago and Atlanta, with Detroit at 0.9 

ug/me higher in Case 2. The impact of the growth sensitivity scenario emissions is less 

in Cleveland and Pittsburgh, compared with other cities. In most of the gird cells in the 

East (over 70 percent) PM 2.5 is higher in Case 2 by 0.5 ug/m3 or less. Fewer than 5 

percent of the grid cells are predicted to have increases in PM 2.5 at or above 0.75 ug/m3. 

The granularity of the patterns shown on the map suggests that many of the areas with the 

largest increases in PM may be affected by differential growth assumptions at non-EGU 

point sources. As shown on Figures 3 and 5 in the west, the largest increases are in the 

South Coast/LA and Central Valley of California. PM 2.5 is higher in the South Coast by 

over 3 ug/m3 in Case 2 compared to Case 1. In the Central Valley, PM 2.5 is higher by 

less than 1.5 ug/m3. Other areas with notably higher PM 2.5 in Case 2 include Salt Lake 

City, southwest Idaho, northern Idaho, and an isolated grid cell in western Oregon. 

Outside of the above areas the impacts of the Case 2 growth assumptions are on the order 

of 0.5 ug/m3 in urban areas and 0.25 ug/m3 or less in rural areas. 

D.4 Discussion and Implication of results 

The air quality modeling above illustrates the implications that assumptions regarding the 

projection of the emissions inventory can have for the “down-stream” emission control 

cost and monetized human health benefit analyses. To the extent that we over-estimate 

growth in future emissions, then we apply emission controls to reduce emissions beyond 

a level necessary to meet attainment. This “over-control” would then bias control costs 

high; it would also bias estimated benefits high, as we would monetize the human health 

benefits of achieving a larger increment of air quality change than necessary to reach 

attainment. 

Conversely, if we under-estimate future emissions growth, then we fail to apply enough 

emission controls to attain fully. This “under-control” would then bias both estimated 

control cost low; it would also bias estimated benefits low, as we would monetize the 

human health benefits of achieving a smaller increment of air quality change than 

necessary to reach attainment. 

As indicated in Chapter 2, EPA used the interim approach instead of the approach used in 

the past since it is in better alignment with historical data. 
D-25

Figure D-2. Increase in PM2.5 Predicted for the Case 2 Growth Scenario vs Case 1 - 

East 

Concentratio

3.2

1.

1.

0.7

0.

0.2

SMAT v2
D-26

Figure D-3. Increase in PM2.5 Predicted for the Case 2 Growth Scenario vs Case 1 

– West 

Concentratio

3.

1.

1.

0.7

0.

0.2

SMAT v2
D-27

Figure D-4. Percent of Model Grids in the East with Higher PM2.5 in the Growth 

Sensitivity Case vs the Base Case 

Figure D-5. Percent of Model Grids in the West with Higher PM2.5 in the Growth 

Sensitivity Case vs the Base Case 

0

10

20

30

40

50

60

70

80

90

100

< 0.25 >=0.25 t o < 0.50 >=0.50 t o < 0.75 >=0.75 t o < 1.00 >=1.00 t o < 1.50 >=1.50

Increase in PM2.5(ug/m3)

Percent of Model G rid Cells

0

10

20

30

40

50

60

70

80

90

100

< 0.25 >=0.25 to < 0.50 >=0.50 to < 0.75 >=0.75 to < 1.00 >=1.00 to < 1.50 >1.50

Increase in PM2.5 (ug/m3)

Percent of Model Grid Cells
D-28

Attachment 1. Background Document Provided to the Council 

Improving EPA Emissions Forecasting 

For Regulatory Impact Analyses 

Summary of the Issue 

The EPA conducts Regulatory Impact Analyses (RIAs) to assess the benefits and 

costs of air regulations. These RIAs require emissions forecasts for all relevant source 

categories. We continually improve these forecasts over time and significant advances 

have been made for major source categories including mobile sources and Electric 

Generating Units (EGUs). However, we have observed a disconnect between our 

emissions forecasts for certain stationary non-EGU source categories and the historical 

record. (For this document, stationary non-EGU or non-utility sources include large 

industrial combustion and process point sources (e.g., industrial boilers, petroleum 

refineries, chemical manufactures, etc.), as well as, small stationary commercial, 

institutional, and residential non-point sources.) This discrepancy appears to have led to 

significant over-prediction of emissions projections in longer-forecast periods required 

for the NAAQS and other programs. We have developed an interim approach for 

addressing this issue and intend to use it to develop a range of forecasts that will provide 

some understanding of the potential uncertainties implied by the past methodology and 

the historical record. This interim application will first be used for the RIA for the 

review of the PM NAAQS. We seek a consultation with the Council to provide advice on 

how to portray the interim approach and the uncertainties involved. We will continue to 

work to develop long-term improved approaches for addressing this issue. 

Background 

Overview of Emission Inventory Forecasts in RIAs 

 EPA has established a tradition of improving the emissions inventory and 

modeling platform for Regulatory Impact Analyses. As new and improved data, 

methods, and models become available, we incorporate this information into the 

emissions estimates and modeling platform at appropriate times. The drivers to the 

updates are the ever-evolving “state of knowledge” and comments received on previous 

analyses. We have placed highest priority on improving data/methods/and models for 

pollutants or sectors impacted by the policy (e.g., EGUs for the Clean Air Interstate Rule 

(CAIR); mobile sources for the Heavy Duty Diesel Engine and Fuel Rule and the Spark 

Ignition Nonroad Engine Rule). 

 For most Regulatory Impact Analyses, we use emissions from a historical year, or 

base year, (e.g., 2001) as the starting point for forecasting potential future-year emissions. 

In evaluating the potential impact of the subject regulation, we develop multiple futureyear emission estimates based on a range of regulatory options. In general, EPA 

estimates the future-year emissions by forecasting changes in the various activities that 

generate emissions and using this forecasted activity to increase (or decrease) emissions. 
D-29

We then reduce forecasted future-year emissions for the impact of mandated Clean Air 

Act (CAA) emission controls. 

 

 Methods Used to Forecast Emissions Inventories 

 

Emissions in the future will differ from current emissions inventories due the 

following factors: 

• Changes (typically growth) in economic activity that influence emissions, 

• Changes in the mix of production activities both within and between 

economic sectors, 

• Changes in vintages of capital equipment, 

• Changes in population, energy use, land use, or motor vehicle miles 

traveled, 

• Technological innovation or changes altering: 

o Production processes for emission sources, 

o Control technologies available, 

o Substitution of inputs to production (e.g., fuel switching), and 

• Emission controls implemented to satisfy CAA regulations, voluntary 

programs and other initiatives expected to reduce air emissions. 

 For many source categories, EPA uses emission factors to relate air pollution to 

emission-generating activities (e.g., production activities of an industry). In previous 

analyses, the method used to project stationary non-utility emissions involves forecasting 

current emissions into the future by considering the following two factors: 

• Changes in economic activity (generally we have assumed a linear 

relationship between economic activity changes and emission changes 

because, as stated above, many of the other factors that may influence 

changes in emissions are difficult to quantify) and 

• Application of emission controls mandated by various parts of the CAA. 

 

 The typical formula for estimating projected inventories follows: 

Projected Future Emissions = Current Emissions * Emission Growth 

Adjustment * Emission Control Adjustment 

The emissions growth adjustment increases or decreases (typically increases) emissions 

in the future from current base year levels due to forecasted changes in economic or other 

activities that impact emission levels (e.g., population). The emission control adjustment 

decreases future-year emissions for expected emissions controls resulting from mandated 

CAA regulations. In the past, the economic growth adjustment for stationary non-EGU 

sources has been based upon the results of the Policy Insight® Model for Regional 

Economic Model, Inc (REMI) by state and Standard Industrial Classification (SIC) codes 

or fuel consumption forecasts by fuel type and energy sector (e.g., industrial, commercial, 

residential) from the US Department of Energy. 
D-30

 For non-EGU stationary source categories, many factors that influence future 

emissions (technology innovations, changes in vintages of capital equipment, energy use, 

etc.) listed above are difficult to quantify and are not adequately captured in current 

models. Our past forecasting approaches for these source categories do appear to model 

economic growth and the impacts of CAA emission controls relatively well, but do not 

address the many other factors affecting emissions (shown above) sufficiently. 

Forecasting emissions for these source categories is further complicated by the multitude 

of non-EGU stationary source categories involved (over 800 industry categories). In 

2002, emissions from non-EGU stationary sources represented approximately 62 percent 

of total direct PM2.5 emissions (excluding emissions from dust and fires) and 

approximately 18 percent and 25 percent of important PM precursors, NOX and SO2, 

respectively. While emissions from these sources are relatively small when compared to 

total emissions from all sources of SO2 and NOX, these sources represent the major 

contributors to direct PM2.5 emissions and are major source categories considered in the 

current PM NAAQS RIA. Emission projections for the stationary non-EGU sources will 

be used to estimate the benefits and costs of the PM NAAQS in the RIA and EPA 

recognizes the immediate need for better future year emissions estimates for these 

categories. 

 Emissions projection methods are less of an issue for mobile sources and EGUs, 

and these sources are not subject to our interim approach. For these sources, EPA has 

developed improved models specific to mobile sources (MOBILE and NONROAD 

models) and EGUs (Integrated Planning Model). These models address many of the 

deficiencies in our current approach for stationary non-EGU sources previously 

discussed. The Integrated Planning Model is a market model of the electric utility 

industry that captures the impact of capital turnover and economically-motivated fuel 

switching on emissions. For EGUs, we also have better emissions source testing due to 

the installation of continuous emissions monitoring for these units. For mobile sources, 

our models directly address equipment turnover and the issue of fuel switching. More 

details may be obtained about these models at www.epa.gov/airmarkets/epa-ipm and 

http://www.epa.gov/OMSWWW/models.htm. In addition to EGUs and mobile sources, 

inventory projections for agricultural ammonia emissions are based on projected animal 

populations provided by US Department of Agriculture, and these sources are also not 

covered by our interim approach. 

 

Problems with Past Projection Approaches 

 Using the approaches described above for stationary non-EGU sources, we 

logically forecast continuing emission increases relating to economic, population, and 

other sources of growth for any given analytical starting point. Such forecasts, however, 

are inconsistent with the relationships we see historically. Figure 1 compares activity 

variables that impact emissions (GDP, energy consumption, population, vehicle miles 

traveled) with historical air emissions from all sources (pollutants include SO2, NOx, 

VOC, PM10, CO, and Pb). Since 1970, air emissions have been steadily declining while 

GDP, population, energy consumption, and vehicle miles traveled all have grown. The 

emissions shown in Figure 1 are dominated by mobile sources emissions. But the trend 
D-31

also exists when focusing on PM-related emissions from EGU or non-EGU stationary 

point and area sources, collectively as well as for key industry. The newly developed 

2002 National Emissions Inventory provides more historical emissions data to 

corroborate the historical decline in emissions we are observing. Figure 2 shows 

decreasing trends in PM2.5 and the primary PM precursors SO2 and NOx for non-EGU 

stationary source emissions from 1990 through 2002. The data source for the historical 

year emissions inventory is the National Emissions Inventory (NEI). The NEI provides 

historical emission estimates for 1990, 1996, 1999, and 2002 that represent measurements 

and estimates of actual emissions for the particular year. The primary data source for the 

NEI emissions are State emission inventories. These data are supplemented by emissions 

estimates developed by EPA to fill gaps in the data provided by the States. Both the State 

and EPA developed emissions are based on actual activity or actual activity surrogate 

data for the given year. Thus emissions estimates in the NEI for 1990, 1996, 1999, 2002 

do not rely upon the application of growth factors to actual emissions from an older 

emissions inventory. 

 Historical emissions trends for key industrial sectors (chemical and allied products, 

petroleum refining and allied products, paper and allied products, and primary metals 

manufacturing) important to the PM NAAQS analysis are shown in Figure 3. We also 

see similar general downward trends in historical emissions across different regions of 

the country. Figure 4 compares historical trends for the stationary non-EGU source 

categories with the CAA baseline (includes control programs that would be implemented 

by 2010) emissions forecast made in the 1997 NAAQS RIA. This figure indicates the 

inconsistency between the forecasts and the trends thus far. 

 

 Our projection methods used to estimate growth for stationary non-EGU sources 

until now have focused on estimates of economic growth and emission reductions 

resulting from CAA mandates. We’ve assumed logically that the “growth” part of 

emission trends correlates linearly with economic or other emission generating activities. 

Our methods have attempted to forecast growth in the general economy and to match this 

growth to those industry sectors that generate air emissions. This approach assumes that 

the emission rate per unit of activity is the same in the base year and future years for the 

stationary non-EGU sources unless emission controls are applied (i.e., emission controls 

are the only factor that reduces emission rates.) Based upon historical data, we recognize 

this assumption is likely incomplete. It is now apparent that the focus exclusively on 

economic growth forecasts and consideration of CAA emission controls overlooks 

important factors that influence emission trends. 

 

 While information needed for a full understanding is lacking, we have several 

plausible explanations for the differences we observe in economic growth projections and 

emission trends and reasons to believe these trends may continue in the future. These 

explanations involve the replacement of older vintages of capital equipment and emission 
D-32

Figure 1

Data Sources: US Department of Commerce, Bureau of Economic Analysis, US Dept. of Transportation, 

Federal Highway Administration, US Census Bureau, and US Department of Energy. 
Figure 2

1990 -2002 Emission Inventories

Non-EGU Stationary Sources Only1

-

1,0002,0003,0004,0005,0006,0007,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Emissions (Thousand Tons) NOx SO2 PM2.5 

1 Emissions shown reflect non-utility stationary point and non-point sources only, excluding fires. Source: National Emissions Invento
Figure 3

Historical SO2 Emission Trends for Large Industrial Categorie

0

100200300400500600700800900

1,000

1990 1993 1996 1999 2002

Year

Emissions (Thousand Tons) Chemical and 

Petroleum RePaper and AllPrimary Meta

1 Emissions shown reflect 2 digit-SIC source categories. Source: National Emissions Inventory 
Figure 4

Comparison of 1997 PM NAAQS RIA Forescasts and NEI Actual Emissions

Non-EGU Stationary Sources Only1 

0

1,0002,0003,0004,0005,0006,0007,0008,000

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Year

Emissions (Thousand Tons) NOx -97 RIA NOx - NEI SO2 - 97 RIA SO2 -NEI

1 Sources: National Emissions Inventory and Regulatory Impact Analysis for the Ozone and PM NAAQS, 1997. 
D-36

rates. Firms replace emission generating equipment for multiple reasons including 

regulatory requirements, enhanced productivity, retirement of obsolete equipment, 

energy efficiency (e.g., fuel switching) and other reasons. Profit seeking firms will 

attempt to maximize profits for the firm with each capital investment. Thus, installation 

of new more efficient equipment may result in an increase in production of goods and 

services without the corollary per unit increase in emissions or in maintenance of current 

levels of production with lower levels of emissions. These outcomes are reasonably 

likely regardless of the rationale for the equipment replacement (i.e., enhanced 

productivity, regulatory requirements, obsolescence of existing equipment, or energy 

efficiency measures such as fuel switching) for firms seeking to maximize profits. Our 

current growth projection methods do not explicitly capture such a phenomenon, and 

there is a lag in our ability to recognize newly installed emission control equipment in our 

current emission inventory process. We have particular difficulty in accounting for 

potential emission reductions from regulatory actions such as CAA New Source Review 

and New Source Performance Standards. In addition, emission rates may not reflect 

current conditions. The emission rates are determined through source testing. Although 

we suspect that average emission rates are declining, we have not been able to verify this 

fact through updated sources testing due to budget constraints. 

 While it is not clear that all of the factors that have served to produce this 

historical decline will continue to operate in the future, it appears unreasonable to assume 

that we currently have arrived at an ‘inflection point’ past which the trend will stop or 

reverse itself. Indeed, because the available data show that a number of large sources in 

the sectors of interest have no or limited pollution controls, it is reasonable to expect 

emissions rates will be steady or decline. Continuing to ignore this factor in future-year 

emission projections may increasingly skew the predicted emissions increase, and the 

farther into the future the forecast the more dramatic the impact. The preceding and other 

explanations suggested that we need to reevaluate our emission forecasting approaches 

for stationary non-EGU sources to incorporate factors not adequately considered in past 

methodologies. 

Interim Approach to Address this Issue 

 We are currently reviewing the PM NAAQS and completing an RIA that 

estimates the benefits and costs of the standard. The stationary non-EGU sectors are 

important sectors for this analysis and emission projections are more important for this 

analysis than they have been in some previous analyses. Over-predicting future 

emissions for these sectors will lead to an over-prediction of the benefits and costs of the 

PM NAAQS. We also believe that potential prediction errors will be greater in distant 

future years (e.g., 2020) due to compounding of growth. As recent and upcoming 

analyses are examining policies that will be implemented in 2020 or later, these overprediction errors have become magnified. As a result, we explored alternative methods 

of addressing this problem. Due to a court-ordered schedule for this analysis, the time 

needed to complete a comprehensive revamp of our forecasting model for these source 

categories was not possible. 
D-37

 As we develop a more comprehensive approach, we are making an interim change 

in our analysis to better align our forecasts of future growth in the stationary non-EGU 

sectors with the historical record. As an interim approach, we will not apply economic 

growth to emissions for many stationary non-EGU sources. Table 1 shows the emission 

forecasting techniques planned for the PM NAAQS RIA. As shown, the interim 

approach affects stationary non-EGU point and non-point sources only. We recognize 

that this solution is a short term one at best, and needs to be improved for the future. Our 

RIA for the PM NAAQS will show a sensitivity analysis of the implications of the 

interim approach relative to our traditional approach. Figure 5 shows the forecasted 

emission trends for the non-utility stationary sources using the old methodology and the 

new interim approach. As depicted in Figure 5, the new interim approach will result in 

lower future-year emission projections for these sources that more closely match the 

observed historical trends. It is worthwhile to recognize that the emissions from these 

stationary non-EGU sectors are a subset of total emissions and the interim approach 

adjustment is minimal when looking at emissions from all source categories (see 

Figure 6). 

 

 In the long term, we recognize the need to improve our forecasting methods and 

models for these important source categories. The technical work needed for a more 

sophisticated and improved approach will take time to develop. In the interim, our 

approach has been implemented in the short time frame needed for our ongoing 

regulatory work. The interim approach minimizes the over-prediction error in future year 

emission estimates for stationary non-utility sources. This approach does not have an a 

priori bias in either direction, as it simply holds non-utility stationary source emissions to 

be consistent with the observed levels in 2001, accounting for known control programs to 

be implemented in future years. The interim approach does not apply the observed 

downward trend in emissions, and as such may still overstate future emissions levels if 

historical trends continue. 

 To develop an improved approach to emission projections, we are focusing first 

on sectors that are the largest contributors to precursors of ozone, PM, regional haze, and 

high risk toxics. Developing the appropriate emissions projection technique is a complex 

process that requires more analysis to first identify and understand the sources of change 

in historical emissions. As previously discussed, our past methods do appropriately 

reflect the impact of economic growth and emission control impacts on future-year 

emissions, but do not adequately reflect the impact of other factors such as technological 

innovation, capital turnover, fuel switching, and other activities that may have significant 

impacts on emissions. After gaining the necessary understanding of these trends, we will 

develop models that better reflect historical and anticipated future trends for key 

stationary non-EGU sectors. This focus on important sectors will provide the most 

benefit for the effort expended to improve emissions projections. 

 After gaining an understanding of historical trends, EPA will evaluate currently 

available forecasting models capable of estimating local, regional, and national economic 

trends. Key considerations will be the efficacy of these models to forecast growth for key 

stationary non-EGU industry sectors. In addition, EPA will consider techniques to model 
D-38

technological innovation and adoption for both productive processes and control 

equipment and models that consider new facility location decision-making. EPA’s goal 

is to implement these improvements as a part of the new 2002 emissions based modeling 

platform. These changes may not be available for the initial version 2002 platform, but 

could be incorporated into the modeling platform along with other updates. When an 

improved approach is formulated, the EPA will consult with the Council to obtain 

feedback on the new methodology prior to its implementation. 

Question for the Council 

Please provide your advice and comments on EPA’s discussion and underlying 

development of the interim forecasting approach for stationary non-EGU sources 

described above. Are there caveats and sensitivities that should be provided in the 

discussion of this interim approach in our analyses? Are there additional suggestions or 

data you could provide to help with the development of a longer term approach? 
D-39

 

Table 1. Emissions Sources and Basis for Current and Future-Year Inventories 

Sector

Interim 

Projection 

Method 

Applied Future-Year Base Case Projections

EGU No Integrated Planning Model (IPM) 

Non-EGU 

Point 

Sources 

Yes Apply CAA mandated controls to base year 

emissions to project future emissions. Projected 

changes in economic activity not applied to 

emission projection. 

Other 

Stationary 

Non-point 

Yes Apply CAA mandated controls to base year 

emissions to project future emissions. Projected 

changes in economic activity not applied to 

emission projection. 

Fires No Average fires from 1996 through 2002 (based on 

state-total acres burned), with the same emissions 

rates and county distributions of emissions as in the 

2001 NEI 

Ag -NH3 No Livestock – USDA projections of future animal 

population 

Fertilizer – Held constant at 2001 level 

On-road No Projected vehicle miles traveled (VMT) DOE 

Energy Outlook VMT projections, future-year 

emissions rates from MOBILE6.2 model via 

National Mobile Inventory Model (NMIM) 

Nonroad No NONROAD 2004 model via NMIM 
Figure 5

2020 Emission Forecasts - Old and Interim Methods

Non-EGU Stationary Sources Only1 

0

1,0002,0003,0004,0005,0006,000

20012002200320042005200620072008200920102011201220132014201520162017201820192020

Year

Emissions (Thousand Tons) NOx (O

NOx (ISO2 (OSO2 (IPM2.5 PM2.5 

Source: Analysis completed for the PM NAAQS RIA (forthcoming). 
Figure 6

2020 Emission Forecasts - Old and Interim Methods

All Sources

0

5,000

10,00015,00020,00025,000

20012002200320042005200620072008200920102011201220132014201520162017201820192020

Year

Emissions (Thousand Tons) NO

NOSOSOPMPM

Source: Analysis completed for the PM NAAQS RIA (forthcoming). 
D-42

References 

U.S. Census Bureau. 2005. Table 4. Population: 1790 to 1990 and Population Projections 

National Summary Table NP-T1. <http://www.census.gov/popest/national/>. 

U.S. Department of Commerce, Bureau of Economic Analysis. (2005) Table 1.1.6 Real 

Gross Domestic Product, Chained Dollars. <http://www.bea.gov/bea/dn/home/gdp.htm>. 

U.S. Department of the Energy. (2005) Table 2.1a. Energy Consumption by Sector, 1949-

2004 and Table 1. Total Energy Supply and Disposition Summary, Reference Case 

Forecast, Annual 2002-2025. 

< www.eia.doe.gov/oiaf/analysispaper/sacsa/pdf/appb_page.pdf - 2004-06-07>.

U.S. Department of Transportation, Federal Highway Administration. 

http://www.fhwa.dot.gov/environment/vmttext.htm. 

US Environmental Protection Agency. National Emissions Inventory. 

http://www.epa.gov/ttn/chief/eiinformation.html

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PM NAAQS. 1997. <http.epa.gov./ttn/ecas/ria.html>.