## D-1 

## Appendix D: Sensitivity of Emissions Inventory

Synopsis

As described in Section 2.3.3 of the RIA, the appr oach 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 so urces and electric generating units) remain

essentially unchanged from recent analyses. Th e methodology used in this RIA to forecast nonEGU stationary source categories recognizes the disconnection betw een prior projection

estimates and the historical reco rd. 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 Mode ling Subcommittee (Council) of the Science

Advisory Board on this interim emissions proj ection approach and requested recommendations

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

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

Council, the Council’s advice to th e EPA, a discussion of the im plication of recommendations by

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

impacts of this interim emissions projection appro ach. The sensitivity anal ysis included in this

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

2015.

D.1 Consultation with the Council

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

the EPA requested advice and comments from th e 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 sensitivitie s that could be provided in the RIA in the

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

term approach to emission forecasting for thes e 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 th e EPA. This alternative would capture the

underlying technological change that the Council c ontends is likely driving the decline in

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

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

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

level of output by sector. The Council recommends th e 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 decl ine (i.e., after removing d eclines 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 recomm endations of longer term approaches were also

discussed and included in the meeting mi nutes. 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 appr oach for three important non-EGU stationary

source sectors including Pulp and Paper Manufac turing, Petroleum Refining, and Chemicals and

Allied Products for SO 2 

## emissions only. The court-ordere d schedule for the PM NAAQS review

does not allow for further investig ation 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 SO 2 

## emissions for these source

categories. Using an approach similar to th e 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 emi ssion trend for these three sectors was assumed to

continue into the future. We recognize the limitations of this an alysis 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 th an 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 at tribution of these historical emission reductions to the Clean

Air Act was not possible.

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

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

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

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

long-term more robust and consistent met hod 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 met hodology for the stationary non-EGU sources for

future analyses.

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

emission inventory analysis ha s 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 develo ping a new and improved emissions projections

methodology that recognizes key co mponents of historical emissi ons changes. This study found

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

showed that even within a speci fic source category the bias in pr ojection methods and historical

data may differ across pollutants demonstrati ng the challenges involve d 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 impr oved 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 econom ic growth term from the emissions projections

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

previous growth assumptions for these sources th at 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 se nsitivity was performed prior to the final

version of the 2015 emissions used for the RIA m odeling, 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 ar e relatively localized to a handful of plants

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

applicable for the purpose of characterizing th e 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 Se nsitivity Comparison to Final Analysis 2015

Baseline Emissions

For both Case 1 and Case 2, there were two diffe rences 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 Ru le”. These were ultimately determined to

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

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

Skies analyses). These were ultimately revise d as described in S ection 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 E GUs 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. Differe nces 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 grow th data is described more fully in Section 4.1

of the CAIR Emission Inventory Techni cal Support Document, available at

http://www.epa.gov/air/interstat eairquality/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 fire s (wildfires, prescribed burning,

agricultural burning, and open burning).

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

between the Case 1 and Case 2 for VOC, NO

x 

## , SO 2 

## , NH 3 

## , and PM 2.5. 

## Table 2(b) provides statetotal differences for the entire state betw een the two cases for the same pollutants.D-7Table D-2(a): Case 1 Compared to Case 2 Emissions by State, Sectors that Changed and PollutantVOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffAlabamaNonpointfugitive dust000%000%000%000%18,82620,0506%Nonpoint152,290196,38329%10,61213,17524%44,89531,436-30%1,3701,63519%11,01513,79125%Point fugitivedust000%000%000%000%447255-43%Non-EGUPoint47,52063,838 34% 89,158 112,327 26% 113,811 128,959 13% 494 589 19% 24,063 30,556 27%Alabama Total 199,810 260,220 30% 99,770 125,50226% 158,706 160,395 1% 1,863 2,225 19% 54,352 64,652 19%Arizona Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 23,394 28,891 23%Nonpoint 91,612 130,416 42% 53,957 67,996 26%3,457 3,504 1% 2,699 3,159 17% 5,025 7,000 39%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 327 413 26%Non-EGUPoint5,706 9,204 61% 29,725 39,710 34% 32,56848,431 49% 41 78 91% 2,145 2,933 37%Arizona Total 97,318 139,620 43% 83,682 107,70629% 36,025 51,935 44% 2,740 3,237 18% 30,891 39,236 27%Arkansas Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 36,892 37,149 1%Nonpoint 92,027 112,573 22% 35,729 42,949 20%19,998 30,680 53% 1,069 1,377 29% 7,003 8,713 24%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 35 42 18%Non-EGUPoint26,495 35,455 34% 54,281 69,560 28% 26,849 33,652 25% 1,235 1,454 18% 17,776 23,912 35%Arkansas Total 118,522 148,028 25% 90,010 112,509 25% 46,846 64,332 37% 2,305 2,832 23% 61,707 69,815 13%California Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 63,469 71,821 13%Nonpoint 415,895 506,517 22% 145,151 167,149 15%10,453 12,196 17% 1,936 2,254 16% 67,190 82,513 23%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 32 42 32%Non-EGUPoint45,589 62,985 38% 101,665 120,422 18% 37,134 44,628 20% 13,902 13,699 -1% 21,630 29,367 36%California Total 461,484 569,502 23% 246,816 287,570 17% 47,586 56,824 19% 15,837 15,953 1% 152,321 183,744 21%Colorado Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 25,524 26,380 3%Nonpoint 84,216 105,390 25% 11,237 15,62239% 1,991 2,361 19% 72 97 35% 12,596 16,245 29%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 45 61 36%D-8VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffNon-EGUPoint33,869 47,126 39% 38,415 48,359 26% 9,19111,137 21% 242 295 22% 11,457 15,169 32%Colorado Total 118,085 152,516 29% 49,65163,981 29% 11,183 13,498 21% 314 392 25% 49,622 57,855 17%Connecticut Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 1,649 1,963 19%Nonpoint 118,202 127,272 8% 13,721 14,873 8%12,121 12,148 0% 2,285 2,766 21% 10,263 12,289 20%Non-EGUPoint5,615 7,596 35% 3,293 4,266 30% 2,9464,097 39% 39 52 34% 1,749 2,506 43%Connecticut Total 123,817 134,868 9% 17,014 19,139 12% 15,066 16,244 8% 2,324 2,818 21% 13,661 16,757 23%D-9VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffDelaware Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 1,664 1,820 9%Nonpoint 14,820 18,917 28% 3,857 5,24736% 10,594 16,086 52% 379 466 23% 2,292 3,141 37%Non-EGUPoint3,641 4,610 27% 8,550 10,363 21% 20,096 22,457 12% 671 762 14% 765 996 30%Delaware Total 18,461 23,527 27% 12,407 15,61026% 30,690 38,543 26% 1,050 1,228 17% 4,721 5,957 26%District of Columbia Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 262 367 40%Nonpoint 9,561 10,898 14% 2,326 3,06132% 5,938 7,448 25% 982 1,133 15% 728 969 33%Non-EGUPoint4 5 24% 477 547 15% 792 924 17% 9 12 38% 29 35 18%District of Columbia Total 9,565 10,903 14%2,803 3,608 29% 6,730 8,371 24% 990 1,145 16% 1,019 1,371 34%Florida Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 22,270 25,783 16%Nonpoint 269,923 359,803 33% 30,248 35,731 18%39,817 62,248 56% 3,389 4,548 34% 14,722 19,891 35%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 19 26 43%Non-EGUPoint40,347 52,998 31% 59,586 76,949 29% 87,311105,004 20% 569 737 29% 29,238 38,988 33%Florida Total 310,270 412,800 33% 89,834 112,68025% 127,128 167,252 32% 3,958 5,284 34% 66,249 84,688 28%Georgia Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 32,708 35,300 8%Nonpoint 167,804 215,134 28% 26,056 32,418 24%4,407 5,934 35% 2,452 3,180 30% 16,968 23,029 36%Non-EGUPoint30,264 40,575 34% 77,356 95,851 24% 84,486 115,947 37% 4,778 6,247 31% 50,009 66,558 33%Georgia Total 198,068 255,710 29% 103,412 128,26924% 88,893 121,881 37% 7,230 9,427 30% 99,686 124,886 25%Idaho Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 26,653 27,428 3%Nonpoint 169,139 261,550 55% 36,323 50,80040% 1,652 2,040 23% 562 740 32% 13,682 19,934 46%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 3 4 42%Non-EGUPoint3,942 5,592 42% 11,298 14,393 27% 18,10923,392 29% 984 1,292 31% 5,828 8,215 41%Idaho Total 173,080 267,142 54% 47,620 65,19237% 19,762 25,432 29% 1,546 2,032 31% 46,166 55,581 20%Illinois Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 92,621 91,260 -1%Nonpoint 272,712 341,249 25% 39,045 46,306 19%41,299 56,996 38% 9,979 12,273 23% 17,107 22,139 29%Point fugitive 0 0 0% 0 0 0% 0 0 0% 0 0 0% 215 260 21%D-10VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffdustNon-EGUPoint82,756 113,170 37% 100,177 127,465 27% 174,790 194,627 11% 9,215 13,490 46% 30,695 42,176 37%Illinois Total 355,468 454,419 28% 139,221 173,77125% 216,089 251,622 16% 19,194 25,763 34% 140,638 155,835 11%Indiana Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 54,764 55,930 2%Nonpoint 190,403 236,149 24% 43,924 46,630 6%8,922 9,015 1% 2,948 3,754 27% 13,691 16,896 23%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 576 744 29%Non-EGUPoint63,344 88,865 40% 89,582 112,702 26% 168,608 194,805 16% 3,460 4,802 39% 46,199 58,815 27%Indiana Total 253,747 325,014 28% 133,506 159,33119% 177,530 203,820 15% 6,409 8,556 34% 115,229 132,386 15%D-11VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffIowa Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 70,799 69,864 -1%Nonpoint 123,428 132,131 7% 29,622 34,596 17%23,947 24,405 2% 7,234 7,583 5% 9,552 9,707 2%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 0 0 0%Non-EGUPoint8,295 11,259 36% 28,043 32,049 14% 54,132 56,262 4% 4,145 5,382 30% 5,223 7,155 37%Iowa Total 131,723 143,389 9% 57,665 66,64516% 78,079 80,667 3% 11,379 12,965 14% 85,574 86,725 1%Kansas Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 102,359 101,325 -1%Nonpoint 88,932 97,015 9% 14,362 16,382 14% 3,800 4,376 15% 1,637 1,895 16% 6,981 6,786 -3%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 213 286 34%Non-EGUPoint22,742 30,772 35% 85,488 108,635 27% 17,16523,787 39% 858 1,116 30% 8,501 11,097 31%Kansas Total 111,674 127,787 14% 99,850 125,01725% 20,965 28,163 34% 2,495 3,011 21% 118,053 119,493 1%Kentucky Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 18,216 20,012 10%Nonpoint 106,387 130,612 23% 73,937 84,608 14% 56,977 56,666 -1% 1,242 1,521 23% 14,301 19,028 33%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 298 325 9%Non-EGUPoint64,477 82,869 29% 35,240 41,784 19% 34,99039,571 13% 575 664 15% 12,712 16,036 26%Kentucky Total 170,865 213,481 25% 109,177 126,392 16% 91,967 96,236 5% 1,817 2,185 20% 45,527 55,401 22%Louisiana Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 19,511 21,177 9%Nonpoint 93,605 110,099 18% 93,604 112,916 21%90,933 135,352 49% 22,828 23,289 2% 9,262 11,070 20%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 3 4 20%Non-EGUPoint55,074 67,429 22% 234,799 282,924 20% 163,566 206,605 26% 8,507 10,777 27% 33,318 42,140 26%Louisiana Total 148,679 177,528 19% 328,402 395,840 21% 254,499 341,956 34% 31,334 34,067 9% 62,094 74,391 20%Maine Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 2,318 2,591 12%Nonpoint 90,770 115,111 27% 8,218 8,804 7%15,722 16,897 7% 1,278 1,574 23% 14,317 17,429 22%Non-EGUPoint4,230 5,638 33% 18,897 25,403 34% 30,59543,305 42% 123 170 39% 10,019 13,328 33%Maine Total 95,001 120,749 27% 27,115 34,20826% 46,317 60,202 30% 1,401 1,744 24% 26,654 33,348 25%D-12VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffMaryland Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 6,375 7,440 17%Nonpoint 80,866 113,550 40% 17,069 20,955 23%41,581 54,861 32% 1,636 2,166 32% 15,145 20,191 33%Non-EGUPoint5,264 6,738 28% 18,529 23,637 28% 22,83628,755 26% 372 511 37% 4,108 5,101 24%Maryland Total 86,130 120,287 40% 35,598 44,59225% 64,416 83,615 30% 2,008 2,677 33% 25,628 32,732 28%Massachusetts Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 10,345 11,249 9%Nonpoint 146,756 178,720 22% 25,595 28,858 13%68,235 85,836 26% 5,665 6,917 22% 18,086 21,941 21%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 0 0 0%Non-EGUPoint9,078 12,524 38% 17,675 22,121 25% 17,90424,535 37% 64 87 35% 2,343 3,208 37%Massachusetts Total 155,835 191,244 23% 43,27150,978 18% 86,140 110,371 28% 5,729 7,004 22% 30,774 36,398 18%Michigan Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 37,834 39,983 6%Nonpoint 249,950 283,840 14% 48,563 55,971 15%34,238 38,797 13% 5,489 6,912 26% 18,175 22,991 26%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 35 44 28%Non-EGUPoint43,667 60,547 39% 90,725 114,013 26% 76,28691,921 20% 393 492 25% 12,928 17,251 33%Michigan Total 293,617 344,387 17% 139,287 169,983 22% 110,524 130,718 18% 5,883 7,404 26% 68,971 80,269 16%Minnesota Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 89,116 87,386 -2%Nonpoint 162,881 171,010 5% 21,747 24,126 11% 5,662 6,122 8% 3,776 4,237 12% 16,131 15,382 -5%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 343 413 20%Non-EGUPoint23,284 30,693 32% 55,734 67,831 22% 21,46625,656 20% 990 1,106 12% 13,987 17,744 27%Minnesota Total 186,165 201,703 8% 77,481 91,958 19% 27,129 31,778 17% 4,766 5,343 12% 119,577 120,924 1%Mississippi Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 25,401 26,098 3%Nonpoint 114,534 141,358 23% 4,154 5,24326% 492 480 -2% 798 1,022 28% 8,714 11,816 36%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 1 1 33%Non-EGUPoint49,184 72,732 48% 103,232 134,960 31% 69,285 81,194 17% 1,146 1,703 49% 21,575 28,569 32%Mississippi Total 163,718 214,090 31% 107,387 140,203 31% 69,777 81,673 17% 1,944 2,725 40% 55,691 66,484 19%D-13VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffMissouri Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 84,991 87,148 3%Nonpoint 141,792 157,892 11% 35,170 37,137 6%34,207 37,589 10% 3,806 4,153 9% 16,723 16,995 2%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 77 101 31%Non-EGUPoint28,479 37,598 32% 31,422 40,255 28% 114,680 142,441 24% 3,968 4,917 24% 10,093 12,664 25%Missouri Total 170,271 195,491 15% 66,593 77,39116% 148,887 180,029 21% 7,774 9,070 17% 111,884 116,907 4%Montana Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 30,128 31,183 3%Nonpoint 41,974 45,221 8% 10,310 13,08227% 1,233 1,248 1% 269 344 28% 3,990 5,457 37%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 125 176 41%Non-EGUPoint3,365 4,480 33% 15,350 18,919 23% 19,80524,536 24% 407 473 16% 5,469 7,143 31%Montana Total 45,339 49,700 10% 25,661 32,001 25% 21,038 25,784 23% 676 816 21% 39,712 43,958 11%Nebraska Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 73,693 74,178 1%Nonpoint 70,366 73,827 5% 13,784 16,51720% 9,850 13,536 37% 598 783 31% 3,769 3,940 5%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 56 74 33%Non-EGUPoint6,702 10,179 52% 11,537 15,194 32% 7,0979,426 33% 14 18 24% 2,519 3,699 47%Nebraska Total 77,068 84,007 9% 25,32131,712 25% 16,948 22,962 35% 612 801 31% 80,037 81,891 2%D-14VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffNevada Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 15,666 19,866 27%Nonpoint 33,547 50,610 51% 7,220 8,992 25%3,452 3,463 0% 915 1,302 42% 2,289 3,036 33%Non-EGUPoint840 1,378 64% 4,693 6,460 38% 656 867 32% 14 21 42% 1,281 1,645 28%Nevada Total 34,387 51,988 51% 11,912 15,45230% 4,108 4,330 5% 929 1,323 42% 19,236 24,546 28%New Hampshire Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 821 964 17%Nonpoint 53,387 67,255 26% 5,385 5,947 10%10,185 11,121 9% 945 1,179 25% 9,446 11,572 22%Non-EGUPoint2,229 3,098 39% 2,743 3,648 33% 5,2507,610 45% 47 69 48% 1,587 2,223 40%New Hampshire Total 55,617 70,353 26% 8,1289,595 18% 15,435 18,731 21% 992 1,248 26% 11,854 14,759 24%New Jersey Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 5,320 6,323 19%Nonpoint 145,975 168,922 16% 37,797 41,146 9%47,838 52,714 10% 4,051 4,809 19% 15,819 18,695 18%Non-EGUPoint19,237 25,132 31% 17,022 20,304 19% 6,5537,451 14% 186 226 22% 1,727 2,090 21%New Jersey Total 165,212 194,054 17% 54,819 61,449 12% 54,391 60,166 11% 4,237 5,035 19% 22,866 27,109 19%New Mexico Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 71,683 74,356 4%Nonpoint 44,554 57,577 29% 25,426 31,398 23%8,451 5,939 -30% 389 487 25% 3,922 5,026 28%Non-EGUPoint12,101 15,477 28% 79,394 100,801 27% 74,580102,463 37% 42 51 22% 2,345 3,441 47%New Mexico Total 56,656 73,054 29% 104,820132,199 26% 83,031 108,402 31% 430 538 25% 77,950 82,824 6%New York Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 24,246 26,938 11%Nonpoint 598,612 715,327 19% 65,289 67,254 3%159,191 159,552 0% 13,437 16,040 19% 71,427 84,611 18%Non-EGUPoint5,465 7,385 35% 37,583 46,857 25% 71,00679,401 12% 972 1,095 13% 3,855 4,679 21%New York Total 604,077 722,712 20% 102,873 114,111 11% 230,197 238,953 4% 14,409 17,135 19% 99,529 116,228 17%North Carolina Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 16,121 18,028 12%Nonpoint 207,535 260,171 25% 14,412 17,774 23%31,822 33,669 6% 2,122 2,657 25% 23,618 31,877 35%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 0 0 50%Non-EGUPoint68,168 93,710 37% 46,995 58,432 24% 66,220 84,852 28% 1,876 2,468 32% 14,571 19,606 35%North Carolina Total 275,703 353,881 28% 61,40776,206 24% 98,042 118,520 21% 3,998 5,125 28% 54,311 69,510 28%D-15VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffNorth Dakota Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 60,541 59,874 -1%Nonpoint 60,442 52,882 -13% 18,442 19,719 7%56,231 52,831 -6% 202 243 21% 2,834 2,771 -2%Non-EGUPoint661 754 14% 10,627 11,688 10% 21,629 24,007 11% 12 14 16% 3,482 4,058 17%North Dakota Total 61,103 53,636 -12% 29,069 31,407 8% 77,860 76,838 -1% 214 258 20% 66,856 66,703 0%D-16VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffOhio Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 45,273 47,795 6%Nonpoint 259,823 319,076 23% 60,160 67,867 13%67,415 75,340 12% 7,196 8,996 25% 22,232 26,832 21%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 695 909 31%Non-EGUPoint33,261 45,189 36% 69,407 80,740 16% 79,844 83,247 4% 2,505 3,070 23% 14,723 18,657 27%Ohio Total 293,084 364,265 24% 129,567 148,60715% 147,259 158,587 8% 9,701 12,065 24% 82,922 94,192 14%Oklahoma Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 76,349 78,323 3%Nonpoint 122,510 150,164 23% 30,256 35,282 17%5,277 6,735 28% 7,736 8,029 4% 6,711 8,114 21%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 13 19 45%Non-EGUPoint19,900 24,599 24% 98,984 116,193 17% 27,498 31,773 16% 3,490 4,268 22% 5,898 7,538 28%Oklahoma Total 142,410 174,764 23% 129,241 151,475 17% 32,774 38,508 17% 11,226 12,297 10% 88,971 93,994 6%Oregon Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 9,487 10,136 7%Nonpoint 252,174 305,486 21% 17,460 20,32516% 22,142 24,124 9% 292 349 20% 40,518 48,832 21%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 4 6 37%Non-EGUPoint11,890 16,225 36% 15,988 19,685 23% 8,93211,003 23% 67 75 12% 8,149 10,938 34%Oregon Total 264,064 321,710 22% 33,448 40,010 20% 31,074 35,126 13% 359 424 18% 58,158 69,911 20%Pennsylvania Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 21,181 23,574 11%Nonpoint 233,160 277,662 19% 53,241 60,281 13%94,191 105,063 12% 6,050 7,261 20% 30,781 35,079 14%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 97 115 19%Non-EGUPoint38,255 51,239 34% 89,806 105,159 17% 82,718 91,483 11% 1,277 1,518 19% 15,182 18,379 21%Pennsylvania Total 271,415 328,901 21% 143,047 165,440 16% 176,909 196,546 11% 7,328 8,779 20% 67,241 77,148 15%Rhode Island Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 528 663 25%Nonpoint 30,425 44,695 47% 4,901 5,98122% 5,263 5,711 9% 97 107 10% 1,232 1,387 13%Non-EGUPoint1,566 2,221 42% 1,650 2,212 34% 2,505 3,493 39% 3 4 47% 127 174 36%Rhode Island Total 31,991 46,916 47% 6,5518,193 25% 7,768 9,204 18% 100 111 11% 1,888 2,224 18%D-17VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffSouth Carolina Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 13,723 14,553 6%Nonpoint 167,921 217,715 30% 18,945 22,279 18%14,763 15,286 4% 1,005 1,268 26% 11,062 14,711 33%Non-EGUPoint25,434 36,526 44% 35,917 42,427 18% 52,420 61,982 18% 1,111 1,470 32% 7,580 9,405 24%South Carolina Total 193,355 254,241 31% 54,86264,706 18% 67,183 77,268 15% 2,116 2,737 29% 32,365 38,669 19%South Dakota Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 45,372 46,022 1%Nonpoint 40,987 38,866 -5% 6,292 6,6576% 20,387 20,634 1% 309 386 25% 3,266 3,266 0%Non-EGUPoint1,256 1,893 51% 4,503 5,965 32% 1,363 1,867 37% 1 1 54% 400 495 24%South Dakota Total 42,243 40,758 -4% 10,795 12,622 17% 21,750 22,502 3% 310 387 25% 49,038 49,783 2%D-18VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffTennessee Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 18,271 20,494 12%Nonpoint 178,994 233,527 30% 23,997 29,385 22%41,818 46,434 11% 3,377 4,946 46% 15,068 20,307 35%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 2 3 55%Non-EGUPoint81,141 116,387 43% 62,850 75,238 20% 75,252 88,682 18% 2,246 2,950 31% 27,675 40,280 46%Tennessee Total 260,134 349,914 35% 86,846 104,62320% 117,069 135,116 15% 5,624 7,897 40% 61,016 81,085 33%Texas Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 231,843 243,614 5%Nonpoint 528,746 636,524 20% 43,589 49,983 15%7,113 8,982 26% 6,917 8,777 27% 27,008 32,678 21%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 88 115 32%Non-EGUPoint118,284 149,692 27% 423,216 495,841 17% 204,910 238,233 16% 0 0 0% 21,869 27,424 25%Texas Total 647,031 786,216 22% 466,804 545,82417% 212,022 247,215 17% 6,917 8,777 27% 280,808 303,832 8%Utah Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 13,334 15,654 17%Nonpoint 47,699 64,544 35% 18,576 23,020 24%10,560 9,720 -8% 632 871 38% 4,199 5,599 33%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 274 381 39%Non-EGUPoint6,751 9,163 36% 24,839 30,025 21% 9,39111,641 24% 785 932 19% 3,873 5,040 30%Utah Total 54,450 73,707 35% 43,415 53,04522% 19,951 21,361 7% 1,417 1,803 27% 21,680 26,674 23%Vermont Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 2,578 2,822 9%Nonpoint 22,491 25,441 13% 3,999 4,65816% 6,988 8,426 21% 272 339 25% 5,200 5,763 11%Non-EGUPoint1,767 2,442 38% 877 1,432 63% 1,294 1,904 47% 1 1 53% 425 600 41%Vermont Total 24,257 27,883 15% 4,876 6,091 25% 8,283 10,331 25% 272 340 25% 8,204 9,185 12%Virginia Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 8,739 9,812 12%Nonpoint 168,516 216,083 28% 43,689 50,56816% 15,237 18,193 19% 685 809 18% 18,707 25,165 35%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 3 4 21%Non-EGUPoint43,536 58,742 35% 68,155 80,635 18% 73,38487,033 19% 727 788 8% 11,739 15,031 28%Virginia Total 212,052 274,825 30% 111,844 131,203 17% 88,622 105,226 19% 1,413 1,597 13% 39,187 50,012 28%D-19VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffWashington Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 14,379 14,265 -1%Nonpoint 156,929 201,834 29% 17,915 21,626 21%3,086 3,291 7% 3,715 4,650 25% 23,540 30,568 30%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 1 2 23%Non-EGUPoint12,290 16,945 38% 31,619 39,609 25% 36,290 44,551 23% 4,206 5,514 31% 10,184 13,445 32%Washington Total 169,219 218,779 29% 49,533 61,235 24% 39,376 47,843 22% 7,921 10,164 28% 48,105 58,280 21%D-20VOCNOXSO2NH3PM2.5StateSectorCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2%DiffCase 1Case 2% DiffCase 1Case 2%DiffWest Virginia Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 2,543 2,760 9%Nonpoint 47,466 55,715 17% 12,988 15,47619% 13,003 14,599 12% 441 522 18% 7,114 8,621 21%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 108 127 18%Non-EGUPoint16,531 20,608 25% 44,318 52,440 18% 51,47059,383 15% 514 587 14% 10,766 13,483 25%West Virginia Total 63,997 76,323 19% 57,306 67,916 19% 64,473 73,983 15% 955 1,109 16% 20,531 24,991 22%Wisconsin Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 23,550 25,417 8%Nonpoint 211,413 257,205 22% 29,434 32,713 11%43,831 57,219 31% 2,596 3,325 28% 29,331 37,898 29%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 2 2 38%Non-EGUPoint31,347 44,210 41% 41,740 49,030 17% 56,80458,937 4% 846 1,045 24% 7,383 9,989 35%Wisconsin Total 242,760 301,416 24% 71,175 81,743 15% 100,634 116,156 15% 3,442 4,370 27% 60,266 73,307 22%Wyoming Nonpointfugitive dust0 0 0% 0 0 0% 0 0 0% 0 0 0% 34,434 35,814 4%Nonpoint 17,354 20,439 18% 60,241 74,895 24%14,903 14,276 -4% 292 353 21% 2,524 3,211 27%Point fugitivedust0 0 0% 0 0 0% 0 0 0% 0 0 0% 0 0 0%Non-EGUPoint11,418 14,085 23% 36,495 42,874 17% 38,12039,916 5% 654 752 15% 15,621 19,575 25%Wyoming Total 28,772 34,524 20% 96,735 117,769 22% 53,023 54,192 2% 946 1,105 17% 52,579 58,600 11%Grand Total 8,467,76610,532,92824% 4,127,6274,962,70620% 3,770,157 4,429,40717% 228,834275,32520% 3,031,9983,455,24514%D-21Table D-2(b): Case 1 Compared to Case 2 Emissions by State and Pollutant (all anthropogenic emission sectors included)VOCNOXSO2NH3PM2.5State Case 1 Case 2 %DiffCase 1 Case 2 %DiffCase 1 Case 2 %DiffCase 1 Case 2 %DiffCase 1 Case 2 %DiffAlabama 286,037 346,447 21% 255,044 280,775 10% 424,702 426,391 0% 85,199 85,561 0% 94,151 104,451 11%Arizona 193,129 235,431 22% 263,284 287,307 9% 100,500 116,410 16% 45,348 45,845 1% 84,187 92,532 10%Arkansas 170,115 199,621 17% 203,948 226,447 11% 74,130 91,616 24% 146,535 147,062 0% 83,121 91,229 10%California 849,540 957,558 13% 825,242 865,997 5% 70,243 79,480 13% 324,066 324,181 0% 291,590 323,013 11%Colorado 191,953 226,383 18% 205,042 219,372 7% 71,260 73,575 3% 78,299 78,377 0% 81,557 89,790 10%Connecticut 160,769 171,820 7% 81,643 83,768 3% 20,352 21,529 6% 9,806 10,299 5% 18,290 21,386 17%Delaware 30,215 35,281 17% 43,139 46,342 7% 59,755 67,608 13% 17,752 17,930 1% 10,769 12,005 11%District ofColumbia12,939 14,277 10% 9,331 10,136 9% 6,780 8,422 24% 1,509 1,664 10% 1,241 1,592 28%Florida 609,012 711,543 17% 432,336 455,182 5% 310,958 351,082 13% 73,489 74,815 2% 210,806 229,245 9%Georgia 355,854 413,495 16% 350,655 375,512 7% 335,024 368,012 10% 124,332 126,529 2% 173,021 198,222 15%Idaho 274,187 368,248 34% 98,419 115,991 18% 23,876 29,546 24% 81,136 81,621 1% 167,056 176,471 6%Illinois 486,738 585,689 20% 444,125 478,674 8% 471,599 507,132 8% 111,111 117,680 6% 167,400 182,597 9%Indiana 344,207 415,474 21% 371,901 397,726 7% 561,507 587,797 5% 100,650 102,797 2% 157,479 174,636 11%Iowa 177,629 189,295 7% 201,599 210,579 4% 243,118 245,707 1% 241,350 242,935 1% 99,948 101,099 1%Kansas 154,747 170,860 10% 227,556 252,723 11% 80,132 87,330 9% 151,498 152,014 0% 136,048 137,488 1%Kentucky 234,191 276,806 18% 310,692 327,907 6% 368,433 372,702 1% 61,098 61,467 1% 83,664 93,537 12%Louisiana 234,179 263,028 12% 613,661 681,099 11% 358,309 445,767 24% 66,430 69,162 4% 121,437 133,733 10%Maine 126,724 152,472 20% 54,420 61,513 13% 52,086 65,971 27% 9,075 9,418 4% 32,013 38,706 21%Maryland 152,199 186,357 22% 157,258 166,252 6% 110,597 129,796 17% 35,107 35,776 2% 41,051 48,155 17%Massachusetts 220,112 255,522 16% 175,130 182,838 4% 105,995 130,226 23% 14,659 15,934 9% 40,897 46,522 14%Michigan 473,221 523,990 11% 423,462 454,158 7% 513,080 533,273 4% 71,642 73,163 2% 107,199 118,497 11%Minnesota 306,630 322,167 5% 270,278 284,755 5% 119,636 124,286 4% 163,719 164,297 0% 153,339 154,686 1%Mississippi 222,793 273,165 23% 210,436 243,252 16% 165,720 177,617 7% 76,176 76,956 1% 89,271 100,064 12%Missouri 259,237 284,456 10% 295,736 306,535 4% 422,967 454,109 7% 121,992 123,288 1% 151,542 156,566 3%Montana 73,469 77,830 6% 127,553 133,893 5% 45,202 49,948 11% 47,456 47,596 0% 65,054 69,300 7%Nebraska 104,123 111,061 7% 152,303 158,693 4% 54,225 60,239 11% 142,849 143,038 0% 88,400 90,254 2%Nevada 76,173 93,774 23% 91,068 94,607 4% 33,406 33,628 1% 11,802 12,195 3% 44,741 50,051 12%New Hampshire 80,159 94,895 18% 38,997 40,464 4%23,356 26,652 14% 3,926 4,182 7% 16,545 19,449 18%New Jersey 249,603 278,446 12% 197,426 204,056 3% 90,234 96,008 6% 18,307 19,105 4% 35,882 40,125 12%New Mexico 116,160 132,559 14% 246,040 273,418 11% 139,821 165,191 18% 52,614 52,722 0% 137,636 142,510 4%New York 787,657 906,292 15% 427,458 438,696 3% 290,825 299,581 3% 75,399 78,125 4% 128,333 145,032 13%D-22VOCNOXSO2NH3PM2.5State Case 1 Case 2 %DiffCase 1 Case 2 %DiffCase 1 Case 2 %DiffCase 1 Case 2 %DiffCase 1 Case 2 %DiffNorth Carolina 471,046 549,225 17% 267,441 282,240 6% 225,881 246,359 9% 198,260 199,387 1% 105,593 120,793 14%North Dakota 76,760 69,292 -10% 117,103 119,4412% 163,147 162,126 -1% 57,449 57,492 0% 76,935 76,782 0%Ohio 436,266 507,447 16% 451,373 470,413 4% 431,072 442,399 3% 86,803 89,168 3% 132,126 143,396 9%Oklahoma 201,035 233,388 16% 272,262 294,496 8% 80,694 86,427 7% 123,609 124,680 1% 113,620 118,643 4%Oregon 365,104 422,750 16% 153,020 159,582 4% 50,840 54,892 8% 49,658 49,724 0% 134,968 146,721 9%Pennsylvania 414,605 472,091 14% 462,695 485,087 5% 328,200 347,837 6% 92,757 94,208 2% 108,787 118,694 9%Rhode Island 41,240 56,165 36% 23,122 24,764 7% 8,431 9,866 17% 1,705 1,716 1% 2,788 3,124 12%South Carolina 261,951 322,836 23% 174,160 184,004 6% 175,256 185,341 6% 37,309 37,930 2% 64,706 71,009 10%South Dakota 60,161 58,676 -2% 46,961 48,788 4% 26,552 27,303 3% 83,011 83,088 0% 58,785 59,530 1%Tennessee 349,379 439,159 26% 250,355 268,131 7% 316,869 334,915 6% 54,005 56,278 4% 90,329 110,399 22%Texas 951,427 1,090,613 15% 1,145,445 1,224,465 7% 620,673 655,866 6% 319,202 321,061 1% 377,140 400,164 6%Utah 112,574 131,831 17% 153,361 162,991 6% 75,448 76,858 2% 31,154 31,540 1% 63,633 68,627 8%Vermont 38,222 41,848 9% 20,185 21,399 6% 8,436 10,484 24% 9,867 9,935 1% 10,422 11,403 9%Virginia 310,041 372,814 20% 319,590 338,950 6% 188,262 204,866 9% 59,252 59,436 0% 66,319 77,143 16%Washington 252,421 301,981 20% 190,411 202,112 6% 57,377 65,843 15% 62,892 65,134 4% 65,546 75,720 16%West Virginia 91,769 104,095 13% 160,080 170,690 7% 183,911 193,420 5% 14,041 14,195 1% 47,537 51,997 9%Wisconsin 346,558 405,213 17% 223,676 234,244 5% 254,576 270,098 6% 86,221 87,150 1% 81,110 94,150 16%Wyoming 51,472 57,223 11% 184,094 205,128 11% 128,540 129,709 1% 17,725 17,884 1% 77,144 83,165 8%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 1% 4,791,155 5,214,402 9%

## D-23 D.3 Impact of Emissions Changes on Air Quality Model Prediction

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

provided in Table 3. This ta ble 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 th ese counties have higher

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

μg/m 3 

## . In over 50 percent of the counties, the increase in PM2.5 is less than 1 μg/m 3



## . The largest differences, which are 3 μg/m 3 

## or more, are predicted for several counties in

California. Between Case 1 and Case 2, th e number of nonattainmen t 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 PM 2.5



## 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 sta tionary sources to the estimates for Case 1

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

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

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

increases in PM 2.5 

## associated with the growth se nsitivity case are predicted in the

Southeast from Arkansas and Louisiana to Ge orgia and Tennessee, and western Kentucky

northward into Illinois, Indiana a nd 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 gr owth 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 pred icted to have increases in PM 2.5 

## at or above 0.75 ug/m3.

The granularity of the patterns shown on the ma p 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 th e 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 il lustrates the implications th at assumptions regarding the

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

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

growth in future emissions, then we appl y 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 emissi ons growth, then we fail to apply enough

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

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

human health benefits of achieving a smalle r increment of air quality change than

necessary to reach attainment.

As indicated in Chapter 2, EPA used the interi m 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 th e 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 th e East with Higher PM2.5 in the Growth

Sensitivity Case vs the Base Case

Figure D-5. Percent of Model Grids in th e 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 Analys es (RIAs) to assess the benefits and

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

categories. We continually improve these fo recasts over time and significant advances

have been made for major source categorie s 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 pr ocess point sources (e.g., industrial boilers, petroleum

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

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

significant over-prediction of emissions pr ojections in longer-for ecast periods required

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

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

some understanding of the potential uncerta inties implied by the past methodology and

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

review of the PM NAAQS. We seek a consul tation 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 appr oaches for addressing this issue.

Background

Overview of Emission Inven tory 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 know ledge” and comments received on previous

analyses. We have placed highest prio rity 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 Di esel 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 s ubject regulation, we develop multiple futureyear emission estimates based on a range of regulatory options. In general, EPA

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

generate emissions and using this forecasted acti vity to increase (or decrease) emissions.

## D-29 We then reduce forecasted future-year emissi ons for the impact of mandated Clean Air

Act (CAA) emission controls.

Methods Used to Foreca st Emissions Inventories

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

following factors:

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

• Changes in the mix of production acti vities both within and between

economic sectors,

• Changes in vintages of capital equipment,

• Changes in population, energy use, la nd 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 producti on (e.g., fuel switching), and

• Emission controls implemented to satisfy CAA regulations, voluntary

programs and other initiatives exp ected to reduce air emissions.

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

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

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

current emissions into the future by c onsidering the following two factors:

• Changes in economic activity (gener ally we have assumed a linear

relationship between economic activ ity changes and emission changes

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

changes in emissions are difficult to quantify) and

• Application of emission controls ma ndated by various parts of the CAA.

The typical formula for estimati ng projected inventories follows:

Projected Future Emissions = Curre nt 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 change s 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 Stan dard Industrial Classi fication (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 vi ntages of capital equipment, energy use,

etc.) listed above are difficul t to quantify and are not ade quately captured in current

models. Our past forecasting approaches fo r these source categorie s do appear to model

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

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

Forecasting emissions for these source categor ies is further complicated by the multitude

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

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

of total direct PM 2.5 

## emissions (excluding emissions from dust and fires) and

approximately 18 percent and 25 percen t of important PM precursors, NO X 

## and SO 2 

## , respectively. While emissions from these sour ces are relatively small when compared to

total emissions from all sources of SO 2 

## and NO X 

## , these sources represent the major

contributors to direct PM 2.5 

## emissions and are major source categories considered in the

current PM NAAQS RIA. Emission projectio ns 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 fu ture 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 inte rim approach. For these sources, EPA has

developed improved models specific to mobile sources (MOBILE and NONROAD

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

deficiencies in our curren t 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 capita l turnover and economically-motivated fuel

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

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

our models directly address equipment turnov er 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 agri cultural ammonia emissions ar e based on projected animal

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

covered by our interim approach.

Problems with Past Projection Approaches

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

logically forecast continuing emission incr eases relating to economic, population, and

other sources of growth for any given analytic al starting point. Su ch 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 (p ollutants include SO 2 

## , NO

x 

## , VOC, PM 10 

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

GDP, population, energy consumption, and vehicl e 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 em issions 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 em issions we are observing. Figure 2 shows

decreasing trends in PM 2.5 

## and the primary PM precursors SO 2 

## and NO

x 

## for non-EGU

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

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

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

and estimates of actual emissions for the partic ular 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 act ual activity or actual activity surrogate

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

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

emissions inventory.

Historical emissions trends for key industrial sectors (chemi cal 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 histor ical emissions across different regions of

the country. Figure 4 compar es 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 ec onomic growth and emission reductions

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

emission trends correlates linear ly with economic or other emission generating activities.

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

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

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

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

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

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

economic growth forecasts and considerat ion of CAA emission controls overlooks

important factors that infl uence emission trends.

While information needed fo r a full understanding is la cking, we have several

plausible explanations for the differences we observe in economic gr owth projections and

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

explanations involve the repla cement 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.D-33Figure 21990 -2002 Emission InventoriesNon-EGU Stationary Sources Only1-1,0002,0003,0004,0005,0006,0007,0001990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002Year

Emissions (Thousand Tons)NOxSO2PM2.51Emissions shown reflect non-utility stationary point and non-point sources only, excluding fires. Source: National Emissions InventoryD-34Figure 3Historical SO2 Emission Trendsfor Large Industrial Categories01002003004005006007008009001,0001990 1993 1996 1999 2002Year

Emissions (Thousand Tons)Chemical and Allied ProductsPetroleum Refining and Related ProductsPaper and Allied ProductsPrimary Metals Industries1Emissions shown reflect 2 digit-SIC source categories. Source: National Emissions InventoryD-35Figure 4Comparison of 1997 PM NAAQS RIA Forescasts and NEI Actual EmissionsNon-EGU Stationary Sources Only101,0002,0003,0004,0005,0006,0007,0008,000 199 0 1991 1992 199 3 199 4 1995 1996 1 997 1 998 199 9 200 0 200 1 2002 2003 200 4 200 5 2006 2007 2 008 2 009 2010Year

Emissions (Thousand Tons)NOx -97 RIANOx - NEISO2 - 97 RIASO2 -NEI1Sources: 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, enha nced productivity, retirement of obsolete equipment,

energy efficiency (e.g., fuel switching) a nd other reasons. Prof it 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 th e equipment replacement (i.e., enhanced

productivity, regulatory requirements, obsoles cence 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 ne wly installed emission control equipment in our

current emission inventory process. We ha ve 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 declin ing, 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 ‘inflec tion 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 po llution controls, it is reasonable to expect

emissions rates will be steady or decline. C ontinuing 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 r eevaluate our emission forecasting approaches

for stationary non-EGU sources to incorporate factors not ade quately 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 co sts of the standard. The st ationary non-EGU sectors are

important sectors for this anal ysis and emission projections are more important for this

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

emissions for these sectors will lead to an ove r-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 cour t-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 a pproach, 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 traditiona l approach. Figure 5 shows the forecasted

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

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

lower future-year emission projections for th ese 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 em issions 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 minimi zes 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 hol ds 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 in terim 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 em ission 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 firs t identify and understand th e sources of change

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

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

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

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

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

develop models that better reflect histori cal 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 histori cal trends, EPA will evaluate currently

available forecasting models capable of estim ating 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 a ddition, EPA will consider techniques to model

## D-38 technological innovation and adoption fo r both productive processes and control

equipment and models that consider new f acility 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 availabl e for the initial vers ion 2002 platform, but

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

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

feedback on the new methodology pr ior to its implementation.

Question for the Council

Please provide your advice and commen ts on EPA’s discussion and underlying

development of the interim forecasting a pproach for stationary non-EGU sources

described above. Are there caveats and sensit ivities 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 fo r 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 futu re 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 futu re 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 c onstant 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 NMIMD-40Figure 52020 Emission Forecasts - Old and Interim MethodsNon-EGU Stationary Sources Only101,0002,0003,0004,0005,0006,000 2 001 2 002 2 003 2 004 2 0 05 2 0 06 2 0 07 2 008 2009 2010 2 011 2 012 2 013 2 014 2 015 2 016 2 0 17 2 0 18 2 0 19 2020Year

Emissions (Thousand Tons)NOx (Old)NOx (Interim)SO2 (Old)SO2 (Interim)PM2.5 (Old)PM2.5 (Interim)Source: Analysis completed forthe PM NAAQS RIA (forthcoming).D-41Figure 62020 Emission Forecasts - Old and Interim MethodsAll Sources05,00010,00015,00020,00025,000 20 0 1

2 0 02 2 0 0 3 2 0 04 2 0 0 5 20 0 6 2 0 07 20 0 8 2 0 09 20 1 0 2 0 11 2 0 1 2 2013 2 0 14 2015 2 0 16 20 1 7 2 0 18 2 0 1 9 2020Year

Emissions (Thousand Tons)NOx (Old)NOx (Interim)SO2 (Old)SO2 (Interim)PM2.5 (Old)PM2.5 (Interim)Source: Analysis completed forthe PM NAAQS RIA (forthcoming).

## D-42 References

U.S. Census Bureau. 2005. Table 4. Populatio n: 1790 to 1990 and P opulation 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

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

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

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



## U.S. Department of Transportati on, Federal Highwa y Administration.

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

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