On behalf of Veto the V.E.T., I am submitting the following information
to support the ending of vehicle emission testing Boone, Kenton and
Campbell counties in Kentucky. It is our assertion that the regulations,
arbitrary air quality standards, and implementation of testing is been
poor public policy resulting in the wasting of millions of consumer
dollars. The auto manufacturer is producing an efficient, durable
emission system that is in fact cleaning the air. Furthermore, it is
apparent few consumers have the ability, knowledge or desire to alter
their emission systems. Since the 1990 Clean Act, every category of air
quality is improving and most experts believe it is a trend to continue
for the next two decades without testing or additional EPA regulations.

The current testing system affects over 93,000 vehicle owners per year.
The new regulations affect less than 250 businesses. Many of the new
regulations are not only improving air quality, they will increase
profits for the businesses involved. The Low Volume High Pressure paint
sprayers use substantially less paint and can be purchased for less than
\$250 each. The use of degreasers has not been objected to in any
meaningful manner by the businesses involved in two public hearings. The
implementation of the proposed regulations meets and exceeds the
reductions required by the EPA, Kentucky's SIP and will continue to do
so in future years.

The driving public does have an incentive to keep their cars well
maintained because of raising fuel cost as well as air quality. The
history of testing has shown most consumers do take care of their
vehicles in an environmentally friendly manner. The real problem and the
one that testing does not address is the gross emitting polluter. The
current Testing Regulations neither removed the vehicle from the
roadways nor requires it to be repaired to the point of passing. A very
large percent of these gross polluting vehicles are operated by
low-income families in need of basic transportation or senior citizens
on a fixed income and long time owners of the vehicle.

I am submitting numerous studies, research summaries or references to
studies that conclude vehicle emission testing should be a thing of the
past. Future emission reductions will come about from technology, older
cars leaving the roadways and alternative hybrid vehicles and the
introduction of technological advancements for diesel vehicles, coal
fired electric plants and other combustion engines, such as aircraft.

To summarize various studies and scientific facts: 98% of tested
vehicles built after 1993 pass the current tailpipe test, two EPA
studies indicate that cars perform almost equally in tested and non
tested areas, technology advancements is the driving force behind the
next 20 years of improving vehicle air quality, the EPA's use of
computer modeling has over stated pollution reduction results from
testing, the EPA method of writing a State Implementation Plan (SIP)
based on emission credits creates emphasis on the process and not
cleaning the air, smog forming ozone is abundant in nature as documented
in the 1991 National Academy of Sciences Report on the Southeast that
stated vegetation produced more such chemicals than cars or factories,
and that the introduction of Reformulated Gas (RFG) is causing
additional Volatile Organic Compound (VOC) and Nitrogen Oxides (NOx)
releases into our air increasing the chances for ozone formation.
Vehicle Testing, Inspection Maintenance and On Board Diagnosis testing
are irrelevant for future reductions of auto emissions and should be
ended.

Larry Brown P O Box 1552 Florence Ky 41042 Co Founder VETO the VET.

**[Table of Contents for References]{.underline}**

Page 3. Breathe Easier: The American Lung Association's misleading
"State of the Air 2001" Report. Joel Schwartz

Page 7. Associated Press & Global Warming: Never Let Facts Get in the
Way of Good Theory.

Page 14. President George Bush Executive Order 8-2602004 allowing local
groups to be included in agency (EPA) regulation decision making.

Page 15. Clean Air Trust 7-04 "Villain of the Month"

Page 18. Reason Public Policy Institute 6-1997 Study: Cost, Economic
Impacts and Benefits of EPA Ozone and Particulate Standards.

Page 151. Bob Brooks Wards 6-3-2003 Tests Don't Affect Emission
Deterioration Rates.

Page 153. Executive Summary: Evaluation of Air Pollutant Emissions from
Commercial Jet Aircraft.

Page 156. Straight Talk About Corn-based Ethanol 1-23-04 Meredith
Corporation.

Page 161. Ozone in the Colorado Front Range Don Stedman 1-23-04

Page 167. Air Scare: Does Air Pollution cause Asthma 2-20-02 Joel
Schwartz

Page 170. Letter Becky Norton Dunlop, Former Secretary Environment
Virginia.

Page 171. N KY Emission Check Annual Report June 2004

Page 194. N KY Emission Check Annual Report June 2003

Page 217. Press Release 5-11-99 National Academy Press: Ozone Forming
Potential of Reformulated Gasoline.

Page 221. No Way Back. Why Air Pollution will Continue to Decline. Joel
Schwartz.

Page 278. Emission Reductions as Result of Automobile Improvements.
2003.

+-----------------------------------------------------------------------+
| **Breathe Easier: The American Lung Association's Misleading \"State  |
| of the Air 2001\" Report**                                            |
|                                                                       |
| By [Joel Schwartz](http://www.rppi.org/schwartz.html)                 |
|                                                                       |
| According to \"State of the Air,\" a report released on May 1 by the  |
| American Lung Association (ALA), \"air pollution remains a major      |
| threat\" to 141 million Americans, up seven percent from last year.   |
| If that were true, air pollution would be one of the most serious     |
| health challenges in the United States. However, the ALA report       |
| vastly overestimates Americans' exposure to unhealthful air, and      |
| misleads the public into believing that air pollution is getting      |
| worse, when in fact it has been improving for more than 20 years. So, |
| how did ALA get the numbers so wrong?                                 |
|                                                                       |
| First, ALA incorrectly counted many clean-air locations as having     |
| unhealthful air. ALA cited a county as having a bad air day when at   |
| least one monitor somewhere in the county measured ozone at a level   |
| greater than or equal to 85 parts-per-billion (ppb) averaged over an  |
| eight-hour period. In fact, many counties with high ozone levels at   |
| some monitoring sites actually have other large areas with clean air. |
| For example, in 1999 and 2000, one third of the monitored locations   |
| in southern California stayed below the level ALA defined as having   |
| an air quality violation. Roughly six or seven million people live in |
| these clean-air locations, but were included by ALA in its tally of   |
| people exposed to polluted air. In a similar manner, ALA              |
| overestimated the extent of polluted air in other populous parts of   |
| the country, inaccurately adding millions of people in clean-air      |
| regions to its polluted-air tally.                                    |
|                                                                       |
| Second, ALA overestimated the amount of time any given person is      |
| exposed to unhealthful air even in regions that exceeded the ozone    |
| standard. ALA calculated the number of bad air days for each county   |
| by counting the days in which *any* location in the entire county     |
| registered an ozone violation. But this number is much greater than   |
| the maximum number of bad air days at any single location, because    |
| ozone levels vary a great deal from place to place.                   |
|                                                                       |
| For example, Table 1 displays the number of ozone violation days      |
| during the three-year period from 1997 to 1999 at each monitoring     |
| location in Los Angeles County. As the table shows, Los Angeles       |
| County as a whole had 110 ozone violation days. However, no single    |
| location had more than 70 violations, and most had far fewer. Thus,   |
| even in the parts of the county that exceeded the ozone standard, ALA |
| overestimated real exposure to polluted air by anywhere from a factor |
| of 1.6 to a factor of 55, depending on the location. The same         |
| overestimation occurred in other parts of the country, including the  |
| Houston area, where the worst site exceeded the ozone standard on 45  |
| percent fewer days than the county as a whole. ALA's erroneous method |
| of evaluation results in a substantial nationwide overestimation of   |
| people's exposure to polluted air.                                    |
|                                                                       |
| **Table 1. Number of Ozone Violation Days for Los Angeles County as   |
| a\                                                                    |
| Whole Compared with Individual Monitoring Locations**                 |
|                                                                       |
| +---------------------------+-------------------------------------+   |
| | **Ozone Monitoring        | **Three-Year Total of Ozone         |   |
| | Location in Los Angeles   | Violation Days, 1997---1999**       |   |
| | County**                  |                                     |   |
| |                           | **(85 ppb, 8-hour ozone standard)** |   |
| +---------------------------+-------------------------------------+   |
| | Anywhere in county        | 110                                 |   |
| +---------------------------+-------------------------------------+   |
| | Santa Clarita             | 70                                  |   |
| +---------------------------+-------------------------------------+   |
| | Glendora                  | 68                                  |   |
| +---------------------------+-------------------------------------+   |
| | Azusa                     | 46                                  |   |
| +---------------------------+-------------------------------------+   |
| | Pomona                    | 38                                  |   |
| +---------------------------+-------------------------------------+   |
| | Pasadena                  | 33                                  |   |
| +---------------------------+-------------------------------------+   |
| | Burbank                   | 22                                  |   |
| +---------------------------+-------------------------------------+   |
| | Pico Rivera               | 21                                  |   |
| +---------------------------+-------------------------------------+   |
| | Downtown Los Angeles      | 14                                  |   |
| +---------------------------+-------------------------------------+   |
| | Reseda                    | 14                                  |   |
| +---------------------------+-------------------------------------+   |
| | Hawthorne                 | 2                                   |   |
| +---------------------------+-------------------------------------+   |
| | Lynwood                   | 0                                   |   |
| +---------------------------+-------------------------------------+   |
| | North Long Beach          | 0                                   |   |
| +---------------------------+-------------------------------------+   |
| | West Los Angeles          | 0                                   |   |
| +---------------------------+-------------------------------------+   |
| | *Source: California Air   |                                     |   |
| | Resources Board*          |                                     |   |
| +---------------------------+-------------------------------------+   |
|                                                                       |
| Third, ALA used a grading system that has little relationship to      |
| actual health risks from air pollution. ALA won't give a county a     |
| clean bill of health, even if its air pollution levels satisfy the    |
| Environmental Protection Agency's (EPA) stringent new ozone health    |
| standard. But EPA chose this standard specifically to protect those   |
| who are most sensitive to the effects of ozone. And even EPA's Clean  |
| Air Science Advisory Committee, an independent panel of health        |
| scientists, concluded that even less stringent standards than the one |
| EPA chose would be equally protective of public health. By using a    |
| standard even tougher than EPA's, ALA was able to classify 30 million |
| additional people as living in areas with poor air quality, even      |
| though air quality in those regions meets EPA's stringent health      |
| standard.                                                             |
|                                                                       |
| Fourth, even though moderate ozone levels don't harm most people, ALA |
| included them in its figure for Americans exposed to unhealthful air. |
| The EPA's new ozone standard is set to protect the most sensitive     |
| groups---those with respiratory diseases, the elderly, and children   |
| with developing lungs. Roughly 90 percent of metropolitan areas that  |
| exceed the EPA standard never reach ozone levels that would harm the  |
| 60 percent of people that don't fall into one of these sensitive      |
| groups. ALA's tally thus includes tens of millions of people who      |
| wouldn't be harmed even if ozone levels occasionally exceed the EPA's |
| new standard.                                                         |
|                                                                       |
| Fifth, ALA blurs the distinction between modest and severe air        |
| pollution problems. In the ALA grading system, counties with no       |
| pollution violations get a grade of A, and those that average 3.3 or  |
| more days per year above the ozone standard get a grade of F. This    |
| lumps Osceola, Florida, averaging 3.3 ozone violations per year, with |
| Houston, which averages 61.5 violations per year. That's a huge       |
| difference in real health risk between the two areas that's not       |
| reflected in the ALA grades. ALA also makes the questionable claim    |
| that three or four days a year of ozone at levels slightly greater    |
| than an already stringent standard should be considered a \"major     |
| threat\" to public health.                                            |
|                                                                       |
| Finally, ALA misleads the public about trends in air pollution        |
| levels. The fight against smog is a great success story in            |
| environmental protection. Southern California, the region with the    |
| worst air in the country, has reduced its number of bad air days by   |
| more than 75 percent between the late 1970s and the last few years.   |
| High ozone levels also occur over a much smaller region than in the   |
| past, exposing far fewer people. Likewise, Houston, the second most   |
| polluted area in the country, reduced ozone violations by about 55    |
| percent over the same period. Most, though not all, metropolitan      |
| areas have seen similar improvements. Tough EPA pollution standards   |
| for new cars and trucks will ensure that air pollution levels         |
| continue to go down for the foreseeable future, as the national fleet |
| turns over to new low-polluting vehicles.                             |
|                                                                       |
| Not only does the ALA report fail to show these ongoing reductions in |
| air pollution, it doesn't reflect current air quality particularly    |
| well either. For example, southern California's ozone levels have     |
| been dropping rapidly for more than 10 years, with 1999 and 2000 the  |
| cleanest ever. ALA's analysis masks these recent improvements.        |
|                                                                       |
| ALA's inaccurate and misleading report could cause tens of millions   |
| of people who breathe clean healthful air year-round to now           |
| incorrectly believe that their air is unsafe. Tens of millions more   |
| might believe that their air poses a major health threat, when in     |
| fact their real risk is minimal.                                      |
|                                                                       |
| Ironically, ALA's efforts could actually reduce Americans' health and |
| safety. The ALA report could cause the public to demand billions of   |
| dollars in expenditures to clean up air that is already clean. These  |
| wasted billions would harm people in two ways. First, in a world of   |
| limited resources, society can only address some of the many risks    |
| people face. Wasting money on phantom risks means that real risks go  |
| unmitigated. Second, health and safety improve over time as talented  |
| people progressively find cheaper and more effective ways of solving  |
| problems. But when people waste effort on fruitless endeavors, fewer  |
| real problems get the attention they deserve, reducing health and     |
| safety not only in the present, but in the long run as well.          |
|                                                                       |
| Everyone deserves to breathe clean air, and nobody wants to see       |
| people suffering from pollution. A few areas of the country have      |
| serious air pollution problems that do threaten the health of people  |
| who live there. But exaggerating the public's risk from air pollution |
| is no better than ignoring real air quality problems. If society      |
| misspends scarce resources based on inaccurate information, more      |
| people will suffer, not fewer.                                        |
|                                                                       |
| **Related Reason Public Policy Institute Publications**               |
|                                                                       |
| [Clearing the Air in Houston: Innovative Strategies for Ozone Control |
| and Air Quality[\                                                     |
| ]{.underline}](http://www.rppi.org/environment/ps273.html)by Kenneth  |
| Green, D.Env. and Lisa Skumatz, Ph.D.                                 |
|                                                                       |
| [Seeking Safety in a Dangerous World: A Risk Reduction Framework for  |
| Policymakers [\                                                       |
| ]{.underline}](http://www.rppi.org/environment/ps261.html)by Kenneth  |
| Green, D. Env.                                                        |
|                                                                       |
| [Costs, Economic Impacts, and Benefits of EPA\'s Ozone and            |
| Particulate Standards[\                                               |
| ]{.underline}](http://www.rppi.org/environment/ps226.html)by Anne     |
| Smith et. al.                                                         |
+-----------------------------------------------------------------------+

+-----------------------------------------------------------------------+
| ![](media/image1.png){width="1.0416666666666666e-2in"                 |
| height="1.0416666666666666e-2in"}                                     |
+-----------------------------------------------------------------------+
| © The Reason Foundation. All rights reserved.\                        |
| Please email <feedback@reason.org> or call 310-391-2245 if you have   |
| questions about the Reason Foundation.                                |
|                                                                       |
| 3415 S. Sepulveda Blvd., Suite 400\                                   |
| Los Angeles, CA 90034\                                                |
| (310) 391-2245                                                        |
+-----------------------------------------------------------------------+

+-----------------------------------------------------------------------+
|                                                                       |
|                                                                       |
| > **Associated Press & Global Warming: Never Let Facts Get in the Way |
| > of a Good Theory**                                                  |
| >                                                                     |
| >                                                                     |
| >                                                                     |
| > **BACKGROUND**: The Associated Press has recently run two global    |
| > warming stories by AP Special Correspondent Charles P. Hanley that  |
| > misrepresent objective facts about climate, apparently for the      |
| > purpose of leading readers to believe that human activities are     |
| > causing the planet to warm significantly.                           |
| >                                                                     |
| > The AP published the same faulty information in another Hanley      |
| > article*1* nearly a year ago (see [Ten Second Response              |
| > #032204](http://www.nationalcenter.org/TSR032204.html), *Global     |
| > Warming: Why Can\'t the Mainstream Press Get Even Basic Facts       |
| > Right?*).                                                           |
| >                                                                     |
| > The re-publication of information the AP should know to be faulty   |
| > falls on the heels of another grossly misleading AP story*2* about  |
| > a global warming report entitled \"Meeting the Climate Challenge.\" |
| > Readers of the AP story about the report would likely conclude the  |
| > report was issued by scientific research organizations - but the    |
| > sponsors were liberal activist groups.                              |
| >                                                                     |
| > **\                                                                 |
| > TEN SECOND RESPONSE**: News organizations using AP materials would  |
| > be well advised to independently confirm information in AP stories  |
| > before publishing.                                                  |
| >                                                                     |
| > **\                                                                 |
| > THIRTY SECOND RESPONSE**: The New York Times and CBS News once were |
| > considered beyond the reach of public criticism. They are no more.  |
| > Wire services such as the Associated Press may be less obviously    |
| > vulnerable because its reporters are less visible personalities     |
| > while its work product tends to be presented in a low-key manner.   |
| > Nonetheless, the AP is hugely influential while the accuracy of its |
| > reporting is questionable at best. If this volatile mix continues,  |
| > increased scrutiny is inevitable.                                   |
| >                                                                     |
| > **\                                                                 |
| > DISCUSSION**:                                                       |
| >                                                                     |
| > The most recent Haney stories (for examples, see \"[Glaciers        |
| > Shrinking in a Warming                                              |
| > World](ht                                                           |
| tp://www.washingtonpost.com/wp-dyn/articles/A47696-2005Jan29.html),\" |
| > Washington Post, January 29, 2005, and \"[Antarctica\'s Ice Seems   |
| > to be Safe, at Least for                                            |
| > Now](http://www.usatoday.com/weat                                   |
| her/resources/coldscience/2005-02-01-warming-2-6-antarctica_x.htm),\" |
| > USA Today, February 7, 2005) contain the following:                 |
| >                                                                     |
| > \"Temperatures globally rose about 1 degree Fahrenheit in the past  |
| > century, most of that attributed by scientific consensus to the     |
| > accumulation in the atmosphere of carbon dioxide and other warming  |
| > \"greenhouse gases,\" mostly from fossil fuel-burning.\"*3*         |
| >                                                                     |
| > *and*                                                               |
| >                                                                     |
| > \"The warming will continue as long as \"greenhouse gases,\"        |
| > primarily carbon dioxide from the burning of fossil fuels,          |
| > accumulate in the atmosphere, say the U.N. panel and other          |
| > authoritative scientific organizations.\"*4*                        |
| >                                                                     |
| > The sentences quoted above are fairly transparent attempts to       |
| > convince the reader that the global warming hypothesis is true.     |
| > They go beyond editorializing to provable inaccuracy, however. Here |
| > are the facts:                                                      |
| >                                                                     |
| > Half or more of 20th century global warming occurred in the first   |
| > few decades of that century,*5* before the widespread burning of    |
| > fossil fuels (and before 82 percent of the increase in atmospheric  |
| > carbon dioxide observed in the 20th century).*6*                    |
| >                                                                     |
| > The primary greenhouse gas is water vapor, not carbon dioxide.*7*   |
| >                                                                     |
| > Most of the carbon dioxide in the atmosphere does not come from the |
| > burning of fossil fuels. Only about 14 percent of it does.*8*       |
| >                                                                     |
| > On the second matter, the AP article on the report \"Meeting the    |
| > Climate Challenge,\" the AP began its story:                        |
| >                                                                     |
| > Global warming is approaching the critical point of no return,      |
| > after which widespread drought, crop failure and rising sea-levels  |
| > would be irreversible, an international climate change task force   |
| > warned Monday.                                                      |
| >                                                                     |
| > The report, \'Meeting the Climate Challenge,\' called on the G-8    |
| > leading industrial nations to cut carbon emissions, double their    |
| > research spending on green technology and work with India and China |
| > to build on the Kyoto Protocol.                                     |
| >                                                                     |
| > \"An ecological time-bomb is ticking away,\" said Stephen Byers,    |
| > who co-chaired the task force with U.S. Republican Senator Olympia  |
| > Snowe, and is a close confidant of British Prime Minister Tony      |
| > Blair. \"World leaders need to recognize that climate change is the |
| > single most important long term issue that the planet faces.\"      |
| >                                                                     |
| > The independent report, by the Institute for Public Policy Research |
| > in Britain, the Center for American Progress in the United States   |
| > and The Australia Institute, is timed to coincide with Blair\'s     |
| > commitment to advance international climate change policy during    |
| > Britain\'s G-8 presidency\...                                       |
| >                                                                     |
| > \...According to the report, urgent action is needed to stop the    |
| > global average temperature rising by 2 degrees Celsius above the    |
| > level in 1750 \-- the approximate start of the Industrial           |
| > Revolution when mankind first started significantly polluting the   |
| > atmosphere with carbon dioxide.                                     |
| >                                                                     |
| > Beyond a 2 degrees rise, \"the risks to human societies and         |
| > ecosystems grow significantly\" the report said, adding there would |
| > be a risk of \"abrupt, accelerated, or runaway climate change.\"    |
| >                                                                     |
| > It warned of \"climatic tipping points\" such as the Greenland and  |
| > West Antarctic ice sheets melting and the Gulf Stream shutting      |
| > down.                                                               |
| >                                                                     |
| > No accurate temperature readings were available for 1750, the       |
| > report said, but since 1860, global average temperature had risen   |
| > by 0.8 percent to 15 degrees Celsius.                               |
| >                                                                     |
| > The two degrees rise could be avoided by keeping the concentration  |
| > of carbon dioxide in the atmosphere below 400 parts per million     |
| > (ppm). Current concentrations of 379 ppm \"are likely to rise above |
| > 400 ppm in coming decades and could rise far higher under a         |
| > business-as-usual scenario,\" the report warned\....\"*9*           |
| >                                                                     |
| > Readers could be forgiven for believing the three organizations     |
| > sponsoring the report are independent, objective scientific         |
| > research organizations. Certainly nothing in the AP\'s text tells   |
| > anyone not already familiar with the groups that they are anything  |
| > but objective:                                                      |
| >                                                                     |
| > The Center for American Progress, in its \"What We\'re About\"      |
| > section on its website, gives one of its four reasons to exist as   |
| > \"responding effectively and rapidly to conservative proposals and  |
| > rhetoric with a thoughtful critique and clear alternatives.\" The   |
| > other three cited reasons are various methods of promoting liberal  |
| > political ideology.*10*                                             |
| >                                                                     |
| > A publication by The Australia Institute openly revealed that the   |
| > report\'s purpose was to influence governmental action, not to      |
| > provide new scientific information about the climate.*11* Other     |
| > Australia Institute publications, including press materials about   |
| > the report,*12* also make this clear. Readers of the AP story,      |
| > however, are never told this.                                       |
| >                                                                     |
| > Britain\'s Institute for Public Policy Research says starkly at the |
| > top left of its Internet home page \"IPPR is the UK\'s leading      |
| > progressive think tank.\"*13* It also says of itself, \"IPPR was    |
| > formed\... to act as a dynamic, independent catalyst for            |
| > progressive thinking on the centre-left\... \[IPPR\'s\] emergence   |
| > was crucial in providing an alternative space to rival the thinking |
| > of free-market think-tanks.\"*14*                                   |
| >                                                                     |
| > The three left-of-center organizations were more open about their   |
| > agenda than the AP was on their behalf.                             |
| >                                                                     |
| >                                                                     |
| >                                                                     |
| > **FOR MORE INFORMATION**:                                           |
| >                                                                     |
| > Charles J. Hanley, \"Antarctica\'s Ice Seems to Be Safe, at Least   |
| > for Now,\" Associated Press, February 2005, available on various    |
| > websites, including USA Today (February 7, 2005) at                 |
| > <http://www.usatoday.com/w                                          |
| eather/resources/coldscience/2005-02-01-warming-2-6-antarctica_x.htm> |
| >                                                                     |
| > Charles J. Hanley, \"Glaciers Shrinking in a Warming World,\"       |
| > Associated Press, January 2005, available on various websites,      |
| > including the Washington Post (January 29, 2005) at                 |
| >                                                                     |
| <http://www.washingtonpost.com/wp-dyn/articles/A47696-2005Jan29.html> |
| > and the Deseret News (January 30, 2005) at                          |
| > <http://deseretnews.com/dn/view/0,1249,600108419,00.html>           |
| >                                                                     |
| > Ed Johnson, \"Report: Global Warming Approaching Critical Point:    |
| > \'An Ecological Time-Bomb is Ticking Away,\'\" Associated Press,    |
| > published on CNN.com at                                             |
| > <http://www.cnn.com/2005/TECH/science/01/24/climate.change.ap/> on  |
| > January 24, 2005; also published online January 24, 2005 online at  |
| > the Natural Resources Defense Council website at                    |
| > <http://www.nrdc.org/news/newsDetails.asp?nID=1596>                 |
| >                                                                     |
| > Charles J. Hanley, \"CO2 Buildup Accelerating in Atmosphere,\"      |
| > Associated Press, as run by USA Today on March 21, 2004 at          |
| > <http://www.usatoday.com/weather/news/2004-03-21-co2-buildup_x.htm> |
| >                                                                     |
| > Dr. Robert Balling, \"The Increase in Global Temperature: What It   |
| > Does and Does Not Tell Us,\" George C. Marshall Institute,          |
| > September 1, 2003 at <http://www.marshall.org/article.php?id=170>   |
| >                                                                     |
| > Summary of remarks of Dr. S. Fred Singer at the Scientific Alliance |
| > conference Apocalypse No! held at the Royal Institution, London,    |
| > January 27, 2005 at <http://www.sepp.org/NewSEPP/Apocalypse-NO.htm> |
| >                                                                     |
| > \"The Increase in Global Temperature: What It Does and Does Not     |
| > Tell Us,\" George C. Marshall Institute, December 7, 2004 at        |
| > <http://www.marshall.org/article.php?id=268>                        |
| >                                                                     |
| > *by Amy Ridenour\                                                   |
| > \                                                                   |
| > Contact the author at: 202-543-4110 or                              |
| > <aridenour@nationalcenter.org>*\                                    |
| > *The National Center for Public Policy Research\                    |
| > 501 Capitol Court, N.E.\                                            |
| > Washington, D.C. 20002*                                             |
| >                                                                     |
| > **Footnotes**:                                                      |
| >                                                                     |
| > \(1\) Charles J. Hanley, \"CO2 Buildup Accelerating in              |
| > Atmosphere,\" Associated Press, March 2004, available on various    |
| > websites, including                                                 |
| >                                                                     |
|  <http://www.usatoday.com/weather/news/2004-03-21-co2-buildup_x.htm>, |
| > <http://www.newsmax.com/archives/articles/2004/3/21/170709.shtml>,  |
| > <http://www.dfw.com/mld/dfw/news/8241534.htm> and many others       |
| > (headlines used vary).                                              |
| >                                                                     |
| > \(2\) Ed Johnson, \"Report: Global Warming Approaching Critical     |
| > Point: \'An Ecological Time-Bomb is Ticking Away,\'\" Associated    |
| > Press, published on CNN.com at                                      |
| > <http://www.cnn.com/2005/TECH/science/01/24/climate.change.ap/> on  |
| > January 24, 2005; also published online January 24, 2005 online at  |
| > the Natural Resources Defense Council website at                    |
| > <http://www.nrdc.org/news/newsDetails.asp?nID=1596>.                |
| >                                                                     |
| > \(3\) Charles J. Hanley, \"Antarctica\'s ice seems to be safe, at   |
| > least for now,\" Associated Press, February 2005, available on      |
| > various websites, including USA Today (February 7, 2005) at         |
| > <http://www.usatoday.com/w                                          |
| eather/resources/coldscience/2005-02-01-warming-2-6-antarctica_x.htm> |
| > (headlines used vary).                                              |
| >                                                                     |
| > \(4\) Charles J. Hanley, \"Glaciers Shrinking in a Warming World,\" |
| > Associated Press, January 2005, available on various websites,      |
| > including the Washington Post (January 29, 2005) at                 |
| >                                                                     |
| <http://www.washingtonpost.com/wp-dyn/articles/A47696-2005Jan29.html> |
| > and the Deseret News (January 30, 2005) at                          |
| > <http://deseretnews.com/dn/view/0,1249,600108419,00.html>.          |
| >                                                                     |
| > \(5\) This is based on a review of global satellite and balloon     |
| > temperature measurements and high-quality U.S.-based surface        |
| > temperature station measurements. For additional details            |
| > understandable to laymen, we recommend the short document \"There   |
| > Has Been No Global Warming for the Past 70 Years,\" published by    |
| > The Center for the Study of Carbon Dioxide and Global Change.       |
| >                                                                     |
| > \(6\) \"Environmental Effects of Increased Atmospheric Carbon       |
| > Dioxide,\" Oregon Institute of Science and Health, 2001,            |
| > <http://www.oism.org/pproject/s33p36.htm>                           |
| >                                                                     |
| > \(7\) See \"The Greenhouse Effect and Greenhouse Gases: An          |
| > Overview,\" Energy Information Administration, U.S. Department of   |
| > Energy (available at                                                |
| > <http://www.eia.doe.gov/cneaf/pubs_html/attf94_v2/chap2.html>) for  |
| > a good summary of this issue understandable to the layman.          |
| >                                                                     |
| > \(8\) \"Frequently Asked Global Change Question: What percentage of |
| > the CO2 in the atmosphere has been produced by human beings through |
| > the burning of fossil fuels?,\" Carbon Dioxide Information Analysis |
| > Center, Oak Ridge National Laboratory, March 2004, available at     |
| > <http://cdiac.esd.ornl.gov/pns/faq.html> as of February 8, 2005.    |
| >                                                                     |
| > \(9\) \"Report: Global warming approaching critical point: \'An     |
| > ecological time-bomb is ticking away,\'\" Associated Press,         |
| > published on CNN.com at                                             |
| > <http://www.cnn.com/2005/TECH/science/01/24/climate.change.ap/> on  |
| > January 24, 2005.                                                   |
| >                                                                     |
| > \(10\) \"What We\'re About,\" Center for American Progress,         |
| > available online at                                                 |
| > <http://www.americanprogress.org/site/pp.asp?c=biJRJ8OVF&b=3459> as |
| > of February 8, 2005.                                                |
| >                                                                     |
| > \(11\) \"Newsletter No. 38, March 2004,\" The Australia Institute,  |
| > available online at <http://www.tai.org.au/> as of February 8,      |
| > 2005.                                                               |
| >                                                                     |
| > \(12\) For example, a December, 2004 Australia Institute press      |
| > release entitled \"International Climate Change Taskforce Meets in  |
| > Sydney,\" available at <http://www.tai.org.au/> as of February 8,   |
| > 2005.                                                               |
| >                                                                     |
| > \(13\) The Institute for Public Policy Research\'s home page at     |
| > <http://www.ippr.org.uk/home/> as of February 8, 2005.              |
| >                                                                     |
| > \(14\) \"About IPPR: IPPR\'s Influence on Policy,\" The Institute   |
| > for Public Policy Research, available online at                     |
| > <http://www.ippr.org.uk/about/index.php?current=influence> as of    |
| > February 8, 2005.                                                   |
| >                                                                     |
| >                                                                     |
|                                                                       |
| [Donate](http://www.nationalcenter.org/ContributionInformation.html)  |
| \|                                                                    |
| [Subscribe](http://gm12.com/app                                       |
| /campaigner/services/optinlist/processoptinrequest.jsp?oilb=58238420) |
| \| [Search](http://www.nationalcenter.org/SearchPage.html) \| [About  |
| Us](http://www.nationalcenter.org/NCPPRHist.html) \| [What\'s         |
| New](http://www.nationalcenter.org/HotIndex.html) \| [Read Our        |
| Blog](http://www.nationalcenter.org/Blog.html) \|                     |
| [Home](http://www.nationalcenter.org/index.html)                      |
|                                                                       |
| > **\                                                                 |
| > \                                                                   |
| > **The National Center for Public Policy Research\                   |
| > 501 Capitol Court, N.E.\                                            |
| > Washington, D.C. 20002\                                             |
| > (202) 543-4110\                                                     |
| > Fax (202) 543-5975\                                                 |
| > E-Mail: <info@nationalcenter.org>\                                  |
| > Web: <http://www.nationalcenter.org/>                               |
+-----------------------------------------------------------------------+

![](media/image2.png){width="0.3020833333333333in"
height="0.22916666666666666in"}

[Issue Alert from Winningreen                 A083004\
]{.underline}**\
President Bush issues executive order to consider local interests in
environmental decisions\
 \
By Gretchen Randall\
 \
Date:** August 30, 2004\
 \
**Issue:  ** Without much public notice, President Bush issued an
executive order August 26, 2004 to the Departments of Interior,
Agriculture, Commerce, Defense and Environmental Protection Agency (EPA)
to include local groups in decision-making that affects land use and
environmental regulations. Specifically the order calls for
\"cooperative conservation\"  among local, state and tribal governments
and for-profit and non-profit organizations in carrying out the
functions of the departments as they relate to the environment and
natural resources.\
 \
The order also requires that these federal agencies \"take account\" of
the interests of local property owners and include them in the decision
making \"to the extent permitted by law.\"  According to the Associated
Press, the Sierra Club claims the order is \"part of the
\'shrink-the-federal-safety-net\' efforts by the Bush administration.\"\
 \
**Comment 1:** This is simply a common sense approach to how all federal
agencies should be approaching their decision making because agency
regulations affect the lives and pocketbooks of local landowners and
businesses.\
 \
**Comment 2:** Liberal groups like the Sierra Club would prefer top-down
decision making from Washington rather than including local citizens in
making policy.  Since when did the bureaucrats in Washington get so
important that they can\'t consider ideas from the people? Isn\'t our
government supposed to be \"of the people, by the people and [for the
people]{.underline}?\"\
 \
 \
**Link:  ** To read the entire executive order, go to:
<http://www.whitehouse.gov/news/releases/2004/08/20040826-11.html>[\
]{.underline}\
Contact: Gretchen Randall\
Winningreen LLC\
3712 N. Broadway -- PMB 279\
Chicago, IL 60613\
Phone: 773-857-5086\
e-mail: grandall@winningreen.com

+---+------------------------------------------------------------------+
|   | Renewable Energy Action Project Named Clean Air Trust \'Villain  |
|   | of the Month\'                                                   |
|   |                                                                  |
|   | 7/19/2004 2:12:00 PM                                             |
|   |                                                                  |
|   | To: National Desk                                                |
|   |                                                                  |
|   | Contact: Frank O\'Donnell of the Clean Air Trust, 301-941-1987   |
|   | or 202-262-6026, Web:                                            |
|   | [http://www.cleanairtrust.org](http://releases.usnewswire.       |
|   | com/redir.asp?ReleaseID=33553&Link=http://www.cleanairtrust.org) |
|   |                                                                  |
|   | WASHINGTON, July 19 /U.S. Newswire/ \-- The following statement  |
|   | on the July \"Villain of the Month\" was released today by the   |
|   | Clean Air Trust:                                                 |
|   |                                                                  |
|   | Why would an organization claiming to represent environmental    |
|   | groups favor dirtier air in California?                          |
|   |                                                                  |
|   | And is there a connection to a race for a U.S. Senate seat from  |
|   | California?                                                      |
|   |                                                                  |
|   | These are mysteries raised by this month\'s \"clean air villain  |
|   | of the month\" for July 2004 \-- the California-based Renewable  |
|   | Energy Action Project (REAP).                                    |
|   |                                                                  |
|   | The organization, whose letterhead says it represents such       |
|   | well-known entities as Greenpeace and the Bluewater Network,     |
|   | raised eyebrows earlier this month when it sided with pro-Bush   |
|   | administration corn growers and attacked a coalition of          |
|   | respected clean-air advocates who seek to permit California gas  |
|   | stations to sell gasoline without the forced use of ethanol.     |
|   |                                                                  |
|   | The clean-air coalition, which includes the Sierra Club,         |
|   | American Lung Association, Physicians for Social Responsibility, |
|   | Natural Resources Defense Council, Environmental Defense, U.S.   |
|   | Public Interest Research Group, National Environmental Trust,    |
|   | Clean Air Trust Education Fund, and Our Children\'s Earth        |
|   | Foundation, had urged EPA Administrator Mike Leavitt to grant    |
|   | California\'s request to waive mandatory use of ethanol in       |
|   | reformulated gasoline sold in California.                        |
|   |                                                                  |
|   | \"In California, the federal oxygen requirement met with ethanol |
|   | exacerbates already unhealthy levels of air pollution throughout |
|   | the state,\" the coalition noted. \"We believe the need to grant |
|   | California\'s oxygen waiver request is urgent. It would benefit  |
|   | public health and the environment and could very well lower      |
|   | gasoline prices almost immediately.\"                            |
|   |                                                                  |
|   | The clean-air coalition\'s request appeared to upset REAP, which |
|   | told Inside EPA\'s Clean Air Report that it was \"at odds\" with |
|   | the clean-air coalition on this issue. \"The national            |
|   | environmental groups have not been involved in the California    |
|   | process and have no understanding of it,\" a REAP source told    |
|   | the publication.                                                 |
|   |                                                                  |
|   | In fact, the clean-air coalition based its comments on detailed  |
|   | research by the California Air Resources Board, which believes   |
|   | that mandatory ethanol use will make it harder to meet clean air |
|   | standards for deadly fine particle matter.                       |
|   |                                                                  |
|   | So why does REAP support the mandatory use of ethanol even when  |
|   | the state of California says that will mean dirtier air? REAP    |
|   | claims it knows more about the issue than the state\'s           |
|   | scientists.                                                      |
|   |                                                                  |
|   | But some people say it\'s because the group is closely           |
|   | affiliated with the ethanol industry. REAP appears on a media    |
|   | \"contact list\" put out by the Renewable Fuels Association,     |
|   | whose board includes not only small ethanol producers, but       |
|   | big-corn growers such as Archer Daniels Midland. And REAP\'s     |
|   | positions on ethanol echo those of the industry.                 |
|   |                                                                  |
|   | We do know the issue is politically volatile \-- at both the     |
|   | national and state levels. It is widely believed that the Bush   |
|   | administration is sitting on California\'s request to avoid      |
|   | offending voters in corn-growing states, including Iowa. There   |
|   | was added evidence for this thesis last week, when the National  |
|   | Corn Growers Association gave the Bush administration the        |
|   | \"friend of corn growers award.\"                                |
|   |                                                                  |
|   | The Iowa media reported the award came in part because the Bush  |
|   | administration had stonewalled California\'s request to permit   |
|   | sales of cleaner gasoline without ethanol. \"By doing so, corn   |
|   | growers said the 650-million-gallon ethanol market was           |
|   | preserved. Iowa continues to build and open new ethanol plants   |
|   | increasing the demand for corn,\" the Waterloo/Cedar Falls       |
|   | (Iowa) Courier reported.                                         |
|   |                                                                  |
|   | The California/ethanol clash is also a point of controversy in a |
|   | race in California for a U.S. Senate seat: Republican candidate  |
|   | Bill Jones is part owner of an ethanol company. His opponent,    |
|   | incumbent Senator Barbara Boxer (D-Calif.) is among those who    |
|   | have repeatedly urged EPA to permit California to sell           |
|   | reformulated gasoline without ethanol.                           |
|   |                                                                  |
|   | One footnote to that Senate race: Jones founded Pacific Ethanol  |
|   | Inc. in 2002, a year after the Bush administration rejected      |
|   | California\'s waiver request. A federal court later ruled the    |
|   | Bush administration acted illegally. Pacific Ethanol and related |
|   | properties are being acquired by Accessity Corp. in a merger     |
|   | said to be worth tens of millions of dollars. Jones announced    |
|   | last week that he would invest \$2 million of his own money in   |
|   | the Senate race. As the Los Angeles Times noted last week, \"His |
|   | wealth \-- at least on paper \-- increased dramatically this     |
|   | spring with the announced merger.\"                              |
|   |                                                                  |
|   | Pacific Ethanol\'s Chairman and CEO is Neil Koehler, who also    |
|   | heads the California Renewable Fuels Partnership. Which is a     |
|   | prominent member of REAP.                                        |
|   |                                                                  |
|   | <http://www.usnewswire.com/>                                     |
|   |                                                                  |
|   | -0-                                                              |
+---+------------------------------------------------------------------+

**Preface**

Reason Public Policy Institute first initiated a project to investigate
the policy implication of proposed tightening of the National Ambient
Air Quality Standards nearly two years ago. EPA's November 1996
announcement of proposed revisions to the ozone and particulate matter
standards helped us refine the focus of our project, and led to our
collaboration with Decision Focus Inc. to produce this report, analyzing
the costs and economic impacts of the proposed ozone and PM~2.5~
National Ambient Air Quality Standards.

 

 

 

Policy Study No. 226, June 1997

**Costs, Economic Impacts, and Benefits of EPA's Ozone and Particulate
Standards**

 

By Anne E. Smith, D. Warner North, James P. Bekemeier, Nathan Y. Chan,
Rana Glasgal, Justin L. Welsh

Project Director: Kenneth Green, D.Env.

 

 

 

 

 

 

 

Part 1

**Costs and Economic Impacts of the Proposed Ozone and PM~2.5~
Standards**

 

**I. Summary of Control-Cost Estimates**

In the analysis described in this section, existing estimates of the
costs of the proposed PM~2.5~ and ozone National Ambient Air Quality
Standards are evaluated in terms of their completeness and
reasonableness. Based on this critique, the analysis provides a synopsis
of the range of cost estimates that can be justified as reasonably
complete and consistent with all the available evidence. These estimates
are intended to reflect a balance of the considerations raised by both
the Environmental Protection Agency (EPA) and by commenters, and the
best judgment of the authors regarding cost assumptions that are either
too high or too low to include in the range.

Estimates of control costs, however, are only an indicator of the scale
of the program that might result from the proposed standards. A key
question of interest to policy makers is what economic impacts may
result from control costs of any given magnitude. This analysis
therefore also includes an economic impact analysis using a regional
economic-demographic model of the United States. The economic impact
analysis takes into account the potential ways that industries and
consumers will have to absorb control costs, and includes the
off-setting benefits to those components of the economy often referred
to as the \"pollution control industry.\" This report summarizes the
nature of the overall potential economic impacts in terms of real
disposable incomes of individual consumers, employment impacts, and the
distribution of these impacts to different income groups, business
sizes, and occupational categories.

For ozone, the only publicly available national cost estimates that
address the proposed standards by making use of air quality models,
engineering cost estimates, and control cost optimization models are
those summarized in EPA's December 1996 *Regulatory Impact Analysis for
Proposed Ozone National Ambient Air Quality Standard* (the \"ozone
RIA\"). For PM~2.5~, there are several such cost estimates other than
those in EPA's December 1996 *Regulatory Impact Analysis for Proposed
Particulate Matter National Ambient Air Quality Standard* (the \"PM
RIA\"). These use a variety of modeling assumptions but were all done by
the same EPA contractor, using the same basic methodology as that used
for the PM RIA cost estimates. None of the available cost estimates has
been interpreted in terms of their potential economic impacts via use of
economic modeling.

None of the ozone and PM cost estimates identifies and applies
sufficient control measures to fully achieve attainment with the
standards. While there are a number of other criticisms that can be made
regarding the cost assumptions and modeling methods, the most important
analysis gap for an independent cost reviewer to address is what the
costs of *full* attainment might really be (and whether attainment is
even feasible). EPA has declined to address the question of full
attainment directly through use of its air quality and cost-assessment
models. However, EPA, as well as a number of organizations that have
commented on the proposed rules, have made attempts to estimate what
those full attainment costs are by extrapolating from the limited
evidence in the RIAs and supporting materials in the dockets.

There is great uncertainty generated by the types of assumptions
required for such extrapolations. After careful review of the evidence
in favor of alternative possible assumptions, this analysis concludes
that:

-   A realistic estimate of the full cost of attaining the proposed
    PM~2.5~ standards (without accounting for any control actions
    associated with ozone) would be in the range of \$70 billion to
    \$150 billion annually. This compares to the cost cited by EPA in
    the PM RIA of \$6.3 billion for partial attainment.

```{=html}
<!-- -->
```
-   A realistic estimate of the costs of fully attaining the proposed
    ozone standard (without accounting for any control actions
    associated with the proposed PM standard) would be \$20 billion to
    \$60 billion. This compares to the partial attainment cost range
    cited by EPA of \$0.6 billion to \$6.3 billion.

The authors do not believe that either of these ranges overstates the
possible costs, as will be explained later in this report. In fact,
there are several cost estimates that are much higher which were not
included in developing these ranges because it is difficult to construct
an argument that they are \"conservative\" (i.e., are more likely to
understate the actual costs than to overstate them).

Determining the total costs of control for the combined standards is not
as straightforward as adding the PM and ozone cost ranges because there
may be overlap in the control measures for PM and ozone that implicitly
lie behind these extrapolations. The control measures for ozone involve
VOC and NO~x~ gases. Organic carbon (OC) particles and nitrate (NO~3~)
particles, which also can form from atmospheric VOC and NO~x~ gases, are
two of the many constituents included in the generic category of
pollution known as PM~2.5~. Thus, although many VOC and NO~x~ control
actions might not be selected as *cost-effective* ways of achieving
PM~2.5~ standards, if they *would be* selected in attempting to control
ozone, then somewhat fewer PM control measures may be required to
achieve a concurrent PM~2.5~ air quality target. To the extent that the
controls of VOCs and NO~x~ do reduce PM~2.5~, the cost estimates for
each standard on its own would not be completely additive. The degree of
cost overlap, however, is not likely to be near 100 percent. For
example:

-   Less than about 1 percent of the manmade VOCs convert to particulate
    OC, yet these same VOCs are a primary target of the ozone control
    costs. Thus, perhaps as much as 99 percent of ozone-motivated VOC
    controls, which are probably a majority of the ozone control costs,
    would not contribute to PM~2.5~ reductions.

```{=html}
<!-- -->
```
-   Some PM-ozone control cost overlap would result from that part of
    the costs of ozone controls related to NO~x~ reductions. However,
    even that overlap is unlikely to be complete because some NO~x~
    reductions are likely to fail to reduce nitrate PM concentrations.

The best examples where cost overlaps may occur are associated with
changes in process or in fuel. Most salient of the possibilities is
switching from oil, coal or wood fuel to natural gas. This control
action would reduce ozone-creating NO~x~ at the same time that primary
PM~2.5~ and possibly secondary organic aerosols and sulfates would be
reduced. It should be noted, however, that switching to natural gas is a
control action that does not feature prominently in either the PM or
ozone cost analyses, probably because it is not a generally
cost-effective measure for either pollutant on its own. If an area were
out of attainment with both standards, then gas switching could become
more relevant to the cost estimates, and thus represent a potential for
cost overlap in extrapolated cost estimates, even if not in the current
\"partial attainment\" cost estimates.

A combined cost range of \$90 billion to \$150 billion per year can be
viewed as accounting for the range of potential cost overlap, while
maintaining conservatism. On the low end, this total cost range is
comparable to the lower bounds of each individual range being additive,
or to a moderately low point estimate for PM of \$90 billion, while also
assuming a conservative 100 percent overlap with ozone control costs. At
the high end, the total cost range is comparable to assuming the
following types of cost combinations: that there is no overlap in costs,
with middling-estimates for the costs of both PM and ozone (e.g., \$120
billion for PM and \$40 billion for ozone); that ozone is high and PM
middling (e.g., \$60 billion for ozone and \$90 billion-120 billion for
PM), with varying degrees of overlap; or that PM is at the upper end of
its realistic range, but there is 100 percent overlap with ozone costs.

The next two sections of Part 1 demonstrate how these cost ranges are
justified as being realistic and conservative by the limited facts
provided to the public by EPA in its *Regulatory Impact Analyses*.
However, it is important to emphasize that these estimates are
extrapolations, not founded on a specific set of identified control
actions. To develop cost estimates with such specific underpinnings
would not be possible to do without access to EPA's models, as well as a
more extensive engineering analysis of control options than EPA has
performed. Nevertheless, it is useful to explore what economic impacts
might result from engaging in an emissions control program of the dollar
magnitude suggested above.

An economic impact analysis, using a widely recognized and validated
regional economic model of the United States, suggests that a major PM
and ozone-oriented control program of the sort that might cost \$90
billion to \$150 billion per year would likely have widespread impacts
throughout all regions and sectors of the economy. Assuming that the
government could be completely effective in eliminating national
employment impacts, *a program consistent with the \$90 billion
estimate* would:

-   Cause lasting (long-run) *average* reductions in the real after-tax
    income *per capita* on the order of \$250 per person, or about 1.2
    percent of their personal budget. The negative net impact on income
    levels reflects both estimated price mark-ups due to the control
    measures, and reduced growth from the diversion of dollars to less
    economically productive investments.

This disposable income reduction may sound small to some, but actually
creates substantial distributional impacts throughout the economy. For
example, even if the federal government were to be able to keep overall
unemployment rates in balance:

-   The largest job losses in the economy will tend to come from the
    service and retail sectors (e.g., restaurants, nonprofit
    organizations, household workers, personal services). Although the
    retail and service sectors contribute the least to the pollution
    that is being reduced, they suffer reduced demand because of the
    real decline in household incomes that is necessary to pay for the
    cleaner air. Consumers have to divert consumption away from
    discretionary expenditures, which affects primarily the service and
    retail sectors.

```{=html}
<!-- -->
```
-   Industries and manufacturing sectors that do not substantially
    participate in supporting pollution control activities also suffer
    job losses.

```{=html}
<!-- -->
```
-   There would be a projected increase of jobs in engineering-related
    services, and in some of the durables industries (e.g., machinery,
    metals fabrication, mineral products, chemicals). In other words,
    those sectors that provide substantial inputs to the pollution
    control activities will grow, even some of those sectors that will
    be carrying a large pollution control cost of their own.

```{=html}
<!-- -->
```
-   Concomitant with the sectoral shifts will be a substantial reduction
    in clerical, sales, and blue collar jobs (about 50,000 to 100,000
    jobs in each year) and an increase in professional, managerial, and
    construction-related jobs.

```{=html}
<!-- -->
```
-   Small establishments (less than 100 employees) will be more likely
    to experience job losses than larger ones.

```{=html}
<!-- -->
```
-   The above impact estimates explicitly include the beneficial
    economic impacts associated with building and maintaining pollution
    control equipment.

Again, all of the above impacts occur even when assuming that the
federal government would be able to successfully keep overall employment
in balance. However, one of the most important impacts of such a control
program would be the difficulty that it would pose for managing the
economy and maintaining stability. To achieve attainment by 2008 would
result in an enormous potential for net job increases during about a
five-year period (starting about 2002), followed suddenly (over the
space of just a couple of years, e.g., between 2008 and 2010) by an
equivalently large and long-term potential for net job losses. Given the
magnitudes of potential net employment effects, their rapid shift in
direction over the space of a few years, and given the many competing
objectives in setting monetary policy, it could be very difficult for
the federal government to effectively manage impacts of this nature,
even without the wide directional swings of the first decade.

Even if one assumes that federal monetary policy will be, for example,
about 90 percent effective in stabilizing unemployment rates, there is a
potential for the early (and temporary) job increases to be on the order
of about 100,000 jobs, and for later job decreases to involve up to
200,000 positions. Put in context with overall employment, this is a
small net effect. It amounts to about 0.1 percent of overall jobs in the
years after 2010 and would translate into about a 0.05 percent change in
the national unemployment rate. Also, the potential for net job losses
might be temporary, until the federal government can gradually steer the
economy back to its target. However, it is also conceivable that some
part of this reduced employment could be more enduring, since the total
effect represents only a fraction of a percentage point in the
observable national unemployment rate. Nevertheless, there are more
significant distributional impacts underlying this net national effect,
similar to but larger than those described above. The economic model
indicates the following quantitative economic effects in years after
2010, if the nation were to be experiencing a net loss of 200,000 jobs:

-   A reduction in the average real disposable per capita income
    amounting to a total annual loss of about \$275 per person.

```{=html}
<!-- -->
```
-   Behind this *average* annual real disposable income loss of \$275
    per person is a situation where individuals with little training or
    education will lose substantially more, and individuals in typically
    higher income categories will lose less, or may even experience
    increases in real income. Model results show that individuals in the
    top-paying 40 percent of jobs will experience net job growth and
    those in the bottom-paying 40--60 percent of jobs will see a net
    decline in jobs in their line of business.

```{=html}
<!-- -->
```
-   There would be a reduction of about 205,000 blue collar, sales, and
    clerical jobs, and an increase of about 5,000 professional,
    managerial, and construction-related jobs.

```{=html}
<!-- -->
```
-   Job reductions would be greatest in the service and retail sectors,
    and in industries that do not also serve the pollution control
    requirements (e.g., furniture, other wood-related products,
    petroleum, motor vehicles, printing, food).

```{=html}
<!-- -->
```
-   Of the total jobs lost, an estimated 85 percent will be associated
    with establishments with fewer than 100 employees. In the baseline,
    about 75 percent of establishments have fewer than 100 employees, so
    this represents a disproportionate impact. This disproportionate
    impact cannot be mitigated by making control requirements apply only
    to larger businesses because most of the disproportionate impact is
    due to losses in the retail and services sector, which are affected
    more by consumer spending impacts than by control requirements *per
    se.*

There is a potential for job *increases* during the years 2002--2006
when construction activities are at their peak (necessary to achieve
attainment by 2008), and O&M costs have yet to take full effect. This
potential also may be difficult to effectively offset through the
government's monetary policies. Should there be an increase in jobs due
to less than fully effective monetary policy, the model indicates that
the following distributional impacts could result:

-   There is still a projected loss in real disposable income, although
    its magnitude may be lessened during the implementation period.

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```
-   The income loss that does occur is likely to fall very
    disproportionately on the lowest income brackets: the model
    indicates net job *losses* in the lowest-paying 20 percent of job
    categories, while net job increases would tend to be shared by all
    other income groups.

```{=html}
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```
-   Some job losses in nondurables manufacturing, service, and retail
    would start to appear, although in a lesser degree than in the case
    of post-implementation years.

The effects of a regulatory program that has a cost estimate in the
range of \$150 billion per year would be qualitatively comparable, but
with impacts roughtly 75 percent larger. For example, the estimated
reduction in real disposable income per capita is estimated to decline
progressively to about \$450 dollars per year, starting from the initial
year of implementation, 2002. This would be a reduction of 2 percent of
incomes on average, with disproportionately greater impacts to lower
income groups.

The rest of this section first summarizes the details of the various
partial- and full-attainment cost estimates for ozone and PM separately.
A critical evaluation of the relative \"quality\" of each estimate is
provided, as well as the rationale for composite \"realistic\" cost
ranges. The section concludes with a more detailed summary of the
economic impacts described above.

 

**II. Ozone Control Costs**

Table 1-1 provides a summary of the reported cost estimates that were
identified and obtained for this analysis of the costs of the ozone
standard. The table summarizes some of the key assumptions in each
estimate, for purposes of comparison. Each of these estimates will be
discussed and critiqued in this section. In addition, DFI will provide a
few additional estimates based on the ones cited in Table 1-1.

The EPA RIA provides cost estimates for two different standards, neither
of which is actually consistent with the proposed standard. They are
supposed to bound the stringency of the actual standard, but the
proposed standard is probably more consistent with the more stringent
(higher cost) of the two. For each of these two standards, EPA also
provides the costs under two different scenarios for controls that will
already be in place for other purposes (such as to achieve the current
ozone standard). Since the United States has not yet determined how it
will achieve the current ozone standard, there is some degree of
uncertainty as to what should be assumed in the baseline. The \"local
control strategy\" assumes only those controls in the current state
implementation plans (SIPs). The \"regional control strategy\" assumes
that, in addition to the well-defined SIPs control baseline, the country
will also embark on a regional reduction of large NO~x~ sources
amounting to a 75 percent reduction in current emissions levels over 37
states, *and* that the California LEV vehicle will be used in all
states. Not surprisingly, the costs of achieving attainment when there
is a pre-existing regional control program of this magnitude is reduced
in some areas. Also, more areas actually come into attainment with this
boost from the baseline assumptions.

<table style="width:100%;">
<colgroup>
<col style="width: 55%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="11"><p>Table 1-1. Ozone Cost Estimates: Cost of compliance
with proposed ozone NAAQS</p>
<p>Cost expressed in 1990 dollars</p></td>
</tr>
<tr class="even">
<td>Label</td>
<td>A</td>
<td>B</td>
<td>C</td>
<td>D</td>
<td>E</td>
<td>F</td>
<td>G</td>
<td>H</td>
<td>I</td>
<td>J</td>
</tr>
<tr class="odd">
<td>Baseline w.r.t. Implementation of CAA Control Requirements</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Incremental to full implementation of CAA control requirements by
2007</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="odd">
<td>Baseline w.r.t. Ozone NAAQS Implementation</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Incremental to the current ozone NAAQS as of the year
2007</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="odd">
<td>Alternative Analyzed</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Eight hour conc based fifth highest avg daily max 0.08 ppm
(8H4AX-80)</p></li>
</ul></td>
<td>X</td>
<td> </td>
<td>X</td>
<td> </td>
<td>X</td>
<td> </td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Eight hour conc based third highest avg daily max 0.08 ppm
(proposed standard)</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Eight hour conc based second highest avg daily max 0.08 ppm
(8H1AX-80)</p></li>
</ul></td>
<td> </td>
<td>X</td>
<td> </td>
<td>X</td>
<td> </td>
<td>X</td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="odd">
<td>Level of Attainment</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Cost corresponds to partial attainment</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Cost corresponds to "full" attainment</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="even">
<td><ul>
<li><p>(Marginal costs used to model "full" attainment may represent
controls that do not exist)</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>Baseline w.r.t. Implementation of a Regional NO<sub>x</sub>
Strategy</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Regional control strategy, incremental to OTAG, CAPI, other
efforts</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X?</td>
<td>X?</td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Local control strategy</p></li>
</ul></td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>Marginal Attainment</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Marginal areas (115% of standard, 0.092 ppm) not subject to
further reductions</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Marginal areas subject to further reduction</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="odd">
<td>Reduction Targets</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Areas failing to meet at least 75% of reduction target labeled
residual nonattainment</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Areas failing to meet 100% of reduction target labeled residual
nonattainment</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="even">
<td>Bounds of Uncertainty Range (if provided)</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Cost estimate represents lower bound</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Cost estimate represents upper bound</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
</tr>
<tr class="odd">
<td>Results</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>No. of Initial Non-Attainment Areas</p></li>
</ul></td>
<td>30</td>
<td>75</td>
<td>30</td>
<td>75</td>
<td>30</td>
<td>75</td>
<td>30</td>
<td>75</td>
<td>75</td>
<td>75</td>
</tr>
<tr class="odd">
<td><ul>
<li><p>No. of Residual Non-Attainment Areas</p></li>
</ul></td>
<td>12</td>
<td>24</td>
<td>21</td>
<td>46</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr class="even">
<td><ul>
<li><p>NO<sub>x</sub> Emission Reduction (1000 tons)</p></li>
</ul></td>
<td>50</td>
<td>290</td>
<td>450</td>
<td>1100</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>VOC Emission Reduction (1000 tons)</p></li>
</ul></td>
<td>115</td>
<td>660</td>
<td>400</td>
<td>1200</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Percent Residual Non-Attainment (%)</p></li>
</ul></td>
<td>40</td>
<td>32</td>
<td>70</td>
<td>61</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Implementation Cost (billion $/year)</p></li>
</ul></td>
<td>0.6</td>
<td>2.5</td>
<td>2.2</td>
<td>6.3</td>
<td>9.1</td>
<td>83.1</td>
<td>53.8</td>
<td>328</td>
<td>11.6</td>
<td>60</td>
</tr>
<tr class="even">
<td><ul>
<li><p>Implementation Cost (billion $/year per nonatt. area)</p></li>
</ul></td>
<td>0.02</td>
<td>0.03</td>
<td>0.07</td>
<td>0.08</td>
<td>0.30</td>
<td>1.11</td>
<td>1.79</td>
<td>4.38</td>
<td>0.15</td>
<td>0.80</td>
</tr>
<tr class="odd">
<td><ul>
<li><p>(estimates A &amp; B do not yet include the cost of the RCS, this
should be added)</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>References</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>A ... EPA's December 1996 "RIA for Proposed Ozone NAAQS" ... page
VI-9</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>B ... EPA's December 1996 "RIA for Proposed Ozone NAAQS" ... page
VI-11</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>C ... EPA's December 1996 "RIA for Proposed Ozone NAAQS" ... page
VI-21</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>D ... EPA's December 1996 "RIA for Proposed Ozone NAAQS" ... page
VI-22</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>E ... Dudley's March 1997 "Comments ..." ... page C-3</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>F ... Dudley's March 1997 "Comments ..." ... page C-3</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>G ... Dudley's March 1997 "Comments ..." ... page C-4</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>H ... Dudley's March 1997 "Comments ..." ... page C-4</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>I ... CEA's December 1996 memo "Ozone NAAQS" ... page 3</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>J ... CEA's December 1996 memo "Ozone NAAQS" ... page 3</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>

The problem with comparing the cost estimates of these various scenarios
is that each one achieves a different amount of control. In effect, each
one represents a different position on the spectrum between no control
and controls consistent with full attainment. To facilitate comparison
of these costs, it is useful to estimate the degree to which each cost
scenario approaches full attainment. In the case of the ozone scenarios,
the ozone RIA provides an estimate of the tons of NO~x~ and VOC
reductions necessary to achieve attainment. After defining the degree of
residual nonattainment in terms of the number of tons remaining to be
reduced once all of the available cost estimates have been taken up,
Figure 1- 1 compares the annual implementation costs for these four
estimates as a function of degree of control. Using notation that may be
useful to those who are familiar with the RIAs, \"4AX-R\" and \"1AX-R\"
reflect the costs of the two bounding standards when the regional
control strategy is in place. \"4AX-L\" and \"1AX-L\" reflect the costs
of the same respective bounding standards when there is no regional
NO~x~ and VOC control scenario in the baseline.

Normally, one would expect to see that the annual costs would increase
with decreasing degree of residual nonattainment. However, that would
only be the case if these estimates were all starting from the same
baseline, which they are not. Since the costs of the regional control
strategy are not included in the incremental costs for attainment of the
proposed new standard, \"1AX-R\" and \"4AX-R\" are lower than \"1AX-L\"
and \"4AX-L,\" respectively. If the costs of the regional control
strategy were added to points \"4AX-R\" and \"1AX-R,\" then a more
typical control cost curve would emerge. For example, cost estimates of
the 75 percent seasonal NO~x~ reduction proposed under OTAG (0.15
lb/MMBtu cap) range from \$2.3 billion (1990\$) to \$5.5 billion
(1995\$). Cost estimates for the LEV program throughout OTAG range from
\$0.6 billion to \$6.2 billion annually. If these control measures were
attributed to \"4AX-R\" and \"1AX-R,\" those points would be raised by
about \$4 billion to \$11 billion, to the range of the bars shown above
them on Figure 1-1, which then present a figure consistent with the
expected increasing incremental cost. Thus, EPA's statement that the
local control strategy \"provides an upper bound to the anticipated
costs of the new ozone NAAQS in the event regional efforts fail to arise
before 2007\" is misleading: although it has higher *incremental* costs
than the regional control scenario baseline, it also provides much less
progress towards attainment. If the controls assumed in the regional
control scenario's baseline do not materialize, then the equivalent
control cost estimates *for a comparable degree of attainment* would be
more like the bars above points \"4AX-R\" and \"1AX-R\" in Figure 1-1.
And the full costs of attainment would be higher still, as is discussed
later.

An appropriate question with regard to whether to use \"4AX-R\" and
\"1AX-R\" or \"4AX-L\" and \"1AX-L\" is whether the cost of the regional
control strategy really should be attributed to the baseline or to the
proposed standard. Some commenters have argued against including the
regional control strategy in the baseline because these control
strategies are not currently mandated, and there is no clear legal basis
for such requirements. Even EPA's RIA acknowledges that their regional
control scenario is more stringent than all but one of the alternatives
that OTAG is currently analyzing. However, it does appear that, from an
attainment standpoint, a good part or all of the NO~x~ portion of these
control costs may be required to achieve the current standard, and
regardless of their current implementation status, should not be
attributed to the *change* in the standard that is being proposed now.
Therefore, if it were not for other major problems that plague *all* of
these cost estimates (and which are described below), it might be
reasonable to accept points \"1AX-R\" and \"4AX-R\" as a reasonable
basis for estimating the *incremental* costs of partial attainment,
rather than \"4AX-L\" or \"1AX-L.\" Further, it seems more reasonable to
use \"1AX-R\" specifically as being closer to a simulation of the
proposed standard. The proposed standard is a \"2AX\" standard, and is
thus probably more like the stringent 1AX form than the 4AX form.
Numerous uses by EPA of the costs of the \"1AX-R\" estimates suggest
that EPA may also be implicitly assuming that this standard is closer to
the proposed standard than the \"1AX\" scenarios.

The more important cost issue, however, is how the costs rise *out of*
the point \"1AX-R\" in the direction of full attainment of the new
proposed standard. The uncertainty on this point has greater impact on
the ultimate cost estimate than whether to accept the regional control
strategy as part of the effort of achieving the current standard. EPA's
cost analysis does not provide that estimate based on detailed modeling.
Instead, EPA's analysis of costs stops counting costs when it has used
up all of the control actions that EPA's analysts have specifically
identified and provided cost estimates for. The limits of this control
action list rather than attainment become the determining factor in the
costs reported in the ozone RIA.

This not only creates an artificial cap on degree of attainment
associated with the cost estimates, but also creates some very
nonsensical cost outcomes. For example, Los Angeles is one of the most
severe ozone attainment problems that must be contended with. Its
problem is so severe that it apparently has already used up all of the
control options that EPA has on its list. Thus, rather than being one of
the areas that will have the highest costs of going the extra mile to
achieve the proposed tightened standard, EPA's estimates attribute
*zero* incremental costs to the Los Angeles area (but many remaining
tons to be removed). Los Angeles, San Diego, and Bakersfield, Calif. do
not even generate incremental costs for attaining the *current*
standard. Instead, they just rack up zero-cost \"credits\" of \"tons per
year of residual nonattainment.\" For the estimated costs of achieving
the \"1AX-R\" standard, these three cities are joined by New York City
and 20 others that contribute relatively little to the total cost
estimate before starting to generate \"tons per year of residual
nonattainment.\" Overall, the costs in the \"1AX-R\" standard are for
950 tons of reduced NO~x~ and VOCs, and the same cost analysis racks up
495 to 753 tons of residual nonattainment that are not reflected in the
cost estimate of \$2.5 billion annually.

In EPA's 4-page *Fact Sheet on EPA's Regulatory Impact Analysis for the
Ozone* *Standard* (but not in its RIA or any other document), EPA
provides an estimated cost of full attainment of up to \$10 billion per
year. Although it is not documented, one can determine that this cost
estimate was based on an assumed \$10,000 per ton for reducing the
remaining 753 \"hard to achieve\" tons. This marginal cost assumption is
inconsistent with other evidence on how costly the last 40 percent of
the attainment effort may be. The marginal costs at the end of the list
of identified controls range up to \$80,000 (and even higher in a few
cases). This provides a different indicator of how costs may escalate
beyond the \$2.5 billion per year \"partial attainment\" estimate.

Several groups have used the information about the marginal control cost
and the remaining tons to be reduced to roughly estimate what the full
attainment cost is. In other words, where EPA estimates zero cost for
some areas like Los Angeles, the extrapolations have attempted to
approximate what it would really cost for \"the L.A.'s\" in EPA's
analysis to get into attainment. The actual control measures associated
with these actions are \"unidentified,\" but under current technological
conditions, it is only reasonable to suspect that on average these
measures will cost more per ton than those control measures that we can
easily identify. It is also likely that any of a range of \"lifestyle
changes\" may become the control measures of choice. While such changes
may have no technical price tag, it is reasonable to believe that our
societal willingness-to-pay to avoid taking these actions is at least as
high as all of the control actions that we are currently engaged in.
Otherwise, severe ozone nonattainment areas would be taking such actions
more enthusiastically already.

Existing estimates of the possible full attainment costs, including
those attributable to EPA, range from \$4 billion to \$328 billion
annually, although the estimates cluster around the range of \$35
billion to \$60 billion. Table 1-2 summarizes the extrapolation
assumptions associated with each estimate. These extrapolations come
from EPA's own estimates, from comments by the Council of Economic
Advisors (CEA) on the proposed rule, and from comments by the Regulatory
Analysis Program of the Center for Study of Public Choice at George
Mason University (RAP). Table 1-2 also shows additional estimates
prepared by DFI, based on our own assessment of appropriate
extrapolation assumptions, to be discussed below. The costs in this
table are also illustrated graphically in Figure 1-2.

  -------------- ---------- ------------------ -------------------------------- ------------
  Table 1-2.                                                                    
  Extrapolated                                                                  
  Estimates of                                                                  
  Cost of Full                                                                  
  Attainment for                                                                
  Proposed Ozone                                                                
  Standard.                                                                     

  Estimate       Baseline   Marginal Cost for  Other Changes in EPA Assumptions Cost of Full
  Source         Standard   Residual Tons                                       Attainment
                                                                                (\$b/yr)

  EPA-1          1AX-R      \$ 3,000/ton       none                             **4.0**

  EPA-2          1AX-R      \$10,000/ton       none                             **10.0**

  CEA-1          1AX-R      \$18,016/ton^††^   may have slightly altered        **11.6**
                                               estimated residual tons in some  
                                               cities                           

  CEA-2          1AX-R      \$74,058/ton^††^   may have slightly altered        **60.0**
                                               estimated residual tons in some  
                                               cities                           

  RAP-1          4AX-L      \$30,000/ton       none                             **9.1**

  RAP-1a         4AX-L      \$80,000/ton       none                             29 ^†^

  RAP-2a         1AX-L      \$30,000/ton       none                             25 ^†^

  RAP-2          1AX-L      \$80,000/ton       none                             83.1

  RAP-3          4AX-L      \$30,000/ton       remove 25% modeling bias         53.8
                                               assumption and 15% cap           

  RAP-3a         4AX-L      \$80,000/ton       remove 25% modeling bias         212 ^†^
                                               assumption and 15% cap           

  RAP-4a         1AX-L      \$30,000/ton       remove 25% modeling bias         87 ^†^
                                               assumption and 15% cap           

  RAP-4          1AX-L      \$80,000/ton       remove 25% modeling bias         **328.3**
                                               assumption and 15% cap           

  DFI-1          1AX-R      \$30,000/ton       mid-range on the estimate of     21
                                               residual tons per year           

  DFI-2          1AX-R      \$90,000/ton       mid-range on the estimate of     59
                                               residual tons per year           
  -------------- ---------- ------------------ -------------------------------- ------------

^†^ These costs are attributed to RAP but were estimated by DFI, working
from the results that are provided in the RAP report and their own
stated assumptions. It is our belief that these values were actually
calculated by RAP, but then lost when results were reported only in the
form of ranges. Values in bold face in Table 1-2 are the values that can
actually be observed in the reference.

^††^ These appear to be the costs per ton used by CEA. The CEA memo
indicates that they used a cost per ton for the second-most expensive
program averaged over three \"representative\" cities (Chicago, Fresno,
Philadelphia). That average value (from Table B-4, see our Appendix 1)
does not reproduce the range that CEA says results. However, the lowest
and highest values of those three second-most expensive programs
[do]{.underline} reproduce the CEA estimates. Although CEA would not
reply to our requests to discuss these numbers, the coincidence is
strong enough for us to assume that these are indeed the marginal costs
used in CEA's estimates.

The main difference between the EPA and CEA estimates appears to be a
difference of opinion about the appropriate estimate for dollars per ton
for all of the residual tons that need to be somehow removed to achieve
full attainment. EPA uses the costs per ton experienced across the bulk
of control actions that are part of partial attainment on the
presumption that additional controls, not yet identifiable, would be
found and used simply because they are less than the marginal cost of
\$30,000 to \$80,000 per ton that *is* experienced in the partial
attainment scenarios. To assume that additional \"unidentified\"
controls would have a cost comparable with the most cost-effective
controls now identifiable is unjustified, as CEA points out,
particularly since much higher control costs are experienced even in the
partial attainment scenarios.

CEA recomputes the costs of full attainment with a cost per ton for the
residual tons that it believes is more justified. For their lower bound,
CEA uses a conservative estimate of the marginal cost that is embodied
in EPA's partial attainment scenarios by looking at the second most
extreme cost estimate in several of EPA's cities. Their upper bound
(resulting in the \$60 billion estimate) is associated with a higher
marginal cost assumption, consistent with the assumption that the
highest marginal costs in the partial attainment scenarios reflect the
true marginal costs. CEA apparently also adjusts for what appear to be
computational errors on the part of EPA, where residual tons required
are reported as zero for some areas that are clearly far from
attainment. Overall, the extrapolations by CEA are more realistic than
those of EPA, and are quite conservative at their low end. Even at the
high end, one could argue that there may be an element of conservatism:
in some cases, the marginal costs for some of the cities for the 1AX-R
standard are in fact much higher than the \$80,000 per ton that EPA
cites. (This can be seen from information in Table B-4 of the ozone RIA,
which is reproduced in marginal cost format for the reader in Appendix 1
of this report).

RAP comments that the local control scenarios are a more appropriate
starting point, given the current absence of any mandate for a regional
control program. RAP provides two separate ranges for extrapolated costs
of full attainment. The first (\$9 billion to \$84 billion) is very
similar in method to the CEA extrapolation except that it uses the local
control scenarios as the starting point, and uses a slightly higher
range for the possible marginal costs of control, based on EPA's own
statement about the range of marginal costs. For these reasons, the
range extends to a higher level than the CEA estimate. On the other
hand, RAP includes the less stringent \"4AX-L\" standard in this range,
which accounts for the lower end of the range being below CEA's, even
though RAP is using higher marginal cost assumptions. The estimated
range specifically for the \"1AX-L\" standard is \$25 billion to \$83
billion.

The second range of estimates provided by RAP (\$54 billion to \$328
billion) attempts to account for another source of underestimation in
the EPA estimates. RAP points out that the definition that EPA uses for
\"full attainment\" is likely to be an understatement, since EPA assumes
that no additional control costs are undertaken once any nonattainment
area gets within 15 percent of its target. RAP also takes issue with an
\"off the top\" 25 percent reduction in model-estimated tons required to
get to attainment, which EPA makes to address asserted (but poorly
documented) bias in the ozone model. The latter cost range redefines
full attainment as requiring the full amount of reduction in tons that
the model estimates as necessary, and attempts to add back into the
residual nonattainment tonnage an estimate of the tons that would be
associated with the 15 percent \"marginal attainment.\"

DFI authors feel that adjustments for model bias, while poorly
documented on the part of EPA, would be reasonable to make if EPA can
show that the model truly does have a persistent upward bias. On the
other hand, it does not seem appropriate for EPA to assume no further
control costs (other than minor administrative ones) whenever any region
gets to the marginal attainment status. However, a close reading of the
RIA and information gathered from EPA staff indicate that the estimates
of residual tons of nonattainment cannot be used to estimate how many
tons of residual nonattainment are associated with areas that were
designated as \"marginal\": no tonnage reductions were ever estimated
for the 35 to 54 counties (for the regional and local control cases,
respectively) that were found by modeling to be within 15 percent of
attainment prior to control estimates. The remaining counties for which
tonnage estimates were developed apparently did not benefit from a
marginal attainment status once they were modeled to be within 15
percent of the required tonnage reduction. Overall, these concerns
suggest that the higher set of RAP's cost extrapolations are overly
conservative.

A realistic estimate of the full attainment costs, given the limited
information currently available to the public, will depend primarily on
making a sound judgment regarding the marginal cost of the controls
necessary to bring all of the residual nonattainment and marginal
attainment areas into compliance. Thus this analysis focused on
determining whether the range of marginal cost estimates used in the
above estimates could be refined further. Data in Appendix B of EPA's
RIA (see Appendix 1 of this report) were used to graph the marginal cost
curves for three typical cities that have residual nonattainment. Figure
1-3 shows the marginal costs as a function of the percent of all
controls explicitly identified by EPA. In Figure 1-3, one can see how
the marginal costs remain under about \$10,000 per ton until the last 10
to 20 percent of the tons reduced. At that point, marginal costs
dramatically escalate in each nonattainment region.

EPA makes two arguments for why costs would not actually escalate as
rapidly as the marginal cost curves of Figure 1-3 indicate.

-   EPA argues that control cost programs are always cheaper than
    anticipated, and also implies that emissions trading would keep
    costs at the average that we see in their data, not at the margin.
    This is not a compelling argument. The control costs have already
    been selected in least-marginal cost order, which is consistent with
    having assumed that emissions trading is occurring. Further, the
    fact that control costs in the past have been less than projected in
    exercises such as these does not imply that actual control costs
    have turned out to be *as cheap as* the cheap actions that have
    already been taken. Costs do still tend to escalate as the
    stringency of the control program has increased.

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-   EPA argues that the control costs in its RIA have been estimated by
    using only those control options that exist literally *within* the
    defined area of nonattainment. EPA argues that, ultimately, control
    options will be taken from a wider area, particularly nearby
    counties that do influence the nonattainment region, but are not
    themselves formally in nonattainment. This does provide a sound
    argument for assuming that cost curves will not escalate exactly as
    shown in Figure 1-3. Without assuming that a truly regional control
    strategy would ensue, states do already have the ability to obtain
    controls from other sources within their own state, even if such
    sources are in attainment areas of the state. The control options in
    the surrounding areas may not be as effective in reducing
    nonattainment area ozone, however, and thus one should still expect
    marginal costs to increase, but in a more measured degree than the
    case where controls are constrained to the nonattainment area
    specifically.

The solid dashed lines in Figure 1-4 show more moderate marginal cost
extrapolations, which are consistent with current data, but still
accommodate the likelihood that there are additional controls in
surrounding areas of influence that are more cost-effective than what
remains within the region. Although there remains much uncertainty about
the exact marginal cost, these costs are far more conservative than
simply assuming that all of the additional tons will cost as much to
reduce as the last control measure identified solely within a
nonattainment region's boundaries. On the other hand, they do not simply
assume that the remaining residual tons can be reduced as easily as the
first few.

These cost curve extrapolations are applicable at the level of the
residual nonattainment area, rather than for the total residual tonnage
at the national level, which is all that EPA reports in its RIA. Using
them thus also requires an understanding of how much *more* control is
needed in each of the 24 residual nonattainment areas associated with
the 1AX-R scenario to achieve attainment. EPA has not released
sufficient information to determine this for any of the cities
individually. However, the RIA does indicate that the total residual
tons necessary over all of the 24 remaining nonattainment regions (after
accounting for the 75 percent model bias) is 495,000 to 753,000 tons per
year. The RIA also indicates that these 24 areas have only achieved
reductions in the partial attainment scenario on the order of 293,000
tons. In other words, the partial attainment cost estimate seems to only
carry the 24 areas that are deemed to remain in nonattainment an average
of about one-third of the way to attainment. If we assume that 90
percent of the additional tonnage that needs to be reduced is associated
with the four worst nonattainment areas (those which cannot even achieve
the current standard), then an additional 293,000 tons would reflect an
average of a 20 percent incremental reduction beyond the 100 percent
point in Figure 1-3 for the other 20 residual nonattainment areas. Using
the two heavy-dashed lines in Figure 1-3, the average incremental cost
associated with another 20 percent of tonnage reduction would range from
\$30,000 to \$60,000 per ton. If we were to reduce the assumption that
90 percent of the national residual tons are associated with the four
worst cities to 80 percent, then the remaining 20 cities would require
an average 40 percent incremental reduction from the 100 percent point,
which the dashed lines imply could cost from \$40,000 to \$90,000 per
ton.

As Table 1-2 shows, even if one applies these lower marginal costs to
the remaining 80 to 90 percent of the residual tons that are associated
with the four cities that cannot even achieve the current standard, full
attainment costs could be between \$20 billion to \$59 billion per
year*.* Note that although these cost estimates are similar to those of
other groups, they were derived by much more detailed efforts to
simulate the actual cost structure, and at each step of the analysis, a
substantial degree of conservatism was built into the estimates. Thus, a
cost range between \$20 billion and \$60 billion is quite realistic and
probably even conservative:

-   To the extent that the actual costs might be higher than this range,
    this would most likely be due to costs associated with the four
    worst-case cities, which are treated in this extrapolation as if
    they would have marginal control costs comparable to what we have
    estimated for the other, less problematic nonattainment areas. This
    is probably a major source of conservatism. The amount of residual
    nonattainment in these four areas, when including their difficulties
    in achieving even the *current* standard, is between 500,000 and
    1,000,000 tons per year, even assuming that they would also benefit
    from a major regional control strategy. It is probably best to
    surmise that these four areas would remain in nonattainment even
    with the controls implicit in the \$20 billion to \$60 billion per
    year range, unless there are substantial changes in infrastructure
    and individual patterns of behavior. The latter changes are
    difficult to quantify except through use of concepts such as
    willingness to pay to avoid these measures. The evidence available
    on this avoidance value is that it appears to currently be higher
    than the marginal control costs currently being experienced, since
    our society has resisted these measures in favor of the quite costly
    technological controls that are the subject of the current
    extrapolations.

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-   This range also fails to account for the 35 \"marginal\"
    nonattainment areas, for which EPA assumed almost zero control
    costs. However, these 35 areas would likely have much lower marginal
    costs as well as fewer tons to reduce. Assuming the average marginal
    nonattainment area would have marginal costs in the range of \$2000
    per ton, and an average per area reduction requirement comparable to
    the residual requirements of 24 residual nonattainment areas of the
    \"1AX-R\" scenario (probably a significant overestimate), the total
    cost for these areas would be only \$2 billion per year. This is
    clearly a small error in comparison to the potential costs
    associated with the less marginal nonattainment areas.

 

**III. Particulate Matter Control Costs**

As with ozone, the only publicly available cost estimates for the
proposed PM~2.5~ standard are those prepared by EPA, and those costs are
for a \"partial attainment\" outcome, when in fact, the costs of
interest to the public should be the full attainment costs. The majority
of this section will also deal with the question of how to extrapolate
the available cost estimates to a full attainment cost. However, there
is a larger record of alternative cost estimates for PM~2.5~, based on a
record in the PM regulatory docket of a series of cost modeling
exercises leading to the actual cost presented in the published RIA.

Before turning to the issue of full attainment extrapolations, a review
of all of the various cost estimates that EPA has produced for PM~2.5~
is made. This review can shed some light on the differences and
similarities in this series of cost estimates, and perhaps narrow some
of the uncertainty regarding the appropriate starting point for
extrapolations.

Table 1-3 compares the assumptions for all the PM cost estimates that
were identified and obtained. The annual cost estimates for \"partial
attainment\" vary from \$4.3 billion to \$17.1 billion. The PM RIA gives
\$6.3 billion as its official estimate of the costs (\"A\" in Table
1-3), although in the same document it suggests that the costs may be as
low as \$4.3 billion (\"b\" in Table 1-3). The latter cost estimate is
supposed to reflect the \"potential impact of additional regional SO~2~
emissions reductions beyond the CAA Title IV requirements.\" However, it
is incorrect to thus suggest that the cost range may be as low as \$4.3
billion. As with ozone, this sets up a situation where the cost curve
appears to be *falling*, when in fact, there are additional costs
associated with the starting point for the lower cost estimate. The
underlying cost of this \"additional SO~2~ control\" program are
anywhere from \$2.2 billion to \$4.8 billion. Unlike in the case of
ozone and its \"regional control scenario,\" this underlying cost
*should* be included in the total estimated cost of the PM standard,
since there is no existing regulatory program or standard that would
mandate this action other than the proposed PM~2.5~ standard itself.
Thus, the appropriate cost for the \"additional SO~2~ control\"
alternative is between \$6.5 billion and \$9.1 billion, and is thus
actually higher than EPA's primary cost estimate of \$6.3 billion.

Many commenters have noted that all earlier drafts of EPA's cost
estimates (as revealed by other documents in the PM Docket) indicated
annualized costs significantly higher than the \$6.3 billion per year in
the PM RIA. However, it is difficult to directly compare these estimates
because they vary in terms of the degree to which they approach full
attainment. Thus, as with the ozone costs, it makes sense to first
assess the degree of residual nonattainment in each estimate. Unlike
with ozone, the PM RIA does not provide a good common metric for
estimating the amount of further reductions needed. There are two
possibilities. The first is to compare the number of counties out of
attainment before and after the controls are applied. The second, which
would be more like the concept of residual tons to reduce for ozone,
would be to use the residual and initial μ g/m^3^ of nonattainment over
all of the areas to determine how much progress there is towards full
attainment. In the case of PM, EPA has not released the necessary
information to perform the latter with much precision. Thus, for
purposes of comparison across various cost estimates, PM cost estimates
were normalized for their degree of attainment using the former
definition based on counties that achieve attainment.

An additional adjustment is necessary before the estimates can be
compared. Some of the estimates in Table 1-3 are based on a list of
nonattainment areas as defined by EPA's air quality model. Later, EPA
limited the areas that need to spend money to achieve attainment to only
those areas that both the model shows to be in nonattainment in 2007,
*and* where there is sufficient current monitoring data to calibrate the
model with local air quality information. This had the effect of cutting
the number of areas that would require control action costs by about
one-half; it also reduced total control cost estimates. To account for
this other key difference, the various cost estimates are also adjusted
to a \"per initial nonattainment county\" basis.

Figure 1-5 plots the resulting costs using the adjustments just
described to improve their comparability. One finds that there does
appear to be a roughly increasing cost per county with increasing degree
of attainment. One also finds that the earlier EPA cost estimates are in
fact *lower* in overall costs after accounting for the decision to only
attribute control actions to the needs of counties that currently have
good monitoring data. Based on a reading of the other modeling
adjustments between the draft and final EPA cost estimates, there is
good reason to believe that the main reason for the underlying increase
in cost per county is related to massive adjustments to reduce the
emissions inventory for dusts.

Unfortunately, these cost estimates do not provide strong evidence for
how to extrapolate to the costs of full attainment. Almost any value
could be obtained based on the fitting of a range of linear to highly
nonlinear curves through these points and then estimating where this
curve would cross the vertical axis. More information is needed from
additional sources on how marginal costs may increase. Although they are
not to be found in the PM RIA, EPA has itself generated two estimates of
the full attainment cost, which are listed as G and H in Table 1-3.
(These correspond to a \$19 billion and \$8.7 billion per year estimate,
respectively).

 

 

<table style="width:100%;">
<colgroup>
<col style="width: 61%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
<col style="width: 4%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="10">Table 1-3. PM Cost Estimates Cost of compliance with
proposed PM<sub>2.5</sub> NAAQS (Cost expressed in 1990 dollars)</td>
</tr>
<tr class="even">
<td>Label</td>
<td>A</td>
<td>b</td>
<td>B</td>
<td>C</td>
<td>D</td>
<td>E</td>
<td>F</td>
<td>G</td>
<td>H</td>
</tr>
<tr class="odd">
<td>Baseline w.r.t. Implementation of CAA Control Requirements</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Incremental to CAPI (i.e., 50% reduction in utility
SO<sub>2</sub> emissions beyond Title IV rqmts)</p></li>
</ul></td>
<td> </td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Incremental to full implementation of CAA control requirements by
the year 2007</p></li>
</ul></td>
<td>X</td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="even">
<td><ul>
<li><p>Incremental to full implementation via CAPI (i.e., 50% reduction
beyond CAA rqmts)</p></li>
</ul></td>
<td> </td>
<td> </td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>Baseline w.r.t. PM NAAQS Implementation</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Incremental to the current PM<sub>10</sub> NAAQS as of the year
2007</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="odd">
<td>Alternative Analyzed</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>PM<sub>2.5</sub> 15 ug/m3 annual</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>PM<sub>2.5</sub> 15 ug/m3 annual, 50 ug/m3 24-hour</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td>X</td>
<td>X</td>
<td> </td>
<td>X</td>
<td>X</td>
</tr>
<tr class="even">
<td><ul>
<li><p>CAPI (i.e., 50% reduction in utility SO<sub>2</sub> emissions
beyond Title IV rqmts)</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>Level of Attainment</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Cost corresponds to partial attainment</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Cost corresponds to "full" attainment</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
</tr>
<tr class="even">
<td><blockquote>
<p>(Marginal costs used to model "full" attainment may represent
controls that do not exist)</p>
</blockquote></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>Exogenous Limits on Cost Effectiveness</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>Set cap on control measure cost at $1 billion per ug/m3
reduced</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Set cap on control measure cost at $2 billion per ug/m3
reduced</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>No cap on control measure cost</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
</tr>
<tr class="odd">
<td>Identification of Initial Non-Attainment Areas</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>From subset of 470 counties with PM<sub>10</sub> monitoring,
predict PM<sub>2.5</sub> exceedences</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Perform concentration modeling for all counties</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>Regionwide/Local Control Measure Constraints</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>In areas exceeding the standard, attainment is sought through
...</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>... control measures within that nonattainment area only
(constrained)</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td>X</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td><ul>
<li><p>... nonutility control measures within area only, utility control
measures regionwide</p></li>
</ul></td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>... control measures throughout the region
(unconstrained)</p></li>
</ul></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td> </td>
<td> </td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
</tr>
<tr class="odd">
<td>Results</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td><ul>
<li><p>No. of Initial Non-Attainment Areas (i.e., counties)</p></li>
</ul></td>
<td>126</td>
<td>126</td>
<td>126</td>
<td>126</td>
<td>510</td>
<td>510</td>
<td>438</td>
<td>126</td>
<td>126</td>
</tr>
<tr class="odd">
<td><ul>
<li><p>No. of Residual Non-Attainment Areas (i.e., counties)</p></li>
</ul></td>
<td>57</td>
<td>53</td>
<td>53</td>
<td>97</td>
<td>378</td>
<td>216</td>
<td>168</td>
<td>0</td>
<td>0</td>
</tr>
<tr class="even">
<td><ul>
<li><p>Percent Residual Non-Attainment (%)</p></li>
</ul></td>
<td>45</td>
<td>42</td>
<td>42</td>
<td>77</td>
<td>74</td>
<td>42</td>
<td>38</td>
<td>0</td>
<td>0</td>
</tr>
<tr class="odd">
<td><ul>
<li><p>Implementation Cost (billion $/year)</p></li>
</ul></td>
<td>6.3</td>
<td>4.3</td>
<td>8.9</td>
<td>4.6</td>
<td>11.3</td>
<td>17.1</td>
<td>17.1</td>
<td>19</td>
<td>8.7</td>
</tr>
<tr class="even">
<td><ul>
<li><p>Implementation Cost (billion $/year per nonatt. area)</p></li>
</ul></td>
<td>0.05</td>
<td>0.03</td>
<td>0.07</td>
<td>0.04</td>
<td>0.02</td>
<td>0.03</td>
<td>0.04</td>
<td>0.15</td>
<td>0.07</td>
</tr>
<tr class="odd">
<td>References</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>A ... EPA's December 1996 "RIA for Proposed PM NAAQS" ... pages 7-9
&amp; 7-12</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>A ... Pechan's September 1996 "Addendum to Complemental ...." ...
page 6</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>b ... EPA's December 1996 "RIA for Proposed PM NAAQS" ... pages
7-19</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>B ... EPA's December 1996 "RIA for Proposed PM NAAQS" ... pages 7-17
&amp; 7-19</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>C ... EPA's "Acid Deposition Standard Feasibility Study Report to
Congress"</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>D ... Pechan's May 1996 "Complemental National Cost Analysis ...."
... page 10</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>E ... Pechan's May 1996 "Complemental National Cost Analysis ...."
... page 28</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>F ... Pechan's January 1996 "Supplemental National Cost Analysis
...." ... page 30</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="even">
<td>G ... EPA Fact Sheet for PM RIA</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr class="odd">
<td>H ... Vatavuk's November 1996 memo "Full Attainment Cost
Estimates"</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>

 

Estimate H was found in the EPA PM Docket and does not appear to have
been cited by EPA in any public communications. DFI reviewed the basis
for this calculation and determined that the method of extrapolation is
flawed. The fact that EPA has not made use of this number probably
indicates that EPA also understands the flaws. Because of the flaws,
this estimate is not used or referred to further in this analysis.

The other EPA-generated full attainment estimate (estimate G) appears in
EPA's 5-page *Fact Sheet on EPA's Regulatory Impact Analysis for the
PM~2.5~ Standard*. It does not appear in the RIA itself, and seems to be
completely undocumented. However, the RIA does provide a table showing
low, high, and average concentrations over all the 57 residual
nonattainment counties before and after implementation to partial
attainment. The residual nonattainment information in that table is
reproduced here in Table 1-4. If one sums the *average* concentration in
each region, the result is 13 μg/m^3^. This is a fairly meaningless
number since there are actually 57 residual nonattainment counties, each
with its own μg/m^3^. However, EPA's marginal cost for each of the 57
counties at the cost \"stopping point\" was \$1 billion per μg/m^3^. If
this marginal cost is multiplied by the 13 μg/m^3^, and added to the
\$6.3 billion, one obtains \$19 billion. Thus we assume that this is the
EPA extrapolation method. EPA asserts in its *Fact Sheet* that this \$19
billion per year is thought to be an overestimate of the likely full
attainment cost for the proposed PM standard. Several other groups have
noted the flaws in the EPA approach. We have added a few to the list:

-   The marginal costs *in any single county* increase dramatically to
    levels such as \$20 billion or \$30 billion per μg/m^3^.

-   The total cost before all controls are exhausted is \$19.6 billion
    in the Midwest/Northeast region and \$3.4 billion per year in the
    Rocky Mountain region. There are five more regions unaccounted for.

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-   There are up to 17 nonattainment counties in most of the regions.
    Although these counties may all be linked as a single nonattainment
    area, EPA provides no information at all on their identity or
    geographical relationships.

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```
-   The 13 μg/m^3^ is only an average over all of the nonattainment
    counties in each region. In fact, the residual μg/m^3^ of
    nonattainment is much higher than the average in some regions. (If
    the worst county's residual μg/m^3^ were added up in each county,
    the 13 μg/m^3^ would be 30 μg/m^3^.)

  ------------------- ------------ ---------------- --------------- --------------
  Table 1-4. Data                                                   
  Provided by EPA on                                                
  Residual                                                          
  Nonattainment                                                     
  Associated with its                                               
  \$6.3 billion per                                                 
  year Cost Estimate                                                
  (partly from Table                                                
  7-4 of the PM RIA)                                                

                      Number of                     Annual PM~2.5~  
                                                    Conc. Achieved  

  Modeling            Residual                      Under Partial   
                      Counties                      Attainment      
                                                    Scenario        

  Regions             Monitored    All Modeled      Average         Maximum

                      Subset       Counties^\*\*^   (μg/m^3^)       (μg/m^3^)

  Midwest/Northeast   10           60               17.8            21.8

  Rocky Mountain      16           36               17.9            22.6

  South Central       1            60               15.1            15.1

  Southeast           1            14               15.1            15.1

  Northwest           11           21               16.9            19.0

  West                17           23               16.2            17.1

  California\*        1            2                19.4            24.0

  Total               57           216                               
  ------------------- ------------ ---------------- --------------- --------------

\* Residual counties in coastal California are assigned to the
California region, residual counties in the remainder of the state are
assigned to the West region.

\*\* Data on number of residual modeled counties is NOT from the RIA.
This is associated with an earlier costs analysis that is available in
the PM Docket.

 

The above list of flaws appears to undermine the suggestion that the
\$19 billion estimate is a realistic full attainment cost. Table 1-5
shows the range of estimates that have been generated by various groups
who have tried to make use of the glimmerings of information that EPA
has released regarding the actual degree of residual nonattainment, and
of its cost modeling results. In attempting to improve the estimates,
DFI made verbal requests to EPA for additional detail on the data
regarding where the residual nonattainment counties are, and what the
residual μg/m^3^ actually is for each of the 57 counties. EPA affirmed
that it does not wish to give out any further information. Ultimately,
therefore, any extrapolations must be based on only this limited set of
information.

Of course, the most appropriate way to determine the full attainment
costs would be to simply run the PM Optimization Model without the
arbitrary cost cut-off of \$1 billion per μg/m^3^ of reduced PM and find
out what the total cost might increase to (as well as whether attainment
might even be feasible within the currently identified set of controls
in the model).

In the case of the ozone full attainment costs, DFI developed estimates
that were similar in magnitude to those found by other analysts. Table
1-5 shows that in the case of PM, DFI has concluded that the potential
control costs could reasonably be expected to be higher than any of the
other cost estimates identified. However, the American Petroleum
Institute (API) estimates are stated as lower bound, based only on costs
of control options that we know are actually in the database of EPA's
cost model. As API points out, this estimate is too low because it does
not reflect any additional costs for five of the seven regions. The
comments submitted by RAP made the same error as the EPA estimates in
assuming that the MC would only need to be applied to a single average
county in each region. On the other hand, RAP's choice of marginal cost
(\$4.28 billion per μg/m^3^) is difficult to justify. It is based on an
average control cost for a single city that itself would not have to
experience that average cost to achieve its own attainment.

  -------------- ---------------------------------------------- -------------
  Table 1-5.                                                    
  Ranges of Full                                                
  Attainment                                                    
  Cost Estimates                                                
  (Each estimate                                                
  has the \$6.3                                                 
  billion added                                                 
  into it for a                                                 
  total full                                                    
  attainment                                                    
  cost; many                                                    
  sources report                                                
  only the                                                      
  incremental                                                   
  cost after the                                                
  \$6.3                                                         
  billion.)                                                     

  Source         Method                                         Full
                                                                Attainment
                                                                Cost Estimate
                                                                (\$billion
                                                                per year)

  EPA Fact Sheet Multiply residual nonattainment for average    \$19 billion
                 county in each region by MC cap                

  API Comments   Take maximum cost that model could produce for \$31.5
                 each region, knowing it is still insufficient  billion
                 to meet standard                               \"+?\"

  RAP Comments   Multiply residual nonattainment for average    \$61.3
                 county in each region by model's maximum       billion
                 average cost from E. Laich letter (i.e.,       
                 \$4.28 billion per μg/m^3^)                    

  DFI \"lower    Multiply residual nonattainment for county     \$36 billion.
  bound\"        that needs to make the greatest residual       
                 reduction by MC cap                            

  DFI            Varying assumptions (see below) regarding      \$70
  \"reasonable   interactions among counties within a region in billion-150
  range\"        their attainment efforts and the possibility   billion
                 that the actual number of residual             
                 nonattainment areas may be as high as that     
                 shown by EPA's PM air quality modeling, rather 
                 than just based on where monitors with good    
                 data currently exist; in all cases, assume     
                 conservatively MCs do not escalate beyond the  
                 \$1 billion per μg/m^3^ cap.                   
  -------------- ---------------------------------------------- -------------

 

The basis for DFI's \"reasonable\" range of full attainment costs is
explained in more detail below. Briefly, it is reasonably conservative
because:

-   It assumes that marginal costs will not increase beyond the level
    that they have already reached in the partial attainment scenario.
    This is like assuming that the control measures not identified and
    included in EPA's model will be more cost-effective than the control
    measures that are in EPA's model but are not selected because they
    exceed a pre-determined marginal cost cap. At the same time, the
    estimate does not assume that there exist many measures that failed
    to be identified in EPA's engineering analysis as viable PM control
    options, yet which will be as cost-effective as those that have been
    more readily identified.

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-   Even though this estimate addresses the fact that there may be many
    more nonattainment counties than EPA has included in its final RIA
    cost analysis, this cost range is still justifiable on the basis of
    only the 57 residual nonattainment areas summarized in Table 1-4
    above. Consideration of potential additional nonattainment areas
    only serves to support the judgment that the upper bound is not
    excessively pessimistic.

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-   The estimate gives substantial credit in our estimation to the
    chances that many actions will provide air quality improvements
    simultaneously to multiple nonattainment areas.

 

***A. DFI's Estimation of Costs of Full Attainment of Proposed PM~2.5~
Standard***

To estimate the cost associated with meeting the proposed PM~2.5~
standard, EPA used an optimization model designed to determine the PM
precursor control measures that would let each nonattainment county meet
the proposed standard at the lowest cost. A critical constraint used in
the PM optimization model is the cost cap on control measures, which is
set at \$1 billion per μg/m^3^ reduced in at least one of the counties
not yet in attainment. This means that if all control measures below the
cost cap are exhausted in an effort to bring a particular county into
attainment, it is assumed that nothing more will be done to help the
county meet the standard, thus labeling it a residual nonattainment
county.

In estimating the cost of full attainment, a particularly significant
source of uncertainty involves control measures which have not been
identified but which will become necessary to advance from partial to
full attainment. EPA has attempted to shed some light on the cost of
pushing residual nonattainment counties from partial to full attainment
by noting that the marginal cost of control may be driven down through
the emergence of more cost-effective control measures that may be
identified in the future. Conversely, EPA points out that the marginal
cost of control would run significantly higher than \$1 billion per
μg/m^3^ in the event that residual nonattainment can be eliminated only
by control measures similar to those that have already been identified.

The lack of firm information relating to the cost-effectiveness of these
unidentified controls suggest that a reasonable middle ground would be
to set marginal cost at the level it reaches in the partial attainment
case, but to not let this marginal cost escalate, as it does even among
EPA's list of identified control measures. Given that the cost cap in
EPA's PM optimization model has been set at \$1 billion per μg/m^3^ of
PM reduced and that all control measures below the cost cap have been
exhausted for those counties in residual nonattainment, it is reasonable
to view this marginal cost of control, \$1 billion per μg/m^3^ reduced,
as a conservative estimate (i.e., more likely to understate cost) for
those unidentified control measures that will become necessary to
proceed from partial to full attainment, especially if attainment
actions must be implemented by 2008 at the latest.

In an effort to arrive at an estimate of the full cost of meeting the
proposed PM~2.5~ standard, it is also necessary to establish the degree
of residual nonattainment by estimating the additional concentration
reduction required to bring residual nonattainment counties into full
attainment. EPA very roughly describes the distribution by region of the
annual PM~2.5~ concentration achieved in the residual nonattainment
counties under the partial attainment scenario (see Table 1-4). These
results relative to the proposed 15 μg/m^3^ standard represent the best
available information regarding the additional concentration reduction
needed to bring the residual nonattainment counties of each region into
full attainment.

Estimating the number of residual nonattainment counties once the
cost-effective control measures have been exhausted is also critical to
estimating the cost of full attainment. By applying its air-quality
modeling methodology to the subset of counties that are currently
monitored as part of the PM~10~ monitoring network, EPA identifies 126
initial nonattainment counties broken down into their respective
regions, 57 of which remain as residual nonattainment counties after
making use of all control measures below the cost cap. Documented in the
PM docket, earlier modeling results for which the scope of analysis is
not restricted to monitored counties provide a significantly different
picture of nonattainment. Based on air quality modeling applied to all
counties in the contiguous United States, reported results indicate that
510 counties initially fail to meet the proposed standard, 216 of which
remained as residual nonattainment counties after exhausting all control
measures below the cost cap. The number of residual nonattainment
counties for each of the individual modeling regions as reported in the
referenced documentation, and the corresponding degree of residual
nonattainment, can also be seen in Table 1-4 above. The key reason for
the rosier picture of initial nonattainment in the RIA is that EPA chose
to ignore over half of the counties even though its model, which is
being used expressly to assess nonattainment problems, indicated that
they too may present nonattainment difficulties.

With these rough estimates of the degree of residual nonattainment, the
number of residual nonattainment counties, and the marginal cost of
control measures necessary to advance from partial to full attainment,
it is possible to develop a range of estimates representing the full
cost of meeting the proposed PM~2.5~ standard. For each region, the
residual nonattainment county furthest from attainment after exhausting
all control measures below the cost cap will require the greatest
additional concentration reduction. That is, it must be reduced from the
maximum PM~2.5~ concentration achieved under the partial attainment
scenario down to the 15μg/m^3^ annual standard. Using the information in
the last column of Table 1-4 one can see that the total cost to
accomplish this for all of the regions, assuming a marginal control cost
of \$1 billion per μg/m^3^ reduced, is approximately \$30 billion.

The control measures employed to bring about full attainment for the
residual nonattainment county furthest from the standard should
reasonably be assumed to produce some collateral concentration reduction
for other residual nonattainment counties in the region. In addition,
subsequent efforts to bring the remaining residual nonattainment
counties into full attainment are also likely to produce some collateral
concentration reduction for other residual nonattainment counties in the
region. Because it is difficult to gauge the extent to which counties
experience collateral benefits due to control measures employed to bring
other counties into full attainment, a range of assumptions is
considered to help develop a plausible range of estimates representing
the full cost of meeting the proposed standard.

As an example, a 50 percent improvement towards attainment may be
experienced by other residual nonattainment counties due to control
measures employed to achieve full attainment for the single worst case
county in the region. Applying a collateral reduction percentage of 50
percent to the remainder of the subset of monitored counties identified
as residual nonattainment counties results in a total cost for all of
the regions of approximately \$54 billion, again assuming a marginal
control cost of \$1 billion per μg/m^3^ reduced. The sum of the cost of
partial attainment across all monitored counties (\$6.3 billion, from
EPA's PM RIA), the cost of full attainment for the county furthest from
the standard (\$30 billion), and the cost of full attainment for the
remainder of the monitored counties assuming a collateral reduction
percentage of 50 percent (\$54 billion) leads to a total cost of \$90
billion for full attainment of the monitored counties within all
regions.

A further step can be taken to address the remaining counties expected
to be in residual nonattainment based on air quality modeling applied to
all counties. These include those designated by the model as residual
nonattainment counties that are not part of the current PM~10~
monitoring network. Like the monitored counties, it is reasonable to
expect that nonmonitored counties will experience collateral benefits
due to control measures employed to achieve full attainment for other
counties in the region. Due to the relatively high number of counties
working simultaneously to achieve full attainment if these additional
counties are to be considered, a somewhat higher level of collateral
concentration reduction may be expected. As an example, applying a
collateral reduction percentage of 75 percent to nonmonitored residual
nonattainment counties results in a total cost for all of the regions of
approximately \$60 billion, again assuming a marginal control cost of
\$1 billion per μg/m^3^ reduced. Adding this to the total cost of full
attainment of the monitored counties within all regions (\$90 billion)
gives \$150 billion.

As mentioned earlier, EPA has provided no information that can help
estimate the extent to which counties experience collateral benefits due
to control measures employed to achieve full attainment for other
counties in the region. For this reason, a range of assumptions
concerning the level of collateral benefits is useful to help establish
a range of estimates representing the full cost of meeting the proposed
standard. Table 1-6 contains estimates of the cost of full attainment
(including the \$6.3 billion annual cost of partial attainment based on
EPA's PM RIA) for a variety of collateral reduction percentage applied
both to monitored and nonmonitored residual nonattainment counties.

  ------------ ------ --------------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ------
  Table 1-6.                                                                                
  Cost of Full                                                                              
  Attainment                                                                                
  as a                                                                                      
  Function of                                                                               
  Collateral                                                                                
  Reduction                                                                                 
  Percentage                                                                                

                      Collateral                                                            
                      Reduction                                                             
                      Percentage                                                            
                      Applied to                                                            
                      Non-Monitored                                                         
                      Counties                                                              

                      50%             55%   60%   65%   70%   75%   80%   85%   90%   95%   100%

  Collateral   100%   154             142   130   119   107   95    83    71    60    48    36

  Reduction    95%    159             147   136   124   112   100   89    77    65    53    41

  Percentage   90%    165             153   141   129   117   106   94    82    70    58    47

  Applied to   85%    170             158   146   135   123   111   99    87    76    64    52

  Monitored    80%    175             163   152   140   128   116   105   93    81    69    57

  Counties     75%    181             169   157   145   133   122   110   98    86    75    63

               70%    186             174   162   151   139   127   115   103   92    80    68

               65%    191             180   168   156   144   132   121   109   97    85    73

               60%    197             185   173   161   150   138   126   114   102   91    79

               55%    202             190   178   167   155   143   131   119   108   96    84

               50%    207             196   184   172   160   148   137   125   113   101   89

               45%    213             201   189   177   166   154   142   130   118   107   95

               40%    218             206   194   183   171   159   147   136   124   112   100

               35%    223             212   200   188   176   164   153   141   129   117   105

               30%    229             217   205   193   182   170   158   146   134   123   111

               25%    234             222   210   199   187   175   163   152   140   128   116

               20%    239             228   216   204   192   180   169   157   145   133   122

               15%    245             233   221   209   198   186   174   162   150   139   127

               10%    250             238   227   215   203   191   179   168   156   144   132

               5%     255             244   232   220   208   197   185   173   161   149   138

               0%     261             249   237   225   214   202   190   178   166   155   143
  ------------ ------ --------------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ------

 

 

 

The cost estimate in the upper, right-hand corner of the table
represents the assumption that the control measures necessary to bring
the county furthest from the standard into full attainment is sufficient
to bring its entire region into attainment. This is the \"lower bound\"
estimate cited in Table 1-5. The cost estimates in the right-most column
of Table 1-6 correspond to the assumption that all nonmonitored residual
nonattainment counties reach the proposed standard due to control
measures employed to achieve full attainment for other counties in the
region. Another way of thinking about this is that the right-most column
provides the cost estimates that would result if the full attainment
extrapolations were to be based solely on the 57 residual nonattainment
areas that are monitored, and thus are covered by the cost estimates
that EPA presented in its RIA. Even under these assumptions, the full
attainment costs could rise as high as \$143 billion per year. It should
also be noted that Table 1-6 does not show the most extreme cost case
where there are less than 50 percent collateral reductions in
nonmonitored counties. The cost estimates could thus rise towards \$300
billion per year. However, it is only realistic to assume a fairly high
degree of collateral PM reductions.

The cost estimates in demarcated in Table 1-6 reflect DFI's view on the
likely range of collateral reduction, and reflect the cost range of \$70
to \$150 billion per year that this analysis has selected. This range
assumes that collateral reductions are between 70 percent and 90 percent
for modeled residual nonattainment areas, and between 50 percent and 90
percent for monitored nonattainment areas. However, individuals who have
alternative viewpoints can determine the cost implications of their own
judgments from Table 1-6.

 

**IV. Economic Impact Analysis Results**

The cost of control measures to reach full attainment of the proposed
PM~2.5~ and ozone standards will have an impact on the overall U.S.
economy. To estimate the magnitude of that impact, a national version of
the EDFS-53 macroeconomic model produced by Regional Economic Models,
Inc. (REMI) was used to characterize important economic indicators such
as real disposable income and distributive impacts such as differential
impacts across regions and income groups. REMI is a 53-sector model
representing interindustry relationships across the various sectors and
regions of the economy, as well as behavioral and demographic
interactions characterized in economic theory. The model developed for
this analysis divided the United States into eight regions, or groups of
states that are likely to have common control needs and have similar
types of economic bases. The REMI regions and the states in each region
are summarized in Table 1-7.

  ------------------- ---------------------------------------------------
  Table 1-7. States   
  in Each of the      
  Eight REMI Model    
  Regions             

  Region 1.           Maine, Vermont, New Hampshire, Massachusetts, Rhode
  Northeast-          Island, Connecticut, New York, New Jersey,
  Mid-Atlantic        Pennsylvania, Delaware, Maryland, West Virginia,
                      Virginia

  Region 2. Southeast North Carolina, South Carolina, Georgia, Florida,
                      Alabama, Mississippi, Tennessee

  Region 3. North     Ohio, Kentucky, Indiana, Illinois, Michigan,
  Central             Wisconsin, Missouri, Iowa, Minnesota

  Region 4. South     Arkansas, Louisiana, Texas, Oklahoma, New Mexico
  Central             

  Region 5. NW        Kansas, Nebraska, South Dakota, North Dakota,
  Central             Montana, Wyoming, Colorado

  Region 6. West      Utah, Arizona, Nevada

  Region 7. Far West  California, Hawaii

  Region 8. Northwest Idaho, Oregon, Washington, Alaska
  ------------------- ---------------------------------------------------

In this analysis, the REMI model was used to obtain some insights about
what the economic effects might look like if the United States were to
embark on a regulatory program to control precursors of PM~2.5~ and
ozone that would have control costs at the level of either \$90 billion
per year or \$150 billion per year. Discussion in earlier parts of this
report indicated that there are no specific control actions explicitly
underlying these cost estimates, because they are based on
extrapolations rather than detailed cost modeling. In addition, there is
very little information available as to the specific nonattainment areas
that would bear these costs. Thus, the economic impact analysis using
REMI is intended as a pro forma exercise to try to understand the nature
and potential magnitude of impacts of a national program of this order
of magnitude.

 

***A. Cost Input Assumptions***

In assigning the estimated total costs to specific REMI sectors and
regions, a number of relevant pieces of information have been used:

-   The control program is likely to have broad regional impacts:
    combinations of ozone and/or PM~2.5~ nonattainment areas appear in
    all regions. In addition, the assumptions used in the cost
    extrapolations relied on the judgment that controls would almost
    certainly be applied in areas of influence outside of the
    nonattainment areas. If this were not the case, then the control
    cost estimates themselves would be significantly higher.

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-   The control activities would tend to be correlated with sources of
    NO~x~, VOCs, SO~2~, and, to a lesser extent, PM~10~. Thus, costs
    were first attributed to individual sectors according to the
    proportion of total relevant emissions accounted for by each sector.
    For emissions associated with the use of a product, the
    redesign/reformulationon costs would be borne by the manufacturer,
    and passed on to the consumer. Thus, in estimating which sectors
    would bear what portion of total control costs, emissions associated
    with use were attributed back to the sector that manufactures the
    product.

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-   For each individual affected sector, the portion of its costs that
    would be borne in each region would tend to correlate with how much
    of that sectoral output occurs within each region. Thus, for
    example, much of the \"control costs\" for redesigning motor
    vehicles to be lower-emitting is concentrated in the North Central
    region (Region 3), although in the model simulation, the costs from
    this production are passed back to consumers of vehicles in all
    regions.

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-   It was assumed that these control programs would be primarily
    technological in nature. No attempts were made to reflect lifestyle
    changes or infrastructure changes, other than to apply costs that
    would be attributed to road dust control measures to local
    government spending.

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-   No changes were made to tax rates.

Table 1-8 presents the resulting cost inputs for each sector associated
with the \$90 billion per year control measures, and shows what percent
of each sector's costs is distributed to each of the eight regions. In
the table, one can see that the costs are actually distributed in a
manner that is reasonably proportionate to regional population, although
population was not used as one of the determining factors. The North
Central states appear to bear proportionately more, consistent with
their large share of utility emissions and the fact that the motor
vehicle industry is centered there. Table 1-9 provides the actual
regional level costs associated with these attributions. (The costs in
this table are still on an annualized basis.)

Computational resource requirements made it infeasible to do the
analysis at the state level. Even an eight region model requires
substantial modeling, data development and computing time. Further,
these analyses of economic impacts are quite illustrative in nature, and
do not have sufficient accuracy to warrant a state-specific modeling
effort. However, Appendix 2 provides an example of how the same
cost-attribution method could be used to generate state-level cost
assumptions. The information in Appendix 2 *was not used as input to any
of these REMI analyses* and is only provided to show how the same \$90
billion per year of costs might have been allocated to states had we
been attempting to perform a state-level analysis of this set of
potential control costs.

The inputs to the REMI model are in fact much more detailed than what
Table 1-9 may seem to indicate. A quick summary of some of the steps and
details are:

-   The annualized costs are converted to 1992 real dollars (which is
    the unit used in the REMI model) and are broken into capital and
    operating cost components.

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-   Capital costs are fully spent during the implementation phase (2002
    to 2008), while operating costs are phased in gradually between 2002
    and 2008, then remain constant (in real terms) over the remainder of
    the model simulation (through 2025). The annual payments for the
    capital expenditures are also phased in like the O&M costs. Thus,
    costs are lower than expenditures prior to about 2007, and then
    become larger than expenditures post-implementation, while companies
    pay off the capital costs, and operate equipment.

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-   Capital expenditures are reflected in the model as increased demand
    to several construction-related sectors. O&M costs are reflected as
    increased demand for a combination of materials-related sectors,
    energy and other public-utility related services, and on-site labor.

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-   Payments of capital charges and O&M are reflected in the model as
    nonproductive cost increases for the sectors that must purchase and
    operate the control equipment, or make other manufacturing changes
    to alter their products (as in the case of reformulated paints,
    aerosols, and lower-emitting vehicles). One exception to this rule
    is that costs related to electric generation are translated into a
    combination of electricity price increases (which is a model input
    variable that affects the costs of all electricity users, including
    households, in proportion to their overall demand for this source of
    energy), and loss to shareholders.

<table>
<colgroup>
<col style="width: 7%" />
<col style="width: 3%" />
<col style="width: 25%" />
<col style="width: 3%" />
<col style="width: 6%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 5%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 4%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="22">Table 1-8. Attribution of the $90 billion Illustrative
Control Cost Scenarios to Regions and Sectors</td>
</tr>
<tr class="even">
<td colspan="3"> </td>
<td colspan="3"> </td>
<td colspan="16">REGION</td>
</tr>
<tr class="odd">
<td colspan="3"> </td>
<td colspan="4">Total</td>
<td colspan="2">1</td>
<td colspan="2">2</td>
<td>3</td>
<td colspan="2">4</td>
<td colspan="2">5</td>
<td colspan="2">6</td>
<td colspan="2">7</td>
<td colspan="2">8</td>
</tr>
<tr class="even">
<td colspan="3"> </td>
<td colspan="4">% of costs</td>
<td colspan="2">22%</td>
<td colspan="2">14%</td>
<td>28%</td>
<td colspan="2">18%</td>
<td colspan="2">3%</td>
<td colspan="2">2%</td>
<td colspan="2">10%</td>
<td colspan="2">3%</td>
</tr>
<tr class="odd">
<td colspan="3"> </td>
<td colspan="4">% population</td>
<td colspan="2">26%</td>
<td colspan="2">17%</td>
<td>23%</td>
<td colspan="2">11%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="2"><p>REMI Sector</p>
<p>No.</p></td>
<td>REMI Sector Name</td>
<td colspan="3">Sector Cost</td>
<td colspan="16">Percent of Sector’s Cost by Region</td>
</tr>
<tr class="odd">
<td colspan="2">1</td>
<td>Lumber</td>
<td colspan="4">$1 billion</td>
<td colspan="2">15%</td>
<td colspan="2">23%</td>
<td>22%</td>
<td colspan="2">10%</td>
<td colspan="2">3%</td>
<td colspan="2">1%</td>
<td colspan="2">8%</td>
<td colspan="2">18%</td>
</tr>
<tr class="even">
<td colspan="2">2</td>
<td>Furniture</td>
<td colspan="4">$0 billion</td>
<td colspan="2">18%</td>
<td colspan="2">30%</td>
<td>31%</td>
<td colspan="2">6%</td>
<td colspan="2">2%</td>
<td colspan="2">1%</td>
<td colspan="2">10%</td>
<td colspan="2">2%</td>
</tr>
<tr class="odd">
<td colspan="2">3</td>
<td>Stone, clay, etc.</td>
<td colspan="4">$2 billion</td>
<td colspan="2">23%</td>
<td colspan="2">19%</td>
<td>28%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
<td colspan="2">2%</td>
<td colspan="2">9%</td>
<td colspan="2">3%</td>
</tr>
<tr class="even">
<td colspan="2">4</td>
<td>Primary metals</td>
<td colspan="4">$3 billion</td>
<td colspan="2">27%</td>
<td colspan="2">12%</td>
<td>41%</td>
<td colspan="2">6%</td>
<td colspan="2">2%</td>
<td colspan="2">2%</td>
<td colspan="2">5%</td>
<td colspan="2">5%</td>
</tr>
<tr class="odd">
<td colspan="2">5</td>
<td>Fabricated metals</td>
<td colspan="4">$3 billion</td>
<td colspan="2">24%</td>
<td colspan="2">12%</td>
<td>42%</td>
<td colspan="2">9%</td>
<td colspan="2">2%</td>
<td colspan="2">1%</td>
<td colspan="2">8%</td>
<td colspan="2">2%</td>
</tr>
<tr class="even">
<td colspan="2">6</td>
<td>Non-electric machine</td>
<td colspan="4">$0 billion</td>
<td colspan="2">23%</td>
<td colspan="2">13%</td>
<td>38%</td>
<td colspan="2">8%</td>
<td colspan="2">3%</td>
<td colspan="2">1%</td>
<td colspan="2">12%</td>
<td colspan="2">2%</td>
</tr>
<tr class="odd">
<td colspan="2">7</td>
<td>Electric equipment</td>
<td colspan="4">$0 billion</td>
<td colspan="2">25%</td>
<td colspan="2">15%</td>
<td>23%</td>
<td colspan="2">13%</td>
<td colspan="2">2%</td>
<td colspan="2">3%</td>
<td colspan="2">16%</td>
<td colspan="2">2%</td>
</tr>
<tr class="even">
<td colspan="2">8</td>
<td>Motor vehicles</td>
<td colspan="4">$9 billion</td>
<td colspan="2">11%</td>
<td colspan="2">10%</td>
<td>68%</td>
<td colspan="2">5%</td>
<td colspan="2">1%</td>
<td colspan="2">1%</td>
<td colspan="2">2%</td>
<td colspan="2">1%</td>
</tr>
<tr class="odd">
<td colspan="2">9</td>
<td>Rest of transportation equip.</td>
<td colspan="4">$2 billion</td>
<td colspan="2">23%</td>
<td colspan="2">13%</td>
<td>16%</td>
<td colspan="2">10%</td>
<td colspan="2">5%</td>
<td colspan="2">4%</td>
<td colspan="2">18%</td>
<td colspan="2">12%</td>
</tr>
<tr class="even">
<td colspan="2">10</td>
<td>Instruments</td>
<td colspan="4">$0 billion</td>
<td colspan="2">39%</td>
<td colspan="2">8%</td>
<td>20%</td>
<td colspan="2">5%</td>
<td colspan="2">3%</td>
<td colspan="2">2%</td>
<td colspan="2">19%</td>
<td colspan="2">2%</td>
</tr>
<tr class="odd">
<td colspan="2">11</td>
<td>Misc. manufacturing</td>
<td colspan="4">$0 billion</td>
<td colspan="2">38%</td>
<td colspan="2">10%</td>
<td>24%</td>
<td colspan="2">7%</td>
<td colspan="2">2%</td>
<td colspan="2">4%</td>
<td colspan="2">12%</td>
<td colspan="2">3%</td>
</tr>
<tr class="even">
<td colspan="2">12</td>
<td>Food</td>
<td colspan="4">$2 billion</td>
<td colspan="2">22%</td>
<td colspan="2">15%</td>
<td>33%</td>
<td colspan="2">9%</td>
<td colspan="2">5%</td>
<td colspan="2">1%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">13</td>
<td>Tobacco manufacturing</td>
<td colspan="4">$0 billion</td>
<td colspan="2">33%</td>
<td colspan="2">53%</td>
<td>14%</td>
<td colspan="2">0%</td>
<td colspan="2">0%</td>
<td colspan="2">0%</td>
<td colspan="2">0%</td>
<td colspan="2">0%</td>
</tr>
<tr class="even">
<td colspan="2">14</td>
<td>Textiles</td>
<td colspan="4">$0 billion</td>
<td colspan="2">23%</td>
<td colspan="2">67%</td>
<td>4%</td>
<td colspan="2">2%</td>
<td colspan="2">0%</td>
<td colspan="2">0%</td>
<td colspan="2">3%</td>
<td colspan="2">0%</td>
</tr>
<tr class="odd">
<td colspan="2">15</td>
<td>Apparel</td>
<td colspan="4">$0 billion</td>
<td colspan="2">28%</td>
<td colspan="2">25%</td>
<td>17%</td>
<td colspan="2">10%</td>
<td colspan="2">1%</td>
<td colspan="2">1%</td>
<td colspan="2">18%</td>
<td colspan="2">1%</td>
</tr>
<tr class="even">
<td colspan="2">16</td>
<td>Paper</td>
<td colspan="4">$2 billion</td>
<td colspan="2">23%</td>
<td colspan="2">24%</td>
<td>31%</td>
<td colspan="2">10%</td>
<td colspan="2">1%</td>
<td colspan="2">1%</td>
<td colspan="2">6%</td>
<td colspan="2">5%</td>
</tr>
<tr class="odd">
<td colspan="2">17</td>
<td>Printing</td>
<td colspan="4">$0 billion</td>
<td colspan="2">33%</td>
<td colspan="2">12%</td>
<td>27%</td>
<td colspan="2">7%</td>
<td colspan="2">5%</td>
<td colspan="2">2%</td>
<td colspan="2">11%</td>
<td colspan="2">3%</td>
</tr>
<tr class="even">
<td colspan="2">18</td>
<td>Chemicals</td>
<td colspan="4">$8 billion</td>
<td colspan="2">32%</td>
<td colspan="2">16%</td>
<td>27%</td>
<td colspan="2">16%</td>
<td colspan="2">1%</td>
<td colspan="2">1%</td>
<td colspan="2">6%</td>
<td colspan="2">1%</td>
</tr>
<tr class="odd">
<td colspan="2">19</td>
<td>Petroleum products</td>
<td colspan="4">$15 billion</td>
<td colspan="2">14%</td>
<td colspan="2">3%</td>
<td>16%</td>
<td colspan="2">49%</td>
<td colspan="2">4%</td>
<td colspan="2">1%</td>
<td colspan="2">11%</td>
<td colspan="2">2%</td>
</tr>
<tr class="even">
<td colspan="2">20</td>
<td>Rubber</td>
<td colspan="4">$0 billion</td>
<td colspan="2">22%</td>
<td colspan="2">19%</td>
<td>38%</td>
<td colspan="2">9%</td>
<td colspan="2">3%</td>
<td colspan="2">1%</td>
<td colspan="2">7%</td>
<td colspan="2">1%</td>
</tr>
<tr class="odd">
<td colspan="2">21</td>
<td>Leather</td>
<td colspan="4">$0 billion</td>
<td colspan="2">47%</td>
<td colspan="2">8%</td>
<td>27%</td>
<td colspan="2">8%</td>
<td colspan="2">3%</td>
<td colspan="2">0%</td>
<td colspan="2">4%</td>
<td colspan="2">1%</td>
</tr>
<tr class="even">
<td colspan="2">22</td>
<td>Mining</td>
<td colspan="4">$1 billion</td>
<td colspan="2">6%</td>
<td colspan="2">5%</td>
<td>11%</td>
<td colspan="2">54%</td>
<td colspan="2">8%</td>
<td colspan="2">8%</td>
<td colspan="2">5%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">23</td>
<td>Construction</td>
<td colspan="4">$1 billion</td>
<td colspan="2">25%</td>
<td colspan="2">19%</td>
<td>20%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="2">24</td>
<td>Railroad</td>
<td colspan="4">$0 billion</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td>na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
</tr>
<tr class="odd">
<td colspan="2">25</td>
<td>Trucking</td>
<td colspan="4">$0 billion</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td>na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
</tr>
<tr class="even">
<td colspan="2">26</td>
<td>Local/interurban transit</td>
<td colspan="4">$0 billion</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td>na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
</tr>
<tr class="odd">
<td colspan="2">27</td>
<td>Air transportation</td>
<td colspan="4">$0 billion</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td>na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
</tr>
<tr class="even">
<td colspan="2">28</td>
<td>Other transportation</td>
<td colspan="4">$0 billion</td>
<td colspan="2">21%</td>
<td colspan="2">14%</td>
<td>17%</td>
<td colspan="2">20%</td>
<td colspan="2">4%</td>
<td colspan="2">1%</td>
<td colspan="2">14%</td>
<td colspan="2">9%</td>
</tr>
<tr class="odd">
<td colspan="2">29</td>
<td>Communication</td>
<td colspan="4">$0 billion</td>
<td colspan="2">28%</td>
<td colspan="2">19%</td>
<td>18%</td>
<td colspan="2">12%</td>
<td colspan="2">6%</td>
<td colspan="2">3%</td>
<td colspan="2">11%</td>
<td colspan="2">3%</td>
</tr>
<tr class="even">
<td colspan="2">30</td>
<td>Public utilities (nonelectric)</td>
<td colspan="4">$3 billion</td>
<td colspan="2">26%</td>
<td colspan="2">15%</td>
<td>22%</td>
<td colspan="2">19%</td>
<td colspan="2">5%</td>
<td colspan="2">2%</td>
<td colspan="2">9%</td>
<td colspan="2">3%</td>
</tr>
<tr class="odd">
<td colspan="2">31</td>
<td>Banking</td>
<td colspan="4">$0 billion</td>
<td colspan="2">29%</td>
<td colspan="2">15%</td>
<td>24%</td>
<td colspan="2">9%</td>
<td colspan="2">5%</td>
<td colspan="2">3%</td>
<td colspan="2">12%</td>
<td colspan="2">3%</td>
</tr>
<tr class="even">
<td colspan="2">32</td>
<td>Insurance</td>
<td colspan="4">$0 billion</td>
<td colspan="2">28%</td>
<td colspan="2">15%</td>
<td>24%</td>
<td colspan="2">11%</td>
<td colspan="2">5%</td>
<td colspan="2">3%</td>
<td colspan="2">11%</td>
<td colspan="2">3%</td>
</tr>
<tr class="odd">
<td colspan="2">33</td>
<td>Credit and finance</td>
<td colspan="4">$0 billion</td>
<td colspan="2">42%</td>
<td colspan="2">10%</td>
<td>18%</td>
<td colspan="2">9%</td>
<td colspan="2">4%</td>
<td colspan="2">2%</td>
<td colspan="2">12%</td>
<td colspan="2">2%</td>
</tr>
<tr class="even">
<td colspan="2">34</td>
<td>Real estate</td>
<td colspan="4">$0 billion</td>
<td colspan="2">31%</td>
<td colspan="2">15%</td>
<td>18%</td>
<td colspan="2">10%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">15%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">35</td>
<td>Eating and drinking</td>
<td colspan="4">$0 billion</td>
<td colspan="2">21%</td>
<td colspan="2">17%</td>
<td>24%</td>
<td colspan="2">12%</td>
<td colspan="2">5%</td>
<td colspan="2">3%</td>
<td colspan="2">14%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="2">36</td>
<td>Rest of retail</td>
<td colspan="4">$0 billion</td>
<td colspan="2">24%</td>
<td colspan="2">18%</td>
<td>23%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">37</td>
<td>Wholesale</td>
<td colspan="4">$0 billion</td>
<td colspan="2">25%</td>
<td colspan="2">17%</td>
<td>23%</td>
<td colspan="2">11%</td>
<td colspan="2">4%</td>
<td colspan="2">2%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="2">38</td>
<td>Hotels</td>
<td colspan="4">$0 billion</td>
<td colspan="2">28%</td>
<td colspan="2">16%</td>
<td>12%</td>
<td colspan="2">7%</td>
<td colspan="2">4%</td>
<td colspan="2">14%</td>
<td colspan="2">15%</td>
<td colspan="2">3%</td>
</tr>
<tr class="odd">
<td colspan="2">39</td>
<td>Personal &amp; repair service</td>
<td colspan="4">$0 billion</td>
<td colspan="2">23%</td>
<td colspan="2">17%</td>
<td>22%</td>
<td colspan="2">12%</td>
<td colspan="2">5%</td>
<td colspan="2">3%</td>
<td colspan="2">15%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="2">40</td>
<td>Private households</td>
<td colspan="4">$0 billion</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td>na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
</tr>
<tr class="odd">
<td colspan="2">41</td>
<td>Auto repair/service</td>
<td colspan="4">$0 billion</td>
<td colspan="2">23%</td>
<td colspan="2">17%</td>
<td>22%</td>
<td colspan="2">12%</td>
<td colspan="2">5%</td>
<td colspan="2">3%</td>
<td colspan="2">14%</td>
<td colspan="2">5%</td>
</tr>
<tr class="even">
<td colspan="2">42</td>
<td>Misc. business service</td>
<td colspan="4">$1 billion</td>
<td colspan="2">27%</td>
<td colspan="2">15%</td>
<td>20%</td>
<td colspan="2">11%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">16%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">43</td>
<td>Amusement &amp; recreation</td>
<td colspan="4">$0 billion</td>
<td colspan="2">26%</td>
<td colspan="2">16%</td>
<td>19%</td>
<td colspan="2">9%</td>
<td colspan="2">4%</td>
<td colspan="2">5%</td>
<td colspan="2">18%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="2">44</td>
<td>Motion pictures</td>
<td colspan="4">$0 billion</td>
<td colspan="2">20%</td>
<td colspan="2">8%</td>
<td>12%</td>
<td colspan="2">6%</td>
<td colspan="2">2%</td>
<td colspan="2">2%</td>
<td colspan="2">48%</td>
<td colspan="2">2%</td>
</tr>
<tr class="odd">
<td colspan="2">45</td>
<td>Medical</td>
<td colspan="4">$1 billion</td>
<td colspan="2">29%</td>
<td colspan="2">15%</td>
<td>23%</td>
<td colspan="2">11%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">11%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="2">46</td>
<td>Misc. professional services</td>
<td colspan="4">$0 billion</td>
<td colspan="2">32%</td>
<td colspan="2">13%</td>
<td>19%</td>
<td colspan="2">11%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">15%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">47</td>
<td>Education</td>
<td colspan="4">$0 billion</td>
<td colspan="2">40%</td>
<td colspan="2">12%</td>
<td>21%</td>
<td colspan="2">7%</td>
<td colspan="2">3%</td>
<td colspan="2">2%</td>
<td colspan="2">12%</td>
<td colspan="2">3%</td>
</tr>
<tr class="even">
<td colspan="2">48</td>
<td>Non-profit organization</td>
<td colspan="4">$0 billion</td>
<td colspan="2">31%</td>
<td colspan="2">14%</td>
<td>24%</td>
<td colspan="2">10%</td>
<td colspan="2">4%</td>
<td colspan="2">2%</td>
<td colspan="2">11%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">49</td>
<td>Agri./forest/fish service</td>
<td colspan="4">$1 billion</td>
<td colspan="2">16%</td>
<td colspan="2">17%</td>
<td>17%</td>
<td colspan="2">12%</td>
<td colspan="2">7%</td>
<td colspan="2">2%</td>
<td colspan="2">18%</td>
<td colspan="2">11%</td>
</tr>
<tr class="even">
<td colspan="2">50</td>
<td>State &amp; Local</td>
<td colspan="4">$4 billion</td>
<td colspan="2">26%</td>
<td colspan="2">16%</td>
<td>24%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">51</td>
<td>Federal, civilian</td>
<td colspan="4">$0 billion</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td>na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
</tr>
<tr class="even">
<td colspan="2">52</td>
<td>Federal, military</td>
<td colspan="4">$0 billion</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td>na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
<td colspan="2">na</td>
</tr>
<tr class="odd">
<td colspan="2">53</td>
<td>Farm</td>
<td colspan="4">$3 billion</td>
<td colspan="2">26%</td>
<td colspan="2">16%</td>
<td>24%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="2">54</td>
<td>Consumers</td>
<td colspan="4">$4 billion</td>
<td colspan="2">26%</td>
<td colspan="2">16%</td>
<td>24%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
</tr>
<tr class="odd">
<td colspan="2">55</td>
<td>Electric utilities</td>
<td colspan="4">$20 billion</td>
<td colspan="2">24%</td>
<td colspan="2">17%</td>
<td>22%</td>
<td colspan="2">12%</td>
<td colspan="2">4%</td>
<td colspan="2">3%</td>
<td colspan="2">14%</td>
<td colspan="2">4%</td>
</tr>
<tr class="even">
<td colspan="22">Table 1-9. Regional Costs (Annualized, in 1990
Dollars)</td>
</tr>
<tr class="odd">
<td colspan="4"> </td>
<td> </td>
<td colspan="17"><p>REGION</p>
<p>(in Billions of dollars)</p></td>
</tr>
<tr class="even">
<td>REMI Sector No.</td>
<td colspan="3">REMI Sector Name</td>
<td>Full Attain.</td>
<td colspan="3">1</td>
<td colspan="2">2</td>
<td colspan="3">3</td>
<td colspan="2">4</td>
<td colspan="2">5</td>
<td colspan="2">6</td>
<td colspan="2">7</td>
<td>8</td>
</tr>
<tr class="odd">
<td> </td>
<td colspan="3">Total</td>
<td>90.0</td>
<td colspan="3">19.6</td>
<td colspan="2">12.2</td>
<td colspan="3">25.2</td>
<td colspan="2">15.9</td>
<td colspan="2">3.0</td>
<td colspan="2">1.8</td>
<td colspan="2">9.1</td>
<td>3.1</td>
</tr>
<tr class="even">
<td>1</td>
<td colspan="3">Lumber</td>
<td>0.8</td>
<td colspan="3">0.1</td>
<td colspan="2">0.2</td>
<td colspan="3">0.2</td>
<td colspan="2">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td>0.1</td>
</tr>
<tr class="odd">
<td>2</td>
<td colspan="3">Furniture</td>
<td>0.2</td>
<td colspan="3">0.0</td>
<td colspan="2">0.1</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>3</td>
<td colspan="3">Stone, clay, etc.</td>
<td>2.3</td>
<td colspan="3">0.5</td>
<td colspan="2">0.5</td>
<td colspan="3">0.7</td>
<td colspan="2">0.3</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.2</td>
<td>0.1</td>
</tr>
<tr class="odd">
<td>4</td>
<td colspan="3">Primary metals</td>
<td>2.6</td>
<td colspan="3">0.7</td>
<td colspan="2">0.3</td>
<td colspan="3">1.1</td>
<td colspan="2">0.2</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td>0.1</td>
</tr>
<tr class="even">
<td>5</td>
<td colspan="3">Fabricated metals</td>
<td>3.0</td>
<td colspan="3">0.7</td>
<td colspan="2">0.4</td>
<td colspan="3">1.3</td>
<td colspan="2">0.3</td>
<td colspan="2">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.2</td>
<td>0.1</td>
</tr>
<tr class="odd">
<td>6</td>
<td colspan="3">Non-electric machine</td>
<td>0.4</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="3">0.2</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>7</td>
<td colspan="3">Electric equipment</td>
<td>0.4</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>8</td>
<td colspan="3">Motor vehicles</td>
<td>9.1</td>
<td colspan="3">1.0</td>
<td colspan="2">0.9</td>
<td colspan="3">6.2</td>
<td colspan="2">0.5</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.2</td>
<td>0.1</td>
</tr>
<tr class="even">
<td>9</td>
<td colspan="3">Rest of transportation equip.</td>
<td>2.5</td>
<td colspan="3">0.6</td>
<td colspan="2">0.3</td>
<td colspan="3">0.4</td>
<td colspan="2">0.2</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.4</td>
<td>0.3</td>
</tr>
<tr class="odd">
<td>10</td>
<td colspan="3">Instruments</td>
<td>0.2</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>11</td>
<td colspan="3">Misc. manufacturing</td>
<td>0.2</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>12</td>
<td colspan="3">Food</td>
<td>2.0</td>
<td colspan="3">0.4</td>
<td colspan="2">0.3</td>
<td colspan="3">0.7</td>
<td colspan="2">0.2</td>
<td colspan="2">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.2</td>
<td>0.1</td>
</tr>
<tr class="even">
<td>13</td>
<td colspan="3">Tobacco manufacturing</td>
<td>0.1</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>14</td>
<td colspan="3">Textiles</td>
<td>0.3</td>
<td colspan="3">0.1</td>
<td colspan="2">0.2</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>15</td>
<td colspan="3">Apparel</td>
<td>0.1</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>16</td>
<td colspan="3">Paper</td>
<td>2.0</td>
<td colspan="3">0.4</td>
<td colspan="2">0.5</td>
<td colspan="3">0.6</td>
<td colspan="2">0.2</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td>0.1</td>
</tr>
<tr class="even">
<td>17</td>
<td colspan="3">Printing</td>
<td>0.3</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>18</td>
<td colspan="3">Chemicals</td>
<td>8.0</td>
<td colspan="3">2.6</td>
<td colspan="2">1.3</td>
<td colspan="3">2.2</td>
<td colspan="2">1.2</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.5</td>
<td>0.1</td>
</tr>
<tr class="even">
<td>19</td>
<td colspan="3">Petroleum products</td>
<td>14.7</td>
<td colspan="3">2.0</td>
<td colspan="2">0.5</td>
<td colspan="3">2.4</td>
<td colspan="2">7.2</td>
<td colspan="2">0.5</td>
<td colspan="2">0.1</td>
<td colspan="2">1.6</td>
<td>0.4</td>
</tr>
<tr class="odd">
<td>20</td>
<td colspan="3">Rubber</td>
<td>0.4</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="3">0.2</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>21</td>
<td colspan="3">Leather</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>22</td>
<td colspan="3">Mining</td>
<td>1.1</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="3">0.1</td>
<td colspan="2">0.6</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>23</td>
<td colspan="3">Construction</td>
<td>1.0</td>
<td colspan="3">0.2</td>
<td colspan="2">0.2</td>
<td colspan="3">0.2</td>
<td colspan="2">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>24</td>
<td colspan="3">Railroad</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>25</td>
<td colspan="3">Trucking</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>26</td>
<td colspan="3">Local/interurban transit</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>27</td>
<td colspan="3">Air transportation</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>28</td>
<td colspan="3">Other transportation</td>
<td>0.2</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>29</td>
<td colspan="3">Communication</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>30</td>
<td colspan="3">Public utilities (nonelectric)</td>
<td>3.0</td>
<td colspan="3">0.8</td>
<td colspan="2">0.5</td>
<td colspan="3">0.7</td>
<td colspan="2">0.6</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.3</td>
<td>0.1</td>
</tr>
<tr class="even">
<td>31</td>
<td colspan="3">Banking</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>32</td>
<td colspan="3">Insurance</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>33</td>
<td colspan="3">Credit and finance</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>34</td>
<td colspan="3">Real estate</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>35</td>
<td colspan="3">Eating and drinking</td>
<td>0.3</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>36</td>
<td colspan="3">Rest of retail</td>
<td>0.1</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>37</td>
<td colspan="3">Wholesale</td>
<td>0.3</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>38</td>
<td colspan="3">Hotels</td>
<td>0.4</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>39</td>
<td colspan="3">Personal &amp; repair service</td>
<td>0.1</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>40</td>
<td colspan="3">Private households</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>41</td>
<td colspan="3">Auto repair/service</td>
<td>0.4</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>42</td>
<td colspan="3">Misc. business service</td>
<td>1.3</td>
<td colspan="3">0.3</td>
<td colspan="2">0.2</td>
<td colspan="3">0.2</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.2</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>43</td>
<td colspan="3">Amusement &amp; recreation</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>44</td>
<td colspan="3">Motion pictures</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>45</td>
<td colspan="3">Medical</td>
<td>0.7</td>
<td colspan="3">0.2</td>
<td colspan="2">0.1</td>
<td colspan="3">0.2</td>
<td colspan="2">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>46</td>
<td colspan="3">Misc. professional services</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>47</td>
<td colspan="3">Education</td>
<td>0.2</td>
<td colspan="3">0.1</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>48</td>
<td colspan="3">Non-profit organization</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>49</td>
<td colspan="3">Agri./forest/fish service</td>
<td>0.7</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="3">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.1</td>
<td>0.1</td>
</tr>
<tr class="odd">
<td>50</td>
<td colspan="3">State &amp; Local</td>
<td>3.9</td>
<td colspan="3">1.0</td>
<td colspan="2">0.6</td>
<td colspan="3">0.9</td>
<td colspan="2">0.5</td>
<td colspan="2">0.2</td>
<td colspan="2">0.1</td>
<td colspan="2">0.5</td>
<td>0.1</td>
</tr>
<tr class="even">
<td>51</td>
<td colspan="3">Federal, civilian</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="odd">
<td>52</td>
<td colspan="3">Federal, military</td>
<td>0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="3">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td colspan="2">0.0</td>
<td>0.0</td>
</tr>
<tr class="even">
<td>53</td>
<td colspan="3">Farm</td>
<td>3.0</td>
<td colspan="3">0.8</td>
<td colspan="2">0.5</td>
<td colspan="3">0.7</td>
<td colspan="2">0.4</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.4</td>
<td>0.1</td>
</tr>
<tr class="odd">
<td>54</td>
<td colspan="3">Consumers</td>
<td>3.6</td>
<td colspan="3">0.9</td>
<td colspan="2">0.6</td>
<td colspan="3">0.9</td>
<td colspan="2">0.4</td>
<td colspan="2">0.1</td>
<td colspan="2">0.1</td>
<td colspan="2">0.4</td>
<td>0.1</td>
</tr>
<tr class="even">
<td>55</td>
<td colspan="3">Electric utilities</td>
<td>20.0</td>
<td colspan="3">4.7</td>
<td colspan="2">3.4</td>
<td colspan="3">4.5</td>
<td colspan="2">2.4</td>
<td colspan="2">0.9</td>
<td colspan="2">0.6</td>
<td colspan="2">2.7</td>
<td>0.9</td>
</tr>
</tbody>
</table>

The rest of this section focuses on the most salient results from the
\$90 billion and \$150 billion per year control cost estimates
summarized earlier in the report. The model runs reported here reflect
an approximate 90 percent success on the part of federal monetary policy
in reducing potential job impacts. The summary to this section also
provides estimates of the nature of the impacts in the event that there
would be *no* residual net job loss in the economy as a whole (i.e., if
monetary policy is fully effective). It is important to note that the
most critical of the impacts that are being reported here would remain
unchanged in large part even if there were no net job loss on a national
scale.

 

***B. Real Disposable Income Impacts Based on Low End of Combined Cost
Range***

One of the most important measures of the impact on the United States
economy of the control costs associated with achieving full attainment
of the proposed PM~2.5~ and ozone standards is the change in real
disposable income per capita. This is because most of the costs of
control will be passed on to consumers, rather than taken from
alternative productive investments. Based on the \$90 billion per year
estimate of the cost of full attainment, Table 1-10 presents the impacts
on personal disposable income per capita by year to illustrate the
time-dependent nature of the effects. Impacts to real disposable income
are softened somewhat in the earlier years by the large capital
expenditures that occur during the implementation phase (2002--2008 in
our model runs).

  --------------------- ---------- --------- -------- --------- ------------
  Table 1-10. Real                                              
  Disposable Income                                             
  Impact Per Capita for                                         
  \$90 billion Scenario                                         
  (1992 real dollars)                                           

                        Modeled                                 
                        Year                                    

                        2005       2010      2015     2020      2025

  Real Disposable       -\$209     -\$259    -\$264   -\$271    -\$276
  Income Per Capita                                             
  --------------------- ---------- --------- -------- --------- ------------

The real disposable income per capita impact may vary to some degree
across regions. Table 1-11 shows the income impacts by region, ordered
from the most adversely affected to least adversely affected region. It
can be seen that the impacts for this initial analysis do not vary
substantially by region. This is because the programs are likely to have
widespread impacts that affect all regions in one way or another.

 

***C. Employment Impacts Based on Low End of Combined Cost Range***

Change in total employment in the United States is another measure that
is useful in characterizing the impact on the United States economy of
control costs. As has been noted, in an ideal world, monetary policy
would effectively erase any net national employment impacts. However,
there is a good chance that the Federal Reserve would fail to be 100
percent effective in the face of a major program and conflicting policy
objectives, if only for a transient period. The results of the model
runs presented here are for a situation where monetary policy is about
90 percent effective in attenuating aggregate employment impacts.

  ---------------------- ------------ -------- -------- ------ --------- --------- --------
  Table 1-11. Real                                                                 
  Disposable Income                                                                
  Impact Per Capita by                                                             
  Region for \$90                                                                  
  billion Scenario (1992                                                           
  real dollars)                                                                    

  Region                 Real                                                      
                         Disposable                                                
                         Income Per                                                
                         Capita by                                                 
                         Region                                                    

                         2005                  2010     2015             2020      2025

  Northwest Central      -\$234       -\$289                   -\$292    -\$302    -\$308

  Northeast-Mid Atlantic -\$213       -\$276                   -\$282    -\$290    -\$294

  Northwest              -\$223       -\$277                   -\$279    -\$283    -\$286

  North Central          -\$213       -\$265                   -\$276    -\$280    -\$284

  South Central          -\$196       -\$255                   -\$256    -\$263    -\$268

  Far West               -\$199       -\$252                   -\$249    -\$256    -\$264

  Southeast              -\$212       -\$234                   -\$244    -\$251    -\$257

  West                   -\$188       -\$230                   -\$225    -\$227    -\$229

  Nationwide             -\$209       -\$259                   -\$264    -\$271    -\$276
  ---------------------- ------------ -------- -------- ------ --------- --------- --------

 

Again focusing on the low end (\$90 billion per year) control cost
estimate, the following table presents the employment impacts for three
time frames, the years 2002, 2010 and 2014. Employment gains in the year
2002 reflect the initial large increase in demand to a number of sectors
associated with implementation of the PM and ozone control measures.
Employment impacts in the later years illustrate the potential effects
following completion of capital projects, as payments on capital
expenditures and maintenance costs begin to outweigh the annual demand
increases associated with operating and maintaining the control measures
that are fully in place. By 2014, projected employment impacts have
leveled off at a net national level of about 200,000 fewer jobs. This
represents less than 0.1 percent of the national baseline employment,
but the job losses are experienced in disproportionate ways by different
groups, sectors, and regions in the economy.

The employment impacts due to the cost of full attainment can be broken
down to the various industrial sectors and regions that make up the
United States economy. Such distributive impacts would remain (although
to a somewhat lesser degree) even if the Federal Reserve were 100
percent effective in balancing aggregate employment impacts. Table 1-12
lists these impacts by sector for 2002, 2010, and 2014 (when projected
impacts stabilize). In addition, it provides the regional disaggregation
of sectoral impacts for 2014, which shows the potential of 200,000 jobs
lost nationally. The sectors are ordered by the most adversely affected
to the most advantageously affected (on an aggregate national basis).

As can be seen in Table 1-12, the net job impact is greater in some
regions than others. This regional disparity would remain even if
Federal monetary measures were able to completely mitigate aggregate
national employment impacts. Future analysis efforts should be targeted
at further refining the relative regional cost burdens, and it is
possible that the disparities between regions would be widened through
such more refined regional analyses.

Many readers may be interested in what these employment impacts may mean
for individual states. The model used for this analysis does not provide
state-level detail. Such modeling detail was infeasible within the
various resource constraints of this analysis. In addition, a
state-level analysis would have been inadvisable, even if it were
feasible, given the informational constraints associated with the full
attainment cost estimates. However, Appendix 2 provides some rough
estimates of state-level employment impacts that are consistent with the
model results for the \$90 billion per year control cost scenario in
2014. Readers are cautioned that the material in Appendix 2 is
speculative, and is not an output of the REMI model.

  ---------------------- --------- ---------- ---------- --------- --------- --------- ---- --------- ---- --------- ---- --------- ---- --------- ---- --------- ---- --------- -------
  Table 1-12. Changes in                                                                                                                                                         
  Available Jobs by                                                                                                                                                              
  Sector and Region for                                                                                                                                                          
  \$90 billion Scenario                                                                                                                                                          

  SECTOR NAME            Total                                     REGIONS                                                                                                       
                         Across                                                                                                                                                  
                         All                                                                                                                                                     
                         Regions                                                                                                                                                 

                                                                   1                   2              3              4              5              6              7              8

                         2002      2010       2014       2014                2014           2014           2014           2014           2014           2014           2014      

  Restretail(52-57,59)   -13,789   -171,508   -173,691   -40,889             -30,638        -41,418        -18,452        -8,044         -5,227         -21,269        -7,754    

  Eating/Drinking(58)    -8,532    -82,680    -87,550    -15,531             -17,694        -19,403        -10,851        -4,203         -2,732         -13,410        -3,725    

  Non-Profit(83,84,86)   -7,057    -63,261    -70,497    -19,445             -10,776        -17,507        -7,236         -3,531         -1,850         -6,714         -3,436    

  Education(82)          -5,224    -48,875    -52,255    -19,839             -5,966         -11,499        -4,293         -1,974         -1,246         -5,574         -1,864    

  Amuse&Recreation(79)   -3,286    -29,904    -32,007    -8,547              -4,632         -5,936         -2,604         -1,528         -1,626         -5,822         -1,313    

  Priv. Household(88)    -5,530    -31,045    -29,083    -6,857              -5,040         -4,397         -4,486         -1,086         -721           -5,474         -1,022    

  Construction(15-17)    41,472    -33,804    -26,251    -4,125              -4,296         -7,050         -2,154         -1,491         -1,141         -4,580         -1,416    

  Per Serv/Rep(72,76)    -754      -18,423    -19,834    -3,564              -3,314         -5,008         -1,865         -874           -565           -3,868         -777      

  Auto Rep/Serv(75)      -1,476    -12,121    -12,336    -2,004              -2,255         -3,084         -1,509         -695           -503           -1,656         -629      

  Credit&Fin(61,62,67)   -1,407    -9,220     -9,836     -2,665              -1,106         -2,103         -1,062         -712           -490           -1,083         -615      

  Food(20)               -1,684    -9,257     -8,948     -1,317              -1,656         -1,930         -1,249         -951           -174           -602           -1,069    

  Motor Veh(371)         -788      -7,237     -7,700     -298                -1,036         -5,647         155            -283           -147           -376           -69       

  Banking(60)            -578      -7,166     -7,664     -1,636              -1,313         -2,143         -739           -428           -295           -744           -364      

  Communication(48)      -1,810    -9,076     -7,242     -1,682              -1,331         -1,597         -948           -404           -256           -748           -275      

  Furniture(25)          -655      -5,414     -6,266     -869                -2,244         -1,795         -228           -174           -128           -664           -163      

  Apparel(23)            -988      -5,946     -5,811     -1,259              -1,927         -788           -517           -73            -84            -985           -178      

  Insurance(63,64)       -1,300    -5,021     -4,926     -501                -855           -1,706         -526           -656           -99            -336           -248      

  Motion Pictures(78)    -685      -3,921     -4,059     -559                -389           -762           -393           -138           -125           -1,531         -164      

  Hotels(70)             -203      -3,242     -3,988     35                  -861           -1,171         184            -68            -2,375         479            -211      

  Rest Trans Equi(R37)   91        -1,421     -2,837     -604                -850           -285           -300           16             120            344            -1,278    

  Agri/F/F Serv(07-09)   73        -2,249     -2,686     -620                -390           -550           -189           -5             -130           -393           -410      

  Textiles(22)           -159      -2,311     -2,495     -460                -1,761         -88            -52            -11            -14            -87            -23       

  Local/Interurban(41)   -318      -2,354     -2,487     -997                -255           -556           -144           -97            -80            -259           -99       

  Misc. Manuf.(39)       -179      -2,069     -2,277     -369                -350           -785           -70            -123           -89            -317           -175      

  Lumber(24)             1,198     -1,829     -1,681     -384                -251           -691           15             -104           -97            -84            -83       

  Paper(26)              343       -1,221     -1,637     -978                856            -1,435         286            -159           -53            -229           77        

  Petro Prod(29)         -107      -1,010     -843       -178                -75            -238           -245           -27            -5             -61            -14       

  Mining(10,12-14)       1,151     -606       -360       -1                  91             -180           -414           16             38             72             18        

  Leather(31)            -65       -236       -203       -37                 -23            -30            -61            -7             -3             -37            -5        

  Tobacco Manuf(21)      -33       -94        -80        -13                 -61            -5             0              0              0              0              0         

  Railroad(40)           464       750        671        142                 113            166            96             80             13             35             26        

  Air Transp(45)         506       973        747        214                 140            -12            140            9              28             217            10        

  Printing(27)           1,044     2,081      1,811      814                 297            76             249            18             44             291            21        

  Other Trsp(44,46,47)   450       1,981      1,998      539                 346            197            242            49             48             467            108       

  Rubber(30)             2,714     4,646      3,527      596                 1,858          -200           815            119            44             311            -16       

  Trucking(42)           3,333     5,111      4,210      963                 1,058          406            923            198            37             484            141       

  Primary Metals(33)     2,072     6,098      4,691      1,126               750            1,447          534            121            166            253            295       

  Misc Prof(81,87,89)    52,749    4,629      4,734      3,341               979            -784           1,369          92             123            -494           106       

  Chemicals(28)          3,136     7,290      5,276      -391                2,999          932            2,938          188            -88            -1,372         69        

  Misc. Busi. Serv(73)   12,266    15,562     8,883      5,858               1,410          -3,593         2,092          -81            194            3,050          -48       

  Public Utilities(49)   6,769     10,467     10,621     2,764               1,544          2,088          2,262          581            257            871            253       

  Instruments(38)        3,045     16,016     14,005     6,443               880            2,449          1,027          572            501            1,868          265       

  Fabricated Metals(34)  6,566     24,083     21,048     4,572               3,071          8,013          2,962          214            255            1,757          205       

  Wholesale(50,51)       19,155    30,035     22,468     6,921               4,825          4,080          2,666          1,244          317            1,410          1,006     

  Stone,Clay,Etc.(32)    4,612     26,659     25,264     5,594               5,188          6,453          3,313          1,040          696            2,209          771       

  Real Estate(65)        336       37,894     41,347     9,892               8,806          8,042          5,234          1,769          1,542          4,218          1,843     

  Mach. & Computers(35   17,552    50,778     45,441     9,799               8,310          12,970         2,229          2,292          731            7,734          1,376     

  Medical(80)            4,103     59,722     58,508     17,937              8,238          12,657         6,021          2,099          1,571          7,920          2,063     

  Elect. Equipment(36)   16,567    71,425     64,704     15,636              12,375         11,805         10,750         1,835          1,813          9,695          795       

  Gov\'t & Farm          7,516     48,765     47,547     14,727              6,908          9,266          7,371          1,250          703            6,455          811       

  Total                  152,676   -147,556   -200,029   -28,706             -30,303        -63,329        -6,714         -14,125        -11,102        -28,629        -17,725   
  ---------------------- --------- ---------- ---------- --------- --------- --------- ---- --------- ---- --------- ---- --------- ---- --------- ---- --------- ---- --------- -------

 

Table 1-13 presents the potential employment impacts in the years 2002,
2010 and 2014 based on establishment size. The change in total
employment across the United States for small establishments (less than
100 employees) and large ones is given.

  ------------------------ --------------- -------------- ----------------
  Table 1-13. Employment                                  
  Changes by Establishment                                
  Size Category for \$90                                  
  billion Scenario\                                       
  (Small Establishments                                   
  are \<100 Employees)                                    

  Establishment Size       Employment                     
                           Impacts by                     
                           Establishment                  
                           Size                           
                           (jobs/year)                    

                           2002            2010           2014

  Small Establishment      114,000         -166,000       -210,000

  Large Establishment      31,000          -30,000        -38,000

  Gov't & Farm             7,500           49,000         48,000
  ------------------------ --------------- -------------- ----------------

 

Table 1-14 presents the potential employment impacts for a number of
broad occupational categories in the years 2002, 2010 and 2014. The
changes in total employment across the United States for these
occupational types reflect increases in employment for all job
categories due to the increased demand for construction and maintenance
activities associated with PM and ozone control measures. Employment
impacts in the year 2010 illustrate the effects following completion of
capital projects aimed at achieving the proposed standards, as payments
on the capital expenditures and O&M costs begin to outweigh the
beneficial economic impacts associated with control measures. The net
job losses stabilize at about -200,000 by 2014. Cross-occupational
impacts of this qualitative nature would persist even if the Federal
Reserve were 100 percent effective in managing aggregate employment
effects.

  ------------------------- --------------- -------------- ---------------
  Table 1-14. Changes in                                   
  Available Jobs by                                        
  Occupational Type for                                    
  \$90 billion Scenario                                    

  Occupational Type         Employment                     
                            Impacts by                     
                            Occupational                   
                            Type (jobs/yr)                 

                            2002            2010           2014

  Blue collar               52,000          -64,000        -90,000

  Clerical/sales            38,000          -101,000       -114,000

  All construction related  27,000          1,400          2,500

  Professional/management   36,000          15,000         2,300

  Total                     153,000         -148,000       -200,000
  ------------------------- --------------- -------------- ---------------

 

The economic analysis also showed the relative employment effects for
jobs in five income ranges. Across all regions the general trend was
that jobs were lost from the lower two income quintiles while the higher
income quintile ranges either maintained or gained jobs.

 

 

 

***D. Personal Disposable Income Impacts Based on High End of Combined
Cost Range***

An economic impact analysis was also conducted for the high estimate in
our combined control cost range (\$150 billion per year). This section
focuses on results based on this \$150 billion per year control cost
estimate, which is incorporated into the economy as if it represents
\$6.3 billion for the PM~2.5~ partial attainment actions, \$60 billion
for VOC and NO~x~ controls to manage ozone, and \$83.7 billion for
additional PM controls. Table 1-15 describes the estimated impacts on
personal disposable income by year. As can be seen, the temporal pattern
of impacts is similar to that for the low cost scenario, but the
magnitude of impacts is increased roughly in proportion to the increase
in the overall annualized control cost. Loss in real disposable income
stabilizes at a level over \$450 *per capita*.

+---------------------+---------+--------+---------+---------+---------+
| Table 1-15. Real    |         |        |         |         |         |
| Disposable Income   |         |        |         |         |         |
| Impact Per Capita   |         |        |         |         |         |
| for \$150 billion   |         |        |         |         |         |
| Scenario            |         |        |         |         |         |
|                     |         |        |         |         |         |
| (1992 real dollars) |         |        |         |         |         |
+---------------------+---------+--------+---------+---------+---------+
|                     | Modeled |        |         |         |         |
|                     | Year    |        |         |         |         |
+---------------------+---------+--------+---------+---------+---------+
|                     | 2005    | 2010   | 2015    | 2020    | 2025    |
+---------------------+---------+--------+---------+---------+---------+
| Real Disposable     | -\$347  | -\$431 | -\$443  | -\$455  | -\$465  |
| Income Per Capita   |         |        |         |         |         |
+---------------------+---------+--------+---------+---------+---------+

 

The relative regional pattern in real disposable income per capita is
quite similar to the corresponding results for the \$90 billion scenario
shown in Table 1-11 above. Though the magnitude of the change in real
disposable income is correspondingly greater for each region in the
\$150 billion scenario, the level of impact for a given region relative
to the other regions across the United States is consistent between the
two scenarios.

 

***E. Employment Impacts Based on High End of Combined Cost Range***

As with the evaluation of employment impacts based on the low end of the
control cost range, the estimates relating to employment in the \$150
billion per year control cost scenario are presented for the years 2002,
2010 and 2014. With about 90 percent effectiveness on the part of
national monetary policy in stabilizing national employment impacts,
this high cost scenario projects potential job losses by 2014 of about
400,000. Table 1-16 displays the potential employment impacts based on
establishment size under the \$150 billion control cost scenario for the
years 2002, 2010 and 2014.

  ------------------------ --------------- ------------ ----------------------
  Table 1-16. Employment                                
  Changes by                                            
  Establishments Size                                   
  Category for \$150                                    
  billion Scenario (Small                               
  Establishments are \<100                              
  Employees)                                            

  Establishment Size       Employment                   
                           Impacts by                   
                           Establishment                
                           Size (job/yr)                

                           2002            2010         2014

  Small Establishments     193,000         -306,000     -389,000

  Large Establishments     52,000          -56,000      -73,000

  Gov't & Farm             13,000          84,000       82,000
  ------------------------ --------------- ------------ ----------------------

 

Table 1-17 shows the potential employment impacts for four broad job
categories for the same years 2002, 2010 and 2014. Note that the change
in 2010 and 2014 in total employment among the blue collar and
clerical/sales occupational categories is a net job loss, while the
construction-related and professional/management occupational categories
still experience some job gains due to the demand for O&M activities
associated with PM and ozone control measures.

 

  ------------------------- -------------- -------------- -----------------
  Table 1-17. Changes in                                  
  Available Jobs by                                       
  Occupational Type for                                   
  \$150 billion Scenario                                  

  Occupational Type         Employment                    
                            Impacts by                    
                            Occupational                  
                            Type (jobs/yr)                

                            2002           2010           2014

  Blue collar               89,000         -123,000       -174,000

  Clerical/sales            64,000         -179,000       -205,000

  All construction related  44,000         380            1,000

  Professional/Management   61,00          23,000         -2,400

  Total                     258,000        -278,000       -381,000
  ------------------------- -------------- -------------- -----------------

 

In conclusion, the qualitative nature of both the \$90 billion per year
and \$150 billion per year control programs is quite similar, with the
key differences being in the quantitative magnitude of each effect.
Although there is a potential for net employment impacts at the national
scale that are small in percentage terms, the most relevant and enduring
impacts are those associated with losses in real disposable income per
capita. Real disposable income declines steadily from the year that
implementation is initiated, regardless of any off-setting economic
activity due to control measures. The losses in real disposable income
are likely to be experienced in all regions, and to fall most heavily on
less skilled labor classes and smaller types of businesses. Ultimately,
the economic burden of the control costs appears to fall
disproportionately on the retail and service sectors, which are the
least commonly associated with the emissions that are the target of the
control programs.

The cost estimates and associated projections of economic impacts
described in this report are the result of a brief initial analysis
conducted with minimal information regarding the specific nature of the
requisite underlying control actions, and minimal information regarding
the specific regions to which they should be attributed. The simulation
of how the costs would filter into the economy was performed using
reasonable assumptions based on the best available data. A number of
sensitivity analyses were performed on the ways that these costs may
filter back into the economy as increased demand, and only minor changes
in the impacts were observed. However, further analysis is warranted,
particularly with regard to alternative ways of attributing the control
costs to economic actors, sectors, and regions. A substantial
improvement in these assumptions could be achieved if EPA were to
release more complete and disaggregated information about the results of
the air quality and control cost modeling results that it only
summarizes in its PM and ozone RIAs. Also, the potential for requisite
lifestyle and infrastructure changes should be considered, and included
in sensitivity cases, as well as the potential for changes in the
relative amenity values of some locations that may have the worst
current air quality levels.

 

Part 2

**Assessing the Benefits of the Proposed Ozone and Particulate
Standards**

**I. Summary**

The ozone and PM Regulatory Impact Analyses (RIAs) provide monetized
estimates of the national benefits of the proposed ozone and PM~2.5~
standards, respectively. Although there is enormous uncertainty
regarding the quantification of health effects from both ozone and
PM~2.5~, EPA estimates and communicates about these benefits primarily
in point estimate form, with minimal reference even in the text to the
nature or implications of the uncertainties. This point estimate
representation conveys an unwarranted impression of precision in the
numerical estimate. The RIAs suggest that EPA's estimates are not
conservative, and that the direction of error is unknown.

This section explores uncertainties in both the ozone and PM benefit
estimates, and re-estimates the national benefits under a range of
alternative scenarios that are each at least as plausible as the
specific point estimates released by EPA. The additional analyses
summarized in this section demonstrate that range of possible benefits
is very wide for both the proposed ozone and PM~2.5~ standards. Most of
scenarios lead to benefit estimates lower than EPA's benefit point
estimates, and many of them are much lower. There are also a few
alternative interpretations of the available health effects evidence
that lead to higher benefit estimates than EPA has published. Overall,
presentation of the full range of possible benefits gives a much more
informative summary of the current state of scientific knowledge than
the point estimates that EPA has provided.

To allow for comparison with the cost estimates developed in Part 1 of
this report, this benefit analysis relates solely to the estimated
benefits of full attainment of the proposed standards. The benefit
estimates are for potential health and welfare benefits that are
incremental to achieving the current ozone and PM~2.5~ standards. As
with EPA's analysis, benefits are only estimated above the background
air concentration level.

Figure 2-1 compares the benefit estimates provided by EPA (the large
stars in the figure) with the re-estimated benefits for the many
alternative plausible scenarios considered in DFI's benefit analysis
(each scenario reflects a separate combination of the key uncertain
assumptions).

![](media/image3.png){width="5.873611111111111in" height="4.5in"}

Insights of this benefit analysis, which are observable in Figure 2-1,
or otherwise described in this section of the report, include:

-   The estimated health benefits from the proposed PM~2.5~ standard
    range from a value well below \$1 billion to a high of about \$100
    billion per year. Most of the estimates are less than \$40 billion
    per year.

```{=html}
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```
-   The range in which the health benefits from the proposed ozone
    standard are likely to be found is similar to that stated by EPA:
    from less than \$0.05 billion to about \$2 billion per year.
    However, EPA did not clearly communicate that its own \"most
    likely\" ozone benefits value is near the low end of this range.

```{=html}
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```
-   The ranges of benefit estimates do not change even if one decides to
    accept the epidemiological findings as a quantified representation
    of fully causal relationships.

```{=html}
<!-- -->
```
-   After accounting for the various uncertainties, the choice of
    \"long-term\" or \"short-term\" mortality in the PM analysis has
    relatively little impact on the estimated range of benefits, even
    though the PM RIA suggests that this is a \"particular\" source of
    uncertainty.

```{=html}
<!-- -->
```
-   There are a substantial number of scenarios in which the welfare
    (nonhealth) benefits of the standard may exceed the health benefits,
    for both PM and ozone. (The welfare benefits are included in the
    benefit estimates shown in Figure 2-1. No uncertainty analysis was
    performed with respect to the welfare benefits in this analysis.)

```{=html}
<!-- -->
```
-   Although EPA argues that its benefit estimates are understatements
    because of the numerous unquantified or nonquantifiable benefits,
    there is little evidence that the ranges would be sensitive to
    addition of these other benefits: the ranges of benefits are
    determined almost entirely by the potential for avoided adverse
    health effects, particularly mortality.

```{=html}
<!-- -->
```
-   No attempt has been made to combine the ranges for PM and ozone.
    However, it is unlikely that the top ends of the ranges should be
    added together. The benefits estimate for ozone is due almost
    entirely to assuming a mortality effect that is highly confounded
    with estimates of potential PM mortality. Thus, if one wishes to
    attribute mortality to ozone, then one must consider reducing the
    mortality benefits attributed to PM.

This re-assessment of EPA's benefit estimate found it useful to
distinguish between (1) errors in, or disagreements about, appropriate
benefit analysis assumptions and (2) uncertainties in the underlying
scientific research that can affect the benefits estimate, but for which
there is little ground for establishing one \"correct\" value within the
range of uncertainty. In re-estimating the benefits, issues of the first
type are communicated by showing the national benefit estimate with and
without the assumptions and judgments preferred in this analysis. The
second type of uncertainty is communicated by displaying the full range
of benefit estimate that result from possible combinations of these
uncertainties. This analysis makes no attempt to assign probabilities to
uncertainties.

This analysis makes adjustments for the following types of errors and
disagreements: use of incorrect relative risk estimate for long-term
mortality for PM~2.5~; excessively high valuation on mortality through
use of \"numbers of deaths\" as opposed to the economically more valid
estimate of \"years of lost life expectancy\"; incorrect estimation of
PM~10~ reductions, and unjustified attribution of benefits to reduction
in the coarse fraction of PM~10~. The uncertainty ranges in Figure 2-1
were estimated after making reasonable adjustments for these
considerations. If the reader wishes to accept all of EPA's judgments on
the above matters, this report also provides uncertainty ranges that
result when using EPA's assumptions.

The ranges shown in Figure 2-1 reflect all the combinations of the
following sources of uncertainty: potential presence of an undetectable
threshold in epidemiologically derived results; alternative estimates of
relative risk across studies; uncertainty ranges on relative risk within
the single study selected by EPA for each benefits estimate; uncertainty
regarding the degree of confounding, and the potential that there is no
causal relationship in epidemiologically derived results. The last of
these uncertainties does not affect the ranges that are derived, and
detailed results are provided in this report both with and without
uncertainty about causality. This is to reassure readers that the lower
estimated benefits ranges have not been predetermined by assumptions
that the relationships may not be causal.

While there are many more sources of uncertainty, those which have been
included in this analysis are the ones that have been demonstrated by
EPA to have the largest potential impact on the estimates (i.e., they
are the most \"sensitive\" of the analysis assumptions). Thus, the
ranges of benefits presented here are unlikely to be dramatically
altered by inclusion of other uncertainties. Again, it is the ranges
rather than any specific point estimates that give the most relevant
context within which to evaluate the merits of the proposed standards.

 

**II. General Quality Concerns**

Before presenting the detailed assumptions and analysis results for
PM~2.5~ and ozone individually, it is worth discussing three key
concerns that are common to both benefit analyses:

1.  the declining quality of oversight and peer review from the
    underlying science summaries (i.e., the Criteria Documents) through
    the RIAs;

2.  lack of accountability and public access; and

3.  the difficulties imposed on benefits estimation when the sole
    underlying evidence is epidemiological.

 

***A. Poor Quality of Oversight and Peer Review in Benefit estimates***

The process of developing a scientific basis for the National Ambient
Air Quality Standards has been held up by EPA as an example of \"peer
review on peer review.\" Indeed, the underlying summary of the science,
called the Criteria Document, does receive an extensive review and
oversight by a panel of prominent professionals known as the Clean Air
Scientific Advisory Committee (CASAC). Further, it is very difficult for
any information or finding to be included in the Criteria Document
unless it has first been published in an accepted peer-reviewed journal.
Thus, the quality of the Criteria Document is usually considered quite
high. The Criteria Document is prepared by EPA's Office of Research and
Development.

The Staff Paper, by contrast, is prepared by a different EPA office, the
Office of Air and Radiation. It is intended as a bridge from the state
of scientific knowledge to a policy interpretation. Although the Staff
Paper does undergo a formal public comment period, the only true peer
review that it receives is related to its summary of the science. CASAC
evaluates the Staff Paper only in terms of whether it properly
summarizes the science, and does not comment on its policy
interpretations.

The information in the Regulatory Impact Analysis, which is released at
the time that a standard is formally proposed, is not peer-reviewed at
all. The Regulatory Impact Analysis (RIA), in fact, is not even a part
of the formal process for setting a National Ambient Air Quality
Standard. Rather, it is required under an Executive Order. Thus, the RIA
is generally given substantially less oversight and peer review, yet the
information in the RIA often receives the greatest amount of public
attention. There are few, if any, procedural mechanisms to assure that
the benefits information used in an RIA properly reflect the state of
science, or properly reflect the warnings and caveats of the Criteria
Document.

As will be discussed in various other parts of this section, this lack
of oversight and peer review seems to result in many areas of criticism
for the RIA benefit estimates. For example, over 95 percent of the
estimated benefits for ozone are based on a single study that was not
performed to address ozone mortality, and which was never cited or
discussed in the ozone Criteria Document. Similarly, there are many
warnings in the PM Criteria Document against using epidemiological
results as well-defined quantitative dose-response relationships; yet
that is exactly what has been done in the benefit estimates of both
RIAs.

 

***B. Lack of Accountability and Public Access***

Lack of peer review is an obviously critical concern. However, this
concern is exacerbated by the fact that there also are no apparent
mechanisms to encourage that EPA be accountable for performing credible
and reproducible benefit or cost estimates. EPA has quite clearly
decided not to release major portions of the intermediate computations
and data that lie behind its national cost and benefit estimates. Within
the limited time frame between the release of the RIAs and the final
rule making, it is infeasible to complete the process that does exist to
obtain the relevant pieces of information from EPA (e.g., via a Freedom
of Information Act request). The public must therefore trust EPA or make
do with the small pieces of the puzzle that EPA voluntarily decides to
release. Thus, it is impossible for any outside party to actually
reproduce any of the national benefit estimates.

Because basic data and assumptions of the RIA are not publicly
available, there have been many computational steps in this
re-assessment that have required analyst judgment, approximations, and
the use of information sources that may not be precisely the same as
those used by EPA. Thus, there is an inherent likelihood that EPA would
generate different results for each uncertainty scenario. However, at
each step of this re-assessment, conservative judgments have been
selected (i.e., judgments that can be expected to overstate the health
benefits). Thus, one can expect that the picture of uncertainty that is
drawn in this analysis would be unlikely to change dramatically if EPA
were to allow broader public access to its data, computations, and
assumptions.

 

***C. Difficulties with Using Epidemiological Results for Benefit
estimates***

Despite warnings in EPA's Criteria Documents about the need for great
caution in using epidemiological results as the sole basis for
quantitative dose-response functions, the vast majority of the PM and
ozone benefit estimates are based solely on such results. In the case of
PM~2.5~, there is no accepted argument for the biological plausibility
that mortality may be affected by changes in low concentrations of
particles, and little credible evidence of a toxicological nature
either. The lack of supporting scientific understanding has caused
numerous scientific experts to suggest that there is not yet even a
strong case that these associations are causal in nature. In other
words, there may not even be a dose-response relationship for these
effects. In the case of ozone, the Criteria Document even states that
there is no clear epidemiological evidence of a relationship of ozone
with mortality, yet mortality benefits based on epidemiological evidence
account for \$2.25 billion of the RIA's \$2.29 billion \"high\" estimate
of the health benefits of the proposed ozone standard. This last example
highlights the types of problems that occur when there is no form of
accountability on the part of EPA to ensure that RIA's benefit estimates
reflect a consistent view of the state of the science as expressed in
the peer-reviewed materials also released by EPA.

There have been several papers published in peer-reviewed journals that
explain how epidemiological studies can produce an erroneous or biased
estimate of risk relationships. There are also statistical proofs of the
potential for bias. Less technical and more intuitive examples can be
provided that demonstrate how easy it would be for epidemiological
evidence of ambient air quality health impacts to appear consistently in
numerous different locations, yet not reflect any true causal
relationship at all. The Criteria Document itself recounts some of these
problems with using epidemiological evidence. Thus, there is a
significant body of evidence that epidemiologically derived risk
estimates are subject to error so substantial as to leave open a
reasonable doubt as to causality itself. Even stronger evidence is
present that there may be substantial bias in the estimated
relationships with PM or ozone, such that one or more other
unaccounted-for factors may be reflected in the PM or ozone risk
estimate. The following uncertainty analyses address the potential
impact of these concerns on potential benefits from the proposed ozone
and PM~2.5~ standards.

 

**III. Benefits Of The Proposed PM**~2.5~ **NAAQS**

 

***A. Current and Proposed Standards***

The current particulate matter standard is based on PM~10~, or particles
less than 10 microns in diameter. The standard is a maximum of 50
μg/m^3^ annual mean, averaged over 3 years, and 150 μg/m^3^ 24-hour
average, with no more than one expected exceedence per year.

The proposed standards would retain the 50 μg/m^3^ annual mean standard
for PM~10~, along with a 24-hour PM~10~ standard of 150 μg/m^3^ at the
98th percentile. Most importantly, new standards for the fine fraction
of particulate matter, PM~2.5~, would also be imposed. These would be at
the levels of 15 μg/m^3^ annual mean, with a 24-hour standard of 50
μg/m^3^ at the 98th percentile. According to the EPA Administrator, this
new combination of annual and peak PM~10~ and PM~2.5~ standards would
\"protect public health with an adequate margin of safety.\"

In the proposed rule, EPA affirms that the current levels of the PM~10~
standards are already adequately protective for any effects that might
be associated with the coarse fraction. For example, it states that
\"the clearest community epidemiological evidence regarding coarse
fraction particles finds such effects only in areas with numerous marked
exceedences of the current PM~10~ standard.\" EPA also indicates that
there is no evidence suggesting that the coarse fraction of PM~10~ might
be associated with mortality: \"in contrast to fine particles, coarse
fraction particles are more clearly linked with certain morbidity
effects at levels above those allowed by the current 24-hour standard.\"
Thus, the only change in the proposed rule that is relevant from the
perspective of calculating benefits is that associated with fine
particles specifically. In the cost and benefit analyses of the PM RIA,
EPA correctly first brings all of the areas that are out of attainment
with the current PM~10~ standard into attainment, and then estimates
benefits (and costs) associated with the new proposed PM~2.5~ standards
incrementally from that starting point.

 

***B. EPA's National Benefits Categories and Estimates***

EPA's PM RIA develops estimates of the national benefits of the proposed
PM~2.5~ standards for a number of health effects categories. In
addition, the national benefits estimate includes two nonhealth benefits
(\"welfare benefits\") categories. The RIA develops estimates of the
avoided incidence or severity of each of the categories when the
proposed standard is applied. It then multiplies each by a monetized
value for the respective endpoints, and sums them to achieve an overall
estimate of total national benefits in the year 2007. It should be noted
that EPA's estimates of avoided incidences in each category, and thus
the estimate of total benefits, provide only a point estimate, with no
range of uncertainty. The overall list of benefits categories used in
EPA's benefits estimate is provided in Table 2-1, along with information
on the basis for the studies.

 

+------------------------+-------------+---------+----------+---------+
| Table 2-1. Summary of  |             |         |          |         |
| EPA's PM~2.5~ National |             |         |          |         |
| Benefit estimates for  |             |         |          |         |
| Full Attainment of the |             |         |          |         |
| Proposed PM~2.5~       |             |         |          |         |
| Standard, and Their    |             |         |          |         |
| Scientific Basis       |             |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Benefits Category      | Estimated   | \% of   | Data     | Sci     |
|                        | Benefits in | Total   | Basis    | entific |
|                        | RIA (\$     |         |          | Basis   |
|                        | millions    |         |          |         |
|                        | p.a.)       |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Mortality              | \$22,485    | 32%     | PM~2.5~  | epidem. |
|                        | (S-T)       | (S-T)   |          |         |
|                        |             |         |          |         |
|                        | \$ 97,319   | 68%     |          |         |
|                        | (L-T)       | (L-T)   |          |         |
+------------------------+-------------+---------+----------+---------+
| Chronic Bronchitis     | \$43,448    | 63 to   | PM~10~   | epidem. |
|                        |             | 30%     |          |         |
+------------------------+-------------+---------+----------+---------+
| All respiratory        | \$63        | \< 0.1% | PM~2.5~  | epidem. |
| hospital admissions    |             |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Congestive heart       | \$35        | \< 0.1% | PM~10~   | epidem. |
| failure hosp. admis.   |             |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Ischemic heart disease | \$49        | \< 0.1% | PM~10~   | epidem. |
| hosp. admis.           |             |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Upper Respiratory      | \$1         | \< 0.1% | PM~10~   | epidem. |
| Symptoms               |             |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Lower Respiratory      | \$3         | \< 0.1% | PM~10,~, | epidem. |
| Symptoms               |             |         | PM~2.5~  |         |
+------------------------+-------------+---------+----------+---------+
| Acute Respiratory      | \$306       | 0.4%    | PM~10~   | epidem. |
| Symptoms               |             |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Acute Bronchitis       | \$2         | \< 0.1% | PM~10~,  | epidem. |
|                        |             |         | PM~2.5~  |         |
+------------------------+-------------+---------+----------+---------+
| Shortness of breath    | \$1         | \< 0.1% | PM~10~   | epidem. |
+------------------------+-------------+---------+----------+---------+
| Moderate or Worse      | \$8         | \< 0.1% | PM~2.5~  | epidem. |
| Asthma Status          |             |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Restricted Activity    | \$54        | \< 0.1% | PM~2.5~  | epidem. |
| Days                   |             |         |          |         |
+------------------------+-------------+---------+----------+---------+
| Visibility             | \$1401      | 1 to 2% | light    | physics |
|                        |             |         | ex       |         |
|                        |             |         | tinction |         |
+------------------------+-------------+---------+----------+---------+
| Household soiling      | \$931       | 1%      | TSP      | econ    |
| damage                 |             |         |          | ometric |
+------------------------+-------------+---------+----------+---------+
| TOTAL                  | \$68,786    | 100%    |          |         |
|                        | (S-T)       |         |          |         |
|                        |             |         |          |         |
|                        | \$143,620   |         |          |         |
|                        | (L-T)       |         |          |         |
+------------------------+-------------+---------+----------+---------+

 

The overall national benefits estimate that results is either \$69
billion or \$144 billion per year. The range depends on what \"choice\"
of mortality estimate is used rather than any form of uncertainty. There
are two types of mortality studies:

-   One type looks at changes in mortality as the daily levels fluctuate
    in a single city. This is called \"short-term mortality\" (S-T)
    because it is associated with day to day variations, and such
    deaths, if they are really *causally* associated with the PM
    variations, would be associated with acute responses to brief but
    high levels of ambient PM.

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-   The second type of mortality study looks at changes in average
    mortality rates from city to city, and associates differences in
    these average rates with differences in average PM levels between
    cities. This is called \"long-term mortality\" (L-T) because it
    appears to reflect the effect of chronically higher exposures to
    ambient PM, and does not necessarily capture only acute responses to
    peak ambient PM episodes.

Assuming that this conceptual construct is a valid one, logic says that
\"long-term\" mortality estimates would encompass \"short-term\" effects
as well as any effects associated with chronically higher exposures.
Thus, one should not add short-term and long-term mortality benefits.
EPA therefore reports two estimates of total benefits, one which uses
short-term mortality, and one which uses long-term mortality.

The higher national benefits estimate of \$144 billion is based on
long-term mortality. EPA cites the fact that it is higher than the
short-term estimate as evidence that there is probably an incremental
chronic mortality effect in addition to an acute response to peak
ambient levels of PM. However, as this analysis will demonstrate, when
uncertainty and errors are accounted for, there is actually quite little
difference between the two types of mortality estimates.

For either estimate of mortality, the combination of mortality and
chronic bronchitis collectively sum to over 95 percent of the total
benefits. Because mortality and chronic bronchitis are by far the most
dominant components of the total benefit estimates, this analysis
focuses on the range of uncertainty and sensitivity brought about by
uncertainty in these two effects categories. Additional uncertainty is
also present in incidence or valuation of other benefits categories, but
it is highly unlikely that these uncertainties would substantially
change the ranges of benefits. It is, however, interesting to note that:

-   The basis for almost all of the benefits is epidemiological studies.
    Not one of the health effects risks appears to be founded on
    evidence of a clinical or laboratory nature. Thus, all of these
    health estimates are subject to the potential biases and causality
    uncertainties described in the preceding section.

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-   Many of the underlying dose-response relationships were derived from
    studies using PM~10~ (or worse, TSP) rather than PM~2.5~. The
    attribution of the effects observed in most of these studies to the
    fine fraction of PM is *postulated* by EPA, rather than supported by
    the underlying epidemiological studies themselves.

These points alone should serve to highlight the degree of uncertainty
that is inherent in EPA's benefit estimates. However, it is still useful
to explore these uncertainties in a quantitative fashion, both with and
without consideration of the problematic issue of whether there is a
causal relationship between these potential benefits categories and
current levels of ambient PM~2.5~.

Figure 2-1 has already provided a summary of the overall quantitative
results of this exploration of uncertainties in the benefit estimates.
The rest of this section will provide a description of the analytical
approach used to derive these results, and will provide more detailed
sets of results that underlie Figure 2-1 for PM. This section is
followed by a similar section that focuses on the uncertainty analysis
of EPA's ozone benefit estimates.

 

***C. Overview of Analysis Methodology for PM Benefit Re-Assessment***

This analysis focused on developing an understanding of the major
uncertainties in EPA's national benefit analysis which could impact the
overall benefit estimates. In addition, several of the assumptions
underlying EPA's analysis were revisited, and in some cases alternative
assumptions were developed which the authors viewed as either more
correct or more appropriate. The ultimate product of this analysis is a
set of national benefit estimates (both in terms of both incidence and
monetized) which reflect a plausible *range* of outcomes if the proposed
rule were to be fully implemented. No single point within any of the
ranges is viewed as \"most likely,\" but the range itself, and the bulk
of the individual alternative scenarios, provide more accurate and
useful information than the single point estimate provided by EPA. The
results presented here are thus more relevant for informing the policy
making process.

The analysis that is reported in EPA's RIA calculates benefits on a
county level, then aggregates to national estimates. However, the
details of the underlying county-level data are not publicly available.
EPA does not even report the identity of the counties that are viewed as
being in nonattainment before or after controls on PM precursors are
applied. This makes it impossible for outside parties to exactly
replicate EPA's numerical results, or to check the accuracy of EPA's own
calculations. Even aspects of the dose-response formulas are
insufficiently documented.

DFI made verbal requests to EPA for relevant pieces of supporting data.
EPA consistently refused to provide underlying data on the basis of such
requests. However, most queries regarding how the calculations were
actually performed were addressed well enough for DFI to develop an
equivalent set of benefits computations for a few individual cities, and
for a few \"representative\" hypothetical cities. The sensitivities of
impacts estimates in this set of real and hypothetical cities were
assessed, and used to estimate the aggregate sensitivity of the
national-scale impacts estimates. Where EPA refused to release certain
source and intermediate-results data necessary to replicate its
methodology, DFI made use of external data or technical judgments, which
are noted in this report. Obviously, the following assessment of
uncertainties could have been much more rigorous if EPA would release
more specific supporting information rather than just a national
aggregate result.

The following steps were undertaken in this analysis. Succeeding
sections provide more discussion of each step.

-   We reviewed EPA assumptions and adjusted for methodological errors
    and inappropriate valuations.

-   We investigated key scientific and epidemiological uncertainties
    which could affect benefits, and developed reasonable ranges for
    uncertain parameters.

-   We determined sub-populations which receive benefits, and developed
    a methodology for aggregating to national estimates.

-   We assessed the sensitivity of health effects incidences to the key
    uncertainties.

-   We aggregated incidences to determine changes in EPA's national
    incidence estimates.

-   We applied values per incident to develop ranges of national
    monetized benefits.

In the discussion, occasional references are made to specific health
effects studies that are used in the benefits estimation process. These
studies are referred to by first author and date. Appendix 3 provides
the full reference for each of these studies.

 

***D. Adjustments to Correct for Errors and Inappropriate Valuation***

There are two basic types of problems with EPA's PM benefit estimates.
The first type is the use of assumptions and judgments that the authors
believe are unjustified or erroneous. The second type is pure
uncertainty that remains in the evidence being used to derive benefit
estimates. In the first step, DFI made adjustments to reflect errors in
assumptions and judgments that a large number of public groups who
submitted formal comments to EPA agree are inappropriate. The result is
an alternative point estimate that is denoted as the \"DFI
valuation/assumptions baseline\" from which uncertainty analysis
scenarios then proceed. The rationale for specific changes to the
valuation or assumptions is detailed in this section. Their impact in
changing EPA's nominal benefit estimates (before any sensitivity
analysis is applied) is also discussed. Figure 2-2 shows the progression
of changes in the nominal estimates as assumptions are changed from EPA
to DFI's baseline.

**PA's estimate of benefits in the RIA.** As was discussed above, Table
9.11 of the PM RIA estimates full attainment benefits using two
different measures of mortality, short-term (S-T) and long-term (L-T).
EPA's point estimates for annual national benefits in the RIA (after
rounding to the nearest billion) are:

Total Benefits of full attainment, counting S-T mortality: \$69 billion

Total Benefits of full attainment, counting L-T mortality: \$144 billion

**Correction of L-T mortality risk calculation.** EPA has acknowledged
an error in its L-T mortality estimates, stemming from an error in its
interpretation of the underlying epidemiological study (i.e., Pope *et
al.*, 1995). That study developed its risk estimates relative to median
concentrations of PM~2.5~, rather than mean concentrations. EPA's
benefits estimation for L-T mortality used the relative risk results
from this study as if they applied to mean concentrations. Roughly
speaking, the mean concentration in ambient PM data is about 25 percent
higher than the median concentration (reflecting the fact that ambient
concentrations tend to be somewhat skewed in distribution, rather than
normally distributed). EPA has publicly announced that correction of
this error will result in an approximate reduction of the avoided L-T
deaths from 20,000 to 15,000---a 25 percent decrease. The uncertainty
analysis in *this* report is based on the corrected estimate for L-T
mortality. After applying this 25 percent to the L-T mortality benefit
estimate in the RIA (\$97 billion), the aggregation benefits estimate
(shown as the second set of bars in Figure 2-2) becomes:

Total benefits of full attainment, counting S-T mortality: \$69 billion

Total benefits of full attainment, counting L-T mortality: \$120 billion

 

We also incorporate this correction into the EPA point estimates which
appear throughout the remainder of this section on particulate matter
benefits.

**Adjustment to Value of Life estimates**. EPA uses \$4.8 million per
estimated reduction in mortality. There are a number of reasons to
believe that this is an overly high value. Economists have long
advocated estimating benefits on the basis of \"years of lost life\" or
\"quality adjusted loss of life-years\" rather than on \"numbers of
lives lost,\" simply because this is the only way to appropriately
reflect the fact that some forms of life risk are clearly of greater
concern than others. This preference among economists is reflected in
key current-Administration communications on this issue:

-   Expert economists reviewing EPA's draft report *The Benefits and
    Costs of the Clean Air Act, 1970-1990* (the \"Retrospective Study\")
    have argued that mortality impacts in that study should be valued
    according to expected years of lost life rather than numbers of
    lives lost.

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-   The Office of the President's guidelines for economic analysis in
    RIAs also suggest the importance of considering value based on
    expected lost life-years in contrast to value based on a random
    \"statistical life.\"

The problems of using \"numbers of lives lost\" are highlighted in the
case of PM. All attempts to develop an argument for biological
plausibility that PM can cause mortality have relied on the idea that
the affected individuals are severely ill, and on the verge of dying due
to pre-existing disease or age; elevated PM effectively becomes the
\"last straw\" rather than a specific precipitating event. Thus, even by
EPA's judgment, any deaths that might (temporarily) be avoided by the
proposed regulation, would accrue disproportionately to the elderly or
very ill. However, the valuation estimate used by EPA reflects a
randomly lost life, and encompasses risks that might involve a much
greater shortening of lifespan than appears to be justified for PM
mortality impacts. For this reason alone, there is an argument for using
a substantially lower value per mortality incident in the PM benefits
estimate.

In addition, some have suggested that the \$4.8 million per
\"statistical life lost\" may itself be too high. One criticism is that
EPA's value-of-life figure is based partly on \"contingent valuation\"
studies rather than market-based behavior. There are many sources of
market-based information about willingness to pay to avoid a loss of
life, and these studies also find a lower value than that produced by
the hypothetical and nonbinding survey approach that contingent
valuation relies on. Even market-based estimates, however, should be
carefully scrutinized for the impact of age on the estimate, and should
be converted into a value per year of lost life expectancy before being
applied to the PM situation.

One set of comments submitted to EPA on the PM RIA noted two alternative
lower values that may be more appropriate, but which also appear to
overstate the benefits if one were to account for likely differences in
years of lost life expectancy. These are: \$2.7 million per statistical
life from the Department of Transportation (which would then need to be
adjusted for the lesser average years lost for pollution rather than
transportation risks), and \$1.3 million for an average of 12 years of
lost life from the National Research Council. This information would
suggest that an equivalent value, after adjusting for a risk that would
imply only a few years of lost life expectancy, might be in the range of
\$1 to 2 million.

Although the single \"correct\" value to use is ultimately a matter of
judgment, there appears to be substantial evidence that the \$4.8
million value in EPA's analysis is inappropriately high for application
in the case of PM-related benefits. The main reason it may be too high
is that it does not account for the much briefer life shortening that
appears to be likely for any potential PM-related deaths. A supporting
but more minor reason is that this estimate does incorporate values
based on contingent valuation, even though there are a substantial
number of market-based value estimates available. Comments filed in the
PM Docket include a substantial effort by the EOP Group, Inc. to develop
and apply an estimate of the PM mortality benefits using the more
desirable \"life-year\" analysis, that reflects the expected years of
life lost rather than numbers of deaths. EOP Group Inc. uses this
measure and specifically re-analyzes EPA's PM mortality benefits. Their
approach results in an effective adjustment to the \$4.8 million value
per \"statistical life lost\": while the dollar value assigned to each
estimated PM-associated \"death\" is lower than \$4.8 million, the
underlying value per random statistical life remains at \$4.8 million,
just as EPA has used. EOP Group Inc. analyzed S-T and L-T benefits
separately, and their estimates of total mortality benefits were:

*For S-T mortality*: RIA estimate \$22.5 billion ⇒ EOP estimate \$9.2
billion (59% less)

*For L-T mortality*: RIA estimate \$97.3 billion ⇒ EOP estimate \$18.6
billion (81% less)

Thus, the valuation suggested is not the \$4.8 million per death of a
random individual in the entire population, but reflects the probably
substantially lesser life-years saved for the particular population
which will be accruing benefits under the proposed standard. The
implicit value per statistical life lost *due to PM impacts* would thus
be \$0.9 million for L-T mortality and \$2.0 million for S-T mortality.
These estimates are clearly within the range of other valuations
suggested above, once those would also be adjusted for an even lesser
life-shortening.

Finally, because of the aforementioned peer-review comments on EPA's
\"Retrospective Study,\" recent revisions to that report have
incorporated sensitivity cases that purport to consider a
life-years-lost approach to valuation. While it was not possible to
review the new EPA method as part of this study, it appears that the
effective valuation for lives estimated to have been prolonged due to
all air quality improvements is about 50 percent of the \$4.8 million
per statistical life lost, or somewhere in the range of \$2.4 million.

Overall, it appears more appropriate to use the implicit values
developed by EOP Group Inc. rather than the \$4.8 million per
statistical life in the RIA. These estimates were founded on a detailed
analysis of the actual studies for PM, are clearly documented, and are
consistent with estimates that have been suggested by a range of others.
EPA's own approach in the \"Retrospective Study\" may result in somewhat
higher values per life-year lost, but (a) it was not possible to review
them during this study, and (b) they apply to all forms of mortality
associated with the past 20 years of air quality improvement, rather
than having been based specifically on the PM studies in question in the
PM benefit analysis.

The third set of bars in Figure 2-2 show the impact of using the EOP
Group Inc. life valuation on the aggregate benefits estimate. With this
change, the \"baseline\" benefit estimates (i.e., without yet accounting
for uncertainties) become:

Total benefits of full attainment, counting S-T mortality: \$56 billion

Total benefits of full attainment, counting L-T mortality: \$60 billion

Interestingly, this adjustment has caused the difference between the L-T
and S-T mortality estimates to be narrowed. This reflects the fact that
the larger numbers of estimated \"long-term\" mortality are actually
accrued over several years, while any \"short-term\" mortality would be
entirely accrued in a single year. It is also interesting to note that
morbidity impacts (specifically chronic bronchitis) now appear to
account for the majority of the total benefits estimate.

**Correction in Estimation of Benefits Associated with Changes in the
Coarse Fraction of PM.** Several of the categories of impact listed in
Table 2-1 are based on studies of PM~10~ rather than on PM~2.5~
specifically. This presents an interesting dilemma for the PM benefits
assessment. On the one hand, it seems more appropriate to apply these
risk associations to the original measure of air quality from which they
were derived. On the other hand, EPA claims quite clearly in its the
*Federal Register* notice of the proposed rule that it believes that the
current PM~10~ standard *does* adequately protect the public health from
the various morbidity impacts of the coarse fraction of particles. For
example:

> *the current annual PM~10~ standard offers substantial protection
> against both long- and short-term effects of coarse fraction
> particles\...qualitative evidence of other long-term coarse particle
> effects\...does not provide evidence of effects below the range of
> 40--50 μ g/m^3^\.... The main quantitative basis for a short-term
> \[PM~10~\] standard\...provide no basis to lower the level of the
> \[24-hour PM~10~\] standard below 150 μ g/m^3^\....retention of a
> 24-hour PM~10~ standard at the level of 150 μ g/m^3^\....would provide
> adequate protection against the short-term effects of coarse particles
> that have been identified to date.*

The appropriate interpretation would be that the only incremental
benefits to assess after the PM~10~ standard has been applied would be
those associated with the fine fraction, PM~2.5~. Nevertheless, in the
RIA, all of the benefits categories identified in Table 2-1 as being
based on PM~10~ data were estimated assuming that there are benefits
associated with reductions in PM~10~ even where PM~10~ is already below
the \"adequately protective\" PM~10~ standard. This portion of the
benefits should be removed from the estimates.

In addition, there is a more direct error in the analysis relating to
how the coarse fraction was handled. Rollback percentages are based on
model outcomes for PM~2.5~. The benefit analysis then assumes that
whatever rollback percentage is estimated for PM~2.5~ can also be
applied to PM~10~. However, most of the control measures being
considered in the RIA apply to precursors of secondary PM, and these
measures will not affect the coarse fraction in any substantial way.
Thus, the rollback for PM~10~ would be a smaller percentage than the
rollback for PM~2.5~. Thus, not only is EPA attributing benefits to
reductions in the coarse fraction that are inconsistent with its
statements that the culprit is in the fine fraction, but it is also
overstating the degree to which the coarse fraction would be reduced
when fine particle control measures are applied.

To adjust for this error, each category which uses PM~10~ in its
benefits calculations is multiplied by the fraction of PM~10~ that is
made up of PM~2.5~. The Natural Resources Defense Council reports that
PM~2.5~ as a fraction of PM~10~ ranges from 55 percent to 80 percent.
The more conservative 80 percent factor was chosen to avoid the chance
that this adjustment to the \"benefits baseline\" would be overly large.
As can be seen in Figure 2-1, the overall adjustment is small. However,
it would be increased substantially if one were to assume that PM~2.5~
were closer to 55 percent than 80 percent under ambient conditions,
since this adjustment affects the chronic bronchitis benefits, which are
now the largest portion of the aggregate benefits.

Benefits categories in the RIA which use PM~10~ as the indicator include
congestive heart failure, Ischemic heart disease, chronic bronchitis,
upper respiratory symptoms, any of 19 acute respiratory symptoms, acute
bronchitis, and shortness of breath, with total estimated benefits of
\$45 billion. Reducing this portion of the benefits by 20 percent gives:

Total Benefits of full attainment, counting S-T mortality: \$47 billion

Total Benefits of full attainment, counting L-T mortality: \$51 billion

The latter benefit estimates reflect what DFI believes are the most
appropriate assumptions and valuations, and thus form the baseline
around which the evaluation of uncertainties takes place. Note that
there are specific incidence estimates underlying these monetized
statements. The following uncertainty analysis is actually applied in
terms of the physical incidence estimates, and the uncertainty ranges on
the monetized benefits that have been reported thus far are built up
from the uncertainty in each type of morbidity or mortality incidence.
Thus, it is possible for readers to apply alternative judgments about
appropriate valuation and still have access to the uncertainty ranges
that result. Although the results presented in the summary section *are*
based on the baseline adjustments for value of mortality described
above, this section also provides uncertainty ranges using EPA's
original values. The fact remains that there is a far more
policy-relevant story to be obtained from the *range* of possible
benefits than from EPA's point estimate alone.

**Adjustments to Chronic Bronchitis Benefits.** Several commenters have
noted another potential error in EPA's benefits estimate for chronic
bronchitis, where it was believed that a higher *prevalence* rate was
being valued as if it reflected a higher number of new cases *annually*.
The net effect of such an error would be an over-valuation of the
chronic bronchitis benefits on the order of a factor of 10--15. If this
were indeed an error in EPA's analysis, then the estimated baseline
benefits would be reduced to as low as \$15 billion to \$20 billion per
year. DFI's efforts to confirm or refute this potential problem through
discussions with EPA, have been equivocal. In the interests of
conservatism, this possible error associated with the estimation of the
chronic bronchitis valuation has *not* been incorporated into the
adjusted benefits baseline described above.

On the other hand, in attempts to obtain proper documentation of the
chronic bronchitis risk analysis, we discovered that the single study on
which the chronic bronchitis benefits were based (Schwartz, 1993) was
not deemed acceptable for use in EPA's \"Retrospective Study\" mentioned
above*.* Another chronic bronchitis study (Abbey *et al.*, 1993) has
been substituted in the \"Retrospective Study.\" EPA has confirmed that
it now also intends to use the Abbey study in the revised PM RIA that
will be released at the time that it announces the final rulemaking
decision in July 1997.

Without the detailed county data, and without a complete documentation
for how the original chronic bronchitis benefits were computed, it is
impossible to estimate with any accuracy what the effect of using the
Abbey *et al.* study would be on the baseline benefits estimate. More
importantly, however, one should note that the change in choice of study
was a direct result of the peer-review process that the \"Retrospective
Study\" must go through, but which the RIA does *not* go through. If
there had not been another benefit analysis effort that *was* subject to
peer review, it is unlikely that there would have been any change made
to the PM RIA's benefit estimates for chronic bronchitis. Overall, this
situation highlights the problems that occur when a major and important
policy-relevant document is produced with little accountability for
accuracy or quality:

-   The chronic bronchitis benefit estimates account for about \$40
    billion of EPA's benefit estimates, yet they were not even mentioned
    in earlier risk estimates leading up to the PM RIA, and first
    appeared in the RIA itself.

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-   The chronic bronchitis benefits are based on a single study that
    appears to have been arbitrarily selected from the record; any
    dose-response estimates from that study has minimal quantitative
    accuracy, as the study controlled only for smoking and not for any
    other environmental consideration, including potentially confounding
    pollutants.

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-   The original chronic bronchitis study did not provide annual
    incidence risks; and the method by which EPA derived annual
    incidence from it (or possibly failed to do so) is not documented in
    any public record.

At this point, initial evidence indicates that the substitution of the
Abbey *et al.* study may reduce the chronic bronchitis benefits by 25
percent or more. But how this study will be used, and what its own
quality is, are also the subject of some concern. These \"shifting
sands\" are indicative of the problems that can occur when a major
document such as the RIA both (1) escapes the rigors of peer-review, and
(2) is not held accountable to supply sufficient documentation to ensure
that outside parties can reproduce its results.

** **

***E. Epidemiological Uncertainties***

The adjusted baseline described in the preceding section reflects
alternative estimates of the benefits after accounting for methodology
errors and inappropriate judgments. In contrast, the uncertainty
analysis around the adjusted impact estimates is intended to address
underlying scientific and statistical uncertainty in the epidemiological
studies of health effects for which it would be very difficult to
suggest that one assumption is any more valid than another. The
uncertainties that are addressed here derive primarily from the fact
that the PM benefit estimates are almost entirely based on
epidemiological studies. The inherent difficulties and uncertainties
that this situation creates were discussed in the introduction to this
section. At this point, we will illustrate how these uncertainties
actually affect results. EPA's RIA and supporting documentation do
mention the uncertainties in the dose-response parameters, but none of
these uncertainties are reflected in the RIA's national benefit numbers.

Epidemiological studies are subject to various types of errors or
uncertainties, ranging from nonrepresentativeness of the air quality
monitors in gauging individual exposure; to \"confounding,\" where
multiple pollutants may be indistinguishable in their effects. Based on
review of the available scientific literature on the problems associated
with epidemiological results, discussion of the impacts of these
potential errors on parameter values in EPA's *Criteria Document* and
*Staff Paper*, and DFI's own previous sensitivity analyses, three key
uncertainties were identified to be the focus of this analysis. The
three types of uncertainty are: dose-response slope (for each of three
health endpoints), in the threshold cutpoint, and in the degree of
potential bias in epidemiologically based slopes. Each of these
parameters is subject to considerable uncertainty even when taking into
account the results of many different published studies, and these
uncertainties have a direct impact on national benefit estimates. A
range of reasonable values was developed for each of these parameters,
and national benefits were then re-estimated for each combination of
these parameters, using the DFI baseline valuations and assumptions
described above.

**L-T and S-T mortality dose-response slope.** One key parameter in
determining the incidence of health effects is the slope of the
dose-response relationship between a particular measure of pollution
(e.g. annual average PM~2.5~) and a particular health endpoint (e.g.
incremental annual mortality). This is also known in various studies as
the \"risk ratio,\" \"odds ratio,\" or \"slope parameter.\" The term
\"relative risk\" is also commonly used to describe the results of these
studies, and this measure can be readily converted into the slope
parameter with a simple numerical manipulation.

Despite EPA's repeated statements that the PM health effects are
supported by dozens of studies, the L-T and S-T mortality risk ratios
and ranges used in estimating benefits in the PM RIA are each based on
the work of a single study team: L-T is based on Pope *et al.* (1995)
and S-T is based on Schwartz *et al.* (1996). The PM *Criteria Document*
(CD) discusses many additional studies for both L-T and S-T mortality.
There is very little explanation for why EPA chose to use only the
particular studies that they did. Figure 2-3 shows the ranges of risk
ratios for L-T mortality, taken from tables and figures in the CD.
Similarly, Figure 2-4 shows the risk ratios in studies available for S-T
mortality. Tables 2-2 and 2-3 identify the studies appearing in the
figures (and the full references for each study are provided in Appendix
3).

-   Note the paucity of studies that were actually based on PM~2.5~. In
    the case of S-T mortality, PM~2.5~-based studies really only consist
    of two independent studies (one with 6 cities, and one with 2
    cities).

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-   Also notice that there are a fairly substantial number of studies,
    both for PM~2.5~ and for PM~10~, that do not find a significant
    association with mortality (i.e., the range of uncertainty
    represented by the vertical bar encompasses a risk ratio of 1.00).

DFI developed a range of uncertainty denoted by the \"overall\" bar in
each figure. This reflects a 90 percent confidence interval (mean ± 1.65
standard deviations) around the mean of means across all of the studies.

![](media/image4.png){width="5.552777777777778in"
height="3.5215277777777776in"}

 

  ----------- ------------------- ----------- ------------ ------------ -------- --------
  Table 2-2.                                                                     
  Long-term                                                                      
  Mortality                                                                      
  Studies                                                                        

                                                           Equivalent            
                                                           for 25 μ              
                                                           g/m^3^                
                                                           PM~2.5~               

  Number      Author              Reference   Location     Risk ratio   Low      High
                                  List Number                                    

  1           Dockery *et al.*    \[16\]      Six city     1.3100       1.1100   1.6800
              1993                                                               

  2           Pope *et al.* 1995  \[17\]      50 Cities    1.1428       1.0760   1.2172

  3           Lipfert 1993        \[18\]      62 SMSAs     1.0700       0.9900   1.1600

  4           Ozkaynak & Thurston \[19\]      36 SMSAs     1.1100       0.9700   1.2200
              1987                                                               

  5           Dockery *et al.*    \[16\]      Six city     1.4200       1.1600   2.0100
              1993                                                               
  ----------- ------------------- ----------- ------------ ------------ -------- --------

The ranges that result for the risk ratios when considering all of the
studies cited in the CD are:

L-T mortality risk ratio: 1.000 1.203 1.467 (per 25 μg/m^3^ annual mean
PM~2.5~)

S-T mortality risk ratio: 1.000 1.033 1.073 (per 25 μg/m^3^ 24-hr.-avg.
PM~2.5~)

It should be noted that the \"medium\" values of relative risk are
actually higher than EPA uses in its RIA for L-T mortality estimates
(compare 1.203 to the Pope value of 1.17 *before* correcting for the
error recently found, and 1.14 *after* correcting for that error). The
\"medium\" value for the S-T risk ratio is slightly lower than that in
the RIA (compare 1.033 to 1.036). These new values, however, are more
representative of the broader set of studies that are cited in the CD.

Although EPA did not estimate uncertainty ranges in the benefit
estimates in the RIA, earlier risk analyses that provided the foundation
for the benefit estimates did include a \"90 percent credible interval\"
supposedly to account for uncertainty. The latter ranges were based
solely on the confidence intervals in the individual studies used for
EPA's point estimates. The uncertainty ranges on dose-response slope
used in this analysis are *wider* than those that EPA used, since this
study accounts for effects estimated over the more comprehensive set of
studies. Further, at the same time that the study accounts for a broader
set of studies, it incorporates studies that address only PM~10~. In
applying the estimated risks from these PM~10~ studies to PM~2.5~, DFI
has attributed *all* of the risk to variations in the fine fraction.
This is a conservative assumption. Uncertainty in this assumption is
addressed by the \"attribution\" parameter described below.

+-------+-------------------+---------+-----------+------+------+----+
| Table |                   |         |           |      |      |    |
| 2-3.  |                   |         |           |      |      |    |
| Short |                   |         |           |      |      |    |
| -term |                   |         |           |      |      |    |
| Mort  |                   |         |           |      |      |    |
| ality |                   |         |           |      |      |    |
| St    |                   |         |           |      |      |    |
| udies |                   |         |           |      |      |    |
+-------+-------------------+---------+-----------+------+------+----+
|       |                   |         |           | Eq   |      |    |
|       |                   |         |           | uiva |      |    |
|       |                   |         |           | lent |      |    |
|       |                   |         |           | for  |      |    |
|       |                   |         |           | 25 μ |      |    |
|       |                   |         |           | g/   |      |    |
|       |                   |         |           | m^3^ |      |    |
|       |                   |         |           | PM~  |      |    |
|       |                   |         |           | 2.5~ |      |    |
+-------+-------------------+---------+-----------+------+------+----+
| N     | Author            | Re      | Location  | Risk | Low  | Hi |
| umber |                   | ference |           | r    |      | gh |
|       |                   |         |           | atio |      |    |
|       |                   | List    |           |      |      |    |
|       |                   | Number  |           |      |      |    |
+-------+-------------------+---------+-----------+------+------+----+
| 1     | Schwartz *et al.* | \[1\]   | Portage   | 1.   | 0.   | 1. |
|       | 1996              |         |           | 0300 | 9930 | 07 |
|       |                   |         |           |      |      | 10 |
+-------+-------------------+---------+-----------+------+------+----+
| 2     | Schwartz *et al.* | \[1\]   | Topeka    | 1.   | 0.   | 1. |
|       | 1996              |         |           | 0200 | 9510 | 09 |
|       |                   |         |           |      |      | 20 |
+-------+-------------------+---------+-----------+------+------+----+
| 3     | Schwartz *et al.* | \[1\]   | Boston    | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0560 | 0380 | 07 |
|       |                   |         |           |      |      | 11 |
+-------+-------------------+---------+-----------+------+------+----+
| 4     | Schwartz *et al.* | \[1\]   | St. Louis | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0280 | 0100 | 04 |
|       |                   |         |           |      |      | 30 |
+-------+-------------------+---------+-----------+------+------+----+
| 5     | Schwartz *et al.* | \[1\]   | Kingston  | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0350 | 0050 | 06 |
|       |                   |         |           |      |      | 60 |
+-------+-------------------+---------+-----------+------+------+----+
| 6     | Schwartz *et al.* | \[1\]   | Ste       | 1.   | 0.   | 1. |
|       | 1996              |         | ubenville | 0250 | 9980 | 05 |
|       |                   |         |           |      |      | 30 |
+-------+-------------------+---------+-----------+------+------+----+
| 7     | Dockery *et al.*  | \[2\]   | St. Louis | 1.   | 0.   | 1. |
|       | 1995              |         |           | 0400 | 9950 | 09 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 8     | Dockery *et al.*  | \[2\]   | E.        | 1.   | 0.   | 1. |
|       | 1995              |         | Tennessee | 0600 | 9700 | 15 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 9     | Pope *et al.*     | \[3\]   | Utah      | 1.   | 1.   | 1. |
|       | 1992              |         |           | 0800 | 0500 | 11 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 10    | Pope & Kalkstein  | \[4\]   | Utah      | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0700 | 0200 | 12 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 11    | Schwartz 1993     | \[5\]   | B         | 1.   | 1.   | 1. |
|       |                   |         | irmingham | 0500 | 0100 | 10 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 12    | Dockery *et al.*  | \[6\]   | St. Louis | 1.   | 1.   | 1. |
|       | 1992              |         |           | 0800 | 0050 | 15 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 13    | Schwartz *et al.* | \[1\]   | St. Louis | 1.   | 0.   | 1. |
|       | 1996              |         |           | 0600 | 9800 | 15 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 14    | Schwartz *et al.* | \[1\]   | St. Louis | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0300 | 0050 | 05 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 15    | Dockery *et al.*  | \[6\]   | Kingston  | 1.   | 0.   | 1. |
|       | 1992              |         |           | 0850 | 9400 | 25 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 16    | Schwartz *et al.* | \[1\]   | Kingston  | 1.   | 0.   | 1. |
|       | 1996              |         |           | 0900 | 9400 | 26 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 17    | Schwartz *et al.* | \[1\]   | Kingston  | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0500 | 0050 | 09 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 18    | Schwartz *et al.* | \[1\]   | Portage   | 1.   | 0.   | 1. |
|       | 1996              |         |           | 0350 | 9800 | 09 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 19    | Schwartz *et al.* | \[1\]   | Boston    | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0600 | 0400 | 09 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 20    | Schwartz *et al.* | \[1\]   | Topeka    | 0.   | 0.   | 1. |
|       | 1996              |         |           | 9800 | 9000 | 05 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 21    | Schwartz *et al.* | \[1\]   | Ste       | 1.   | 1.   | 1. |
|       | 1996              |         | ubenville | 0500 | 0050 | 08 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 22    | Ozkaynak *et al.* | \[7\]   | Toronto   | 1.   | 1.   | 1. |
|       | 1994              |         |           | 0250 | 0150 | 03 |
|       |                   |         |           |      |      | 40 |
+-------+-------------------+---------+-----------+------+------+----+
| 23    | Kinney *et al.*   | \[8\]   | Los       | 1.   | 1.   | 1. |
|       | 1995              |         | Angeles   | 0250 | 0000 | 05 |
|       |                   |         |           |      |      | 50 |
+-------+-------------------+---------+-----------+------+------+----+
| 24    | Kinney *et al.*   | \[8\]   | Los       | 1.   | 0.   | 1. |
|       | 1995              |         | Angeles   | 0170 | 9900 | 03 |
|       |                   |         |           |      |      | 60 |
+-------+-------------------+---------+-----------+------+------+----+
| 25    | Ito *et al.* 1995 | \[9\]   | Chicago   | 1.   | 1.   | 1. |
|       |                   |         |           | 0250 | 0050 | 05 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 26    | Styer *et al.*    | \[10\]  | Chicago   | 1.   | 1.   | 1. |
|       | 1995              |         |           | 0400 | 0000 | 08 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 27    | Ito & Thurston    | \[11\]  | Chicago   | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0250 | 0050 | 04 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 28    | Ito & Thurston    | \[11\]  | Chicago   | 1.   | 1.   | 1. |
|       | 1996              |         |           | 0200 | 0050 | 03 |
|       |                   |         |           |      |      | 50 |
+-------+-------------------+---------+-----------+------+------+----+
| 29    | Ostro *et al.*    | \[12\]  | Santiago  | 1.   | 0.   | 1. |
|       | 1996              |         |           | 0257 | 9913 | 05 |
|       |                   |         |           |      |      | 86 |
+-------+-------------------+---------+-----------+------+------+----+
| 30    | Ostro 1993        | \[13\]  | London    | 1.   | 1.   | 1. |
|       |                   |         |           | 0019 | 0018 | 00 |
|       |                   |         |           |      |      | 19 |
+-------+-------------------+---------+-----------+------+------+----+
| 31    | Ostro 1993        | \[13\]  | Ste       | 1.   | 1.   | 1. |
|       |                   |         | ubenville | 0049 | 0036 | 00 |
|       |                   |         |           |      |      | 69 |
+-------+-------------------+---------+-----------+------+------+----+
| 32    | Ostro 1993        | \[13\]  | Phi       | 1.   | 1.   | 1. |
|       |                   |         | ladelphia | 0143 | 0114 | 01 |
|       |                   |         |           |      |      | 72 |
+-------+-------------------+---------+-----------+------+------+----+
| 33    | Ostro 1993        | \[13\]  | Santa     | 1.   | 1.   | 1. |
|       |                   |         | Clara     | 0149 | 0099 | 02 |
|       |                   |         |           |      |      | 05 |
+-------+-------------------+---------+-----------+------+------+----+
| 34    | Dockery & Pope    | \[14\]  | St. Louis | 1.   | 1.   | 1. |
|       | 1994              |         |           | 0269 | 0018 | 05 |
|       |                   |         |           |      |      | 24 |
+-------+-------------------+---------+-----------+------+------+----+
| 35    | Dockery & Pope    | \[14\]  | Kingston  | 1.   | 0.   | 1. |
|       | 1994              |         |           | 0268 | 9784 | 07 |
|       |                   |         |           |      |      | 78 |
+-------+-------------------+---------+-----------+------+------+----+
| 36    | Dockery & Pope    | \[14\]  | B         | 1.   | 1.   | 1. |
|       | 1994              |         | irmingham | 0104 | 0021 | 01 |
|       |                   |         |           |      |      | 56 |
+-------+-------------------+---------+-----------+------+------+----+
| 37    | Dockery & Pope    | \[14\]  | Utah      | 1.   | 1.   | 1. |
|       | 1994              |         |           | 0160 | 0096 | 02 |
|       |                   |         |           |      |      | 24 |
+-------+-------------------+---------+-----------+------+------+----+
| 38    | Dockery & Pope    | \[14\]  | Phi       | 1.   | 1.   | 1. |
|       | 1994              |         | ladelphia | 0150 | 0088 | 02 |
|       |                   |         |           |      |      | 13 |
+-------+-------------------+---------+-----------+------+------+----+
| 39    | Dockery & Pope    | \[14\]  | Detroit   | 1.   | 1.   | 1. |
|       | 1994              |         |           | 0104 | 0052 | 01 |
|       |                   |         |           |      |      | 67 |
+-------+-------------------+---------+-----------+------+------+----+
| 40    | Dockery & Pope    | \[14\]  | Ste       | 1.   | 1.   | 1. |
|       | 1994              |         | ubenville | 0057 | 0033 | 00 |
|       |                   |         |           |      |      | 82 |
+-------+-------------------+---------+-----------+------+------+----+
| 41    | Dockery & Pope    | \[14\]  | Santa     | 1.   | 1.   | 1. |
|       | 1994              |         | Clara     | 0114 | 0029 | 02 |
|       |                   |         |           |      |      | 15 |
+-------+-------------------+---------+-----------+------+------+----+
| 42    | Moolgavkar *et    | \[15\]  | Ste       | 1.   | 1.   | 1. |
|       | al.* 1995         |         | ubenville | 0300 | 0050 | 06 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+
| 43    | Moolgavkar *et    | \[15\]  | Ste       | 1.   | 1.   | 1. |
|       | al.* 1995         |         | ubenville | 0250 | 0000 | 05 |
|       |                   |         |           |      |      | 00 |
+-------+-------------------+---------+-----------+------+------+----+

**Chronic bronchitis dose-response slope.** EPA's chronic bronchitis
risks are derived again from a single study, Schwartz (1993). For the
present analysis, DFI chose to use the risk ratio range from the
Schwartz study:

CB risk ratio: 1.02 1.07 1.12 (per 25 μg/m^3^ PM~2.5~)

This again, is a conservative assumption for several reasons:

-   A review of the Schwartz (1993) study shows that it does not account
    or control for any environmental factors other than smoking. It
    warranted only one paragraph of discussion in the CD, and was never
    mentioned in the *Staff Paper*. The record of supporting studies in
    the PM Docket indicate that it was not used in any of the original
    risk analyses, and suddenly appeared in one of the last \"updates\"
    of the risk analyses that were delivered just prior to the release
    of the PM RIA.

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-   The Schwartz (1993) study is a cross-sectional study, implying that
    its results indicate overall differences in incidence rates. EPA
    states that the estimates it has derived from this study are
    actually supposed to be annual *new cases* of chronic bronchitis.
    DFI has been unable to obtain useful documentation to determine
    whether a reasonable conversion was applied. Thus, there is some
    chance that the estimates from this study substantially overstate
    the annual incidence. However, this analysis gives the benefit of
    the doubt that the conversion was done in a reasonable manner.

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-   In trying to obtain better documentation of how the Schwartz (1993)
    study was obtained, DFI learned that this study had been removed
    from EPA's \"Retrospective Study*\"* as a result of that study's
    peer review process. It was replaced by benefit estimates based on
    another study, Abbey *et al.* (1993), and DFI has learned that EPA
    is also making the same replacement in its revised PM RIA. The Abbey
    *et al.* usage also requires some complex conversions to obtain
    estimates of annual new cases of chronic bronchitis, and EPA's
    method is not yet documented. However, based on preliminary evidence
    in a recent draft of the \"Retrospective Study,\" it appears that
    use of the Abbey results may reduce the dose-response slope by about
    25 percent. Because of the highly speculative nature of this
    estimate, this report continues to use the uncertainty ranges solely
    from Schwartz (1993), but it should be noted that doing so is
    probably a conservative step.

**Threshold cutpoint.** A key uncertainty in the epidemiological studies
is whether there exists a \"cutpoint\" or \"threshold,\" which is a
level of the pollutant below which there is not a relationship between
pollutant concentration and health effects. This is essentially a \"safe
level\" of the pollutant, and there would be no incremental benefits for
ambient reductions below such a level.

Most of the epidemiological studies use a dose-response form which
implicitly assumes that there is no such threshold or safe level above
zero. That is, the incidence of adverse health effects continues to
decline as pollution levels decline, all the way to a pollution level of
zero. However, it is well-known that the types of measurement errors
that are endemic to all of these studies would be sufficient to mask the
presence of any threshold, even when using sophisticated techniques such
as quintile-partitioned data, or nonparametric smoothing. Thus, the fact
that no threshold has been in evidence does not provide any confidence
that a threshold above background does not exist, as EPA acknowledges in
a number of locations.

This uncertainty is of prime importance, as acknowledged by EPA: \"The
single most important factor influencing the uncertainty associated with
the risk estimates is whether or not a threshold concentration exists
below which PM-associated health risks are not likely to occur.\"
Although EPA obviously understands this point, the magnitude of this
uncertainty is not reported anywhere in the benefit analysis. It is,
however, incorporated into this analysis of uncertainty.

There are a number of ways that EPA suggests a potential \"reasonable\"
level for the threshold. These are best summarized in the Proposed Rule
itself: \"The Staff Paper assessment\...concluded that the evidence for
increased risk was more apparent at annual concentrations at or above 15
μ g/m^3^\...however, the estimated magnitude of effects \[in those
studies\] may be related to somewhat higher historical concentrations
than the affected communities experienced during the time period of the
studies; this consideration suggests that a level of 15 μ g/m^3^ *would
incorporate a margin of safety*\" \[emphasis added\]. The point about
there being an adequate margin of safety at about 15 μ g/m^3^ annual
average is reiterated a couple of times in the following pages of the
Proposed Rule, along with a suggestion that there is very weak evidence
of a short-term effect below 35 μ g/m^3^ on a 24-hour average basis.

There is no mistaking that EPA is suggesting that it believes that a
threshold may be conceivable somewhere at or even above this level on
the annual average. To add to this, a good part of the arguments for the
threshold being possibly at or somewhat above 15 μ g/m^3^ is based on
the Pope *et al.* (1995) study. This is the very study that EPA has
since admitted it misinterpreted (see discussion in earlier section on
errors in the baseline benefit estimates). When correcting for that
error in EPA's understanding of the data in this study, one finds that
EPA is actually arguing that there is some suggestion or possibility of
a threshold at or slightly above 18.8 μ g/m^3^.

In addition to the actual statements by EPA on the matter of a
threshold, one of the supporting documents for EPA's RIA is a risk
assessment for Philadelphia and Los Angeles by Abt Associates. That
analysis investigated various threshold scenarios of up to 30 μg/m^3^
for PM~2.5~ for short-term effects, and up to 18 μ g/m^3^ for long-term
effects. Consistent with these scenarios and EPA statements, this
analysis used the following ranges to reflect the threshold uncertainty:

Cutpoint for L-T effects: 0 15 18 μg/m^3^

Cutpoint for S-T mortality: 0 18 30 μg/m^3^

**Attribution of Health Effects to PM~2.5~.** As mentioned previously,
some epidemiological studies find health effects which their authors
attribute to PM~2.5~. Although the study designs should attempt to
isolate the effect of this one pollutant, there are inevitable
confounding issues which could lead to errors in attributing health
effects to PM~2.5~. It is very difficult to determine how much this bias
could be. Note that in the estimates for mortality, the range estimated
for slope was based on a number of PM~10~ studies, and *all* of the
incremental risk in those studies was attributed to PM~2.5~ in that step
of this analysis. To the extent that the coarse fraction might still
account for an equal share of the risk in those studies, up to as much
as half of that risk might actually be attributable to the coarse
fraction. Further, measurement error differentials between PM~2.5~ and
the coarse fraction could cause the statistical estimation process to
falsely attribute risk associated with PM~10~ entirely to PM~2.5~. Thus,
even those studies that *were* based on PM~2.5~ could be falsely
attributing effects of the coarse fraction entirely onto the fine
fraction. These types of problems might be addressed by scenarios that
attribute as much as 50 percent of the estimated risk to concerns other
than PM~2.5~. This scenario may be thought of as reflecting the
potential impact of a moderate amount of bias due to confounding by
other pollutants.

Further, it is even possible that some other pollutant or some other
environmental factor is at work. This reflects the active debate of
whether these associations are even causal in nature. EPA has argued
that the case for causality can be made by the fact that the PM
association is quite stable over numerous locations. There are many
counter-arguments that remain prevalent. Without trying to repeat all of
the many counter-arguments, this analysis has attempted to reflect the
fact that this uncertainty does exist with a scenario where none of the
epidemiologically derived estimates of benefits should be attributed to
PM~2.5~.

Given what is known about the types of measurement errors, with the
relative exposure misclassification errors for PM~2.5~ probably being
small compared to other pollutants, then it is doubtful that there is
much potential bias in the other direction. However, it should be noted
that the uncertainty ranges on the risk ratio do account for the chances
that the actual dose-response slope estimate is higher than that used by
EPA.

Thus, for purposes of this analysis, DFI used three alternative values
to reflect the attribution uncertainty:

Attribution: 0% 50% 100%

The 0 percent scenario represents the possibility that PM is not to
blame at all for the health effects; the 100 percent means that all of
the health effects attributed to fine PM in the epidemiological studies
are correctly attributed, with no other hidden confounders. 100 percent
is the most conservative value, and is the choice that EPA used in its
analysis. 0 percent is the most extreme scenario, and since most of the
PM~2.5~ benefits are in fact based solely on epidemiological evidence,
it will obviously have significant impact on benefit estimates. It is,
however, an assumption that is still within the realm of reason.

Some readers may feel uncomfortable with this particular form of
uncertainty, or may themselves feel quite confident that causality and
bias are not in question. In the uncertainty scenarios that are
presented below, ranges are provided both with and without the
attribution uncertainty included. One will find that accounting for
potential bias or noncausality alters the balance of results, but not
the overall range of uncertainty. Since this analysis does not attempt
to assign any probabilities to individual uncertainties, the balance of
scenarios has little direct import on conclusions of this report.

**Scenarios.** A scenario is a particular combination of the values of
each of the relevant parameters discussed above. For estimates of
incidence, there are 27 scenarios. For example, for S-T mortality, the
attribution can take on any of 3 values, the dose-response can take on
any of 3 values, and the cutpoint can take on any of 3 values. This
makes 3^3^=27 possible combinations.

When monetized benefits are calculated, incidence of multiple health
endpoints are combined. For example, the total monetized benefits
including L-T mortality would involve uncertainties both on L-T
mortality and chronic bronchitis. These uncertainties are assumed to be
independent, so each scenario would consist of selecting one of the
three values of attribution, L-T mortality risk ratio, CB risk ratio,
L-T mortality cutpoint, and CB cutpoint (five parameters). The total
number of monetized benefit scenarios is therefore 3^5^=243.

 

***F. Populations Benefiting from the Proposed Standard***

EPA's RIA analysis presents national estimates of reduced incidence and
monetized benefits. However, EPA will not release sufficient data for
any external group to accurately replicate its national estimates. To
arrive at comparable national estimates in the present analysis, it is
necessary to determine which populations will be affected by the
proposed standards, and which sub-populations will receive various
levels of air quality improvements.

**Affected population.** EPA splits its analysis into seven regions,
defined in the RIA in Figure 7-1. Table 6-7 of the RIA shows how many
counties in each region are considered in EPA's 470-county analysis. For
instance, for the Midwest/Northeast region, 210 counties were
considered. Since EPA will not disclose which counties were actually
considered, it was assumed that they are the most populous counties in
the region. The population in these counties is taken to be EPA's
analyzed population.

EPA's RIA summary tables also indicate the number of counties in each
region, out of the number that EPA analyzed, that were projected to be
out of attainment with the proposed standard in 2007, given
implementation of currently mandated controls. These counties will be
the primary beneficiaries of the new standard since, under the full
attainment scenario, PM~2.5~ concentrations in these counties will be
reduced to meet the standard. Since EPA will not release information on
exactly which counties these are, it was assumed that they are the most
populous counties in each region.

In addition to these nonattainment areas, however, there may be
additional indirect benefits to counties which are already in
attainment. This is because control measures implemented in
nonattainment areas will likely improve air quality in neighboring
counties as well, because of the long-range transport of PM~2.5~. EPA
evidently counts improvements in attainment areas as part of its
national benefits estimate. The authors developed approximate estimates
of the population in such areas by using judgmental assumptions
regarding the population in areas near nonattainment areas.

EPA's tallies of the number of counties out of attainment are based on
their limited 470-county analysis, which encompasses about 60 percent of
the national population. In the present analysis, simple scaling and
proportionality assumptions were used to develop estimates of the number
of affected individuals receiving benefits when the entire national
population is considered.

**Selection of representative cities.** EPA will not release its
assumptions regarding air quality in 2007 from which improvements are
made to generate health benefits. DFI's approach was to analyze
sensitivities to the uncertain parameters in a small set of four
representative cities, then use proportionality assumptions to roll-up
to a national estimate.

Specific geographical areas in 2007 were categorized according to the
extent of 2007 PM concentrations. The PM RIA (Table 6-7) indicates the
number of counties projected to violate each of three alternative
PM~2.5~ standards. Consistent with this breakdown, this analysis
categorized the United States population receiving benefits from the
proposed standard into four groups:

**Category Initial Air Quality in 2007:**

A Annual average PM**~2.5~** \>20 μg/m^3^

B Between 15 and 20 μg/m^3^

C Between 12.5 and 15 μg/m^3^

D Under 12.5 μg/m^3^

E Attainment areas that do not experience any PM~2.5~ reductions due to
neighboring region control strategies

In the absence of knowing which particular areas or cities are projected
to have various levels of PM~2.5~, DFI used external data to select a
set of representative cities.

-   The Abt Risk Assessment projects that Los Angeles will not be in
    attainment of the current PM~10~ standard in 2007. That document
    analyzes the effect of bringing Los Angeles into attainment of the
    current standard, and estimates that doing so will lead to an annual
    average PM~2.5~ concentration of 24.1 μg/m^3^. Therefore, Los
    Angeles was designated as the representative city for category A.

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-   Supporting documentation in the PM Docket indicates that the 2007
    PM~2.5~ concentration in Philadelphia is estimated at 18.6 μg/m^3^,
    after implementation of CAA controls. Philadelphia was thus assigned
    as the representative city for category B.

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```
-   No specific information was available on projected air quality in
    2007 for any areas in categories C and D. Therefore, two
    hypothetical \"attainment\" cities were created, denoted \"Hyp. C\"
    and \"Hyp. D\" which have annual average PM~2.5~ concentrations of
    13.5 and 11.5 μg/m^3^, respectively.

 

***G. Sensitivity Analysis for Each Representative City***

The goal is to determine the plausible range of *national* benefit
estimates. To do that, this analysis first investigated the *percentage*
sensitivities in avoided incidence of health effects (not monetized yet)
for each of the representative cities described above. As mentioned
previously, attention was focused on mortality (L-T and S-T) and chronic
bronchitis, since these make up the largest portion of EPA's benefit
estimates.

The EPA methodology for determining air concentrations was followed as
closely as practicable, given the available data. Gamma distributions
were fitted to the annual average and 98th percentile daily PM~2.5~
concentrations, and then 365 daily values of PM~2.5~ were generated.
Proportional rollbacks were carried out to reduce concentrations to
attain the proposed standard, and logistic dose-response relationships
were applied to determine the change in incidence for the health
endpoints.

Figures 2-5 to 2-8 show the sensitivity ranges (in percent of the
baseline incidence estimate) for a number of different scenarios for
each city. The horizontal line at 0 percent on the figures represents
the \"baseline\" scenario consistent with what would be the RIA result
(i.e., using the base case risk ratios, zero threshold cutpoint, and the
assumption that the level of effect which the statistical studies assign
to fine PM is in fact 100 percent due to fine PM). Thus, a sensitivity
of 0 percent implies that that scenario for that type of city produces
results consistent with EPA's benefit estimates in the RIA. The ranges
represented by the vertical lines indicate how much the health effect in
question might increase or decrease as each uncertainty parameter is
moved (one at a time) to the ends of its range, all else being held
equal. The sensitivities are labeled in terms of the health endpoint in
question and the parameter being varied. For example, the first bar in
Figure 2-5 shows that, for Los Angeles, as the cutpoint scenario for L-T
mortality is ranged from 0 (no threshold) to 15 and then to 18 μg/m^3^,
the estimated avoided incidence (i.e., benefits due to the proposed
standard) of L-T mortality drops by about 35 percent.

One can see that the degree of sensitivity tends to increase as the
region has lower and lower initial PM~2.5~ concentrations. For example,
the sensitivity of the Los Angeles L-T mortality estimate to the
threshold cutpoint uncertainty is from 0 percent to about -35 percent.
The same uncertainty results in up to a -80 percent effect in
Philadelphia. Threshold uncertainty can rise to as much as -100 percent
for all the other, cleaner cities. EPA acknowledged this same fact in
the Proposed Rule: \"Alternative assumed threshold
concentrations\...result in as much as a three- to four-fold difference
in estimated risk associated with PM exposures in Los Angeles
County\.... In an area with PM concentrations well below the current PM
standards (e.g., Philadelphia County), differences in risk\...may be
even greater.\" The difference is that this analysis actually estimates
and reports that change in sensitivity, and it is shown that the
threshold assumption can actually cause mortality estimates to drop to
zero in most of the areas of the country. Few areas are as
\"insensitive\" to the threshold uncertainty as Los Angeles.

![](media/image5.png){width="4.364583333333333in"
height="4.010416666666667in"}

 

 

Taken collectively, it is evident that each of the cities has quite a
high range of sensitivities to the parameters. As has been discussed
above, the high and low scenarios are plausible based on information in
the scientific record for the PM standard, and based on EPA's own
discussions of the evidence in the Proposed Rule and supporting
documents. These results indicate that the estimated benefits, in terms
of the avoided incidence of health effects, will vary widely as these
alternative key parameters are accounted for in the analysis. In some
cases the benefits may disappear completely, in some cases the benefits
can more than double.

 

***H. National Aggregation of Health Effect Incidences***

Given the sensitivity ranges developed for the representative cities, a
national aggregate estimate is developed by assigning fractions of the
national population to each of the area categories A-E, and
disaggregating EPA's overall incidence estimate to the same categories.
This was done based on both population and anticipated air quality
improvement.

With the national incidence estimates thus attributed to individual
categories, the sensitivity ranges of the representative cities were
applied, then aggregated on a population-weighted basis to form revised
estimates of national estimates of the reduced incidence of S-T
mortality, L-T mortality, and chronic bronchitis. Figures 2-9 to 2-11
show the outcomes for each of the 27 scenarios (reflecting combinations
of three possible values for three variables: attribution, cutpoint, and
risk ratio). Figures 2-12 to 2-14 are equivalent but show only the nine
scenarios in which full attribution of epidemiological risk is made to
PM~2.5~.

For more detail, Tables 2-4 to 2-6 show the equivalent information as in
the figures, but in addition allow for identification of the specific
scenario corresponding to any particular estimate.

Figure 2-15 illustrates the way that the chronic bronchitis estimates of
Figure 2-11 might change once they have been re-estimated using the
Abbey *et al.* (1993) study in place of Schwartz (1993). (As discussed
above, EPA has decided to make this change in its revised RIA, to be
released in July 1997.) One can see that the avoided cases of chronic
bronchitis can be expected to drop by about 25 percent. However, these
are much more speculative sensitivity estimates since there is almost no
useful documentation available on how EPA has actually applied either
study. For the remainder of the analysis and discussion, the current
RIA's chronic bronchitis estimates will be used. This adds a fair degree
of conservatism to the final uncertainty ranges.

 

 

 

 

 

** **

+-----------+-----------+---+-------------+----------------+--------------+
| Table     |           |   |             |                |              |
| 2-4.      |           |   |             |                |              |
| National  |           |   |             |                |              |
| Avoided   |           |   |             |                |              |
| Incidence |           |   |             |                |              |
| of        |           |   |             |                |              |
| S         |           |   |             |                |              |
| hort-term |           |   |             |                |              |
| Deaths in |           |   |             |                |              |
| 2007, 27  |           |   |             |                |              |
| Scenarios |           |   |             |                |              |
|           |           |   |             |                |              |
| (Shaded   |           |   |             |                |              |
| block is  |           |   |             |                |              |
| scenario  |           |   |             |                |              |
| closest   |           |   |             |                |              |
| to RIA    |           |   |             |                |              |
| estimate) |           |   |             |                |              |
+-----------+-----------+---+-------------+----------------+--------------+
|           |           |   | Risk ratio  |                |              |
+-----------+-----------+---+-------------+----------------+--------------+
| At        | Cutpoint  | L |             | Med.           | High         |
| tribution |           | o |             |                |              |
|           |           | w |             |                |              |
+-----------+-----------+---+-------------+----------------+--------------+
|           | Low       | 0 |             | 0              | 0            |
+-----------+-----------+---+-------------+----------------+--------------+
| 0%        | Medium    | 0 |             | 0              | 0            |
+-----------+-----------+---+-------------+----------------+--------------+
|           | High      | 0 |             | 0              | 0            |
+-----------+-----------+---+-------------+----------------+--------------+
|           | Low       | 0 |             | 2,500          | 5,394        |
+-----------+-----------+---+-------------+----------------+--------------+
| 50%       | Medium    | 0 |             | 1,692          | 3,645        |
+-----------+-----------+---+-------------+----------------+--------------+
|           | High      | 0 |             | 880            | 1,891        |
+-----------+-----------+---+-------------+----------------+--------------+
|           | Low       | 0 |             | 5,000          | 10,789       |
+-----------+-----------+---+-------------+----------------+--------------+
| 100%      | Medium    | 0 |             | 3,384          | 7,290        |
+-----------+-----------+---+-------------+----------------+--------------+
|           | High      | 0 |             | 1,759          | 3,783        |
+-----------+-----------+---+-------------+----------------+--------------+

+----------+----+----+---+--------------+--------------+---------------+
| ** **    |    |    |   |              |              |               |
|          |    |    |   |              |              |               |
| **Table  |    |    |   |              |              |               |
| 2-5.     |    |    |   |              |              |               |
| National |    |    |   |              |              |               |
| Avoided  |    |    |   |              |              |               |
| I        |    |    |   |              |              |               |
| ncidence |    |    |   |              |              |               |
| of       |    |    |   |              |              |               |
| L        |    |    |   |              |              |               |
| ong-term |    |    |   |              |              |               |
| Deaths   |    |    |   |              |              |               |
| in 2007, |    |    |   |              |              |               |
| 27       |    |    |   |              |              |               |
| Sce      |    |    |   |              |              |               |
| narios** |    |    |   |              |              |               |
|          |    |    |   |              |              |               |
| *        |    |    |   |              |              |               |
| *(Shaded |    |    |   |              |              |               |
| block is |    |    |   |              |              |               |
| scenario |    |    |   |              |              |               |
| closest  |    |    |   |              |              |               |
| to RIA   |    |    |   |              |              |               |
| est      |    |    |   |              |              |               |
| imate)** |    |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          |    |    | * |              |              |               |
|          |    |    | * |              |              |               |
|          |    |    | R |              |              |               |
|          |    |    | i |              |              |               |
|          |    |    | s |              |              |               |
|          |    |    | k |              |              |               |
|          |    |    | r |              |              |               |
|          |    |    | a |              |              |               |
|          |    |    | t |              |              |               |
|          |    |    | i |              |              |               |
|          |    |    | o |              |              |               |
|          |    |    | * |              |              |               |
|          |    |    | * |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| **Attri  | ** |    | * |              | **Med.**     | **High**      |
| bution** | Cu |    | * |              |              |               |
|          | tp |    | L |              |              |               |
|          | oi |    | o |              |              |               |
|          | nt |    | w |              |              |               |
|          | ** |    | * |              |              |               |
|          |    |    | * |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          | L  |    | 0 |              | 0            | 0             |
|          | ow |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| 0%       | Me |    | 0 |              | 0            | 0             |
|          | d. |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          | Hi |    | 0 |              | 0            | 0             |
|          | gh |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          | L  |    | 0 |              | 7,500        | 15,328        |
|          | ow |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| 50%      | Me |    | 0 |              | 6,186        | 12,615        |
|          | d. |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          | Hi |    | 0 |              | 1,816        | 3,718         |
|          | gh |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          | L  |    | 0 |              | 15,000       | 30,655        |
|          | ow |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| 100%     | Me |    | 0 |              | 12,373       | 25,230        |
|          | d. |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          | Hi |    | 0 |              | 3,633        | 7,435         |
|          | gh |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| **Table  |    |    |   |              |              |               |
| 2-6.     |    |    |   |              |              |               |
| National |    |    |   |              |              |               |
| Avoided  |    |    |   |              |              |               |
| I        |    |    |   |              |              |               |
| ncidence |    |    |   |              |              |               |
| of       |    |    |   |              |              |               |
| Chronic  |    |    |   |              |              |               |
| Br       |    |    |   |              |              |               |
| onchitis |    |    |   |              |              |               |
| Cases in |    |    |   |              |              |               |
| 2007, 27 |    |    |   |              |              |               |
| Sc       |    |    |   |              |              |               |
| enarios\ |    |    |   |              |              |               |
| (Shaded  |    |    |   |              |              |               |
| block is |    |    |   |              |              |               |
| scenario |    |    |   |              |              |               |
| closest  |    |    |   |              |              |               |
| to RIA   |    |    |   |              |              |               |
| est      |    |    |   |              |              |               |
| imate)** |    |    |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          |    |    |   | **Risk       |              |               |
|          |    |    |   | ratio**      |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| **Attri  |    | ** |   | **Low**      | **Med.**     | **High**      |
| bution** |    | Cu |   |              |              |               |
|          |    | tp |   |              |              |               |
|          |    | oi |   |              |              |               |
|          |    | nt |   |              |              |               |
|          |    | ** |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          |    | L  |   | 0            | 0            | 0             |
|          |    | ow |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| 0%       |    | Me |   | 0            | 0            | 0             |
|          |    | d. |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          |    | Hi |   | 0            | 0            | 0             |
|          |    | gh |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          |    | L  |   | 11,235       | 37,000       | 59,897        |
|          |    | ow |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| 50%      |    | Me |   | 9,318        | 30,519       | 49,151        |
|          |    | d. |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          |    | Hi |   | 2,782        | 9,197        | 14,943        |
|          |    | gh |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          |    | L  |   | 22,471       | 74,000       | 119,794       |
|          |    | ow |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
| 100%     |    | Me |   | 18,636       | 61,038       | 98,302        |
|          |    | d. |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+
|          |    | Hi |   | 5,564        | 18,394       | 29,886        |
|          |    | gh |   |              |              |               |
+----------+----+----+---+--------------+--------------+---------------+

 

A recent study has suggested that the national L-T mortality estimate of
15,000 deaths should actually be a value \"less than 1000.\" To obtain
that estimate, the study assumed that the correct standard (after
accounting for the error that EPA made in interpreting the Figure 2 in
Pope *et al.*) should be set at 18.7 μg/m^3^ on the annual average. The
study also argued that since EPA has stated that this level is
adequately protective of the public health, then no benefits should be
estimated for any of the cities that are below that standard. It is
worth noting that an equivalent to this \"less than 1000\" mortality
estimate would be a scenario that applies a long-term mortality
threshold at the level of 18.7 μg/m^3^. The closest approximation here
of such a scenario would be the national L-T mortality estimate with a
threshold at 18 μg/m^3^ in Table 2-5, for \"100 percent attribution\"
and \"medium\" risk ratio. One can see that a quite comparable estimate
of about 3,600 lives is associated with that scenario. There are a
couple of reasons why one would expect the estimate in Table 2-5 to be
higher. First, the \"medium\" risk ratio assumption is higher than the
risk ratio used in Pope *et al.* (see Figure 2-3 above). Second, the
threshold assumption is lower than that used by Jones, and the estimates
are quite sensitive to the threshold in this range. Finally, our
estimates for developing a national extrapolation from the four
representative cities have been made with conservative assumptions. DFI
also investigated a scenario with a cutpoint of 18.7 μg/m^3^ and the
Pope *et al.* risk ratio, and arrived at a estimate of 1,930 avoided
annual L-T mortality. Generally, given the conservatism in this
analysis' methodology, it appears that a fairly comparable estimate to
Jones has been derived through a very different estimation process.

 

***I. National Monetized Benefits***

With national incidences calculated from the scenarios, the final step
is to monetize and add the benefits to get a national total such as that
provided in the PM RIA. Consistent with EPA, two totals are calculated,
one including S-T mortality and one including L-T mortality. The
analysis retains the full range of combinations of scenarios. For
example, when deriving the S-T total, there are separate scenarios for
S-T mortality threshold and chronic bronchitis threshold. There is not a
convincing *a priori* reason to exclude the possibility that the S-T
mortality threshold could be high at the same time that the chronic
bronchitis threshold is low. So, whereas the individual health endpoints
had 27 different scenarios, the monetized aggregate benefit has 243
scenarios each for S-T and L-T.

The calculation of the monetized benefits for any particular scenario is
as follows:

-   The appropriate mortality and chronic bronchitis benefits (national
    avoided incidences) are calculated.

```{=html}
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```
-   The incidences are multiplied by the appropriate valuation
    (depending on the assumptions baseline).

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-   Other RIA benefits categories which are dependent on PM~10~, such as
    respiratory symptoms (see Table 9.4 of RIA) are adjusted for the
    overcounting of coarse fraction benefits, if the DFI assumptions
    baseline is used.

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```
-   Other RIA benefits categories which are epidemiological, such as
    hospital admissions, are adjusted if the scenario assumes less than
    full attribution of the health effects to PM.

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```
-   Other RIA benefits categories which are not based on PM~10~ and
    which are not epidemiological, (e.g., visibility), are added without
    modification.

This yields the total dollar benefits for one scenario. This is repeated
for each scenario, separately for S-T and L-T mortality.

Figure 2-16 shows two estimated ranges of total monetized benefits of
the proposed standard, when short-term or long-term mortality is
included. The initial RIA estimates, and the ones corrected as per EPA's
April 2, 1997 press release, are also shown for comparison. As mentioned
previously, it is unclear how EPA arrived at its chronic bronchitis
incidences and it is possible that they were computed incorrectly. At
this point, EPA says that it will be basing chronic bronchitis estimates
on yet a different study. Preliminary indications are that this will
make the top end of the ranges in Figure 2-16 decline by about another
\$10 billion.

Figures 2-17 and 2-18 provide more detail on specific outcomes within
the range, for short-term and long-term mortality, respectively. Each
point in the scatter plots represents the outcome from a specific
uncertainty scenario.

This analysis does *not* assign probabilities to the scenarios, since
those are individual judgments that may vary. Thus, the distribution of
points in any of the figures contained in this report should not be
interpreted as a probability distribution. However, it is usually the
case that the most extreme scenarios are the ones with the very smallest
probability associated with them. That is certainly the case here since
the ranges associated with the risk ratios are themselves only a 90
percent confidence interval on the means of many studies. To avoid
overstating extremes when stating a range, one might eliminate a certain
number of the top and bottom points. For example, discarding the top and
bottom 6 data points would provide a 95 percent confidence interval *if
every single scenario has equal probability*. In fact, as we have noted,
the top and bottom 6 scenarios should be attributed less than equal
probability than more intermediate scenarios. The ranges in Figure 2-16
were created in this way, and thus provide a probably conservative
estimate of the 95 percent confidence interval. However, attempts to
even approximate an \"expected value\" within the range would require
that the reader personally apply probabilities to each of the three
uncertainty ranges and then assess the probability distribution that
results for each scenario. Such examples are not provided in this
analysis, but can be performed readily.

The uncertainties are naturally large, but overall one can see that the
national benefit estimates range substantially lower than the EPA
estimates, even after correcting for the error that EPA has acknowledged
on long-term benefits. DFI's re-calculated benefits range (encompassing
95 percent of the scenarios) is from a low of \$2 billion per year to a
high of \$76 billion when S-T mortality is included, and up to a high of
\$80 billion when L-T mortality is included. The lower bound of \$2
billion consists primarily of visibility rather than health benefits.
(The full range, including all scenarios, not just 95 percent of them,
leads to a benefits range of \$2 billion to \$94 billion when counting
S-T mortality, and \$2 billion to \$101 billion when counting L-T
mortality.) In contrast, EPA's RIA estimates (corrected for the
acknowledged error in the epidemiological study) are \$69 billion when
counting S-T mortality, and \$119 billion when counting L-T mortality.
These are near or above the high end of the uncertainty range that has
been assessed.

Figures 2-19 and 2-20 show similar scenario results, but only include
those scenarios that assume full attribution of the health effects to
PM~2.5~. That is, this is what the range of uncertainty looks like if
one wants to assume that there is no possibility of confounding, and the
epidemiological studies correctly attribute the risk to PM~2.5~. Even
with this conservative assumption, total benefits based on both S-T and
L-T mortality have the majority of scenarios leading to benefits which
lie below EPA's point estimate.

The results presented below have been based on DFI's assumptions and
valuation baseline, as discussed above. Some people may wish to reject
the mortality valuation and other assumptions that DFI feels are best
justified. It is also possible to show the uncertainty in the benefit
range using EPA's own baseline valuations and assumptions, but still
accounting for the uncertainties due to the epidemiological foundation.
This is provided in Figures 2-21 and 2-22. Because EPA's assumptions are
used, this range reflects only the epidemiological uncertainties (for
which there is underlying statistical uncertainty, not based on
judgments or assumptions). Although the ranges now encompass both of
EPA's own original dollar benefit estimates, it is clear that the range
of plausible benefits is very wide, extending downwards to well less
than \$5 billion per year. Even with EPA's judgments, most of the
estimates fall in the lower end of the range.

** **

***J. Conclusions of the PM Benefit analysis***

EPA's point estimates of the benefits of the proposed PM~2.5~ standard
imply substantial benefits from the proposed PM~2.5~ standard. However,
by presenting only a point estimate, without adequate discussion of the
uncertainties involved, EPA does not provide the proper context to
understand these benefits. The range of potential benefits is so wide
that a single point estimate provides little information. This analysis
has identified how a number of key uncertainties, which the EPA has
acknowledged but not presented in its benefit estimates, can
substantially affect the projected benefits of the regulation.

The resulting range of benefits from a number of plausible scenarios is
very large, ranging from near zero to significantly larger than EPA's
estimate. Although DFI has not attempted to determine which outcome is
\"most likely,\" and has not assigned probabilities to the various
scenarios, the majority of the scenarios imply benefits significantly
lower than EPA's point estimate. The ranges generated from this analysis
give a larger and more realistic representation of the potential
benefits, and thus provide a more accurate context within which to
evaluate the merits of the proposed standard.

 

 

 

**IV. Benefits of the Proposed Ozone NAAQS**

 

***A. Current and Proposed Standards***

The current EPA ozone standard is 120 ppb, measured as the maximum
concentration over one hour during a day, with one exceedence of the
standard allowed on average per year (averaged over three years). EPA's
proposed new standard is 80 ppb, measured by taking the third highest
daily 8-hour average concentration in each year, and then averaging that
value over three years. The current standard is estimated to correspond
to an 8-hour, two exceedence standard of about 90--95 ppb, so that the
proposed new standard would require an incremental reduction in ozone
levels of about 10 percent beyond attainment with the current standard.

Some U.S. metropolitan areas, such as Los Angeles, have ozone levels far
above the current standard. Other areas are in attainment for the
current standard, but would be out of attainment under the proposed new
standard. The proposed new standard would require those areas now in
attainment to carry out additional control measures in order to reduce
the emissions of ozone precursors (i.e., volatile organic compounds
(VOCs) and nitrogen oxides (NO~x~) that react in the atmosphere to form
ozone). For the \"full attainment\" benefits calculations in the RIA,
EPA has assumed that all areas will reduce precursor emissions to meet
the new standard. (For the Los Angeles area and for some other U.S.
metropolitan areas with high ozone levels, it is highly doubtful that
feasible methods exist to accomplish the reduction in precursor
emissions that would be needed to meet either the current ozone standard
or the proposed new ozone standard.)

 

***B. Epidemiological Evidence***

**Mortality.** Ozone-related mortality is suspected from observed
association, but multiple uncertainties remain*.* EPA's Air Quality
*Criteria Document* for Ozone, EPA's Ozone *Staff Paper,* and the RIA
each state that ozone is not known to cause premature deaths.
Epidemiological studies in many metropolitan areas inside and outside
the United States have examined the relationship of ozone and other air
pollutants with day-to-day fluctuations in observed deaths. Many of the
studies show that air pollution is associated with elevated daily
mortality. With existing information it is not possible to determine
what is responsible, a constituent of air pollution such as ozone, or a
factor that is correlated with elevated air pollution but which would
not be affected by control measures to reduce emissions. No one knows
today whether ozone or other constituents of air pollution at the
concentrations found in and downwind of U.S. urban areas cause premature
death. If a causal relationship between air pollution and daily
mortality does exist, it is not known whether the death should be
attributed to ozone, to particulate matter, or to other constituents of
air pollution. The Health Effects Institute recently studied the effect
of ozone on mortality, and in their report, the authors state: \"In
summary, although the investigators found an increase in mortality
associated with periods during which ambient levels of ozone were
elevated, this increase cannot necessarily be attributed to ozone
because it was not statistically significant when other air pollutants
were included in the analysis\...further research is needed to
disentangle the effects of the various pollutants and to gain insights
into the association of individual pollutants with morbidity and
mortality.\"

Moolgavkar states in his 1995 study (on which the EPA's mortality
estimate was based): \"Our findings in Philadelphia were similar to our
findings in Steubenville: although there was an association between air
pollution and mortality, it was not possible to isolate one component of
air pollution as being responsible.\"

If ozone as a component of air pollution does cause premature death,
this effect is most likely to occur in individuals whose health is
already severely compromised through disease or aging. In such cases,
the onset of death may be advanced by a small amount of time (days,
weeks, months, or a few years) relative to total life span. In the event
that a causal relationship does exist, it is not known whether exposure
to the air pollution at *any* level will hasten death, or whether there
is a *threshold***,** below which the effect does not occur.

**Morbidity.** There is much stronger evidence for ozone-related
morbidity impacts, but the extent and severity at ambient concentrations
are not well known. At levels in the 70 ppb to 90 ppb ozone exposure
range there is no evidence of a threshold for the onset of measurable
symptoms or changes in lung function. Clinical experiments on healthy
human subjects indicate that ozone exposure during moderate or heavy
exercise for periods of four to six hours at levels of 80 ppb cause
changes in lung function (narrowing of airways) and inflammation of
airway tissue. As a result, EPA is concerned with the effect of ozone on
groups that exercise out-of-doors, such as children or outdoor workers.
Both the narrowing of airways and the inflammation are believed to last
only a short time, a few hours to at most a few days. The health
significance of repeated exposure to ozone at these levels, or at higher
levels such as occurring in Los Angeles, remains unknown. Scientists
suspect that ozone may be associated with increases in asthma attacks,
hospital admissions, and premature aging of the lung.

**Uncertainty and Peer Review.** Extensive discussion of what is known
and suspected about the health impacts of ozone exposure is found in
EPA's *Criteria Document* (CD) and EPA's OAQPS *Staff Paper,* both of
which underwent extensive peer review by the Ozone Panel of the Clean
Air Scientific Advisory Committee (CASAC). CASAC is a part of EPA's
Science Advisory Board, which is composed of expert scientists from
outside of the federal government. CASAC is charged by the Clean Air Act
with reviewing available scientific information and making
recommendations to the EPA Administrator on National Ambient Air Quality
Standards. The letter from CASAC chair George Wolff to EPA Administrator
Carol Browner of November 30, 1995, stated the CASAC Ozone Panel's
conclusion as follows:

-   The Panel felt that the weight of the health effects evidence
    indicates that there is no threshold concentration for the onset of
    biological responses due to exposures to ozone above background
    concentrations. Based on information now available, ozone may elicit
    a continuum of biological responses down to background
    concentrations.

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-   Based on the results\... presented in the *Staff Paper*, the Panel
    concluded there is no \"bright line\" which distinguishes any of the
    proposed standards (either the level or the proposed number of
    exceedences) as being significantly more protective of public
    health\.... Consequently, the selection of a specific level and the
    number of allowable exceedences is a policy judgment.

The CASAC review as well as the wording in the *Criteria Document* and
the *Staff Paper* stress that the health impacts of ozone exposure
remain uncertain. There is no known threshold of no effect, or of a
serious adverse effect, from ozone at the concentrations occurring in
the United States. EPA is charged by law to set a standard that protects
health with \"an adequate margin of safety.\" EPA's proposed new
standard reducing allowable ozone concentrations is a *policy judgment*
about how far, in the face of scientific uncertainty, EPA should go to
protect against health effects that *might* be avoided by reducing ozone
concentrations.

 

 

***C. EPA's National Benefits Categories and Estimates***

**Overview.** EPA has calculated the benefits from the proposed new
National Ambient Air Quality Standard for ozone. The methodology for the
calculation is discussed in the December 1996 *Regulatory Impact
Analysis for Proposed Ozone National Ambient Air Quality Standards*
(RIA) and in more detail in supporting contractor reports. Estimates of
ambient ozone concentrations are calculated using projections of
precursor emissions at the county level. Ozone concentrations are
projected using the Regional Oxidant Model (ROM) and extrapolating its
results from the eastern U.S. to the entire nation. Projections of ozone
concentrations under the proposed new standard are made for the year
2007.

Benefits of lowered ozone concentrations are derived by estimating
decreases in the following health effects:

-   mortality;

-   hospital admissions for respiratory illnesses, pneumonia, and
    chronic obstructive pulmonary disease (COPD); and

-   incidences of acute respiratory symptoms and asthma attacks.

In addition, increased worker productivity is considered.

The impact of control strategies in lowering ozone concentrations is
assessed in EPA's \"full attainment\" scenario. A rollback procedure is
applied to precursor emissions, independent of the cost and feasibility
control measures identified in EPA's cost analysis. This procedure is
used to determine how much emissions reduction is needed to meet the
attainment criteria of the proposed standard.

The RIA uses dose-response relationships to estimate the incidence of
symptoms and mortality, given projected hourly ozone concentrations in
each nonattainment area. These are then aggregated to national incidence
estimates. EPA has then assigned monetary values to symptoms avoided and
to deaths avoided as the result of reductions in ozone concentrations.
The benefits ascribed to the change from the existing ozone standard are
then estimated as the sum of monetary values from symptoms avoided plus
monetary values from deaths avoided. In this manner, EPA has calculated
an overall estimate of total monetized national health benefits from the
proposed new ozone standard in the year 2007. Welfare benefits are also
calculated in the RIA. The welfare benefits are primarily from damage
avoided to crops and vegetation.

**Basis of EPA's Mortality Estimates.** EPA's RIA does not use the exact
form of the proposed new standard in the mortality and other benefits
calculations (i.e., a third-highest average concentration). Rather, a
second-highest average concentration and a fifth-highest average
concentration at 80 ppb for the 8-hour daily maximum are used. In its
summary presentations EPA presents a range of monetized benefits. This
report will focus on the upper end of EPA's range, the second-highest
average concentration form (\"1AX\") using the \"highest estimate\"
benefits for the Regional Control Strategy. In this re-assessment,
however, DFI directly assesses benefits of the actual proposed form.

The ozone RIA cites four epidemiological studies as the basis for its
mortality estimates, but only two of these actually enter into the
calculations. EPA's low estimate of zero mortality benefits is based on
the Kinney *et al.* (1995) study of Los Angeles. EPA does not give a
nominal estimate for mortality; it was \"not estimated due to
uncertainty considerations.\" EPA's high estimate is based on a 1995
study by Moolgavkar, *et al.* of Philadelphia. Using the \"1AX\" form
with the regional controls strategies baseline, EPA calculates 470
deaths avoided annually from the new standard compared to the current
standard. As for PM~2.5~, EPA has used the figure of \$4.8 million as a
willingness-to-pay estimate per death avoided. Thus, the \"high\"
mortality benefits estimate is \$2.3 billion per year.

This estimate of deaths avoided is the primary basis for EPA's high-end
estimate of annual monetized benefits of \$2.8 billion in 1990 dollars.
Of this amount, only \$0.5 billion is attributed to welfare, and only
\$0.03 billion to nonmortality health effects. The benefit ascribed to
mortality is by far the dominant term in the RIA benefit calculation.

 

***D. Overview of Analysis Methodology for Ozone Benefits
Re-Assessment***

**Focus on mortality.** The analysis in this report focuses only on the
mortality portion of the RIA's estimated benefits. The benefits ascribed
to other health effects are less than \$50 million, of the order of 0.1
percent of the incremental costs associated with the proposed rule. The
RIA estimates that welfare benefits from the proposed new standard are
in the range of \$195 million to \$520 million. *Without including the
[high]{.underline} estimate for mortality, total health and welfare
benefits for EPA's proposed new ozone standard are well under \$1
billion.*

This summary of the RIA's benefit estimates was based on the 1AX version
of the Regional Control Strategy. This baseline was selected because it
was decided in the course of this study's cost analysis (Part 1) that
the 1AX version of the Regional Control Strategy was the most
appropriate baseline for the cost of the proposed ozone standard. Thus,
the ozone benefits estimate described in this section are comparable to
the cost estimates for ozone described in Part 1 of this report.

**Steps to calculate national monetized benefits.** Insufficient
information and data was made available by EPA to precisely replicate
its methodology. Where data gaps occurred, DFI developed comparable
methodology to calculate mortality incidence and aggregate to national
monetized benefits:

-   We evaluated EPA's assumptions and valuation, and where necessary,
    developed alternative assumptions or valuation.

-   We investigated key scientific and epidemiological uncertainties
    which could affect benefits, and developed reasonable ranges for
    uncertain parameters.

-   We determined sub-populations which will experience benefits.

-   We calculated the avoided incidence of mortality for each
    sub-population.

-   We aggregated to national estimates of incidence under multiple
    scenarios of uncertainty.

-   We applied values per incident to develop ranges of national
    monetized benefits.

Succeeding sections provide more discussion of each step.

 

***E. Evaluate and Modify Baseline for Valuation and Assumptions***

EPA assumes a willingness-to-pay to avoid a mortality incident at \$4.8
million per life. However, this is primarily based on literature studies
investigating workplace or accidental deaths. The population most at
risk from increased ozone exposure is not necessarily a representative
cross-section of the overall population. As with PM, if there is a
mortality effect from ozone, those individuals who are physiologically
compromised, or over the age of 65 would be the most likely to die from
high ozone concentrations, and their lives may be shortened by only
weeks, months or a few years. The HEI Mexico City study (described
below) examined both total mortality and mortality among persons over 65
years of age. In this latter age group relative risk is increased by
about 70 percent to 1.0715 compared to the relative risk of 1.0415 for
total mortality.

In this context of greatly skewed distribution of mortality, a valuation
of *life-years* saved, rather than lives *per se*, is more appropriate.
Further discussion of this point can be found in the PM benefits section
of this report. For consistency with the PM assessment, DFI uses the
same valuation selected for PM \"short-term\" mortality and explained in
the previous section. Thus, \$2 million per ozone-related death avoided
is used in the DFI baseline for ozone. The DFI analysis continues to use
avoided incidence of mortality as its health effects endpoint, but the
multiplication by the lower valuation reflects the life-year perspective
applied to the skewed age distribution of effects.

** **

***F. Epidemiological Studies Used in This Analysis***

In contrast to the EPA *Criteria Document*, the RIA is not peer-reviewed
in its use of available scientific information. A number of recent
epidemiological studies examining the association between daily
mortality and ozone exposure have become available since the EPA
*Criteria Document* was produced. These include the Kinney *et al*.
(1995) study of Los Angeles and the Moolgavkar *et al.* (1995) study of
Philadelphia used in the RIA, and a study of mortality in Mexico City
supported by the Health Effects Institute. The RIA used the first two of
these studies, and this report will use these two plus the Mexico City
study as the main basis for mortality estimates. Some other recent
epidemiological studies for other cities will be considered more briefly
in the discussion following the presentation of the main results for
ozone mortality benefits. Appendix 3 provides a full reference list of
all the ozone mortality studies mentioned in this section.

The Moolgavkar *et al.* (1995) study is one of a number of studies that
were carried out to examine the relationship of daily mortality in
Philadelphia to particulate matter. An association between mortality and
ozone was found when ozone, particulate matter (as Total Suspended
Particulates, TSP), and sulfur dioxide were considered simultaneously.
The association between ozone and mortality was significant only for the
highest 20 percent of ozone exposures in the highest exposure season,
the summer, when ozone and TSP are correlated. The measure of ozone
exposure used was averages throughout each day and for all monitors in
the metropolitan area. The authors of the study concluded, \"Because the
pollution covariates are highly correlated, it is not possible to single
out one specific component as being responsible for the observed
association between air pollution and mortality.\"

The Kinney *et al.* (1995) study reexamined a potential relationship
between mortality and ozone levels in Los Angeles using new statistical
methods and data from 1985--1990. An earlier study by Kinney and
Ozkaynak (1991) had examined 1970--1979 data from Los Angeles and had
found an association between total oxidants (including ozone) and
mortality. The 1995 Kinney *et al.* study examined ozone (measured using
one-hour daily maximum concentrations), particulate matter (measured as
PM~10~), and carbon monoxide (CO) for associations with mortality,
individually and in combination. Ozone was found to be associated with
mortality if the other pollutants were not included, but when all three
measures were included, the mortality was associated with CO and PM~10~
rather than with ozone. Ozone may have been acting as a surrogate for
particulate matter. The authors wrote, \"Definitive conclusions
regarding a possible role of O~3~ \[ozone\] in daily mortality cannot be
drawn from this small data set, nor from the existing literature.\"

Mexico City was selected as the site for an extensive investigation of
the relation of mortality to air pollution. Mexico City has very high
levels of ozone, comparable to those in Los Angeles and far above the
current U.S. EPA standard. Philadelphia, by comparison, is
representative of many cities in the eastern U.S. that are much closer
to attainment of the current United States standard. A team of
investigators led by Loomis and Shy were selected by the Health Effects
Institute to carry out this investigation, which used advanced
statistical methods and considered different indices of ozone
concentration: a one-hour maximum level, similar to that used in both
Kinney studies, a 24-hour average such as used in the Moolgavkar study
of Philadelphia, and two measures similar to the eight-hour maximum in
the proposed new EPA standard: an average between the hours of 8 a.m.
and 6 p.m. and an 8-hour moving average around the daily maximum. The
results of the Mexico City analysis showed that ozone is associated with
daily mortality using any of the four indices if ozone is considered
alone. However, if other air pollutants (TSP and sulfur dioxide) are
also included in the analysis, the finding of an association of
mortality with ozone disappears.

In each of these epidemiological studies, the authors calculate a
relative risk: the amount by which mortality is observed to increase per
unit of increase in ozone level. (The unit of increase is 100 ppb.) In
comparing the results, it is important to recognize that the number
depends on the index for ozone: whether the daily maximum one-hour
average, 24-hour daily average, or daily maximum 8-hour average is used.
This report uses a combination of the measures from the Mexico City
study similar to the daily maximum 8-hour average of EPA's proposed
standard. Comparison of the results from Mexico City with the studies of
Los Angeles and Philadelphia shows surprisingly good quantitative
agreement in the relative risk for association of mortality with ozone
level, given that an association exists. The main uncertainties are
whether ozone actually *causes* premature mortality, and whether there
is a threshold below which the mortality does not occur.

 

***G. Epidemiological Uncertainties***

The analysis of this report captures the major uncertainties present in
current scientific studies regarding the effects of ozone on mortality
by calculating the benefits of the proposed ozone standard under a set
of different scenarios. As with the PM benefits re-assessment, three
sources of uncertainty in the extent of mortality are examined
explicitly in this analysis: whether ozone is causally related to
mortality, the relative risk describing the increase in mortality for a
given increase in ozone exposure, and the possibility that ozone causes
mortality only above a threshold concentration level. Each of these
uncertainties is characterized by three possible values. The resulting
27 combinations give a set of scenarios that can readily be examined to
illustrate the implications of uncertainty in current knowledge of ozone
mortality.

**Relative risk.** The association of mortality to ozone alone in Mexico
City has been used as the basis for the relative risk. The relative risk
estimate and the 95 percent confidence limits are used for the three
values to represent the uncertainty. This analysis uses the two
intermediate indices of ozone exposure in the Mexico City study, the
8-hour moving average and the 8 a.m. to 6 p.m. average. The values are
quite close: the two relative risk estimates are 1.040 and 1.043. The
average of 1.0415 is used as the middle value in this analysis. The
lower value is 1.02 and the upper value is 1.063. These values
approximate the 95 percent confidence intervals reported in the Mexico
City study for the 8-hour moving average and the 8 a.m. to 6 p.m.
average. Thus, the three scenarios used in this analysis are:

> Relative risk: 1.020 1.042 1.063 (per 100 ppb ozone daily maximum
> 8-hour average)

The Moolgavkar *et al.* (1995) study of Philadelphia used a 24-hour
daily average for ozone exposure. Measured against this index of ozone
exposure, the study found a relative risk of 1.063 (with 95 percent
confidence limits of 1.018 to 1.108). The relative risk in the Mexico
City study for ozone alone using a 24-hour average ozone measurement is
1.058 (with 95 percent confidence limits of 1.022 to 1.094). The Kinney
*et al.* (1995) study of Los Angeles used a 1-hour maximum for ozone
exposure. The relative risk for ozone alone was 1.02 (95 percent
confidence limits of 1.00 to 1.05). The Mexico City relative risk using
one-hour maximum is 1.024 (with 95 percent confidence limits of 1.011 to
1.039). The agreement in relative risk among the epidemiological studies
of the three different cities is seen to be close.

**Threshold cutpoint**. The possible existence of a threshold, a level
below which ozone has no mortality consequences, is a key uncertainty in
this analysis. There are reasons to believe that a threshold may exist
in epidemiological studies of air pollution, even if one cannot be
observed in the data. This point was discussed in more detail in the PM
benefits section. However, in the case of ozone, the epidemiology itself
seems to point to the possibility of a threshold. The three scenarios
investigated were:

> Threshold: 40 70 100 ppb

40 ppb is equivalent to background concentration level, so that
ozone-induced mortality occurs for any exposure elevated over natural
background levels. HEI's study of mortality in Mexico City shows
evidence of a threshold between 117.3 ppb and 142.8 ppb 1-hr daily
maximum, assumed to be at 130 ppb. This translates to approximately 100
ppb using an eight-hour maximum. Finally, 70 ppb corresponds to the
midpoint between the low and high threshold values.

**Attribution of mortality to ozone.** As mentioned previously, there is
only equivocal evidence that ozone is a causal factor of premature
mortality. Most epidemiological studies have other potentially
confounding pollutants which could be underlying the observed mortality.
DFI developed three scenarios for attributing the observed mortality
association to ozone:

> Attribution: 0% 50% 100%

The low scenario is that there is no causal association. The middle
scenario is that there is 50 percent attribution of the observed
mortality to ozone exposure, so that a reduction in ozone levels would
result in 50 percent less decrease in deaths than predicted by the
relative risk. The high case is 100 percent attribution; that is,
mortality would be reduced as predicted by the relative risk. The 100
percent case is the most conservative and is what is assumed by EPA.
This substantial evidence of confounding in the mortality studies
provided ample reason to believe that a 50 percent attribution or less
could be realistic.

 

***H. Populations Benefiting from the Proposed Standard***

The EPA's RIA process first estimates which areas will be out of
attainment with the current ozone standard in 2007. These nonattainment
areas are the only areas in which incremental benefits are estimated to
accrue due to the proposed standard. Supporting documentation from the
EPA contractor, Mathtech, Inc., gives a listing of the complete set of
these projected nonattainment areas.

Rather than computing avoided mortality for each of the areas listed by
Mathtech, DFI selected a set of six representative cities. Each city
represents a \"category\" of ozone problem, which is defined by EPA's
nonattainment status. The cities and categories are listed in the Table
2-7 below:

  -------------------- --------------------------------------------------
  **Table 2-7.         
  Representative       
  Cities**             

  Representative City  Current EPA Non-Attainment Classification

  Los Angeles          Extreme

  Philadelphia         Severe

  Baton Rouge          Serious

  Dallas               Moderate

  Albany, N.Y.         Marginal

  Denver               Transitional
  -------------------- --------------------------------------------------

 

To calculate avoided mortality across all of Mathtech's nonattainment
areas, DFI assigned each nonattainment area to one of the six
categories. The assignment was based on each area's current
nonattainment status. So, for example, if Atlanta is a projected
nonattainment area for the 1AX Regional Control Scenario, and its
current EPA classification is \"severe,\" then it would be assigned to
the Severe category and represented by analysis results for
Philadelphia. DFI then estimated the total population falling into each
of the six categories. These populations receive different levels of
benefits (avoided deaths) due to full attainment of the proposed
standard.

 

***I. Air Quality and Avoided Mortality for Representative Cities***

**Projected air quality.** Following EPA's methodology, this analysis
determined 2007 ozone concentrations based on recent measurement data.
The RIA analysis used hourly data for 1990, but these data were not
available to DFI. Thus, calculations in this analysis are an
approximation to EPA's calculations. This analysis used eight-hour daily
maximum ozone concentrations from EPA's Aerometric Information and
Retrieval System (AIRS) database for the years 1991--1995. The cities
selected had essentially complete ozone concentration measurements for
these years. The base year used for calculations is 1994, an average
ozone year inthe six cities studied. Neither extremely high
concentrations nor extremely low concentrations were observed in
comparison to other years in the data base.

The 1994 ozone concentrations were transformed to 2007 concentrations
using the same method as in the RIA. The ROM Regional Control Strategy
coefficients from the RIA were used in a EPA's linear equation to
extrapolate the ozone levels appropriately.

The amount of rollback required to meet the current standard and the
proposed standard (eight-hour maximum, 80 ppb, 2 exceedences per year)
was determined using a quadratic rollback technique similar to EPA's
technique. This technique lowers the peak ozone days proportionally more
than it lowers the low ozone days.

**Mortality.** Having calculated the daily ozone concentrations for each
of the representative cities, the relative risk scenarios and baseline
mortality incidences were then applied to calculate mortality under both
the current and proposed standards, for each city. The difference
between these two is the avoided deaths (benefits) attributable to the
proposed standard (above and beyond the benefits of the current
standard). These deaths avoided were normalized to deaths per 100,000
population.

 

***J. National Aggregation of Mortality Incidence***

With the standardized mortality rate (avoided deaths per 100,000
population due to the proposed standard) for each representative city,
and the populations assigned to each of the corresponding six
categories, the national aggregate deaths avoided was calculated by
simply multiplying the rate by the population for each category, then
summing across categories.

The results of the 27 scenarios for deaths avoided are shown in Figure
2-23. The EPA estimate for deaths avoided is approximately bracketed:
the highest scenario gives an estimate of approximately twice EPA's
figure of 470 deaths. The set of low scenarios show no deaths avoided if
ozone exposure does not cause premature mortality.

 

***K. National Monetized Benefits***

The valuation per ozone-related mortality incident was applied to
calculate monetized benefits of avoided mortality. The range of benefits
is from \$0 billion to \$1.96 billion, as shown in Figure 2-24. The
graph also shows EPA's mortality benefits estimate of \$2.3 billion. The
resulting benefits for each of the 27 scenarios is shown in Figure 2-25.
This analysis has used the value of \$2 million per death avoided,
which, as discussed above, is less than half of EPA's estimate. Since
the estimates for the number of deaths range from 0 to twice EPA's
estimate of 470 deaths, all of the 27 scenarios fall below EPA's
estimate of \$2.3 billion. The figure shows that 10 scenarios involve no
deaths and no mortality benefits. Fifteen more scenarios fall below \$1
billion, for a total of 25 of the 27.

Eighteen of the 27 scenarios have less than 100 percent attribution of
mortality effects to ozone. As mentioned previously, EPA's conservative
assumption is that all of the risk determined by the underlying
epidemiological studies is in fact attributable to ozone (i.e., no
hidden confounders). Figure 2-26 and 2-27 show avoided incidence and
benefits, respectively, for only the scenarios in which 100 percent
attribution is made. Clearly, EPA's estimates still remain at the high
end or outside of the range of scenarios.

 

* *

Some may choose not to accept the life-year valuation methodology which
leads to the \$2 million per \"statistical life.\" Figure 2-28
illustrates the range of mortality benefits using the EPA's value of
life (\$4.8 million per \"statistical life\"). In this case, the range
is widened, and 3 scenarios produce benefits above EPA's estimate. These
are the scenarios with high relative risk, low threshold, and high
attribution of mortality to ozone. However, most of the scenarios are
still below EPA's estimate. Twenty-one scenarios are below \$1.5
billion.

 

***L. Additional Sensitivity Analysis: Effects of Background Ozone
Level***

Although not considered as a primary uncertainty in the development of
the 27 scenarios, the effect of background ozone level could affect the
incidence results. The EPA's *Staff Paper* states, \"it is reasonable to
estimate that the eight-hour daily maximum O~3~ \[concentration\] during
the summer is in the range of 0.03 to 0.05 ppm.\" Based on this
information, DFI used the middle of this range, 0.04 ppm (40 ppb) for
its analysis. The EPA also used 0.04 ppm in its analysis. In the
calculation of rollback, no rollback was necessary if the ozone
concentration was already below background. If the ozone concentration
was above background, rollback is calculated as a percentage of the
difference between the current level and background, multiplied by the
concentration.

Use of different background levels changes the benefits by approximately
25 percent. Figure 2-29 illustrates the difference between using 30 ppb,
40 ppb, and 50 ppb. (These values are the low, middle, and high end of
the range cited in the *Staff Paper*.)

 

** **

***M. Other Epidemiological Mortality Studies***

This analysis has been based upon the 1996 Mexico City study by Loomis
*et al.* The relative risk for ozone alone from this Mexico City study,
using a 24-hour daily average measure of ozone, closely matches the
relative risk from the 1995 Moolgavkar *et al.* study of Philadelphia
used for the RIA high estimate of mortality. The relative risk for ozone
alone, using a one-hour maximum measure, closely matches the relative
risk for ozone alone in the 1995 Kinney *et al*. study of Los Angeles.
When particulate matter was included in addition to ozone, the
significance of the association between mortality and ozone disappeared
in both the Mexico City and in the 1995 Kinney *et al.* Los Angeles
study. This result from the Kinney study is the basis of the RIA low
estimate of zero mortality from ozone exposure.

This section briefly reviews other epidemiological studies that have
explored the association between ozone and mortality (see Table 2-8
below). The RIA references two studies by Dockery *et al.* (1992).
Dockery, Schwartz, and Spengler investigated the association of
mortality with ozone and other indices of air pollution in St. Louis and
eastern Tennessee in a one-year period of 1985--1986. They found an
association between several measures of particulate matter and
mortality, but the association for ozone and for other gaseous
pollutants (sulfur dioxide, nitrogen dioxide) was far from statistical
significance. St. Louis had five violations of the current ozone
standard during the one-year investigation period, while eastern
Tennessee had none. There are several other studies of other areas
(Detroit, Steubenville, Ohio*,* Philadelphia) published by Schwartz and
Dockery, in which associations between mortality and particulate matter
were found, but not associations with ozone. The reference to the second
Dockery *et al.* (1995) study in the RIA is not clear.

Samet *et al.* (1997) have carried out further investigation of the
Philadelphia data for 1974-1988 in their Phase I-B report to the Health
Effects Institute. This study used a 24-hour average as the index for
ozone exposure, and the findings are similar to those of Moolgavkar *et
al.* (1995) with a larger relative risk: 1.118 for ozone alone and 1.099
with other pollutants included. With other pollutants included in the
model, the relative risk translates to approximately 1.072 for 8-hour
maximum ozone concentrations. This relative risk would imply an increase
in avoided mortality of 12 percent to 20 percent compared to the high
relative risk value of 1.063 used in this analysis.

Thurston recently submitted comments to EPA in which he claims that
research he and colleagues at New York University are conducting shows a
significant association of mortality with ozone for nine U.S. cities.
(Neither a published version nor an unpublished version with details of
the procedures used was available for this analysis.) Thurston's results
show much lower relative risk for cities with high ozone levels (Los
Angeles, 1.03; Houston 1.02), compared to cities with moderate ozone
levels (Atlanta, 1.08; Chicago, 1.07; Detroit, 1.10; New York, 1.08; St.
Louis, 1.05), or low ozone levels in compliance with the current
standard (Minneapolis, 1.07; San Francisco, 1.09). (All relative risks
are per 100 ppb increase, using a 1-hour daily maximum measure of
ozone). This pattern of results of higher associations in the cities
with lower ozone levels is counter-intuitive. One would expect a
stronger association in locations where the pollutant concentrations are
higher. The fact that Thurston finds almost exactly the opposite pattern
is highly suggestive of one or more confounding factors correlated to
variations in ozone level within each city. These factors could involve
weather, the quality of indoor air, or other air pollutants.

Several recent studies outside the United States cited by Thurston show
the same pattern: that higher relative risks occur in cities with lower
ozone levels. The Anderson *et al*. study of London, a city whose
maximum 8-hour ozone level of 74 ppb and maximum one-hour level meet
both the current and proposed U.S. standards, calculated a relative risk
of 1.11 for 100 ppb, 1-hour daily max. The Verhoeff *et al.* study of
Amsterdam, a city whose maximum 1-hour level of ozone at 153 ppb
indicates nonattainment of the current U.S. standard similar to the
moderate U.S. cities above, had a relative risk for current day ozone
(1-hour maximum) of 1.04, and for ozone with a two-day lag, 1.10. A
study of Belgium by Sartor *et al*. during the 1994 summer heat wave,
with 24-hour average concentrations of ozone reaching 53 ppb, very low
in comparison with U.S. cities, found a 16 percent increase in daily
mortality, equivalent to a 1.37 relative risk. This seasonal relative
risk number for a 100 ppb increase in the 24-hour average ozone level
cannot be compared directly with the other relative risk numbers for
one-hour maximum ozone increases of 100 ppb, but it is clearly very
high. In the Mexico City study, the relative risk was 1.058 for a 100
ppb increase in 24-hour averaged ozone.

The distinguished scientists on the review panel for the Health Effects
Institute comment as follows in their review of the Samet *et al.*
(1997) Phase I-B re-evaluation of the Philadelphia data set:

> *No single pollutant by itself accounts for the increase in daily
> mortality\.... In reality, we do not know which variables have a
> causal function; indeed, one of the purposes of the analysis is to
> explore competing hypotheses in an attempt to infer which combination
> of variables is more likely to cause health effects\.... Ultimately,
> it will require joint analysis of data sets from multiple cities with
> different copollutant correlations (such as planned for Phase II) to
> address further the role of multiple pollutants\.... Statistical
> analyses of these complex, multivariable data cannot be expected to
> provide certainty concerning the causal role of particulate air
> pollution in daily mortality.*

This conclusion seems appropriate for ozone as well as for particulate
matter. There is much less evidence accumulated from epidemiological
studies associating ozone with mortality than there is evidence
implicating particulate matter. Further, the recent epidemiological
studies have not undergone peer review within EPA's *Criteria Document*
and *Staff Paper* process.

  -------------------- --------- ---------- --------------- --------------- --------------
  **Table 2-8. Summary                                                      
  of Other Studies of                                                       
  Ozone and Mortality                                                       
  (Full References Are                                                      
  in Appendix 3)**                                                          

  Study                Years of  Relative   95% Conf        Type of O~3~    Study Site
                       Study     Risk       Interval for    Concentration   
                                            Relative Risk   Measurement     

  Schwartz (1991)      1973-82   1.00                       avg of 1-hr max Detroit
                                                            and 24-hr avg   

  Dockery, Schwartz, & 1985-86   1.00                       24-hour average MO and TN
  Spengler (1992)                                                           

  Sartor *et al.*      1994      1.37                       24-hour average Belgium
  (1995)                                                                    

  Kinney & Ozkaynak    1970-79   1.02                       1-hour maximum  Los Angeles
  (1991)                                                                    

  Kinney, Ito, &       1985-90   1.02       (1.00,1.05)     1-hour maximum  Los Angeles
  Thurston (1995)                                                           

  HEI (1996)           1990-92   1.024      (1.015,1.044)   1-hour maximum  Mexico City

  Ozkaynak (1995)      1972-90   1.04                                       Toronto

  Thurston (1997)      1981-90   1.06       (1.02,1.10)     1-hour maximum  9 cities
                                            (range, not CI)                 

  Moolgavkar *et al.*  1973-88   1.063      (1.018,1.108)   24-hour average Philadelphia
  (1995)                                                                    

  Verhoeff *et al*.    1986-92   1.10                       1-hour maximum  Amsterdam
  (1995)                                                                    

  Anderson *et al.*    1987-92   1.10                       8-hour maximum  London
  (1996)                                                                    

  HEI (1997)           1974-88   1.118                      24-hour average Philadelphia
  -------------------- --------- ---------- --------------- --------------- --------------

* *

***N. Conclusions of the Ozone Benefit Analysis***

This analysis has presented a set of calculations of the benefits for
EPA's proposed new ozone standard of 80 ppb for the three-year average
of the annual third-highest maximum eight-hour daily average. The
analysis has focused only on mortality, since if mortality is not
included, the benefits from the proposed new standard are, by EPA's
calculations, well below \$0.05 billion for health impacts avoided and
up to an additional \$0.5 billion for welfare impacts avoided, such as
damage to crops and vegetation.

The mortality calculations are consistent with the methods, assumptions,
and data used in the RIA. These calculations illustrate that EPA's point
estimate of \$2.3 billion in benefits can be obtained by making two sets
of assumptions: (1) a high value should be assigned per \"death
avoided,\" and (2) the imposition of additional controls to meet the
proposed ozone standard will actually reduce deaths.

This analysis has used a lower value per death avoided than the \$4.8
million used by EPA. The observations from epidemiological studies
strongly suggest that deaths associated with high air pollution are
occurring primarily in elderly and sick individuals, and that the
shortening of life involved is far less than in situations such as
highway traffic accidents, where values such as \$4.8 million per death
avoided would be appropriate.

A causal relationship between ozone exposure and mortality is not
established at the present time. Neither EPA's carefully peer-reviewed
*Criteria Document* nor its *Staff Paper* show significant scientific
support for such a causal relationship. Much of the more recent
epidemiological research has investigated whether ozone and other air
pollutants might explain some of the association that has been observed
between mortality and particulate matter. The review presented above
notes that the highest predictions for an association between mortality
and ozone have come from the cities with the lowest ozone levels. If
ozone is actually linked with premature deaths, one would expect that
the strongest evidence would instead come from studies of Los Angeles
and Mexico City, where ozone levels remain far above current U.S.
standards.

This report has considered the uncertainty in ozone mortality benefits
by examining 27 scenarios, representing three alternative assumptions
for important uncertain factors: causality (the attribution of deaths
avoided to reductions in ozone exposure), the magnitude of the relative
risk relationship, and the potential that ozone causes mortality only
when a threshold is exceeded, rather than at levels of ozone extending
down to natural background. The results show that high benefits only
occur if ozone is responsible for a large fraction of the deaths
associated with air pollution, and that these deaths relate to ozone
exposure at low levels.

The range of benefits calculated in this analysis is wide, ranging from
near zero benefits to significantly higher than EPA's point estimate.
This range stems from substantial uncertainties in key factors that
drive the benefit estimates. The authors believe that each of the
scenarios considered is plausible, and that the resulting range of
national monetized benefits more accurately represents the state of
knowledge than EPA's single point estimate. This range of benefits
provides a more realistic context within which to evaluate the merits of
the proposed standard.

 

 

 

 

Part 3

**About the Authors**

**Dr. Anne E. Smith**, a Vice President of Decision Focus Incorporated,
is an economist and risk analyst specializing in the integrated
assessment of major policy issues. She has been analyzing and producing
expert testimony on the benefits, costs, and economic impacts of ambient
particulate matter controls over the past several years. Prior to her
work on the NAAQS for PM, she performed the primary integrated and
economic analyses for the Grand Canyon Visibility Transport Commission.

**Dr. D. Warner North**, a Sr. Vice President of Decision Focus
Incorporated, is an international leader in the development of risk
analysis techniques. Dr. North served with the Clean Air Scientific
Advisory Committee (CASAC) in its most recent review of the scientific
basis for the ozone NAAQS standard.

**Dr. Kenneth Green** is Environmental Studies Director and a Senior
Policy Analyst at the Reason Foundation. Dr. Green has published three
previous studies on the linkage between transportation and air quality:
\"Looking Beyond ECO,\" \"Defending Automobility,\" and \"Checking Up on
Smog Check.\" He has also directed studies on electric vehicles and on
roadway finance reform. Dr. Green serves on the California Department of
Transportation Advisory Committee and also on the REACH Commission, a
task force sponsored by the Federal Highway Administration and the
Southern California Association of Governments to design pricing
approaches for roadways in the South Coast Air Basin.

 

**Acknowledgement**

The authors would like to acknowledge the intensive research and
technical support provided by Mr. **Charles J. Helman** on this project.
We also commend him for his exceptional independent management of
logistics and communications, which made possible the prompt completion
of this rapid turnaround, wide-ranging sutdy.

Appendix 1

Marginal Control Cost Estimates from EPA's Ozone RIA

  ---------------- -------------------- ------------------------- ------------ ------------ ------------- ------------- ----------- ------------- ---------
  Alternative                                                                                                                                     
  8H1AX-80:                                                                                                                                       
  Marginal                                                                                                                                        
  Emission                                                                                                                                        
  Reductions and                                                                                                                                  
  Costs by                                                                                                                                        
  Nonattainment                                                                                                                                   
  Area and Control                                                                                                                                
  Measure Under                                                                                                                                   
  the RCS                                                                                                                                         

  Nonattainment    Source Category      Control Measure Control   Reductions   Reductions   Costs         Costs         VOC+NO~x~   VOC+NO~x~     \$/ton
  Area                                  Measure                   (tons/yr.)   (tons/yr.)   (1990\$) VOC  (1990\$)      (tons)      (\$)          
                                                                  VOC          NO~x~                      NO~x~                                   

  Atlanta, GA      Open Burning         Episodic Ban              2,436.90     462.1        0             0             2899        0             0

                   Motor Vehicles       Enhanced I/M (w/49 State  4,867.60     3,819.70     952,657       1,905,313     8687.3      2,857,970     329
                                        LEV)                                                                                                      

                   Industrial Boiler -  LNB                       0            175.3        0             129,287       175.3       129,287       738
                   Residual Oil                                                                                                                   

                   Cement               LNB                       0            141.1        0             118,154       141.1       118,154       837
                   Manufacturing - Dry                                                                                                            

                   Industrial Boiler -  LNB                       0            76.1         0             69,300        76.1        69,300        911
                   Natural Gas                                                                                                                    

                   Cement               SNCR - Urea based         0            141.1        0             181,642       141.1       181,642       1,287
                   Manufacturing - Dry                                                                                                            

                   Industrial Boiler -  LNB                       0            16.2         0             22,570        16.2        22,570        1,393
                   Distillate Oil                                                                                                                 

                   Industrial Boiler -  LNB                       0            229.5        0             369,572       229.5       369,572       1,610
                   PC                                                                                                                             

                   Area Source          RACT to small sources     0            40           0             68,692        40          68,692        1,717
                   Industrial NG Comb                                                                                                             

                   Glass                LNB                       0            339.2        0             814,003       339.2       814,003       2,400
                   Manufacturing -                                                                                                                
                   Container                                                                                                                      

                   Glass                SCR                       0            296.8        0             1,141,514     296.8       1,141,514     3,846
                   Manufacturing -                                                                                                                
                   Container                                                                                                                      

                   Nonroad gasoline     Reformulated gasoline     286.4        0            1,432,000     0             286.4       1,432,000     5,000

                   Motor Vehicles       California Reform         -715         14,570.60    0             75,035,816    13855.6     75,035,816    5,416

                   Industrial Boiler -  LNB + FGR                 0            38.3         0             209,562       38.3        209,562       5,472
                   Residual Oil                                                                                                                   

                   Industrial Boiler -  LNB + FGR                 0            38.1         0             237,338       38.1        237,338       6,229
                   Natural Gas                                                                                                                    

                   Motor Vehicles       Federal Reform            9,600.30     2,839.10     78,262,901    0             12439.4     78,262,901    6,292

                   Industrial Boiler -  SCR                       0            76.6         0             545,406       76.6        545,406       7,120
                   Residual Oil                                                                                                                   

                   Nonroad Diesels      CARB Stds for \> 175 HP   0            326.6        0             2,664,317     326.6       2,664,317     8,158

                   Industrial Boiler -  SCR                       0            76.2         0             702,239       76.2        702,239       9,216
                   Natural Gas                                                                                                                    

                   Industrial Boiler -  LNB + FGR                 0            4.5          0             44,896        4.5         44,896        9,977
                   Distillate Oil                                                                                                                 

                   Industrial Boiler -  SCR                       0            9.1          0             95,822        9.1         95,822        10,530
                   Distillate Oil                                                                                                                 

                   Cement               SCR                       0            169.3        0             1,980,056     169.3       1,980,056     11,696
                   Manufacturing - Dry                                                                                                            

                   Industrial Boiler -  SNCR                      0            45.9         0             569,962       45.9        569,962       12,417
                   PC                                                                                                                             

                   Industrial Boiler -  SCR                       0            68.9         0             1,770,659     68.9        1,770,659     25,699
                   PC                                                                                                                             

                   Glass                Oxy-Firing                0            84.8         0             2,819,334     84.8        2,819,334     33,247
                   Manufacturing -                                                                                                                
                   Container                                                                                                                      

                   Motor Vehicles       Reform Diesel             0            520.4        0             27,274,484    520.4       27,274,484    52,411

                   Utility Boiler -     SCR                       0            3.4          0             2,423,615     3.4         2,423,615     712,828
                   Oil-Gas/Tangential                                                                                                             

                                        Total                     16,476.20    24,608.90    80,647,558    121,193,553   41085.1     201,841,111   4,913

                                                                                                                                                   

  Atlantic City,   Open Burning         Episodic Ban              243.5        46.2         0             0             289.7       0             0
  NJ                                                                                                                                              

                   Service stations -   Vapor balance & P-V       321.6        0            8,040         0             321.6       8,040         25
                   stage I-truck un     valves                                                                                                    

                   Wood product surface Reformulationon           0.4          0            21            0             0.4         21            53
                   coating                                                                                                                        

                   Metal product        VOC content limits &      7.7          0            845           0             7.7         845           110
                   surface coating      improved                                                                                                  

                   Recreational         CARB standards            40.2         0            21,229        0             40.2        21,229        528
                   vehicles                                                                                                                       

                   Wood furniture       Reformulationon           8.6          0            10,454        0             8.6         10,454        1,216
                   surface coating                                                                                                                

                   Point Sources        RE Improvements           139.4        0            278,860       0             139.4       278,860       2,000

                   Aerosols             CARB Tier 2 Standards -   42.3         0            105,864       0             42.3        105,864       2,503
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         18.8         0            69,071        0             18.8        69,071        3,674
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         166.3        1,274.40     0             6,071,805     1440.7      6,071,805     4,214

                   Nonroad gasoline     Reformulated gasoline     25.9         0            129,500       0             25.9        129,500       5,000

                   Pesticide            Reformulationon - FIP     6.7          0            62,831        0             6.7         62,831        9,378
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        42.3         0            423,456       0             42.3        423,456       10,011
                                        Reformulation                                                                                             

                   Automobile           FIP Rule (VOC Content &   71.7         0            1,292,122     0             71.7        1,292,122     18,021
                   refinishing          TE)                                                                                                       

                   marine surface       Add-on control levels     31.3         0            722,827       0             31.3        722,827       23,094
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     3.8          0            221,789       0             3.8         221,789       58,366
                   surface coating                                                                                                                

                                        Total                     1,170.50     1,320.60     3,346,909     6,071,805     2491.1      9,418,714     3,781

                                                                                                                                                   

  Bakersfield, CA  Aerosols             CARB Tier 2 Standards -   0            0            0             0             0           0             0
                                        Reform                                                                                                    

                   Aerosols             SCAQMD Standards -        0            0            0             0             0           0             0
                                        Reformulation                                                                                             

                   Aircraft surface     Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Automobile           CARB BARCT limits         0            0            0             0             0           0             0
                   refinishing                                                                                                                    

                   Automobile           FIP Rule (VOC Content &   0            0            0             0             0           0             0
                   refinishing          TE)                                                                                                       

                   Metal product        VOC content limits &      0            0            0             0             0           0             0
                   surface coating      improved                                                                                                  

                   Miscellaneous        Add-on control levels     0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Miscellaneous        MACT level of control     0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Nonroad gasoline     Reformulated gasoline     0            0            0             0             0           0             0

                   Open Burning         Episodic Ban              0            0            0             0             0           0             0

                   Pesticide            Reformulationon - FIP     0            0            0             0             0           0             0
                   Application          rule                                                                                                      

                   Point Sources        RE Improvements           0            0            0             0             0           0             0

                   Recreational         CARB standards            0            0            0             0             0           0             0
                   vehicles                                                                                                                       

                   Service stations -   Vapor balance & P-V       0            0            0             0             0           0             0
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Wood product surface Reformulationon           0.7          0            16            0             0.7         16            23
                   coating                                                                                                                        

                                        Total                     0.7          0            16            0             0.7         16            23

                                                                                                                                                   

  Baltimore-D.C.   Adhesives -          RACT                      2.9          0            7,333         0             2.9         7,333         2,529
                   industrial                                                                                                                     

                   Web Offset           New CTG (carbon adsorber) 0.4          0            -45           0             0.4         -45           -113
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      11.6         0            0             0             11.6        0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              829.7        158.1        0             0             987.8       0             0

                   Point Source Metal   FIP VOC Limits            824.2        0            0             0             824.2       0             0
                   Surface Coating                                                                                                                

                   Point Source Wood    FIP VOC Limits            7.3          0            183           0             7.3         183           25
                   Product Coating                                                                                                                

                   Service stations -   Vapor balance & P-V       2,761.00     0            80,261        0             2761        80,261        29
                   stage I-truck un     valves                                                                                                    

                   Wood product surface Reformulationon           18.6         0            602           0             18.6        602           32
                   coating                                                                                                                        

                   Metal product        VOC content limits &      600.4        0            26,412        0             600.4       26,412        44
                   surface coating      improved                                                                                                  

                   Motor Vehicles       Enhanced I/M (w/49 State  4,219.60     4,304.60     1,004,762     2,009,525     8524.2      3,014,287     354
                                        LEV)                                                                                                      

                   Wood furniture       Reformulationon           1,033.90     0            395,887       0             1033.9      395,887       383
                   surface coating                                                                                                                

                   Recreational         CARB standards            785.1        0            416,107       0             785.1       416,107       530
                   vehicles                                                                                                                       

                   Point Sources        RE Improvements           5,980.00     0            11,960,320    0             5980        11,960,320    2,000

                   Miscellaneous        MACT level of control     131.2        0            327,585       0             131.2       327,585       2,497
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   838.8        0            2,096,568     0             838.8       2,096,568     2,499
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         441.1        0            1,622,379     0             441.1       1,622,379     3,678
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     541.9        0            2,709,500     0             541.9       2,709,500     5,000

                   Motor Vehicles       Federal Reform            449.5        131          3,989,469     0             580.5       3,989,469     6,872

                   Pesticide            Reformulationon - FIP     272.8        0            2,535,796     0             272.8       2,535,796     9,295
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        838.9        0            8,386,272     0             838.9       8,386,272     9,997
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     116.2        0            1,679,127     0             116.2       1,679,127     14,450
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   1,683.90     0            30,350,042    0             1683.9      30,350,042    18,024
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     147.5        0            2,713,264     0             147.5       2,713,264     18,395
                   Surface Coating                                                                                                                

                   Miscellaneous        Add-on control levels     260.3        0            5,369,000     0             260.3       5,369,000     20,626
                   surface coating                                                                                                                

                   Aircraft surface     Add-on control levels     20.3         0            727,841       0             20.3        727,841       35,854
                   coating                                                                                                                        

                   Paper surface        Add-on control levels     40           0            2,915,609     0             40          2,915,609     72,890
                   coating                                                                                                                        

                                        Total                     22,857.10    4,593.70     79,314,274    2,009,525     27450.8     81,323,799    2,963

                                                                                                                                                   

  Baton Rouge, LA  Open burning         Seasonal/episodic ban     42.5         8.1          0             0             50.6        0             0

                   Wood furniture       Reformulationon           0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Wood product surface Reformulationon           0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       11.4         0            286           0             11.4        286           25
                   stage I-truck un     valves                                                                                                    

                   Metal product        VOC content limits &      45.7         0            1,419         0             45.7        1,419         31
                   surface coating      improved                                                                                                  

                   Recreational         CARB standards            23.1         0            12,266        0             23.1        12,266        531
                   vehicles                                                                                                                       

                   Point Sources        RE Improvements           902.3        0            1,804,560     0             902.3       1,804,560     2,000

                   Miscellaneous        MACT level of control     1.8          0            4,378         0             1.8         4,378         2,432
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   5.3          0            13,278        0             5.3         13,278        2,505
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         0.3          0            1,078         0             0.3         1,078         3,593
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     5            0            25,000        0             5           25,000        5,000

                   Motor Vehicles       Federal Reform            1,260.20     385.9        10,568,265    0             1646.1      10,568,265    6,420

                   Pesticide            Reformulationon - FIP     36.9         0            343,654       0             36.9        343,654       9,313
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        91.6         0            916,440       0             91.6        916,440       10,005
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     20.4         0            295,101       0             20.4        295,101       14,466
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   84.7         0            1,526,835     0             84.7        1,526,835     18,026
                   refinishing          TE)                                                                                                       

                   Miscellaneous        Add-on control levels     2.3          0            160,685       0             2.3         160,685       69,863
                   surface coating                                                                                                                

                   Paper surface        Add-on control levels     4.5          0            467,671       0             4.5         467,671       103,927
                   coating                                                                                                                        

                                        Total                     2,538.00     394          16,140,916    0             2932        16,140,916    5,505

                                                                                                                                                   

  Beaumont, TX     Web Offset           New CTG (carbon adsorber) 2.6          0            -326          0             2.6         -326          -125
                   Lithography                                                                                                                    

                   Open Burning         Episodic Ban              313.4        59.3         0             0             372.7       0             0

                   Point Source Metal   FIP VOC Limits            22.6         0            0             0             22.6        0             0
                   Surface Coating                                                                                                                

                   Cutback Asphalt      Switch to emulsified      27.3         0            0             0             27.3        0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              2,378.80     451.4        0             0             2830.2      0             0

                   Point Source Metal   FIP VOC Limits            6,953.00     0            0             0             6953        0             0
                   Surface Coating                                                                                                                

                   Service stations -   Vapor balance & P-V       220          0            5,502         0             220         5,502         25
                   stage I-truck un     valves                                                                                                    

                   Point Source Wood    FIP VOC Limits            78.8         0            1,971         0             78.8        1,971         25
                   Product Coating                                                                                                                

                   Wood product surface Reformulationon           25.3         0            650           0             25.3        650           26
                   coating                                                                                                                        

                   Wood product surface Reformulationon           0.8          0            27            0             0.8         27            34
                   coating                                                                                                                        

                   Metal product        VOC content limits &      3,362.50     0            119,678       0             3362.5      119,678       36
                   surface coating      improved                                                                                                  

                   Metal product        VOC content limits &      43.3         0            1,746         0             43.3        1,746         40
                   surface coating      improved                                                                                                  

                   Service stations -   Vapor balance & P-V       3,158.00     0            139,715       0             3158        139,715       44
                   stage I-truck un     valves                                                                                                    

                   Motor Vehicles       Enhanced I/M (w/49 State  357.5        304.4        75,325        150,650       661.9       225,975       341
                                        LEV)                                                                                                      

                   Recreational         CARB standards            69.9         0            36,998        0             69.9        36,998        529
                   vehicles                                                                                                                       

                   Recreational         CARB standards            1,358.20     0            719,732       0             1358.2      719,732       530
                   vehicles                                                                                                                       

                   Wood furniture       Reformulationon           1,097.10     0            1,485,499     0             1097.1      1,485,499     1,354
                   surface coating                                                                                                                

                   Wood furniture       Reformulationon           6.7          0            9,574         0             6.7         9,574         1,429
                   surface coating                                                                                                                

                   Bulk Terminals       RACT                      89           0            148,271       0             89          148,271       1,666

                                        Total                     97,369.90    59.3         193,440,337   0             97429.2     193,440,337   1,985

                                                                                                                                                   

  Chicago, IL      Point Sources        RE Improvements           9,246.90     0            18,493,820    0             9246.9      18,493,820    2,000

                   Point Sources        RE Improvements           96,693.20    0            193,386,490   2,009,525     96693.2     195,396,015   2,021

                   Aerosols             CARB Tier 2 Standards -   1,188.10     0            2,969,826     0             1188.1      2,969,826     2,500
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     1,271.30     0            3,177,986     0             1271.3      3,177,986     2,500
                   surface coating                                                                                                                

                   Adhesives -          RACT                      17.5         0            43,823        0             17.5        43,823        2,504
                   industrial                                                                                                                     

                   Automobile           CARB BARCT limits         662.6        0            2,436,920     0             662.6       2,436,920     3,678
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         3,819.60     21,388.40    0             110,921,385   25208       110,921,385   4,400

                   Nonroad gasoline     Reformulated gasoline     567.8        0            2,839,000     0             567.8       2,839,000     5,000

                   Motor Vehicles       Federal Reform            70.8         24.8         679,832       0             95.6        679,832       7,111

                   Pesticide            Reformulationon - FIP     314.4        0            2,923,605     0             314.4       2,923,605     9,299
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        1,188.10     0            11,879,304    0             1188.1      11,879,304    9,999
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     99.5         0            1,440,735     0             99.5        1,440,735     14,480
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   2,529.60     0            45,587,801    0             2529.6      45,587,801    18,022
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     3,315.70     0            61,008,144    0             3315.7      61,008,144    18,400
                   Surface Coating                                                                                                                

                   Miscellaneous        Add-on control levels     1,416.80     0            36,920,959    0             1416.8      36,920,959    26,059
                   surface coating                                                                                                                

                   Aircraft surface     Add-on control levels     8.5          0            288,264       0             8.5         288,264       33,913
                   coating                                                                                                                        

                   Paper surface        Add-on control levels     25.4         0            2,636,415     0             25.4        2,636,415     103,796
                   coating                                                                                                                        

                                        Total                     44,630.70    22,169.00    196,016,949   111072035     66799.7     307,088,984   4,597

                                                                                                                                                   

  Cincinnati, OH   Web Offset           New CTG (carbon adsorber) 8            0            -989          0             8           -989          -124
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      70           0            0             0             70          0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              1,973.10     375.1        0             0             2348.2      0             0

                   Point Source Metal   FIP VOC Limits            455.9        0            0             0             455.9       0             0
                   Surface Coating                                                                                                                

                   Wood product surface Reformulationon           8.7          0            228           0             8.7         228           26
                   coating                                                                                                                        

                   Metal product        VOC content limits &      233.8        0            6,817         0             233.8       6,817         29
                   surface coating      improved                                                                                                  

                   Motor Vehicles       Enhanced I/M (w/49 State  18,368.90    13,119.30    675,395       1,350,789     31488.2     2,026,184     64
                                        LEV)                                                                                                      

                   Service stations -   Vapor balance & P-V       852.4        0            119,526       0             852.4       119,526       140
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           746.9        0            315,871       0             746.9       315,871       423
                   surface coating                                                                                                                

                   Recreational         CARB standards            202.5        0            107,319       0             202.5       107,319       530
                   vehicles                                                                                                                       

                   Bulk Terminals       RACT                      134.5        0            224,072       0             134.5       224,072       1,666

                   Point Sources        RE Improvements           857.1        0            1,714,040     0             857.1       1,714,040     2,000

                   Miscellaneous        MACT level of control     42.5         0            106,099       0             42.5        106,099       2,496
                   surface coating                                                                                                                

                   Adhesives -          RACT                      79.4         0            198,526       0             79.4        198,526       2,500
                   industrial                                                                                                                     

                   Aerosols             CARB Tier 2 Standards -   270.2        0            675,798       0             270.2       675,798       2,501
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         100.6        0            370,237       0             100.6       370,237       3,680
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         955.8        6,134.80     0             30,656,109    7090.6      30,656,109    4,323

                   Nonroad gasoline     Reformulated gasoline     138.6        0            693,000       0             138.6       693,000       5,000

                   Motor Vehicles       Federal Reform            3,876.30     938.1        26,812,203    0             4814.4      26,812,203    5,569

                   Pesticide            Reformulationon - FIP     95.9         0            890,829       0             95.9        890,829       9,289
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        270.2        0            2,703,192     0             270.2       2,703,192     10,004
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     9.9          0            143,004       0             9.9         143,004       14,445
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   384.2        0            6,926,038     0             384.2       6,926,038     18,027
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     155.1        0            2,854,300     0             155.1       2,854,300     18,403
                   Surface Coating                                                                                                                

                   Aircraft surface     Add-on control levels     51.8         0            1,873,198     0             51.8        1,873,198     36,162
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     323.3        0            11,922,127    0             323.3       11,922,127    36,876
                   surface coating                                                                                                                

                   Paper surface        Add-on control levels     43           0            3,019,753     0             43          3,019,753     70,227
                   coating                                                                                                                        

                                        Total                     30,708.60    20,567.30    62,350,583    32,006,898    51275.9     94,357,481    1,840

                                                                                                                                                   

  Dallas, TX       Petroleum refinery   RACT                      167.1        0            -75,186       0             167.1       -75,186       -450
                   fugitives                                                                                                                      

                   Web Offset           New CTG (carbon adsorber) 30.3         0            -3,789        0             30.3        -3,789        -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      348.1        0            0             0             348.1       0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              2,152.80     408.2        0             0             2561        0             0

                   Point Source Metal   FIP VOC Limits            204          0            0             0             204         0             0
                   Surface Coating                                                                                                                

                   Point Source Open    Episodic Ban              9.1          0            0             0             9.1         0             0
                   Burning                                                                                                                        

                   Point Source Wood    FIP VOC Limits            208          0            5,202         0             208         5,202         25
                   Product Coating                                                                                                                

                   Metal product        VOC content limits &      1,160.40     0            41,375        0             1160.4      41,375        36
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           15           0            857           0             15          857           57
                   coating                                                                                                                        

                   Motor Vehicles       Enhanced I/M (w/49 State  32,776.90    29,743.00    2,029,995     4,059,989     62519.9     6,089,984     97
                                        LEV)                                                                                                      

                   SOCMI fugitives      RACT                      1            0            141           0             1           141           141

                   Service stations -   Vapor balance & P-V       2,505.10     0            592,835       0             2505.1      592,835       237
                   stage I-truck un     valves                                                                                                    

                   Pharmaceutical       RACT                      1.3          0            417           0             1.3         417           321
                   manufacture                                                                                                                    

                   Oil and natural gas  RACT                      34.9         0            13,809        0             34.9        13,809        396
                   production fiel      (equipment/maintenance)                                                                                   

                   Recreational         CARB standards            761.1        0            403,346       0             761.1       403,346       530
                   vehicles                                                                                                                       

                   Wood furniture       Reformulationon           695          0            586,526       0             695         586,526       844
                   surface coating                                                                                                                

                   Bulk Terminals       RACT                      443.3        0            738,704       0             443.3       738,704       1,666

                   Point Sources        RE Improvements           14,125.20    0            28,250,270    0             14125.2     28,250,270    2,000

                   Adhesives -          RACT                      206.5        0            516,108       0             206.5       516,108       2,499
                   industrial                                                                                                                     

                   Miscellaneous        MACT level of control     836.2        0            2,090,260     0             836.2       2,090,260     2,500
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   569.3        0            1,423,242     0             569.3       1,423,242     2,500
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         275.8        0            1,014,479     0             275.8       1,014,479     3,678
                   refinishing                                                                                                                    

                   SOCMI batch reactor  New CTG                   1.7          0            7,007         0             1.7         7,007         4,122
                   processes                                                                                                                      

                   Motor Vehicles       Federal Reform            1,320.50     283.2        7,756,926     0             1603.7      7,756,926     4,837

                   Nonroad gasoline     Reformulated gasoline     478.5        0            2,392,500     0             478.5       2,392,500     5,000

                   Pesticide            Reformulationon - FIP     153.4        0            1,426,100     0             153.4       1,426,100     9,297
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        569.2        0            5,692,968     0             569.2       5,692,968     10,002
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     15.1         0            218,518       0             15.1        218,518       14,471
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   1,053.20     0            18,977,998    0             1053.2      18,977,998    18,019
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     4,042.70     0            74,386,416    0             4042.7      74,386,416    18,400
                   Surface Coating                                                                                                                

                   Miscellaneous        Add-on control levels     738          0            17,418,524    0             738         17,418,524    23,602
                   surface coating                                                                                                                

                   Aircraft surface     Add-on control levels     10.9         0            344,747       0             10.9        344,747       31,628
                   coating                                                                                                                        

                   Paper surface        Add-on control levels     69.3         0            6,065,109     0             69.3        6,065,109     87,520
                   coating                                                                                                                        

                                        Total                     65,978.90    30,434.40    172,315,404   4,059,989     96413.3     176,375,393   1,829

                                                                                                                                                   

  Eugene, OR       Web Offset           New CTG (carbon adsorber) 67.6         0            -8,447        0             67.6        -8,447        -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      351.5        0            0             0             351.5       0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              473.9        89.9         0             0             563.8       0             0

                   Metal product        VOC content limits &      92.8         0            2,314         0             92.8        2,314         25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           58.6         0            1,464         0             58.6        1,464         25
                   coating                                                                                                                        

                   Wood furniture       Reformulationon           114.8        0            43,029        0             114.8       43,029        375
                   surface coating                                                                                                                

                   Oil and natural gas  RACT                      1.2          0            470           0             1.2         470           392
                   production fiel      (equipment/maintenance)                                                                                   

                   Service stations -   Vapor balance & P-V       358.9        0            186,182       0             358.9       186,182       519
                   stage I-truck un     valves                                                                                                    

                   Recreational         CARB standards            75.8         0            40,095        0             75.8        40,095        529
                   vehicles                                                                                                                       

                   Motor Vehicles       Enhanced I/M              2,659.90     2,365.10     951,535       1,903,069     5025        2,854,604     568

                   Bulk Terminals       RACT                      173.6        0            289,200       0             173.6       289,200       1,666

                   Miscellaneous        MACT level of control     49.2         0            122,853       0             49.2        122,853       2,497
                   surface coating                                                                                                                

                   Adhesives -          RACT                      398.9        0            997,164       0             398.9       997,164       2,500
                   industrial                                                                                                                     

                   Aerosols             CARB Tier 2 Standards -   67.3         0            168,276       0             67.3        168,276       2,500
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         27.4         0            100,653       0             27.4        100,653       3,673
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     34.8         0            174,000       0             34.8        174,000       5,000

                                        Total                     5,006.20     2,455.00     3,068,788     1,903,069     7461.2      4,971,857     666

                                                                                                                                                   

  Fairfield, CT    Service stations -   Vapor balance & P-V       349.8        0            8,745         0             349.8       8,745         25
                   stage I-truck un     valves                                                                                                    

                   Wood product surface Reformulationon           3.1          0            80            0             3.1         80            26
                   coating                                                                                                                        

                   Metal product        VOC content limits &      82.5         0            11,089        0             82.5        11,089        134
                   surface coating      improved                                                                                                  

                   Recreational         CARB standards            218          0            115,514       0             218         115,514       530
                   vehicles                                                                                                                       

                   Wood furniture       Reformulationon           23.6         0            37,782        0             23.6        37,782        1,601
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   104.9        0            262,200       0             104.9       262,200       2,500
                                        Reform                                                                                                    

                   Motor Vehicles       California Reform         414.2        2,580.70     0             14,343,658    2994.9      14,343,658    4,789

                   Nonroad gasoline     Reformulated gasoline     71.6         0            358,000       0             71.6        358,000       5,000

                   Pesticide            Reformulationon - FIP     1            0            8,816         0             1           8,816         8,816
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        104.9        0            1,048,800     0             104.9       1,048,800     9,998
                                        Reformulation                                                                                             

                   Point Source Ind.    Add-on Control Levels     39.4         0            725,328       0             39.4        725,328       18,409
                   Surface Coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   173.8        0            5,062,064     0             173.8       5,062,064     29,126
                   refinishing          TE)                                                                                                       

                   Miscellaneous        Add-on control levels     45           0            2,937,767     0             45          2,937,767     65,284
                   surface coating                                                                                                                

                   Paper surface        Add-on control levels     3.1          0            313,572       0             3.1         313,572       101,152
                   coating                                                                                                                        

                                        Total                     1,634.90     2,580.70     10,889,757    14,343,658    4215.6      25,233,415    5,986

                                                                                                                                                   

  Fresno, CA       Web Offset           New CTG (carbon adsorber) 4.1          0            -508          0             4.1         -508          -124
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      57           0            0             0             57          0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              276.4        52.4         0             0             328.8       0             0

                   Point Source Metal   FIP VOC Limits            52.6         0            0             0             52.6        0             0
                   Surface Coating                                                                                                                

                   Wood product surface Reformulationon           6.2          0            154           0             6.2         154           25
                   coating                                                                                                                        

                   Metal product        VOC content limits &      77.5         0            1,931         0             77.5        1,931         25
                   surface coating      improved                                                                                                  

                   Service stations -   Vapor balance & P-V       454.2        0            103,431       0             454.2       103,431       228
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           102.1        0            38,279        0             102.1       38,279        375
                   surface coating                                                                                                                

                   Motor Vehicles       Enhanced I/M              750.9        1,264.00     298,651       597,301       2014.9      895,952       445

                   Recreational         CARB standards            60.8         0            32,225        0             60.8        32,225        530
                   vehicles                                                                                                                       

                   Bulk Terminals       RACT                      57.2         0            95,380        0             57.2        95,380        1,667

                   Point Sources        RE Improvements           3,519.30     0            7,038,660     0             3519.3      7,038,660     2,000

                   Miscellaneous        MACT level of control     27.8         0            69,321        0             27.8        69,321        2,494
                   surface coating                                                                                                                

                   Adhesives -          RACT                      14.2         0            35,444        0             14.2        35,444        2,496
                   industrial                                                                                                                     

                   Aerosols             CARB Tier 2 Standards -   119.6        0            298,638       0             119.6       298,638       2,497
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         58.9         0            217,170       0             58.9        217,170       3,687
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     63           0            315,000       0             63          315,000       5,000

                   Pesticide            Reformulationon - FIP     742.3        0            6,903,465     0             742.3       6,903,465     9,300
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        119.6        0            1,194,552     0             119.6       1,194,552     9,988
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     11.1         0            160,048       0             11.1        160,048       14,419
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     93.6         0            1,460,681     0             93.6        1,460,681     15,606
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   225.5        0            4,062,635     0             225.5       4,062,635     18,016
                   refinishing          TE)                                                                                                       

                   Paper surface        Add-on control levels     4.5          0            298,193       0             4.5         298,193       66,265
                   coating                                                                                                                        

                                        Total                     6,898.40     1,316.40     22,623,350    597,301       8214.8      23,220,651    2,827

                                                                                                                                                   

  Grand Rapids, MI Web Offset           New CTG (carbon adsorber) 8.5          0            -1,067        0             8.5         -1,067        -126
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      85.3         0            0             0             85.3        0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              648.1        122.9        0             0             771         0             0

                   Service stations -   Vapor balance & P-V       292.2        0            7,305         0             292.2       7,305         25
                   stage I-truck un     valves                                                                                                    

                   Metal product        VOC content limits &      199.1        0            4,981         0             199.1       4,981         25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           6.5          0            298           0             6.5         298           46
                   coating                                                                                                                        

                   Motor Vehicles       Enhanced I/M (w/49 State  8,035.80     5,841.00     409,302       818,603       13876.8     1,227,905     88
                                        LEV)                                                                                                      

                   Pharmaceutical       RACT                      138.2        0            46,285        0             138.2       46,285        335
                   manufacture                                                                                                                    

                   Wood furniture       Reformulationon           4,967.90     0            1,862,987     0             4967.9      1,862,987     375
                   surface coating                                                                                                                

                   Oil and natural gas  RACT                      37.2         0            14,770        0             37.2        14,770        397
                   production fiel      (equipment/maintenance)                                                                                   

                   Recreational         CARB standards            548.1        0            290,468       0             548.1       290,468       530
                   vehicles                                                                                                                       

                   Point Sources        RE Improvements           6,025.10     0            12,050,110    0             6025.1      12,050,110    2,000

                   Aerosols             CARB Tier 2 Standards -   102.6        0            256,368       0             102.6       256,368       2,499
                                        Reform                                                                                                    

                   Adhesives -          RACT                      130.6        0            326,390       0             130.6       326,390       2,499
                   industrial                                                                                                                     

                   Automobile           CARB BARCT limits         62.2         0            228,904       0             62.2        228,904       3,680
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         364.6        2,984.10     0             13,408,026    3348.7      13,408,026    4,004

                   Nonroad gasoline     Reformulated gasoline     101.6        0            508,000       0             101.6       508,000       5,000

                   Motor Vehicles       Federal Reform            1,844.40     488.6        13,996,171    0             2333        13,996,171    5,999

                   Pesticide            Reformulationon - FIP     72.5         0            673,506       0             72.5        673,506       9,290
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        102.6        0            1,025,472     0             102.6       1,025,472     9,995
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     68.1         0            985,510       0             68.1        985,510       14,472
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   237.6        0            4,282,146     0             237.6       4,282,146     18,023
                   refinishing          TE)                                                                                                       

                   Miscellaneous        Add-on control levels     1,366.50     0            31,117,326    0             1366.5      31,117,326    22,772
                   surface coating                                                                                                                

                   Aircraft surface     Add-on control levels     3.7          0            132,865       0             3.7         132,865       35,909
                   coating                                                                                                                        

                   Paper surface        Add-on control levels     22.2         0            1,402,309     0             22.2        1,402,309     63,167
                   coating                                                                                                                        

                                        Total                     25,471.20    9,436.60     69,620,406    14,226,629    34907.8     83,847,035    2,402

                                                                                                                                                   

  Hartford, CT     Open Burning         Episodic Ban              890.4        168.9        0             0             1059.3      0             0

                   Point Source Metal   FIP VOC Limits            102.9        0            0             0             102.9       0             0
                   Surface Coating                                                                                                                

                   Wood product surface Reformulationon           5.5          0            136           0             5.5         136           25
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       508.9        0            12,723        0             508.9       12,723        25
                   stage I-truck un     valves                                                                                                    

                   Metal product        VOC content limits &      103.8        0            13,957        0             103.8       13,957        134
                   surface coating      improved                                                                                                  

                   Recreational         CARB standards            342          0            181,179       0             342         181,179       530
                   vehicles                                                                                                                       

                   Wood furniture       Reformulationon           92           0            146,938       0             92          146,938       1,597
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   159          0            397,518       0             159         397,518       2,500
                                        Reform                                                                                                    

                   Motor Vehicles       California Reform         708          4,400.20     0             22,365,569    5108.2      22,365,569    4,378

                   Nonroad gasoline     Reformulated gasoline     112.5        0            562,500       0             112.5       562,500       5,000

                   Pesticide            Reformulationon - FIP     15.3         0            141,676       0             15.3        141,676       9,260
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        159.1        0            1,590,072     0             159.1       1,590,072     9,994
                                        Reformulation                                                                                             

                   Point Source Ind.    Add-on Control Levels     5.8          0            107,456       0             5.8         107,456       18,527
                   Surface Coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   298.6        0            8,697,450     0             298.6       8,697,450     29,127
                   refinishing          TE)                                                                                                       

                   Aircraft surface     Add-on control levels     116.4        0            6,094,635     0             116.4       6,094,635     52,359
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     88.4         0            6,479,450     0             88.4        6,479,450     73,297
                   surface coating                                                                                                                

                   Paper surface        Add-on control levels     22.7         0            2,278,121     0             22.7        2,278,121     100,358
                   coating                                                                                                                        

                                        Total                     3,731.30     4,569.10     26,703,811    22,365,569    8300.4      49,069,380    5,912

                                                                                                                                                   

  Houston, TX      Web Offset           New CTG (carbon adsorber) 10.5         0            -1,319        0             10.5        -1,319        -126
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      112.6        0            0             0             112.6       0             0
                                        asphalts                                                                                                  

                   Open burning         Seasonal/episodic ban     73.7         13.8         0             0             87.5        0             0

                   marine surface       Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Paper surface        Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Point Source Metal   FIP VOC Limits            0            0            0             0             0           0             0
                   Surface Coating                                                                                                                

                   Point Sources        RE Improvements           0            0            0             0             0           0             0

                   Point Source Wood    FIP VOC Limits            62.8         0            1,570         0             62.8        1,570         25
                   Product Coating                                                                                                                

                   Metal product        VOC content limits &      5.1          0            129           0             5.1         129           25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           1.5          0            38            0             1.5         38            25
                   coating                                                                                                                        

                   Motor Vehicles       Enhanced I/M (w/49 State  1,635.50     1,168.20     291,551       583,101       2803.7      874,652       312
                                        LEV)                                                                                                      

                   Wood furniture       Reformulationon           12.1         0            4,536         0             12.1        4,536         375
                   surface coating                                                                                                                

                   Oil and natural gas  RACT                      11.8         0            4,674         0             11.8        4,674         396
                   production fiel      (equipment/maintenance)                                                                                   

                   Recreational         CARB standards            21.5         0            11,363        0             21.5        11,363        529
                   vehicles                                                                                                                       

                   Service stations -   Vapor balance & P-V       260.9        0            195,467       0             260.9       195,467       749
                   stage I-truck un     valves                                                                                                    

                   Bulk Terminals       RACT                      203.7        0            339,507       0             203.7       339,507       1,667

                   Miscellaneous        MACT level of control     2.2          0            5,479         0             2.2         5,479         2,490
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   17.9         0            44,754        0             17.9        44,754        2,500
                                        Reform                                                                                                    

                   Adhesives -          RACT                      27.2         0            68,115        0             27.2        68,115        2,504
                   industrial                                                                                                                     

                   Automobile           CARB BARCT limits         2.7          0            10,196        0             2.7         10,196        3,776
                   refinishing                                                                                                                    

                   Motor Vehicles       Federal Reform            456.6        99           2,622,337     0             555.6       2,622,337     4,720

                   Nonroad gasoline     Reformulated gasoline     13.7         0            68,500        0             13.7        68,500        5,000

                   Pesticide            Reformulationon - FIP     105.6        0            981,616       0             105.6       981,616       9,296
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        17.8         0            179,016       0             17.8        179,016       10,057
                                        Reformulation                                                                                             

                   Automobile           FIP Rule (VOC Content &   10.6         0            190,748       0             10.6        190,748       17,995
                   refinishing          TE)                                                                                                       

                   Miscellaneous        Add-on control levels     2.6          0            46,902        0             2.6         46,902        18,039
                   surface coating                                                                                                                

                   Point Source Ind.    Add-on Control Levels     85           0            1,564,828     0             85          1,564,828     18,410
                   Surface Coating                                                                                                                

                                        Total                     3,153.60     1,281.00     6,630,007     583,101       4434.6      7,213,108     1,627

                                                                                                                                                   

  Huntington, WV   Web Offset           New CTG (carbon adsorber) 8.7          0            -1,087        0             8.7         -1,087        -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      77.5         0            0             0             77.5        0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              443          83.6         0             0             526.6       0             0

                   Metal product        VOC content limits &      43.3         0            1,721         0             43.3        1,721         40
                   surface coating      improved                                                                                                  

                   SOCMI fugitives      RACT                      61.3         0            8,345         0             61.3        8,345         136

                   Motor Vehicles       Enhanced I/M (w/49 State  3,256.80     2,435.50     263,196       526,392       5692.3      789,588       139
                                        LEV)                                                                                                      

                   Oil and natural gas  RACT                      0.5          0            188           0             0.5         188           376
                   production fiel      (equipment/maintenance)                                                                                   

                   Service stations -   Vapor balance & P-V       241.6        0            93,577        0             241.6       93,577        387
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           26.8         0            10,523        0             26.8        10,523        393
                   surface coating                                                                                                                

                   Recreational         CARB standards            34           0            18,019        0             34          18,019        530
                   vehicles                                                                                                                       

                   Bulk Terminals       RACT                      36.6         0            60,913        0             36.6        60,913        1,664

                   Point Sources        RE Improvements           6,281.00     0            12,561,840    0             6281        12,561,840    2,000

                   Aerosols             CARB Tier 2 Standards -   51.1         0            127,602       0             51.1        127,602       2,497
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     13.7         0            34,223        0             13.7        34,223        2,498
                   surface coating                                                                                                                

                   Adhesives -          RACT                      23.8         0            59,522        0             23.8        59,522        2,501
                   industrial                                                                                                                     

                   Automobile           CARB BARCT limits         12.6         0            46,575        0             12.6        46,575        3,696
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         145.1        1,267.40     0             5,466,889     1412.5      5,466,889     3,870

                   SOCMI batch reactor  New CTG                   105.1        0            426,152       0             105.1       426,152       4,055
                   processes                                                                                                                      

                   Nonroad gasoline     Reformulated gasoline     23.7         0            118,500       0             23.7        118,500       5,000

                   Motor Vehicles       Federal Reform            729.3        205.3        5,708,990     0             934.6       5,708,990     6,108

                   Pesticide            Reformulationon - FIP     4.3          0            39,618        0             4.3         39,618        9,213
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        51.1         0            510,408       0             51.1        510,408       9,988
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     5.7          0            82,393        0             5.7         82,393        14,455
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     10           0            164,121       0             10          164,121       16,412
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   48.4         0            871,265       0             48.4        871,265       18,001
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     12.4         0            228,344       0             12.4        228,344       18,415
                   Surface Coating                                                                                                                

                   Paper surface        Add-on control levels     0.5          0            38,060        0             0.5         38,060        76,120
                   coating                                                                                                                        

                                        Total                     11,747.90    3,991.80     21,473,008    5,993,281     15739.7     27,466,289    1,745

                                                                                                                                                   

  Knoxville, TN    Web Offset           New CTG (carbon adsorber) 79.9         0            -9,997        0             79.9        -9,997        -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      280.6        0            0             0             280.6       0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              938.2        177.9        0             0             1116.1      0             0

                   Service stations -   Vapor balance & P-V       358.2        0            8,956         0             358.2       8,956         25
                   stage I-truck un     valves                                                                                                    

                   Metal product        VOC content limits &      167.1        0            8,216         0             167.1       8,216         49
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           2.1          0            135           0             2.1         135           64
                   coating                                                                                                                        

                   Motor Vehicles       Enhanced I/M (w/49 State  8,942.20     6,407.40     1,648,631     3,297,263     15349.6     4,945,894     322
                                        LEV)                                                                                                      

                   Wood furniture       Reformulationon           284.6        0            106,743       0             284.6       106,743       375
                   surface coating                                                                                                                

                   Recreational         CARB standards            42.8         0            22,703        0             42.8        22,703        530
                   vehicles                                                                                                                       

                   Process Heaters -    ULNB                      0            62.7         0             40,020        62.7        40,020        638
                   Natural Gas                                                                                                                    

                   Industrial Boiler -  LNB                       0            16.2         0             12,168        16.2        12,168        751
                   Residual Oil                                                                                                                   

                   Cement               LNB                       0            262.9        0             210,670       262.9       210,670       801
                   Manufacturing - Dry                                                                                                            

                   Industrial Boiler -  LNB                       0            31.2         0             28,823        31.2        28,823        924
                   Natural Gas                                                                                                                    

                   Cement               SNCR - Urea based         0            262.9        0             323,865       262.9       323,865       1,232
                   Manufacturing - Dry                                                                                                            

                   Industrial Boiler -  LNB                       0            2.2          0             3,080         2.2         3,080         1,400
                   Distillate Oil                                                                                                                 

                   Industrial Boiler -  LNB                       0            884.5        0             1,421,744     884.5       1,421,744     1,607
                   PC                                                                                                                             

                   Industrial Boiler -  SNCR                      0            113.6        0             244,779       113.6       244,779       2,155
                   Stoker                                                                                                                         

                   Area Source          RACT to small sources     0            0.8          0             1,758         0.8         1,758         2,198
                   Industrial Oil Comb                                                                                                            

                   Miscellaneous        MACT level of control     32.9         0            82,138        0             32.9        82,138        2,497
                   surface coating                                                                                                                

                   Adhesives -          RACT                      329.4        0            823,688       0             329.4       823,688       2,501
                   industrial                                                                                                                     

                   Aerosols             CARB Tier 2 Standards -   108.5        0            271,524       0             108.5       271,524       2,503
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         33.1         0            121,990       0             33.1        121,990       3,685
                   refinishing                                                                                                                    

                   Area Source          RACT to small sources     0            22.7         0             88,259        22.7        88,259        3,888
                   Industrial Coal Comb                                                                                                           

                   Motor Vehicles       California Reform         -113.2       3,111.80     0             14,082,175    2998.6      14,082,175    4,696

                   Nonroad gasoline     Reformulated gasoline     58.7         0            293,500       0             58.7        293,500       5,000

                   Industrial Boiler -  LNB + FGR                 0            6.2          0             34,572        6.2         34,572        5,576
                   Residual Oil                                                                                                                   

                   Motor Vehicles       Federal Reform            1,943.40     541.8        14,703,239    0             2485.2      14,703,239    5,916

                   Industrial Boiler -  LNB + FGR                 0            6.2          0             38,107        6.2         38,107        6,146
                   Natural Gas                                                                                                                    

                   Industrial Boiler -  SCR                       0            12.6         0             89,980        12.6        89,980        7,141
                   Residual Oil                                                                                                                   

                   Nonroad Diesels      CARB Stds for \> 175 HP   0            67.8         0             553,880       67.8        553,880       8,169

                   Industrial Boiler -  LNB + FGR                 0            0.5          0             4,283         0.5         4,283         8,566
                   Distillate Oil                                                                                                                 

                   Industrial Boiler -  SCR                       0            12.6         0             112,742       12.6        112,742       8,948
                   Natural Gas                                                                                                                    

                   Pesticide            Reformulationon - FIP     9.3          0            86,601        0             9.3         86,601        9,312
                   Application          rule                                                                                                      

                   Industrial Boiler -  SCR                       0            90.9         0             905,202       90.9        905,202       9,958
                   Stoker                                                                                                                         

                   Commercial Marine    Emission Fees             0            33.2         0             331,535       33.2        331,535       9,986
                   Vessels                                                                                                                        

                   Aerosols             SCAQMD Standards -        108.5        0            1,086,096     0             108.5       1,086,096     10,010
                                        Reformulation                                                                                             

                   Industrial Boiler -  SCR                       0            0.9          0             9,136         0.9         9,136         10,151
                   Distillate Oil                                                                                                                 

                   Cement               SCR                       0            315.5        0             3,530,444     315.5       3,530,444     11,190
                   Manufacturing - Dry                                                                                                            

                   Industrial Boiler -  SNCR                      0            176.9        0             2,192,644     176.9       2,192,644     12,395
                   PC                                                                                                                             

                   marine surface       Add-on control levels     122.4        0            1,771,300     0             122.4       1,771,300     14,471
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   126.6        0            2,282,141     0             126.6       2,282,141     18,026
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     864          0            15,896,772    0             864         15,896,772    18,399
                   Surface Coating                                                                                                                

                   Paper surface        Add-on control levels     13.5         0            308,857       0             13.5        308,857       22,878
                   coating                                                                                                                        

                   Industrial Boiler -  SCR                       0            265.4        0             6,811,724     265.4       6,811,724     25,666
                   PC                                                                                                                             

                   Process Heaters -    LNB + SCR                 0            10.9         0             304,598       10.9        304,598       27,945
                   Natural Gas                                                                                                                    

                   Aircraft surface     Add-on control levels     4            0            125,719       0             4           125,719       31,430
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     179.3        0            8,570,343     0             179.3       8,570,343     47,799
                   surface coating                                                                                                                

                   Motor Vehicles       Reform Diesel             0            131          0             6,648,656     131         6,648,656     50,753

                                        Total                     14,916.10    13,029.20    48,209,295    41,322,107    27945.3     89,531,402    3,204

                                                                                                                                                   

  Los Angeles, CA  Aerosols             CARB Tier 2 Standards -   0            0            0             0             0           0             0
                                        Reform                                                                                                    

                   Aerosols             SCAQMD Standards -        0            0            0             0             0           0             0
                                        Reformulation                                                                                             

                   Aircraft surface     Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Automobile           CARB BARCT limits         0            0            0             0             0           0             0
                   refinishing                                                                                                                    

                   Automobile           FIP Rule (VOC Content &   0            0            0             0             0           0             0
                   refinishing          TE)                                                                                                       

                   marine surface       Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Metal product        VOC content limits &      0            0            0             0             0           0             0
                   surface coating      improved                                                                                                  

                   Miscellaneous        Add-on control levels     0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Miscellaneous        MACT level of control     0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Nonroad gasoline     Reformulated gasoline     0            0            0             0             0           0             0

                   Open Burning         Episodic Ban              0            0            0             0             0           0             0

                   Paper surface        Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Pesticide            Reformulationon - FIP     0            0            0             0             0           0             0
                   Application          rule                                                                                                      

                   Point Source Ind.    Add-on Control Levels     0            0            0             0             0           0             0
                   Surface Coating                                                                                                                

                   Point Source Metal   FIP VOC Limits            0            0            0             0             0           0             0
                   Surface Coating                                                                                                                

                   Point Source Wood    FIP VOC Limits            0            0            0             0             0           0             0
                   Product Coating                                                                                                                

                   Point Sources        RE Improvements           0            0            0             0             0           0             0

                   Recreational         CARB standards            0            0            0             0             0           0             0
                   vehicles                                                                                                                       

                   Service stations -   Vapor balance & P-V       0            0            0             0             0           0             0
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Wood product surface Reformulationon           0            0            0             0             0           0             0
                   coating                                                                                                                        

                                        Total                     0            0            0             0             0           0             0

                                                                                                                                                   

  Manitowoc, WI    Web Offset           New CTG (carbon adsorber) 2.5          0            -312          0             2.5         -312          -125
                   Lithography                                                                                                                    

                   Open Burning         Episodic Ban              163.5        30.9         0             0             194.4       0             0

                   Point Source Metal   FIP VOC Limits            16.4         0            0             0             16.4        0             0
                   Surface Coating                                                                                                                

                   Metal product        VOC content limits &      34.7         0            865           0             34.7        865           25
                   surface coating      improved                                                                                                  

                   Point Source Wood    FIP VOC Limits            17.9         0            447           0             17.9        447           25
                   Product Coating                                                                                                                

                   Wood product surface Reformulationon           2            0            50            0             2           50            25
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       48.6         0            1,220         0             48.6        1,220         25
                   stage I-truck un     valves                                                                                                    

                   Motor Vehicles       Enhanced I/M (w/49 State  1,274.40     1,030.20     91,780        183,560       2304.6      275,340       119
                                        LEV)                                                                                                      

                   Wood furniture       Reformulationon           116.4        0            43,659        0             116.4       43,659        375
                   surface coating                                                                                                                

                   Recreational         CARB standards            40.7         0            21,584        0             40.7        21,584        530
                   vehicles                                                                                                                       

                   Aerosols             CARB Tier 2 Standards -   19.9         0            49,596        0             19.9        49,596        2,492
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     56.8         0            142,041       0             56.8        142,041       2,501
                   surface coating                                                                                                                

                   Motor Vehicles       California Reform         56.6         580.5        0             2,299,274     637.1       2,299,274     3,609

                   Automobile           CARB BARCT limits         8.1          0            29,881        0             8.1         29,881        3,689
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     15.4         0            77,000        0             15.4        77,000        5,000

                   Motor Vehicles       Federal Reform            304.4        88.5         2,403,498     0             392.9       2,403,498     6,117

                   Pesticide            Reformulationon - FIP     34.9         0            324,459       0             34.9        324,459       9,297
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        19.9         0            198,384       0             19.9        198,384       9,969
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     96.5         0            1,395,871     0             96.5        1,395,871     14,465
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     31.5         0            547,300       0             31.5        547,300       17,375
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   31           0            558,983       0             31          558,983       18,032
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     104.4        0            1,920,776     0             104.4       1,920,776     18,398
                   Surface Coating                                                                                                                

                   Paper surface        Add-on control levels     1.1          0            75,421        0             1.1         75,421        68,565
                   coating                                                                                                                        

                                        Total                     2,497.60     1,730.10     7,882,503     2,482,834     4227.7      10,365,337    2,452

                                                                                                                                                   

  Modesto, CA      Open Burning         Episodic Ban              97.6         18.5         0             0             116.1       0             0

                   Point Source Metal   FIP VOC Limits            61           0            0             0             61          0             0
                   Surface Coating                                                                                                                

                   Metal product        VOC content limits &      352.8        0            8,725         0             352.8       8,725         25
                   surface coating      improved                                                                                                  

                   Service stations -   Vapor balance & P-V       131.9        0            3,297         0             131.9       3,297         25
                   stage I-truck un     valves                                                                                                    

                   Wood product surface Reformulationon           2.5          0            63            0             2.5         63            25
                   coating                                                                                                                        

                   Wood furniture       Reformulationon           78.4         0            29,408        0             78.4        29,408        375
                   surface coating                                                                                                                

                   Recreational         CARB standards            24.4         0            12,907        0             24.4        12,907        529
                   vehicles                                                                                                                       

                   Aerosols             CARB Tier 2 Standards -   46.7         0            116,760       0             46.7        116,760       2,500
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     17.1         0            42,760        0             17.1        42,760        2,501
                   surface coating                                                                                                                

                   Automobile           CARB BARCT limits         30           0            110,381       0             30          110,381       3,679
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     25.1         0            125,500       0             25.1        125,500       5,000

                   Pesticide            Reformulationon - FIP     108.9        0            1,012,621     0             108.9       1,012,621     9,299
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        46.7         0            467,040       0             46.7        467,040       10,001
                                        Reformulation                                                                                             

                   Miscellaneous        Add-on control levels     64.2         0            1,006,908     0             64.2        1,006,908     15,684
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   114.6        0            2,064,926     0             114.6       2,064,926     18,019
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     74.1         0            1,363,348     0             74.1        1,363,348     18,399
                   Surface Coating                                                                                                                

                   Paper surface        Add-on control levels     9.4          0            632,619       0             9.4         632,619       67,300
                   coating                                                                                                                        

                                        Total                     1,285.40     18.5         6,997,263     0             1303.9      6,997,263     5,366

                                                                                                                                                   

  Muskegon, MI     Open Burning         Episodic Ban              190.1        35.9         0             0             226         0             0

                   Service stations -   Vapor balance & P-V       63.7         0            1,594         0             63.7        1,594         25
                   stage I-truck un     valves                                                                                                    

                   Motor Vehicles       Enhanced I/M (w/49 State  1,589.50     1,175.30     47,591        95,182        2764.8      142,773       52
                                        LEV)                                                                                                      

                   Wood furniture       Reformulationon           406.8        0            152,532       0             406.8       152,532       375
                   surface coating                                                                                                                

                   Recreational         CARB standards            111.9        0            59,310        0             111.9       59,310        530
                   vehicles                                                                                                                       

                   Aerosols             CARB Tier 2 Standards -   22.1         0            55,344        0             22.1        55,344        2,504
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         11.9         0            43,723        0             11.9        43,723        3,674
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         60.2         608.9        0             2,717,140     669.1       2,717,140     4,061

                   Nonroad gasoline     Reformulated gasoline     20.9         0            104,500       0             20.9        104,500       5,000

                   Motor Vehicles       Federal Reform            350.5        99.1         2,836,015     0             449.6       2,836,015     6,308

                   Pesticide            Reformulationon - FIP     8.6          0            79,850        0             8.6         79,850        9,285
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        22.1         0            221,376       0             22.1        221,376       10,017
                                        Reformulation                                                                                             

                   Automobile           FIP Rule (VOC Content &   45.4         0            817,916       0             45.4        817,916       18,016
                   refinishing          TE)                                                                                                       

                   Miscellaneous        Add-on control levels     98.5         0            3,018,808     0             98.5        3,018,808     30,648
                   surface coating                                                                                                                

                   Aircraft surface     Add-on control levels     1.9          0            67,602        0             1.9         67,602        35,580
                   coating                                                                                                                        

                   Paper surface        Add-on control levels     6.2          0            414,248       0             6.2         414,248       66,814
                   coating                                                                                                                        

                                        Total                     3,010.30     1,919.20     7,920,409     2,812,322     4929.5      10,732,731    2,177

                                                                                                                                                   

  Nashville, TN    Open Burning         Episodic Ban              746.4        141.4        0             0             887.8       0             0

                   Motor Vehicles       Enhanced I/M (w/49 State  11,604.10    8,719.20     567,807       1,135,614     20323.3     1,703,421     84
                                        LEV)                                                                                                      

                   Process Heaters -    ULNB                      0            5.9          0             3,734         5.9         3,734         633
                   Natural Gas                                                                                                                    

                   Industrial Boiler -  LNB                       0            6.8          0             6,100         6.8         6,100         897
                   Natural Gas                                                                                                                    

                   Area Source          RACT to small sources     0            0.5          0             1,073         0.5         1,073         2,146
                   Industrial Oil Comb                                                                                                            

                   IC Engines - Natural NSCR                      0            491.3        0             1,241,469     491.3       1,241,469     2,527
                   Gas                                                                                                                            

                   Municipal Waste      SNCR                      0            131.6        0             439,269       131.6       439,269       3,338
                   Combustors                                                                                                                     

                   Area Source          RACT to small sources     0            27.7         0             107,750       27.7        107,750       3,890
                   Industrial Coal Comb                                                                                                           

                   Nonroad gasoline     Reformulated gasoline     91.6         0            458,000       0             91.6        458,000       5,000

                   Motor Vehicles       California Reform         -162.8       4,007.30     0             20,150,267    3844.5      20,150,267    5,241

                   Industrial Boiler -  LNB + FGR                 0            1.7          0             9,893         1.7         9,893         5,819
                   Residual Oil                                                                                                                   

                   Motor Vehicles       Federal Reform            2,630.30     768.2        21,020,978    0             3398.5      21,020,978    6,185

                   Industrial Boiler -  LNB + FGR                 0            69           0             438,362       69          438,362       6,353
                   Natural Gas                                                                                                                    

                   Industrial Boiler -  SCR                       0            3.5          0             25,747        3.5         25,747        7,356
                   Residual Oil                                                                                                                   

                   Nonroad Diesels      CARB Stds for \> 175 HP   0            104.6        0             852,643       104.6       852,643       8,151

                   Industrial Boiler -  SCR                       0            138          0             1,297,032     138         1,297,032     9,399
                   Natural Gas                                                                                                                    

                   Commercial Marine    Emission Fees             0            27.6         0             274,516       27.6        274,516       9,946
                   Vessels                                                                                                                        

                   Industrial Boiler -  LNB + FGR                 0            6.3          0             64,698        6.3         64,698        10,270
                   Distillate Oil                                                                                                                 

                   Industrial Boiler -  SCR                       0            39.7         0             413,030       39.7        413,030       10,404
                   Stoker                                                                                                                         

                   Industrial Boiler -  SCR                       0            12.6         0             138,075       12.6        138,075       10,958
                   Distillate Oil                                                                                                                 

                   Industrial Boiler -  SNCR                      0            44.4         0             577,548       44.4        577,548       13,008
                   PC                                                                                                                             

                   Gas Turbines -       SCR + STEAM INJECTION     0            37.3         0             801,585       37.3        801,585       21,490
                   Natural Gas                                                                                                                    

                   Gas Turbines - Oil   SCR + WATER INJECTION     0            246.5        0             5,652,452     246.5       5,652,452     22,931

                   Industrial Boiler -  SCR                       0            66.6         0             1,794,222     66.6        1,794,222     26,940
                   PC                                                                                                                             

                   Process Heaters -    LNB + SCR                 0            5.4          0             170,509       5.4         170,509       31,576
                   Natural Gas                                                                                                                    

                   Motor Vehicles       Reform Diesel             0            152.3        0             7,739,306     152.3       7,739,306     50,816

                   Process Heaters -    LNB + SCR                 0            0            0             1,637         0           1,637         #DIV/0!
                   Distillate Oil                                                                                                                 

                                        Total                     14,909.60    15,255.40    22,046,785    43,336,531    30165       65,383,316    2,168

                                                                                                                                                   

  New London, CT   Open Burning         Episodic Ban              849.4        161.1        0             0             1010.5      0             0

                   Wood product surface Reformulationon           5.3          0            130           0             5.3         130           25
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       460.7        0            11,517        0             460.7       11,517        25
                   stage I-truck un     valves                                                                                                    

                   Metal product        VOC content limits &      283.2        0            16,501        0             283.2       16,501        58
                   surface coating      improved                                                                                                  

                   Recreational         CARB standards            604.1        0            320,133       0             604.1       320,133       530
                   vehicles                                                                                                                       

                   Wood furniture       Reformulationon           77.4         0            122,710       0             77.4        122,710       1,585
                   surface coating                                                                                                                

                   Point Sources        RE Improvements           1,007.70     0            2,015,530     0             1007.7      2,015,530     2,000

                   Miscellaneous        MACT level of control     19.7         0            49,140        0             19.7        49,140        2,494
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   158.8        0            396,912       0             158.8       396,912       2,499
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         6.5          0            24,006        0             6.5         24,006        3,693
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         630.1        3,961.20     0             20,401,080    4591.3      20,401,080    4,443

                   Nonroad gasoline     Reformulated gasoline     164.1        0            820,500       0             164.1       820,500       5,000

                   Pesticide            Reformulationon - FIP     10.1         0            93,725        0             10.1        93,725        9,280
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        158.8        0            1,587,648     0             158.8       1,587,648     9,998
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     2.9          0            41,886        0             2.9         41,886        14,443
                   coating                                                                                                                        

                   Point Source Ind.    Add-on Control Levels     97.8         0            1,799,888     0             97.8        1,799,888     18,404
                   Surface Coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   238.2        0            6,660,863     0             238.2       6,660,863     27,963
                   refinishing          TE)                                                                                                       

                   Aircraft surface     Add-on control levels     13.4         0            704,628       0             13.4        704,628       52,584
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     71.7         0            6,276,407     0             71.7        6,276,407     87,537
                   surface coating                                                                                                                

                   Paper surface        Add-on control levels     24.2         0            2,346,314     0             24.2        2,346,314     96,955
                   coating                                                                                                                        

                                        Total                     4,884.10     4,122.30     23,288,438    20,401,080    9006.4      43,689,518    4,851

                                                                                                                                                   

  New Orleans, LA  Web Offset           New CTG (carbon adsorber) 115.6        0            -14,447       0             115.6       -14,447       -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      1,068.40     0            0             0             1068.4      0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              1,167.10     221.5        0             0             1388.6      0             0

                   Point Source Metal   FIP VOC Limits            20.4         0            0             0             20.4        0             0
                   Surface Coating                                                                                                                

                   Service stations -   Vapor balance & P-V       598.5        0            14,963        0             598.5       14,963        25
                   stage I-truck un     valves                                                                                                    

                   Metal product        VOC content limits &      277.9        0            9,701         0             277.9       9,701         35
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           1.4          0            89            0             1.4         89            64
                   coating                                                                                                                        

                   Motor Vehicles       Enhanced I/M (w/49 State  11,508.40    6,549.10     1,773,573     3,547,145     18057.5     5,320,718     295
                                        LEV)                                                                                                      

                   Pharmaceutical       RACT                      5.7          0            1,878         0             5.7         1,878         329
                   manufacture                                                                                                                    

                   Oil and natural gas  RACT                      268.9        0            106,656       0             268.9       106,656       397
                   production fiel      (equipment/maintenance)                                                                                   

                                        Total                     15,032.30    6,770.60     1,892,413     3,547,145     21802.9     5,439,558     249

                                                                                                                                                   

  New York, NY     Aerosols             CARB Tier 2 Standards -   0            0            0             0             0           0             0
                                        Reform                                                                                                    

                   Aerosols             SCAQMD                    0            0            0             0             0           0             0
                                        Standards-Reformulation                                                                                   

                   Aircraft surface     Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Automobile           CARB BARCT limits         0            0            0             0             0           0             0
                   refinishing                                                                                                                    

                   Automobile           FIP Rule (VOC Content &   0            0            0             0             0           0             0
                   refinishing          TE)                                                                                                       

                   marine surface       Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Metal product        VOC content limits &      0            0            0             0             0           0             0
                   surface coating      improved                                                                                                  

                   Miscellaneous        Add-on control levels     0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Miscellaneous        MACT level of control     0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Motor Vehicles       California Reform         0            0            0             0             0           0             0

                   Nonroad gasoline     Reformulated gasoline     0            0            0             0             0           0             0

                   Open Burning         Episodic Ban              0            0            0             0             0           0             0

                   Paper surface        Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Pesticide            Reformulationon - FIP     0            0            0             0             0           0             0
                   Application          rule                                                                                                      

                   Point Source Ind.    Add-on Control Levels     0            0            0             0             0           0             0
                   Surface Coating                                                                                                                

                   Point Source Metal   FIP VOC Limits            0            0            0             0             0           0             0
                   Surface Coating                                                                                                                

                   Point Source Wood    FIP VOC Limits            0            0            0             0             0           0             0
                   Product Coating                                                                                                                

                   Point Sources        RE Improvements           0            0            0             0             0           0             0

                   Recreational         CARB standards            0            0            0             0             0           0             0
                   vehicles                                                                                                                       

                   Service stations -   Vapor balance & P-V       0            0            0             0             0           0             0
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Wood product surface Reformulationon           0            0            0             0             0           0             0
                   coating                                                                                                                        

                                        Total                     0            0            0             0             0           0             0

                                                                                                                                                   

  Philadelphia, PA Open Burning         Episodic Ban              2,582.60     489.7        0             0             3072.3      0             0

                   Point Source Metal   FIP VOC Limits            366.2        0            0             0             366.2       0             0
                   Surface Coating                                                                                                                

                   Point Source Wood    FIP VOC Limits            97.1         0            2,427         0             97.1        2,427         25
                   Product Coating                                                                                                                

                   Service stations -   Vapor balance & P-V       2,028.70     0            50,721        0             2028.7      50,721        25
                   stage I-truck un     valves                                                                                                    

                   Metal product        VOC content limits &      661.9        0            23,283        0             661.9       23,283        35
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           13.2         0            477           0             13.2        477           36
                   coating                                                                                                                        

                   Recreational         CARB standards            799.6        0            423,651       0             799.6       423,651       530
                   vehicles                                                                                                                       

                   Wood furniture       Reformulationon           841.2        0            884,447       0             841.2       884,447       1,051
                   surface coating                                                                                                                

                   Point Sources        RE Improvements           30,474.20    0            60,948,430    0             30474.2     60,948,430    2,000

                   Miscellaneous        MACT level of control     49           0            122,400       0             49          122,400       2,498
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   849.5        0            2,124,030     0             849.5       2,124,030     2,500
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         550.3        0            2,023,964     0             550.3       2,023,964     3,678
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         2,768.40     16,305.20    0             83,136,982    19073.6     83,136,982    4,359

                   Nonroad gasoline     Reformulated gasoline     516.8        0            2,584,000     0             516.8       2,584,000     5,000

                   Pesticide            Reformulationon - FIP     122.7        0            1,140,144     0             122.7       1,140,144     9,292
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        849.6        0            8,496,120     0             849.6       8,496,120     10,000
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     138.1        0            2,413,661     0             138.1       2,413,661     17,478
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   2,101.00     0            37,862,493    0             2101        37,862,493    18,021
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     5,061.80     0            93,137,488    0             5061.8      93,137,488    18,400
                   Surface Coating                                                                                                                

                   Aircraft surface     Add-on control levels     28           0            1,012,040     0             28          1,012,040     36,144
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     369.8        0            19,157,461    0             369.8       19,157,461    51,805
                   surface coating                                                                                                                

                   Paper surface        Add-on control levels     68.2         0            5,050,766     0             68.2        5,050,766     74,058
                   coating                                                                                                                        

                                        Total                     51,337.90    16,794.90    237,458,003   83,136,982    68132.8     320,594,985   4,705

                                                                                                                                                   

  Phoenix, AZ      Web Offset           New CTG (carbon adsorber) 6.4          0            -795          0             6.4         -795          -124
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      109.7        0            0             0             109.7       0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              1,589.90     303.4        0             0             1893.3      0             0

                   Metal product        VOC content limits &      521.6        0            12,986        0             521.6       12,986        25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           23.1         0            576           0             23.1        576           25
                   coating                                                                                                                        

                   Motor Vehicles       Enhanced I/M              21,094.90    12,639.60    1,199,840     2,399,679     33734.5     3,599,519     107

                   Service stations -   Vapor balance & P-V       1,131.70     0            212,983       0             1131.7      212,983       188
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           891          0            334,128       0             891         334,128       375
                   surface coating                                                                                                                

                   Recreational         CARB standards            318.6        0            168,875       0             318.6       168,875       530
                   vehicles                                                                                                                       

                   Motor Vehicles       Federal Reform            36,455.70    3,002.30     47,793,100    0             39458       47,793,100    1,211

                   Bulk Terminals       RACT                      115.1        0            191,712       0             115.1       191,712       1,666

                   Aerosols             CARB Tier 2 Standards -   402          0            1,004,856     0             402         1,004,856     2,500
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     417.7        0            1,044,188     0             417.7       1,044,188     2,500
                   surface coating                                                                                                                

                   Adhesives -          RACT                      39.2         0            98,028        0             39.2        98,028        2,501
                   industrial                                                                                                                     

                   Automobile           CARB BARCT limits         182.6        0            671,796       0             182.6       671,796       3,679
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     306.9        0            1,534,500     0             306.9       1,534,500     5,000

                                        Total                     63,606.10    15,945.30    54,266,773    2,399,679     79551.4     56,666,452    712

                                                                                                                                                   

  Portland, ME     Open Burning         Episodic Ban              801.7        152.3        0             0             954         0             0

                   Area Source          RACT to small sources     0            1.6          0             2,349         1.6         2,349         1,468
                   Industrial NG Comb                                                                                                             

                   Residential NG       LNB Space heaters         0            12.5         0             20,258        12.5        20,258        1,621
                   Consumption                                                                                                                    

                   Area Source          RACT to small sources     0            3.7          0             7,486         3.7         7,486         2,023
                   Industrial Oil Comb                                                                                                            

                   Area Source          RACT to small sources     0            0.1          0             235           0.1         235           2,350
                   Industrial Coal Comb                                                                                                           

                   Motor Vehicles       California Reform         208.9        1,844.30     0             7,957,384     2053.2      7,957,384     3,876

                   Nonroad gasoline     Reformulated gasoline     23.3         0            117,000       0             23.3        117,000       5,021

                   Industrial Boiler -  LNB + FGR                 0            66.3         0             379,442       66.3        379,442       5,723
                   Residual Oil                                                                                                                   

                   Industrial Boiler -  LNB + FGR                 0            0.9          0             5,706         0.9         5,706         6,340
                   Natural Gas                                                                                                                    

                   Industrial Boiler -  SCR                       0            132.5        0             987,553       132.5       987,553       7,453
                   Residual Oil                                                                                                                   

                   Nonroad Diesels      CARB Stds for \> 175 HP   0            4            0             33,035        4           33,035        8,259

                   Industrial Boiler -  SCR                       0            1.8          0             16,880        1.8         16,880        9,378
                   Natural Gas                                                                                                                    

                   Commercial Marine    Emission Fees             0            90.8         0             907,683       90.8        907,683       9,997
                   Vessels                                                                                                                        

                   Industrial Boiler -  LNB + FGR                 0            2.9          0             29,602        2.9         29,602        10,208
                   Distillate Oil                                                                                                                 

                   Industrial Boiler -  SCR                       0            5.8          0             63,175        5.8         63,175        10,892
                   Distillate Oil                                                                                                                 

                   Utility Boiler -     SCR                       0            181.7        0             2,235,995     181.7       2,235,995     12,306
                   Oil-Gas/Tangential                                                                                                             

                   Utility Boiler -     LNB + FGR + OFA           0            58.2         0             977,858       58.2        977,858       16,802
                   Oil-Gas/Tangential                                                                                                             

                   Motor Vehicles       Reform Diesel             0            81.5         0             4,121,838     81.5        4,121,838     50,575

                                        Total                     1,033.90     2,640.90     117,000       17,746,479    3674.8      17,863,479    4,861

                                                                                                                                                   

  Portland, OR     Web Offset           New CTG (carbon adsorber) 259.3        0            -32,399       0             259.3       -32,399       -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      320.2        0            0             0             320.2       0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              2,265.50     429.5        0             0             2695        0             0

                   Point Source Metal   FIP VOC Limits            139.8        0            0             0             139.8       0             0
                   Surface Coating                                                                                                                

                   Metal product        VOC content limits &      406.7        0            10,136        0             406.7       10,136        25
                   surface coating      improved                                                                                                  

                   Point Source Wood    FIP VOC Limits            217.2        0            5,429         0             217.2       5,429         25
                   Product Coating                                                                                                                

                   Wood product surface Reformulationon           43.4         0            1,779         0             43.4        1,779         41
                   coating                                                                                                                        

                   SOCMI fugitives      RACT                      12.7         0            1,729         0             12.7        1,729         136

                   Motor Vehicles       Enhanced I/M              11,068.20    10,483.50    1,747,796     3,495,591     21551.7     5,243,387     243

                   Pharmaceutical       RACT                      4.2          0            1,408         0             4.2         1,408         335
                   manufacture                                                                                                                    

                   Wood furniture       Reformulationon           487.7        0            182,937       0             487.7       182,937       375
                   surface coating                                                                                                                

                   Service stations -   Vapor balance & P-V       1,432.70     0            562,989       0             1432.7      562,989       393
                   stage I-truck un     valves                                                                                                    

                   Recreational         CARB standards            403.8        0            214,057       0             403.8       214,057       530
                   vehicles                                                                                                                       

                   Bulk Terminals       RACT                      364.1        0            606,885       0             364.1       606,885       1,667

                   Point Sources        RE Improvements           19,187.00    0            38,373,910    0             19187       38,373,910    2,000

                                        Total                     36,612.50    10,913.00    41,676,656    3,495,591     47525.5     45,172,247    950

                                                                                                                                                   

  Providence, RI   Open Burning         Episodic Ban              510.9        97.1         0             0             608         0             0

                   Point Source Metal   FIP VOC Limits            129.6        0            0             0             129.6       0             0
                   Surface Coating                                                                                                                

                   Metal product        VOC content limits &      285.3        0            7,100         0             285.3       7,100         25
                   surface coating      improved                                                                                                  

                   Service stations -   Vapor balance & P-V       313          0            7,823         0             313         7,823         25
                   stage I-truck un     valves                                                                                                    

                   Wood product surface Reformulationon           5.1          0            130           0             5.1         130           25
                   coating                                                                                                                        

                   Wood furniture       Reformulationon           402.2        0            150,805       0             402.2       150,805       375
                   surface coating                                                                                                                

                   Recreational         CARB standards            545.9        0            289,257       0             545.9       289,257       530
                   vehicles                                                                                                                       

                   Point Sources        RE Improvements           2,381.30     0            4,762,520     0             2381.3      4,762,520     2,000

                   Aerosols             CARB Tier 2 Standards -   133.1        0            332,580       0             133.1       332,580       2,499
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     129.2        0            323,006       0             129.2       323,006       2,500
                   surface coating                                                                                                                

                   Automobile           CARB BARCT limits         83.6         0            307,511       0             83.6        307,511       3,678
                   refinishing                                                                                                                    

                   Motor Vehicles       California Reform         410.5        2,548.70     0             13,834,606    2959.2      13,834,606    4,675

                   Nonroad gasoline     Reformulated gasoline     101.8        0            509,000       0             101.8       509,000       5,000

                   Pesticide            Reformulationon - FIP     1.6          0            15,196        0             1.6         15,196        9,498
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        133.1        0            1,330,320     0             133.1       1,330,320     9,995
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     131.6        0            1,905,058     0             131.6       1,905,058     14,476
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     193.3        0            3,130,460     0             193.3       3,130,460     16,195
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   319.2        0            5,752,666     0             319.2       5,752,666     18,022
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     177.8        0            3,270,692     0             177.8       3,270,692     18,395
                   Surface Coating                                                                                                                

                   Aircraft surface     Add-on control levels     0.2          0            4,764         0             0.2         4,764         23,820
                   coating                                                                                                                        

                   Paper surface        Add-on control levels     0.8          0            50,406        0             0.8         50,406        63,008
                   coating                                                                                                                        

                                        Total                     6,389.10     2,645.80     22,149,294    13,834,606    9034.9      35,983,900    3,983

                                                                                                                                                   

  Redding, CA      Web Offset           New CTG (carbon adsorber) 14.3         0            -1,791        0             14.3        -1,791        -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      180.7        0            0             0             180.7       0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              15.6         3            0             0             18.6        0             0

                   Metal product        VOC content limits &      18.8         0            468           0             18.8        468           25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           8.3          0            208           0             8.3         208           25
                   coating                                                                                                                        

                   Wood furniture       Reformulationon           7.4          0            2,761         0             7.4         2,761         373
                   surface coating                                                                                                                

                   Motor Vehicles       Enhanced I/M              2,316.30     3,420.10     841,481       1,682,962     5736.4      2,524,443     440

                   Recreational         CARB standards            12.8         0            6,824         0             12.8        6,824         533
                   vehicles                                                                                                                       

                   Service stations -   Vapor balance & P-V       731          0            547,670       0             731         547,670       749
                   stage I-truck un     valves                                                                                                    

                   Bulk Terminals       RACT                      303.5        0            505,794       0             303.5       505,794       1,667

                   Miscellaneous        MACT level of control     3.7          0            9,223         0             3.7         9,223         2,493
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   26.1         0            65,190        0             26.1        65,190        2,498
                                        Reform                                                                                                    

                   Adhesives -          RACT                      97           0            242,361       0             97          242,361       2,499
                   industrial                                                                                                                     

                   Automobile           CARB BARCT limits         12.7         0            46,813        0             12.7        46,813        3,686
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     13.7         0            68,500        0             13.7        68,500        5,000

                   Pesticide            Reformulationon - FIP     24.2         0            225,693       0             24.2        225,693       9,326
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        26.1         0            260,760       0             26.1        260,760       9,991
                                        Reformulation                                                                                             

                   Paper surface        Add-on control levels     64.3         0            774,483       0             64.3        774,483       12,045
                   coating                                                                                                                        

                   marine surface       Add-on control levels     6.4          0            93,094        0             6.4         93,094        14,546
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     8.6          0            133,220       0             8.6         133,220       15,491
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   48.6         0            875,746       0             48.6        875,746       18,019
                   refinishing          TE)                                                                                                       

                                        Total                     3,940.10     3,423.10     4,698,498     1,682,962     7363.2      6,381,460     867

                                                                                                                                                   

  Reno, NV         Web Offset           New CTG (carbon adsorber) 97           0            -12,130       0             97          -12,130       -125
                   Lithography                                                                                                                    

                   Open Burning         Episodic Ban              82.3         15.6         0             0             97.9        0             0

                   Metal product        VOC content limits &      37.5         0            934           0             37.5        934           25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           2            0            50            0             2           50            25
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       153.1        0            3,845         0             153.1       3,845         25
                   stage I-truck un     valves                                                                                                    

                   Motor Vehicles       Enhanced I/M              1,537.10     1,483.50     94,145        188,291       3020.6      282,436       94

                   Wood furniture       Reformulationon           68.8         0            25,794        0             68.8        25,794        375
                   surface coating                                                                                                                

                   Recreational         CARB standards            118.5        0            62,805        0             118.5       62,805        530
                   vehicles                                                                                                                       

                   Motor Vehicles       Federal Reform            1,919.70     357.6        5,606,016     0             2277.3      5,606,016     2,462

                   Aerosols             CARB Tier 2 Standards -   44.1         0            110,244       0             44.1        110,244       2,500
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     30.7         0            76,867        0             30.7        76,867        2,504
                   surface coating                                                                                                                

                   Automobile           CARB BARCT limits         30.8         0            113,240       0             30.8        113,240       3,677
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     39.8         0            199,000       0             39.8        199,000       5,000

                                        Total                     4,161.40     1,856.70     6,280,810     188,291       6018.1      6,469,101     1,075

                                                                                                                                                   

  Sacramento, CA   Web Offset           New CTG (carbon adsorber) 6.6          0            -825          0             6.6         -825          -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      72.2         0            0             0             72.2        0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              444          84           0             0             528         0             0

                   Point Source Metal   FIP VOC Limits            313.9        0            0             0             313.9       0             0
                   Surface Coating                                                                                                                

                   Metal product        VOC content limits &      236.7        0            5,867         0             236.7       5,867         25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           16           0            399           0             16          399           25
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       799          0            130,285       0             799         130,285       163
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           252.5        0            94,698        0             252.5       94,698        375
                   surface coating                                                                                                                

                   Motor Vehicles       Enhanced I/M              828.8        1,377.30     326,808       653,615       2206.1      980,423       444

                   Recreational         CARB standards            102.4        0            54,329        0             102.4       54,329        531
                   vehicles                                                                                                                       

                   Bulk Terminals       RACT                      12.8         0            21,347        0             12.8        21,347        1,668

                   Point Sources        RE Improvements           40.5         0            81,030        0             40.5        81,030        2,001

                   Aerosols             CARB Tier 2 Standards -   201.3        0            503,034       0             201.3       503,034       2,499
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     105.2        0            262,951       0             105.2       262,951       2,500
                   surface coating                                                                                                                

                   Adhesives -          RACT                      34.5         0            86,360        0             34.5        86,360        2,503
                   industrial                                                                                                                     

                   Automobile           CARB BARCT limits         127.1        0            467,563       0             127.1       467,563       3,679
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     106.5        0            532,500       0             106.5       532,500       5,000

                   Pesticide            Reformulationon - FIP     114.2        0            1,062,488     0             114.2       1,062,488     9,304
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        201.2        0            2,012,136     0             201.2       2,012,136     10,001
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     4.2          0            60,687        0             4.2         60,687        14,449
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     119.5        0            1,938,485     0             119.5       1,938,485     16,222
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   485.3        0            8,746,744     0             485.3       8,746,744     18,023
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     361.7        0            6,655,556     0             361.7       6,655,556     18,401
                   Surface Coating                                                                                                                

                   Paper surface        Add-on control levels     3.1          0            206,239       0             3.1         206,239       66,529
                   coating                                                                                                                        

                                        Total                     4,989.20     1,461.30     23,248,681    653,615       6450.5      23,902,296    3,705

                                                                                                                                                   

  San Diego, CA    Aerosols             CARB Tier 2 Standards -   0            0            0             0             0           0             0
                                        Reform                                                                                                    

                   Aerosols             SCAQMD Standards -        0            0            0             0             0           0             0
                                        Reformulationon                                                                                           

                   Automobile           CARB BARCT limits         0            0            0             0             0           0             0
                   refinishing                                                                                                                    

                   Automobile           FIP Rule (VOC Content &   0            0            0             0             0           0             0
                   refinishing          TE)                                                                                                       

                   Metal product        VOC content limits &      0            0            0             0             0           0             0
                   surface coating      improved                                                                                                  

                   Miscellaneous        Add-on control levels     0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Miscellaneous        MACT level of control     0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Nonroad gasoline     Reformulated gasoline     0            0            0             0             0           0             0

                   Open Burning         Episodic Ban              0            0            0             0             0           0             0

                   Paper surface        Add-on control levels     0            0            0             0             0           0             0
                   coating                                                                                                                        

                   Pesticide            Reformulationon - FIP     0            0            0             0             0           0             0
                   Application          rule                                                                                                      

                   Point Source Ind.    Add-on Control Levels     0            0            0             0             0           0             0
                   Surface Coating                                                                                                                

                   Point Sources        RE Improvements           0            0            0             0             0           0             0

                   Recreational         CARB standards            0            0            0             0             0           0             0
                   vehicles                                                                                                                       

                   Service stations -   Vapor balance & P-V       0            0            0             0             0           0             0
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           0            0            0             0             0           0             0
                   surface coating                                                                                                                

                   Wood product surface Reformulationon           0            0            0             0             0           0             0
                   coating                                                                                                                        

                                        Total                     0            0            0             0             0           0             0

                                                                                                                                                   

  Santa Barbara,   Open Burning         Episodic Ban              134.4        25.5         0             0             159.9       0             0
  CA                                                                                                                                              

                   Metal product        VOC content limits &      44           0            1,098         0             44          1,098         25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           1.6          0            40            0             1.6         40            25
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       196          0            4,901         0             196         4,901         25
                   stage I-truck un     valves                                                                                                    

                   Motor Vehicles       Enhanced I/M              1,827.60     2,680.20     106,112       212,224       4507.8      318,336       71

                   Wood furniture       Reformulationon           52.3         0            19,616        0             52.3        19,616        375
                   surface coating                                                                                                                

                   Recreational         CARB standards            5.8          0            3,072         0             5.8         3,072         530
                   vehicles                                                                                                                       

                   Point Sources        RE Improvements           91.6         0            183,230       0             91.6        183,230       2,000

                   Miscellaneous        MACT level of control     45.7         0            114,149       0             45.7        114,149       2,498
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   50.1         0            125,256       0             50.1        125,256       2,500
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         25.1         0            92,413        0             25.1        92,413        3,682
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     26           0            130,000       0             26          130,000       5,000

                   Pesticide            Reformulationon - FIP     34.8         0            323,156       0             34.8        323,156       9,286
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        50.1         0            501,024       0             50.1        501,024       10,000
                                        Reformulationon                                                                                           

                   Miscellaneous        Add-on control levels     18.2         0            303,110       0             18.2        303,110       16,654
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   95.9         0            1,728,785     0             95.9        1,728,785     18,027
                   refinishing          TE)                                                                                                       

                   Aircraft surface     Add-on control levels     10.5         0            330,223       0             10.5        330,223       31,450
                   coating                                                                                                                        

                   Paper surface        Add-on control levels     1.3          0            84,224        0             1.3         84,224        64,788
                   coating                                                                                                                        

                                        Total                     2,711.00     2,705.70     4,050,409     212,224       5416.7      4,262,633     787

                                                                                                                                                   

  Seattle, WA      Web Offset           New CTG (carbon adsorber) 532.5        0            -66,548       0             532.5       -66,548       -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      454.5        0            0             0             454.5       0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              1,232.70     233.9        0             0             1466.6      0             0

                   Point Source Metal   FIP VOC Limits            8.8          0            0             0             8.8         0             0
                   Surface Coating                                                                                                                

                   Metal product        VOC content limits &      578.7        0            14,445        0             578.7       14,445        25
                   surface coating      improved                                                                                                  

                   Point Source Wood    FIP VOC Limits            93.1         0            2,327         0             93.1        2,327         25
                   Product Coating                                                                                                                

                   Wood product surface Reformulationon           22.6         0            1,182         0             22.6        1,182         52
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       2,104.60     0            699,089       0             2104.6      699,089       332
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           989.4        0            371,013       0             989.4       371,013       375
                   surface coating                                                                                                                

                   Motor Vehicles       Enhanced I/M              4,292.70     3,491.00     1,372,151     2,744,303     7783.7      4,116,454     529

                   Recreational         CARB standards            628.9        0            333,244       0             628.9       333,244       530
                   vehicles                                                                                                                       

                   Motor Vehicles       California LEV            8,992.70     15,879.00    12,434,797    12,434,797    24871.7     24,869,594    1,000

                   Bulk Terminals       RACT                      248.4        0            413,881       0             248.4       413,881       1,666

                   Adhesives -          RACT                      105.8        0            264,172       0             105.8       264,172       2,497
                   industrial                                                                                                                     

                   Aerosols             CARB Tier 2 Standards -   467.2        0            1,167,756     0             467.2       1,167,756     2,499
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     240.4        0            601,215       0             240.4       601,215       2,501
                   surface coating                                                                                                                

                   Automobile           CARB BARCT limits         250.9        0            922,119       0             250.9       922,119       3,675
                   refinishing                                                                                                                    

                   Motor Vehicles       Federal Reform            11,865.00    3,331.60     67,981,645    0             15196.6     67,981,645    4,473

                   Nonroad gasoline     Reformulated gasoline     292.5        0            1,462,500     0             292.5       1,462,500     5,000

                   Pesticide            Reformulationon - FIP     71.3         0            662,233       0             71.3        662,233       9,288
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        467          0            4,671,024     0             467         4,671,024     10,002
                                        Reformulation                                                                                             

                   Motor Vehicles       California Reform         1,615.10     4,241.60     0             65,175,421    5856.7      65,175,421    11,128

                                        Total                     35,554.80    27,177.10    93,308,245    80,354,520    62731.9     173,662,765   2,768

                                                                                                                                                   

  St. Louis, MO    Web Offset           New CTG (carbon adsorber) 2.2          0            -281          0             2.2         -281          -128
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      48.5         0            0             0             48.5        0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              1,607.90     304.9        0             0             1912.8      0             0

                   Point Source Metal   FIP VOC Limits            874.2        0            0             0             874.2       0             0
                   Surface Coating                                                                                                                

                   Wood product surface Reformulationon           6.8          0            168           0             6.8         168           25
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       1,235.80     0            30,896        0             1235.8      30,896        25
                   stage I-truck un     valves                                                                                                    

                   Metal product        VOC content limits &      836.5        0            35,843        0             836.5       35,843        43
                   surface coating      improved                                                                                                  

                   Motor Vehicles       Enhanced I/M (w/49 State  26,691.50    19,604.60    948,486       1,896,971     46296.1     2,845,457     61
                                        LEV)                                                                                                      

                   Oil and natural gas  RACT                      4.2          0            1,690         0             4.2         1,690         402
                   production fiel      (equipment/maintenance)                                                                                   

                   Recreational         CARB standards            253          0            133,902       0             253         133,902       529
                   vehicles                                                                                                                       

                   Wood furniture       Reformulationon           335.3        0            482,482       0             335.3       482,482       1,439
                   surface coating                                                                                                                

                   Point Sources        RE Improvements           3,298.80     0            6,597,740     0             3298.8      6,597,740     2,000

                   Adhesives -          RACT                      18.4         0            45,877        0             18.4        45,877        2,493
                   industrial                                                                                                                     

                   Aerosols             CARB Tier 2 Standards -   364.1        0            910,368       0             364.1       910,368       2,500
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     176          0            440,252       0             176         440,252       2,501
                   surface coating                                                                                                                

                   Automobile           CARB BARCT limits         183.9        0            676,359       0             183.9       676,359       3,678
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     151.4        0            757,000       0             151.4       757,000       5,000

                   Motor Vehicles       California Reform         24.8         8,761.50     0             45,829,827    8786.3      45,829,827    5,216

                   Motor Vehicles       Federal Reform            6,064.00     1,717.00     47,795,117    0             7781        47,795,117    6,143

                   Pesticide            Reformulationon - FIP     217.1        0            2,019,308     0             217.1       2,019,308     9,301
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        364.1        0            3,641,472     0             364.1       3,641,472     10,001
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     17.2         0            249,014       0             17.2        249,014       14,478
                   coating                                                                                                                        

                   Automobile           FIP Rule (VOC Content &   702.2        0            12,652,662    0             702.2       12,652,662    18,019
                   refinishing          TE)                                                                                                       

                   Point Source Ind.    Add-on Control Levels     7,192.00     0            132,332,064   0             7192        132,332,064   18,400
                   Surface Coating                                                                                                                

                   Aircraft surface     Add-on control levels     120.4        0            3,790,114     0             120.4       3,790,114     31,479
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     241.9        0            15,971,880    0             241.9       15,971,880    66,027
                   surface coating                                                                                                                

                   Paper surface        Add-on control levels     23.6         0            2,442,361     0             23.6        2,442,361     103,490
                   coating                                                                                                                        

                                        Total                     51,055.80    30,388.00    231,954,774   47,726,798    81443.8     279,681,572   3,434

                                                                                                                                                   

  Stockton, CA     Open Burning         Episodic Ban              146.1        27.8         0             0             173.9       0             0

                   Metal product        VOC content limits &      315.6        0            7,806         0             315.6       7,806         25
                   surface coating      improved                                                                                                  

                   Point Source Wood    FIP VOC Limits            138          0            3,449         0             138         3,449         25
                   Product Coating                                                                                                                

                   Service stations -   Vapor balance & P-V       184.4        0            4,610         0             184.4       4,610         25
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           214          0            80,240        0             214         80,240        375
                   surface coating                                                                                                                

                   Recreational         CARB standards            31.5         0            16,720        0             31.5        16,720        531
                   vehicles                                                                                                                       

                   Miscellaneous        MACT level of control     38           0            94,975        0             38          94,975        2,499
                   surface coating                                                                                                                

                   Aerosols             CARB Tier 2 Standards -   63.9         0            159,828       0             63.9        159,828       2,501
                                        Reform                                                                                                    

                   Automobile           CARB BARCT limits         30.9         0            113,829       0             30.9        113,829       3,684
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     32.9         0            164,500       0             32.9        164,500       5,000

                   Pesticide            Reformulationon - FIP     162.5        0            1,510,952     0             162.5       1,510,952     9,298
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        63.9         0            639,312       0             63.9        639,312       10,005
                                        Reformulation                                                                                             

                   marine surface       Add-on control levels     2.6          0            37,680        0             2.6         37,680        14,492
                   coating                                                                                                                        

                   Miscellaneous        Add-on control levels     86.9         0            1,429,779     0             86.9        1,429,779     16,453
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   118.2        0            2,129,416     0             118.2       2,129,416     18,015
                   refinishing          TE)                                                                                                       

                   Paper surface        Add-on control levels     5.3          0            358,421       0             5.3         358,421       67,627
                   coating                                                                                                                        

                                        Total                     1,634.70     27.8         6,751,517     0             1662.5      6,751,517     4,061

                                                                                                                                                   

  Tell City        Petroleum refinery   RACT                      52.5         0            -23,622       0             52.5        -23,622       -450
  (IN-KY)          fugitives                                                                                                                      

                   Web Offset           New CTG (carbon adsorber) 14.8         0            -1,846        0             14.8        -1,846        -125
                   Lithography                                                                                                                    

                   Cutback Asphalt      Switch to emulsified      40.9         0            0             0             40.9        0             0
                                        asphalts                                                                                                  

                   Open Burning         Episodic Ban              73.8         13.9         0             0             87.7        0             0

                   Point Source Metal   FIP VOC Limits            220.4        0            0             0             220.4       0             0
                   Surface Coating                                                                                                                

                   Metal product        VOC content limits &      9.8          0            323           0             9.8         323           33
                   surface coating      improved                                                                                                  

                   Motor Vehicles       Enhanced I/M (w/49 State  626.6        531          130,727       261,453       1157.6      392,180       339
                                        LEV)                                                                                                      

                   Wood furniture       Reformulationon           118.8        0            44,537        0             118.8       44,537        375
                   surface coating                                                                                                                

                   Oil and natural gas  RACT                      2.2          0            840           0             2.2         840           382
                   production fiel      (equipment/maintenance)                                                                                   

                                        Total                     1,159.80     544.9        150,959       261,453       1704.7      412,412       242

                                                                                                                                                   

  Visalia, CA      Open Burning         Episodic Ban              93.7         17.7         0             0             111.4       0             0

                   Metal product        VOC content limits &      49.9         0            1,238         0             49.9        1,238         25
                   surface coating      improved                                                                                                  

                   Wood product surface Reformulationon           2.7          0            67            0             2.7         67            25
                   coating                                                                                                                        

                   Service stations -   Vapor balance & P-V       110.8        0            2,770         0             110.8       2,770         25
                   stage I-truck un     valves                                                                                                    

                   Wood furniture       Reformulationon           8.2          0            3,088         0             8.2         3,088         377
                   surface coating                                                                                                                

                   Recreational         CARB standards            22.4         0            11,867        0             22.4        11,867        530
                   vehicles                                                                                                                       

                   Aerosols             CARB Tier 2 Standards -   42.6         0            106,380       0             42.6        106,380       2,497
                                        Reform                                                                                                    

                   Miscellaneous        MACT level of control     19.7         0            49,270        0             19.7        49,270        2,501
                   surface coating                                                                                                                

                   Automobile           CARB BARCT limits         11.2         0            41,327        0             11.2        41,327        3,690
                   refinishing                                                                                                                    

                   Nonroad gasoline     Reformulated gasoline     23.1         0            115,500       0             23.1        115,500       5,000

                   Pesticide            Reformulationon - FIP     250.4        0            2,329,073     0             250.4       2,329,073     9,301
                   Application          rule                                                                                                      

                   Aerosols             SCAQMD Standards -        42.6         0            425,520       0             42.6        425,520       9,989
                                        Reformulation                                                                                             

                   Miscellaneous        Add-on control levels     22           0            333,737       0             22          333,737       15,170
                   surface coating                                                                                                                

                   Automobile           FIP Rule (VOC Content &   42.9         0            773,106       0             42.9        773,106       18,021
                   refinishing          TE)                                                                                                       

                                        Total                     742.2        17.7         4,192,943     0             759.9       4,192,943     5,518
  ---------------- -------------------- ------------------------- ------------ ------------ ------------- ------------- ----------- ------------- ---------

Source: Ozone RIA, Table B-4. Alternative 8H1AX-80: Marginal Emission
Reductions and Costs by Nonattainment Area and Control Measure Under the
RCS.

 

Appendix 2

**Potential State-Level Disaggregations**

**I. Potential State-Level Cost Allocation**

The economic analysis is performed with an eight-region version of the
model. For a pro forma economic assessment of a control program of the
sort that the \$90 billion annual cost reflects, it does not make sense
to suggest that costs could be attributed to regions with great
accuracy. Further, the exceptional resource requirements of developing
and applying a 50 region model, and the time constraints of this project
made it simply infeasible to consider an economic analysis at the state
level of detail. However, many may be interested in understanding the
potential relative costs at the level of the individual state. This
appendix provides a possible set of such inputs for the reader's
interest, but it is emphasized that these state-level data were not used
as inputs to any REMI model run. They should be thought of as the
comparable regional breakdown of costs that might have been used if the
analysis were being performed with a 50-state model.

As the main text of this report indicates, regional cost inputs were
developed from information about relative contributions to ozone and
PM~2.5~ precursors of each of 53 sectors of the economy, rather than
from specific engineering-based cost estimates that could be linked to
specific facilities in specific regions. This same attribution method
was applied to the regional costs that were the actual REMI model inputs
for the illustrative \$90 billion per year cost scenario. In this case,
the percent contribution of each state to its region's total weighted
relevant emissions were used to attribute costs back to the individual
states. The results of this attribution are presented in Table A2-1.
Again, the reader should note that the data in this table were not used
as actual model inputs.

+------------------------+----------------------+---------------------+
| **Table A2-1.          |                      |                     |
| Potential State-Level  |                      |                     |
| Control Costs          |                      |                     |
| Consistent with a \$90 |                      |                     |
| billion per year Cost  |                      |                     |
| Estimate**             |                      |                     |
|                        |                      |                     |
| **(Note: These are     |                      |                     |
| [not]{.underline}      |                      |                     |
| model input            |                      |                     |
| assumptions)**         |                      |                     |
+------------------------+----------------------+---------------------+
| **State**              | **Cost by State      | **Rank**            |
|                        | (\$b)**              |                     |
+------------------------+----------------------+---------------------+
| Alabama                | 1.8                  | 18                  |
+------------------------+----------------------+---------------------+
| Alaska                 | 0.0                  | 50                  |
+------------------------+----------------------+---------------------+
| Arizona                | 1.0                  | 30                  |
+------------------------+----------------------+---------------------+
| Arkansas               | 1.0                  | 31                  |
+------------------------+----------------------+---------------------+
| California             | 9.1                  | 1                   |
+------------------------+----------------------+---------------------+
| Colorado               | 0.5                  | 37                  |
+------------------------+----------------------+---------------------+
| Connecticut            | 0.6                  | 35                  |
+------------------------+----------------------+---------------------+
| Delaware               | 0.4                  | 42                  |
+------------------------+----------------------+---------------------+
| District of Columbia   | 0.1                  | 49                  |
+------------------------+----------------------+---------------------+
| Florida                | 2.6                  | 11                  |
+------------------------+----------------------+---------------------+
| Georgia                | 1.9                  | 16                  |
+------------------------+----------------------+---------------------+
| Hawaii                 | 0.0                  | 50                  |
+------------------------+----------------------+---------------------+
| Idaho                  | 0.6                  | 34                  |
+------------------------+----------------------+---------------------+
| Illinois               | 3.9                  | 6                   |
+------------------------+----------------------+---------------------+
| Indiana                | 4.1                  | 5                   |
+------------------------+----------------------+---------------------+
| Iowa                   | 1.3                  | 26                  |
+------------------------+----------------------+---------------------+
| Kansas                 | 0.7                  | 33                  |
+------------------------+----------------------+---------------------+
| Kentucky               | 2.6                  | 9                   |
+------------------------+----------------------+---------------------+
| Louisiana              | 2.5                  | 13                  |
+------------------------+----------------------+---------------------+
| Maine                  | 0.4                  | 41                  |
+------------------------+----------------------+---------------------+
| Maryland               | 1.5                  | 21                  |
+------------------------+----------------------+---------------------+
| Massachusetts          | 1.3                  | 27                  |
+------------------------+----------------------+---------------------+
| Michigan               | 2.6                  | 10                  |
+------------------------+----------------------+---------------------+
| Minnesota              | 1.4                  | 24                  |
+------------------------+----------------------+---------------------+
| Mississippi            | 1.1                  | 29                  |
+------------------------+----------------------+---------------------+
| Missouri               | 2.5                  | 12                  |
+------------------------+----------------------+---------------------+
| Montana                | 0.4                  | 39                  |
+------------------------+----------------------+---------------------+
| Nebraska               | 0.4                  | 43                  |
+------------------------+----------------------+---------------------+
| Nevada                 | 0.3                  | 45                  |
+------------------------+----------------------+---------------------+
| New Hampshire          | 0.3                  | 44                  |
+------------------------+----------------------+---------------------+
| New Jersey             | 1.5                  | 23                  |
+------------------------+----------------------+---------------------+
| New Mexico             | 1.4                  | 25                  |
+------------------------+----------------------+---------------------+
| New York               | 3.3                  | 7                   |
+------------------------+----------------------+---------------------+
| North Carolina         | 1.8                  | 19                  |
+------------------------+----------------------+---------------------+
| North Dakota           | 0.5                  | 38                  |
+------------------------+----------------------+---------------------+
| Ohio                   | 4.9                  | 3                   |
+------------------------+----------------------+---------------------+
| Oklahoma               | 2.1                  | 15                  |
+------------------------+----------------------+---------------------+
| Oregon                 | 0.9                  | 32                  |
+------------------------+----------------------+---------------------+
| Pennsylvania           | 4.9                  | 4                   |
+------------------------+----------------------+---------------------+
| Rhode Island           | 0.1                  | 48                  |
+------------------------+----------------------+---------------------+
| South Carolina         | 1.1                  | 28                  |
+------------------------+----------------------+---------------------+
| South Dakota           | 0.2                  | 46                  |
+------------------------+----------------------+---------------------+
| Tennessee              | 1.9                  | 17                  |
+------------------------+----------------------+---------------------+
| Texas                  | 9.0                  | 2                   |
+------------------------+----------------------+---------------------+
| Utah                   | 0.5                  | 36                  |
+------------------------+----------------------+---------------------+
| Vermont                | 0.1                  | 47                  |
+------------------------+----------------------+---------------------+
| Virginia               | 2.3                  | 14                  |
+------------------------+----------------------+---------------------+
| Washington             | 1.5                  | 22                  |
+------------------------+----------------------+---------------------+
| West Virginia          | 2.6                  | 8                   |
+------------------------+----------------------+---------------------+
| Wisconsin              | 1.6                  | 20                  |
+------------------------+----------------------+---------------------+
| Wyoming                | 0.4                  | 40                  |
+------------------------+----------------------+---------------------+
| **National**           | 90.0                 |                     |
+------------------------+----------------------+---------------------+

** **

***A. Potential State-Level Employment Impact Allocation***

For the same reason that the REMI model used in this analysis does not
use state-level cost inputs, the model also does not provide estimates
of impacts at the state-level. The only way to estimate specific
state-level impacts is to make a range of rough judgments about how
impacts would be spread among states. Such judgments should be
consistent with the way that model inputs were developed, but even that
consistency does not ensure accuracy in estimating what individual
states may experience. Although this appendix provides the results of
one way of making such attributions, readers should be aware that these
estimates are quite speculative.

It is also important to point out that there is no linkage between the
potential state-level costs of Table A2-1 above and the state-level
employment estimates provided below. The model was run on an
eight-region level with only regional-scale cost inputs, and the results
are reported only at the eight region level. The state-level employment
impacts are estimated using assumptions that are consistent with the
model results for the \$90 billion per year cost scenario, and using an
understanding of the root economic causes in the model of the potential
net job losses. However, it is emphasized that the state-level estimates
are not themselves results of the economic model.

There are several possible ways to allocate the potential 200,000 net
national job losses that are found in later, post-implementation years
of the model results for the \$90 billion per year scenario. One could
simply allocate them back to states according to their relative
population sizes. Or, one could allocate them back to states
proportionately to the sources of relevant emissions that need to be
controlled. Each method generates quite different results. However, one
can make use of the sectoral detail behind these employment impacts, and
an understanding of the causes for each type of impact. Specifically,
behind a net national impact of 200,000 jobs is an approximate loss of
385,000 jobs in retail and service sectors. These losses stem primarily
from the loss of real disposable income among consumers, and as such,
the majority of such job losses would tend to be located near consumers,
or roughly proportional to population. In turn, there are net job
increases of about 185,000 in the manufacturing sectors. Even though
these sectors are where the pollution control costs are applied, as a
group they also benefit relatively more from the increased demand
associated with pollution control measures. Thus, one would expect jobs
in these sectors to be more closely aligned with where the control
measures are being applied, or roughly proportional to the location of
the relevant emissions.

Table A2-2 shows the net job losses by region for sectors that are
categorized as \"population-oriented\" and those that are categorized as
\"emissions-oriented.\" As can be seen, the relative proportions differ
by region. Net state-level impacts involved attributing these two types
of job impacts by each state's proportion of its regional population and
weighted emissions, respectively. Table A2-3 summarizes the net job
impact categories by state that the procedure generates.

  -------------- -------------- --------------------- ----------------------
  **Table A2-2.                                       
  Regional Job                                        
  Category                                            
  Impacts                                             
  (Thousands of                                       
  Jobs)**                                             

  **Region**     **Net Impact** **Job Impacts in      **Job Impacts in
                                Population-Oriented   Emissions-Oriented
                                Sectors**             Sectors**

  1\. NE/MidAtl  -29            -77                   48

  2\. Southeast  -30            -64                   34

  3\. North      -63            -100                  36
  Central                                             

  4\. South      -7             -35                   28
  Central                                             

  5\. NW Central -14            -21                   7

  6\. West       -11            -15                   4

  7\. Far West   -29            -51                   23

  8\. Northwest  -18            -19                   2

  **National**   **-201**       **-383**              **182**
  -------------- -------------- --------------------- ----------------------

**[ ]{.underline}**

**[ ]{.underline}**

 

+-----------------+----------------+-----------------+----------------+
| **Table A2-3:   |                |                 |                |
| Rough Estimates |                |                 |                |
| of Potential    |                |                 |                |
| State-Level Job |                |                 |                |
| Impacts         |                |                 |                |
| Consistent with |                |                 |                |
| 200,000 Net Job |                |                 |                |
| Loss at the     |                |                 |                |
| National Scale\ |                |                 |                |
| **(Note: These  |                |                 |                |
| are             |                |                 |                |
| [n              |                |                 |                |
| ot]{.underline} |                |                 |                |
| model outputs.  |                |                 |                |
| They are        |                |                 |                |
| approximated,   |                |                 |                |
| based on        |                |                 |                |
| analyst         |                |                 |                |
| judgments using |                |                 |                |
| model results   |                |                 |                |
| at the          |                |                 |                |
| eight-region    |                |                 |                |
| level:          |                |                 |                |
+-----------------+----------------+-----------------+----------------+
| States That     | States That    | States That     | States That    |
| Might           | Might          | Might           | Might          |
| Experience Job  | Experience Job | Experience Job  | Experience Job |
| Losses Less     | Losses Between | Losses Between  | Losses Greater |
| than 1,000      | 1,000 and      | 5,000 and       | than 10,000    |
|                 | 5,000          | 10,000          |                |
+-----------------+----------------+-----------------+----------------+
| 1.  Delaware    | 1.  Alabama    | 1.  Arizona     | 1.  California |
+-----------------+----------------+-----------------+----------------+
| 1.  District of | 1.  Arkansas   | 1.  Colorado    | 1.  Florida    |
|     Columbia    |                |                 |                |
+-----------------+----------------+-----------------+----------------+
| 1.  Louisiana   | 1              | 1.  Minnesota   | 1.  Illinois   |
|                 | .  Connecticut |                 |                |
+-----------------+----------------+-----------------+----------------+
| 1.  Maine       | 1.  Georgia    | 1.  Missouri    | 1.  Michigan   |
+-----------------+----------------+-----------------+----------------+
| 1.  Montana     | 1.  Idaho      | 1.  New Jersey  | 1.  New York   |
+-----------------+----------------+-----------------+----------------+
| 1.  New         | 1.  Indiana    | 1.  North       | 1.  Ohio       |
|     Hampshire   |                |     Carolina    |                |
+-----------------+----------------+-----------------+----------------+
| 1.  New Mexico  | 1.  Iowa       | 1.  Oregon      | 1.  Washington |
+-----------------+----------------+-----------------+----------------+
| 1.  North       | 1.  Kansas     | 1.  Texas       |                |
|     Dakota      |                |                 |                |
+-----------------+----------------+-----------------+----------------+
| 1.  Oklahoma    | 1.  Kentucky   | 1.  Wisconsin   |                |
+-----------------+----------------+-----------------+----------------+
| 1.  Rhode       | 1.  Maryland   |                 |                |
|     Island      |                |                 |                |
+-----------------+----------------+-----------------+----------------+
| 1.  Vermont     | 1.             |                 |                |
|                 |  Massachusetts |                 |                |
+-----------------+----------------+-----------------+----------------+
| 1.  West        | 1              |                 |                |
|     Virginia    | .  Mississippi |                 |                |
+-----------------+----------------+-----------------+----------------+
| 1.  Wyoming     | 1.  Nebraska   |                 |                |
+-----------------+----------------+-----------------+----------------+
|                 |  Nevada       |                 |                |
+-----------------+----------------+-----------------+----------------+
|                 |  Pennsylvania |                 |                |
+-----------------+----------------+-----------------+----------------+
|                 |  South        |                 |                |
|                 | Carolina       |                 |                |
+-----------------+----------------+-----------------+----------------+
|                 |  South Dakota |                 |                |
+-----------------+----------------+-----------------+----------------+
|                 |  Tennessee    |                 |                |
+-----------------+----------------+-----------------+----------------+
|                 |  Utah         |                 |                |
+-----------------+----------------+-----------------+----------------+
|                 |  Virginia     |                 |                |
+-----------------+----------------+-----------------+----------------+

(Note: These are [not]{.underline} model outputs. They are approximated,
based on analyst judgments using model results at the eight-region
level)

Appendix 3

**Reference List of PM and Ozone Mortality Studies**

**Reference List of PM and Ozone Mortality Studies**

** **

***A. Short-term PM Mortality Studies***

> \[1\] Schwartz, J., Dockery, D.W., Neas, L.M. Is daily mortality
> associated specifically with fine particles? *J. Air Waste Manage.
> Assoc*., 46:927-939, 1996.
>
> \[2\] Dockery, D.W. *Particle/Mortality Associations in St. Louis and
> Eastern Tennessee; Elaboration of Published Results*. Prepared for:
> EPA critical evaluation workshop on particulate matter-mortality
> epidemiology studies; November, 1994; Raleigh, NC. Harvard School of
> Public Health, Boston, MA, 1995.
>
> \[3\] Pope, C.A., III, Schwartz, J., Ransom, M.R. Daily mortality and
> PM~10~ pollution in Utah valley. *Arch. Environ. Health*, 47:211-217,
> 1992.
>
> \[4\] Pope, C.A., III, Kalkstein, L.S. Synoptic weather modeling and
> estimates of the exposure-response relationship between daily
> mortality and particulate air pollution. *Environ. Health Perspect*.,
> 104, 1996.
>
> \[5\] Schwartz, J. Air pollution and daily mortality in Birmingham,
> Alabama. *Am. J. Epidemiol*. 137:1136-1147, 1993.
>
> \[6\] Dockery, D.W., Schwartz, J., Spengler, J.D. Air pollution and
> daily mortality: associations with particulates and acid aerosols.
> *Environ. Res*., 59:362-373, 1992.
>
> \[7\] Ozkaynak, H., Xue, J., Severance, P., Burnett, R., Raizenne, M.
> *Associations between daily mortality, ozone, and particulate air
> pollution in Toronto, Canada*. University of California, Irvine, Air
> Pollution Health Effects Laboratory, report no. 94-02, 1994.
>
> \[8\] Kinney, P.L., Ito, K., Thurston, G.D. A sensitivity analysis of
> mortality/PM~10~ associations in Los Angeles. *Inhalation Toxicol*.,
> 7:59-69, 1995.
>
> \[9\] Ito, K., Kinney, P., Thurston, G.D. Variations in PM~10~
> concentrations within two metropolitan areas and their implications
> for health effects analyses. *Inhalation Toxicol*., 7:735-745, 1995.
>
> \[10\] Styer, P., McMillan, N., Gao, F., Davis, J., Sacks, J. The
> effect of airborne particulate matter on daily death counts. *Environ.
> Health Perspect*., 13: 490-497, 1995.
>
> \[11\] Ito, K., Thurston, G.D. Daily PM~10~/mortality associations: an
> investigation of at-risk sub-populations. *J. Exposure Anal. Environ.
> Epidemiol*., 1996.
>
> \[12\] Ostro, B., Sanchez, J.M., Aranda, C., Eskeland, G.S. Air
> pollution and mortality: results from a study of Santiago, Chile. *J.
> Exposure Anal. Environ. Epidemiol*., 1996.
>
> \[13\] Ostro, B. The association of air pollution and mortality:
> examining the case for inference. *Arch. Environ. Health*, 48:336-342,
> 1993.
>
> \[14\] Dockery, D.W., Pope, C.A., III. Acute respiratory effects of
> particulate air pollution. *Annu. Rev. Public Health*, 15:107-132,
> 1994.
>
> \[15\] Moolgavkar, S.H., Luebeck, E.G., Hall, T.A., Anderson, E.L.
> Particulate air pollution, sulfur dioxide, and daily mortality: a
> reanalysis of the Steubenville data. *Inhalation Toxicol*., 7:35-44,
> 1995.
>
>  

***B. Long-term PM Mortality Studies***

> \[16\] Dockery, D.W., Pope, C.A., III, Xu, X., Spengler, J.D., Ware,
> J.H., Fay, M.E., Ferris, B.G., Jr., Speizer, F.E. An association
> between air pollution and mortality in six U.S. cities. *N. Engl. J.
> Med*., 329:1753-1759, 1993.
>
> \[17\] Pope, C.A., III, Thun, M.J., Namboodiri, M.M., Dockery, D.W.,
> Evans, J.S., Speizer, F.E., Heath, C.W., Jr. Particulate air pollution
> as a predictor of mortality in a prospective study of U.S. adults. *A.
> J. Respir. Crit. Care Med*., 151: 669-674, 1995.
>
> \[18\] Lipfert, F.W. *Community Air Pollution and Mortality: Analysis
> of 1980 Data from U.S. Metropolitan Areas. I. Particulate Air
> Pollution.* U.S. Dept. of Energy, Brookhaven National Laboratory,
> Report No. BNL 48446-R.
>
> \[19\] Ozkaynak, H., Thurston, G.D. Associations between 1980 U.S.
> mortality rates and alternative mesaures of airborne particle
> concentration. *Risk Anal*., 7:449-461, 1987.
>
>  

***C. Chronic Bronchitis Studies***

1.  Schwartz, J. Particulate air pollution and chronic respiratory
    disease. *Environ. Res.*, 62:7-13, 1993.

2.  Abbey, D. E.; Peterson, F.; Mills, P.K.; Beeson, W.L. Long-term
    ambient concentration of total suspended particles, ozone and sulfur
    dioxide and respiratory sysmptoms in a nonsmoking population. *Arch.
    Environ. Health*., 48:Jan./Feb(1) 33-46., 1993.

 

***D. Ozone Mortality Studies***

1.  Anderson, H.R.; Ponce de Leon, A; Bland, J. M.; Bower, J.S.;
    Strachan, D.P. Air pollution and daily mortality in London: 1987-92.
    *British Medical Journal*, 312:665-669, 1996.

2.  Dockery, D.W.; Schwartz, J.; Spengler, J.D. Air pollution and daily
    mortality: associations with particulates and acid aerosols.
    *Environmental Research*, 59:362-373, 1992.

3.  Loomis, D.; Borja-Aburto, V.; Bangdiwala, S.; Shy, C. *Ozone
    Exposure and Daily Mortality in Mexico City: A Time-Series
    Analysis*. Health Effects Institute. Cambridge, MA. Research Report
    No. 75, October 1996.

4.  Samet, J.; Zeger, S; Kelsall, J.; Xu, J.; Kalkstein, L. *Particulate
    Air Pollution and Daily Mortality: Analyses of the Effects of
    Weather and Multiple Air Pollutants*, The Phase I.B Report of the
    Particle Epidemiology Evaluation Project*.* Health Effects
    Institute, Cambridge, MA. March 1997.

5.  Kinney, P.L.; Ito, K.; Thurston, G.D. A sensitivity analysis of
    mortality/PM~10~ associations in Los Angeles. *Inhalation Toxicol.*,
    7:59-69, 1995.

6.  Kinney, P.L.; Ozkaynak, H. Associations of Daily Mortality and Air
    Pollution in Los Angeles County. *Environmental Res.*, 54:99-120,
    1991.

7.  Moolgavkar, S.H.; Luebeck, E.G.; Hall, T.A.; Anderson, E. L. Air
    pollution and daily mortality in Philadelphia. *Epidemiology*,
    September 1995, pp. 476-484.

8.  Sartor, F.; Snacken, R.; Demuth, C.; Walckiers, D. Temperature,
    ambient ozone levels, and mortality during summer, 1994, in Belgium.
    *Environmental Res.*, 70:105-113, 1994.

9.  Schwartz, J. Particulate air pollution and daily mortality in
    Detroit. *Environmental Res.*, 56:204-213, 1991.

10. Thurston, G. *Ozone Air Pollution and Human Mortality*. Presentation
    to the Air & Waste Management Association's 90th Annual Meeting and
    Exhibition, June 8-13, 1997, Toronto, Ontario, Canada (and submitted
    to the Ozone docket).

11. Verhoeff, A.P.; Hoek, G.; Schwartz, J.; Van Wijnen, J.H. Air
    pollution and daily mortality in Amsterstdam. *Epidemiology*,
    7:225-230, 1995.

Table of Contents

**Preface**

**Costs, Economic Impacts, and Benefits of EPA's Ozone and Particulate
Standards**

> **Introduction**
>
> **EPA Estimates of Costs and Benefits of its Proposed NAAQS**
>
> **Scope of This Study**
>
> **Problems with EPA's RIA Process**
>
> **Alternative View of Costs and Benefits**
>
> **Explanation of Reasons for Altered Cost and Benefit Estimates**
>
> **Implications for the National Economy**
>
> **Organization of the Study**

**Costs and Economic Impacts of the Proposed Ozone and PM~2.5~
Standards**

> **I. Summary of Control-Cost Estimates**
>
> **II. Ozone Control Costs**
>
> **III. Particulate Matter Control Costs**
>
> A. DFI's Estimation of Costs of Full Attainment of Proposed PM~2.5~
> Standard
>
> **IV. Economic Impact Analysis Results**
>
> A. Cost Input Assumptions
>
> A. Real Disposable Income Impacts Based on Low End of Combined Cost
> Range
>
> B. Employment Impacts Based on Low End of Combined Cost Range
>
> D. Personal Disposable Income Impacts Based on High End of Combined
> Cost Range
>
> E. Employment Impacts Based on High End of Combined Cost Range

**Assessing the Benefits of the Proposed Ozone and Particulate
Standards**

> **I. Summary**
>
> **II. General Quality Concerns**
>
> A. Poor Quality of Oversight and Peer Review in Benefit estimates
>
> B. Lack of Accountability and Public Access
>
> C. Difficulties with Using Epidemiological Results for Benefit
> estimates
>
> **III. Benefits Of The Proposed PM**~2.5~ **NAAQS**
>
> A. Current and Proposed Standards
>
> B. EPA's National Benefits Categories and Estimates
>
> C. Overview of Analysis Methodology for PM Benefit Re-Assessment
>
> D. Adjustments to Correct for Errors and Inappropriate Valuation
>
> E. Epidemiological Uncertainties
>
> F. Populations Benefiting from the Proposed Standard
>
> G. Sensitivity Analysis for Each Representative City
>
> H. National Aggregation of Health Effect Incidences
>
> I. National Monetized Benefits
>
> J. Conclusions of the PM Benefit analysis
>
> **III. Benefits of the Proposed Ozone NAAQS**
>
> A. Current and Proposed Standards
>
> B. Epidemiological Evidence
>
> C. EPA's National Benefits Categories and Estimates
>
> D. Overview of Analysis Methodology for Ozone Benefits Re-Assessment
>
> E. Evaluate and Modify Baseline for Valuation and Assumptions
>
> F. Epidemiological Studies Used in This Analysis
>
> G. Epidemiological Uncertainties
>
> H. Populations Benefiting from the Proposed Standard
>
> I. Air Quality and Avoided Mortality for Representative Cities
>
> J. National Aggregation of Mortality Incidence
>
> K. National Monetized Benefits
>
> L. Additional Sensitivity Analysis: Effects of Background Ozone Level
>
> M. Other Epidemiological Mortality Studies
>
> N. Conclusions of the Ozone Benefit analysis

**About the Authors**

**Acknowledgement**

**APPENDIX 1: Marginal Control Cost Estimates From EPA's Ozone RIA**

**Appendix 2: Potential State-Level Disaggregations**

> **I. Potential State-Level Cost Allocation**
>
> A. Potential State-Level Employment Impact Allocation

**Appendix 3: Reference List of PM and Ozone Mortality Studies**

> **Reference List of PM and Ozone Mortality Studies**
>
> A. Short-term PM Mortality Studies
>
> B. Long-term PM Mortality Studies
>
> C. Chronic Bronchitis Studies
>
> D. Ozone Mortality Studies

**Bob Brooks WARD\'S has sent you this article from PRIMEDIA Business\'s
[WardsAuto.com](http://subscribers.wardsauto.com/microsites/index.asp?SrID=10088&MagazineID=1004&SiteID=26)**

[**Study: Tests Don't Affect Emissions Deterioration
Rate**](http://subscribers.wardsauto.com/microsites/newsarticle.asp?newsarticleid=2684710&srid=10088&pageid=594&Siteid=26&magazineid=1004)\
\
By Bob Brooks\
\
, Jun 3, 2003, 12:00 a.m. ET

Real-time measurement of in-use vehicle emissions verifies that the
emissions deterioration rate of vehicles in areas with emissions-testing
programs is no better than the deterioration rate in areas with no
emissions testing.

The testing was funded by the Environmental Protection Agency and the
Coordinating Research Council (CRC).

Using remote sensors to test the emissions of vehicles during driving
time, the project data show there is no difference in the emissions
deterioration rate of vehicles as they age in Omaha, NE -- which has no
emissions-test program -- compared with Chicago and Phoenix, both of
which do have test programs.

Emissions of thousands of light-duty vehicles in the three cities
recently were reported by University of Denver researchers Gary Bishop,
Donald Stedman and Mitchell Williams. Stedman tells *Ward's* that
differences in emissions output between vehicles of the same age in the
three cities could not be attributed to inspection/maintenance (I/M)
emissions testing.

He says a variety of factors, including speed, acceleration, vehicle mix
and other variables caused emissions differences in different locations.

The crucial finding, says Stedman, is that the critical emissions
deterioration rate as vehicles age was the same whether or not an
emissions testing program is in operation.

Controlling the deterioration of emissions as vehicles age was a
fundamental goal underlying the EPA's startup of mandated emissions
testing in the mid 1980s.

Essentially, the same finding as determined by the new EPA/CRC study
came out of so-called \"tampering studies\" performed over a decade ago,
where vehicles were randomly selected for roadside inspection and
testing.

Doug Lawson, a researcher then with the California state environmental
agency, found that whether a vehicle was registered in an
emissions-testing area or a non-test area had no significant impact on
its in-use emissions levels.

The current measurements in Omaha respond to a recommendation of the
National Research Council published in July 2001 that the real-world
value of I/M emissions-test programs should be determined on a
comparative basis.

Lori Stewart, EPA deputy director, Vehicle Certification and Compliance
Section, told *Ward's* last year that new comparative measurements of
effectiveness in I/M vs. non-I/M areas would be made. The EPA\'s
director of the section, Gregory Green, recently said the agency would
announce changes in the program by the end of this summer. [(See related
story: EPA Plans Strategic Shift in Emissions-Test
Programs)](http://subscribers.wardsauto.com/microsites/newsarticle.asp?newsarticleid=2682425&siteid=26&magazineid=1004&instanceid=8674&pageid=824&srid=10088)

In addition to the emission-control value of I/M, the EPA has voiced
concern with escalating repair costs expected to further increase as
vehicles equipped with onboard diagnostic systems (OBD) get older.

Illustrating the EPA\'s cost concerns, an analysis two years ago by
Lawson (now with the National Renewable Energy Laboratory) of Denver
Dept. of Health and Environment data indicated \$24 per gram per mile of
reduced pollution as the cost for IM-240 (dynamometer) testing and
repairs and \$885 per gram per mile for OBD-based emissions testing and
repairs.

Adding to this concern, Wisconsin\'s I/M program compliance investigator
Charles Rhodes says all OBD-indicated faults (1996 and eligible newer
cars) must be repaired regardless of cost, and the state's \$450
repair-cost waiver limit does not apply to vehicles that fail an
OBD-based emissions test.

© 2003, PRIMEDIA Business Magazines & Media Inc. All rights reserved.
This article is protected by United States copyright and other
intellectual property laws and may not be reproduced, rewritten,
distributed, redisseminated, transmitted, displayed, published or
broadcast, directly or indirectly, in any medium without the prior
written permission of PRIMEDIA Business Corp.

EPA420-R-99-013

April 1999

Final Report

Evaluation of Air Pollutant Emissions

from Subsonic Commercial Jet Aircraft

Engine Programs and Compliance Division

Office of Mobile Sources

U.S. Environmental Protection Agency

Ann Arbor, Michigan 48105

Prepared for EPA by

ICF Consulting Group

EPA Contract No. 68-C-98-170

Work Assignment No. 0-3

EXECUTIVE SUMMARY

The U.S. Environmental Protection Agency\'s (EPA\'s) Office of

Mobile Sources initiated this study in order to assess the

existing and potential impact of aircraft emissions on local air

quality at ten selected cities. Aircraft emissions and airport

related emissions have received considerable attention in recent

years, both on national and international agendas.

Recent activities, such as the Clean Airport Summit (held in

Denver December 1997), a National Resources Defense Council

(NRDC) report, and correspondence from state and local air

quality agencies, reflect increased awareness of ground-level

aircraft emissions. State and local air quality officials are

seeking strategies for cost-effective emissions reductions to

comply with National Ambient Air Quality Standards. Perhaps most

significant on the national agenda, EPA and Federal Aviation

Administration (FAA) have convened a multi-stakeholder process to

seek a voluntary agreement on ground-level emissions reductions

actions for commercial aircraft and aviation-related emissions.

In order to focus the analysis, EPA made the following decisions

regarding the scope of this study:

\* Estimate the emissions from commercial jet aircraft only

(exclude emissions from on board auxiliary power units)

\* Select ten cities with current or potential local air quality

problems, as indicated by compliance with the ozone National

Ambient Air Quality Standards

\* Rely on the methodology presented in EPA Procedures for

Emission Inventory Preparation, Volume 4 : Mobile Sources,

dated 1992

\* Seek data sources that are national in scope and readily

available

\* Use 1990 as a base year, and 2010 as the projection year due

to availability of total regional emission data for these

years, and the desire to identify potential long-term trends

in emissions growth.

In the study, one portion of the airport-related emissions,

commercial aircraft, were projected to the year 2010. After an

initial draft of the study was prepared, EPA invited comments on

the draft report from the multi-stakeholder group. The most

significant comments are included in text boxes throughout the

report.

The analytical results of the study confirm that commercial

aircraft emissions have the potential to significantly contribute

to air pollution in the ten study areas. Study results indicate

that in 1990, for NOx, the aircraft component of the regional

mobile source emissions ranged from 0.6% to 3.6%. In the 2010

projection year for all cities studied, the projected

ground-level emissions from commercial aircraft increased in

absolute terms. The proportion of total urban emissions

attributable to aircraft also increased for all ten cities (range

from 1.9% to 10.4% of the regional mobile source NOx emissions);

these proportions were calculated using aircraft emissions

calculated in this study and total emissions from 1990 and 2010

inventories previously developed by EPA. While there is

uncertainty associated with these estimates for the projection

year, they generally suggest an increase in ground-level

emissions from commercial aircraft as a result of forecast growth

in the aviation sector.

Comments received from reviewers of the draft study indicated

that uncertainty may exist in the national forecasts of growth in

aircraft activity, on future composition of the aircraft fleet,

and on the accuracy of a default mixing height. Such

uncertainties carry over into projections of future emissions,

and resolution of uncertainties may result in higher or lower

ground-level emissions estimates from future aircraft. In order

to reduce the uncertainty of the results presented, additional

areas for investigation would be

\* Improvements in activity forecasts to account for supply-side

constraints that could dampen growth rates (e.g.,

infrastructure limitations, funding limitations, limited gate

availability, regulatory constraints)

\* Improvements in forecasts of national level fleet turnover

\* Addition of sensitivity analyses for the above key parameters

and others such as mixing height

Thus, this study has achieved its initial goals and creates a

basic understanding of ground-level aircraft emissions

contribution. It provides an estimation of the contribution of

aircraft to air quality emissions in ten urban areas, confirms

that investigation of cost-effective control options on aircraft

emissions is warranted, and highlights the need for improvements

in the quality of national level data as noted by reviewers of

the draft study if more certainty is desired. Reliance upon the

study\'s conclusions should take into account the caveats noted in

this report.

+-----------------------------------------------------------------------+
| # Straight talk about corn-based ethanol                              |
|                                                                       |
| 2000-08-08 13:34:16.0\                                                |
| \                                                                     |
| With the significant variations in the price of crude oil and auto    |
| fuel of the last few months, the use of ethanol made from US-grown    |
| corn as auto fuel has become a popular topic of discussion -- as it   |
| does every time that fuel prices increase.\                           |
| \                                                                     |
| Corn-based ethanol is often described in terms of its benefits to the |
| environment, its positive impact upon the US appetite for foreign     |
| oil, and the effects that the increased demand for corn has for corn  |
| farmers. However, questions remain about whether corn-based ethanol   |
| really does have all of the benefits which are often claimed for it.\ |
| \                                                                     |
| Plenty of people will tell you, in plenty of detail, about the        |
| positive benefits of ethanol. For example, you can visit the web-site |
| of the pro-ethanol lobbying group, the American Coalition for         |
| Ethanol, at [www.ethanol.org](http://www.ethanol.org/), where all of  |
| these claims are put forth in great detail.\                          |
| \                                                                     |
| For a contrasting view, I contacted Dr. David Pimentel, who is        |
| Professor of Ecology and Agricultural Sciences at Cornell University, |
| and asked him for an interview, which he was kind enough to grant me. |
| Our conversation follows.\                                            |
| \                                                                     |
| **llamas --** Professor Pimentel, you have been publishing research   |
| and studies about corn-based ethanol as auto fuel for at least 15     |
| years that I know of. Would you please describe your present, general |
| opinions of the economics, politics and ecology of corn-based ethanol |
| as auto fuel as it is presently implemented in the USA?\              |
| \                                                                     |
| **David Pimentel -** I have been investigating the energetics,        |
| economics, and environmental aspects of ethanol production for about  |
| 20 years. I currently have another paper in press. Although there     |
| have been some improvements in the technology of producing corn, and  |
| in producing ethanol from it, the latest data confirm that about 70%  |
| more fossil energy is required to produce a gallon of corn-based      |
| ethanol than is obtained in from that gallon as auto fuel. The reason |
| that nearly \$1 billion in Federal and state subsidies is spent each  |
| year to support ethanol production is that it costs about 3 times     |
| more to produce ethanol than gasoline. There are numerous             |
| environmental and social problems associated with ethanol production, |
| including soil erosion, heavy pesticide use, air pollutants, and      |
| about 160 gallons of effluent per gallon of ethanol produced.\        |
| \                                                                     |
| **llamas -** Your studies of corn-based ethanol continue to show a    |
| negative energy balance for its manufacture - in other words, that it |
| takes a lot more energy to make ethanol than you get out of it as     |
| auto fuel. Pro-ethanol lobbying groups, such as the Renewable Fuels   |
| Association and the American Coalition for Ethanol, claim that modern |
| production methods now give corn -based ethanol a positive energy     |
| balance \-- as high as 34%, according to one claim. How do you        |
| account for this apparent disagreement?\                              |
| \                                                                     |
| **David Pimentel -** The data that I include for corn production and  |
| ethanol production are detailed in my published papers. I have seen   |
| some assessments that provide a positive return in energy but these   |
| assessments do not include all the inputs for corn production or for  |
| ethanol production.\                                                  |
| \                                                                     |
| **llamas -** Critics of your studies have claimed that you employ     |
| statistics which show much more energy used in the growing of corn    |
| for ethanol than is presently the case. How do you respond to those   |
| criticisms?\                                                          |
| \                                                                     |
| **David Pimentel -** The inputs I use for corn production are average |
| data for corn production obtained from the U.S. Department of         |
| Agriculture\'s published data. Where else is there a better source of |
| data?\                                                                |
| \                                                                     |
| Having said that, the energy input figures for corn grain production  |
| have improved (gone down) since I began my research because corn      |
| yields have increased and some of the inputs, like the rate of        |
| nitrogen fertilizer application, have decreased. And I have reduced   |
| those factors in my analyses, based upon the best data available. For |
| example, my 1991 study showed nitrogen fertilizer being used at the   |
| average rate of 136 pounds per acre. My latest analysis used the      |
| latest rate given by the USDA, which is 129 pounds per acre.\         |
| \                                                                     |
| However, I have found that the studies which show much lower energy   |
| costs for the growing of corn use figures which are open to question. |
| For example, we could consider the comparative study by Shapouri et   |
| al (USDA AERS 721, 1995), which showed some scenarios in which        |
| corn-based ethanol appeared to have a small but positive energy       |
| balance. However, this study shows the energy cost of a kilogram of   |
| nitrogen production at 12,000 kcal/kg, considerably lower than Food   |
| and Agriculture Organization of the United Nations which lists the    |
| cost of nitrogen production at 18,590 kcal per kg. Using the lower    |
| figure reduces the nitrogen-energy inputs in corn production by about |
| 50%.\                                                                 |
| \                                                                     |
| Some studies also fail to consider all of the energy input costs      |
| associated with US-grown corn. Taking the Shapouri study again as an  |
| example, I note that it includes no energy inputs for machinery,      |
| irrigation, or for transportation, all of which are major energy      |
| input costs in U.S. corn production.\                                 |
| \                                                                     |
| Reports indicate that most corn used in ethanol production is         |
| irrigated. Because I was not satisfied with the data, I used the      |
| average figure of 16% for irrigated corn in my ethanol analysis.      |
| Irrigation energy inputs are especially costly.\                      |
| \                                                                     |
| **llamas -** Critics similarly claim that you employ statistics which |
| show much more energy used in the processing of finished corn into    |
| ethanol than is presently the case. How do you respond to those       |
| criticisms?\                                                          |
| \                                                                     |
| **David Pimentel -** The data that I use for processing corn into     |
| ethanol are the best published data available in the literature. I    |
| have heard about other claims of greater efficiency but when I have   |
| asked for the published data, I never receive it.\                    |
| \                                                                     |
| A factor that often reduces the claimed energy inputs in production   |
| is the assumption that the 95% ethanol obtained from the beer (8%     |
| ethanol and 92% water) with a single distillation is adequate as a    |
| finished product. But to mix ethanol with gasoline for auto fuel, the |
| ethanol must be greater than 99% pure. To achieve this at least 3     |
| distillations are required - all of which increase the manipulations  |
| and energy inputs.\                                                   |
| \                                                                     |
| **llamas -** Your latest studies continue to show a negative picture  |
| of corn-based ethanol as it relates to greenhouse gasses (GHGs). A    |
| recent study by Dr Michael Wang et al, of the Argonne National        |
| Laboratories, using what appears to be a very comprehensive           |
| statistical model, appears to show a modest GHG benefit from the use  |
| of gasoline blends using corn-based ethanol made by viable near-term  |
| production methods. How do you account for this apparent              |
| disagreement?\                                                        |
| \                                                                     |
| **David Pimentel -** If more energy is utilized to produce ethanol    |
| than we obtain in a gallon of ethanol, it is in fact contributing to  |
| greenhouse gases. Again, I stand by my published data that has been   |
| reviewed by specialists around the world and published in             |
| peer-reviewed journals.\                                              |
| \                                                                     |
| **llamas -** What can we learn from the experiences of other nations  |
| that have adopted the use of ethanol as auto fuel?\                   |
| \                                                                     |
| **David Pimentel -** Brazil has extensive experience in producing     |
| ethanol and I have been to Brazil several times to investigate        |
| ethanol production. They use sugarcane as the feedstock and because   |
| the cane residue, known as "bagasse", is used as a fuel, the energy   |
| return in ethanol production is slightly better than producing        |
| ethanol using corn. However, the last time I examined their data, it  |
| was costing the Brazilians 33¢ to produce a liter of ethanol that was |
| selling for 22¢ per liter. As another example, a 1998 paper in the    |
| Journal of Energy by L.P. Rosa of Brazil reported that a              |
| barrel-equivalent of gasoline in Brazil sold for between \$36 and     |
| \$54. He indicated that without large subsidies, ethanol from         |
| sugarcane is too costly an alternative to gasoline.\                  |
| \                                                                     |
| Clearly, Brazilian ethanol production has been heavily subsidized in  |
| the past. With the removal of those subsidies (in November of 1999),  |
| the cost of ethanol immediately rose to a level which is above the    |
| cost of production, and also well above the cost of gasoline, with    |
| the result that the market for ethanol fuel in Brazil has collapsed.\ |
| \                                                                     |
| Brazil has also had serious environmental and social costs associated |
| with ethanol production.\                                             |
| \                                                                     |
| **llamas -** Professor, in your opinion, who is benefiting from the   |
| production of corn-based ethanol in the USA today?\                   |
| \                                                                     |
| **David Pimentel -** The people who are benefiting from the           |
| production of corn-based ethanol in the U.S. are the processors of    |
| corn to ethanol \-- not the farmers who grow the corn.\               |
| \                                                                     |
| **llamas -** Having regard to all that you have said, what future do  |
| you see for corn-based ethanol in the USA?\                           |
| \                                                                     |
| **David Pimentel -** The politics of ethanol production is such that  |
| taxpayers will continue to subsidize ethanol production in the        |
| future. Without subsidies there would be no ethanol produced.\        |
| \                                                                     |
| **llamas -** Professor, is there anything else you would like to add  |
| on this subject that may be of particular interest, either to farmers |
| in general (as heavy users of auto fuel), or specifically to corn     |
| farmers (as the growers of the feedstock for corn-based ethanol)?\    |
| \                                                                     |
| **David Pimentel -** There is nothing more that I want to add. As a   |
| scientist, I am only interested in reliable scientific data related   |
| to the energetics, economics, and environmental aspects of ethanol    |
| production.\                                                          |
| \                                                                     |
| **llamas -** Professor, thank you very much for taking the time to    |
| talk with me today.\                                                  |
| \                                                                     |
| There\'s nothing I want to add to that.\                              |
| \                                                                     |
| Have a fine day,\                                                     |
| \                                                                     |
| llater,\                                                              |
| \                                                                     |
| llamas                                                                |
+-----------------------------------------------------------------------+

© Copyright [Meredith Corporation](http://www.meredith.com/)
![](media/image6.png){width="0.20833333333333334in"
height="0.15625in"}20042000. All rights reserved

**OZONE IN THE COLORADO FRONT RANGE**

**Donald H. Stedman**

**Chemistry and Biochemistry Dept.**

**University of Denver**

**CO 80208**

**303 871-2580 FAX 2587**

[**dstedman@du.edu**](mailto:dstedman@du.edu)

**January 23, 2004.**

A position paper to be presented to the AQCC by D.H. Stedman as a
private citizen and scientist with experience in photochemical smog
chemistry and motor vehicle emissions**.**

![](media/image7.png){width="5.999305555555556in"
height="4.1715277777777775in"}

The diagram above was presented to the RAQC by Gordon Pierce of CDPHE
October 2, 2002. Note that two of the top three molecules forming ozone
are the carbonyls formaldehyde and acetaldehyde. The former may be, and
the latter certainly is, markedly enhanced in automobile exhaust if the
fuel is oxygenated. Notice how importantly this diagram differs from the
one presented by Jana Milford which only included hydrocarbons.

**Policy recommendation**: Do not allow or only allow a minimal (1% or
less for instance) fuel oxygenation in the summer months. We were the
first state in the union to mandate oxygenated fuel to improve our air
quality, we can be the first to require its absence for the same reason.

Dec. 11 2003, AQCC meeting Rick Barrett (CDPHE) presented an important
study of IM240 "false failures" of new cars on hot days \[the worst days
for ozone formation\] emitting large hydrocarbon concentrations. As he
agreed, these are real emissions of real VOCs, they are only "false
failures" when you wish to compare the IM240 results to the FTP test and
determine whether there is a repairable fault in the vehicle (there is
not). The fault is the fuel. The fuel is too volatile. It floods the
evaporative canister with excess vapor such that upon purge, the fuel
load is too much for the vehicle's control system to handle.

**Policy recommendation**: Mandate a low fuel RVP in summer and if
ethanol is allowed to remain in the fuel in summer at all (see
recommendation above) do not allow that mix to have one pound increased
RVP.

The professional ethanol supporters state that computer models show that
ethanol is net neutral relative to ozone formation, largely because of
the CO reduction. (CO is very low in reactivity but it is not zero and
there is a lot of it). This statement is correct. The computer models do
not show ethanol blending as bad for ozone. This is because the computer
models ARE WRONG. They do not use Rick Barrett\'s data which show very
large HC emissions from 2-3% of new vehicles on hot days. There are
about 1,000,000 new vehicles in Denver and 30,000 of these emitting
excess HC when they purge on particularly hot days is a lot of HC and
can be eliminated by either removing the 1 lb RVP waiver for the summer
or removing ethanol for the summer. The 2-3% problem that Rick showed is
probably too low, and on the road, the fuel problem is probably worse
because not all vehicles purge on IM240 to the same extent, and the
amount of purge depends upon the previous driving/idling in line
history.

**Policy recommendation**: If ethanol is allowed to remain in the fuel
in summer at all (see recommendation above) do not allow that mix to
have one pound increased RVP.

![](media/image8.jpeg){width="6.0in" height="3.839583333333333in"}

The figure above is a highly simplified diagram of ozone formation which
nevertheless includes many of the otherwise obscure features of a more
complete photochemical model. For the most part, the understanding comes
from G.M. Johnson ("A simple model for predicting the ozone
concentration of ambient air". Proc. 8^th^ International Clean Air
Conference pp 715-731, H.F. Hartmann et al eds. 1984). There are four
recognizably different regions lettered A-D.

A\) **UPWIND** There is always ozone coming in from upwind ozone
sources, and even from the stratosphere. It does matter how much ozone
comes in from upwind. The more upwind ozone, the shorter is the
titration region (B).

B\) **DOWNTOWN** This is the downtown titration region in which nitric
oxide (NO) removes ozone, turning it into NO~2~. The reaction is NO +
O~3~ =\> NO~2~ + O~2~. Almost all NOx is emitted as NO. The more NO is
emitted the longer the ozone stays down. Since time and distance are
related by wind, we expect to see weekend ozone building up sooner in
time and closer to downtown in space than during weekdays. This is
observed. The simplest approximation would place the length of time in
(B) as only dependent upon the NO emissions. This is not quite true. As
noted above, more ozone coming in from upwind shortens this time. More
HC shortens the time somewhat, and most relevant to Denver, less HC
lengthens it. All computer models and on-road data suggest that mobile
source HC emissions in Denver will continue to decline.

C\) **HC LIMITED** This is the time when the complex photochemistry
takes over and ozone builds up. Graham Johnson's breakthrough in
understanding is that the rate of buildup (the angle at which the line
goes up) is dependent only upon the VOC (amount and reactivity) and
independent of the NOx. It is also dependent upon the intensity of
sunlight and larger at higher temperatures. All the regions under the
sloping line are hydrocarbon limited. Lower HC and everyone is better
off. As HC emissions have gone down nationwide, the rate of ozone
buildup has gone down nationwide. As a result, even places like Los
Angeles (let alone Denver) rarely even get as far as the ozone level
section (D). Section (C) is hydrocarbon limited and is getting larger
and larger as HC emissions are reduced.

\(D\) **NOx LIMITED** Graham's other breakthrough observation is that if
the sun stays out long enough and the hydrocarbon is reactive enough you
get to section (D) which has a level ozone concentration proportional
only to the amount of NOx input. The important understanding of recent
trends is that hydrocarbon concentrations and reactivity have gone down
so much that even Riverside hardly ever gets to this NOx limited ozone
level before the sun goes down (which also stops the ozone buildup, even
though the amount obtained remains in the (C) hydrocarbon limited
regime). The continued trend to lower mobile source HC emissions (where
most people are breathing) means that there are fewer and fewer places
which benefit from NOx reduction and everywhere else there are people
whose ozone levels go up in NOx is reduced. NOx does contribute to
pollution all on its own. PAN is a NOx derived molecule of no benefit.
NOx acidifies rain, fertilizes high mountain lakes and mobilizes soil
aluminum. However, the focus of this discussion is ozone.

**Policy recommendation:** Take no steps to reduce NOx at this time.
Control measures such as RICE which promise mostly NO reductions should
not be carried out, or at least should be postponed until we are well
within ozone compliance.

This diagram shows (Lower HC in red compared to the black line) why
hydrocarbon reduction alone as a control measure makes everyone better
off. When hydrocarbon reduction is coupled with similar NO reduction,
(Lower HC and NO in blue) the downtown titration region (B) is shortened
and most areas actually receive a **higher** ozone dose than they would
have received had the similar NO reduction not taken place. The only
place where anyone is better off with HC and NO reduction together is
all the way out on the right (far downwind where the red and blue lines
cross) if we ever get there before the sun goes down. I believe that we
do not in Denver or in LA.

This diagram also shows how extra ozone from Weld County can be removed
by NO titration when crossing the Denver Basin, but reappear as more
ozone in the west because section (B) is shortened by the increased
incoming ozone.

Compared to L.A., Denver has slightly longer hours of sunlight (bad) but
is a much smaller urban center (good) with much less traffic (good) and
with somewhat newer vehicles (good). L.A. is rapidly approaching
"weekends are worse everywhere" I would expect any statistically
significant data set to show that Denver and environs are already there,
only more so.

**Policy recommendation:** It is more important to reduce HC (aka VOC)
than to reduce NO emissions. If it were possible to put back the recent
NO reductions (without HC reductions) from power plants you should do so
for the benefit of our ozone readings. Control measures such as RICE
which promise mostly NO reductions should not be carried out, or at
least should be postponed until we are well within ozone compliance.

Do not believe simple arguments showing that 5 Tons per day of NO
emissions in Denver would only make 4ppb of NO if diluted to Chatfield
in the absence of photochemistry, when the next step of the argument is
to turn on the chemistry and state that 4ppb of NO would only remove
4ppb of ozone which is negligible. The fact is that the NO and the ozone
both travel together to Chatfield. There are regions where the NO was 20
and even 50 ppb and that much ozone would be removed and that would be
important. Only trust the result of a full photochemical model run with
and without realistic NO emissions reductions from power plants with no
other parameter varied.

By now the AQCC probably realizes that MOBILE modeling of motor vehicle
emissions inventories is something of a joke (the official Tons per Day
CO inventory for Colorado Springs was doubled! by going from MOBILE5 to
MOBILE6 at your December 11 meeting). According to Oliver Houck "SIP
programs eat up heroic amounts of money, remain information starved,
feature shameless manipulation of the data, face crippling political
pressure, and produce little abatement." Science Dec. 12 p 1928.

**Policy recommendation:** If you desire actual mobile source emission
inventories use real on-road data. Our work is limited in scope but
published in a peer-reviewed journal (S.S. Pokharel, G.A. Bishop and
D.H. Stedman, "An on-road motor vehicle emissions inventory for Denver:
an efficient alternative to modeling", *Atmos. Environ*. 36, 5177-5184,
2002). There is now however a wealth of data from the Clean Screen vans
from which could be derived an excellent, up to date, fuel based,
emission inventory for the Denver region.

It may seem hypocritical to recommend trusting a photochemical model,
but also arguing that the inventory model upon which it is based is
certainly flawed. Photochemical modeling has been subjected to a large
amount of checking and the errors are known and recognized. MOBILE
modeling is largely unchecked and certainly contains several of the
items mentioned by Houck above concerning manipulation of the "data" in
the face of political pressure (National Academy Press 2000).

According to Lawson at the October 22 RAQC meeting:

-   The majority of on-road exhaust HC emissions comes just a few
    vehicles. These are the "low-hanging fruit."

-   Remote sensing is the silver bullet.

I agree. We find about three gross HC vehicles each day with our SMART
SIGN remote sensor out of about 12,000 passing vehicles. One of the most
recent would have measured about 250 gm/mile CO and 50 gm/mile HC (The
HC standard for new cars is 0.04 gm/mile). I know of no state which has
found the right combination of penalties and incentives to successfully
fix/remove these gross emitters from their roadways. We currently spend
at least \$50,000,000 per annum in Colorado on oxygenated fuels in the
winter and inspection and maintenance year round. I have suggested ways
that their emission reduction goals might be achieved for a lot less
dollar outlay,
([www.feat.biochem.du.edu](http://www.feat.biochem.du.edu/) click on
link to Independence Institute report) but these are policy suggestions
from a Chemist, not from a person with qualifications in the policy
arena.

**Policy recommendation:** If you can not find a way to repair or remove
from the fleet the on-road gross HC emitters easily identified by
on-road remote sensing, then it is unlikely that any of your other
control measures will be successful.

**Air Scare: Does Air Pollution Cause Asthma?**

By [Joel Schwartz](http://www.rppi.org/schwartz.html)

February 20, 2002.

While previous studies showed that air pollution can aggravate
pre-existing lung ailments, a new study of children in southern
California reports that frequent, strenuous, outdoor exercise, combined
with high levels of ozone air pollution, can more than triple children's
risk of developing asthma. This study, published in the Lancet, a
prestigious British medical journal, is the first study to find that air
pollution might actually cause asthma, not just aggravate it. Officials
of the California Air Resources Board, which paid for the study, were
quick to claim that its results applied not only to the six southern
California communities where it was performed, but also to many cities
across the country. ARB officials also maintained that the study
justifies additional air pollution regulations in order to protect
children's health. State and local public health officials and advocates
echoed these sentiments in the many newspapers that carried the story.

But pollution-control advocates who invoke this study to justify ever
more intrusive air pollution regulations are playing fast and loose with
the numbers. Though high ozone levels of the past may have caused asthma
in some parts of southern California, children exposed to current levels
of air pollution are at virtually no additional risk of developing
asthma.

In the Lancet asthma study, researchers followed two groups of children
in southern California from 1993 to 1998. One group lived in six
communities with low pollution levels; the other lived in six
communities with high pollution levels. After accounting for factors
besides pollution that could account for differences in asthma rates
(such as income, ethnicity, parental smoking, etc.) the study found
that:

-   Children in high-ozone communities who participated in three or more
    team sports (about 8 percent of all children in the study) were 3.3
    times as likely to become asthmatic when compared with less-active
    children. Asthma was unrelated to sports participation in the
    low-ozone communities.

-   Children who spent the most time outdoors were 1.4 times more likely
    than other children to become asthmatic in the six high-ozone
    communities, but not in the low-ozone communities.

-   The study also measured other pollutants, such as airborne
    particulates and nitrogen dioxide, but found no relationship between
    these pollutants and asthma.

-   Despite the increased asthma rate found for very-active children in
    polluted areas, asthma rates did not differ overall between the
    high- and low-pollution areas.

The asthma study estimated children's ozone exposure using pollution
measurements from 1994 to 1997. But southern California has made great
progress on air pollution in the intervening years. The communities in
the study went from an average of about 34 days per year exceeding the
federal ozone health standard, down to an average of just 4 during the
last two years---a reduction of almost 90 percent. Even Crestline,
California, which was not part of the study, but has the worst ozone in
the nation, exceeded federal standards an average of 23 days per year
for the last two years---about a third less often than the asthma study
areas.

Ozone levels are far lower everywhere else. For example, a few other
areas of California---San Bernardino, Riverside, Fresno, eastern Los
Angeles County and Kern County---have about 5 to 15 high ozone days per
year. Ozone across the Houston metro area exceeds the standard from one
to 10 days per year. Beyond these regions, no other part of the country
averages more than 4 high-ozone days per year, and most have one or
none.

In other words, no American experiences ozone at levels similar to those
of the asthma study, and more than 97 percent of Americans don't even
experience one-tenth as many high ozone days. Clearly, regulators and
public health advocates are wrong when they claim either that the asthma
study has something to say about the risks of current air pollution
levels, or that current regulations are not sufficient to reduce air
pollution.

Proponents of the pollution-causes-asthma hypothesis also err in
claiming that air pollution is partly to blame for rising asthma rates
during the last 20 years. The Environmental Protection Agency reports
ozone levels dropped an average of 24 percent between 1980 and 1999, and
other pollutants also declined substantially. Areas with the most ozone
pollution experienced the greatest improvement. And the asthma study
itself also found no difference in overall asthma incidence when
comparing high- and low-ozone communities.

Asthma is a serious disease and no one wants to take risks with their
children's health. But for children to be at risk, they'd need to be
frequently exposed to high ozone levels. Fortunately, they're not.
Recent air quality improvements mean that no one in America is now
exposed to high ozone as often as in the southern California study.

Asthma exacts a large health toll on our society, making it urgent that
we learn what causes the disease and how to neutralize it. But
regulators and activists do the public a disservice when they exaggerate
health threats. Not only do they scare people for no good reason, they
also divert attention and resources from real threats to people's
welfare.

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Joel Schwartz is a Senior Scientist at Reason Public Policy Institute.
Prior to joining Reason, Schwartz was Executive Officer of the
California Inspection and Maintenance Review Committee, the group
charged with evaluating California\'s Smog Check program and advising
the Legislature and Governor on Smog Check policy. He also previously
served as the first Staff Scientist at the Coalition for Clean Air, in
Los Angeles.

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Mr. Brown\...I encourage you in your efforts but I must tell you that I
had the support of the governor (George Allen) and both Senators (one
Republican and one Democrat) in our effort to stop the EPA push to
implement centralized emissions testing in Northern Virginia.  We
already had a decentralized program in place and the push was on to make
it centralized and tougher.   Because of the air quality standards that
we were required by law to achieve, we agreed to the tougher standards
but fought to keep the testing de-centralized.  We won that battle and
it is working quite well.   In Richmond and the Tidewater areas of
Virginia, we did not want to institute any auto emissions testing so we
established some aggressive pr efforts to alert citizens to actions that
could cause our air quality in those area to worsen, actions they could
take to avoid that, and outreach to manufacturing facilities to the
same.  The governor was also involved in these efforts (which included
limited state government activities when necessary) and we got great
cooperation.  The result was air quality improved in those two areas of
the state and we were able to fend off EPA intentions that would have
led to auto emissions testing in those areas.    For you, much would
depend on the air quality in recent years, the requirements of the law
versus EPA mandates and the support of some elected officials.  Good
luck to you.  Becky Dunlop\
\
\
Becky Norton Dunlop\
Vice President\
www.heritage.org

**Northern Kentucky Emissions** **Check**

**2003 Annual Report**

***Prepared by Kentucky Division for Air Quality***

**June 2004**

2

**Table of Contents**

Tables and
Figures\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
3

Executive Summary
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
4

Emissions Check Program Background and Overview
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\..... 5

Operations Statistics
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
6

Test Activities
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
6

Test Center
Activities\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
7

Vehicle Repair
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
7

Referee Activity
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
8

Customer Service
Activities\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
8

Quality Assurance/Quality
Control\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
8

Calibrations and
Checks\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
8

Equipment Audits
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
8

Enforcement\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
8

Improving Repair
Effectiveness\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
12

Appendices
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
13

A Initial Tests by Vehicle Type and Model Year
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\... 14

B First Retests by Vehicle Type and Model Year
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.... 16

C Subsequent Retests by Vehicle Type and Model Year
\...\...\...\...\...\...\...\...\...\...\.... 18

D Waiver Activities by Vehicle Type and Model Year
\...\...\...\...\...\...\...\...\...\...\...\...\.... 20

E Hired and Self Repairs by Vehicle Type and Model Year
\...\...\...\...\...\...\...\...\...\... 21

F Semiannual Lane Audits by Division for Air Quality
\...\...\...\...\...\...\...\...\...\...\...\...\... 22

G HC and CO Emission-Reduction Calculations
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\..... 23

3

**Tables and Figures**

**Tables**

Table 1: Overall Testing Activity
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
6

Table 2: Hotline Call Report
2003\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
9

Table 3: Northern Kentucky Calibrations and System Checks
\...\...\...\...\...\...\...\...\...\...\...\...\.... 10

**Figures**

Figure 1: Initial Test Results, All Parameters
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
6

Figure 2: Emissions Fail Breakdown by Pollutant
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
6

Figure 3: Pass/Fail Breakdown, by Vehicle Type
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
7

Figure 4: Pass/Fail Breakdown, by
Station\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
7

Figure 5: Average Repair Costs
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
7

Figure 6: Waivers & Appeal Waivers, by
Station\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
8

Figure 7: Northern Kentucky Emissions Check Performance
Audit\...\...\...\...\...\...\...\...\...\... 11

Figure 8: Extensions Issued, by Station
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
12

4

**Executive Summary**

The United States Environmental Protection Agency (USEPA) requires
annual reports from Vehicle

Inspection/Maintenance (I/M) programs, pursuant to 40 CFR 51.366. This
report for calendar year 2003

focuses on program statistics, quality assurance and quality control
(QA/QC), and enforcement.

The Kentucky Division for Air Quality (DAQ) administers the centralized,
"improved basic" Northern

Kentucky Emissions Check Program in the Cincinnati-airshed counties of
Boone, Campbell, and Kenton,

with one testing station in each county. DAQ personnel maintain offices
in each of the three stations to

perform oversight and referee functions. The testing contractor is
Envirotest Systems Corp. (ESC).

Enforcement is based on denial of vehicle-registration renewals.

Biennial testing of the area's approximately 190,000 1968-and-newer
vehicles began September 1, 1999.

For gasoline-fueled vehicles, the tests include analysis of idle
emissions of hydrocarbons (HC) and carbon

monoxide (CO), an evaporative-system pressure check (1981 and newer
model years), and an antitampering

check. For diesel vehicles, an opacity standard of 10% is in place.

The Emissions Check program performed a total of 100,935 tests on 96,540
vehicles in 2003. The fail rate

for initial tests during the year was 4.8%; 2,431 (52.3%) of the 4,646
initial fails passed subsequent retests,

and 1,213 (26.1%) were issued repair-cost waivers for having spent the
repair \"cap\" (\$75 for 1980-andolder

vehicles, \$200 for 1981-and-newer vehicles) but still not passing the
test. The overall waiver rate was

1.3% for 2003. Initial fail rates for pre-1981 vehicles averaged 23.3%;
for 1981 and newer vehicles, 4.6%.

Of the 4,646 initial fails, 3,518 (75.7%) were emissions fails, 1,269
(10.5%) were evaporative system fails,

74 (1.6%) were tampering fails, and 16 (0.9%) were opacity fails on
diesel-fueled vehicles. (Totals exceed

100% because of multi-parameter fails.)

Emissions reductions in a basic program such as this can be quantified
in terms of percent reductions, but

not in terms of actual amounts reduced in Tons per Summer Day.
Nevertheless, it can be reported that

emissions for all repaired vehicles improved 71.9% for HC and 49.6% for
CO (see Appendix G).

Envirotest's quality-control protocols for 2003, including all
calibrations performed and their frequency, are

listed in Table 3. DAQ's quality-assurance protocols for 2003 included
equipment audits in each of the

program's 11 lanes (see Appendix F). DAQ performed overt performance
audits in 2003 to review staff and

test procedures, per 40 CFR 51.363(a)(3). A covert audit was performed
on February 25, 2003 by de la

Torre Klausmeier Consulting, Inc. (dKC). The audit identified several
deficiencies to which Envirotest

responded in the finalized audit publication dated July 25, 2003.

Public education and outreach are important elements of the Emissions
Check program. In 2003,

awareness of the program and its benefits for public health and the
environment were promoted by

collaboration between DAQ and Envirotest through community partnerships
and public relations projects

such as:

A 24-hour toll-free hotline to answer customers' questions

Informational posters and pamphlets at County Clerks\' offices to
promote compliance

A quarterly Repair Effectiveness Handbook that rates repair shops\'
handling of test failures

A section in the Kentucky Drivers Manual that explains vehicle emissions
and ozone

Sponsorship of the annual Clean Air-a-thon organized by the Regional
Ozone Coalition

Participation in quarterly meetings of a Repair Advisory Committee

A monthly newsletter for County Clerks to enhance program enforcement.

5

**Emissions** **Check Program Background and Overview**

The Northern Kentucky counties of Boone, Campbell, and Kenton are a part
of the Greater Cincinnati

airshed. The area has violated the National Ambient Air Quality Standard
for ozone periodically for the past

20 years. The levels at which the ozone standard has been exceeded
warranted the "moderate nonattainment"

designation. Under the federal Clean Air Act Amendments of 1990, areas
with this designation

are required to reduce emissions of ozone precursors by at least 15%,
and a vehicle inspection and

maintenance (I/M) program must be in place in the area. Even though the
area was re-designated to

attainment in 2002, the program has been kept in place to ensure that
compliance with the standard is

maintained.

Kentucky's 1990 General Assembly authorized an I/M program for Northern
Kentucky with the enactment

of KRS 224.20-710 through 224.20-765. In turn, DAQ promulgated an
administrative regulation, 401 KAR

65:010, to carry out the statutory mandate and implement an "improved
basic" program. For gasolinefueled

vehicles, test parameters include hydrocarbon and carbon monoxide
emissions analysis, an

evaporative system pressure test (1981 and newer model years), and a
tampering check of the catalytic

converter, fuel filler inlet restrictor, and gas cap. Diesel vehicles
receive an opacity test. The test fee is \$20,

of which \$17.78 goes to Envirotest and \$2.22 to the Commonwealth to
recover administrative costs.

By 1998, Kentucky awarded a 10-year contract to Envirotest to construct
and operate one testing station in

each county. Together, the stations offer 11 testing lanes for biennial
testing of the area's approximately

190,000 subject vehicles (1968 and newer model years, 18,000 pounds or
less Gross Vehicle Weight

Rating, or GVWR, and four wheels or more). There is a DAQ office in each
station, where DAQ staffs

perform referee, oversight, and customer-assistance functions.

Area motorists are given a three-month period in which to test. This
testing period begins two months

before the registration-renewal month. Odd-model-year vehicles have
testing deadlines in odd-numbered

years; even-model years have deadlines in even-numbered years.
Envirotest mails testing notices to

vehicle owners about one week before their testing cycles begin.
Additionally, DAQ publishes a test

notification in the local newspaper each month. Legislation passed in
the 2003 Session changed the

frequency of testing for the area's approximately 1,950 official
vehicles from annual to biennial.

The program is enforced through denial of registration renewal. A
telecommunications network links the

Commonwealth, the testing stations, and the County Clerks (where
registration renewal takes place). Test

transactions are electronically transmitted from the stations to
Envirotest's central computer in Twinsburg,

Ohio, where databases are updated. From Twinsburg, test data are
transmitted to the Commonwealth's

computers in Frankfort, where the vehicle-registration database that the
County Clerks access to renew

registrations resides. The telecommunications system blocks registration
renewals for non-compliant

motorists and resets blocks for compliant motorists to two years in the
future.

The following exemptions are available to accommodate various
situations:

Permanent exemptions for vehicles that operate exclusively on fuels
other than gasoline or diesel.

Reciprocal exemptions for vehicles that have complied with another I/M
program in the past year.

Absence exemptions for vehicles that are driven less than 30 days in the
three-county program

area during the two-year certification period.

Temporary extensions of varying duration for vehicles that are unable to
comply with the testing

requirement during the three-month testing period.

Repair-cost waivers for 1980-and-older vehicles and diesel vehicles on
which at least \$75 has

been spent on appropriate repairs after a failed test and for
1981-and-newer vehicles on which at

least \$200 has been spent on appropriate repairs after a failed test.

6

**Operations Statistics**

***Test Activities***

The statistics in Table 1 show overall numbers and types of tests
performed in 2003. The program also

processed 2,189 certification period exemptions and 92 permanent
exemptions in 2003.

**Table 1: Overall Testing Activity**

**\# Tested \# Passed \# Failed % Passed**

First Test 96,540 91,894 4,646 95.2

Free Retest 3,843 2,182 1,661 56.8

Subsequent Retests 552 249 303 45.1

***Total All Tests*** 100,935 94,325 6,610 93.5

Of the 96,540 initial tests, 91,894 passed and 4,646 failed, for an
initial fail rate of 4.8%. During the year,

initial tests were received by 1,719 diesel-fueled vehicles. Of that
number, 16 (0.9%) failed the opacity test.

Of the 93,580 1981-and-newer gasoline-fueled vehicles that were
initially subject to pressure tests, only

12,085 vehicles were given the test because of the inaccessibility or
untestability of the evaporative

systems. Of the tests that were performed, 10,816 passed (89.5%) and
1,269 failed (10.5%)**.** Of 94,753

initial emissions tests performed, 3,518 failed (3.7%). Figure 1 shows
the results of all initial tests

performed in 2003.

**0**

**20,000**

**40,000**

**60,000**

**80,000**

**100,000**

**Pass Fail**

**Figure 1: Initial Test Results, All Parameters**

**HC**

**CO**

**Opacity**

**Pressure**

**Tamper**

As shown in Figure 2, of 3,518 initial emissions fails, 1,733 failed HC,
326 failed CO, and 1,459 failed both

HC and CO.

**0**

**1000**

**2000**

**3000**

**HC Only CO Only HC & CO**

**Figure 2: Emissions Fail Breakdown by Pollutant**

Different standards are set for four different kinds of gasoline-fueled
vehicles. They are light-duty gasoline

vehicles (LDGV), light-duty gasoline trucks that are 6,000 pounds or
less (LDGT1), light-duty gasoline

trucks that are 10,000 pounds or less (LDGT2), and heavy-duty gasoline
trucks (HDGT) that are 18,000

pounds or less. Figure 3 provides a pass/fail breakdown for initial
tests by vehicle type.

7

**0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100**

**LDGV**

**LDGT1**

**LDGT2**

**HDGT**

**Figure 3: Pass/Fail Breakdown by Vehicle Type**

\% Fail

\% Pass

Average emissions improvements on the 1,611 first retests for HC were
303.1 ppm HC (70.9%

improvement); on 259 subsequent retests, 168.4 ppm HC (80.0%
improvement). Average improvements

on the 1237 first retests for CO were 2.8% CO (48.3% improvement); on
189 subsequent retests, 2.0% CO

(58.5% improvement). Overall, repairs yielded reductions averaging 284.4
ppm for HC and 2.7% for CO

(improvements of 71.9% for HC and 49.6% for CO). Details on emissions
improvements can be found in

Appendix G.

Appendices A through C contain details on all test results, broken down
by vehicle type and model year.

***Test Center Activities***

Northern Kentucky Emissions Check operates three test centers, one in
each of the three Northern

Kentucky counties. At Station 1 (Boone), where 33,524 vehicles took
their initial tests, 96.2% passed and

3.8% failed inspection; at Station 2 (Campbell), 95.4% of the 26,564
initial tests passed and 4.6% failed;

and at Station 3 (Kenton), 94.1% of the 36,452 initial tests passed and
5.9% failed. The percentages of all

fails system-wide were: Boone-27.2%, Campbell-26.3%, and Kenton-46.5%.
The pass/fail percentages for

each station are presented graphically in Figure 4.

**0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100**

**Boone**

**Campbell**

**Kenton**

**Figure 4: Pass/Fail Breakdown by Station**

\% Fail

\% Pass

***Vehicle Repair Data***

Of 96,540 initial tests performed in 2003, 4,646 vehicles failed
inspection and 3,843 returned for a first

retest. Figure 5 shows the average repair costs for vehicles that had
retests, broken down by parts and

labor and by self repairs compared to hired repairs. (Self-repairs are
not eligible for labor costs.)

Additionally, Appendix E provides details on self vs. hired repairs,
broken down by model year and vehicle

type.

**\$0**

**\$50**

**\$100**

**\$150**

**Self Repair Hired Repair**

**Figure 5: Average Repair Costs**

Parts Costs

Labor Costs

8

***Referee Activity***

Vehicles that cannot pass the emissions test may be given a waiver if at
least \$75 (1980 and older models)

or \$200 (1981 and newer models) has been spent on emissions-related
repairs. Appendix D summarizes

waiver activity by vehicle type and model year. Envirotest issues a
regular waiver if there has been an

improvement in emissions, and DAQ issues an appeal waiver if there has
been no improvement. Figure 6

shows the number of regular waivers and appeal waivers for each station
in 2003. Regular waivers

amounted to 45.1% of the 1,213 waivers issued in 2003.

**0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300**

**All Waivers**

**Appeal Waivers**

**Regular Waivers**

**Figure 6: Waivers and Appeal Waivers by Station**

**Totals**

**Kenton**

**Campbell**

**Boone**

**Customer Service Activities**

The Northern Kentucky Emissions Check program is committed to providing
customer service in an

efficient, constructive, and professional manner. Both DAQ and
Envirotest personnel are trained

extensively in all aspects of customer service. The program provides an
around-the-clock hotline, with

Envirotest personnel staffing the hotline during regular business hours.
DAQ personnel are available at

each station not only to oversee the tests, but also to provide referee
services when warranted between

customers and the contractor; to issue extensions, exemptions, and
appeal waivers; and to provide

information, advice, and other appropriate customer service. Table 2
gives a breakdown of operatorassisted

hotline calls.

**Quality Assurance/Quality Control**

***Calibrations and Checks***

Quality-control activities are performed by Envirotest to ensure the
accuracy and reliability of equipment

and testing materials. The frequency of these activities is specified in
Table 3.

***Equipment Audits***

Semiannual equipment audits for each of the program's 11 lanes at the
three stations are included in

Appendix F. The results of these audits provide quality assurance for
the program and ensure proper

operation of station equipment. Monthly performance audits are logged on
the form shown in Figure 7.

**Enforcement**

Beyond registration-renewal blocks for non-compliers, the program uses
other forms of enforcement to

ensure compliance.

DAQ monitors County Clerks' block/override activity statewide, with
particular focus on the program-area

clerks, for overrides that appear to be unjustifiable (i.e., clerk
renews registration of non-compliant motorist

whose renewal is blocked). In almost all cases, the clerks' overrides
have been found to be due to

temporary failures by the telecommunications network to transmit
compliant test results. Less than 1% of

the overrides were found to be improper, and DAQ's monthly override
audits revealed that all of the

improper overrides were due to errant keystrokes or training oversights.

9

**Table 2: Hotline Call Report**

**2003**

**Date**

**Total Daily Calls**

**Already Tested**

**Appt./**

**Bring What**

**Complaints**

**Cost**

**Extensions/**

**Waivers**

**Failures**

**Hiring**

**Hours of**

**Operation**

**Location**

**Not Running**

**Needed/When/**

**Early**

**Notice not**

**Received**

**Overweight**

**Vehicles**

**Sold**

**Technical**

**Test Process/**

**Length**

**Vouchers**

**Other**

**Total Questions**

**Daily**

January 808 40 85 7 60 142 0 8 140 70 0 179 23 0 89 30 14 14 27 928

February 602 33 97 5 57 113 0 6 123 40 0 104 6 0 35 35 13 5 18 690

March 717 46 115 11 51 168 0 1 101 49 0 100 13 0 97 40 10 4 25 831

April 771 59 66 15 49 125 2 14 143 59 0 218 10 0 98 33 4 2 10 907

May 577 44 60 9 60 105 3 4 109 48 7 134 4 0 71 26 5 3 11 703

June 636 45 86 15 51 117 2 7 85 50 4 115 8 1 114 19 9 2 10 740

July 625 32 110 11 55 127 3 4 98 63 0 134 34 0 63 31 16 1 10 792

August 593 38 94 5 56 102 9 3 82 44 1 124 13 2 97 22 13 1 44 717

September 577 26 113 6 61 89 1 0 80 57 1 139 23 9 0 98 30 18 0 751

October 580 32 101 24 36 93 0 4 77 50 0 118 13 5 5 99 22 13 0 692

November 403 20 71 10 52 69 1 7 144 38 1 67 7 11 0 40 29 12 2 581

December 503 23 86 12 52 73 0 7 170 38 0 94 11 14 0 35 21 6 3 645

**Totals** 7,392 438 1,084 130 640 1,323 21 65 1,352 606 14 1,526 165 42
669 508 186 81 127 8,977

Daily Average 616 37 90 11 53 110 2 5 113 51 1 127 14 4 56 42 16 7 11
748

\% Questions Mthly 5% 12% 1% 7% 15% 0% 1% 15% 7% 0% 17% 2% 0% 7% 6% 2%
1% 1% 100%

10

**Table 3: Northern Kentucky Calibrations and System Checks**

**Test or Calibration Periodicity Notes**

Horiba Zero Calibration Before Test

Hang-Up Check Before Test

\"Before Test\" refers to checks the computer

accomplishes before testing a vehicle.

Position 3 Zero Pressure Check Before Test

Sample System Leak Check 4 Hours

Analyzer 2-pt Span Check 4 Hours

Opacity Daily Calibration Daily

Dyne Warm Up Check Daily

Cap Pressure Leak Check Daily

Tank Pressure Leak Check Daily

Nitrogen Bottle Pressure Check Daily

Span Bottle Pressure Check Daily

Position 3 Gauge Calibration Semimonthly

1-pt Span Check Monthly

5-pt Audit Monthly

"Semimonthly," \"Before Test,\" \"4 Hour,\" and \"Daily\" are

also completed during the monthly calibration routine.

Tail Pipe State Audit Monthly

Dual Hose Sample System Leak Check Monthly

Opacity Span Calibration Monthly

Check Purge Pump for Leaks Monthly

Check RPM Probes for Damage Monthly

Coast Down Check Monthly

Parasitic Losses Calibration Monthly

Dyne Roller Speed Check Monthly

Check All Dyne Bolts Monthly

Horiba Cabinet Pressure Check Monthly

Calibrate CO Monitor Monthly

Calibrate PAU Load Cell Semiannual

Calibrate KB Board Semiannual

\"Semiannuals\" are accomplished around the months of

April and October.

Calibrate Zero and Span Voltages Semiannual

Check and Grease all Dyne Fittings Semiannual

Change Compressor Oil and Filter Semiannual

Change Desiccant Dryer Filters Semiannual

State Audit Scheduled

State audits may be surprise or scheduled in advance.

Change Replaceable Filters As Needed

Clean Reusable Filters As Needed

All filters are checked on a monthly basis but may be

cleaned or replaced at any time.

11

Figure 7:

**Northern Kentucky Emissions** **Check**

**Performance Audit**

***Results key:** 1 -- Fails to meet standards 2 -- Meets standards 3 --
Exceeds standards*

**TEST PROCEDURES (performance)**

Procedure Result Inspector \# Vehicle ID

Greeting 1 2 3

Data Entry 1 2 3

Document Security/Correlate ID 1 2 3

Tampering Inspection 1 2 3

Testing Prep. (hangup check, etc.) 1 2 3

Emission Test 1 2 3

Pressure Test 1 2 3

Info/Comment Cards 1 2 3

Presentation of VIR/Correlate ID 1 2 3

Recordkeeping Practices (in lane) 1 2 3

Inspector Professionalism 1 2 3

**Comments**:

**RECORDS RETENTION (completeness & accuracy)**

Record Type Result

OIP Test Procedures Audits (ESC documentation) 1 2 3

Daily Analyzer calibrations (2-point span, leak & pressure checks) 1 2 3

Performance Audits (ESC Q/A documentation) 1 2 3

**Comments:**

**SAFETY CONDITIONS**

Condition Result

Customers only in approved areas 1 2 3

Fire extinguishers 1 2 3

Walkways 1 2 3

Calibration gas cylinders 1 2 3

CO monitor 1 2 3

**Comments:**

**STATION APPEARANCE**

Station Area Result

Facility grounds 1 2 3

Interior station appearance 1 2 3

Rest rooms 1 2 3

Signs 1 2 3

**Comments:**

Facility Facility \# Lane \# Time Date

Contractor Rep. Auditor

Analyzer calibration (2-point span & leak check) 4-hour results:

12

DAQ issues temporary extensions for vehicles that cannot be made
available for testing by the biennial

testing deadline. Figure 8 shows that of the 3,362 extensions issued for
2003, Boone issued 768 (22.8%),

Campbell issued 730 (21.7%), and Kenton issued 1,864 (55.5%). These
extensions reset the registrationrenewal

block ahead one year. When extension recipients comply with the testing
requirement, the block is

reset ahead another year, returning the vehicle to its normal biennial
testing cycle.

**0**

**1000**

**2000**

**3000**

**4000**

**Boone Campbell Kenton Total**

**Figure 8: Extensions Issued, by Station**

On the other hand, extension violators who have taken advantage of the
extension-triggered block removal

without honoring their commitment to return for a test will have their
registration renewals blocked in their

off-cycle year and will have to pay a \$25 penalty as well as comply
with the testing requirement before they

can renew their registrations in the off-cycle year. Of the 3,362
extensions issued in 2003, 1,145 were

violated (34%), an improvement from 41% in 2002. In 2003, only 430
extension violators returned and

complied with the testing requirement, a slight increase (5.6%) from the
406 that complied in 2002. It is

presumed that the non-compliers sold the vehicle outside the program
area, disposed of it, or continued to

drive it in the program area on the extension-enabled renewed
registration. State law prohibits extension

violators from receiving any further extensions.

Vehicles that are expected to appear for the initial test but never do
are pursued by researching registration

databases and performing parking-lot surveys.

**Improving Repair Effectiveness**

Because repairing failed vehicles could be considered the cornerstone of
the program, improving repair

effectiveness is a major program objective. Northern Kentucky Emissions
Check addresses this objective

through outreach activities, annual repair-industry expositions, and
meetings with a Repair Industry

Advisory Committee. Additionally, a quarterly Repair Effectiveness
Handbook, nearing the final stages of

development by the end of 2003, will provide motorists and
program-oversight personnel with a "report

card" showing success rates for repair shops.

The percentage of initially failing vehicles that passed retests in 2003
was 47.0%, up from 44.3% in 2002

(803 of the 4,646 initial fails did not return for a first retest). This
may indicate that motorists are finding

more skilled repair technicians experienced in repairing emissions
problems and that repair technicians are

becoming more proficient in performing emissions-related repairs.
Another indication of increasing

proficiency within the repair industry is the ratio of appeal waivers
(no emissions improvement) to regular

waivers (some emissions improvement, but not enough to pass). The ratio
of appeal waivers to regular

waivers decreased from 815:536 (or 1.52:1) in 2002 to 666:547 (or
1.22:1) in 2003. It is likely that the

accountability provided by the new Repair Effectiveness Report improved
repair effectiveness in 2003.

13

**Appendices**

A Initial Tests by Vehicle Type and Model Year

B First Retests by Vehicle Type and Model Year

C Subsequent Retests by Vehicle Type and Model Year

D Waiver Activities by Vehicle Type and Model Year

E Hired and Self Repairs by Vehicle Type and Model Year

F 2003 Semiannual Lane Audits by Division for Air Quality

G HC and CO Emission-Reduction Calculations

14

**Appendix A: Initial Tests by Vehicle Type and Model Year**

**Emissions Test Pressure Test Vehicle**

**Type**

**Total**

**Tested**

**Initial Passed %Passed Failed %Failed Passed %Passed Failed %Failed**

HDGT 549 250 88.3 33 11.7 5 45.5 6 54.5

LDGT1 24721 23255 95.1 1203 4.9 3733 88.7 475 11.3

LDGT2 10069 8430 93.0 635 7.0 216 56.5 166 43.5

LDGV 61201 59300 97.3 1647 2.7 6862 91.7 622 8.3

**Total 96540 91235 96.3 3518 3.7 10816 89.5 1269 10.5**

Emissions Test Pressure Test Vehicle

Year

Total

Tested

Initial Passed %Passed Failed %Failed Passed %Passed Failed %Failed

1968 6 5 83.3 1 16.7 0 0.0 0 0.0

1969 77 58 75.3 19 24.7 0 0.0 0 0.0

1970 12 9 75.0 3 25.0 0 0.0 0 0.0

1971 85 63 75.0 21 25.0 0 0.0 0 0.0

1972 13 10 76.9 3 23.1 0 0.0 0 0.0

1973 114 85 74.6 29 25.4 0 0.0 0 0.0

1974 6 3 50.0 3 50.0 0 0.0 0 0.0

1975 72 53 77.9 15 22.1 0 0.0 0 0.0

1976 11 7 63.6 4 36.4 0 0.0 0 0.0

1977 249 199 80.2 49 19.8 0 0.0 0 0.0

1978 34 28 82.4 6 17.6 0 0.0 0 0.0

1979 546 412 79.7 105 20.3 0 0.0 0 0.0

1980 26 17 68.0 8 32.0 0 0.0 0 0.0

1981 266 163 65.5 86 34.5 68 69.4 30 30.6

1982 32 19 63.3 11 36.7 9 81.8 2 18.2

1983 562 373 71.3 150 28.7 166 79.8 42 20.2

1984 93 58 63.7 33 36.3 27 71.1 11 28.9

1985 1624 1208 76.6 368 23.4 498 80.8 118 19.2

1986 231 159 70.4 67 29.6 72 79.1 19 20.9

1987 3115 2561 83.4 511 16.6 993 84.9 177 15.1

1988 331 271 82.6 57 17.4 88 84.6 16 15.4

1989 5124 4500 89.2 544 10.8 1293 87.7 181 12.3

1990 435 366 84.5 67 15.5 93 92.1 8 7.9

1991 6664 6149 93.0 466 7.0 1744 89.1 213 10.9

1992 556 496 90.0 55 10.0 126 94.7 7 5.3

1993 9842 9311 95.7 417 4.3 1825 93.2 133 6.8

1994 666 634 95.6 29 4.4 117 93.6 8 6.4

1995 12717 12256 97.6 300 2.4 1862 93.6 128 6.4

1996 681 669 99.1 6 0.9 67 90.5 7 9.5

1997 14664 14341 99.6 57 0.4 1305 92.4 107 7.6

1998 806 798 99.8 2 0.3 17 70.8 7 29.2

1999 16611 16171 99.9 21 0.1 272 87.7 38 12.3

2000 908 896 99.7 3 0.3 18 90.0 2 10.0

2001 15036 14647 100.0 2 0.0 154 92.2 13 7.8

2002 638 630 100.0 0 0.0 2 66.7 1 33.3

2003 3661 3584 100.0 0 0.0 0 0.0 1 0.0

2004 26 26 100.0 0 0.0 0 0.0 0 0.0

**Total 96540 91235 96.3 3518 3.7 10816 89.5 1269 10.5**

15

**Appendix A: *continued***

**Catalytic Converter Test Opacity Test Tampering Test**

**Passed %Passed Failed %Failed Passed %Passed Failed %Failed Passed
%Passed Failed %Failed**

212 100.0 0 0.0 264 99.2 2 0.8 212 100.0 0 0.0

24352 100.0 1 0.0 240 98.8 3 1.2 24342 99.9 23 0.1

8832 100.0 0 0.0 974 99.2 8 0.8 8823 99.7 23 0.3

60679 100.0 0 0.0 225 98.7 3 1.3 60672 100.0 28 0.0

**94075 99.9 53 0.1 1703 99.1 16 0.9 94049 99.9 74 0.1**

**Catalytic Converter Test Opacity Test Tampering Test**

**Passed %Passed Failed %Failed Passed %Passed Failed %Failed Passed
%Passed Failed %Failed**

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 1 100.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

37 100.0 3 7.5 1 100.0 0 0.0 37 92.5 3 7.5

9 100.0 0 0.0 0 0.0 0 0.0 9 100.0 0 0.0

148 100.0 1 0.7 0 0.0 0 0.0 148 99.3 1 0.0

18 94.7 1 5.3 0 0.0 0 0.0 18 94.7 1 0.0

467 100.0 13 2.7 8 0.0 0 0.0 455 95.2 23 0.0

23 100.0 1 4.2 0 0.0 0 0.0 23 95.8 1 0.0

229 100.0 5 2.1 12 100.0 0 0.0 228 97.9 5 2.1

25 100.0 1 3.8 1 100.0 0 0.0 24 96.0 1 4.0

485 100.0 2 0.4 37 100.0 0 0.0 484 99.6 2 0.4

89 100.0 0 0.0 2 100.0 0 0.0 89 100.0 0 0.0

1515 100.0 3 0.2 42 93.3 3 6.7 1514 99.7 4 0.3

219 100.0 1 0.5 4 100.0 0 0.0 219 99.5 1 0.5

3044 100.0 5 0.2 37 97.4 1 2.6 3044 99.8 5 0.2

328 100.0 2 0.6 1 100.0 0 0.0 328 99.4 2 0.6

5039 100.0 8 0.2 70 98.6 1 1.4 5038 99.8 9 0.2

433 100.0 0 0.0 2 100.0 0 0.0 433 100.0 0 0.0

6615 100.0 0 0.0 49 100.0 0 0.0 6615 100.0 0 0.0

551 100.0 0 0.0 5 100.0 0 0.0 551 100.0 0 0.0

9728 100.0 1 0.0 113 100.0 0 0.0 9727 100.0 2 0.0

663 100.0 0 0.0 2 66.7 1 33.3 663 100.0 0 0.0

12556 100.0 2 0.0 156 99.4 1 0.6 12554 100.0 4 0.0

675 100.0 0 0.0 6 100.0 0 0.0 675 100.0 0 0.0

14397 100.0 2 0.0 259 98.5 4 1.5 14396 100.0 3 0.0

800 100.0 0 0.0 6 100.0 0 0.0 800 100.0 0 0.0

16194 100.0 2 0.0 410 99.0 4 1.0 16190 100.0 6 0.0

899 100.0 0 0.0 9 100.0 0 0.0 899 100.0 0 0.0

14649 100.0 0 0.0 385 99.7 1 0.3 14648 100.0 1 0.0

630 100.0 0 0.0 8 100.0 0 0.0 630 100.0 0 0.0

3584 100.0 0 0.0 77 100.0 0 0.0 3584 100.0 0 0.0

26 100.0 0 0.0 0 0.0 0 0.0 26 100.0 0 0.0

**94075 99.9 53 0.1 1703 99.1 16 0.9 94049 99.9 74 0.1**

16

**Appendix B: First Retests by Vehicle Type and Model Year**

**Emissions Test Pressure Test Vehicle**

**Year**

**Total**

**Tested**

**1st**

**Retests Passed %Passed Failed %Failed Passed %Passed Failed %Failed**

HDGT 40 14 36.8 24 63.2 5 100.0 0 0.0

LDGT1 1329 790 59.6 535 40.4 364 76.3 113 23.7

LDGT2 674 377 56.5 290 43.5 61 71.8 24 28.2

LDGV 1800 1141 63.5 656 36.5 518 86.6 80 13.4

**Total 3843 2322 60.7 1505 39.3 948 81.4 217 18.6**

**Emissions Test Pressure Test Vehicle**

**Year**

**Total**

**Tested**

**1st**

**Retests Passed %Passed Failed %Failed Passed %Passed Failed %Failed**

1968 1 1 100.0 **0** 0.0 0 0.0 0 0.0

1969 14 7 50.0 7 50.0 0 0.0 0 0.0

1970 4 1 25.0 3 75.0 0 0.0 0 0.0

1971 20 6 30.0 14 70.0 0 0.0 0 0.0

1972 3 1 33.3 2 66.7 0 0.0 0 0.0

1973 20 11 55.0 9 45.0 0 0.0 0 0.0

1974 3 1 33.3 2 66.7 0 0.0 0 0.0

1975 11 6 54.5 5 45.5 0 0.0 0 0.0

1976 7 3 42.9 4 57.1 0 0.0 0 0.0

1977 35 13 37.1 22 62.9 0 0.0 0 0.0

1978 10 4 44.4 5 55.6 0 0.0 0 0.0

1979 105 54 51.4 51 48.6 0 0.0 0 0.0

1980 6 2 33.3 4 66.7 0 0.0 0 0.0

1981 81 32 39.5 49 60.5 23 79.3 6 20.7

1982 18 3 16.7 15 83.3 3 50.0 3 50.0

1983 125 57 45.6 68 54.4 27 84.4 5 15.6

1984 63 26 41.9 36 58.1 19 79.2 5 20.8

1985 340 182 53.8 156 46.2 102 76.1 32 23.9

1986 83 38 45.8 45 54.2 31 86.1 5 13.9

1987 506 276 54.7 229 45.3 152 74.1 53 25.9

1988 76 34 44.7 42 55.3 20 87.0 3 13.0

1989 531 332 62.6 198 37.4 160 86.0 26 14.0

1990 80 42 52.5 38 47.5 24 88.9 3 11.1

1991 516 337 65.3 179 34.7 174 77.7 50 22.3

1992 92 59 64.1 33 35.9 24 80.0 6 20.0

1993 424 285 67.4 138 32.6 87 87.9 12 12.1

1994 52 28 54.9 23 45.1 12 100.0 0 0.0

1995 360 255 71.0 104 29.0 71 91.0 7 9.0

1996 13 11 84.6 2 15.4 2 100.0 0 0.0

1997 152 134 89.9 15 10.1 13 92.9 1 7.1

1998 9 8 88.9 1 11.1 0 0.0 0 0.0

1999 60 52 91.2 5 8.8 3 100.0 0 0.0

2000 5 5 100.0 0 0.0 1 100.0 0 0.0

2001 16 14 93.3 1 6.7 0 0.0 0 0.0

2002 1 1 100.0 0 0.0 0 0.0 0 0.0

2003 1 1 100.0 0 0.0 0 0.0 0 0.0

**Total 3843 2322 60.7 1505 39.3 948 81.4 217 18.6**

17

**Appendix B: continued**

**Catalytic Converter Test Opacity Test Tampering Test**

**Passed %Passed Failed %Failed Passed %Passed Failed %Failed Passed
%Passed Failed %Failed**

19 100.0 0 0.0 2 100.0 0 0.0 19 100.0 0 0.0

1302 100.0 0 0.0 3 100.0 0 0.0 1299 99.9 1 0.0

607 99.8 1 0.2 6 100.0 0 0.0 605 99.7 2 0.3

1743 99.9 1 0.1 2 100.0 0 0.0 1741 99.9 2 0.0

**3671 99.9 2 0.1 13 100.0 0 0.0 3664 99.9 5 0.1**

**Catalytic Converter Test Opacity Test Tampering Test**

**Passed %Passed Failed %Failed Passed %Passed Failed %Failed Passed
%Passed Failed %Failed**

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

37 92.5 3 7.5 0 0.0 0 0.0 7 100.0 0 0.0

9 100.0 0 0.0 0 0.0 0 0.0 2 100.0 0 0.0

148 99.3 1 0.7 0 0.0 0 0.0 22 100.0 0 0.0

18 94.7 1 5.3 0 0.0 0 0.0 4 80.0 1 0.0

467 97.3 13 2.7 0 0.0 0 0.0 89 98.9 1 0.0

23 95.8 1 4.2 0 0.0 0 0.0 5 100.0 0 0.0

229 97.9 5 2.1 0 0.0 0 0.0 76 100.0 0 0.0

25 96.2 1 3.8 0 0.0 0 0.0 16 100.0 0 0.0

485 99.6 2 0.4 0 0.0 0 0.0 111 100.0 0 0.0

89 100.0 0 0.0 0 0.0 0 0.0 61 98.4 1 0.0

1515 99.8 3 0.2 2 100.0 0 0.0 321 100.0 0 0.0

219 99.5 1 0.5 0 0.0 0 0.0 81 100.0 0 0.0

3044 99.8 5 0.2 1 100.0 0 0.0 497 100.0 0 0.0

328 99.4 2 0.6 0 0.0 0 0.0 76 100.0 0 0.0

5039 99.8 8 0.2 1 100.0 0 0.0 527 100.0 0 0.0

433 100.0 0 0.0 0 0.0 0 0.0 80 100.0 0 0.0

6615 100.0 0 0.0 0 0.0 0 0.0 516 100.0 0 0.0

551 100.0 0 0.0 0 0.0 0 0.0 92 100.0 0 0.0

9728 100.0 1 0.0 0 0.0 0 0.0 423 99.8 1 0.0

663 100.0 0 0.0 1 100.0 0 0.0 51 100.0 0 0.0

12556 100.0 2 0.0 1 100.0 0 0.0 359 100.0 0 0.0

675 100.0 0 0.0 0 0.0 0 0.0 13 100.0 0 0.0

14397 100.0 2 0.0 3 100.0 0 0.0 149 100.0 0 0.0

800 100.0 0 0.0 0 0.0 0 0.0 9 100.0 0 0.0

16194 100.0 2 0.0 3 100.0 0 0.0 57 100.0 0 0.0

899 100.0 0 0.0 0 0.0 0 0.0 5 100.0 0 0.0

14649 100.0 0 0.0 1 100.0 0 0.0 13 92.9 1 7.1

630 100.0 0 0.0 0 0.0 0 0.0 1 100.0 0 0.0

3584 100.0 0 0.0 0 0.0 0 0.0 1 100.0 0 0.0

**26 100.0 0 0.0 13 100.0 0 0.0 3664 99.9 5 0.1**

18

**Appendix C: Subsequent Retests by Vehicle Type and Model Year**

**Emissions Test Pressure Test Vehicle**

**Type**

**Total**

**Tested**

**2+**

**Retests Passed %Passed Failed %Failed Passed %Passed Failed %Failed**

HDGT 5 3 60.0 2 40.0 0 0.0 2 100.0

LDGT1 220 118 53.6 102 46.4 71 71.0 29 29.0

LDGT2 112 53 47.3 59 52.7 3 30.0 7 70.0

LDGV 215 110 51.2 105 48.8 74 85.1 13 14.9

**Total 552 284 51.4 268 48.6 148 74.4 51 25.6**

**Emissions Test Pressure Test Vehicle**

**Year**

**Total**

**Tested**

**2+**

**Retests Passed %Passed Failed %Failed Passed %Passed Failed %Failed**

1968 **0** 0 0.0 0 0.0 0 0.0 0 0.0

1969 1 0 0.0 1 100.0 0 0.0 0 0.0

1970 0 0 0.0 0 0.0 0 0.0 0 0.0

1971 0 0 0.0 0 0.0 0 0.0 0 0.0

1972 2 0 0.0 0 0.0 0 0.0 0 0.0

1973 0 1 0.0 1 0.0 0 0.0 0 0.0

1974 0 0 0.0 0 0.0 0 0.0 0 0.0

1975 0 0 0.0 0 0.0 0 0.0 0 0.0

1976 3 0 0.0 0 0.0 0 0.0 0 0.0

1977 0 2 66.7 1 33.3 0 0.0 0 0.0

1978 17 0 0.0 0 0.0 0 0.0 0 0.0

1979 0 9 52.9 8 47.1 0 0.0 0 0.0

1980 12 0 0.0 0 0.0 0 0.0 0 0.0

1981 5 4 33.3 8 66.7 1 100.0 0 0.0

1982 26 0 0.0 5 100.0 2 66.7 1 33.3

1983 16 5 19.2 21 80.8 7 87.5 1 12.5

1984 48 6 37.5 10 62.5 5 83.3 1 16.7

1985 22 27 56.3 21 43.8 10 58.8 7 41.2

1986 93 8 36.4 14 63.6 11 91.7 1 8.3

1987 33 54 58.1 39 41.9 35 72.9 13 27.1

1988 60 16 48.5 17 51.5 9 75.0 3 25.0

1989 9 30 50.0 30 50.0 16 72.7 6 27.3

1990 73 5 55.6 4 44.4 2 100.0 0 0.0

1991 23 50 68.5 23 31.5 29 78.4 8 21.6

1992 53 11 47.8 12 52.2 4 44.4 5 55.6

1993 11 24 45.3 29 54.7 11 84.6 2 15.4

1994 32 7 63.6 4 36.4 5 100.0 0 0.0

1995 6 18 56.3 14 43.8 1 100.0 0 0.0

1996 6 4 66.7 2 33.3 0 0.0 2 100.0

1997 0 2 33.3 4 66.7 0 0.0 1 0.0

1998 1 0 0.0 0 0.0 0 0.0 0 0.0

1999 0 1 0.0 0 0.0 0 0.0 0 0.0

2000 0 0 0.0 0 0.0 0 0.0 0 0.0

2001 0 0 0.0 0 0.0 0 0.0 0 0.0

**Total 552 284 51.4 268 48.6 148 74.4 51 25.6**

19

**Appendix C: *continued***

**Catalytic Converter Test Opacity Test Tampering Test**

**Passed %Passed Failed %Failed Passed %Passed Failed %Failed Passed
%Passed Failed %Failed**

4 100.0 0 0.0 0 0.0 0 0.0 4 100.0 0 0.0

219 100.0 0 0.0 0 0.0 0 0.0 219 100.0 0 0.0

103 100.0 0 0.0 0 0.0 0 0.0 103 100.0 0 0.0

211 100.0 0 0.0 0 0.0 0 0.0 211 100.0 0 0.0

**537 100.0 0 0.0 0** 0.0 **0 0.0 537** 100.0 **0 0.0**

**Catalytic Converter Test Opacity Test Tampering Test**

**Passed %Passed Failed %Failed Passed %Passed Failed %Failed Passed
%Passed Failed %Failed**

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

2 100.0 0 0.0 0 0.0 0 0.0 2 100.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

14 100.0 0 0.0 0 0.0 0 0.0 14 100.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

12 100.0 0 0.0 0 0.0 0 0.0 12 100.0 0 0.0

5 100.0 0 0.0 0 0.0 0 0.0 5 100.0 0 0.0

23 100.0 0 0.0 0 0.0 0 0.0 23 100.0 0 0.0

15 100.0 0 0.0 0 0.0 0 0.0 15 100.0 0 0.0

45 100.0 0 0.0 0 0.0 0 0.0 45 100.0 0 0.0

22 100.0 0 0.0 0 0.0 0 0.0 22 100.0 0 0.0

92 100.0 0 0.0 0 0.0 0 0.0 92 100.0 0 0.0

33 100.0 0 0.0 0 0.0 0 0.0 33 100.0 0 0.0

60 100.0 0 0.0 0 0.0 0 0.0 60 100.0 0 0.0

9 100.0 0 0.0 0 0.0 0 0.0 9 100.0 0 0.0

73 100.0 0 0.0 0 0.0 0 0.0 73 100.0 0 0.0

23 100.0 0 0.0 0 0.0 0 0.0 23 100.0 0 0.0

53 100.0 0 0.0 0 0.0 0 0.0 53 100.0 0 0.0

11 100.0 0 0.0 0 0.0 0 0.0 11 100.0 0 0.0

32 100.0 0 0.0 0 0.0 0 0.0 32 100.0 0 0.0

6 100.0 0 0.0 0 0.0 0 0.0 6 100.0 0 0.0

6 100.0 0 0.0 0 0.0 0 0.0 6 100.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

1 0.0 0 0.0 0 0.0 0 0.0 1 100.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

**537 100.0 0 0.0 0 0.0 0 0.0 537 100.0 0 0.0**

20

**Appendix D: Waiver Activities by Vehicle Type and Model Year**

**Total Initial Failures Waivers Granted Vehicle Type**

**Total Tested Total Failed % Failed Initial Total Granted % of Total
Initial**

HDGT 549 38 6.9 24 4.4

LDGT1 24721 1606 6.5 427 1.7

LDGT2 10069 787 7.8 233 2.3

LDGV 61201 2215 3.6 529 0.9

**Total 96540 4646 4.8 1213 1.3**

**Total Initial Failures Waivers Granted Vehicle Year**

**Total Tested Total Failed % Failed Initial Total Granted % of Total
Initial**

1968 6 1 16.7 **0** 0.0

1969 77 19 24.7 6 7.8

1970 12 3 25.0 2 16.7

1971 85 21 24.7 11 12.9

1972 13 3 23.1 2 15.4

1973 114 29 25.4 5 4.4

1974 6 3 50.0 1 16.7

1975 72 18 25.0 4 5.6

1976 11 4 36.4 4 36.4

1977 249 50 20.1 18 7.2

1978 34 6 17.6 4 11.8

1979 546 126 23.1 33 6.0

1980 26 9 34.6 4 15.4

1981 266 106 39.8 38 14.3

1982 32 12 37.5 15 46.9

1983 562 170 30.2 49 8.7

1984 93 41 44.1 34 36.6

1985 1624 452 27.8 124 7.6

1986 231 80 34.6 43 18.6

1987 3115 651 20.9 171 5.5

1988 331 71 21.5 40 12.1

1989 5124 701 13.7 149 2.9

1990 435 71 16.3 39 9.0

1991 6664 652 9.8 147 2.2

1992 556 59 10.6 33 5.9

1993 9842 536 5.4 114 1.2

1994 666 38 5.7 20 3.0

1995 12717 429 3.4 76 0.6

1996 681 13 1.9 5 0.7

1997 14664 171 1.2 15 0.1

1998 806 9 1.1 1 0.1

1999 16611 68 0.4 4 0.0

2000 908 5 0.6 1 0.1

2001 15036 17 0.1 1 0.0

2002 638 1 0.2 0 0.0

2003 3661 1 0.0 0 0.0

2004 26 0 0.0 0 0.0

**Total 96540 4646 4.8 1213 1.3**

21

**Appendix E: Hired and Self Repairs by Vehicle Type and Model Year**

**Self Repairs Hired Repairs**

**Vehicle**

**Type**

**Self repair**

**count**

**Total cost**

**for parts**

**Average**

**cost (parts)**

**Hired repair**

**count**

**Total cost**

**for parts**

**Average**

**cost (parts)**

**Total cost**

**for labor**

**Average**

**cost (labor)**

HDGT 17 1557 92 26 2928 113 3351 129

LDGT1 743 54271 73 881 125130 142 110340 125

LDGT2 369 37388 101 462 72443 157 57028 123

LDGV 934 52420 56 1188 155934 131 143911 121

**Total 2063 \$145,636 \$71 2557 \$356,435 \$139 \$314,630 \$123**

**Self Repairs Hired Repairs**

**Vehicle**

**Year**

**Self repair**

**count**

**Total cost**

**for parts**

**Average**

**cost (parts)**

**Hired repair**

**count**

**Total cost**

**for parts**

**Average**

**cost (parts)**

**Total cost**

**for labor**

**Average**

**cost (labor)**

1968 1 50 50 0 0 0 0 0

1969 14 850 61 6 156 26 390 65

1970 3 338 113 0 0 0 0 0

1971 10 544 54 12 637 53 1135 95

1972 3 262 87 1 24 24 74 74

1973 15 674 45 12 1296 108 1097 91

1974 0 0 0 2 96 48 255 128

1975 6 329 55 7 471 67 453 65

1976 3 305 102 4 632 158 564 141

1977 17 1804 106 23 1977 86 2215 96

1978 5 608 122 5 454 91 467 93

1979 71 3632 51 62 4989 80 7373 119

1980 6 347 58 4 105 26 268 67

1981 44 4358 99 53 6765 128 6066 114

1982 7 958 137 18 2369 132 2756 153

1983 67 5113 76 97 9173 95 11326 117

1984 42 9459 225 42 5675 135 4707 112

1985 182 12892 71 246 30984 126 30238 123

1986 48 5196 108 70 8806 126 10089 144

1987 275 20673 75 365 51414 141 39546 108

1988 44 5956 135 70 10466 150 7171 102

1989 259 18663 72 361 45275 125 42642 118

1990 34 3004 88 59 15412 261 8074 137

1991 245 13961 57 359 50310 140 45018 125

1992 45 7453 166 75 10090 135 10035 134

1993 207 12087 58 289 47074 163 38499 133

1994 26 1750 67 32 6079 190 6210 194

1995 193 8235 43 208 36263 174 29129 140

1996 9 2347 261 8 995 124 380 48

1997 119 2545 21 39 5159 132 5437 139

1998 6 45 8 2 238 119 330 165

1999 40 1040 26 20 2884 144 2489 124

2000 2 60 30 3 155 52 136 45

2001 14 89 6 2 12 6 61 31

2002 1 9 9 0 0 0 0 0

2003 0 0 0 1 0 0 0 0

**Total 2,063 \$145,636 \$71 2557 \$356,435 \$139 \$314,630 \$123**

22

**Appendix F: 2003 Semiannual Lane Audits by Division for Air Quality**

**Station/Lane**

**Date**

**2-Point Gas Calibration**

**Sample Leak Check**

**State Audit Check HC**

**State Audit Check CO**

**State Audit Check CO2**

**Roll Speed Check**

**Para. Loss & Coastdown**

**Opacity Check**

**Pressure System**

**Total Pass**

**Total Fail**

**Percent Tests Passed**

61/1 7/21/03 P P P P P P P P P 9 0 100%

61/2 7/21/03 P P P P P P P P P 9 0 100%

61/3 7/21/03 P P P P P P P P P 9 0 100%

61/4 7/21/03 P P P P P P P P P 9 0 100%

62/1 7/16/03 P P P P P P P P P 9 0 100%

62/2 7/16/03 P P P P P P P P P 9 0 100%

63/1 6/25/03 P P P P P P P P P 9 0 100%

63/2 6/25/03 P P P P P P P P P 9 0 100%

63/3 6/25/03 P P P P P P P P P 9 0 100%

63/4 6/25/03 P P P P P P P P P 9 0 100%

63/5 6/25/03 P P P P P P P P P 9 0 100%

61/1 12/11/03 P P P P P P P P P 9 0 100%

61/2 12/11/03 P P P P P P P P P 9 0 100%

61/3 12/11/03 P P P P P P P P P 9 0 100%

61/4 12/11/03 P P P P P P P P P 9 0 100%

62/1 12/11/03 P P P P P P P P P 9 0 100%

62/2 12/11/03 P P P P P P P P P 9 0 100%

63/1 12/16/03 P P P P P P P P P 9 0 100%

63/2 1/8/04 P P P P P P P P P 9 0 100%

63/3 12/19/03 P P P P P P P P P 9 0 100%

63/4 12/30/03 P P P P P P P P P 9 0 100%

63/5 12/16/03 P P P P P P P P P 9 0 100%

**Total Passed** 22 22 22 22 22 22 22 22 22 198 100%

**Total Failed** 0 0 0 0 0 0 0 0 0 0 0%

23

**Appendix G: HC and CO Emission-Reduction Calculations**

**Overall Hydrocarbon (HC) Improvements**

**Vehicle Year Initial HC Final HC Avg. Initial HC Avg. Final HC Tests
Avg. HC Diff**

1969 12816.0 20350.6 2136.0 3391.8 6 -1255.8

1970 16621.3 6418.6 4155.3 1604.7 4 2550.7

1971 38104.4 39439.7 2721.7 2817.1 14 -95.4

1972 4582.3 3460.4 2291.2 1730.2 2 561.0

1973 30121.1 20121.5 2738.3 1829.2 11 909.1

1974 2822.1 2948.4 1411.1 1474.2 2 -63.2

1975 17390.5 17567.4 3478.1 3513.5 5 -35.4

1976 14750.0 3793.1 2950.0 758.6 5 2191.4

1977 37847.8 34096.8 1992.0 1794.6 19 197.4

1978 14318.4 8815.1 2045.5 1259.3 7 786.2

1979 58966.3 54475.0 1371.3 1266.9 43 104.4

1980 9107.9 10850.0 1518.0 1808.3 6 -290.4

1981 54191.3 39467.1 1231.6 897.0 44 334.6

1982 14749.4 11099.3 1340.9 1009.0 11 331.8

1983 81629.5 63116.0 1218.4 942.0 67 276.3

1984 47528.3 47655.3 1131.6 1134.7 42 -3.0

1985 146573.5 133501.2 916.1 834.4 160 81.7

1986 62763.9 54973.5 1082.1 947.8 58 134.3

1987 211383.5 183177.8 785.8 681.0 269 104.9

1988 46617.8 38975.6 764.2 638.9 61 125.3

1989 255759.8 151391.2 979.9 580.0 261 399.9

1990 44448.9 29819.6 926.0 621.2 48 304.8

1991 167520.1 121492.7 698.0 506.2 240 191.8

1992 51662.7 41557.6 974.8 784.1 53 190.7

1993 162613.9 102368.2 785.6 494.5 207 291.0

1994 29543.7 19037.5 923.2 594.9 32 328.3

1995 201020.9 76303.5 1386.4 526.2 145 860.1

1996 11328.7 2562.3 1888.1 427.1 6 1461.1

1997 23343.7 18910.7 972.7 787.9 24 184.7

1998 1380.4 1709.4 690.2 854.7 2 -164.5

1999 22028.0 3638.8 1573.4 259.9 14 1313.5

2000 1592.3 193.2 796.2 96.6 2 699.6

**Totals: 1895128.4 1363287.1 1013.4 729.0 1870 284.4**

**Average HC Improvement: 284.4**

**Average % Improvement: 71.9%**

24

**Overall Carbon Monoxide (CO) Improvement Calculations**

**Vehicle Year Initial CO Final CO Avg. Initial CO Avg. Final CO Tests
Avg. CO Diff**

1968 9.83 7.31 9.8 7.3 1 2.5

1969 55.39 42.19 9.2 7.0 6 2.2

1970 11.49 5.09 11.5 5.1 1 6.4

1971 30.25 17.97 10.1 6.0 3 4.1

1972 17.93 16.59 9.0 8.3 2 0.7

1973 158.16 103.15 9.9 6.4 16 3.4

1975 40.81 29.19 8.2 5.8 5 2.3

1976 46.36 31.83 9.3 6.4 5 2.9

1977 153.61 99.81 9.6 6.2 16 3.4

1978 48.6 19.96 8.1 3.3 6 4.8

1979 401.51 228.79 7.6 4.3 53 3.3

1980 31.18 15.88 6.2 3.2 5 3.1

1981 274.15 123.89 5.8 2.6 47 3.2

1982 64.29 69.24 4.9 5.3 13 -0.4

1983 336.97 233.73 5.3 3.7 64 1.6

1984 177.66 114.46 5.6 3.6 32 2.0

1985 632.26 377.33 4.5 2.7 140 1.8

1986 206.91 125.26 4.9 3.0 42 1.9

1987 841.11 466.29 4.5 2.5 185 2.0

1988 190.88 84.75 4.7 2.1 41 2.6

1989 1041.58 446.3 5.2 2.2 201 3.0

1990 129.33 68.32 4.5 2.4 29 2.1

1991 870.3 364.14 4.9 2.0 179 2.8

1992 180.29 82.03 5.0 2.3 36 2.7

1993 867.45 328.78 5.7 2.1 153 3.5

1994 95.93 43.39 6.4 2.9 15 3.5

1995 376.06 117.64 4.3 1.4 87 3.0

1996 35.3 17.84 5.0 2.5 7 2.5

1997 86.48 44.37 4.1 2.1 21 2.0

1998 1.37 0.31 1.4 0.3 1 1.1

1999 86.6 3.22 7.9 0.3 11 7.6

2000 22.77 0.5 11.4 0.3 2 11.1

2001 2.14 1.86 2.1 1.9 1 0.3

**Totals: 7524.95 3731.41 5.3 2.6 1426 2.7**

**Average CO Improvement: 2.7**

**Average % Improvement: 49.6%**

25

**First Retest Hydrocarbon (HC) Calculations**

**Vehicle Year Initial HC Final HC AVG Initial HC AVG Final HC Tests AVG
HC Diff**

1969 11210.1 16656.5 2242.0 3331.3 5 -1089.3

1970 16621.3 6418.6 4155.3 1604.7 4 2550.7

1971 38104.4 39439.7 2721.7 2817.1 14 -95.4

1972 4582.3 3460.4 2291.2 1730.2 2 561.0

1973 27789 16585.8 2778.9 1658.6 10 1120.3

1974 2822.1 2948.4 1411.1 1474.2 2 -63.2

1975 17390.5 17567.4 3478.1 3513.5 5 -35.4

1976 14750 3793.1 2950.0 758.6 5 2191.4

1977 36909.2 33991 2050.5 1888.4 18 162.1

1978 14318.4 8815.1 2045.5 1259.3 7 786.2

1979 54203.3 49954.9 1389.8 1280.9 39 108.9

1980 9107.9 10850 1518.0 1808.3 6 -290.4

1981 52262.1 38280.6 1306.6 957.0 40 349.5

1982 12257.3 7830.1 1751.0 1118.6 7 632.5

1983 66744.6 49936.1 1259.3 942.2 53 317.1

1984 37154.3 42090.9 1125.9 1275.5 33 -149.6

1985 131971.4 122704.6 916.5 852.1 144 64.4

1986 51800.9 46071.6 1102.1 980.2 47 121.9

1987 181337.9 156049.3 798.8 687.4 227 111.4

1988 32845.1 26809 763.8 623.5 43 140.4

1989 234966 130470.6 1035.1 574.8 227 460.3

1990 43344.4 27702.1 942.3 602.2 46 340.1

1991 154295.8 112359.3 727.8 530.0 212 197.8

1992 44172.3 31445 1077.4 767.0 41 310.4

1993 130366.5 82387.5 740.7 468.1 176 272.6

1994 25427.9 15868.2 941.8 587.7 27 354.1

1995 173535.8 63610.3 1345.2 493.1 129 852.1

1996 10760.4 2162.1 2690.1 540.5 4 2149.6

1997 21470.8 17485.8 1073.5 874.3 20 199.3

1998 1380.4 1709.4 690.2 854.7 2 -164.5

1999 22028 3638.8 1573.4 259.9 14 1313.5

2000 1592.3 193.2 796.2 96.6 2 699.6

**Totals: 1677522.7 1189285.4 1041.3 738.2 1611 303.1**

**Average HC Improvement: 303.1**

**Average % Improvement: 70.9%**

26

**First Retest Carbon Monoxide (CO) Calculations**

**Vehicle Year Initial CO Final CO Avg. Initial CO Avg. Final CO Tests
Avg. CO Diff**

1968 9.83 7.31 9.8 7.3 1 2.5

1969 55.39 42.19 9.2 7.0 6 2.2

1970 11.49 5.09 11.5 5.1 1 6.4

1971 30.25 17.97 10.1 6.0 3 4.1

1972 17.93 16.59 9.0 8.3 2 0.7

1973 136.28 87.42 9.7 6.2 14 3.5

1975 40.81 29.19 8.2 5.8 5 2.3

1976 46.36 31.83 9.3 6.4 5 2.9

1977 135.94 87.36 9.7 6.2 14 3.5

1978 48.6 19.96 8.1 3.3 6 4.8

1979 363.5 207.05 7.6 4.3 48 3.3

1980 31.18 15.88 6.2 3.2 5 3.1

1981 223.95 91.9 6.2 2.6 36 3.7

1982 40.39 42.85 5.0 5.4 8 -0.3

1983 271.44 178.58 5.1 3.4 53 1.8

1984 143.7 103.39 5.5 4.0 26 1.6

1985 528.76 311.09 4.5 2.7 117 1.9

1986 170.79 105.67 4.9 3.0 35 1.9

1987 717.98 398.12 4.6 2.6 156 2.1

1988 158.23 59.3 5.1 1.9 31 3.2

1989 971.69 418.02 5.3 2.3 185 3.0

1990 126.99 67.12 4.5 2.4 28 2.1

1991 777.06 317.34 5.0 2.1 154 3.0

1992 146.38 69.15 4.9 2.3 30 2.6

1993 750.86 259.15 5.6 1.9 134 3.7

1994 88.33 42.98 6.8 3.3 13 3.5

1995 340.95 106.22 4.2 1.3 82 2.9

1996 24.7 4.41 6.2 1.1 4 5.1

1997 82.59 44.37 4.1 2.2 20 1.9

1998 1.37 0.31 1.4 0.3 1 1.1

1999 86.6 3.22 7.9 0.3 11 7.6

2000 22.77 0.5 11.4 0.3 2 11.1

2001 2.14 1.86 2.1 1.9 1 0.3

**Totals: 6605.23 3193.39 5.3 2.6 1237 2.8**

**Average CO Improvement: 2.8**

**Average % Improvement: 48.3%**

27

**Subsequent Retest Hydrocarbon (HC) Calculations**

**Vehicle Year Initial HC Final HC Avg. Initial HC Avg. Final HC Tests
Avg. HC Diff**

1969 1605.9 3694.1 1605.9 3694.1 1 -2088.2

1973 2332.1 3535.7 2332.1 3535.7 1 -1203.6

1977 938.6 105.8 938.6 105.8 1 832.8

1979 4763 4520.1 1190.8 1130.0 4 60.7

1981 1929.2 1186.5 482.3 296.6 4 185.7

1982 2492.1 3269.2 623.0 817.3 4 -194.3

1983 14884.9 13179.9 1063.2 941.4 14 121.8

1984 10374 5564.4 1152.7 618.3 9 534.4

1985 14602.1 10796.6 912.6 674.8 16 237.8

1986 10963 8901.9 996.6 809.3 11 187.4

1987 30045.6 27128.5 715.4 645.9 42 69.5

1988 13772.7 12166.6 765.2 675.9 18 89.2

1989 20793.8 20920.6 611.6 615.3 34 -3.7

1990 1104.5 2117.5 552.3 1058.8 2 -506.5

1991 13224.3 9133.4 472.3 326.2 28 146.1

1992 7490.4 10112.6 624.2 842.7 12 -218.5

1993 32247.4 19980.7 1040.2 644.5 31 395.7

1994 4115.8 3169.3 823.2 633.9 5 189.3

1995 27485.1 12693.2 1717.8 793.3 16 924.5

1996 568.3 400.2 284.2 200.1 2 84.1

1997 1872.9 1424.9 468.2 356.2 4 112.0

**Totals: 217605.7 174001.7 840.2 671.8 259 168.4**

**Average HC Improvement: 168.4**

**Average % Improvement: 80.0%**

28

**Subsequent Retest Carbon Monoxide (CO) Calculations**

**Vehicle Year Initial CO Final CO Avg. Initial CO Initial Final CO
Tests Avg. CO Diff**

1973 21.9 15.7 10.9 7.9 2 3.1

1977 17.7 12.5 8.8 6.2 2 2.6

1979 38.0 21.7 7.6 4.3 5 3.3

1981 50.2 32.0 4.6 2.9 11 1.7

1982 23.9 26.4 4.8 5.3 5 -0.5

1983 65.5 55.2 6.0 5.0 11 0.9

1984 34.0 11.1 5.7 1.8 6 3.8

1985 103.5 66.2 4.5 2.9 23 1.6

1986 36.1 19.6 5.2 2.8 7 2.4

1987 123.1 68.2 4.2 2.4 29 1.9

1988 32.7 25.5 3.3 2.5 10 0.7

1989 69.9 28.3 4.4 1.8 16 2.6

1990 2.3 1.2 2.3 1.2 1 1.1

1991 93.2 46.8 3.7 1.9 25 1.9

1992 33.9 12.9 5.7 2.1 6 3.5

1993 116.6 69.6 6.1 3.7 19 2.5

1994 7.6 0.4 3.8 0.2 2 3.6

1995 35.1 11.4 7.0 2.3 5 4.7

1996 10.6 13.4 3.5 4.5 3 -0.9

1997 3.9 0.0 3.9 0.0 1 3.9

**Totals: 919.7 538.0 4.9 2.8 189 2.0**

**Average CO Improvement: 2.0**

**Average % Improvement: 58.5%**

**Northern Kentucky Emissions** **Check**

**2002 Annual Report**

***Prepared by Kentucky Division for Air Quality***

**August 2003**

2

**Table of Contents**

Tables and Figures
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....3

Executive Summary
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....4

Emissions Check Program Background and
Overview\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...5

Operations Statistics
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...6

Test Activities
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...6

Test Center Activities
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....7

Vehicle Repair
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...7

Referee Activity
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...8

Customer Service Activities
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....8

Quality Assurance/Quality
Control\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....8

Quality Assurance: Calibrations and
Checks\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....8

Equipment
Audits\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....8

Enforcement
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...8

Improving Repair
Effectiveness\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....12

Appendices
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....13

A Initial Tests by Vehicle Type and Model
Year\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....14

B First Retests by Vehicle Type and Model
Year\...\...\...\...\...\...\...\...\...\...\...\...\...\.....16

C Subsequent Retests by Vehicle Type and Model Year
\...\...\...\...\...\...\...\...\...\.....18

D Waiver Activities by Vehicle Type and Model Year
\...\...\...\...\...\...\...\...\...\...\...\.....20

E Hired and Self Repairs by Vehicle Type and Model
Year\...\...\...\...\...\...\...\...\.....21

F Semiannual Lane Audits by Division for Air Quality
\...\...\...\...\...\...\...\...\...\...\...\....22

G HC and CO Emission-Reduction Calculations
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...23

3

**Tables and Figures**

**Tables**

Table 1: Overall Testing
Activity\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...6

Table 2: Hotline Call Report
2002\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...9

Table 3: Northern Kentucky Calibrations and System Checks
\...\...\...\...\...\...\...\...\...\...\...\.....10

**Figures**

Figure 1: Initial Test Results, All
Parameters\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....6

Figure 2: Emissions Fail Breakdown by Pollutant
\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....6

Figure 3: Pass/Fail Breakdown, by Vehicle
Type\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....7

Figure 4: Pass/Fail Breakdown, by
Station\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....7

Figure 5: Average Repair
Costs\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....7

Figure 6: Waivers & Appeal Waivers, by
Station\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...8

Figure 7: Northern Kentucky Emissions Check Performance Audit
\...\...\...\...\...\...\...\...\....11

Figure 8: Extensions Issued, by
Station\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....12

4

**Executive Summary**

The United States Environmental Protection Agency (USEPA) requires
annual reports from Vehicle

Inspection/Maintenance (I/M) programs, pursuant to 40 CFR 51.366. This
report for calendar year 2002

focuses on program statistics, quality assurance and quality control
(QA/QC), and enforcement.

The Kentucky Division for Air Quality (DAQ) administers the centralized,
"improved basic" Northern

Kentucky Emissions Check Program in the Cincinnati-airshed counties of
Boone, Campbell, and Kenton,

with one testing station in each county. DAQ personnel maintain offices
in each of the three stations to

perform oversight and referee functions. The testing contractor is
Envirotest Systems Corp. (ESC).

Enforcement is based on denial of vehicle-registration renewals.

Biennial testing of the area's approximately 190,000 1968-and-newer
vehicles began September 1, 1999.

For gasoline-fueled vehicles, the tests include analysis of idle
emissions of hydrocarbons (HC) and carbon

monoxide (CO), an evaporative-system pressure check (1981 and newer
model years), and an antitampering

check. For diesel vehicles, an opacity standard of 10% is in place.

The Emissions Check program performed a total of 98,174 tests on 93,900
vehicles in 2002. The fail rate

for initial tests during the year was 5.1%; 2114 (44.4%) of the 4757
initial fails passed subsequent retests,

and 1351 (28.4%) were issued repair-cost waivers for having spent the
repair \"cap\" (\$75 for 1980-and-older

vehicles, \$200 for 1981-and-newer vehicles) but still not passing the
test. The overall waiver rate was 1.4%

for 2002. Fail rates for pre-1981 vehicles averaged 23.3%; for 1981 and
newer vehicles, 4.8%. Of the 4757

initial fails, 4082 (88.1%) were emissions fails, 541 (11.4%) were
evaporative system fails, 309 (1.5%) were

tampering fails, and 6 (0.1%) were opacity fails on diesel-fueled
vehicles. (Totals exceed 100% because of

multi-parameter fails.)

Emissions reductions in a basic program such as this cannot be
accurately quantified in terms of amounts

reduced. Nevertheless, it can be reported that emissions for all
repaired vehicles improved 37.83% for HC

and 48.74% for CO (see Appendix G).

Envirotest's quality-control protocols for 2002, including all
calibrations performed and their frequency, are

listed in Table 3. DAQ's quality-assurance protocols for 2002 included
equipment audits in each of the

program's 11 lanes (see Appendix F). DAQ performed overt performance
audits in 2002 to review staff and

test procedures, per 40 CFR 51.363(a)(3). A contract for covert auditing
services, to begin in February

2003, was signed in 2002 with de la Torre Klausmeier Consulting, Inc.
(dKC). Under the contract, dKC is to

determine how Envirotest can maintain or improve services by grading
inspectors' ability to enter data,

perform testing procedures, and explain to motorists any issues
concerning vehicle failure.

Public education and outreach are important elements of the Emissions
Check program. In 2002,

awareness of the program and its benefits for public health and the
environment were promoted by

collaboration between DAQ and Envirotest through community partnerships
and public relations such as:

A 24-hour toll-free hotline to answer customers' questions

Informational posters and pamphlets at County Clerks\' offices to
promote compliance

A quarterly Repair Effectiveness Handbook that rates repair shops\'
handling of test failures

A section in the Kentucky Drivers Manual that explains vehicle
emissions and ozone

Sponsorship of the annual Clean Air-a-thon organized by the Regional
Ozone Coalition

Participation in quarterly meetings of a Repair Advisory Committee

A monthly newsletter for County Clerks to enhance Emissions Check
enforcement.

5

**Emissions** **Check Program Background and Overview**

The Northern Kentucky counties of Boone, Campbell, and Kenton are a part
of the Greater Cincinnati

airshed. The area has violated the National Ambient Air Quality Standard
for ozone periodically for the past

20 years. The levels at which the ozone standard has been exceeded
warranted the "moderate

nonattainment" designation. Under the federal Clean Air Act Amendments
of 1990, areas with this

designation are required to reduce emissions of ozone precursors by at
least 15%, and a vehicle inspection

and maintenance (I/M) program must be in place in the area.

Kentucky's 1990 General Assembly authorized an I/M program for Northern
Kentucky with the enactment

of KRS 224.20-710 through 224.20-765. In turn, DAQ promulgated an
administrative regulation, 401 KAR

65:010, to carry out the statutory mandate and implement an "improved
basic" program. For gasolinefueled

vehicles, test parameters include hydrocarbon and carbon monoxide
emissions analysis, an

evaporative system pressure test (1981 and newer model years), and a
tampering check of the catalytic

converter, fuel filler inlet restrictor, and gas cap. Diesel vehicles
receive an opacity test. The test fee is \$20,

of which \$17.78 goes to Envirotest and \$2.22 to the Commonwealth to
recover administrative costs.

By 1998, Kentucky awarded a 10-year contract to Envirotest to construct
and operate one testing station in

each county. Together, the stations offer 11 testing lanes for biennial
testing of the area's approximately

190,000 subject vehicles (1968 and newer model years, 18,000 pounds or
less Gross Vehicle Weight

Rating, or GVWR, and four wheels or more). There is a DAQ office in each
station, where DAQ staff

perform referee, oversight, and customer-assistance functions.

Area motorists are given a three-month period in which to test. This
testing period begins two months

before the registration-renewal month. Odd-model-year vehicles have
testing deadlines in odd-numbered

years; even model years have deadlines in even-numbered years.
Envirotest mails testing notices to

vehicle owners about one week before their testing cycles begin;
additionally, DAQ publishes a notification

in the local newspaper each month.

The program is enforced through denial of registration renewal. A
telecommunications network links the

Commonwealth, the testing stations, and the County Clerks (where
registration renewal takes place). Test

transactions are electronically transmitted from the stations to
Envirotest's central computer in Twinsburg,

Ohio, where databases are updated. From Twinsburg, test data are
transmitted to the Commonwealth's

computers in Frankfort, where the vehicle-registration database that the
County Clerks access to renew

registrations resides. The telecommunications system blocks registration
renewals for non-compliant

motorists and resets blocks for compliant motorists to two years in the
future.

The following exemptions are available to accommodate various
situations:

Permanent exemptions for vehicles that operate exclusively on fuels
other than gasoline or diesel.

Reciprocal exemptions for vehicles that have complied with another I/M
program in the past year.

Absence exemptions for vehicles that are driven less than 30 days in
the three-county program

area during the two-year certification period.

Temporary extensions of varying duration for vehicles that are unable
to comply with the testing

requirement during the three-month testing period.

Repair-cost waivers for 1980-and-older vehicles and diesel vehicles on
which at least \$75 has

been spent on appropriate repairs after a failed test and for
1981-and-newer vehicles on which at

least \$200 has been spent on appropriate repairs after a failed test.

6

**Operations Statistics**

***Test Activities***

The statistics in Table 1 show overall numbers and types of tests
performed in 2002. The program also

processed 6101 certification period exemptions and 86 permanent
exemptions in 2002.

**Table 1: Overall Testing Activity**

**\# Tested \# Passed \# Failed % Passed**

First Test 93,900 89,143 4757 94.9

Free Retest 3721 1879 1842 50.5

Subsequent Retests 553 235 318 42.5

***Total All Tests*** 98,174 N/A N/A N/A

Of the 93,900 initial tests, 89,143 passed and 4757 failed, for a fail
rate of 5.1%. During the year, initial

tests were received by 1213 diesel-fueled vehicles. Of that number, six
failed the opacity test. Of the

91,090 vehicles that were initially subject to pressure tests, only
16,573 vehicles were given the test

because of the inaccessibility or untestability of the evaporative
systems. Of the tests that were performed,

16,032 passed (96.7%) and 541 failed (3.3%)**.** Figure 1 shows the
results of all initial tests performed in

2002\.

As shown in Figure 2, of 4330 initial emissions fails, 2101 failed HC,
392 failed CO, and 1837 failed both

HC and CO.

Different standards are set for four different kinds of gasoline-fueled
vehicles. They are light-duty gasoline

vehicles (LDGV), light-duty gasoline trucks that are 6,000 pounds or
less (LDGT1), light-duty gasoline

trucks that are 10,000 pounds or less (LDGT2), and heavy-duty gasoline
trucks (HDGT) that are 18,000

pounds or less. Figure 3 provides a pass/fail breakdown for initial
tests by vehicle type.

**0**

**1,000**

**2,000**

**3,000**

**HC Only CO Only HC&CO**

Figure 2: Emissions Fail Breakdown by Pollutant

7

Average emissions improvements on the 2187 first retests for HC were
355.0 ppm HC (37.92%

improvement); on 353 subsequent retests, 309.3 ppm HC (37.18%
improvement). Average improvements

on the 1263 first retests for CO were 2.60% CO (48.74% improvement); on
217 subsequent retests, 2.35%

CO (48.75% improvement). Overall, repairs yielded reductions averaging
348.7 ppm for HC and 2.56% for

CO (improvements of 37.83% for HC and 48.74% for CO). Details on
emissions improvements can be

found in Appendix G.

Appendices A through C contain details on all test results, broken down
by vehicle type and model year.

***Test Center Activities***

Northern Kentucky Emissions Check operates three test centers, one in
each of the three Northern

Kentucky counties. At Station 1 (Boone), 95.6% passed and 4.4% failed
inspection; at Station 2

(Campbell), 95.8% passed and 4.2% failed; and at Station 3 (Kenton),
93.8% passed and 6.2% failed.

These pass/fail percentages for each station are presented graphically
in Figure 4.

**0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100**

**Boone**

**Campbell**

**Kenton**

Figure 4: Pass/Fail Breakdown by Station

**% Fail**

**% Pass**

***Vehicle Repair Data***

Of 93,900 initial tests performed in 2002, 4757 vehicles failed
inspection and 3721returned for a first retest.

Figure 5 shows the average repair costs for vehicles that had retests,
broken down by parts and labor and

by self repairs compared to hired repairs. (Self-repairs are not
eligible for labor costs.) Additionally,

Appendix E provides details on self vs. hired repairs, broken down by
model year and vehicle type.

**0**

**50**

**100**

**150**

**Self Repair Hired Repair**

Figure 5: Average Repair Costs

**Parts Costs**

**Labor Costs**

**0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100**

**LDGV**

**LDGT1**

**LDGT2**

**HDGT**

Figure 3: Pass/Fail Breakdown by Vehicle Type

**% Fail**

**% Pass**

8

***Referee Activity***

Vehicles that cannot pass the emissions test may be given a waiver if at
least \$75 (1980 and older models)

or \$200 (1981 and newer models) has been spent on emissions-related
repairs. Appendix D summarizes

waiver activity by vehicle type and model year. Envirotest issues a
regular waiver if there has been an

improvement in emissions, and DAQ issues an appeal waiver if there has
been no improvement. Figure 6

shows the number of regular waivers and appeal waivers for each station
in 2002. Regular waivers

amounted to 39.7% of the 1351 waivers issued in 2002.

**0 200 400 600 800 1,000 1,200 1,400**

**All Waivers**

**Appeal Waivers**

**Regular Waivers**

Figure 6: Waivers & Appeal Waivers, by Station

**Totals**

**Kenton**

**Campbell**

**Boone**

**Customer Service Activities**

The Northern Kentucky Emissions Check program is committed to providing
customer service in an

efficient, constructive, and professional manner. Both DAQ and
Envirotest personnel are trained

extensively in all aspects of customer service. The program provides an
around-the-clock hotline, with

Envirotest personnel staffing the hotline during regular business hours.
DAQ personnel are available at

each station not only to oversee the tests, but also to provide referee
services when warranted between

customers and the contractor; to issue extensions, exemptions, and
appeal waivers; and to provide

information, advice, and other appropriate customer service. Table 2
gives a breakdown of operatorassisted

hotline calls.

**Quality Assurance/Quality Control**

***Quality Assurance: Calibrations and Checks***

Quality-control activities are performed by Envirotest to ensure the
accuracy and reliability of equipment

and testing materials. The frequency of these activities is specified in
Table 3.

***Equipment Audits***

Semiannual equipment audits for each of the program's 11 lanes at the
three stations are included in

Appendix F. The results of these audits provide quality assurance for
the program and ensure proper

operation of station equipment. Monthly performance audits are logged on
the form shown in Figure 7.

**Enforcement**

Beyond registration-renewal blocks for non-compliers, the program uses
other forms of enforcement to

ensure compliance.

DAQ monitors County Clerks' block/override activity statewide, with
particular focus on the program-area

clerks, for overrides that appear to be unjustifiable (i.e., clerk
renews registration of non-compliant motorist

whose renewal is blocked). In almost all cases, the clerks' overrides
have been found to be due to

temporary failures by the telecommunications network to transmit
compliant test results. Less than 1% of

the overrides were found to be improper, and a DAQ investigation
revealed that all of the improper

overrides were due to errant keystrokes or training oversights.

**Table 2: Hotline Call Report**

**2002**

**Date**

**Total Daily Calls**

**Location**

**Needed / When /**

**Early**

**Sold / Turned In**

**Voucher**

**Modified Vehicle /**

**Not Running**

**Extension / Waiver**

**Failure**

**Hours of Operation**

**Cost**

**Already Tested**

**Test Process /**

**Length**

**Technical**

**Notice not**

**received**

**Appt. / Bring What**

**Overweight**

**Vehicles**

**Complaints**

**Hiring**

**Other**

**Total Questions**

**Daily**

January 811 48 87 32 1 7 32 11 52 33 9 11 11 12 90 0 0 0 412 848

February 491 43 49 20 5 3 16 3 45 37 14 8 5 7 76 0 0 0 238 569

March 776 79 135 66 1 23 54 5 140 52 0 6 16 8 144 0 0 11 173 913

April 538 66 107 22 1 8 74 1 71 41 2 11 18 9 130 1 8 12 71 653

May 569 62 142 66 1 7 37 0 97 34 0 6 17 4 99 0 10 1 83 666

June 487 50 106 59 0 3 26 0 82 33 3 8 2 8 64 0 3 1 106 554

July 583 70 114 78 0 4 38 3 126 48 0 7 10 9 102 0 12 2 80 703

August 478 60 102 98 0 0 45 0 92 33 0 2 4 4 71 0 0 0 63 574

September 487 50 106 59 0 3 26 0 82 33 3 8 2 8 64 0 3 1 106 554

October 612 79 82 87 1 13 76 1 87 40 25 11 15 6 98 0 6 0 80 707

November 394 40 60 51 2 1 62 0 65 41 16 5 18 11 58 0 6 3 20 459

December 617 45 85 74 6 0 124 0 133 61 36 15 29 13 89 0 9 1 15 735

**Totals** 6843 692 1175 712 18 72 610 24 1072 486 108 98 147 99 1085 1
57 32 1447 7935

Daily Average 570 58 98 59 2 6 51 2 89 41 9 8 12 8 90 0 5 3 121 661

\% Questions Mthly 0.86238 9% 15% 9% 1% 1% 8% 0% 14% 6% 1% 1% 2% 1% 14%
0% 1% 2% 18% 103%

9

10

**Table 3: Northern Kentucky Calibrations and System Checks**

**Test or Calibration Periodicity Notes**

Horiba Zero Calibration Before Test

Hang-Up Check Before Test

\"Before Test\" refers to checks the computer

accomplishes before testing a vehicle.

Position 3 Zero Pressure Check Before Test

Sample System Leak Check 4 Hours

Analyzer 2-pt Span Check 4 Hours

Opacity Daily Calibration Daily

Dyne Warm Up Check Daily

Cap Pressure Leak Check Daily

Tank Pressure Leak Check Daily

Nitrogen Bottle Pressure Check Daily

Span Bottle Pressure Check Daily

Position 3 Gauge Calibration Semimonthly

1-pt Span Check Monthly

5-pt Audit Monthly

"Semimonthly," \"Before Test,\" \"4 Hour,\" and \"Daily\" are

also completed during the monthly calibration routine.

Tail Pipe State Audit Monthly

Dual Hose Sample System Leak Check Monthly

Opacity Span Calibration Monthly

Check Purge Pump for Leaks Monthly

Check RPM Probes for Damage Monthly

Coast Down Check Monthly

Parasitic Losses Calibration Monthly

Dyne Roller Speed Check Monthly

Check All Dyne Bolts Monthly

Horiba Cabinet Pressure Check Monthly

Calibrate CO Monitor Monthly

Calibrate PAU Load Cell Semiannual

Calibrate KB Board Semiannual

\"Semiannuals\" are accomplished around the months of

April and October.

Calibrate Zero and Span Voltages Semiannual

Check and Grease all Dyne Fittings Semiannual

Change Compressor Oil and Filter Semiannual

Change Desiccant Dryer Filters Semiannual

State Audit Scheduled

State audits may be surprise or scheduled in advance.

Change Replaceable Filters As Needed

Clean Reusable Filters As Needed

All filters are checked on a monthly basis but may be

cleaned or replaced at any time.

11

Figure 7:

**Northern Kentucky Emissions** **Check**

**Performance Audit**

***Results key:** 1 -- Fails to meet standards 2 -- Meets standards 3 --
Exceeds standards*

**TEST PROCEDURES (performance)**

Procedure Result Inspector \# Vehicle ID

Greeting 1 2 3

Data Entry 1 2 3

Document Security/Correlate ID 1 2 3

Tampering Inspection 1 2 3

Testing Prep. (hangup check, etc.) 1 2 3

Emission Test 1 2 3

Pressure Test 1 2 3

Info/Comment Cards 1 2 3

Presentation of VIR/Correlate ID 1 2 3

Recordkeeping Practices (in lane) 1 2 3

Inspector Professionalism 1 2 3

**Comments**:

**RECORDS RETENTION (completeness & accuracy)**

Record Type Result

OIP Test Procedures Audits (ESC documentation) 1 2 3

Daily Analyzer calibrations (2-point span, leak & pressure checks) 1 2 3

Performance Audits (ESC Q/A documentation) 1 2 3

**Comments:**

**SAFETY CONDITIONS**

Condition Result

Customers only in approved areas 1 2 3

Fire extinguishers 1 2 3

Walkways 1 2 3

Calibration gas cylinders 1 2 3

CO monitor 1 2 3

**Comments:**

**STATION APPEARANCE**

Station Area Result

Facility grounds 1 2 3

Interior station appearance 1 2 3

Rest rooms 1 2 3

Signs 1 2 3

**Comments:**

Facility Facility \# Lane \# Time Date

Contractor Rep. Auditor

Analyzer calibration (2-point span & leak check) 4-hour results:

12

DAQ issues temporary extensions for vehicles that cannot be made
available for testing by the biennial

testing deadline. Figure 8 shows that of the 3903 extensions issued for
2002, Boone issued 822 (21.1%),

Campbell issued 726 (18.6%), and Kenton issued 2355 (60.3%). These
extensions reset the registrationrenewal

block ahead one year. When extension recipients comply with the testing
requirement, the block is

reset ahead another year, returning the vehicle to its normal biennial
testing cycle.

**0**

**1,000**

**2,000**

**3,000**

**4,000**

**Boone Campbell Kenton Total**

Figure 8: Extensions Issued, by Station

On the other hand, extension violators who have taken advantage of the
extension-triggered block removal

without honoring their commitment to return for a test will have their
registration renewals blocked in their

off-cycle year and will have to pay a \$25 penalty as well as comply
with the testing requirement before they

can renew their registrations in the off-cycle year. In 2002, about 41%
of the extensions issued were

violated. Of 1619 extensions violated in 2002, only 415 violators
eventually complied with the testing

requirement. It is presumed that the non-compliers sold the vehicle
outside the program area, disposed of

it, or continued to drive it in the program area on the
extension-enabled renewed registration. State law

prohibits extension violators from receiving any further extensions, but
identifying them was a tedious,

manual process until 2002, when DAQ began using a computerized tracking
system that identifies

extension violators and helps to ensure that the extension privilege is
not abused.

Vehicles that are expected to appear for the initial test but never do
are pursued by researching registration

databases and performing parking-lot surveys.

**Improving Repair Effectiveness**

Because repairing failed vehicles could be considered the cornerstone of
the program, improving repair

effectiveness is a major program objective. Northern Kentucky Emissions
Check addresses this objective

through outreach activities, annual repair-industry expositions, and
meetings with a Repair Industry

Advisory Committee. Additionally, a quarterly Repair Effectiveness
Handbook, nearing the final stages of

development by the end of 2002, will provide motorists and
program-oversight personnel with a "report

card" showing success rates for repair shops.

The percentage of initially failing vehicles that passed retests in 2002
decreased slightly from the previous

year to 44.3%, down from 44.6% in 2001. This may indicate that motorists
are finding fewer skilled repair

technicians experienced in repairing emissions problems. Another
indication of decreasing proficiency

within the repair industry is the ratio of appeal waivers (no emissions
improvement) to regular waivers

(some emissions improvement, but not enough to pass). The ratio of
appeal waivers to regular waivers

increased slightly from 1012:678 (or 1.49:1) in 2002 to 815:536 (or
1.52:1) in 2002. However, it is

expected that the accountability provided by the new Repair
Effectiveness Handbook will improve repair

effectiveness in 2003 and thereafter.

13

**Appendices**

A Initial Tests by Vehicle Type and Model Year

B First Retests by Vehicle Type and Model Year

C Subsequent Retests by Vehicle Type and Model Year

D Waiver Activities by Vehicle Type and Model Year

E Hired and Self Repairs by Vehicle Type and Model Year

F Semiannual Lane Audits by Division for Air Quality

G HC and CO Emission-Reduction Calculations

14

**Appendix A: Initial Tests by Vehicle Type and Model Year**

**Emissions Test Pressure Test**

**Vehicle Type Total Tested**

**Initial Passed% Passed Failed% Failed Passed % Passed Failed %
Failed**

HDGT 889 429 83.6 84 16.4 22 78.6 6 21.4

LDGT1 25841 24525 95.2 1231 4.8 5353 96.0 222 4.0

LDGT2 10243 8939 92.8 689 7.2 864 92.6 69 7.4

LDGV 56927 54712 96.3 2078 3.7 9793 97.6 244 2.4

**Total 93900 88605 95.6 4082 4.4 16032 96.7 541 3.3**

**Emissions Test Pressure Test Vehicle Model**

**Year**

**Total Tested**

**Initial Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

1968 77 65 84.4 12 15.6 0 0.0 0 0.0

1969 14 8 57.1 6 42.9 0 0.0 0 0.0

1970 87 61 70.1 26 29.9 0 0.0 0 0.0

1971 14 9 69.2 4 30.8 0 0.0 0 0.0

1972 121 90 74.4 31 25.6 0 0.0 0 0.0

1973 12 10 83.3 2 16.7 0 0.0 0 0.0

1974 121 95 78.5 26 21.5 0 0.0 0 0.0

1975 11 9 90.0 1 10.0 0 0.0 0 0.0

1976 200 159 81.1 37 18.9 0 0.0 0 0.0

1977 34 28 82.4 6 17.6 0 0.0 0 0.0

1978 555 428 77.5 124 22.5 0 0.0 0 0.0

1979 77 58 75.3 19 24.7 0 0.0 0 0.0

1980 274 184 70.5 77 29.5 0 0.0 0 0.0

1981 39 23 60.5 15 39.5 9 75.0 3 25.0

1982 440 282 69.1 126 30.9 142 92.2 12 7.8

1983 78 54 74.0 19 26.0 28 90.3 3 9.7

1984 1387 945 69.8 408 30.2 534 94.8 29 5.2

1985 228 160 70.8 66 29.2 77 88.5 10 11.5

1986 3065 2350 78.0 664 22.0 1259 93.2 92 6.8

1987 361 272 76.0 86 24.0 128 93.4 9 6.6

1988 5148 4432 86.8 675 13.2 1856 95.0 98 5.0

1989 536 441 82.4 94 17.6 171 94.0 11 6.0

1990 6794 6161 91.3 586 8.7 2405 96.0 101 4.0

1991 589 523 89.1 64 10.9 191 93.6 13 6.4

1992 8782 8197 94.2 502 5.8 2537 97.2 74 2.8

1993 706 658 93.9 43 6.1 184 97.4 5 2.6

1994 12167 11767 97.9 248 2.1 3106 98.2 57 1.8

1995 811 784 97.9 17 2.1 125 98.4 2 1.6

1996 13252 13030 99.6 58 0.4 1962 99.1 17 0.9

1997 847 829 99.8 2 0.2 90 100.0 0 0.0

1998 14846 14712 99.9 20 0.1 668 99.6 3 0.4

1999 963 949 99.7 3 0.3 23 100.0 0 0.0

2000 16290 15952 99.9 13 0.1 517 99.6 2 0.4

2001 654 624 99.8 1 0.2 8 100.0 0 0.0

2002 4289 4225 100.0 1 0.0 12 100.0 0 0.0

2003 31 31 100.0 0 0.0 0 0.0 0 0.0

**Total 93900 88605 95.6 4082 4.4 16032 96.7 541 3.3**

15

**Appendix A: *continued***

**Catalytic Converter Test Opacity Test**

**Vehicle Type Total Tested**

**Initial Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

HDGT 889 431 99.8 1 0.2 373 99.2 3 0.8

LDGT1 25841 25598 100.0 11 0.0 85 100.0 0 0.0

LDGT2 10243 9432 100.0 3 0.0 613 99.7 2 0.3

LDGV 56927 56401 100.0 8 0.0 136 99.3 1 0.7

**Total 93900 91862 100.0 23 0.0 1207 99.5 6 0.5**

**Catalytic Converter Test Opacity Test Vehicle Model**

**Year**

**Total Tested**

**Initial Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

1968 77 0 0.0 0 0.0 0 0.0 0 0.0

1969 14 0 0.0 0 0.0 0 0.0 0 0.0

1970 87 0 0.0 0 0.0 0 0.0 0 0.0

1971 14 0 0.0 0 0.0 1 100.0 0 0.0

1972 121 0 0.0 0 0.0 0 0.0 0 0.0

1973 12 0 0.0 0 0.0 0 0.0 0 0.0

1974 121 0 0.0 0 0.0 0 0.0 0 0.0

1975 11 9 100.0 0 0.0 1 100.0 0 0.0

1976 200 144 98.6 2 1.4 4 100.0 0 0.0

1977 34 24 100.0 0 0.0 0 0.0 0 0.0

1978 555 396 99.5 2 0.5 3 100.0 0 0.0

1979 77 70 100.0 0 0.0 0 0.0 0 0.0

1980 274 242 99.6 1 0.4 12 92.3 1 7.7

1981 39 37 97.4 1 2.6 1 100.0 0 0.0

1982 440 398 99.5 2 0.5 32 100.0 0 0.0

1983 78 71 100.0 0 0.0 5 100.0 0 0.0

1984 1387 1319 99.9 1 0.1 34 100.0 0 0.0

1985 228 216 99.5 1 0.5 2 100.0 0 0.0

1986 3065 2951 99.9 4 0.1 51 100.0 0 0.0

1987 361 356 99.7 1 0.3 3 100.0 0 0.0

1988 5148 5099 100.0 2 0.0 40 97.6 1 2.4

1989 536 534 100.0 0 0.0 1 100.0 0 0.0

1990 6794 6746 100.0 2 0.0 47 100.0 0 0.0

1991 589 587 100.0 0 0.0 2 100.0 0 0.0

1992 8782 8699 100.0 0 0.0 82 98.8 1 1.2

1993 706 701 100.0 0 0.0 5 100.0 0 0.0

1994 12167 12015 100.0 0 0.0 152 100.0 0 0.0

1995 811 801 100.0 0 0.0 10 100.0 0 0.0

1996 13252 13088 100.0 3 0.0 163 99.4 1 0.6

1997 847 831 100.0 0 0.0 16 100.0 0 0.0

1998 14846 14732 100.0 0 0.0 113 99.1 1 0.9

1999 963 951 100.0 0 0.0 11 100.0 0 0.0

2000 16290 15963 100.0 1 0.0 324 99.7 1 0.3

2001 654 625 100.0 0 0.0 29 100.0 0 0.0

2002 4289 4226 100.0 0 0.0 63 100.0 0 0.0

2003 31 31 100.0 0 0.0 0 0.0 0 0.0

**Total 93900 91862 100.0 23 0.0 1207 99.5 6 0.5**

16

**Appendix B: First Retests by Vehicle Type and Model Year**

**Emissions Test Pressure Test Vehicle**

**Type**

**Total**

**Tested 1st**

**Retests Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

HDGT 79 20 26.3 56 73.7 9 56.3 7 43.8

LDGT1 1165 619 53.1 546 46.9 477 87.8 66 12.2

LDGT2 611 291 47.8 318 52.2 118 82.5 25 17.5

LDGV 1866 1065 57.1 801 42.9 725 92.0 63 8.0

**Total 3721 1995 53.7 1721 46.3 1329 89.2 161 10.8**

**Emissions Test Pressure Test Vehicle**

**Model**

**Year**

**Total**

**Tested 1st**

**Retests Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

1968 9 0 0.0 9 100.0 0 0.0 0 0.0

1969 5 3 60.0 2 40.0 0 0.0 0 0.0

1970 15 10 66.7 5 33.3 0 0.0 0 0.0

1971 3 0 0.0 3 100.0 0 0.0 0 0.0

1972 21 12 57.1 9 42.9 0 0.0 0 0.0

1973 1 0 0.0 1 100.0 0 0.0 0 0.0

1974 20 9 45.0 11 55.0 0 0.0 0 0.0

1975 1 0 0.0 1 100.0 0 0.0 0 0.0

1976 26 12 46.2 14 53.8 0 0.0 0 0.0

1977 8 4 50.0 4 50.0 0 0.0 0 0.0

1978 99 42 42.4 57 57.6 0 0.0 0 0.0

1979 19 11 57.9 8 42.1 0 0.0 0 0.0

1980 54 21 38.9 33 61.1 0 0.0 0 0.0

1981 16 9 56.3 7 43.8 5 71.4 2 28.6

1982 96 41 42.7 55 57.3 40 93.0 3 7.0

1983 24 11 45.8 13 54.2 9 75.0 3 25.0

1984 305 128 42.0 177 58.0 114 92.7 9 7.3

1985 78 35 44.9 43 55.1 25 92.6 2 7.4

1986 564 263 46.6 301 53.4 237 89.8 27 10.2

1987 114 53 46.5 61 53.5 43 82.7 9 17.3

1988 579 319 55.2 259 44.8 242 85.8 40 14.2

1989 114 54 47.4 60 52.6 44 83.0 9 17.0

1990 535 307 57.4 228 42.6 239 90.2 26 9.8

1991 95 58 61.1 37 38.9 37 88.1 5 11.9

1992 446 275 61.8 170 38.2 148 90.8 15 9.2

1993 51 32 62.7 19 37.3 21 95.5 1 4.5

1994 240 155 64.6 85 35.4 84 91.3 8 8.7

1995 37 21 56.8 16 43.2 6 100.0 0 0.0

1996 85 62 73.8 22 26.2 29 93.5 2 6.5

1997 6 5 83.3 1 16.7 0 0.0 0 0.0

1998 30 25 86.2 4 13.8 2 100.0 0 0.0

1999 5 5 100.0 0 0.0 0 0.0 0 0.0

2000 16 9 60.0 6 40.0 4 100.0 0 0.0

2001 2 2 100.0 0 0.0 0 0.0 0 0.0

2002 2 2 100.0 0 0.0 0 0.0 0 0.0

**Total 3721 1995 53.7 1721 46.3 1329 89.2 161 10.8**

17

**Appendix B: continued**

**Catalytic Converter Test Opacity Test Vehicle**

**Type**

**Total**

**Tested 1st**

**Retests Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

HDGT 79 59 100.0 0 0.0 3 100.0 0 0.0

LDGT1 1165 1137 99.9 1 0.1 0 0.0 0 0.0

LDGT2 611 567 100.0 0 0.0 2 100.0 0 0.0

LDGV 1866 1797 100.0 0 0.0 0 0.0 0 0.0

**Total 3721 3560 100.0 1 0.0 5 100.0 0 0.0**

**Catalytic Converter Test Opacity Test Vehicle**

**Model**

**Year**

**Total**

**Tested 1st**

**Retests Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

1968 9 0 0.0 0 0.0 0 0.0 0 0.0

1969 5 0 0.0 0 0.0 0 0.0 0 0.0

1970 15 0 0.0 0 0.0 0 0.0 0 0.0

1971 3 0 0.0 0 0.0 0 0.0 0 0.0

1972 21 0 0.0 0 0.0 0 0.0 0 0.0

1973 1 0 0.0 0 0.0 0 0.0 0 0.0

1974 20 0 0.0 0 0.0 0 0.0 0 0.0

1975 1 1 100.0 0 0.0 0 0.0 0 0.0

1976 26 18 100.0 0 0.0 0 0.0 0 0.0

1977 8 7 100.0 0 0.0 0 0.0 0 0.0

1978 99 73 100.0 0 0.0 0 0.0 0 0.0

1979 19 17 100.0 0 0.0 0 0.0 0 0.0

1980 54 48 100.0 0 0.0 0 0.0 0 0.0

1981 16 16 100.0 0 0.0 0 0.0 0 0.0

1982 96 94 100.0 0 0.0 0 0.0 0 0.0

1983 24 23 100.0 0 0.0 0 0.0 0 0.0

1984 305 292 100.0 0 0.0 0 0.0 0 0.0

1985 78 77 100.0 0 0.0 0 0.0 0 0.0

1986 564 547 100.0 0 0.0 0 0.0 0 0.0

1987 114 113 100.0 0 0.0 0 0.0 0 0.0

1988 579 575 99.8 1 0.2 1 100.0 0 0.0

1989 114 114 100.0 0 0.0 0 0.0 0 0.0

1990 535 535 100.0 0 0.0 0 0.0 0 0.0

1991 95 95 100.0 0 0.0 0 0.0 0 0.0

1992 446 445 100.0 0 0.0 1 100.0 0 0.0

1993 51 51 100.0 0 0.0 0 0.0 0 0.0

1994 240 240 100.0 0 0.0 0 0.0 0 0.0

1995 37 37 100.0 0 0.0 0 0.0 0 0.0

1996 85 84 100.0 0 0.0 1 100.0 0 0.0

1997 6 6 100.0 0 0.0 0 0.0 0 0.0

1998 30 29 100.0 0 0.0 1 100.0 0 0.0

1999 5 5 100.0 0 0.0 0 0.0 0 0.0

2000 16 14 100.0 0 0.0 1 100.0 0 0.0

2001 2 2 100.0 0 0.0 0 0.0 0 0.0

2002 2 2 100.0 0 0.0 0 0.0 0 0.0

**Total 3721 3560 100.0 1 0.0 5 100.0 0 0.0**

18

**Appendix C: Subsequent Retests by Vehicle Type and Model Year**

**Emissions Test Pressure Test Vehicle**

**Type**

**Total**

**Tested 2+**

**Retests Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

HDGT 15 5 33.3 10 66.7 4 100.0 0 0.0

LDGT1 197 96 48.7 101 51.3 84 84.0 16 16.0

LDGT2 104 42 40.4 62 59.6 21 87.5 3 12.5

LDGV 237 110 46.4 127 53.6 102 91.9 9 8.1

**Total 553 253 45.8 300 54.2 211 88.3 28 11.7**

**Emissions Test Pressure Test Vehicle**

**Model**

**Year**

**Total**

**Tested 2+**

**Retests Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

1968 1 0 0.0 1 100.0 0 0.0 0 0.0

1969 2 1 50.0 1 50.0 0 0.0 0 0.0

1970 1 0 0.0 1 100.0 0 0.0 0 0.0

1971 1 0 0.0 1 100.0 0 0.0 0 0.0

1972 2 0 0.0 2 100.0 0 0.0 0 0.0

1973 0 0 0.0 0 0.0 0 0.0 0 0.0

1974 1 0 0.0 1 100.0 0 0.0 0 0.0

1975 0 0 0.0 0 0.0 0 0.0 0 0.0

1976 1 0 0.0 1 100.0 0 0.0 0 0.0

1977 2 0 0.0 2 100.0 0 0.0 0 0.0

1978 10 5 50.0 5 50.0 0 0.0 0 0.0

1979 2 1 50.0 1 50.0 0 0.0 0 0.0

1980 4 3 75.0 1 25.0 0 0.0 0 0.0

1981 5 3 60.0 2 40.0 2 50.0 2 50.0

1982 11 3 27.3 8 72.7 7 100.0 0 0.0

1983 10 0 0.0 10 100.0 5 83.3 1 16.7

1984 45 15 33.3 30 66.7 20 90.9 2 9.1

1985 26 8 30.8 18 69.2 13 92.9 1 7.1

1986 91 41 45.1 50 54.9 39 92.9 3 7.1

1987 29 14 48.3 15 51.7 16 94.1 1 5.9

1988 82 48 58.5 34 41.5 34 89.5 4 10.5

1989 26 13 50.0 13 50.0 8 72.7 3 27.3

1990 75 36 48.0 39 52.0 30 83.3 6 16.7

1991 25 12 48.0 13 52.0 12 100.0 0 0.0

1992 53 29 54.7 24 45.3 14 93.3 1 6.7

1993 9 3 33.3 6 66.7 3 60.0 2 40.0

1994 24 9 37.5 15 62.5 6 75.0 2 25.0

1995 6 3 50.0 3 50.0 1 100.0 0 0.0

1996 7 5 71.4 2 28.6 1 100.0 0 0.0

1997 1 1 100.0 0 0.0 0 0.0 0 0.0

1998 0 0 0.0 0 0.0 0 0.0 0 0.0

1999 0 0 0.0 0 0.0 0 0.0 0 0.0

2000 1 0 0.0 1 100.0 0 0.0 0 0.0

2001 0 0 0.0 0 0.0 0 0.0 0 0.0

**Total 553 253 45.8 300 54.2 211 88.3 28 11.7**

19

**Appendix C: *continued***

**Catalytic Converter Test Opacity Test Vehicle**

**Type**

**Total**

**Tested 2+**

**Retests Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

HDGT 15 11 100.0 0 0.0 0 0.0 0 0.0

LDGT1 197 195 100.0 0 0.0 0 0.0 0 0.0

LDGT2 104 98 100.0 0 0.0 0 0.0 0 0.0

LDGV 237 230 100.0 0 0.0 0 0.0 0 0.0

**Total 553 534 100.0 0 0.0 0** 0.0 **0 0.0**

**Catalytic Converter Test Opacity Test Vehicle**

**Model**

**Year**

**Total**

**Tested 2+**

**Retests Passed % Passed Failed % Failed Passed % Passed Failed %
Failed**

1968 1 0 0.0 0 0.0 0 0.0 0 0.0

1969 2 0 0.0 0 0.0 0 0.0 0 0.0

1970 1 0 0.0 0 0.0 0 0.0 0 0.0

1971 1 0 0.0 0 0.0 0 0.0 0 0.0

1972 2 0 0.0 0 0.0 0 0.0 0 0.0

1973 0 0 0.0 0 0.0 0 0.0 0 0.0

1974 1 0 0.0 0 0.0 0 0.0 0 0.0

1975 0 0 0.0 0 0.0 0 0.0 0 0.0

1976 1 1 100.0 0 0.0 0 0.0 0 0.0

1977 2 2 100.0 0 0.0 0 0.0 0 0.0

1978 10 7 100.0 0 0.0 0 0.0 0 0.0

1979 2 2 100.0 0 0.0 0 0.0 0 0.0

1980 4 4 100.0 0 0.0 0 0.0 0 0.0

1981 5 5 100.0 0 0.0 0 0.0 0 0.0

1982 11 10 100.0 0 0.0 0 0.0 0 0.0

1983 10 10 100.0 0 0.0 0 0.0 0 0.0

1984 45 42 100.0 0 0.0 0 0.0 0 0.0

1985 26 26 100.0 0 0.0 0 0.0 0 0.0

1986 91 89 100.0 0 0.0 0 0.0 0 0.0

1987 29 29 100.0 0 0.0 0 0.0 0 0.0

1988 82 80 100.0 0 0.0 0 0.0 0 0.0

1989 26 26 100.0 0 0.0 0 0.0 0 0.0

1990 75 75 100.0 0 0.0 0 0.0 0 0.0

1991 25 25 100.0 0 0.0 0 0.0 0 0.0

1992 53 53 100.0 0 0.0 0 0.0 0 0.0

1993 9 9 100.0 0 0.0 0 0.0 0 0.0

1994 24 24 100.0 0 0.0 0 0.0 0 0.0

1995 6 6 100.0 0 0.0 0 0.0 0 0.0

1996 7 7 100.0 0 0.0 0 0.0 0 0.0

1997 1 1 100.0 0 0.0 0 0.0 0 0.0

1998 0 0 0.0 0 0.0 0 0.0 0 0.0

1999 0 0 0.0 0 0.0 0 0.0 0 0.0

2000 1 1 100.0 0 0.0 0 0.0 0 0.0

2001 0 0 0.0 0 0.0 0 0.0 0 0.0

**Total 553 534 100.0 0 0.0 0** 0.0 **0 0.0**

20

**Appendix D: Waiver Activities by Vehicle Type and Model Year**

**Total Initial Failures Waivers Granted Vehicle Type**

**Total Tested Total Failed % Failed Initial Total Granted % of Total
Initial**

HDGT 889 95 10.7 48 5.4

LDGT1 25842 1477 5.7 436 1.7

LDGT2 10243 766 7.5 243 2.4

LDGV 56927 2419 4.2 624 1.1

**Total 93900 4757 5.1 1351 1.4**

**Total Initial Failures Waivers Granted Vehicle Model**

**Year**

**Total Tested Total Failed % Failed Initial Total Granted % of Total
Initial**

1968 77 12 15.6 8 10.4

1969 14 6 42.9 1 7.1

1970 87 26 29.9 5 5.7

1971 14 4 28.6 2 14.3

1972 121 31 25.6 7 5.8

1973 12 2 16.7 1 8.3

1974 121 26 21.5 9 7.4

1975 11 1 9.1 0 0.0

1976 200 37 18.5 11 5.5

1977 34 6 17.6 4 11.8

1978 555 124 22.3 50 9.0

1979 77 19 24.7 8 10.4

1980 274 78 28.5 31 11.3

1981 39 18 46.2 8 20.5

1982 441 134 30.4 44 10.0

1983 78 22 28.2 15 19.2

1984 1387 440 31.7 136 9.8

1985 228 73 32.0 48 21.1

1986 3065 753 24.6 223 7.3

1987 361 93 25.8 53 14.7

1988 5148 797 15.5 191 3.7

1989 536 107 20.0 52 9.7

1990 6794 694 10.2 172 2.5

1991 589 79 13.4 37 6.3

1992 8782 607 6.9 112 1.3

1993 706 49 6.9 22 3.1

1994 12167 326 2.7 68 0.6

1995 811 22 2.7 12 1.5

1996 13252 98 0.7 12 0.1

1997 847 6 0.7 1 0.1

1998 14846 37 0.2 5 0.0

1999 963 5 0.5 0 0.0

2000 16290 21 0.1 3 0.0

2001 654 2 0.3 0 0.0

2002 4289 2 0.0 0 0.0

2003 31 0 0.0 0 0.0

**Total 93900 4757 5.1 1351 1.4**

21

**Appendix E: Hired and Self Repairs by Vehicle Type and Model Year**

**Self Repairs Hired Repairs Vehicle**

**Type Self repair**

**count**

**Total cost**

**for parts**

**Average**

**cost (parts)**

**Hired repair**

**count**

**Total cost**

**for parts**

**Average**

**cost (parts)**

**Total cost**

**for labor**

**Average**

**cost (labor)**

HDGT 37 \$3,177 \$86 65 \$10,281 \$158 \$8,830 \$136

LDGT1 586 \$40,054 \$68 889 \$113,528 \$128 \$99,732 \$112

LDGT2 319 \$30,643 \$96 458 \$78,257 \$171 \$57,771 \$126

LDGV 783 \$49,611 \$63 1441 \$199,632 \$139 \$165,172 \$115

**Total 1725 \$123,485 \$72 2853 \$401,698 \$141 \$331,505 \$116**

**Self Repairs Hired Repairs Vehicle**

**Model Year Self repair**

**count**

**Total cost**

**for parts**

**Average**

**cost (parts)**

**Hired repair**

**count**

**Total cost**

**for parts**

**Average**

**cost (parts)**

**Total cost**

**for labor**

**Average**

**cost (labor)**

1968 9 \$972 \$108 7 \$78 \$11 \$150 \$21

1969 3 \$516 \$172 4 \$304 \$76 \$275 \$69

1970 11 \$640 \$58 10 \$571 \$57 \$648 \$65

1971 2 \$115 \$58 2 \$400 \$200 \$0 \$0

1972 12 \$2,262 \$189 16 \$986 \$62 \$849 \$53

1973 0 \$0 \$0 1 \$196 \$196 \$238 \$238

1974 7 \$344 \$49 9 \$822 \$91 \$1,436 \$160

1975 1 \$0 \$0 0 \$0 \$0 \$0 \$0

1976 17 \$1,090 \$64 49 \$1,025 \$21 \$684 \$14

1977 7 \$1,222 \$175 10 \$286 \$29 \$401 \$40

1978 49 \$4,513 \$92 49 \$2,883 \$59 \$4,613 \$94

1979 9 \$674 \$75 13 \$1,140 \$88 \$1,624 \$125

1980 30 \$1,749 \$58 50 \$4,389 \$88 \$2,676 \$54

1981 6 \$239 \$40 17 \$1,641 \$97 \$1,532 \$90

1982 42 \$4,251 \$101 139 \$9,913 \$71 \$9,537 \$69

1983 18 \$1,994 \$111 34 \$2,211 \$65 \$2,190 \$64

1984 140 \$12,581 \$90 362 \$32,245 \$89 \$29,185 \$81

1985 30 \$3,684 \$123 78 \$17,579 \$225 \$11,100 \$142

1986 274 \$19,927 \$73 427 \$60,186 \$141 \$50,977 \$119

1987 66 \$5,284 \$80 100 \$11,847 \$118 \$10,048 \$100

1988 261 \$16,312 \$62 440 \$55,902 \$127 \$49,156 \$112

1989 58 \$4,306 \$74 85 \$17,611 \$207 \$16,838 \$198

1990 227 \$16,551 \$73 380 \$58,566 \$154 \$47,584 \$125

1991 44 \$2,872 \$65 76 \$14,214 \$187 \$8,639 \$114

1992 195 \$11,770 \$60 263 \$51,016 \$194 \$36,497 \$139

1993 22 \$1,625 \$74 38 \$7,550 \$199 \$6,163 \$162

1994 92 \$4,848 \$53 113 \$28,196 \$250 \$23,008 \$204

1995 16 \$507 \$32 22 \$3,915 \$178 \$3,538 \$161

1996 43 \$1,064 \$25 33 \$9,644 \$292 \$6,982 \$212

1997 3 \$20 \$7 3 \$1,316 \$439 \$1,158 \$386

1998 18 \$163 \$9 12 \$2,429 \$202 \$2,022 \$169

1999 1 \$8 \$8 5 \$1,788 \$358 \$1,049 \$210

2000 9 \$1,382 \$154 5 \$615 \$123 \$657 \$131

2001 1 \$0 \$0 1 \$100 \$100 \$51 \$51

2002 2 \$0 \$0 0 \$134 \$0 \$0 \$0

**Total 1,725 \$123,485 \$72 2853 \$401,698 \$141 \$331,505 \$116**

22

**Appendix F: 2002 Semiannual Lane Audits by Division for Air Quality**

**Station / Lane**

**Date**

**2-Point Gas Calibration**

**Sample Leak Check**

**State Audit Check HC**

**State Audit Check CO**

**State Audit Check CO2**

**Roll Speed Check**

**Para. Loss & Coastdown**

**Opacity Check**

**Pressure System**

**Total Pass**

**Total Fail**

**Percent Tests Passed** 61/1 06/20/02 P P P P P P P P P 9 0 100.0%

61/2 06/20/02 P P P P P P P P P 9 0 100.0%

61/3 06/21/02 P P P P P P P P P 9 0 100.0%

61/4 06/21/02 P P P P P P P P P 9 0 100.0%

62/1 06/20/02 P P P P P P P P P 9 0 100.0%

62/2 06/20/02 P P P P P P P P P 9 0 100.0%

63/1 06/19/02 P P P P P P P P P 9 0 100.0%

63/2 06/19/02 P P P P P P P P P 9 0 100.0%

63/3 06/19/02 P P P P P P P P P 9 0 100.0%

63/4 06/19/02 P P P P P P P P P 9 0 100.0%

63/5 06/19/02 P P P P P P P P P 9 0 100.0%

61/1 12/23/02 P P P P P P P P P 9 0 100.0%

61/2 12/23/02 P P P P P P P P P 9 0 100.0%

61/3 12/23/02 P P P P P P P P P 9 0 100.0%

61/4 12/23/02 P P P P P P P P P 9 0 100.0%

62/1 12/17/02 P P P P P P P P P 9 0 100.0%

62/2 12/17/02 P P P P P P P P P 9 0 100.0%

63/1 12/18/02 P P P P P P P P P 9 0 100.0%

63/2 12/18/02 P P P P P P P P P 9 0 100.0%

63/3 12/18/02 P P P P P P P P P 9 0 100.0%

63/4 12/20/02 P P P P P P P P P 9 0 100.0%

63/5 12/20/02 P P P P P P P P P 9 0 100.0%

**Total Passed** 23 23 23 23 23 23 23 23 23 207 100.0%

**Total Failed** 0 0 0 0 0 0 0 0 0 0 0.0%

23

**Appendix G: HC and CO Emission-Reduction Calculations**

**Overall Hydrocarbon (HC) Improvements**

**Vehicle Year Initial HC Final HC Tests HC Diff.**

1968 22639.7 23354.5 9 -714.8

1969 6538.8 2793.4 3 3745.4

1970 34238.2 12696.7 10 21541.5

1971 7202.8 6572.4 3 630.4

1972 33536.8 24865.5 13 8671.3

1973 6175.8 3337.9 1 2837.9

1974 68412.8 34340 15 34072.8

1975 979.9 847.9 1 132

1976 47030.4 23654.4 16 23376

1977 7721.9 5035.3 3 2686.6

1978 145591.5 118552.8 69 27038.7

1979 24600 13240.6 8 11359.4

1980 57079.9 52042.4 44 5037.5

1981 8139.3 7526.3 10 613

1982 107559.9 60151.4 76 47408.5

1983 6986.9 3450 10 3536.9

1984 244145.9 149487.4 236 94658.5

1985 43642 20735.6 29 22906.4

1986 337740.1 210604.9 424 127135.2

1987 29830.5 24542.3 39 5288.2

1988 310902.2 193111.1 434 117791.1

1989 67125.2 31290.7 55 35834.5

1990 269477.3 138706.9 383 130770.4

1991 21425.1 14455.4 42 6969.7

1992 212477.6 126511.8 341 85965.8

1993 24171.4 11922.6 26 12248.8

1994 146847.8 115662.6 163 31185.2

1995 3814.8 6828.8 9 -3014

1996 24778 11264.1 41 13513.9

1997 1784.6 1599 2 185.6

1998 6600.9 2599.1 14 4001.8

1999 1146.9 240.8 3 906.1

2000 9825.8 3327.6 6 6498.2

2001 354.4 114.9 1 239.5

2002 592.4 9.8 1 582.6

**Totals: 2341117.5 1455476.9 2540 885640.6**

**Avg HC Improvement: 348.68**

**Avg % Improvement: 37.83%**

24

**Overall Carbon Monoxide (CO) Improvement Calculations**

**Vehicle Year Initial CO Final CO AVG Initial CO AVG Final CO Tests CO
Diff**

1969 9.26 6.43 9.26 6.43 1 2.83

1970 49.34 23.56 9.868 4.712 5 5.156

1971 8.96 8.88 8.96 8.88 1 0.08

1972 114 86.9 9.5 7.241666667 12 2.258333333

1973 8.99 6.31 8.99 6.31 1 2.68

1974 101.79 65.77 10.179 6.577 10 3.602

1976 126.15 77.79 9.010714286 5.556428571 14 3.454285714

1977 11.62 9.98 5.81 4.99 2 0.82

1978 419.44 262.87 8.56 5.364693878 49 3.195306122

1979 62.42 33.38 7.8025 4.1725 8 3.63

1980 219.98 116.43 6.666060606 3.528181818 33 3.137878788

1981 62.6 16.72 8.942857143 2.388571429 7 6.554285714

1982 323.49 180.57 5.881636364 3.283090909 55 2.598545455

1983 29.88 18.6 4.98 3.1 6 1.88

1984 820.29 516.28 5.159056604 3.247044025 159 1.912012579

1985 118.23 100.05 5.374090909 4.547727273 22 0.826363636

1986 1268.6 689.77 4.629927007 2.517408759 274 2.112518248

1987 133.12 80.45 5.12 3.094230769 26 2.025769231

1988 924.65 466.38 4.280787037 2.159166667 216 2.12162037

1989 175.04 83.88 5.001142857 2.396571429 35 2.604571429

1990 1236.52 468.84 5.026504065 1.905853659 246 3.120650407

1991 87.31 42.51 4.3655 2.1255 20 2.24

1992 847.87 352.71 5.332515723 2.218301887 159 3.114213836

1993 104.2 53.16 6.5125 3.3225 16 3.19

1994 289.49 134.82 5.360925926 2.496666667 54 2.864259259

1995 8.37 0.17 8.37 0.17 1 8.2

1996 121.18 43.31 4.039333333 1.443666667 30 2.595666667

1998 47.99 19.73 5.332222222 2.192222222 9 3.14

1999 12.59 0.37 4.196666667 0.123333333 3 4.073333333

2000 15.05 16.67 3.7625 4.1675 4 -0.405

2001 2.27 0.09 2.27 0.09 1 2.18

2002 10.69 0 10.69 0 1 10.69

**Totals: 7771.38 3983.38 6.538576273 3.460994582 1480 98.48261412**

**Avg CO Improvement: 2.56**

**Avg % Improvement: 48.74%**

25

**First Retest Hydrocarbon (HC) Calculations**

**Vehicle Year Initial HC Final HC Tests HC Diff.**

1968 20814.5 22146.5 8 -1332

1969 6538.8 2793.4 3 3745.4

1970 32936.6 11181.8 9 21754.8

1971 4756.5 5264.5 2 -508

1972 30938.3 20948.6 11 9989.7

1973 6175.8 3337.9 1 2837.9

1974 65535.2 33273.2 14 32262

1975 979.9 847.9 1 132

1976 43878.4 20795.4 15 23083

1977 4318 1848.8 2 2469.2

1978 132003.6 115621.9 64 16381.7

1979 24600 13240.6 8 11359.4

1980 54211.9 50741.7 40 3470.2

1981 8139.3 7526.3 10 613

1982 95772.3 54540.9 68 41231.4

1983 5487.7 2522.1 8 2965.6

1984 215297.6 127434.3 203 87863.3

1985 37132.4 17083.7 23 20048.7

1986 277284.6 164651.1 352 112633.5

1987 22044.9 19489.5 29 2555.4

1988 266079.8 170400.7 380 95679.1

1989 58079.5 28789.8 47 29289.7

1990 237831.7 113132.9 325 124698.8

1991 21425.1 14455.4 42 6969.7

1992 186610.1 109927.9 290 76682.2

1993 17810.2 8176.6 23 9633.6

1994 134797.7 109585.9 143 25211.8

1995 3814.8 6828.8 9 -3014

1996 20050.4 8798.6 33 11251.8

1997 1784.6 1599 2 185.6

1998 6600.9 2599.1 14 4001.8

1999 1146.9 240.8 3 906.1

2000 1648.6 1057.5 3 591.1

2001 354.4 114.9 1 239.5

2002 592.4 9.8 1 582.6

**Totals: 2047473.4 1271007.8 2187 776465.6**

**Avg HC Improvement: 355.04**

**Avg % Improvement: 37.92%**

26

**First Retest Carbon Monoxide (CO) Calculations**

**Vehicle Year Initial CO Final CO AVG Initial CO AVG Final CO Tests CO
Diff.**

1969 9.26 6.43 9.26 6.43 1 2.83

1970 49.34 23.56 9.868 4.712 5 5.156

1971 8.96 8.88 8.96 8.88 1 0.08

1972 104.34 78.34 9.485454545 7.121818182 11 2.363636364

1973 8.99 6.31 8.99 6.31 1 2.68

1974 101.79 65.77 10.179 6.577 10 3.602

1976 110.05 67.65 9.170833333 5.6375 12 3.533333333

1977 11.62 9.98 5.81 4.99 2 0.82

1978 349.56 225.35 8.525853659 5.496341463 41 3.029512195

1979 62.42 33.38 7.8025 4.1725 8 3.63

1980 205.68 107.63 6.856 3.587666667 30 3.268333333

1981 62.6 16.72 8.942857143 2.388571429 7 6.554285714

1982 286.34 153.57 5.843673469 3.134081633 49 2.709591837

1983 17.37 13.09 4.3425 3.2725 4 1.07

1984 679.95 417.74 5.312109375 3.26359375 128 2.048515625

1985 94.24 82.74 5.235555556 4.596666667 18 0.638888889

1986 1083.88 586.36 4.795929204 2.594513274 226 2.201415929

1987 93.76 57.41 4.688 2.8705 20 1.8175

1988 791.41 416.03 4.187354497 2.201216931 189 1.986137566

1989 165.73 80.59 5.524333333 2.686333333 30 2.838

1990 1061.1 392.33 5.101442308 1.886201923 208 3.215240385

1991 87.31 42.51 4.3655 2.1255 20 2.24

1992 754.04 328.17 5.464057971 2.378043478 138 3.086014493

1993 87.28 39.25 6.234285714 2.803571429 14 3.430714286

1994 243.66 117.2 5.184255319 2.493617021 47 2.690638298

1995 8.37 0.17 8.37 0.17 1 8.2

1996 103.86 42.03 3.994615385 1.616538462 26 2.378076923

1998 47.99 19.73 5.332222222 2.192222222 9 3.14

1999 12.59 0.37 4.196666667 0.123333333 3 4.073333333

2000 10.98 8.93 5.49 4.465 2 1.025

2001 2.27 0.09 2.27 0.09 1 2.18

2002 10.69 0 10.69 0 1 10.69

**Totals: 6727.43 3448.31 6.577281241 3.477088475 1263 3.100192766**

**Avg CO Improvement: 2.60**

**Avg % Improvement: 48.74%**

27

**Subsequent Retest Hydrocarbon (HC) Calculations**

**Vehicle Year Initial HC Final HC Tests HC Diff.**

1968 1825.2 1208 1 617.2

1970 1301.6 1514.9 1 -213.3

1971 2446.3 1307.9 1 1138.4

1972 2598.5 3916.9 2 -1318.4

1974 2877.6 1066.8 1 1810.8

1976 3152 2859 1 293

1977 3403.9 3186.5 1 217.4

1978 13587.9 2930.9 5 10657

1980 2868 1300.7 4 1567.3

1982 11787.6 5610.5 8 6177.1

1983 1499.2 927.9 2 571.3

1984 28848.3 22053.1 33 6795.2

1985 6509.6 3651.9 6 2857.7

1986 60455.5 45953.8 72 14501.7

1987 7785.6 5052.8 10 2732.8

1988 44822.4 22710.4 54 22112

1989 9045.7 2500.9 8 6544.8

1990 31645.6 25574 58 6071.6

1992 25867.5 16583.9 51 9283.6

1993 6361.2 3746 3 2615.2

1994 12050.1 6076.7 20 5973.4

1996 4727.6 2465.5 8 2262.1

2000 8177.2 2270.1 3 5907.1

**Totals: 293644.1 184469.1 353 109175**

**Avg HC Improvement: 309.28**

**Avg % Improvement: 37.18%**

28

**Subsequent Retest Carbon Monoxide (CO) Calculations**

**Vehicle Year Initial CO Final CO AVG Initial CO AVG Final CO Tests CO
Diff.**

1972 9.66 8.56 9.66 8.56 1 1.1

1976 16.1 10.14 8.05 5.07 2 2.98

1978 69.88 37.52 8.735 4.69 8 4.045

1980 14.3 8.8 4.766666667 2.933333333 3 1.833333333

1982 37.15 27 6.191666667 4.5 6 1.691666667

1983 12.51 5.51 6.255 2.755 2 3.5

1984 140.34 98.54 4.527096774 3.178709677 31 1.348387097

1985 23.99 17.31 5.9975 4.3275 4 1.67

1986 184.72 103.41 3.848333333 2.154375 48 1.693958333

1987 39.36 23.04 6.56 3.84 6 2.72

1988 133.24 50.35 4.934814815 1.864814815 27 3.07

1989 9.31 3.29 1.862 0.658 5 1.204

1990 175.42 76.51 4.616315789 2.013421053 38 2.602894737

1992 93.83 24.54 4.468095238 1.168571429 21 3.29952381

1993 16.92 13.91 8.46 6.955 2 1.505

1994 45.83 17.62 6.547142857 2.517142857 7 4.03

1996 17.32 1.28 4.33 0.32 4 4.01

2000 4.07 7.74 2.035 3.87 2 -1.835

**Totals: 1043.95 535.07 5.658035119 3.409770454 217 2.248264665**

**Avg CO Improvement: 2.35**

**Avg % Improvement: 48.75%**

Date: May 11, 1999\
Contacts: Molly Galvin, Media Relations Officer\
Dumi Ndlovu, Media Relations Assistant\
(202) 334-2138; e-mail \<news@nas.edu\>\
\
**[EMBARGOED: NOT FOR PUBLIC RELEASE BEFORE 5 P.M. EDT TUESDAY, MAY
11]{.underline}**\
\
**Commonly Available Ethanol and MTBE Gasoline Blends**\
**Do Little to Reduce Smog**\
\
WASHINGTON \-- The two principal types of oxygen additives used in
reformulated gasolines in the United States contribute little to
reducing ozone pollution, says a new report by a committee of the
National Research Council. In a study undertaken to examine the
differences between the additives ethanol and methyl tertiary-butyl
ether (MTBE), the committee found that reformulated gasoline made with
ethanol is less effective, but that the overall impact of either oxygen
additive on reducing ozone \-- a major component of smog \-- is very
small.\
\
\"Motor-vehicle emissions of chemicals that form ozone pollution have
decreased in recent years,\" said committee chair William Chameides,
Regents Professor of Earth and Atmospheric Sciences at the Georgia
Institute of Technology, Atlanta. [\"But that\'s largely because of
better emissions control equipment and components of reformulated
gasolines \-- other than oxygen additives \-- that improve air
quality]{.underline}. Although additives do reduce some pollutants from
motor vehicles emissions**[, the oxygenates appear to have little impact
on lowering ozone levels]{.underline}**. Moreover, it is not possible to
attribute a significant portion of past reductions in smog to the use of
these gasoline additives.\"\
\
The Clean Air Act amendments of 1990 require the use of reformulated
gasoline with oxygen additives in areas of the United States that have
substantial ozone pollution. These gasolines are sold in cities on the
East Coast, in the Midwest, Texas, and California \-- particularly
during the summer months, when near-ground ozone is most prevalent. This
ozone is formed when pollutants from many different sources, including
automobiles, react chemically in the presence of sunlight. Reformulated
gasolines are designed to lower the emissions of vehicle pollutants,
including those that contribute to ozone formation. In addition to
oxygen additives, the fuels have a number of other characteristics that
lower emissions.\
\
But the oxygen additives in reformulated gasolines have raised
environmental concerns. MTBE, for example, has leaked into drinking
water in California, leading the state to phase out the use of the
additive. Because questions persist over which types of reformulated
gasolines are preferable in improving air quality, the Environmental
Protection Agency (EPA) asked the Research Council to study methods for
certifying gasoline blends with oxygen additives.\
\
The committee found that, compared with MTBE blends, ethanol blends
result in more pollutants evaporating from vehicle gas tanks[. Ethanol
blends also increase the overall potential of emissions to form
ozone.]{.underline} However, available data indicate that the potential
for either additive to lower smog levels is small and will continue to
decrease as other measures to reduce vehicle emissions take effect, the
committee said. Tougher air-quality regulations and improvements to
vehicles over the last few decades have substantially reduced emissions
that help create near-ground ozone.\
\
Projections indicate that motor vehicle emissions could decrease further
in the next several years as more new technologies are incorporated, the
report says. If these projections are correct, the impact that
reformulated gasolines have in reducing ozone concentrations will
continue to decline. Even if the contributions that motor vehicle
emissions make to ozone formation have been underestimated \-- which has
occurred in the past \-- [oxygen additives in reformulated gasolines
will have little impact on reducing smog, the committee
said]{.underline}.\
\
The committee noted that available data on reformulated gasolines are
limited because they are mostly derived from tests on properly
functioning motor vehicles. [A disproportionate amount of pollutants
originate from a small number of high-emitting vehicles, such as older
vehicles that have malfunctioning catalytic converters. There is
considerable uncertainty surrounding the effects of reformulated
gasolines on these vehicles, making it difficult to quantify total
overall vehicle emissions and evaluate the effects of different fuel
blends]{.underline}.\
\
**Certifying Fuels**\
\
Peak ozone concentrations in many U.S. metropolitan areas declined by
about 10 percent from 1986 to 1997, but approximately 48 million people
still live in areas where ozone concentrations exceed EPA\'s standard
for health protection. Most of the Earth\'s ozone is found in the
stratosphere, where the gas protects against harmful ultraviolet
radiation from the sun. But ozone that is formed and trapped near the
ground can present a significant health and environmental hazard,
impairing lung function and damaging forests, crops, and other
vegetation.\
\
The Clean Air Act, which mandated the reformulated gasoline program,
stipulated a minimum oxygen content in the reformulated blends to help
reduce emissions. EPA must certify reformulated blends to ensure that
they meet program standards. The agency currently certifies reformulated
gasoline blends by measuring the total mass of volatile compounds that
are emitted, rather than by examining the potential for these compounds
to form ozone. Because [ethanol-blended gasoline is more volatile,
meaning that more pollutants are evaporated into the atmosphere, it is
difficult for such blends to meet EPA\'s standards]{.underline} unless
the ethanol is blended with a more expensive, lower-volatility gasoline
that is not readily available in many markets. Ethanol proponents have
argued that EPA should evaluate the fuels by the extent to which their
emissions will react in the atmosphere and contribute to ozone
formation.\
\
Reactivity has reached a level of substantial rigor as a tool for
assessing the ozone-forming potential of emissions, the committee said,
but there are no compelling scientific reasons at this time to recommend
that reformulated fuels be certified based on reactivity. The basic
results of comparing the MTBE and ethanol fuel blends were the same
regardless of whether they were evaluated by reactivity or the mass of
their emissions. In addition, an evaluation of carbon monoxide emissions
should be included in assessments of the effects of reformulated
gasoline, the committee said. [Carbon monoxide can be a significant
factor in ozone formation, but often it is overlooked in assessments of
reformulated gasolines.]{.underline}\
\
The study was funded by the Environmental Protection Agency. The
National Research Council is the principal operating arm of the National
Academy of Sciences and the National Academy of Engineering. It is a
private, non-profit institution that provides independent advice on
science and technology issues under a congressional charter. A committee
roster follows.\
\
Read the full text of ***[Ozone-Forming Potential of Reformulated
Gasoline](http://books.nap.edu/catalog/9627.html?onpi_newsdoc070199)***
for free on the Web, as well as more than 1,800 other publications from
the National Academies. Printed copies are available for purchase from
the [**National Academy Press**](http://www.nap.edu/) Web site or at the
mailing address in the letterhead; tel. (202) 334-3313 or
1-800-624-6242. Reporters may obtain a pre-publication copy from the
Office of News and Public Information at the letterhead address
(contacts listed above).\
\
\
\
\
\
NATIONAL RESEARCH COUNCIL\
Commission on Geosciences, Environment, and Resources\
Board on Environmental Studies and Toxicology\
and\
Board on Atmospheric Sciences and Climate\
\
**Committee on Ozone-Forming Potential of Reformulated Gasoline**\
\
**William L. Chameides ^1^ (chair)**\
Regents Professor of Earth and Atmospheric Sciences\
School of Earth and Atmospheric Sciences\
Georgia Institute of Technology\
Atlanta\
\
**Charles A. Amann ^2^**\
Principal Engineer\
KAB Engineering\
Bloomfield Hills, Mich.\
\
**Roger Atkinson**\
Research Chemist\
Statewide Air Pollution Resource Center\
University of California\
Riverside\
\
**Nancy J. Brown**\
Senior Scientist\
Ernest Orlando Lawrence Berkeley\
National Laboratory\
Berkeley, Calif.\
\
**Jack G. Calvert**\
Senior Scientist\
National Center for Atmospheric Research\
Boulder, Colo.\
\
**Fred C. Fehsenfeld**\
Senior Scientist and Program Leader\
Aeronomy Laboratory\
National Oceanographic and Atmospheric Administration\
Boulder, Colo.\
\
**John P. Longwell ^2^**\
Professor Emeritus\
Department of Chemical Engineering\
Massachusetts Institute of Technology\
Cambridge\
\
**Mario J. Molina^1^**\
Professor of Atmospheric Chemistry\
Department of Earth, Atmospheric, and Planetary Sciences\
Massachusetts Institute of Technology\
Cambridge\
\
**S. Trivikrama Rao**\
Assistant Commissioner\
Office of Science and Technology\
New York State Department of\
Environmental Conservation\
Albany\
\
**Armistead G. Russell**\
Professor of Environmental Engineering\
Department of Civil and Environmental Engineering\
Georgia Institute of Technology\
Atlanta\
\
**Christopher L. Saricks**\
Transportation Systems Analyst\
Center for Transportation Research\
Energy Systems Division\
Argonne National Laboratory\
Argonne, Ill.\
\
RESEARCH COUNCIL STAFF\
\
**Raymond Wassel**\
Study Director\
\
\
^1^ Member, National Academy of Sciences\
^2^ Member, National Academy of Engineering

No Way Back

Why Air Pollution Will Continue to Decline

Joel Schwartz

The AEI Press

*Publisher for the American Enterprise Institute*

WA S H I N G T O N , D . C .

*2003*

Available in the United States from the AEI Press, c/o Client
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This publication is a project of the National Research Initiative, a
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is designed to support, publish, and disseminate research by
university-based scholars and other independent

researchers who are engaged in the exploration of important public
policy issues.

**Library of Congress Cataloging-in-Publication Data**

Schwartz, Joel, 1947-

No way back : why air pollution will continue to decline / Joel
Schwartz.

p\. cm.

ISBN 0-8447-7166-X (pbk.)

1\. Air---Pollution---United States. 2. Air quality

management---Government policy---United States. I. Title.

TD883.2.S35 2003

363.739\'2\'0973---dc21

2003056030

© 2003 by the American Enterprise Institute for Public Policy Research,
Washington, D.C. All rights reserved. No

part of this publication may be used or reproduced in any manner
whatsoever without permission in writing from

the American Enterprise Institute except in the case of brief quotations
embodied in news articles, critical articles, or

reviews. The views expressed in the publications of the American
Enterprise Institute are those of the authors and do

not necessarily reflect the views of the staff, advisory panels,
officers, or trustees of AEI.

*Printed in the United States of America*

NRINATIONAL

RESEARCH

INITIATIVE

iii

**Contents**

**LIST OF FIGURES AND TABLES v**

**INTRODUCTION 1**

**PAST AND FUTURE AIR POLLUTION EMISSIONS 5**

*Gasoline Vehicles* 5

*Diesel Trucks* 15

*Power Plants and Industrial Boilers* 18

**POLICY IMPLICATIONS 19**

**APPENDIX: DATA ANALYSIS METHODS FOR VEHICLE EMISSIONS 22**

*Remote Sensing Data Preparation* 22

*Data Analysis* 22

*Statistical Issues* 24

**NOTES 27**

**ABOUT THE AUTHOR 35**

**Figures and Tables**

**FIGURES**

1a. Trends in Average Ambient Levels of Gaseous Air Pollutants 1

1b. Trends in Average Ambient Levels of Airborne

Particulate Matter 2

2a. Light-Duty Vehicle Emission Trends Measured

in a San Francisco Bay Area Tunnel 5

2b. Light-Duty Vehicle Emission Trends Measured by

On-Road Remote Sensing in Chicago 6

2c. Light-Duty Vehicle Emission Trends Measured by

I/M Testing and On-Road Remote Sensing in Denver 6

3\. Effect of Growth in Fuel Consumption on Emissions 7

4\. Percent Change in Total Vehicle Emissions Compared with

Percent Change in Gasoline Use and in SUVs and Pickups

as a Fraction of the Vehicle Fleet, 1994--2001 8

5a. Comparison of Average Car and SUV/Pickup Emissions,

from Phoenix I/M Data 9

5b. Comparison of Average Car and SUV/Pickup Emissions,

from Denver I/M Data 9

6a. Federal VOC Emission Standards for Cars and Light Trucks 10

6b. Federal NOx Emission Standards for Cars and Light Trucks 10

7a. VOC Emissions vs. Vehicle Age, for Calendar Years

1997 and 2000, from Chicago Remote Sensing Data 11

7b. NOx Emissions vs. Vehicle Age, for Calendar Years

1997 and 2000, from Chicago Remote Sensing Data 11

8a. NOx Emissions vs. Vehicle Age for Model Years

1993--1999, from Chicago Remote Sensing Data 12

8b. VOC Emissions vs. Vehicle Age for Model Years

1991--1999, from Chicago Remote Sensing Data 12

9a. VOC Emissions vs. Vehicle Age from Chicago Remote

Sensing Data 13

9b. VOC Emissions vs. Vehicle Type and Age from Denver I/M Data 13

v

vi

10a. Future NOx Emissions under Four Scenarios 14

10b. Future VOC Emissions under Four Scenarios 14

11\. Fleet-Average Emissions Projected by CARB and

EPA Mobile-Source Emissions Models 15

12\. Future Emissions Under the LEV II/Tier 2 Scenario,

Including Growth in Vehicle Travel 15

13\. Heavy-Duty Diesel Truck PM10 Emissions Measured

in Pennsylvania Turnpike Tunnels, 1975--1999 16

14\. Federal NOx and PM Standards for Heavy-Duty Diesel Trucks 16

15\. Projections of Heavy-Duty Truck NOx Emissions with

and without CY2007 New-Vehicle Standards 17

16\. Power Plant Sulfur Dioxide Emissions 17

17\. VOC Emissions from Vehicles, Ranked from Cleanest

to Dirtiest, Based on Chicago Remote Sensing Data 20

A-1a. VOC Emissions vs. Vehicle Age with 95 Percent Confidence

Intervals, from Chicago Remote Sensing Data 24

A-1b. NOx Emissions vs. Vehicle Age with 95 Percent Confidence

Intervals, from Chicago Remote Sensing Data 25

**TABLES**

1\. Average Annual Percent Reduction in Emissions for

Various Time Periods, Locations, and Data Sources 7

2\. Effect of Growth in Fuel Consumption on Vehicle Emission

Trends from 1994 to 2001 in the San Francisco Bay Area 8

1

The United States has made tremendous progress in

reducing air pollution during the last forty years. Air

pollution has declined dramatically since the 1960s

and 1970s, and virtually the entire nation now meets

federal health standards for carbon monoxide (CO),

sulfur dioxide (SO2), and nitrogen dioxide (NO2).1

Many areas of the country still exceed health standards

for ground-level ozone ("smog") and airborne

particulate matter (PM), but both of these pollutants

continue to decline as well. Half of

the nation's ozone monitoring locations

exceeded the federal one-hour

ozone standard in the early 1980s,

but only 13 percent exceeded the

standard by the end of 2002.2 PM

measurement methods have changed

a number of times during the last

forty years, but all trend data show

PM levels dropping. Average levels

of PM2.5---the form of PM now of

greatest regulatory concern---have

declined by a third during the last

twenty years.3 Figures 1a and 1b

summarize pollution trend data.4

Will air pollution continue to

improve? Many opinion leaders

do not think so. After the Bush

administration announced plans to

modify certain provisions of the

Clean Air Act's New Source Review

(NSR) program, which requires

large industrial plants to install

state-of-the-art pollution controls if

they make a "major modification"

to their facilities, *New York Times*

columnist and Nobel Prize--winning

economist Paul Krugman suggested

"it might be a good idea to breathe deeply now,

while you still can."5

Newspaper headlines and editorials likewise

declared that the administration was working

to roll back years of progress on air pollution at

the behest of large utilities and other regulated

industries.6 Environmental activists also turned up

the heat, asserting the NSR changes "would allow millions

more tons of soot, smog, and toxic pollution to

**Introduction**

**Figure 1a. Trends in Average Ambient Levels of Gaseous Air
Pollutants**

*Source:* Based on data reported in F. W. Lipfert and S. C. Morris,
"Temporal and Spatial Relations

between Age Specific Mortality and Ambient Air Quality in the United
States: Regression Results for

Counties, 1960--97," *Occupational and Environmental Medicine* 59, no. 3
(2002): 156--74.

*Note:* Averages were calculated for periods of several years. The
markers on the graph are placed at

the mid-point of each averaging period (e.g., 1972 represents 1970--74).
Brackets for 1960s values

are included as a reminder that only a few dozen counties were monitored
at the time, while later

averages are based on monitoring data from hundreds of counties.

SO2 Sulfur dioxide (average of annual means from all monitoring
locations)

NO2 Nitrogen dioxide (average of annual means from all monitoring
locations)

CO Carbon monoxide (average of the 95th percentile of daily readings for
each monitoring

location)

Ozone Average of the 95th percentile of peak daily one-hour-average
readings for each monitoring

location

ppm Parts per million

ppb Parts per billion

0

10

20

30

40

50

60

1960 1965 1970 1975 1980 1985 1990 1995 2000

SO2 and NO2 (ppb) CO (ppm)

0

20

40

60

80

100

120

140

160

Ozone (ppb)

NO2

CO (95th percentile)

SO2

Ozone (1-hour, 95th percentile)

Calendar Year

NO WAY BACK

2

be spewed into our air each year," and dubbing the

Bush Environmental Protection Agency (EPA) the

"Environmental Pollution Agency."7 And nine attorneys

general from northeastern states sued EPA over

the new NSR rules, asserting that "the Bush administration

has taken an action that will bring more acid

rain, more smog, more asthma and more respiratory

disease to millions of Americans."8

The public is also pessimistic on air pollution---

presumably at least partly as a result of gloomy and

alarming forecasts from activists and the media.

Polls consistently show that a large majority of

Americans believe air pollution has

been increasing and will continue

to worsen in the future.9

Despite the heated rhetoric on

New Source Review and the Bush

administration's approach to environmental

protection, the concerns

raised by the media, environmentalists,

and opinion leaders reflect a

profound misunderstanding of the

processes that have improved air

quality in the past and will continue

to in the future. Policy choices can

indeed affect the rate of future

progress, but it would be virtually

impossible for anyone, no matter

how tenacious and determined, to

prevent continued reductions in air

pollution. This is because of three

simple facts:

• Most air pollution comes from

motor vehicles.10 But the actions

necessary to clean up the vast

majority of vehicle pollution

have already been taken. The car

and light-truck (i.e., SUV and

pickup) fleet turns over every

twenty years or so, which means

that progressively tougher vehicle

emissions standards implemented

during the last decade have yet

to fully come to fruition.11 Onroad

pollutionmeasurements and emissions test

data from vehicle inspection programs show that

with each new model-year, motor vehicles start

out and stay cleaner than previous models. This

means that we will be reaping the benefits of progressively

cleaner vehicles for decades to come.

These data also show that emissions from SUVs are

converging with emissions from cars, muting the

effect on air quality of the increasing popularity of

larger automobiles. Furthermore, these pollution

reductions are unstoppable, because they depend

only on older vehicles being retired, rather than on

**Figure 1b. Trends in Average Ambient Levels of Airborne Particulate**

**Matter**

*Sources:* TSP and early sulfate trend, Lipfert and Morris, (2002).
Recent sulfate trend is based on 42

EPA CASTNET sites in the eastern United States that had data for each
year from 1988 to 2001.

CASTNET data were downloaded from www.epa.gov/castnet/data.html. PM10
trend is based on 199

monitoring sites around the United States that had data for each year
between 1988 and 2001. PM10

data were downloaded from EPA's AIRData Web site,
www.epa.gov/aqspubl1/select.html. Average

PM2.5 levels for 1979--1983 and 1999--2000 are reported in C. A. Pope et
al., "Lung Cancer,

Cardiopulmonary Mortality, and Long-Term Exposure to Fine Particulate
Air Pollution," *Journal of*

*the American Medical Association* 287, no. 9 (2002): 113--41.

Trend lines were derived from least-squares linear regressions. The
early sulfate trend is based on

data collected on glass-fiber filters, which are now known to create
"artifactual" sulfate from ambient

SO2 gas drawn through the filter. Thus, these early sulfate data are
biased high in terms of

absolute sulfate values; the trend in sulfate levels from these data is
less likely to suffer from this bias

since all the data were collected with the same technique. Data points
for TSP, the earlier sulfate values,

and PM2.5 represent averages over two to four years. The year given in
the chart is the average

year for each point.

PM10 Particulate matter less than 10 micrometers in diameter

PM2.5 Particulate matter less than 2.5 micrometers in diameter

TSP Total suspended particulates (roughly equivalent to PM30)

Sulfate The sulfate component of airborne particulates

*∝*g/m3 Micrograms per cubic meter

0

10

20

30

40

50

60

70

80

90

100

110

1960 1965 1970 1975 1980 1985 1990 1995 2000

0

1

2

3

4

5

6

7

8

9

10

11

TSP

PM10

PM2.5

Sulfate

TSP and PM (∝g/m3)

Sulfate (∝g/m3)

Calendar Year

newer vehicles becoming cleaner. Likewise, the

heavy-duty diesel truck fleet turns over on an even

longer time scale than for cars, yet diesel standards

have been tightened three times in the last fifteen

years.

• In addition to the future pollution reductions we

will see from already implemented standards

and existing technologies, EPA has also promulgated

additional regulations for both automobiles

and heavy-duty trucks that will begin to phase in,

respectively, in 2004 and 2007. Depending on

pollutant and type of vehicle, these new standards

will lower emissions by 70 to 90 percent

below the stringent levels already achieved during

the last decade. While these upcoming standards

could theoretically be repealed, many auto

manufacturers are already selling some models

that meet the new standards, and there is no significant

political support for weakening these

standards. Furthermore, California adopts its

own stringent vehicle standards independent of

federal actions, and other states, including New

York and Massachusetts, have adopted California's

Low Emission Vehicle (LEV) regulations.12 The

2004 standards also require big SUVs to achieve

the same low emissions as regular cars, so the relative

popularity of SUVs will not affect future air

quality. Even after accounting for growth, total

vehicle emissions are certain to decline more than

80 percent during the next twenty years or so.

• While the Bush administration has tinkered with

New Source Review requirements for existing

facilities, other laws and regulations require systemwide

emission reductions from the same large

industrial facilities regardless of NSR requirements.

EPA's NOx "SIP Call" regulation requires a

60 percent reduction in warm-season emissions

of nitrogen oxides (NOx) from Eastern power

plants and industrial boilers starting in 2004.13

EPA is enforcing this regulation. The Clean Air

Act's Title IV acid rain program requires a 20 percent

reduction in SO2 emissions from power

plants between 2000 and 2010. This program

has broad, bipartisan support and has no chance

of being repealed. Both of these programs put

firm, declining caps on systemwide emissions

that cannot be exceeded, regardless of New

Source Review requirements.

Together, these sources account for more than

three-fourths of ozone- and PM-forming emissions.

Since future reductions from these sources are all but

guaranteed, air pollution in America will decline dramatically

in coming years. Because already adopted

requirements will eliminate most remaining air pollution,

the challenge of ensuring that every American

breathes healthy air has been met---or rather, it will be

met when we see the full effect of existing programs.

Yet public debate on air pollution policy is being

dominated by the false premise that air pollution will

rise unless we redouble our efforts to reduce it.

Instead, policymakers should be thinking about

remaining air pollution as a near-term problem and

seek ways to cost-effectively achieve more rapid nearterm

pollution reductions in areas that still have substantial

air pollution problems. For example, on-road

emission studies show that the worst 5 percent of

volatile organic compound (VOC) emitters accounts

for about half of all automotive VOC emissions, so

appropriate policies would probably best be focused

on speeding the demise of the remaining stock of

high-polluting, older vehicles, and repairing the

smaller number of newer high polluters. This

approach could achieve rapid, inexpensive pollution

reductions, without imposing new long-term costs.

If already adopted regulations will eliminate

the vast majority of remaining vehicle pollution,

then measures based on the premise that air pollution

is a continuing long-term problem are likely

to be both expensive and relatively ineffective.

For example, the Clean Air Act's transportation

conformity requirement and other regional growth

management policies are based on the premise

that transportation infrastructure decisions should

be driven by air quality concerns. But such policies

are at best superfluous if air pollution has

already been dealt with through technological

advancements.

INTRODUCTION

3

In addition, since the rate of future pollution

reductions depends on how fast the vehicle fleet

turns over, policymakers should be particularly

cautious about imposing regulatory costs that

would artificially slow the purchase of new vehicles.

For example, the California Air Resources

Board estimates that electric vehicles will cost

about twice as much as a comparable gasolinepowered

car. Mandating such vehicles would slow

new-car purchases, slowing down turnover of the

fleet and making future air pollution worse than it

would otherwise be.

The rest of this report presents the analysis behind

these conclusions.

NO WAY BACK

4

**Gasoline Vehicles**

Gasoline vehicles include light-duty vehicles, such as

cars, minivans, SUVs, and pickup trucks, as well as

some larger medium- and heavy-duty vehicles powered

by gasoline. Gasoline vehicles are the major

source of VOCs and CO in metropolitan areas, and

are also a substantial contributor to NOx emissions.

NOx and VOCs combine to form ground-level ozone

(commonly known as smog) on hot, sunny days, and

are therefore known as "ozone precursors." NOx can

also be converted into nitrates, which contribute significantly

to PM in the western United States. VOCs

can also be chemically converted to organic PM

through atmospheric reactions, and are a significant

component of PM in most metropolitan areas. NOx

also contributes to acid rain.

"Source apportionment" studies

have found that gasoline exhaust and

evaporation accounts for 50 to 80

percent of total anthropogenic VOC

emissions in metropolitan regions.14

On a nationwide basis, gasoline vehicles

also account for about 20 percent

of NOx emissions, and probably

a greater percentage in metropolitan

areas where gasoline vehicles are

concentrated.15 About 85 to 95 percent

of CO comes from gasolinevehicle

exhaust as well.16

Tunnel studies, on-road remote

sensing, and vehicle emissions

inspection and maintenance (I/M)

programs are the main sources of

data on gasoline-vehicle emission

trends. Figure 2a displays average

vehicle emissions rates, in grams of

pollutant emitted per gallon of fuel burned, from

1994 to 2001, as measured in the Caldecott Tunnel

in the San Francisco Bay Area.17 The point markers

represent the actual measured values, and the

curves were derived from a least-squares regression

of the data using an exponential function.

Emission rates declined substantially for all three

pollutants during the seven-year measurement

period---67 percent for VOCs, 49 percent for NOx,

and 62 percent for CO. Based on the exponential

trend fit to the data, these represent annualized

reductions in average vehicle emission rates of 15, 9,

and 13 percent, respectively.18 Although not shown

in the graph, benzene emissions declined 82 percent.

Figure 2b presents similar data for Chicago,

based on remote sensing measurements taken at

5

**Past and Future Air Pollution Emissions**

**Figure 2a. Light-Duty Vehicle Emission Trends Measured in a San**

**Francisco Bay Area Tunnel**

*Source:* Based on data in A. J. Kean et al., "Trends in Exhaust
Emissions from In-Use California Light-

Duty Vehicles, 1994--2001" (Warrendale, Penn.: Society of Automotive
Engineers, 2002).

0

5

10

15

20

25

30

35

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Calendar Year

0

60

120

180

240

300

360

420

CO (grams/gallon)

VOC or NOx (grams/gallon)

NOx

VOC

CO

the same location each year from

1997 through 2000, while Figure 2c

displays trend data for Denver from

vehicle emissions inspection tests as

well as remote sensing data for CO

and NOx.19 The Chicago and Denver

data also show substantial declines

in vehicle emission rates. Once

again, the lines in each graph represent

a least-squares regression using

an exponential function.

Table 1 summarizes emission

trends from these data and also compares

them with predictions of EPA's

and the California Air Resources

Board's (CARB) vehicle emissions

models, MOBILE6 and EMFAC-

2000, respectively.20 For each pollutant,

the table gives the annualized

percent decline in emissions for the

given time period, and the number

of years during each time period in

which measurements were made.

Average emission rates declined rapidly

for all pollutants, and output

from the emission models parallels

these declines.21

**Effect of Growth in Miles Driven**

**and the Popularity of SUVs.** The

discussion above focused on emission

*rates* from vehicles, either in

grams per mile driven or grams per

gallon of fuel consumed. All other

things being equal, declines in *total*

emissions would match declines in

emission rates. But total emissions

are also affected by several other factors,

including:

• *Changing composition of the vehicle*

*fleet.* SUVs and pickups---

often referred to as "light

trucks"---have higher per-mile

emissions than cars (although, as

NO WAY BACK

6

**Figure 2b. Light-Duty Vehicle Emission Trends Measured by On-Road**

**Remote Sensing in Chicago**

*Source:* Based on remote sensing data provided by Don Stedman and Gary
Bishop, University of

Denver, and downloaded from www.feat.biochem.du.edu/.

0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001

0

30

60

90

120

150

180

NOx

VOC

CO

Calendar Year

CO (grams/gallon)

VOC or NOx (grams/gallon)

**Figure 2c. Light-Duty Vehicle Emission Trends Measured**

**by I/M Testing and On-Road Remote Sensing**

**in Denver**

*Source:* Based on remote sensing data provided by Don Stedman and Gary
Bishop, University of

Denver, and I/M data supplied by Tom Wenzel, Lawrence Berkeley National
Laboratory.

I/M = inspection and maintenance program data; RSD = on-road remote
sensing data. In terms

of absolute emissions, I/M and remote sensing data are not directly
comparable for two reasons.

First, the I/M data span vehicles of age zero to fourteen years, while
the remote sensing data

include cars of all ages. Second, the I/M data are based on the IM240
test, which does not necessarily

include the same distribution of engine loads as observed at the remote
sensing sites.

0

2

4

6

8

10

12

14

16

18

20

22

24

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

0

40

80

120

160

200

240

280

320

360

400

440

480

NOx (I/M)

NOx (RSD)

VOC (I/M)

CO (I/M)

CO (RSD)

Calendar Year

CO (grams/gallon)

VOC or NOx (grams/gallon)

will be shown below, light-truck

emissions have been approaching

those of cars, and all light trucks

will be held to the same emissions

and durability requirements as

cars under EPA standards that

begin phasing in for the 2004

model year).

• *Increased per-capita driving.* If people

drive longer distances, total

emissions will also increase.

Suburbanization might increase

commuting distances, resulting in

more driving per capita.

• *Population growth.* More people

in a metropolitan area means

more cars on the road, adding

additional emissions.

Let us assess the net effect of

these factors on total vehicle emissions.

The Caldecott Tunnel data

discussed earlier provide emission

trends in grams per gallon of fuel

consumed. Total fleet emissions in a

given year are then equal to this

emission rate times the total amount

of fuel consumed. The effects of

SUVs, suburbanization, and population

growth would all be reflected in

their combined effect on total fuel

consumption.

Between 1994 and 2001, fuel

consumption in California increased

13 percent, or about 1.7 percent per

year.22 Figure 3 shows the effect of

increasing fuel consumption on

total emissions for a hypothetical fleet with emissions

arbitrarily set at 1,000 tons of VOC and NOx

in 1994. The solid lines show emissions each year at

constant annual fuel consumption, but including

the observed annual reduction in emissions per gallon

of fuel consumed. The dashed line adds in the

observed growth in fuel consumption of 13 percent

from 1994 to 2001. As the graph shows, rapid

declines in vehicle emission rates overwhelmed the

effects of more SUVs, more cars on the road, and

more per-capita miles of driving. Indeed, as actually

measured in the Caldecott Tunnel, SUVs and pickups

PAST AND FUTURE AIR POLLUTION EMISSIONS

7

**Table 1. Average Annual Percent Reduction in Emissions for**

**Various Time Periods, Locations, and Data Sources**

**Number of**

**Location Time Period VOC NOx CO Years Measured**

Bay Area

Tunnel 1994--2001 14.9% 9.4% 12.8% 6

Chicago RSD 1997--2000 13.3% 8.5% 14.9% 4\*

Denver RSD 1996--2003 NA 3.4% 8.3% 6\*\*

Denver I/M 1996--2002 13.2% 4.7% 10.1% 7

Phoenix I/M 1995--1999 11.5% 5.3% 10.3% 5

EMFAC2000 1990--2000 9.0% 6.8% 9.0% NA

MOBILE6 1990--2000 8.2% 8.4% 9.3% NA

*Sources:* Based on Bay Area tunnel data in Kean et al. (2002). Chicago
and Denver remote sensing

data (RSD) provided by Don Stedman and Gary Bishop, University of
Denver. Denver and

Phoenix I/M data supplied by Tom Wenzel, Lawrence Berkeley National
Laboratory.

EMFAC2000 and MOBILE6 output were reported in Darlington et al. (2001).

\* 3 for VOC, spanning 1997 to 2000

\*\* 4 for NOx, spanning 1999 to 2003

**Figure 3. Effect of Growth in Fuel Consumption on Emissions**

*Source:* Based on Bay Area tunnel data in Kean et al. (2002). Solid
line is what the total emissions

trend would look like if fuel consumption remained constant from 1994 to
2001. The

dashed line gives the actual trend in total emissions from 1994 to 2001,
given actual growth in

gasoline consumption.

0

100

200

300

400

500

600

700

800

900

1000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

VOC

Calendar Year

Emissions (tons)

Add 13% growth in fuel use, 1994--2001

Constant Fuel Use

NOx

went from 31 to 38 percent of the vehicle fleet

between 1994 and 2001, but this had little effect on

overall emission reductions. Table 2 compares

changes in emission rates with changes in total

emissions after accounting for growth in fuel consumption.

Figure 4 compares emission reductions

with growth in gasoline consumption and in SUVs

and pickups as a fraction of the vehicle fleet.

The increasing popularity of SUVs has done little

to slow the declining trend in vehicle emissions,

because the absolute difference in emissions of cars

and SUVs has also been declining. Figures 5a and

5b demonstrate this using data from the Phoenix

and Denver I/M programs, respectively. Each graph

displays NOx and VOC emissions by model year.23

The Phoenix graph divides the vehicles into three

categories---traditional cars, small and medium

SUVs and pickups, such as the

Toyota RAV-4 or the Ford Explorer,

and large SUVs and pickups, such

as the Chevrolet Suburban.24 The

Denver data include all light trucks

in a single category.25

As the graphs show, emissions of

SUVs and pickups are getting closer

and closer to car emissions with

each successive model year, such

that there is now little difference

between cars and light trucks. This

result is not an artifact of the more

recent model years' also being

younger at the time they were

measured, as both the Denver and

Phoenix data show that the difference

in car and SUV/pickup emissions

remained stable with vehicle

age for all model years.26

In addition, figures 5a and 5b

display emissions in grams per mile,

so the relative emissions levels inherently

account for the lower fuel

economy of larger vehicles relative to

cars. VOC emissions from cars and

trucks are virtually indistinguishable

in recent model years.27 Another

factor to keep in mind is that federal and California

emission regulations eliminate the distinction

between car and SUV/pickup emission standards

starting with the 2004 model year.28 Overall, the

data clearly show that the increasing popularity of

larger vehicles will have little impact on future pollution

emissions from the vehicle fleet.

**Projecting Future Emissions.** Although emissions

have declined in the past, can we expect continued

declines in the future? The following factors guarantee

substantial reductions in future automobile

pollution:

• *On average, more recent vehicle models start out and*

*stay cleaner than earlier models.* This means that

even if future vehicles have the same emissions

NO WAY BACK

8

**Figure 4. Percent Change in Total Vehicle Emissions Compared with**

**Percent Change in Gasoline Use and in SUVs and Pickups as**

**a Fraction of the Vehicle Fleet, 1994--2001**

*Source:* Based on data in Kean et al. (2002).

--63%

--55%

--43%

23%

13%

--70%

--60%

--50%

--40%

--30%

--20%

--10%

0%

10%

20%

30%

VOC CO NOx SUVs/Pickups Gasoline Use

**Table 2. Effect of Growth in Fuel Consumption on Vehicle Emission**

**Trends from 1994 to 2001 in the San Francisco Bay Area**

**Pollutant**

**HC NOx CO**

Change in Fleet Emission Rate -- 67% -- 49% -- 62%

Change in Total Fleet Emissions -- 63% -- 43% -- 55%

*Source:* Based on data in Kean et al. (2002).

performance as vehicles sold in

2003, emissions will continue to

decline for the roughly twenty

years required for the fleet to turn

over to vehicles with current

emissions control technology and

durability.

• *"Exogenous" improvements in vehicle*

*performance.* Vehicle emissions

have been continuously improving

for at least two decades, even

during multiyear periods during

which regulatory standards stayed

the same. This suggests that regulatory

standards are not the

only factors that spur automakers

to improve vehicle emissions

performance.

• *Progressively more stringent emission*

*standards.* Both federal and

California emission standards and

durability requirements continue

to become more stringent, culminating

with the federal "Tier 2"

standards and California "LEV II"

standards that begin phasing

in for the 2004 model year.

Continued improvements in

vehicle emissions and durability

ensure that fleet turnover will

continue to improve air quality

for decades to come.

Figures 6a and 6b display federal

VOC and NOx emission standards

by model year for cars and light

trucks.29 The designations along

the bottom of each graph give EPA's

names for its emission standards

categories and the model years covered

by each standards category.30

CO standards have become similarly more stringent

with time. California standards followed a

similar path but declined more rapidly than the

federal standards. Durability requirements have

PAST AND FUTURE AIR POLLUTION EMISSIONS

9

**Figure 5b. Comparison of Average Car and SUV/Pickup Emissions,**

**from Denver I/M Data**

*Source:* Based on IM240 test data collected in 2002. Data provided by
Tom Wenzel, Lawrence

Berkeley National Laboratory.

.

SUV/Pickup

Car

VOC NOx

0

0.5

1

1.5

2

2.5

3

3.5

4

.

Emissions (grams/mile)

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

Model Year

**Figure 5a. Comparison of Average Car and SUV/Pickup Emissions,**

**from Phoenix I/M Data**

*Source:* Based on IM147 test data collected in 2001. Data provided by
Tom Wenzel, Lawrence

Berkeley National Laboratory.

0

0.5

1

1.5

2

2.5

3

3.5

4

Large SUV/Pickup

Small/Medium SUV/Pickup

Car

NOx VOC

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Model Year

Emissions (grams/mile)

also become more stringent with time. Through

1993, vehicles had to meet emission standards

only up to 50,000 miles. Starting

with the 1994 model year, the durability

requirements were extended to

100,000 miles. The 2004 standards

extend the durability requirement

up to 120,000 miles.31 When compared

with the Tier 1 standards,

which phased in starting in 1994,

the Tier 2 standards represent VOC

and NOx reductions of 80 to 90

percent. The increased durability

requirements make the true percentage

reduction requirements

even greater.

Remote sensing and I/M emissions

data can show us how vehicle

emissions change with age, and

how emissions vs. age changes with

calendar year. In the following

charts, the abbreviation CY refers to

a calendar year in which emissions

were measured, and MY refers to

a given model year of vehicles---for

example, "MY1994" refers to the

cohort of vehicles built in the 1994

model year.

Figure 7a displays VOC emissions

vs. vehicle age for CY1997 and

CY2000, based on Chicago remote

sensing data. MY1994 and MY1997

are marked on the graph to show

the relationship between age, calendar

year, and model year in these

data. MY1994 was three years old in

CY1997 and six years old in CY2000,

so this graph shows emissions of

each model year at two different

ages. This graph also shows emissions

of *different model years at the*

*same age.* For example, when compared

at three years of age, MY2000

emits much less VOC than MY1997.

Note that at any given age, vehicles

were usually much cleaner in CY2000 than in

CY1997. Figure 7b displays similar data for NOx.32

NO WAY BACK

10

**Figure 6b. Federal NOx Emission Standards for Cars and Light Trucks**

*Sources:* Davis and Siegel, "Transportation Energy Data Book: Edition
22," (2002); EPA, "Federal and

California Exhaust and Evaporative Emission Standards for Light-Duty
Vehicles and Light-Duty

Trucks," (2000).

NLEV = National Low Emission Vehicle

0

0.5

1

1.5

2

2.5

3

3.5

1970 1975 1980 1985 1990 1995 2000 2005 2010

Large SUV/Pickup

Small/Medium SUV/Pickup

Car

Model Year

Tier 1 Tier 2 NLEV Pre-Tier 0 Tier 0

Emissions (grams/mile)

**Figure 6a. Federal VOC Emission Standards for Cars and Light Trucks**

*Sources:* S. C. Davis and S. W. Siegel, "Transportation Energy Data
Book: Edition 22" (Oak Ridge,

Tennessee: Oak Ridge National Laboratory, September 2002); EPA, "Federal
and California Exhaust

and Evaporative Emission Standards for Light-Duty Vehicles and
Light-Duty Trucks" (Washington,

DC: February 2000).

NLEV = National Low Emission Vehicle

0

0.5

1

1.5

2

2.5

3

3.5

1970 1975 1980 1985 1990 1995 2000 2005 2010

Model Year

Large SUV/Pickup

Small/Medium SUV/Pickup

Car

Tier 1 Tier 2 NLEV Pre-Tier 0 Tier 0

Emissions (grams/mile)

Figure 8a presents the same data

but with NOx emissions plotted by

age for each model year.33 This

format shows how a given model

year's emissions change with age.

More crucially, this graph shows

why average vehicle emissions have

declined---improved performance

and durability---and why we should

expect additional pollution reductions

in the future. As Figure 8a

shows, at any given age, more recent

vehicle model years generally have

lower emissions than earlier model

years.34 For example, looking at

three-year-old vehicles, MY1994

averages 14.7 grams per gallon, but

MY1997 averages only 8.3 grams

per gallon---about 45 percent lower.

In general, more recent MYs start out

cleaner and stay cleaner than previous

MYs. As a result, future vehicle

fleets will have lower emissions than

the current fleet. Figure 8b shows

the same pattern for VOC emissions.

35 The Chicago remote sensing

data are not unique; remote sensing

and I/M data show similar patterns

in all cities for which such data are

available.

At least two factors ensure that

emissions performance will continue

to improve for future vehicle

model years. First, both federal

and California emission requirements

continue to become progressively

more stringent, both in

terms of allowable emissions levels

and of the length of time and

mileage over which automakers

must warranty vehicle emission

control systems (see discussion

above). To the extent that past

improvements in emissions performance have

been driven by these regulatory standards, we can

probably expect substantial pollution reductions

to continue in the future.

PAST AND FUTURE AIR POLLUTION EMISSIONS

11

**Figure 7b. NOx Emissions vs. Vehicle Age, for Calendar Years 1997**

**and 2000, from Chicago Remote Sensing Data**

*Source:* Remote sensing data collected by Don Stedman and Gary Bishop
of the University of Denver,

and downloaded from www.feat.biochem.du.edu/.

Age 14 represents an average for ages 13--15; age 18 represents an
average of ages 16--20.

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

2000

Calendar Year

1997

Age (years)

Emissions (grams/gallon)

**Figure 7a. VOC Emissions vs. Vehicle Age, for Calendar Years 1997**

**and 2000, from Chicago Remote Sensing Data**

*Source:* Remote sensing data collected by Don Stedman and Gary Bishop
of the University of Denver,

and downloaded from www.feat.biochem.du.edu/.

Age 14 represents an average of ages 13--15; age 18 represents an
average of ages 16--20.

0

10

20

30

40

50

60

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Age (years)

1997

2000

Calendar Year

MY 1994

MY 1997

Emissions (grams/gallon)

Other regulatory factors might

also encourage automakers to improve

the durability of emission

control systems. For example,

MY1996 and newer vehicles are

equipped with the second generation

of on-board diagnostics

(OBDII)---a system of sensors that

monitors various aspects of engine

performance and illuminates the

"check engine" light if a potential

problem occurs. EPA and CARB

require the OBDII system to be set

with very stringent tolerances, and

many I/M programs now "test" vehicles'

emission control systems by

querying the OBDII system. This

may encourage manufacturers to

over-engineer their cars beyond

the nominal emission certification

requirements in order to reduce the

likelihood that the OBDII system

will be triggered.36

Second, automakers have improved

the emissions performance of

their vehicles beyond regulatory

requirements. Figures 9a and 9b display

VOC emissions data from,

respectively, Chicago and Denver in

the same format as Figures 8a and

8b. The Denver data show emissions

separately for cars and light trucks.

For ease of legibility, the graphs

include only even model years.

Regulatory standards for VOC emissions

were constant for model years

1982 through 1992, yet all three

graphs show substantial improvement

in the emissions performance

of vehicles built during this period. A

detailed understanding of the reasons

for these emission improvements

would require a separate

analysis. However, one might surmise

that the same competitive forces

NO WAY BACK

12

**Figure 8b. VOC Emissions vs. Vehicle Age for Model Years**

**1991--1999, from Chicago Remote Sensing Data**

*Source:* Remote sensing data collected by Don Stedman and Gary Bishop
of the University of Denver,

and downloaded from www.feat.biochem.du.edu/.

0

2

4

6

8

10

12

14

16

18

20

22

24

0 1 2 3 4 5 6 7 8 9 10

Age (years)

MY 91

MY 92

MY 93

MY 94

MY 95

MY 96

MY 97

MY 98

MY 99

X

X

X

Emissions (grams/gallon)

X

**Figure 8a. NOx Emissions vs. Vehicle Age for Model Years 1993--1999,**

**from Chicago Remote Sensing Data**

*Source:* Remote sensing data collected by Don Stedman and Gary Bishop
of the University of Denver,

and downloaded from www.feat.biochem.du.edu/.

0

2

4

6

8

10

12

14

16

18

20

22

24

0 1 2 3 4 5 6 7 8

Age (years)

MY 93

MY 94

MY 95

MY 96

MY 97

MY 98

MY 99

X

\+

\+ +

\+ +

X X

X

X

Emissions (grams/gallon)

that spurred manufacturers to

improve the general reliability, durability,

and performance of their vehicles

would also benefit emissions

performance. For example, the widespread

introduction of fuel injection

and computerized engine control

during the 1980s also permitted finer

control of the fuel-air mixture, which

benefits emissions performance. The

Denver I/M data also amplify the

points made earlier on the declining

difference in emissions between

SUVs and cars and on the ongoing

improvement in emissions with each

successive model year.

We can use existing information

to make reasonable projections of

how emissions will evolve in the

future. As a start, we can ask what

would happen if emissions performance

had stopped improving

after the 1999 model year. If this

happened, emissions would have

declined until the current fleet was

replaced with vehicles built from

CY1999 onwards---say about fifteen

to twenty years from now.

Predicting emissions from such a

fleet requires an estimate of

emissions from new vehicles, and a

deterioration function to estimate

emissions as the fleet ages.

Figure 10a displays projections

of future NOx emissions under four

scenarios. The top curve in the

graph represents actual emissions

in CY2000 from the Chicago

remote sensing data.37 The next

four curves represent scenarios

described in the following paragraphs.

The values at the right of

the graph give, for each scenario,

the percentage reduction in fleet

emissions from the CY2000 base.

PAST AND FUTURE AIR POLLUTION EMISSIONS

13

**Figure 9b. VOC Emissions vs. Vehicle Type and Age from Denver**

**I/M Data**

*Source:* Data provided by Tom Wenzel, Lawrence Berkeley National
Laboratory.

The zigzag pattern in the data results from differences in the vehicles
between adjacent ages. The

Denver program requires testing every two years and at change of
ownership. Even-model years

receive a biennial test in even years, while odd-model years receive a
biennial test in odd years.

But vehicles with an even model year can be tested in an odd calendar
year if they are sold, and

these "change-of-ownership" vehicles have higher emissions, on average,
than vehicles that are

not sold.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1 3 5 7 9 11131517 19 1 3 5 7 9 1113 1517 19

Age (years)

Emissions (grams/mile)

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

SUV/Pickup Car

X

X

X

X X

X X

X

X

X

X

X

X X

X

Model Year

Car SUV/Pickup

**Figure 9a. VOC Emissions vs. Vehicle Age from Chicago Remote**

**Sensing Data**

*Source:* Remote sensing data collected by Don Stedman and Gary Bishop
of the University of Denver,

and downloaded from www.feat.biochem.du.edu/.

0

5

10

15

20

25

30

35

40

45

50

55

60

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Vehicle Age (years)

MY 84

MY 86

MY 88

MY 90

MY 92

MY 94

MY 96

MY 98

X

X

X

Emissions (grams/gallon)

The "High Deterioration" scenario begins with

a fleet that has the same emissions as MY1999

at one year of age. This pessimistically assumes

MY1999 vehicles will deteriorate at a rate similar

to that of much earlier model years.38 This scenario

is intended to be a conservative "worst-case"

for how recent model years will deteriorate over

time. The scenario is worst-case, because the deterioration

rate is based on the deterioration rate

of vehicles built years ago, even though newer

vehicles deteriorate more slowly. Even so, NOx

emissions still decline 35 percent under this

scenario.

The "Moderate Deterioration" scenario assumes

linear deterioration with age, and the deterioration

slope is the average of the measured deterioration

slopes for MYs 1995 and 1996, based on a linear

extrapolation of the data.39 Under this scenario,

fleet emissions would decline 52

percent from the level measured in

CY2000.

The "No Deterioration" scenario

is a completely artificial model

intended to illustrate an underappreciated

fact about vehicle pollution:

Most emission reductions

come not from making relatively

low-emitting new cars just a little

bit cleaner, but by reducing the rate

at which emissions deteriorate with

age. Under this scenario, even if

vehicles' starting emissions don't fall

below the MY1999 level, two-thirds

of NOx emissions are nevertheless

eliminated.

The scenario labeled "LEV II/

Tier 2" is intended to project what

is actually likely to happen in

coming years. Emissions in this

scenario are based on the maximum

emissions allowed under federal

Tier 2 and California LEV II

standards that begin phasing in

with the 2004 model-year.40 The

federal and California standards are

similar, so I used the LEV II ultralow-

emission-vehicle (ULEV) standards

to represent both.41 Figure

10b displays the results of the same

four scenarios for VOC emissions.

Under the LEV II/Tier 2 standards,

fleet-average emissions will decline

about 90 percent from current levels

as the current fleet is replaced

NO WAY BACK

14

**Figure 10b. Future VOC Emissions under Four Scenarios**

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

--38%

--61%

--89%

--73%

CY2000 Actual Emissions

High Deterioration

Moderate Deterioration

No Deterioration

LEV II/Tier 2

Age (years)

Emissions (grams/gallon)

**Figure 10a. Future NOx Emissions under Four Scenarios**

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age (years)

CY2000 Actual Emissions

High Deterioration

Moderate Deterioration

No Deterioration

LEV II/Tier 2

--52%

--35%

--90%

--67%

Emissions (grams/gallon)

PAST AND FUTURE AIR POLLUTION EMISSIONS

15

with vehicles meeting LEV II/Tier 2 standards during

the next twenty years or so.

Even the two unrealistically pessimistic scenarios

would result in substantial improvements in

vehicle emissions during the next two decades.

The LEV II/Tier 2 scenario would

eliminate almost all vehicle emissions

by the time the fleet turned

over to these vehicles. Even this

scenario may be conservative,

because it assumes that emissions

would always be at the allowable

limit. Actual emissions could be

below this level if manufacturers

included a safety margin to ensure

they do not exceed the standards

and risk fines or recalls.

The vehicle emission models

used by CARB and EPA, EMFAC

and MOBILE, also project large

declines in future emissions,

though not quite as large as one

might expect by a simple comparison

of current on-road emissions

with the upcoming Tier 2 and

LEV II emission standards. Figure

11 displays the emission trends predicted

by the regulatory agencies'

models.

Figure 12 projects future effects

of growth in vehicle travel given a

90 percent reduction in gram per

gallon vehicle emissions under the

LEV II/ Tier 2 scenario. The reductions

are assumed to occur during

a twenty-year period starting in

2004.42 The lower curve shows

the reduction in grams per gallon

emissions, while the upper curve

factors in a 2 percent per year

growth in gasoline use, to account

for increases in driving.43 As the

graph shows, growth turns a 90

percent reduction in the emission

rate into an 85 percent reduction

in total emissions---in other words, just as shown

earlier, growth in driving will do little to offset

gains from improved vehicle emissions performance.

Based on declining trends in vehicle emissions

and the requirements of standards that come

**Figure 12. Future Emissions Under the LEV II/Tier 2 Scenario,**

**Including Growth in Vehicle Travel**

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024

Calendar Year

Emissions Relative to Base Year Reduction in Total Emissions with
2%/year Growth in Gasoline

Use

LEV II/Tier 2 Scenario: Reduction in Average Emissions Rate

**Figure 11. Fleet-Average Emissions Projected by CARB and EPA**

**Mobile-Source Emissions Models**

*Source:* T. L. Darlington et al., "Comparison of EMFAC2000 and
MOBILE6," 11th CRC On-Road

Vehicle Emissions Workshop, San Diego, CRC, 2001.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1985 1990 1995 2000 2005 2010 2015 2020

Calendar Year

EMFAC2000

MOBILE6

VOC

NOx

Emissions (grams/mile)

NO WAY BACK

16

into effect starting in 2004, we should therefore

conservatively expect total automobile emissions

to decline more than 80 percent during the next

twenty years or so.

**Diesel Trucks**

Diesel trucks are a major source of

NOx emissions, and also a significant

contributor to PM. On a nation-wide

basis, diesel trucks account for an

estimated 25 percent of total NOx

emissions.44 Studies of particulate

matter during the late 1980s to late

1990s suggest that diesel PM typically

contributes a few micrograms per

cubic meter to daily average PM levels,

and roughly 5 to 20 percent of

typical total PM2.5 levels in metropolitan

areas.45 Diesel trucks also emit

VOCs, but typically contribute only

about one-sixth to one-seventh the

amount from gasoline vehicles.46

Trend data from tunnel studies

suggest that per-mile diesel truck PM

emissions have declined more than

80 percent during the last twentyfive

years (see figure 13).47 Unfortunately,

there are not similar long-term

trend data for diesel NOx emissions.

However, measurements in the

Tuscarora Mountain Tunnel in

Pennsylvania in 1992 and 1999 suggest

that average diesel-truck NOx

emissions *increased* almost 14 percent,

even though roughly a 9

percent decline would have been

expected, based on tighter diesel

emission standards implemented in

1991.48

The NOx increase can be

explained as follows: The certification

test for heavy-duty diesel engines

does not include a steady-state cruise

mode---which is the kind of driving

that occurs on uncongested freeways,

such as the Tuscarora Tunnel. Engine

**Figure 13. Heavy-Duty Diesel Truck PM10 Emissions Measured in**

**Pennsylvania Turnpike Tunnels, 1975--1999**

*Source:* Based on data from A. W. Gertler et al., "Measurements of
Mobile Source Particulate

Emissions in a Highway Tunnel," *International Journal of Vehicle
Design* 27 (2002): 86--93.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1970 1975 1980 1985 1990 1995 2000 2005

Calendar Year

Emissions (grams/mile)

**Figure 14. Federal NOx and PM Standards for Heavy-Duty Diesel**

**Trucks**

*Source:* Based on heavy duty vehicle emissions standards in EPA,
"Health Assessment Document for

Diesel Engine Exhaust" (Washington, DC: May 2002): 2-16.

0

1

2

3

4

5

6

7

8

9

10

11

12

1984 1988 1992 1996 2000 2004 2008 2012

Model Year

NOx

PM

NOx Standard (grams/bhp-hr)

PM Standard (grams/bhp-hr)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

manufacturers had programmed the engine-control

systems in 1990s trucks to go into a fuel-saving mode

during steady-state driving; a side effect of this

mode of operation was increased

NOx emissions.49 This probably

explains the observed increase in

NOx emission rates between 1992

and 1999.

EPA and CARB have since ruled

that programming for fuel savings

under steady-state driving violated

the emission certification requirements,

and have required that the

affected trucks---mainly those built

for the 1992 through 1998 model

years---have their engine programming

re-adjusted at their next engine

rebuild.50

There are no trend measurements

of emissions from diesel trucks driving

under the more "transient" conditions

that would occur on urban

freeways and streets (and that are

included in the regulatory certification

test for engine emissions), but it

is probable that emissions under

these conditions have declined due to

the tightened standards, just as was

the case with diesel PM emissions.

The expectation that truck NOx emissions

have declined overall is consistent

with the measured decline in

ambient NO2 levels at monitoring

locations around the country (see figure

1a).

Figure 14 displays federal NOx

and PM emission certification standards

for heavy-duty diesel trucks

from 1986 onward.51 Late 1990s

standards are more than 60 and 80

percent below 1980s levels, respectively,

for NOx and PM. EPA in 2000

also promulgated regulations requiring

an additional 90 percent reduction

in PM and NOx from current

levels, starting with the 2007 model year, which

would put emissions of future trucks about 99 percent

below 1980s levels.52

PAST AND FUTURE AIR POLLUTION EMISSIONS

17

**Figure 15. Projections of Heavy-Duty Truck NOx Emissions with and**

**without CY2007 New-Vehicle Standards**

*Source:* Based on model output reported in J. G. Heiken and T. L.
Darlington, "Comparison of

MOBILE6 and EMFAC2000 Heavy-Duty Diesel Emissions," 11th CRC On-Road
Vehicle Emissions

Workshop, San Diego, CRC, 2001; and tunnel data reported in A. W.
Gertler et al., "Trends in

Emissions from In-Use Light- and Heavy-Duty Vehicles," *Journal of the
Air & Waste Management*

*Association* (2003): submitted.

0

5

10

15

20

25

30

1985 1990 1995 2000 2005 2010 2015 2020 2025

Calendar Year

Without 2007 Standards

(from EMFAC2000)

With 2007 Standards (from MOBILE6)

Measured in Tuscarora Tunnel

Emissions (grams/mile)

**Figure 16. Power Plant Sulfur Dioxide Emissions**

*Source:* Based on data in EPA, "EPA's Acid Rain Program: Results of
Phase I, Outlook for Phase II"

(Washington, DC: October 2001).

0

2

4

6

8

10

12

14

16

18

20

1975 1980 1985 1990 1995 2000 2005 2010 2015

Calendar Year

Emissions (million tons)

Unlike gasoline vehicles, for which there is a

wealth of information on emissions by age and

model year, we have only the two Tuscarora

Mountain Tunnel point estimates of fleet-average

diesel-truck NOx emissions. I have therefore relied

on the CARB and EPA emission models for projections

of future diesel-truck emissions. Figure 15

displays projections of average diesel-truck NOx

emission rates with and without the 2007 standards,

along with the tunnel measurements.53 These projections

also include the projected effect of engine

rebuilds in reducing future NOx emissions from

MY1992--MY1998 truck engines, as well as the effect

of ultra-low-sulfur diesel (ULSD) fuel in reducing

emissions from existing diesel trucks when ULSD

becomes a national requirement in 2006.

Based on CARB's projections from the EMFAC-

2000 model, per-mile truck NOx emissions would

decline by about 65 percent between 2000 and 2020

without the 2007 standards. Including the 2007 standards,

EPA's MOBILE6 model projects that per-mile

truck NOx emissions will decline 87 percent between

2000 and 2020, with continued reductions beyond

2020, as pre-2007 truck engines are retired.

Assuming a 50 percent increase in truck travel

between 2000 and 2020, total truck emissions would

decline about 80 percent under this scenario.

Likewise, EPA projects a 75 percent reduction in total

diesel truck PM emissions between 2000 and 2020

when the 2007 standards are included.54

**Power Plants and Industrial Boilers**

EPA estimates that about two-thirds of SO2 emissions

come from coal-fired power plants, almost all in the

eastern United States, where coal is a much more

common fuel for electricity generation than in the

West.55 Although SO2 levels have been well below

federal health standards for some time, some SO2 is

converted to particulate sulfate, which contributes to

elevated PM levels and acid rain in the eastern United

States.56 Title IV of the Clean Air Act Amendments of

1990 requires that power-plant SO2 emissions be

reduced 50 percent below 1980 levels by 2010.57 The

reductions are achieved through a "cap-and-trade"

program that, beginning in 1995, established a

mandatory, annually declining cap on total SO2 emissions,

but allows utilities to trade allowances among

themselves to meet the cap at the lowest possible cost.

Figure 16 displays the trend in utility SO2 emissions

from 1980 to 2000, along with reductions

required by 2010 because of the continued decline

in the systemwide emissions cap. The hash mark in

2010 marks the ultimate cap on SO2 emissions that

will be achieved within a few years after 2010.58

According to EPA's 1999 NOx emissions inventory,

utilities and industrial electricity boilers contribute

about one-third of NOx emissions in the eastern

United States.59 Under Title IV of the Clean Air Act,

NOx emissions from electric utilities were reduced 25

percent between 1990 and 2000.60 Under EPA's "SIP

Call" regulation, warm-season NOx emissions from

utilities, as well as industrial boilers, will be capped at

60 percent below current levels starting in 2004.61

In addition to these preexisting requirements,

there is a great deal of momentum for additional federal

action on power plant pollution. Compared with

estimated emissions in 2000, the Bush administration's

Clear Skies Initiative would reduce power

plant NOx emissions by 67 percent, and SO2 emissions

by 73 percent by 2018.62 Senator James

Jeffords's Clean Power Act would go even further,

reducing NOx by 70 percent and SO2 by 80 percent

in 2008.63 Given the substantial bipartisan support

for new power plant emission reduction requirements,

it seems likely that Congress and the Bush

administration will adopt legislation reducing power

plant emissions below the limits set by current regulatory

requirements.

Some states have already chosen to go beyond

current federal requirements. For example, North

Carolina's Clean Smokestacks Act requires a 77 percent

reduction in NOx emissions in 2009 and a

73 percent reduction in SO2 emissions in 2013.64

New York State's Department of Environmental

Conservation recently adopted rules that phase in

starting in 2005 and require a 50 percent reduction

in SO2 emissions below levels currently

allowed under the Clean Air Act's Title IV acid rain

program.65

NO WAY BACK

18

19

We can draw the following conclusions from the

analysis above:

• Motor vehicle emissions will decline at least 80

percent during the next twenty years or so, as

older-technology vehicles are continually replaced

by more recent models that stay cleaner throughout

their lives. Because most NOx and VOC pollution

comes from motor vehicles, these virtually

unstoppable reductions in vehicle pollution are

enough by themselves to keep air pollution in

retreat for years to come.

• Average vehicle emission rates are declining

much more rapidly than driving is increasing.

Vehicle emissions are declining about 5 to 15 percent

per year due to fleet turnover, while driving

is increasing less than 2 percent per year. In other

words, total vehicle emissions will continue to

decline in spite of population growth, suburbanization,

and other trends toward increased

driving.

• From the late 1990s onward, the popularity of

SUVs and other larger vehicles has made virtually

no difference for air quality, as there is now

little difference between emissions of cars and

light trucks. Starting in 2004, both federal

and California regulations require the same

tough emission standards for all cars, SUVs,

and pickups.

• Current federal laws and regulations require

substantial reductions in power plant NOx and

SO2 emissions during the next few years, and

there is substantial bipartisan support for

requiring even greater emissions reductions.

Coal-fired power plants and industrial boilers

are under declining caps on systemwide emissions

that will not only prevent net increases

in emissions but ensure continued decreases,

regardless of changes to programs such as New

Source Review. In addition, some states have

already adopted new emission reduction

requirements for power plants that go well

beyond current federal requirements.

Corollaries to these conclusions include the

following:

• The long-term problem of air pollution in

America has essentially been solved by actions

that have already been taken but have not yet

come fully to fruition. If so, then additional

air pollution regulations that impose large

ongoing costs on the public could end up providing

few additional benefits. Instead, policymakers

should focus on ways to cost-effectively

mitigate remaining near-term air pollution

problems. Appropriate policies would probably

best be focused on speeding the repair, retrofit,

or demise of the remaining stock of older, highemitting

vehicles. Indeed, another striking

result from vehicle emission studies is that

a small number of "gross polluters" account

for most vehicle emissions. For example, in

Chicago, the worst 5 percent of vehicles generate

46 percent of all tailpipe VOC emissions,

and the worst 10 percent contribute 65 percent

of tailpipe VOC emissions (see figure 17).66 A

comparison of median and average emissions

also shows how skewed the distribution of

emissions is among different vehicles in the

fleet. The median car in this dataset emits only

**Policy Implications**

NO WAY BACK

20

3.8 grams per gallon, while the average car

emits 7.7 grams per gallon. NOx emissions are

also highly skewed, but not to as great an extent

as for VOC. The worst 5 percent of NOx emitters

cause 27 percent of total NOx emissions.

Data from other cities and from other types of

emission tests give similar results.67

• Anything that makes new vehicles artificially more

expensive, or that unnecessarily reduces people's

disposable income---and therefore their purchasing

power---could retard fleet turnover, thereby

slowing the most important process for cleaning

the air. CARB's zero-emission vehicle (ZEV)

requirement is one such policy. According to

analysis by CARB staff, ZEVs will likely cost

\$17,000 more than a comparable gasoline vehicle.

68 Automakers would therefore likely need to

sell them at a loss in order to meet their ZEV

quota. The only way to make this work financially

would be to raise the price of gasoline vehicles

in order to "cross-subsidize" ZEVs. The result

would be reduced new-car sales and slower fleet

turnover. But as shown above, the

gasoline cars that ZEVs would

replace will already eliminate

roughly 90 percent of existing

emissions from the vehicle fleet.

Because of the small marginal benefits

of ZEVs and their large costs

(which, perversely, slow fleet turnover),

the ZEV requirement risks

making future air quality worse

that it would otherwise be.69

• If growth in vehicle travel has little

effect on future air pollution levels,

then efforts to reduce driving

through lifestyle restrictions or

provision of expensive fixed-rail

transit systems will do relatively

little to clean the air, even if they

work as intended. In addition,

such measures are far more expensive

than alternatives, such as

dealing directly with gross-polluting vehicles, and

would take far longer to implement. For example,

rail projects typically cost about \$1 million per ton

of ozone precursors eliminated,70 yet regulators do

not consider an air pollution reduction measure to

be cost effective unless it costs less than about

\$10,000 to \$20,000 per ton of pollution eliminated.

71 This means that every dollar spent on rail

would achieve at least 50 to 100 times the pollution

reduction if spent on almost any other pollution

reduction measure. In particular, scrapping or

repairing gross polluters would achieve at least

200 times the pollution reductions from rail for

each dollar spent.72

• A Clean Air Act provision known as transportation

conformity requires that regional transportation

planning and infrastructure decisions be guided

by air quality goals.73 The intent of the conformity

requirement is to prevent expansion of automobile

infrastructure if it is expected to increase

future emissions enough to harm air quality. But

if technological advances coupled with fleet

**Figure 17. VOC Emissions from Vehicles, Ranked from Cleanest to**

**Dirtiest, Based on Chicago Remote Sensing Data**

*Source:* Based on remote sensing data supplied by Don Stedman and Gary
Bishop, University of

Denver and downloaded from www.feat.biochem.du.edu/.

Data are from calendar year 2000. Engine loads were restricted to
between 5 and 15 kilowatts per

metric ton to avoid bias due to load variation. Only vehicles with two
or more measurements are

included (1,643 vehicles). Negative values do not indicate negative
emissions (which is physically

impossible), but all measurements include some random scatter due to
instrument noise.

0 10 20 30 40 50 60 70 80 90 100

0

100

200

300

400

500

600

700

Emissions (grams/gallon)

Percent Rank

POLICY IMPLICATIONS

21

turnover will solve the long-term problem of

automotive air pollution, and gross-polluter

identification can mitigate the short-term problem,

then it is foolish to give substantial weight

to air quality considerations in high-stakes transportation

infrastructure decisions.

• Claims that air quality will decline in the future

are not only incorrect, they are the polar opposite

of what will actually occur. Emissions from motor

vehicles will continue to fall by several percent

per year and will be more than 80 percent below

current levels within the next twenty years.

Likewise, power plant and industrial emissions

will continue to decline simply as a result of continuing

implementation of existing laws and regulations.

Together, these sources account for more

than three-fourths of ozone- and PM-forming

pollution. The only way air pollution could

increase is if emissions from *all other sources*

increased by at least a few hundred percent---a

prospect that is absurd on its face and completely

at odds with historical data showing declines in

most emission sources over time, because of both

regulations and technological advancements.

Indeed, EPA and CARB already regulate emissions

from virtually all sources of air pollution, and

existing or planned regulations require continued

reductions from these sources.74

More generally, public debate on air pollution

policy is being driven by the false premise that

air pollution will rise unless we redouble our efforts

to reduce it.75 In reality, no one can stop continued

improvements in air quality in America. There is

truly no way back to the smog levels of yesterday.

22

**Remote Sensing Data Preparation**

Remote sensing data were downloaded from a

website maintained by Dr. Gary Bishop at the

University of Denver Department of Chemistry

(www.feat.biochem.du.edu/light_duty_vehicles.html).

The data are provided by location and "campaign"---

that is, the time period when the measurements

were taken. Data for the Arlington Heights site

in Chicago were available for calendar years

1997, 1998, 1999, and 2000 for all three pollutants

(VOC, NOx, and CO). Data for the 6th Ave./I-25 site

in Denver were available for calendar years 1996,

1997, 1999, 2000, and 2001, and for the Speer Blvd.

and I-25 site for 2003 for CO, while NOx data were

available only for 1999 onward.76

• The datasets provide emission readings in

parts per million. These were converted to

grams per gallon using conversion equations

that are available at www.feat.biochem.du.edu/

assets/reports/ftmath.pdf.

• Vehicles driving by a remote sensor can also be

under a wide range of engine loads. Engine load

is a function of speed, acceleration, and road

grade, and is usually reported in units of kilowatts

per metric ton of vehicle mass (kW/tonne).

The remote sensing data include variables for

speed and acceleration, while road grade is provided

in reports that accompany the datasets.

• The remote sensing data include flags for

whether a reading is valid for each pollutant and

whether the speed/acceleration reading was valid.

Only valid readings were used in the analysis for

this study.

• Remote sensing data for vehicles of age zero (that

is, vehicles for which the calendar year is the same

as the model year) cannot be used for the following

reason: Between the time a vehicle is measured

with remote sensing on the road and the

time its license plate is matched with state registration

data a couple of months later, a few older

vehicles measured on the road are scrapped, and

their license plates are turned in and assigned to a

new vehicle. These few old vehicles that were

measured on the road then appear to be new

vehicles in the remote sensing database. This creates

spuriously high average emissions in new

vehicles, making the zero-age vehicles unusable

for analysis. Thus, for the scenarios depicted in

Figures 10a and 10b, I took the conservative

approach of assigning the average emissions of

one-year-old vehicles to zero-age vehicles as well.

**Data Analysis**

**Remote Sensing Data.** The remote sensing data

were "sliced" to provide emissions estimates categorized

in various ways, including:

• Fleet-average emissions by calendar year

• Emissions vs. age by calendar year

• Emissions vs. age by model year

Because engine load affects emissions, and the

load distribution varies from calendar year to calendar

year, all data were adjusted to a common load distribution

to allow apples-to-apples comparisons. This

was done by taking averages of emissions grouped (or

"binned" in research parlance) by vehicle age and

**Appendix A**

**Data Analysis Methods for Vehicle Emissions**

load, with load divided into the following bins (in

kW/tonne): --60, --5, 0, 5, 10, 15, 20, 60, where each

bin falls between the listed values, and values less

than --60 or greater than 60 were excluded. The average

emissions in each load bin for vehicles of age X

were then weighted by a load distribution for age X to

get average emissions for vehicles of age X in each calendar

year.

For the Chicago data, the weighting function for

engine load was the average load distribution by age

for the entire four years of data. For Denver, which

had relatively low loads in some years (due to stopand-

go traffic as a result of construction), 2000, a

calendar year with more typical average loads, was

used.

To calculate fleet-average emissions for each calendar

year, emissions also must be weighted by the

"travel fraction"---that is, the percent of vehicles of

each age driving by the remote sensor. For each city,

the travel fraction was derived from the most recent

calendar year's remote sensing data. Fleet-average

emissions were then calculated by applying the

travel-fraction weighting to the load-adjusted average

emissions by age.

Remote sensing VOC data suffer from an offset

that made the Denver VOC data unusable for the

purposes of this study. The 1998 Chicago VOC data

were not usable due to an apparent instrument malfunction.

The remaining Chicago VOC data were

offset-adjusted by shifting all of the VOC readings in

a given calendar year so as to set the mode for that

calendar year to zero.77

The ranked emission results presented in Figure

17 were generated using data collected in CY2000,

restricting vehicle engine loads to between 5 and 15

kW/tonne, and also to vehicles with two or more

measurements, and then ranking emissions for each

unique vehicle from lowest to highest for a given

pollutant. The load restriction was intended to

ensure that the emission rankings would not be

biased by systematic differences in engine load

between vehicles with high and low emissions.

Restricting the data to vehicles with at least two

measurements controls for a statistical phenomenon

called "regression to the mean."78 When vehicles

with only one emission measurement are also

included, the worst 5 percent contribute 53 percent

of VOC emissions, and the worst 10 percent contribute

76 percent of VOC emissions.

**I/M Data.** I/M data for Phoenix for calendar years

1995--2001 were provided by Tom Wenzel of

Lawrence Berkeley National Laboratory. The I/M

test was changed in 2000, so post-1999 data are

not comparable with previous data.79 The data are

in the form of grams per mile emissions on the

IM240 test---a test in which a vehicle is driven on

a treadmill-like machine called a dynamometer for

four minutes over a standardized driving cycle.

The data were provided as averages by model year

and vehicle type (car, small/medium SUV/pickup

("LDT1"), and large SUV/pickup ("LDT2")) for

each calendar year, as well as weighted averages by

model year for the fleet as a whole. Data were also

available for 2000 and 2001 for the IM147 test---

the successor to the IM240 in Phoenix.

I/M data for Denver for calendar years 1996--2002

were provided by Tom Wenzel. These data were

similar to the Phoenix data, except that they were

divided only into "cars" and "light trucks," the latter

category including all SUVs and pickups.

To generate fleet average emission rates by calendar

year, the Phoenix data were weighted by a travel

fraction derived from remote sensing data collected in

Phoenix in 2000. Likewise, the Denver data were

weighted by the 2001 remote sensing travel fraction

at the 6th Ave./I-25 site in Denver.

The average emissions from a fleet of vehicles

depends on how much they are driven. Older vehicles

are driven fewer miles, on average, than newer

vehicles, and this is reflected in a lower representation

of older vehicles on the road than would be

naively expected from the percentage of older vehicles

calculated from vehicle registrations.

Almost all vehicles either pass or fail the IM240

test prior to completion of the full four-minute test

due to use of a "fast-pass/fast-fail" algorithm used in

order to shorten the time motorists spend going

through the testing process (the same is true for

Phoenix's IM147 test). The test data from Phoenix

APPENDIX A

23

and Denver represent mainly measurements that

have been statistically adjusted to full IM240 equivalent

emissions. This adjustment method is not perfect

and has been shown, based on comparison with

random samples of vehicles given a full IM240 test,

to overstate hydrocarbons (HC) and NOx emissions

of the most recent model-years.80

**Statistical Issues**

For the purposes of this analysis, there are two

major statistical concerns---random error and bias.

Bias can occur if the sample of vehicles for which

we have emission measurements differs in some

important way(s) from the full population of vehicles

on the road.

For example, although data from I/M programs are

useful for assessing various aspects of vehicle emissions,

they are also subject to significant sampling

bias, and must be interpreted cautiously. Stedman et

al. showed using on-road remote sensing measurements

that after Denver's enhanced I/M program

began in 1995, many motorists re-registered highemitting

cars outside the area, but continued to drive

in Denver. As a result, the fleet tested

in the Denver I/M program became

progressively depleted in highemitters

(relative to the actual onroad

fleet) during the first few years of

the program. There may be other

types of "gaming" that make emissions

on I/M tests different from emissions

on the road.81 Also, as noted

above, the algorithm to convert fastpass/

fast-fail emissions to full IM240

emissions overestimates the emissions

of more recent model years relative

to earlier ones, which could

cause an underestimate of the rate of

emission reductions. In addition,

there is evidence that some vehicles'

emissions during the I/M test are not

representative of their true emissions

on the road, because some motorists

take steps to pass the test without

performing lasting repairs on their vehicles.82

Remote sensing data are unlikely to suffer from

this type of bias, because vehicles are measured

at random in their unprepared on-road state.

Furthermore, the data were collected at the same

location and the same time of year in each calendar

year. Data from different calendar years were also all

adjusted to the same age and engine-load distribution

to remove bias due to these factors. Nevertheless,

there are other potential sources of bias in

remote sensing measurements. For example, driver

income has previously been shown to be inversely

correlated with vehicle emissions, even after controlling

for vehicle age.83 If the income distribution of

the vehicle owners driving by the remote sensor

changed over time, this would introduce bias not

accounted for in the analysis presented here. In addition,

the remote sensing data were collected from

vehicles entering or leaving freeways, which could

result in undersampling of older vehicles since they

are probably less likely to be driven on freeways. If

such a bias exists, it would be less likely to affect

emission trend estimates, since the bias would likely

be similar from year to year.

NO WAY BACK

24

**Figure A-1a. VOC Emissions vs. Vehicle Age with 95 Percent**

**Confidence Intervals, from Chicago Remote Sensing Data**

*Source:* Remote sensing data collected by Don Stedman and Gary Bishop
of the University of Denver,

and downloaded from www.feat.biochem.du.edu/.

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Age (years)

1997

Calendar Year

MY 1994

MY 1997

Emissions (grams/gallon)

2000

APPENDIX A

25

While sample selection bias is

always a concern, the weight of the

evidence suggests it is unlikely that

selection bias could be causing spurious

results. All geographic locations

and data collection methods gave

qualitatively similar results in terms

of emission trends, concentration of

most emissions in a few high emitters,

progressive improvement in

emissions with each model year, etc.

Random error results from

unavoidable random fluctuations

inherent in any sampling process. If

you have a large bowl filled with

equal numbers of red and blue marbles

and you draw ten at random,

you will most often get five of each

color. But sometimes the breakdown

will be six to four, seven to three, etc.,

just due to random chance.

The concept of "statistical significance" addresses

the concern over whether a numerical result

reflects a genuine property of a population of, say,

vehicles or people, or whether it is likely to have

resulted from random sampling error. In this study,

the concern is over whether a given difference in

emissions between vehicles from different model

years measured at the same age is likely to reflect a

real trend in emissions over time, or to be the

chance result of random fluctuations in the sample

of vehicles measured in each calendar year.

By convention, a result is considered statistically

significant if an appropriate statistical test suggests

that the result has no more than a 5 percent likelihood

of having resulted due to random sampling

error, and therefore a 95 percent chance of representing

a real trend in vehicle emissions. The term

"statistically significant" is a term of art in statistical

analysis, and the word "significant" in this context

does not in any way mean "important" or

"noteworthy" as it would in everyday use. The fact

that a result is statistically significant thus provides

no information either way on whether it is of practical

significance. All other things being equal, a

measured difference between two groups of vehicles

is more likely to become statistically significant

as the sample size gets larger and as the spread (in

technical parlance, the standard deviation) of the

measurements gets smaller.

One way to determine statistical significance is to

determine whether the "95 percent confidence intervals"

(95 percent CI) of two different measurements

overlap. Figures A-1a and A-1b display the same

data as figures 7a and 7b, but this time with the 95

percent CI displayed for each data point as bars jutting

up and down from each data point. For each

data point, there is a 95 percent chance that the true

value of that data point---in this case, the average

emissions of vehicles of the given age in a given calendar

year---lies within the range of the 95 percent

CI.84 Differences between groups are statistically significant

when their 95 percent CIs do not overlap.

Thus, as the graphs show, at most ages, the differences

between vehicles measured in 2000 and 1997

are statistically significant. As can be seen by the

range of the 95 percent CIs, emissions differences of

several percent or more are coincidentally also statistically

significant.

**Figure A-1b. NOx Emissions vs. Vehicle Age with 95 Percent**

**Confidence Intervals, from Chicago Remote Sensing Data**

*Source:* Remote sensing data collected by Don Stedman and Gary Bishop
of the University of Denver,

and downloaded from www.feat.biochem.du.edu/.

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Age (years)

1997

Calendar Year

2000

Emissions (grams/gallon)

27

1\. Three of 557 monitoring locations exceed the CO

health standard. Two of 667 monitoring locations exceed

the SO2 standard. The entire country meets the NO2 standard.

(Based on analysis of AirData pollution monitoring

data reports downloaded from EPA, www.epa.gov/air/

data/moncols.html?us\~USA\~United%20States.)

2\. Based on analysis of ozone monitoring data for 1982

through 2002 downloaded from www.epa.gov/aqspubl1/

select.html. EPA recently promulgated the more stringent

"eight-hour" ozone standard, which is exceeded at about

40 percent of monitoring locations. The eight-hour

standard is significantly more stringent than the one-hour

standard.

3\. PM2.5 denotes all PM up to 2.5 micrometers in diameter.

C. A. Pope et al., "Lung Cancer, Cardiopulmonary

Mortality, and Long-Term Exposure to Fine Particulate Air

Pollution," *Journal of the American Medical Association* 287,

no. 9 (2002): 1132--41.

4\. For a more detailed analysis of air pollution trends,

see J. Schwartz, "Understanding Air Pollution: Trends,

Health Effects, and Current Issues" (Washington, DC:

Cato, forthcoming).

5\. P. Krugman, "Every Breath You Take," *New York*

*Times,* 26 November 2002, A27.

6\. See, for example, Editorial, "Christie Whitman's

Tribulations," *New York Times,* 27 November, 2002, A22;

Editorial, "Empty Promises," *New York Times,* 31 January

2003, A28; Editorial, "Unclear Skies," *Dallas Morning*

*News,* 4 August, 2002, 2J; J. Kay, "EPA Plans Rollback of

Clean Air Act," *San Francisco Chronicle,* 14 June 2002, A1.

7\. S. Borenstein and P. Rogers, "Easing Air Rules

for Power Plants Generates Scorn," *San Jose Mercury News,*

14 June 2002, A4; SaveTheCleanAirAct.org, "Stop the

Attack on the Clean Air Act!" www.savethecleanairact.org/

public/.

8\. E. Pianin, "New Pollution Standards Prompt

Suit, 9 States Challenge U.S. Decision to Relax

Rules," *Washington Post,* 1 January 2003, A1. The

quote is from New York State Attorney General Eliot

Spitzer.

9\. See, for example, Clean Air Trust, "Entering the

New Millennium: Americans Want More Done to

Protect the Environment," www.cleanairtrust.org/

survey.environment.html; Foundation for Clean

Air Progress, "Survey of Air Pollution Perceptions:

Final Report," www.cleanairprogress.org/news/

quorum_res_01_14_02.asp. On the role of environmental

activists and the media in public misperceptions about air

pollution also see J. Schwartz, "Clearing the Air," *Regulation*

26, no. 2 (2003): 22--29.

10\. In metropolitan areas, on-road motor vehicles

contribute about 50 to 80 percent of VOCs, 45 to 65

percent of NOx, 40 to 60 percent of PM2.5, and 85 to 95

percent of CO. (See, for example, G. R. Cass et al.,

"Determination of Fine Particle and Coarse Particle

Concentrations and Chemical Composition in the

Northeastern United States, 1995" (Pasadena: California

Institute of Technology, December 1999); EPA, "Latest

Findings on National Air Quality, 2001 Status and

Trends" (Washington, DC: September 2002); South

Coast Air Quality Management District, "Draft 2003

Air Quality Management Plan, Appendix III: Base

and Future Year Emission Inventories" (Diamond Bar,

CA: February 2003); J. G. Watson et al., "Review of

Volatile Organic Compound Source Apportionment

by Chemical Mass Balance," *Atmospheric Environment*

32 (2001): 1567--84; M. Zheng et al., "Source

Apportionment of PM2.5 in the Southeastern United

States Using Solvent-Extractable Organic Compounds

as Tracers," *Environmental Science & Technology* 36

(2002): 2361--71.

11\. EPA's "Tier 1" standards were implemented in

1994, and National Low Emission Vehicle (NLEV) standards

were implemented in 2000.

**Notes**

NOTES TO PAGES 3--6

28

12\. EPA, "Section 177 States Vehicle Emissions Control

Requirements---Status on California Rules Adoption,"

undated, www.epa.gov/otaq/cert/ ststatus.pdf.

13\. NOx is a shorthand for NO2 + NO (nitrogen dioxide

and nitric oxide, respectively). Most NOx is emitted as

NO, but is converted to NO2 in the atmosphere.

14\. J. G. Watson et al., "Review of Volatile Organic

Compound Source Apportionment by Chemical Mass

Balance," *Atmospheric Environment* 32 (2001): 1567--84.

EPA's official inventory claims that on-road motor vehicles

contribute only about 30 percent of VOC, but "real-world"

source-apportionment studies during the last fifteen years

reviewed in Watson et al., cited above, show that EPA's

official inventory underestimates the percentage contribution

of motor vehicles to total VOCs, often by a large margin.

I added the caveat "anthropogenic" because VOC

emissions from trees and vegetation can dominate the VOC

inventory in some areas, particularly in the southeastern

United States. See L. M. Hagerman et al., "Characterization

of Non-Methane Hydrocarbons in the Rural Southeast

United States," *Atmospheric Environment* 31, no. 23 (1997):

4017--38; B. K. Pun et al., "Contribution of Biogenic

Emissions to the Formation of Ozone and Particulate

Matter in the Eastern United States," *Environmental Science*

*& Technology* 36 (2002): 3586--96.

15\. EPA's emissions inventory estimates were downloaded

from www.epa.gov/air/data/repsst.html. As an

example of a metropolitan inventory, the South Coast

Air Quality Management District (SCAQMD) estimates

that about 35 percent of NOx in the Los Angeles region

comes from cars and light trucks.

16\. EPA, "Latest Findings on National Air Quality, 2001

Status and Trends"; South Coast Air Quality Management

District, "Draft 2003 Air Quality Management Plan,

Appendix III: Base and Future Year Emission Inventories"

(Diamond Bar, California: February 2003).

17\. Data for this chart come from A. J. Kean et al.,

"Trends in Exhaust Emissions from In-Use California

Light-Duty Vehicles, 1994--2001" (Warrendale,

Pennsylvania: Society of Automotive Engineers, 2002).

In each year, sampling was done at the same time of

year over a ten-day period. Researchers measure emissions

of cars going through a tunnel by comparing the

increase in the concentration of VOC, NOx, CO, and

CO2 between the tunnel inlet and outlet. Measuring

CO2 allows a determination of emissions per gallon of

fuel burned, because all of the carbon in the fuel must

be emitted as either VOC, CO, or CO2.

18\. These percentages are the percent reduction in

each year from the previous years' emission rate---the

equivalent of a compounded growth (or in this case,

decline) calculation. When I refer to an "annualized"

percent reduction at other points in this paper, I mean

the year-over-year percent reduction rate that gives the

observed overall percent reduction in emissions for a

given dataset. I use this method so that data from different

places, collected over different time periods, can

be placed on a comparable footing.

There is no obvious trend in the rate of change in emissions

from year to year for any of the pollutants. HC and

CO decreased more from 1995 to 1996 than in other years,

probably due to the introduction of reformulated gasoline.

VOC emissions declined more from 1999 to 2001 than

1997 to 1999, while the opposite was true for NOx and

CO. On the other hand, NOx declined more (on an annualized

basis) from 1997 to 1999 than in earlier years.

Additional years of data will be necessary to determine

whether the rate of emissions decline is changing systematically

over time for any of the pollutants.

19\. The Chicago remote sensing emissions data are in

grams per gallon of fuel burned (grams/gallon). The

Denver I/M emissions data are normally reported in

grams per mile, but are in grams per gallon on the chart.

I converted the Denver data from grams per mile to

grams per gallon (grams/mile multiplied by miles/gallon

= grams/mile). Fuel economy during the emissions test

was included as part of the Denver I/M data, allowing

this conversion to be made. See Appendix A for details

on how the various emissions datasets were analyzed.

20\. Predicted emission trends from EMFAC2000 and

MOBILE6 were derived from T. L. Darlington et al.,

"Comparison EMFAC2000 and MOBILE6," 11th CRC

On-Road Vehicle Emissions Workshop, San Diego,

CRC, 2001.

21\. There are also some trend data from earlier time

periods. For example, based on measurements in the Van

Nuys Tunnel in Los Angeles in 1987 and 1995, VOC,

NOx, and CO emissions declined 9 percent, 2.5 percent,

and 6 percent per year. Based on data from the Allegheny

and Tuscarora Tunnels on the Pennsylvania Turnpike, CO

emissions declined an average of 8.6 percent per year

between 1973 and 1999 (almost a 90 percent decline

overall). (A. W. Gertler et al., "Trends in Emissions from

In-Use Light- and Heavy-Duty Vehicles," *Journal of the Air*

*& Waste Management Association* (2003): submitted.)

Regarding the emission models, it is worth noting that

although the models are in the ballpark on the relative

rate of change in emissions, the models overpredict

absolute emission rates by a large margin. For example,

compared with the data presented here, the models overpredict

VOC and CO emission rates by factors of about 4

and 3, respectively. These data do not include cold-start

and non-tailpipe emissions (except for the tunnel data,

which does include non-tailpipe emissions) but the discrepancy

between the data and models is several times

larger than the potential effect of these excluded emissions.

The models come closer on NOx but still overpredict

emissions by about a factor of 1.4.

22\. Kean et al., "Trends in Exhaust Emissions from In-

Use California Light-Duty Vehicles, 1994--2001".

23\. Results are similar for CO (data not shown).

24\. For each model year, the reported emissions were

measured during calendar year 2001 on the IM147 test

(the successor to the IM240 test). The IM147 test uses

the same equipment as the IM240 test, but a different

driving cycle. The difference in car and SUV/pickup

emissions for each model year was stable with vehicle

age, so this study uses the most recent calendar year for

which data were available.

25\. For each model year, the reported emissions were

measured during calendar year 2002. The difference in

car and SUV/pickup emissions for each model year was

stable with vehicle age, so this study uses the most

recent calendar year for which data were available.

26\. While I/M data suffer from underrepresentation of

high-emitting vehicles, this likely does not have much

effect on the SUV/pickup versus car analysis presented

here. First, the sampling bias would presumably affect all

vehicles to a similar extent. Second, most high-emitters

are older vehicles. Newer vehicles rarely have excessive

emissions and therefore rarely fail an I/M test. Thus, sampling

bias affects mainly model years that are older at the

time they are measured in an I/M program.

27\. The California Bureau of Automotive Repair in

1998 and 1999 collected several thousand emissions

measurements by pulling cars over at random at various

roadside locations and giving them an I/M test. These data

also indicate that SUV/pickup emissions have gotten much

closer to car emissions in recent years (data not shown).

28\. In fact, small and medium SUVs and pickups have

had to meet the same standards as cars since 1994. The

2004 standards will include the largest SUVs under the

same standards umbrella as other automobiles. CARB,

"The California Low-Emission Vehicle Regulations"

(Sacramento: May 2001); S. C. Davis and S. W. Siegel,

"Transportation Energy Data Book: Edition 22" (Oak

Ridge, Tennessee: Oak Ridge National Laboratory,

September 2002); EPA, "Tier 2 Motor Vehicle Emissions

Standards and Gasoline Sulfur Control Requirements;

Final Rule" (Washington, DC: 10 February 2000).

29\. "Light truck" refers to SUVs and pickup trucks.

30\. EPA generally phases new standards in over a

period of three to four years. For example, 40 percent of

vehicles had to meet Tier 1 standards in model-year

1994, 80 percent in 1995, and 100 percent in 1996. For

Tier 2, 25 percent must meet the standards in model

year 2004, 50 percent in 2005, 75 percent in 2006, and

100 percent in 2007.

31\. The standards in Figures 6a and 6b are the 50,000-

mile standards. EPA also included a 100,000-mile durability

requirement for vehicles built to Tier 1 standards, which

took effect in 1994. EPA's new Tier 2 requirements, which

begin phasing in starting with the 2004 model year,

include a 120,000-mile durability requirement. These

longer-term durability requirements allow emissions to rise

by up to about 50 percent above the 50,000-mile caps.

32\. Note that Figure 2b showed that, despite an

aggregate decline in NOx emissions from 1997 to 2000,

fleet-average NOx emissions in this dataset increased

slightly from CY1998 to CY1999, and the average for each

of these calendar years falls in between those for CY1997

and CY2000. Thus, if figures 7a and 7b included curves

for CY1998 and CY1999, those curves would fall between

the curves for CY1997 and CY2000, and would be relatively

close together at any given age.

33\. To my knowledge, Peter McClintock of Applied

Analysis, Inc. was the first person to create this type of

graphic for presenting vehicle emissions data.

34\. Figure 8a displays NOx data back to MY 1993. The

Chicago remote sensing data suggest little improvement

NOTES TO PAGES 6--11

29

in NOx emissions with model years before the early

1990s. Phoenix I/M data give similar results; however, the

Denver I/M data suggest some progressive improvement

in NOx for 1980s model years (data not shown).

35\. CO emissions follow a similar trend to NOx and

VOC (data not shown).

36\. Personal communication with Tom Wenzel,

Lawrence Berkeley National Laboratory.

37\. Emissions at age zero were set equal to emissions at

age one in both figures 11a and 11b. See appendix A for a

discussion of this.

38\. At each age, the deterioration slope is derived

from the deterioration rate of vehicles of the given age

that were measured by remote sensing. For example, the

deterioration rate for vehicles at four years of age is

the average deterioration slope for all model years that

were measured at four years of age---in this case

MY1993--MY1996 (see Figure 8a for a graphic representation).

Likewise, the deterioration slope for tenyear-

old vehicles is the average deterioration slope

measured for MY1987--MY1990, and so on.

39\. Figure 8a displays NOx emissions deterioration vs.

age for MYs 1995 and 1996.

40\. The phase-in schedule is 25 percent in 2004, 50 percent

in 2005, 75 percent in 2006, and 100 percent meeting

LEV II/Tier 2 standards in 2007.

41\. The LEV II/ULEV standards require NOx to be below

0.05 gram/mile at 50,000 miles, and below 0.07 gram/mile

at 120,000 miles. The corresponding values for VOCs are

0.04 and 0.055. To be conservative, I assumed that emissions

started out at the 50,000-mile level until four years of

age, rose linearly up to the 120,000-mile level at ten years

of age, and continued rising linearly with age afterwards.

To put these grams/mile emissions into grams/gallon, I

assumed an average fuel economy of 23 miles/gallon.

42\. Of course, actual future emissions would not decline

along a smooth exponential curve as projected here. Future

emissions will depend on regulatory and macroeconomic

factors that affect the rate at which motorists purchase new

vehicles and retire old ones, so the real trend will be

"bumpy" (see figure 3 for an example of an actual trend).

43\. EPA assumed a 1.7 percent per year increase in

driving when modeling future vehicle emissions for its

Tier 2 regulation, so this estimate is more conservative

than EPA's (EPA, "Tier 2 Motor Vehicle Emissions

Standards and Gasoline Sulfur Control Requirements;

Final Rule").

44\. Two caveats here: First, this is a national inventory.

Local and regional contributions of diesel trucks likely

differ from the national average. For example, electricity

generation contributes about a third of NOx emissions

in the eastern United States, but only 10 to 15 percent

in the western United States, and less than 2 percent in

California. Thus, diesel trucks account for a relatively

greater percentage of NOx in the West than in the East.

Also, in metropolitan areas, gasoline vehicles---mainly

cars, light trucks, and delivery trucks---likely contribute

a greater fraction of NOx, relative to diesel

trucks, than would be expected based on the national

inventory. This is because much truck travel occurs on

rural highways and interstates, and these emissions are

included in the national inventory, while gasoline vehicles

log relatively more of their miles within metropolitan

areas.

Second, EPA's official national NOx inventory for

1999 has only 14 percent of NOx coming from diesel

trucks---just over half the contribution claimed here.

Recent research on "real-world" emissions of diesel

vehicles indicates that EPA has underestimated diesel

truck NOx emissions by a factor of 2 in its official inventory,

while overestimating emissions from off-road

diesel equipment by a factor of about 2.2. To derive the

25 percent contribution from diesel trucks, this study

took the official EPA inventory breakdown and adjusted

the on- and off-road diesel contributions based on these

real-world estimates, while implicitly assuming that

other portions of the official inventory are accurate. (A.

J. Kean et al., "A Fuel-Based Assessment of Off-Road

Diesel Engine Emissions," *Journal of the Air & Waste*

*Management Association* 50 (2000): 1929--39.)

45\. J. C. Chow and J. G. Watson, "Review of PM2.5 and

PM10 Apportionment for Fossil Fuel Combustion and

Other Sources by the Chemical Mass Balance Receptor

Model," *Energy & Fuels* 16 (2002): 222-60; A. C. Lloyd and

T. A. Cackette, "Diesel Engines: Environmental Impact and

Control," *Journal of the Air & Waste Management Association*

51 (2001): 809--47.

46\. Watson et al., "Review of Volatile Organic

Compound Source Apportionment by Chemical Mass

Balance."

NOTES TO PAGES 11--16

30

NOTES TO PAGES 16--19

47\. A. W. Gertler et al., "Trends in Emissions from In-

Use Light- and Heavy-Duty Vehicles," *Journal of the Air &*

*Waste Management Association* (2003): submitted.

48\. Ibid.

49\. See www.epa.gov/compliance/civil/programs/caa/

diesel/ for more information.

50\. Ibid.

51\. Heavy-duty diesel emission standards are denoted in

units of grams per brake-horsepower-hour (grams/bhphr),

which means emissions per unit of power output

(horsepower) and time (hours). Thus, an engine operating

at 200 horsepower that emits 1 gram/bhp-hr would

emit 200 grams per hour.

52\. EPA, "Regulatory Impact Analysis: Heavy-Duty

Engine and Vehicle Standards and Highway Diesel Fuel

Sulfur Control Requirements" (Washington, DC:

December 2000).

53\. J. G. Heiken and T. L. Darlington, "Comparison of

MOBILE6 and EMFAC2000 Heavy-Duty Diesel

Emissions," 11th CRC On-Road Vehicle Emissions

Workshop, San Diego, CRC, 2001. The two projections

come from two different emission models, and therefore

differ in a number of ways besides inclusion of the 2007

standards.

54\. EPA, "Regulatory Impact Analysis: Heavy-Duty

Engine and Vehicle Standards and Highway Diesel Fuel

Sulfur Control Requirements."

55\. EPA's pollutant emissions inventory estimates were

downloaded from www.epa.gov/air/data/repsst.html.

56\. EPA, "Latest Findings on National Air Quality,

2001 Status and Trends."

57\. EPA, "EPA's Acid Rain Program: Results of Phase

I, Outlook for Phase II" (Washington, DC: October

2001).

58\. The final cap won't be reached until a few years

after 2010, because utilities are allowed to "bank" some

SO2 credits generated in a given year for use in a future

year. It will likely take a few years for these banked credits

to be depleted after 2010.

59\. Coal is much less common in the West, and,

according to EPA's inventory, electricity generation

contributes only 10 to 15 percent of NOx emissions

in the West. In California, power plants account for

only about 2 percent of NOx. (The western NOx inventory

breakdown comes from EPA's 1999 emission

inventory. CARB estimates that only about 2 percent of

NOx emissions in southern California and the Central

Valley come from electricity generation. California

regional emission inventories can be downloaded from

www.arb.ca.gov/emisinv/maps/statemap/abmap.htm.

For sources of electricity in California, see California

Energy Commission, "California Gross System Electricity

Production for 2001," www.energy.ca.gov/electricity/

gross_system_power.html.)

As for diesel truck NOx emissions, the value presented

here is based on the EPA inventory, but modified by the

Kean et al. adjustment factors for actual NOx emissions

from on- and off-road diesel sources. (A. J. Kean et al.,

"A Fuel-Based Assessment of Off-Road Diesel Engine

Emissions," *Journal of the Air & Waste Management*

*Association* 50 (2000): 1929--39.)

60\. EPA, "EPA's Acid Rain Program: Results of Phase I,

Outlook for Phase II" (Washington, DC: October 2001).

61\. EPA, "Addendum to the Regulatory Impact Analysis

for the NOx SIP Call, FIP, and Section 126 Petitions"

(Washington, DC: September 1998). The rationale for

capping only warm-season (May-- September) NOx emissions

is that NOx contributes to ozone formation, but at

least in the East it is a minor contributor to PM.

62\. The Clear Skies Initiative is Senate Bill 485 and

House of Representatives Bill 999.

63\. The Clean Power Act is embodied in Senate Bill 366.

64\. The percentage reductions are relative to 1998

levels. Information on the Clean Smokestacks Act can be

downloaded from the North Carolina Department of

Environment and Natural Resources at daq.state.nc.us/

news/leg/.

65\. K. Johnson, "Rules Approved to Reduce Pollutants

at Power Plants," *New York Times*, 27 March 2003.

66\. Recent remote sensing data for other cities, such as

Phoenix, Arizona and Riverside, California, show a similarly

skewed distribution of emissions among vehicles

(data not shown). The highly skewed nature of vehicle

emissions is a general property of all vehicle fleets and

has been demonstrated using all types of vehicle emission

tests (see, for example, D. R. Lawson, "The Costs of

'M' in I/M---Reflections on Inspection/Maintenance

Programs," *Journal of the Air & Waste Management*

*Association* 45 (1995): 465--76; J. Schwartz, "Clarification

of Misconceptions Regarding the Sacramento RSD Pilot

31

Program" (Sacramento: California I/M Review Committee,

31 March 1995). In addition, the skewed nature of the

emissions distribution has increased with time. For fleets

measured during the early 1990s, the worst 10 percent of

VOC emitters typically contributed about 50 percent of

total VOC emissions (D. H. Stedman et al., "On-Road

Remote Sensing of CO and HC Emissions in California---

Final Report" (Sacramento: California Air Resources

Board, February 1994).

67\. See previous footnote.

68\. CARB, "Description and Rationale for Staff's

Additional Proposed Modifications to the January 10, 2003

ZEV Regulatory Proposal" (Sacramento, CA: 2003).

69\. H. Gruenspecht, "Zero Emission Vehicles: A Dirty

Little Secret," *Resources* (Winter 2001): 7--10. See also L.

Dixon et al., "Driving Emissions to Zero: Are the

Benefits of California's Zero Emission Vehicle Program

Worth the Cost?" (Santa Monica, CA: RAND, 2002).

70\. Calculated based on capital and operating costs

and estimated emission reductions for a sample of

rail projects. Data were obtained from the Federal

Transportation Administration's "Annual Report on

New Starts," downloaded from www.fta.dot.gov/library/

policy/ns/annreports.htm. Capital and operating costs

were included in the cost effectiveness calculation, on

the assumption of a 40-year amortization of capital costs

and a 7 percent interest rate.

71\. For example, EPA estimated that measures needed

to comply with the new eight-hour ozone standard

would have a cost effectiveness averaging about \$13,000

for NOx and \$5,000 for VOC. Measures proposed in

the latest plan to clean up Southern California's air range

in cost effectiveness from a few hundred dollars up to

\$20,000 per ton. Voluntary scrap programs using age or

failure of an emissions inspection test as eligibility criteria

have an estimated cost effectiveness of about \$5,000 per

ton in reducing ozone precursors. A pilot program that

used remote sensing to identify high-emitting vehicles for

repair had a similar cost effectiveness. (Eastern Research

Group, "Overview of Voluntary Vehicle Scrap Programs for

Reducing in-Use Vehicle Emissions" (Austin, Texas: June

2002); EPA, "Regulatory Impact Analysis: Tier 2/Gasoline

Sulfur Final Rulemaking" (Washington, DC: December

1999); D. R. Lawson et al., "Program for the Use of Remote

Sensing Devices to Detect High-Emitting Vehicles,

Prepared for the South Coast Air Quality Management

District" (Reno: Desert Research Institute, 16 April 1996);

South Coast Air Quality Management District, "Draft

2003 Air Quality Management Plan" (Diamond Bar, CA:

February 2003).

72\. Previous scrap programs have estimated cost

effectiveness to be about \$5,000 per ton of ozone precursors

eliminated. These programs generally used age

as the eligibility criterion. A program that targeted the

highest emitters based on remote sensing would likely

have even better cost effectiveness.

73\. See 40 CFR 93 Subparts A and B of the Code of

Federal Regulations.

74\. Regulations that EPA has already adopted can

be browsed and downloaded from www.epa.gov/

docs/epacfr40/chapt-I.info/subch-C.htm. EPA's agenda

for future proposed air pollution control rules and

information about the proposed rules can be downloaded

from ciir.cs.umass.edu/ua/Spring2003/tables/

ENVIRONMENTAL_PROTECTION_AGENCY\_(EPA).html.

75\. See notes 5 through 9 for examples.

76\. NOx measurements were collected in 1996 and

1997, but these data suffer from VOC interference and

are therefore biased high (personal communication with

Professor Don Stedman, University of Denver).

77\. Personal communication with Professor Don

Stedman, University of Denver. The mode is the most

common value in a given set of data.

78\. Regression to the mean occurs whenever one selects

the extreme values in a distribution, say, the highestemitting

10 percent of vehicles, or students scoring in the

lowest 10 percent on a test, and retests them. If either of

these groups are retested, their scores will, on average, be

closer to the population mean on the retest. For a lucid

explanation of why regression to the mean occurs, see

trochim.human.cornell.edu/kb/regrmean.htm.

79\. However, the 2001 Phoenix data were used to

determine the ratio of emissions of light trucks to cars,

since comparability with other calendar years wasn't an

issue for that analysis.

80\. Eastern Research Group, "Analysis of Historical

Remote Sensing and I/M Emissions Data in Arizona"

(Austin, TX: 2002).

81\. D. H. Stedman et al., "On-Road Evaluation of

an Automobile Emission Test Program," *Environmental*

NOTES TO PAGES 19--24

32

NOTES TO PAGES 24--25

33

*Science & Technology* 31, no. 3 (1997): 927--31; D. H.

Stedman et al., "Repair Avoidance and Evaluating

Inspection and Maintenance Programs," *Environmental*

*Science & Technology* 32, no. 10 (1998): 1544--45.

82\. D.R. Lawson, "Passing the Test-Human Behavior

and California's Smog Check Program," *Journal of*

*the Air & Waste Management Association* 43 (1993):

1567--75.

83\. B. C. Singer et al., "A Fuel-Based Assessment

of Motor Vehicle Emissions in Southern California,"

*Journal of the Air & Waste Management Association* 49

(1999): 125--35.

84\. It is worth stressing again that the idea of statistical

significance and the 95 percent CI is premised on the

assumption that the samples of vehicles are drawn at

random from the on-road vehicle population. The 95

percent CI represents the uncertainty solely due to random

sampling variation and does not provide any information

on whether the sampling method itself suffers

from systematic bias.

35

**JOEL SCHWARTZ** is an independent scientist and

policy researcher, and an adjunct fellow at the

American Enterprise Institute.

Mr. Schwartz formerly directed the Reason

Public Policy Institute's Air Quality Project and

has also published studies on chemical risks and

extended producer responsibility. Prior to joining

Reason, he was Executive Officer of the California

Inspection and Maintenance Review Committee, a

government agency charged with evaluating

California's vehicle emissions inspection program

and making recommendations to the legislature

and Governor on program improvements. He has

also worked at the RAND Corporation, the South

Coast Air Quality Management District, and the

Coalition for Clean Air.

Mr. Schwartz received his bachelor's degree in

chemistry from Cornell University and his master's

degree in planetary science from the California

Institute of Technology. He was a German Marshall

Fund fellow in 1993, during which he studied

European approaches to transportation and air

quality policy. He lives and works in Sacramento,

California.

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**Emissions Reductions as a Result**

**of Automobile Improvement**

S A J A L S . P O K H A R E L ,

G A R Y A . B I S H O P , A N D

D O N A L D H . S T E D M A N \*

*Department of Chemistry and Biochemistry,*

*University of Denver, Denver, Colorado*

R O B E R T S L O T T

*71 Hawes Avenue, Hyannis, Massachusetts 02601*

Remote sensing of light duty vehicle on-road tailpipe

exhaust has been used to measure on-road mass emissions

of automobile fleets in Denver for 13 years and in two

other U.S. cities for 5 years. Analysis of these fleets shows

that newer automobiles, during a period of fairly constant

new car standards, have become continually less

polluting independent of measurement location. Improving

emissions control technology spurred by federal regulations

is thought to have brought about these trends.

**Introduction**

The U.S. Environmental Protection Agency (EPA) estimates

on-road motor vehicle emissions to be the single largest

contributor of major atmospheric pollutants. As of 1998,

automobiles contributed 60% of the carbon monoxide (CO),

44% of the hydrocarbons (HC), and 31% of the oxides of

nitrogen (NO*x*) to the national emissions inventory in the

United States (*1*). In urban areas the contribution of motor

vehicles is even higher; for instance, the Colorado 1998 Air

Quality Data Report states that 86% of Denver's CO comes

from motor vehicles (*2*).

Since the 1960s, various strategies have been employed

to control automobile emissions in the United States. This

control has been made more necessary by the substantial

increase in automobile use since that time. A two-pronged

approach was adopted. First, new vehicles were required to

be built to be much lower emitting. Advances in emission

control technology were often enabled by additional regulations

controlling fuel composition. Second, emissions from

older vehicles were to be reduced through scheduled,

mandatory vehicle emission inspection, and maintenance

(I/M) programs. In many states, including Colorado, Illinois,

and California, new vehicles are exempt from I/M emissions

testing for 4 years unless they change ownership.

In 1970, Congress adopted the first major Clean Air Act

and established the U.S. EPA, which required new vehicles

to meet certain emission standards. Throughout the next 2

decades, these emission standards were revised and new

technologies were devised tomeetthe standards. Certification

standards for new vehicles outside California remained

constant from 1981 to 1999, with the exception that in 1994

the NO standard was reduced and a 100 000 mile, 10-year

durability standard was phased in for NO, CO, and HC (*3*).

By the 1996 model year, all light duty vehicles were required

to have on-board diagnostic systems (OBDII). The intent of

this system is early detection of potential emission control

component failures (*4*). In 1990, congressmadeamendments

to the Clean Air Act and required "enhanced" (I/M) programs

involving loadedmodetesting to "closely reflecthowvehicles

perform under actual driving conditions" (*4*).

Recent observations indicate that the 4 decades of

regulations have made an impact. For example, Figure 1

illustrates that motor fuel consumption by the transportation

sector has been increasing (*5*), while CO concentrations in

the air have been decreasing (*6*). Note that in the past decade

there has been a 4.1% annual decrease in average ambient

COconcentrations in the United States. The EPA also reports

a decrease in national average ambient NO2 concentrations

of 1.6% per year during the same time period (*6*). Furthermore,

a recent review of ambient CO trends at various

locations in California's South Coast Air Basin shows declines

between 4% and 9% per annum over the last 12 years (*7*).

This paper presents evidence based on on-road remote

sensing that lower emitting newer vehicles have contributed

to these reduced urban ambient pollutant concentrations

even as fuel use has increased.

The on-road evidence comes from remote sensing

measurements of vehicle emissions made over a decade in

several U.S. cities. Remote sensing of automobile emissions

involves the use of open-path infrared and ultraviolet

spectroscopy to measure the concentration of pollutant

species relative to the concentration of carbon dioxide (CO2)

in a section of the exhaust plume behind an automobile.

From the ratio of pollutant to CO2 one can then derive the

fuel specific mass emissions of pollutant. Detailed discussions

of remote sensing technology have been presented previously

(*8*-*10*).

Using on-road remote sensing, we have measured fuel

specific mass emissions of CO since 1989. Fuel specific

emissions depend on load, HC and NO more so than CO.

Load can be estimated from on-road speed, acceleration,

and road grade measurements. Furthermore, CO and HC

light-duty vehicle emissions may be large, even for wellmaintained

vehicles, if the vehicle is in cold start. A cold start

is when a vehicle's catalytic converter and emission control

systems have not reached operational temperature.

With careful site selection, measurement of high load and

cold start driving modes can be prevented. By selecting

measurement sites that are heavily trafficked, curved, uphill

freeway off-ramps, we limit vehicles in cold start mode and

those under high load. When light duty fleets are measured

over several years at the same site with similar load

distributions, fleet performance over time and under actual

driving conditions can be studied.

Statistics from such measurements indicate that every

year there exists a relatively small number of very high

emitting vehicles, which are responsible for most of the total

emissions of each pollutant (*11*). A "gross polluter" of a

particular pollutant is defined (*11*) as one of the relatively

few vehicles in the emissions category that together cause

half the total on-road emissions of that particular pollutant.

For example, in Denver in 2000, the 6% of the measured fleet

that wereCOgross polluters by this definition were measured

with average emissions of 480 g CO/kg fuel, which is almost

9 times greater than the mean and 32 times greater than the

median for the same fleet.

The fraction of gross polluters, however, has been

decreasing over the years of measurement. This decrease is

illustrated in Figure 2. In Denver in 1989, half of the total CO

emissions came from about 9% of the vehicles (*12*). In 2000,

half of the total came from only about 6% of the fleet (*13*).

\* Corresponding author phone: (303)871-2580; fax: (303)871-2587;

e-mail: dstedman@du.edu.

Environ. Sci. Technol.**2003**, 37,5097-5101

10.1021/es026340x CCC: \$25.00  2003 American Chemical Society VOL. 37,
NO. 22, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 **5097**

Published on Web 10/04/2003

The average rate of decrease is 0.30 ( 0.10% per year, where

the uncertainty is the 95% confidence interval. Thus, most

automobiles are low emitting and collectively emit a relatively

small portion of total emissions. Furthermore, the fraction

of low emitting vehicles continues to increase with time.

When emissions are studied as a function of model year,

the newest vehicles are always low emitting. Additionally,

our most recent data suggest that emissions deterioration

over time has slowed. This trend was first observed with

remote sensing measurements between 1989 and 1995 (*14*),

and the trend has continued (*13*). In 1989, the results, when

plotted versus model year, showed a steep, approximately

linear increase of CO emissions with age such that, after 10

years, the average emissions of 1979 model year vehicles was

240 g CO/kg of fuel. In 2000, the increase was much slower,

and after 10 years 1990 model year vehicles (which in 1992

averaged 39 g CO/kg) emitted an average of only 80 g CO/kg.

The remote sensing data also indicate that the small fraction

of broken older vehicles has lower average emissions today

than 13 years ago.

These observations led us to study our large remote

sensing dataset using a new approach. We present here a

study of the emissions of relatively new vehicles over several

years of measurement. This approach highlights improvements

in emission control strategies over the time period of

the study.

**Method of Data Collection and Mining**

Wehave obtained large data sets of emissions measurements

from busy curved uphill freeway ramps in Denver (*13*, *15*,

*16*), Chicago (*17*), and Los Angeles (*18*). All of the Chicago

and Los Angeles measurements and the Denver measurements

since 1996 have been made at the exact same location

in each city. The earlier Denver measurements were made

on another very similar off-ramp three miles north on the

same freeway near downtown Denver.

The timing of the yearly measurements has also been

controlled. The Chicago, Los Angeles, and Denver data since

1996 were all obtained during measurement campaigns that

lasted several days each. In Denver, these were days in late

December or early January, the middle of mandatory

oxygenated fuel season. The Denver mandate has been

essentially unchanged from 1988 onward. We made measurements

during September in Chicago and during June

and July in Los Angeles. Measurements made during the

same time of year help control for seasonal variables. Such

variables include atmospheric temperature, presence of

oxygenated fuels, and use of air conditioning.

The fleets analyzed are comprised almost entirely of

passenger cars and light duty trucks. Furthermore, vehicles

up to 4-years old are studied. Model years that are up to

4-years old comprise a large fraction of the on-road fleet in

the United States and, thus, are most useful as subsets that

have statistically significant sample sizes. Large sample size

is crucial with automobile emissions data because of the

highly skewed nature of their distribution, and the fact that,

regardless of testing procedure, high emitters generally show

very highly variable emissions results (*19*-*21*). Additionally,

in the states of interest, vehicles up to 4 years of age are

mostly exempt from I/M testing. Thus, in the study presented

here, only vehicles up to 4-years old are analyzed. Analysis

of the data is also limited to whole model year groups. Table

1 shows typical data from Chicago demonstrating that from

1997 to 2001, these newer vehicles contributed about 50%

of the on-road measurements. Their contribution to the

overall emissions however decreased significantly.

To observe the trend in average emissions of successive

model year vehicles, the average emissions of vehicles of a

certain age is studied over several years of measurement.

For example, we look athowaverageCOemissions of 3-yearold

cars and light duty trucks measured in Chicago between

1997 and 2000 have changed. Thus, we compare 1994 model

year vehicles measured in 1997 to 1995 model year vehicles

measured in 1998, etc. This type of analysis tracks the

emissions improvement from one model year to the next

without interference from the effect of deterioration. The

grouping of the data in this study yields sample sizes between

1500 and 2500 emissions readings for all measurement

**FIGURE 1. Opposite trends in gasoline consumption and CO ambient**

**concentration. The decreasing plot shows EPA reported data on**

**second maximum measured carbon monoxide concentrations in**

**the air of the United States ( 6), while gasoline consumption by**

**motor vehicles has been increasing ( 5).**

**FIGURE 2. Decrease in fraction of CO gross polluters during**

**measurements in Denver from 1989 to 2001. Gross polluters are**

**defined as the percentage of the total measured fleet responsible**

**for 50% of total measured CO emissions.**

**TABLE 1. Chicago Data Showing the Percentage of the Fleet**

**and the Percentage of the Various Emissions from the**

**Newest Four Years of Vehicles**

**measurement year % of fleet % of CO % of HC % of NO**

2002 48 16 23 19

1999 49 20 32 24

1997 53 29 37 35

**5098** 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 37, NO. 22, 2003

campaigns except for some of the older measurements in

Denver, which had smaller sample sizes.

Three pollutants were measured in these studies: CO,

HC, and NO.Wehave observed and reported a measurement

offset, restricted to theHCchannel (*13*, *17*, *18*), but diagnosis

has proved difficult. In the absence of a true diagnosis of the

problem, we proposed a remedy to remove the offset and

obtain data that can be compared from the several years of

study. This adjustment is to subtract a predetermined offset

from the averaged data. The offset is determined as the

average emissions of the cleanest model year and make of

vehicles from each data set. Since we assume the cleanest

vehicles to emit next to nothing, such an approximation will

only err slightly toward clean because the true offset will be

a value somewhat less than the average of the cleanest model

year and make. This procedure is used to adjust the HC data

so that measurements from different years can be compared

without interference from an artificial offset.

**Results**

Figure 3 tracks the on-road, fuel specific CO emissions of

vehicles that are less than 5-years old over a 13-year period

in Denver.Atrend of continually decreasing emissions during

successive years of measurements is observed. In Figure 3,

we plot the natural log of average grams CO emissions per

kilogram of fuel used as a function of the year of measurement.

We define the age of a vehicle as the year of

measurement minus the model year. For example, during

the 2001 Denver fieldwork a 2000 model year vehicle would

be considered 1-year old.

Alogarithmic scale is used because the natural log function

most closely models the data. The slope of the best-fit

regression line then shows the proportionate annual decrease

in average emissions. For example, in Figure 3 the slope of

the regression line to the CO data for 4-year-old vehicles in

Denver is -0.12, which corresponds to an average annual

decrease in CO emissions of 12%.

Similar studies of trends in average emissions of successive

model year vehicles were conducted with data from Chicago

and Los Angeles. We have made four sets of yearly measurements

in Chicago starting in 1997 and three sets in Los Angeles

beginning in 1999. Each year of measurement has been

written up previously as a report, peer-reviewed by two levels

of committee of the Coordinating Research Council, and

published at the CRC Web site www.crcao.com and at

www.feat.biochem.du.edu (*13*, *17*, *18*). The HC and NO

emissions trends were also studied for all three cities. Figures

4 and 5 illustrate the HC and NO data from Chicago. Again,

we can observe decreasing trends in both HC and NO

emissions over successive years of measurement.

The average annual decreases in CO, HC, and NO

emissions for light duty vehicle fleets aged 1-4 years in

Denver, Chicago, and Los Angeles are tabulated in Table 2.

The reported percentage values are obtained from the slopes

of regression fits similar to and including Figures 3-5. The

reported uncertainty values are derived from the standard

errors of the slopes. Notice that almost every decreasing trend

is significant. In Denver, theCOandHCrates of improvement

**FIGURE 3. Decreasing CO emissions from new vehicles. The natural**

**log of grams CO emissions per kilogram of fuel used is plotted by**

**year of measurement for vehicles aged 1**-**4 years measured between**

**1989 and 2001 in Denver. Age of vehicle is calculated by subtracting**

**model year from measurement year. Regression lines for each age**

**are drawn and R2 values are included. As a reference, the tier 0**

**and tier 1 federal 50 000 mile standard of 3.3 g/mi CO emissions for**

**light duty vehicles transforms to 28 g CO/kg fuel, assuming a fuel**

**economy of 23 mpg. This standard would appear at 3.3 on the log**

**scale in the figure.**

**FIGURE 4. The HC data from four measurement campaigns in**

**Chicago. As a reference, the tier 0 and tier 1 federal 50 000 mile**

**standard of 0.41 g/mi HC emissions for light duty vehicles
transforms**

**to 3.4 g HC/kg fuel, assuming a fuel economy of 23 mpg. This
standard**

**would appear at 1.2 on the log scale in the figure.**

**FIGURE 5. The NO data from four measurement campaigns in**

**Chicago. As a reference, the tier 0 federal 50 000 mile standard of**

**1.0 g/mi emissions as (NO2) for light duty vehicles transforms to
5.4**

**gNO/kg fuel, assuming a fuel economy of 23 mpg. This standard**

**would appear at 1.7 on the log scale in the figure. The 0.4 g/mi
Tier**

**1 standard would be 0.74 on the log scale.**

VOL. 37, NO. 22, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 **5099**

are less than NO. In Chicago, the NO improvement rate is

comparable to Denver, but the HC and CO rates are higher

than NO. Los Angeles is intermediate, but relative uncertainties

are high in the Los Angeles data because only three

measurement campaigns have been conducted at that

location.

Figures 3-5 can also be used to look at the deterioration

in fleet emissions. Deconstruction of the plots reveals the

trend in emissions of a specific model year fleet as it ages

over the years of the study. This deconstruction involves

ignoring the trend lines and tracking a specific model year

from one calendar year to the next. For example in Figure

5, we see that the 1-year olds measured in 1997 (i.e. the 1996

model year fleet) have average NO emissions very close to

that of 2-year olds measured in 1998 (again, the 1996 model

year fleet). Thus, the 1996 model year vehicles' emissions

did not deteriorate significantly as they aged 1 year between

1997 and 1998. We can follow the 1996 model year fleet

through the 1999 and 2000 measurements and observe that

there is only a sight increase in average NO emissions. This

lack of significant deterioration holds true for other model

year fleets and with CO and HC as well. It is also interesting

that after 4 years of "deterioration" (or lack thereof) newer

model year fleets are still lower emitting than older model

year vehicles when they were only a year old.

**Discussion**

This analysis of on-road remote sensing data shows continual

improvement in fleet average emissions of new vehicles over

several years of measurement in three separate cities. Several

factors may have contributed to these decreasing emissions.

Three possibilities are that the states' I/M programs have

become more effective, that gasoline supplied in these areas

has become cleaner burning, and that vehicle technologies

to reduce emissions have improved.

The effect of I/M programs have been effectively removed

in this analysis by selecting vehicles that are up to 4-years

old and, thus, mostly exempt from I/M testing and unaffected

by I/M programs. The only notable exceptions were CO

measurements made in Denver before 1995. Denver vehicles

were subject to the Colorado annual idle I/M program

between 1990 and 1995. During that time, there was only a

1-year exemption and then biennial testing for the next 2

years. While this I/M program may have influenced the

average emissions of the fleets measured early on, the

"enhanced" I/M program actually became progressively less

involved in the newer vehicles, which nevertheless became

lower and lower emitting. Thus, the enhanced I/M program

cannot account for the decreased emissions.

The evidence of a lack of significant deterioration

combined with the observation that newer vehicles that have

aged several years are still lower emitting than new vehicles

from a few years ago is important in discerning the influence

of fuels on emissions. If cleaner burning fuels were the only

cause of emissions improvements, one would expect emissions

deterioration to offset the effect of cleaner burning

fuels so that newer vehicles that have aged would have higher

emissions than new vehicles measured a few years earlier.

These observations suggest that improvements in emissions

control technologies have played a significant role in

lowering fleet average on-road automobile emissions. Automobile

manufacturers havemadeinnovations in emissions

control technologies in part to meet federal automobile

emissions standards. The standards for new vehicles have

remained fairly constant outside of California since the 1981

model year. Details are given in an NRC report (*3*) and by

the EPA (*22*). The ability of the manufacturers to meet these

standards on-road has apparently improved, however. New

technologies continue to be developed tomeetmorestringent

standards as discussed by Mondt (*23*). While the observed

trends in Table 2 are mostly consistent from one age group

to then next, they are different at the different locations. We

do not know the cause of this difference. Further research

will result in longer records at all locations which will lower

the uncertainties and hopefully provide further insights.

Even as manufacturers meet current emission standards,

they are compelled to produce cleaner technologies by the

approach of future standards. OBDII has been installed on

all 1996 and newer vehicles (*4*), and starting in the 2001 model

year, new light duty vehicles will continue to have tighter

emissions standards under a federal program called the

national lower-emissions vehicle program (NLEV) (*3*, *22*). In

fact, the motivation for auto manufacturers to produce

cleaner vehicles may be also customer focused. They may

not wish their customers to fail I/M tests and, with 1996 and

newer vehicles, not have OBDII malfunction indicator lights

go on.

**Literature Cited**

\(1\) U.S. Environmental Protection Agency. *National Air Quality*

*andEmissions Trends, 1998;*EPAPublication 454-R-00-003; 2000.

\(2\) Colorado Department of Public Health and Environment. *Air*

*Quality Data Report*-*1998*; Publication APCD-TS-B1; CDPHE,

Denver, 1998.

\(3\) *Evaluating Vehicle Emissions Inspection and Maintenance*

*Programs*; National Research Council, National Academy

Press: Washington, DC, 2001.

\(4\) U.S. Environmental Protection Agency. "Requirements For

Preparation, Adoption, and Submittal of Implementation Plans"

**TABLE 2. Annual Percent Reductions in Emissions of Newer Vehicles
during Their First 4 Years*a***

**location**

**(duration)**

**fleet**

**age yr**

**annual % change**

**in CO emissions**

**annual % change**

**in HC emissions**

**annual % change**

**in NO emissions**

Denver 4 -12 ( 1 -10 ( 3 -15 ( 3

(1989-2001 for CO) 3 -10 ( 1 -9 ( 2 -19 ( 2

(1996-2001 for 2 -12 ( 1 -12 ( 3 -21 ( 2

HC and NO) 1 -11 ( 2 -15 ( 8 -18 ( 4

Chicago (1997-2000) 4 -29 ( 7 -25 ( 12 -19 ( 5

3 -35 ( 6 -32 ( 13 -21 ( 3

2 -29 ( 2 -23 ( 7 -23 ( 7

1 -33 ( 7 -24 ( 6 -21 ( 3

Los Angeles (1999-2001) 4 -22 ( 5 -19 ( 0.1 13 ( 4

3 -15 ( 5 -16 ( 6 -4 ( 8

2 -16 ( 1 -19 (2 6( 4

1 -24 ( 2 -48 ( 25 11 ( 24

a Data are grouped by age of vehicles. The period of measurement varies
among locations and is indicated below the location names. There

were 10 sets of CO measurements in Denver (five sets for HC and NO), 4
sets in Chicago, and 3 sets each in Los Angeles. Uncertainty values are

standard errors of the mean. Note that only one of the regression fits
indicates an emissions increase that is statistically significant.

**5100** 9 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 37, NO. 22, 2003

(U.S. Code of Federal Regulations, Title 40, Part 51, 2002; http://

www.epa.gov/epahome/cfr40.htm).

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index2000.htm).

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*on National Air Quality: 2000* - *Status and Trends* -

*Carbon Monoxide*; EPA: 2001; http://

www.epa.gov/oar/aqtrnd00/carbon.html.

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*Waste Manage. Assoc.* **2002**, *52,*1012.

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Stedman, D. H. *Anal. Chem.* **1989**, *61*, 671A.

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843\.

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*sensing of automobile emissions in the Denver area: year 3*;

Technol. Rep. Coordinating Research Council E-23-4; University

of Denver: Denver, CO, 2002; http://www.feat.biochem.du.edu.

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*1989*-*1995*; Preprint extended abstract, American Chemical

Society: New Orleans, LA, March 24-26, 1996.

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of Denver: Denver, CO, 2001; http://www.feat.biochem.du.edu.

\(18\) Pokharel, S. S.; Bishop, G. A.; Stedman, D. H. *On-road remote*

*sensing of automobile emissions in the Los Angeles area: year*

*3*; Technol. Rep. Coordinating Research Council E-23-4;

University of Denver: Denver, CO, 2002; http://

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*Received for review November 18, 2002. Revised manuscript*

*received July 28, 2003. Accepted August 26, 2003.*

ES026340X

VOL. 37, NO. 22, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 9 **5101**