Environmental Policy Challenges of the Future:
A Longitudinal Analysis of Atlanta’s Air Quality
Jennifer Chirico
Douglas Noonan
Georgia Institute of Technology
______________________________________________________________________________
Abstract
Determining the future of Atlanta’s air quality is paramount to public policy makers as they
make decisions about the appropriate resource allocations for air quality improvements. The
EPA recently strengthened standards for ozone and lead in 2008, creating greater challenges for
the City of Atlanta, which was out of attainment for ozone most years in the 1990s. We conduct a
longitudinal study examining the air quality of Atlanta over a 50 year time frame and the extent
to which the recent increase in air quality standards might affect the City of Atlanta. Our study is
two fold: i) we compare and contrast the history of major air pollutants in Atlanta to key air
quality determinants such as population, vehicle miles traveled (VMT), industrial activity, and
technology developments; ii) we utilize a multivariate fractional polynomial regression that
forecasts a relationship between the economic indicators and ambient concentrations based on
our historical data. Our findings reveal that Atlanta has experienced great advances in air
quality, yet rising populations, VMT, and industrial activity are not positively related to ambient
concentration levels, making forecasting air quality a difficult task. While our estimations reveal
that ambient concentrations will tend to consistently fall and then asymptote near a low level,
our results point to technology and policy as playing a dominant role in the air quality system.
We conclude that forecasting air quality appears to pose an enormous practical challenge to any
agency tasked with monitoring it and achieving greater standards.
This research is based in part on research funded by EPA STAR grant, “Air Quality, Emissions,
Growth, and Change.”
The U.S. Environmental Protection Agency (EPA) made recent modifications to national
air quality standards for several criteria air pollutants. These modifications may create greater
challenges for cities in the U.S. that were previously out of attainment in one or more of the
criteria air pollutants. The City of Atlanta was out of attainment for ozone for most of the years
in the 1990s, and now it must reevaluate its goals for air quality and determine how it will work
to meet the new standards. Determining the key determinants of poor air quality in Atlanta is
paramount for policy makers as they make decisions about the appropriate resource allocations
for meeting these new federal standards. This study examines the air quality in Atlanta over the
last 50 years and uses this information to estimate the future of Atlanta’s air quality through
2050. There are two primary components of this study: i) to compare and contrast the history of
major air pollutants in Atlanta to key air quality determinants such as population, vehicle miles
traveled (VMT), industrial activity, and technology developments; ii) to utilize a multivariate
fractional polynomial regression that forecasts a relationship between the economic indicators
and ambient concentrations based on historical data.
Section I of this paper provides background information on air quality and how it has
changed over time in Atlanta. Sections II discusses relevant literature on air quality standards
and determinants. Section III provides the data and methodology utilized. Section IV analyzes
provides the results of the historical analysis and the forecasted data analysis of the six criteria
air pollutants and key determinants of air quality. Section V provides a discussion of the results,
and Section VI concludes with the policy implications and recommendations for Atlanta.
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I. Air Quality Background
Up until the 20th century, the term “air pollution” did not exist. Air pollution was referred
to as ‘smoke,’ ‘kiln,’ ‘nuisance,’ and ‘coal’ (Bowler and Brimblecombe, 1992). During the first
hundred years of the United State’s existence, “smoke” complaints were considered a common
law nuisance and were resolved by litigation among involved parties instead of through
legislation. Around 1881, the first legislation was enacted that declared smoke emissions a public
nuisance, and by the early 1900s, smoke was prohibited if it was considered “dense,” “black,” or
“grey.” Even as late as the 1950s when air control programs were becoming more common at the
city level, “air pollution” was not yet an official term and was still referred to as “dirty air arising
from coal combustion” (Sabatier, 1988).
Federal legislation on air pollution in the U.S. was not enacted until 1970. After the
passage of the Clean Air Act (CAA) in 1970, air pollution control became the responsibility of
the U.S. Environmental Protection Agency (EPA). The new, strict regulations imposed by the
CAA coincided with a change in the public perception of air quality. The public began to
perceive air pollution as not only dirty, but also unhealthy. In addition, the perception of the
sources of air pollution shifted from coal-burning and factory emissions to automobiles
(Sabatier, 1988). The passage of the CAA also accompanied the first Earth Day celebration, a
rise in environmental concerns that coincided with rising affluence and scientific understanding
of air pollution problems. Under the CAA, the EPA established National Ambient Air Quality
Standards (NAAQS) that included six criteria air pollutants: lead (pb), particulate matter (pm),
sulfur dioxide (SO2), and nitrogen dioxide (NOx), ozone (O3), and carbon monoxide (CO) (EPA,
2006a, 1999). The National Ambient Air Quality Standards (NAAQS) provides national air
quality standards for each pollutant.
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Prior to the passage of the CAA, direct historical evidence of Atlanta’s air quality is quite
limited. This poses a serious challenge for understanding Atlanta’s air pollution problems in a
longer context. With the exception of lead, no systematic, direct measures of concentrations of
other criteria pollutants are publicly available. Furthermore, it poses an enormous problem
identifying the determinants of air quality changes and, in particular, the role of the 1970
extensions to the Clean Air Act. The conventional wisdom and mental models of discussants (as
well as many formal air quality models) posit very close relationships between pollution and
these measures of economic activity.1 The relationship is often thought to be quite direct, even if
a bit noisy. Holding all else equal, it is commonly expected that ambient concetrations of
pollution will rise when population, industrial activity, transportation, and temperatures rise.
Population in Metro Atlanta has been on the rise since the 1940s, and industrial activity is
a primary component. Metro Atlanta is home to more than 137,000 businesses and serves as the
primary hub for transportation and manufacturing in the Southeastern U.S. (Georgia Department
of Labor, 2005). Trade, transportation, and utilities (TTU) occupy the largest percentage (22.9%)
of the industry sector, and currently employ about 23% of the workforce (City of Atlanta
Chamber of Commerce, 2006). Atlanta has the largest rail center hub in the South, is home to
four major interstates for truck and car transport, and has the busiest airport in the world, with
more than 1,200 flights departing per day and more than 85 million passengers traveling through
the Atlanta airport per year (Metro Atlanta, Chamber of Commerce, 2006). Manufacturing is the
fifth largest industry sector in Metro Atlanta (Metro Atlanta Chamber of Commerce, 2006). In
addition, technological advances in the automobile may also play a significant role in the air
1
Even the simple heuristic common to environmental studies of I=PAT (where I=impact, P=population,
A=affluence, and T=technology) supports the intuition that as Atlanta’s population, affluence, and use of emitting
technologies increases, its air will become more polluted. Obviously, a key variable in this relationship is
technology, which has changed dramatically over time.
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quality of Atlanta. With the extensive suburban growth in Atlanta, this rate has continued to
grow, and today Metro Atlanta residents utilize the private automobile more than ever. Against
this trend in rising automobile use are the changes in automobile technologies, most notably the
introduction of the catalytic converters and new fuel mixes (e.g., unleaded and reformulated
gasolines). In addition, the EPA claims that vehicle miles traveled (VMT) will continue to rise,
and that the VMT growth rate for Atlanta is expected to be 103 percent between 1996 and 2030
(EPA, 2006c). According the Census, average commute times in Metro Atlanta grew by 20%
from 1990 to 2000, compared to a 14% growth nationwide. Atlanta now has the highest traffic
congestion growth rate (76% between 1993 and 2003) in the country (Metro Atlanta Chamber of
Commerce, 2006). It has surpassed large urban cities, such as Denver, Houston, and Dallas. The
roads in Atlanta are more congested, and the average number of hours commuters are delayed
due to congestion increased by 43% between 1993 and 2003 (Metro Atlanta Chamber of
Commerce, 2006). Atlanta also experiences very hot summers. The climate in Atlanta can
directly affect ozone (which requires favorable weather conditions for its formation) and
indirectly weather affects emissions (e.g., more air conditioning and driving in the hot summer).
II. Literature
Air quality standards for ozone, particulate matter, and lead were recently strengthened
by the EPA. Ozone standards decreased from .08 to .075 ppm and lead decreased from 1.5 ug/m3
to .15 ug/m3 in 2008. Particulate matter (pm) standards also strengthened in 2006. Several
reports have analyzed the effects on this change and found that there are many cities and counties
who are projected to not be able to meet the new standards (EPA, 2008). The PAG Southwest
Air Quality Review (2008) issued a report claiming that the number of counties out of attainment
for ozone would increase from 104 counties to 398 counties with the new legislation. When the
5
particulate matter standard changed, the EPA speculated that the number of counties out of
attainment for pm would increase from 39 to 141 (EPA, 2006f). Other state and municipality
reports have found similar results with other pollutants, such as carbon monoxide (Municipality
of Anchorage, 2008; Utah Division of Air Quality, 2008) and nitrogen oxides (Utah Division of
Air Quality, 2008).
Recent literature has also been conducted on the determinants of air quality and how they
might affect future air quality. Warm temperatures are a necessary condition for some air
pollutants and, therefore, warming trends could lead to greater amounts of air pollution (Georgia
State Climatology Office, 2006). Climate change leading to warming temperatures in the future
can potentially affect air quality (Tagaris, 2007), transportation, and emissions (Tagaris, 2006).
Tagaris et al. (2007) conducted a study that simulated future pollutant concentrations based on
past concentrations to examine potential changes in temperatures. They found that most
pollutants, including ozone, will continue to decrease into the future in Atlanta. Transportation is
also deemed important to air quality, as a recent report in Bridgeport, Connecticut claimed that
transportation is the major contributor its air pollution (Bridgeport Regional Planning, 2007).
This study observes air quality in concert with key determinants of air quality to
determine what affects Atlanta’s air quality and what policy makers can expect air quality to be
in the future. It is unique in that it is timely to the recent changes to air quality standards and
examines Atlanta’s air quality over a 100 year time period. Air quality regulators are projecting
that many places will be out of attainment and violate NAAQS into the distant future under these
new standards. Being out of attainment is costly for cites and requires great effort to meet
standards. Therefore, this study analyzes the history of air quality followed by a statistical
analysis that forecasts the future of Atlanta’s air quality. Results indicate that, while air quality is
6
improving generally, measuring air quality is not a simple matter and that policy and technology
may play the most prominent role in achieving desired air quality goals.
III. Data and Methodology
Data for this study was collected from several resources. The dependent variables in the
models are the air pollutants monitored by the EPA and the independent variables are the
indicators of air quality.
Dependent Variables
The historical criteria air pollutant concentrations were collected from archive records at
the EPA. The concentrations date back to 1958 for some variables (e.g., tsp, lead) and are coded
based on the EPA’s corresponding measuring unit (e.g, ppm).
Lead: Lead is one of the earliest pollutants measured. Historically, lead emissions came
from leaded gasoline from cars and trucks, and today most lead emissions come from industrial
operations (EPA, 2006b). As illustrated in Figure 1, annual lead concentrations in Atlanta
substantially decreased in the late 1970s and continued to steadily decline through 2005 (EPA,
2003, 2007 ). However, despite the substantial decline, the new NAAQS standard is only slightly
above current lead levels.
Particulate Matter: Particulate matter (PM) measurements are usually split into two
categories: 1) PM10 which is made up of inhalable coarse particles that are smaller than 10 and
larger than 2.5 micrometers; and 2) PM2.5 which is made up of fine particles that are 2.5
micrometers in diameter or smaller. PM10 was not systematically measured by the U.S. EPA until
the late 1980s, and PM2.5 was not measured until the late 1990s. Prior to formal measurements of
PM10 and PM2.5, the EPA measured particulate matter in the form of total suspended particles
(TSP). TSP, while not a criteria pollutant as such, is a common measure of air pollution in
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international studies. TSP is made up of particles that are suspended in the air and are larger than
10 micrometers in diameter. Public measurement records of TSP date back into the 1950s, which
provides a good indicator for examining older trends in PM10 and PM2.5. Emissions of PM2.5
are usually in the form of smoke coming from power plants, industries, and automobiles reacting
with the air (EPA, 2006b). Figure 2 depicts the TSP and particulate matter concentrations for
Atlanta. Overall, TSP varied dramatically between 1957 and 1975 and then steadily declined
from 1975 into the 1990s. PM10 decreased by about 55% from 1986 to 2005 and remained well
below NAAQS (the EPA standard). PM2.5 only slightly decreased (11.6%) from 1999 to 2005
and remained consistently above NAAQS (EPA, 2007).
Ozone: Ozone is one of the most discussed pollutants due to its negative health
consequences, such as asthma, wheezing, and reduced lung capacity (EPA, 2006b). It is created
when volatile organic compounds and nitrogen oxide react with sunlight. The focus here is on
ground-level ozone rather than stratospheric ozone.2 Figure 3 illustrates Atlanta’s ozone trends.
From 1987 to 2005, the ozone concentration fluctuated up and down and had an overall decrease
of about 17 percent. For most of these years, Atlanta’s peak ozone concentrations were above the
NAAQS and well above the new NAAQS standard.
Sulfur dioxide and Nitrogen Dioxide: Sulfur dioxide (SO2) is in all raw materials that
contain common metals. It is an air pollutant that is formed when fuel is burned. It can cause
serious respiratory problems to humans, and it facilitates the production of acid rain, which can
cause considerable damage to plants and water animals (EPA, 2006b). Nitrogen dioxide (NO2) is
a part of the nitrogen oxide (NOx) family of reactive gases that contain various amounts of
nitrogen and oxygen. NO2 often creates a reddish-brown layer of air over urban areas and is a
component of automobile exhaust fumes. Figure 4 depicts the trends in Atlanta’s SO2 and NO2
2
“Good” ozone rests 10-30 miles above ground in the stratosphere, and “bad” ozone is at ground level (EPA, 2006).
8
levels over the past 30 years. The average annual SO2 concentration dropped dramatically in
1973 and then steadily decreased through 2005, while NO2 steadily decreased over the thirty year
time period (EPA, 2007).
Carbon monoxide: Carbon monoxide (CO) is the final of the six criteria pollutants
regulated by the EPA. Nationwide, the EPA estimates that 56% of CO emissions come from onroad vehicles, while 22% derive from non-road vehicles (e.g., construction equipment, boats). In
Atlanta, CO concentrations gradually decreased since 1972, falling below the NAAQS level in
the early 1980s (EPA, 2007). Figure 6 shows some of the population trends from Census data.
Atlanta’s population peaked in 1970 (496,973), at which point the population began to drop until
1990 (394,017).
--Insert Figures 1-5 Here—
Independent Variables
Population, industrial and manufacturing activity, VMT and transportation, climate, and
technology are contributing factors which routinely receive the bulk of regulatory and planning
attention. Understanding the determinants of air quality can help one predict or forecast air
quality trends. These determinants are the independent variables in our model. By observing the
relationship between these indicators and measured air quality in Atlanta, trends in Atlanta’s air
quality prior to formal measuring (“backcasting” before 1970) and in air quality after today
(forecasting after 2005) can be estimated.
Population: Population data was collected from the U.S. Census (1998 2006). There was
a resurgence of Atlanta’s population in the late 1990s and early twenty-first century, reaching
almost 471,000 in 2005 (U.S. Census Bureau, 1998, 2006). The Metro Atlanta region
experienced similar growth, without the decline after the 1970s. Today, Metro Atlanta includes
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8,376 square miles, has a population of nearly 5 million, and is expected to grow to nearly 6
million by 2015 (City of Atlanta Chamber of Commerce, 2006).
Industrial Activity: Manufacturing data was found on the Bureau of Economic Analysis
on Regional Economic Information System (BEA 2006). Metro Atlanta’s industrial activity
generally increased along with its population. The manufacturing industry grew from 3,513
establishments in 1990 to 4,997 in 2006 (Georgia Department of Labor, 2005). Interestingly,
employment in the manufacturing industry had the opposite effect—overall, it slightly decreased
over the last several decades from about 190,000 employees in 1969 to approximately 185,000 in
2004 (see Figure 7). More, smaller establishments are producing increasingly valuable
manufacturing output. Although the manufacturing sector in Metro Atlanta is hardly shrinking in
raw size (measured in terms of real earnings), its share of total earnings has declined from 22
percent in 1969 to under 8 percent today (see Figure 7).
VMT: Vehicle miles traveled data was collected from the Georgia Department of
Transportation (GDOT 2006). Atlanta’s VMT increased by 55% between 1990 and 2004 (see
Figure 8). The 3.2% annual growth rate for VMT for the City of Atlanta lags slightly behind
Metro Atlanta’s 3.4% population growth rate over the same time span (Georgia Department of
Transportation, 2006).
Climate: Atlanta climate data was found at the Georgia State Climatology Office (2006)
out of the University of Georgia The temperatures in Atlanta fluctuate from year to year, but
have experienced a slight overall increase since 1940 (see Figure 9). Atlanta experienced its
highest recorded average annual temperatures in the 1990s.
--Insert Figure 6-9 Here—
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At a national level, the EPA regularly produces reports to tout the efficacy of their
efforts. Figure 10 reprints a graph from one of the EPA’s recent reports (U.S. EPA, 2006a).
While reductions in emissions are not the same as reductions in ambient concentrations, the
steady declines over the past 25 years suggest some success. This accomplishment occurs
alongside fairly steady increases in population, VMT, energy use, and economic activity.
Comparable measures for the Atlanta area suggest a similar story by overlaying Figures 1-5 on
top of Figures 6-9.
--Insert Figure 10 Here—
IV. Methodology
The methodology for this study uses a straightforward and flexible statistical modeling
approach to forecast Atlanta’s air quality. A multivariate fractional polynomial regression is
estimated separately for each of the major pollutants. This approach fits a regression of the
annual ambient concentration measure on several economic construction earnings, real
transportation earnings, and average temperatures). In fitting the regression, the best-fitting
fractional polynomials of the predictors are found.3 This allows us to estimate the ambient
concentration measure for each year as a flexible nonlinear function of the economic indicators.
While this approach offers a richer description of the data and enables the estimation of
confidence intervals, it maintains its naïveté regarding atmospheric chemistry, regulatory
changes, technological innovation, and many other important details. Rather, this parsimonious
model incorporates only a handful of variables that are commonly linked to air pollution policy
and debates. It does so without restrictions on the way in which they might enter the model or
explain data. If, controlling for other factors, increases in VMT are associated with cleaner air,
3
The polynomials are chosen from powers of -2, -1, -0.5, 0.5, 1, 2, 3, and ln. Up to four power terms can be chosen
for each predictor.
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then the model estimates will reflect this. Interpreting the model, therefore, requires some
caution, because important intervening factors may not be controlled for and might account for
the observed relationships.
Before proceeding with the multivariate forecasting exercise, an additional remark is
necessary about forecasts of the economic indicators that serve as “independent variables” or
predictors. The fractional polynomial regressions are based on observed values during roughly
thirty years (1972-2005), with a few missing values imputed. The model estimates a relationship
between the economic indicators and ambient concentrations. To predict the concentrations in
the future requires some knowledge about the future values of the predictors.4 We accomplished
this by producing forecasts of the economic indicators in a straightforward manner. A basic
(univariate) VAR model is estimated for the Metro Atlanta population with a three period lag,
and then forecast out 50 years. The same is done for the temperature and VMT variables. Then, a
two-equation VAR model is estimated for the Metro Atlanta population variable and one other
economic indicator (e.g., real per-capita earnings). This allows for the economic indicator to be
determined by its own time path and the time path of the metropolitan population. This twoequation VAR model is repeated for the population variable and for each of the other economic
indicators (i.e., real earnings in manufacturing, construction, and transportation). Annual
forecasts are then made out 50 years. These forecasted values are used to generate the forecasts
of air quality measures after estimating the fractional polynomial regression models. A sample of
the forecasts is depicted in Figure 11. In Figure 11A, the gray and blue dots reflect actual
populations for the city and the MSA, respectively, while the orange and maroon dots are their
forecasted values. In Figure 11B, the gray and maroon dots are historical income and VMT
4
This approach does assume that these predictors are exogenous, or that the economy is not itself influence by air
quality. There is some evidence to suggest that this assumption may not hold. Addressing the endogeneity of
economic indicators is beyond the scope of this paper.
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values, respectively, while red and green dots represent their forecasts. In Figure 11C, the blue,
green, and gray dots represent historical real earnings in manufacturing, construction, and
transportation, respectively. Their forecasted counterparts are in maroon, orange, and red.
--Insert Figures 11A-11C-V. Results
The resulting forecasts of air quality measures offer some insight beyond what is already
suspected. Perhaps, however, the biggest lesson is not in the central tendency of the forecasts but
rather the scope of the uncertainty (i.e., confidence intervals) around those forecasts. These
forecasts treat the projected trends in population and economic indicators as though they are
certain or inevitable. Even with this unrealistic assumption of certainty, the fractional polynomial
regressions produce forecasts with enormous uncertainty. Forecasts for lead, ozone, SO2, NOx,
and CO fall into three general categories.
Forecasts for lead and ozone both show rapidly rising levels with overwhelming noise in
the prediction. For (8-hour) ozone, the polynomial fits the data fairly well (adjusted-R2 = 0.69),
although notably worse than for the other criteria pollutants that follow. Most terms enter
linearly, although only VMT and construction earnings do so significantly at the 0.10 level.5 The
forecasted ozone levels are presented in Figure 12A. Here, the forecasted levels (in maroon)
quickly fall below zero before rising rapidly after 2030. Although the VAR forecasts predicts
leveling off of ozone levels, the multivariate analysis with continued growth of the economic
indicators (e.g., VMT) overwhelms the ozone trend in the coming decades. For lead, the
polynomial regression also fits the data well (adjusted R2=0.79). Here, temperatures (negatively)
and transportation (positively) predict lead levels. The forecasted values in Figure 12B shown,
5
Curiously, there is a positive time trend and temperature is negatively related to ozone, although neither is
significant beyond the 0.30 level.
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similar to ozone in 12A, rapidly rising lead levels in coming decades. Even though recent history
suggests lead levels are asymptoting near zero, the growth in the transportation sector augurs
higher lead levels in this approach. These inauspicious forecasts must be interpreted in light of
the models’ limitations as well as the other dominant feature of Figures 12A and 12B: the
enormous confidence intervals. The models yield very little precision in their forecasts of ozone
and lead, even when their prominent economic determinants are assumed with certainty.
The second set of models, for SO2 (see Figure 12C) and NOx (see Figure 12D), continue
the bleak forecasts. These forecasts resemble those for ozone and lead, except that their
confidence intervals are somewhat smaller and tend to not include negative values of ambient
concentrations. Accordingly, the forecasts for SO2 and NOx are both fit with more precision
(adjusted R2=0.96 for both). Income and VMT, nonlinearly, predict increases in SO2, while
transportation earnings predict decreases. NOx levels decline over time and, nonlinearly, in
VMT, but rise in income and also depend significantly on the manufacturing sector. SO2 levels
are forecast to rise, surpassing the NAAQS threshold before 2020. NOx levels are also forecast
to rise, surpassing the threshold after 2030. Both of these trends diverge from the VAR forecasts
that predicted concentrations asymptoting to much lower levels. Furthermore, considerable
uncertainty in the estimates remain, although less so than for ozone and lead.
The forecasts for CO (Figure 12E) resemble the others in its uncertainty, but differ in the
expectation of rapidly falling CO levels. The model fit for CO is good (adjusted R2=0.91). There
is a positive time trend in CO levels, which are expected to fall at an increasing rate as
population rises and to rise at a decreasing rate as VMT grows. Thus, over the projected growth
scenario for Atlanta, the CO concentrations are forecast to continue falling. While the forecast of
negative CO levels makes little sense, the prevailing negative relationship between Metro
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Atlanta population size and CO concentrations does suggest that further growth may not
exacerbate CO pollution.
--Insert Figures 12A-12E-VI. Discussion
Prior to 1970, very little was known about the distribution of the criteria pollutants in
many areas. However, since the 1970s, Atlanta has experienced a downward trend in most air
pollutants. Lead decreased significantly between 1971 and 1987 and then remained relatively
stable through 2005. Recent concentrations for lead, however, are only slightly below the new
policy standards, which could potentially pose threats to unattainment for Atlanta. PM10
decreased overall since 1986, and PM2.5 only decreased slightly since 1999, thereby making it
borderline for meeting NAAQS standards. Ozone fell before 1987 and then fluctuated between
1987 and 2005. While there was an overall decrease (17%) in ozone, the concentrations were
above the NAAQS for most years and well above the recent NAAQS modifications. Sulfur
dioxide dropped significantly in 1973 and then steadily decreased through 2005. Nitrogen
dioxide and carbon monoxide steadily decreased between 1972 and 2005.
For the key determinants, results suggest that rising populations, VMT, and industrial
activity are not positively related to ambient concentration levels. If anything, they are negatively
related to them.6 As Atlanta has experienced a population and transportation boom since 1970,
6
Glancing at the pairwise correlations between ambient concentrations of the six criteria pollutants and economic
measures (population, real per capita earnings, real manufacturing earnings, real transportation earnings, and
average temperatures) confirms this. Each of the pollution levels has a negative and statistically significant
correlation (at a 5% level) with each of the economic variables; the only exception being PM 10, which lacks
sufficient observations. Many of these correlations are quite strong, and all are negative. The same set of pairwise
correlations, when examined in first-differences, exhibits no significant correlations. Annual changes in ambient
concentrations, up or down, appear uncorrelated with annual changes in the economic indicators. This suggests that
the air quality and economic variables may be structurally unrelated, but both enjoy their own independent (and
opposite) trends. Interestingly, this pattern also holds for the pairwise correlations between the changes in
concentrations and the changes in economic indicators in the preceding year. One exception to this arises for NOx,
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its air quality has noticeably improved (or, perhaps in the case of ozone, stagnated). The data
might better support a conclusion that people and traffic cause cleaner air, not dirtier air.
While the recent trends clearly depict improving air quality and increasing economic
trends, perhaps the most noteworthy finding is that the new modifications to the NAAQS
standards for ozone, lead, and particulate matter may be difficult to overcome for Atlanta.
Currently, Atlanta does not pass the new standard for ozone and only barely passes the standard
for lead and particulate matter. These findings suggest potential challenges for the City of
Atlanta.
However, the limitations of this study prevent us from making forecasts with any degree
of certainty. The graphs and bivariate correlations are inadequate to form predictions of what
might be a much more complex system. It is possible that there is no relation between the
economic indicators and the air quality measures. But it is also possible that the relationship
appears in more subtle ways. For instance, a steady decline in ambient pollutant levels associated
with the passage of time (perhaps because of technological innovation or regulation) might well
fade over time and be overcome by the effects of population, VMT, or some other determinant
experience rapid growth.
In addition, measuring air quality is not a simple matter in the best circumstances. Air
quality may vary substantially over space. The airshed in Atlanta is quite large, and a single “air
quality index” value might not do well to describe the air quality in the area. Air quality can also
vary widely over time, whether time is measured in hours or in months. The graphs above depict
variation at an annual level, but they obscure important and dramatic variations at smaller time
scales. Ozone levels, for instance, fluctuate dramatically over the course of a day, can vary
where NOx levels tend to rise in the year following increases in income and manufacturing and decreases in
temperatures.
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widely from day to day, and certainly have important seasonal fluctuations as well. Air quality is
also multidimensional with respect to the many chemicals that constitute the ground-level
atmosphere. Atlanta may be relatively clean with respect to some pollutants, like lead, but
remains polluted with respect to others, like PM2.5 or even airborne carcinogens.7 Measuring air
quality, across the many dimensions of time, space, and chemicals, poses an enormous practical
challenge to any agency tasked with monitoring it. It may be little wonder that the data that do
exist suffer from coding errors and other possible outliers. (e.g., the average and maximum SO2
levels fell sixfold from 1972 to 1973). Furthermore, omitted variables, such as technological and
policy events can lead to biases.
VII. Conclusion
Overall, the story of Altanta’s air quality is dynamic and, thus far, promising. Despite
powerful growth trends in most of the major urban air pollution indicators, Atlanta has
experienced little deterioration and, in most instances, great advances in quality. Recent history
has seen major improvements in many air pollution levels. This has occurred against a backdrop
of growing population, rising VMT, and growing manufacturing and construction sectors.
Clearly, either the simple model used in policy discussions (more cars and people and
industry means worsened air quality) has not applied to Atlanta or something is missing. Most
likely, the story of Atlanta’s air quality history has been dominated by technological shifts. These
changes have made the other factors second-order at best. Whether the technologies have been
“forced” via regulation (e.g., unleaded gasoline, catalytic converters, smokestack scrubbers) or
arisen with macroeconomic shifts (e.g., growth of service- and information-based industries) or
7
This analysis has focused on the criteria air pollutants. These chemicals fall under a separate regulatory category
from air toxics, or cancer-causing chemicals. These air toxics might pose some of the greatest threats to public
health, although they are only more recently receiving much attention. For the purposes of this paper, however, it
should suffice to note that trends in air toxics in Atlanta have roughly followed similar patterns to criteria pollutants
like SO2.
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something else is unclear from this analysis. Moreover, simply examining trends cannot identify
the counterfactual or “what would have happened” in the absence of one factor or another. At
this point, even after 30 or more years of intensive federal air quality regulation, there are a great
many unanswered questions about urban air quality.
These ambiguous and mixed results point to technology and policy as playing a dominant
role in the air quality system. Understanding the innovation and diffusion of new technologies
that impact air quality is no small feat, and predicting it is still tougher. Regulations can favor
certain technologies, with both positive and negative impacts on air quality. A holistic approach
to the air quality system appreciates the complex interactions between technology, policy,
economics, and the environment. Just as one affects the other, those changes are likely to
feedback and influence the other aspects of the system.
These feedback loops can be particularly important in making long-term forecasts about
Atlanta’s air quality. And, to date, very little is understood about these mechanisms. Most air
quality models take economic and policy trends as “exogenous” to the model, or completely
determined from outside the system. Yet, as Atlanta struggles to manage its growth, it is clear
that policies react to changes in both environmental quality and economic developments. Many
economic development models likewise take air quality as fixed. Integrating the subsystems
involves a major interdisciplinary undertaking. To date, far too little effort has gone into
understanding how (technology and policy) innovation responds to environmental and economic
dynamics, and vice versa. The shortcomings of the conventional predictions (more traffic,
population, construction leads to worsening air quality) reflect this “partial” modeling of the
situation.
18
The rest of the system remains a mystery – one for political debate and opportunism.
Lacking the ability to forecast air quality with much confidence, we might be content in knowing
that the sky is neither falling nor pristine. Our air quality fate is likely to be determined by the
winds of change that blow elsewhere. This may take the form of larger technological innovations
(perhaps induced by policy, perhaps by continuing deindustrialization) that are difficult to
foresee by just looking at air quality monitoring stations and regional planning documents. While
we have made tremendous progress in improving air quality over the last several decades,
important gaps remain in our understanding of the system that generates that air quality. It seems
we understand least the most salient determinants of air quality. In the coming decades, Atlanta
faces a formidable task in filling in those knowledge gaps and achieving additional
improvements. If the past three decades are any guide, there is cause for guarded optimism.
19
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23
Figure 1: Annual Lead Concentrations, Atlanta, GA,
1958-2005 Based on Annual Maximum Average8
Lead Concentration, ug/m3
3
2.5
2
1.5
1
0.5
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
0
Year
Concentration, ug/m3
"Old" NAAQS Standard
"New" NAAQS Standard
Figure 2: Annual TSP, PM 2.5, and PM 10 Concentrations, Atlanta, GA, 1958-2005
Based on Annual 4th Maximum Average
250
200
150
PM TSP
PM 2.5
100
PM 10
50
19
57
19
61
19
65
19
69
19
73
19
77
19
81
19
85
19
89
19
93
19
97
20
01
20
05
0
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
Ozone 8hr Concentration, ppm
Figure 3: Annual Ozone Concentrations, Atlanta, GA, 1987-2005
Based on Annual 8 Hour 4th Maximum Average
Year
Annual Ozone Concentrations
"Old" NAAQS Standard
"New NAAQS Standard
8
The lead values between the years 1959-1962 were unavailable. The average of 1958 and 1963 were used for these
years.
24
Figure 4: Annual NO2 and SO2 Concentrations, Atlanta, GA, 1972-2005
Based on Annual Averages
0.1200
0.1000
0.0800
SO2
0.0600
NO2
0.0400
0.0200
19
72
19
75
19
78
19
81
19
84
19
87
19
90
19
93
19
96
19
99
20
02
20
05
0.0000
20
15
10
5
0
19
7
19 2
7
19 3
7
19 4
7
19 5
7
19 6
7
19 7
7
19 8
7
19 9
8
19 0
8
19 1
82
19
8
19 3
8
19 4
8
19 5
8
19 6
8
19 7
8
19 8
8
19 9
9
19 0
9
19 1
9
19 2
9
19 3
94
19
9
19 5
9
19 6
9
19 7
9
19 8
9
20 9
0
20 0
0
20 1
0
20 2
0
20 3
0
20 4
05
CO Concentration, ppm
Figure 5: Annual CO Concentrations, Atlanta, GA, 1972-2005
Based on 8-Hour Second Maximum Annual Averages
Year
Annual CO Concentrations, Atlanta
NAAQS CO
Atlanta population
4
3
2
1
0
0
MSA population, in millions
5
100000 200000 300000 400000 500000
Figure 6: Population Trends, Atlanta, GA, 1860-2005
1850
1900
1950
2000
year
MSA population, in millions
25
Atlanta population
.25
.2
.1
.15
Share of earnings
220000
200000
180000
160000
1970
1980
1990
Year
2000
2010
Employment in manufacturing
Share of earnings from manufacturing
40000000
35000000
30000000
25000000
20000000
15000000
10000000
5000000
0
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
VMT
Figure 11: VMT Trends, Atlanta, GA, 1984-2004
Year
Figure 12: Climate Trends, Atlanta, GA, 1940-2003
66
65
Average Temperature
Employment
240000
Figure 10: Manufacturing Employment and Sector Size,
Metro Atlanta, 1969-2005
64
63
62
61
60
59
58
57
56
1940
1950
1960
1970
Year
26
1980
1990
2000
Figure 13: National Trends in Emissions and Other Factors
400000
0
0
200000
ATL city population
5
10
ATL MSA population
15
20
600000
Figure 11A: Historical and Forecasted Populations
1850
1900
1950
year
2000
2050
ATL MSA population (millions) forecasted metro pop (millions)
predicted censusATL
forecasted city pop
ATL city population
80000
0
20000
40000
60000
per capita income (1982-83$)
6.00e+07
2.00e+07
4.00e+07
3.00e+07
5.00e+07
1.00e+07
Vehicle miles traveled
Figure 11B: Historical and Forecasted Income and VMT
1960
1980
2000
2020
2040
2060
year
fitted VMT line
VAR VMT forecast
real income/capita
VMT
fitted income/capita
VAR income/capita forecast
Figure 11C: Historical and Forecasted Earnings
27
0
1980
2000
2020
2040
2060
year
real manufacturing earnings
real transportation earnings
real construction earnings
VAR forecast of manuf. earnings
VAR forecast of transport. earnings
VAR forecast of construct. earnings
-.5
0
.5
1
Figure 12A: Historical and Forecast Ozone Levels, from
Multivariate Regression
1960
1980
2000
2020
2040
2060
Year
95% confidence interval for ozone forecast
VAR ozone forecast
Prediction
NAAQS Ozone (8hr)
5
10
Figure 12B: Historical and Forecast Lead Levels, from
Multivariate Regression
0
earnings (1982-83$)
2.00e+07 4.00e+07 6.00e+07 8.00e+07
1960
1960
1980
2000
Year
95% confidence interval for lead forecast
VAR lead forecast
2020
2040
Prediction
NAAQS Lead
Figure 12C: Historical and Forecast SO2 Levels, from
Multivariate Regression
28
.6
.4
.2
0
-.2
1960
1980
2000
2020
2040
2060
Year
95% confidence interval for SO2 forecast
VAR SO2 forecast
Prediction
NAAQS SO2
0
.1
.2
.3
.4
Figure 12D: Historical and Forecast NOx Levels, from
Multivariate Regression
1980
2000
2020
Year
2040
95% confidence interval for NOx forecast
VAR NOx forecast
2060
Prediction
NAAQS NO2
-60
-40
-20
0
20
Figure 12E: Historical and Forecast CO Levels, from
Multivariate Regression
1960
1980
2000
Year
95% confidence interval for CO forecast
VAR CO forecast
29
2020
2040
Prediction
NAAQS CO