Andras Herczeg:
Air dispersion modeling as a support tool for
air pollution regulations
(Final Report – Air)
Rensselaer Polytechnic Institute
MANE-6960H01
Air and Water Pollution Prevention and Control Engineering
Fall 2013
Professor Ernesto Gutierrez-Miravete
Introduction
Environmental impact assessments, risk analysis and emergency planning are all rely on air
pollution models that measure the emission of chemicals, particulates, or biological materials,
which may cause discomfort, disease, or death to humans, damage other living organisms such
as food crops, or damage the natural environment or built environment. The models cover a
broad range of scales from local to global, lead to distinct modelling requirements. In highly
polluted cities (such as Beijing, Los Angeles and Mexico City) regional scale air quality models
are used to forecast air pollution episodes, which results may initiate compulsory shutdown of
industries or vehicle restrictions (Macdonald, 2003; Fay and Golomb, 2012).
Photos taken by Bobak Ha'Eri
Beijing China air on a day after rain (left) and a sunny but smoggy day (right)
Air pollutants are classified in two categories: primary (emitted directly from the sources) and
secondary (transformed by chemical reactions in the atmosphere from primary pollutants).
Examples of primary pollutants are sulfur dioxide, nitric oxide, carbon monoxide, organic
vapors, and particles. Particles may be composed of inorganic material, such as fuel ash, organic
compounds originating in the fuel, and elemental carbon, commonly called soot. Examples of
secondary pollutants are higher oxides of sulfur and nitrogen, ozone and other oxidants, and
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particles that are formed in the atmosphere by condensation of vapors or coalescence of primary
particles. Silva et al. (2013) described serious health concern of both types of air pollution and
pointed out that according to their estimation anthropogenic changes to air pollutant
concentrations since the preindustrial are associated annually with 470 000 (95% CI, 140 000 to
900 000) premature respiratory deaths related to ozone, and 2.1 (1.3 to 3.0) million CPD and LC
deaths related to PM2.5. Also, the research suggests that climate change might influence air
quality. As the awareness of the air pollution risks grew, legislators and regulators started to
address the root causes.
Regulatory considerations regarding air quality issues
Fossil fuel use and its environmental effects triggered public awareness concerning impair air
quality that became one of the most problematic consequence of fossil fuel combustion (Levy et
al., 2007). Residents are especially sensitive to the visible smoke that emanates from
smokestacks, fireplaces, and diesel truck exhaust pipes; while the regulators even more profound
of the excess of invisible pollutants is emitted from the different sources. The emission of air
pollutants may occur in different forms, such as during extraction, transport, refining,
manufacturing, usage and even storage phases (Helsen, 2005). Fossil fuel examples include
fugitive coal dust emissions from crude and refined oil storage tanks, as well as from oil and
gasoline spills; evaporative emissions from gasoline tanks on board vehicles and during
refueling; natural gas leaks from storage tanks and pipelines; fugitive dust from ash piles; etc.
To address the growing challenge, in the United States the first Clean Air Act (CAA) was passed
by Congress in 1963. Subsequently, the Clean Air Act was amended in 1970, 1977, and 1990.
During this time, other developed countries also enacted their own version of clean air acts and
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various legislation and regulations pertaining to reducing air pollution, which resulted in steadily
improving air quality in these countries. The prescribed maximum amount of pollutants that
allowed to be emitted from the source called emission standards. For large sources (e.g. power
plants), the emission standards set at a level that - after dispersion in the air within a reasonable
distance - the pollutants will not cause significant human health and environmental effects. In the
case of the small sources (e.g. vehicles), the goal of the emission standards is to prevent health
effects from the cumulative emissions of all sources. While these standards helped to reduce
emissions from centralized sources; yet, the number of dispersed sources are often everincreasing.
To implement a consistent and rigorous regulation, the National Ambient Air Quality Standards
(NAAQS) and other similar requirements - such as New Source Review (NSR) and Prevention
of Significant Deterioration (PSD) - have been established by the US Environmental Protection
Agency (EPA) for pollutants considered harmful to public health and environment under the
latest amendment of the Clean Air Act to promote prevention and to protect human health (EPA,
1990).
Two types of NAAQS are identified:
a) the primary standards provide public health protection, including protecting the health of
"sensitive" populations such as asthmatics, children, and the elderly;
b) the secondary standards provide public welfare protection, including protection against
decreased visibility and damage to animals, crops, vegetation, and buildings.
EPA has set National Ambient Air Quality Standards for six principal pollutants, which are
called "criteria" pollutants (see table below).
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Source: EPA
National Ambient Air Quality Standards for six principal pollutants
To protect human health, most countries also prescribe maximum tolerable concentrations in the
air. The emission and ambient standards are legal parameters, published in laws and decrees. If
these standards are exceeded, the causative sources can be penalized, and their licenses can be
revoked.
While National Ambient Air Quality Standards (NAAQS) address concerns related to the human
activities that have significantly increased the concentrations of ozone and fine particulate matter
in both urban and rural regions; yet, it also has limitations. EPA has regulatory rights only
regarding the US and cannot prevent global influences. Moreover, even within the US additional
legislation is constantly required to address different kind of – e.g. interstate - pollution. For
instance the Clean Air Interstate Rule (CAIR) which was issued in 2005 by the EPA provides
states with a solution to the problem of power plant pollution that drifts from one state to another
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(EPA, 2005). Air dispersion models are used, among the variety of reasons, to ensure compliance
with these particular regulations.
The types of air dispersion models
The estimated of the concentration of pollutants in space and time is called air quality modeling
or source-receptor-modeling (Moreira et al., 2009). Air dispersion models are used to measure
primary air pollutants disperse into the atmosphere by turbulent diffusion, advert by winds, and
transform into secondary pollutants by chemical reactions among themselves and with other
atmospheric species.
There are three broad, well-cited types of air quality models (Srivastava and Rao, 2011):
1. Physical models: involve reproducing urban area in the wind tunnel, which results in
scale reduction of both the replica and the actual atmospheric flows. Additionally, high
cost is associated with these methods.
2. Mathematical / numerical models: use mathematical representation and/or use statistics to
analyze the available data. Another type focuses on representing the physical and
chemical processes in equations without assumptions.
3. Statistical models: are simple but lack the description of causal relationships. Also, they
are highly rely on past data and cannot be extrapolated beyond limits of data used in their
derivation. Therefore these are generally not used for planning purposes, since they
cannot predict effect of changes in emissions.
Furthermore, several other classifications exist. For example the models are grouped into five
types (Box model, Gaussian model, Lagrangian model, Eulerian model, Dense gas model), as
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well as some hybrids these. (Cheremisinoff et al., 2008). All of these dispersion models are
capable of describe the different emission sources, such as point (smokestack), line (highway), or
area (an urban area) sources, and their effect on the receptors, such as designated human habitats
or ecologically sensitive areas (Jones et al., 2007).
Still, the Gaussian model, which incorporates the Gaussian distribution equation (GPE, see
below), is the most commonly used (Lushi and Stockie, 2010).
Gaussian distribution equation (GPE)
Several parameters (e.g. temperature, wind speed, velocity, stability, sideways, stack height)
have an effect on the GPE. Also, the Gaussian plume model is only an approximation, which
works best on level ground. Since the wind speed u appears in the denominator, the GPE cannot
be used for calms, when the wind speed is less than approximately 2 m/s. Also, the modeling
distance should not be extended further than 20-30 km, because wind direction and speed, as
well as the dispersion characteristics (atmospheric stability category), may change over long
distances. Overall, Gaussian models are considered only accurate for determining pollutant
concentrations up to 50 km away from the source.
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Source: Srivastava and Rao (2011)
Gaussian air pollutant dispersion plume
It should be noted that GPE is a steady state model; therefore the emission rate Qp and plume rise
∆h should also remain constant. On the other hand, significant reduction in the maximum ground
concentrations may be achieved with the initial selection of a taller stack, as the stack height has
negligible influence at far field locations. However, the increase in ground level concentrations
due to a reduced stack height can occur within couple hundred meters. Fay and Golomb (2012)
points out that within a limited distance, and on level terrain, the GPE gives concentrations on
the ground that are within a factor of 2 of measurements. In valleys, hills, and urban areas,
aerodynamic obstacle effects need to be considered. Numerous equations exist that work
reasonably well when corrections for terrain complexities are incorporated into the Gaussian
plume model.
Air Pollution Meteorology
Gaussian and other air dispersion models depend on correct and precise input data, therefore air
pollution meteorology is a crucial element of the air quality modeling. The required
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meteorological data are available from numerous weather stations operating around the world.
The weather stations measure and record data that is rendered twice daily at 0000 and 1200
Greenwich Mean Time (GMT) for global synchronization. These data include surface and upper
air winds, atmospheric pressure, humidity, precipitation, insolation, temperature on the ground,
and the temperature gradient in the atmosphere – the temperature variation with altitude.
Wind statistics is especially vital for the modeling domain and the dispersion characteristics of
the atmosphere. The term dispersion is used to describe the combination of diffusion (due to
turbulent eddy motion) and advection (due to the wind) that occurs within the air near the earth’s
surface (Stockie, 2010). Winds blow from high- to low-pressure regions on the earth and since
the earth is a rotating body revolving around the sun, any spot on the earth receives constantly
changing insolation over day and night and over the seasons. Additionally, orographic effects,
surface friction, sea-land interfaces, street canyons alter the course of winds, which requires a
multiyear wind statistic approach to accurately predict the advection by the winds of pollutants
from the sources to the receptor. In the atmosphere, dispersion occurs mostly by turbulent or
eddy diffusion that has a magnitude faster than molecular or laminar diffusion.
The cause of turbulent diffusion is either mechanical or thermal shear (Fay and Golomb, 2012).
Mechanical turbulence is caused by wind shear in the free atmosphere (adjacent layers of the
atmosphere move in different directions or speeds, or friction experienced by winds blowing
over the ground surface and obstacles, such as tree canopies, mountains, and buildings). Thermal
gradient is in the lower troposphere; usually the temperature is higher near the ground and
declines with altitude. In a dry atmosphere, the gradient amounts to approximately – 10°C/km
and is called the dry adiabatic lapse rate. In a moist atmosphere, the gradient is less steep because
of the addition of the latent heat of condensation of water vapor. At night inversion might happen
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because of radiative cooling of the surface: the gradient may become positive with temperature
increasing with altitude. Inversions can also occur aloft, when a negative gradient is interrupted
by a positive one. The mixing layer is defined as the bottom layer up to the inversion. With an
inversion, atmospheric conditions are especially prone to air pollution episodes, because
pollutants emitted at the ground are concentrated in the shallow mixing layer. Later in the day, as
the sun rises, the inversion layer may break up, allowing pollutants to escape aloft and thus
alleviating the pollution episode. Valleys and other areas surrounded by mountain experience
frequent inversion layers and these may result in pollution issues (e.g. Los Angeles, Mexico
City). When the temperature in the upper layers is colder than in the lower layers, upper air
parcels fall downward because of their larger density, and lower air parcels move upwards. This
movement generates turbulent or eddy diffusion. Unstable conditions occur when the
temperature gradient becomes steeper, which causes a greater turbulent intensity. On the other
hand, neutral condition appears when a temperature gradient is present that is equal to the dry
adiabatic lapse rate and may lead to moderate turbulence. A temperature gradient that is less
steep than the dry adiabatic lapse rate, or even a positive gradient, is called a stable condition, in
which there is minimal or no turbulence at all.
The turbulent conditions of the atmosphere are classified into six Pasquill-Gifford stability
categories (Pasquill, 1961; Gifford, 1976) (see below).
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Source: Burton (2010)
Estimating the stability class from the wind speed, cloud cover and time of day
They range from A, very unstable (very turbulent) to F, very stable (little turbulence). The
neutral is the Category D with moderate turbulence. The negative temperature gradient (lapse
rate) of category D coincides with the dry adiabatic lapse, about – 10°C/km. For categories A, B
and C, the magnitude of the negative gradient is greater than D, for E the gradient is smaller than
for D, and for F the gradient is positive.
Source: Burton (2010)
Variation σy and σz with downwind distance x for the six Pasquill-Gifford stability classes
The stability categories can be approximate by knowing the surface wind speed, insolation, and
cloud cover. In daytime, low wind speeds and strong insolation lead to unstable categories A or
B; high wind speeds and moderate to slight insolation lead to neutral categories C or D. At night,
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the stability categories are almost always neutral or stable, D, E, or F. Overall, with proper data
air quality models, including the Gaussian model, are capable to describe well air pollution in
every meteorological conditions.
Conclusion
Dispersion modeling uses mathematical formulations to characterize the atmospheric processes
that disperse a pollutant emitted by a source. Based on emissions and meteorological inputs, a
dispersion model can be used to predict concentrations at selected downwind receptor locations.
Several air quality models exist and the Gaussian model is the most commonly used due to its
simple but flexible design. However, with proper modifications almost all of these models can be
used to determine compliance with National Ambient Air Quality Standards and other regulatory
requirements. Due to their powerful attributes, air dispersion models play a critical role to ensure
a fair and feasible framework for air pollution regulation and regulators should aim for
incorporating the latest modeling results to promote ever improving air quality.
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