Technical support document (TSD) for NO2-

related AERMOD modifications

EPA- 454/B-15-004

July 2015

Technical support document (TSD) for NO2-related AERMOD modifications

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Analysis Division

Air Quality Modeling Group

Research Triangle Park, North Carolina
Preface 

This document provides a review the scientific merit of the NO2 options in AERMOD, summarizes 

existing evaluations of these options, and presents additional testing used to determine appropriate 

application of these options for these options as part of the regulatory default version of AERMOD.
Contents 

Preface ..........................................................................................................................................................4

Contents........................................................................................................................................................5

Figures...........................................................................................................................................................6

Tables............................................................................................................................................................7

1. Introduction ..............................................................................................................................................8

2. Background ...............................................................................................................................................8

2.1 NO2 chemistry ....................................................................................................................................8

2.2 Ambient Ratio Method .....................................................................................................................10

2.3 Ambient Ratio Method 2 ..................................................................................................................11

2.4 Ozone Limiting Method ....................................................................................................................13

2.5 Plume Volume Molar Ratio Method.................................................................................................13

2.6 Existing evaluations of OLM and PVMRM.........................................................................................14

3. Regulatory status of ARM, ARM2, OLM, and PVMRM............................................................................15

3.1 Regulatory requirements..................................................................................................................15

3.2 ARM versus ARM2.............................................................................................................................16

3.3 Status of OLM and PVMRM ..............................................................................................................17

3.4 The 3-Tiered approach......................................................................................................................17

4. Proposed regulatory options for NO2 modeling in AERMOD.................................................................17

4.1 ARM2 options ...................................................................................................................................17

4.2 Default options for OLM and PVMRM..............................................................................................19

4.3 Appropriate applications of OLM and PVRMR..................................................................................21

4. PVMRM2 .................................................................................................................................................21

4.1 Comparison of PVMRM vs. PVMRM2 Options in AERMOD..............................................................22

4.2 Comparisons of PVMRM and PVMRM2 for select field studies .......................................................26

References ..................................................................................................................................................30
Figures 

Figure 1 – NO2/NOx equilibrium ratios based on the PSS assumption from Equation 1...........................10

Figure 2 – NO2/NOx ratios from AQS for 2012...........................................................................................12

Figure 3 - Comparison of modeled NO2 concentrations from ARM2 and PVMRM ...................................18

Figure 4 - Distribution of NO2 ISRs from EPA's database as of June, 2015. ...............................................20

Figure 5 - Comparison of PVMRM and PVMRM2 for the Empire Abo north monitor................................27

Figure 6 - Comparison of PVMRM and PVMRM2 for the Empire Abo south monitor ...............................28

Figure 7 - QQ plot of PVMRM for the Palaau monitor................................................................................28

Figure 8 - QQ plot of PVMRM2 for the Palaau monitor..............................................................................29
Tables 

Table 1 - Comparison of ARM and ARM2 estimated NO2 concentrations.................................................17
1. Introduction 

The proposed revisions to Appendix W to CFR 40 Part 51—Guideline on Air Quality Models (Appendix 

W), includes a new version of AERMOD (ADD VERSION #). This version of AERMOD reflects the proposed 

new regulatory default options for NO2, including the new Ambient Ratio Method 2 (ARM2, (Podrez, 

2015)), the existing Ozone Limiting Method (OLM, (Cole & Summerhays, 1979)), and a revised version of 

the Plume Volume Molar Ratio Method (PVMRM, (Hanrahan, 1999a)). This TSD reviews the scientific 

merit of these methods, summarizes existing model evaluations, and presents additional testing used to 

determine appropriate application of these options for use as part of the regulatory default version of 

AERMOD. 

2. Background 

The development of the original Ambient Ratio Method (ARM), OLM, and PVMRM precedes both the 

development of AERMOD in the late 1990’s and its promulgation for regulatory use in 2005. Though 

there are aspects of the implementation of these methods in AERMOD that are specific to AERMOD, the 

basic approach to NO2 speciation used by ARM/ARM2, OLM, and PVMRM are not inherent to AERMOD. 

As a result, there is a notable body of literature focused on the more fundamental science behind these 

NO2/NOx speciation approaches. This section provides a review of the scientific background available 

for ARM/ARM2, OLM and PVMRM as well as the basics of NO2 chemistry relevant for these methods. 

2.1 NO2 chemistry

Nitrogen oxides (NOx) consist of two species, NO2 and NO. Anthropogenic NOx is typically formed 

during high-temperature combustion processes via three main reaction pathways. The most prominent 

and consistent production route is the thermal oxidation of N2 via the Zeldovich mechanism (Zel'dovich, 

Sadonikov, & Frank-Kamenetskii, 1946). The thermal production of NOx is fairly predictable and the total 

amount of NOx and the speciation of NO and NO2 in the exhaust gas stream from “thermal NOx” is 

controlled by the reaction temperature and the amount of available oxygen in the gas stream. NOx can 

also be produced when nitrogen-containing fuels, such as oil and coals, are used and the nitrogen 

contained in the fuel is liberated and subsequently oxidized. “Fuel NOx” can be minimal or a major 

contributor to NOx emissions, depending on the fuel type (e.g., natural gas has virtually no nitrogen, 

resulting in no fuel NOx production). The last production route is known as prompt production, which 

involves the reaction of N2 with oxygen and hydrocarbon radicals resulting from fuel combustion. 

“Prompt NOx” can involve hundreds of reactions of very short-lived species in the combustion chamber 

and exhaust gas stream and is generally difficult to characterize and predict and instead is estimated as 

the amount of NOx that is present that cannot be accounted for as thermal or fuel NOx. The speciation 

of NO and NO2 in the exhaust gas stream from prompt and fuel NOx is a function of the fuels and the 

type of combustion process and is very difficult to generalize. NOx production can continue in the 

exhaust gas stream until temperatures drop low enough to end combustion. 

Once NOx is in the atmosphere, there a number of potential chemical reactions that can occur, 

depending on the relative amounts of NO and NO2, the total NOx, the ambient meteorological 

conditions, and other atmospheric trace gasses available for reaction. In most cases, the fastest and 

most important reactions of NOx involve ozone (O3). When there is sufficient ozone present, Reaction 1 

dominates all other NOx chemistry:

NNNN + 33 → NN 22 + 22 Reaction 1
In fairly concentrated plumes, this reaction is initially ozone limited, such that all available ozone will be 

consumed. As a plume becomes more diluted and more ozone is mixed in, the reaction eventually 

becomes NO limited. During daytime, NO2 can be photolyzed back to NO and subsequently reform 

ozone:

NN 22 + hhhh → NNNN + ( PP33 ) Reaction 2

� PP

33 � + 22

MM

→ 33 Reaction 3

Though Reaction 1 is the fastest of the three, a quasi-equilibrium state, also referred to as the 

photostationary state (PSS) due to Reaction 2 being driven by sunlight, can be achieved during the 

daytime between NO, NO2 and ozone. While the PSS can occur within 1-3 minutes, it must also compete 

with changes in plume concentrations due to mixing, i.e., entrainment of additional ozone and dilution 

of NOx towards ambient levels. Thus, for a fairly concentrated plume, the rate of mixing and the rate of 

the PSS reactions will compete on relatively equal time scales and Reaction 1 will usually dominate the 

chemistry. Once a plume becomes relatively diluted, the PSS can be reached, which is dictated by the 

total amount of NOx, background ozone and the ambient meteorological conditions. Assuming an equal 

rate of NO2 formation and destruction, Reactions 1 and 2 can be used to determine the equilibrium 

ratio of NO2/NOx (Seinfeld & Pandis, 2006):

NN 22

NN xx

= (KK11)[ 33]/(KK22 + KK11 ∗ [ 33]) Equation 1

Where K1 and K2 are the reaction rates for reaction 1 and 2:

KK11 = �

1111.3333

TT � ∗ ee ee �− 11111111TT � Equation 2

KK22 = . 0000 ∗ ee ee(− .

cccccc(θθ)

) Equation 3

The units for K1 are ppb-1*sec-1 and sec-1 for K2, where T is the ambient temperature in Kelvin and θ is 

the solar zenith angle (SZA) in radians from zenith1

. The result from this equation is that the equilibrium 

NO2/NOx ratio is solely a function of ozone, sunlight, and temperature and not a function of the total 

NOx. The equilibrium NO2/NOx ratios determined from Equation 1 for a variety of conditions are shown 

in Figure 1. There are clearly a wide range of potential equilibrium ratios. As the available sunlight 

decreases (95% cloudy) or as there is more ozone available, the preference is for NOx to remain as NO2.

It is important to note that the solution to Equation 1 holds ozone constant, such that ozone titration is 

not taken into account within the setting of a plume undergoing dilution and reaction. Thus, for any 

background ozone concentration, the corresponding equilibrium ratio in Equation 1 is a conservative 

estimate of conditions present in a plume in the atmosphere. 

 1 The rates for K1 and K2 given here are taken from (Hanrahan, 1999a). However, it should be noted that there is 

notable variation in the reported reaction rates for K1 from various sources. Additionally, K2 is an estimate of the 

photolysis rate of NO2, which was stated as accurate to about 10% for a SZA between 0 and 65 degrees (Dickerson, 

Stedman, & Delany, 1982). More recent references (e.g., (Atkinson, et al., 2004)) indicate that the rate for K2 

requires an integration of the absorptions rates at the range of wavelengths present for any scenario. Despite 

these differences in rate constants, the resulting NO2/NOx ratio is not anticipated to vary greatly between 

estimates of the rate for either reaction and that the results indicated in Figure 1 are reasonably representative. 
Figure 1 – NO2/NOx equilibrium ratios based on the PSS assumption from Equation 1. 

Though generally ignored in the realm of NO2 modeling with Gaussian plume-based models, there are a 

number of other chemical reactions that can convert NO to NO2, convert NO and NO2 reservoir species 

(e.g., nitrous and nitric acid), and remove NOx from the atmosphere entirely. At nighttime, there is 

essentially no ozone production, such that in urban areas (where NOx emissions continue throughout 

the day and night), background ozone is generally quite low. As a result, there are fundamentally a 

different set of daytime and nighttime reactions. In general, nighttime NOx chemistry favors conversion 

of NO to NO2 and conversion of NO2 to NO2 reservoir species, which may be removed from the 

atmosphere or converted back to NO2 at sunrise. 

2.2 Ambient Ratio Method

The original ARM is based on (Chu & Meyer, 1991) and is focused on long-term averages of NOx

concentrations. The basis for the approach assumes that for long-term averages, the partitioning of NO 

and NO2 at any location is controlled by the NO/NO2 partitioning in the ambient background air 

because: 1) plumes are mixing with ambient air, and 2) as plumes photochemically age, they will

ultimately approach the background equilibrium. It can also be assumed than any sources near a 

monitor will periodically impact a monitor and as a result, the average NO2/NOx ratio will reflect plumes 

in various stages of mixing and photochemical aging. Since most sources are expected to have relatively 

low in-stack ratios (ISR) of NO2/NOx, such periodic impacts of a source on a monitor would be expected 

to reduce the long-term average NO2/NOx ratio. While this would make the NO2/NOx ratio from the 

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90 100

NO2/NOx equilibrium ratio

ozone (ppb)

SZA 0°, 95% cloudy, 30° C SZA 0°, 50% cloudy, 30° C SZA 0°, 5% cloudy, 30° C

SZA 60°, 95% cloudy, 0° C SZA 60°, 50% cloudy, 0° C SZA 60°, 5% cloudy, 0° C
monitor more representative of those local course impacts, it would make the NO2/NOx ratio less 

conservative when predicting impacts from new or modified sources. Nevertheless, a NO2/NOx ratio 

controlled by background conditions would still be expected to be conservative for estimating the longterm average impacts from a new or modifying source. Thus, long-term averages of the NO2/NOx ratio 

at any monitor can be used as a surrogate for estimating the long-term NO2/NOx ratio at other 

locations. 

The validity of the ARM assumptions were also confirmed with several sets of ambient data. First, (Chu 

& Meyer, 1991) evaluated 10 years of ambient data from nationwide US networks to determine trends 

of the long-term averages of the NO2/NOx ratios. Their analysis had four major conclusions:

1. The annual NO2/NOx ratios ranged from 0.48 to 0.74 over the 10-year period. 

2. There was relatively little variation in the median, maximum and minimum ratios over the data 

period. 

3. The year-to-year variability at any single monitor was less than 10%. 

4. The 90th percentile value for the most recent 3-years of data (1987-1989) was 0.75.

The fundamental conclusion drawn from the ambient data was that the annual average of the ambient 

NO/2NOx ratio was fairly stable and robust, and 0.75 was recommended as a national default value. 

Second, in addition to their own analysis of the available ambient data, several older studies, which 

focused on sampling aging plumes as they were transported downwind, were also cited ( (Keifer, 1977), 

(Janssen, Wakeren, Duuren, & Elshout, 1988), (Builtjes, 1981). These studies generally showed that the 

NO2/NOx ratio tends to increase with increasing distance from major sources. 

Based on the behavior of the nationwide monitoring data and source-specific studies, the recommended 

application for ARM included:

1. Use the 3-year average of annual NO2/NOx ratios in the vicinity of the new or modifying source 

(within 15 to 90 km) to apply to annual averaged modeled NOx to determine the annual NO2. 

2. Monitored values below 20 ppb of NO2 should be excluded when calculating the annual 

averages due to uncertainty at these lower concentrations.

3. If the monitor is located in a major urban area, then only daytime measurements should be used 

in the annual averages. 

4. The national default should be used in absence of any nearby monitoring data. 

ARM was implemented in the 2005 version of Appendix W with the national default value of 0.75 for 

annual averages, with a mention of using regional ratios as an alternative. Given that there was no 

hourly standard available at the time, there was no national default given for short-term standards. 

However, the EPA issued a guidance memorandum that recommended a national default NO2/NOX 

ratio for usage for the hourly standard (0.8) (U. S. EPA, 2011). 

2.3 Ambient Ratio Method 2

The updated ARM2 was developed by (Podrez, 2015) and is focused on short-term (i.e., hourly) averages 

of NO2 concentrations. The basis for the approach assumes that due to the nature of NOx chemistry and 

typical NO2/NOx emission ratios, that the ambient NO2/NOx ratios will exhibit a predictable pattern, 

which will in large part, be a function of the total NOx present. Given this assumption, a 10-year record 

(2001-2010) of ambient data from the Air Quality System (AQS, 2014) was analyzed to characterize the 
behavior of these data. The analysis showed a decreasing NO2/NOx ratio with increasing total NOx 

concentrations. In order to develop a NO2/NOx ratio response curve, the ambient data was binned 

based on the NOx concentrations, the 98th percentile NO2/NOx ratio was determined for each NOx bin, 

and a 6th order polynomial regression was performed on the selected values from each NOx bin. The 

resulting polynomial is used to predict the NO2/NOx ratio based on the modeled NOx concentration, 

though maximum and minimum limits on the NO2/NOx ratio are used at the upper and lower ends of 

the range of potential NOx concentrations (0.9 maximum and 0.2 minimum). Figure 2 shows an example 

of AQS data, comparing the NO2/NOx ratio to the total NOx, demonstrating the relationship found by 

(Podrez, 2015). 

Figure 2 – NO2/NOx ratios from AQS for 2012

In addition to developing the ARM2 response curve, (Podrez, 2015) evaluated ARM2 against PVMRM 

and OLM for three ambient datasets and performed a large number of sensitivity tests with modelbased evaluations. The ambient datasets include: 1) a gas processing plant in Empire Abo, NM; 2) a coalfired power plant in Palaau, HI; and 3) a small diesel-fired power plant in Wainwright, AK. The sensitivity 

tests included scenarios taken from an earlier report on OLM and PVMRM performance (MACTEC, 2004)

and tests to address generalized facilities representative of several other source (diesel generators, an 

oil refinery, a gas pipeline compressor station, natural gas production fields and processing plants, and a 

large boiler). These comparisons showed that ARM2 gave generally conservative estimates of the 

NO2/NOx ratio and the total NO2 as compared to the measurements as well as OLM and PVMRM. The 

model evaluation report, (RTP Environmental Associates, Inc., 2013), provides more complete details on 

the evaluation results than the journal article. 
2.4 Ozone Limiting Method

(Cole & Summerhays, 1979) is most often cited as the origin of OLM even though this particular paper is 

a review paper of methods to estimate NO2 concentrations since there are no earlier citations for OLM. 

As outlined by (Cole & Summerhays, 1979), OLM assumes NO conversion to NO2 by reaction with 

ambient ozone (NO + O3 → NO2 + O2 Reaction 1), according to the 

following procedure:

1. Estimate total NOx using the dispersion model of choice.

2. Divide the total NOx into two components:

a. Component 1 is the “thermal” NO2 that is emitted directly as NO2 from the stack 

(assumed to be 0.10 for most sources)

b. The remaining NOx is assumed to be NO that is available for reaction with ozone.

3. If the ambient [ozone] > 0.9*[NOx] (the portion of NOx present as NO), then assume that all NO 

is converted to NO2, such that [NO2] = [NOx]. If the ambient [ozone] < 0.9*[NOx], then assume 

that the amount of NO converted is equal to the available ozone, such that [NO2] = 0.1*[NOx] + 

[ozone].

4. The NO2 concentration from (3) is added to the background [NO2] to determine the total [NO2]. 

The reaction is assumed to be instantaneous and irreversible and can be applied on an hourly basis (or 

shorter or longer, depending on the resolution of the model run and background [NO2] and [ozone] 

data). 

(Cole & Summerhays, 1979) presented no evaluations of OLM, but there have been a number of formal 

and informal studies that have evaluated OLM based on AERMOD and other dispersion models. The 

majority of these studies have simultaneously performed evaluations of OLM and PVMRM with results 

of these studies being summarized below for both OLM and PVMRM. 

2.5 Plume Volume Molar Ratio Method

PVMRM was developed by (Hanrahan, 1999a) and is based on the same fundamental chemistry as OLM 

(NO + O3 → NO2 + O2 Reaction 1), which is also assumed to be 

instantaneous and irreversible and can be applied on an hourly basis (or shorter or longer, depending on 

the resolution of the model run and background [NO2] and [ozone] data). In contrast to OLM, though, 

PVMRM attempts to estimate the amount of ozone that has been entrained into the plume(s), rather 

than assuming all ambient ozone is available for NO conversions. (Hanrahan, 1999a) describes the 

methodology for PVMRM as follows:

1. Calculated the plume segment volume at the receptor

2. Determine the moles of NOx present in the plume segment volume

3. Determine the moles of ozone present in the plume segment volume

4. Calculate the ratio of ozone moles to NOx moles

5. Multiple the ratio found in (4) to the NOx moles to determine the moles of NO2

a. The original PVMRM assumed that 10% of the NOx was initially NO2 upon release, so 

0.1 was added to the ratio in (4) to account for the initial NO2. 

Since PVMRM can account for NO conversion in overlapping plumes, PVMRM includes a methodology 

for determining the volume of overlapping plumes. When considering multiple plumes, the plume 

volume at each receptor is computed by first identifying the primary source contributing to the NOx at 
the receptor. Then, all additional sources contributing at least half of the amount of NOx as the primary 

contributing source are identified as “major contributing sources.” Third, the maximum width between 

the major contributing sources is found. Finally, the maximum width found in the third step is added to 

the width of the primary contributing plume to determine the combined plume width. Similarly, for area 

sources, PVMRM uses the projected width of the area source as part of determining the combined

plume width. 

2.6 Existing evaluations of OLM and PVMRM

The number of field studies available to evaluate refined NO2 modeling in the near-field are fairly 

limited. As a result, the measurement-based model evaluations available for OLM and PVMRM tend to 

include both models. There are also a number of “sensitivity studies”, that are limited to comparisons 

modeling scenarios without any measurement data to benchmark model performance. A selection of 

the model evaluations are summarized here:

1. (Hanrahan, 1999b) presented an evaluation of PVMRM for several filed studies, including 

airplane-based sampling of power plumes in the Netherlands and ground-based measurements 

from power plants in Hawaii (Palaau) and New Mexico (Empire Abo) as well as a comparison 

with a large eddy simulation (LES) model. The evaluations were focused on NO2/NOx ratios 

rather than absolute NO2 and NOx concentrations and the underlying dispersion model was the 

Industrial Source Complex (ISC) model. The results of their evaluation showed that the model 

had some skill in replicating NO2/NOx ratios, though there were obvious deficiencies in the 

model. 

2. (MACTEC, 2004) presented sensitivity tests for a variety of single and multi-source scenarios for 

OLM and PVMRM based on an early version of AERMOD and AERMET. The results of their 

evaluation showed that both models were reasonably sensitive to source parameters, the 

NO2/NOx ratios tended to be lower for PVMRM than for OLM, and that the NO2/NOx ratio 

tended to be controlled by the volume of the plume for PVMRM versus being controlled by the 

total NOx concentration for OLM. 

3. (Hendrick, Tino, Hanna, & Egan, 2013) evaluated OLM and PVMRM using a relatively new field 

dataset from a small diesel-fired power generation facility in Wainwright, AK with a relatively 

recent version of AERMOD (11103). Additionally, (Hendrick, Tino, Hanna, & Egan, 2013)

suggested several improvements to the model code for PVMRM. This study was based on an 

earlier report (Hendrick, Tino, Hanna, & Egan, 2012), which includes additional details on the 

Wainwright comparison and a review of other ambient databases used for model evaluations. 

Three main conclusions were drawn from the study:

a. OLM has a tendency to overpredict the NO2/NOx ratios, while PVMRM has a tendency 

to overpredict the ratio at smaller NOx concentrations and underpredict the ratio at 

larger NOx concentrations.

b. Neither OLM nor PVMRM have any skill in predicting NO2 when paired in time and 

space with measurements. This appears to be driven more by the model’s underlying 

NOx prediction rather than with the NO/NO2 speciation. 

c. When comparing only the highest unpaired concentrations, both models have a 

tendency to overpredict the NO2 concentrations. 
4. (Schroeder, 2012) evaluated OLM and PVMRM for a single hypothetical source, but with 

meteorology and background ozone taken from 4 different locations (Illinois, New Mexico, Utah, 

and Wisconsin). The source was modeled with 2 emissions rates (1 g/s and 100 g/s) and with 2 

ISRs (0.5 and 0.1), resulting in 16 sensitivity tests. The results of the study showed that at lower 

emission rates, all options showed relatively similar maximum NO2 concentrations, as there was 

sufficient ozone relative to NOx for high conversion rates, while the lower emission rates were 

sensitive to the ISRs and the ambient ozone. 

5. (Podrez, 2015) and the companion report (RTP Environmental Associates, Inc., 2013), though 

focused on the development of ARM2, also performed extensive testing of OLM and PVMRM

with the more recent versions of AERMOD. The analysis completed model-to-monitor 

comparisons for the field studies from Alaska, New Mexico, and Hawaii, reran the model 

sensitivity tests initially done by (MACTEC, 2004), and added a number of additional sensitivity 

tests to include other typical NO2 sources. The results of the study showed that OLM and 

PVMRM tended to slightly overestimate the highest NO2 as compared to the measurement 

data. All methods tended to show relatively similar maximum NO2 concentrations when NOx 

concentrations were lower, as there was sufficient ozone relative to NOx for high conversion 

rates, while the lower emission rates were sensitive to the ISRs and the ambient ozone.

3. Regulatory status of ARM, ARM2, OLM, and PVMRM 

EPA is proposing to adopt ARM2, OLM, and PVMRM as non-default options in AERMOD, such that the 

alternative model approval currently needed for these options is no longer required. However, these 

methods are still considered screening techniques, and as such, will still require the approval of the

appropriate review authority (paragraph 3.0(b) of the proposed Appendix W). This section discusses 

several aspects of this proposal. 

3.1 Regulatory requirements

Under EPA’s Guideline on Air Quality Models published as Appendix W to 40 CFR Part 51, AERMOD is the 

preferred model for satisfying New Source Review (NSR) requirements, including Prevention of 

Significant Deterioration (PSD) for inert pollutants. For inert pollutants, AERMOD is considered to be a 

refined model, such that within the regulatory framework of a NSR or PSD demonstration, the model is 

considered not to be biased high or low. NO and NO2, however, are not inert and conversion from NO 

to NO2 must be estimated. At present, under Appendix W, the EPA recommends a 3-Tiered screening 

approach. The three Tiers should be most conservative at Tier 1 and least conservative at Tier 3, though 

the selection of any option or setting at any Tier should strive towards the most accurate estimates, 

rather than the most conservative. While all three Tiers available for NO2 modeling in AERMOD are 

considered screening techniques, each Tier has increasing complexity, which includes more refined (and 

presumably more accurate) estimates of NO and NO2 speciation, but also increasing data input 

requirements. Screening models and techniques should not be biased to under predict, i.e., should give 

conservative modeled estimates. Thus, AERMOD is considered unbiased in its estimates of NOx 

concentrations, but estimates of NO2 concentrations should be either unbiased or conservative. For the 

Tier 2 and 3, the additional data inputs required may not be available for all facilities, necessitating the 
determination of default values to insure that modeled concentrations do not underestimate facility 

impacts. 

3.2 ARM versus ARM2

The original ARM was developed exclusively for long-term average concentrations of NO2 and the intent 

was to use national default only when nearby monitoring was not available or at longer transport 

distances from a source, when a plume would have mixed towards background. The historical 

implementation of ARM in the NSR and PSD programs, however, has been to use the national default at 

all distances and all locations, regardless of the availability of nearby monitoring. Fundamentally, this 

approach for long-term averages is likely appropriately conservative for a Tier 2 method. However, ARM 

was not intended to estimate short-term average concentrations of NO2. On the other hand, ARM2 was 

developed exclusively for short-term average concentrations of NO2, specifically 1-hour averages. 

Assuming the implementation of ARM2 is appropriately conservative for estimates of 1-hour NO2 

concentrations, then ARM2 would be preferred to any modifications of ARM for short-term averages. It 

is also logical that if the 1-hour estimates of NO2 derived from ARM2 are appropriate, then any longterm averages calculated from ARM2-adjusted 1-hour concentrations should also be appropriate. Given 

this fact, there is a question as to what method should be used for long term estimates of NO2 or 

whether both ARM and ARM2 should be viable options. There are two arguments in favor of ARM2 over 

ARM. Firstly, ARM has not been implemented according to the recommendation of (Chu & Meyer, 

1991), which would a case-by-case analysis of nearby monitoring data to determine the equilibrium 

ratio rather than using the national default for all cases. Secondly, long-term averages based on ARM 

and ARM2 would not be identical. Thus, if ARM were used for long-term averages and ARM2 were used 

for short-term, there would be a discrepancy between the underlying data in hourly values and the longterm averages that could be calculated. This is demonstrated in Table 1 - Comparison of ARM and ARM2 

estimated NO2 concentrationswhich has an average NOx concentration of 235 ppb. Using the ARM 

national default of 0.75, the estimated NO2 concentration would be 176 ppb, while the average 

concentration from ARM2 would be 76.3 ppb. Therefore, if ARM2 is an appropriate tool for estimating 

1-hour concentrations, it would be preferred to use the hourly estimates from ARM2 to compute longerterm averages over the application of ARM on average NOx concentrations. 

NOx NO2/NOx ARM2 

curve

ARM2 default 

applied

NO2 ARM2 EPA 

applied

NO2

50 96% 0.90 45.00 0.90 45.00

100 70% 0.70 69.74 0.70 69.74

150 50% 0.50 75.37 0.50 75.37

200 38% 0.38 76.24 0.50 100.00

250 31% 0.31 78.64 0.50 125.00

400 22% 0.22 89.37 0.50 200.00

500 19% 0.20 100.00 0.50 250.00

Average Average Average Average Average Average
235.71 0.47 0.46 76.34 0.59 123.59

Table 1 - Comparison of ARM and ARM2 estimated NO2 concentrations

3.3 Status of OLM and PVMRM

Currently, OLM and PVMRM are only available for regulatory usage as alternative models, requiring an 

formal request to the appropriate reviewing authority and technical demonstration for their usage. (U. 

S. EPA, 2010) and (U. S. EPA, 2011) provided the bulk of the technical demonstration, showing that both 

models were appropriate as “alternative models”, when applied to the appropriate source 

configurations. As a result, the usage of OLM and PVMRM has mainly required an administrative request 

for approval from the appropriate reviewing authority. However, there is a reasonably strong case that 

both models also meet the requirements of preferred models. Both models are written in a common 

programming language; the models are well documented; test datasets available for model evaluation;

the model is useful to typical users; there are robust model-to-monitor intercomparisons; and the 

source code is freely available. Given that the EPA has stated that the technical demonstrations 

supporting the usage of OLM and PVMRM are broadly acceptable for regulatory applications, there is 

notable scientific merit behind OLM and PVMRM. Additionally, the available databases to test the model 

indicate that both perform reasonably well and are not biased to underpredict. Therefore, it stands to 

reason that both methods should be included in AERMOD as regulatory, non-beta preferred options for 

modeling NO2. 

3.4 The 3-Tiered approach

Despite the fact that OLM and PVMRM are clearly more refined and accurate than full conversion, ARM, 

and ARM2, both OLM and PVMRM are still considered screening rather than refined models at this 

point. Thus, a case could be made to eliminate the Tier 1 and 2 methods in preference of OLM and 

PVMRM. However, the intent in the tiered screening process is to reduce the burden of performing an 

NO2 modeling demonstration by providing easier to implement methods. As a result, the EPA intends to 

maintain the 3-tiered screening for NO2 modeling but changing ARM2, OLM, and PVMRM to non-beta 

options. 

4. Proposed regulatory options for NO2 modeling in AERMOD 

The Tier 2 and Tier 3 options for NO2 available within AERMOD have several settings that can affect 

their results. For some of these settings, facility or area-specific data is not always available, requiring 

considerations that are appropriate for default regulatory applications. This section reviews these

technical details within the context of regulatory requirements. 

4.1 ARM2 options

The equation that was derived for ARM2 will indicate NO2/NOx ratios above 1 for lower NOx 

concentrations and less than 0 for higher NOx concentrations. As a result, maximum and minimum 

NO2/NOx ratios were selected for each end of the distribution. These maximum and minimum values 

are discussed further here. 

(Podrez, 2015) and (RTP Environmental Associates, Inc., 2013) recommended 0.9 as the maximum ratio, 

as this ratio has generally been considered an appropriate equilibrium ratio for background air and 
diluted plumes. The ambient data shown in Figure 2 – NO2/NOx ratios from AQS for 2012indicates that 

NO2/NOx ratios do exceed 0.9 and achieve a maximum of 1.0. Many of these high NO2/NOx ratios occur 

at lower NOx levels, where instrument uncertainty can be a significant source of measurement error. 

Additionally, the PSS analysis shown in Figure 1 – NO2/NOx equilibrium ratios based on the PSS 

assumption from Equation 1. also indicates that when solar radiation levels are relatively low (2 cases 

with 95% cloud cover) that the equilibrium ratio can approach 1.0. However, the PSS values shown in 

Figure 1 – NO2/NOx equilibrium ratios based on the PSS assumption from Equation 1. assume that 

ozone concentrations remain constant, i.e., ozone depletion is not accounted for in the calculations. As a 

result, the NO2/NOx ratios shown in Figure 1 – NO2/NOx equilibrium ratios based on the PSS 

assumption from Equation 1. overestimate what would happen in a plume, with ozone titration 

occurring while converting NO to NO2.

In addition, the EPA has established that the equilibrium ratio of 0.9 is appropriate for Tier 3 methods

based on the recommendations for PVMRM given by (Hanrahan, 1999a). Though there is evidence that 

equilibrium ratios can be above 0.9, these cases are generally at lower NOx concentrations and only 

occur when ozone levels are very high or when ambient light is very low. Since ozone has a daytime 

maximum and a nighttime minimum, the NO2/NOx ratios for higher ozone levels for even moderate 

light conditions maximize at 0.9. Again, this is an overestimate, as ozone is assumed to be held constant 

rather than titrated by NO). Likewise, in lower light levels and at nighttime, the ambient ozone will be 

low and titrated quickly. The resulting NO2/NOx ratios for the lower-light scenarios are shown in Figure 

1. These ratios would be associated with the lower ozone concentrations only, resulting in a 

conservative estimate of NO2/NOx ratios around 0.9. Thus, based on the information from the PSS 

analysis, the maximum NO2/NOx ratio of 0.9 is reasonable and appropriate as a maximum ratio for 

ARM2. 

Figure 3 - Comparison of modeled NO2 concentrations from ARM2 and PVMRM

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 200 400 600 800 1000 1200

NO2/NOx

NOx (ppb)

ARM2 PVMRM, 0.5 ISR max PVMRM, 0.5 ISR DV

PVMRM, 0.2 ISR max PVMRM, 0.2 ISR DV
(Podrez, 2015) and (RTP Environmental Associates, Inc., 2013) recommended a minimum ratio of 0.2, as 

this value seemed to be the maximum ratio from the ambient data at the higher NOx concentrations. 

The minimum ratio of 0.2 appears to be fairly well supported by the ambient data in that there are few 

data points above the 0.2 NO2/NOx ratio at higher NOx concentrations. However, the regulatory 

requirements are important to consider when determining a national default value for the minimum 

ratio. (Owen & Brode, 2014) discuss at length the regulatory implications of the minimum NO2/NOx 

ratio, concluding that the default 0.2 cannot appropriately account for all sources. While there is general 

agreement that most sources have relatively low ISRs, there are sources with higher ISRs and it would be 

inappropriate to allow sources to base their modeling on lower ISRs than they actually have. As 

discussed in (Owen & Brode, 2014), the minimum ambient NO2/NOx ratios in a plume are first and 

foremost driven by the ISR and that the NO2/NOx ratio can only increase from the ISR as mixing and 

conversions occurs in the atmosphere. (U. S. EPA, 2011) established a national default ISR of 0.5 for 

sources that did not have site-specific data (which is always preferred over default values) for the Tier 3 

methods. This value was considered appropriately conservative, given that most sources are likely to 

have much lower ISR. (Owen & Brode, 2014) showed that a minimum NO2/NOx ratio in ARM2 of 0.5 

produced appropriately conservative results for a Tier 2 method relative to sources modeled with 

PVMRM and an IRS of 0.5, as shown in Figure 3 - Comparison of modeled NO2 concentrations from 

ARM2 and PVMRM. Based on the assumption that the 0.5 ISR is appropriate for OLM and PVMRM, then 

a minimum NO2/NOx ratio of 0.5 should also be appropriate as a minimum ratio for ARM2 in evaluating 

sources with an unknown ISR. 

4.2 Default options for OLM and PVMRM

There are two additional inputs required for OLM and PVMRMR that are not required for the other NO2 

options. In addition to the equilibrium ratio discussed above, the source’s ISR and background ozone 

concentrations are also required model inputs. 

The NO2/NOx equilibrium ratio of 0.9 recommended for ARM2 is also recommended for OLM and 

PVMRM, based on the information from the PSS analysis and ambient data considerations presented in 

Section 4.1.
Figure 4 - Distribution of NO2 ISRs from EPA's database as of June, 2015.

When modeling with OLM and PVMRM, each source requires an ISR be specified. As detailed above, the 

ISR is the initial driving factor in the ambient NO2/NOx ratios and thus can have a major impact on 

estimated NO2 concentrations nearest each source. The EPA recently initiated the collection of 

NO2/NOx ISR measurements for compilation in a publically available database for reference in 

regulatory applications (http://www.epa.gov/scram001/no2_isr_database.htm). The database 

represents one of the larger collection of samples of NO2/NOx ISRs and provides an opportunity to 

evaluate the relative conservativeness of any particular ISR. The database has over 2300 entries, though 

only 1580 are non-zero entries. The entries with an ISR of 0 indicate that there is no NO2 in the exit gas 

stream, a case that is extremely unlikely. Most NO2 instruments would still report some low level of 

NO2 even when no NO2 is present (e.g., less than the detection limit, commonly 1 ppb). The fact that 

zero values (0 ppb of NO2) are reported for roughly 700 entries suggests that NO2 was not actually 

measured (rather than measurement being collected and concentrations of 0 ppb of NO2 being 

reported). Therefore, the focus here is on the remaining non-zero entries.

As shown in Figure 4, over 93% of the remaining ISR are less than the traditional default of 0.5, which 

strongly indicates that this default value will be conservative for most sources and is a reasonable 

default to insure model results do not underpredict potential source impacts. (Owen & Brode, 2014)

recommended that, in the absence of source-specific data, the national default of 0.5 be applied to the 

new or modifying source and sources in the immediate vicinity of the new or modifying source. 

However, since the ISR has a diminishing impact on ambient NO2/NOx ratios as a plume is transported 

farther downwind due to mixing and reaction towards background ambient NO2/NOx ratios, (Owen & 

Brode, 2014) recommended that more distant sources could use a default ISR of 0.2 in lieu of source 

specific data. According to the EPA ISR database, 67% of the tests indicated ISRs less than of 0.2. Given 

that the greatest impact of the NO2/NOx in-stack ratio is typically on the closest/closer receptors to a 

source and that the highest modeled impact also generally is close to a source, it is reasonable to set 0.2 

as a default for the more distant nearby sources. (Owen & Brode, 2014) suggested sources that use a 

0

0.2

0.4

0.6

0.8

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cumulative Frequency

NO2/NOx In-stack Ratio

All ratios Non-zero ratios
default of 0.2 will generally be greater than 1 to 3 km away from the primary source, with the distance 

dependent upon the relative strength of the primary and background sources as well as the relative 

location of the background sources with respect to the prominent wind direction and location of 

expected maximum impacts from the primary source.

The third required input for OLM and PVMRM are background ozone concentrations. Traditionally, there 

have not been default inputs for background ozone. Instead, hourly data from a nearby ozone monitor is 

used (e.g., ozone data taken from AQS (AQS, 2014)). Data gaps from a particular monitor can be filled 

with: 1) data from another monitor, 2) interpolation between existing data, or 3) substitution with 

maximum data from the same monitor for similar time periods (e.g., maximum ozone concentrations 

from nighttime winter might be used to fill a missing hour from 3 am in January). It would be possible to 

set a conservative national default ozone value (e.g., 90 ppb would likely be conservative in most cases). 

However, ozone monitors are ubiquitous throughout the nation and ozone is a regional pollutant. Thus, 

ambient monitoring data from a more distant monitor could easily be appropriate. These facts make it 

relatively straight-forward to find reasonably representative background ozone data. Given this

situation, no default background ozone concentration is suggested for implementation in the model and 

hourly, site-specific data is recommended. 

4.3 Appropriate applications of OLM and PVMRM

The Tier 3 methods of OLM and PVMRM each have strengths and weaknesses that should be considered 

when applied to a particular modeling scenario. (U. S. EPA, 2010), (U. S. EPA, 2011), and (Owen & Brode, 

2014) discussed these aspects of the two models at length. In summary, these memos recommend the 

following applications for each model:

• PVMRM is recommended for relatively isolated, elevated sources. 

• PVMRM is not recommended for area or line sources, near-surface releases, or groups of 

sources with moderate distances between them due to the potential to overestimate plume 

volumes in these cases. 

• OLM is recommended for all sources where PVMRM is anticipated to overestimate plume 

volumes, including roadway emissions from mobile sources. 

• For most cases, the OLMGROUP ALL option is recommended when using OLM. 

4. PVMRM2 

In addition to changing the status of PVMRM from an alternative model to an accepted Tier 3 screening 

technique for NO2, we are proposing modifications to the current PVMRM formulation. The new 

PVMRM, called PVMRM2, has 3 major modifications from the current PVMRM formulation:

1. The current formulation of PVMRM uses relative dispersion coefficients, which are most 

appropriate for convective conditions but are likely to overestimate the plume volume in stable 

conditions, and therefore the NO2 concentrations during stable conditions. The PVMRM2 option 

utilizes the original relative coefficients for convective conditions, but uses a new formulation 

based on total dispersion coefficients for stable conditions, with appropriate adjustments to the 

number of sigmas used to define the plume volume.

2. The original PVMRM formulation uses a radial distance from the source to receptor to define 

the plume volume, which determines the amount of O3 available for NO-NO2 conversion. 
However, due to the meander component, plume impacts can occur upwind of the source 

resulting from upwind dispersion. These impacts can occur at receptors that are far off the 

plume centerline. Using the radial distance does not take into account the plume width 

associated with the concentrations far from the plume centerline. PVMRM2 includes 

modifications to more accurately account for plume volumes in these situations. 

3. In the process of implementing and testing the PVMRM2 option, a number of issues associated 

with the treatment of penetrated plumes were address that also affected the PVMRM option.

4.1 Comparison of PVMRM vs. PVMRM2 Options in AERMOD

DISTDOM – distance from source to receptor used to calculate the plume volume and the moles of NOx 

contained in the plume:

 PVMRM:

For POINT & VOLUME sources: DISTDOM = radial distance from dominant source to

receptor (using center for VOLUMEs);

For AREA/LINE/OPENPIT source: DISTDOM = radial distance from upwind corner of

dominant source to receptor.

 PVMRM2:

For POINT & VOLUME sources: DISTDOM = downwind distance from dominant

source (center for VOLUMEs) to 

receptor, with minimum of 1m;

For AREA/LINE/OPENPIT source: DISTDOM = downwind distance from center of 

dominant source to receptor, with 

minimum of 1m.

 Rationale: DISTDOM determines the volume of the plume and therefore the moles of O3 within 

the plume. DISTDOM is also used to calculate the moles of NOx contained in the plume based 

on the wind speed. Applying the original approach for receptors located upwind of the source 

may produce physically unrealistic estimates of NO to NO2 conversion, and using the downwind 

distance provides a more realistic estimate of NOx conversion consistent with a straight-line, 

steady-state plume model.

domNOxmoles – moles of NOx in the plume from source to receptor for dominant source based on 

emission rate, distance and wind speed:

 PVMRM:

For all source types: domNOxmoles = Q * DISTDOM/UDOM

 PVMRM2:
For all source types: domNOxmoles = Q * (1-NO2Ratio)*DISTDOM/UDOM

 Rationale: By using the total NOx emission rate (Q), the original PVMRM formulation effectively 

double counts a portion of the NO2 emissions by applying the domO3moles/domNOxmoles 

ratio to determine the conversion of NO to NO2, and adding that to the in-stack NO2/NOx ratio. 

A similar approach is used for sumNOxmoles (moles of NOx in the “combined” plume) except 

that AVE_NO2RATIO is used to account for the combined in-stack ratios of NO2 across all 

contributing plumes.

CWMIN/CWMAX – Minimum and maximum crosswind distances of major contributing sources used to 

determine the “box” within which emissions from all sources are included:

 PVMRM:

For POINT & VOLUME sources: CWMIN/CWMAX = range of crosswind distances for

contributing sources (using center

for VOLUMEs);

For AREA/LINE/OPENPIT sources: CWMIN/CWMAX = range of crosswind distances for

contributing sources (using center of

source), including the projected width

for AREA, LINE and OPENPIT sources.

 PVMRM2:

For POINT & VOLUME sources: CWMIN/CWMAX = range of crosswind distances for

contributing sources (using center for

VOLUME sources);

For AREA/LINE/OPENPIT sources: CWMIN/CWMAX = range of crosswind distances for

contributing sources (using center of

source), without including projected 

width for AREA, LINE, and OPENPIT

sources.

 Rationale: Including the projected width of AREA/LINE/OPENPIT source to determine the “box” of 

major contributing sources could significantly increase the moles of O3 within the combined 

plume, and therefore increase the conversion of NO to NO2.

DWMIN/DWMAX – Minimum and maximum downwind distances of major contributing sources used to 

determine the “box” within which emissions from all sources are included:

 PVMRM:

For POINT & VOLUME sources: DWMIN/DWMAX = range of downwind distances for
contributing sources (using center

for VOLUME);

For AREA/LINE/OPENPIT sources: DWMIN/DWMAX = range of downwind distances for

contributing sources (using center of

source), including the projected length

for AREA, LINE and OPENPIT sources.

 PVMRM2:

For POINT & VOLUME sources: DWMIN/DWMAX = range of downwind distances for

contributing sources (using center for

VOLUME sources);

For AREA/LINE/OPENPIT sources: DWMIN/DWMAX = range of crosswind distances for

contributing sources (using center of

source), without including projected 

length for AREA, LINE, and OPENPIT

sources.

 Rationale: Including the projected length of AREA/LINE/OPENPIT source to determine the “box” of 

major contributing sources could significantly increase the moles of O3 within the combined 

plume, and therefore increase the conversion of NO to NO2.

Dispersion coefficients used to calculate the volume of the plume:

 PVMRM:

For all source types: Relative dispersion coefficients are used to calculate the plume

volume for stable and convective conditions; however, these

coefficients are likely to overestimate the plume volume under

stable conditions, and therefore overestimate NO2 

concentrations.

 PVMRM2:

For all source types: Relative dispersion coefficients are used to calculate the plume 

volume for convective conditions, but the “standard” total 

dispersion coefficients are used to calculate plume volume for

stable conditions. However, since total dispersion coefficients

are bimodal (i.e., separate lateral and vertical coefficients) the

stable dispersion coefficients are calculated as SQRT(Φy*Φz).
 Rationale: The relative dispersion coefficients used for the original PVMRM option are not 

appropriate for stable conditions, and will tend to overestimate plume volume and NO2 

concentrations under stable conditions.

Defining the extent of the plume used to calculate the plume volume:

 PVMRM:

For all source types: The plume volume calculation is based on a radius of four (4) 

times the relative dispersion coefficient, which accounts for 

about 99.994% of the plume. A minimum value of 5m is 

assumed for the radius of the plume to account for near-source 

effects (e.g., downwash), which is consistent with the initial 

plume volume assumed by Hanrahan (1999),i.e., 

(1.282/4.0)*15 = 4.8 (rounded to 5)

 PVMRM2:

For all source types: The radius used to define the plume volume varies based on 

stability. For stable conditions, the radius of the plume is 

assumed to be 1.282 times the total dispersion coefficient, 

consistent with the original PVMRM approach proposed by 

Hanrahan (1999). This accounts for about 80% of the plume.

For unstable conditions, the radius of the plume is defined as 

2.58 times the relative dispersion coefficient, which accounts 

for about 99% of the plume. A minimum value of 9m is assumed 

for the radius of the plume to account for near-source effects 

(e.g., downwash).

In addition, since AERMOD incorporates a horizontal meander 

meander algorithm that increases lateral plume spread beyond 

that accounted for based on dispersion coefficients, the number 

of sigmas used to define the plume volume is adjusted to 

account for meander, i.e.,

NSUBZ = MIN( 2.15, 1.282 * (SYEFF/SY),

where SYEFF is the effective sigma-y value that replicates the 

plume centerline concentration associated with meander, but 

based on a standard Gaussian plume calculation. An upper 

bound of 2.15 is applied, which corresponds with the point at 

which the concentration is 10 percent of the centerline value, 

and is used to define the extent of the plume in other contexts 
(e.g., initial dispersion coefficients for volume sources). The 

minimum value of the dispersion coefficient is also adjusted 

based on the ratio 1.282/NSUBZ, where NSUBZ is the adjusted 

value. 

Since the portion of the plume that penetrates above the mixing 

height during unstable conditions is treated as a stable plume, 

the minimum relative dispersion coefficient for penetrated 

plumes (SRMIN3) is a weighted average of the stable and 

unstable values based on plume penetration factor (PPF), i.e.,

SRMIN3 = PPF*SRMINS + (1-PPF)*SRMINU

In addition, the value of NSUBZ for penetrated plumes is

adjusted based on PPF-weighted mean of stable and unstable 

values of NSUBZ.

 Rationale: The approach for the original PVMRM option of using 4 times the dispersion coefficients 

to define the volume of the plume is excessive. It accounts for about 99.994% of the plume, and 

corresponds to the point at which the concentration is about 0.00005 times the centerline 

concentration. That approach effectively assumes full and instant entrainment of ozone within 

the plume which is physically unrealistic. The PVMRM2 option provides a more reasonable 

account of the plume volume for purposes of NOx conversion.

4.2 Comparisons of PVMRM and PVMRM2 for select field studies

Comparisons of PVMRM and PVMRM2 were conducted for the Empire Abo gas plant in New Mexico and 

the Palaau generating station on the island of Molokai. The Empire Abo data set consists of data from 

two monitors located near the Empire Abo gas plant, with one monitor located 1.6 km north of the 

plant, and the other located 2.5 km south of the plant. Data collection included ozone, NOx, and 

meteorological data. Ambient data is available for a two year period, from June, 1993, through June, 

1995. Emissions from the 20 stacks at the facility were assumed to be constant. The Palaau generating 

station data set consists of data from one monitor 240 meters from the generating stations. Data 

collection included ozone, NOx, and meteorological data. Ambient data is available for a one year 

period, from January, 1993, through December, 1993. 
Figure 5 - Comparison of PVMRM and PVMRM2 for the Empire Abo north monitor

10

100

1000

10 100 1000

Predicted Conc (μg/m3)

Observed Conc (μg/m3)

AERMOD Model - NM N Monitor w/S-O3 - 1hr NO2 Q-Q Plot - PVMRM2 w/Adj &BulkRn

FULL

PVMRM2

OLMGRP

PVMRM1
Figure 6 - Comparison of PVMRM and PVMRM2 for the Empire Abo south monitor

Figure 7 - QQ plot of PVMRM for the Palaau monitor

10

100

1000

10 100 1000

Predicted Conc (μg/m3)

Observed Conc (μg/m3)

AERMOD - NM S Monitor w/N-O3 - 1hr NO2 Q-Q Plot - PVMRM2 wAdj & BulkRn

FULL

PVMRM2

OLMGRP

PVMRM1

1

10

100

1000

1 10 100 1000

Predicted Conc (μg/m3)

Observed Conc (μg/m3)

AERMOD - Palaau, HI - PVMRM1 Opt v15DFT w/Adj

FULL

OLM

OLMGRP

PVMRM1
Figure 8 - QQ plot of PVMRM2 for the Palaau monitor

There is clearly a substantial improvement in the results for the Empire Abo field study. Both the north 

and south monitor show PVMRM2 significantly outperforms PVMRM and is the best performing model 

from the comparison. For the south monitor, PVMRM is outside the factor of 2 line for highest 

concentrations, while the PVMRM2 results are within 20% of the measurements. For Palaau, PVMRM2 

performs slightly better for the highest few concentrations, but tends to underpredict slightly for much 

of the upper end of the distribution. Overall, the PVMRM2 performance for Palaau seems to be slightly 

worse, but the difference is marginal, particularly when compared to the model performance found in 

the Empire Abo data set. Based on these findings, along with the technical bases for these changes, the 

EPA believes that it is appropriate to make PVMRM2 the new default, replacing the current 

implementation of PVMRM. 

1

10

100

1000

1 10 100 1000

Predicted Conc (μg/m3)

Observed Conc (μg/m3)

AERMOD - Palaau, HI - PVMRM2 Opt v15DFT w/Adj

FULL

OLM

OLMGRP

PVMRM2
References 

AQS. (2014). Download Detailed AQS Data. Retrieved from 

http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm

Atkinson, R., Baulch, D. L., Cox, R. A., Crowley, J. N., Hampson, R. F., Hynes, R. G., . . . Troe, J. (2004). 

Evaluated kinetic and photochemical data for atmospheric chemistry: Volume I – gas phase 

reactions of Ox, HOx, NOx and SOx species. Atmos. Chem. Phys, 4, 1461–1738.

Builtjes, P. J. (1981). Chemically reacting plume experiments for the wind tunnel. Netherlands 

Organization for Applied Scientific Research. Division of Technology for Society Ref. No. 81-

013563, File No. 8710-20330.

Chu, S., & Meyer, E. (1991). Use of Ambient Ratios to Estimate Impact of NOx Sources on Annual N02 

Concentrations. Proceedings of the 84th Annual Meeting & Exhibition, (pp. 91-180.6). Pittsburgh, 

PA: Air & Waste Management Association.

Cole, H. S., & Summerhays, J. E. (1979). A Review of Techniques Available for Estimating Short-Term NO2 

concentrations. J. Air Poll. Cont. Assoc., 29:8, 812-817. doi:10.1080/00022470.1979.10470866

Dickerson, R., Stedman, D., & Delany, A. (1982). Direct Measurements of Ozone and Nitrogen Dioxide 

Photolysis Rates in the Troposphere. J. Geophys. Res., 87(C7), 4933-4946.

Hanrahan, P. L. (1999a). The Plume Volume Molar Ratio Method for Determining NO2/NOx Ratios in 

Modeling—Part I: Methodology. J. Air & Waste Manage. Assoc., 1324-1331.

Hanrahan, P. L. (1999b). The Plume Volume Molar Ratio Method for Determining NO2/NOx Ratios in 

Modeling—Part II: Evaluation Studies. J. Air & Waste Manage. Assoc., 1332-1338.

Hendrick, E., Tino, V., Hanna, S., & Egan, B. (2012). Review and Evaluation of the Plume Volume Molar 

Ratio Method (PVMRM) and Ozone Limiting Method (OLM) for short-term (1-hour average) NO2 

Impacts. Washington, DC: The American Petroleum Institute.

Hendrick, E., Tino, V., Hanna, S., & Egan, B. (2013). Evaluation of NO2 predictions by the plume volume 

molar ratio method (PVMRM) and ozone limiting method (OLM) in AERMOD using new field 

observations. J. Air & Waste Mgt. Assoc., 844-854. doi:10.1080/10962247.2013.798599

Janssen, L., Wakeren, J. V., Duuren, H. V., & Elshout, A. (1988). A classification of no oxidation rates in 

power plant plumes based on atmospheric conditions. Atm. Env., 43-53.

Keifer, W. S. (1977). The generation of ozone in plumes from large point sources dealing with power 

plants and other rural point sources. PhD dissertation, University of Maryland.

MACTEC. (2004). Sensitivity Analysis of PVMRM and OLM in AERMOD. Contract No. 18-8018-04: Alaska 

DEC.

Owen, R. C., & Brode, R. (2014, Sept. 30). Clarification on the Use of AERMOD Dispersion Modeling for 

Demonstrating Compliance with the N02 National Ambient Air Quality Standard.

Podrez, M. (2015). An update to the ambient ratio method for 1-h NO2 air quality standards dispersion 

modeling. Atm. Env., 163-170.
RTP Environmental Associates, Inc. (2013, March 3). Ambient Ratio Method Version 2 (ARM2) for use 

with AERMOD for 1-hr NO2 Modeling. Retrieved from 

http://www.epa.gov/ttn/scram/models/aermod/ARM2_Development_and_Evaluation_ReportSeptember_20_2013.pdf

Schroeder, A. J. (2012). AERMOD tiering approach case study for 1-hour NO2. 105th Air and Waste 

Management Association Annual Conference and Exhibition, (p. Paper No 481). San Antonio, TX .

Seinfeld, J., & Pandis, S. (2006). Atmospheric Chemistry and Physics: From Air Pollution to Climate 

Change. New York: John Wiley & Sons.

U. S. EPA. (2010, June 28). Applicability of Appendix W Modeling Guidance for the 1-hour NO2 National 

Ambient Air Quality Standard.

U. S. EPA. (2011, March 1). Additional Clarification Regarding Application of Appendix W Modeling 

Guidance for the 1-hour NO2 ,National Ambient Air Quality Standard.

Zel'dovich, Y., Sadonikov, P. Y., & Frank-Kamenetskii, D. A. (1946). The Oxidation of Nitrogen in 

Combustion Explosions. Acta Physicochimica U.S.S.R. 21, 577–628.

United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Air Quality Analysis Division

Research Triangle Park, NC

Publication No. EPA- 454/B-15-004

[June, 2015]