Appendix L.12: New Improve Equation Rationale 

Appendix L.12 contents 

L.12.1 Using the new IMPROVE equation 

L.12.2 “Revised IMPROVE Algorithm for Estimating Light Extinction from Particle 

 Speciation Data” by Dr. Ivar Tombach 

L.12.3 Justification for using new IMPROVE equation by Dr. Ivar Tombach 

L.12.4 “Instructions: A Postprocessor for Recalculating CALPOST Visibility Outputs 

 with the New IMPROVE Algorithm” by Dr. Ivar Tombach 

L.12.5 Letter to EPA concerning the use of the New IMPROVE equation 

L.12.6 Letter from EPA concerning the use of the New IMPROVE equation 

 

 
Appendix L. 12: Use of New IMPROVE Equation 

During the regional haze SIP development process, the IMPROVE Steering Committee made 

recommendations for a new IMPROVE equation. Among other things, the new equation includes 

components that take into account the effects of sea salt and site specific Rayleigh scattering. These 

are important components to appropriately consider for Mississippi’s coastal facilities. 

VISTAS contracted Dr. Ivar Tombach to develop a post processor to allow CALPOST outputs to be 

used with the new IMPROVE equation. Dr. Tombach’s discussion of the new IMPROVE equation 

and the post processor he developed is included in appendix L.12.2, L.12.3, and L.12.4. A brief 

discussion of the rationale for use of the new IMPROVE equation follows. 

Rationale for Need for Use of New IMPROVE Equation

The new IMPROVE equation is a much better representation of the effects of particulate matter on 

light extinction than the old equation and takes into account the latest scientific understanding of 

several parameters. 

1. The new algorithm overcomes biases of the old algorithm on the haziest days and the 

clearest days as demonstrated by comparing the measured light extinction from 

nephelometers at Class I areas to light extinction calculated using each of the equations. 

2. The new algorithm recognizes spatial and temporal variation in light extinction as size 

distribution of the aerosol changes by increasing extinction efficiency as sulfate, nitrate and 

organics concentrations increase. 

3. The new algorithm incorporates a term to reflect the contribution of fine sea salt and its 

hygroscopic growth with increasing relative humidity recognizing research findings 

showing that fine sea salt can be an important contributor to light extinction in coastal 

areas. 

4. The new algorithm reflects research finding that the mass concentration of particulate 

organic matter in rural areas is greater than represented by the old equation. 

5. The new algorithm includes a NO2 term to represent times when light absorption by 

NO2 is a meaningful contributor to light extinction. 

6. The new algorithm incorporates site-specific Rayleigh scattering values to better 

represent sites close to sea level or with very hot or cold climates. 

With this combination of revisions to the IMPROVE equation, the resulting apportionment of 

extinction to various components is more accurate on the haziest and clearest days. This is 

important for development of emission control strategies since the benefits of control of 

concentrations of each species will be represented more correctly with the new algorithm. 
Three of Mississippi’s BART eligible sources located in coastal areas, MS Power – Plant 

Daniel, Plant Watson, and Chevron Cogeneration, chose to use the new IMPROVE equation. 

The ability to appropriately represent the contribution of sea salt for these sources is important 

in determining their contribution to haze and evaluating the benefit of potential control 

strategies. US EPA Region 4, National Park Service, and the US Fish & Wildlife Service 

determined that adding site-specific data to account for the impact of sea salt and Rayleigh 

scattering to the old IMPROVE equation is not appropriate, but rather the entire new 

IMPROVE equation should be used. The spreadsheet developed by Dr. Tombach to 

implement the entire new IMPROVE equation for use with CALPUFF contains a 

modification of CALPOST that calculates the light extinction from the CALPUFF output. In 

their review of the spreadsheet and supporting documentation, EPA Region 4 staff 

commented that the VISTAS methodology contained in the spreadsheet developed by Dr. 

Tombach appropriately implements the features of the new IMPROVE equation, with the 

exception of the nitrogen dioxide term. EPA Region 4 further stated that with the addition of 

their recommendation for addressing the NO2 term, the VISTAS methodology to incorporate 

the new IMPROVE equation is acceptable on a case-by-case basis upon consultation with the 

EPA Regional Office. 
Revised IMPROVE Algorithm for Estimating Light 

Extinction from Particle Speciation Data 

I. Introduction 

Light Extinction

Atmospheric light extinction is a fundamental metric used to characterize air pollution 

impacts on visibility. It is the fractional loss of intensity in a light beam per unit distance 

due to scattering and absorption by the gases and particles in the air. Light extinction 

(bext) can be expressed as the sum of light scattering by particles (bs,p), scattering by gases 

(bs,g), absorption by particles (ba,p) and absorption by gases (ba,g).* 

Light extinction due to the gaseous components of the atmosphere are relatively well 

understood and well estimated for any atmospheric conditions. Absorption of visible 

light by gases in the atmosphere is primarily by NO2, and can be directly and accurately 

estimated from NO2 concentrations by multiplying by the absorption efficiency. 

Scattering by gases is described by the Rayleigh scattering theory (van de Hulst, 1981). 

Rayleigh scattering depends on the density of the atmosphere, with highest values at sea 

level (about 12Mm-1) and diminishing with elevation (8Mm-1 at about 12,000’), and 

varies somewhat at any elevation due to atmospheric temperature and pressure variations. 

Rayleigh scattering can be accurately determined for any elevation and meteorological 

conditions. 

Particle light extinction is more complex than that caused by gaseous components. Lightabsorbing carbon (e.g. diesel exhaust soot and smoke) and some crustal minerals are the 

only commonly occurring airborne particle components that absorb light. All particles 

scatter light, and generally particle light scattering is the largest of the four light 

extinction components. If the index of refraction as a function of particle size is well 

characterized, Mie theory can be used to accurately calculate the light scattering and 

absorption by those particles. However, it is rare that these particle properties are known, 

so assumptions are used in place of missing information to develop a simplified 

calculation scheme that provides an estimate of the particle light extinction from the 

available data set. 

Current Algorithm

IMPROVE particle monitoring provides 24-hour duration mass concentrations for PM10

and PM2.5 as well as most of the PM2.5 component concentrations on a one day in three 

schedule. These data are routinely available at each IMPROVE monitoring site for use in 

estimating light extinction for the IMPROVE program. At 21 IMPROVE monitoring 

sites (Table A1 in appendix), hourly-averaged nephelometer and relative humidity data 

are also routinely available. Data from these sites have been key to evaluate the 

performance of the current IMPROVE algorithm, as well as for development and 

 

*

 Light is a wavelength-dependent portion of the electro-magnetic spectrum. Traditionally for 

visibility-protection applications, the most sensitive portion of the spectrum for human vision 

(550nm) has been used to characterize light extinction and its components. For NO2 light 

absorption, a photopic-weighted approach is used, as shown in section III. 
performance evaluation of proposed revised algorithms. 

The current IMPROVE algorithm for estimating light extinction from IMPROVE particle 

monitoring data assumes that absorption by gases (ba,g) is zero, that Rayleigh scattering 

(bs,g) is 10Mm-1 for each monitoring site regardless of site elevation and meteorological 

condition, and that particle scattering and absorption (bs,p and ba,p) can be estimated by 

multiplying the concentrations of each of six major components by typical componentspecific light extinction efficiencies. The six major components are sulfate (assumed to 

be ammonium sulfate), nitrate (assumed to be ammonium nitrate), organic compounds 

(based on measured organic carbon mass), elemental or black carbon (directly measured), 

fine soil (crustal elements plus oxides) and coarse mass (the difference between PM10 and 

PM2.5 mass concentrations). The component extinction efficiency values are constants, 

except for the sulfate and nitrate extinction efficiency terms that include a water growth 

factor that is a function of relative humidity (displayed as f(RH)) multiplied by a constant 

dry extinction efficiency. Monthly averaged water growth terms for each site were 

developed because most monitoring sites don’t include on-site relative humidity 

monitoring. Expressed as an equation, the current algorithm for estimating light 

extinction from IMPROVE data takes the following form where the particle component 

concentrations are indicated in the brackets. The formulas for the composite components 

are available elsewhere (IMPROVE web site). 

The units for light extinction and Rayleigh scattering are inverse megameters (1/106

m 

usually written Mm-1); component concentrations shown in brackets are in microgram per 

meter cubed (μg/m3

); dry efficiency terms are in units of meters squared per gram (m2/g); 

and the water growth terms, f(RH), are unitless. 

Among the implicit assumptions for this formulation of the algorithm are that 

• the six particle component terms plus a constant Rayleigh scattering term are 

sufficient for a good estimate of light extinction; 

• constant dry extinction efficiency terms rounded to one significant digit for each 

of the six particle components (i.e. for both sulfate and nitrate the value is 3) 

works adequately for all locations and times; and 

• light extinction contributed by the individual particle components can be 

adequately estimated as separate terms as they would if they were in completely 

separate particles (externally mixed), though they often are known to be internally 

mixed in particles. 

[ ]

[ ]

[ ]

[ ]

[ ]

[ ]

10

0.6

1

10

4

3 ( )

3 ( )

+

+ ×

+ ×

+ ×

+ ×

+ × ×

≈ × ×

Coarse Mass

Fine Soil

Elemental Carbon

Organic Carbon

f RH Nitrate

b f RH Sulfate ext
A relatively simple algorithm for estimating light extinction using only the available 

monitoring data requires assumptions such as these. 

Estimates of particle scattering by this algorithm (i.e. excluding the light absorbing 

carbon and Rayleigh terms) have been compared to directly-measured particle scattering 

data at the 21 monitoring sites that have hourly-averaged nephelometer and relative 

humidity data. As shown in Figure 1 below, the algorithm performs reasonably well over 

a broad range of particle light scattering values and monitoring locations. The algorithm 

tends to under-estimate the highest extinction values and over-estimate the lowest 

extinction values. Since it first use (IMPROVE Report, 1993), the current algorithm has 

been a useful tool that contributed significantly to a better understanding of haze levels 

and the relative magnitude of haze contribution by the various particle components. 

0

50

100

150

200

250

300

350

0 50 100 150 200 250 300 350

Measured Bsp

IMPROVE Bsp

Figure 1. A scatter plot of the current IMPROVE algorithm estimated particle light 

scattering versus measured particle light scattering. 

Review and Revision 

The IMPROVE algorithm for estimating light extinction was adopted by the EPA as 

basis for the regional haze metric used to track progress in reducing haze levels for 

visibility-protected areas under the 1999 Regional Haze Rule (RHR). As a result the 

IMPROVE algorithm has been scrutinized carefully to assess deficiencies that could bias 

the implementation of the RHR. 

The RHR uses the IMPROVE algorithm to estimate light extinction, which is then 

converted to the deciview haze index (i.e. a logarithmic transformation of bext). The RHR 

then calls for the determination of the mean of the annual 20% best and 20% worst haze 

days for each of the IMPROVE monitoring sites that represent the visibility-protected 

areas. States are asked to manage emissions so that over a 60-year period the worst haze 
days will improve to natural conditions without degrading visibility conditions for the 

best haze days. For consistency, the same approach (i.e. IMPROVE algorithm and 

conversion to the deciview haze index) is also used to estimate natural haze levels for 

each representative monitoring site using estimates of the natural concentration levels for 

the major particle components. For each location, the linear rate of reduction of the 

deciview values for the worst haze days during the baseline period (2000 to 2004) that is 

needed to reach the estimated worst haze days under natural conditions by 2064 must be 

determined. This linear rate is used as a guide to pace the desired rate of haze reduction 

and to determine interim visibility goals that are compared to the monitoring data trends 

of the best and worst haze days. 

The RHR emphasizes the extremes of light extinction through its requirement to estimate 

best and worst haze days for the baseline period and for estimates of natural worst haze 

conditions. Also, the use of the deciview index means that additive biases in the light 

extinction estimates (e.g. the use of a standard Rayleigh scattering term for all sites 

regardless of elevation) will affect the calculation of a linear glide slope, which is used to 

set the pace of emission reductions. Use of the IMPROVE algorithm for the RHR 

elicited concerns about possible biases in the apportionment among the various major 

particle components. Such issues have been the subject of a number of critical reviews of 

the use of the IMPROVE algorithm in the RHR (Lowenthal and Kumar, 2003; Ryan et 

al., 2005). 

In light of the concerns raised by its use in the RHR, the IMPROVE Steering Committee 

initiated an internal review including recommendations for revisions of the IMPROVE 

algorithm for estimating light extinction. The review team (composed of National Park 

Service and Cooperative Institute for Research in the Atmosphere scientists) reviewed 

pertinent literature and employed both Mie theory modeling and statistical assessment 

methods to identify deficiencies in the current algorithm and evaluate possible 

refinements. The goal was to develop a revised algorithm that reduces biases in light 

extinction estimates, and is as consistent as possible with the current scientific literature 

while constrained by the need to use only those data that are routinely available from the 

IMPROVE particle monitoring network. A preliminary report by this team was 

presented in June, 2005 at a national Regional Planning Organization workshop in 

Denver Colorado hosted by the Western Regional Air Partnership (WRAP) and broadly 

participated in by those with an interest in the regional haze rule. The preliminary results 

were also summarized in presentations at the July 2005 IMPROVE Steering Committee 

meeting in Acadia Maine. The full report of this review is available elsewhere (Hand and 

Malm, 2005). 

Purpose and Organization

This document is a summary report by a subcommittee established by the IMPROVE 

Steering Committee at their July 2005 meeting to recommend a refined algorithm that 

would replace or be made available as an alternative to the current approach. The 

subcommittee included those who worked on the internal review as well as scientists who 
have been critical of the original IMPROVE algorithm.†

 The primary purpose of this 

document is to describe the subcommittee’s recommended revised algorithm, 

characterize its performance, and summarize the rationale for each of the changes from 

the currently used algorithm. This document is the principal means to communicate the 

recommendations to the IMPROVE Steering Committee prior to their deliberation and 

vote on the adoption of a new algorithm. Others with an interest in this topic, including 

those who have responsibilities or interests associated with the RHR may also find it to 

be useful in understanding the technical issues and how the recommended algorithm 

addresses them. 

Section II of the report describes the recommended revised algorithm for estimating light 

extinction using IMPROVE particle data and shows its performance compared to that of 

the currently used approach. Section III provides the technical justification of each of the 

revised terms in the recommended algorithm. 

REFERENCES 

Hand, J.L. and W.C. Malm, 2005: Review of the IMPROVE Equation for Estimating 

Ambient Light Extinction Coefficients, on the IMPROVE web site at 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm

Lowenthal, D.H. and Kumar, N., 2003: PM2.5 mass and light extinction 

reconstruction in IMPROVE. J. Air & Waste Manage. Assoc. 53, 1109-1120. 

Ryan, P.A., Lowenthal, D., and Kumar, N., 2005: Improved light extinction 

reconstruction in Interagency Monitoring of Protected Visual 

Environments. J. Air & Waste Manage. Assoc. 55, 1751-1759. 

van de Hulst, H. C., Light scattering by small particles, New York: Dover, 1981 

II. Overview of the Revised Algorithm 

The recommended revised algorithm is shown in the equation below with revised terms 

in bold font. The total sulfate, nitrate and organic carbon compound concentrations are 

each split into two fractions, representing small and large size distributions of those 

components. Though not explicitly shown in the equation, the organic mass 

concentration used in this new algorithm is 1.8 times the organic carbon mass 

concentration, changed from 1.4 times carbon mass concentration as used for input for 

the current IMPROVE algorithm. New terms have been added for sea salt (important for 

coastal locations) and for absorption by NO2 (only used where NO2 data are available). 

Site-specific Rayleigh scattering is calculated for the elevation and annual average 

temperature of each of the IMPROVE monitoring sites as shown in the Table A at the 

 

†

 Subcommittee members concurring in these recommendations are William Malm and Bret 

Schichtel, NPS; Marc Pitchford, NOAA; Naresh Kumar, EPRI; Douglas Lowenthal, DRI; and 

Jenny Hand, CIRA 
end of the document. 

The apportionment of the total concentration of sulfate compounds into the 

concentrations of the small and large size fractions is accomplished using the following 

equations. 

 

 

 

 

The same equations are used to apportion total nitrate and total organic carbon 

concentrations into the small and large size fractions. 

Sea salt is calculated as 1.8 x [Chloride], or 1.8 x [Chlorine] if the chloride measurement 

is below detection limits, missing or invalid. The algorithm uses three water growth 

adjustment term as shown in the Figure 2 and Table 1. They are for use with the small 

size distribution and the large size distribution sulfate and nitrate compounds and for sea 

salt (fS(RH), fL(RH) and fSS(RH) respectively). 

[ ] [ ]

[] [ ]

[ ][ ]

[ ]

[ ]

[ ]

[ ]

[ ] p p b

) (

0. 3 3 N O

S p e cifi c

)

S i t e (R a yl ei g h S c a tt e ri n g

R H S e a S alt ) (

1 . 7 f

2 . 8 S m al l O r g a ni c C a r b o n 6 . 1 L a r g e O r g a ni c C a r b o n

R H L a r g e Ni tr at e ) (

R H S m al l Ni tr a t e 5. 1 f ) (2 . 4 f

R H L a r g e S ulf at e (

)

R H S m al l S ul f a t e 4 . 8 f (

)

2. 2 f

2

S S

S L

S L

+ ×

+

+ ×

+ × ×

+ ×

+ ×

+ × + ×

+ × × + × ×

≈ × × + × ×

Coarse Mass

Fine Soil

Elemental Carbon

bext

0.6

1

10

[ ][ ] [ ] 3 Larg e Sultate = Total Sulfate , for Total Sulfate ≥ 20 μg / m

[ ] [ ] [ ][ ] 3

3 , 20 /

20 / arg Total Sulfate for Total Sulfate g mg m

Total Sulfate L e Sulfate μ

μ = × <

[ ][ ] Small Sulfate = Total Sulfate −[Large Sulfate]
Water Growth Curves

0

1

2

3

4

5

6

7

8

9

10

0 10 20 30 40 50 60 70 80 90 100

Relative Humidity (%)

f(RH)

fS(RH)

fL(RH)

Original

fSS(RH)

Figure 2. Water growth curves for small and large size distribution sulfate and nitrate, sea 

salt and the original IMPROVE algorithm sulfate and nitrate. 

Table 1. f(RH) for small and large size distribution sulfate and nitrate, an sea salt. 

RH (%) fS(RH) fL(RH) fSS(RH) RH (%) fS(RH) fL(RH) fSS(RH) RH (%) fS(RH) fL(RH) fSS(RH) 

0 to 36 1.00 1.00 1.00 56 1.78 1.61 2.58 76 2.60 2.18 3.35 

37 1.38 1.31 1.00 57 1.81 1.63 2.59 77 2.67 2.22 3.42 

38 1.40 1.32 1.00 58 1.83 1.65 2.62 78 2.75 2.27 3.52 

39 1.42 1.34 1.00 59 1.86 1.67 2.66 79 2.84 2.33 3.57 

40 1.44 1.35 1.00 60 1.89 1.69 2.69 80 2.93 2.39 3.63 

41 1.46 1.36 1.00 61 1.92 1.71 2.73 81 3.03 2.45 3.69 

42 1.48 1.38 1.00 62 1.95 1.73 2.78 82 3.15 2.52 3.81 

43 1.49 1.39 1.00 63 1.99 1.75 2.83 83 3.27 2.60 3.95 

44 1.51 1.41 1.00 64 2.02 1.78 2.83 84 3.42 2.69 4.04 

45 1.53 1.42 1.00 65 2.06 1.80 2.86 85 3.58 2.79 4.11 

46 1.55 1.44 1.00 66 2.09 1.83 2.89 86 3.76 2.90 4.28 

47 1.57 1.45 2.36 67 2.13 1.86 2.91 87 3.98 3.02 4.49 

48 1.59 1.47 2.38 68 2.17 1.89 2.95 88 4.23 3.16 4.61 

49 1.62 1.49 2.42 69 2.22 1.92 3.01 89 4.53 3.33 4.86 

50 1.64 1.50 2.45 70 2.26 1.95 3.05 90 4.90 3.53 5.12 

51 1.66 1.52 2.48 71 2.31 1.98 3.13 91 5.35 3.77 5.38 

52 1.68 1.54 2.50 72 2.36 2.01 3.17 92 5.93 4.06 5.75 

53 1.71 1.55 2.51 73 2.41 2.05 3.21 93 6.71 4.43 6.17 

54 1.73 1.57 2.53 74 2.47 2.09 3.25 94 7.78 4.92 6.72 

55 1.76 1.59 2.56 75 2.54 2.13 3.27 95 9.34 5.57 7.35 

Algorithm Performance Evaluation

Performance of the current and proposed new algorithm for estimating extinction can be 
assessed in a number of ways each of which serves to answer different questions. 

Reduction of the biases in light scattering estimates at the extremes (i.e. underestimation 

of the high values and over estimation of the low values) when compared to 

nephelometer measurements was one of the most compelling reasons for development of 

a new algorithm, so comparisons of bias for the current and proposed new algorithm are 

one way to evaluate performance. 

The fractional bias for each sample period was calculated as the difference in light 

scattering (i.e. estimated bsp minus the measured bsp) divided by the measured light 

scattering. These biases were then averaged in each quintile to indicate the bias in those 

five subsets of the data from the lowest to the highest light scattering values. Two 

different approaches to this grouping by quintiles were performed, referred to as criteria 1 

and 2. 

Criterion 1 used the measured light scattering to determine which sample periods were in 

each quintile. Since we think of the nephelometer as the better measure of light 

scattering, bias by this criterion better addresses the question of algorithm performance 

with regards to the haze conditions. Criterion 2 uses the algorithm-estimated light 

extinction to determine which sample periods were in each quintile. The Regional Haze 

Rule index is based on the highest and lowest haze levels as determined by the algorithm, 

so criterion 2 better addresses the haze rule application of the algorithm. Tables 2 

through 5 show the bias results by both criteria for the current and new algorithm for sites 

averaged by RPO. 

Table 2. Averaged fractional bias by RPO for the current IMPROVE algorithm 

with quintiles based on measured light scattering (criterion 1). Bold font highlights 

the bias values that are lower than corresponding values in Table 3. 

RPO Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Average 

CEN 0.67 0.18 0.10 0.02 -0.11 0.17 

MANE 0.93 0.27 0.19 0.10 0.01 0.28 

VISTAS 0.59 0.21 0.11 0.02 -0.13 0.16 

WRAP 1.07 0.37 0.18 0.07 -0.08 0.32 

Table 3. Averaged fractional bias by RPO for the current new proposed algorithm 

with quintiles based on measured light scattering (criterion 1). Bold font highlights 

the bias values that are lower than corresponding values in Table 2. 

RPO Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Average 

CEN 0.51 0.08 0.02 -0.03 -0.08 0.10 

MANE 0.74 0.14 0.06 0.01 -0.02 0.17 

VISTAS 0.50 0.16 0.11 0.06 -0.01 0.16 

WRAP 0.84 0.25 0.08 0.01 -0.10 0.21 

Table 4. Averaged fractional bias by RPO for the current IMPROVE algorithm 

with quintiles based on estimated light scattering (criterion 2). Bold font highlights 

the bias values that are lower than corresponding values in Table 5. 

RPO Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Average 
CEN 0.42 0.19 0.18 0.09 -0.01 0.17 

MANE 0.35 0.27 0.30 0.33 0.15 0.28 

VISTAS 0.38 0.19 0.14 0.08 0.01 0.16 

WRAP 0.58 0.37 0.26 0.25 0.15 0.32 

Table 5. Averaged fractional bias by RPO for the current new proposed algorithm 

with quintiles based on estimated light scattering (criterion 2). Bold font highlights 

the bias values that are lower than corresponding values in Table 4. 

RPO Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Average 

CEN 0.23 0.11 0.08 0.06 0.02 0.10 

MANE 0.15 0.14 0.15 0.19 0.21 0.17 

VISTAS 0.25 0.14 0.08 0.13 0.21 0.16 

WRAP 0.37 0.19 0.19 0.16 0.17 0.21 

These tables show that the new algorithm has lower fractional bias than the current 

IMPROVE algorithm in all but the haziest conditions (i.e. quintile 5) regardless of the 

criterion used to sort the data into quintiles. By criterion 1, the two algorithms perform 

about the same for haziest days except for the sites in the southeastern U.S. (i.e. the 

VISTA RPO), where the new algorithm has much lower bias (1% compared to 13%). 

Using criterion 2, the current algorithm has consistently lower bias compared with the 

new algorithm for the haziest days (i.e. quintile 5) for each of the RPOs. This seeming 

paradox is the result of the somewhat greater imprecision of the new algorithm compared 

to the current algorithm, which results in somewhat larger errors in selecting worst haze 

sample periods for the new algorithm compared with the current algorithm. 

Scatter plots (Figures 1 and 3) of light scattering estimates from the current and new 

proposed algorithms versus nephelometer data for all available data at 21 monitoring 

sites are one way to view the overall performance differences between the two. These 

figures show that the bias at the extremes is reduced using the new algorithm compared 

to the original IMPROVE algorithm (i.e. the points tend to be better centered on the oneto-one line). They also show that the somewhat reduced precision of the new algorithm 

compared to the original IMPROVE algorithm (i.e. points are more broadly scattered). 
0

50

100

150

200

250

300

350

0 50 100 150 200 250 300 350

Measured Bsp

Split Component Method Bsp

Figure 3. Scatter plot of the recommended revised algorithm estimates of light scattering 

versus measured light scattering. 

Similar pairs of scatter plots were prepared for each individual monitoring site (available 

in the appendix). Figures 4 and 5 are example plots for Shenandoah and Grand Canyon 

National Parks. The logarithmic scales on these plots exaggerate the scatter for low 

values compared to high values. The individual-site scatter plots have the 80th percentile 

values indicated on the graphs for the predicted and measured values by horizontal and 

vertical lines respectively. Points that are to the right of the vertical line have 

nephelometer values that are among the 20% worst light scattering for that monitoring 

sites. Points that are above the horizontal line have algorithm determined values that are 

among the 20% worst estimated light scattering for that monitoring site. 

Scatter Plot for SHEN using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted Bsp

Scatter Plot for SHEN using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted Bsp

Figure 4. Scatter plots of the current IMPROVE and new recommended algorithm 

estimates of light scattering versus measured light scattering for Shenandoah National 

Park. Horizontal and vertical lines are at the 80th percentile for estimated and measured 

light scattering. 
Scatter Plot for GRCA using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted Bsp

Scatter Plot for GRCA using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted Bsp

Figure 5. Scatter plots of the current IMPROVE and new recommended algorithm 

estimates of light scattering versus measured light scattering for Grand Canyon National 

Park. Horizontal and vertical lines are at the 80th percentile for estimated and measured 

light scattering. 

The proposed new algorithm performs noticeably better with respect to having data 

points more centered on the one-to-one line at the high and low haze level extremes than 

the current IMPROVE algorithm for Shenandoah National Park, which is typical for the 

high haze level locations in the southeast U.S. A large number of the measured worst 

haze sample periods are correctly identified by both algorithms (these are the points 

above and to the right of the two 80th percentile lines). The differences between the two 

algorithms for Grand Canyon National Park and most of the other less hazy locations are 

not apparent in these scatter plots. 

The final approach for evaluating the relative performance of the two algorithms is to 

compare the average composition of the best haze days and the worst haze days as 

selected using each algorithm and using the measured light scattering. Table 6 and 7 

contain the average composition by RPO for days selected as best and worst by these 

three methods. Similar results for each of the 21 nephelometer monitoring locations are 

shown in tables in the appendix. 

Table 6. Mean light scattering and percent PM2.5 composition for the five major 

components for 20% best days as determined by measurement, the current 

IMPROVE algorithm and the proposed new algorithm. 

RPO 

Mean 

Bsp 

(Mm-1) 

Percent 

Ammonium 

Sulfate 

Percent 

Ammonium 

Nitrate 

Percent 

OCM 

Percent 

Soil 

Percent 

Coarse 

Percent 

EC 

Measured 6.8 19 2 15 8 54 2 

CENRAP IMPROVE 9.1 20 2 15 7 53 2 

 NEW 8.1 21 3 16 7 51 2 

MANEVU Measured 6.1 22 3 22 4 47 3 
IMPROVE 8.4 21 3 22 4 47 3 

NEW 7.4 22 4 22 4 45 3 

Measured 13.8 25 7 21 4 40 3 

VISTAS IMPROVE 18.4 25 7 21 4 40 3 

 NEW 17.0 25 8 21 4 39 3 

Measured 3.4 13 3 18 8 55 3 

WRAP IMPROVE 5.2 14 3 19 8 53 3 

NEW 4.5 15 3 19 8 52 3 

Table 7. Mean light scattering and percent PM2.5 composition for the five major 

components for 20% worst days as determined by measurement, the current 

IMPROVE algorithm and the proposed new algorithm. 

RPO 

Mean 

Bsp 

(Mm-1) 

Percent 

Ammonium 

Sulfate 

Percent 

Ammonium 

Nitrate 

Percent 

OCM 

Percent 

Soil 

Percent 

Coarse 

Percent 

EC 

Measured 76 34 6 19 5 34 2 

CENRAP IMPROVE 67 34 6 19 5 33 2 

 NEW 72 34 6 19 6 34 2 

Measured 61 36 6 23 3 30 3 

MANEVU IMPROVE 61 36 6 22 3 30 3 

 NEW 63 35 6 23 3 31 3 

Measured 120 46 5 21 3 22 2 

VISTAS IMPROVE 106 47 4 21 3 23 2 

 NEW 127 47 3 22 3 22 2 

Measured 36 15 6 27 7 42 3 

WRAP IMPROVE 33 14 6 27 6 44 3 

 NEW 33 13 6 27 6 44 3 

These tables demonstrate that the composition associated with the best and worst haze 

days are not very sensitive to the method of identifying the sample periods that fit in best 

and worst categories. Some of the individual sites (e.g. Grand Canyon) have somewhat 

larger variations in the composition between measurement-selected days compared to 

algorithm-selected days, though there’s little difference between the average composition 

comparing the two algorithms on the best and worst days. The contributions to light 

extinction by the various components were not explicitly calculated, but are inherently 

somewhat different because of the explicit differences in the two algorithms. 

In summary, the proposed new algorithm for estimating haze reduces the biases 

compared to measurements at the high and low extremes. This is most apparent for the 

hazier eastern sites. The composition of days selected as best and worst by the current 

and the new algorithm are very similar, and similar to days selected by measurements. 

Most of the reduction of bias associated with the new algorithm is attributed to the use of 

the split component extinction efficiency method for sulfate, nitrate and organic 

components that permitted variable extinction efficiency depending on the component 

mass concentration. Though not subject to explicit performance testing, the proposed 

new algorithm also contains specific changes from the current algorithm that reflect a 

better understanding of the atmosphere as reflected in the more recent scientific literature 
(e.g. change to 1.8 from 1.4 for organic compound mass to carbon mass ratio) and a more 

complete accounting for contributors to haze (e.g. sea salt and NO2 terms), and use of site 

specific Rayleigh scattering terms to reduce elevation-related bias. 

III. Technical Justification for Revisions 

Five major revisions to the IMPROVE algorithm for estimating light extinction from 

IMPROVE particle speciation data are incorporated into the recommended approach. 

They include 

• addition of a sea salt term which is a particular concern for coastal monitoring 

locations where the sum of the major components of light extinction and mass 

have been deficient; 

• change the assumed organic mass to organic carbon ratio from 1.4 to 1.8 to 

reflect more recent peer-reviewed literature on the subject; 

• use of site-specific Rayleigh scattering based on the elevation and annual 

average temperature of the monitoring sites; 

• development and use of a split component extinction efficiency model for 

sulfate, nitrate and organic carbon components including new water growth 

terms for sulfate and nitrate to better estimate light extinction at the high and low 

extremes of the range; and 

• addition of a NO2 light absorption term that would only be used at sites with 

available NO2 concentration data. 

A summary of the technical rationale for making each of these changes is described in 

separate sections below. 

Sea Salt 

The current IMPROVE protocol for estimating light extinction does not include light 

scattering (bsp) by sea salt aerosols. Lowenthal and Kumar (2003) demonstrated that 

inclusion of elements from sea salt (e.g., Na, Cl) increased the accuracy of mass 

reconstruction at coastal IMPROVE sites. Contributions of sea salt particles to light 

extinction at some coastal IMPROVE sites may be significant, especially since bsp by sea 

salt particles should be significantly enhanced by hygroscopic growth in humid 

environments. Lowenthal and Kumar (2005) found that fine sea salt aerosols accounted 

for 43% of estimated bsp at the U.S. Virgin Islands IMPROVE site. 

To include sea salt in the IMPROVE light extinction equation, it is necessary to: 1) 

estimate the sea salt mass concentration; 2) specify a dry sea salt scattering efficiency; 

and 3) specify an f(RH) curve for sea salt representing the enhancement of sea salt 

scattering by hygroscopic growth as a function of relative humidity (RH). 

Sea Salt Mass Concentration 

Estimating sea salt mass requires a sea salt marker species measured in IMPROVE 

aerosol samples. The most obvious such markers are sodium (Na) and chlorine (Cl), 

since NaCl is the main component in sea water and sea salt. Based on the composition of 
sea water, pure sea salt mass is Na multiplied by 3.1 or Cl multiplied by 1.8 (Pytkowicz 

and Kester, 1971). However, Na is poorly quantified by x-ray fluorescence (XRF) and Cl 

can be depleted in ambient aerosol samples by acid-base reactions between sea salt 

particles and sulfuric and nitric acids (McInnes et al., 1994). Without accurate 

measurement of both Na (or other conservative tracers) and Cl, it is not possible to 

estimate how much Cl has been replaced by nitrate and/or sulfate in ambient samples. 

Further, without chemical speciation of the PM10 sample (Module D of the IMPROVE 

sampler), it is not possible to estimate coarse sea salt scattering. 

Given these limitations, it is recommended that the PM2.5 sea salt concentration be 

estimated as the concentration of chloride ion (Cl-) measured by ion chromatography 

multiplied by 1.8. If the chloride measurement is below the detection limit, missing or 

invalid then the PM2.5 sea salt concentration should be estimated as the concentration of 

chlorine (Cl) measured by XRF multiplied by 1.8. 

Although the XRF measurement can detect chlorine (Cl) at lower concentrations, the Amodule sample for XRF is more exposed to reactive losses because acidic gases are not 

removed from the air-stream and any HCl they release from the sample is not retained by 

the Teflon filter. Unless speciated data become available for PM10, coarse sea salt mass 

and light scattering will not be considered. To the degree that chloride has been replaced 

by sulfate or nitrate in ambient particles, this approach will underestimate the mass and 

scattering contributed by the substituted sea salt that results (e.g. NaNO3, NaHSO4, or 

Na2SO4). This mass is partially accounted for by ammonium sulfate and ammonium 

nitrate in the IMPROVE equation. However, the substituted Na salt mass is underestimated because ammonium is lighter than sodium. The scattering is also underestimate because the sodium salts absorb more water than does ammonium sulfate above 

60% RH. Given the limitations of the available data, 1.8 times chloride provides a 

reasonable lower-limit to the fine sea salt mass. 

Dry Scattering Efficiency 

In order to estimate the dry scattering efficiency and f(RH) for sea salt aerosols, their dry 

mass size distribution must be known. While this has not been measured at most 

IMPROVE sites, extensive sea salt size distribution measurements have been made in the 

remote marine environment during cruised-based experiments (Quinn et al., 1995, 1996, 

1998). Based on these studies, a dry log-normal mass size distribution with a geometric 

mean diameter (Dg) of 2.5 μm and geometric standard deviation (σg) of 2 is 

recommended. A dry scattering efficiency for PM2.5 sea salt of 1.7 m2

/g was calculated 

using Mie theory based on this size distribution assuming a sea salt refractive index of 

[1.55+ i0] and a density of 1.9 g cm-3 recommended by Quinn et al. (1995). 

Sea Salt f(RH) 

Tang et al. (1997) determined hygroscopic growth curves for aerosols generated from 

Long Island, NY and Atlantic Ocean seawater. The water absorption curves for sea salt 

were nearly identical to that of NaCl. The NaCl growth factors derived from the AIM3 

thermodynamic equilibrium model (Clegg et al., 1998) are shown in Table 8 as a function 

of relative humidity (RH). Below the crystallization point (RH = 47%), the growth factor 
set to one. Values are presented to RH = 95% , to which higher RH are “rolled back” 

under the Regional Haze Rule protocol (USEPA, 2001). Dry (RH=0%) light scattering 

(bsp(Dry)) was calculated using Mie theory for sea salt at unit PM2.5 mass concentration 

with the dry mass size distribution, refractive index, and density described above. Light 

scattering at RH = 46-95% at unit RH intervals (bsp(RH)) was calculated by applying the 

NaCl growth curve (Table 8) to the dry mass size distribution using Mie theory, 

accounting for the change in particle volume and refractive index from the addition of 

water. The f(RH) values, defined as bsp(RH)/bsp(Dry), are listed in Table 8. The f(RH) 

values in Table 8 will be converted to monthly, site-specific “climatological” values, as 

was done for ammonium sulfate/ammonium nitrate. Light scattering by sea salt (SS) 

aerosols is estimated as: 

 bsp(SS) = 1.7 fSS(RH) [1.8 * Cl]. 

Table 8. Sea Salt particle diameter growth and water growth function. 

RH (%) Growth Factora f(RH) RH (%) Growth Factor f(RH)

 

1-46 1.0000 1.0000 71 1.8434 3.1269 

47 1.5922 2.3584 72 1.8589 3.1729 

48 1.6001 2.3799 73 1.8751 3.2055 

49 1.6081 2.4204 74 1.8921 3.2459 

50 1.6162 2.4488 75 1.9100 3.2673 

51 1.6245 2.4848 76 1.9288 3.3478 

52 1.6329 2.5006 77 1.9488 3.4174 

53 1.6415 2.5052 78 1.9700 3.5202 

54 1.6503 2.5279 79 1.9925 3.5744 

55 1.6593 2.5614 80 2.0166 3.6329 

56 1.6685 2.5848 81 2.0423 3.6905 

57 1.6779 2.5888 82 2.0701 3.8080 

58 1.6875 2.6160 83 2.1001 3.9505 

59 1.6974 2.6581 84 2.1328 4.0398 

60 1.7075 2.6866 85 2.1684 4.1127 

61 1.7179 2.7341 86 2.2077 4.2824 

62 1.7286 2.7834 87 2.2512 4.4940 

63 1.7397 2.8272 88 2.2999 4.6078 

64 1.7511 2.8287 89 2.3548 4.8573 

65 1.7629 2.8594 90 2.4174 5.1165 

66 1.7751 2.8943 91 2.4898 5.3844 

67 1.7877 2.9105 92 2.5749 5.7457 

68 1.8008 2.9451 93 2.6769 6.1704 

69 1.8145 3.0105 94 2.8021 6.7178 

70 1.8286 3.0485 95 2.9610 7.3492 

 a

 Diameter at RH/Dry Diameter 

REFERENCES 

Clegg, S.L.; Brimblecombe, P.; Wexler, A.S., A Thermodynamic Model of the System 

H+

-NH4

+

-Na+-SO4

2--NO3--Cl-

-H2O at 298.15 K. J. Phys. Chem., 1998, 102, 2155-

2171. 
Lowenthal, D.H.; Kumar, N. PM2.5 Mass and Light Extinction Reconstruction in 

IMPROVE; J. Air & Waste Manage. Assoc., 2003, 53, 1109-1120. 

Lowenthal, D.H.; Kumar, N. Light Scattering from Sea Salt Aerosols at IMPROVE Sites; 

J. Air & Waste Manage. Assoc., 2005 (in press). 

McInnes, L.M.; Covert, D.S.; Quinn, P.K.; Germani, M.S. Measurements of Chloride 

Depletion and Sulfur Enrichment in Individual Sea-Salt Particles Collected from 

the Remote Marine Boundary Layer; J. Geophys. Res., 1994, 99, 8257-8268. 

Pytkowicz, R.M.; Kester, D.R. The Physical Chemistry of Sea Water; Oceanogr. Mar. 

Biol. Ann. Rev., 1971, 9, 11-60. 

Quinn, P.K.; Marshall, S.F.; Bates, T.S.; Covert, D.S.; Kapustin, V.N. Comparison of 

Measured and Calculated Aerosol Properties Relevant to the Direct Radiative 

Forcing ot Tropospheric Sulfate Aerosol on Climate. J. Geophys. Res., 1995, 100, 

8977-8991. 

Quinn, P.K.; Kapustin, V.N.; Bates, T.S.; Covert, D.S. Chemical and Optical Properties 

of Marine Boundary Layer Aerosol Particles of the Mid-Pacific in Relation to 

Sources and Meteorological Transport; J. Geophys. Res., 1996, 101, 6931-6951. 

Quinn, P.K.; Coffman, D.J.; Kapustin, V.N.; Bates, T.S.; Covert, D.S. Aerosol Optical 

Properties in the Marine Boundary Layer During the First Aerosol 

Characterization Experiment (ACE 1) and the Underlying Chemical and Physcial 

Aerosol Properties. J. Geophys. Res., 1998, 103, 16,547-16,563. 

Tang, I.N.; Tridico, A.C.; Fung, K.H. Thermodynamic And Optical Properties of Sea Salt 

Aerosols; J. Geophys. Res., 1997, 102, 23,269-23,275. 

USEPA. Draft Guidance for Tracking Progress Under the Regional Haze Rule; U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards: 

Research Triangle Park, NC, September 27, 2001. Available at: 

Http:/Vista.Cira.Colostate.Edu/IMPROVE/Publications/Guidancedocs/Guidanced

ocs.Htm. 

Organic Mass to Organic Carbon Ratio 

A factor of 1.4 is currently used in the “IMPROVE equation” to convert OC to organic 

mass (OM) to account for unmeasured elements (e.g. O, H, N) in OM. The value of 1.4 

was based on an experiment conducted by Grosjean and Friedlander (1975) in urban 

Pasadena, CA in 1973. They found that the carbon content of these samples averaged 

73%. White and Roberts(1977) suggested an OC to OM conversion factor (OM/OC) of 

1.4 on the reciprocal of 0.73. Andrews et al. (2000) attempted to explain the 

reconstructed mass deficit during SEAVS (Southeastern Aerosol and Visibility Study) at 

Great Smoky Mountains National Park in terms of underestimation of OM. 

Turpin and Lim (2001) recommended the use of OM/OC factors of 1.6±0.2 and 2.1 ±0.2 

for urban and non-urban aerosol, respectively, based on the chemical structure of 

organics compounds found in such environments. This is consistent with an expectation 

that OM/OC ratio should increase as aerosols age during transport and photochemical 
reactions produce secondary organic compounds that are more oxygenated than their 

primary precursors. Krivácsy et al. (2001) isolated the polar, water-soluble organic 

carbon fraction of aerosols from the Jungfraujoch, Switzerland using solid phase 

extraction. An OM/OC ratio of 1.91 was inferred from elemental composition (C, N, H, 

and S). Poirot and Husar (2004) found that agreement between reconstructed and 

measured PM2.5 was closer with an OM/OC ratio of 1.8 than with the factor of 1.4 for 

samples from the IMPROVE and STN networks in the northeastern U.S. during summer, 

2002, when large impacts from forest fires in Quebec were observed. Malm et al. (2005) 

found that PM2.5 mass and light scattering closure was achieved assuming an OM/OC 

ratio of 1.8 during a two-month study at Yosemite National Park in summer, 2002. ElZanan et al. (2005) derived OM/OC ratios of 1.92±0.40 from solvent extracts of archived 

filter samples from five IMPROVE sites and 2.07±0.32 from chemical mass balance in 

40,532 daily IMPROVE samples at 50 sites from 1988-2003. 

While additional experimental work is needed to further explore this issue, it is clear that 

an OC conversion factor or 1.4 is not applicable for remote U.S. national parks. A 

consensus value of 1.8 is recommended for use in the proposed new algorithm. 

REFERENCES 

Andrews, E., Saxena, P., Musarra, S., Hildemann, L.M., Koutrakis, P., McMurry, P.H., 

Olmez, I., and White, W.H., 2000: Concentration and composition of atmospheric 

aerosols from the 1995 SEAVS experiment and a review of the closure between 

chemical and gravimetric measurements. J. Air & Waste Manage. Assoc. 50, 648-

664. 

Grosjean, D., and Friedlander, S. K. 1975: Gas-particle distribution factors for organic 

and other pollutants in the Los Angeles atmosphere. J. Air Pollut. Control Assoc., 

25, 1038-1044. 

Krivácsy, Z., Gelencsér, A., Kiss, G., Mézáros, E., Molnár, A., Hoffer, A., Mézáros, T., 

Sárvári, Z., Temesi, D., Varga, B., Baltensperger, U., Nyeki, S., and Weingartner, 

E., 2001: Study on the chemical character of water soluble organic compounds in 

fine atmospheric aerosol at the Jungfraujoch. J. Atmos. Chem., 39, 235-259. 

Malm, W.C., Day, D.E., Carrico, C., Kreidenweis, S.M., Collett, J.L., Jr., McMeeking, 

G., Lee, and Carillo, J., 2005: Intercomparison and closure calculations using 

measurements of aerosol species and optical properties during the Yosemite 

Aerosol Characterization Study, J. Geophys. Res., doi:10.1029/2004JD005494, in 

press. 

Poirot, R.L., and Husar, R.B., 2004: Chemical and physical characteristics of wood 

smoke in the northeastern U.S. during July 2002: Impacts from Quebec forest 

fires, Paper #94, A&WMA Specialty Conference: Regional and Global 

Perspectives on Haze: Causes, Consequences and Controversies, Ashville, NC, 

October 25-29, 2004. 

Turpin, B.J., and Lim, H.-J., 2001: Contributions to PM2.5 mass concentrations: revisiting 

common assumptions for estimating organic mass. Aerosol. Sci. Technol., 35, 

602-610. 

White, W. H., and Roberts P. T., 1977: On the nature and origins of visibility-reducing 

aerosols in the Los Angeles air basin. Atmos. Environ., 11, 803-812. 
Split Component Extinction Efficiency Model

Concentration-Varying Dry Scattering Efficiencies 

The current IMPROVE algorithm employs dry scattering efficiencies (E) of 3 m2

/g for 

ammonium sulfate and ammonium nitrate and 4 m2/g for organic matter (OM). Data from 

IMPROVE special studies suggest that dry extinction efficiencies are variable. 

Lowenthal and Kumar (2005) found that PM2.5 mass scattering efficiencies increased 

with increasing levels of particle light scattering and mass concentration. This was 

attributed to growth of the dry particle size distribution into size ranges with higher 

scattering efficiencies under more-polluted conditions, which is related to a higher degree 

of cloud processing during transport. Malm et al. (2003) estimated dry ammoniated 

sulfate scattering efficiencies ranging from 2.4-4.1 m2/g during the Big Bend Aerosol and 

Visibility Observational Study (BRAVO). A weak relationship between efficiency and 

ammoniated sulfate mass concentration was reported. 

The proposed new IMPROVE algorithm accounts for the increase of ammonium 

sulfate/ammonium nitrate and organic matter (OM) efficiencies with concentration using 

a simple mixing model where the concentrations of ammonium sulfate, ammonium 

nitrate, and OM are each comprised of external mixtures of mass in small and large 

particle size modes. The large mode represents aged and/or cloud processed particles, 

while the small mode represents freshly formed particles. These size modes are described 

by log-normal mass size distributions with geometric mean diameters (Dg) and geometric 

standard deviations (σg) of 0.2 μm and 2.2 for small mode and 0.5 μm and 1.5 for the 

large mode, respectively. The dry PM2.5 scattering efficiencies for small- and large-mode 

ammonium sulfate (2.2 and 4.8 m2

/g), ammonium nitrate (2.4 and 5.1 m2/g), and OM (2.8 

and 6.1 m2/g) were calculated using Mie theory at a wavelength of 550 nm based on the 

log-normal mass size distribution parameters described above. The ammonium sulfate, 

ammonium nitrate, and OM densities and refractive indexes used in this calculation are 

1.77, 1.73, and 1.4 g/cm3

, respectively, and 1.53+i0, 1.55+i0, and 1.55+i0, respectively. 

No attempt was made to account for possible difference in composition between the two 

size modes of these particles.

f(RH) 

The current IMPROVE algorithm applies a single f(RH) curve to ammonium sulfate and 

ammonium nitrate scattering which is based on a hygroscopic growth curve (D(RH)/D(Dry)) 

(particle diameter at ambient RH divided by the dry particle diameter) for pure 

ammonium sulfate that was smoothed between the deliquescence and efflorescence 

branches (USEPA, 2001). The proposed new IMPROVE algorithm contains f(RH) 

curves for small- and large-mode ammonium sulfate that are also applied to small and 

large mode ammonium nitrate. The f(RH) for OM is assumed to be one at all RH for 

small and large OM modes. The f(RH) for ammonium sulfate and ammonium nitrate are 

based on the hygroscopic growth curve for pure ammonium sulfate derived from the AIM 

thermodynamic equilibrium model (Clegg et al., 1998). This growth curve represents the 

upper branch, also referred to as the efflorescence or hysteresis branch, of the ammonium 

sulfate growth curve. The upper branch is used because deliquescence is rarely observed 
in the environment. Because pure ammonium sulfate crystallizes at 37% RH, it is 

assumed that there is no hygroscopic growth and that the f(RH) is one below this RH. 

Dry (RH=0%) light scattering (bsp(Dry)) was calculated using Mie theory for small- and 

large-mode ammonium sulfate. Light scattering at RH = 37-95% at unit RH intervals 

(bsp(RH)) was calculated by applying the AIM ammonium sulfate growth curve to the 

small and large dry mode size distributions using Mie theory, accounting for the change 

in particle volume and refractive index from the addition of water. The f(RH), defined as 

bsp(RH)/bsp(Dry), are listed in Table 9 for the small (f(S)RH) and large (f(L)RH) modes. 

Values are presented to RH = 95%, to which higher RH are “rolled back” under the 

Regional Haze Rule protocol (USEPA, 2001). The same f(RH) are applied to small- and 

large-mode ammonium sulfate and ammonium nitrate. 

Table 9. Water growth for the small and large sized distribution sulfate and nitrate 

components. 

RH (%) fS(RH) fL(RH) RH (%) fS(RH) fL(RH) RH (%) fS(RH) fL(RH)

0 to 36 1.00 1.00 56 1.78 1.61 76 2.60 2.18 

37 1.38 1.31 57 1.81 1.63 77 2.67 2.22 

38 1.40 1.32 58 1.83 1.65 78 2.75 2.27 

39 1.42 1.34 59 1.86 1.67 79 2.84 2.33 

40 1.44 1.35 60 1.89 1.69 80 2.93 2.39 

41 1.46 1.36 61 1.92 1.71 81 3.03 2.45 

42 1.48 1.38 62 1.95 1.73 82 3.15 2.52 

43 1.49 1.39 63 1.99 1.75 83 3.27 2.60 

44 1.51 1.41 64 2.02 1.78 84 3.42 2.69 

45 1.53 1.42 65 2.06 1.80 85 3.58 2.79 

46 1.55 1.44 66 2.09 1.83 86 3.76 2.90 

47 1.57 1.45 67 2.13 1.86 87 3.98 3.02 

48 1.59 1.47 68 2.17 1.89 88 4.23 3.16 

49 1.62 1.49 69 2.22 1.92 89 4.53 3.33 

50 1.64 1.50 70 2.26 1.95 90 4.90 3.53 

51 1.66 1.52 71 2.31 1.98 91 5.35 3.77 

52 1.68 1.54 72 2.36 2.01 92 5.93 4.06 

53 1.71 1.55 73 2.41 2.05 93 6.71 4.43 

54 1.73 1.57 74 2.47 2.09 94 7.78 4.92 

55 1.76 1.59 75 2.54 2.13 95 9.34 5.57 

REFERENCES 

 Clegg, S.L.; Brimblecombe, P.; Wexler, A.S., A Thermodynamic Model of the 

System H+

-NH4

+

-Na+-SO4

2--NO3--Cl-

-H2O at 298.15 K. J. Phys. Chem., 1998, 102, 2155-

2171. 

 Lowenthal, D.H.; Kumar, N., 2004: Variation of Mass Scattering Efficiencies in 

IMPROVE; J. Air & Waste Manage. Assoc., 2005, 54, 926-934. 

 Malm, W.C.; Day, D.E.; Kreidenweis, S.M.; Collett, J.L.; Lee, T. Humiditydependent optical properties of fine particles during the Big Bend Regional Aerosol and 
Visibility Study. J. Geophys. Res., 2003, 108, 4279, doi: 10.1029/2002JD002998. 

 USEPA. Draft Guidance for Tracking Progress Under the Regional Haze Rule; 

U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards: 

Research Triangle Park, NC, September 27, 2001. Available at: 

Http:/Vista.Cira.Colostate.Edu/IMPROVE/Publications/Guidancedocs/Guidancedocs.Ht

m. 

Site Specific Rayleigh Scattering

Rayleigh scattering refers to the scattering of light from the molecules of the air, and a 

constant value of 10 Mm-1 is used in the current IMPROVE algorithm. However, 

Rayleigh scattering depends on the density of the air and thus varies with temperature and 

pressure. Site-specific Rayleigh scattering was estimated using a Rayleigh Scattering 

Calculator developed by Air Resource Specialists, Inc. that calculates Rayleigh scattering 

as a function of temperature and pressure. For each IMPROVE site, we used the standard 

U.S. atmospheric pressure corresponding to the monitoring site elevation, and an 

estimated annual mean temperature. The temperature data were obtained from the nearest 

weather stations for time periods encompassing 10 to 30 years and were interpolated to 

the monitoring site location. Table A2 (at the end of the document) shows the sitespecific Rayleigh scattering calculated using this procedure. The recommended integerrounded site-specific values are shown in the last column of the table. They range from 

12Mm-1 for sites near sea level to 8Mm-1 for sites at about 12,000 feet elevation. 

NO2 Absorption

The NO2 absorption efficiency term (i.e. 0.33Mm-1/ppm) in the proposed new algorithm 

is a photopic-weighted absorption efficiency value (PAENO2). It was calculated by 

dividing the sum of the products of the relative observer photopic response values 

(PR(8)) for viewing an image of 2o angular size and the spectral NO2 absorption 

efficiency values (AE(8)) by the sum of the photopic response values, as shown in the 

equation below. 

∑

∑ ×

= 750

350

750

350

( )

( ) ( )

2

λ

λ λ

PR

PR AE

PAENO

The spectral NO2 absoption efficiency values are from Dixon (1940) and available in 

PLUVUE Users Manual (1980), where they were giving in 10ηm increments that were 

interpolated to generate 1ηm values. The photopic response values are from the CIE Ybar 

function downloaded directly from the CVRL Color and Vision database. Both are 

shown in the figure below. 
REFERENCES 

J.K. Dixon, "The Absorption Coefficient of Nitrogen Dioxide in the Visible Spectrum", 

Journal of Chemical Physics, Volume 8, February, 1940. pp 157-161 

Draft Final Report: User's Manual For The Plume Visibility Model (PLUVUE), July, 

1980, Systems Applications, Inc. 

CVRL Color & Vision database, CIE (1931) 2-deg color matching function, http://wwwcvrl.ucsd.edu/cmfs.htm
Table A1. IMPROVE monitoring sites with nephelometers used to evaluate 

algorithm performance. 

Abbreviation Name State 

ACAD Acadia National Park Maine 

BIBE Big Bend National Park Texas 

BOWA Boundary Waters Canoe Area Minnesota 

CORI Columbia River Gorge Washington 

DOSO Dolly Sods/Otter Creek Wilderness West Virgina 

GICI Gila Wilderness New Mexico 

GRCA Grand Canyon National Park Arizona 

GRGU Great Gulf Wilderness New Hampshire 

GRSM Great Smoky Mountains Tennessee 

JARB Jarbidge Wilderness Nevada 

LOPE Lone Peak Wilderness Utah 

LYBR Lye Brook Wilderness Vermont 

MACA Mammoth Cave National Park Kentucky 

MORA Mount Rainier National Park Washington 

MOZI Mount Zirkel Wilderness Colorado 

OKEF Okefenokee National Wildlife Refuge Florida 

SHEN Shenandoah National Park Virginia 

SHRO Shining Rock Wilderness North Carolina 

SNAP Snoqualamie Pass Wilderness Washington 

THIS Three Sisters Wilderness Oregon 

UPBU Upper Buffalo Wilderness Arkansas 
Table A2. Site-specific Rayleigh values for all IMPROVE monitoring sites. Revised algorithm uses values roun

values (last column). 

Monitoring Site Name Elevation 

(ft) 

Latitude Longi- tude Standard U.S. Atmospher 

Temp. (C) 

Standard U.S. Atmosphere Pressure (mb) 

Rayleigh at Standard Atmosphere (Mm-1) 

Annual Average Temp. (C) 

CorRay(M

Acadia National Park 492 44.4 68.3 14.2 997 11.5 7.4 Addison Pinnacle 1732 42.1 77.2 11.8 951 11.0 7.1 Agua Tibia 1663 33.5 117 11.9 954 11.1 16.7 Arches National Park 5648 38.8 109.6 4 820 9.8 10.0 Arendtsville 879 39.9 77.3 13.5 983 11.3 10.4 Badlands National Park 2414 43.7 101.9 10.4 927 10.8 10.3 Bandelier National Monument 

6517 35.8 106.3 2.3 793 9.5 8.7 

Big Bend National Park 3526 29.3 103.2 8.2 889 10.5 19.4 Bliss State Park (TRPA) 6940 39 120.1 1.4 780 9.4 4.5 Bondville 692 40.1 88.4 13.8 990 11.4 11.0 Bosque del Apache 4536 33.9 106.9 6.2 855 10.1 14.0 Boundary Waters Canoe Area 

1719 47.9 91.5 11.8 952 11.1 2.4 

Breton 7 29.1 89.2 15.2 1016 11.7 21.0 Bridger Wilderness 8551 43 109.8 -1.7 734 9.0 2.0 Bridgton 794 44.1 70.7 13.6 986 11.4 6.1 Brigantine National Wildlife Refuge 

16 39.5 74.4 15.2 1015 11.6 12.7 

Brooklyn Lake 10483 41.4 106.2 -5.6 682 8.4 0.5 Bryce Canyon National Park 8125 37.6 112.2 -0.9 746 9.1 4.1 Cabinet Mountains 4704 48 115.7 5.9 850 10.1 3.2 Cadiz 617 36.8 87.9 14 992 11.4 13.8 Caney Creek 2263 34.5 94.1 10.7 932 10.9 13.1 
Canyonlands National Park 5901 38.5 109.8 3.5 812 9.7 11.5 

Cape Cod 134 42 70 14.9 1011 11.6 9.8 Cape Romain National Wildlife Refuge 

10 32.9 79.7 15.2 1016 11.7 17.9 

Capitol Reef (CAPI1) 6199 38.3 111.3 2.9 803 9.6 9.2 Capitol Reef (CARE1)+F76 6199 38.3 111.3 2.9 803 9.6 9.2 Casco Bay 49 43.8 70.1 15.1 1014 11.6 7.6 Chassahowitzka National Wildlife 

7 28.7 82.6 15.2 1016 11.7 21.4 

Chiricahua National Monument 

5150 32 109.4 5 835 9.9 15.5 

Cohutta 2437 34.8 84.6 10.4 926 10.8 11.9 Columbia Gorge 738 45.6 122.2 13.7 988 11.4 11.6 Columbia River Gorge 659 45.7 121 13.9 991 11.4 10.5 Connecticut Hill 1656 42.4 76.7 11.9 954 11.1 7.0 Crater Lake National Park 6439 42.9 122.1 2.4 795 9.6 3.2 Craters of the Moon NM(US DOE) 

5960 43.5 113.6 3.4 810 9.7 6.2 

Death Valley Monument 410 36.5 116.8 14.4 1000 11.5 22.6 Denali National Park 2158 63.7 149 10.9 936 10.9 -3.0 Dolly Sods /Otter Creek Wilderness 

3798 39.1 79.4 7.7 879 10.4 6.6 

Dome Lands Wilderness 2942 35.7 118.2 9.4 909 10.6 15.0 Dome Lands Wilderness 3034 35.7 118.1 9.2 905 10.6 15.3 Everglades National Park 10 25.4 80.7 15.2 1016 11.7 23.7 Gates of the Mountains 7846 46.8 111.7 -0.4 754 9.2 -1.1 Gila Wilderness 5825 33.2 108.2 3.7 814 9.7 10.6 Glacier National Park 3211 48.5 114 8.8 899 10.6 5.7 Great Basin National Park 6783 39 114.2 1.8 785 9.5 8.8 Great Gulf Wilderness 1460 44.3 71.2 12.3 961 11.1 5.0 Great Sand Dunes National Monument 

8213 37.7 105.5 -1.1 744 9.0 6.0 
Great Smoky Mountains 

National Park 

2673 35.6 83.9 9.9 918 10.7 11.2 

Guadalupe Mountains National Park 

5491 31.8 104.8 4.3 825 9.8 15.2 

Haleakala National Park 3798 20.8 156.3 7.7 879 10.4 15.0 Hance Camp at Grand Canyon NP 

7436 36 112 0.5 766 9.3 6.6 

Hawaii Volcanoes National Park 

3949 19.4 155.3 7.4 874 10.3 16.3 

Hells Canyon 2050 45 116.8 11.1 940 10.9 11.5 Hercules-Glades 1394 36.7 92.9 12.4 964 11.2 13.7 Hillside 4953 34.4 113 5.4 842 10.0 9.9 Hoover 8416 38.1 119.2 -1.5 738 9.0 3.5 Hopi Point #1 7098 36.1 112.2 1.1 776 9.4 7.6 Hopi Point #2 (High Sensitivity) 

7098 36.1 112.2 1.1 776 9.4 7.6 

Ike's Backbone 4274 34.3 117.9 6.7 864 10.2 14.0 Indian Gardens 3824 36.1 112.1 7.6 879 10.4 16.7 Indian Gardens 2 (High Sensitivity) 

3824 36.1 112.1 7.6 879 10.4 16.7 

Isle Royale National Park 699 47.9 89.2 14 993 11.4 3.5 Isle Royale National Park (New) 

610 47.5 88.1 13.8 989 11.4 3.7 

James River Face 981 37.6 79.5 13.3 979 11.3 13.0 

Jarbidge Wilderness 6173 41.9 115.4 3 803 9.6 7.1 Jefferson/James River Face Wilderness 

918 37.7 79.4 13.4 981 11.3 13.1 

Joshua Tree 4028 34.1 116.4 7.2 872 10.3 14.9 Joshua Tree National Monument 

4028 34.1 116.4 7.2 872 10.3 14.9 

Kaiser 8439 37.2 119.2 -1.5 737 9.0 3.4 Kalmiopsis 295 42.6 124.1 14.6 1005 11.5 13.1 Lassen Volcanic National Park 

5756 40.5 121.6 3.8 816 9.8 5.3 
Lava Beds 4818 41.7 121.5 5.7 846 10.0 8.7 

Linville Gorge 3234 36 81.9 8.8 898 10.5 10.0 Livonia 977 38.5 86.3 13.3 979 11.3 12.2 Lone Peak Wilderness 5799 40.4 111.7 3.7 815 9.7 9.1 Lostwood 2270 48.6 102.4 10.7 932 10.9 3.2 Lye Brook Wilderness 3300 43.1 73.1 8.7 896 10.5 2.4 Lynden 92 49 122.6 15 1013 11.6 10.1 M.K. Goddard 1269 41.4 80.1 12.7 968 11.2 9.0 Mammoth Cave National Park 

813 37.1 86.1 13.6 985 11.4 13.7 

Mauna Loa National Observatory (MALO1) 

11152 19.5 155.6 -6.9 665 8.3 7.0 

Meadview 2959 36 114.1 9.3 908 10.6 16.2 Medicine Lake 1984 48.5 104.5 11.3 942 11.0 4.8 Mesa Verde National Park 7141 37.2 108.5 1 774 9.4 8.7 Mingo 367 37 90.1 14.5 1002 11.5 14.3 Mohawk Mt. 1745 41.8 73.3 11.7 951 11.0 6.3 Monture 4241 47.1 113.2 6.8 865 10.2 3.5 Moosehorn NWR 308 45.1 67.3 14.6 1004 11.5 6.0 Mount Baldy 8243 34.1 109.4 -1.1 743 9.0 7.6 Mount Hood 4398 45.3 121.8 6.5 860 10.2 4.3 Mount Rainier National Park 1401 46.8 122.1 12.4 963 11.2 8.9 Mount Zirkel Wilderness 10637 40.5 106.7 -5.9 678 8.4 0.2 Mount Zirkel Wilderness (Storm Peak) 

10562 40.5 106.7 -5.7 680 8.4 0.5 

North Absaroka 8134 44.7 109.4 -0.9 746 9.1 -0.9 North Cascades 1889 48.7 121.1 11.5 946 11.0 7.2 Okefenokee National Wildlife Refuge 

161 30.7 82.1 14.9 1010 11.6 20.3 

Old Town 213 44.9 68.6 14.8 1008 11.6 6.2 Olympic 1968 48 123 11.3 943 11.0 10.9 
Organ Pipe 1847 32 113 11.5 947 11.0 21.0 

Pasayten 5360 48.4 119.9 4.6 829 9.9 4.8 Petrified Forest National Park 

5796 35.1 109.8 3.7 815 9.7 11.7 

Phoenix 1109 33.5 112.1 13 974 11.3 23.4 Pinnacles National Monument 

1040 36.5 121.2 13.1 980 11.3 16.0 

Point Reyes National Seashore 

279 38.1 122.9 14.6 1005 11.6 11.3 

Presque Isle 535 46.7 68 14.1 996 11.5 4.8 Proctor Maple R. F. 1322 44.5 72.9 12.6 966 11.2 4.5 Puget Sound 262 47.6 122.3 14.7 1006 11.6 10.7 Quabbin Summit 1033 42.3 72.3 13.2 977 11.3 6.7 Quaker City 1233 39.9 81.3 12.8 970 11.2 9.8 Queen Valley 2158 33.3 111.3 10.9 936 10.9 21.8 Redwood National Park 804 41.6 124.1 13.6 985 11.4 8.7 Rocky Mountain National Park 

9036 40.3 105.5 -2.7 721 8.8 1.9 

Rocky Mountain National Park (Headquarters) 

7872 40.4 105.6 -0.4 753 9.1 5.5 

Saguaro National Monument 3060 32.2 110.7 9.1 904 10.6 18.1 Saguaro West 2355 32.2 111.2 10.5 929 10.8 21.5 Salmon National Forest 9145 45.2 114 -2.9 718 8.8 -7.6 Salt Creek 3533 33.5 104.4 8.2 888 10.5 15.3 San Andres 4674 32.7 106.5 5.9 851 10.1 13.3 San Gabriel 5874 34.3 118 3.6 813 9.7 13.1 San Gorgonio Wilderness 5592 34.2 116.9 4.1 821 9.8 12.0 San Pedro Parks 9574 36 106.8 -3.8 706 8.7 -0.3 San Rafael 3126 34.7 120 9 902 10.6 16.7 Sawtooth National Forest 6494 44.2 114.9 2.3 794 9.5 1.1 Scoville 4920 43.7 113 5.4 843 10.0 6.1 
Seney 708 46.3 85.9 13.8 989 11.4 5.8 

Sequoia National Park 1755 36.5 118.8 11.7 950 11.0 17.0 Shenandoah National Park 3601 38.5 78.4 8.1 886 10.4 8.2 Shining Rock Wilderness 5317 35.4 82.8 4.7 830 9.9 7.4 Sierra Ancha 5232 34.1 110.9 4.8 833 9.9 12.5 Sikes 148 32.1 92.4 14.9 1010 11.6 18.2 Simeonof 98 55.3 160.5 15 1012 11.6 4.5 Sipsy Wilderness 915 34.3 87.3 13.4 981 11.3 15.5 Snoqualamie Pass, Snoqualamie N.F 

3805 47.4 121.4 7.7 879 10.4 3.2 

South Lake Tahoe 6232 38.9 120 2.8 802 9.6 6.1 Spokane Res. 1797 47.9 117.9 11.6 949 11.0 10.0 St. Marks 7 30.1 84.2 15.2 1016 11.7 19.8 Starkey 4126 45.2 118.5 7 869 10.3 4.2 Sula (Selway Bitteroot Wilderness) 

6242 45.9 114 2.8 801 9.6 -0.5 

Swanquarter 7 35.5 76.2 15.2 1016 11.7 16.9 Sycamore Canyon 6691 35.1 112 1.9 788 9.5 9.8 Theodore Roosevelt 2798 46.9 103.4 9.7 914 10.7 5.1 Three Sisters Wilderness 2903 44.3 122 9.4 910 10.7 6.8 Tonto National Monument 2578 33.6 111.1 10.1 921 10.8 18.7 Trapper Creek 479 62.3 150.3 14.3 998 11.5 2.3 Trinity 3303 40.8 122.8 8.7 896 10.5 12.9 Tuxedni 33 60 152.6 15.1 1015 11.6 1.8 UL Bend 2929 47.6 108.7 9.4 909 10.6 5.2 Upper Buffalo Wilderness 2371 35.8 93.2 10.5 928 10.8 12.5 Virgin Islands National Park 210 18.3 64.8 14.8 1008 11.6 26.1 Voyageurs National Park 1 1138 48.6 93.2 12.9 973 11.3 3.1 Voyageurs National Park 2 1407 48.4 92.8 12.4 963 11.2 2.6 Washington D.C. 52 38.9 77 15.1 1014 11.6 14.5 
Weminuche Wilderness 9069 37.7 107.8 -2.8 720 8.8 1.5 

Wheeler Peak 11060 36.6 105.5 -6.7 667 8.3 -3.0 White Mountain 6724 33.5 105.5 1.9 787 9.5 9.9 White Pass 6002 46.6 121.4 3.3 809 9.7 1.4 White River National Forest 11211 39.2 106.8 -7 663 8.2 -0.3 Wichita Mountains 1699 34.7 98.7 11.8 952 11.1 15.7 Wind Cave 4264 43.6 103.5 6.7 864 10.2 8.1 Yellowstone National Park 1 7744 44.6 110.4 -0.1 757 9.2 -0.2 Yellowstone National Park 2 7954 44.6 110.4 -0.6 751 9.1 -0.8 Yosemite National Park 5297 37.7 119.7 4.7 831 9.9 8.0 Zion 5068 37.5 113.2 5.2 838 10.0 13.2 
Scatter Plot for Big Bend using IMPROVE Algorithm

1

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1 10 100 1000

Measured Bsp

Predicted Bsp

Scatter Plot for Big Bend using New Algor

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Measured Bsp

Predicted Bsp

Scatter Plot for BOWA using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for BOWA using New Algorit

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Measured Bsp

Predicted Bsp
Scatter Plot for UPBU using IMPROVE Algorithm

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Predicted Bsp

Scatter Plot for UPBU using New Algorith

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Predicted Bsp

Scatter Plot for ACAD using IMPROVE Algorithm

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Predicted Bsp

Scatter Plot for ACAD using New Algorithm

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Predicted Bsp
Scatter Plot for GRGU using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for GRGU using New Algorith

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Measured Bsp

Predicted Bsp

Scatter Plot for LYBR using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for LYBR using New Algorith

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Predicted Bsp
Scatter Plot for DOSO using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for DOSO using New Algorith

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Predicted Bsp

Scatter Plot for GRSM using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for GRSM using New Algorit

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Measured Bsp

Predicted Bsp
Scatter Plot for MACA using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for MACA using New Algorit

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Measured Bsp

Predicted Bsp

Scatter Plot for OKEF using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for OKEF using New Algorit

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1 10 100 

Measured Bsp

Predicted Bsp
Scatter Plot for SHEN using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for SHEN using New Algorit

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Measured Bsp

Predicted Bsp

Scatter Plot for SHRO using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for SHRO using New Algorit

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Measured Bsp

Predicted Bsp
Scatter Plot for CORI using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for CORI using New Algorit

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Measured Bsp

Predicted Bsp

Scatter Plot for GICL using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for GICL using New Algorit

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Measured Bsp

Predicted Bsp
Scatter Plot for GRCA using IMPROVE Algorithm

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Measured Bsp

Predicted Bsp

Scatter Plot for GRCA using New Algori

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Predicted Bsp

Scatter Plot for JARB using IMPROVE Algorithm

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Predicted Bsp

Scatter Plot for JARB using New Algorit

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Predicted Bsp
Scatter Plot for LOPE using IMPROVE Algorithm

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Predicted Bsp

Scatter Plot for LOPE using New Algorit

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Predicted Bsp

Scatter Plot for MORA using IMPROVE Algorithm

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Predicted Bsp

Scatter Plot for MORA using New Algori

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Predicted Bsp
Scatter Plot for MOZI using IMPROVE Algorithm

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Predicted Bsp

Scatter Plot for MOZI using New Algori

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Predicted Bsp

Scatter Plot for SNPA using IMPROVE Algorithm

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Predicted Bsp

Scatter Plot for SNPA using New Algor

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Predicted Bsp
Scatter Plot for THSI using IMPROVE Algorithm

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Predicted Bsp

Scatter Plot for THSI using New Algorit

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Predicted Bsp
Table A3. Mean light scattering and percent PM2.5 composition for the five major 

components for 20% best days as determined by measurement, the current IMPROVE 

algorithm and the proposed new algorithm. 

Site 

Mean 

Bsp 

(Mm-1) 

Percent 

Ammonium 

Sulfate 

Percent 

Ammonium 

Nitrate 

percent 

OCM 

Percent 

Soil 

Percent 

Coarse 

Percent 

EC 

Measured 6.3 20 3 21 4 50 3 

IMPROVE 8.6 20 3 21 4 49 3 

ACAD1 NEW 7.7 20 3 22 4 49 3 

Measured 6.3 17 2 11 10 59 2 

IMPROVE 8.7 19 2 11 9 57 2 

BIBE1 NEW 7.9 18 2 11 9 57 2 

Measured 5.4 26 3 21 5 43 2 

IMPROVE 7.7 24 2 23 5 44 2 

BOWA1 NEW 6.6 26 3 20 5 44 2 

Measured 5.7 10 3 17 5 61 3 

IMPROVE 8.8 9 4 18 4 62 3 

CORI1 NEW 8.4 10 4 18 5 60 4 

Measured 15 26 5 20 4 42 3 

IMPROVE 20 24 6 20 4 42 3 

DOSO1 NEW 19 25 6 19 4 42 3 

Measured 2.9 16 2 19 8 52 3 

IMPROVE 4.9 16 2 20 8 50 3 

GICL1 NEW 4.1 17 2 20 8 48 3 

Measured 2.0 14 3 15 9 55 2 

IMPROVE 2.8 18 4 16 11 48 3 

GRCA2 NEW 2.5 18 5 15 10 48 3 

Measured 5.4 26 3 23 2 43 2 

IMPROVE 8.0 25 4 21 3 44 3 

GRGU1 NEW 6.8 25 4 22 3 44 3 

Measured 15 24 6 23 4 39 4 

IMPROVE 20 24 6 24 4 38 4 

GRSM1 NEW 20 25 7 23 3 38 4 

Measured 1.8 11 2 21 8 56 2 

IMPROVE 3.6 12 3 22 7 54 2 

JARB1 NEW 3.2 12 3 22 7 54 2 

Measured 5.3 12 6 19 9 50 4 

IMPROVE 8.2 12 6 18 10 50 4 

LOPE1 NEW 7.2 13 7 18 10 49 4 

Measured 5.3 24 6 25 4 38 3 

IMPROVE 8.2 24 4 27 4 38 3 

LYBR1 NEW 7.0 25 5 26 4 38 3 

Measured 18 27 7 21 4 37 3 

IMPROVE 22 25 9 21 4 36 4 

MACA1 NEW 19 26 9 20 4 37 4 

MORA1 Measured 5.5 14 3 31 4 45 4 

IMPROVE 6.8 14 3 27 4 49 3 
NEW 6.2 14 3 28 4 48 4 

Measured 2.8 12 3 18 8 57 2 

IMPROVE 4.8 13 4 18 8 55 3 

MOZI1 NEW 4.1 13 4 17 7 56 3 

Measured 19 21 3 19 3 50 3 

IMPROVE 23 20 3 19 3 52 3 

OKEF1 NEW 21 22 3 20 3 49 3 

Measured 11 27 10 18 4 39 3 

IMPROVE 14 26 10 19 4 38 3 

SHEN1 NEW 13 26 10 19 4 38 3 

Measured 4.6 16 2 21 6 53 3 

IMPROVE 8.9 19 2 22 4 50 3 

SHRO1 NEW 8.0 19 2 21 4 51 3 

Measured 7.3 14 5 22 5 49 4 

IMPROVE 10.0 13 6 26 5 44 5 

SNPA1 NEW 8.7 15 7 25 5 43 5 

Measured 5.4 10 2 23 4 58 3 

IMPROVE 7.1 12 3 23 5 54 3 

THSI1 NEW 6.4 11 3 22 4 57 3 

Measured 13 15 4 16 5 57 2 

IMPROVE 14 15 6 17 5 55 2 

UPBU1 NEW 13 15 6 17 5 55 2 

Table A4. Mean light scattering and percent PM2.5 composition for the five major 

components for 20% worst days as determined by measurement, the current IMPROVE 

algorithm and the proposed new algorithm. 

Site 

Mean 

Bsp 

(Mm-1) 

Percent 

Ammonium 

Sulfate 

Percent 

Ammonium 

Nitrate 

percent 

OCM 

Percent 

Soil 

Percent 

Coarse 

Percent 

EC 

Measured 58 34 6 22 3 32 3 

IMPROVE 59 34 6 21 3 33 3 

ACAD1 NEW 62 34 6 22 3 33 3 

Measured 49 34 2 16 7 39 2 

IMPROVE 44 33 2 15 8 41 2 

BIBE1 NEW 46 33 2 15 8 41 2 

Measured 53 25 13 23 4 32 2 

IMPROVE 49 27 13 22 4 32 2 

BOWA1 NEW 45 23 13 23 4 35 2 

Measured 56 13 11 25 5 43 3 

IMPROVE 48 13 10 24 5 45 3 

CORI1 NEW 48 12 10 24 5 46 3 

Measured 109 44 2 21 3 28 2 

IMPROVE 105 47 2 21 3 26 2 

DOSO1 NEW 121 46 1 20 3 29 2 

Measured 25 18 2 25 8 44 2 

IMPROVE 24 19 2 26 8 43 2 

GICL1 NEW 23 18 2 26 9 44 2 
Measured 19 16 3 19 11 50 2 

IMPROVE 19 11 2 18 10 57 1 

GRCA2 NEW 18 11 2 18 10 57 1 

Measured 58 35 3 24 3 33 3 

IMPROVE 56 36 4 25 3 30 3 

GRGU1 NEW 59 35 3 26 3 30 3 

Measured 153 52 1 22 3 20 2 

IMPROVE 125 51 1 22 3 21 2 

GRSM1 NEW 163 52 1 22 3 21 2 

Measured 24 8 3 21 12 54 2 

IMPROVE 21 7 3 20 12 57 1 

JARB1 NEW 21 7 3 21 12 57 1 

Measured 35 13 14 20 9 41 3 

IMPROVE 33 12 15 20 9 41 3 

LOPE1 NEW 31 11 13 21 9 42 3 

Measured 83 42 8 23 4 21 3 

IMPROVE 75 44 7 21 4 21 3 

LYBR1 NEW 77 45 6 22 4 21 3 

Measured 106 42 11 23 3 19 3 

IMPROVE 96 42 9 22 3 21 3 

MACA1 NEW 102 42 7 24 3 21 3 

Measured 42 20 3 37 5 31 4 

IMPROVE 42 23 4 35 5 30 4 

MORA1 NEW 42 20 3 37 5 32 4 

Measured 24 15 4 23 8 47 2 

IMPROVE 21 15 4 22 8 49 2 

MOZI1 NEW 20 13 3 24 9 49 2 

Measured 87 31 3 17 4 43 2 

IMPROVE 75 29 3 18 6 42 2 

OKEF1 NEW 76 31 3 17 5 42 2 

Measured 95 46 4 20 4 23 3 

IMPROVE 94 48 5 20 3 22 2 

SHEN1 NEW 108 47 3 21 3 24 2 

Measured 77 36 5 20 7 29 3 

IMPROVE 63 35 3 18 7 35 3 

SHRO1 NEW 62 36 3 20 6 32 3 

Measured 46 17 7 39 5 26 5 

IMPROVE 36 17 7 35 6 31 5 

SNPA1 NEW 37 15 6 37 6 32 5 

Measured 40 12 3 40 5 38 3 

IMPROVE 34 12 3 36 5 41 3 

THSI1 NEW 37 12 2 38 5 40 3 

Measured 123 42 3 20 5 27 2 

IMPROVE 104 41 3 20 6 29 2 

UPBU1 NEW 124 43 2 21 5 28 2 
DRAFT 10/26/06 

Why Use the New IMPROVE Algorithm? 

The new IMPROVE algorithm, which has been approved by the IMPROVE Steering Committee, 

is a much better representation of the effects of particulate matter on light extinction than the old 

IMPROVE algorithm. Excellent justification of this conclusion is provided in the November 

2005 report of the IMPROVE Steering Committee, “Revised IMPROVE Algorithm for 

Estimating Light Extinction from Particle Speciation Data.” 

The new algorithm incorporates changes that represent much of what has been learned about the 

relationship between particulate matter component concentrations and light extinction in the 

more than a decade since the old algorithm was developed. The main reasons that justify using 

the new algorithm instead of the old one are the following: 

1. The new algorithm overcomes the biases of the old algorithm on the haziest days (when 

the old algorithm underestimates light extinction) and on the clearest days (when the old 

algorithm overestimates extinction). This improvement can be seen by comparing Figures 

1 and 3 in the report mentioned above. 

2. The new algorithm recognizes that light extinction efficiencies vary from location to 

location and time to time as the size distribution of the aerosol changes, and it reflects 

this variation by increasing the extinction efficiency as the concentrations of sulfates, 

nitrates, and organics increase. It also recognizes that the humidity-related growth rates of 

particles differ depending on their sizes. As a result, the new algorithm generally does a 

better job of representing light extinction at individual sites than does the old algorithm, 

as indicated by the reduced fractional biases in each RPO region shown in Tables 3 and 5 

(versus Tables 2 and 4) in the report mentioned above 

3. The new algorithm recognizes research findings that show that fine sea salt can be an 

important contributor to light extinction in coastal areas, and thus includes a new term 

that reflects its contribution and its hygroscopic growth with increasing relative humidity. 

The old algorithm ignores sea salt, probably because it was originally developed mainly 

for inland Class I areas in the inter-mountain West (although incorporating sea salt into 

the calculation was already considered in a University of California at Davis memo in 

1995). 

4. The new algorithm acknowledges that substantial recent research has shown that the mass 

concentration of particulate organic matter (often denoted by OMC in IMPROVE 

publications) in rural areas is a greater multiple of the measured OC concentration than 

the 1.4 value used in the old algorithm. The new algorithm updates the factor to a moreappropriate1.8. 
5. The new algorithm recognizes that there may be times (near urban areas or in some 

source emission plumes) when light absorption by NO2 gas is a meaningful contributor to 

light extinction, and thus includes a new term based on the NO2 concentration. 

6. The new algorithm recognizes that the assumption of constant Rayleigh scattering of 10 

Mm-1 (representative of an elevation of about 5000 feet) may have been adequate when 

only inter-mountain West sites were considered, but is a distortion for sites close to sea 

level or with very hot or cold climates. It thus allows for site-specific values of the 

Rayleigh scattering. 

At any given location, the net result of all these changes is typically small on average because 

some changes result in increases in extinction and some in decreases, but the apportionment of 

that extinction to the various components is more accurate with the new algorithm, especially on 

the haziest and clearest days. This is particularly important for the development of emission 

management strategies, because the benefits of control of concentrations of each species will be 

represented more correctly with the new algorithm. 
SrATE OF MISSw1PPI 

HN EY 6-u~ 

GOVERNOR 

MISSISSIPPI DEPARTMENT OP ENVIRONMENTAL QUALITY 

TRunv D FISHER. EXECWIVE DIRE~R 

August 14,2008 

Mr. James 1. Palmer, Jr. 

Regional Administrator 

US. Environmental Protection Agency 

61 Forsyth Street 

Atlanta, Georgia 30303-8960 

Re: Request for Approval to Use New 

IMPROVE Equation for Bart 

Modeling 

Dear Mr. Palmer: 

The Mississippi Department of Environmental Quality (MDEQ) has been working with 

Visibility Improvement States and Tribal Association of the Southeast (VISTAS) and 

U.S. EPA Region 4 staff to implement the Best Available Retrofit Technology (BART) 

requirements of the Regional Haze Rule. As part of this process, the need for a 

methodology to post-process CALPUFF/CALPOST modeling data to implement the new 

IMPROVE equation was identified. VISTAS tasked Dr. Ivar Tombach to develop such 

methodology. The resulting spreadsheet and supporting information has been reviewed 

by EPA and the Federal Land Managers (FLMs). EPA Region 4 staff commented that 

they consider this post-processing methodology a non-regulatory modeling application 

requiring compliance with section 3.2 of Appendix W Part 5 1 : Guideline on Air Quality 

and that MDEQ must obtain approval for its use from the EPA Regional Adminstrator. 

In response to these comments and for the reasons discussed below, the following 

additional information is provided and formal approval of use of this alternative by the 

affected sources for BART exemption and demonstration modeling is requested. 

Rationale for Need for Use of the New IMPROVE Eauation 

The New IMPROVE equation is a much better representation of the effects of particulate 

matter on light extinction than the old equation and takes into account the latest scientific 

understanding of several parameters. 

1 The New algorithm overcomes biases of the old algorithm on the haziest 

days and the clearest days as demonstrated by comparing the measured 

light extinction from nephalometers at Class I areas to light extinction 

calculated using each one of the equations. 

OFFICE OF POLLUTION CONTROL 

POST OFFICE B(JX 2261 JACKSON. Msnsr~rr~ 39225-2261- TEL. (601) 961-5171 .FAX. (641) 354-6612. wdrq rtmc ms us 

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The New algorithm recognized spatial and temporal variations in light 

extinction as size distribution of the aerosol changes by increasing 

extinction efficiency of sulfate, nitrate, and organics concentration 

increase. 

3. The New algorithm incorporates a term to reflect the contribution of fine 

sea salt and its hygroscopic growth with increasing relative humidity 

recognizing research findings showing the sea salt can be an important 

contributor to light extinction in coastal areas. 

4. The New algorithm reflects research findings that the mass concentration 

of particulate organic matter in rural areas is greater than represented by 

the old equation. 

5. The New algorithm includes an NO2 term to represent times when light 

absorption by NO2 is a meaningful contributor to light extinction. 

6. The New algorithm incorporates site-specific Rayleigh scattering values to 

better represents sites close to sea level or with very hot or cold climates. 

With this combination of revisions to the IMPROVE equation, the resulting 

apportionment of extinction to various components is more accurate on the haziest and 

clearest days. This is important for development of emission control strategies since the 

benefits of controls of concentrations of each species will be represented more correctly 

with the new algorithm. The spreadsheet developed by Dr. Tombach implements the 

entire New IMPROVE equation for use with CALPUFF and contains a modification of 

CALPOST that calculates the light extinction from the CALPUFF output. 

The Primary Class 1 area that Mississippi is concerned with is the Breton National 

Wildlife Rehge, which is a chain of islands south of Mississippi in the Gulf of Mexico. 

The ability to appropriately represent the contribution of sea salt and Raleigh scattering 

for this site is important in determining their contribution to haze and evaluating the 

benefit of potential control strategies. There is no site specific sea salt data available for 

Breton, so the values for St. Marks in Florida which is the closest Gulf coastal site were 

used. Mississippi is requesting approval to use the New IMPROVE Equation for 

Mississippi Power Corporation (MPC), Plant Watson in Harrison County, and MPC 

Plants Chevron and Daniels in Jackson County. It should be noted that the Breton Class 1 

area is in the State of Louisiana and they are allowing the use of the New IMPROVE 

equation as well. 
We look forward to your response to this request for approval to use the VISTAS 

methodology incorporating the new IMPROVE equation by Mississippi Power. Attached 

are the spreadsheet instructions developed by Dr. Tornbach which include greater detail 

on the New IMPROVE algorithm. Should you have any questions regarding this matter. 

please contact me at (601) 961 - 5242 or Elliott Bickerstaff at (601) 961 - 5 176. Thank 

you for your continued participation in this process. 

Sincerlev. 

Maya Rao, PE, BCEE 

Chief, Air Division 

Enclosure 

c: Brenda Johnson, EPA Region 4 
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

REGION 4 

ATLANTA FEDERAL CENTER 

61 FORSYTH STREET 

ATLANTA GEORGIA 30303-8960 

SEP - 3 2008 

Maya Rao, PE, BCEE, Chief 

Air Division 

Mississippi Department of 

Environmental Quality 

Post Office Box 2261 

Jackson, Mississippi 39225-2261 

Dear Ms. Rao: 

Thank you for your August 14,2008, letter concerning the application of the new 

Interagency Monitoring of Protected Visual Environments (IMPROVE) equation. You 

requested approval to use this new methodology to estimate visibility impacts from three 

facilities performing modeling related to Best Available Retrofit Technology (BART) 

requirements for the Regional Haze Rule. You assert that the new IMPROVE equation could 

more appropriately represent the contribution of sea salt for the Breton National Wildlife Refuge 

Class I area Finally, the State of Louisiana (which is responsible for the Breton Class I area) has 

also approved the use of the new IMPROVE equation. 

The new methodology uses a spreadsheet developed by the Visibility Improvement - 

State and Tribal Association of the Southeast (VISTAS) to post-process the visibility modeling 

output from the CALPUFF and CALPOST models. The spreadsheet implements the new 

IMPROVE equation for calculating visibility impacts that was adopted by the IMPROVE 

Steering Committee in December 2005. As noted in your letter, US. Environmental Rotection 

Agency (EPA), Region 4, has previously provided comments indicating that new methodology is 

an alternative modeling approach and should meet the requirements in Section 3.2 of Appendix 

W, Guideline on Air Quality Models, contained in 40 C.F.R. Part 51. We have reviewed your 

demonstration for using the new methodology, which included a rationale for the need to use the 

new IMPROVE equation, a discussion of the performance of the equation versus measured 

values, and a submittal of the modified post-processing methodology. Based on the 

demonstration provided in your letter, we approve your request. 

We appreciate your desire to protect and preserve the environment. If we may be of 

further assistance, please contact Beverly Banister of my staff at (404) 562-9077. 

Sincerely, 

Regional Administrator 

Internet Address (URL) . http ilwww epa gov 

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