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-1at 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. Light-absorbing 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 

 

*

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.
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 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-1for 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 component-specific 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 

(1/106m 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; 

 

 

 

 

 

 

10

0.6

1

10

4

3 ( )

3 ( )



 

 

 

 

  

  

Coarse Mass

Fine Soil

Elemental Carbon

Organic Mass

f RH Nitrate

b f RH Sulfate ext
 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. 

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

IMPR

O

VE 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

 

†

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
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 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 mass

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 

   

   

   

 

 

 

 

0 . 3 3 N O (p p b )

R a y le ig h S c a t t e r in g (S it e S p e c if ic )

1 . 7 f (R H )S e a S a lt

2 . 8 S m a l l O r g a n ic M a s s 6 . 1 L a r g e O r g a n ic M a s s

2 . 4 f (R H )S m a l l N itr a t e 5 . 1 f (R H )L a r g e N itr a t e

2 . 2 f (R H )S m a l l S u lf a t e 4 . 8 f (R H )L a r g e S u lf a t e

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 g / m

 

 

   

3

3

, 20 /

20 /

arg Total Sulfate for Total Sulfate g m

g m

Total Sulfate L e Sulfate 



  

Small Sulfate  Total SulfateLarge Sulfate
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).

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(R

H)

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 algorithmestimated 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 one-to-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 individualsite 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

CENRAP

Measured 6.8 19 2 15 8 54 2

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

VISTAS

Measured 13.8 25 7 21 4 40 3

IMPROVE 18.4 25 7 21 4 40 3

NEW 17.0 25 8 21 4 39 3

WRAP

Measured 3.4 13 3 18 8 55 3

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

CENRAP

Measured 76 34 6 19 5 34 2

IMPROVE 67 34 6 19 5 33 2

NEW 72 34 6 19 6 34 2

MANEVU

Measured 61 36 6 23 3 30 3

IMPROVE 61 36 6 22 3 30 3

NEW 63 35 6 23 3 31 3

VISTAS

Measured 120 46 5 21 3 22 2

IMPROVE 106 47 4 21 3 23 2

NEW 127 47 3 22 3 22 2

WRAP

Measured 36 15 6 27 7 42 3

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 A-module 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 under-estimated because ammonium is lighter than sodium. The

scattering is also under-estimate 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-3recommended 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 Factoraf(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/Guid

ancedocs.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. El-Zanan 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 visibilityreducing 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 m

2

/g), ammonium nitrate (2.4 and 5.1 m

2

/g), and 

OM (2.8 and 6.1 m

2

/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 smalland 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/Guidanced

ocs.Htm.

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 site-specific Rayleigh scattering calculated using this procedure. The 

recommended integer-rounded site-specific values are shown in the last column 

of the table. They range from 12Mm-1for sites near sea level to 8Mm-1for 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()) for viewing an image of 2

o

angular size and the spectral 

NO2 absorption efficiency values (AE()) by the sum of the photopic response 

values, as shown in the equation below. 



 

 750

350

750

350

( )

( ) ( )

2



 

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://www-cvrl.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 rounded to 

whole integer values (last column).

Monitoring Site Name Elevation 

(ft)

LatitudeLongitudeStandard 

U.S. 

Atmospher 

Temp. (C)

Standard 

U.S. 

Atmosphere 

Pressure 

(mb)

Rayleigh 

at 

Standard 

Atmosphere 

(Mm

-1

)

Annual 

Average 

Temp. (C)

Corrected 

Rayleigh 

(Mm

-1

)

Corrected 

Rayleigh 

rounded 

to integer 

(Mm

-1

)

Acadia National Park 492 44.4 68.3 14.2 997 11.5 7.4 11.8 12

Addison Pinnacle 1732 42.1 77.2 11.8 951 11.0 7.1 11.2 11

Agua Tibia 1663 33.5 117 11.9 954 11.1 16.7 10.9 11

Arches National Park 5648 38.8 109.6 4 820 9.8 10.0 9.6 10

Arendtsville 879 39.9 77.3 13.5 983 11.3 10.4 11.5 11

Badlands National Park 2414 43.7 101.9 10.4 927 10.8 10.3 10.8 11

Bandelier National 

Monument

6517 35.8 106.3 2.3 793 9.5 8.7 9.3 9

Big Bend National Park 3526 29.3 103.2 8.2 889 10.5 19.4 10.1 10

Bliss State Park (TRPA) 6940 39 120.1 1.4 780 9.4 4.5 9.3 9

Bondville 692 40.1 88.4 13.8 990 11.4 11.0 11.5 12

Bosque del Apache 4536 33.9 106.9 6.2 855 10.1 14.0 9.9 10

Boundary Waters Canoe 

Area

1719 47.9 91.5 11.8 952 11.1 2.4 11.4 11

Breton 7 29.1 89.2 15.2 1016 11.7 21.0 11.4 11

Bridger Wilderness 8551 43 109.8 -1.7 734 9.0 2.0 8.8 9

Bridgton 794 44.1 70.7 13.6 986 11.4 6.1 11.7 12

Brigantine National 

Wildlife Refuge

16 39.5 74.4 15.2 1015 11.6 12.7 11.8 12

Brooklyn Lake 10483 41.4 106.2 -5.6 682 8.4 0.5 8.2 8

Bryce Canyon National 

Park

8125 37.6 112.2 -0.9 746 9.1 4.1 8.9 9

Cabinet Mountains 4704 48 115.7 5.9 850 10.1 3.2 10.2 10

Cadiz 617 36.8 87.9 14 992 11.4 13.8 11.4 11
Caney Creek 2263 34.5 94.1 10.7 932 10.9 13.1 10.8 11

Canyonlands National 

Park

5901 38.5 109.8 3.5 812 9.7 11.5 9.49

Cape Cod 134 42 70 14.9 1011 11.6 9.8 11.8 12

Cape Romain National 

Wildlife Refuge

10 32.9 79.7 15.2 1016 11.7 17.9 11.5 12

Capitol Reef (CAPI1) 6199 38.3 111.3 2.9 803 9.6 9.2 9.49

Capitol Reef 

(CARE1)+F76

6199 38.3 111.3 2.9 803 9.6 9.2 9.49

Casco Bay 49 43.8 70.1 15.1 1014 11.6 7.6 11.9 12

Chassahowitzka National 

Wildlife

7 28.7 82.6 15.2 1016 11.7 21.4 11.4 11

Chiricahua National 

Monument

5150 32 109.45 835 9.9 15.5 9.6 10

Cohutta 2437 34.8 84.6 10.4 926 10.8 11.9 10.8 11

Columbia Gorge 738 45.6 122.2 13.7 988 11.4 11.6 11.5 11

Columbia River Gorge 659 45.7 121 13.9 991 11.4 10.5 11.6 12

Connecticut Hill 1656 42.4 76.7 11.9 954 11.1 7.0 11.3 11

Crater Lake National Park 6439 42.9 122.1 2.4 795 9.6 3.2 9.5 10

Craters of the Moon 

NM(US DOE)

5960 43.5 113.6 3.4 810 9.7 6.2 9.6 10

Death Valley Monument 410 36.5 116.8 14.4 1000 11.5 22.6 11.2 11

Denali National Park 2158 63.7 149 10.9 936 10.9-3.0 11.5 11

Dolly Sods /Otter Creek 

Wilderness

3798 39.1 79.4 7.7 879 10.4 6.6 10.4 10

Dome Lands Wilderness 2942 35.7 118.2 9.4 909 10.6 15.0 10.4 10

Dome Lands Wilderness 3034 35.7 118.1 9.2 905 10.6 15.3 10.4 10

Everglades National Park 10 25.4 80.7 15.2 1016 11.7 23.7 11.3 11

Gates of the Mountains 7846 46.8 111.7-0.4 754 9.2-1.1 9.29

Gila Wilderness 5825 33.2 108.2 3.7 814 9.7 10.6 9.5 10

Glacier National Park 3211 48.5 114 8.8 899 10.6 5.7 10.7 11

Great Basin National Park 6783 39 114.2 1.8 785 9.5 8.8 9.29
Great Gulf Wilderness 1460 44.3 71.2 12.3 961 11.1 5.0 11.4 11

Great Sand Dunes 

National Monument

8213 37.7 105.5-1.1 744 9.0 6.0 8.89

Great Smoky Mountains 

National Park

2673 35.6 83.9 9.9 918 10.7 11.2 10.7 11

Guadalupe Mountains 

National Park

5491 31.8 104.8 4.3 825 9.8 15.2 9.59

Haleakala National Park 3798 20.8 156.3 7.7 879 10.4 15.0 10.1 10

Hance Camp at Grand 

Canyon NP

7436 36 112 0.5 766 9.3 6.6 9.19

Hawaii Volcanoes 

National Park

3949 19.4 155.3 7.4 874 10.3 16.3 10.0 10

Hells Canyon 2050 45 116.8 11.1 940 10.9 11.5 10.9 11

Hercules-Glades 1394 36.7 92.9 12.4 964 11.2 13.7 11.1 11

Hillside 4953 34.4 113 5.4 842 10.0 9.9 9.8 10

Hoover 8416 38.1 119.2-1.5 738 9.0 3.5 8.89

Hopi Point #1 7098 36.1 112.2 1.1 776 9.4 7.6 9.19

Hopi Point #2 (High 

Sensitivity)

7098 36.1 112.2 1.1 776 9.4 7.6 9.19

Ike's Backbone 4274 34.3 117.9 6.7 864 10.2 14.0 10.0 10

Indian Gardens 3824 36.1 112.1 7.6 879 10.4 16.7 10.0 10

Indian Gardens 2 (High 

Sensitivity)

3824 36.1 112.1 7.6 879 10.4 16.7 10.0 10

Isle Royale National Park 699 47.9 89.2 14 993 11.4 3.5 11.8 12

Isle Royale National Park 

(New)

610 47.5 88.1 13.8 989 11.4 3.7 11.9 12

James River Face 981 37.6 79.5 13.3 979 11.3 13.0 11.3 11

Jarbidge Wilderness 6173 41.9 115.43 803 9.6 7.1 9.59

Jefferson/James River 

Face Wilderness

918 37.7 79.4 13.4 981 11.3 13.1 11.3 11

Joshua Tree 4028 34.1 116.4 7.2 872 10.3 14.9 10.0 10

Joshua Tree National 

Monument

4028 34.1 116.4 7.2 872 10.3 14.9 10.0 10

Kaiser 8439 37.2 119.2-1.5 737 9.0 3.4 8.89
Kalmiopsis 295 42.6 124.1 14.6 1005 11.5 13.1 11.6 12

Lassen Volcanic National 

Park

5756 40.5 121.6 3.8 816 9.8 5.3 9.7 10

Lava Beds 4818 41.7 121.5 5.7 846 10.0 8.7 9.9 10

Linville Gorge 3234 36 81.9 8.8 898 10.5 10.0 10.5 11

Livonia 977 38.5 86.3 13.3 979 11.3 12.2 11.4 11

Lone Peak Wilderness 5799 40.4 111.7 3.7 815 9.7 9.1 9.6 10

Lostwood 2270 48.6 102.4 10.7 932 10.9 3.2 11.2 11

Lye Brook Wilderness 3300 43.1 73.1 8.7 896 10.5 2.4 10.8 11

Lynden 92 49 122.6 15 1013 11.6 10.1 11.8 12

M.K. Goddard 1269 41.4 80.1 12.7 968 11.2 9.0 11.4 11

Mammoth Cave National 

Park

813 37.1 86.1 13.6 985 11.4 13.7 11.4 11

Mauna Loa National 

Observatory (MALO1)

11152 19.5 155.6-6.9 665 8.3 7.0 7.98

Meadview 2959 36 114.1 9.3 908 10.6 16.2 10.4 10

Medicine Lake 1984 48.5 104.5 11.3 942 11.0 4.8 11.2 11

Mesa Verde National 

Park

7141 37.2 108.51 774 9.4 8.7 9.19

Mingo 367 37 90.1 14.5 1002 11.5 14.3 11.5 12

Mohawk Mt. 1745 41.8 73.3 11.7 951 11.0 6.3 11.3 11

Monture 4241 47.1 113.2 6.8 865 10.2 3.5 10.3 10

Moosehorn NWR 308 45.1 67.3 14.6 1004 11.5 6.0 11.9 12

Mount Baldy 8243 34.1 109.4-1.1 743 9.0 7.6 8.89

Mount Hood 4398 45.3 121.8 6.5 860 10.2 4.3 10.3 10

Mount Rainier National 

Park

1401 46.8 122.1 12.4 963 11.2 8.9 11.3 11

Mount Zirkel Wilderness 10637 40.5 106.7-5.9 678 8.4 0.2 8.28

Mount Zirkel Wilderness 

(Storm Peak)

10562 40.5 106.7-5.7 680 8.4 0.5 8.28

North Absaroka 8134 44.7 109.4-0.9 746 9.1-0.9 9.19

North Cascades 1889 48.7 121.1 11.5 946 11.0 7.2 11.2 11
Okefenokee National 

Wildlife Refuge

161 30.7 82.1 14.9 1010 11.6 20.3 11.4 11

Old Town 213 44.9 68.6 14.8 1008 11.6 6.2 11.9 12

Olympic 1968 48 123 11.3 943 11.0 10.9 11.0 11

Organ Pipe 1847 32 113 11.5 947 11.0 21.0 10.7 11

Pasayten 5360 48.4 119.9 4.6 829 9.9 4.8 9.9 10

Petrified Forest National 

Park

5796 35.1 109.8 3.7 815 9.7 11.7 9.59

Phoenix 1109 33.5 112.1 13 974 11.3 23.4 10.9 11

Pinnacles National 

Monument

1040 36.5 121.2 13.1 980 11.3 16.0 11.2 11

Point Reyes National 

Seashore

279 38.1 122.9 14.6 1005 11.6 11.3 11.7 12

Presque Isle 535 46.7 68 14.1 996 11.5 4.811.8 12

Proctor Maple R. F. 1322 44.5 72.9 12.6 966 11.2 4.5 11.5 12

Puget Sound 262 47.6 122.3 14.7 1006 11.6 10.7 11.7 12

Quabbin Summit 1033 42.3 72.3 13.2 977 11.3 6.7 11.6 12

Quaker City 1233 39.9 81.3 12.8 970 11.2 9.8 11.3 11

Queen Valley 215833.3 111.3 10.9 936 10.9 21.8 10.5 11

Redwood National Park 804 41.6 124.1 13.6 985 11.4 8.7 11.6 12

Rocky Mountain National 

Park

9036 40.3 105.5-2.7 721 8.8 1.9 8.79

Rocky Mountain National 

Park (Headquarters)

7872 40.4 105.6-0.4 753 9.1 5.5 8.99

Saguaro National 

Monument

3060 32.2 110.7 9.1 904 10.6 18.1 10.3 10

Saguaro West 2355 32.2 111.2 10.5 929 10.8 21.5 10.4 10

Salmon National Forest 9145 45.2 114-2.9 718 8.8-7.6 8.99

Salt Creek 3533 33.5 104.4 8.2 888 10.5 15.3 10.2 10

San Andres 4674 32.7 106.5 5.9 851 10.1 13.3 9.8 10

San Gabriel 5874 34.3 118 3.6 813 9.7 13.1 9.49

San Gorgonio Wilderness 5592 34.2 116.9 4.1 821 9.8 12.0 9.5 10
San Pedro Parks 9574 36 106.8

-3.8 706 8.7-0.3 8.69

San Rafael 3126 34.7 1209 902 10.6 16.7 10.3 10

Sawtooth National Forest 6494 44.2 114.9 2.3 794 9.5 1.1 9.6 10

Scoville 4920 43.7 113 5.4 843 10.0 6.1 10.0 10

Seney 708 46.3 85.9 13.8 989 11.4 5.8 11.7 12

Sequoia National Park 1755 36.5 118.8 11.7 950 11.0 17.0 10.8 11

Shenandoah National 

Park

3601 38.5 78.4 8.1 886 10.4 8.2 10.4 10

Shining Rock Wilderness 5317 35.4 82.8 4.7 830 9.9 7.4 9.8 10

Sierra Ancha 5232 34.1 110.9 4.8 833 9.9 12.5 9.7 10

Sikes 148 32.1 92.4 14.9 1010 11.6 18.2 11.5 11

Simeonof 98 55.3 160.5 15 1012 11.6 4.5 12.1 12

Sipsy Wilderness 915 34.3 87.3 13.4 981 11.3 15.5 11.2 11

Snoqualamie Pass, 

Snoqualamie N.F

3805 47.4 121.4 7.7 879 10.4 3.2 10.5 11

South Lake Tahoe 6232 38.9 120 2.8 802 9.6 6.1 9.5 10

Spokane Res. 1797 47.9 117.9 11.6 949 11.0 10.0 11.1 11

St. Marks7 30.1 84.2 15.2 1016 11.7 19.8 11.5 11

Starkey 4126 45.2 118.57 869 10.3 4.2 10.4 10

Sula (Selway Bitteroot 

Wilderness)

6242 45.9 114 2.8 801 9.6-0.5 9.7 10

Swanquarter7 35.5 76.2 15.2 1016 11.7 16.9 11.6 12

Sycamore Canyon 6691 35.1 112 1.9 788 9.5 9.8 9.29

Theodore Roosevelt 2798 46.9 103.4 9.7 914 10.7 5.1 10.9 11

Three Sisters Wilderness 2903 44.3 122 9.4 910 10.7 6.8 10.8 11

Tonto National Monument 2578 33.6 111.1 10.1 921 10.8 18.7 10.4 10

Trapper Creek 479 62.3 150.3 14.3 998 11.5 2.3 12.0 12

Trinity 3303 40.8 122.8 8.7 896 10.5 12.9 10.4 10

Tuxedni 33 60 152.6 15.1 1015 11.6 1.8 12.2 12

UL Bend 2929 47.6 108.7 9.4 909 10.6 5.2 10.8 11
Upper Buffalo Wilderness 2371 35.8 93.2 10.5 928 10.8 12.5 10.8 11

Virgin Islands National 

Park

210 18.3 64.8 14.8 1008 11.6 26.1 11.1 11

Voyageurs National Park 11138 48.6 93.2 12.9 973 11.3 3.1 11.7 12

Voyageurs National Park 21407 48.4 92.8 12.4 963 11.2 2.6 11.6 12

Washington D.C. 52 38.9 77 15.1 1014 11.6 14.5 11.7 12

Weminuche Wilderness 9069 37.7 107.8-2.8 720 8.8 1.5 8.79

Wheeler Peak 11060 36.6 105.5-6.7 667 8.3-3.0 8.28

White Mountain 6724 33.5 105.5 1.9 787 9.5 9.9 9.29

White Pass 6002 46.6 121.4 3.3 809 9.7 1.4 9.8 10

White River National 

Forest

11211 39.2 106.8-7 663 8.2-0.3 8.08

Wichita Mountains 1699 34.7 98.7 11.8 952 11.1 15.7 10.9 11

Wind Cave 4264 43.6 103.5 6.7 864 10.2 8.1 10.2 10

Yellowstone National 

Park 1

7744 44.6 110.4-0.1 757 9.2-0.2 9.29

Yellowstone National 

Park 2

7954 44.6 110.4-0.6 751 9.1-0.8 9.19

Yosemite National Park 5297 37.7 119.7 4.7 831 9.9 8.0 9.8 10

Zion 5068 37.5 113.2 5.2 838 10.0 13.2 9.7 10
Scatter Plot for Big Bend using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for Big Bend using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for BOWA using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for BOWA using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
Scatter Plot for UPBU using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Pre

dicte

d

Bsp

Scatter Plot for UPBU using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for ACAD using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for ACAD using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
Scatter Plot for GRGU using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for GRGU using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for LYBR using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for LYBR using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
Scatter Plot for DOSO using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for DOSO using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for GRSM using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for GRSM using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
Scatter Plot for MACA using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for MACA using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for OKEF using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for OKEF using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
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

Scatter Plot for SHRO using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for SHRO using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
Scatter Plot for CORI using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for CORI using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for GICL using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for GICL using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
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

Scatter Plot for JARB using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for JARB using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
Scatter Plot for LOPE using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for LOPE using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for MORA using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for MORA using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
Scatter Plot for MOZI using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for MOZI using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for SNPA using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for SNPA using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp
Scatter Plot for THSI using IMPROVE Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

Predicted

Bsp

Scatter Plot for THSI using New Algorithm

1

10

100

1000

1 10 100 1000

Measured Bsp

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

ACAD1

Measured 6.3 20 3 21 4 50 3

IMPROVE 8.6 20 3 21 4 49 3

NEW 7.7 20 3 22 4 49 3

BIBE1

Measured 6.3 17 2 11 10 59 2

IMPROVE 8.7 19 2 11 9 57 2

NEW 7.9 18 2 11 9 57 2

BOWA1

Measured 5.4 26 3 21 5 43 2

IMPROVE 7.7 24 2 23 5 44 2

NEW 6.6 26 3 20 5 44 2

CORI1

Measured 5.7 10 3 17 5 61 3

IMPROVE 8.8 9 4 18 4 62 3

NEW 8.4 10 4 18 5 60 4

DOSO1

Measured 15 26 5 20 4 42 3

IMPROVE 20 24 6 20 4 42 3

NEW 19 25 6 19 4 42 3

GICL1

Measured 2.9 16 2 19 8 52 3

IMPROVE 4.9 16 2 20 8 50 3

NEW 4.1 17 2 20 8 48 3

GRCA2

Measured 2.0 14 3 15 9 55 2

IMPROVE 2.8 18 4 16 11 48 3

NEW 2.5 18 5 15 10 48 3

GRGU1

Measured 5.4 26 3 23 2 43 2

IMPROVE 8.0 25 4 21 3 44 3

NEW 6.8 25 4 22 3 44 3

GRSM1

Measured 15 24 6 23 4 39 4

IMPROVE 20 24 6 24 4 38 4

NEW 20 25 7 23 3 38 4

JARB1

Measured 1.8 11 2 21 8 56 2

IMPROVE 3.6 12 3 22 7 54 2

NEW 3.2 12 3 22 7 54 2

LOPE1

Measured 5.3 12 6 19 9 50 4

IMPROVE 8.2 12 6 18 10 50 4

NEW 7.2 13 7 18 10 49 4

LYBR1

Measured 5.3 24 6 25 4 38 3

IMPROVE 8.2 24 4 27 4 38 3

NEW 7.0 25 5 26 4 38 3

MACA1

Measured 18 27 7 21 4 37 3

IMPROVE 22 25 9 21 4 36 4

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

MOZI1 Measured 2.8 12 3 18 8 57 2
IMPROVE 4.8 13 4 18 8 55 3

NEW 4.1 13 4 17 7 56 3

OKEF1

Measured 19 21 3 19 3 50 3

IMPROVE 23 20 3 19 3 52 3

NEW 21 22 3 20 3 49 3

SHEN1

Measured 11 27 10 18 4 39 3

IMPROVE 14 26 10 19 4 38 3

NEW 13 26 10 19 4 38 3

SHRO1

Measured 4.6 16 2 21 6 53 3

IMPROVE 8.9 19 2 22 4 50 3

NEW 8.0 19 2 21 4 51 3

SNPA1

Measured 7.3 14 5 22 5 49 4

IMPROVE 10.0 13 6 26 5 44 5

NEW 8.7 15 7 25 5 43 5

THSI1

Measured 5.4 10 2 23 4 58 3

IMPROVE 7.1 12 3 23 5 54 3

NEW 6.4 11 3 22 4 57 3

UPBU1

Measured 13 15 4 16 5 57 2

IMPROVE 14 15 6 17 5 55 2

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

ACAD1

Measured 58 34 6 22 3 32 3

IMPROVE 59 34 6 21 3 33 3

NEW 62 34 6 22 3 33 3

BIBE1

Measured 49 34 2 16 7 39 2

IMPROVE 44 33 2 15 8 41 2

NEW 46 33 2 15 8 41 2

BOWA1

Measured 53 25 13 23 4 32 2

IMPROVE 49 27 13 22 4 32 2

NEW 45 23 13 23 4 35 2

CORI1

Measured 56 13 11 25 5 43 3

IMPROVE 48 13 10 24 5 45 3

NEW 48 12 10 24 5 46 3

DOSO1

Measured 109 44 2 21 3 28 2

IMPROVE 105 47 2 21 3 26 2

NEW 121 46 1 20 3 29 2

GICL1

Measured 25 18 2 25 8 44 2

IMPROVE 24 19 2 26 8 43 2

NEW 23 18 2 26 9 44 2

GRCA2

Measured 19 16 3 19 11 50 2

IMPROVE 19 11 2 18 10 57 1

NEW 18 11 2 18 10 57 1

GRGU1 Measured 58 35 3 24 3 33 3
IMPROVE 56 36

4 253 303

NEW 59 353 263 303

GRSM1

Measured 153 521 223 202

IMPROVE 125 511 223 212

NEW 163 521 223 212

JARB1

Measured 2483 21 12 542

IMPROVE 2173 20 12 571

NEW 2173 21 12 571

LOPE1

Measured 35 13 14 209 413

IMPROVE 33 12 15 209 413

NEW 31 11 13 219 423

LYBR1

Measured 83 428 234 213

IMPROVE 75 447 214 213

NEW 77 456 224 213

MACA1

Measured 106 42 11 233 193

IMPROVE 96 429 223 213

NEW 102 427 243 213

MORA1

Measured 42 203 375 314

IMPROVE 42 234 355 304

NEW 42 203 375 324

MOZI1

Measured 24 154 238 472

IMPROVE 21 154 228 492

NEW 20 133 249 492

OKEF1

Measured 87 313 174 432

IMPROVE 75 293 186 422

NEW 76 313 175 422

SHEN1

Measured 95 464 204 233

IMPROVE 94 485 203 222

NEW 108 473 213 242

SHRO1

Measured 77 365 207 293

IMPROVE 63 353 187 353

NEW 62 363 206 323

SNPA1

Measured 46 177 395 265

IMPROVE 36 177 356 315

NEW 37 156 376 325

THSI1

Measured 40 123 405 383

IMPROVE 34 123 365 413

NEW 37 122 385 403

UPBU1

Measured 123 423 205 272

IMPROVE 104 413 206 292

NEW 124 432 215 282