# Re **vi sed IMPROVE**  **Al gorithm for Estimating Light** 

**Extinction from Particle Speciation Dat a**  **I. Introduction**  Light Extinction Atmospheric light extinction is a fundamental metric used to characterize air pollution impact s on visibility. It is the fractional l oss of intensity in a light beam per unit distance due to scattering and absorption by the gases and particles in the air. Light extinction ( b ext 

### ) can be expressed as the sum of light scattering by particles ( b s,p 

### ), scattering by gases ( b s,g 

### ), absorption by particles ( b a,p 

### ) and absorption by gases ( b a,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 atmosphe re is primarily by NO 2 

### , and can be di rectly and accurately estimated from NO 2 

### 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 atmospher e , with highest values at sea level (about 12Mm - 1 

### ) and diminishing with elevation ( 8Mm - 1 

### at about 1 2 ,000’) , and varies somewhat at any elevation due to atmospheric temperature and pressure variations

. Rayleigh scattering ca n 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 on ly commonly occurring airborne particle components that absorb light

. A ll 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 i s 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 pla ce 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 PM 10 

### and PM 2.5 

### as well as most of the PM 2.5 

### component concentrations on a one day in three schedule

. T hese data are routinely available at each IMPROVE

* Light is a waveleng th

- dependent portion of the electro

- ma gnetic spectrum. T raditional ly for visibility

- protection applications, the mo st sensitive portion of the spectrum for human vision (550nm) has been used to characterize light extinction and its components . For NO 2 li ght 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) , hourl y - av eraged 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 al gorithms. The current IMPROVE algorithm for estimating light extinction from IMPROVE particle monitoring data assumes that absorption by gases ( b a,g 

### ) is zero, that Rayleigh scattering ( b s,g 

### ) is 10Mm - 1 

### for each monitoring site regardless of site elevation and meteorological condition, and that particle scattering and absorption ( b s,p 

### and b a,p 

### ) can be estimated by multiplying the concentrations of each of six major components by typical component

- specific light extinction efficiencies. The six major compone nts 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 PM 10 

### and PM 2.5 

### 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 cur rent 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/ 10 6 

### m usually written Mm - 1 

### ); component concentrations shown in brackets are in microgram per meter cubed ( μg/m 3 

### ); dry efficiency terms are in units of meters squared per gram (m 2 

### /g); and the water growth terms, f(RH), are unitless.
A mong the implicit assumption s for this formulation of the algorithm are that

 the six particle component term s plus a constant R ayleigh scattering term are sufficient for a good estimate of light extinction; 

#             10 6

. 0 1 10 4 ) ( 3 ) ( 3

              

Mass Coarse Soil Fine Carbon Elemental Mass Organic Nitrate RH f Sulfate RH f b ext

###  constant dry extinction efficiency term s rounded to one significant digit for each of the six particle component s (i.e. for both sulfate and nitrate the value is 3) works adeq uately for all location s 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 th e light absorbing ca rbon and Rayleigh terms) ha ve been compared to directly

- measured particle scattering data at the 2 1 monitoring sites that have hourly

- averaged nephelometer and relative humidity data . As shown in F igure 1 below , the algorithm performs reasonably well over a broad range of particle light scattering values and monitoring locations. T he 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 I** **M P** **R O** **V E** **B s** **p**

**Figure 1**  **. A**

**scatter plo t 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 b ext 

### ). The RHR then calls for the determination of the mean of the annual 2 0% best and 20% worst haze da ys for each of the IMPROVE monitoring sites that represent the visibility

- protected areas. States are asked to manage emissions so that o ver a 60

- year pe riod the worst haze days will improve to natural conditions without degrading visibility conditions fo r 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 l evels 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 condition s by 2064 mu st be determined . This linear rate is used as a guide to pace the desire d 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 t he 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 elic ited concerns about possible biases in the apportionment among the various major particle components. Such issues have been the subject of a number of critical review s 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 Nati onal 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 pos sible refine ments . 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 routine ly available from the IMPROVE particle monitoring network. A preliminary report by this team was presented in June, 200 5 at a national Regional Planning Organization workshop in Denver Colorado hosted by the W estern Regional Air Partnership ( WR AP) and bro adly participated in by those with an interest in the regional haze rule. The preliminary

### results were also summarized in presentations at the July 200 5 IMPROVE

Steering Committee meeting in Acadia Maine. The full report of this review is available elsewh ere ( 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 200 5 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 th is document is to describe the subcommitte e’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 Committ ee 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 appr oach. 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://v ista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm Lowenthal, D.H. and Kumar, N., 2003: PM 2.5 

### 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 member s concurring in these recommendations are Wi lliam Malm and Bret Schichtel, NPS; Marc Pitchford, NOAA; Naresh Kumar, EPRI; Douglas Lowenthal, DRI; and Jenny Hand, CIRA

# II. **Ov er**  **vi ew**  **of the Re vi**  **sed Al**  **gorithm**

The recommended revised algorithm is shown in the equation below with revised term s 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 co ncentration 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 NO 2 

### (only used where NO 2 

### 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 T able A at the end of t he document

.

The apportionment of the total concentration of sulfate compounds into the concentrations of the small and large size fractions is accomplish ed 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 [ Chlorid e ] , or 1.8 x [ Chlorine ] if the chloride measurement is below detection l imits, missing or invalid

. The algorithm uses three water growth adjustment term as shown in the F igure 2 and T able 1 . They



#                       p p b)( 

### N O 0

. 3 3 S p e c i f i c) 

### i t e S( 

### S c a t t e r i n g R a y l e i g h S a l t

S e a R H)( 

### f 1

. 7 M a s s O r g a n i c a r g e L 6

. 1 M a s s O r g a n i c S m a l l 2

. 8 N i t r a t e a r g e L R H)( 

### f 5

. 1 N i t r a t e S m a l l R H)( 

### f 2

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

### f 4

. 8 S u l f a t e S m a l l R H)( 

### f 2

. 2

2

S S L S L S



###                            

Mass

Coarse Soil Fine Carbon Elemental b ext



### 6

. 0 1 10



#       3 

### / 20 , rg a m g Sulfate Total for Sulfate Total Sultate e L    

#         3 3 

### / 20 , / 20 arg m g Sulfate Total for Sulfate Total m g Sulfate Total Sulfate e L 

 

 



#       Sulfate e L Sulfate Total Sulfate Small arg

 

### are for use with the small size distribution and the large size distribution sulfate

and nitrate compounds and for sea salt ( f S 

### (RH ), f L 

### (RH) and f

SS 

### (RH) respectively)

.

**Water Grow**

**th C**  **urves**

0 1 2 3 4 5 6 7 8 9 10

0 10 20 30 40 50 60 70 80 90 100 **Rel at**  **ive Humidity (% )** **f (** **R H** **)** fS(RH) fL(R H) Ori gin al fSS(R H)

**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 s ize distribution sulfate and nitrate , an sea salt



### . **RH (%) f** 

**S (RH)**  **f L**  **(RH) f**  **SS (RH)**  **RH (%) f**  **S (RH)**  **f L**  **(RH) f**  **SS (RH)**  **RH (%) f**  **S (RH)**  **f L**  **(RH) f**  **SS (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 es timates 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 cur rent 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 b sp 

### minus the measured b sp 

### ) divided by the measured light scattering. T hese 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 algori thm 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 determin ed 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** 

**. A** 

**verage d**  **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 A verage 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 WR AP 1.07 0.37 0.18 0.07 **- 0.08**  0.32


### Table 3

**. A** 

**verage d**  **fractional bias**  **by RPO**  **for the**  **current new proposed algorithm**  **with quintiles base**  **d 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 0.1**  **4 0.06**  **0.01**

- 0.02 **0.17** VISTAS **0.50 0.16**  0.11 0.06 **- 0.01**  0.16 WR AP **0.84 0.25**  **0.08 0.01** 

- 0.10 **0.21**

### Table 4

**. A**  **verage d**  **fractional bias**  **by RPO**  **for the**  **current IMPROVE algorithm**  **with quintiles based on estimated**  **light scattering ( criterion 2**  **)**

**. Bold font hig**  **hlights 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 WR AP 0.58 0.37 0.26 0.25 **0.15** 0.32


### Table 5

**. A** 

**verage d**  **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 WR AP **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 criteri on 1, the two algorithms perform about the same for haziest days ex cept for the sites in the south eastern 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 fo r 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 sampl e periods for the new algorithm compared with the current algorithm. Scatter plots (Figures 1 and 3) of light scatter ing estimates from the current and new proposed algorithms vers us 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 p recision 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 S** **p l** **i t** **C o** **m p** **o n** **e n** **t M** **e t** **h o** **d B** **s p** 

**Figure 3**  **. Scatter plot of the recommended revised algorithm est**  **imates 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 p lot s for Shenandoah and Grand Canyon National Parks . T he logarithmic scales on these plots exaggerate the scatter for low values compared to high values. The individual

site scatter plots have the 80 th 

### percentile values indicated on the graphs for the predicted and measured values by horizontal and vertical l ines 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 amo ng the 20% worst estimated light scattering for that monitoring site.

**Scatte r Plot for SHEN using IM**  **PROV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asured Bsp** **P r** **e d** **i c** **t e** **d B** **s p**  **Sc atte**  **r Plot for SHEN using Ne w A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Measured Bsp P** **r e** **d i** **c t** **e d** **B s** **p**

**Figure 4. Scatter plots of the current IMPROVE and new recommended algorithm**  **estimates of light scattering versus measured light scattering for Shenandoah National Park. Horizon**  **tal and vertical lines are at the 80 th**  **percentile for estimated and measured light scattering.** **Scatte r Plot for G**  **RCA using IM**  **PROV**

**E A lgor**  **ithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for GRCA using Ne**  **w Al**  **gorithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bs p** **P r** **e d** **i c** **t e** **d B** **s p** 

**Figure 5. Scatter plots of the current IMPROVE and new recommended algorithm**  **estimates of light scattering versus measured light scattering for Grand Canyo n National**  **Park. Horizontal and vertical lines are at the 80 th**  **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 bo th algorithms (these are the points above and to the right of the two 80 th 

### 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 scatteri ng. Table 6 and 7 co ntain 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 show n in tables in the appendix.

**T able 6.**  **Mean light scattering**  **and percent PM**  **2.5** 

### composition for the **five major components for 20% best day**  **s 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 NE W 8.1 21 3 16 7 51 2MANEVU Measured 6.1 22 3 22 4 47 3 IMPROVE 8.4 21 3 22 4 47 3 NE W 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 NE W 17.0 25 8 21 4 39 3

WR AP Measured 3.4 13 3 18 8 55 3

IMPROVE 5.2 14 3 19 8 53 3 NE W 4.5 15 3 19 8 52 3


### Table 7. **Mean light scattering**  **and percent PM**  **2.5** 

### composition for the **five major components for 20%**  **wo rst**  **day s**  **as determined b y**  **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 NE W 72 34 6 19 6 34 2

MANEVU Measured 61 36 6 23 3 30 3

IMPROVE 61 36 6 22 3 30 3 NE W 63 35 6 23 3 31 3

VISTAS Measured 120 46 5 21 3 22 2

IMPROVE 106 47 4 21 3 23 2 NE W 127 47 3 22 3 22 2

WR AP Measured 36 15 6 27 7 42 3

IMPROVE 3 3 14 6 27 6 44 3 NE W 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 algorithm s 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 algor ithm 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 simila r , 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 variab le 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 atm osphere 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 NO 2 

### terms), and use of site specific Rayle igh 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 recommend ed a pproach . 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 rat**  **io** from 1.4 to 1.8 to reflect more recent peer - reviewed literature on the subject;

 use of **site -**  **specific Ray leigh scattering**  based on the elevation and annual average temperature of the monitoring sites;

 development and use of a **split component extinction e fficiency**  **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 **NO 2**  

### light absorption term that woul d only be used at sites with available NO 2 

### 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 ( b sp 

### ) 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 particle s to light extinction at some coastal IMPROVE sites may be significant, especially since b sp 

### by sea salt particles should be significantly enhanced by hygroscopic growth in humid environments. Lowenthal and Kumar (2005) found that fine sea salt aerosols ac counted for 43% of estimated b sp 

### 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 S alt M ass Concentration Estimating sea salt mass requires a sea salt marker species measured in I MPROVE 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). Wi tho ut a ccurate 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 PM 10 

### sample (Module D of the IMPR OVE sampler), it is not possible to estimate coarse sea salt scattering. Given these limitations, it is recommended that the PM 2.5 

### 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 PM 2.5 

### 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 PM 10 

### , coarse sea salt mass and light scattering will not be c onsidered. To the degree that chloride has been replaced by sulfate or nitrate in ambient particles , this approach will underestimate the mass and scatterin g contribut ed by the substituted sea salt that results (e.g. NaNO 3 

### , NaHSO 4 

### , or Na 2 

### SO 4 

### ). 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 fin e 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. Wh ile this has not been measured at most IMPROVE sites, extensive sea salt siz e 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

(D g 

### ) of 2.5 μm an d geometric s tandard deviation (σ g 

### ) of 2 is recommended. A dry scattering efficiency for PM 2.5 

### sea salt of 1.7 m 2 

### /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 

### recommend ed 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. Th e 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 t o RH = 95% , to which higher RH are “rolled back” under the Regional Haze Rule protocol (USEPA, 2001). Dry (RH=0%) light scattering ( b sp (Dry) 

### ) was calculated using Mie theory for sea salt at unit PM 2.5 

### mass concentration with the dry mass size distribution , refractive index, and density described above. Light scattering at RH = 46

- 95% at unit RH intervals ( b sp (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 par ticle volume and refractive index from the addition of water

. The f(RH) values , defined as b sp (RH) 

### / b sp (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 s ulfate/ammonium nitrate. Light scattering by sea salt (SS) aerosols is estimated as:

b sp (SS) 

### = 1.7 f

SS 

### (RH) [1.8 * Cl]. Table 8 . Sea Salt particle diameter growth and water growth function. RH (%) Growth Factor a 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 4.857365 1.7629 2.8594 90 2.4174 5.1165 66 1.7751 2.8943 91 2.4898 5.3844 67 1.78 77 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 Diam eter at RH/Dry Diameter


### REFERENCES Clegg, S.L.; Brimblecombe, P.; W exler, A.S., A Thermodynamic Mod el of the System H +



### - NH 4 +



### - Na +



### - SO 4 2

-



### - NO 3

-



### - Cl -



### - H 2 

### O at 298.15 K. J. Phys. Chem. , **1998** , 102 , 2155

- 2171

. Lowenthal, D.H.; Kumar, N. PM 2.5 

### Mass and Light Extinction Reconstruction in IMPROVE; J. Air & Waste Manage. Assoc. , **2003** , 53, 1109

- 1120. Lowenthal, D.H.; Ku mar, 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 Partic les 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 Wa ter; 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. , **199 8**  , 10 3 , 16,547

- 16,563. Tang, I.N.; Tridico, A.C.; Fun g, 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 Haz e Rule ; U.S. Environmental Protection Agency, Office of Air Quality Planning an d 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 equa tion” 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%. Wh ite 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 Visibi lity 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 or ganics 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 primar y 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 PM 2.5 

### 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 i mpacts from forest fires in Quebec were observed. Malm et al. (2005) found that PM 2.5 

### 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) d erived 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.
Wh ile 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., M ézáros, E., Molnár, A., Hoffer, A., Mézáros, T., Sárvári, Z., Temesi, D., Varga, B., Baltensperger, U., Nyeki, S., and We ingartner, 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 dur ing 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&W MA 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 PM 2.5 

### mass concentrations: revisit ing common assumptions for estimating organic mass. Aerosol. Sci. Technol., **35** , 602

- 610. Wh ite, 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 Com ponent Extinction Efficiency Model
Concentration

- Varying Dry Scattering Efficiencies The current IMPROVE algorithm employs dry scattering efficiencies (E) of 3 m 2 

### /g for ammonium sulfate and ammonium nitrate and 4 m 2 

### /g for organic matter (OM). Data from I MPROVE special studies suggest that dry extinction efficiencies are var iable

. Lowenthal and Kumar (2005) found that PM 2.5 

### mass scattering efficiencies increased with increasing levels of particle light scattering and mass concentration. This was attribut ed 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 s ulfate scattering efficiencies ranging from 2.4

- 4.1 m 2 

### /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 alg orithm 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 repr esents 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 g eometric mean diameters (D g 

### ) and geometric standard deviations (σ g 

### ) of

### 0.2 μm and 2.2 for small mode and 0.5 μm and 1.5 for the large mode ,

respectively

. The dry PM 2.5 

### 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 a nd refractive indexes used in this calculation are 1.77, 1.73, and 1.4 g/cm 3 

### , 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 particle s. 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 equ ilibrium 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 e nvironment. 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 ( b sp (Dry) 

### ) was calculated using Mie theory for small

and large

- mode ammonium sulfate. Light scattering at RH = 37

- 95% at unit RH intervals ( b sp (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 b sp (RH) 

### / b sp (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 Region al Haze Rule protocol (USEPA, 2001). The same f(RH) are applied to small

- and large

- mode ammonium sulfate and ammonium nitrate.
Table 9 . W ater growth for the small and large sized distribution sulfate and nitrate components. **RH (%) f** 

**S** 

### (RH) **f L**  

### (RH) **RH (%) f**  **S** 

### (RH) **f L**  

### (RH) **RH (%) f**  **S** 

### (RH) **f L**  

### (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.; W exler, A.S., A Thermodynamic Model of the System H +



### - NH 4 +



### - Na +



### - SO 4 2

-



### - NO 3

-



### - Cl -



### - H 2 

### O at 298.15 K. J. P hys. Chem. , **1998** , 102 , 2155

- 2171. Lowenthal, D.H.; Kumar, N., 2004: Variation of Mass Scattering Efficiencies in IMPROVE; J. Air & Waste Manage. Assoc. , 2 **005** , 54 , 926

- 934. Malm, W. C.; Day, D.E.; Kreidenweis, S.M.; Collett, J.L.; Lee, T. Humidity

dependen t 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 Haz e Rule ; U.S. Environmental Pr otection 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 sca ttering 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

. S ite

- specific Rayleigh scattering was estimated using a Rayleigh Scattering Calculator developed by A ir R esource S pecialists, Inc. that calculates Rayleigh scattering as a function of temperature and pressure. For each IMPROVE site, we used the standar d 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 A 2 (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 f rom 12Mm - 1 

### for sites near sea level to 8Mm - 1 

### for sites at about 12,000 feet elevation.
NO 2 

### Absorption
The NO 2 

### absorption efficiency term (i.e. 0.33Mm - 1 

### /ppm) in the proposed new a lgorithm is a photopic

- weighted abso r ption efficiency value ( PAE NO2 

### ) . It wa s calculated by dividing the sum of the product s of the relative observer photopic response values ( PR(

 ) ) for viewing an image of 2 o 

### angular size and the spectral NO 2 

### absorption efficiency values ( AE(  ) ) by the sum of the photopic response values , as show n in the equation below

.



#   

 750

350 750

350 

### )

( ) ( ) (

2 

###  



PR AE PR PAE NO



### The spectral NO 2 

### absoption efficiency values are from Dixon (1940) and available in PLUVUE Users Manual (1980), where they were giving in 10 η m increments that were interpolated to generate 1 η m values. The photopic response values are fr om the CIE Ybar function downloaded directly from the CVRL Color and Vision database. Both are show n in the figure below.

### REFERENCES

J.K. Dixon, "The Absorp tion 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. Ab**  **brev iation**  **Na me**  **State** ACAD Acadia National Park Maine BIBE Big Bend National Park Texas BO WA Boundary W aters Canoe Area Minnesota CORI Columbia River Gorge Wa shington DOSO Dolly Sods/Otter Creek W ilderness We st Virgina GICI Gila W ilderness New Mexico GRCA Grand Canyon National Park Arizona GRGU Great Gulf Wi lderness New Hampshire GRSM Great Smoky Mountains Tennessee JARB Jarbidge W ilderness Nevada LOPE Lone Peak Wi lderness Utah LYBR Lye Brook Wi lderness Vermont MACA Mammoth Cave National Park Kentucky MORA Mount Rainier National Park Wa shington MOZI Mount Zirkel Wi lderness Colorado OKEF Okefenokee National W ildlife Refuge Florida SHEN Shenandoah National Park Virginia SHRO Shining Rock Wi lderness North Carolina SNAP Snoqualamie Pass Wi lderness Wa shingt on THIS Three Sisters W ilderness Oregon UPBU Upper Buffalo W ilderness Arkansas

### Table A 2

. Site

- specific Rayleigh values for all IMPROVE monitoring sites. Revised algorithm uses values rounded to

whole integer values (last column)

. **Monitoring Site Na me**  **Elevation (ft)**  **Lati**  

** tude** **Longi**  

** tude** **Standard**

**U.S. At**  **mo**   ** spher** **Temp. (C) Standard** 

**U.S. At**  **mo**   ** sphere** **Pressure (mb)**  **Rayleigh**

**at Standard**  **At mo**  **** **sphere (M**  **m -**  **1 )**  **An nual** 

**Av erage**  **Temp. (C) Corrected** 

**Rayleigh (M**  **m -**  **1 )**  **Corrected**

**Rayleigh rounded**  **to integer (M**  **m -**  **1 )** 
Acadi a 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 Monum ent 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 Boundar y W aters 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 Wi lderness 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 Wi ldlife 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 Cany on 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 11Caney Creek 2263 34.5 94.1 10.7 932 10.9 13.1 10.8 11

Cany onlands National Park 5901 38.5 109. 8 3.5 812 9.7 11.5 9.4 9 Cape Cod 134 42 70 14.9 1011 11.6 9.8 11.8 12 Cape Romain National Wi ldlife 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 9.4 9 Capitol Reef (CARE1)+F76 6199 38.3 111.3 2.9 803 9.6 9.2 9.4 9 Casco Bay 49 43.8 70.1 15.1 1014 11.6 7.6 11.9 12 Chassahowitzka National Wi ldlife 7 28.7 82.6 15.2 1016 11.7 21.4 11.4 11
Chiricahua National Monum ent 5150 32 109.4 5 835 9.9 15.5 9.6 10
Cohutta 2437 34.8 84.6 10.4 926 10.8 11.9 10. 8 11 Colum bia Gorge 738 45.6 122.2 13.7 988 11.4 11.6 11.5 11 Colum bia 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 Crate rs 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 Wi lderness 3798 39.1 79.4 7.7 879 10. 4 6.6 10.4 10
Dome Lands Wi lderness 2942 35.7 118.2 9.4 909 10.6 15.0 10.4 10 Dome Lands Wi lderness 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 9.2 9 Gila W ilderness 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 9.2 9Great Gulf Wi lderness 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 8.8 9 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 9.5 9
Haleakala National Park 3798 20.8 156.3 7.7 879 10.4 15.0 10.1 10 Hance Camp at Grand Cany on NP 7436 36 112 0.5 766 9.3 6.6 9.1 9
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

- Glade s 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 8.8 9 Hopi Point #1 7098 36.1 112.2 1.1 776 9.4 7.6 9.1 9 Hopi Point #2 (High Sensitivity) 7098 36.1 112.2 1.1 776 9.4 7. 6 9.1 9
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
Jam es River Face 981 37.6 79.5 13.3 979 11.3 13.0 11.3 11 Jarbidge W ilderness 6173 41.9 115.4 3 803 9.6 7.1 9.5 9 Jefferson/James River Face Wi lderness 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 Monum ent 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 8.8 9Kalm iopsis 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 Wi lderness 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 Ly e Brook Wi lderness 3300 43.1 73.1 8.7 896 10.5 2.4 10.8 11 Ly nden 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 Ca ve National Park 813 37.1 86.1 13.6 985 11.4 13.7 11.4 11
Mauna Loa National Observator y (MALO1) 11152 19.5 155.6

- 6.9 665 8.3 7.0 7.9 8
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 37.2 108.5 1 774 9.4 8.7 9.1 9 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 NW R 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 8.8 9 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 Wi lderness 10637 40.5 106.7

- 5.9 678 8.4 0.2 8.2 8 Mount Zirkel Wi lderness (Storm Peak) 10562 40.5 106.7

- 5.7 680 8.4 0.5 8.2 8
North Absaroka 8134 44.7 109.4

- 0.9 746 9.1

- 0.9 9.1 9 North Cascades 1889 48.7 121.1 11.5 946 11.0 7.2 11.2 11Ok efenokee National

Wi ldlife 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 Oly mp ic 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 9.5 9
Phoenix 1109 33.5 112.1 13 974 11.3 23.4 10.9 11 Pinnacles National Monum ent 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 4.8 1 1.8 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 2158 3 3.3 111.3 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 Rock y Mountain National Park 9036 40.3 105.5

- 2.7 721 8.8 1.9 8.7 9
Rock y Mountain National Park (Headquarters) 7872 40.4 105.6

- 0.4 753 9.1 5.5 8.9 9
S aguaro National Monum ent 3060 32.2 110.7 9.1 904 10.6 18.1 10.3 10 Saguaro W est 2355 32.2 111.2 10.5 929 10.8 21.5 10.4 10 Salm on National Forest 9145 45.2 114

- 2.9 718 8.8

- 7.6 8.9 9 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 9.4 9 San Gorgonio W ilderness 5592 34.2 116.9 4.1 821 9.8 12.0 9.5 10San Pedro Parks 9574 36 106.8

- 3.8 706 8.7

- 0.3 8.6 9

San Rafael 3126 34.7 120 9 902 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 36 01 38.5 78.4 8.1 886 10.4 8.2 10.4 10 Shining Rock Wi lderness 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 Sim eonof 98 55.3 160.5 15 1012 11.6 4.5 12.1 12 Si ps y W ilderness 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. Marks 7 30.1 84.2 15.2 1016 11.7 19.8 11.5 11 Starkey 4126 45.2 118.5 7 869 10.3 4.2 10.4 10 Sula (Selway Bitteroot Wi lderness) 6242 45.9 114 2.8 801 9.6

- 0.5 9.7 10 Swanquarter 7 35.5 76.2 15.2 1016 11.7 16.9 11.6 12 Sy camore Canyon 6691 35.1 112 1.9 788 9.5 9.8 9.2 9 Theodore Roosevelt 2798 46.9 103.4 9.7 914 10.7 5.1 10.9 11 Three Sisters Wi lderness 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 11Upper Buffalo Wi lderness 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
Vo yageurs National Park 1 1138 48.6 93.2 12.9 973 11.3 3.1 11.7 12
Vo yageurs National Park 2 1407 48.4 92.8 12.4 963 11.2 2.6 11.6 12
Wa shington D.C. 52 38.9 77 15.1 1014 11.6 14.5 11.7 12 We mi nuche Wi lderness 9069 37.7 107.8

- 2.8 720 8.8 1.5 8.7 9 Wh eeler Peak 11060 36.6 105.5

- 6.7 667 8.3

- 3.0 8.2 8 Wh ite Mountain 6724 33.5 105.5 1.9 787 9.5 9.9 9.2 9 Wh ite Pass 6002 46.6 121.4 3.3 809 9.7 1.4 9.8 10 Wh ite River National Forest 11211 39.2 106.8

- 7 663 8.2

- 0.3 8.0 8
Wi chita Mountains 1699 34.7 98.7 11.8 952 11.1 15.7 10.9 11 Wi nd 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 9.2 9
Yellowstone National Park 2 7954 44.6 110.4

- 0.6 751 9.1

- 0.8 9.1 9
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**Scatte r**  **Plot for Big Be nd using IM**  **PROV E A**  **lgor ithm** 

1 10 100 1000

1 10 100 1000 **Measured Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for Big Be nd using Ne**  **w Al**  **gorithm**

1 10 100 1000

1 10 100 1000 **Me asured Bsp** **P r** **e d** **i c** **t e** **d B** **s p** 

**Sc at**  **te r**  **Plot for BO WA**  **using IM PROV**  **E A**

**lgorithm**

1 10 100 1000

1 10 100 1000 **Measured Bsp** **P r** **e d** **i c** **t e** **d B** **s p**  **Scatte r Plot for BOW**  **A using N**  **ew**

**A lgorithm** 

1 10 100 1000

1 10 100 1000 **Me asured**  **Bs p** **P r** **e d** **i c** **t e** **d B** **s p** **Sc atte**  **r Plot for UPBU using IM PROV**  **E A lgorithm** 

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bsp** **P r** **e d** **i c** **t e** **d B** **s p**  **Sc atte**  **r Plot for UPBU using Ne w**  **Al gorithm** 

1 10 100 1000

1 10 100 1000 **Measured Bsp** **P r** **e d** **i c** **t e** **d B** **s p** 

**Sc at**  **te r**  **Plot for A**

**CA D**  **using IM PRO**  **VE A** 

**lgorithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bs p** **P r** **e d** **i c** **t e** **d B** **s p**  **Scatte r Plot for A**  **CA D us**  **ing Ne w**  **Al gorithm** 

1 10 100 1000

1 10 100 1000 **Measured Bsp P** **r e** **d i** **c t** **e d** **B s** **p Sc**  **atte r**  **Plot for GRGU using IM PR**  **OV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for GRGU using Ne**  **w Al**  **gor ithm** 

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p**

**Sc atte**  **r Plot for LYBR using IM PR**  **OV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bs p** **P r** **e d** **i c** **t e** **d B** **s p**  **Scatte r**  **Plot for LYBR using Ne w**  **Al gor**  **ithm**

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bsp** **P r** **e d** **i c** **t e** **d B** **s p** **Sc atte**  **r Plot for DOSO using IM PR**  **OV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bsp** **P r** **e d** **i c** **t e** **d B** **s p**  **Sc atte**  **r Plot for DOSO us ing Ne**  **w Al**  **gorithm**

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bsp** **P r** **e d** **i c** **t e** **d B** **s p** 

**Scatte r Plot for GRSM**  **using IM PR**  **OV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bsp** **P r** **e d** **i c** **t e** **d B** **s p**  **Scatte r**  **Plot for GRSM using Ne w A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p Sc**  **atte r Plot for M**  **AC A**  **using IM PROV**  **E A lgorithm** 

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p Sc**  **atte r**  **Plot for M AC**  **A using Ne**  **w Al**  **gorithm**

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bsp** **P r** **e d** **i c** **t e** **d B** **s p** 

**Sc at**  **te r**  **Plot for O KEF using IM**  **PROV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for OKEF using Ne w**  **Al gorithm** 

1 10 100 1000

1 10 100 1000 **Measured Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Sc**  **at te**  **r Plot for SHEN using IM**  **PROV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for SHEN using Ne w A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p**

**Scatte r Plot for SHRO**  **using IM PROV**  **E A lgorithm** 

1 10 100 1000

1 10 100 1000 **Measured Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for SHRO using Ne w**  **Al gorithm** 

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Pl**  **ot for CO RI using IM**  **PROV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Measured Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for CORI using Ne w**  **Al gorithm** 

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p**

**Scatte r Plot for GICL us**  **ing IM PROV**  **E A lgorithm** 

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Sc**  **atte r Plot for GICL using Ne**  **w A lgorithm** 

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for GRCA**  **using IM PROV**  **E A**

**lgorithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p Sc**  **atte r**  **Plot for GRCA using Ne**  **w Al**  **gorithm**

1 10 100 1000

1 10 100 1000 **Measured Bsp** **P r** **e d** **i c** **t e** **d B** **s p** 

**Sc atte**  **r Plot for JA** 

**RB using IM PR**  **OV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Measured Bs**  **p P** **r e** **d i** **c t** **e d** **B s** **p Sc**  **atte r Plot for JA**  **RB using N ew** 

**A lgor**  **ithm**

1 10 100 1000

1 10 100 1000 **Measured Bsp** **P r** **e d** **i c** **t e** **d B** **s p** **Sc atte**  **r Plot for LOPE using IM**  **PROV E A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Measured Bsp P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for LOPE using Ne**  **w Al**  **gor ithm** 

1 10 100 1000

1 10 100 1000 **Measured Bsp** **P r** **e d** **i c** **t e** **d B** **s p** 

**Scatte r Plot for M**  **ORA using IM**  **PRO VE**  **A**

**lgorithm**

1 10 100 1000

1 10 100 1000 **Measured Bsp P** **r e** **d i** **c t** **e d** **B s** **p Sc**  **atte r Plot for M**  **ORA using Ne**  **w A**

**lgorithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bs p** **P r** **e d** **i c** **t e** **d B** **s p** **Sc atte**  **r Plot for M OZ**  **I using IM PRO**  **VE A**  **lgorithm**

1 10 100 1000

1 10 100 1000 **Measured Bsp** **P r** **e d** **i c** **t e** **d B** **s p**  **Sc atte**  **r Plot for M OZ**  **I using N ew**  **A lgorithm** 

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p**

**Sc atte**  **r Plot for SNPA**  **using IM PR**  **OV E A** 

**lgor ithm** 

1 10 100 1000

1 10 100 1000 **Me asured**  **Bsp P** **r e** **d i** **c t** **e d** **B s** **p Scatte**  **r Plot for SNPA using Ne**  **w Al**  **gorithm**

1 10 100 1000

1 10 100 1000 **Me asured**  **Bs p** **P r** **e d** **i c** **t e** **d B** **s p** **Sc atte**  **r Plot for T HSI using IM**  **PROV E A**  **lgor ithm** 

1 10 100 1000

1 10 100 1000 **Me asur**  **ed Bsp** **P r** **e d** **i c** **t e** **d B** **s p**  **Sc atte**  **r Plot for T**  **HSI using Ne w A**  **lgor it**  **hm**

1 10 100 1000

1 10 100 1000 **Measured Bsp** **P r** **e d** **i c** **t e** **d B** **s p** 

### Table A **3**

**. Mean**  **ligh t scattering**  **and percent PM**  **2.5** 

### composition for the **fiv e** 

**major components for 20% best day s**  **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 NE W 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 NE W 7.9 18 2 11 9 57 2

BOW A1 Measured 5.4 26 3 2 1 5 43 2

IMPROVE 7.7 24 2 23 5 44 2 NE W 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 NE W 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 NE W 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 NE W 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 NE W 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 NE W 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 NE W 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 NE W 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 NE W 7.2 13 7 18 10 4 9 4

LYBR1 Measured 5.3 24 6 25 4 38 3

IMPROVE 8.2 24 4 27 4 38 3 NE W 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 NE W 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 NE W 6.2 14 3 28 4 48 4 MOZI1 Measured 2.8 12 3 18 8 57 2IMPROVE 4.8 13 4 18 8 55 3 NE W 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 NE W 21 22 3 20 3 49 3

SHEN1 Measured 11 27 10 18 4 39 3

IMPROVE 14 26 10 19 4 38 3 NE W 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 NE W 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 NE W 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 NE W 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 NE W 13 15 6 17 5 55 2



### Table A **4**

**. Mean**  **light scattering and percent**  **PM 2.5**  

### composition for the **fiv e**  **major components for 20% wo**  **rst day**  **s as determined by**  **measur ement, 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 NE W 62 34 6 22 3 33 3

BIBE1 Measured 49 34 2 16 7 39 2

IMPROVE 44 33 2 15 8 41 2 NE W 46 33 2 15 8 41 2

BOW A1 Measured 53 25 13 23 4 32 2

IMPROVE 49 27 13 22 4 32 2 NE W 45 23 13 23 4 35 2

CORI1 Measured 56 13 11 25 5 43 3

IMPROVE 48 13 10 24 5 45 3 NE W 48 12 10 24 5 46 3

DOSO1 Measured 109 44 2 21 3 28 2

IMPROVE 105 47 2 21 3 26 2 NE W 121 46 1 20 3 29 2

GICL1 Measured 25 18 2 25 8 44 2

IMPROVE 24 19 2 26 8 43 2 NE W 23 18 2 26 9 44 2

GRCA2 Measured 19 16 3 19 11 50 2

IMPROVE 19 11 2 18 10 57 1 NE W 18 11 2 18 10 57 1 GRGU1 Measured 58 35 3 24 3 33 3IMPROVE 56 36 4 25 3 30 3 NE W 59 35 3 26 3 30 3

GRSM1 Measured 153 52 1 22 3 20 2

IMPROVE 125 51 1 22 3 21 2 NE W 163 52 1 22 3 21 2

JARB1 Measured 24 8 3 21 12 54 2

IMPROVE 21 7 3 20 12 57 1 NE W 21 7 3 21 12 57 1

LOPE1 Measured 35 13 14 20 9 41 3

IMPROVE 33 12 15 20 9 41 3 NE W 31 11 13 21 9 42 3

LYBR1 Measured 83 42 8 23 4 21 3

IMPROVE 75 44 7 21 4 21 3 NE W 77 45 6 22 4 21 3

MACA1 Measured 106 42 11 23 3 19 3

IMPROVE 96 42 9 22 3 21 3 NE W 102 42 7 24 3 21 3

MORA1 Measured 42 20 3 37 5 31 4

IMPROVE 42 23 4 35 5 30 4 NE W 42 20 3 37 5 32 4

MOZI1 Measured 24 15 4 23 8 47 2

IMPROVE 21 15 4 22 8 49 2 NE W 20 13 3 24 9 49 2

OKEF1 Measured 87 31 3 17 4 43 2

IMPROVE 75 29 3 18 6 42 2 NE W 76 31 3 17 5 42 2

SHEN1 Measured 95 46 4 20 4 23 3

IMPROVE 94 48 5 20 3 22 2 NE W 108 47 3 21 3 24 2

SHRO1 Measured 77 36 5 20 7 29 3

IMPROVE 63 35 3 18 7 35 3 NE W 62 36 3 20 6 32 3

SNPA1 Measured 46 17 7 39 5 26 5

IMPROVE 36 17 7 35 6 31 5 NE W 37 15 6 37 6 32 5

THSI1 Measured 40 12 3 40 5 38 3

IMPROVE 34 12 3 36 5 41 3 NE W 37 12 2 38 5 40 3

UPBU1 Measured 123 42 3 20 5 27 2

IMPROVE 104 41 3 20 6 29 2 NE W 124 43 2 21 5 28 2