## Revised IMPROVE Algorithm for Estimating Light Extinction from Particle Speciation Data

I. Introduction Light Extinction Atmospheric light extinction is a fundament al 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 ( 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 component s of the atmosphere are relatively well understood and well estimated for any at mospheric conditions. Absorption of visible light by gases in t he atmosphere is primarily by NO 2 , and can be directly 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 atmosphere, with highes t values at sea level (about 12Mm -1 ) and diminishing with elevation (8Mm -1 at about 12,000’), and varies somewhat at any elevation due to atmospheric temperatur e 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 parti cles 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 scatte ring 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 particl e 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. These data are routinely availabl e 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 NO 2 light absorption, a photopic-wei ghted 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 (Tabl e A1 in appendix), hourly-averaged nephelometer and relative humidit y data are also routinely available. Data from these sites have been key to evaluate t he performance of the current IMPROVE algorithm, as well as for development and performance evaluation of proposed revised algorithms.

The current IMPROVE algorithm for esti mating 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 condit ion, 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 typica l component-specific light extinction efficiencies. The six major components are sulfate (assumed to be ammonium sulfate), nitrate (assumed to be ammoni um nitrate), organic compounds (based on measured organic carbon mass), elem ental 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 constant s, except for the sulfate and nitrate extinction efficiency terms that include a wate r growth factor that is a function of relative humidity (displayed as f(RH) ) multiplied by a cons tant dry extinction efficiency. Monthly averaged water growth terms for each si te were developed because most monitoring sites don’t include on-site relative humidity monitoring. Expressed as an equation, the current al gorithm for estimating light extinction from IMPROVE data takes the followi ng form where the particle component concentrations are indicated in the bra ckets. The formulas for the composite components are available else where (IMPROVE web site).

The units for light extinction and Rayl eigh scattering are inverse megameters (1/10 6 m usually written Mm -1 ); component concentrations shown in brackets are in microgram per meter cubed ( μ g/m 3 ); dry efficiency terms are in units of meters squared per gram (m 2 /g); and the water growth te rms, f(RH), are unitless.

Among the implicit assumptions for this formulation of the al gorithm are that

• the six particle component terms plus a constant Rayleigh scattering term are sufficient for a good estima te 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 te rms 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 t he individual particle components can be adequately estimated as separate terms as they would if they were in completely separate particles (exter nally mixed), though they often are known to be internally mixed in particles. A relatively simple algorithm for esti mating 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) hav e been compared to directly-measured particle scattering data at the 21 monito ring 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 t he lowest extinction values. Since it first use (IMPROVE Report, 1993), the curr ent algorithm has been a useful tool that contributed significantly to a better understanding of haze levels and the relative magnitude of haze contributi on by the various particle components.

0 50 100 150 200 250 300 350

0 50 100 150 200 250 300 350 Measured BspIMPROVE Bsp

Figure 1. A scatter plot of the current IM PROVE 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 us ed 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 esti mate 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 determinati on of the mean of the annual 20% best and 20% worst haze days for each of t he IMPROVE monitoring sites that represent the visibility-prot ected 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 cond itions for the best haze days. For consistency, the same approach (i.e. IM PROVE algorithm and conversion to the deciview haze index) is also used to estimate natural haze levels for each representative monitoring site using estima tes of the natural concentration levels for the major particle components. For eac h 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. Th is 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 tr ends 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 t he baseline period and for estimates of natural worst haze conditions. Also, the use of the deciview index means that additive biases in the light extincti on estimates (e.g. the use of a standard Rayleigh scattering te rm for all sites regardless of elevation) will affect the calculation of a linear g lide slope, which is used to set the pace of emission reductions. Use of the IM PROVE algorithm for the RHR elicited concerns about possible biases in the apportionment among the various major particle components. Such issues have been the subj ect of a number of critical reviews of the use of the IMPROVE algorith m in the RHR (Lowenthal and Kumar, 2003; Ryan et al., 2005).

In light of the concerns raised by it s use in the RHR, the IMPROVE Steering Committee initiated an internal review incl uding recommendations for revisions of the IMPROVE algorithm for estimating lig ht extinction. The review team (composed of National Park Service and Co operative Institute for Research in the Atmosphere scientists) reviewed per tinent literature and employed both Mie theory modeling and statistical assessment methods to iden tify deficiencies in the current algorithm and evaluat e 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 sci entific literature while constrained by the need to use only those data that are routinely available from the IMPROVE particle monitoring network. A preliminar y report by this team was presented in June, 2005 at a national Regional Planni ng Organization workshop in Denver Colorado hosted by the Western Regi onal Air Partnership (WRAP) and broadly participated in by those with an interest in the regional haze rule. The preliminaryresults were also summarized in pr esentations 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 thei r July 2005 meeting to recommend a refined algorithm that would replace or be made available as an alternative to the current approach. The subcommittee incl uded 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 algorith m, characterize its performance, and summarize the rationale for each of the changes from the currently used algorithm. This document is the pr incipal 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 underst anding 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 IM PROVE particle data and shows its performance compared to t hat of the currently us ed 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 Coeffi cients, on the IMPRO VE web site at http://vista.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 members concurring in thes e recommendations are William Malm and Bret Schichtel, NPS; Marc Pitchford, NOAA; Na resh 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 sulf ate, nitrate and organic carbon compound concentrations are each split into two fr actions, representing small and large size distributions of those components. Though not explicitly show n in the equation, the organic mass concentration used in th is new algorithm is 1.8 times the organic carbon mass concentration, c hanged 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 elevat ion and annual average te mperature of each of the IMPROVE monitoring sites as s hown in the Table A at the end of the document.

The apportionment of the total concentra tion 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, mi ssing or invalid. The algorithm uses three water growth adjustment term as s hown in the Figure 2 and Table 1. They 

## [ ] [ ] [] [ ] [ ][ ] [ ] [ ] [ ] [ ]

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. 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

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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 μ ≥ = 

## [ ] [ ] [ ][ ] 3
3 

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, / 20 arg m g Sulfate Total for Sulfate Total

m g Sulfate Total Sulfate e L μ μ < × =



## [ ][ ] [ ] Sulfate e L Sulfate Total Sulfate Small arg

− =are for use with the small size distributi on 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 Growth Curves

0 1 2 3 4 5 6 7 8 9 10

0 10 20 30 40 50 60 70 80 90 100 Relative Humidity (%)f(RH) fS(RH) fL(RH) Original fSS(RH)

Figure 2. Water growth curves for small and large size distribution sulfate and nitrate, sea salt and the original IMPROVE algorithm sulfate and nitrate.

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

. RH (%) 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 7.35Algorithm 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 li ght scattering estimates at the extremes (i.e. underestimation of the high values and over estimation of the low values) when compared to nephelometer measurement s was one of the most compelling reasons for development of a new algorit hm, so comparisons of bias for the current and proposed new algorithm are one way to evaluate performance.

The fractional bias for each sample peri od was calculated as the difference in light scattering (i.e. estimated b sp minus the measured b sp ) 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 scatteri ng 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 criter ion better addresses the question of algorithm performance with regards to the haze conditions. Criteri on 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 algor ithm. Tables 2 through 5 show the bias results by both criteria for the curr ent and new algorithm for sites averaged by RPO.

Table 2. Averaged fractional bias by RPO for the current IMPROVE algorithm with quintiles based on measu red light scattering (criterion 1). Bold font highlights th e 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 measu red light scattering (criterion 1). Bold font highlights th e 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 0.21Table 4. Averaged fractional bi as by RPO for the current IMPROVE algorithm with quintiles b ased on estimated light scattering (criterion 2). Bold font highlights th e 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 b ased on estimated light scattering (criterion 2). Bold font highlights th e 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 lowe r fractional bias than the current IMPROVE algorithm in all but t he haziest conditions (i.e. quintile 5) regardless of the criterion us ed to sort the data into quint iles. 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 RP O), where the new al gorithm 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 co mpared with the cu rrent algorithm.

Scatter plots (Figures 1 and 3) of light scattering estimates from the current and new proposed algorithms versus nephelomet er 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 t he 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 precis ion of the new algorithm compared to the original IMPROVE algorithm (i.e. point s are more broadly scattered).0 50 100 150 200 250 300 350

0 50 100 150 200 250 300 350 Measured BspSplit 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 pr epared for each individual monitoring site (available in the appendix). Figures 4 and 5 are example pl ots for Shenandoah and Grand Canyon National Parks. The logarithmic scales on these plots exaggerate the scatter for low values co mpared to high values. The individualsite scatter plots have the 80 th percentile values indicated on the graphs for the predicted and measured values by horizont al and vertical lines respectively. Points that are to the right of the vertical line have neph elometer values that are among the 20% worst light scattering for that monitoring sites. Points that are above the horizontal line hav e algorithm determined val ues 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 BspPredicted Bsp Scatter Plot for SHEN using New Algorithm

1 10 100 1000

1 10 100 1000 Measured BspPredicted Bsp

Figure 4. Scatter plots of the current IMPROVE and new recommended algorithm estimates of light scattering versus measure d light scattering for Shenandoah National Park. Horizontal and vertical lines are at the 80 th percentile for estimated and measured light scattering.Scatter Plot for GRCA using IMPROVE Algorithm

1 10 100 1000

1 10 100 1000 Measured BspPredicted Bsp Scatter Plot for GRCA using New Algorithm

1 10 100 1000

1 10 100 1000 Measured BspPredicted Bsp

Figure 5. Scatter plots of the current IMPROVE and new recommended algorithm estimates of light scattering versus measure d light scattering for Grand Canyon National Park. Horizontal and vertical lines are at the 80 th percentile for estimated and measured light scattering. The proposed new algorithm performs not iceably 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 80 th percentile lines). The di fferences between the two algo rithms 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 rela tive 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 composit ion by RPO for days selected as best and worst by these three methods. Si milar results for each of the 21 nephelometer monitoring locations are shown in tables in the appendix.

Table 6. Mean light scattering and percent PM 2.5 composition for the five major components for 20% best days as determined by measurement, the current IMPROVE algorithm a nd 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 2MANEVU 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 PM 2.5 composition for the five major components for 20% worst days as determined by measurement, the current IMPROVE algorithm a nd 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 t hat the composition asso ciated 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 categorie s. Some of the i ndividual sites (e.g. Grand Canyon) have somewhat larger vari ations in the composition between measurement-selected days compared to algorithm-selected days, though there’s little difference between the av erage composition comparing the two algorithms on the best and worst days. The contributions to light extinction by the various components were not explic itly 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 com position of days selected as best and worst by the current and the new algorithm ar e 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 organi c components that permitted variable extinction efficiency depending on the co mponent mass concentration. Thoughnot subject to explicit performance test ing, the proposed new algorithm also contains specific changes from the current algorit hm that reflect a better understanding of the atmospher e 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 accountin g for contributors to haze (e.g. sea salt and NO 2 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 al gorithm for estimating light extinction from IMPROVE particle speciation dat a 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 nitrat e to better estimate light extinction at the high and low extr emes of the range; and

• addition of a NO 2 light absorption term that would only be used at sites with available NO 2 concentration data. A summary of the technical rational e for making each of these changes is described in separate sections below.

Sea Salt

The current IMPROVE protocol for esti mating 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 coasta l IMPROVE sites. Contributions of sea salt particles 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 accounted for 43% of estimated b sp at the U.S. Virgin Islands IMPROVE site.

To include sea salt in the IMPROVE light extinction equat ion, it is necessary to: 1) estimate the sea salt mass concentrati on; 2) specify a dry sea salt scattering efficiency; and 3) specify an f(RH) cu rve for sea salt representing theenhancement of sea salt scattering by hy groscopic growth as a function of relative humidity (RH).

Sea Salt Mass Concentration Estimating sea salt mass requires a s ea salt marker species measured in IMPROVE aerosol samples. The most obv ious such markers are sodium (Na) and chlorine (Cl), since NaCl is the ma in 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 nitr ate and/or sulfate in am bient samples. Further, without chemical speciation of the PM 10 sample (Module D of the IMPROVE sampler), it is not possible to es timate 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 chlori ne (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 ex posed to reactive losses because acidic gases are not removed from the air-str eam 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 considered. To the degree that chloride has been replaced by sulfate or nitrate in ambient particles, this approach wil l underestimate the mass and scattering contributed by the substituted s ea salt that results (e.g. NaNO 3 , NaHSO 4 , or Na 2 SO 4 ). This mass is partially acc ounted for by ammonium sulfate and ammonium nitrate in the IM PROVE 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 provi des 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, ex tensive sea salt size distribution measurements have been made in the remote marine environment during cruised-based experiment s (Quinn et al., 1995, 1996, 1998). Based on thesestudies, a dry log-normal mass size dist ribution with a geomet ric mean diameter (D g ) of 2.5 μm and geometri c standard 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 assu ming a sea salt refractive index of [1.55+ i0] and a density of 1.9 g cm -3 recommended by Quinn et al. (1995).

Sea Salt f(RH) Tang et al. (1997) determined hygroscopic growth curves for aerosols generated from Long Island, NY and Atlantic Ocean seaw ater. The water absorption curves for sea salt were nearly identical to t hat of NaCl. The NaCl growth factors derived from the AIM3 thermodynamic equi librium model (Clegg et al., 1998) are shown in Table 8 as a function of relati ve 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 ba ck” 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 ma ss size distribution using Mie theory, accounting for the change in particle vo lume 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 sulfate/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 1.7877 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 + -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.; 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 Sulf ur Enrichment in Indivi dual 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 Aero sol 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 Bound ary 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 Progre ss Under the Regional Haze Rule ; U.S. Environmental Protec tion Agency, Office of Air Quality Planning and Standards: Research Triangle Park, NC, September 27, 2001. Available at: Http:/Vista.Cira.Colostate.Edu/IMPR OVE/Publications/Guidancedocs/Guid ancedocs.Htm.Organic Mass to Organic Carbon Ratio

A factor of 1.4 is current ly used in the “IMPROVE equat ion” to convert OC to organic mass (OM) to account for unmeasur ed 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 av eraged 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 expl ain the reconstruct ed mass deficit during SEAVS (Southeastern Aerosol and Visibili ty 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, re spectively, 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 prim ary precursors. Kr ivácsy et al. (2001) isolated the polar, water-soluble organic carbon fraction of aerosols from the Jungfraujoch, Switzerland using solid phas e 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 betw een reconstructed and measured PM 2.5 was closer with an OM/OC ratio of 1.8 than with the fa ctor of 1.4 for samples from the IMPROVE and STN networks in the nort heastern U.S. during summer, 2002, when large impacts 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. (20 05) 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 ex plore 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., Hild emann, 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 an d 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., Kr eidenweis, S.M., Co llett, J.L., Jr., McMeeking, G., Lee, and Carillo, J ., 2005: Intercomparison and closure calculations using measurements of aer osol 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: C hemical and physical characteristics of wood smoke in the northeastern U.S. during July 2002: Impacts from Quebec forest fires, Paper #94, A&WMA Specialty Confer ence: 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: 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 m 2 /g for ammonium sulfate and ammonium nitrate and 4 m 2 /g for organic matter (OM). Data from IMPROVE special studies suggest that dry extinction efficiencies are variable. Lowenthal and Kumar (2005) found that PM 2.5 mass scattering efficiencies increased with increasing leve ls of particle light scattering and mass concentration. This was attributed to grow th of the dry partic le size distribution into size ranges with higher scatteri ng efficiencies under more-polluted conditions, which is related to a higher degree of cloud processing during transport. Malm et al. (2003) esti mated dry ammoniated sulfate scattering efficiencies ranging from 2.4-4.1 m 2 /g during the Big Bend Aerosol and Visibility Observational Study (BRAVO). A w eak relationship between efficiency and ammoniated sulfate mass concen tration was reported.

The proposed new IMPROVE algorithm acc ounts for the increase of ammonium sulfate/ammonium nitrate and organic matte r (OM) efficiencies with concentration using a simple mixing model where the concentrations of ammonium sulfate, ammonium nitrate, and OM are each comp rised 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 (D g ) and geometric standard deviations ( σ g ) of0.2 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 dist ribution parameters described above. The ammonium sulfate, ammonium nitr ate, and OM densities and refractive indexes used in this calculat ion 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 m ade to account for possible difference in composition bet ween 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 ambien t RH divided by the dry particle diameter) for pure ammonium sulf ate 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 a ll RH for small and large OM modes. The f(RH) for ammonium sulfat e and ammonium nitrate are based on the hygroscopic growth curve for pure ammonium sulfat e derived from the AIM thermodynamic equilibrium model (Clegg et al., 1998). This growth curve represents the upper branch, also referred to as the efflor escence 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 assu med 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 smalland large-mode ammonium sulf ate. Light scattering at RH = 37-95% at unit RH intervals (b sp(RH) ) was calculated by applying the AIM ammonium su lfate growth curve to the small and large dry mode size distributions using Mie theory, accounting for the change in particle vo lume 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 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 dist ribution 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 2.2739 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.; Wexl er, 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. Phys. 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. Humiditydependent optical properties of fine parti cles during the Big Bend Regional Aerosol and Visibility Study. J. Geophys. Res

., 2003

, 108 , 4279, doi: 10.1029/2002JD002998. USEPA. Draft Guidance for Tracking Progr ess 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/IMPRO VE/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 us ed 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 scatt ering 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. T he temperature data were obtained from the nearestweather stations for time periods encompassing 10 to 30 years and were interpolated to the monitori ng 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 val ues are shown in the last column of the table. T hey range from 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 algorithm is a photopic-weighted absorption efficiency value ( PAE NO2 ). It was calculated by dividing the sum of the produc ts of the relative observer photopic response values ( PR( 8 ) ) for viewing an image of 2 o angular size and the spectral NO 2 absorption efficiency values ( AE( 8 ) ) by the sum of the photopic response values, as shown in the equation below.



## ∑ ∑ ×

= 750

350 750

350 ) ( ) ( ) (

2 λ λ λ

PR AE PR
PAE NO

The spectral NO 2 absoption efficiency values are from Dix on (1940) and available in PLUVUE Users Manual ( 1980), where they were giving in 10 η m increments that were in terpolated to generate 1 η m values. The photopic response values are from the CIE Ybar function downloaded directly from the CVRL Color and Vision database. Bo th are shown in the figure below.REFERENCES J.K. Dixon, "The Absorption Coefficient of Nitrogen Dioxide in the Visible Spectrum", Journal of Chemical Physi cs, Volume 8, February, 1940. pp 157-161

Draft Final Report: User's Manual For T he 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.htmTable A1. IMPROVE monito ring 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 Nati onal Park Kentucky MORA Mount Rainier National Park Washington MOZI Mount Zirkel Wilderness Colorado OKEF Okefenokee National W ildlife Refuge Florida SHEN Shenandoah Nationa l Park Virginia SHRO Shining Rock Wilderness North Carolina SNAP Snoqualamie Pass Wilderness Washington THIS Three Sisters Wilderness Oregon UPBU Upper Buffalo Wilderness ArkansasTable A2. Site-specific Rayleigh values for all IMPROVE monitoring sites. Revised algorithm uses values rounded towhole integer values (last column).Monitoring Site Name Elevation(ft)LatitudeLongi-tudeStandardU.S.AtmospherTemp. (C)StandardU.S.AtmospherePressure(mb)RayleighatStandardAtmosphere(Mm-1)AnnualAverageTemp. (C)CorrectedRayleigh(Mm-1)CorrectedRayleighroundedto integer(Mm-1)Acadia National Park 492 44.4 68.3 14.2 997 11.5 7.4 11.8 12Addison Pinnacle 1732 42.1 77.2 11.8 951 11.0 7.1 11.2 11Agua Tibia 1663 33.5 117 11.9 954 11.1 16.7 10.9 11Arches National Park 5648 38.8 109.6 4 820 9.8 10.0 9.6 10Arendtsville 879 39.9 77.3 13.5 983 11.3 10.4 11.5 11Badlands National Park 2414 43.7 101.9 10.4 927 10.8 10.3 10.8 11Bandelier NationalMonument6517 35.8 106.3 2.3 793 9.5 8.7 9.3 9Big Bend National Park 3526 29.3 103.2 8.2 889 10.5 19.4 10.1 10Bliss State Park (TRPA) 694039 120.1 1.4 780 9.4 4.5 9.3 9Bondville 692 40.1 88.4 13.8 990 11.4 11.0 11.5 12Bosque del Apache 4536 33.9 106.9 6.2 855 10.1 14.0 9.9 10Boundary Waters CanoeArea1719 47.9 91.5 11.8 952 11.1 2.4 11.4 11Breton 7 29.1 89.2 15.2 1016 11.7 21.0 11.4 11Bridger Wilderness 8551 43 109.8 -1.7 734 9.0 2.0 8.8 9Bridgton 794 44.1 70.7 13.6 986 11.4 6.1 11.7 12Brigantine NationalWildlife Refuge16 39.5 74.4 15.2 1015 11.6 12.7 11.8 12Brooklyn Lake 10483 41.4 106.2 -5.6 682 8.4 0.5 8.2 8Bryce Canyon NationalPark8125 37.6 112.2 -0.9 746 9.1 4.1 8.9 9Cabinet Mountains 4704 48 115.7 5.9 850 10.1 3.2 10.2 10Cadiz 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 11Canyonlands NationalPark5901 38.5 109.8 3.5 812 9.7 11.5 9.4 9Cape Cod 134 42 70 14.9 1011 11.6 9.8 11.8 12Cape Romain NationalWildlife Refuge10 32.9 79.7 15.2 1016 11.7 17.9 11.5 12Capitol Reef (CAPI1) 6199 38.3 111.3 2.9 803 9.6 9.2 9.4 9Capitol Reef(CARE1)+F766199 38.3 111.3 2.9 803 9.6 9.2 9.4 9Casco Bay 49 43.8 70.1 15.1 1014 11.6 7.6 11.9 12Chassahowitzka NationalWildlife7 28.7 82.6 15.2 1016 11.7 21.4 11.4 11Chiricahua NationalMonument5150 32 109.4 5 835 9.9 15.5 9.6 10Cohutta 2437 34.8 84.6 10.4 926 10.8 11.9 10.8 11Columbia Gorge 738 45.6 122.2 13.7 988 11.4 11.6 11.5 11Columbia River Gorge 659 45.7 121 13.9 991 11.4 10.5 11.6 12Connecticut Hill 1656 42.4 76.7 11.9 954 11.1 7.0 11.3 11Crater Lake National Park 6439 42.9 122.1 2.4 795 9.6 3.2 9.5 10Craters of the MoonNM(US DOE)5960 43.5 113.6 3.4 810 9.7 6.2 9.6 10Death Valley Monument 410 36.5 116.8 14.4 1000 11.5 22.6 11.2 11Denali National Park 2158 63.7 149 10.9 936 10.9 -3.0 11.5 11Dolly Sods /Otter CreekWilderness3798 39.1 79.4 7.7 879 10.4 6.6 10.4 10Dome Lands Wilderness 2942 35.7 118.2 9.4 909 10.6 15.0 10.4 10Dome Lands Wilderness 3034 35.7 118.1 9.2 905 10.6 15.3 10.4 10Everglades National Park 10 25.4 80.7 15.2 1016 11.7 23.7 11.3 11Gates of the Mountains 7846 46.8111.7 -0.4 754 9.2 -1.1 9.2 9Gila Wilderness 5825 33.2 108.2 3.7 814 9.7 10.6 9.5 10Glacier National Park 3211 48.5 114 8.8 899 10.6 5.7 10.7 11Great Basin National Park 6783 39 114.2 1.8 785 9.5 8.8 9.2 9Great Gulf Wilderness 1460 44.3 71.2 12.3 961 11.1 5.0 11.4 11Great Sand DunesNational Monument8213 37.7 105.5 -1.1 744 9.0 6.0 8.8 9Great Smoky MountainsNational Park2673 35.6 83.9 9.9 918 10.7 11.2 10.7 11Guadalupe MountainsNational Park5491 31.8 104.8 4.3 825 9.8 15.2 9.5 9Haleakala National Park 3798 20.8 156.3 7.7 879 10.4 15.0 10.1 10Hance Camp at GrandCanyon NP7436 36 112 0.5 766 9.3 6.6 9.1 9Hawaii VolcanoesNational Park3949 19.4 155.3 7.4 874 10.3 16.3 10.0 10Hells Canyon 2050 45 116.8 11.1 940 10.9 11.5 10.9 11Hercules-Glades 1394 36.7 92.912.4 964 11.2 13.7 11.1 11Hillside 4953 34.4 113 5.4 842 10.0 9.9 9.8 10Hoover 8416 38.1 119.2 -1.5 738 9.0 3.5 8.8 9Hopi Point #1 7098 36.1 112.2 1.1 776 9.4 7.6 9.1 9Hopi Point #2 (HighSensitivity)7098 36.1 112.2 1.1 776 9.4 7.6 9.1 9Ike's Backbone 4274 34.3 117.9 6.7 864 10.2 14.0 10.0 10Indian Gardens 3824 36.1 112.1 7.6 879 10.4 16.7 10.0 10Indian Gardens 2 (HighSensitivity)3824 36.1 112.1 7.6 879 10.4 16.7 10.0 10Isle Royale National Park 699 47.9 89.2 14 993 11.4 3.5 11.8 12Isle Royale National Park(New)610 47.5 88.1 13.8 989 11.4 3.7 11.9 12James River Face 981 37.6 79.5 13.3 979 11.3 13.0 11.3 11Jarbidge Wilderness 6173 41.9 115.4 3 803 9.6 7.1 9.5 9Jefferson/James RiverFace Wilderness918 37.7 79.4 13.4 981 11.3 13.1 11.3 11Joshua Tree 4028 34.1 116.4 7.2 872 10.3 14.9 10.0 10Joshua Tree NationalMonument4028 34.1 116.4 7.2 872 10.3 14.9 10.0 10Kaiser 8439 37.2 119.2 -1.5 737 9.0 3.4 8.8 9Kalmiopsis 295 42.6 124.1 14.6 1005 11.5 13.1 11.6 12Lassen Volcanic NationalPark5756 40.5 121.6 3.8 816 9.8 5.3 9.7 10Lava Beds 4818 41.7 121.5 5.7 846 10.0 8.7 9.9 10Linville Gorge 3234 36 81.9 8.8 898 10.5 10.0 10.5 11Livonia 977 38.5 86.3 13.3 979 11.3 12.2 11.4 11Lone Peak Wilderness 5799 40.4 111.7 3.7 815 9.7 9.1 9.6 10Lostwood 2270 48.6 102.4 10.7 932 10.9 3.2 11.2 11Lye Brook Wilderness 3300 43.1 73.1 8.7 896 10.5 2.4 10.8 11Lynden 92 49 122.6 15 1013 11.6 10.1 11.8 12M.K. Goddard 1269 41.4 80.1 12.7 968 11.2 9.0 11.4 11Mammoth Cave NationalPark813 37.1 86.1 13.6 985 11.4 13.7 11.4 11Mauna Loa NationalObservatory (MALO1)11152 19.5 155.6 -6.9 665 8.3 7.0 7.9 8Meadview 2959 36 114.1 9.3 908 10.6 16.2 10.4 10Medicine Lake 1984 48.5 104.5 11.3 942 11.0 4.8 11.2 11Mesa Verde NationalPark7141 37.2 108.5 1 774 9.4 8.7 9.1 9Mingo 367 37 90.1 14.5 1002 11.5 14.3 11.5 12Mohawk Mt. 1745 41.8 73.3 11.7 951 11.0 6.3 11.3 11Monture 4241 47.1 113.2 6.8 865 10.2 3.5 10.3 10Moosehorn NWR 308 45.1 67.3 14.6 1004 11.5 6.0 11.9 12Mount Baldy 8243 34.1 109.4 -1.1 743 9.0 7.6 8.8 9Mount Hood 4398 45.3 121.8 6.5 860 10.2 4.3 10.3 10Mount Rainier NationalPark1401 46.8 122.1 12.4 963 11.2 8.9 11.3 11Mount Zirkel Wilderness 10637 40.5 106.7 -5.9 678 8.4 0.2 8.2 8Mount Zirkel Wilderness(Storm Peak)10562 40.5 106.7 -5.7 680 8.4 0.5 8.2 8North Absaroka 8134 44.7 109.4 -0.9 746 9.1 -0.9 9.1 9North Cascades 1889 48.7 121.111.5 946 11.0 7.2 11.2 11Okefenokee NationalWildlife Refuge161 30.7 82.1 14.9 1010 11.6 20.3 11.4 11Old Town 213 44.9 68.6 14.8 1008 11.6 6.2 11.9 12Olympic 1968 48 123 11.3 943 11.0 10.9 11.0 11Organ Pipe 1847 32 113 11.5 947 11.0 21.0 10.7 11Pasayten 5360 48.4 119.9 4.6 829 9.9 4.8 9.9 10Petrified Forest NationalPark5796 35.1 109.8 3.7 815 9.7 11.7 9.5 9Phoenix 1109 33.5 112.1 13 974 11.3 23.4 10.9 11Pinnacles NationalMonument1040 36.5 121.2 13.1 980 11.3 16.0 11.2 11Point Reyes NationalSeashore279 38.1 122.9 14.6 1005 11.6 11.3 11.7 12Presque Isle 535 46.7 68 14.1 996 11.5 4.8 11.8 12Proctor Maple R. F. 1322 44.572.9 12.6 966 11.2 4.5 11.5 12Puget Sound 262 47.6 122.3 14.7 1006 11.6 10.7 11.7 12Quabbin Summit 1033 42.3 72.3 13.2 977 11.3 6.7 11.6 12Quaker City 1233 39.9 81.3 12.8 970 11.2 9.8 11.3 11Queen Valley 2158 33.3 111.3 10.9 936 10.9 21.8 10.5 11Redwood National Park 804 41.6 124.1 13.6 985 11.4 8.7 11.6 12Rocky Mountain NationalPark9036 40.3 105.5 -2.7 721 8.8 1.9 8.7 9Rocky Mountain NationalPark (Headquarters)7872 40.4 105.6 -0.4 753 9.1 5.5 8.9 9Saguaro NationalMonument3060 32.2 110.7 9.1 904 10.6 18.1 10.3 10Saguaro West 2355 32.2 111.2 10.5 929 10.8 21.5 10.4 10Salmon National Forest 9145 45.2 114 -2.9 718 8.8 -7.6 8.9 9Salt Creek 3533 33.5 104.4 8.2 888 10.5 15.3 10.2 10San Andres 4674 32.7 106.5 5.9 851 10.1 13.3 9.8 10San Gabriel 5874 34.3 118 3.6 813 9.7 13.1 9.4 9San Gorgonio Wilderness 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 9San Rafael 3126 34.7 120 9 902 10.6 16.7 10.3 10Sawtooth National Forest 6494 44.2114.9 2.3 794 9.5 1.1 9.6 10Scoville 4920 43.7 113 5.4 843 10.0 6.1 10.0 10Seney 708 46.3 85.9 13.8 989 11.4 5.8 11.7 12Sequoia National Park 1755 36.5 118.8 11.7 950 11.0 17.0 10.8 11Shenandoah NationalPark3601 38.5 78.4 8.1 886 10.4 8.2 10.4 10Shining Rock Wilderness 5317 35.4 82.8 4.7 830 9.9 7.4 9.8 10Sierra Ancha 5232 34.1 110.9 4.8 833 9.9 12.5 9.7 10Sikes 148 32.1 92.4 14.9 1010 11.6 18.2 11.5 11Simeonof 98 55.3 160.5 15 1012 11.6 4.5 12.1 12Sipsy Wilderness 915 34.3 87.3 13.4 981 11.3 15.5 11.2 11Snoqualamie Pass,Snoqualamie N.F3805 47.4 121.4 7.7 879 10.4 3.2 10.5 11South Lake Tahoe 6232 38.9 1202.8 802 9.6 6.1 9.5 10Spokane Res. 1797 47.9 117.9 11.6 949 11.0 10.0 11.1 11St. Marks 7 30.1 84.2 15.2 1016 11.7 19.8 11.5 11Starkey 4126 45.2 118.5 7 869 10.3 4.2 10.4 10Sula (Selway BitterootWilderness)6242 45.9 114 2.8 801 9.6 -0.5 9.7 10Swanquarter 7 35.5 76.2 15.2 1016 11.7 16.9 11.6 12Sycamore Canyon 6691 35.1 112 1.9 788 9.5 9.8 9.2 9Theodore Roosevelt 2798 46.9 103.4 9.7 914 10.7 5.1 10.9 11Three Sisters Wilderness 2903 44.3 122 9.4 910 10.7 6.8 10.8 11Tonto National Monument 2578 33.6 111.1 10.1 921 10.8 18.7 10.4 10Trapper Creek 479 62.3 150.3 14.3 998 11.5 2.3 12.0 12Trinity 3303 40.8 122.8 8.7 896 10.5 12.9 10.4 10Tuxedni 33 60 152.6 15.1 1015 11.6 1.8 12.2 12UL Bend 2929 47.6 108.7 9.4 909 10.6 5.2 10.8 11Upper Buffalo Wilderness 2371 35.8 93.2 10.5 928 10.8 12.5 10.8 11Virgin Islands NationalPark210 18.3 64.8 14.8 1008 11.6 26.1 11.1 11Voyageurs National Park11138 48.6 93.2 12.9 973 11.3 3.1 11.7 12Voyageurs National Park21407 48.4 92.8 12.4 963 11.2 2.6 11.6 12Washington D.C. 52 38.9 77 15.1 1014 11.6 14.5 11.7 12Weminuche Wilderness 9069 37.7 107.8 -2.8 720 8.8 1.5 8.7 9Wheeler Peak 11060 36.6 105.5 -6.7 667 8.3 -3.0 8.2 8White Mountain 6724 33.5 105.5 1.9 787 9.5 9.9 9.2 9White Pass 6002 46.6 121.4 3.3 809 9.7 1.4 9.8 10White River NationalForest11211 39.2 106.8 -7 663 8.2 -0.3 8.0 8Wichita Mountains 1699 34.7 98.7 11.8 952 11.1 15.7 10.9 11Wind Cave 4264 43.6 103.5 6.7 864 10.2 8.1 10.2 10Yellowstone NationalPark 17744 44.6 110.4 -0.1 757 9.2 -0.2 9.2 9Yellowstone NationalPark 27954 44.6 110.4 -0.6 751 9.1 -0.8 9.1 9Yosemite National Park 5297 37.7 119.7 4.7 831 9.9 8.0 9.8 10Zion 5068 37.5 113.2 5.2 838 10.0 13.2 9.7 10Scatter Plot for Big Bend using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for Big Bend using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for BOWA using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for BOWA using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for UPBU using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for UPBU using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for ACAD using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for ACAD using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for GRGU using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for GRGU using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for LYBR using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for LYBR using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for DOSO using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for DOSO using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for GRSM using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for GRSM using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for MACA using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for MACA using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for OKEF using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for OKEF using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for SHEN using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for SHEN using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for SHRO using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for SHRO using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for CORI using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for CORI using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for GICL using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for GICL using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for GRCA using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for GRCA using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for JARB using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for JARB using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for LOPE using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for LOPE using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for MORA using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for MORA using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for MOZI using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for MOZI using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for SNPA using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for SNPA using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for THSI using IMPROVE Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspScatter Plot for THSI using New Algorithm11010010001 10 100 1000Measured Bsp

Predicted BspTable A3. Mean light scattering and percent PM 2.5 composition for the five major components for 20% best days as determined by measurement, the current IMPROVE algorithm a nd 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 2IMPROVE 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 PM 2.5 composition for the five major components for 20% worst days as determined by measurement, the current IMPROVE algorithm a nd 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 3IMPROVE 56 36 4 25 3 30 3 NEW 59 35 3 26 3 30 3

GRSM1 Measured 153 52 1 22 3 20 2 IMPROVE 125 51 1 22 3 21 2 NEW 163 52 1 22 3 21 2

JARB1 Measured 24 8 3 21 12 54 2 IMPROVE 21 7 3 20 12 57 1 NEW 21 7 3 21 12 57 1

LOPE1 Measured 35 13 14 20 9 41 3 IMPROVE 33 12 15 20 9 41 3 NEW 31 11 13 21 9 42 3

LYBR1 Measured 83 42 8 23 4 21 3 IMPROVE 75 44 7 21 4 21 3 NEW 77 45 6 22 4 21 3

MACA1 Measured 106 42 11 23 3 19 3 IMPROVE 96 42 9 22 3 21 3 NEW 102 42 7 24 3 21 3

MORA1 Measured 42 20 3 37 5 31 4 IMPROVE 42 23 4 35 5 30 4 NEW 42 20 3 37 5 32 4

MOZI1 Measured 24 15 4 23 8 47 2 IMPROVE 21 15 4 22 8 49 2 NEW 20 13 3 24 9 49 2

OKEF1 Measured 87 31 3 17 4 43 2 IMPROVE 75 29 3 18 6 42 2 NEW 76 31 3 17 5 42 2

SHEN1 Measured 95 46 4 20 4 23 3 IMPROVE 94 48 5 20 3 22 2 NEW 108 47 3 21 3 24 2

SHRO1 Measured 77 36 5 20 7 29 3 IMPROVE 63 35 3 18 7 35 3 NEW 62 36 3 20 6 32 3

SNPA1 Measured 46 17 7 39 5 26 5 IMPROVE 36 17 7 35 6 31 5 NEW 37 15 6 37 6 32 5

THSI1 Measured 40 12 3 40 5 38 3 IMPROVE 34 12 3 36 5 41 3 NEW 37 12 2 38 5 40 3

UPBU1 Measured 123 42 3 20 5 27 2 IMPROVE 104 41 3 20 6 29 2 NEW 124 43 2 21 5 28 2