Appendix L.12: New Improve Equation Rationale

Appendix L.12 contents

L.12.1 Using the new IMPROVE equation

L.12.2 “Revised IMPROVE Algorithm for Estimating Light Extinction from Particle Speciation Data” by Dr. Ivar Tombach

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

L.12.4 “Instructions: A Postprocessor for R ecalculating CALPOST Visibility Outputs with the New IMPROVE Algorithm” by Dr. Ivar Tombach

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

L.12.6 Letter from EPA concerning the use of the New IMPROVE equationAppendix L. 12: Use of New IMPROVE Equation

During the regional haze SIP development proce ss, the IMPROVE Steering Committee made recommendations for a new IMPROVE equation. Am ong other things, the new equation includes components that take into account th e effects of sea salt and site sp ecific Rayleigh scattering. These are important components to appropriately cons ider for Mississi ppi’s coastal fac ilities.

VISTAS contracted Dr. Ivar Tomb ach to develop a post processor to allow CALPOST outputs to be used with the new IMPROVE equation. Dr. Tomb ach’s discussion of th e new IMPROVE equation and the post processor he devel oped is included in appendix L. 12.2, L.12.3, and L.12.4. A brief discussion of the rationale for use of the new IMPROVE equation follows.

Rationale for Need for Us e of New IMPROVE Equation The new IMPROVE equation is a much better repres entation of the effects of particulate matter on light extinction than the old equa tion and takes into account the la test scientific understanding of several parameters.

1. The new algorithm overcomes biases of the old algorithm on the haziest days and the clearest days as demonstrated by comparing the measured light extinction from nephelometers at Class I areas to light extinction calculated using each of the equations.

2. The new algorithm recognizes sp atial and temporal variation in light extinction as size distribution of the aerosol changes by increasing extinction efficiency as sulfate, nitrate and organics concentrations increase.

3. The new algorithm incorporates a term to reflect the contribu tion of fine sea salt and its hygroscopic growth with incr easing relative humidity rec ognizing research findings showing that fine sea salt can be an important cont ributor to light exti nction in coastal

areas.
4. The new algorithm refl ects research finding that the mass concentration of particulate organic matter in rural areas is greater than represented by the old equation.

5. The new algorithm includes a NO 2 term to represent times when light absorption by NO 2 is a meaningful contribu tor to light extinction.

6. The new algorithm incorporates site-specifi c Rayleigh scattering values to better represent sites close to sea level or with very hot or cold climates.

With this combination of revisions to the IM PROVE equation, the resu lting apportionment of extinction to various components is more accurate on the haziest and clearest days. This is important for develo pment of emission contro l strategies since the benefits of control of concentrations of each species will be represen ted more correctly with the new algorithm.Three of Mississippi’s BART eligible sources located in coastal areas, MS Power – Plant Daniel, Plant Watson, and Chevron Cogenerati on, chose to use the new IMPROVE equation. The ability to appropriately repr esent the contribution of sea salt for these sources is important in determining their contribution to haze and evaluating the benefit of potential control strategies. US EPA Region 4, National Park Se rvice, and the US Fish & Wildlife Service determined that adding site-specific data to account for the impact of sea salt and Rayleigh scattering to the old IMPROVE equation is not appropriate, but rather the entire new IMPROVE equation should be used. The sp readsheet developed by Dr. Tombach to implement the entire new IMPROVE equa tion for use with CALPUFF contains a modification of CALPOST that calculates the light extinction from the CALPUFF output. In their review of th e spreadsheet and s upporting documentation, EPA Region 4 staff commented that the VISTAS methodology contained in the spreadsheet developed by Dr. Tombach appropriately implements the featur es of the new IMPROVE equation, with the exception of the n itrogen dioxide term. EPA Region 4 furt her stated that with the addition of their recommendation for addressing the NO 2 term, the VISTAS meth odology to incorporate the new IMPROVE equation is acceptable on a case-by-case basis upon consultation with the EPA Regional Office.

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

I. Introduction Light Extinction Atmospheric light extinction is a fundamental metric used to ch aracterize ai r 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 an d 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 atmos pheric conditions. Absorption of visible light by gases in the atmosphere is primarily by NO 2 , and can be direc tly and accurately estimated from NO 2 concentrations by multiplying by the absorption efficiency. Scattering by gases is described by the Raylei gh scattering theory (van de Hulst, 1981). Rayleigh scattering depends on the density of th e atmosphere, with highest values at sea level (about 12Mm -1 ) and diminishing with elevation (8Mm -1 at about 12,000’), and varies somewhat at any elevation due to atmo spheric temperature and pressure variations. Rayleigh scattering can be accurately determ ined for any elevation and meteorological conditions.

Particle light extinction is mo re complex than that caused by gaseous components. Lightabsorbing carbon (e.g. diesel exhaust soot and smoke) and some crustal minerals are the only commonly occurring airborne particle co mponents that absorb light. All particles scatter light, and generally par ticle light scattering is the largest of the four light extinction components. If the index of refr action as a function of particle size is well characterized, Mie theory can be used to accurately calculate the light scattering and absorption by those particles. However, it is rare that these partic le properties are known, so assumptions are used in place of mi ssing 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. These data are routinely available at each IMPROVE monitoring site for use in estimating light extinction for the IMPR OVE program. At 21 IMPROVE monitoring sites (Table A1 in appendix), hourly-average d nephelometer and relative humidity data are also routinely available. Data from th ese sites have been key to evaluate the performance of the current IMPROVE algorithm, as well as for development and

* Light is a wavelength-dependent portion of the electro-magnetic spectrum. Traditionally for visibility-protection applications , the most sensitive portion of the spectrum for human vision (550nm) has been used to characterize light extinction and its components. For NO 2 light absorption, a photopic-wei ghted approach is used, as shown in section III.performance evaluation of pr oposed revised algorithms.

The current IMPROVE algorithm for estimating light extinction from IMPROVE particle monitoring data assumes th at absorption by gases ( b a,g ) is zero, that Ra yleigh 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 componentspecific light extinction effici encies. The six major components are sulfate (assumed to be ammonium sulfate), nitrat e (assumed to be ammonium 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 exti nction efficiency values are constants, except for the sulfate and nitrate extinction e fficiency terms that include a water growth factor that is a function of re lative 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, th e current algorithm for estimating light extinction from IMPROVE data takes the follo wing 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 ( μ g/m 3 ); dry efficiency terms are in un its of meters squared per gram (m 2 /g); and the water growth terms, f(RH), are unitless.

Among the implicit assumptions for this fo rmulation of the algorithm are that

• the six particle component terms plus a constant Rayleigh scattering term are sufficient for a good estimate of light extinction;

• constant dry extinction efficiency terms rounded to one significant digit for each of the six particle components (i.e. for both sulfate and nitrate the value is 3) works adequately for all locations and times; and

• light extinction contributed by the indi vidual particle components can be adequately estimated as separate terms as they would if they were in completely separate particles (externally mixed), though they often are known to be internally mixed in particles. 

## [ ] [ ] [ ]

[ ] [ ] [ ]

10

6

. 0 1 10 4 ) ( 3 ) ( 3

+ × + × + × + × + × × + × × ≈

Mass Coarse Soil Fine Carbon

Elemental Carbon Organic Nitrate

RH f Sulfate RH f b
extA relatively simple algorithm for estimati ng light extinction usi ng only the available monitoring data requires assumptions such as these.

Estimates of particle scattering by this al gorithm (i.e. excluding the light absorbing carbon and Rayleigh terms) have been compared to directly-measured particle scattering data at the 21 monitoring sites that ha ve hourly-averaged nephelometer and relative humidity data. As shown in Figure 1 belo w, the algorithm performs reasonably well over a broad range of particle light scattering values and monito ring locations. The algorithm tends to under-estimate the highest extinc tion values and over-estimate the lowest extinction values. Since it first use (IMPR OVE Report, 1993), the current algorithm has been a useful tool that contributed signifi cantly to a better understanding of haze levels and the relative magnitude of haze contri bution 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 used to track progress in reducing haze levels for visibility-protected areas under the 1999 Regi onal Haze Rule (RHR). As a result the IMPROVE algorithm has been scru tinized carefully to assess de ficiencies 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 determinati on of the mean of the annual 20% best and 20% worst haze days for each of the IMPROVE monitoring site s that represent the visibility-protected areas. States are asked to manage emissions so that over a 60-year period the worst hazedays will improve to natural c onditions without degrading visibility conditions for the best haze days. For consistency, the sa me approach (i.e. IMPROVE algorithm and conversion to the deciview haze index) is also used to estimate natural haze levels for each representative monitoring site using estimates of the natural concentration levels for the major particle components. For each lo cation, the linear rate of reduction of the deciview values for the worst haze days duri ng the baseline period (2000 to 2004) that is needed to reach the estimated worst haze da ys under natural conditions by 2064 must be determined. This linear rate is used as a gui de to pace the desired rate of haze reduction and to determine interim visibility goals that are compared to the monitoring data trends of the best and worst haze days.

The RHR emphasizes the extremes of light extin ction 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 i ndex means that additive biases in the light extinction estimates (e.g. the use of a standard Rayleigh s cattering term for all sites regardless of elevation) will affect the calculation of a linear glide slope, which is used to set the pace of emission reductions. Use of the IMPROVE algorithm for the RHR elicited concerns about possible biases in the apportionment among the various major particle components. Such issues have been th e subject of a number of critical reviews of the use of the IMPROVE algorithm in the RHR (Lowenthal and Kumar, 2003; Ryan et al., 2005).

In light of the concerns raised by its us e in the RHR, the IMPROVE Steering Committee initiated an internal review including reco mmendations for revisions of the IMPROVE algorithm for estimating light extinction. The review team (composed of National Park Service and Cooperative Institute for Research in the Atmosphere scientists) reviewed pertinent literature and empl oyed both Mie theory modeling and statistical assessment methods to identify deficiencies in the current algorithm and evaluate possible refinements. The goal was to develop a revi sed algorithm that reduces biases in light extinction estimates, and is as consistent as pos sible with the current scientific literature while constrained by the need to use only those data that are routinely available from the IMPROVE particle monitoring network. A preliminary report by this team was presented in June, 2005 at a national Re gional Planning Organization workshop in Denver Colorado hosted by the Western Region al Air Partnership (WRAP) and broadly participated in by those with an interest in the regional haze rule. The preliminary results were also summarized in presentations at the July 2005 IMPROVE Steering Committee meeting in Acadia Maine. The full report of th is review is available elsewhere (Hand and Malm, 2005).

Purpose and Organization This document is a summary report by a subcommittee established by the IMPROVE Steering Committee at their July 2005 meeti ng 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 in ternal review as well as scientists whohave been critical of the original IMPROVE algorithm. † The primary purpose of this document is to describe the subco mmittee’s recommended revised algorithm, characterize its performance, and summarize th e rationale for each of the changes from the currently used algorithm. This document is the principal means to communicate the recommendations to the IMPROVE Steering Committee prior to their deliberation and vote on the adoption of a new algor ithm. Others with an interest in th is 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 reco mmended revised algorithm for estimating light extinction using IMPROVE particle data and sh ows its performance co mpared to that of the currently used approach. Section III provide s the technical justifi cation of each of the revised terms in the r ecommended algorithm.

REFERENCES

Hand, J.L. and W.C. Malm, 2005: Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients , on the IMPROVE web site at http://vista.cira.colostate.edu/improve/P ublications/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 li ght extinction reconstruction in Interagency Monitoring of Protected Visual Environments. J. Air & Waste Manage. Assoc. 55 , 1751-1759.

van de Hulst, H. C.

, Light scattering by small particles, New York: Dover, 1981



## II. Overview of the Revised Algorithm

The recommended revised algorithm is shown in the equation below with revised terms in bold font. The total sulfate, nitrate and organic carbon compound concentrations are each split into two fractions, representing sm all and large size distributions of those components. Though not explicitly s hown in the equation, the organic mass concentration used in this new algorit hm 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 scatte ring is calculated for the elevation and annual average temperature of each of the IMPROVE monitori ng sites as shown in the Table A at the

† 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, CIRAend of the document.

The apportionment of the total concentr ation 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 apporti on total nitrate and total organic carbon concentrations into the small and large size fractions.

Sea salt is calculated as 1.8 x [ Chloride ], or 1.8 x [ Chlorine ] if the chloride measurement is below detection limits, missing or invali d. The algorithm uses three water growth adjustment term as shown in the Figure 2 and Table 1. They are for use with the small size distribution and the large size distribution sulfate and nitrate compounds and for sea salt ( f

S (RH), f L (RH) and f SS (RH) respectively). 

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

[ ] 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 C a r b o n O r g a n i c a r g e L 6

. 1 C a r b o n 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 μ ≥ = 

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

### / 20

, / 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

− =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 7.35

Algorithm Performance Evaluation

Performance of the current and proposed new algorithm for estimating extinction can beassessed in a number of ways each of whic h 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 mo st compelling reasons for development of a new algorithm, so comparisons of bias for the current a nd 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. These biases were then averaged in each quintile to indi cate 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 perfor med, 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 nephe lometer as the better measure of light scattering, bias by this crite rion better addresses the quest ion of algorithm performance with regards to the haze conditions. Crite rion 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 addresse s the haze rule application of the algorithm. Tables 2 through 5 show the bias results by both criteria for the current and new algorithm for sites averaged by RPO.

Table 2. Averaged fractional bias by RPO for the current IMPROVE algorithm with quintiles based on measured light scatt ering (criterion 1). Bold font highlights the bias values that are lower than corresponding values in Table 3.

RPO Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Average CEN 0.67 0.18 0.10 0.02 -0.11 0.17 MANE 0.93 0.27 0.19 0.10 0.01 0.28 VISTAS 0.59 0.21 0.11 0.02 -0.13 0.16 WRAP 1.07 0.37 0.18 0.07 -0.08 0.32

Table 3. Averaged fractional bias by RP O for the current new proposed algorithm with quintiles based on measured light scatt ering (criterion 1). Bold font highlights the bias values that are lower than corresponding values in Table 2.

RPO Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Average CEN 0.51 0.08 0.02 -0.03 -0.08 0.10 MANE 0.74 0.14 0.06 0.01 -0.02 0.17 VISTAS 0.50 0.16 0.11 0.06 -0.01 0.16 WRAP 0.84 0.25 0.08 0.01 -0.10 0.21 Table 4. Averaged fractional bias by RPO for the current IMPROVE algorithm with quintiles based on estimated light scatt ering (criterion 2). Bold font highlights the bias values that are lower than corresponding values in Table 5.

RPO Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 AverageCEN 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 RP O for the current new proposed algorithm with quintiles based on estimated light scatt ering (criterion 2). Bold font highlights the bias values that are lower than corresponding values in Table 4.

RPO Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Average CEN 0.23 0.11 0.08 0.06 0.02 0.10 MANE 0.15 0.14 0.15 0.19 0.21 0.17 VISTAS 0.25 0.14 0.08 0.13 0.21 0.16 WRAP 0.37 0.19 0.19 0.16 0.17 0.21

These tables show that the new algorithm has lower fractional bias than the current IMPROVE algorithm in all but the haziest cond itions (i.e. quintile 5) regardless of the criterion used to sort the da ta into quintiles. By criteri on 1, the two algorithms perform about the same for haziest days except for th e sites in the southeastern U.S. (i.e. the VISTA RPO), where the new al gorithm has much lower bias (1% compared to 13%). Using criterion 2, the current algorithm has c onsistently 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 greate r imprecision of the new algorithm compared to the current algorithm, which results in somewhat larger errors in selecting worst haze sample periods for the new algorithm compared with the current algorithm.

Scatter plots (Figures 1 and 3) of light sc attering estimates from the current and new proposed algorithms versus nephelometer data for all available data at 21 monitoring sites are one way to view the overall perf ormance 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. th e points tend to be be tter centered on the oneto-one line). They also show that the some what reduced precision of the new algorithm compared to the original IMPROVE algorithm (i.e. points are more broadly scattered).0 50 100 150 200 250 300 350

0 50 100 150 200 250 300 350 Measured 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 prepared fo r each individual mon itoring site (available in the appendix). Figures 4 and 5 are example plots for Shenandoah and Grand Canyon National Parks. The logarithmic scales on these plots exaggerate the scatter for low values compared to high values. The i ndividual-site scatte r plots have the 80 th percentile values indicated on the graphs for the predic ted and measured values by horizontal and vertical lines respectively. Points that ar e to the right of th e vertical line have nephelometer values that are among the 20% wo rst light scattering for that monitoring sites. Points that are above the horizontal line have algorithm determined values that are among the 20% worst estimated light scattering for that monitoring site.

Scatter Plot for SHEN using IMPROVE Algorithm

1 10 100 1000

1 10 100 1000 Measured 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 noticeab ly 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 Shenandoa h 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 differences between the two algorithms for Grand Canyon National Park and mo st of the other less hazy locations are not apparent in these scatter plots.

The final approach for evaluating the relative performance of the two algorithms is to compare the average composition of the best haze days and the worst haze days as selected using each algorithm and using the measured light scattering. Table 6 and 7 contain the average composition by RPO for da ys selected as best and worst by these three methods. Similar results for each of the 21 nephelometer monitoring locations are shown in tables in the appendix.

Table 6. Mean light scattering and percent PM 2.5 composition for the five major components for 20% best days as de termined by measurement, the current IMPROVE algorithm and the proposed new algorithm.

RPO Mean Bsp (Mm-1) Percent Ammonium Sulfate Percent Ammonium Nitrate Percent OCM Percent Soil Percent Coarse Percent EC Measured 6.8 19 2 15 8 54 2 IMPROVE 9.1 20 2 15 7 53 2 CENRAP NEW 8.1 21 3 16 7 51 2 MANEVU Measured 6.1 22 3 22 4 47 3IMPROVE 8.4 21 3 22 4 47 3 NEW 7.4 22 4 22 4 45 3 Measured 13.8 25 7 21 4 40 3 IMPROVE 18.4 25 7 21 4 40 3 VISTAS NEW 17.0 25 8 21 4 39 3 Measured 3.4 13 3 18 8 55 3 IMPROVE 5.2 14 3 19 8 53 3 WRAP 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 de termined by measurement, the current IMPROVE algorithm and the proposed new algorithm.

RPO Mean Bsp (Mm-1) Percent Ammonium Sulfate Percent Ammonium Nitrate Percent OCM Percent Soil Percent Coarse Percent EC Measured 76 34 6 19 5 34 2 IMPROVE 67 34 6 19 5 33 2 CENRAP NEW 72 34 6 19 6 34 2 Measured 61 36 6 23 3 30 3 IMPROVE 61 36 6 22 3 30 3 MANEVU NEW 63 35 6 23 3 31 3 Measured 120 46 5 21 3 22 2 IMPROVE 106 47 4 21 3 23 2 VISTAS NEW 127 47 3 22 3 22 2 Measured 36 15 6 27 7 42 3 IMPROVE 33 14 6 27 6 44 3 WRAP NEW 33 13 6 27 6 44 3

These tables demonstrate that the composition associated with the best and worst haze days are not very sensit ive to the method of identifying the sample periods that fit in best and worst categories. Some of the indivi dual sites (e.g. Grand Canyon) have somewhat larger variations in the composition between measurement-selected days compared to algorithm-selected days, though there’s little difference between the average composition comparing the two algorithms on the best and worst days. The contributions to light extinction by the various components were not explicitly calculated, but are inherently somewhat different because of the explic it differences in the two algorithms.

In summary, the proposed new algorithm for estimating haze reduces the biases compared to measurements at the high and low extremes. This is most apparent for the hazier eastern sites. The composition of days selected as best and worst by the current and the new algorithm are very similar, and si milar to days selected by measurements. Most of the reduction of bias a ssociated with the new algorithm is attributed to the use of the split component extinction efficiency method for sulfate, nitrate and organic components that permitted variable extinction efficiency depending on the component mass concentration. Though not subject to explicit performance testing, the proposed new algorithm also contains sp ecific changes from the current algorithm that reflect a better understanding of the atmosphere as reflected in the more recent sc ientific literature(e.g. change to 1.8 from 1.4 for organic com pound 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 Rayleigh scattering terms to reduce elevation-related bias.



### III. Technical Justification for Revisions

Five major revisions to the IMPROVE algor ithm for estimating light extinction from IMPROVE particle speciation data are incor porated 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 extincti on efficiency model for sulfate, nitrate and organic carbon components including new water growth terms for sulfate and nitrate to better es timate light extinction at the high and low extremes 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 rationale for ma king each of these changes is described in separate sections below.

Sea Salt

The current IMPROVE protocol for estimati ng light extinction does not include light scattering (b sp ) by sea salt aerosols. Lowenthal an d Kumar (2003) demonstrated that inclusion of elements from sea salt (e .g., Na, Cl) increased the accuracy of mass reconstruction at coastal IMPROVE sites. Cont ributions 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 e nhanced by hygroscopic growth in humid environments. Lowenthal and Kumar (2005) f ound 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 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 f unction of relative humidity (RH).

Sea Salt Mass Concentration Estimating sea salt mass requires a sea salt marker species measured in IMPROVE aerosol samples. The most obvious such ma rkers are sodium (Na) and chlorine (Cl), since NaCl is the main component in sea wa ter and sea salt. Based on the composition ofsea 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 sample s 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 conservati ve tracers) and Cl, it is not possible to estimate how much Cl has been replaced by nitr ate and/or sulfate in ambient samples. Further, without chemical speciation of the PM 10 sample (Module D of the IMPROVE sampler), it is not possible to estimate coarse sea salt scattering.

Given these limitations, it is recommended that the 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 chlori ne (Cl) at lower concentrations, the Amodule sample for XRF is more exposed to re active 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 considered. To the degree that chloride has been replaced by sulfate or nitrate in ambient particles, this approach will underestimate the mass and scattering contributed by the substituted sea salt that results (e.g. NaNO 3 , NaHSO 4 , or Na 2 SO 4 ). This mass is partially accounted for by ammonium sulf ate and ammonium nitrate in the IMPROVE equation. Howeve r, the substituted Na salt mass is underestimated because ammonium is lighter th an sodium. The scattering is also underestimate because the sodium salts absorb more water than does am monium sulfate above 60% RH. Given the limitations of the avai lable data, 1.8 times chloride provides a reasonable lower-limit to the fine sea salt mass.

Dry Scattering Efficiency In order to estimate the dry scattering efficien cy and f(RH) for sea salt aerosols, their dry mass size distribution must be known. While this has not been measured at most IMPROVE sites, extensive sea salt size distribu tion measurements have been made in the remote marine environment during cruise d-based experiments (Quinn et al., 1995, 1996, 1998). Based on these studies, a dry log-norma l mass size distribution with a geometric mean diameter (D g ) of 2.5 μm and geometric 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 distribu tion assuming a sea salt refractive index of [1.55+ i0] and a density of 1.9 g cm -3 recommended by Quinn et al. (1995).

Sea Salt f(RH) Tang et al. (1997) determined hygroscopic gr owth 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 crystall ization point (RH = 47%), the growth factorset to one. Values are presented to RH = 95% , to which higher RH are “rolled back” under the Regional Haze Rule protocol (USE PA, 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 distribu tion, refractive index, and dens ity 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 dr y mass size distributi on using Mie theory, accounting for the change in particle 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 m onthly, site-specific “clim atological” values, as was done for ammonium sulfate/ammonium nitr ate. 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 4.8573 65 1.7629 2.8594 90 2.4174 5.1165 66 1.7751 2.8943 91 2.4898 5.3844 67 1.7877 2.9105 92 2.5749 5.7457 68 1.8008 2.9451 93 2.6769 6.1704 69 1.8145 3.0105 94 2.8021 6.7178 70 1.8286 3.0485 95 2.9610 7.3492

a Diameter at RH/Dry Diameter

REFERENCES Clegg, S.L.; Brimblecombe, P.; Wexler, A.S., A Thermodynamic Model of the System H + -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.; Ge rmani, M.S. Measurements of Chloride Depletion and Sulfur Enrichme nt in Individual Sea-Salt Particles Collected from the Remote Marine Boundary Layer; J. Geophys. Res.

, 1994

, 99 , 8257-8268. Pytkowicz, R.M.; Kester, D.R. The Physical Chemistry of Sea Water; Oceanogr. Mar. Biol. Ann. Rev.

, 1971

, 9 , 11-60. Quinn, P.K.; Marshall, S.F.; Bates, T.S.; Covert, D.S.; Kapustin, V.N. Comparison of Measured and Calculated Aerosol Propert ies 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.; Cove rt, D.S. Chemical and Optical Properties of Marine Boundary Layer Aerosol Partic les 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) a nd 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. Thermody namic And Optical Properties of Sea Salt Aerosols; J. Geophys. Res.

, 1997

, 102 , 23,269-23,275. USEPA. Draft Guidance for Tracking Progress Under the Regional Haze Rule ; U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards: Research Triangle Park, NC, Se ptember 27, 2001. Available at: Http:/Vista.Cira.Colostate.Edu/IMPROVE /Publications/Guidancedocs/Guidanced ocs.Htm.

Organic Mass to Organic Carbon Ratio

A factor of 1.4 is currently used in the “I MPROVE equation” to convert OC to organic mass (OM) to account for unmeasured elements (e.g. O, H, N) in OM. The value of 1.4 was based on an experiment conducted by Gr osjean and Friedlander (1975) in urban Pasadena, CA in 1973. They found that the carbon content of these samples averaged 73%. White and Roberts (1977) suggested an OC to OM conversion factor (OM/OC) of 1.4 on the reciprocal of 0.73. Andrews et al. (2000) attempted to explain the reconstructed mass deficit during SEAVS (South eastern Aerosol and Visibility Study) at Great Smoky Mountains National Park in terms of underestimation of OM.

Turpin and Lim (2001) recommended the use of OM/OC factors of 1.6±0.2 and 2.1 ±0.2 for urban and non-urban aeros ol, respectively, based on th e chemical structure of organics compounds found in such environments. This is cons istent with an expectation that OM/OC ratio should incr ease as aerosols age during transport and photochemicalreactions produce secondary organic compounds that are more oxygenated than their primary precursors. Krivácsy et al. (2001) isolated the polar, water-soluble organic carbon fraction of aerosols from the Jungfra ujoch, Switzerland using solid phase extraction. An OM/OC ratio of 1.91 was infe rred from elemental co mposition (C, N, H, and S). Poirot and Husar (2004) found th at 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 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 Yo semite National Park in summer, 2002. ElZanan et al. (2005) derived OM /OC ratios of 1.92±0.40 from so lvent extracts of archived filter samples from five IMPROVE sites and 2.07±0.32 from chemical mass balance in 40,532 daily IMPROVE samples at 50 sites from 1988-2003.

While additional experimental work is needed to further explore this issue, it is clear that an OC conversion factor or 1.4 is not applicable for remote U.S. national parks. A consensus value of 1.8 is recommended for use in the proposed new algorithm.

REFERENCES Andrews, E., Saxena, P., Musarra, S., Hild emann, L.M., Koutrakis, P., McMurry, P.H., Olmez, I., and White, W.H., 2000: Concen tration 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: Ga s-particle distributi on 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., Baltens perger, 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., Carric o, C., Kreidenweis, S.M., Collett, J.L., Jr., McMeeking, G., Lee, and Carillo, J., 2005: Intercompa rison and closure calculations using measurements of aerosol species and op tical properties during the Yosemite Aerosol Characterization Study, J. Geophys. Res. , doi:10.1029/2004JD005494, in
press. Poirot, R.L., and Husar, R.B., 2004: Chemi cal and physical characteristics of wood smoke in the northeastern U.S. during July 2002: Impacts from Quebec forest fires, Paper #94, A&WMA Specialty Conference: Regional and Global Perspectives on Haze: Causes, Consequences and Controversies, Ashville, NC, October 25-29, 2004. Turpin, B.J., and Lim, H.-J ., 2001: Contributions to 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 visibility-reducing aerosols in the Los Angeles air basin. Atmos. Environ.

, 11 , 803-812.Split Component Extinction Efficiency Model

Concentration-Varying Dry Scattering Efficiencies The current IMPROVE algorithm employs dr y scattering efficiencies (E) of 3 m 2 /g for ammonium sulfate and a mmonium nitrate and 4 m 2 /g for organic matter (OM). Data from IMPROVE special studies sugge st that dry extinction effi ciencies are variable. Lowenthal and Kumar (2005) found that PM 2.5 mass scattering efficiencies increased with increasing levels of pa rticle light scattering and ma ss concentration. This was attributed to growth of the dry particle size distribution into size ranges with higher scattering efficiencies under more-polluted c onditions, which is related to a higher degree of cloud processing during transport. Malm et al. (2003) estimated dry ammoniated sulfate scattering efficien cies 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 algorithm accounts for the increase of ammonium sulfate/ammonium nitrate and organic matter (O M) efficiencies with concentration using a simple mixing model where the concentra tions of ammonium sulfate, ammonium nitrate, and OM are each comprised of exte rnal mixtures of mass in small and large particle size modes. The large mode repres ents aged and/or cloud processed particles, while the small mode represents freshly form ed particles. These size modes are described by log-normal mass size distributions with geometric 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 fo r 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 de scribed above. The ammonium sulfate, ammonium nitrate, and OM densities and refr active 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 possibl e difference in composition between the two size modes of these particles.

f(RH) The current IMPROVE algorithm applies a sing le f(RH) curve to ammonium sulfate and ammonium nitrate s cattering which is based on a hygroscopic growth curve (D (RH) /D (Dry) ) (particle diameter at ambient RH divide d by the dry particle diameter) for pure ammonium sulfate that was smoothed betw een 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) fo r 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 pur e ammonium sulfate derived from the AIM thermodynamic equilibrium model (Clegg et al ., 1998). This growth curve represents the upper branch, also referred to as the efflorescence or hysteresis branch, of the ammonium sulfate growth curve. The upper branch is us ed because deliquescence is rarely observedin the environment. Because pure ammonium sulfate crystallizes at 37% RH, it is assumed that there is no hygroscopic growth a nd 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 sca ttering at RH = 37-95% at unit RH intervals (b sp(RH) ) was calculated by applying the AIM a mmonium sulfate growth curve to the small and large dry mode size distributions using Mie theory, account ing for the change in particle volume and refractive index from th e 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 wh ich higher RH are “rolled back” under the Regional Haze Rule protocol (USEPA, 2001). Th e same f(RH) are applied to small- and large-mode ammonium sulfat e and ammonium nitrate.

Table 9. Water growth for the small and la rge 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.; 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., 2004: Variation of Mass Scatte ring 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 partic les during the Big Bend Regional Aerosol andVisibility Study. J. Geophys. Res

., 2003

, 108 , 4279, doi: 10.1029/2002JD002998. USEPA. Draft Guidance for Tracking Progress Under the Regional Haze Rule ; U.S. Environmental Protection Agency, Offi ce of Air Quality Planning and Standards: Research Triangle Park, NC, Se ptember 27, 2001. Available at: Http:/Vista.Cira.Colostate.Edu/IMPROVE/P ublications/Guidancedocs/Guidancedocs.Ht

m.

Site Specific Rayleigh Scattering

Rayleigh scattering refers to th e 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 th e air and thus varies with temperature and pressure. Site-specific Rayl eigh scattering was estimated using a Rayleigh Scattering Calculator developed by Air Res ource Specialists, Inc. that calculates Rayleigh scattering as a function of temperature and pressure. Fo r each IMPROVE site, we used the standard U.S. atmospheric pressure corresponding to the monitoring site elevation, and an estimated annual mean temperature. The temper ature data were obtained from the nearest weather stations for time periods encompassi ng 10 to 30 years and were interpolated to the monitoring site location. Table A2 (at the end of the document) shows the sitespecific Rayleigh scattering calculated using this procedure. The recommended integerrounded site-specific values are shown in the la st column of the table. They range from 12Mm -1 for sites near sea level to 8Mm -1 for sites at about 12,000 feet elevation.

NO 2 Absorption

The NO 2 absorption efficiency term (i.e. 0.33Mm -1 /ppm) in the proposed new algorithm is a photopic-weighted abso rption 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 res ponse 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 Dixon (1940) and available in PLUVUE Users Manual (1980), wh ere they were giving in 10 η m increments that were interpolated to generate 1 η m values. The photopic response values are from the CIE Ybar function downloaded directly from the CV RL Color and Vision database. Both are shown in the figure below.REFERENCES J.K. Dixon, "The Absorption Coefficient of Nitrogen Dioxide in the Visible Spectrum", Journal of Chemical Physics, Volume 8, February, 1940. pp 157-161

Draft Final Report: User's Manual For The Plume Visibility Model (PLUVUE), July, 1980, Systems Applications, Inc.

CVRL Color & Vision database, CIE (1931) 2-deg color matching function, http://wwwcvrl.ucsd.edu/cmfs.htmTable A1. IMPROVE monitoring sites wi th nephelometers used to evaluate algorithm performance. Abbreviation Name State ACAD Acadia National Park Maine BIBE Big Bend National Park Texas BOWA Boundary Waters Canoe Area Minnesota CORI Columbia River Gorge Washington DOSO Dolly Sods/Otter Creek Wilderness West Virgina GICI Gila Wilderness New Mexico GRCA Grand Canyon National Park Arizona GRGU Great Gulf Wilderness New Hampshire GRSM Great Smoky Mountains Tennessee JARB Jarbidge Wilderness Nevada LOPE Lone Peak Wilderness Utah LYBR Lye Brook Wilderness Vermont MACA Mammoth Cave National Park Kentucky MORA Mount Rainier National Park Washington MOZI Mount Zirkel Wilderness Colorado OKEF Okefenokee National Wildlife Refuge Florida SHEN Shenandoah National Park Virginia SHRO Shining Rock Wilderness North Carolina SNAP Snoqualamie Pass Wilderness Washington THIS Three Sisters Wilderness Oregon UPBU Upper Buffalo Wilderness ArkansasTable A2. Site-specific Rayleigh values for all IMPROVE monitoring sites. Revised algorithm uses values rounded to whole integervalues (last column).Monitoring Site Name Elevation(ft)LatitudeLongi-tudeStandardU.S. AtmospherTemp. (C)StandardU.S.AtmospherePressure(mb)Rayleigh atStandardAtmosphere(Mm-1)AnnualAverageTemp. (C)CorrectedRayleigh(Mm-1)CorrectedRayleighrounded tointeger(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) 6940 39 120.1 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 National WildlifeRefuge16 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 National Park 8125 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 National Park 5901 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)+F76 6199 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 Moon NM(USDOE)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 46.8 111.7 -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 Dunes NationalMonument8213 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 Volcanoes NationalPark3949 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 92.9 12.4 964 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 79.513.3 979 11.313.0 11.3 11Jarbidge Wilderness 6173 41.9 115.4 3 803 9.6 7.1 9.5 9Jefferson/James River FaceWilderness918 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 National Park 7141 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 National Park 1401 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 48.7 121.1 11.5 946 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 44.5 72.9 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 National Monument 3060 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 44.2 114.9 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 National Park 3601 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 38.9 120 2.8 802 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 National Park 210 18.3 64.8 14.8 1008 11.6 26.1 11.1 11Voyageurs National Park 1 1138 48.6 93.2 12.9 973 11.3 3.1 11.7 12Voyageurs National Park 2 1407 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 National Forest 11211 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 National Park 1 7744 44.6 110.4 -0.1 757 9.2 -0.2 9.2 9Yellowstone National Park 2 7954 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 determ ined by measurement, the current IMPROVE algorithm and the proposed new algorithm.

Site Mean Bsp (Mm-1) Percent Ammonium Sulfate Percent Ammonium Nitrate percent OCM Percent Soil Percent Coarse Percent EC Measured 6.3 20 3 21 4 50 3 IMPROVE 8.6 20 3 21 4 49 3 ACAD1 NEW 7.7 20 3 22 4 49 3 Measured 6.3 17 2 11 10 59 2 IMPROVE 8.7 19 2 11 9 57 2 BIBE1 NEW 7.9 18 2 11 9 57 2 Measured 5.4 26 3 21 5 43 2 IMPROVE 7.7 24 2 23 5 44 2 BOWA1 NEW 6.6 26 3 20 5 44 2 Measured 5.7 10 3 17 5 61 3 IMPROVE 8.8 9 4 18 4 62 3 CORI1 NEW 8.4 10 4 18 5 60 4 Measured 15 26 5 20 4 42 3 IMPROVE 20 24 6 20 4 42 3 DOSO1 NEW 19 25 6 19 4 42 3 Measured 2.9 16 2 19 8 52 3 IMPROVE 4.9 16 2 20 8 50 3 GICL1 NEW 4.1 17 2 20 8 48 3 Measured 2.0 14 3 15 9 55 2 IMPROVE 2.8 18 4 16 11 48 3 GRCA2 NEW 2.5 18 5 15 10 48 3 Measured 5.4 26 3 23 2 43 2 IMPROVE 8.0 25 4 21 3 44 3 GRGU1 NEW 6.8 25 4 22 3 44 3 Measured 15 24 6 23 4 39 4 IMPROVE 20 24 6 24 4 38 4 GRSM1 NEW 20 25 7 23 3 38 4 Measured 1.8 11 2 21 8 56 2 IMPROVE 3.6 12 3 22 7 54 2 JARB1 NEW 3.2 12 3 22 7 54 2 Measured 5.3 12 6 19 9 50 4 IMPROVE 8.2 12 6 18 10 50 4 LOPE1 NEW 7.2 13 7 18 10 49 4 Measured 5.3 24 6 25 4 38 3 IMPROVE 8.2 24 4 27 4 38 3 LYBR1 NEW 7.0 25 5 26 4 38 3 Measured 18 27 7 21 4 37 3 IMPROVE 22 25 9 21 4 36 4 MACA1 NEW 19 26 9 20 4 37 4 Measured 5.5 14 3 31 4 45 4 MORA1 IMPROVE 6.8 14 3 27 4 49 3NEW 6.2 14 3 28 4 48 4 Measured 2.8 12 3 18 8 57 2 IMPROVE 4.8 13 4 18 8 55 3 MOZI1 NEW 4.1 13 4 17 7 56 3 Measured 19 21 3 19 3 50 3 IMPROVE 23 20 3 19 3 52 3 OKEF1 NEW 21 22 3 20 3 49 3 Measured 11 27 10 18 4 39 3 IMPROVE 14 26 10 19 4 38 3 SHEN1 NEW 13 26 10 19 4 38 3 Measured 4.6 16 2 21 6 53 3 IMPROVE 8.9 19 2 22 4 50 3 SHRO1 NEW 8.0 19 2 21 4 51 3 Measured 7.3 14 5 22 5 49 4 IMPROVE 10.0 13 6 26 5 44 5 SNPA1 NEW 8.7 15 7 25 5 43 5 Measured 5.4 10 2 23 4 58 3 IMPROVE 7.1 12 3 23 5 54 3 THSI1 NEW 6.4 11 3 22 4 57 3 Measured 13 15 4 16 5 57 2 IMPROVE 14 15 6 17 5 55 2 UPBU1 NEW 13 15 6 17 5 55 2

Table A4. Mean light scattering and percent PM 2.5 composition for the five major components for 20% worst days as determ ined by measurement, the current IMPROVE algorithm and the proposed new algorithm.

Site Mean Bsp (Mm-1) Percent Ammonium Sulfate Percent Ammonium Nitrate percent OCM Percent Soil Percent Coarse Percent EC Measured 58 34 6 22 3 32 3 IMPROVE 59 34 6 21 3 33 3 ACAD1 NEW 62 34 6 22 3 33 3 Measured 49 34 2 16 7 39 2 IMPROVE 44 33 2 15 8 41 2 BIBE1 NEW 46 33 2 15 8 41 2 Measured 53 25 13 23 4 32 2 IMPROVE 49 27 13 22 4 32 2 BOWA1 NEW 45 23 13 23 4 35 2 Measured 56 13 11 25 5 43 3 IMPROVE 48 13 10 24 5 45 3 CORI1 NEW 48 12 10 24 5 46 3 Measured 109 44 2 21 3 28 2 IMPROVE 105 47 2 21 3 26 2 DOSO1 NEW 121 46 1 20 3 29 2 Measured 25 18 2 25 8 44 2 IMPROVE 24 19 2 26 8 43 2 GICL1 NEW 23 18 2 26 9 44 2Measured 19 16 3 19 11 50 2 IMPROVE 19 11 2 18 10 57 1 GRCA2 NEW 18 11 2 18 10 57 1 Measured 58 35 3 24 3 33 3 IMPROVE 56 36 4 25 3 30 3 GRGU1 NEW 59 35 3 26 3 30 3 Measured 153 52 1 22 3 20 2 IMPROVE 125 51 1 22 3 21 2 GRSM1 NEW 163 52 1 22 3 21 2 Measured 24 8 3 21 12 54 2 IMPROVE 21 7 3 20 12 57 1 JARB1 NEW 21 7 3 21 12 57 1 Measured 35 13 14 20 9 41 3 IMPROVE 33 12 15 20 9 41 3 LOPE1 NEW 31 11 13 21 9 42 3 Measured 83 42 8 23 4 21 3 IMPROVE 75 44 7 21 4 21 3 LYBR1 NEW 77 45 6 22 4 21 3 Measured 106 42 11 23 3 19 3 IMPROVE 96 42 9 22 3 21 3 MACA1 NEW 102 42 7 24 3 21 3 Measured 42 20 3 37 5 31 4 IMPROVE 42 23 4 35 5 30 4 MORA1 NEW 42 20 3 37 5 32 4 Measured 24 15 4 23 8 47 2 IMPROVE 21 15 4 22 8 49 2 MOZI1 NEW 20 13 3 24 9 49 2 Measured 87 31 3 17 4 43 2 IMPROVE 75 29 3 18 6 42 2 OKEF1 NEW 76 31 3 17 5 42 2 Measured 95 46 4 20 4 23 3 IMPROVE 94 48 5 20 3 22 2 SHEN1 NEW 108 47 3 21 3 24 2 Measured 77 36 5 20 7 29 3 IMPROVE 63 35 3 18 7 35 3 SHRO1 NEW 62 36 3 20 6 32 3 Measured 46 17 7 39 5 26 5 IMPROVE 36 17 7 35 6 31 5 SNPA1 NEW 37 15 6 37 6 32 5 Measured 40 12 3 40 5 38 3 IMPROVE 34 12 3 36 5 41 3 THSI1 NEW 37 12 2 38 5 40 3 Measured 123 42 3 20 5 27 2 IMPROVE 104 41 3 20 6 29 2 UPBU1 NEW 124 43 2 21 5 28 2

### DRAFT 10/26/06

Why Use the New IMPROVE Algorithm?

The new IMPROVE algorithm, which has been approved by the IMPROVE Steering Committee, is a much better representation of the effects of particulate matte r on light extinction than the old IMPROVE algorithm. Excellent just ification of this conclusion is provided in the November 2005 report of the IMPROVE Steering Comm ittee, “Revised IMPROVE Algorithm for Estimating Light Extinction from Particle Speciation Data.”

The new algorithm incorporates changes that repr esent much of what has been learned about the relationship between particulate matter component concentrations and lig ht extinction in the more than a decade since the old algorithm was developed. The main reasons that justify using the new algorithm instead of the old one are the following:

1. The new algorithm overcomes the biases of the old algorithm on the haziest days (when the old algorithm underestimates light extincti on) and on the clearest days (when the old algorithm overestimates extinction). This impr ovement can be seen by comparing Figures 1 and 3 in the report mentioned above.

2. The new algorithm recognizes that light extinc tion efficiencies vary from location to location and time to time as the size distribution of the aerosol changes, and it reflects this variation by increasing the extinction effi ciency as the concentr ations of sulfates, nitrates, and organics increase. It also recognizes that the hum idity-related growth rates of particles differ depending on th eir sizes. As a result, the new algorithm generally does a better job of representing light extinction at individual sites than doe s the old algorithm, as indicated by the reduced fractional biases in each RPO region shown in Tables 3 and 5 (versus Tables 2 and 4) in the report mentioned above

3. The new algorithm recognizes research findings that show that fine sea salt can be an important contributor to light extinction in coastal areas, a nd thus includes a new term that reflects its contribution a nd its hygroscopic growth with increasing relative humidity. The old algorithm ignores sea salt, probably because it was originally developed mainly for inland Class I areas in the inter-mountain West (although incor porating sea salt into the calculation was already considered in a Un iversity of California at Davis memo in 1995).

4. The new algorithm acknowledges that substantia l recent research has shown that the mass concentration of particulate organic ma tter (often denoted by OMC in IMPROVE publications) in rural areas is a greater multiple of the measured OC concentration than the 1.4 value used in the old algorithm. The new algorithm updates the factor to a moreappropriate1.8.5. The new algorithm recognizes that there may be times (near urban areas or in some source emission plumes) when light absorption by NO 2 gas is a meaningful contributor to light extinction, and thus incl udes a new term based on the NO 2 concentration.

6. The new algorithm recognizes that the assump tion of constant Rayleigh scattering of 10 Mm -1 (representative of an elev ation of about 5000 feet) may have been adequate when only inter-mountain West sites were considered, but is a distortion for sites close to sea level or with very hot or cold climates. It thus allows for site-specific values of the Rayleigh scattering.

At any given location, the net result of all thes e changes is typically small on average because some changes result in increases in extinction and some in d ecreases, but the apportionment of that extinction to the various components is more accurate with the new algorithm, especially on the haziest and clearest days. This is particul arly important for the development of emission management strategies, because the benefits of co ntrol of concentrations of each species will be represented more correctly with the new algorithm.

### SrATE 

### OF MISSw1PPI

HN EY 6-u~

GOVERNOR 

### MISSISSIPPI DEPARTMENT OP 

### ENVIRONMENTAL 

### QUALITY

TRunv D FISHER. EXECWIVE DIRE~R 

### August 14,2008

Mr. James 1. 

### Palmer, Jr. Regional Administrator US. Environmental Protection Agency 61 Forsyth Street Atlanta, Georgia 30303-8960 Re: Request for Approval to Use New IMPROVE Equation for Bart Modeling Dear Mr. Palmer:

The Mississippi Department of Environmental Quality (MDEQ) has been working with Visibility Improvement States and Tribal Association of the Southeast (VISTAS) and U.S. 

### EPA Region 4 staff to implement the Best Available Retrofit Technology (BART) requirements of the Regional Haze Rule. As part of this process, the need for a methodology to post-process CALPUFF/CALPOST modeling data to implement the new IMPROVE equation was identified. VISTAS tasked Dr. Ivar Tombach to develop such methodology. The resulting spreadsheet and supporting information has been reviewed by EPA and the Federal Land Managers (FLMs). EPA Region 4 staff commented that they consider this post-processing methodology a non-regulatory modeling application requiring compliance with section 3.2 

### of Appendix 

### W Part 

### 5 1 : Guideline 

### on 

### Air Quality 

### and that MDEQ must obtain approval for its use from the EPA Regional Adminstrator. In response to these comments and for the reasons discussed below, the following additional information is provided and formal approval of use of this alternative by the affected sources for BART exemption and demonstration modeling is requested.

Rationale for Need for Use of the New IMPROVE Eauation

The New IMPROVE equation is a much better representation of the effects of particulate
matter on light extinction than the old equation and takes into account the latest scientific understanding of several parameters.

1 

### The New algorithm overcomes biases of the old algorithm on the haziest days and the clearest days 

### as demonstrated by comparing the measured light extinction from nephalometers at Class 

### I areas to light extinction calculated using each one of the equations.

OFFICE OF POLLUTION CONTROL

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

AM i.nt~rr Clnmmr8 #urn Ftm-vrn

### The New algorithm recognized spatial and temporal variations in light extinction as size distribution of the aerosol changes by increasing extinction efficiency of sulfate, nitrate, and organics concentration increase. 

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

sea salt and its hygroscopic growth with increasing relative humidity recognizing research findings showing the sea salt can be an important contributor to light extinction in coastal areas. 4. 

### The New algorithm reflects research findings that the mass concentration of particulate organic matter in rural areas is greater than represented by the old equation. 5. 

### The New algorithm includes an NO2 term to represent times when light absorption by NO2 is a meaningful contributor to light extinction. 

### 6. The New algorithm incorporates site-specific Rayleigh scattering values to better represents sites close to sea level or with very hot or cold climates.

With this combination of revisions to the IMPROVE equation, the resulting apportionment of extinction to various components is more accurate on the haziest and clearest days. This is important for development of emission control strategies since the benefits of controls of concentrations of each species will be represented more correctly with the new algorithm. The spreadsheet developed by Dr. Tombach implements the entire New IMPROVE equation for use with CALPUFF and contains a modification of CALPOST that calculates the light extinction from the CALPUFF output.

The Primary Class 1 

### area that Mississippi is concerned with is the Breton National Wildlife Rehge, which is a chain of islands south of Mississippi in the Gulf of Mexico. The ability to appropriately represent the contribution of sea salt and Raleigh scattering for this site is important in determining their contribution to haze and evaluating the benefit of potential control strategies. There is no site specific sea salt data available for Breton, so the values for St. 

### Marks in Florida which is the closest Gulf coastal site were used. Mississippi is requesting approval to use the New IMPROVE Equation for Mississippi Power Corporation (MPC), Plant Watson in Harrison County, and MPC Plants Chevron and Daniels in Jackson County. It should be noted that the Breton Class 

### 1

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

### well.

### We look forward to your response to this request for approval to use the VISTAS methodology incorporating the new IMPROVE equation by Mississippi Power. Attached are the spreadsheet instructions developed by Dr. Tornbach which include greater detail on the New IMPROVE algorithm. Should you have any questions regarding this matter. please contact me at (601) 961 

### - 5242 

### or Elliott Bickerstaff at (601) 961 

### - 

### 5 176. Thank
you for your continued participation in this process.

Sincerlev.

Maya Rao, PE, BCEE Chief, Air Division

Enclosure

c: Brenda Johnson, EPA Region 4UNITED STATES ENVIRONMENTAL PROTECTION AGENCY REGION 4 ATLANTA FEDERAL CENTER 61 FORSYTH STREET ATLANTA GEORGIA 30303-8960 

## SEP 

# - 3 2008

Maya Rao, PE, BCEE, Chief Air Division Mississippi Department of

Environmental Quality Post Office Box 2261 Jackson, Mississippi 39225-2261

Dear Ms. Rao:



### Thank you for your August 14,2008, letter concerning the application of the new Interagency Monitoring of Protected Visual Environments (IMPROVE) equation. You requested approval to use this new methodology to estimate visibility impacts from three facilities performing modeling related to Best Available Retrofit Technology (BART) requirements for the Regional Haze Rule. You assert that the new IMPROVE equation could more appropriately represent the contribution of sea salt for the Breton National Wildlife Refuge Class I area Finally, the State of Louisiana (which is responsible for the Breton Class I area) 

### has also approved the use of the new IMPROVE equation.

The new methodology uses a spreadsheet developed by the Visibility Improvement 

### - State and Tribal Association of the Southeast (VISTAS) to 

### post-process the visibility modeling output from the CALPUFF and CALPOST models. The spreadsheet implements the new IMPROVE equation for calculating visibility impacts that was adopted by the IMPROVE Steering Committee in December 2005. As noted in your letter, US. Environmental Rotection Agency (EPA), Region 4, has previously provided comments indicating that new methodology is an alternative modeling approach and should meet the requirements in Section 

### 3.2 of Appendix 

### W, Guideline on 

### Air Quality Models, contained in 40 C.F.R. Part 51. We have reviewed your demonstration for using the new methodology, which included a rationale for the need to use the new IMPROVE equation, a discussion of the performance of the equation versus measured values, and a submittal of the modified post-processing methodology. Based on the demonstration provided in your letter, we approve your request.

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

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

Sincerely,

Regional Administrator

Internet Address (URL) 

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