U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration

Air Resources Laboratory Special Operations and Research Division 232 Energy Way North Las Vegas, NV 89030

Telephone: (702) 295-1232 Fax: (702) 295-3068 Website: www.sord.nv.doe.gov MEMO

November 17, 2010

Subject: Assessment of the Use of Speciated PM 2.5 Mass-Calculated Li ght Extinction as a Secondary PM NAAQS Indi cator of Visibility

From: Marc Pitchford, NOAA /s/

To: PM NAAQS Review Docket (EPA-HQ-OAR-2007-0492)

This memo documents the results of some of the assessment of the use of hourly PM 2.5 mass concentration and relative humidity data and monthly-averaged PM 2.5 composition to estimate daylight hourly-averaged PM 2.5 light extinction (LE), base d on the original IMPROVE algorithm. Hourly PM 2.5 light extinction estimated in this ma nner is a contemplated indicator for a secondary PM national ambient air quality stan dard (NAAQS) to protect visibility. This memo addresses the effect of the monthly averaging as pect of this approach versus a day-by-day
treatment. It also addresses the issue of whether certain f eatures in the revised IMPROVE algorithm, where they differ from the original algorithm, should be incorporated into the speciated PM 2.5 mass-calculated light extinction indica tor. These features are (1) explicit
treatment of “sea salt” in the approach, (2) site-specific valu es for Rayleigh light scattering, and (3) the use of the split com ponent mass extinction efficiency approach. A more complete description of the original a nd revised IMPROVE algorithms is available in the literature. 1

The Original IMPROVE Algorithm

The original IMPROVE algorithm fo r estimating light extinction (LE) is a simple linear equation that multiplies each of the concentrations of the major PM 10 components by an extinction efficiency value to estimate its contribution to LE. The hygros copic components (i.e., ammonium sulfate and ammonium nitrate) have extinction efficiencies th at are dependent on the ambient relative humidity, while the other component extincti on efficiencies are assumed to be constants.
The sum of these terms is the estimate of the PM 10 LE. The IMPROVE data managers routinely

1 Revised algorithm for estimating light extinction from IM PROVE particle speciation data. M. Pitchford et al., Journal of the Air Waste Management Association, 2007.- 2 -

use 1-day-in-3 24-hour-averaged speciation data along with location-specific, monthly-averaged long-term (10 years) relative humidity (RH) valu es with the IMPROVE algorithm to generate daily visibility conditions needed to track visibil ity trends in federally pr otected remote areas as required for the Regional Haze Rule. 2

More specifically, the original IMPROVE algorithm for estima ting LE includes separate terms
for five PM 2.5 components, a term for PM 10-2.5 mass, and a constant th at accounts for Rayleigh light scattering by gaseous components of the atmo sphere. When this algorithm is stripped of the

PM 10-2.5 and Rayleigh terms, it can be used to estimate PM 2.5 LE in units of Mm -1 as shown in equation (1) below.

PM 2.5 LE = 3f(RH) [AS] + 3f(RH) [AN] + 4[OM] + 10[EC] + 1[FS] (1)

where f(RH) is the relative humidity-dependent water growth function (i.e., the ratio of the hygroscopic fraction’s LE at ambient relative humi dity to its LE when dry) and [AS], [AN], [OM], [EC] and [FS] are the concentrations in μ g/m 3 of ammonium sulfate, ammonium nitrate, organic matter, elemental carbon, and fine soil respectively. The water growth function is nonlinear with a value 1.0 for relative humidity belo w 37% and up to ~4 at a relative humidity of
90%. 3 The IMPROVE algorithm displayed as show n in equation (1) makes clear that the LE contributions by the various major component are separable. This is the basis for determining extinction budgets that have been widely us ed to approximately a pportion LE to various PM 2.5 components.

However, this five-factor algorithm for estimating hourly PM 2.5 LE from hourly PM 2.5 mass concentration and relative humidity data with auxiliary PM 2.5 composition data can be expressed in a two-factor form by dividing both sides by PM 2.5 mass and rearranging the terms into two extinction efficiency factors that depend on PM 2.5 composition: one is the dry extinction efficiency and the other is the extinction efficiency increment due to the water content of the

PM 2.5 at ambient relative humidity. PM 2.5 LE efficiency is the ratio of PM 2.5 LE to the PM 2.5 mass concentration, which in this case is the sum of the major PM 2.5 components (i.e., [AS] + [AN] + [OM] + [EC] + [FS]), that when multiplied by the appropriately measured hourly PM 2.5 mass will give PM 2.5 LE. Rearranging the terms in equati on (1) and dividing by the sum of the components yields the two terms that added toge ther give extinction efficiency as shown in equation (2) below.

PM 2.5 LE Efficiency = 3{(f(RH) -1)([AS] + [AN])} / ([AS] + [AN] + [OM] + [EC] + [FS]) +

(3[AS] + 3[AN] + 4[OM] + 10[EC] + 1[FS]) / ([AS] + [AN] + [OM] + [EC] + [FS]) (2)

2 40 CFR 51 subpart P.

3 Values for f(RH) are available at http://vista.cira. colostate.edu/improve/Tools/humidity_correction.htm.- 3 -

The numerator of the first term, the extinction efficiency incremen t due to the water content of
the ambient PM 2.5 mass, is the product of a relative hum idity function (i.e., 3(f(RH) – 1)) and the hygroscopic fraction (HF) of the PM 2.5 components mass (i.e., HF = ([AS] + [AN])/([AS] + [AN] + [OM] + [EC] + [FS])). Division by the reconstructed PM 2.5 mass makes the first term have units of light extinction e fficiency. The second term in equa tion (2), the dry light extinction efficiency (DLEE), is the component concentrat ion-weighted average of the individual dry component extinction coefficients (i.e., 3 for [A S] and [AN], 4 for [OM], 10 for [EC] and 1 for [FS]). PM 2.5 LE efficiency in equation (2) can be rewr itten in terms of the HF and DLEE as

PM 2.5 LE Efficiency = 3((f(RH) -1)HF + DLEE (3)

where the first term is the extinction efficiency increment due to PM 2.5 -bound water that depends

on PM 2.5 composition and relative humidity, and the second term (DLEE) only depends on composition. With this form for expressing PM 2.5 LE efficiency, equation (1) can be re-written

as

PM 2.5 LE = (PM 2.5 mass) * [3((f(RH) -1)HF + DLEE] (4)

Both of the terms in the square brackets of equation (4) have a limited range of possible values
within the constraints that we are interested i n. The extinction efficiency water increment term ranges from zero, when either the relative hum idity is below 37% or when HF = 0, to a
maximum of about 9.5 m 2 /g for relative humidity at 90% and HF = 1 as shown in Figure 1. The uncertainty in the extinction efficiency water increment term caused by within-month variability
of HF is assessed in the next section using 24-hour average con centration data. While the effect of relative humidity measurement uncertainty on the water growth term is not separately assessed, we can point out that the steep rise in the water growth function for high relative humidity conditions makes the case for ta king and using accurate relative humidity measurements. The extinction efficiency water increment and its uncertainty are only of importance during periods with moderate to high relative humidity. As show in Figure 1, relative humidity must exceed ~50% for the extinction effi ciency water increment to contribute at most 1

m 2 /g (for HF = 1) to the PM 2.5 LE efficiency. The assessment desc ribed in the subsequent section shows the range and variability of the extincti on efficiency water increment for a number of
urban areas.

It should be noted that strictly speaking, equatio ns (1) through (4) are al gebraically consistent with one another only if PM 2.5 mass in equation (4) is the su m of the mass of the five PM2.5 components used to derive the PM 2.5 LE efficiency in equation (2) from the IMPROVE algorithm in equation (1). However, for the purposes of defining a PM 2.5 light extinction
indicator to be the basis for a secondar y standard, another quantification of PM 2.5 mass is anticipated in order to achieve hourly time resolution. Measurements of hourly time resolution
for individual PM 2.5 component mass are not practical or co st-effective. Also, while the sum of these components typically accounts for most of the dry PM 2.5 mass in ambient air, especially in
the remote rural environments for which the IMPROVE algorithm has been validated against- 4 -

optical measurements, it will not necessarily be the same as the actual ambient PM 2.5 mass that contributes to light extinction in urban areas, especially where road salt, sea salt, or industrial
emissions with a different elemental mix than th e "crustal" component in rural areas, may be present. (An assessment of the consequences of using PM 2.5 mass for sites with significant contributions by PM 2.5 sea salt is described below in this memorandum.) A gravimetric
measurement of PM 2.5 mass such as a measurement by the filter-based FRM, or by a continuous method well correlated to the FRM, would captu re these other contributors to ambient PM 2.5 mass, but may also result in reported mass values that include – as with the FRM -- some particle-bound water and/ or that can have negative artifact s for ammonium nitrate under some conditions. Estimation of hourly PM 2.5 mass on a day-specific and si te-specific basis presumably
must make use of data from on-site continuous PM 2.5 instruments approved as FEM, in some way. Such data could be used directly, or norma lized in some manner in an attempt to be more consistent with the sum of the five components. It remains to be investigated and considered what specific approach to using FEM data in equa tion (4) will work best in defining an indicator for hourly PM 2.5 light extinction.

Figure 1. Water growth term from equation (3) which is the maximum water extinction efficiency for periods where the HF = 1.

The maximum range of DLEE is from 1, if all of the PM 2.5 were fine soil, to 10 if all of the PM 2.5 were elemental carbon. In urban areas, fine soil and elemental carbon tend to be the smallest two components of PM 2.5 mass, which is typically dominate d by some combination of ammonium sulfate, ammonium nitrate, and organic mass co mponents. Since these ha ve species extinction
efficiencies of 3 m 2 /g, 3 m 2 /g, and 4 m 2 /g, the DLEE is generally near 3 m 2 /g to 4 m 2 /g. The 0 1 2 3 4 5 6 7 8 9 10

0 10 20 30 40 50 60 70 80 90 1003*(f(RH) -1) in units of m2/g

Relative Humidity (%) Maximum Water Growth Term versus Relative Humidity

3*(f(RH) -1)- 5 -

assessments described in the subsequent sections show the within-month variability for a number of urban areas of the HF and DLEE values, as well the performance of using monthly-averaged values in reproducing sample-day PM 2.5 LE.

Performance Assessment for PM 2.5 LE Using Monthly-Averaged Composition

“Performance” as used here refers to the degr ee of agreement between estimates of daily 24-hour

PM 2.5 light extinction efficiency based on mont hly-average values of HF and DLEE with estimates of the same parameter based on same -day data. For simplicity, bounding values are assumed for relative humidity, rather than actual daily or hourly RH values from the four study

areas.

One year (2008) of IMPROVE PM 2.5 speciation data from four urban monitoring sites (Fresno, CA; Phoenix, AZ; New York, NY, and Washington, DC) were used to assess the ability of PM 2.5 LE determined using monthly-averaged HF and DLEE to reproduce day-specific PM 2.5 LE. Relative humidity data is not readily avai lable at IMPROVE monitoring sites, so PM 2.5 LE
values were calculated for the two extremes of dry condition (i.e., RH < 37%) where only the DLEE is used, and moist conditions (i.e., RH ≈ 89.3%), where the maximum extinction
coefficient water increment is 9 m 2 /g times the HF. Performance at any other relative humidity
condition between these two extremes will be bo unded by the performance at the two extremes.

The sample day-specific and monthly-averaged values of the maxi mum light extinction efficiency water increment, DLEE, and the sum of these that corresponds to the maximum total

PM 2.5 LE efficiency values for the four urban area s are shown as line plots in Figures 2, 3, 4, and 5. The time plots for the four urban areas show a greater day-to-day and seasonal variability in the maximum extinction efficiency water increm ent than for the DLEE. As anticipated, the DLEE tends to be in a range from 3 m 2 /g to 4 m 2 /g for each of the urban areas, though in Phoenix it drops to a value nearer to 2 m 2 /g in June due to higher re lative contributions to PM 2.5 by fine soil and to values somewhat higher than 4 m 2 /g in winter because of higher relative contributions by elemental carbon. The DLEE values for the other three urban areas have less seasonal variability.- 6 -

Figure 2. Fresno monthly averaged and sample-day spec ific maximum extinction efficiency water increment (i.e., for RH ≈ 89%) and dry light extinction efficiency (DLEE). Monthly averaged values are dashed lines. Red, blue, and black colored lines are for dry, wet, and total light extinction efficiency values respectively.

Figure 3. Phoenix monthly averaged and sample-day sp ecific maximum extinction e fficiency water increment (i.e., for RH ≈ 89%) and dry light extinction efficiency (DLEE). Monthly averaged values are dashed lines. Red, blue, and black colored lines are for dry, wet, and total light extinction efficiency values respectively.- 7 -

Figure 4. New York monthly averaged and sample-da y specific maximum extinct ion efficiency water increment (i.e., for RH ≈ 89%)and dry light extinction efficiency (DLEE). Monthly averaged values are dashed lines. Red, blue, and black colored lines are for dry, wet, and total light extinction efficiency values respectively.

Figure 5. Washington monthly averaged and sample- day specific maximum extin ction efficiency water increment (i.e., for RH ≈ 89%)and dry light extinction efficiency (DLEE). Monthly averaged values are dashed lines. Red, blue, and black colored lines are for dry, wet, and total light extinction efficiency values respectively.- 8 -

Table 1 shows the coefficient of variation (i.e ., standard deviation divided by the mean times 100%) for the DLEE and maximum extinction effici ency water increment values by month, the mean of the 12 monthly values, and the annual va lues. These give a more quantitative view of the variability patterns seen in Figures 2 thr ough 5. The within-month variabilities of the dry extinction efficiency values are generally less than about 10%, except at Phoenix during a few
months when the coefficient of variation is ne arer 20%, due to periods where the day-specific values are low because of higher relative contributions to PM 2.5 by fine soil. The within-month
variabilities of the maximum extinction efficien cy water increment are generally less than 15% except for a few months at Fresno where the coeffi cient of variation is cl oser to 20%, due to large within-month variations in the HF duri ng the winter months. The San Joaquin Valley where Fresno is located is known to experience epis odes of high nitrate concentrations in cooler months. The means of the monthly extinction effici ency water increment coefficient of variation values are significantly lower than the annual va lues, while the mean monthly DLEE coefficient of variation values are more comparable to th e annual values. This indi cates that the use of monthly-averaged values is important to account for seasonal patte rns in the maximum extinction efficiency water increment, while annual-averaged DLEE would perform almost as well as monthly averaged values.

Table 1. Monthly and annual coefficient of variation (%) for the dry and maximum water extinction coefficients for four urban areas.

Fresno Phoenix New York Washington

Dry Water Dry Water Dry Water Dry Water

January 10.8 17.1 4.9 13.5 6.1 5.5 4.2 6.8

February 7.9 16.2 6.9 10.4 10.1 5.7 3.7 7.7

March 9.1 10.4 10.3 5.8 6.2 9.7 3.1 5.6

April 4.2 11.2 12.8 5.7 4.6 9.0 5.4 12.7

May 13.1 14.2 21.1 12.4 9.5 13.3 6.9 6.8

June 9.2 12.6 26.7 10.0 4.0 8.7 6.1 10.6

July 5.3 8.0 7.3 3.8 10.0 11.9 5.9 7.3

August 6.8 6.1 8.4 8.4 7.3 10.6 4.7 7.8

September 4.8 7.3 11.2 13.2 5.9 8.2 4.3 5.7

October 9.3 8.7 10.7 4.2 6.8 8.9 5.4 10.0

November 5.8 12.1 18.1 6.2 6.7 8.4 1.7 7.7

December 8.7 20.9 5.4 8.1 7.2 5.5 4.9 6.1

Mean Monthly 7.9 12.1 12.0 8.5 7.0 8.8 4.7 7.9

Annual 10.2 38.7 18.4 40.5 7.7 19.8 5.7 17.4- 9 -

Figures 6, 7, 8, and 9 are paired scatter plots of PM 2.5 LE calculated using monthly-averaged

PM 2.5 LE efficiencies versus those calculated using sample-day specific PM 2.5 LE efficiencies for dry and moist conditions. In all cases , the sum of the day-specific PM 2.5 component
concentrations were used as the PM 2.5 mass concentration that was multiplied by the extinction efficiency values to calculate PM 2.5 LE. The simple linear re gression equation and the R 2 values (i.e., the fraction of the variance explained, where R 2 = 1.0 is a perfect fit) for each are shown on the plots. The scatter plots and regression fits fo r each site indicate a strong correlation between
the PM 2.5 LE calculated using monthly mean co mposition values with day-specific PM 2.5 LE. The performance is somewhat better when relativ e humidity is assumed to be low compared to when it is assumed to be high. Among these four urban areas, the fit fo r Phoenix is the worst
with R 2 = 0.93 for assumed dry conditions and 0.87 for the high humidity condition case. The relationship using actual rela tive humidity conditions will be bounded by these two extremes, so for the generally dry conditions in Phoenix us ing ambient relative humidity should result in a relationship closer to the dry extreme R 2 value. The R 2 values for the other three urban areas range from 0.96 to 0.98.

Apparent from the Figures 6 through 9 is that for each urban location the PM 2.5 LE values range
over a factor of about 10. This corr esponds well to the range of 24-hour PM 2.5 mass concentration values for these urban areas, with their range of about a factor of 10. Though not explicitly evaluated in this assessment, the role of relative humidity c onditions in the calculation of light extinction can be appreciated by noting th e doubling or more of the axis scales for the scatter plots displaying the highe st humidity cases compared with the corresponding axis scales for dry case scatter plots in Figures 6 through 9. This implies that va riations in relative humidity alone can change the water content of PM 2.5 in these urban enough to result in as much as a factor of two range in PM 2.5 LE values. Based on these observations and the fact that PM 2.5 LE determined using monthly averaged compositi on (i.e., HF and DLEE values) does not much
differ from PM 2.5 LE values calculated with day-specifi c composition, it would appear that the
most influential factors that influence variability of PM 2.5 LE are PM 2.5 mass, followed by relative humidity, with composition being the leas t important so long as monthly-averaged HF
and DLEE are used in the calcula tion, rather than any longer- term average values. Use of monthly-averaged values for each site seems to sufficiently capture the seasonal and regional differences in the relationship between PM 2.5 LE and measured PM 2.5 mass and relative humidity data. The largest differences caused by using mo nthly-mean composition percentages instead of day-specific percentages are for Fresno under co nditions of high relative humidity and high

PM 2.5 concentrations. This is consistent with the known occurrence of high nitrate concentrations on some but not all days during cool months. The use of monthl y averages underestimates HF (and hence the light extinction) on days with the highest nitrate concentra tions and overestimates it on other days.- 10 -

Figure 6. Scatter plots of monthly averaged composition calculated PM 2.5 LE versus day sp ecific composition calculated light extinction for dry (top) and high humidity (bottom) conditions, us ing Fresno 2008 data.- 11 -

Figure 7. Scatter plots of monthly averaged composition calculated PM 2.5 LE versus day specific composition calculated light extinction for dry (top) and high humidity (bottom) conditions, using Phoenix 2008 data.- 12 -

Figure 8. Scatter plots of monthly averaged composition calculated PM 2.5 LE versus day sp ecific composition calculated light extinction for dry (top) and high humidity (bottom) conditions, us ing New York 2008 data.- 13 -

Figure 9. Scatter plots of monthly averaged composition calculated PM 2.5 LE versus day sp ecific composition calculated light extinction for dry (top) and high humidity (bottom) c onditions, using W ashington 2008 data.

Sea Salt Assessment- 14 -

The original IMPROVE equation has been revised to include sea salt contributions, which can be significant at some coastal locations and contri bute significantly to remote area visibility conditions that are of concern for the Regional Haze Rule. 4 A sea salt term could be added to the algorithm shown above for extinction efficiency (equation 3). Because sea salt has a significantly different water growth curve than that used for AS and AN, a versi on of equation (4) that included sea salt explicitly would be a three-factor instead of tw o-factor algorithm. The question
at issue here is whether sea sa lt should be explicitly included in the calculation of light
extinction.

To examine this question, data from the Chem ical Speciation Network (2000 to 2008) for ten monitoring sites (see Table 2) with high annual sea salt concentrati ons were used to calculate the
sea salt contribution to PM 2.5 LE. Sea salt concentration and contribution to PM 2.5 LE were based on the chlorine concentration using the method in the revised IMPROVE algorithm. Day-specific relative humidity data for these sites were not re adily available so the long-term monthly mean relative humidity conditions as used in Regiona l Haze Rule calculations were used for each sampling day in a month in pl ace of day-spec ific data.

Table 2 Site codes and locations for sites with high annual sea salt concentrations.

AQS Site ID Location AQS Site ID Location
530530029 Tacoma, WA 150032004 Pearl City, HI
530330080 Seattle, WA 121030026 Tampa, FL
482011039 Houston, TX 120573002 Central Florida 410390060 Eugene, OR 120111002 Miami, FL
330150014 Portsmouth, NH 060850005 San Jose, CA

Figure 10 contains scatter plot s of the percent contributions by sea salt to 24-hour PM 2.5 LE
versus the 24-hour PM 2.5 LE level for each of the ten sites. As seen in these figures, sea salt can contribute substantially (i.e., >25%) to PM 2.5 LE on days with low to moderate PM 2.5 LE levels. However except for a few outliers, the contributions to PM 2.5 LE are well below 5% for PM 2.5 LE greater than 100 Mm -1 . From these plots it would seem that not explicitly including a sea salt
term in the calculated PM 2.5 LE does not have an important effect on LE estimates on days that would likely exceed a new secondary NAAQS, even for locations that have the greatest average
concentrations of PM 2.5 sea salt.

4 Pitchford M; Maim W; Schich tel B; Kumar N; Lowenthal D; Hand J (2007). Re vised algorithm for estimating light extinction from IMPROVE particle spec iation data. J Air Waste Manag Assoc, 57: 1326-36.- 15 -- 16 -

Figure 10. Percent contributions to PM 2.5 LE by sea salt versus PM 2.5 LE levels for ten CSN monitoring sites with the highest annual sea salt concentrations.

It should be noted that not explic itly including a sea sa lt term in the calculation of the water extinction efficiency and DLEE does not mean that the contributions of sea salt are entirely
excluded in the calculated PM 2.5 LE, if the measured hourly PM 2.5 mass concentration that includes the mass of the sea salt is used directly in equation (4). In other words, the additional

PM 2.5 mass from sea salt is transformed into PM 2.5 LE using the extinction efficiency values calculated from monthly–averaged non-sea salt PM 2.5 component composition and relative humidity. A logical question is how mu ch uncertainty and/or bias in PM 2.5 LE does this
contribute.

To assess the effect of calculating PM 2.5 LE using extinction efficiencies that do not explicitly include a sea salt term, but have a substa ntial contribution by sea salt to the PM 2.5 mass, a sensitivity analysis was conducted using hypothetic al data over a range of conditions. Three hypothetical PM 2.5 compositions were assumed (i.e., low, medium, and high non-sea salt HF values of 0.2, 0.5, and 0.7 respectively), each with PM 2.5 mass = 50 μ g/m 3 of which 10 μ g/m 3 is- 17 -

sea salt, and a DLEE value for th e non-sea salt co mponents of 4 m 2 /g. PM 2.5 LE levels were calculated for each of these for six relative humidity values (i.e., RH = 40%, 50%, 60%, 70%, 80%, and 90%) using two different approaches . One of the two approaches (shown as LE original ) multiplies the PM 2.5 mass (i.e., 50 μ g/m 3 ) times PM 2.5 LE efficiency from equation 3 that does not include sea salt explicitly and where the exti nction efficiency is calculated using the fivecomponent reconstructed mass. The other approach (LE revised ) is calculated using the algorithm with the sea salt term and water growth func tions from the revised IMPROVE algorithm. All other aspects for the two approach es are identical (e.g., same dry sp ecies extinction efficiencies).
The results of these calculations ar e shown in Figure 11 as plots of PM 2.5 LE by both methods as a function of relative humidity for each of th e three non-sea salt HF component levels.

The two algorithms give broadly si milar results with the greatest absolute differences for the case with low non-sea salt HF when the relative humidity is at or below 40% and at 90%. The average
fractional differences across the six relative humidity values are 0.4%, 4.9%, and 6.7% for the low, medium, and high non-sea salt HF cases respec tively, with the greatest fractional errors at RH=40% where the original algor ithm approach overestimates PM 2.5 LE by about 13% for each
of the HF cases.- 18 -

Figure 11. PM2.5 LE calculated using the original IMPR OVE algorithm without an explicit sea salt term and using the sea salt term from the revised IMPROVE algorithm versus relative humidity for three HF conditions, HF = 0.2 (top), HF = 0.5 (middle), and HF = 0.7 ( bottom). PM10 and sea salt concentrations set at 50 μ g/m3 and 10 μ g/m3 respectively for all calculations.- 19 -

Rayleigh Light Scattering

The revised IMPROVE algorithm includes the us e of standardized site-specific Rayleigh scattering values rounded to the neares t integer Mm -1 (revised algorithm), while 10 Mm -1 is used for all sites in the original IMPROVE algorithm. The standardized site-specific Rayleigh values depend on the typical density of the atmosphere which is primarily a function of elevation and temperature. The resulting values range from about 8 Mm -1 at about 4 km elevation to about 12

Mm -1 at sea level. The calculation of PM 2.5 LE does not include a Rayleigh term, however 10

Mm -1 was subtracted from the LE values that co rresponded to the 20 dv to 30 dv range from the preference studied (UFVA Chapter 2) used to de fine the CPL. The maximum difference between the two approaches for treating Rayleigh scattering is 2 Mm -1

.
Split Component Mass Extinction Efficiency

The most significant difference between the origin al and refined IMPROVE algorithm is the use of a more complex approach in the revised algorithm to account for biases between direct measurements of light scattering and original algorithm estimates of light scattering extinction. These biases are largest at the low and high ends of the range of light extinction for remote areas monitoring sites. In essence, the original algorith m’s set of dry extinction efficiency values for the major species were selected to work well across the range of conditions in available data sets. While the original IMPROVE algorithm is succes sful for most conditions, for the lowest PM
concentration conditions it tends to overesti mate the PM LE, while for the highest PM
concentrations it tends to underestim ate the PM LE. The best explana tion for this behavior is that
the size distribution and hence the dry light extinction efficiency of ambient particles tend to be
smaller when the concentrations are low and la rger when the concentrations are larger. How
much of this behavior is due to differences in particle format ion mechanisms or the result of aging of the particles is unclear. It is also unclear whether this behavior applies in urban areas,
where more of the ambient PM is attributable to recent or “fresh ” local emissions, compared to
remote areas farther from emission sources.

The revised algorithm uses a so-called “split co mponent mass extinction efficiency model” to account for this tendency for lower dry extinction efficiencies at low c oncentrations and higher dry extinction efficiencies at high concentratio ns. Three species, ammoni um sulfate, ammonium nitrate, and organic mass compound s (i.e., AS, AN, OMC) are each assumed to be a mixture of two extreme particle size distributions, one labe led “small” and the other “large”. The extinction efficiencies were determined based on typical me asured small and large size distributions, so that
the extremes would be anchored to realisti c ambient particle properties. The relative concentrations of the small and large particle distributions of each species was determined empirically using a simple lin ear relationship to species conc entrations that provided an improved correspondence between nephelometer- measured and algorithm-estimated light scattering at 21 monitoring s ites in remote areas.- 20 -

The revised algorithm light extinction terms fo r ammonium sulfate, ammonium nitrate, and organic mass components are as follows

LE AS ≈ 2.2 x f s (RH) x [AS s ] + 4.8 x f L (RH) x [AS L ] (4)

LE AN ≈ 2.4 x f s (RH) x [AN s ] + 5.1 x f L (RH) x [AN] (5)

LE OMC ≈ 2.8 x [OMC s ] + 6.1 x [OMC L ] (6)

where subscripts S and L denote small and larg e particle size distributions for the three components and for the water growth functions of relative humidity. The concentrations of the large particle size distributions for each species is set equal to the ratio of its total concentration divided by 20 μ g/m 3 times the total concentration of the species, and the con centration of the small particle size distribution is the difference be tween the total concentration of the species and
the concentration of the large size distribution. Fo r example if [OMC] = 4 μ g/m 3 , then [OMC L ] =
4/20 x 4 μ g/m 3 or 0.8 μ g/m 3 and [OMC s ] = 4 μ g/m 3 – 0.8 μ g/m 3 or 3.2 μ g/m 3 . If the concentration of one of the species exceeds 20 μ g/m 3 then all of that sp ecies concentration is assumed to be in the larg e particle size distribution.

As is contemplated using the original IM PROVE algorithm, the revised IMPROVE algorithm could be used to estimate hourly PM 2.5 LE by employing the same a ssumption that the monthly
mean relative PM 2.5 composition adds little uncertain ty when applied to hourly PM 2.5 mass concentration and relative humidity data. It doe s, however, add to the complexity of the computations, since the concentrations and resulti ng extinction efficiencies of the large and small particle size distributions need to be determined for each of the three split species for each hour.
For the two-factor version of the original IMPROVE algorithm as shown in equation (4), the algorithm can be reformulated so that a pair of monthly-averaged terms (i.e., HF and DLEE) would be all that is needed to determine hourly PM 2.5 LE from hourly PM 2.5 concentration and relative humidity data for each monitoring site. In contrast, if the split component approach were used, each hour would require calculations using the five monthly-averaged composition percentages to apportion the hourly PM 2.5 mass concentrations into the five components, followed by apportion of the AS, AN, and OMC components into large and small size distribution split species, followe d by the calculation of hourly PM 2.5 LE. This process could be incorporated into a software pr ogram so the routine burden for those responsible for making the calculation would be minimal, though it would be more complicated than use of the original algorithm approach formul ated in equation (4).

The magnitude of the difference between LE calculated using the revised and original IMPROVE algorithms is demonstrated in Figur e 12 using 2004 24-hour da ta from two urban IMPROVE monitoring sites (Phoeni x and Washington). These are data taken from the VIEWS website. LE is calculated using the 10-year aver aged monthly relative humidity growth factors and different assumed OMC to OC ratios (1.4 for the original and 1.8 for the revised algorithms respectively). As can be seen in the plots, the revised and original algor ithm calculations diverge little from the 1-to-1 line for days with LE up to about 50 Mm -1 but by 200 Mm -1 using the- 21 -

original algorithm, the revised algorithm estimated LE is about 250 Mm -1 . From the power law regression equation fits and R 2 values shown for the two ur ban areas, the site-specific relationships between the revised and original algorithm are str ong, but differ for the two urban areas. Analysis of results from other locations can be expected to demonstrate similar differences at high LE values as seen in this assessment.

Figure 12 Scatter plot of LE estimates from the revised (y-axis) versus the original (x-axis) IMPROVE algorithms for Washington (blue) and Phoenix (red) based on 2004 IMPROVE data downloaded from the VIEWS website. The power law regression lines, equations and R2 values for the two data sets are shown as is a 1 to 1 line.

The revised IMPROVE algorithm was developed us ing data from remote area locations with collocated nephelometers. There is only limited urban data available upon which to check its applicability. In comments received on the s econd draft PAD from UARG, an assessment using- 22 -

data from the Atlanta, GA SEARCH site show ed that the revised algorithm with the split component mass extinction efficiency and a OM C to OC ratio of 1.8 performed better at predicting nephelometer-measured light scattering than the original algorithm using either 1.4 or
1.8 for the OMC to OC ratio. 5 The results of this one-site assessment suggest that the LE estimates from the revised IMPROVE algorithm may be more appropriate for Atlanta than estimates from the original algorithm.

Summary and Conclusions

The original IMPROVE algorithm can be reformulated to calculate PM 2.5 LE by multiplying

PM 2.5 mass times a term that depends on part icle composition and relative humidity. PM 2.5 LE efficiency is the sum of two factors. The first is the water extinction efficiency which is the product of the hygroscopic fraction (HF) and a function of relative humidity that accounts for the

PM 2.5 -bound water at any elevated humidity level. The second term is the dry light extinction efficiency (DLEE) which is a component concen tration-weighted average of the individual component extinction efficiencies (i.e., 3 m 2 /g for ammonium sulf ate and nitrate, 4 m 2 /g for organic mass, 10 m 2 /g for elemental carbon, and 1 m 2 /g for fine soil). While HF and DLEE vary when the composition of the PM 2.5 changes, they are bound by the constraints of their definition (i.e., 0 < HF < 1, and 1 m 2 /g < DLEE < 10 m 2 /g) and tend to be suffici ently well behaved that monthly-averaged values of both can be combined with day-specific 24-hour PM 2.5 mass and bounding values for relative humidity to generate 24-hour PM 2.5 LE values that agree quite well
with 24-hour PM 2.5 LE values using day-specific compos ition and the same bounding values of relative humidity. Though not explicitly demo nstrated here, this good performance using monthly-averaged HF and DLEE should tr ansfer to the estimation of hourly PM 2.5 LE when combined with hourly PM 2.5 mass concentration and hourly relative humidity data. 6

Contributions to PM 2.5 LE by sea salt at the coastal areas where it might be important were examined several ways. Assessment of sea salt’s contribution to PM 2.5 LE at 10 CSN monitoring sites with high annual sea salt contributions were shown to be minor for high haze levels (i.e.,
sea salt PM 2.5 LE < 5% when total PM 2.5 LE > 100 Mm -1 ). The IMPROVE algorithm can be modified to explicitly include sea salt by a dding an additional water growth curve and using
calculated sea salt mass from PM 2.5 chlorine concentration. A sensitivity assessment showed that use of the original algorithm w ithout an explicit sea salt term performs well at estimating PM 2.5 LE over a range of composition and relative humidity conditions. Given the small influence of sea salt at the likely NAAQS control levels, that it is emitted from a natural process (i.e., spray

5 Comments of the Utility Air Regulatory Group on Chapter 4 of the Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standa rds – Second External Re view Draft (June 2010), Attachment A, August 30, 2010.

6 Appendix F of the final Policy Assessment Document examines the results of using hourly relative humidity data instead of bounding values. It also comp ares an approach (“approach T”) that uses monthly-averaged composition to an approach (“approach W”) that uses only same-day composition.- 23 -

and breaking waves), and the ab ility of the simpler algorithm to adequately calculate PM 2.5 LE for cases with high sea salt, there seems to be little reason to employ the more complicated algorithm that explicitly incl udes a sea salt term.

The maximum difference between the two approaches for treating Rayleigh light scattering (i.e., site-specific or constant = 10 Mm -1 ) is 2 Mm -1 , which is only a few per cent of the values being considered for the protection level of the standa rd, so accounting for it is not thought to warrant the extra complication of having differ ent values for different locations.

The simplicity of the single species extinction efficiency of the orig inal IMPROVE algorithm compared to the split component mass extinction efficiency a pproach used in the revised algorithm is an attractive feature. The differen ce between the two approa ches is only significant at the higher PM 2.5 LE levels where the revised algorithm a pproach would estimate larger values than those from the original algorithm. Given the limitations of available directly measured

PM 2.5 LE in urban areas upon which to base a ch oice between the two approaches, EPA should pursue the simpler approach, but also solicit comments on the appropriateness of using the revised algorithm in place of the original algorithm as the basis for the speciated PM 2.5 masscalculated LE indicator.