## Regional Haze Rule Natural Level Estimates Using the Revised IMPROVE Aerosol Reconstructed Light Extinction Algorithm

Final Paper # 48

S. A. Copeland , CIRA/USDA Forest Service, 333 Ea st Main Street, Lander, WY 82520;
M. Pitchford , Air Resources Laboratory, NOAA, 755 East Flamingo Rd, Las Vegas, NV 89119;
R. Ames , Department of Atmospheric Science, Colorado State University, Fort Collins,
CO 80523



### INTRODUCTION In 1999 the Environmental Protection Agency (EPA) promulgated the Region Haze Rule (RHR) 1 that established a program to mitigat e existing visibility impairment in 156 visibility-protected federal class I areas (i .e. certain large nationa l parks and wilderness areas as specified by the Clean Air Act). RHR implementation guidance requires states
to determine the uniform rate of haze reduc tion that would result in reaching natural haze level from current conditions for the five-y ear mean of the annual 20% most impaired visibility condition fo r each protected area. 2 They must also show that the five-year mean of the annual 20% least impai red visibility conditions at each protected area do not degrade. For the RHR, haze levels are expres sed using units of deciview, which is a logtransformation of light extincti on that results in a more per ceptually uniform metric of haze than either light extinction or visual range. 3 Light extinction is estimated from particle speciation data generated by the In teragency Monitoring of Protected Visual Environments (IMPROVE) 4 monitoring network that samples on a one day in three schedule at 110 monitoring site s representative of the cl ass I protected areas. Though states may ultimately justify adoption of an alternative rate of progress in their RHR implementation plans, they must first determ ine the uniform rate of progress for their
state’s class I areas.

Haze levels for the 20% worst na tural haze conditions need to be estimated in order to determine the 60-year uniform rate of progr ess for each protected area. EPA provided a default approach 5 for estimating worst natural haze le vels that involves a three step process. First the mean natural haze levels are estimated using the original IMPROVE algorithm 6 for estimating light extinction from PM speciation data applied to estimates of natural PM species concentra tions developed by John Trijonis 7 and a spatiallyinterpolated 10-year measured relative humidity data set that provide s distinct values for each visibility protected area. The second st ep is to convert the mean natural light extinction levels for each pr otected area to deciview values using the simple logtransformation that defines the deciview scale. 3 The final step is to add 1.28 times the
assumed standard deviation of the natural haze distribution in deciview units to the mean value from the second step to get an estimate of the 90 th percentile that was thought to be a reasonable approximation of the mean of th e 20% highest haze levels. Estimates of the20% least impaired natural conditions were made in a similar way by subtracting 1.28
times the assumed standard deviation of the natural haze distribution in deciview units.
The assumed standard deviation of 3dv for eas tern class I areas and 2dv for western class
I areas was based on the distributi on of data at the most pristi ne locations in the east and

west. 8 Following this procedure, EPA provided estimated default mean, worst and best natural haze levels for all of the protected ar eas as part of their guidance which states could use unless they chose to refine the approach.

The default approach for estimating natura l conditions was the s ubject of technical
reviews as a result of its role in determin ing the uniform rate of progress for the RHR. 9, 10,

11 The Trijonis estimates of typical natural species concentrations which provided one set
of values for the eastern U.S. and another set for the western U.S. were based on the limited available PM composition monitoring data in the late 1980s, so were seen as not likely to be representative of a ll of the visibility protected ar eas. The statistical approach
that calculated the 90 th and 10 th percentile natural levels for use as estimates of the mean
of the 20% best and 20% worst haze levels was shown to be flawed in a number of ways. The IMPROVE algorithm was shown to overestimat e light extinction at low levels and to underestimate it at high levels. It also didn’ t work well for data from coastal monitoring sites because it didn’t account for light scattering by sea salt.

The IMPROVE algorithm was revised in 2005 to reduce biases at the high and low light extinction extremes, to incorporate more recen t information from the literature, to include extinction from sea salt (an important component at some coastal s ites), and to include elevation-specific estimates of Rayleigh light scattering. 12 Many states indicated their intent to adopt the new algorithm for RH R assessment and planning purposes. For consistency in the determination of a uniform rate of progress, the use of the revised algorithm for estimating current haze levels implies the need to use it to estimate natural
conditions.

In 2006 the Regional Planning Orga nizations (i.e. five regional organizations of state, tribal, federal and stakeholder organizations established to pr omote regionally consistent RHR implementation) established an ad hock committee to establish a uniform approach for the application of the revised IMPROVE al gorithm to estimate natural levels and to remedy other flaws in the default methodology where possible. A presentation of the
results of their work is available on the IMPROVE web site. 13 In the sections that follow is a description of the revised natural haze estimation methodology, the results of its application, and a discussion of the differen ces between the default and revised natural haze estimates and uniform rate of progress results.



### METHODOLOGY

There are two fundamental differences between the default and the revised approach for estimating the 20% highest and 20% lowest natural haze levels for each of the visibility protected areas. The first difference involve s the use of the revised IMPROVE algorithm for estimating light extinction from PM speciation data in place of the original IMPROVE algorithm. The second differen ce addresses the flaw in the default approach’s use of a statistical method to estimate the highest and lowest natural hazelevels. The revised approach adjusts each sample period’s species concentration to generate a simulated natural haze distribu tion with the annual mean for each species being equivalent to the Trijonis estimated natu ral concentration for that species. The step by step details of this approach are described below.

1. Start with the “Daily Values Including Patched Values” data set from the on the Visibility Information Exchange We b Sites (VIEWS) data summary website 14

. 2. For sites with fewer than three valid sample years from the 2000-2004 baseline period,
use the substituted data 15 set in place of the standard VIEWS data from step 1. 3. Select records with sample dates fr om 2000-2004 for each site of interest. 4. Retain only records from “valid ” years based on EPA RHR guidance 2

. 5. Retain only records that have “valid” sample codes for each of the seven aerosol species (i.e. sulfate, nitrate, organic carbon, elemental carbon, fine soil, coarse mass, and sea salt) needed to reconstruct aeroso l extinction using the revised IMPROVE algorithm 12

. 6. Discard records which have any patched values from the EPA RHR patching technique 2

. 7. Calculate arithmetic means for each of the seven species for each site and each calendar year. 8. Divide the resultant means by the Trijo nis-based EPA default natural conditions
estimates 5 for the six species (i.e., all except sea salt, which is included in the revised IMPROVE algorithm but not included in the default estimates). “East” and “West” are
defined as east or west of the 98 th meridian respectively for purposes of determining
which default natural conditions to use. Th e ratio of baseline annual mean to default
natural annual mean becomes the sca ling factor for each of the species.
9. In cases where the baseline annual mean c oncentration of a species is less than the EPA default estimated concentration, the base line values are retained (i.e. the scaling factor is 1). Similarly, sea sa lt concentrations have a sca ling factor of 1, which defines
the baseline condition as the natural condition. 10. Every observation is divided by the appr opriate species specific scaling factor, creating a new distribution for each species which has the same annual mean as the EPA default natural annual mean (o r the baseline value when a scaling factor of 1 is used) while retaining the shape of the baseline di stribution. Figure 1 illustrates the adjustment of species concentration for one site-year.Figure 1. Frequency distribution of the current (i.e. measured) species concentration for Sipsey Alabama (top) and the species concentrations adjusted to Trijonis-based natural levels (bottom). The use of a logarithmic concentration scale means that the distributions a re the same shape for each current and natural species, though tr anslated horizontally. No log transformation is performed in the approach however. The hanging bars at the top of each plot show the mean for each species (see color key) with short solid lines being current and longer dotted lines being adjusted means.

11. Keeping the observations grouped by the samp le date, convert the scaled masses of each species to extinction values using the revised IMPROVE algorithm. 12. Sum the resultant species specific extincti ons for each sample date, and add the site specific Rayleigh scattering value. 13. Convert this daily extincti on value to a deciview value. For pristine conditions at high elevation sites (i.e. >2200m) these decivi ew values are sometimes negative. While counterintuitive, this is mathematically a ppropriate and negative or zero values are retained. The result is up to five years wo rth of daily deciview values adjusted to be
simulated natural haze levels at each IMPROVE site. Figure 2 illustrates the current and
natural haze distributions that result from this process.Figure 2. Frequency distribution of current and ad justed natural haze levels in deciview units for Sipsey Alabama. Notice that the distribution shape is not the same.

14. Determine the highest and lowest 20% simu lated natural deciview days for each year and site in the same way that the highest a nd lowest 20% deciview days are determined
for baseline haze levels. Note that these sa mple dates will probably not be the same sample dates that were the highest and lowest 20% dates for the base line calculations. 15. Calculate the arithmetic mean for each year and each species for sample dates which comprise the lowest 20% and highest 20% haze days.
16. Calculate the arithmetic mean across the fi ve annual means for each species including
deciview.

The results of these steps are lowest 20% and highest 20% natural conditions estimates for each site with species compositions, and the natural deciview values needed for glide slope calculation. Note that since the deciview transfor mation is non-linear, the mean deciview value will not equal the deciview va lue calculated from the sum of the mean
aerosol extinction valu es plus Rayleigh.



### RESULTS Estimates of the mean, highest 20% and lowest 20% haze levels for each of the IMPROVE monitoring sites repres enting visibility protected areas is available as the Natural Haze Levels II (version 2) spreadsheet on the VIEWS website 16 . Figure 3 contains contour maps that show the spatia l distribution of the EPA default and revised approach estimates for the highest 20% values. Both have the same striking east – west gradient that results directly from the di fferent Trijonis estimates of natural species
concentrations for the eastern and western states. The greatest difference between the patterns on the two maps is along the Calif ornia coast where sea salt contributes significantly to the PM 2.5 concentration and hence to the h aze at coastal monitoring sites. The default approach doesn’t account for sea salt while the revised approach explicitly
includes it. The natural haze levels from the revised approach at some high elevation sites are lower due to the use of a lower, elevation-dependent Rayl eigh light extinction value compared to the default approach. However many other high elevation sites havehigher natural haze level estimates using the re vised approach (principally those in the Pacific Northwest), probably due to the broade r distribution of current and thus simulated
natural haze levels associat ed with a higher incidence of wildfire influence.

Figure 3. Contour maps of the revised (top) an d default (bottom) natural haze level estimate.

Perhaps of greater interest is a comparison of the uniform rate of progress values for the revised approach compared to the default a pproach. Figure 4 shows contour maps of the uniform rate of progress in units of deciview per decade. As with the natural haze level contour maps, the two algorithms yield similar looking spatial patterns , with the greatest
rates centered on the Ohio River basin in the East and Southern California in the West. Notice that the revised approach uniform rate of progress values for California coastal sites are not obviously affected by sea salt sinc e its contribution to h aze is counted in both
the natural and baseli ne condition estimates.Figure 4. Contour maps of the 10 year haze reduc tion in deciview per decade units for a uniform rate of progress for the revised (top) and default (b ottom) approaches of es timating natural levels
and current conditions.



### DISCUSSION & CONCLUSIONS The decision by a number of states to adopt the revised IMPROVE algorithm to estimate haze levels from IMPROVE speciation data result ed in the need for a consistent approach for estimating natural haze leve ls. In addition to using th e new IMPROVE algorithm, the methodology for estimating revised natural haze levels changed the method of estimating
the 20% haziest and 20% clearest conditions to avoid the flawed statistical approach that was used to generate the de fault natural haze levels.

Both the default and revised approaches for estimating natural haze levels rely extensively on the Trijonis Ea st and West estimates of na tural species concentration levels, which is undoubtedly why the spatial di stributions in Figure 3 show the similarstrong gradients along the arbitr arily selected demarcation li ne between east and west. The only justification for using only two geogr aphically distinct sets of natural PM speciation concentration estimates for all of the visibility pr otected areas is the lack of estimates for other smaller regions. Tombach 9 shows that up to 15 geographically distinct regions are justified based on sim ilar characteristic of current PM species concentrations, but his work doesn’t offer estim ates of natural levels for these regions. Global scale modeling may ultima tely provide better estimates of natural haze levels on a more spatially and temporally resolved basi s, as well as address the related issue of estimating haze from non-U.S. man-made emi ssions for each visibility protected area. 15

The inclusion in the revised a pproach of sea salt with the assumption that all sea salt is from natural sources demonstrates how samp le-period-specific monitoring data can be used to refine the estimate of natural levels. However, most PM species are from a
combination of natural and man-made sources so some type of attrib ution analysis would be needed to apportion how much of each sp ecies is from either source category on a sample-specific basis. It’s unclear whether application of either receptor or air quality simulation modeling would sufficiently im prove the accuracy of natural level concentrations over those provided by Trijonis or similar estimates that could be developed for a greater number of regions.

The 60-year schedule of the RHR with its periodic planning a nd technical review provides the time needed to further expand our knowledge and technology to improve our estimates of natural levels. Ultimately as ma n-made emissions that contribute to haze in visibility protected areas are reduced, current conditions will more closely match natural haze levels, perhaps making the task of specifying them somewhat easier.



### References 1. 40CFR Part 51: Regional H aze Regulation: Final Rule; Fed. Regist. 1999 , 64 (126),
35714-35774
2. Guidance for Tracking Progress Under the Regional Haze Rule , EPA-454/B-03-004, September 2003, available at the IMPROVE web site, http://vista.cira.colostate.edu/improve/P ublications/GuidanceDocs/guidancedocs.htm (accessed 3/19/2008). 3. Pitchford, M.L.; Malm, W.C., Development and Application of a Standard Visual Index, Atmos. Environ., 28: 5, pp1049-1054, 1994. 4. Interagency Monitoring of Protected Vi sual Environments (IMPROVE) web site, http://vista.cira.colostate.edu/improve/Default.htm (accessed 3/19/2008).
5. Guidance for Estimating Natural Visibil ity Conditions Under the Regional Haze Program , EPA-454/B-03-005 September 2003, availa ble at the IMPROVE web site, http://vista.cira.colostate.edu/improve/P ublications/GuidanceDocs/guidancedocs.htm (accessed 3/19/2008). 6. Sisler, J.F., Spatial and Seasonal Patterns and L ong Term Variability of the Composition of the Haze in the United States: an Analysis of Data from the
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