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Estimates of worst 20% natural condition deciview: application of the new IMPROVE algorithm and a revised statistical approach

Rodger Ames, CIRA (rames@cira.colostate.edu)Marc Pitchford DRI, NOAA

RPO Monitoring and Data Analysis Call April 26, 2006

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Reasons to revise

• new IMPROVE algorithm (NIA)– new OC mass conversion factor, mass scattering

efficiencies, etc.• more monitoring site provide better spatially resolved

statistical parameters – 55 sites available for 1996-2000 analysis, 141 for

2000-20041 – old method based on simplistic east/west division of

frequency distribution standard deviation. In new approach every site has statistical underpinning

• revised statistical approach uses new insights– Gaussian dv frequency distribution assumption can

be improved upon

1. Five urban sites were not used for 2000-2004 statistical analysis. Results for four AK sites, two Hawaii sites, and VIIS are not presented here. Two sites failed RHR criteria for baseline period.

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Steps1. Estimate aerosol light extinction (aerosolbext)

and deciview (dv) frequency distribution parameters by simulating natural conditions from current data.

2. Determine appropriate statistical approach to convert natural condition mean aerosol mass concentrations to worst 20% natural condition dv.

3. Simulate worst 20% natural condition dv using key frequency distribution parameters obtained from 2000-2004 IMPROVE data.

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Step 1: simulate natural conditions scenario from current data

• roll back current sulfate and nitrate aerosol mass concentrations to natural levels– assume nitrates and sulfates are predominately

anthropogenic in origin, while carbon and crustal species are predominately natural origins

– for each IMPROVE site (n obs > 300, 129 sites) during 2000-2004, daily nitrate and sulfate concentrations are scaled so that the 5-year mean equals the estimated annual average natural sulfate and nitrate levels for the east and west U.S.1

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Step 1: natural condition aerosol mass concentrations, east/west U.S.

1. Trijonis J.C.; Malm W.C.; Pitchford M.; White W.H., Chapter 24 of Acidic Deposition: State of Science and Technology, vol. 3 Terrestrial, Materials, Health and Visibility Effects, edited by P. M. Irving, U.S. Natl. Acid Precip. Assess. Prog. (NAPAP)., Washington D.C., 1990.

Table 1. Estimated particle mass concentrations (g/m3) for the east and west U.S. under natural conditions1

East West Error Factor

Ammonium bisulfate 0.2 0.1 2

Ammonium nitrate 0.1 0.1 2

Organic carbon mass (1.5*OC) 1.5 0.5 2

Elemental carbon 0.02 0.02 2-3

Soil 0.5 0.5 1.5-2

Coarse Mass 3 3 1.5-2

Mass conversion factor Mass scattering efficiency

East West Old IA New IA Old IA New IA

Ammonium sulfate 0.23 0.115 Small size Large size

Organic carbon 1 0.333 1.4 1.8 4 2.8 6.1

Table 2. modifications to natural conditions mass and key differences in new IMPROVE algorithm in revised approach

East is defined as “basically up to one tier of states west of the Mississippi” and west as “basically the desert/mountain areas of the Mountain and Pacific time zones”1

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Step1: roll back current sulfate and nitrate aerosol mass concentrations to natural levels

Figure 1. current dv standard deviation. Figure 2. dv standard deviation for natural condition scenario.

Current and natural condition scenario daily dv frequency distribution standard deviation using new IMPROVE algorithm and 2000-2004 data.

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Step1: natural condition scenario and recap of old default approach

• natural condition scenario dv standard deviation (s) is between 3-4 for most of the continental U.S. IMPROVE sites, with a region of lower dv s between 2-3 along the Colorado Plateau

• regions of higher dv s, between 4-5, along the CA Sierra and Northern Rockies. Other localized areas with dv s in the 4-5 range occur, for example in FL at Everglades and sites in the southwest and mountain regions

• old approach assumed east and west U.S. dv standard deviations of 3 and 2, respectively to conform to east/west mass concentrations. Also assumed Gaussian dv frequency distribution (FD) to estimate distribution tails from FD standard deviation and mean

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Step 2: revise statistical approach - dv frequency distributions

log (abext + R)

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0 1 2 3 4 5

skewness

kurto

sis

Skew greater than 0 indicates the distribution is skewed to the right, or towards higher values. A normal distribution has a skewness of 0. Positive kurtosis indicates distributions with large tails. As presented, a normal distribution will have kurtosis of zero.

Examine natural condition scenario FD shape parameters. Natural condition scenario dv FDs all have positive skew and kurtosis.

Figure 1. FD kurtosis vs. skewness Figure 2. map of FD skewness

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Step 2: revise statistical approach - log(aerosolbext) frequency distributions

log abext

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-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

skewness

kurt

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Natural condition scenario log(aerosolbext) frequency distribution skewness and kurtosis.

Figure 1. FD kurtosis vs. skewness Figure 2. map of FD skewness

Skew is symmetric about zero and has less magnitude than in dv FDs. Some outliers exist, possibly due to a few outlying datapoints.

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Step 2: revise statistical approach - log(aerosolbext) frequency distributions

log abext

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

mean

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Natural condition scenario log(aerosolbext) frequency distribution mean and standard deviation

Figure 1. FD standard deviation vs. mean

Figure 2. map of FD mean

Figure 3. map of FD standard deviation

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Step 3: Simulate worst 20% natural condition deciview using frequency distribution parameters

• generate random Gaussian (n=500) frequency distributions to simulate natural log(aerosolbext) FD.– use natural condition aerosol mass (east/west), site specific fRHs,

site or regionally specific FD standard deviations. • add site specific rayleigh to each (daily)

simulated abext FD value• calculate daily simulated worst 20% dv

– FD(dv,nc)=10*ln(FD(FDbext/10)• calculate mean of worst 20% dv

– P80 = floor(.8*n)+1– G90= mean(FD(dv,nc(p80…n))

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Step 3: natural condition scenario log(abext) frequency distribution standard deviation

Figure 1. 2000-2004 log aerosol bext natural condition scenario frequency distribution standard deviation, same as previous figure but scaled to observed range.

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Step 3: natural condition scenario log(abext) frequency distribution standard deviation

Natural condition scenario log(aerosolbext) frequency distribution standard deviation at 3 and 2 step values.

Use site specific log(aerosolbext) standard deviation for NC simulations.

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Recap: old default method, mean of worst 20% natural condition dv

Figure 1. Ames and Malm (2001, Bend OR, AWMA conference proceeding). Modified to better estimate worst 20% mean from normal distribution parameters (92 percentile rather than 90th).

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Recap: old default method, mean of worst 20% natural condition dv

Figure 1. Ames and Malm (2001 Bend OR, AWMA conference proceeding). Modified to better estimate worst 20% mean from normal distribution parameters (92 percentile rather than 90th).

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Step 3: new approach, mean of worst 20% natural condition dv

Figure 1. Use NIA, site specific rayleigh, site specific standard deviation, Rand500 to generate normal NC dv FD. Units are dv. Rand500 overestimating FD s by ~3%.

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Step 3: new statistical approach vs. old default method, natural condition mean of worst 20% dv

Figure 1. Fraction which the new NC G90 differs from the old approach (expressed as new/old-1)

Regions in darker blue indicate ~< 10% change old default estimates. Positive values indicate higher values from the new approach.

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Recap: glide slopes from current 2000-2004 conditions to default natural conditions

Figure 1. Glide slopes from the old default approach. Uses old IMPROVE algorithm for baseline and natural condition values. Units are dv/10 years.

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Step 3: glide slopes using new statistical approach

Figure 1. Glide slopes from the new approach. Uses new IMPROVE algorithm for baseline and natural condition values. Units are dv/10 years.

Regions in dark blue indicate current conditions are close to natural condition estimates, or glide slope ~ zero.

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Step 3: new statistical approach vs. old default method, glide slope comparison.

Figure 1. Fraction which the new glide slope differs from the old approach (expressed as new/old-1)

Regions in red indicate little modification to glide slope using new approach. Negative values indicate decrease in glide slope magnitude for new approach.

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Conclusions

• little change in NC estimates from NIA alone.• log (aerosolbext) better approximates Gaussian

distribution than dv.• new approach worst 20% dv higher by 30-80% than old

default approach along mountains of CA, northwest, and northern U.S. Rockies.

• new glide slopes close to zero in some western regions, suggesting current worst day conditions are near natural estimates.

• using FD parameters derived from 2000-2004 data in natural condition estimates makes sense because the baseline data contain similar FD characteristics. That is, sites with higher baseline dv (due to fire or dust) should get higher natural conditions value. But do they?

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Future workSensitivity analysis• Gaussian FD assumption is a better approximation for log

transformed aerosolbext metric however, some deviations occur– sensitivity to changes in natural condition mass concentrations– sensitivity to changes in skew, kurtosis parameters – examine sites with large magnitude FD skew, kurtosis - what is driving

negative skew at some sites?

Refinements to new natural condition estimation approach • incorporate observed dist skew, kurtosis, and other distribution

parameters• incorporate longer time period into NC scenario – better averaging

for fires, etc…• how much can we rely on model data from 2000-2004 period for NC

estimates? (fire emissions in WRAP Plan02b, GEOS-Chem output)

Species specific worst 20% natural condition estimates• decompose aerosolbext FD into component species• species GP = (species base line abext- species NC abext)/60 yrs

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2000-2004 log(abext) FD mean and standard deviation

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2000-2004 log(abext) FD mean and standard deviation under natural condition scenario

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Recap: mean of worst 20% natural condition dv using old method and new IMPROVE algorithm

Figure 1. New IMPROVE algorithm used in default approach. Rayleigh = 10

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Step 3: new statistical approach natural condition mean of worst 20% dv

Figure 1. Uses NIA, site rayleigh, Rand10K to generate NC dv FD. Units are dv.

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2000-2004 worst 20% dv baseline

Figure 1. Current conditions baseline for 2000-2004 worst 20% dv. Units are dv.