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RESOLVE Project Final Report

Visibility Conditions and Causes of Visibility Degradation

In the Mojave Desert of California

by

John Trijonis, Santa Fe Research Corp

zz Michael McGown and Marc Pitchford, EPA-EMSL

Donald Blumenthal and Paul Roberts, Sonoma Technology

Warren White and Edward Macias, Washington University

Raymond Weiss and Alan Waggoner, Waggoner Electronics

John Watson and Judith Chow, Desert Research Institute

Robert Flocchini, University of California at Davis

D F)T' Cforthe

f'7" R2508 Joint Planning and Policy Board

Visibility Protection Program

Environmental Division

Public Works Department

D CXL JULY 1988

NAVAL WEAPONS CENTER

CHINA LAKE, CA 93555-6001 e NO

Approved for Public release; distrbution is unlimited

i i ii F
Naval Weapons Center

FOREWORD

This report documents air quality studip in the Mojave De3ert, of CaliFh'rnla :,•JIia±d

and authorized by the Joint Policy Planning Board (JPPB) of the R-2508 Restricted

Airspace. This study, called RESOLVE (Research on Operations Limiting V~ijaI

Extinction), is part of a Department of Defense (DOD) program to protect flight and

weapons test operations in this airspace from adverse impacts caused by degraded visibilitv

The RESOLVE study was carried out in three overlapping phases: the design and

planning phase from 1983 to mid-1984: the data collection phase from August 1983 through

August 1985, with various special field experiments carried out intermittently during 1984

through 1986. and midcourse, on-line data analysis begun in late 1984 and completed in late

1985

Approved by Under authority of

Capt. K. C. KELLEY, Head J. A. BURT

Public Works Department Capt., U S. Navy

30 June 1988 Commander

Released for publication by

G. R. SCHIEFER

Technical Director

NWC Technical Publication 6869

Published by ................ Technical Information Department

Collation . . . . . . . . . . . . . . . . . . . . . .. . Cover, 92 leaves

First printing .................... .......................... .. 400 copies
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4 -ERFORMING ORGANIZATION REPORT NUMBER(S) 5 MONITORING ORGANIZATION REPORT NUMBER(S)

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ia NAME OF OERFORMING ORGANIZATION 60 OFFICE SYMBOL 7a NAME OF MONITORING ORGANIZATION

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",c 4.OORESS (Cze, State. ara ZIP Code) 7b, ADDRESS (C:ty, State. and ZIP Code)

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PROGRAM PROJECT T ASK NORK UNIT

ELEMENT NO. NO. NO. NO

11 TITLE TncludeSecunty Classtficatuin)

RESOLVE PROJECT FINAL REPORT, Visibility Conditions and Causes of Visibility Degradation in the Mojave

Desert of California

12 PERSON-L AUtHOR(S

Trijonis, McGown, Pitchford, Blumenthal, Roberts, White, Macias, Weiss, Waggoner, Watson, Chow. Flocchini

13a TYPE OF REPORT 1bTIME COVERED 14. DATE OF REPORT (Year. MOnth. Day) 15. PAGE COUNT

Final 1;Fromn 1983 To 1986 1988, July 182

I6 SUPPLEMENTARY NOTATION

17 COSA T? CODES iS. SUBJECT TERMS (Contnue on rvuerse side if necessary and ainfo by block number)

-,FIELD GROUP SUB.GROUP Degradation, visibility, aerosol mass, extinction, emission,

variations, air particles, particulate matter, nitrates, organic

19 ABSTRACT (Con•nu oan revers side ctnecessary and Idenify by block number)

(U) The Department of Defense has been conducting an extensive air-quality study called RESOLVE

(Research On Operations Limiting Visual Extinction). The RESOLVE study is part of a DOD program to protect

flight and test operations in the Mojave Desert of California from adverse impacts caused by degraded visibility.

Historically, the Mojave Desert in Southern California has represented an ideal region for test and training

operations because of excellent visibility, dependably good weather, and large areas of unused land and airspace.

Since the 1940s a decrease in visibility in the airspace has been occurring and DOD is concerned about potential

adverse effects on operations from further declines in visibility. Highlighted in this study are the major conclusions

that pertain directly to the two RESOLVE objectives: documenting baseline visibility conditions in the study region

and characterizing the causes and source areas of visibility degradation.

jJ OISTRIBUTiON/AVAILABI1ITY OF ABSTRACT 21 ASTRACT SECURITY CLASSIFICATION

I UNCLASSIFIED/UNLIMITED [] SAME AS RPT. C DTIC USERS UNCLASSIFIED

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DO FORM 1473, 84 MAR All other editions are obsolete UNCLASSIFIED
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CONTENTS

1.0 In trod uction ............................................................. 3

1.L B ackground .......................................................... 3

1.2 Study P hilosophy ............. ............................ .......... 4

1.3 Terminology and Basic Concepts of Visibility ............................ 5

1.4 Sum mary of M ajor Conclusions ......................................... 6

2.0 M onitoring and Data Overview ............................................ 9

2.1 M onitoring Design Considerations ...................................... 9

2.2 Routine M onitoring Network . ............... ........................ 10

2.3 Monitoring Schedule and Data Recovery ................................ i 1

2.4 Special Studies ....................................................... 15

3.0 Data Quality and Representativeness ..................................... 19

3.1 Data-Screening Procedures ........................................... 19

3.2 Precision and Accuracy ....................................... ....... 24

3.3 M ass and Extinction Balances ......................................... 28

3.4 Interparam eter Correlations .......................................... 31

3.5 Diurnal and Vertical Representativeness ............................... 33

4.0 Clim ate and M eteorology ................................................. 35

4.1 Climate of the RESOLVE Study Region ................................ 35

4.2 RESOLVE Meteorology and Comparison to Long-Term Averages ......... 41

4.3 Relationship of Visibility to Meteorology ............................... 56

4.4 Definition of Flow Regim es ............................................ 65

5.0 Spatial Patterns of Visibility ............................................. 66

5.1 A ir-Q uality M aps .................................................... 66

5.2 Interstation Relationships ............................................ 70

6.0 Temporal Patterns of Visibility ........................................... 72

6.1 Seasonal Variations............................................... 742

6.2 Diurnal V ariations .................................................. 77

6.3 T im e-Series Plots .................................................... 78

7.0 Causes of Light Extinction ............................................... 84

7.1 Scope of the Analysis ................................................. 84

7.2 Extinction Allocation Procedure..................................... 85

7.3 Extinction Budget Resultso ............................................ 90

7.4 U ncertainty Estim ates ............................................... 93

8.0 Sources of AtmosDheric Light Extinction ... .......................... 94

8.1 Natural Versus Man-Made Sources .................................... 94

8.2 Tracer-Element Model for Source Areas .............................. 95

8.3 Conclusions Regarding Source Areas ................................ 105

8.4 Extinction Contributions from Major Anthropogenic Sources ............ 106

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9.0 Illustrative Case Studies .......................................... 108

9.1 Selection of Case Study Days .................................... .. 108

9.2 Discussion of the Case Study Days .... .... ........ ................. 109

9.3 Conclusions ......... ................................. 113

Appendixes:

A. Precision Estimates for RESOLVE Data Sets ......................... 115

B. Estimation of Extinction Contributions from Major Source Categories ..... 131

C. Uncertainty Analysis for the Extinction Budget . ............ ... 139

D. Theory for the NiIPb Tracer Model ..................................... 145

E. Chemical Mass Balance Approach to Source Apportionment .............. 151

F. Detailed Analysis of Case Study Days .................................. 161

R eferen ces ................................................................... 17 7

(

. . t 12

]2
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1.0 INTRODUCTION

For several years, the Department of Defense (DOD) has been conducting an extensive

air-quality study in the Mojave Desert of California. This study, called RESOLVE

(Research On Operations Limiting Visual Extinction), is part of a DOD program to protect

flight and test operations in the desert from adverse impacts caused by degraded visibility.

Within the overall program to protect operations, the purpose of RESOLVE is to provide a

fundamental understanding of visibility conditions and visibility-related atmospheric

processes. The two major objectives of RESOLVE are (1) to document baseline visibility

conditions in the study region and (2) to characterize the causes and source areas of

visibility degradation.

The RESOLVE study was carried out in three overlapping phases: design, data

collection, and data analysis. The design and planning phase took a year and a half (1983 to

mid-1984), although additions and modifications to the plan occurred throughout the course

of the study. The data-collection phase included routine monitoring from August 1983

through August 1985 at seven locations, plus various special field experiments carried out

intermittently from 1984 through 1986. Midcourse, online data analysis was begun in late

1984 and was concluded a year later. This document is the culmination of the data analysis

effort.

1.1 BACKGROUND

Historically, the Mojave Desert in Southern California has represented an ideal region

for DOD test and training operations because of excellent visibility, dependably good

weather, and large areas of unused land and airspace. Because of these advantages, DOD

located a number of major facilities in the region (designated the R-2508 airspace) shown in

Figure 1. The DOD facilities include Edwards and George Air Force Bases, the Naval

Weapons Center (NWC), and Fort Irwin Army National Training Center (FINTC).

Together, these facilities employ approximately 37,000 people with a budget of 1.5 billion

dollars per year.

Since the mid-1940s, when the facilities were established, a decrease in visibility in the

R-2508 airspace has been occurring. Sometimes visibility degradation is now severe enough

to adversely affect the optical data for certain tests or to force changes in operational

procedures. Visibility conditions are still generally adequate for the types of test and

training activities conducted at the facilities. However, DOD is concerned about potential

adverse effects on operations from further declines in visibility.

The Joint Policy Planning Board (JPPB) of DOD has the goal of developing a

management strategy to maintain and optimize the operational capabilities of the test

facilities. In addressing this goal, JPPB initiated studies and discussions of the visibility

issue, which, in turn, have led to the formation of a DOD Visibility Protection Program

(VPP). The VPP consists of four parts: (1) a project to evaluate the operational impacts of

visibility impairment at the DOD facilites, (2) the RESOLVE study to document baseline

visibility and characterize sources of visibility impairment, (3) a long-term monitoring

network to measure future visibility trends and associated causes, and (4) a policy

3
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ZIA

.N... C,,-N.'

I E'.

'" ,.CHINA '

SAN JOAQUINA F' VALLEY " RA 

A IRWIN

TEH~ACHAPI RASý

&EDWARDS LEGEN AIR FORCE BASE . CC.... . ,S!tIOI

395. Jj DaS5 s "o ... 9 S' rsI

.......... -. .... .' ,, . . , ..... S "O " "L " ... .A N % . .:

± CAJON PAS6

':. ..... .... .. .. ......

S... ... ... ...

NLOS -';

FIGURE 1. RESOLVE Study Area Monitoring Sites.

planning study to identify the best strategies for protecting operations. As noted, this report presents the results of the second part of the program-the RESOLVE visibility stud~y.

Several publications have been issued as part of the RESOLVE program. The most

significant are a protocol for the monitoring program (Reference 1), a data analysis plan (Reference 2), a design evaluation based on historical data and error analysis (Reference 3), a quality assurance paper (Reference 4), and annual/quarterly data summary reports (References 5 and 6). The monitoring program protocol (Reference 1) is particularly important because it explains the assumptions, design considerations, and plans behind

RESOLVE.

1.2 STUDY PHILOSOPHY

The RESOLVE program interests many organizations, among which are potential conflicts. In addition to DOD, numerous Federal and state agencies are concerned. Some of

these are the U.S. Environmental Protection Agency (EPA), the National Park Service

(NPS), the Forest Service (FS), the Bureau of Land Management (BLyM), the California Air

Resources Board (CARB), the Department of Forestry, the Department of Food and

Agriculture, and the Energay Commission. nterested parties also include industry,

4
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agriculture, utilities, developers, and local and county governments in the Mojave Desert

and in the upwind air basins. The two upwind regions of most concern are the Los Angeles

basin (the South Coast Air Basin of California) and the San Joaquin Valley.

Because of the potential for conflicts, a statement about study philosophy is included

here. To deal effectively with visibility as a resource, DOD needs to develop an

understanding of visual air quality that is not only comprehensive but also accurate. If the

results of RESOLVE are biased in any way, DOD will be hampered in its management

effort. In other words, DOD has a vested interest in not wrongly implicating any one type of

pollutant or any one source area. In designing the RESOLVE program, conducting the

monitoring, and analyzing the data, the basic philosophy has always been to seek the best

possible, unbiased scientific results.

To achieve the best possible technical results from RESOLVE, DOD gathered input

from other interested parties. One area where other organizations have given assistance is

in research support. Western Oil and Gas Association (WOGA) sponsored four important

projects in support of RESOLVE: (1) high-sensitivity X-ray fluorescence IXRF) analysis of

RESOLVE particle filters, (2) a study of organic particle origins, (3) a heated/ambient

nephelometer experiment, and (4) a data-base documentation effort. The Las Vegas EPA

Laboratory contributed personnel, computer time, and equipment that went well above the

requirements of the RESOLVE Interagency Agreement between EPA and DOD.

The second major area of input was in the review process. To keep other parties abreast

of RESOLVE, DOD members participated in SCENES (Subregional Cooperative Electric

Utility, Department of Defense, National Park Service, Environmental Protection Agency

Study) and CDAWG (California Desert Air Working Group). The RESOLVE program plan

(Reference 1) and data analysis plan (Reference 2) were widely disseminated among

interested parties and publicly presented at workshops. These plans were significantly

modified based on resulting comments. Interim products from the study (e.g., data

summaries and research papers) were also subject to public scrutiny. This final report has

undergone a similar process of review and modification.

An idea that was highlighted in the review of the program protocol was the need to

quantify uncertainties. This idea was made a part of the RESOLVE approach and, in fact,

comprehensive treatment of uncertainties is one of the unique features of RESOLVE. The

precisions of the data sets have been documented, and key policy results (e.g., causes and

source areas of visiblity degradation) have been subjected to error analyses. All the

uncertainties are specified as single standard errors.

1.3 TERMINOLOGY AND BASIC CONCEPTS OF VISIBILITY

The air quality issue of interest in this study is atmospheric visibility. Visibility can be

defined quantitatively in terms of contrast (the relative brightness of various features in a

scene), discoloration (shifts in the wavelength distribution of light produced by the

atmosphere), visual range (the farthest distance that an observer is able to distinguish a

large black object against the horizon sky), and extinction coefficient (the fraction of light

that is attenuated per unit distance as a light beam traverses the atmosphere). As

illustrated in the following paragraphs, these visibility indices are all interrelated.

Among the four visibility indices, extinction coefficient is most directly related to the

composition of the atmosphere. Visib~lity through the atmosphere is restricted by the

scattering and absorption of light by both gases and particles. The extinction coefficient, "B"
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in conventional units of Mm- 1, is simply the sum of those four components, B = Bsg - Bag

+ Bsp -r Bap. Here,

Bsg = light scattering by air molecules (Rayleigh or natural blue-sky scatter).

This term is on the order of 10 to 12 Mm - 1, depending on altitude.

Bag = light absorption by gases. Nitrogen dioxide (NO2,) is the only common

gaseous species that significantly absorbs light.

Bsp = light scattering by particles. This term is usually dominated by fine

particles because scattering efficiency per unit particle mass exhibits a

pronounced peak in the size range of 0.1 to 1.0 micrometers.

Bap = light absorption by particles. This term arises nearly entirely from

elemental carbon (soot) particles.

Once the extinction coefficient and the properties of a scene have been fully

characterized, the other three indices of visibility can be calculated in a straightforward

manner. For example, in a uniform atmosphere, visual range (V) and extinction coefficient

are related by the simple Koschmeider formula, V = k/B, where k is a constant that

depends on the contrast detection threshold of the observer. A "standard" observer is defined

as one with a 2% contrast threshold; in this case, k = 3.9.

Because extinction coefficient is directly related to atmospheric composition, and

because other visibility indices can be calculated from extinction coefficient, the visibility

results in this report are presented in terms of extinction coefficient (or its simple inverse,

standard visual range). In some cases, we report specific measured components of

extinction, such as nephelometer particle scattering (Bscat). Note, however, that Bscat is

not exactly equal to real particle scattering (Bsp) because of measurement limitations of the

nephelometer. As explained in Section 3.3 of this report, adjustments are made for

measurement biases when calculating extinction coefficient or visual range.

Recognizing that Rayleigh scatter by air molecules is purely natural and basically

constant, some of the analyses herein focus on non-Rayleigh extinction, rather than total

extinction (or visual range). Essentially all of non-Rayleigh extinction in the RESOLVE

area comes from atmospheric particles (gaseous NO2 is found to contribute only about 3% on

the average). Furthermore, although coarse-particle extinction is not negligible, fine

particles (52.5 micrometers in diameter) usually dominate non-Rayleigh extinction.

Because of these reasons, and because the size and chemical composition of particles reflect

their origin, many of the analyses in this report revolve around the mass, size (fine versus

coarse), and chemical composition of ambient particles.

With respect to chemical composition, in most areas only five types of species account for

essentially all of particle mass and associated light extinction. These five types are organics,

sulfates, nitrates, elemental carbon, and soil dust. Formulas for calculating the mass of

these species from the RESOLVE data are given later in the text.

1.4 SUMMARY OF MAJOR CONCLUSIONS

We highlight here the major conclusions that pertain directly to the two RESOLVE

objectives: documenting baseline visibility conditions in the study region and

characterizing the causes and source areas of visibility degradation.

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Baseline Visibility Conditions

* For average extinction levels, visual range at NWC is 89 kilometers (55 miles).

Worst tenth-percentile days have a visual range of 59 kilometers, with the best

tenth-percentile days at 143 kilometers. Edwards AFB averages about 15% worse

than NWC, while Fort Irwin NTC averages about 15% better. Edwards AFB

experiences especially severe worst-case days, while Fort Irwin NTC has especially

clear best-case days.

* A map was prepared illustrating the geographical pattern of visibility within the

"study region (Figure 2). This map should provide a reasonable estimate of average

visibility at any point of interest in the study region.

* From lowest visibility to highest visibility, the ranking of the calendar quarters is

Jul.-Sep., Apr.-Jun., Oct.-Dec., and Jan.-Mar. The summer quarter average for total

extinction is one and a half times the winter extinction average. The seasonal

pattern for worst-case conditions is similar to, but not as pronounced as, the

seasonal pattern for average conditions.

* At the desert receptor locations, visibility tends to be lowest just before midnight

and highest in the early afternoon. The daytime (0800 to 1600) average for light

extinction is about 10% lower than the 24-hour average.

Causes and Sources of Visibility Degradation

" Averaged over the three receptor sites, the allocation of visibility degradation by

species is as follows: organics 26% ± 9%; sulfates 24% ± 5%; elemental carbon 19%

± 6%; soil dust 18% ± 8%; and nitrates plus NO 2 13% ± 5%. The extinction budget

shows some minor variations from site to site. The main seasonal variation occurs

for the nitrates plus NO2 percentage, which becomes as large as the other categories

in the winter, but which is especially small in the summer. The extinction budget

for worst-case days is almost identical to the one for average conditions.

" Various analyses indicate that organics sulfates, elemental carbon, and nitrates

plus NO2 are dominated by man-made sources from upwind air basins. Estimates

indicate that slightly over three-fourths of non-Rayleigh extinction is from

anthropogenic sources.

" On the average, the single most significant source of visibility degradation in the

study region appears to be anthropogenic transport from the San Joaquin Valley,

accounting for about one-half (maybe somewhat more) of non-Rayleigh extinction.

In the northern majority of the study region (NWC), the remainder of non-Rayleigh

extinction comes from natural sources. Along the southern edge of the study region,

contributions from Los Angeles and local and natural sources are significant. For

the worst-case days at Edwards AFB and Fort Irwin NTC, transport from the Los

Angeles basin appears to be more important than it is for average contributions.

"* Three individual source categories contribute significantly to man-made extinction

in the RESOLVE region: diesel vehicles and equipment, the petroleum industry,

and gasoline vehicles. Together, these sources account for about 60% of

anthropogenic extinction, with the diesel category alone contributing about onequarter of man-made extinction. No other source categories individually account for

more than 5% of man-made extinction in the region as a whole.

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30 40 6 0,. 8 k m .100 150km

A% • % A

99RA

59• S•OL 75

A A

2 " C' P"4" 

27 23 • - • .. ,21 " C ^ . S17 ,D 16

.. ~~_*•._~23 ~• 26

NOTES:

* Circled data points represent RESOLVE monitoring data. The entire 2 years of RESOLVE data were used, with

nephelometer scattering factored to non-Rayleigh extinction using site-specific RESOLVE base-year

,elationships and 11 Mm-' added for Rayleigh scatter

"* Smaller print data Doints are based on particle-scattering data in and near Searles Valley. The data are

factored to total extinction using RESOLVE relationships.

"* Other data points are based on median airport data calibrated to RESOLVE data.

"* The dashed lines indicate uncertainty in the isopleths near Searles Valley "* Isopleths near the border of the map are guided by nephelometer data at Death Valley as well as data from

other airports in California and Nevada

"* The Beaumont (Banning Pass) value, shown in parentheses, is inconsistent (higher) than surrounding stations

This inconsistency may be because of the high altitude of that site (790 meters) or because of uncertainties

associated with the limited visibility markers at that site. "* The following abbreviations are used in this figure for the stations: Edwards AFB. EDW; NWC, CHL; Fort Irwin

NTC, FOR; Soledad Pass, SOL; Cajon Pass, CAP; Tehachapi Pass, TEH; and Randsburg Wash, RAW

FIGURE 2. Spatial Pattern of Visual Range (for 2% Contrast and

Average Extinction Levels) In and Near the RESOLVE Study Area.

8
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2.0 MONITORING AND DATA OVERVIEW

2.1 MONITORING DESIGN CONSIDERATIONS

Most of the military operations sensitive to visibility occur in the southern half of the

R-2508 restricted airspace (see Figure 1). Aircraft measurements in the region show that

visibility degradation is primarily confined to the lower troposphe:e below the level of the

maximum afternoon miying heights, i.e., less than about 3,000 n.eters mean sea level

(MSL) altitude* (References 7 and 8). Thus, the RESOLVE moniLoring program was

focused within the mixing layer in the southern R-2508 region.

In designing the RESOLVE monitoring network, the results of prior aerometric studies

in the region were reviewed and critical aspects of the design were evaluated using

historical data and error analysis (Reference 3). Based on this review and evaluation,

several assumptions were made in formulating the monitoring program. These

assumptions were subsequently reassessed during RESOLVE through data analyses and

special measurements. Some of the critical assumptions affecting the program design were

as follows:

" It was assumed that particulate matter, not NO 2, is the dominant contributor to

non-Rayleigh extinction in the study region (Reference 3). It was further

understood that, although fine particles should dominate non-Rayleigh extinction,

coarse particle contributions would not be negligible (References 9 and 10). The

major aerosol constituents of interest should include organics, sulfates, elemental

carbon, soil dust, and nitrates (References 10 and 11).

" It was assumed that the visibility degrading aerosol in the Mojave Desert would

include contributions from the Los Angeles urban area (References 7, 12, and 13);

the San Joaquin Valley (References 14 and 15); wind-blown dust from desert

sources (References 16 and 10); and, on a localized basis, certain industrial

complexes, urban areas, and roads in the desert.

" Since, at least under some circumstances, the aerosol species from the Los Angeles

basin and the San Joaquin Valley should have different chemical compositions, it

was assumed that a combination of chemically resolved particle data (from both

source and receptor regions) and meteorological data would help to distinguish

aerosols from the Los Angeles basin from those originating in the San Joaquin

Valley.

" Because of high correlations between daytime and 24-hour particle averages,

because of the similarity between daytime and 24-hour particle composition, and

because of some military interest in nighttime as well as daytime visibility, it was

assumed that 24-hour particle samples (used in conjunction with continuous

nephelometer data) would be appropriate for characterizing the nature and scope of

the visibility-reducing aerosol (Reference 3). Particulate data on a 24-hour basis

(rather than on an 8-hour basis) should also provide greater measurement

precision with less operational difficulties and smaller expenses.

" Based on the results of preliminary error analysis (Reference 3), it was understood

that the monitoring program should minimize measurement uncertainties,

maximize the number of sampling cases, and provide documentation on precision

and accuracy.

All references to altitude in this report refer to height above sea level.

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2.2 ROUTINE MONITORING NETWORK

The routine RESOL'vE monitoring network, operated from August 1983 through

August 1985, included continuous measurements of light scattering, contrast, and

meteorological parameters as well as daily particle sampling. Monitoring was conducted at

three important receptor sites in the desert (Edwards AFB, NWC at China Lake, and Fort

Irwin NTC); three pass sites leading into the desert (Soledad, Cajon, and Tehachapi): and

Randsburg Wash, part of NWC. Refer to Figure 1 for the location of the RESOLVE

network. Note that the following abbreviations will sometimes be used herein for the

stations: Edwards AFB, EDW; NWC, CHL; Fort Irwin NTC, FOR; Soledad Pass, SOL;

Cajon Pass, CAP; Tehachapi Pass, TEH: and Randsburg Wash, RAW.

The monitoring equipment operated at each site is listed in Table 1. The measurements

at all sites included continuous light scattering coefficient, continuous meteorological

parameters, and daily collection of fine (< 2.5 micrometers) and total (< 10 micrometers)

particle filter samples. In addition, contrast measurements and/or photographs were

obtained at the receptor sites. Radiosonde measurements of wind, temperature, and

relative humidity were made on most days at both Edwards AFB and NWC by DOD; and

National Weather Service (NWS) synoptic weather data were acquired for all study days.

TABLE 1. RESOLVE Monitoring Stations and Equipment.

Monitoring stations

E~luiment

EDW CHL FOR RAW TEH SOL CAP Total

VISIBILITY

Nephelometer (continuous) 1 1 1 1 1 1 1 7

Teleradiometer (hourly) 2 1 3 6

Camera

AEROSOL 2 1 3 1 7

RESOLVE 2x4 samplera

(24-hour) 1 1 1 1 1 1 7

DRUMb particle sampler

(4-hour) 1 4

METEOROLOGY

Wind speed/direction and

temperature 1 1 1 1 1 1 1 7

Oewpoint I 1 1 1 4

Relative humidity 1 1 1 3

Radiosonde (upper air

wind, temperature, and

RH) IC IC 2

DATA RECORDING

Satellite data system 1 1 1 1 1 1 1 7

Cassette tape data system 1 1 1 1 4

Strip chart recorders 1 1 1 1 1 1 1 7

'2x4refersto. ,Ilctionin twosize rangesofa totaloffoursimultaneoussamples(1 totai, d < 10

micrometers; 3 fine. d < 2.5 micrometers

0 Multistage rotating DRUM impactor. These samplers were usually at the sites indicated, but were

occasionally moved between sites.

Not co-located wich monitoring site. Operated by DOD personnel.

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The fine and total particle measurements at all sites were made using the RESOLVE

2x4 sampler, which acquired one total and three fine filter samples. The 2x4 samplers were

operated on a 24-hour basis (midnight to midnight*) each day. The total filter and two of

the fine filters were made of Teflon and provided data on mass, absorption, and elemental

composition. The second fine Teflon filter served as a data quality check and as a means to

document precision. The third fine particle filter, composed of quartz, was used for

elemental and organic carbon determination. In addition, five Davis Rotating Universal

Multistage (DRUM) impactor samplers were opf;rated at various sites during the program.

The DRUM samplers obtained size-segregated samples for elemental analysis by protoninduced X-ray emission (PIXE) with a time resolution of 4 hours. These samplers are

described in more detail in other RESOLVE reports (References 3 and 4).

For each site/day, all three Teflon filters (one total and two fine) were analyzed for

particle mass and light absorption. For every third day at the receptor sites, for every sixth

and twelfth day at the other sites, and for selected other days, one fine Teflon filter was

analyzed for elemental composition by PIXE and high-sensitivity XRF, and the fine quartz

filter was analyzed for organic and elemental carbon. The substrates and types and

frequency of analyses for the 2x4 samples at the various sites are summarized in Table 2.

The existence of both PIXE and XRF data for some elements raised the question as to

what values to use in the data analysis. Table 3 lists the measured elements and the

percent of values above the detection thresholds for both methods. For each element, we

chose to use the values from the method that had the lower detection threshold for that

element (i.e., the method with the higher percentage in Table 3).

Extensive quality-control procedures were developed and documented for all

measurements, and quality-assurance audits were performed semiannually at all sites by a

contractor (References 4 and 17). To document the performance of the 2x4 particle

samplers, the seven RESOLVE samplers were operated side-by-side in a comparison test

with two virtual impactors (Sierra Instruments Model 244) at EPA in Las Vegas, Nev.

(Reference 18). The 2x4 sampler was also included in another comparison test at Reno,

Nev., using various samplers involved in the SCENES program (Reference 19). In addition,

the 2x4 sampler was designed so that one fine filter was redundant. Accordingly, the

quality of the data could be checked and the internal precision of the sampler could be

assessed by comparing the mass and absorption measurements on the simultaneously

collected filters.

2.3 MONITORING SCHEDULE AND DATA RECOVERY

Figure 3 illustrates the period covered by the RESOLVE monitoring program. The

RESOLVE network operated from August 1983 through August 1985. Because of initial

difficulties with the 2x4 samplers, however, the 2x4 samplers were not deployed until

April, May, or June 1984, depending on the site. Thus, the daily particle sampling

encompasses about 15 to 17 months of the 2-year program.

As shown in Figure 3, the period July 1984 through June 1985 is designated as the

"RESOLVE base year." This period consists of the 4 calendar quarters when the particle

samplers were fully operational. To avoid seasonal biases, many of the analyses reported

* All references to hours in this report refer to Pacific Standard Time.

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TABLE 2. RESOLVE 2x4 Sampler Filter Analyses.

2x4 f,iter Size of particles f Graimetric PIXE,a OAT, Quality control and

numoer on filter, pmin mass I XRFc Carbond special studies

NWC, Edwards AFB, Fort irwin NTC

1 < 10 All filters Every third filter

Total Teflon (day) plus -40

additionai filters

per site.e

2 <2 5 All fiters Every third filter Selected filters

Fine Teflon (day) plus-40

additionai filters

per site.,

3 <2 5 All filters

Fine Teflon

4 < 2.5 Every third filter Selected filters

Fine quartz (day) plus -40

additional filters

per site.e

Tehachapi, Soledad Pass, Cajon Pass. Randlsburg Wash

1 <10 All filters Routine plus -4

rotal Teflon additiona) fiters

per slte.e

2 < 2.5 All filters Routine plus - Selected filters

Fine Teflon additional filters

per site.e

3 <2.5 All filters

Fine Teflon

4 <2.5 Routine plus -40 Selected filters

Fine quartz additional filters

per site.(*

a PIXE - Elemental analysis by proton-induced X-ray emission technique

b OAT - Light absorption and elemental carbon by optical absorption technique.

c XRF -special sensitivity X-ray fluorescence; performed on fine filters only.

d Organic, elemental, and total.

e Selected based on the results of the routine analyses.

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TABLE 3. Percent of XRF and PIXE Data

Above Detection Thresholds.

Chemical XRF PIXE

S1 --- 996

A) -89.4

Na 82.0

Mg 445

P - 39.2

Au 5.1

Pt 9

Ru -- .6

S 100.0 100.0

Fe 99.7 99.3

Ca 99.6 99.0

K 100.0 98.4

Zn 100.0 80.7

Pb 99.9 72.2

Ti 85.6 72.3

Br 100.0 54.2

Cu 96.6 54.5

Ni 95.8 52.4

Mn 91.7 43.1

V 87.8 53.0

Cr 77.2 40.0

Sr 974 2.1

Se 93.9 3.3

Rb 93.6 .9

CI 37.4 38.8

Hg 33.1 .6

Cd 8.3 .5

Pd 3.7 .6

As 1 8 1.4

Ag 2.4 .5

Sn 2.0 .2

8a 1,2 .3

Mo 7 .8

Zr 4 .6

Y 76.7 -

Ga 10.8

In 1.2

Sb 10 --

La .8

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19.!3 .9 8. 1985 S 0 N D J F N JA M A AS J F M A MJ J AO

EDWARDS ...........

FORT IRWIN - . . . .

CHINA LAKE . . ... . ... ................... .. .

RANDSBURG .......... ...... ..........

TEHACHAPI ....... . ..............

S O L E D A D ............... ........ .......... ..... .. .

CAJON ...... -.......... 

* .- Otlif Oat& A"IYSIS0Jt jfC -

NOTES. e Operationwithout2x4particlesampler . .............

"* Operation with 2x4 particle sampler _

"* Special study experiments = a

"* In addition to the special study experiments indicated above, a heated/ambient nephelometer

study was conducted at Edwards AFB and NWC from November 1984 to August 1985. an insulated

nephelometer study was conducted at Edwards AFB during the fall/winter of 1985-86 and summer

of 1986, a winter nitrate study was conducted at Edwards AF8 and NWC in January-February 1986.

and an organic origins study was conducted at Edwards AFB and NWC in the winter and summer of

1986.

"* Chart includes all data gaps exceeding 7 days.

"* Absorption measurements are missing for November-December 1984 at all sites and also for

January 1985 at NWC

FIGURE 3. RESOLVE Monitoring Schedule.

herein are restricted to data from the RESOLVE base year. When seasonal biases are

unimportant, or when the biases can be easily discounted, the analysis is conducted for the

entire 15- to 17-month program, or for the entire 2 years of the program.

Figure 3 also illustrates all significant data gaps that occurred during the monitoring

program. The two most noteworthy data gaps were

1. In late March 1985, the 2x4 samplers were inoperative for about I to 3 weeks

(depending on the site) because of a temporary disruption in the supply of Teflon filters.

2. Absorption measurements were missing during November and December 1984

because the quality assurance program detected unreliable blank values for that period

caused by a deteriorating laboratory instrument.

No serious consequences developed from either data gap because the RESOLVE data set

consists of a large number of measurements and the gaps did not represent a critical

fraction of the data.*

* The one data gap with potentially serious consequences was the missing absorption data set that might

have introduced a significant seasonal bias. However, we tested our major findings using explicit seasonal

adjustments and found that the effect ofthe missing absorption data on our conclusions was negligible.

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The period for the Online Data Analysis Project is indicated on Figure ,eiow the last

entry on the chart. The purpose of the Online Data Analysis Project was to analyze the

incoming data and recommend midcourse correction to the program-extensions to the

routine monitoring and additions to the special intensive studies. As it turned out, the

Online Data Analysis Project was critical to the successful completion of the RESOLVE

program (see further discussion in Section 2.4).

Figure 4 illustrates the completeness of the data for daily averages. We chose to define

a daily average as valid or nonmissing if the instrument operated for at least two-thirds of

the day. Figure 4 shows percent completeness on a monthly basis for the 2x4 samplers,

meteorological sensors, nephelometers, and teleradiometers. With the exception of start-up

months and the March 1985 gap for the 2x4 sampler, the monthly data recovery always

exceeded 80% and usually exceeded 90% for all four types of instrumentation. Considering

the entire period after instrument start-up, the average fractions of daily data recovery

were as follows:

2x4 samplers 90%

meterological data 95%

nephelometers 92%

teleradiometers 95%

For the continuous monitors, if we examine data completeness on an hourly basis rather

than a daily average basis, the fractions of data recovery were as follows:

meteorological data 95%

nephelometers 95%

teleradiometers 95%

Note the size of the routine RESOLVE data set with respect to the number of recorded

observations. For the 2 years of continuous monitoring, about 115,000 hourly nephelometer

averages, 463,000 meteorological data points (114,000 to 117,000 each for four

meteorological variables), and 32,000 teleradiometer observations were recorded. For the

16-month period of particle sampling, approximately 3100 site-days, when three Teflon

filters and one quartz filter were exposed in the 2x4 samplers, led to about 9000 filter mass

measurements and 8000 absorption measurements. Analyses by PIXE, high-sensitivity

XRF, and the carbon technique were performed on about 800 of the 3100 site-days with

particle sampling.

2.4 SPECIAL STUDIES

Although the routine monitoring network provides most of the data needed to attain

the RESOLVE objectives, the network does not provide all of the measurements required

for the program goals. The remainder of the requisite information is furnished by intensive

field experiments and other special studies carried out for limited time periods and/or at a

limited number of sites. In the planning work for RESOLVE, the decision as to whether a

certain measurement should be done routinely or as part of a special study was based on an

evaluation of the trade-offs between the. needs for complete data coverage and the costs of

conducting the measurements.

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100% •"'"•

50%

• 2x4 SAMPLERS

50%

MTOROLOGICAL DATA

100% " U 111t I..

50%

NEPHELQI4ETERS

TELERAOIOMETERS

A SO0 N D J F M A MJ J A SO0 ND0J F MA Mj JJA

1983 1984 198S

FIGURE 4. Completeness of RESOLVE Data Collection.

Some of the special studies (Organic Origins, Insulated Nephelometer, Local Emission

Inventory, Nuclepore Filter Data) were not formulated until the majority of the routine

monitoring was completed. The need for these studies was identified by the Online Data

Analysis Project carried out from late 1984 through the middle of 1985. The Online Data

Analysis Project also led to midcourse adjustments in the routine monitoring program (e.g.,

a time extension from April 1985 to August 1985, and the addition of high-sensitivity XRF

analysis for the Teflon filters).

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The RESOLVE special studies are briefly described in the following paragraphs. The

descriptions cover when, where, and why the studies were performed as well as how the

data are used in this report. All of the field experiments except for the Organic Origins

Study were carried out by Alan Waggoner and Raymond Weiss. The last two studies deal

with literature reviews of emission-type data rather than field measurements. The Organic

Origins and Heated/Ambient Nephelometer Studies, plus the high-sensitivity XRF

analysis in the routine program, were funded by WOGA as research contributions in

support of RESOLVE.

High-Sensitivity NO 2

In the summer of 1984, a high-sensitivity instrument was used to measure NO2

concentrations at Edwards AFB, NWC, and Tehachapi. These measurements, along with

data from other previous studies, provided information on the contribution of NO 2

absorption to the light extinction budget (see Section 7.2. 1).

True Particulate Nitrate

During the summers of 1984 and 1985 and the winter of 1986, particulate nitrate

concentrations at Edwards AFB and NWC were measured using a nylon filter preceded by

a nitric-acid denuder. These data were used to estimate nitrate contributions to the light

extinction budget (see Section 7.2.3) and to investigate the role of nitrate in the aerosol

mass balance (see Section 3.3).

Thermidograph Nephelometer

A cyclone (fine particle) nephelometer that heated atmospheric samples to 350°C was

deployed at Edwards AFB in the summer of 1984. This instrument indicated what fraction

of fine particle scattering was caused by refractory materials as opposed to evaporable

compounds. The data provided an important check on the allocation of fine-particle light

scattering among aerosol components (see Section 7.2.3).

Cyclone Nephelometer

During the summers of 1984 and 1985, a nephelometer with an on-off cyclone to

eliminate coarse particles was operated at Edwards AFB and NWC. The data from this

experiment were used to estimate the contribution of coarse-particle scattering to the light

extinction budget (see Section 7.2.3) and to evaluate the accuracy of the nephelometer as a

measure of total particle scattering (see Section 3.2.2).

Organic Origins

During the winter and summer of 1986, organic particle samples were collected on an

episodic basis at Edwards AFB and NWC under a WOGA contract to Global Geochemistry

Corp. These samples were analyzed with respect to carbon-12/carbon-14 isotope ratios and

with respect to molecular composition. The results provided information on the origin of

the organic aerosol in terms of contemporary versus fossil and in terms of natural versus

anthropogenic (see Section 8.1).

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Coarse-Particle Carbon

Coarse-particle samples for carbon analysis were collected on quartz fiber filters at

Edwards AFB in the summer of 1985. This data set was used to help determine the relative

contributions of elemental carbon and soil dust to coarse-particle absorption (see Section

7.2.2).

Heated/Ambient Nephelometer

Special nephelometers were operated at Edwards AFB and NWC from November 1984

through August 1985 under a WOGA contract to Waggoner and Weiss. These

nephelometers sampled air that was cycled from near-ambient temperature to an elevated

temperature (+ 15°C) that would evaporate absorbed water in the aerosol. 17he resulting

information on light scattering by water was useful in interpreting the light scattering

budget (see Section 7.2.3) and in evaluating the accuracy of the routine RESOLVE

nephelometers (see Section 3.2.2).

Nuclepore- Filter Data

During the summer of 1985, fine-particle samples were collected at Edwards AFB on

nuclepore filters. The nuclepore filter data were used to evaluate the accuracy of the

routine RESOLVE Teflon data. This accuracy evaluation was especially important with

respect to the Teflon absorption measurements (see Section 3.2.2).

Insulated Nephelometer

For 9 months during the fall and winter of 1985 and 1986 and the summer of 1986, an

insulated/ambient nephelometer was operated in conjunction with the routine RESOLVE

nephelometer at Edwards AFB. This experiment provided information on the accuracy of

the RESOLVE nephelometer relative to ambient conditions and on the precision of the

nephelometer data (see Section 3.2.2 and Appendix A).

Diurnal Particle Composition

In the summer of 1985, the 2x4 samplers at Edwards AFB and NWC were operated on

an 8-hour basis rather than on a 24-hour basis. Particle composition analyses were

conducted on all samples over a 3-week period. The purpose of this intensive study was to

investigate whether the 24-hour composition data were representative of shorter time

periods (see Section 3.5).

Dust Characterization

A literature review was conducted by AeroVironment, Inc., to characterize the

:hemical composition of dust sources in the RESOLVE study region. The information was

needed to help evaluate the origins of soil dust particles (see Section 8.1).

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Local Emission Inventory

An emission inventory for sources of primary particulate matter in the RESOLVE

study region was assembled by Nero and Associates, Inc. This inventory included

resolution by particle size and chemical composition. The results were useful in evaluating

the contributions of local desert source categories to extinction (see Section 8.4 and

Appendix B)

3.0 DATA QUALITY AND REPRESENTATIVENESS

Chapter 2.0 dealt with the scope and quantity of the RESOLVE data. Chapter 3.0

focuses on the quality of the data set. Understanding the quality of the data is important in

providing a sense of the amount of confidence that can be placed in the subsequent

conclusions.

Several reasons account for the good quality of the RESOLVE data base: care in

designing the monitoring network, especially the selection of instrumentation and the

provision for a quality assurance program; attentiveness in operating and maintaining the

monitoring stations; using the On-Line Data Analysis Program; examining the data set

while the data were being collected; and subjecting the final data set to an intense

screening process to flag erroneous or suspicious values.

3.1 DATA-SCREENING PROCEDURES

Once the RESOLVE data base was assembled, the critical air quality parameters (daily

averages of nephelometer data and particle mass/absorption/carbon/P[CE/XRF data) were

subjected to a careful screening process to flag erroneous values. Such a screening

procedure is an absolute necessity with most air-quality data sets because erroneous values

do become recorded and because just a few such outliers can have a substantial effect on the

data analysis results (especially those that involve least-squares methods or variancerelated techniques.) However, to avoid biases produced by preconceived ideas as to what

relationships or models the data should follow, the screening process was performed before

starting any of the key data-analysis studies (i.e., extinction budget regressions, receptor

modeling analyses, spatial/temporal descriptions, etc.).

The screening process for the RESOLVE data was facilitated and made especially

purposeful by several duplications built into the monitoring system. List No. 1 in Table 4

indicates pairs of data-base parameters that were essentially duplicate measurements of

the same phenomenon. Scrutinizing these duplicate measurements was the most

important aspect of the screening procedure.

The screening process included the following steps to identify and flag outliers:

1. For each of the seven sites individually, special scatterplot outputs were prepared

for 24 pairs of closely associated parameters. These pairs included the duplicate parameters

(List No. I of Table 4) as well as other closely related parameters (List No. 2 of Table 4). The

scatterplot outputs contained not only a routine scatter diagram of the data but a listing of

the date and data values as well as markings indicating the number of standard deviations

that each observation varied from a least-squares line fit to the data.

2. For each site, the scatterplot outputs were scrutinized to identify anomalous data

points. When an anomaly was identified on one scatterplot, the anomaly was then circled

and marked on all scatterplots pertaining to that parameter.

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TABLE 4. Plots Prepared for Each Site as Part of

the Data-Screening Process.

List No 1: Scatterplots of Duplicate Parameters

Two alternate fine masses

Each fine mass versus sum of chemical speeies (organics + sulfates + elemental carbon + soil)

Two alternate fine absorptions

Fine absorption versus elemental carbon

PIXE S versus XRF S

PIXE Fe versus XRF Fe

PIXE Ca versus XRF Ca

PiXE K versus XRF K

PIXE Ti versus XRF Ti

PIXE Pb versus XRF Pb

PIXE Zn versus XRF Zn

List No. 2: Scatterplots of Closely Related Parameters

Total mass versus each fine mass

Each fine mass versus Bsp

Total mass versus Bsp

Total absorption versus each fine absorption

Each fine mass versus each fine absorption

Total mass versus total absorption

Each fine mass versus organics

Each fine mass versus sulfur

Each fine mass versus elemental carbon

8sp versus organics

Dsp versus sulfur

8sp versus elemental carbon

Organics versus elemental carbon

List No. 3: Time Series Plots of Daily Averaqes

Ratio of each fine mass to 8sp

Ratio of each fine mass to total mass

Ratio of each fine absorption to 8sp

3. The day in question was found on three time-series plots of daily "key ratios" (see

List No. 3 in Table 4; also see Figure 5 for an example). This information allowed a test to

be made as to whether the anomaly occurred just for the date in question (or also for

surrounding dates) and whether the anomaly occurred just at the site in question (or also

for surrounding sites). In the process of checking the scatterplots of duplicate parameters,

the scatterplots of other closely related parameters, and the spatial/temporal patterns in

the time series plots, it often became obvious that a specific data value was actually

erroneous.

4. The fourth step was to make a judgment whether the data value was erroneous and

to write down the reasons for the error. A file of these reasons was assembled.

5. The last step was to label certain data values as "definitely" or "probably"

erroneous and to flag them as such in the RESOLVE data archive.

The causes for the erroneous data were investigated by checking the monitoring

station logs and the original data recordings. In some cases, a correctable mistake (e.g., a

transcription error) was found and the flagged data point was fixed. For some cases, a likely

cause was found, but no way was found to correct the data. Sometimes, no obvious cause

was identified.

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FINE MASS/BSCAT 03,84

EDW

FOR

CHL

RAW

TEH

SOL

CAP

AVG

Uy Auguet ptm r

FIGURE 5. Time Series Plot For Data Screening. The two lines

for each site represent the ratio of each fine-mass channel to

nephelometer scattering.

Table 5 summarizes the amount of data points flagged by the above procedure. Generally, about I to 2% of the data points were flagged for particle mass, particle absorption, and PIXE analysis. None of the XRF data and almost none of the nephelometer data appeared to be erroneous. About 5 to 7% of the carbon data were flagged. The thermal

separation technique for organic/elemental carbon is an evolving and uncertain approach

21
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that has not yet been standardized and carbon measurements tend to be the least precise of

all the RESOLVE data sets.

TABLE 5. Percent of Daily Average Data Flagged

By the Data Quality Screening Process.

Nephelometer Scattering 0 1

F,ne-Particie Mass 2 2

Total Particle Mass 1 0

F,ne-Particle Absorption 2 2

Total Particle Absorotion 1 3

Fine-Organic Carbon 5 4

Fine-Elemental Carbon 6 9

PIXE Analysis 0 8

XRF Analysis 0 0

With respect to erroneous values for fine-particle mass and absorption. the flagging

process produced essentially no loss in the total number of daily observations. For the data

set used in this report (which exciudes flagged values), the two simultaneous fine mass or

absorption measurements were averaged together, but just the single valid measurement

was used if the other measurement was missing or erroneous.

Figures 6, 7, and 8 illustrate some of the flagged data by showing scatterplots (for all

sites combined) of the two alternative fine-mass values, the two alternative fine-absorption

values, and fine-absorption versus fine-elemental carbon. The flagging procedure was not

based on these scatterplots alone, but rather on an examination of all the graphic outputs

listed in Table 4.

A.L SITES

I..

4 0

0

s ee

* + + 4

C 4 * 4"

II'

4, f. C.9/.--33 .-2 . -

go 4

FIGURE 6. Scatterplot of Alternate Fine-Mass

Channels, Showing Flagged Data I + ).

22
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%LL SITES

V2.

F U .o.

* - 4 .

"F -o e , .. 4 Fg-a

.. ÷ ,.. 4

* ÷+

*6 ÷ +

F UR 7. Sctepo4 fAlent ie

Absorption Channels, Showing Flagged Data ( +).

ALL. SITES

I I

I.. ÷ . •. .'

"3. "4

44 .".. * S.. .

oo0 I I " " I" I l I

.861 4* O3 II 00 4 3 *3 3I 3 ? 5j *6l i

FIGURE 8. Scatterplot or" Fine-Absorption Versus

Fine-Elemental Carbon, Showing Flagged Data (-/ + .

23
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In addition to the discussed process for identifying erroneous daily averages, other

screening and flagging procedures were applied to the RESOLVE data set. For example, all

daily averages based on less than 16 hours of sampler operation were flagged as

incomplete. Also, certain teleradiometer measurements were flagged as erroneous when

extremely low toften negative) extinction coefficients resulted because of a problem with

the inherent contrast values in the fall season under certain sky and cloud conditions. A

brief screening process was also applied to the meteorological data set, but none of the

meteorological data that had passed the initial field examination were found to be

erroneous.

3.2 PRECISION AND ACCURACY

This section discusses the precision and accuracy of the RESOLVE measurements.

Precision refers to the degree of reproducibility of the measurement; imprecision thus

represents random error. Accuracy refers to the lack of a consistent bias in the

measurements; inaccuracy thus represents systematic error.

3.2.1 Precision Estimates

Precision is defined here as ± 1 standard error of measurement reproducibility.

Precision can be conceptualized as the standard deviation of the data set that would occur if

a particular parameter were measured simultaneously by a large number of equivalent

monitors. In practice, precision is often determined by operating two equivalent monitors

together over a large number of sampling periods. In the latter case, the deviation between

the two monitors includes the imprecision of both, and the total deviation is thus (by simple

error propagation) v'-times the individual imprecision. Thus, for the two-monitor case,

precision is estimated as the standard deviation of the difference between the two monitors

divided by V_27

Appendix A derives precision estimates for the critical air-quality parameters in

RESOLVE-particle mass, absorption, scattering, and composition (carbon/PIXE/XRF). For

particle mass and absorption, the precision is determined directly from the duplicate

measurements provided by the 2x4 sampler. For the other parameters, total imprecision is

estimated by error propagation techniques applied to various components of the

measurement uncertainty.

Table 6 summarizes the precision results (and, for convenient reference, average

parameter values at the receptor sites). The precisions for most of the key air-quality

parameters are generally about 5 to 10%. The main exceptions are fine elemental carbon

and coarse absorption, both of which have high relative imprecision because they are of

small magnitude. For fine elemental carbon, an alternative, more precise measurement is

available (fine-particle absorption). Coarse absorption is very imprecise (on a percentage

basis) because it is obtained by subtraction of two parameters of relatively large

magnitude. Fortunately, it is not critical to the data analysis that coarse absorption be

known precisely on a daily basis.

To get a better feeling for the precision estimates, refer to the scatterplots in Figures 6,

7, and 8 (neglecting the flagged, erroneous data). The high precision of particle mass and

absorption is evident in Figures 6 and 7. Figure 8 reflects the lower precision of the

elemental carbon data. Figure 9 illustrates the precision of the PIXE and XRF analyses for

the four elements (S, K, Ca, and Fe) that are nearly always above detection limits for both

methods.

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TABLE 6. Precision of RESOLVE 24-Hour Data.

Average base-year concentrations

Elements Average

precisiona Edwards AF8 NWC Fort Irwin NTC

Scattering and Absorption

Particlescattering ± 2% 31.2 Mm- 25.7 Mm- 20 3 Mm -

Fine-particle absorptionb ± 5% 5,2 Mm - 5 8Mm 4 1 Mm -

Total particle absorption . ± 7% 6.4 7 2 5 1

Coarse-particle absorption . . ( _ 45%)c 1 2 1.4 1 0

Particle Mass

Fine-particle massb ......... 5% 8 3 V±g/m3 9.0 Vig/m 3 6.5 pig/mJ

Total particle mass ...... ±.. 5% 178 18.0 13 6

Coarse-particle mass . . 12%)c 9 5 90 7 1

Critical Elements (Fine)

Fine-organic carbon ....... ±. 8% 2.1 vg/mi3 1.9 Pg/m 3 1 1 pg/m 3

Fine-elemental carbon ... _+ 23% 54 49 42

XRF S ... .... 5% 51 .67 45

PIXE Si .±....... 10% 51 35 47

PIXE Al ........ ± 12% 21 .14 .17

XRF Ca .......... 6% 093 .073 088

XRF Fe ......... 5% .11 .082 085

XRF Mn ......... ± 11% .0037 .0023 0024

PIXE Mg ±......... 12% .042 .032 062

XRF K ± 5% .095 078 .070

XRF Pb . ±..... 8% .016 .021 0094

XRF Ni ........... 6% .0028 .0032 0019

Other elements of interertd

XRF V ........ ± 25% .0022 Vg/m 3 0022 pg/m3 0015 pg/m3

PIXE Cl ........... 8% .067 .034 025

PIXE Na .......... ± 15% .22 .19 15

XRF Zn ......... ± 16% .0068 0099 0076

XRF 8r ......... ± 7% .0043 0051 0029

a Precision uncertainty represents a single standard error.

b Reported precision pertains to the average of the two fine 2x4 values (as used in the RESOLVE DATA

ANALYSIS). The imprecision of a single fine channel would be greater by a factor of V2

c Obtained by subtraction of fine from total.

d The RESOLVE special experiments for fine particulate nitrate involved just a few weeks of sampling at

Edwards AF8 and NWC. The data suggest annual average NO3 concentrations of about 1.2 gg/m 3 at

Edwards AFS and 0.5 pg/M3 at NWC (see Section 7.3). The precision of an individual 24-hour sample is

dominated by flow rate uncertainties and is estimated as ± 5% (Reference 20).

25
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$. I FPtee , .

C J 

-C I - C

/C

0.

s C J ,

XRF xftF

Cole, I,.-,ra

FIGURE 9. Comparison of PIXE and XRF Analyses for Pour Elements (in

pg/m3) That Are Highly Detectable by Both Methods.

All of the major conclusions of the data analysis are based on longer term averages of

the RESOLVE data. For a longer term average of N. data points, the error in the average

due to measurement imprecision is reduced by a factor of ý N Because of this effect, and

because measurement precision is generally quite good, we found that measurement

imprecision is a negligibie component of the overall uncertainty level in our conclusions.

3.2.2 Accuracy Considerations

In analyzing the RESOLVE data, we adopted the approach of correcting for any

inaccuracies that were known to be significant. We do not mean that all inaccuracies were

removed from the data. On the contrary, the potential for unknown or miscorrected

inaccuracies represents one of the main sources of uncertainty in the results. Nevertheless,

we did attempt to make corrections whenever they were appropriate.

Two of the important RESOLVE measurements are known to be inaccurate-particle

absorption and particle scattering. Absorption measurements on Teflon f~ilters tend to

overestimate atmospheric particle absorption because of internal filter reflectance

(References 21, 22, and 23). Nephelometers may mismeasure scattering from volatile

components (especially water) by altering the temperature and relative humidity of the

26
NWC TP 6869

sample. Nephelometers also miss much of coarse particle scattering because of a loss of

coarse particles in the sampling train and because of a systematic underestimate of the

scattering from coarse particles.

Two methods are used here to estimate an accuracy calibration factor for the Teflon

absorption measurements. The first is based on a comparison of RESOLVE fine absorption

levels to RESOLVE fine-elemental-carbon concentrations. Although the RESOLVE

elemental carbon data are not very precise, they are (on the average) accurate, at least in

terms of agreement with other researchers (Reference 24). To make the RESOLVE

absorption data set consistent with values found in the literature, the absorption data set

can be calibrated against elemental carbon concentrations using the literature consensus

of 9 m2/g as the absorption efficiency for fine elemental carbon ',Reference 25). This

procedure yields a correction factor of 0.6 for the Teflon absorption data.

The second method is based on a 3-week experiment at Edwards AFB comparing

RESOLVE Teflon absorption to nuclepore absorption using the integrating plate method,

which, in turn, has been calibrated against an optical extinction cell (Reference 26). Some

problems developed in intei preting the data from this experiment, but the result yielded

the same correction factor-0.6. The 0.6 accuracy factor (applied to all particle absorption

values before dlacing the values into the RESOLVE data base) is not without uncertainty,

despite the fact that the two methods agreed.

The issue of volatile aerosols in the nephelometer was addressed by two RESOLVE

special studies. The Insulated Nephelometer Study compared a routine RESOLVE

nephelometer to an ambient nephelometer (one that is designed to produce only a very

slight temperature change from ambient conditions). This study indicated that, on the

average, the RESOLVE nephelometer was only about 1 to 2% lower than the ambient

nephelometer. The difference (which showed considerable random scatter) was greater at

high relative humidities. However, even above 70% relative humidity, which occurred less

than 10% of the time, the average difference was only a factor of about 1.1. The

Heater/Ambient Nephelometer Study cycled nephelome*ters from near-ambient conditions

to a heated condition (+ 15'C) to measure the percent of scattering by water and other

easily volatilized species. The volatilized component averaged less than 3% at NWC and

less than 7% at Edwards AFB, and was almost always less than 20%.

The Insulated Nephelometer Study demonstrated that the RESOLVE nephelometers

closely approximate ambient scattering conditions with respect to volatile aerosols (to

within 1 to 2% on the average). The Heated/Ambient Nephelometer Study explained why

this was reasonable by showing that water and other easily volatilized species are not very

significant aerosol components in the study region. We decided not to apply any accuracy

correction factor to the RESOLVE nephelometer data to account for volatilized species

because the observed correction factor was so small and variable.

With respect to the issue of missing coarse-particle scattering, the inaccuracy of the

RESOLVE nephelometer data is more significant. We estimate that the RESOLVE

nephelometers miss about one-half of coarse-particle scattering based on Stokes theory

calculations of sampling train losses, Mie theory results for scattering efficiencies, Mie

theory calculations of nephelometer truncation errors, and crude size distribution data

available in the literature. In Section 7.2.3, we conclude that average coarse-particle

scattering is about 0.6 m2

/g times average coarse (2.5 to 10 micrometers) mass, which

amounts to 5 Mm-1 as a mean value over the three RESOLVE receptor locations. This

information implies that 2.5 Mm- I of coarse-particle scattering is missing on the average,

which is about 10% of the three-site mean nephelometer value (25.5 Mm- 1). The corrected

three-site mean for total-particle scattering would thus be 28 Mm - 1.

27
NWC TP 6869

One method to use to correct for the coarse-scattering inaccuracy would be to adjust

each individual 24-hour nephelometer value in the data base by adding 0.3 m2/g times the

daily coarse-particle mass. We decided against this approach, however, for the following

three reasons: (1) The outputs based on 24-hour nephelometer data would not be consistent

with outputs based on the hourly nephelometer data (which could not be corrected because

(," the tack ot hourly coarse-mass data), (2) a multiplication of missing values would be

present because both nephelometer and coarse-mass data would have to be available on a

given day, and (3) significant imprecision would be added to the nephelometer data base

because the coarse-particle scattering correction of 0.3 m2/g times coarse-particle mass is

very imprecise on a daily basis (because of imprecision in coarse-particle mass data and

because of variations in daily average coarse-scattering efficiency). Rather, we decided to

add the correction during the data analysis whenever we calculated total extinction or

visual range. Thus, when nephelometer readings are reported here (sometimes labeled as

Bscat), they represent raw nephelometer data with no correction made for coarse-particle

scattering. But, whenever we sum all extinction components or report visual range, the

results always include the correction for missing coarse-particle scatter.

3.3 MASS AND EXTINCTION BALANCES

One of the most important quality checks for the particle and extinction data bases in

RESOLVE involves comparing the whole to the sum of the parts. Does the sum of the major

aerosol chemical constituents approximate measured aerosol mass? Does the sum of

particle scattering and absorption plus gaseous scattering and absorption equal measured

total extinction?

Table 7 presents average fine-particle mass balances for all seven stations based on the

routine data during the RESOLVE base year (July 1984 to June 1985). About 10 to 20% of

the fine-particle mass is not accounted for by organics, sulfates, soil, and elemental carbon.

This remaining mass is believed to be mostly nitrates* plus very minor contributions from

miscellaneous species. For Edwards AFB and NWC, if we add the measured particle

nitrate mass (using the denuder technique that should yield a somewhat greater value

than the nitrate on Teflon filters) and the mass of other miscellaneous elements (about 3%),

the total mass accounted for is 104% at Edwards AFB and 97% at NWC. Thus, the average

mass balance for fine particles seems to be quite reasonable.

Figure 10 shows how the sum of chemical components (organics plus sulfates plus

elemental carbon plus soil dust plus miscellaneous measured elements) compares to fine

mass on a daily basis for the entire data set. Although the relationship is not extremely

precise, the correlation is fairly good (R = 0.79). Thus, both for the average and individual

days, the fine-particle-mass balance is indicative of good data quality.

Table 8 compares teleradiometer measurements of total extinction (available for the

three RESOLVE receptor sites) to the sum of extinction components. As discussed in

Section 7.2, the sum of extinction components-averaged over the three sites-consists of

24% from Rayleigh scatter by air molecules (assumed to be a constant 11 Mm- 1), 2% from

light absorption by gaseous NO 2 (based on special study NO2 data), 61% from light

scattering by particles (50% from fine particles and 11% from coarse particles based on

nephe!ometry and coarse-particle data), and 13% from light absorption by elemental

"Several indications are present that the remaining mass mostly represents nitrates. One important piece of

evidence is that nitrates and unaccounted-for mass are the only two aerosol components that show a winter

maximum rather than a summer maximum (see data and discussion in Section 7.3.2).

28
NWC TP 6869

TABLE 7. Fine-Particle Mass Balances Calculated

for the RESOLVE Base Year.

Elements Mass componentsaivided by total measured fine mass by site

EDW CHLJ FOR RAW TEl- SOL CAP

Organicsd 32% 37%/ 29% 30% 34% 30% 36%

Sulfatesd 30% 24% 28% 32% 26% 32% 27%

Elemental carbon 6% 6% 7% 5% 4% 8% 7%

Soils 16% 20% 27% 17% 15% 10% 8%

Unaccounted forb 16% 13% 9% 16% 21% 20% 22 %

Number of days( 85 87 80 26 26 30 33

a To include the mass of associated ions, the following conventions are adopted

throughout this report: sulfates = 4 12 x S (assuming ammonium sulfate), organics =

1 .5xOrganic C (accounting for associated hydrogens and other ions), and soil = 2.14

Si + 1.67 Al + 1 44 Ca + 1 40 Fe * 1.66 Mg + 1.22 K + 1 44 Mn (accounting for

common soil compounds).

b This category represents the fine-particle mass not accounted for by the other

four categories. It is thought to be mostly nitrates. True particulate nitrate by the

denuder/nylon-filter method, which would tend to be greater than the nitrate

collected on the routine Teflon filters, is approximately 17% of fine mass at Edwards

AF8 and 7% at NWC. The mass of miscellaneous other species measured by PIXE or

XRF (e.g., lead salts) accounts for about 3% of the total at all seven sites.

c This table is restricted to days in the RESOLVE base year with data availabLe for

fine mass and all chemical components.

ALL SZTES

nol

as

I I I i i I

4 °

a 0 40 00 . a M. 0 ns m n

ii B S, °* . ". .,, 3

FIGURE~ 10. ~~• ofSmo ,ctepo esrdFn eoo

Component V0 ctersusota Finem o Mass. rdFn eoo

29
NWC TP 6869

TABLE 8. Comparison of Teleradiometer Total Extinction to

the Sum of Extinction Components.

Ratio of teleradiometer Correlation of daily teleradiometer

Site and teleradiometer targets to sum of components* to sum of components

All data Clear skies All data Clear skies

Edwards AFB

Target1 88 87 90 (335)'* 92 (98)

Target 2 84 85 78 (308) 70 (35)

Fort Irwin NTC

Target1 84 81 78 (188) 84 (10)

Target 2 82 82 82 (259) 89 (33)

Target3 73 79 77 (261) 91 (18)

NWC

Target1 78 83 79 (322) 89 (47)

"Comparisons are carried out using daily averages. The sum of components is calculated in Mm - I as

11+ f(Bca0 24 + .3CP) + .04 B + B Bap2at8

Iscat24 B 4p24 

The first term is Rayleigh scatter. The value in parentheses represents particle scatter, adjusted to account

for the missing part of coarse-particle scattering. The second and third terms in the brackets are NO2 and

particle absorption, respectively. The subscript *24" refers to 24-hour averages. The last ratio in the formula

factors the result to an 8-hour daytime basis to be consistent with the daytime data for teleradiometer

extinction. For the short period of missing absorption data, B.,24 is estimated as a constant fraction of 9

Scat24-

"* 0 = Sample size.

carbon (based on routine particle absorption data). Results are presented for all

teleradiometer data (regardless of sky conditions) and for clear skies (when the data

quality should be best). The data are for daily averages, with the sum of extinction

components adjusted from a 24-hour basis to an 8-hour basis using the diurnal pattern of

Bscat (see footnote to Table 8).

As indicated in the left-hand side of Table 8, teleradiometer extinction is generally

about 15 to 25% less than the sum of components. This amount is qualitatively consistent

with expectations. The sum of extinction components is based on ground-level, point

measurements. The teleradiometer, on the other hand, represents an average over a line of

sight from ground level to elevated targets (mountains). It is very reasonable to expect

somewhat lower extinction levels at higher elevations (particularly at elevations above the

mixing layer).

The right-hand side of Table 8 presents the correlation between daily values for

teleradiometer extinction and sum of components. Most of the correlations range from

about 0.75 to 0.9, which is good, especially considering the potential problems with

30
NWC TP 6869

teleradiometer data* (References 27, 28, and 29) and the fact that point measurements are

being correlated with line-of-sight measurements. Thus, both with respect to averages and

daily correlations, the teleradiometer data appear to corroborate the good quality of the

data sets for extinction components (i.e., scattering and absorption).

3.4 INTERPARAMETER CORRELATIONS

Table 9 summarizes the correlations that exist among the important aerosol and

extinction parameters. The correlations are based on all daily averages from the three

receptor sites during the RESOLVE base year. For each parameter, the other parameters

are listed below it in order of descending degree of correlation. The correlations can be

interpreted in terms of physical and chemical relationships as well as in terms of data

quality. Here, we focus mainly on the data quality implications.

One feature shown in Table 9 is the consistently good correlation (generally about 0.75

to 0.80) that exists among particle scattering (Bscat), fine-particle absorption (Fabs), fineparticle mass (Fmass), organics (Org), and sulfates (Sul). These are the main measures or

components of fine-particle mass and fine-particle extinction-they all show summertime

peaks and they all are thought to originate mostly from man-made sources. That the

parameters show fairly good levels of correlation despite the fact that none are really

duplicate measures of the same phenomenon suggests good data quality. More

significantly, the fact that each of these parameters consistently shows about the same

degree of correlation with the others suggests that none of these parameters has uniquely

poor data quality.

Elemental carbon shows a somewhat lesser correlation with the above five

parameters-about 0.6 to 0.7. Even for fine absorption, a duplicate measure of the same

phenomenon, only a 0.6 correlation with elemental carbon is present. The reason for this is

the relatively low precision of the elemental carbon data (see Table 6).

The soil variable and the major soil elements (Si, Al, Fe, and Ca) all intercorrelate at

very high levels-generally 0.9 to 1.0. This high intercorrelation is a reflection of the good

precision in the PIXE and XRF analyses.

The elements Ni and Pb do not correlate as high with the other key air-quality

parameters (Bscat, Fabs, Fmass, Org. Sul, and EC) as the soil variable and soil elements

intercorrelate. Generally, Ni and Pb correlate at a level of 0.25 to 0.40. This fact should not

represent a data quality problem, because precision is quite good for Ni and Pb (see Table

6). Rather, the explanation for the lower correlations lies in some of the findings of Chapter

8. Ni and Pb are used as tracer elements to investigate contributions from different source

areas (San Joaquin Valley (Ni rich) versus Los Angeles and desert sources (both Pb rich)).

Tracer element fraction of fine mass in air leaving the source areas exhibits a significant

* The potential for imprecision in the teleradiometer measurements is suggested by some correlation studies

with the RESOLVE data sets. Comparing hourly teleradiometer data for different targets at the same site yields

correlations around 0.65 to 0.80. Comparing hourly teleradiometer data with hourly nephelometer data yields

correlations around 0.6 to 0.8.

31
NWC TP 6869

TABLE 9. Correlation Coefficients Between Key Extinction

and Aerosol Parameters.

Mean Std. Dev. Mean Std Dev. Mean Std. Dev. Mean Std. Dev Mean Std. Dev.

248 Bscat 15.7 2.7 Org 1.8 1.7 Soil 2.0 0.10 Fe 0.11 004 No 003

Fmass 0 84 Fabs 0.79 So 1 00 Soil 0 98 Fabs 0 43

Fabs 0.79 Fmass 0 76 Fe 0.98 So 0 97 Pb 0,36

Sul 0 79 Bscat 0 74 Al 0.95 Ca 0.95 Oc 0 36

Oc 0.74 Ec 0.68 Ca 0.94 Al 094 Sul 0.30

Ec 061 Sul 0.65 K 088 K 089 Bscat 028

Rem 040 Pb 0,38 Fmass 0.54 Fmass 0 59 Fmass 0,22

Pb 040 K 0.36 Sul 0.16 Sul 0 23 Ec 0 21

K 0.36 Ni 0.36 Bscat 0.16 Bscat 022 Rem 0 16

Ni 0.28 Fe 0.18 Oc 0.10 Oc 0 18 K -04

Fe 0.22 Al 0 12 Ec 0.08 Ec 0.14 Fe -.09

Al 0.19 Rem 0.11 Fabs 0.06 Fabs 0.14 Soil -15

Soil 0.16 Soil 010 N, -15 Ni -.09 Si -.16

Si 0.14 Ca 0.10 Pb -.19 Pb -.16 Ca -17

Ca 0.13 Si 0.07 Rem -23 Rem -22 Al - 17

5.1 Fabs 2.0 2.2 Sul 1.4 0.44 Si 0.55 0.10 Ca 0.11 .018 Pb .017

Bscat 0.79 8scat 0.79 Soil 1.00 Fe 0.95 Fabs 0.46

Oc 0.79 Fabs 0.78 Fe 0.97 Soil 0.94 Bscat 0.40

Sul 0.78 Fmass 0.76 Al 0.94 Si 0.92 Oc 0.38

Fmass 0.76 Oc 0.65 Ca 0.92 Al 0.88 Ni 0.36

Ec 0.66 Ec 0.59 K 0.86 K 0.82 Ec 0.33

Pb 0.46 Pb 0.32 Fmass 0.52 Fmass 0.47 Sul 0.32

Ni 043 K 0.31 8scat 0.14 Sul 0.16 Rem 0.30

Rem 027 Ni 0.30 Sul 0.14 Bscat 0.13 Fmass 0.28

K 0.2S Fe 0.23 Oc 0.07 Oc 0.10 K -12

Fe 0.14 Al 0.20 Ec 0 06 Ec 0.07 Fe -.16

Al 0.07 Soil 0.16 Fabs 0.04 Fabs 0.04 Al -.17

Soil 0.06 Ca 0I.16 Ni -.16 Ni -.17 Soil - 19

Si 0.04 Si 0.14 Pb -.20 Pb -21 Si -20

Ca 0.04 Rem 0.08 Rem -.22 Rem -.32 Ca -.21

8.1 Fmass 4.4 0.48 Ec 0.34 0.17 Al 0.21 0.09 K 0.09 0.79 Rem 1.6

8scat 0.84 Oc 0.68 Soil 0.95 Fe 0.89 Bscat 0.40

Fabs 0.76 Fabs 0.66 Si 0.94 Soil 0.88 Fmass 0.33

Oc 0.76 Fmass 0.61 Fe 0.94 Si 0.86 Pb 0.30

Sul 0.76 8scat 0.61 Ca 0.88 Al 0.83 Fabs 0.27

K 0.69 Sul 0.59 K 0.83 Ca 0.82 Ni 0.16

Ec 0.61 Pb 0.33 Fmass 0.54 Fmass 0.69 Oc 0.11

Fe 0.59 K 0.23 Su. 0.20 8scat 0.36 Sul 0.08

Al 0.54 Ni 0.21 8scat 0.19 Oc 0.36 Ec 0.08

Soil 0.54 Fe 0.14 Oc 012 Sul 0.31 K -.08

Si 0.52 Al 011 Ec 0.11 Fabs 0.25 Fe -.22

Ca 0.47 ;oil 0.08 Fabs 0.07 Ec 0.23 Si -.22

Rem 0.33 Rem 0.08 Pb - 17 Ni -04 Al -23

Pb 0.28 Ca 007 No - 17 Rem -.08 Soil -.23

Ni 022 Si 006 Rem -23 Pb - !2 Ca -32

NOTES:

"* All means and standard deviations are in Mm -I or Vg/m2

"* Data are for all 24-hour averages during the RESOLVE base year at the three receptor sites.

32
NWC TP 6869

degree of variability.* In fact, at the passes, the critical tracer elements (Ni at Tehachapi

and Pb at Soledad and Cajon) exhibit correlations of only 0.3 to 0.7 with the other key airquality variables. At the receptor sites, the correlations should be even lower, because Ni

(or Pb) can further vary independently of the other air-quality parameters, depending on

the relative contributions of the various source areas.

3.5 DIURNAL AND VERTICAL REPRESENTATIVENESS

In this section we discuss two aspects of the temporal/spatial representativeness of

RESOLVE data. Specifically, we address the questions (1) are the 24-hour particulate

samples adequate for characterizing air quality during daytime hours (when visibility is of

special concern), and (2) are the ground-level measurements adequate to characterize

visibility at higher altitudes within the mixing layer?

Particle scattering levels at the RESOLVE receptor sites show a distinct diurnal

pattern. A minimum occurs in the early afternoon, so that daytime (0800 to 1600) particle

scattering averages about 10 to 15% lower than the 24-hour mean. This fact, in itself, does

not negate the use of 24-hour particle data to characterize daytime air quality. Perhaps the

24-hour particle data can simply be factored by the diurnal variations in the nephelometer

if daytime visibility per se becomes an issue. The key to this question is whether or not

particle composition (which serves as the basis for the extinction budget allocations and

aerosol origin allocations) shows a significant diurnal variation.

To address the question of diurnal variations in particle composition, a special field

experiment was conducted at Edwards AFB and NWC for 3 weeks during August 1985.

The experiment involved operating the 2x4 particle samples on an 8-hour basis rather than

a 24-hour basis. Table 10 compares average fine-particle composition for the daytime and

24-hour periods. With only two exceptions-denuder nitrates at Edwards AFB and organics

at NWC (both of which may have involved sampling problems)**-the average particle

compositions are nearly identical for the daytime and 24-hour periods. These data are

consistent with the conclusions of a RESOLVE planning study (Reference 3) that found

identical daytime and nighttime particle compositions at NWC based on the data by

Ouimette (Reference 30).

The earlier RESOLVE planning study by Trijonis, et al. (Reference 3) also noted a close

coupling of daytime and 24-hour air quality as evidenced by high correlations between

0800-1600 and 0000-2400 averages of nephelometer data. These high correlation levels,

typically about 0.9, are confirmed in the RESOLVE data set.** Thus, both with respect to

correlation of loadings and overall aerosol composition, we conclude that the 24-hour

particle samples are adequate for characterizing air quality during daytime hours.

* Day-to-day fluctuations in the Ni mass fraction at Tehachapi or the Pb mass fraction at the Los Angeles

passes can be explained by at least three factors: (1) variation in the secondary versus primary makeup of fine

mass, (2) variation in trajectories (and therefore source exposure) within the upwind air basins, and (3) variations

in source emissions.

** The RESOLVE planning study has also been verified in another sense. The planning study warned of data

quality and precision problems for 8-hour sampling as opposed to 24-hour sampling. During the 8-hour sampling of

August 1985, it was found that (I) precision was generally a factor of 2 or 3 worse than for the routine 24-hour

sampling; (2) the number of values flagged by the data-screening procedure increased by an order of magnitude;

and (3) the sampling protocol was more problematical, leading to several questions regarding data quality.

33
NWC TP 6869

TABLE 10. Fine-Particle Mass Balances,

Daytime (0800-1600) Versus 24-Hour.

Mass components plus total measured fine mass

by site and period

Edwards AFB NWC

Elements

Daytime I 24-Hour Daytime 24-Hour

Mass components + total measured fine mass

Organicsa 35% 36% 50% 41%

Sulfatesa 37% 36% 32% 32%

Elemental carbon b 7% 6% 6% 6%

Sola 15% 13% 24% 22%

Unaccounted forc 6% 9% - 12% - 1%

Total fine mass 7.1 10.0 8.8 10 3(pg/m3)

Days of samplingd 14 14 12 14

a To account for the mass of associated ions, the following conventions were

adopted throughout this report: sulfates = 4.2 S, organics - 1.5 Organic C, and soil

2.14Si + 1,67Al + 1 44Ca + 1.40Fe + 1,66Mg + 122K + 1 44Mn.

b Computed from particle absorption because of poor-quality, elemental-carbon

data during the special study.

c This category represents the fine-particle mass not accounted for by the other

four categories. It is thought to be mostly nitrates. True particulate nitrate by the

denuder/nylon-filter method, which would tend to be greater than the nitrate

collected on the routine Teflon filters, averaged 12% over the 24-hour periods and

21% over the daytime periods during this set of sampling days.

d Sample is restricted to periods without missing data for all key parameters.

The question of vertical representativeness can be investigated using data from a

nephelometer network operated by NWC. In addition to a station at NWC adjacent to the

RESOLVE site, NWC operates stations at B Mountain (370 meters higher than NWC at 7

kilometers SSE) and Laurel Mountain (710 meters higher than NWC at 19 kilometers

south). To compare NWC with the two elevated sites, particle scattering data were

assembled for the three sites over the 2-year RESOLVE period (September 1983 to August

1985).

Over the 2 years, particle scattering at B Mountain and Laurel Mountain averaged

64% and 89%, respectively, of that at NWC. These averages are consistent with the

conclusion that extinction levels are somewhat lower at higher elevations. It is puzzling

that the higher mountain (Laurel) shows the higher particle scattering, but this could be

explained by the closer proximity of Laurel Mountain to flow from the San Joaquin Valley.

The correlation coefficient for daily average particle scattering is 0.77 between NWC

and B Mountain, and 0.75 between NWC and Laurel Mountain. These correlations are

quite good, especially considering that this situation represents a "worst-case" or

"conservative" evaluation of vertical representativeness. As noted in Sections 4.3.2 and

4.3.3, NWC is the RESOLVE receptor site that is most subject to influence by local sources.

NWC is also separated from the other two sites, not only vertically, but horizontally.

Horizontal inhomogeneities seem significant because a correlation of only 0.79 is obtained

even when the two mountain sites are compared. Good correlations, despite the horizontal

distances, show that (at least on a daily basis, on an hourly basis, the correlations are not as

34
NWC TP 6869

good-0.66 between NWC and B Mountain, and 0.61 between NWC and Laurel Mountain)

ground-level air quality is not severely decoupled from elevated air quality.

4.0 CLIMATE AND METEOROLOGY

The two major objectives of RESOLVE are to document existing visibility conditions in

the study region and to characterize the causes and sources of visibility degradation. In

addressing each of these objectives, it is important to have an understanding of the

meteorology of the study region.

4.1 CLIMATE OF THE RESOLVE STUDY REGION

4.1.1 Temperature and Moisture

The RESOLVE study region encompasses much of the California high desert, including

the western half of the Mojave Desert and the southern portion of the Owens Valley (see

Figure 1). The region includes many desert valleys and dry lakes with elevations of 600 to

1000 meters and several mountain ranges with peaks of 1500 to 2500 meters.

The climate of the study area is strongly influenced by the mountain ranges that border

the desert on the south and west. The Sierra Nevada and Tehachapi Mountains on the west

and the San Gabriel Mountains on the south intercept the storms that bring rain to the

coastal regions of California. Air masses crossing these mountains typically drop much of

their moisture on the mountain barriers and become warmer and drier as they move down

the slopes into the desert.

Average rainfall in the desert region ranges from about 10 to 25 centimeters per year

(Reference 31). Most of the rain occurs from December through March in winter storms that

come from the Pacific. Some rainfall occurs in the summer and is associated with tropical,

unstable air masses coming from the southeast. An average of only about 25 days per year

having over. 025 centimeter of precipitation occurs at Edwards AFB.

As a result of the trapping of moisture by the mountains, the desert region is relatively

dry and free of clouds. Solar heating and nighttime radiational cooling result in average

diurnal temperature variations of 15 to 20°C. As an example of the desert climate, the

monthly temperature, precipitation, and relative humidity averages for Edwards AFB are

presented in Table 11.

TABLE 11. Climatic Data for Edwards AFB.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Surface temperature

summary, °C

Average maximum 13.3 161 178 22.2 267 32.2 36.7 35.6 32.8 25.6 189 13.9

Averageminimum -1 1 1 1 3.9 6.7 10.0 144 18.9 178 144 7.8 22 -1 1

Mean hourly 6.1 8.9 111 144 18.9 23 3 27.8 26.7 23.9 16.7 106 6.7

Average surface relative

humidity, % 56 52 52 45 38 33 27 31 40 37 47 52

Average precipitation.

mm(monthytotals) 226 236 218 66 18 10 1.3 48 46 0.0 140 175

NOTE: Data obtained from monthly weather summaries (Reference 32)

35
NWC TP 6869

4.1.2 Wind-Flow Patterns

The air masses that affect California tend to travel from west to east, bringing clean

ocean air into the state. In the desert region, above the influence of the terrain, the

prevailing flows tend to be from the west or northwest most of the year (Reference 33), with

a shift to southwesterly flows in the summer (Reference 34). The actual flow patterns and

wind directions in the lower levels of the atmosphere are controlled by the lucations of highand low-pressure systems and by the topography.

The weather in California is strongly affected by the location of the semipermanent

high-pressure area in the northern Pacific Ocean (Reference 33) In summer, this pressure

center remains to the north, blocking Pacific storms and low-pressure systems. In the

winter, this pressure center weakens and moves south, allowing storms to enter California.

As storms and low-pressure systems cross the state, the upper air winds can vary greatly in

speed and direction. However, since storin periods tend to be times of good ventilation and

good visibility, most of our discussion will focus on nonstorm flow patterns.

In the absence of storms in the summer, and between storms in the winter, the

prevailing flow patterns tend to be dominated in the lower levels by the topography and by

thermal effects. Without the convective mixing associated with storms, the air in the

RESOLVE region and in the air basins to the west and south tends to be stable, with mixing

depths well below the heights of the mountain ranges that separate the desert from the

coastal valley (Reference 35). Thus the prevailing westerly flows tend to be channeled into

the desert through passes in the mountain ranges. The most frequent morning and

afternoon surface streamlines for January and July in the southern part of the study region

have been determined by Smith, et al. (References 7 and 35) and are shown in Figures 11

and 12. Winter and summer predominant 24-hour surface wind flow patterns for all of

California are illustrated in Figures 11 and 12.

The winter months are characterized by calm winds in the desert and in the mountain

passes during the night and morning, and light flows into the desert during the afternoon

(see Figure 11). In the winter, radiative cooling creates very stable air near the surface, with

the surface flows essentially decoupled from the flows aloft during much of the day. In fact,

near the surface in winter, drainage flows from the mountains tend to counteract the

prevailing westerlies. As seen in Figure 13, the result on a 24-hour-average basis is a

tendency for offshore flows out of the coastal valleys. Even in winter, however, by

midafternoon, solar heating tends to dissipate the drainage flows, and the prevailing

westerlies, reinforced by heating inland, push some air into the desert from the San Joaquin

Valley and the Los Angeles basin (see Figure 11).

An important feature of the winter regime in the desert is the relatively light wind at

the surface. Calms are reported 26% of the hours in November, December, and January at

NWC (Reference 36) and 37% at Edwards AFB (Reference 37). This percentage contrasts

with May through July, when only 6% of the hours at NWC and 10% at Edwards AFB are

reported as calm. The large percentage of winter calms allows the potential buildup of local

emissions from wood burning, motor vehicles, and industrial sources to supplement the

pollutants transported into the desert from upwind source areas.

In the summer months (see Figures 12 and 14), the transport is more consistent

throughout the day. Solar heating in the desert creates a thermal low-pressure area that

tends to persist through the night, generating flow into the desert for almost the entire 24-

hour day. The wind speeds, however, are typically much higher in the afternoon than in the

late night and morning.

36
NWC TP 6869

Figures 12 and 14 indicate that the San Joaquin Valley is the primary source of summer

air for most of the study region, with the Los Angeles basin (and possibly Ventura Countvi

influencing the southern part of the region. The San Joaquin Valley is the dominant source

of summer air at NWC and the primary source at Fort Irwin NTC. The San Joaquin Valley

may also be the primary source of summer air at Edwards AFB, although Edwards AFB

frequently receives air from the Los Angeles basin late in the day.

The convergence zone to the east of Fort Irwin NTC in Figure 12 is a typical summer

feature. On occasion, this zone shifts to the west, allowing emissions transported from the

Imperial Valley (southeast of Palm Springs) to impact the Fort Irwin NTC region.

"". •0800 PST

, NWC

-. -oBAKERSFIELD SAK•RSFI "INYOKERN D * 0

a ," * TEHACHAPI FT. IRWIN NTC

S-• -" ... " EDWARDS , -. AFB * - a.

OXNARO; - /. 1 .) L:Y,-' NSE D AGGETT %

"•T.•; A • SOLEDADe ,'

"BURBANK VICTORVILLE

SONTARIO CAJON PT .... 1" _•* 0 BEAUMONT •''

1 4iI OELMONTE • -. ". "

L~ MARCH AF: . PALM SPRINGS " SANTA ANA / *. • '... .

**** "-', '"" 160 , P ST •• • '" /-% • NWC " " t •

,. • • BAKERSFIELD NYOE.-,, mI• \

" * TEHACHAPI FT. IRWIN NTC

" EDWARDSAFB J

O*,;R" .. '5 a.

e . S O L E DA D O 9O W G. . BURBANK * LE C-

.' BEAUMONT -0 .

• gumuLAX llm !L MONT • • ll •" '

LA A F•, MARCH AFB S J SANTA S" ANA r. P~~A M SPRINGS * '

SCALE

I I I ( 1) i10 SPEED LESS IIIAN 3 vO.)TS

o so 100 mlesi

FIGURE 11. Most Frequent Wind Directions in the Mojave

Desert for 0800 and 1600 PST for January 1979 and 1980. Heavy

arrows indicate observed most-frequent wind directions.

37
NWC TP 6869

aim 15U 1200 PST

SO A

Sm * y *.

ammi itMPS

muss) ~ ~ ,.DJAK ,, ~

am t lptI

iikq

FIGURE 12. M 0eostFeun idDrcin nteMjv

Deer for 10an160 US JuyadAgut17 0S 0o n 1980. Covegec zone ar4niae yhthdlns

38 U
N;WC TP 6869

- - NORTHE(AST

- tKw PLATEAU

* ...~SACRAMENTO * J~~~~~ALLEY = cataWVa

~4~~COI -MOUNTAIN "uTI"

N\'A~ p~ C0UNT113. ZONab

~ - !;,Kr TANCIE

LAmE COUNTY--

- ~-MN JOAOSJIN VALLEY

- AYAANC&I .~ ~ .~ .GREAT SAW%VALLEYS

NORTH CENTRAL COAST

DESERET

AST.W

FIGURE 13. California Predominant Surface Wind-Flow

Patterns for the Winter (December to February). (Reference 38).

39
NWC TP 6869

S -NORTHIEAST

PLATEAU

, ~VAL.LEY

tnp COASTAL VFAI•M

LAML COUNTY'LK/%

NORt MALm COAStI

SOUTH C.qTn'AL COAST • . [FA

FIGURE 14. Calif'ornia Predominant Surface Wind-Flow

Patterns for the Summer (June to August). (Reference 38.)

40
NWC TP 6869

4.1.3 Source Regions and Resulting Visual Range Distribution

Figure 15 shows 1983 emissions reported for the RESOLVE study area (western

Southeast Desert Air Basin and southop" Great Ra-in Air Basin) and the major upwind

basins (San Joaquin Valley, Los Angeles, and South Central Coast) for PM%10. SO,, and

NO,. Although emissions of SO, in the San Joaquin Valley and the South Coast changed

somewhat between 1983 and the 1984-85 RESOLVE period, the general spatial distribution

remained similar. The emission estimates are those reported by the local air pollution

control districts, and the methods of estimating emissions may not be consistent between

districts. Nevertheless, it is clearly shown in Figure 15 that reported emissions of the air

basins upwind of the RESOLVE region (San Joaquin Valley, Los Angeles, and South

Central Coast) are much greater than those of the RESOLVE study area. Total emissions

reported for the three upwind basins are a factor of 13 times greater than those for the

RESOLVE study area for SO,, a factor of 12 times greater for NO., and a factor of 17 times

greater for PM 10.

The emission patterns are reflected in the spatial distribution of visibility illustrated in

Figure 16 (Reference 40). The median airport visual range for the upwind air basins is in the

15- to 40-kilometer range. Airport visual ranges in the RESOLVE area are on the order of

50 to 100 kilometers, with the worst visibility in the areas nearest the upwind air basins. It

is clear from Figures 15 and 16 that air masses transported into the desert from the upwind

counties have the potential to degrade visibility in the desert.

4.2 RESOLVE METEOROLOGY AND COMPARISON

TO LONG-TERM AVERAGES

In this section we describe the meteorology of the RESOLVE study period and compare

the RESOLVE period to long-term averages. We made the comparisons using parameters

for which historical data are readily available at Edwards AFB and NWC.

4.2.1 Temperature and Moisture

Annual and quarterly maps for temperature and relative humidity at the RESOLVE

measurement sites for July 1984 through June 1985 are presented in Figures 17 and 18. It is

apparent from Figure 17 that the desert sites average 3 to 5°C warmer than the pass sites

during all seasons. This difference in temperature reflects three factors: (1) Cooler marine

air from the upwind valleys is transported through the passes, (2) increased solar heating is

present in the desert, and (3) the pass sites are higher in elevation than the desert.

The transport of air from the upwind valleys into the desert is also evident in the

relative humidity spatial distribution shown in Figure 18. The pass sites have higher

average relative humidity values than the desert sites. Relative humidity at Edwards AFB

is higher than at the other desert sites, reflecting closer proximity of Edwards AFB to the

passes and the impact of moist marine air from the Los Angeles basin.

Average diurnal patterns for temperature and relative humidity are plotted in Figure

19 for Edwards AFB, NWC, and Fort Irwin NTC. NWC has the greatest daily temperature

extremes, probably because of its location in a dry lake bed where the effects of daytime

heating and nighttime drainage are both maximized. The impact of marine air at Edwards

AFB late in the afternoon is indicated by the temperature starting to decrease earlier in the

day than at the other sites, and by the relative humidity being higher (especially in the late

afternoon).

41
NWC TP 6869

M OMN W&UT 10

270

a. Particulate Matter less than 10 jam (1000 tons/yr)

W owed miss, 0.6

58 . %11111 ai111f cost

12

b. Sulfur Oxides (1000 tons/yr)

aii nal "1.1logiIN NOW OWI WLI 2

I" '186 'A

SSo

3655

C. Nitrogen Oxides (1000 tons/yr)

FIGURE 15. 1983 Emissions in the RESOLVE Region and

Upwind Air Basins (Reference 39).

42
NWC TP 6869

25 45 65

25!

64 3

-m 15 4,0

Caiori1 167 t 197 90a)

436
NWC TP 6869

MEAN TEMPERATURE ( 0C

-- a 18 - CHL

17 a. RAW

'T .. a 12 T TF

17 -w FOR

a6 > a ECDW-

-t 13 13 *-aSOL CAP

8 u- - :j .

8 il, a a!

a In -2

,.. a T 22 - ?2 aaa -

2 •, A23

22a 4 4

a2 23 7 a

a~~ 6 5Z e 7 I

ýýLA

FIUE 7 An al n urel vrg Tmeatr (T a

the REOV Sie fo Jul 95 a94truhJn 

44a
NWC TP 6869

MEAN RELATIVE HUMIDITY (W)

-40 - CHL.

. . 32 a -. RU

38 W a a- OeW FOR .- o46 > X OE - "a a., a a ,. W

47 52 CAP

S..4 a a . a aa a a a. a a..

-- 43 "2" T ... 3 a T 2.5.J

51 4 Z -.. 36 28 "

I .. •-- aaaa -2

•3 2 305- 

0 ca

T aa 

aa252a . 3" t .Ta.. 2 .34

aIUE .AnuladQ arelAeagRetie umdy

a% at EOV ie o 45 ah auy18 houhJn 9

36 cL T 69 4

a7 a 57 4

a40 45 adI1 61

LA 1-- co

FIGURE 18. Annual and Quarterly Average Relative Humidity

M% at the RESOLVE Sites for July 1984 through June 1985.

45
NWC TP 6869

EDW

CHL_ ... FOR•..

.30

20

-E

b4

I SSI !.. ..

Hours (PSI)

FIGURE 19. Annual Average Temperature an 4 Relative

Humidity Diurnal Patterns at Edwards AFB, NWC, and Fort

Irwin NTC.

46
NWC TP 6869

The surface temperature, precipitation, and relative humidity for the RESOLVE year

(July 1984 to June 1985) are compared with the long-term averages at Edwards AFB and

NWC in Figures 20 through 22 (References 32 and 41). From Figure 20, the average

maximum temperature at both Edwards AFB and NWC was within 3°C of the long-term

averages for all months except December 1984 and April and June 1985. The December

average maximum was substantially cooler than average at both locations. This cooling was

because December was exceptionally stormy, with record rainfall (see Figure 21) (Reference

41) and very high average relative humidity (see Figure 22). April and June at both sites

were somewhat warmer than normal. During the late winter and spring, both sites

experienced below normal rainfall and relative humidity.

The NWC precipitation and relative humidity distributions are similar to those at

Edwards AFB for the RESOLVE period except for July and August 1984. During the

summer of 1984, unusual thunderstorms occurred over the Sierra Nevada. These storms

affected the NWC site but not the Edwards AFB site.

Upper-air temperatures are an indication of the stability of the atmosphere. Typically,

temperatures above about 20'C at 850 mb (about 1500 m/ms 1) in the summer are associated

with high photochemical pollution potential in Southern California. The average monthly

850 mb temperatures from the Edwards AFB and Vandenberg AFB soundings during

RESOLVE are plotted in Figure 23, along with the long-term averages. (Vandenberg AFB

is located on the coast, west of the RESOLVE study area.) Generally, significantly warmer

850 mb temperatures than the rest of the year occur from June through September. These

months can be considered a "summer" regime with the greatest potential for photochemr('al

pollutants.

For the RESOLVE period, the 850 mb temperatures for most months at both sites were

reasonably close to the long-term averages. However, the October through December 1984

period averaged about 4 degrees cooler than normal, reflecting increased frontal system

activity during that period. The May 1985 850 mb temperatures at Edwards AFB were over

5 degrees higher than normal, making the air in the desert more stable than usual during

that month.

4.2.2 Winds and Flow Patterns

The predominant flow patterns for winter and summer are shown in Figures 11 to 14.

Annual wind roses for the RESOLVE sites are shown in Figure 24. The positions of the roseg

on the page reflect their geographic locations. The predominant wind directions in Figure 24

are consistent with the summer flow patterns in Figures 12 and 14, and with the general

winter afternoon flows in Figures 11 and 13.

Long-term monthly average wind data from Edwards AFB (Reference 31) and NWC

(Reference 41) are compared with data obtained during the RESOLVE year in Figures 25

and 26, respectively. During RESOLVE, the wind directions in all months at both sites were

quite close to their long-term means, but a slight variation toward more southerly flow is

evident for a few months at both sites. This more southerly flow might have slightly

increased the effect of the Los Angeles basin compared to the San Joaquin Valley at

Edwards AFB during RESOLVE.

47
NWC TP 6869

.* 40

. -a- Long Term Average

-- RESOLVE Year Average /-j 30

cc 20

10 * Edwards AFB

1 1 S0 - I 1 I I I I I I

Jul. Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

a. 40 S -a- Long Term Average

.- RESOLVE Year Average

30

,o 10

China Lake NWC

0 - 1 I 1 I I I I t L I I I I

Jul. Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

Month

FIGURE 20. Comparison of Long-Term and RESOLVE Year

Average Daily Maximum Surface Temperatures at Edwards

AFB and NWC.

48
NWC TP 6869

120

100 -W. Long-Term Average

S0A-*- RESOLVE Year Aver a

Edwards AFB

60

40

20

0*

Jul. Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

120

100

80 -a- LongL-Term Average

.. China Lake NWC A - RESOLVE Year Average

.2 60

~40 A

20

0

Jul. Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

Month

FIGURE 21. Comparison of Long-Term and RESOLVE Year

Precipitation at Edwards AFB and NWC.

49
NWC TP 6869

80

~70

.-A§ Lon .Te Average

Is 6 RESO V Y a Aver a EdwardS AFB erAvr

' 40

>/ a:30

20

JUL Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

S70 LVTr vrg

I China Lake NWC

x so

$ 4 0 ' 

30

JuL Aug. Sep. Oct Nov. Dec. Jan. Feb. Mar.,Apr. May Jun.

Month

FIGURE 22. Comparison of Long-Term and RESOLVE Year Relative Hunrdity at Edwards AFB and NWC. Long-term RESOLVE measurements and

at NWC are from different measurement stations.

.50
NWC TP 6869

U 30 - Long-Term Averages

-0- Resolve Year Averages

2..

0.

S10

E Edwards AFB

Jul. Aug. Sep. Oct. Nov Dec. Jan. Feb. Mar. Apr. May Jun.

U30

-&" Long-Term Averages

-- Resolve Year Averages

S20

E 10

E Vandenberg AFB

C2 Ln# 0-

Jul. Aug. Sep. Oct. Nov Dec. Jan. Feb. Mar. Apr. May Jun.

Month

FIGURE 23. Comparison of Long-Term and RESOLVE Year 850

mb Temperatures From the Edwards AFB and Vandenberg AFB

Soundings (References 32, 42 and 43). Error bars for Vandenberg

AFB are I standard deviation of 30-year monthly averages.

51
NWC TP 6869

ANIMAL 7/84-6/85 HOUR 3:60 TO 24:@8

Nhtm. Lake

San Joaqufm valley I tandsburg wash

\ fachapf

•1 / Fort Irwin

Edwards I

2-3o,.

L. A. Basin

FIGURE 24. Annual Surface Wind Roses for the

RESOLVE Network. Average wind speed for each

direction is indicated by the bar style; and the

percent of hours with wind in the direction is shown

by the length of the bar. The percentages of hours

with calm winds are shown in Lhe circles.

52
NWC TP 6869

360

o£ -ow Longj Term Averages

- -e- RESOLVE Yew Averages

240 _ _ _ _ v-ariable

180

Edwards AFB

120

60

0

Jul. Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

6

411 Lana Tetrfl Averaes__ 5 Ga -4- RESOLVE Yew Averages

44

W~ 3

Edwards AFB

Jul. Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

60

50 Ln emAeae -0 RESOLVE Year Averages

S40

*30

S20

10

Edwards AFB

0 I I I I I _ I

Jul. A4g.Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

Month

FIGURE 25. Comparison of Long-Term and RESOLVE Year

Surface Winds at Edwards AFB. Median direction and average

speed are shown.

53
NWC TP 6869

360 c LonW TtmAvermao , o 315 -e- RESOLVE Year Avwages

q;270

- 225 _ _ _ _ _ _ _ _ _ _; Sj180

-e 13590

M 4 China Lake NWC 45

a I * I I I I ! I' I I I

Jui. Aug. Sep. Oct. Nov Dec. Jan. Feb. Mm. Apr. May Jun.

6

5 4- Long Term Averages 5 -0- RESOLVE Year Averages

.4

China Lake NWC

Jul. Aug. Sep. Oct. Nov Dec. Jan. Feb. Mar. Apr. May Jun.

Month

NOTE: No data on RESOLVE year calms were easily available from NWC.

FIGURE 26. Comparison of Long-Term and RESOLVE Year

Surface Winds at NWC. Prevailing direction and average speed

are shown.

The average wind speeds at NWC during RESOLVE were representative of normal

condition:. At Edwards AFB, however, the wind speeds appeared to be lower than normal

during RESOLVE, and the percentage of calms reported was higher. These differences are

not consistent with the data from NWC, and we do not have an obvious explanation for

them. It is possible that the differences are an artifact of instrument placement or

instrument type. Small changes in instrument placement can cause large changes in speed

and direction. (An example of substantial differences between nearby sites can be seen by

comparing the Edwards AFB airfield data in Figure 25 with the RESOLVE site data in

Figure 24. However, the weather office at Edwards AFB says that the sensor type and

placement has remained consistent for the period of record.)

4.2.3 Visual Range

To examine the climatological representativeness of visibility conditions during

RESOLVE, nephelometer data obtained by a long-term DOD monitor adjacent to the

RESOLVE NWC site are plotted in Figure 27. Monthly averages of scattering coefficient for

1979 through 1985 at the site are compared to monthly averages from the same site during

the RESOLVE year. With the exception of December 1984, light scattering during

RESOLVE was similar to, but slightly greater than, the longer term data. The base-year

mean is 7% higher than the long-term mean. The December average during RESOLVE was

54
NWC TP 6869

47 Mm- 1 compared to the 7-year average of 34 Mm-1. December 1984 was the wettest

December on record at NWC, and the high Bscat levels are probably related to the high

relative humidity and fog recorded that month.

50 -C- '79*85 Averages

440

~30

:2 20

Values include

o 10 Mm-11 lot

Rayleigh Scatter

00 ' 1 2 I I I I I 1 2 1 I

Jul. Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun.

Month

FIGURE 27. Comparison of 1979 to 1985 Average Bscat Data at

NWC Range Trailer With RESOLVE Year Monthly Averages.

Error bars are 1 standard deviation of the 7-year monthly

averages.

4.2.4 Summary of Climatological Representativeness

Earlier, we compared the RESOLVE year to climatic norms for several meteorological

indicators. In general, surface temperature, upper air temperature, and wind-flow patterns

during the RESOLVE year were reasonably normal. In contrast, the rainfall amounts and,

in some months, the relative humidity were quite different from long-term averages,

especially in July 1984, which had near-record rain at NWC, and December 1984, which

had record precipitation. Rainfall, however, is highly variable from year to year, and rain

typically falls during short storm periods in any one year. It is not unusual to have the

rainfall concentrated during a few months, and summer rains are also not rare.

To help assess the representativeness of the study period, it is useful to know the

standard deviations of the long-term averages. For the parameters discussed in the first

three sections of this chapter, we were able to determine the standard deviations only for

850 mb temperature at Vandenberg AFB and Bscat at NWC. For these parameters, the

RESOLVE year months were generally within 1 standard deviation, and always within 2

standard deviations, of the long-term monthly averages. Thus, except for the heavy rainfall

in 2 months, it does not appear that the RESOLVE year was an outlier.

For a visibility study, the major climatological norm of interest is the one for Bscat. The

nephelometer monthly averages at NWC were similar to their 7-year averages for all

months except December 1984. The conclusions about visibility, drawn from the RESOLVE

data, should generally be representative, although some caution should be exercised in

interpreting data from December 1984, especially at NWC.

55
NWC TP 6869

4.3 RELATIONSHIP OF VISIBILITY TO METEOROLOGY

Within any season, visibility fluctuates substantially day to day because of

meteorological variations. In this section, we examine the relationships between visibility

and several meteorological parameters to identify meteorological situations that produce

degraded visibility in the desert and to develop hypotheses about the sources of visibility

degrading aerosols.

4.3.1 Synoptic Conditions Leading to Visibility Degradation

A synoptic typing scheme developed for use in the California Central Valley (Reference

44) was applied for each day of the 2-year RESOLVE nephelometer record. It was found that

low visibility in the desert occurs primarily under two types of synoptic patterns, but the

occurrence of these patterns does not always result in low visibility. Certain mesoscale

features that happen frequently on low-visibility days during both synoptic patterns were

also identified. These features are onshore flow below about 3000 m/ms I and a deep enough

mixing layer to allow marine air to flow through the passes or over the mountains into the

desert. Examples of the two synoptic conditions, under which similar mesoscale transport of

pollutants from the upwind basins to the desert can occur, are shown in Figures 28A and

28B. The thermal low-pressure condition in Figure 28A is frequent in the summer, while

the prefrontal condition in Figure 28B typically occurs in other seasons.

Another condition that occasionally leads to degraded visibility in the desert is a strong

pressure gradient aligned with the north-south desert valleys (see Figure 28C). This

condition generates strong northerly winds that entrain and transport dust from dry lake

beds.

4.3.2 Surface Flow Patterns Related to High Bscat

Wind roses for the days from August 1983 through August 1985 that had the 25%

highest and 25% lowest maximum hourly Bscat for each RESOLVE site are shown in

Figure 29. The wind roses for the days of hiF lest Bscat (see Figure 29a) show that the winds

on the days of worst visibility are similar to the annual average winds shown in Figure 24,

and reflect the prevailing transport from the Los Angeles basin and San Joaquin Valley.

Wind roses for the best visibility days (see Figure 29b) are significantly different, however,

and reflect the desert origin of the air on clean days. The high percentage of calms at NWC

in Figure 29a is indicative of a potential contribution to visibility impairment from local

sources on the worst days. The reduction in the percent of flow from the southwest at

Edwards AFB on the best days is indicative of the reduced importance of the Los Angeles

basin on clean days at that site.

4.3.3 Relationship of Diurnal Air-Quality Variations to Flow Patterns

The diurnal patterns of light scattering coefficient on best-case and worst-case days at

the RESOLVE pass and receptor sites are presented in Figures 30 and 31. These figures are

consistent with the streamlines in Figures 11 and 12 as well as with the wind roses for the

high Bscat days in Figure 29a.

Figure 30 shows that, on hazy (high Bscat) days, the Bscat values in the passes peak in

the evening. This is consistent with typical ventilation patterns of the upwind basins

(References 7, 35, and 45). Figure 31 shows that, again on hazy days, the maximum Bscat

56
NWC TP 6869

(A) kjIJ-.-~ 1 Thermial Low

-1 (8/20/84)

(B) Prefrontal

(2/07/85)

.. .... .. .

1 - Blowing Dust - -* . I- (2/20/85)

Surface LEGEND 500 mb

H , L ......Surface pressure centers

T-d6 .... Surface fronts

- o .... Surface isobars (every 4 mb)

.... Flow lines at 500mrnb

FIGURE 28. Example of Synoptic Conditions Leading to

Degraded Visibility in the California Deserts.

57
NWC TP 6869

RESOLVE 25Z HIGH HOUR dil TO 24,UU RESOLVE 252 LOW HOUR I10l 10 24,91

China Labe Chis Lak..

Sa Joaquin Viille•llssisValIley landsburg Sao Joaquin ~ *a5 Randsbvjrl tr

Valley washy /j wash

Fert Irwin

le(ha9S lehachapi 1

Edwards E~mh.j'dwards

- I004 I )PO-,.

3 Soledad $24Seledid

2i-3ad e ý )Cao 23-owe

A. 3sl. 4. ,L. A. leasi I <2I 

5 602 j 0 6411

(a) Highest Bscat. (b) Lowest Bscat

FIGURE 29. Twenty-Four-Hour Surface Wind Roses for the RESOLVE

Network for the Days of the 25% Highest (a) and Lowest (b) Daily Maximum

Bscat Values at Each Site. Average wind speed for each direction is indicated

by the bar style, and percent of hours with wind in the direction is shown by

the length of the bar. The percentages of hours with calm winds are shown in

the circles.

58
NWC TP 6869

Tehachapi Highest 25A 71 dad*11h~ S

as_ _ _ _ _ _ _ _ _ _ _ I

or 76 Tehachapi Lowest 25%17

Of

Hour (PST) Hour (PST)

a.Soledad Highest 251 76 China Lake Highest 25%

as

or ~~~Soladad Lowest 251or7ChnLaeows S

aan

Hour (PST) Hour (PST)

lago

arCajon Lowest 251 Fort Irwin Lowest 251

Hour (PST) Hour (PST)

FIGURE 30. Diurnal Average Bscat for FIGURE 31. Diurnal Average Bscat for

RESOLVE Sampling Period for 25% RESOLVE Sampling Period for 25%

Highest and 25% Lowest Daily Average Bscat at Highest and 25% Lowest Daily

Bscat at RESOLVE Pass Sites. Average RESOLVE Receptor Sites.

59
NWC TP 6869

values at the receptor sites occur during the late night and early morning. This phenomena

is consistent both with transport of the evening peaks from the upwind passes and with the

added early morning contribution of local sources (such as wood burning and traffic in the

residential areas near NWC). For clean days, the diurnal variations are much less

pronounced, or even absent, suggesting that different transport processes may be occurring

on those days.

4.3.4 Relationship of Mean Bscat to Wind Speed

Figures 32 and 33 display the relationship of 24-hour mean Bscat to wind speed at the

pass and receptor sites, respectively. Three types of relationships are apparent in the

figures. The Soledad and Cajon Pass sites are on the desert sides of the passes. For these

sites, Bscat increases with wind speeds up to 4 or 5 m/s and then decreases with high wind

speeds. This phenomenon can be interpreted as follows. At very low speeds, Bscat is low

because the effects of transport are minimal and because local sources are evidently

unimportant. At slightly higher speeds, the impact of the Los Angeles basin becomes more

apparent. At still higher speeds, the decrease in Bscat is probably due to better ventilation

related to the higher winds.

A second type of pattern is a decrease in Bscat as speed increases from calm to relatively

low speeds, followed by a subsequent increase with wind speed. This phenomen occurs at

Edwards, NWC, Randsburg Wash, and Tehachapi. The initial decrease may be from a

dilution of emissio.-.s from local sources (however, it is not obvious what the local sources at

Tehachapi are). The subsequent increases with speed might be attributed to transport of

pollutants from other locations, and to wind-blown dust at higher speeds.

At Fort Irwin NTC, the trend is a steady increase with wind speed suggesting a lack of

local sources.

4.3.5 Transport Direction for Days of High and Low Bscat

To assess the relative frequency of occurrence of air mass transport into the desert from

the various upwind regions, we examined the 850 mb winds measured in the morning at

Edwards AFB. Edwards AFB is in flat terrain at the center of the RESOLVE monitoring

region, so the winds there should be reasonably representative of the transport in the

region. The morning soundings (between midnight and noon) were chosen to be

representative of synoptically driven transport without distortion by afternoon thermally

driven flows. The 850 mb height was chosen to be above local nighttime drainage flows but

still representative of the afternoon mixed layer.

The frequencies and average speeds of the 850 mb winds for each direction are shown in

Figure 34. It is evident that, on an annual basis, the prevailing boundary layer flow is from

the southwest through west. However, in the winter months, the flow is bimodal with about

equal frequency from the northeast and west.

Figure 35 presents similar frequency distributions of the Edwards AFB 850 mb winds

for the days with the 25% highest and 25% lowest 24-hour average Bscat at the three

RESOLVE receptor sites. From Figure 35, it is evident that the days of worst visibility at

the receptor sites are dominated by flow from the southwest, and the days of best visibility

typically occur with flow from the northeast. This distribution is consistent with a strong

contribution to the desert Bscat from the San Joaquin Valley and/or Los Angeles basin.

Then, the flow on 61% of all days originates in these regions. In contrast, 77% of the worstcase days and only 26% of the best-case days at Edwards AFB have flow from these regions.

The results are similar for the other receptor sites.

60
NWC TP 6869

60I

Iii Soledad Pas sCajon I Pass II

40 60

4'

4 J

Li0U I | 40

209 5 11 17 23 29

wind speed-(m/s) wind speed (m/s)

50 ' I I ' I

IIiI [ Tehachapi

~I I Pass Illooo iI I

Bscat numbers include

10 Mm-1 for Rayleigh

scatter. k40

4.'

io

30 , , I I , I i I

5 11 17 23 29

wind speed (m/s)

FIGURE 32. Mean Bscat for Each Wind Speed Category at the Pass Sites.

Error bars indicate t 1 standard deviation of Bscat. Note that Bscat scales vary

between plots.

61
NWC TP 6869

50 40 , ,

IiI~l

40 30

It I

.1Edwards caChina Lake

30 ..... z.... 2 n a a t a a

5 11 17 23 29 5 11 17 23 29 wind speed (mi/s) wind speed (mis)

40 40 I I I

;30 ,• 30

RandsburgII Fort Irwin

Wash

20 t I I I I I I 1 20 1 1 _1 1 1 5 11 17 23 29 5 11 17 23 29" wind speed (m/s) wind speed (m/s)

Bscat numbers include 10 Mm- 1 for Rayleigh scatter

FIGURE 33. Mean Bscat for Each Wind Speed Category at the Receptor Sites.

Error bars indicate ± I standard deviation of Bscat. Note that Bscat scales

vary between plots.

62
NWC TP 6869

45, April - September A; 7

( "Sume r" )

40.

c 35,

S30. U

2C 5 4 3 M 6 2 7--2

so.c

Olober M March

25. 7 Winter*)

303

E -

5

70

All Data

60.

U0

5 0.

40

L. 20 77

E 4 4

S0.

0 45 90 135 180 2f5 270 315 360

S* Wind Direction (Degrees)

(Wind Speeds Shown above bars in m/s)

FIGURE 34. Frequency and Average Speed of Edwards AFB 850

mb Winds for Each Direction for AM Soundings. Data are from

August 1983 through July 1985. Note that 417 days of soundings

are available. The missing days include February through April

1984 plus other randomly distributed days.

63
NWC TP 6869

23.

251 41 ries 24 hr/~21Ni~s 4N

Average 81c~t Oays/ 13

Average 'scat Days a _ __ _ __ _ _

4 P/7 7 13

25. ' Z4 P/ Loes 

Aea* 251a 3ighst21

12 AvtraragesciD ay

C2

2 7;__________________________

0~ ~ 90 135 45 180 225 270 315 3600 s 9 13 10 22 27 35 36

Mb insfo Direction Ec for~ AWSonings onrethen 25%r~

NWC, and ~Fort IrwinNT.Dtbaeithsmesfo

Figur 34.et24h

264
NWC TP 6869

Some exceptions exist to these generalizations. In Figure 35, on days of high Bscat with

winds from the north through east, the 850 mb winds are often very light, especially in

winter. These stagnation conditions may allow the buildup of local desert emissions and

may represent a significant, though infrequent, contribution to visibility impairment by

local emissions. On a few other of the low-visibility days with northerly winds, the wind

speeds were quite high (above 15 m/s), indicating visibility impairment due to wind-blown

dust. This condition occurred only a few days per year, however. From Figure 35, it is also

evident that, when clean days occurred with winds from the southwest, the wind speeds

were high, suggesting possible frontal conditions with good mixing and ventilation.

4.4 DEFINITION OF FLOW REGIMES

In analyzing the contributions of various regions to visibility degradation at the

RESOLVE desert sites, it is useful to have a daily indicator of air mass origin for each site.

The simplest approach is to use the direction of the upper winds in the region as an indicator

of air mass movement. The most easily accessible and appropriate parameter is the morning

850 mb wind at Edwards AFB.

The definitions of the source regions and associated wind sectors are presented in Table

12. At each desert site, the days are divided into five classes, each representing potential

transport from a specific source region.

It is interesting to assess the percent of variance that can be explained by the classes for

various measures of aerosol mass, extinction, and composition. In general, the classes are

not very successful at explaining the variance in air quality. About 7 to 20% of the variance

at Edwards AFB and NWC and 20 to 40% of the variance at Fort Irwin NTC is explained by

the classification scheme. This result is not unexpected, however, since the purpose of the

scheme is to assess transport directions, not aerosol concentrations.

TABLE 12. Definition of Wind-Flow Classes for Desert Sites.

Wind direction sectors for each site

Class adbr Edwards AFB NWC Randsburg Wash Fort Irwin NTC

Northern desert sources and local 3150 -67.5° 3150 -45° 315' -22.5' 3150 -450

(includes Searles Valley at Randsburg Wash)

Eastern desert sources and local 67 5' - 135' 450 - 1350 22.5' - 135' 45° - 180°

(includes Searles Valley at NWC and

Imperial Valley at Fort Irwin)

Southern Mojave Desert. Los Angeles 135' - 247 5' 135' - 225' 135' - 225' 180' - 202.5'

basin, and upwind coastal counties

(Ventura, Santa Barbara) sources

Los Angeles basin, coastal counties. 247 5' - 270' 225' -270' 225' -270' 247 5' - 270'

San Joaquin Valley, and western desert

sources

San Joaquin Valley and western desert 270' -315' 270' -315' 270' -315' 270' -315'

sources

65
NWC TP 6869

5.0 SPATIAL PATTERNS OF VISIBILITY

The first objective of the RESOLVE program is to characterize baseline visibility in the

RESOLVE study region. One of the most important aspects of this objective is to develop an

understanding of geographical air-quality patterns.

5.1 AIR-QUALITY MAPS

The most straightforward method of illustrating the spatial features of the RESOLVE

data is to plot averages (or other statistics) for each site on a map of the study area. The

RESOLVE Annual and Quarterly Data Summary Reports (References 5 and 6) include

numerous maps of this type. Figure 36 presents some examples- maps of annual averages

for total scattering (particle plus Rayleigh), particle absorption, fine mass, coarse mass,

organics, and sulfates. The Data Summary Reports also include tables listing statistical

parameters for selected particle and extinction measurements; Table 13 presents some

examples for total scattering and particle absorption. Using the data in such tables, it is

possible to prepare maps for many statistical parameters.

Figure 36 does reveal some of the more salient spatial features of air quality in the

RESOLVE study area. For example, it is obvious that the worst visual air quality occurs

along the southern edge of the study area (at the pass sites near the Los Angeles basin), and

that the best visual air quality occurs in the northeastern part of the study area. However,

the maps of Figure 36 are not really satisfactory in terms of giving a comprehensive and

vivid picture of spatial air-quality patterns.

To obtain a better picture of visual air quality in and around the RESOLVE study

region, the RESOLVE data were combined with data from other sources. Most of the other

data were based on airport visibility observations (References 40 and 46), although we also

used nephelometer recordings from Death Valley (Reference 47) and Searlec Valley

(Reference 48). In combining the data sets, the most significant step was to calibrate the

airport data to the more accurate RESOLVE instrumental observations for total extinction.

The airport data are not equivalent to the RESOLVE data for at least the following three

reasons.

1. Airport visibility readings depend on the properties of the observer, the target, and

the illumination conditions. The airport readings generally correspond to an observer

contrast threshold of at least 5% (i.e., a Kischmeider constant at least 1.3 times smaller than

the constant for a 2% contrast threshold).

2. The airport data taken from the literature represent median extinction levels, not

average extinction levels.

3. The published airport data correspond to the periods 1977 to 1979 in Los Angeles

and 1974 to 1976 at the other California sites, and do not correspond to the RESOLVE study

period.

The calibration factor for the airport data was derived by comparing the RESOLVE data

to nearby airport visibilities. We found that median airport visual range should be

multiplied by a factor of 1.4 to be equivalent to a visual range based on average RESOLVE

extinction data (sum of Rayleigh scatter, fine scatter, coarse scatter, NO 2 absorption, and

particle absorption). The most significant uncertainty in the resulting visibility map

(Figure 37) is whether the spatial patterns of airport visibility in the southern half of

66
NWC TP 6869

RESOLVE YEAR (7/84 to 6/85)

3a iT *6.4

a33 5.

N T ^ 0" N ^ r .39 " .. 8

3 30 " t

,41 " , * 7.2

a a 0

a51 69 a*7.9 10.6

a sek a ak .a .

LA Aa

LQ

--- , , * ^,,-, * 3.3

a a 7

N

8 7 2.4 I

- ,..9 1.8

a2 .

a (A ,.10 13 . 2.7 4.2

o 50 km , a e 50km a-, L. •-• LA a i \LA "" a

" * 9 a" a * 2.2

a7a 2 1

N aa N

, a * 2.7

• "a*1 aa 9 11 - a *2.

W 3.2 3.1 a at a S.a a^ Q I.-4--I-- a^ a * a aFIGLIaE 36. Maps of Air Quaity for Selected Parameters and Annual

Averages Over the RESOLVE Base Year.

67
NWC TP 6869

TABLE 13. Examples of Tables for Statistical Parameters and Distributions.

RESOLVE year (7/84 to 6/85) - Distribution of data.

Arithmetic Geometric Values equaled or exceeded by stated

percentage of observations

S,te obs. of Max Min Mean dev Sdt Mean dev 1 1 10 25 150 75 90 95 99

Hourly nephelometer scattering (Mm - 1) (includes 10 Mm - for Rayleigh scatter)

EawardsAFB 8132 318 10 40.9 266 344 1 8 141 91 73 52 34 23 15 14 12

FortIrwin 8313 237 10 303 192 260 1.7 100 65 54 38 25 17 13 12 11

NWC 8226 369 6 35 7 21 8 31 4 1.6 125 71 58 43 31 22 17 15 12

Randsburg 8282 396 11 332 229 285 1.7 118 71 57 41 27 19 15 13 12

Tehachapi 7844 373 8 390 28.5 313 1.9 132 93 74 52 32 18 13 12 10

Soledad 8523 398 10 51.3 445 387 2-1 217 139 106 65 37 22 15 13 11

Cajon 8541 396 1 699 65.7 476 2.4 311 212 162 93 45 23 15 13 11

Daily nephelometer scattering (Mm - 1) (includes 10 Mm - for Rayleigh scatter)

EdwardsAFB 356 167 13 41.2 19.9 370 1.6 105 81 65 51 39 29 19 16 13

FortIrwin 363 91 11 30.3 14.1 27.3 1 6 75 56 51 39 28 19 14 13 11

NWC 358 110 14 35.7 15.1 33.2 1.5 95 62 52 43 33 26 21 19 15

Randsburg 355 148 12 33.2 16.3 30.0 1.6 90 62 52 41 31 22 16 15 13

Tehachapi 343 111 10 38.8 199 33.6 1.7 91 78 67 51 38 21 15 13 12

Soledad 365 228 12 51.2 30.9 43.0 1.8 137 107 92 69 44 29 17 15 13

Cajon 365 275 12 70.4 44.1 56.1 2.1 184 147 134 98 65 32 18 15 12

Daily total particle absorption (Mm - 1)

EdwardsAFB 256 14 1 7.2 26 6.6 1.5 13 11 10 9 7 5 4 3 2

FortIrwin 246 13 0 5.1 2.5 4.4 1.9 12 9 8 7 5 3 2 1 0

NWC 228 14 1 6.4 2.3 5.9 1.5 13 10 9 8 6 5 4 3 2

Randsburg 251 12 0 5.2 2.7 4.1 2.3 11 10 9 7 5 3 2 1 0

Tehachapi 225 16 0 6.8 32 5.6 2.0 13 12 11 9 7 4 2 1 0

Soledad 245 16 0 7.9 3.2 6.9 1,8 15 13 12 10 8 6 3 2 0

Cajon 250 19 1 10.6 3.9 9.5 1.7 18 17 15 13 11 9 5 3 2

California have changed substantially from the mid- to late 1970s to the early to mid-1980s.

Available data (References 40, 46, 49, and 50) suggest that such changes were probably not

large.

In essence, Figure 37 is a more detailed, "blown-up" picture of the California visibility

map presented previously as Figure 16 and is also the same as Figure 2, repeated here for

the convenience of the reader. The California visibility map shows that two pockets of low

visibility exist in the state: the Los Angeles basin and the San Joaquin Valley. Trijonis

(Reference 40) demonstrated that these two pockets of low visibility are caused by manmade air-quality variations rather than purely natural factors. Figures 16 and 37 reveal

that the RESOLVE study area is in the air-quality gradient associated with those two

regions. The shape of the isop!eths (i.e., the form of the gradient) suggests that the NWC

receptor site is much more affected by the San Joaquin Valley than by the Los Angeles

basin. Fort Irwin NTC and Edwards AFB appear to be affected significantly by both source

regions. These conclusions agree quite well with the qualitative description of transport

relationships found in Section 4.1.2.

68
NWC TP 6869

30 40kg 60, j".km 100 150 km

1, RAW

32 -

17 2

NOTES;

* Circled data points represent RESOLVE monitoring data. The entire 2 years of RESOLVE data were used, with

nephelometer scattering factored to non-Rayleigh extinction using site-specific RESOLVE base-year

relationships and 11IMm-' added for Rayleigh scatter.

* Smaller print data points are based on particle-scattering data in and near Searles Valley. The data are

factored to total extinction using RESOLVE relationships.

* Other data points are based on median airport data calibrated to RESOLVE data.

* The dashed lines indicate uncertainty in the isopleths near Searles Valley.

* isopleths near the border of the map are guided by nephelometer data at Death Valley as well as data from

other airports in California and Nevada.

* The Beaumont (Banning Pass) value, shown in parentheses, is inconsistent (higher) than surrounding stations.

This inconsistency may be because of the high altitude of that site (790 meters) or because of uncertainties

associated with the limited visibility markers at that site

FIGU'RE 37. Spatial Pattern of Visual Range (for 2% Contrast and Average

Extinction Levels) In and Near the RESOLVE Study Area.

69
NWC TP 6869

5.2 INTERSTATION RELATIONSHIPS

This section examines the degree to which air quality at each site is related to air

quality at the other sites. One measure of this commonality is the correlation coefficient

between daily average concentrations for each pair of sites- The correlation coefficient

indicates the correspondence between high and low concentrations at two sites or, because

correlations usually are dominated by the highest concentrations, the correspondence

between episode conditions at the two sites. Correlations between sites are not necessarily

representative of source/receptor relationships because of lag times in source-to-receptor

impacts, and because of potential negative aspects of source/receptor associations (for

example, a receptor site being impacted on days when strong transport (and good

ventilation) exists at a pass site).

Table 14 presents interstation correlation coefficients for daily averages of particle

scattering and particle absorption. All data available from the 2 years of routine monitoring

are used to derive the table, so the sample sizes are quite large - around 600 to 700 for Bsp,

and 250 to 350 for Bap. The greatest correlations are marked by circles (R > .70) and

underlining (R > .60).

Two important features appear in Table 14: (1) All of the stations correlate fairly well,

with even the smallest correlation coefficients (NWC versus the Los Angeles pass sites)

being about 0.40; (2) the four desert sites generally tend to correlate higher than pass sites

versus desert sites or pass sites versus pass sites.

TABLE 14. Interstation Correlation Coefficients for Daily Particle

Scattering and Daily Particle Absorption.

Particle scattering Total absorption

Site

EDW FOR CHL RAW TEH SOL EDW FOR CHL RAW TEH SOL

EDW

FOR

CHL 55 59 .60 66

RAW _L80 .6-900G

TEH 57 51 .43 48 68 61 55 0

SOL 0 66 .41 52 44 66 59 44 58 67

CAP 54 58 36 44 45 Q 59 63 45 63 65

70
NWC TP 6869

These two observations indicate that the whole study region, especially the desert part

of the study region, tends to have at least some degree of homogeneity in terms of air-quality

variations. That the air-quality variations are not perfectly uniform, however, can be

appreciated by noting that a correlation of .80 represents only 64% variance explained, and

a correlation of .40 represents only 16% variance explained. Among the three key receptor

sites (Edwards AFB, Fort Irwin NTC, and NWC) it is NWC that shows the most

independence from the other two (i.e., the least correlations). Upon examining the pass

sites, we find a salient feature is the high correlation between Soledad and Cajon, indicating

that (when the Los Angeles basin ventilates to produce high concentrations at the passes)

the ventilation tends to occur at both passes together. Other features are the relatively high

correlation between Soledad and Edwards AFB, and the tendency for Cajon to be the pass

site least correlated with the desert sites (except Cajon versus Fort Irwin NTC).

Factor analysis was also used to investigate the spatial interrelationships of daily

particle scattering among the seven sites. The analysis was segregated into annual

(September 1984 to August 1985), quarterly, and monthly data sets. The annual results

place all sites into one factor with Edwards AFB, Fort Irwin NTC, and Randsburg Wash

having the highest factor loadings. These results are consistent with the discussion of Table

14 and reflect the general correlation of all the sites, especially the desert sites.

iThe results for quarters 2 and 3 look very similar to those of the annual data set; again

all sites fall into 1 factor. Quarters I and 4 (fall and winter) divide the sites into 2 factors. In

general, factor 1 includes the pass sites, and factor 2 the desert sites. In quarter 4, Edwards

AFB could be grouped with either pass or desert sites; and Fort Irwin NTC is grouped with

the pass sites.

Factor analysis performed month by month provides 1, 2, or 3 factors depending on the

particular month. Site groupings within these factors vary and it is difficult to interpret

what the factors actually represent. Table 15 is a summary of the monthly factor analysis

and shows how often one site is in the same factor with another. Table 15 shows that NWC,

Randsburg Wash, and Fort Irwin NTC are normally in the same factor as Soledad-Cajon,

Edwards AFB-Soledad, and Edwards AFB-Randsburg Wash. Also, NWC and Randsburg

Wash are not highly correlated with either of the Los Angeles pass sites. All of these results

seem consistent with the previous interpretation of Table 14.

TABLE 15. Summary of the Monthly Factor Analyses.

Number of months in which sites occur in the same factor (24 months total).

Site EDW FOR CHL RAW TEH SOL CAP

EDW

CHL 15

FOR 16 18

RAW 18 21 21

TEH 15 15 13 13

SOL 18 8 15 11 13

CAP is 11 13 10 13 20

71
NWC TP 6869

6.0 TEMPORAL PATTERNS OF VISIBILITY

Two temporal patterns of interest exist: seasonal variations over the time of year and

diurnal variations over the time of day.

6.1 SEASONAL VARIATIONS

Figure 38 presents the seasonal pattern for particle scattering at the three receptor sites

on a quarterly basis. The average, the best-case tenth percentile, and the worst-case tenth

percentile are presented both for hourly averages and daily averages during the entire 2

years of nephelometer monitoring. Incidentally, the observed seasonal patterns in particle

scattering are representative of seasonal patterns in total extinction because the seasonal

variation in particle absorption closely resembles that of particle scattering,

HOURLY AVERAGES DAILY AVERAGES

70 -EDW 70

60. 60 EDW

-- o . CH 50 - so0

CHL

F40FOR FOR

0 40.

30 -

S30

a.e

20

Jan-Mar Apr-Jun Jul-%ep Oct-Dec Jan-Mar Apr-'Jun Jul-2Sep Oct-Dec

FIGURE 38. Quarterly Patterns in Hourly and Daily Averages

for Particle Scattering

At least four major points are to be made with respect to Figure 38.

1. The ranking of the three receptor sites with respect to light extinction is Edwards

AFB highest, then NWC, then Fort Irwin NTC. Basically, NWC experiences about average

72
NWC TP 6869

particle scattering among the three receptor sites, with Edwards AFB about 20% worse, and

Fort Irwin NTC about 20% better.

2. Average light scattering at all three sites demonstrates the same distinct seasonal

pattern. Average visibility is worse in the summer (third) quarter and best in the winter

(first) quarter. Average visibility in the spring and fall quarters is intermediate, with the

spring quarter tending to be slightly worse than the fall quarter.

3. An interesting relationship exists among the sites for the best-case and worst-case

tenth percentiles. For the worst tenth percentile, NWC is about the same as Fort Irwin

NTC, with Edwards AFB showing more severe worst-case conditions than those two sites.

For the best tenth percentile, NWC and Edwards AFB are close together, with Fort Irwin

NTC demonstrating especially good best-case conditions. Basically, regarding NWC as the

average site, apparently Edwards AFB has lower average visibility than NWC, mostly

because Edwards AFB has more severe worst-case conditions. On the other hand, Fort Irwin

NTC has higher average visibility than NWC, mostly because Fort Irwin NTC experiences

especially good best-case conditions.

4. The seasonal pattern is not as constant or strong for the worst-case tenth percentile

as it is for the average. Essentially, this fact reflects the phenomena that worst-case

conditions are nearly as frequent in fall and other seasons as they are in summer, even

though average visibility is lowest during summer.

Figures 39, 40, and 41 illustrate the seasonal variation in the key atmospheric

parameters on a monthly basis. The data in these figures cover the entire 17-month period

of particle sampling. The figures reveal the following features:

* Particle scattering peaks in the third quarter (July through September). The local

maximum in July is due to data from July 1985 when the RESOLVE network was affected

by very severe brush and forest fires in Southern California.

0 Organics, sulfates, and elemental carbon (or equivalently, particle absorption) all

show a broad summertime maximum from May through Sepember. This condition may

reflect the strong transport conditions from the upwind source regions during summer (see

Section 4.1.2) and may suggest the importance of transport for each of these three aerosol

components. The localized peak for organics during July is associated with the wildfires of

July 1985.

* Fine-particle soil reaches a maximum from March through May when wind speeds

are at a maximum. Coarse-particle mass (dominated by soils) shows a broad summertime

peak. The disagreement in seasonal patterns, for fine-soil and coarse particles may reflect

different source processes for fine soil versus coarse soil and/or strong seasonal patterns in

the small nonsoil component of coarse particles (e.g., coarse nitrates, organics, sulfates, and

elemental carbon.)

* The remaining unaccounted-for fine mass is slightly greater in winter than in

summer. As discussed in Section 3.3, che unaccounted-for component probably mostly

represents nitrate aerosol (which was found to have a winter peak-see Section 7.3). Higher

nitrate concentrations in winter than in summer would likely reflect Lhe strong

temperature dependence of the equilibrium between ammonium nitrate aerosol and its

precursor gases, HNO 3 and NH 3 (References 51 and 52). That is, even though transport

from the upwind source regions is greatest during summer, the thermodynamic processes

seem to be the controlling factor for the seasonal pattern of aerosol nitrate.

73
NWC TP 6869

* Fine-mass concentration exhibits a broed maximum from April through October

Essentially, the May through September peak for organics, sulfates, and elemental carbon

is extended to April because of fine soil in spring, and to October because of unaccounted-for

fine mass (nitrates) in fall.

An additional aspect of seasonal patterns is the seasonal variation in the light

extinction budget. This aspect is discussed in Section 7.3.2.

450 __ I I I I I I I I I I I I -

, 460

- 50

.S 40~

a 3*

ggl • Ij

$@

9 4

30

720

0 0

I 2 3 4 S 0 7 a I is II t 2.

MONTH

FIGURE 39. Monthly Variations in Particle Scattering, Absorption, and Mass

Averaged Over the Three Receptor Sites.

74
NWC TP 6869

i |,

2.1S 

4 0

SIs

2 S

41

4.

23.6

2.1

2-S

S$ ___ 

1r

a.i

u w ai 31Sl l r

"n. 210

am f-, M M

2. 3. . O Is o 1$ oo 12? •.

MONTH

FIGURE 40. Monthly Variations in the Chemical Components of FineParticle Mass Averaged Over the Three Receptor Sites (All in lag/m3).

75
NWC TP 6869

I I I i I I I I | i I

4. a

S 0

"3

2.a

264S

Ovr..Ilfrnnr1D Is

tu13 2

72.0 -

3 4. S, a. 7 a. 9. Is. 1 t. 12.

MONTH

FIGURE 41. Monthly Variations in Meteorological Parameters Averaged

Over the Three Receptor Sites.

76T
NWC TP 6869

6.2 DIURNAL VARIATIONS

Diurnal variations in air quality were discussed in previous chapters. In this section, we

present the overall average diurnal patterns for particle scattering. As indicated on the

right-hand side of Figure 42, the diurnal patterns at the pass sites show increasing

concentrations, starting at midday, that lead to peaks in aerosol scattering during the

evening. These data are consistent with the typical ventilation pattern for air masses

leaving the upwind source areas. At the desert sites, aerosol scattering tends to decrease in

late morning, leading to a minimum in early afternoon. This fact most likely reflects greater

dispersion in the morning because of the elevation of mixing heights and the increase in

wind speeds. Aerosol concentrations in the desert start to rise in the afteroon, leading to a

peak late at night and early in the morning. This phenomenon is consistent with transport

from the upwind basins during the afternoon and evening (References 7 and 14). Also

consistent with the transport hypothesis is the fact that the diurnal variations are most

pronounced at the sites nearest the upwind basins, and least pronounced at the sites most

distant from the upwind basins.

g/83-6/a$ Pýt¢* $~cl S"tt',- o*t9 . (",-I) /13iS Part~c,ti ~ t•,$C~t•et¢*

LOU TEN

FOR SOL

71 71

s. o.

21 2S

S4

a 1 9 * 1$ .6 12 3 .1 6Is I 6I I 26 21 20223 9 f a 3 a 7 0 0 ' 6 .3 1.Is 176 s a .9 62621 22 2

FIGURE 42. Average Diurnal Patterns for Particle Scattering.
NWC TP 6869

6.3 TIME-SERIES PLOTS

Figures 43 through 45 provide time-series plots of extinction and aerosol data for

Edwards AFB, NWC, and Fort Irwin NTC, respectively. The top row in each figure presents

nephelometer particle scattering as well as the sum of nephelometer particle scattering and

particle absorption. The second row pertains to fine-particle mass, and the next five rows

give the components of fine mass (all in tag/m 3 ). The last row presents the ratio of nickel to

lead.

The top two rows are used to identify episodes of poor air quality. The next two rows

characterize the aerosol components causing the episodes. The Ni/Pb ratio provides

information on source areas (see Section 8.2).

Table 16 provides a list of days when standard visual range was 50 kilometers or less at

any of the three RESOLVE receptor sites. As discussed previously (see Figure 38), Edwards

AFB has significantly more low-visibility days than NWC or Fort Irwin NTC-the latter two

sites being about equivalent in frequency of low-visibility days. It is also apparent that

worst-case days can occur in any season. Table 16 and Figures 43 through 45 were used to

select episodes for the illustrative case studies of Chapter 9.

Bscat+1abstI$ I a o(uM-1)

Bscat s* o0

- (ug/m3 )

Fine Mass to

0

Or2anics 2

44

Sul fate * 2

Soil A

EC

Rem

.2 ,

Ni/Pb ..

Apr. May Jun. Jul. Aug. Sep.

NOTES:

"* Bscat represents nephelometer particle scattering without correction for coarse-particle

scattering. The dashed line in the first row is Bscat plus particle absorption.

" -Rem," fine mass minus the next 4 components, should qualitatively reflect tine nitrates.

FIGURE 43a. Time Series of Daily Averages at Edwards AFB (April 1984

to September 1984).

78
NWC TP 6869

Fine . 1

Organic$ I

Sul fate4

Soil I

TM. A4-4 NL R.3I

Oct. Now. Dec. Jan. Feb. Mr.

NOTES:

*Bscat represents nephelometer particle scattering without correction for coarse-particle

scattering. The dashed line in the first row is Bscat plus particle absorption.

' Rem," fine mass minus the next 4 components, should qualitatively reflect fine nitrates.

FIGURE 43b. Time Series of Daily Averages at Edwards AFB (October

1984 to March 1985).

sicat

a.(ug/m3 )

Fine Mass~ I

Organics n

S~ulfate2

$oil ft A2 LAArA.ANAJ da

Ap.PyOw. Jl Aug Sa.

tor Augus 1985J).A.. O

NOTES9

* sa ersnsnphlmtrpril cttrn ihu orcio o orepril
NWC TP 6869

s~cat t

N".~ Pass to3

Organics 3

Su Ifate n

Soil

4aMl/Pb

Apr. Key Jun. Jul. Aug. Se..

NOTES:

411 Bscat represents nephelometer particle scattering without correction for coarse-particle

scattering. The dashed line in the first row is Bscat plus particle absorption.

* -Rem," fine mass minus the next 4 components, should qualitatively reflect Fine nitrates.

FIGURE 44a. Time Series of Daily Averages at NWC (April 1984 to

September 1984).

oscat'.i. 1 So13I

Fine N isi .a..... ..

Organilcs Wa oo

Swlfate T n

Soil a

Oct Now De.I Fb ar

NOTES.

* 9scat represents nephelometer particle scatterirg wvithout correction for coarst particle

attering. Th~e dashed line in the first row is Bscat plus particle absorption.

* Aem," fine mass minus the next 4 components. stiould qualitativeiy reflect tine nitrates.

FIGURE 44b. Time Series of Dai[Y Averages at NWC (October 1984 to

March 1985).

80
NWC TP 6869

sulfate ARM

Sole A~s AM'i anna A4R

* C

* j . _LA L i , aAL 4LXrai cs 2

......

Ap PlyJn4ul uýSo

NOTES

"Sl ersnsnpeo ee atcesateigwtotcreto o orepril *sa 

scteig Th4ahdln ntefrtrwi sa lspril bopin

"'Rem"fn asmnstenx o pnns hudqaiaieyrtetfn irts

FIUR 44.Tm4eiso al vrgsa W Arl18 oAgs

1985)

la/ b s'::.

~ l~I~ '~~q,-, Al0a flsh~n.I1 i

19i Apre Au.Ag Muss 

NOrgEis:ngR1

Sulscate rpeet ehlmtrpril cteigwtotcreto o orepril

scattering.~ ~ ~ ~ ~ Th aa dahdln3ntefrtrwi lspril ~~~~li bopin

Sa IRm, msmiuthnet4cmoethulqulttelreetneirAtes AAne 

FIUR 1c TieSre3fDiyAeae a W Arl18 oAgs

1985).

*icai.Bb i*

4* 5A

Apr. iuftJul. Ny Ag./Soo

NOTES:ii4

" m R "4 fiem s iu4h et4cm oet.hul L2iai yrfetfn irts

FIU REa~ 45.Tm2eiso al vrge tFr ri A rl'8

to b 94See 

Sc'i 81
NWC TP 6869

Scat 4M/)

Fine. Mass .2

Organic$ 11,a AA j 3L

Sulfate

soil 2

Row

Oct. Nov. Dec. Jan. Feb. Ksar.

NOTES:

"* Bscat represents nephelometer particle scattering without correction for coarse-particle

scattering. The dashed line in the first row is Bscat plus particle absorption.

" "Rem," fine mass minus the next 4 components, should qualitatively reflect fine nitrates.

FIGUJRE 45b. Time Series of Daily Averages at Fort Irwin NTC (October

1984 to.March 1985).

ascot n -.

Fine Mlass s4

Organics In it

Sulfate .A , l , n n4

EC 4

AM. Ma Jun. Ju.A"

NOTES:

"* Bscat represents nephelometer particle scattering without correction for coarse-particle

scattering. The dashed line in the first row is Bscat plus particle absorption.

"* 'Rem," fine mass minus the next 4 components, should qualitatively reflect fine nitrates.

FIGURE 45c. Time Series of Daily Averages at Fort Irwin NTC (April 1985

to August 1985).

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TABLE 16. Days When Standard Visual Range* at the RESOLVE Receptor

Sites Was 50 Kilometers or Less-April 1984 to August 1985.

Visual range. Km Visual range. Km

Date Date

EDW NWC FOR EDW NWC FOR

Seccnd Quarter 1984 First Quarter 1985

4/25 50 --- 1/21 21 ---

5/29 -.. 49 1/22 37

5/31 47 ... 1/27 47

6 ,2 46 2/7 42 .

63 50 . -. 2,20 28 28 3:

6,6 50 --- 16 2/21 - 30 ---

3/1 38 ......

Third Quarter 1984 3/5 -..... 50

76 --- 46 36 3/16 38 ---

7/7 43 40 3/17 39 ......

7,11 --- 49 - 3/25 --- 39 ---

7/18 50 ---..

7/19 .. .--- 44 Second Quarter 1985

7/20 49 4/6 46

7/21 49 4/10 36

7/23 49 - 4/16 45 ......

7/26 45 4/19 --- 49

7/28 38 46 4/25 43 ---

7/29 47 --- 4/26 -- 46

7/31 49 5i6 40 ---..

811 50 -- 507 47

8/9 50 - 5/25 43

8/17 -- 47 48 5/26 40 --- ..

8/21 44 -.... 5/31 38

8/22 42 6/15 47

8/31 --- 50 6/19 40

9/10 49

9/11 38 Third Quarter 1985

9/22 33 7/5 48

9/23 23 --- 38 7/6 -..... 41

7/9 --- 47

Fourth Quarter 1984 7/10 41 45

10/4 38 7/11 -- 44 39

10/5 30 30 43 7/12 38 27 33

10/6 37 36 - 7/13 36 40 43

10/11 49 39 7/14 30 42 37

10/31 41 -- 7/15 35 41 42

11/6 35 - 47 7/16 42 41 43

11/12 37 --- 7/19 44 49

11/16 49 - 7/20 44 --- 45

12/4 -- 33 7/24 49 ......

12/5 30 40 --- 7/25 37 ---

12/23 - 47 --- 7/26 49 .

12/24 -- 36 - 8/8 --- 45

12/25 36 - 8/9 --- 46

12/26 31 - 8/24 50 ...

12/27 26 -

*Standard visual range is 3 9/8ext. Nmere Bext is total instrumental extinction Based on the discussions

of Sections 3 22 and 7 2. total extinction -s calculated as the sum of 11 Mm-1 (Rayleigh scatter), Bext

(nephelometer Darticle scattering), 3 CP (the sum of coarse-Oarticle scattering missed by the nepnetometer),

04 Bext (an estimate of NO2 absorotion), and Bao (total oarticle aosorption) If CP and/or Sap are missing,

total extinction is estimated as 11 + 1 4 8scat (see Secton 7 2)

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7.0 CAUSES OF LIGHT EXTINCTION

The results for the light extinction budget-the allocation of extinction contributions

among atmospheric species-is presented in this chapter. In addition to natural Rayleigh

scattering by air molecules, just five types of aerosol (plus gaseous NO 2 ) usually contribute

nearly all of ambient light extinction. These five categories are sulfates (from SO,

emissions), nitrates plus NO 2 (from NO, emissions), elemental carbon, organics, and soil

dust. The first three categories are generally dominated by man-made emissions, while the

last two aerosol types (organics and soil dust) arise from a wide variety of both natural and

anthropogenic sources. Knowledge of the extinction contributions from Rayleigh scatter and

these five categories already provide important insights about the nature and origins of

visibility degradation in the Mojave Desert.

7.1 SCOPE OF THE ANALYSIS

The right-hand column of Table 17 lists annual average non-Rayleigh extinction for

Edwards AFB, NWC, and Fort Irwin NTC. As explained later in this chapter, these

estimates of non-Rayleigh extinction are calculated from RESOLVE data for particle

scattering, particle absorption, and NO 2 concentrations, with appropriate accuracy and

correction factors applied. Moving leftward in the table, the addition of Rayleigh scatter

yields total extinction coefficients that can, in turn, be translated into visual ranges using

the Koschmeider relationship.

TABLE 17. Average Extinction Coefficients for RESOLVE Base Year.

Bext - Bs9 + Bag + 8sp + p

Rayleigh Scatter Non-Rayleigh

Visual Range Units - [(m-I]

(2% contrast) Site 8ext

73 km EDW 53 - 1 * 42

83 km CHL 47 • 11 + 36

100 kn FOR 39 11 + 28

83 k. Average 46 , 11 + 35

Several aspects of the scope of this chapter can be explained in relation to Table 17.

1. N"early all of the discussion herein pertains to average results over the three

receptor locations.

2. The analysis focuses on a budget for non-Rayleigh extinction. Although Rayleigh

scatter contributes 20 to 30% of average extinction at the receptor sites, the Rayleigh scatter

component is natural and constant.

34
NWC TP 6869

3. The results presented here are for the RESOLVE base year, July 1984 through June

1985. The RESOLVE particle samplers operated for 16 months (May 1984 through August

1985), but we have restricted the data to the four complete quarters to avoid seasonal bias.

4. The definition of aerosol species deserves a brief explanation. To account for the

mass of associated ions, the following conventions are adopted:

Sulfates = 4.12[S]

Nitrates = 1.29[NO5]

Organics = 1.5[Organic C]

Soil = 2.14Si÷1.67Al+1.44Ca+1.40Fe+1.66 Mg+1.22K1.44 Mn

7.2 EXTINCTION ALLOCATION PROCEDURE

Figure 46 presents an overview of the entire extinction allocation procedure. Total nonRayleigh extinction at the three desert receptor sites, 35 Mm-1, consists of contributions

from absorption by gases (I Mm- 1 ), scattering by particles (28 Mm- 1 ), and absorption by

particles (6 Mm- 1 ). The boxes in the figure indicate the techniques used to determine and

allocate the contributions. The following three subsections expound on these determinations

and allocation methods.

7.2.1 Nitrogen Dioxide Absorption

The only gas that contributes significantly to atmospheric light absorption is nitrogen

dioxide. Absorption by NO 2 is determined from measured NO 2 concentrations by the

formula, Bag = .33 [NO2], where the units of absorption are Mm-I and the units of [NO2 ]

are ppb2 ,3,4. Special high-sensitivity NO 2 data collected for RESOLVE at Edwards AFB and

NWC during the summer/fall 1984 indicate that NO 2 absorption is about 4% of particle

scattering in the Mojave Desert. This result is consistent with an analysis of long-term

routine data sets for the desert by Trijonis, et al. (Reference 3) as well as with other special

field programs (References 53 and 54). Applying this 4% factor to the average particle

scattering measured at the receptor sites yields a value for NO 2 absorption of only 1 Mm- I

(see Figure 46).

7.2.2 Particle Absorption

The extinction budget analysis for particle absorption is illustrated on the right-hand

side of Figure 46. Particle absorption measurements with the daily RESOLVE Teflon filters

were made at the University of California, Davis, using the integrating plate method and

were corrected for accuracy as described in Section 3.2.2. Based on the routine RESOLVE

data, total parti le absorption is 6 Mm- 1 , consisting of 5 Mm- t from fine particles and I

Mm- I from coarse particles.

The next step is to allocate the absorption among aerosol components. In the literature,

it is almost always assumed that particle absorption arises entirely from elemental carbon

concentrations. Some speculation, however, has been made that soil dust might also

contribute significantly to absorption, especially in the coarse-particle size range. Three

methods were used to estimate soil dust contributions to coarse-particle absorption. As

indicated by Table 18, all three methods implied that absorption from coarse soil dust is

small, only about 20% of the 1 Mm -I total coarse absorption. The same methods show that

absorption from fine soil dust is negligible. Accordingly, assuming that 20% of coarseparticle absorption is from soil dust, we arrive at the result in Figure 46 that absorption

arises almost entirely from elemental carbon.

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NWC TP 6869

BNon.Rayie,gh = Sag Bsp + Bap

Routine RESOLVE data. Routine RESOLVE data.

Analysis of existing NO 2 data. RESOLVE special RESOLVE special

RESOLVE E special N02 data. experiment experiments. I T - -

35 Mm 1 + 28 6

RESOLVE special experiment.

Literature review. Routine RESOLVE data.

RESOLVE Mie Theory.

FINE COARSE FINE COARSE

23 S 5 7

RESOLVE regressions. Routine Literature review.

Mie Theory. RESOLVE RESOLVE experiment.

Literature review, data. RESOLVE Mie Theory.

Sloane Interactive Theory.

Species contributions = NO2 1 Organics: 8+ Soi dust: 4* Elemental carbon: 6 -

(Mm - 1) Sulfates: 8 Organics ) Soil dust: 0

Soil dust: 2 Sulfates :I -

Elemental carbon: T Elemental C

(RESOLVE special experiment) Nitrates: 4 Nitrates

FIGURE 46. Extinction Allocation Procedure.

36
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TABLE 18. Estimation of Soil Dust Contribution to Coarse Absorption.

Percentage of coarse

Methodology absorption allocated

to soil dust

Coarse EC absorption estimated from RESOLVE special study measurements of 0-40%

coarse EC with published absorption efficiencies for coarse EC (References 11

and 57). Coarse-soil absorption obtained by subtraction.

Coarse-soil absorption calculated from Mie theory using RESOLVE DRUM 0-30%

sampler data for soil elements (Reference 58), with imaginary refractive index

for soil of :5.0041 (Reference 59), and with adjustments to account for entire

coarse-soil distribution.

Coarse-soil absorption estimated from experimental measurements of soil 10-30%

absorption efficiency by Draftz (Reference 60), with RESOLVE soil

concentrations adjusted to account for entire coarse-soil distribution.

7.2.3 Particle Scattering

Particle scattering is the most complex part of the extinction budget analysis. The

average particle scattering measured by the routine RESOLVE nephelometers at the three

receptor sites is 25.5 Mm-1. As discussed in Section 3.2.2, special intensive experiments

demonstrated that the RESOLVE nephelometers operate near ambient conditions with

respect to relative humidity. Consequently, no significant accuracy correction is required

for changes in water scattering. Calculations regarding the collection and measurement

efficiency of the nephelometer for coarse particles, however, indicate that the nephelometer

misses one-half of coarse-particle scattering (later estimated to be 5 Mm- 1). When adjusted

for this missing coarse-particle scattering, the corrected total particle scattering becomes 28

Mm- 1 (see Figure 46).

Three methods are used here to estimate the portion of particle scattering due to coarse

particles. To derive a factor that can be applied to averages of the routine RESOLVE coarsemass data, and to provide a common basis for comparison, the results of all three methods

are specified in terms of a pseudo coarse-particle scattering efficiency in m2/g, defined as

[total coarse-particle scattering]/[particle mass from 2.5 to 10 Vml. Table 19 describes the

three methods and summarizes the result-,. In our opinion, the most relevant result is that

from the first method (RESOLVE Cyclone Nephelometer Study) that was designed

specifically to address this issue. As a concensus finding, we have chosen a pseudo coarseparticle scattering efficiency of 0.6 m2/g.

Applying the 0.6 m2/g pseudo coarse-scattering efficiency to the average coarse mass at

the three receptor sites yields a coarse-scattering value of 5 Mm-I. This 5 Mm-i is

allocated among species according to their percentage contributions to measured coarse

mass (2.5 to 10 micrometers): 85% soil dust*, 5% nitrates **, 4% organics", 3% sulfates*,

and 3% elemental carbon*, where * refers to values based on RESOLVE monitoring data

and ** refers to values estimated from engineering judgments. Two major potential sources

of error exist in this allocation, but, fortunately, they tend to cancel. First, soil dust

contributions may be underestimated because soil dust might account for even more han

85% of the coarse mass greater than 10 micrometers. Second, soil dust contributions may be

overestimated because coarse scattering efficiencies should be greater for the other four

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components. The other four components will tend to occur in smaller coarse particles, and

coarse-particle scattering efficiency is approximately proportional to the reciprocal of

particle diameter.

TABLE 19. Estimation of Pseudo Coarse-Particle Scattering Efficiency.

Methodology Pseudo coarse-scattering

efficiency*

RESOLVE cyclone nepheiometer experiment, with adjustments for 6 m2

/g

nephelometer collection efficiency and nephelometer measurement

efficiency for coarse particles.

Mie theory calculation (Reference 58) based on RESOLVE DRUM sampler 3- 5 m2/g

data for soil, with adjustments to account for entire coarse-particle size

distribution.

Regressions based on teleradiometer data and fine-/coarse-particle data.

Conventional regression (Reference 61) 4 m2

/g

Zero interception (References 9 and 61) 7-13 m2/g

"Total coarse-particle scattering/Particle mass from 2.5 to 10vim

The fine scattering of 23 Mm-I in Figure 46 is allocated among species according to

fine-particle scattering efficiencies. As shown in Table 20, four methods are used to

determine the scattering efficiencies of fine organics, sulfates, elemental carbon, and soil

dust. The scattering efficiency of fine nitrates is assumed to be the same as that of fine

sulfates (References 11, 55, 56, and 57).

Note that good agreement is found among the methods, within about ± 1 m2/g, on the

scattering efficiencies for organics, sulfates, and soil dust. High uncertainty exists with

respect to the scattering efficiency of elemental carbon, but this is of little consequence

because elemental carbon occurs in such small concentrations. The consensus scattering

efficiencies are based on discussions among RESOLVE team members about the merits and

limitations of the various methods. The consensus values are reported as whole numbers

with fractions rather than decimals as a better reflection of the uncertainties. The

uncertainties in the scattering efficiencies and the resulting potential errors in the

extinction budget are quantified in Appendix A.

In Table 20 we chose to include the scattering contribution from aerosol water as a part

of the scattering efficiencies (i.e., the scattering efficiencies are inflated above "dry

efficiencies" to represent aerosol water). With respect to regression formulations, this

means that we are using strictly linear regression forms rather than regression forms that

include nonlinear relative humidity (RH) terms. The strictly linear regressions achieve a

slightly better statistical fit with the RESOLVE data. In any case, aerosol water is not a

critical factor in the RESOLVE study area. The Heated/Ambient Nephelometer Special

Study indicated that, on the average, aerosol water contributes only 7% to fine-particle

scattering at Edwards AFB and 3% at NWC.

To obtain a fine-scattering budget, the consensus scattering efficiencies of Table 20 are

multiplied by average concentrations for organics, sulfates, elemental carbon, and soil dust

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TABLE 20. Determination of Fine-Particle Scattering Efficiencies.

Scattering efficiencies (m2

/g)

Methodology

Organics Sulfates Elemental Soil dust

carbon

REGRESSION ANALYSIS (based on routine RESOLVE data

at the three receptor sites)

Conventional regression 3.7 5.0 0 6b 0.4

Structural regressiond 3.8 5.1 - 1 .8b 0,5

LITERATURE REVIEW (20 studies with the following

adjustments for consistency)

Nephelometer calibration 2-3 3-6 (2-3)c 1-2f

Airport contrast = 5%

Nephelometer A = 530 nm

Organics = 15 OC

Relative humidity = 40%

MIE THEORY (for Molave Desert data)

Ouimette and Flagan (Reference 49) 2.5d 3.2 NA 1 4

RESOLVE DRUM sampler data NA 3.2 NA 1.4

INTERACTIVE MIE THEORY (based on Detroit size

distributions, Reference 62) 3.8 4.7 (3.8)c 1.3

CONSENSUS 31 4-J 1- 1

a As in Madansky (Reference 63) and White and Macias (Reference 64).

b Difference from zero not statistically significant.

c Elemental carbon grouped with organic carbon

d Mie theory based on volume size distribution for all material, not just organics.

as well as special-study concentrations for denuder difference nitrates.* The addition of the

five resulting components yields a scattering level that is 13% greater than measured fine

scattering. To achieve the species allocation of the 23 Mm - I listed in Figure 46, the five

calculated components are each divided by a factor of 1.13.

* The RESOLVE special studies for fine particulate nitrate using the denuder difference method found the

following (sample size in parentheses):

Fine NO3 concentrations, pIg/m 3

Edwards AFB NWC

SUMMER

1984 Study 5 (27 day) .4 (25 day)

1985 Study 1 1 (22 day/20 night)

W'NTER

1986 Study 1 6 (12 day/9 night) 6 (9 day/8 night)

Multiplying by 1.29 to account for the cation mass, we conclude that the average fine NH 4NO 3 concentrations are

approximately 1.55 pig/m 3 (11.0 lig/rm summer/2.0 pig/mJ winter) at Edwards AFB and .65 pig/m 3 (.5 lg/mi3 summer.

.8 pig/m 3 winter) at NWC. Nitrate data were not gathered at Fort Irwin NTC. Fort Irwin NTC is more similar to

NWC than to Edwards AFB both in terms of distance from upwind source areas and in terms of percentage of fine

mass not accounted for by non-nitrate components. Scaling the NWC data by fine mass, we estimate that average

fine NH 4NO1 concentrations are approximately .46 pg/mr3 (.4 ig/mJ summer, .5 pig/m,3 winter) at Fort Irwin NTC.

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A special experiment with a thermidograph nephelometer that heats samples to 350'C

provides a check on the nature of the fine-particle-scattering budget. According to the

RESOLVE thermidograph experiment, 87% of the fine aerosol scattering should be

nonrefractory species-organics, sulfates, and nitrates. The corresponding result predicted

by our fine-scattering budget is 89%. Basically, the thermidograph nephelometer verifies

that the refractory species (soil dust and elemental carbon) do have much lower scattering

efficiencies than fine organics, sulfates, and nitrates, and that these latter three species do

dominate fine scattering in the RESOLVE area.

7.3 EXTINCTION BUDGET RESULTS

Table 21 presents the final extinction budget obtained by adding all of the species

contributions listed along the bottom of Figure 46. (Table 21 is more precise than Figure 46

in the sense that a decimal point was added to the calculations.) The results indicate that all

five major species contribute significantly to non-Rayleigh extinction, with organics and

sulfates most important, and the nitrates plus NO_2 category least important.

TABLE 21. Average Extinction Budget Calculated

for the Three RESOLVE Receptor Sites.

Total Non-Rayleigh: 35 4 Mm=_I

Organics: 91(26%)

Sulfates: 8 5(24%)

Elemental carbon: 6.8(19%)

Soil dust: 6.4(18%)

NOZ + nitrates: 4 6(13%)

It is interesting to compare the extinction budget results with the previous extinction

budget published by Ouimette and co-workers (References 10 and 49) using data for NWC

during 1979. Our results are similar to those of Ouimette in terms of fine-particle

scattering, fine-particle absorption, and the extinction contributions from sulfates and

elemental carbon. Ouimette, however, had a much smaller contribution from organics and a

larger contribution from coarse-particle scattering (and therefore soil dust). His low organic

concentrations may represent a measurement problem for organics (note that the aerosol

chemical species fell substantially short of closing a mass balance in his data). The coarseparticle scattering in the 1979 study may be overestimated because of potential problems

with the optical particle counter in the coarse-particle range.

7.3.1 Site-to-Site Variations

Table 22 presents extinction budgets for the three sites individually. The values in

Table 22 were derived by using the assumptions and parameters of Sections 7.2.1 to 7.2.3 in

conjunction with aerosol composition data (e.g., FP versus CP, Bsp versus Bap, and FP

chemical speciation) that is specific to each site.

The most notable site-to-site variations in the extinction budgets are the extra

importance of organics at NWC and nitrates at Edwards AFB. Generally, however, the

percentage contributions are fairly similar at all three locations, a reflection of similar

aerosol composition at the three locations.

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TABLE 22. Site-to-Site Variations in Extinction Budgets.

Non- E Elemental Iuefate Nitrates

Rayleigh Org anics Sulfates carbn Si dust +N2

total ao o NO 2

Absolute contribution (Mm - 1)

EDW 42 10 10+ 8 67

CHL 36 701- 8 7 I 7 3 j

FOR 28 6+ 7i 5i 5j 3

Relative contribution (%)

EDW 100 24 25 19 15 17

CHL 100 29 22 19 20 10

FOR 100 23 26 20 21 10

7.3.2 Seasonal Variations

Table 23 presents extinction budgets for summer and winter.* Total non-Rayleigh

extinction is nearly 50% higher in summer than in winter, basically because of a near

doubling of organic, sulfate, and soil dust concentrations in summer compared to winter. In

contrast, nitrate concentrations are higher in winter than in summer. As a result, the

nitrate plus NO 2 category, that accounts for only 9% of the total in summer, contributes

about 20% in winter.

TABLE 23. Extinction Budgets for Summer Versus Winter.

Non- Elemental Nitrates

Rayleigh Organics Sulfates c Soil dust Nia

total ttlcarbon j÷___ NO2

Absolute contribution (Mm - 1)

Summer (April-September) 41• 11j I 10+ 7+- 8 4

Winter (October-March) 28+ 6 6 5+ 5 J 5i

Relative contribution (%)

Summer (April-September) 100 28 [ 25 f 18 209

Winter (October-March) 100 23 [ 21 19 17 20

= In this section, to make maximum use of the RESOLVE data set, all 17 months (6 winter and 11 summer) of

particle data are used in sorting by winter/summer or calendar quarter.

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Complete extinction budgets are not possible on a quarterly basis because of the lack of

quarterly nitrate data. As the next best alternative, Table 24 lists the components of

particle extinction and fine-particle mass on a quarterly basis. Table 24 shows that the

ranking of particle extinction and mass (from the highest quarter to the lowest quarter) is 3-

2-4-i. The percentage contributions of absorption, coarse scatter, and fine scatter to total

particle extinction are generally about the same for all quarters, with the exception of a

greater fraction of coarse-particle scatter in spring (because of relatively higher dust

concentrations during that windy season). Organics and sulfates show a second-third

quarter peak (especially the third quarter). Fine soil dust, like coarse-particle mass, peaks

in the second quarter. The unaccounted-for fine mass is greatest during the first and fourth

quarters, these data agree with the special-study data that show a wintertime nitrate peak.

TABLE 24. Quarterly Variations in the Components of Particle

Extinction and Fine Particle Mass.

Calendar quarter

January-March April-June July-Seotember October-December

Contributions - absolute (/)

Total partic'e extinction 25 Mm - 36 Mm - 1 44j Mm - 1 30+- Mm -

Particle absorption 4 (17%) 6- (17%) 7+ (17%) 6 (19%)

Coarse-oartile scatter 3j (14%) 7j (21%) 5j (1 3

0%) 4 (13%)

Fne-oart~cIe scatter 17j (69%) 22 (62%) 31j (70%) 20j (68%)

Total fine-particle mass 5 7 pg/m 3 10 2 Vg/m 3 10.6 pgimJ 6.4 gg/m3

Organics 1 4 (25%) 3.3 (32%) 40 (38%) 1 9 (30%)

Sulfates 1 3 (23%) 2.6 (26%) 3 1 (29%) 1 5 (23%)

Elemental carbon 4 (6%) 6 (5%) 7 (7%) 3 (5%)

Soil dust 1 4 (25%) 2.7 (27%) 1 7 (16%) 9 (14%)

Unaccounted-for 1 2 (21%) 1 0 (10%) 1 1 (10%) 1,8 (28%)

7.3.3 Quartile Variations

Although a complete extinction budget sorted by percentiles of visibility is not possible

because of the lack of detailed nitrate data, it is possible to examine how the components of

particle extinction and fine-particle mass vary with overall visibility levels. Table 25 lists

extinction components and fine-mass components averaged over the best 25% of days, all

days, and worst 25% of days.

Proceeding from best-case days, to average days, to worst-case days, a tendency exists

for an increased fraction of extinction caused by fine-particle scatter, and decreased

fractions caused by particle absorption and coarse-particle scatter Also, a tendency exists

for a higher percentage fine-mass contribution from "unaccotated-cor" (nitrates?), and a

lower percentage contribution from soil dust. Although these tendencies are statistically

significant, they are of minor practical importance. The average of worst-case days closely

resembles the average of all days in terms of extinction contributions and is nearly identical

in terms of fine-mass contributions. Even examining the highest 10% of worst-case days, we

do not find dramatic shifts in the composition of particle extinction or fine mass.

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TABLE 25. Components of Particle Extinction and Fine Particle Mass

for Best, Average, and Worst Visibility Conditions.*

Average of best Average of ail Average of worst

25% of days days 25% of days

Contributions = absolute (%)

Total particle extinction 13 Mm - I 34.5 Mm - 1 59 5 Mm -

Particle absorption 3 (23) 6 5 (18) 8.5 (15)

Coarse-particle scatter 2.5 (18) 5 (15) 8 (13)

Fine-particle scatter 7 5 (59) 23 (67) 43 (72)

Total fine-particle mass 3 6 pg/M 3 8 4 lig/m3 '3.0 pg/ml

Organics 1 1 (30) 28 (33) 4.1 (32)

Sulfates 9 (25) 2.3 (27) 3.7 (28)

Elemental carbon 2 (6) 5 (6) 8 (6)

Soil dust 1 0 (28) 1 7 (20) 2.4 (19)

Unaccounted-for 4 (11) 1.1 (13) 2.0 (15)

"Sorting of days at each site is according to the sum of all extinction components

at that site. Averages are then taken over the three sites together

7.4 UNCERTAINTY ESTIMATES

Appendix C presents a detailed uncertainty analysis for the extinction budget results.

Error propagation techniques are applied to judgmental estimates of uncertainty that

encompass instrument imprecisions, climatological variations, potential measurement

inaccuracies, and potential modeling errors (e.g., in the extinction efficiencies). The

estimated uncertainties (single standard error) for the non-Rayleigh extinction budget are

as follows:

Species Contribution ± uncertainty

Organics 26% t 9%

Sulfates 24% t 5%

Elemental carbon 19% ± 6%

Soil dust 18% ± 8%

Nitrates plus NO 2 13% ± 5%

The errors in the species contributions range from 5% to 9% of non-Rayleigh extinction,

with the greatest absolute errors being in the organics and soil-dust categories.

An important benefit of the error propagation analysis is that it reveals the relative

magnitude of the different sources of uncertainty (i.e., imprecision versus climatological

representativeness versus measurement inaccuracy versus modeling error). For example,

the uncertainties in the organic and sulfate categories are dominated by potential errors in

the scattering efficiencies to be used with these components. (Note that errors introduced by

sampling artifacts are included within the uncertainties of the scattering efficiencies. See

Appendix B for details.) Uncertainty in the accuracy correction factor for Teflon abosrption

accounts 'or the major part of the error in the elemental-carbon category. Error in the soildust category arises essentially all from uncertainty in our estimate of total coarse-particle

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scattering. Uncertainty in the nitrate-plus-NO2 category is about one-half because of error

in the scattering efficiency for nitrates, and one-half because of error in the concentration of

nitrate particles. Uncertainties related to measurement imprecision and climatological

representativeness are quite negligible (except for the sparse nitrate sampling) because of

the large number of observations in the RESOLVE data set.

8.0 SOURCES OF ATMOSPHERIC LIGHT EXTINCTION

One of the main objectives of the RESOLVE program is to determine the sources of light

extinction-to understand the origins, source areas, and source categories of visibility

reducing particles in the R-2508 study region.

The source allocation techniques used in Sections 8.1 and 8.2 are founded on

measurements of aerosol composition-composition either with respect to the five major

aerosol components or with respect to tracer elements. As such, these techniques fall within

the framework of "receptor" modeling (modeling centered on empirical data collected at

receptor sites). The RESOLVE monitoring program was designed specifically around the

receptor modeling approach (Reference 1). An alternative approach, "source-oriented" or

"mathematical diffusion" modeling, may also be possible in the long term. The development

of source-oriented models, however, may be extremely difficult.*

8.1 NATURAL VERSUS MAN-MADE SOURCES

In Chapter 7, contributions to non-Rayleigh extinction were allocated among five

categories: sulfates, nitrates plus NO 2 , elemental carbon, organics, and soil dust. By

considering the origins of each of these categories individually, we can derive an overall

estimate regarding natural versus man-made contributions to light extinctior.

Trijonis (Reference 40) evaluated natural versus anthropogenic origins for sulfates,

nitrates, and elemental carbon. All three of these aerosol categories are dominated by manmade sources. Based on a review of that study, a reasonable judgment for the RESOLVE

region is that man-made sources contribute about 85% ± 10% of combined extinction from

sulfates, nitrates plus NO 2 , and elemental carbon. (Note that the ± 10% uncertainty is a

judgmental estimate of a single standard error, i.e., a 68% confidence interval.)

The origin of organic aerosols in the Mojave Desert was the subject of a RESOLVE

special study funded by WOGA and carried out by Global Geochemistry Corp. (GGC). That

study included measurements of carbon isotope ratios and analyses of organic molecular

composition. Overall, the data from that study suggest that about 80% ± 15% of organic

aerosols are from man-made sources.**

* The difficulties concern the requirements for (1) detailed emissions data regarding all five aerosol

components, (2) aerometric data on transformation/deposition processes for all five components, (3) detailed wind

data to characterize transport and diffusion, and (4) a model framework capable of handling the complex terrain of

the study region.

** The basis for this estimate is as follows: The GGC isotope tests consistently show that about 65% ( ± 5%) of

total carbon is fossil. Assuming that 85% of elemental carbon is fossil (a noncritical assumption), and noting that

80% of total carbon is organic, we find that an estimated 60% of organic carbon is fossil. All these fossil organics are

assumed to be anthropogenic. In addition, some of the contemporary organics are anthropogenic (i.e., residential

wood burning, agricultural burning, food cooking) as opposed to natural (wild or controlled forest burning, plant

waxes, secondary aerosols from plant terpenes). Actually, the 60% fossil fraction is representative of anthropogenic

primary organic emissions (Reference 65), so one could argue that the organics are essentially all anthropogenic.

However, natural sources are known to be non-negligible based both on a literature review iReference 38) and on

obvious wild-fire impacts seen in the RESOLVE data (e.g., July 1985). Somewhat arbitrarily, we will assume that

the 40% contemporary fraction ;- half man-ma.e ai,a half natural. yielding an 80% total for anthropogenic. A high

anthropogenic percentage is supported qualitatively by the results of the molecular composition studies that

suggest that organic aerosols are dominated by urban sources.

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With respect to soil dust, it is highly problematical to quantify the relative

contributions of man-made sources (e.g., road dust, construction, mineral industry,

agriculture, etc.) and natural sources (wind-blown dust). First, the definition of natural is

ambiguous. Should wind-blown dust be considered natural if it is significantly affected by

land-use practices such as disruption of soil surfaces or drying of lake beds? Second, an

extensive literature review regarding dust sources in the RESOLVE region was unable to

produce reliable estimates of emission rates for wind-blown dust (Reference 66). Faced with

these difficulties, we will assume an arbitrary value with a large uncertainty, 50% + 25%,

as the anthropogenic contribution to soil dust. Fortunately, the assumption here is not

critical because soil dust does not account for a large part of light extinction.

Combining the results of the last three paragraphs with the extinction budget of

Chapter 7, and using routine error propagation methods, yields an overall estimate of manmade contributions to light extinction. Averaged over the three RESOLVE receptor sites,

the estimate is that 77% ± 14% of non-Rayleigh extinction is from man-made sources.

Adding in natural Rayleigh scatter, the fraction of total extinction from man-made sources

is 59% ± 12%.

The above line uf analysis can be taken a step further. The emissions data in Section

4.1.3 indicate that the two upwind air basins (Los Angeles and San Joaquin Valley) have

man-made emissions about 10 to 20 times greater than those of the RESOLVE region (bee

Figure 15). If, based on emissions data, we assume that 90% of the man-made nonsoil

components are from transport, we arrive at the estimate that about five-eighths of nonRayleigh extinction is transported anthropogenic material from upwind air basins, and

three-eighths local or natural.

Because sulfates, nitrates, organics, and elemental carbon are apparently dominated by

man-made transport, a review of the major source categories for these species (or their

precursors) in the upwind air basins was conducted. Table 26 summarizes 1983 SO,• and

NO, emissions for both the Los Angeles basin and San Joaquin Valley (Reference 39) as

well as 1982 organic and elemental carbon emissions for Los Angeles (Reference 67).

Several features stand out in the table. The petroleum industry is the largest single source

category for SO,, especially in the San Joaquin Valley. Emissions of NO, are dominated by

three categories: gasoline-powered vehicles, the petroleum industry, and diesel trucks.

Primary organic emissions come from a large number of relatively small source categories.

Elemental carbon comes mostly from engines burning diesel type fuel.

8.2 TRACER-ELEMENT MODEL FOR SOURCE AREAS

An important aspect of source characterization is the quantification of the contributions

of major source areas to visibility degradation in the RESOLVE study area. The two obvious

upwind source areas are the Los Angeles basin (LAB) and the San Joaquin Valley (SJV).

The local Mojave Desert region, i.e., the study region itself, must also be considered. The

proximity of these areas creates the necessity to separate the individual contributions using

some chemical or tracer-element approach instead of relying on a purely meteorological

approach. In this section, measurements at the Cajon and Soledad Passes are used to

determine the elemental composition of particles leaving the Los Angeles basin, while

Tehachapi Pass is used to characterize particles from the San Joaquin Valley (see Figure 1).

These elemental signatures are then combined with particle composition measurements in

the high desert to estimate Ute contributions of the source areas to aerosol loadings at the

receptor sites.

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TABLE 26. Emissions Inventories for SO,, NOx, Primary

Organics, and Elemental Carbon.

SO, emissionso NO, emissionsa

LAB SJV LAB SJV

Petroleum industry 27% 47% Gasoline vehicles 47% 22%

Diesel road vehicles 16% 16% Petroleum industry 9% 31%

Gasoline vehicles 18% 5% Diesel road vehicles 16% 20%

Ships 15% 0 Off-road equipment 7% 13%

Miscellaneous 25% 32% Miscellaneous 21% 14%

Fine organic particle emissionsb Pine elemental carbon emissionsb

LAB LAB

Forest fires 16% Diesel road vehicles 44%

Paved road dust 14"% Off-road equipment 12%

Charcoal broilers 12% Gasoline vehicles 10%

Gasoline vehicles 10% Miscellaneous 33%

Primary metallurgical 8%

Fireplaces 8%

Miscellaneous 28%

NOTE: All source categories contributing greater than 8% of the total in either air basin

are listed. The gasoline vehicles category includes motorcycles. The off-road equipment

category is dominated by diesels. The petroleum industry category includes both fuel

combustion and processes in both production and refining.

a 1983 inventory by CARB (Reference 39), except petroleum SO, emissions in the San

Joaquin Valley. were updated to 1986 to reflect the very large recent changes in that

category.

b 1982 Tnventory by Gray (Reference 67).

8.2.1 Analysis Overview

The basis for distinguishing the contribution of the San Joaquin Valley to the Mojave

Desert aerosol is displayed in Figure 47. Fine particles from the San Joaquin Valley exhibit

consistently higher Ni/Pb ratios than do fine particles from the Los Angeles basin. The ratio

of average Ni and Pb contents measured on days with concurrent outflow from both source

basins was 0.64 at Tehachapi and 0.062 at Soledad, an order-of-magnitude difference.

The Ni/Pb ratios measured at the receptor sites range between those characteristic of

the San Joaquin Valley and of the Los Angeles basin. Figure 48 shows an example (for

Edwards AFB) of these ratios. These intermediate ratios reflect varying mixes of Ni-rich

San Joaquin Valley aerosol with relatively Pb-rich aerosols from the Los Angeles basin

and the local desert. High Ni/Pb ratios at the receptor must represent large relative

contributions from the San Joaquin Valley, which is the only source of Ni-rich aerosol.

Nickel and lead were chosen as the basis for the analysis after screening the

composition profiles at the pass sites for elemental indicators of the source region.. The NiPb pair yielded the clearest separation between Tehachapi and the Los Angeles or local

desert source signatures.

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U-

= 1.2

UO TEHACHAPI

o 0 0 SOLEDAD

.• 1.0-0 A CAJON

W1

0

~0.8- 0

C

la.-

0 0.6-

z, 0o 8 00

S0.4

0

0

" 0.2 -

010 3 6 9

Lead as a fraction of Fine Mass (thousandths)

NOTE: Each point represents a 24-hour period with no backflow and at least 18 hours

(Tehachapi and Soledad) or 12 hours (Cajon) of outflow.

FIGURE 47. Trace-Element Content of Fine-Particle Samples Collected in Passes on

Outflow Days Leading from the San Joaquin Valley (Tehachapi) and the Los

Angeles Basin (Soledad and Cajon).

12

0 LAB

10 x SJV

x

8-

E N. ox* 0

6- ..X6 '

0 9 0

2 '40 0A 0 0

o o

0 30 60 90

Pb, ng/m 3

NOTES: * The Ni and Pb scales are in the same ratio as those of Figure 47.

* The symbols distinguish days on which morning 850 mb winds were from the

direction of the Los Angeles basin (SE-WSW), the San Joaquin Valley (W.NW), or

other.

FIGURE 48. Trace-Element Concentrations of Fine-Particle Samples at

Edwards AFB.

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No elements were found that reliably resolved Soledad, Cajon, or the local desert from

each other. Nickel and lead were also among the most consistently detected and precisely

measured of all elements. The average precision was ± 6% for Ni and ± 8% for Pb (see

Table 6). A final consideration is that Ni and Pb come from known sources, the combustion,

respectively, of residual oil and leaded gasoline, and the physical basis for the observed

geographic differences is thus understood (see, for example, the relative contributions of

gasoline vehicles and the petroleum industry in the NO, emission inventories of Table 26).

The correlation level with extinction and fine mass was not one of the criteria for tracer

selection. For example, bromine accounts for 55% of tne observed variance in fine mass at

Edwards AFB, whereas nickel and lead together account for only 25%. However, the

statistical relationship of fine mass to bromine offers little guidance in source-area

apportionment because bromine composition does not vary greatly between the regions.

With respect to our approach, information on air mass origin does not appear to be

readily available in the synoptic regimes based on Edwards AFB upper-air winds. As seen

in the data plotted with various symbols in Figure 48, the synoptic regimes are poor

predictors of the Ni/Pb ratio.* Thus, the following analysis does not use data stratifications

based on synoptic regimes.

8.2.2 Source Area Characterization

To obtain a representative characterization of the Ni and Pb composition of particles

leaving the source areas, pass measurements were chosen based on two pass wind

conditions. Individual outflow days had no backflow and substantial outflow from the pass

in question; data that met this requirement were used in Figure 47. Joint outflow days had

no hours of backflow at any of the three passes. Elemental data that meet both criteria are

given in Table 27. Note that the mean composition on joint outflow days is similar to that

on individual outflow days.

8.2.3 Theory

The theory for the method employed here is described in detail in Appendix D. This

section provides a brief overview and explanation.

Elemental concentrations at each receptor site are assumed to be linear combinations

of source contributions from the San Joaquin Valley, the Los Angeles basin, and the high

desert. The percentage of Ni and Pb in the primary aerosol from San Joaquin Valley and

Los Angeles basin are supplied from measurements in the passes on outflow days. The

source contributions represent only material that was already in the particle phase on

leaving the source basins. This material can be considered "primary" in the sense that it is

directly "emitted" by the source basins. To the degree that gas-to-particle conversion

continues downwind of the passes, the primary contributions thus understate the full

impact of the source basins.

* In retrospect, this is not surprising because air masses from both the Los Angeles basin and the San Joaquin

Valley arrive at Edwards AFB from the west. It is difficult for the synoptic regimes to resolve the two source areas

under westerly flows.

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TABLE 27. Concurrent Ni and Pb Data from Three Pass

Sites and One Receptor Site.

Tehachapi Soledad Cajon EdwardsAFB

n n =8 n= 10

Date [

Ni/FM PbiFM Ni/FM PbiFM Ni/FM Pb/FM Ni Pb FM

ppm(m)J ppm(m) ppm(m) ppm(m) ppm(m) ppm(m) ng/m3 ng/m 3 .±g/m3

Joint outflow days

6/29/84 1041 1675 133 3520 77 3853 6.7 185 1 05

5/19/85 835 1204 156 1871 114 2601 6.5 15.2 11 26

5/25/85 1025 1378 146 2327 * 2.1 21 7 12.06

6/12/85 504 696 114 1710 106 2235 3.4 174 13 72

6/24/85 535 1055 136 1895 * 2.3 15 7 12.43

7/12/85 320 635 54 579 81 1720 4.1 12.2 23.33

Average 710 1107 123 1984 95 2602 42 16.8 1413

CV 39% 33% 27% 44% 17% 30% 44% 18% 30%

Individual outflow days

Average 682 1144 119 2111 102 4382

CV 38% 34% 28% 45% [ 26% 42%

" Not sampled.

CV = Coefficient of variation.

n - Sample size.

Estimates of the elemental content of primary aerosol from the high desert are

available from measurements at the receptor sites under conditions minimizing transport

from the San Joaquin Valley and Los Angeles basin. These measurements indicate that the

Ni/Pb ratio of local source emissions is lower than that of the Los Angeles basin. This

conclusion is consistent with available composition measurements for prominent local

desert sources (References 66 and 68).

The method becomes understandable when you consider the fact that the San Joaquin

Valley is the only source of high Ni/Pb ratios. Thus the Ni/Pb ratio at a receptor can be

lowered by various combinations of aerosols from the Los Angeles basin and the high

desert, but the Ni/Pb ratio can be raised only by aerosol for the San Joaquin Valley. The

upper and lower bounds of the contribution of the San Joaquin Valley at a receptor are

determined by assuming alternately that only one of the two other source areas exists in

addition to the San Joaquin Valley-either the local high desert or the Los Angeles basin.

Figure 49 provides geometric insight into the dependence of the calculated San Joaquin

Valley contribution on Ni/Pb ratios from the other source areas. In Figure 49, the point

labeled EDWARDS shows the measured concentrations of Ni and Pb at Edwards AFB on

12 July 1985. The solid lines labeled TEHACHAPI, SOLEDAD, and CAJON show the Ni

and Pb concentrations corresponding to varying dilutions of the aerosols measured at these

passes on this date. The solid line labeled DESERT shows the Pb concentrations

corresponding to varying dilutions of the average aerosol measured at Edwards AFB on

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other days when outflow from all three passes was minimal. The fine-mass concentrations

of the pass and desert aerosols are indicated by ticks at I pg/m3 intervals.

The combining of aerosols in the atmosphere corresponds to the addition of composition

vectors in Figure 49 The dotted and dashed lines indicate two methods of representing the

measured Edwards AFB aerosol as a sum of source aerosols. Just under 30 ligimJ of the

Tehachapi aerosol is required to reproduce the Ni and Pb concentrations observed at

Edwards AFB if the Ni-poor high desert is taken as the only other source (dashed line)

Somewhat less is required if some of the Ni is contributed by the Los Angeles basin via

Soledad (dotted line).

To determine the San Joaquin Valley contribution at the receptors throughout the

year, we must extract, from the limited source measurements, a statistical

characterization of source composition and its variability. We made two assumptions: 1 1)

that the measured source (pass site) variability is genuine, and (2) that this source

variability is uncorrelated with receptor concentrations. Each of these assumptions is

examined briefly here. A more complete discussion is presented in Appendix E.

7

TEHACHAPI

6

5

S4 --. ?EDWARDS( 7/12 /85)

S3 v

CAO

,,• ,,•SOLEDAD

0 DESERT

0 20 40 60

Pb, ng/m 3

FIGURE 49. A Geometric fnterpretation of Source Apportionment. See text

for explanation of symbols.

1. Probably, the bulk of the observed variability of Ni and Pb contents in the passes

represents real variations in source composition. Measurement precision was good, and

averaging over 24 hours of outflow should have minimized the impact of inhomogeneities.

Substantial correlations between passes, particularly for Pb content, support the reality of

source variability. Note, for example, the uniformly low Ni and Pb contents at all three

passes on 12 July 1985, which is suggestive of synoptic c(,,,dittons favorable to gas-toparticle conversion.

2. The characteristics of the Ni and Pb fractions of fine mass of an upwind basin are

controlled by the within-basin source mix and gas-to-particle conversion, while the

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concentrations of Ni and Pb at a receptor are controlled by interbasin transport factors. In

this case, the covariance of source characteristics and receptor concentration can be

expected to be small.

8.2.4 Results

At Edwards AFB during the RESOLVE base year, 84 routine samples were analyzed.

Average concentrations for the base year were Ni =3.1 ng/m3 , Pb = 19.3 ng/m3, and FM =

8.5 g/rm3 .

A lower bound for the San Joaquin Valley contribution is obtained by attributing all of

the remaining aerosol to the Los Angeles basin emissions vented from Soledad. Based on

the above averages at Edwards AFB, and the observed Ni and Pb contents at Tehachapi

and Soledad on joint outflow days, this lower bound for the average cGntribution of the San

Joaquin Valley primary fine mass at Edwards AFB is 3.2 (± 0.5) Pg/m 3 , or 38% of fine mass.

The indicated uncertainty in this estimate is the standard deviation attributable to the

small sample (n = 61 of source characterizations on which this estimate is based.

An upper bound for the San Joaquin Valley contribution is obtained by attributing all

Ni to the San Joaquin Valley. Based on the observed Ni content at Tehachapi on individual

outflow days and the average Ni concentration at Edwards AFB, this upper bound for the

average contribution of the San Joaquin Valley to primary fine mass at Edwards AFB is

5.3 (±0.7) ig/m 3 , or 63% of fine mass. Again, the indicated uncertainty is the standard

deviation attributable to the small sample (n = 11) of source characterizations on which

this estimate is based.

The statistical uncertainty of the above bounds is substantially smaller than their

spread. Consequently, we shall be more concerned in what follows with possible biases in

characterizing the source areas and implementing the model rather than with random

sampling fluctuations.

Table 28 shows results for Edwards AFB under various specific conditions: base year

(July 1984 to June 1985), grand average (April 1984 to August 1985), summer (April to

September average), easterly upper winds (morning 850 mb mind at Edwards AFB from

ENE to SE), no Tehachapi outflow (no outflow for 48 hours on the sampling day and the

previous day), and 48-hour Tehachapi outflow (continuous outflow for 48 hours on the

sampling day and the previous day). The estimated San Joaquin Valley contributions in

Table 28 are consistent with expectations and add confidence to the receptor model results.

Meteorological conditions, such as easterly upper winds or an absence of outflow at

Tehachapi, should isolate the high desert from thn San Joaquin Valley. The San Joaquin

Valley contributions estimated for these conditions are, in fact, indistinguishable from zero

at all three receptors. Conversely, sustained outflows from Tehachapi should maximize the

impact of the San Joaquin Valley on the high desert. The estimated San Joquin Valley

contributions for this condition are indeed above average.

Table 29 (calculated as in Table 28) summarizes results for all three RESOLVE

receptor sites. Two conditions are considered for each site, the average of all routine

measurement days and the average over the 25% of days with highest Bscat. As a fraction

of observed fine-particle mass, the estimated contribution from the San Joaquin Valley is

fairly uniform overall. However, nazy days at Edwards AFB and Fort Irwin NTC (the

receptors nearest the Los Angeles basin) are accompanied by higher than average

estimated relative contributions from the Los Angeles basin and high desert.

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TABLE 28. Estimated Contributions of the San Joaquin Valley to Fine-Particle

Mass Concentrations at Edwards AFB Under Various Conditions.

Estimated contributions of

Number of days Measured fine San Joaquin Valley. %

Lower bound Upper bound

EdwardsAFB

RESOLVE base year 84 8.5 38 63

Grand average

(4/84-8185) 104 9 3 40 62

April-September

average 57 1 1 7 39 59

Easterly upper

winds 5 46 0 24

No Tehachapi

outflow 5 60 0 13

48-hour Tehachapi

outflow 24 10 1 66 77

NOTES:

* Lower bounds are based on the data from Tehachapi and Soledad for the 6 joint outflow days and

the average concentrations at Edwards AFB for the indicated subsample. Upper bounds are based on the

data from Tehachapi for the 11 individual outflow days and the average concentrations at Edwards AF8 for

the indicated subsample.

* The source characterization days are not necessarily included among the receptor subsamples, for

example, the category "No Tehachapi outflow" obviously includes no source-characterization days.

TABLE 29. Estimated Contributions of the San Joaquin Vailey to

Fine-Particle Mass Concentrations at Three Receptor Sites.

Estimated contributions of

Measured fine San Joaquin Valley, %

mass, Vg/mJ

Lower bound Upper bound

Edwards AF8

RESOLVE base year 8.5 38 63

Worst quartie 8sp 13.6 30 51

NWC

RESOLVE base year 8.9 36 57

Worst quartile Bsp 12,6 42 56

Fort Irwin NTC

RESOLVE base year 6 5 37 53

Worst quartile 8sp Il 8 32 46

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8.2.5 Assumptions and Limitations

The apportionments presented in Tables 27 and 28 rest on the following two critical

assumptions:

1. The San Joaquin Valley is the only source of Ni/Pb ratios higher than those

measured at Soledad.

2. The Ni/fine-mass (Ni/FM) ratios measured at Tehachapi are representative of the

San Joaquin Valley contributions at the receptors.

It is appropriate at this point to reconsider these assumptions and the consequences of any

violations.

The assumption that the San Joaquin Valley is the only Ni-rich source is strongly

supported by the available data. We have seen that windflows minimizing transport from

the San Joaquin Valley were consistently associated with low Ni/Pb ratios at the receptors.

The conditions considered here include minimal outflow from all passes (2 days), no outflow

from Tehachapi for 48 hours (5 days), and easterly morning 850 mb winds (5 days, 3 with

no Tehachapi outflow). The only serious qualification to this result is that all 9 of these

minimal-transport days were from 3 winter months: December, January, and February.

The assumption that the Tehachapi Ni/FM ratios are representative of the San Joaquin

Valley contributions is more difficult to test. At least four sources of bias can be imagined.

1. San Joaquin Valley aerosol on outflow days might be unrepresentative of San

Joaquin Valley aerosol on other days. The identification of outflow days was based solely on

wind data, without requiring high mass concentrations or scattering coefficients, precisely

to avoid the introduction of a downward bias in Ni/FM and Pb/FM ratios. Seasonal

variations do not appear to be a major problem; although 9 of the II Tehachapi outflow

days were from 4 summer months (May to August), estimated relative contributions for

April to September averages differ little from estimated relative contributions for the

RESOLVE base year (Table 27). The possibility remains that ventilation itself could, by

decreasing residence times in the San Joaquin Valley, produce unreprenenratively high

Ni/FM ratios.

2. San Joaquin Valley aerosol might be modified downwind of Tehachapi by gas-toparticle conversion. Little indication exists in the data of significant gas-to-particle

conversion downwind of the source basins; "primary" aerosols from the San Joaquin Valley

and Los Angeles basin together can account for most or all of the fine mass observed in the

high desert. If some residual conversion exists, its effect is to reduce Ni/FM ratios below

those observed at Tehachapi.

3. Aerosol leaving the San Joaquin Valley via Tehachapi might be unrepresentative

of aerosol leaving the San Jaquin Valley via other routes. Oil recovery operations, the

presumed source of high Ni concentrations from the San Joaquin Valley, are clustered in

certain southern areas of the valley, e.g., near Lake Isabella Pass. Aerosols leaving the

valley at Lake Isabella Pass might be even more rich in Ni, while aerosols from further

north might be less rich in Ni.

4. Also, the possibility exists of particle-to-gas conversion (e.g., nitrates and organics).

However, the RESOLVE thermidograph nephelometer experiments suggest that the effect

on total fine mass should be rather small.

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The first two of these sources of bias imply that the model would underestimate San

Joaquin Valley contributions. The third source of bias could produce either an overestimate

(if hypothesized Lake Isabella Pass effects were critical) or an underestimate (if overall

northisouth effects were dominant). The last minor point would imply an overestimate.

Our qualitative judgment is that the overall bias is probably in the direction of

underestimating San Joaquin Valley contributions.

8.2.6 An Alternative Tracer Element Model

To provide a quality assurance check on the Ni/Pb tracer model and to quantify some of

the uncertainties in the results, an alternative tracer element model was applied with

different solution methods and different data configurations. Appendix D gives a detailed

description of this alternative analysis. Only a synopsis will be presented here.

The EPA chemical mass balance (CMB) software (Reference 69) was used to apportion

fine mass at each receptor site among the source regions. The EPA CMB software

calculates source contributions and associated uncertainties by applying the effective

variance solution method (Reference 70) to the CMB equations. The software includes

several performance measures that can be used to evaluate the validity of the model

application. The most important performance measures with respect to the RESOLVE

applications are the "source uncertainty clusters" that identify source profiles that are very

similar to each other and thereby produce large uncertainties. The CMB application and

validation protocol of Pace and Watson (Reference 71) was followed to assess the validity of

the input data and compliance with model assumptions.

The EPA CMB model requires source profiles and ambient concentration profiles, both

with specified uncertainties. These were determined from the ambient data at the pass and

receptor sites. The source profiles needed selection criteria to identify days thought to be

representative of source area composition. These selection criteria were chosen

independently from (and differed considerably from) those used in the Ni/Pb tracer model.

Furthermore, the data sets for the EPA CMB model were varied to investigate how

different averaging methods and different numbers of species affected the solution.

The first part of the analysis involved feasibility testing using the methodology from an

early RESOLVE design evaluation study (Reference 3). The conclusions were as follows: (1)

the RESOLVE pass site profiles from the Los Angeles basin and the San Joaquin Valley

can be used to distinguish these source areas; (2) the key distinguishing species appear to

be lead and nickel (or vanadium); (3) profiles at the Soledad and Cajon sites are too similar

to be separated from each other; and '4) the addition of a third (local) source profile would

interfere with the source apportionment.

The second, more extensive, part of the analysis was to apply the EPA CMB model for

various receptor sites, averaging periods, source area profiles, and sets of elemental

species. The major findings from this application were as follows:

"• Only two source areas can be resolved from each other regardless of the receptor

site, averaging period, or set of elemental species. This is consistent with the

feasibility testing and with the results of the Ni/Pb tracer model.

"* The source contributions for the San Joaquin Valley calculated by the EPA CMB

model using only Ni and Pb are generally consistent with those of the Ni/Pb tracer

model. The EPA CMB model usually yields values toward the lower end of the

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range produced by the Ni/Pb tracer model, but the uncertainties calculated by the

EPA CMB model cover most of those ranges.

0 The San Joaquin Valley contributions from the EPA CMB model for either 17 or 20

elemental species and two averaging times are generally similar to those of the

Ni/Pb tracer model when a nonwinter, local-source profile is included. When such a

source profile is excluded, the San Joaquin Valley contributions are much higher,

exceeding the upper bound of the Ni/Pb tracer model. The uncertainties are large

(about 50% of the source contribution), however, because of the great similarity

among the source profiles used with the EPA CMB model.

The overall conclusion of this complementary analysis is that the San Joaquin Valley can

be resolved from other source areas but only with uncertainties of 50% or more. The San

Joaquin Valley contribution is probably in the range of 25 to 75% of fine-particle mass at

the receptor sites. Some of the conclusions may change if the estimates of the local source

profile are refined and improved. In addition to the uncertainties discussed in this section,

the above results are subject to all the limitations described in Section 8.2.5.

8.3 CONLCUSIONS REGARDING SOURCE AREAS

The purpose of this section is to synthesize all available information about source area

contributions to arrive at the most reasonable conclusions possible based on the current

data and understanding. Unlike many other areas of this report (e.g., spatial visibility

patterns, temporal visibility patterns, or light extinction budget) where firm quantitative

conclusions are attained, the findings regarding source area contributions are only

semiquantitative. Although the individual analyses concerning source areas are

quantitative and objective, they all involve significant uncertainties, and none provides a

complete answer covering all source areas. Engineering judgments are required to

synthesize the results. The format here will be to state the conclusion and then to list the

evidence and considerations behind the conclusion.

On the average at NWC, about one-half (maybe somewhat more) of non-Rayleigh

extinction appears to be anthropogenic transport from the San Joaquin Valley, with the

remainder of non-Rayleigh extinction arising from local and natural sources.

Contributions from the Los Angeles basin seem to be very minor. The evidence is as

follows:

"* The Ni/Pb tracer model of Section 8.2 predicts average fine-mass contributions

from the San Joaquin Valley of 36 to 57%. The tracer model, however, includes

natural as well as anthropogenic contributions from the San Joaquin Valley.

" The line of reasoning in Section 8.1 suggests that about five-eighths of nonRayleigh extinction at NWC is anthropogenic transport from upwind air basins.

with the remaining three-eighths being local and/or natural. Wind-flow patterns

(Section 4.1.2), spatial visibility patterns (Section 5.1), and SF 6 tracer studies

(Reference 14) all indicate that anthropogenic transport at NWC is on the average

dominated by the San Joaquin Valley with relatively infrequent contributions

from the Los Angeles basin.

"* Particle scattering at remote elevated sites near NWC is 64 to 89% of that at NWC

itself (Section 3.5). Among other things, this 64 to 89% may be a reflection of

regional sources (basically transport) as opposed to local sources.

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On the average at Edwards AFB, about one-half of non-Rayleigh extinction seems to

represent anthropogenic transport from the San Joaquin Valley, with the Los Angeles

basin and natural/local sources also contributing significantly. The source area allocation

at Fort Irwin NTC seems similar to that at Edwards AFB, with a slight shift from the

anthropogenic transport category to the natural and local categories.

" The Ni/Pb tracer model for Edwards AFB indicated average fine-mass

contributions from the San Joaquin Valley of 38 to 63%. (Note that the tracer

model results include natural as well as anthropogenic transport from the San

Joaquin Valley.)

"* Wind-flow patterns (Section 4.1.2), spatial visibility patterns (Section 5.1 ), and SFr

tracer studies (Reference 14) all indicate that Edwards AFB is affected

significantly by anthropogenic transport from both the San Joaquin Valley and the

Los Angeles basin. Local emissions data (Reference 68), the calculation methods of

Section 8.1, and various Bscat/meteorology relationships in Chapter 4 suggest that

Edwards AFB should have slightly greater total anthropogenic transport (relative

to local and/or natural sources) than NWC. However, local and/or natural

categories (e.g., portions of organics and soil dust) are certainly not negligible at

Edwards AFB.

"* Wind-flow patterns and geographical visibility patterns indicate that Fort Irwin

NTC, like Edwards AFB, is affected significantly by anthropogenic transport from

both the San Joaquin Valley and the Los Angeles basin. The lower pollutant

concentrations and geographic location of Fort Irwin NTC, however, suggest that

anthropogenic transport should be somewhat less important (relatively) there than

at Edwards AFB. This is consistent with the slightly lower San Joaquin Valley

contributions predicted by the Ni/Pb model at Fort Irwin NTC (37 to 53%, as

opposed to 38 to 63% at Edwards AFB).

For worst-case days at Edwards AFB and Fort Irwin NTC, transport from the Los

Angeles basin appears to be more important than it is for average contributions.

"* The Ni/Pb tracer model for Edwards AFB and Fort Irwin NTC indicates fine-mass

contributions from the San Joaquin Valley of about 40 to 60% on the average, but

only 30 to 50% for the worst-case quartile (see Table 29).

"* The illustrative case studies of Chapter 9, although very limited in scope, feature

the importance of transport from Los Angeles under worst-case conditions at

Edwards AFB and Fort Irwin NTC.

"* For worst-case days, light extinction at Edwards AFB is slightly greater than at

Tehachapi, suggesting the importance of sources other than the San Joaquin

Valley (see Table 13).

8.4 EXTINCTION CONTRIBUTIONS FROM MAJOR

ANTHROPOGENIC SOURCES

It is interesting to identify the important source categories that contribute to light

extinction in the RESOLVE study region. Appendix B presents estimates of the

contributions from individual source categories to man-made light extinction. The results

pertain to the study region as a whole (i.e., to an average of the three RESOLVE receptor

sites) and to annual mean conditions (rather than worst-case days).

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The estimation procedure involves three steps:

1. Allocating anthropogenic extinction in the RESOLVE region among three source

areas (San Joaquin, Los Angeles, and local desert)

2. For each source area, apportioning man-made extinction among the five aerosol

components (organics, sulfates, elemental carbon, soil dust, and nitrates plus NO2)

3. For each source-area/aerosol-component, allocating the relevant emissions among

source categories

In the last step, emission inventories representative of the early/mid-1980s are used,

with SO, emissions in the San Joaquin Valley updated to 1986 to include the large recent

decreases in petroleum industry SO, (References 72 and 73).*

The results indicate that only three individual source categories exist that contribute

significantly to man-made extinction in the RESOLVE region as a whole. These source

categories are diesel vehicles/equipment (basically highway trucks and off-road mobile

equipment but excluding planes/ships/trains), the petroleum industry (mostly from the San

Joaquin Valley), and gasoline vehicles (including both exhaust and suspended dust).

Together, these sources account for about 60% of anthropogenic extinction, with the diesel

category alone contributing about one-quarter of man-made extinction. No other source

categories individually account for more than about 5% of man-made extinction in the

region as a whole. These findings are consistent with our discussion of emission inventories

in Section 8.1. (There we noted that diesels, gasoline vehicles, and the petroleum industry

dominated the emission inventories for NO,, SO., and elemental carbon, and that

emissions of primary organics came from a large number of relatively small source

categories.)

Table 30 summarizes the results, along with overall uncertainties and a breakdown

according- to aerosol components. The overall uncertainties are based on propagation of

judgmental error estimates for the various parameters in the analysis.

We emphasize that these results represent percentage contributions to average

anthropogenic extinction. To obtain percentage contributions to non-Rayleigh extinction

(including natural aerosols) or total extinction (including natural aerosols and Rayleigh

scatter), the above results should be multiplied by 0.77 or 0.59, respectively. For example,

the overall allocation for average total extinction is estimated as 24% from natural

Rayleigh scatter, 17% from natural particles, 15% from diesel vehicles/equipment, 11%

from the petroleum industry, 9% from gasoline vehicles, and 24% from other man-made

sources.

We stress that the above results refer to the impact of aggregated source categories on

the region as a whole. Historically, DOD personnel have been interested in the effect of

specific local sources at individual receptor sites, particularly the impacts of Owens Valley

dust storms and Searles Valley air pollution at NWC. Particulate air quality standards

have been severely violated in the Owens Valley (Reference 74) and Searles Valley

(Reference 75) and concern exists regarding spillover effects at NWC. Qualitatively, this

report has provided some insights into air quality effects from the Owens and Searles

Valleys. Examples are the visibility isopleths around the Searles Valley in Figure 37, the

* From 1983 to 1986, petroleum industry emissions for SO, in the San Joaquin Valley were approximately as

follows (rounded to the nearest 5 tons per day;: 1983 = 110 TID, 1984 = 85 TMD, 1985 = 55 T[D, and t986 = 45

T/D.

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mention of Searles Valley trajectories in Section 9.2.3, and the various discussions

throughout this report of suspended dust (which might involve Owens Valley). However, to

estimate quantitatively the impact of local source categories at individual receptor sites

would require different methodologies than those used herein.

TABLE 30. Man-Made Extinction Allocated Among Major Source Categories.

Total Contribution via each aerosol component

contribution to Source category man-made

extinction Elemental Nitrates exml•n Organics Sulfates Soil dust

carbon NO2

Diesel vehicles/ equipment 26% :t 7% 3% 6% 12% % 4%

Petroleum industry 18% ± 5% 4% 11% 0 0 3%1o

Gasoline vehicles 16% ± 5% 3% 2% 1% 6% 4%

9.0 ILLUSTRATIVE CASE STUDIES

We have shown that visibility in the RESOLVE region is affected by at least three

major source areas-the San Joaquin Valley, the Los Angeles basin, and local desert

sources. On many days, the impacts of these source areas at a given receptor site may be

combined. However, on some days, only one or two of these sources affect a particular

receptor site. A detailed analysis of these latter days can lead to a better understanding of

the atmospheric processes that link emissions in the source area to air quality at the

receptor site. This is the objective of illustrative case study analysis.

The analyses here will focus on flow patterns, trajectories, transport times, and aerosol

compositions for a few days. This approach is different from that of Chapter 8, where

general statistical associations and long-term averages were considered. The shorter time

scale allows the possibility of direct evidence of the path and time of transport from source

region to receptor. However, since the detailed evaluations are time-consuming and

expensive, only 4 specific days are analyzed. These days are fairly clear examples of

particular types of impacts at the receptors. Keep in mind that the days represent only

those specific types of impacts, not the average mixture of sou:ce area impacts at the

receptor sites.

Appendix F presents a detailed description of the wind trajectories and diurnal airquality patterns for each day.

9.1 SELECTION OF CASE STUDY DAYS

The days for the illustrative case studies were selected by scrutinizing time series plots

of aerosol and extinction data (e.g., Figures 43 through 45) as well as data on hourly/daily

wind flows throughout the region. This process not only revealed days when one or two of

the three source areas seemed to predominate, but also, in a few cases, even implicated

specific source categories, such as dust from dry lake beds (e.g., 20 February and 25 April

1985) and smoke from forest fires (e.g., the second week of July 1985).

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General criteria for choosing the case study days were established so that the goals

could be achieved within project resources. These criteria included (1) availability of data

for the sites of interest, (2) meteorology that allows isolation of only one or two specific

source areas at each receptor, (3) a large impact of a particular source area, and (4) time

duration of the impact long enough to dominate the 24-hour particle samples. We also

wanted to ensure that the examples had variety, including impacts from all the major

source areas.

Table 31 lists the days chosen for the analysis and some of the basic characteristics of

those days. The days represent transport from the San Joaquin Valley, the Los Angeles

basin, and local sources. The days also include examples of wind-blown dust and forest

fires. Significant impacts occur at all of the RESOLVE receptor sites on the days shown.

TABLE 31. The RESOLVE Case Study Days.

Date Characteristics

4 October 1984 Transport from both upwind basins into the desert. All desert sites affected.

14 July 1985 Transport from both upwind basins into the high desert, with all receptor sites

affected. Impacts from Ojai forest fires.

25 December 1984 Transport effects at Edwards AFB early in the day. Local and regional impacts at

Edwards AFB the rest of the day and at other receptor sites all day.

20 February 1985 Wind-blown dust impact at all receptor sites and the two Los Angeles pass sites.

Some transport im pacts at receptor sites early in the day.

9.2 DISCUSSION OF THE CASE STUDY DAYS

The following subsections discuss the results for the 4 case study days. Most of the

discussion centers on wind trajectories and diurnal patterns of particle scattering.

Trajectories are intended to represent the general path of air parcels on their way to the

sampling sites. The details of this analysis are presented in Appendix F.

9.2.1 4 October 1984

4 October 1984 was a day of transport to the RESOLVE receptor sites from both the San

Joaquin Valley and the Los Angeles basin. On this day, all four RESOLVE desert sites

showed a dramatic increase in particle scattering in the early-to-late afternoon. Particle

scattering at both Tehachapi and Cajon Passes slowly increased most of the day, while the

particle scattering at Soledad Pass increased dramatically in the afternoon. Outflow (flow

into the high desert) occurred all day from all three passes.

Trajectories for Edwards AFB indicate that morning air parcels took about 6 hours to

travel from the pass regions. However, by late afternoon, the trajectories were significantly

shorter and traveled along a more direct route from the Los Angeles basin. An increase in

particle scattering at Edwards AFB (between 1400 and 1600 PST) occurred in an air parcel

that had passed into the desert about the same time as a large increase in particle

scattering was measured at Soledad. According to the 1600 PST trajectory, this air parcel

was in the San Fernando Valley area most of the morning before crossing the mountains

near Castaic Lake (northwest of Soledad Canyon) and arriving at Edwards AFB.

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The Ni/Pb ratio at Edwards AFB on 4 October was slightly higher than the Soledad

Pass Ni/Pb, but still significantly lower than the Tehachapi Pass Ni/Pb. (See Section 8.2

and Appendix F for discussion of the significance of the NiUPb ratio.) This situation shows

that, even in a case when the trajectories indicate transport from the Los Angeles basin all

day, the 24-hour average sample still can show a small influence from the San Joaquin

Valley.

The wind at NWC was light and variable until about 1600 PST, then the wind

increased in velocity and came from the south-southwest. The trajectories indicate that the

air parcels had been within about 15 miles of NWC for the last day or more until late

afternoon. By 2000 PST, trajectories indicate that transport from the Terachapi Pass

region was established. This occurrence matches a significant increase in particle

scattering that lasted the rest of the day. This action is a typical pattern at NWC.

The particle scattering diurnal pattern at Fort Irwin NTC was very similar to that at

NWC. However, the wind was higher than at NWC and was consistently from the west to

southwest all day. Trajectories were from Tehachapi until late in the day, when transport

shifted to the Soledad region, the transport time decreased, and a sharp increase in particle

scattering occurred. Later trajectories indicate transport from Cajon Pass.

The data for Fort Irwin NTC on 4 October contain an interesting phenomenon. During

the worst particle scattering hours of the day, the Los Angeles basin was the major

contributor to the aerosols measured at Fort Irwin NTC (as indicated by the trajectories).

However, the major source area contributing to the 24-hour average concentrations (as

indicated by the Ni/Pb ratio) was the San Joaquin Valley. Thus, the source area

contributions during the worst hours of this particular day seem to be very different than

the source area contributions averaged over the complete day. This may not be the typical

situation at Fort Irwin NTC, since we have only looked at a very small subset of the

RESOLVE data in this manner.

9.2.2 14 July 1985

14 July 1985 is another day of transport from both the San Joaquin Valley and the Los

Angeles basin to the RESOLVE receptor sites. However, this day provides an interesting

contrast, since many of the characteristics are significantly different than those of 4

October 1984.

At NWC, winds were light and variable until almost noon. Then the winds picked up

and came from the south to the southwest for the rest of the day. The particle scattering

was modest and steady until the wind speed increased and a pulse arrived from the

Tehachapi region. This particle scattering pulse coincided with a shorter transport time

and an air parcel that had been in the southern San Joaquin Valley during the early

morning. NWC particle scattering dropped back to its prenoon value in the midafternoon,

even though transport was still quick (about 4 hours) and from the Tehachapi region.

These later air parcels were in the southern San Joaquin Valley about noon, rather than

early morning, and may have experienced less emissions and less reacting time before

arriving at NWC.

Trajectories show that a similar air parcel, along with a scattering pulse, arrived at

both Edwards AFB and Randsburg Wash. However, right after this pulse, an air parcel

from the Los Angeles basin began arriving at both Edwards AFB and Randsburg Wash.

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The trajectories indicate that all air parcels arriving at Fort Irwin NTC on this day had

originated in the Los Angeles basin. The particle scattering pulse in the late afternoon

corresponds to a shorter transport time. The interpretation of the Fort Irwin Ni/Pb data is

again confusing (see discussion for 4 October 1984). The hourly trajectories indicate

transport from Los Angeles all day, while the 24-hour Ni/Pb ratio suggests significant San

Joaquin Valley influence.

The case study days were chosen to represent specific types of effects at the RESOLVE

receptor sites, not to represent average impacts. However, a comparison of the chemistry

measurements for the case study days and the RESOLVE base year is valuable. Table 32

presents the chemical component data for this comparison.

TABLE 32. Percentage of Various Components at the RESOLVE Sites.

Day Component EDW CHL FOR RAW TEH SOL CAP

RESOLVE Organics 32 37 29 30 34 30 36

base year Sulfate 30 24 28 32 26 32 27

,C 6 6 7 5 4 8 7

Soil 16 20 27 17 15 10 8

Remainder 16 13 9 16 21 20 22

10/4/84 Organics 43 44 40 36 34 39 40

Sulfate 43 32 39 28 27 39 40

EC 3 3 3 2 1 2 2

Soil 9 9 9 12 9 6 5

Remainder 2 12 9 22 29 14 13

7114/85 Organics 59 65 56 61 43 58 59

Sulfate 19 17 19 15 21 15 23

EC 2 2 2 2 0 2 2

Soil 8 11 10 18 13 6 6

Remainder 12 5 13 4 23 19 10

12/25/84 Organics 28 37 -"- -- -- 57

Sulfate 11 2 -....... 9

EC 0 0 -..... 6

Soil 2 2 -...... 11

Remainder 59 59 - -- - - 17

2/20/85 Organics 33 -- 12 11 52 21 26

Sulfate 20 - 19 -- 32 19 22

EC 2 -- 1 1 3 2 2

Soil 38 - 77 - 16 44 51

Remainder 7 -- -5 -- -3 14 -1

Fine mass less than 1.5 pg/m 3

The two transport days (4 October 1984 and 14 July 1985) had markedly different

chemical patterns. Organics were much higher than average at all receptor sites on 14

July, while sulfates were somewhat higher than average at three of the four sites on 4

October. Organic aerosol from the Ojai fires was a major contributor on 14 July (the Ojai

fires had gone into a smoldering stage about 10 July and were emitting copius smoke

without strong vertical venting from fire heat). It appears that atmospheric conditions

were especially conducive to producing sulfate aerosol on 4 October 1984.

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9.2.3 25 December 1984

In general, 25 December 1984 was a day with mostly local, high-desert effects on the

RESOLVE receptor sites. The wind speeds were light most of the day (and the previous

day) and wind direction was more variable than on other days.

Air parcels arriving at NWC apparently spent the last 10 to 20 hours within about 25

miles of that site. The trajectories indicate that the air parcels arriving between 1500 and

2000 PST probably passed through Searles Valley.

Even on this day, with slow winds and mostly local effects, the situation at Edwards

AFB is complicated because Edwards AFB is so close to the passes. The morning

trajectories indicate slow transport from either the San Joaquin Valley or Los Angeles

basin. However, the air parcels that arrived in the late afternoon and evening probably

spent at least the last 16 hours in the desert.

Air parcels arriving at Randsburg Wash between about 1300 and 1800 PST had come

from the northwest and spent the last 9 to 24 hours within about 45 miles of the monitoring

site. Two of the trajectories indicate that air parcels passed through the Searles Valley.

Fort Irwin NTC was much cleaner on this day than the other sites. The morning

trajectories indicate that the air parcels had spent at least the last 6 hours north of the

sampling site. The air parcels that arrived around noon had spent 10 to 16 hours traveling

from the south and southwest, possibly passing through the Barstow area. Late in the day,

the air parcels came directly from the east.

The chemical components on this day show an extremely large unmeasured component

at Edwards AFB and NWC. Other parts of this report indicate that this unmeasured

component may be an indicator of nitrate aerosols.

9.2.4 20 February 1985

20 February 1985 was a day when very high wind speeds and blowing dust were

reported at many locations in the high desert and much of Southern California. The

synoptic conditions showed strong winds from the north early in the day. However, surface

winds were from the west in the western part of the desert until early afternoon, even

though extremely strong winds from the north covered the remainder of the desert. On a

day such as this, surface winds and trajectories can be misleading about the origin of air

parcels. Upper-level winds can carry aerosols long distances, after which the aerosols can

be mixed down into a surface layer that had traveled in the opposite direction.

The trajectories indicate transport from the upwind basins at Edwards AFB,

Randsburg Wash, and Fort Irwin NTC early in the morning. However, trajectories for

NWC (all day) and the other three sites after 1000 PST show strong winds and transport

from the north. The high particle scattering levels that occurred throughout the day at all

of the sites reflect the influence of wind-blown dust.

The measurement of the soil component at Edwards AFB and Fort Irwin NTC

(measurements are not available at NWC and Randsburg Wash) were much higher than

normal, confirming a large wind-blown dust component. On this day, even Soledad and

Cajon Passes show the strong influence of wind-blown dust (they both had flow from the

desert late in the day). The only exception is Tehachapi Pass, where the chemical

compositior measurements and wind data suggest impacts from the San Joaquin Valley.

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9.3 CONCLUSIONS

The case study analysis evaluated a number of days where fairly obvious source

impacts existed at the RESOLVE receptor sites. This information helped to provide a better

understanding of how visibility impacts occur at the RESOLVE receptor sites. The analysis

of flow patterns, transport routes and times, and aerosol concentration patterns in the

RESOLVE region provided direct evidence of transport from both the San Joaquin Valley

and the Los Angeles basin to the receptor sites.

Large increases in particle scattering at the RESOLVE receptor sites can occur when

arriving air parcels contain early-morning concentrations from either the San Joaquin

Valley or Los Angeles. Air parcel trajectories indicate that this increase often happens

when the wind speed increases and aerosols are quickly transported from the upwind

basins to the receptor sites. Under these conditions, transport time from the pass regions to

Edwards AFB is often 2 or 3 hours, whereas the corresponding transport time to the more

distant RESOLVE receptor sites is often 6 to 8 hours. Particle scattering is generally lower

at the receptor sites when the transport time is longer.

On the four case study days, NWC was never impacted by transport of aerosols from the

Los Angeles basin. Edwards AFB was often impacted by aerosols transported from the San

Joaquin Valley early in the day and by aerosols transported from Los Angeles later in the

day. On the case study days, Edwards AFB was never impacted by Los Angeles via Cajon

Pass. Randsburg Wash was impacted by both upwind basins. Since Fort Irwin NTC is so far

from the San Joaquin Valley, it was hard to document transport from the San Joaquin

Valley. However, the large increases in particle scattering at Fort Irwin NTC did occur

during transport of aerosols from Los Angeles via Soledad Pass or Cajon Pass.

On one case study day, only local impacts were documented at NWC, Randsburg Wash,

and Fort Irwin NTC. Some of the air parcel trajectories passed through the Searles Valley

before they arrived at NWC and Randsburg Wash.

Some of the case study analyses are consistent with the concept that, to some extent,

the study region is a large, fairly uniform mixing vessel with a relatively long

characteristic residence time (on the average, greater than I day). The significant

residence time implies that daily carryover and background concentrations are

substantial. Shorter-term pulses are added to this background from the upwind basins (or

local sources) to produce a daily maximum. These pulses and some of the background that

day, in turn, become part of the next day's background.

Other aspects of this report that support this view are as follows: (I) the consistent, but

weak, average diurnal variations of aerosol concentrations (Section 6.2); (2) the lack of a

correlation between Ni/Pb ratios and current-day wind direction (Section 8.2.1); (3) the low

spatial variability in extinction budgets and Ni/Pb ratio (Sections 7.3.1 and 8.2.4); and (4)

the fact that worst-case conditions at the receptors are more dominated by the nearest air

basins than are average conditions (Section 8.2.4).

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Appendix A

PRECISION ESTIMATES FOR RESOLVE DATA SETS

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The purpose of this appendix is to estimate the precision of various RESOLVE data sets.

The discussion is organized according to four types of data: mass/absorption samples, carbon

measurements, PIXE/XRF data, and nephelometer measurements.

In general, precision refers to the reproducibility of measurements ithe case of random

error as opposed to the case of systematic error). Here, precision is defined as one standard

error of random deviation away from the stable value (the value with no imprecision). We

can estimate from a series of independent, simultaneous, duplicate measurements as the

standard deviation of the difference between the two duplicates divided by the square root of

two, that is

(X - X )2 (A.1)

Precision-

-2 N

The square root of 2 factor is needed because the standard error of the difference between

the duplicates should be a V2 greater than the standard error of either measurement

relative to the stable value.

A-1 MASS AND ABSORPTION DATA

The duplicate fine samples taken throughout RESOLVE should represent all three

fundamental sources of error: mass (or absorption) determination, flow rate measurement,

and blank uncertainty. Accordingly, the overall precision should be completely

characterized by the errors observed in comparing the alternative fine samples. In this

section we use the duplicate 2x4 samples to estimate the precision of the 2x4 sampler data

for mass and absorption, including the fine, coarse, and total mass fractions.

Figures A. 1 and A.2 illustrate the 2783 duplicate samples for fine mass and the 2333

duplicate samples for fine absorption, respectively. The relevant statistics are presented in

Table Al. From Table A.1, it is apparent that the average precision of an individual

RESOLVE fine mass sample is .73 pg/m 3 (7.4%), while the average precision of an

individual fine absorption sample is .45 Mm- 1 (7.5%)

Table A.2 shows how precision varies with overall loading. Approximate formulas for

the absolute precision of an individual sample as a function of mass or absorption can be

derived from Table A.2:

FP: Precision = .15 + .082*FP.84 (pg/m3 ) (A.2a)

ABFL: Precision = .24 + .034*ABF (Mm-') (A.2b)

Note that the absorption formula is linear, while the fine mass formula is slightly

nonlinear.

Table A.2 and the formulas given above should also characterize the precision of the

measurements for total mass and total absorption. At the average TP level of 19.5 lpg/m 3 ,

the formula indicates that the precision of total mass is 1.0 Vg/m 3 (5. 1%). Similarly, at the

average ABT level of 7.3 Mm-l, the precision for total absorption is .49 Mm-1 (6.7%).

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53.81"

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.31 5.78 11.23 16.69 22.15 27.61 33.07 38.52 43.98 49.44 54.90

FINE MASS

FIGURE A.1 Scatterplot of Simultaneous Fine Mass Measurements

From the Two Alternate Channels of the RESOLVE 2x4 Sampler.

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NWC TP 6869

17.37

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.10 1.87 3.64 5.41 7.19 8.96 10.73 12.50 14.27 16.04 17.82

FINE ABSORPTION

FIGURE A.2 Scatterplot of Simultaneous Fine Absorption

Measurements From the Two Alternate Channels of the RESOLVE 2x4

Sampler.

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TABLE A. 1. Statistics from Duplicate 2x4 Samples for Mass and Absorption.

Fine mass Fine absorption

Sample size 2783 2333

Mean concentration ( ± standard deviation) 9 93 t 6 2 pg/m 3 6 00 ± 2.8 Mm-'

R (R2) for duplicate samples 986 (973) (981) (962)

Precision standard error* 73 pg/m 3 (7 4%) 45 Mm'1 (7 5%)

The precision standard error is defined as o/-,/ vvhere o is the standard deviation of

the difference between the two duplicates.

TABLE A.2. Variation of Precision with Overall Loadings.

Fine mass Fine absorption

Concentration. Precision, Level, Precision,

Pg/m3 ipg/mi (%) Mm-i Mm-1 (%)

3 3 36 (10.9) 2 7 33 (12 2)

7 4 59 (80) 5 0 41 (82)

109 86 (7 8) 66 46 (6ý9)

18 1 96 (5.3) 96 57 (5.9)

Note: The results in Table A 2 and Equation 2 pertain to individual fine-particle samples. The fine

mass and fine absorption data actually used in the RESOLVE data analysis represent averages of

two samples, so that uncertainty is decreased by a factor of v-from the values listed above.

Actually, the RESOLVE data analysis uses averages of the two fine samples for mass and

absorption. Thus, for fine mass and fine absorption, the error of the data in the analysis is

decreased b a factor of V"2. The precision of the data in the RESOLVE analysis is thus .73

pg/rm3 V"= 2- 52 pg/m 3 (5.2%) for fine mass and .45 Mm-1 IV2 = .32 Mm- 1 (5.3%) for

fine absorption.

Coarse mass and coarse absorption are obtained by subtraction, so the precision is just

the root mean square of the fine and total errors. At the average coarse mass of 9.6 pg/M3 ,

the coarse mass precision is N/.522 + 1.02 = 1.13 pg/rm3 (1Z%). At the average coarse

absorption of 1.3 Mm- 1 , the coarse absorption precision is V.322 + .492 = .59 Mm-I (45%).

To summarize briefly, the average precision of the data for the RESOLVE analysis is as

follows: FP (5.2%), TP (5.1%), CP (12%), ABF (5.3%), ABT (6.7%), and ABC (45%). The

formulas given can be used to estimate the precision of individual samples at various overall

loadings.

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A.2 ORGANIC AND ELEMENTAL CARBON

This section summarizes the results of the GGC duplicate carbon analyses and derives

overall precision estimates based on those results. Figures A.3 and A.4 illustrate the 68

duplicate analyses for organic carbon and elemental carbon, respectively. The relevant

statistics are presented in Table A.3. From Table A.3 we see that the precision of the GGC

organic carbon analysis is. 13 pg/m3 (4%), while the precision of the GGC elemental carbon

is .14 pg/m 3 (19%).

The above error figures for analysis do not represent total errors for organic and

elemental carbon concentrations. To derive the precision for the concentrations, we must

add the errors for the blank values and the flow rates. From a RESOLVE memorandum on

carbon blanks (see Section A.5), the blank uncertainty is about. 15 Pg/im 3 for organic carbon

and .1 pg/m3 for elemental carbon. The uncertainty in blank adjusted carbon, obtained by

subtraction, equals the root mean square of the analysis and blank uncertainties,

v/.132 .152 = .20 pg/m3 for organic carbon, and .142 + .12 = .17 pg/m3 for elemental

carbon. The flow rate error is about 5% (Reference 76). Because the flow rate enters as a

division, the total relative uncertainties, v'.202/3.162 + 705 2 ± 8% for organic carbon and

V.17 2/. 7 2 2 +- .052 = ± 24% for elemental carbon.

How the precision varies with overall loading was investigated by sorting the data

according to quartiles. Table A.4 presents the results. The absolute error decreases with

reduced loading, but not in direct proportion, so the percentage error increases at lower

loadings. In fact, the generally low concentration of elemental carbon in the RESOLVE

samples is a basic cause of the relatively high percentage error for elemental carbon

analysis.

A.3 XRF AND PIXE ELEMENTAL CARBON

As was the case with the carbon measurements, the total uncertainty in the elemental

data represents the compounded errors in blank values, flow rates, and laboratory analysis.

The uncertainty in the blank value is negligible compared to the other two sources of error

(Reference 77). The flow rate precision is ± 5% (Reference 78). For the XRF data, the

precision of the laboratory analysis was estimated by using the results of 18 duplicate

analyses of RESOLVE filters. For the PIXE data, Flocchini (Reference 75) provided

estimates of the analytical precision for various elements.

Table A.5 summarizes the total uncertainty for elements of greatest interest in the

RESOLVE program. The middle column of the table denotes the analytical technique (XRF

versus PIXE) that provides the greatest percent of data above detection threshold. This is

the analytical technique that was chosen for the final RESOLVE data set whenever both

XRF and PIXE data are available for an element.

Table A.5 shows that, with the exception of vanadium, most of the random uncertainties

in the elemental data range from about 5 to 15%. Several of the elements (S, Ca, Fe, K. and

Ni) have such low analytical uncertainties that the total imprecision is nearly equal to the

volume (flow rate) uncertainty.

[.21
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P.;

9+

8+

A

7+

I-

-6+

-- S

= s AA

2+

o~ ~ A,

,..l,

P44

Q , AB

A SB 

c i C

0+ -

B

0) 1 2 4 5 6 71 3 10 .

ORGANIC CARBON (,ug/m3)

FIGURE A.3. Duplicate Organic Carbon Analyses..

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4.5+

'-

4.0 +

3.5 +

3.0 +

L2.5 +

I-..

a. I

z2.0 +

S86 1.0 +

S SA

0 .0 +6

0.0 0.6 1.2 1 .8 .2.-4 3.0 -3 . Z 4..2

ELEMENTAL CARBON (Uglm3)

FIGU°RE A.4. Duplicate Elemental Carbon Analyses.

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TABLE A.3. Statistics from GGC Duplicate Carbon Analyses.

Organic carbon Elemental carbon

Sample size 68 68

Mean concentration (_ standard deviation) 3.16 t 1 90g Ig/m3 72 ± 59 Vgim 3

R (R2) for duplicate analysis 995(991) 947 (898)

Standard error of analysis'(%) 127 pg/M3 (4.0%) 136 pg/m3 (18 9%)

" The standard error of the analysis is defined as o/V-,where o is the root mean square error of the

difference between the two duplcates.

TABLE A.4. Precision of Carbon Concentrations as a Function of Loading.

Organic carbon Elemental carbon

Average concentration, Precision absolute, Average concentration Precision absolute,

gg/mr3 pg/m3 (%) gg/m 3 Vg/m 3 (%)

1st quartile 1.24 17 (14) 1st quartile 24 13 (54)

2nd quartile 2.18 22 (10) 2nd quartile .46 17 (38)

3rd quartile 3.47 28 (8) 3rd quartile 77 18 (24)

4th quartile 5.78 35 (6) 4th quartile 1 40 18 (13)

TABLE A.5. Precision of RESOLVE Elemental Data.

Element Analysis method Precision, t %

Sulfates S XRF 5

Soil elements Si PIXE 10

Al PIXE 12

Ca XRF 6

Fe XRF 5

Mn XRF 11

Mg PIXE 12

K XRF 5

Others Pb XRF 8

Ni XRF 6

V XRF 25

Cl PIXE 8

Na PIXE 15

Zn XRF 16

8r XRF 7

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A.4 NEPHELOMETER DATA

In the nephelometer measurements of particle scattering (Bscat), two sources of

imprecision exist-zero uncertainty and span uncertainty. The zero uncertainty is additive,

while the span uncertainty is multiplicative. Results from RESOLVE special studies

(Reference 79) indicate that, for hourly Bscat, the zero uncertainty is ± 1.5 Mm- 1 , and the

span uncertainty is ± 4.1%. Because 24-hour averages involve 8 independent zero and span

calibrations, the uncertainties for 24-hour averages are less by a factor of V-8.

Based on the above information, the precision of hourly and 24-hour nephelometer data

can be expressed in units of Mm-I as follows:

Hourly: PI = V/1.5 + .041lBscat 2 (A.3a)

24-hour: P24 = P//'' (A.3b)

At the average RESOLVE Bscat level of 33 Mm-1, the hourly precision is 2.0 Mm- 1 (6.1%),

while the 24-hour precision is 0.7 Mm-1 (2.2%).

A.5 RESOLVE MEMO ON CARBON BLANKS (Reference80)

As part of the RESOLVE monitoring program, quartz filters were exposed on a weekly

basis to represent field blanks for the carbon analyses. Of these weekly field blanks, 151

filters-scattered among the monitoring sites and seasons-were analyzed for organic and

elemental carbon. The purpose of this memorandum is to investigate the blank values

statistically and to derive blank adjustment factors for the RESOLVE carbon data.

Blank values should be independent of the actual sampler flow rates. The blank values

physically represent mass per filter or mass per filter area. To allow more insightful

interpretation, however, we will present the blank data in units of pig/m 3 by normalizing all

values to a standard daily flow of 32 m3 .

Averaged over all sites and seasons, the mean blank values for organic carbon and

elemental carbon are 1.13 pg/m 3 and .27 p1g/m 3 , respectively. The question that

immediately arises is do the blank values depend on the site and season?. Figure A.5 shows

a plot of the seasonal variations (averaged for all sites) as well as a plot of the site variations

(averaged for all seasons).* Obviously, a pronounced, statistically significant seasonal

pattern exists for organic carbon blanks. The seasonal variations for elemental carbon as

well as the site variations for both organic and elemental carbon are of marginal statistical

significance (many t-scores on the order of 1.0 to 1.5).

It seems reasonable that both seasonal and site variations should be included in the

specification of carbon blanks.

One possible method to include both seasonal and site variations in the specification of

carbon blanks is to use the average blank values by site and by seasonal quarter, as shown

in Table A.6. However, the individual quarterly averages contain large statistical

* Note that, in the production of Figure A.5, biases introduced by different sample sizes for various seasons and

sites were eliminated. For example, the seasonal patterns were computed by first averaging within each site and

then averaging the 7 site values each season. Also, the site patterns were determined by first averaging within each

season and then averaging the 4 seasons for each site.

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1.5

oORGA IC CRBON

0..

EL EME RJNTAL CARBON

.' -I !

CALNDA QUARIGSTER

.0.5

i

" E OERGNAL CARBON

-, I

ELRGENIC CARBON

! i

.33

~~126

• m mlll l l i l I I l l l / l l I 1
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TABLE A.6. Average Carbon Blank Values by Site and Quarter.

i All values correspond to a standard flow of 32 cubic meters per day.

Organic carbon (pg/m 3

), Elemental carbon (pg/m 3

),

mean ± standard error of mean mean - standard error of mean

(sample size) (sample size)

Q1 Q2 Q3 Q4 QI Q2 Q3 Q4

EDW .72 ± 10 1,15-±08 1 36_±.14 122_±,10 EDW 13±07 35±.09 47-± 10 .37± 02 (3) 011) (8) (4) () (1 1) (8) (4)

CHL 80±.12 1 12±,14 1 15±.05 .82 ±.11 CHL 17±09 21± 07 18± 06 26-±07

(4) (7) (9) (4) (4) (7) (9) (4)

RAW 1 12-±.11 .95-±.12 1 20 ±02 1 23-±.06 RAW 2 07 26±,06 .19 ± 05 47-± 12

(4) (5) (5) (4) (4) (5) (5) (4)

FOR 70± 04 1 39±.10 102± 05 .90± 04 FOR 23±07 23±08 05-±.07 28± 06

(5) (5) (7) (4) (5) (5) (7) (4)

TEH 78 ±.07 1.39_±.13 1 53-±28 84±.10 TER .35±06 30± 08 32-± 10 .35± 03

(4) (6) (6) (4) (4) (6) (6) (4)

SOL 67-±.06 1.69 ±.22 1.71 ±.28 95±08 SOL 26±08 16± 03 23± 07 26± 07

(4) (7) (6) (4) (4) (7) (6) (4)

CAP 59_±.11 1.24± 21 1.26±.28 79-±06 CAP 36±03 .18-±.03 35±05 .31 ± 04

(4) (7) (6) (4) (4) (7) (6) (4)

aberrations because of the small sample sizes (e.g., third quarter for elemental carbon at

Fort Irwin NTC) and substantial discontinuities from quarter to quarter (e.g., organic

carbon at Soledad going from .67 11g/m 3 to 1.69 pg/m3 from 31 March to 1 April).

A second possibility involves seeking more general patterns in the data. For example,

perhaps all sites have similar seasonal patterns so that the yearly mean at each site should

be adjusted by the same general set of seasonal factors. This possibility, however, does not

appear defensible because the seasonal patterns are, in actuality, not that constant from site

to site (see Figure A.6).

A third possibility (the author's recommended possibility) is the scheme presented in

Table A.7. Here, to increase the sample size yet include the basic summer/winter dichotomy

for organics shown in Figure A.5, the blanks have been averaged over the entire winter 6

months and the entire summer 6 months. To avoid large discontinuities, transition months

are taken as the average of the summer and winter values.

A separate question involves the issue as to whether the carbon blank values changed

significantly at some time during the monitoring program. This question can be addressed

by comparing the average blank values for the summers of 1984 and 1985 (Quarters 2 and

3), the first 2 and last 2 quarters of sampling. These values (including standard errors of the

means) are as follows:

Summer 1984 Summer 1985

Organic carbon blanks 1.33 ±.06 1.18 ±.07

Elemental carbon blanks .25 ±.03 .27 t.04

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2.5.

2.0-

I I I

I..0

EOW CHL RAW FOýR TIEH SOL CAP

NOTE: Uncertainty bars represent standard S~errrors of the estimated ratios.

-l

S1.5-

I-'- ~j1.0

o .5

0

I-6

EDW CHI RAW FOR TEN SOL CAP

FIGUREA.6. Summer[Winter Ratios of Carbon Blank

Values for the RESOLVE Sites.

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TABLE A.7. Average Carbon Blank Values by Site and Seasons.

(All values correspond to a standard flow of 32 cubic meters per day.)

Organic carbon (gg/m 3 ), Elemental carbon (.gim 3

),

Winter Summer Transition Winter Summer Transition

Nov-Feb May-Aug Mar/Apr/Sep/Oct Nov-Feb May-Aug Mar/Apr/Sep/Oct

EDW 97 1.25 111 EDW .25 .41 33

CHL 81 1 14 98 CHL .22 .19 20

RAW 1 18 108 1.13 RAW 38 22 30

FOR 80 1 20 1 00 FOR .25 .16 21

TEH 81 1 46 1 14 TEH .35 .31 33

SOL 81 1 70 1 26 SOL 26 .19 23

CAP .69 1 25 .97 CAP 34 .26 30

No differences are apparent in these numbers.

A final question is what is the uncertainty in the blank values that should be used in

assessing the overall uncertainty of carbon concentrations. Apparently, no definitive

answer is given by the data. The blank uncertainties are surely not as high as the standard

deviations of individual blanks (about 4 pg/m 3 for organic carbon and .2 pg/m3 for elemental

carbon) because these individual data points contain the carbon analysis uncertainties that

are treated separately (about .25 pg/m3 , see Reference 80). On the other hand, the blank

uncertainties may be greater than the standard errors of the site/quarter means given in

Table A.6 because there will be some real week-to-week variations in actual blank

exposures. The uncertainty in carbon blanks, in the end, is a judgment call. The author's

judgment is that the uncertainty approximately corresponds to a .2 pg/m3 standard error for

organic carbon and a. 1 pg/m3 standard error for elemental carbon.

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Appendix B

ESTIMATION OF EXTINCTION CONTRIBUTIONS FROM

MAJOR SOURCE CATEGORIES

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This appendix presents calculations to estimate the most important source categories

contributing to man-made visibility impairment in the RESOLVE study region. The

calculations pertain to an average of the three RESOLVE receptor sites (i.e., to the study

region as a composite) and to annual mean conditions (rather than worst-case days).

The procedure for estimating source category contributions to man-made light

extinction involves three parts. First, we allocate man-made extinction in the RESOLVE

study region among three source areas-San Joaquin Valley, Los Angeles basin, and local

desert region. Second, for each of these source areas, man-made extinction contributions are

apportioned among the five aerosol components: organics, sulfates, elemental carbon, soil

dust, and nitrates plus NO2 . Third, for each source area/aerosol type, the corresponding

emissions are allocated among the source categories.

B.1 SOURCE AREA CONTRIBUTIONS TO ANTHROPOGENIC EXTINCTION

In Section 8.3 we concluded that, on the average, approximately one-half of nonRayleigh extinction represents anthropogenic transport from the San Joquin Valley.

Section 8.1 includes the estimate that, on the average, three-fourths of non-Rayleigh

extinction is anthropogenic. Combining these results, we conclude that about two-thirds of

anthropogenic extinction in the RESOLVE region comes from the San Joaquin Valley.

The remaining one-third of anthropogenic extinction is assumed to come from local

desert sources and the Los Angeles basin. Although our analyses indicate that both of these

sources are significant, we do not have an objective method of apportionment for them. We

assume that the remaining one-third of anthropogenic extinction arises equally from the

local sources and the Los Angeles basin. Accordingly, the overall, regional source area

allocation for anthropogenic extinction is as follows: San Joaquin Valley = 2/3, local desert

sources = 1/6, and Los Angeles basin = 1/6.

B.2 ANTHROPOGENIC EXTINCTION COMPONENTS

The average non-Rayleigh extinction budget for the RESOLVE region is as follows (see

Table 21): organics 26%, sulfates 24%, elemental carbon 19%, soil dust 18%, and nitrates

plus NO 2 13%, with a total non-Rayleigh extinction of 35 Mm-1 . It is argued in Section 8.1

that 85% of sulfates/elemental-carbon/nitrates-plus-N02, 80% of organics, and 50% of soil

dust are anthropogenic. When we combine these two results, the budget for anthropogenic

extinction is organics 27%, sulfates 26%, elemental carbon 21%, soil dust 12%, and nitrates

plus NO 2 14%, with a total anthropogenic extinction of 27 Mm- 1 .

For the purpose of this appendix, we need extinction budgets for anthropogenic

contributions from each of the three source areas individually. A reasonable estimate of

these extinction budgets can be made by noting three constraints:

1. The weighted sum of the three source areas must equal the total anthropogenic

extinction budget listed at the end of the first paragraph in Section B.2.

2. The coarse soil dust component should be assigned predominantly to local sources

because of the relatively short atmospheric life of coarse particles.

3. With minor exceptions, the budget for transported San Joaquin extinction should

resemble that of transported Los Angeles extinction because of similar "five-component"

composition for the two source regions (see Table 7). Table B.1 presents source-area

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anthropogenic extinction budgets that meet these constraints. All values in the table are

stated to the nearest 5 or 10% to more appropriately reflect uncertainties.

TABLE B.I. Extinction Budgets for Anthropogenic

Contributions in the RESOLVE Region.

San Joaquin Valleya Los Angeles basinad Local desert sources' RESOLVE region

Organics 30% 25% 20%c 27%

Sulfates 30% 30% 10% 26%

Elemental carbon 20%d 30%c 10% 21%

Soil dust 5%e 0%d 50% 12%

Nitrates plus NO2 15% 15% 10% 14%

SWith the minor exceptions discussed below, budgets for these two source areas should be similar

because of similar "five-component" Composition at the pass sites Because together they dominate

the total, they each should be similar to the RESOLVE region anthropogenic budget (except for soil

dust).

b Coarse soil dust is basically all assigned to the local source category

I Emission and ambient data for the desert suggest the possibility of relatively high organics

compared to sulfates and elemental carbon.

d Tehachapi pass shows relatively less elemental carbon than the Los Angeles passes.

e Tehachapi pass shows relatively more soil dust than the Los Angeles passes Also, transport

distances are shorter from Tehachapi. a factor that may be more critical for soil com ponents.

B.3 ANTHROPOGENIC EMISSION INVENTORIES

The third part of the engineering calculation is to assign the anthropogenic components

(by source area) to emission source categories. For each source area, man-made sulfates,

elemental carbon, and soil dust will be allocated according to anthropogenic inventories for

SOx, elemental carbon, and soil dust, respectively. Based on the assumption that two-thirds

of man-made organics are primary (Reference 65), two-thirds of organics will be allocated by

primary organic aerosol emissions, with one-third apportioned by reactive organic gas

(ROG) emissions.

It is reasonable to allocate NO 2 according to NOx emissions (Reference 81), but the

situation for nitrates is problematical. Fine aerosol nitrates predominately consist of

ammonium nitrate that forms in the atmosphere from gaseous nitric acid and ammonia.

Ammonia availability, rather than NO, availability, may be the controlling factor for

aerosol nitrates (References 51 and 82). Nevertheless, because of the lack of ammonia

emission inventories, we will apportion nitrates according to NO, emissions.

An initial set of rough calculations demonstrates that only three source categories

contribute significantly to anthropogenic extinction, with any other source category

individually accounting for at most about 5% of man-made extinction in the region as a

whole. The three important source categories are diesel vehicles/equipment (highway

trucks and off-road mobile equipment, but not planes, ships, or trains), the petroleum

industry (mostly from the San Joaquin Valley), and gasoline-powered vehicles (including

exhaust and suspended dust). The calculations here are restricted to these three source

categories.

Table B.2 presents the required emission data. As indicated by the footnotes to the table,

three critical references exist: a 1983 emission inventory for California (Reference 39), a

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1982 carbon-particle emission inventory for Los Angeles (Reference 67), and an early/mid1980s particle inventory for the RESOLVE region (Reference 68). The SO, emissions

inventory for the San Joaquin Valley was updated to 1986 to include the large recent

decreases in petroleum industry SO, (References 72 and 73). (See footnote in Section 8.4.)

TABLE B.2. Percent of Emissions from Major Source Categories.

ROgaGcSO. Organic Elemental NO, Soil

Source particles carbon dust

category L (Siv +

Siva LABlLocala S.JVb LAS Localu SJVa LAB! Locald ;JVb LABc .ocaid SJva LABa Local, Local)d

Diesel

vehicles

equipment 4 3 7 14 5 9 22 20 22 66 57 43 33 23 27 5

Petroleum

industry 48 8 4 4 1 0 47 27 0 1 0 0 31 9 0 0

Gasoline

vehicles 19 46 29 5 10 3 5 18 3 2 10 1 22 47 13 50

a Based on the 1983 inventory by CARS (Reference 39), the SO, inventory for the San Joaquin Valley

has been updated to 1986 using data from Cleary (Reference 72) and Ouimette (Reference 73) T

o

separate out diesel tignt/medium-duty vehicles, it is assumed that diesel vehicles account for 10% of

total light/medium-duty SO,, 5% of total light/medium duty NO., and a negligible amount of total

light/medium-duty ROG (Reference 83).

b Based on applying the fine-organic carbon and fine-elemental carbon fractions of Gray (Reference

67) to all source categories in the PM-10 inventory by CARB (Reference 39).

c Based on the 1982 inventory by Gray (Reference 67).

d Based on the early/mid-1980s inventory by Nero (Reference 68). Vehicular emissions are split

between gasoline and diesel using data in CARD (Reference 39) and Gray (Reference 67).

B.4 RESULTS AND UNCERTAINTIES

The anthropogenic contribution of each source category via each aerosol component

from each source area is obtained by multiplying the factors of Sections B.1, B.2, and B.3.

For example, diesel vehicles/equipment via elemental carbon from the San Joaquin Valley

contribute 9% of man-made extinction-67% (contribution of San Joaquin Valley to manmade extinction) times 20% (portion of San Joaquin Valley man-made extinction due to

elemental carbon) times 66% (portion of San Joaquin elemental carbon from diesels). Table

B.3 presents the composite results for each source category, with a breakdown according to

aerosol components.

Table B.3 indicates that the three source categories together account for about 60% of

anthropogenic extinction. The diesel vehicle/equipment category alone accounts for about

one-quarter of man-made extinction.

One method of estimating uncertainties for the total contribution from each source

category is to apply simple error propagation to potential errors in all three sets of factors

discussed in Sections B.1, B.2, and B.3. However, this method would not be strictly correct

because some of the errors are mutually dependent. Moreover, it would not take advantage

of an important simplification that is usually possible in error propagation. This

simplification is that, because errors are added according to root mean sums of squares, one

or two sources of error usually dominate the total uncertainty. This phenomen is illustrated

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in Appendix C, where only one source of error was found to dominate for four of the five

extinction budget categories, and only two sources of error were significant in the fifth

category.

TABLE B-3. Man-Made Extinction Allocated Among Source Categories.

SoeTotal contribution Contribution via each aerosol component Source ~ mnmd

category extinction Organics Sulfates EC Soil dust Nitrates + NO2

Diesel vehiclesequipment 25.7% 2,7% 5_8% 12.3% 6% 4 3%

Petroleum industry 18 0% 3 8% 10.8% 1% 0 3 3%

Gasoline vehicles 15 7% 3 5% 2.0% 8% 5 8% 3 611,

A review of potential errors for the diesel vehicles/equipment category indicates that the

predominant source of error is in the largest component, elemental carbon. For this

component, errors in the source area contributions (Section B.1) are not important because

elemental carbon from diesels has similar significance in all three source areas (so that the

result is not very sensitive to source area allocation). The relative error for elemental carbon

in the anthropogenic extinction budget allocation (Section B.2) should be nearly the same as

the relative error for elemental carbon in the extinction budget (t 32%, see Appendix C). For

all three source areas, diesels account for about one-half of elemental carbon emissions

(Section B.3); the relative error in this third factor is judged to be ±35% based on experience

with emission inventories for elemental carbon (References 67 and 83). Accordingly, the

total relative error for the elemental carbon category is V-32%2 '+35%2 = t47%, and the

absolute error on the diesel elemental carbon component is ±47% X 12.3% = t5.8%.

Arbitrarily assuming a ±50% relative error for the other diesel components, and assuming

the errors can be combined by root mean square, gives an overall absolute error of ±7% for

the diesel category.

For the petroleum industry, the largest error is in the sulfate contribution from the San

Joaquin Valley (the estimated contribution for this category is 9.4%). The relative error in

the first factor, source area contribution (Section B.1), is judged to be =25% based on a

review of Chapter 8. The relative error in the sulfate component of the extinction budget

(Section B.2) is ±21% (see Appendix C). From experience with emission inventories, the

relative error in the percentage contribution by the petroleum industry is judged to be

± 30%. Propagating these errors yields a relative error of ±44% for the San Joaquin Valley

petroleum SO, category, or an absolute error of 4.2%. Arbitrarily assuming a ± 50% relative

error for the organics and NOx categories brings the total root mean square absolute error

up to only t_5%.

The contribution from gasoline vehicles is generally insensitive to errors in the source

area allocation and extinction budgets because gasoline vehicles are significant in all three

source areas and in nearly all components of the extinction budget. Also, emissions data are

fairly well documented for motor vehicles. The one major exception is in the soil dust

category. Uncertainty exists regarding the portion of soil dust that is anthropogenic and the

fraction of anthropogenic dust that is caused by motor vehicles. Allowing for a ±50%

relative error in both these fractions yields an overall relative error of ±71% and an

absolute error of ±-71% X 5.8% = ± 4. 1%. Assuming a relative error of ± 40% for the other

four gasoline vehicle components, and assuming root mean square propagation, we arrive at

a total absolute error of ± 5%.

In summary, the contributions of the three source categories to average, regional

anthropogenic extinction are as follows:

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Diesel vehicles/equipment 26% ± 7%

Petroleum industry 18% ± 5%

Gasoline vehicles 16% ± 5%

To obtain the contributions to average non-Rayleigh extinction (including natural aerosols),

these results should be multiplied by 0.77. To obtain the contributions to average total

extinction (including natural aerosols and Rayleigh scatter), the above results should be

multiplied by 0.59.

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Appendix C

UNCERTAINTY ANALYSIS FOR THE EXTINCTION BUDGET

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The purpose of this appendix is to estimate the uncertainty in the RESOLVE

extinction budget. Specifically, error propagation methods are used to determine the

standard errors in the average percentage contributions to non-Rayleigh extinction for five

components: organics (ORG = 1.5-OC), sulfates (SUL = 4.12.S), elemental carbon

(ELC), soil (SOIL), and nitrates (NO 3 ) plus NO2 . The error propagation methods are applied

to judgmental estimates of uncertainty that encompass instrument imprecisions,

climatological variations, potential measurement inaccuracies, and potential modeling

errors (e.g., in extinction efficiencies).

Estimates of uncertainty are themselves extremely uncertain. A minimal estimate of

uncertainty is supplied by the analytical uncertainty (imprecision) that is determined by

replicate measurements of the same observable. The temporal representativeness of the

samples contributes an additional uncertainty to average values derived from individual

measurements. Further uncertainty is added when measurement and modeling

assumptions are not entirely met. The uncertainties here reflect an aggregate of these

individual causes of uncertainty. Quantitative values for imprecision were drawn from

analytical error propagation specific to RESOLVE measurements and from measurement

methods tests performed in RESOLVE and other studies. A year-to-year climatological

variability in average values of ± 10% was applied to all data based on previous analysis of

long-term air quality data.

These estimates have been justified, and modified, in a peer review process that

examined the rationale for their selection. A conservative approach was taken, so, if

anything, these uncertainties probably overestimate the true uncertainty. Note that all

units are pg/m3 , Mm- 1 , and m2/g and that all uncertainties pertain to long-term averages.

For total non-Rayleigh extinction, we have BNR = BNO2 + Bsp + Bap. The three terms

and the associated uncertainties are as follows:

1. NO 2 absorption, BNO, = (4% ± 1.5%).Bscat. The uncertainty of B.No4 as a

percentage of nephelometer scattering (Bscat) represents judgment of the net effects of

instrument accuracy, instrument precision (minor), and climatological representativeness

for various data sets. For this formula, Bscat = 25.6 ± 2.5 Mm- 1 , where the uncertainty

here represents judgment of climatological representativeness (inflated well above simple

o/-'Nbecause of nonindependent sampling).

2. Particle scattering, Bsp = 28.2 ± 4.5 Mm- 1 .The significant uncertainties here are

climatological representativeness (3 Mm- 1) and potential error in our estimate as to

how the nephelometer must be corrected for missing coarse-particle scaLtering and water

(±3 Mm- 1).

3. Particle absorption, Bap = 6.2 ± 2 Mm-1. The significant uncertainties here are the

accuracy correction factor for Teflon absorption (± 30%) and climatology (± 10%).

Scattering and absorption by particles are allocated among aerosol constituents as described

in Section 7.2. The uncertainties are quantified in the following paragraphs.

For organics, the contribution to extinction is BORG = (3.75 ± 1.25)IORGIl. 13 + (4% _

3%) Bspc. The first term represents fine-particle scattering. The uncertainty in the finescattering efficiency (3.75 ± 1.25 m2/g) is meant to represent all errors in the scattering

efficiency appropriate to (ORGI, including errors produced by sampling artifacts. The value

of 1.13 is a correction factor that makes fine-particle scattering be exactly apportioned (see

Section 7.2.3). The error in the concentration of fine organics, [ORGJ = 2.7 ± .1 pg/m 3 ,

should represent the error in the relative contribution of organics to fine mass. This error is

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extremely small because of the large sample size and because accuracy considerations have

been obviated by considering the scattering efficiency as appropriate to wlatever [ORGI

represents. The second term is coarse-particle scattering, with 4% ± 3% assumed to be from

organics. Total coarse scattering, Bspc = 5.1 ± 3 Mm-1, has a substantial uncertainty

because of the large potential error in our estimate of this parameter.

For sulfates, we again have fine and coarse scattering, BSUL = (4.25 ± 1)[SULJ/1.13 +

(3% ± 1.5%)Bspc. The uncertainty in the fine-scattering efficiency (4.25 ± I m2/g) is meant

to represent all errors in the scattering efficiency appropriate to SO41. The error in the

concentration of fine sulfates, [SUL] = 2.2 ± .1 lIgIm3 , again represents the error in the

relative contribution of sulfates to fine mass. The second term is scattering by coarse

sulfates, 3% ± 1.5% of total coarse scattering.

For elemental carbon, BELC = (1.5 ± 2)IELCl/1.13 + (3% ± 3%) Bspc + Bapf + (80% ±

10%)Bapc. The first term represents fine scattering, with [ELCI = .5 ± .04 Pg/M3 . The

second term is coarse-particle scattering. The third term is fine-particle absorption, with

Bapf = 5.0 ± 1.5 Mm- 1 (see previous discussion for total absorption). The fourth term is

coarse-particle absorption, Bapc = 1.2 ± .5 Mm- 1 , with 80% ± 10% of this assumed to be

from elemental carbon.

For soil dust, BSOIL = (1.25 ± .5)[SOILI/1.13 + (85% ± 10%) Bspc + (20% t 10%) Bapc.

The three terms represent fine scattering ([SOIL] = 1.7 .2 lig/M3 ), coarse scattering, and

coarse absorption.

For nitrates, BNO3 - (4.25 ± 1.5)[NO 3 /I.13 + (5% ± 4%) Bspc. The first term, fine

scattering, includes a relatively high uncertainty in the scattering efficiency (± 1.5 m2/g)

because of the lack of a specific modeling analysis for nitrates. The error in the

concentration term, (NO 3] = 0.9 ± .3, is also large because of uncertainties as to the

climatological representativeness of the sparse winter sampling. The second term

represents coarse scattering.

Carrying out the uncertainty analysis using routine error propagation techniques leads

to the following conclusions. Average non-Rayleigh extinction is BNR = 35.4 ± 5 Mm-1,

with the dominant source of error being the uncertainty in the degree to which

nephelometer scattering needs to be increased to account for missing coarse-particle

scattering, and a secondary source of error being the uncertainty in the accuracy correction

for particle absorption. The light extinction budget, including one-standard-error

uncertainties, is

Organics 25% ± 9%

Sulfates 24% ± 5%

Elemental carbon 19% ± 6%

Soil dust 18% ± 8%

Nitrates plus NO 2 13% ± 5%

It is apparent that the errors for the species contributions range from 5 to 9% of total nonRayleigh extinction, with the greatest absolute errors being in the organics and soil-dust

categories.

The analysis further demonstrates that the error in the extinction budget is dominated

by only four types of uncertainty as follows:

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1. Uncertainties in the fine-scattering efficiencies for organics, sulfates, and nitrates

account for basically all of the error in the organic and sulfate categories, and about half of

the error in the nitrate category. (Note that potential errors introduced by sampling

artifacts have been included within the uncertainty of the scattering efficiencies.)

2. Uncertainty in the accuracy correction factor for Teflon absorption accounts for the

major part of the error in the elemental carbon category.

3. Uncertainty in our estimate of coarse-particle scattering dominates the error in the

soil-dust category.

4. Uncertainty in the concentration of nitrate particles contributes about half of the

error in the nitrate category.

With respect to the extinction budget, the uncertainties related to measurement

imprecision and climatological representativeness are quite negligible (except for the sparse

nitrate sampling) because of the large number of observations in the RESOLVE data set.

Furthermore, uncertainties related to the allocation of coarse scattering among the five

species as well as to the allocation of fine scattering and coarse absorption between

elemental carbon and soil dust are of minor importance because of the small magnitude of

most of these terms.

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Appendix D

THEORY FOR THE Ni/Pb TRACER MODEL

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This appendix presents a detailed discussion of the theory behind the Ni/Pb tracer

model. Essentially, the discussion here is an expanded, more mathematical version of

Section 8.2.3.

The elemental concentrations at a receptor can be expressed as linear combinations

from various sources as follows:

CNi = aNi.SJ sSJ + aNUf.SLAs + aNiIAc sLAc + aNi.HD SHD and

(D.1)

CPb = aPb.SjsSJ + apb,LAs SLAs + aPb,LAc SLAc + aPb.HD SHD where

CNi and cpb = elemental concentrations (mass of element per volume of air)

at the receptor.

sSj, sLAs, SLAc and sHD = source contributions (mass of primary aerosol per volume of

air) at the receptor from the San Joaquin Valley, the Los

Angeles basin via Soledad Pass, the Los Angeles basin via

Cajon Pass, and the high desert.

aij = content (mass of element per mass of primary aerosol) of

element i in the aerosol from sourcej.

The coefficients aiSj, aiLAs', and ai.LAc in Equation D. 1 are supplied by the measurements

in the passes on outflow days. The coefficients aiWD are available from measurements at the

receptor sites under conditions minimizing transport from the San Joaquin Valley and Los

Angeles basin. The concentrations ci are measured at the receptor. This leaves four

unknowns in the two equations, the source contributions sj.

D.A BOUNDING CONTRIBUTIONS

Although Equation D.1 is underdetermined, admitting an infinite number of solutions,

it yields useful bounds in the San Joaquin Valley contribution ssj because the San Joaquin

Valley is the only source of Ni/Pb ratios higher than those typical of the receptors. The

Ni/Pb ratio of a trial mixture can be lowered by various combinations of aerosols from

Soledad Pass, Cajon Pass, and the high desert, but it can be raised only by aerosol from the

San Joaquin Valley. It is hard to show that the nonnegative solution to Equation D.1

yielding the minimum value of ssj is that for which the non-SJV source with the highest

Ni/Pb ratio is the only other contributor. Similarly, the nonnegative solution to Equation

D. 1 yielding the maximum value of sSj is that for which the non-SJV source with the lowest

Ni/Pb ratio is the only other contributor. Figure 49 provides geometric insight into this

dependence of the calculated SJV contribution on other sources' Ni/Pb ratios.

For an example of the calculations involved we will bound the contribution of the San

Joaquin Valley to Edwards on 12 July 1985. This was an outflow day at all three passes,

with Ni/Pb ratios ranked Tehachapi, Edwards, Soledad, Cajon (see Table D. 1). The Ni/Pb

ratio attributable to local high desert sources can be assumed to be similar to the NuPb

ratios measured at Edwards AFB on other days when transport from the San Joaquin

Valley and Los Angeles basin was minimal. On two days (23 December 1984 and 16

January 1985), when no more than 4 hours of outflow had been through any of the three

passes during a 48-hour period, the average contents Ni/FM and Pb/FM were 86 and 6361

ppm(m), yielding an Ni/Pb ratio that is lower than either of the ratios for the Los Angeles

basin.

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TABLE D. 1 Estimated Contributions of the San Joaquin Valley and Los Anr-%'eF

Basin to Fine-Particle Mass Concentrations at Edwards AFB on 6 Joint Outflow Days.

Estimated contributions to Edwards AF8 fine mass

San Joaquin Valley Los Angeles basin

Input data Calculation

Lower Upper Lower Upper

bound bound bound bound

Source distributions and

receptor distributions S - A-C 39 47 0 45

Source distributions and

receptor averages S - (A-D)C 40 50 0 53

Source averages and

receptor averages S - (A)- 1 C 35 42 0 41

It follows that the lower bound for the contribution ssj is provided by the (unique)

solution to Equation D. 1 with SLAC = SHD = 0. Setting the contributions from Cajon and the

high desert to zero leaves two equations in two unknowns, easily solved for

aNij., pPb P.LAs N.i (D.2)

S (lower) =

aNi.As aPBa.& - pbLAs aNi '

The upper bound for ssj is provided by the solution to Equation D. 1 with SLAS = SLAc = 0.

Significant uncertainties exist in the Ni and Pb contents of the high desert contribution that

were not measured directly as those of the San Joaquin Valley and Los Angeles basin were.

The most conservative bound for sSj is obtained by assuming the Ni content aNijHD to be

zero, so that the San Joaquin Valley is the sole source of all Ni. The San Joaquin Valley

contribution is then simply

C~. (D.3)

s s,(upper) = -

The contribution of the San Joaquin Valley at Edwards AFB on 12 July 1985 is thus

estimated to have been between 49 and 55% of the observed fine-particle mass.

The contributions of the Los Angeles basin and high desert are only loosely constrained

by Equation D.1 because they are largely interchangeable. For example, attributing all of

the non-SJV aerosol at Edwards AFB on 12 July 1985 to transport through Soledad yields

the following estimates: sSj = 11.4 pg/m3 , SLAs + SLAc = 8.6 pg/m3 , and SilD = 0 pg/m3 .

Alternatively, attributing all of the non-SJV aerosol to transport through Cajon yields ssj

= 12.2 gg/m3 , SLAs + SLAc = 2.6 pg/m3 , and SHD = 0 pg/mr3 . Alternatively, attributing all of

the non-SJV aerosol to high desert aerosol of the type measured at Edwards AFB on the two

minimal outflow days yields ssj = 12.6 pg/m3 , SLAS + sLAc = 0 pg/m3 , and SHD = 0.7

pg/m3 . The estimates for the San Joaquin Valley contribution to fine-particle mass fall

within the relatively narrow bounds of 49 and 55% determined earlier, while those for the

Los Angeles basin range from 37% down to 0%. Even the sum of all primary contributions is

comparatively poorly determined, ranging between 86% down to 57% of the observed total.

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This last observation is consistent with the results of regression analysis that shows that Ni

and Pb together account for only 25% of the observed variance in fine mass at Edwards AFB.

D.2 AVERAGING CONTRIBUTIONS

We want to determine the San Joaquin Valley contribution at the receptors throughout

the year, not just for the few joint outflow days shown in Table D.1. Most receptor

measurements were made without concurrent characterization of the source basins.

However, sampling frequencies at the passes were about one-third those at the receptors,

and many of the pass measurements were compromised by backflow. The full set of receptor

measurements can be utilized only by extracting from the limited source measurements a

statistical characterization of source composition and its variability.

The equations giving rise to the bounds 2 and 3 can be expressed in matrix terms as C =

AS, where

C = cN., A = a vi.s,aNiX, and S = sS, (D.4)

c Pb a PbSJ ,a PbX SX

where the subscript X denotes a non-SJV source. The solution to Equation D.4 then has the

simple form

S = A-'C (D. 5)

We write daily values in terms of their deviation from the mean, the mean denoted by an

overbar

S = S+(S-h),A-' = A-I + (A-'-A-'),C= C+ (C--C)

The mean source contribution S can be written in terms of the means and covariance of A-,

and C as follows:

S = A-1C = (A-1 +(A-1-A- 1 ))(C+C-EC)) = (A- 1 )C+(A-1 -A-')(C-C) (D.6)

Equation D.6 shows that the mean contribution S of a source to a receptor can be

accurately estimated from mean source characteristicsA-I and mean source receptor

concentrations C only if the covariance (A-I - A-I) (C - C) between source characteristics

Ni/FM and Pb/FM of an upwind basin are controlled by the within-basin source mix and

gas-to-particle conversion, while the concentrations Ni and Pb at a receptor are controlled

by interbasin transport and dispersion. Only indirect linkages exist between these two sets

of factors so that there is reason to expect the covariance of source characteristics and

receptor concentrations to be small.

The only data available for calculating source-receptor covariance are those from the 6

joint outflow days of Table D.1, when both source basins were characterized concurrently

with measurements at Edwards AFB. The effects of the observed covariance on these days

are summarized in the top two rows of Table D.1, which compare the mean source

contributions calculated according to the exact Equation D.6 with those calculated

neglecting the covariance term. The simplest example is provided by the upper bound for

the San Joaquin Valley contribution, for which the matrix equation, Equation D.5, reduces

to the scalar equation, Equation D.3: ssj(upper) = aviSj-1c.v. The true mean is sSj(upper)

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= 6-1(0.001041-10.0067 + ... + 0.000320-10.0041) = 6.7 pg/m3 while the estimate derived

from mean values is 6-1(0.001041-' + ... + 0.000320-1)0.0042 = 7.0 pg/m3 . The difference

arises from a slight negative correlation (r = -0.26) between aN.vSj-'and c,vi. Ni

concentrations aL Edwards AFB tended to be a little higher than average when tne Ni

content of the San Joaquin Valley aerosol was higher than average. This correlation is

statistically insignificant, as are the others underlying the differences between rows one

and two. Six samples are too few to provide a reliable test of independence, however, and the

decision to neglect source-receptor covariance ultimately must rest on the a priori reasoning

outlined in the preceding paragraph.

Another point in Equation D.6 concerns the proper averaging of source characteristics.

One interpretation of the observed variability of Ni/FM and Pb/FM in the passes is that it

stems from measurement errors and local inhomogeneities, and represents random

deviations from true source compositions that are more or less constant. This interpretation

underlies the "effective variance" method of Watson, et al., (Reference 70) and leads to the

use of (A}-H, the inverse of the mean source composition matrix, in calculating source

contributions. An alternative interpretation is that the observed variability reflects real

variations in source composition, arising from variations in emissions and gas-to-particle

conversion. This interpretation leads to the use of (A-1), the mean of the inverse source

composition matrixes as in Equation D.6. The bottom two rows of Table D.1 compare the

source contributions calculated according to the two interpretations. The upper bound for

the San Joaquin Valley contribution again provides a simple illustration of the distinction.

The inverse mean San Joaquin Valley Ni content on the 6 joint outflow days is (aNi.sJ)-' =

0.000710-1 = 1408, while the mean inverse is (aNisj-1) = 6-1(0.001041-1 - ... +

0.000320-1) = 1685.

It seems possible that the bulk of the observed variability of Ni and Pb contents in the

passes represents real variations in source composition. Measurement precision was good,

and averaging over 24 hours of outflow should have minimized the impact of

inhomogeneities. Substantial correlations between passes, particularly for Pb content,

support the reality of source variability. (Note, for example, the uniformly low Ni and Pb

contents at all three passes on 12 July 1985, suggesting synoptic conditions favorable to gasto-particle conversion.) The estimates of average source contributions to follow will

accordingly be calculated by the formula

S = (A)1 (D.7)

To summarize the discussion in this subsection, use of Equation D.7 implies an

assumption that measured source variability is genuine, and uncorrelated with receptor

concentrations.

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Appendix E

CHEMICAL MASS BALANCE APPROACH TO SOURCE APPORTIONMENT

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Section 8.2 described a tracer solution to the CMB equations for determining the upper

and lower limits of contributions from the San Joaquin Valley to fine-particle

concentrations at RESOLVE receptor sites. This appendix reports the results of the same

conceptual Litodel applied with different solution methods and different data configurations.

The purpose of this independent application is to provide quality assurance for the results

reported in Section 8.2.4 and to provide quantitative estimates of the uncertainties of those

results.

E.A CHEMICAL MASS BALANCE MODELING

The CMB software developed for EPA (Reference 69) was used to apportion annual

average fine-particle mass concentrations at Edwards AFB, Fort Irwin NTC, and NWC to

regional sources. This software applies the effective variance solution (Reference 70) to a set

of chemical mass balance equations to calculate source contributions and their

uncertainties. The software provides several performance measures that indicate the

validity of each application. The most useful of these performance measures are the "source

uncertainty clusters" that identify source profiles that are v-ery -imilar to each nther and

cannot be resolved without large degrees of uncertainty. A separate CMB applications and

validation protocol (Reference 71) was followed to estimate the validity of the input data and

the compliance with model assumptions.

The fundamental assumptions of the CMB are

1. Compositions of source emissions, as perceived at the receptor, are

constant over the period of ambient and source sampling.

2. Chemical species concentrations add linearly.

3. All sources with a potential for significantly contributing to the receptor

have been identified and had their emissions characterized.

4. Source compositions are linearly independent of each other.

5. The number of sources is less than or equal to the number of pollutant

properties.

6. Measurement uncertainties are random, uncorrelated, and normally

distributed.

These as•amptions are never completely complied with in actual practice. Model testing

studies show that substantial deviations from them can be tolerated. The degree of this

tolerance is situation-specific, however, and some testing is required for each application of

the model. Assumption 4 is the most critical assumption for source apportionment in the

RESOLVE study, and model testing and uncertainty analysis is focused on this assumption.

E.2 MODEL INPUT DATA

The CMB receptor model requires source profiles and their uncertainties, and ambient

concentrations and their uncertainties. Source profiles were derived from ambient

measurements at the source and receptor sampling sites and are presented in Table E.l.

Profiles for air exiting the South Coast Air Basin via the Soledad and Cajon Passes and for

air exiting the San Joaquin Valley Air Basin via the Tehachapi Pass were calculated from

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days in which wind directions pointed from each basin to the desert for more than two-thirds

of the 24-hour sampling period. The average fraction of fine mass and the standard

deviaiion were caiculated for each of these subsets of samples, with the results appearing in

the first three columns of Table E. 1.

"Local" source profiles were derived from receptor data at Edwards AFB, NWC, and

Fort Irwin NTC by selecting cases for which flow from the deserts into the air basins was

greater than one-third of the 24-hour sampling period at all three pass sites simultaneously.

These subsets were further divided into winter (November through February, indicated

with a W suffix) and nonwinter (March through September, indicated with an NW suffix)

subsets prior to calculation of the average and standard deviation of fractional fine-mass

composition. Undoubtedly, some contamination of these local profiles exists with

contributions from the Los Angeles and San Joaquin Valley air basins, since there was some

outflow from these basins during the sampling periods. However, these are the best

nonregional profiles obtainable with current data.

Annual averages for the base year for Al, Si, S, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br,

and Pb were calculated for each receptor site. The standard error of these averages

(standard deviation divided by the square root of the number of samples) was used as an

estimate of their uncertainty. Cases were eliminated when any single species did not have

valid data reported. Another set of annual averages was calculated for the above species

plus Cl, Oc, and EC for the entire data set from April 1984 through August 1985. All valid

data were included in each average. The CMB applied to these two different data sets allows

the effects of different averaging methods and different numbers of species in the solution to

be determined.

E.3 FEASIBILITY TESTING

The source apportionment of nonlocal fine particles at Edwards AFB, NWC, and Fort

Irwin NTC depended on the uniqueness of fine-particle chemical composition exiting the

South Coast Air Basin and the San Joaquin Valley. These are the only potentially

significant regional source contributors to desert sampling sites. The analytical approach

applied by Trijonis, et al. (Reference 3), to hypothetical RESOLVE data was applied a

posteriori to the actual data taken at Cajon, Soledad, and Tehachapi Passes.

Coefficients of variation for most species in the Soledad, Cajon, and Tehachapi source

profiles are generally in a range of 30 to 80%, a range that Trijonis, et al. (Reference 3),

found marginal for the resolution of five different sources used in their tests. However, the

fractional compositions for the Tehachapi Pass source profile (representing the San Joaquin

Valley) is significantly different from the Soledad and Cajon source profiles. The Tehachapi

profile was enriched in vanadium and nickel with respect to the other profiles. The Cajon

profile (representing the eastern South Coast Air Basin) was enriched in chromium,

chlorine, bromine, and lead with respect to the other profiles. The Soledad profile

(representative of the northern South Coast Air Basin) exhibited levels for the measured

species that were similar to one or the other of the remaining two profiiles.

Random uncertainty proportional to the coefficients of variation was applied to ambient

concentrations generated from these three source profiles and equal contributions from each

source. The procedure of Trijonis, et al. (Reference 3), was followed to maintain equivalence

to the a priori tests made for RESOLVE. The results were that contributions from

Tehachapi and Cajon could be resolved from each other at an uncertainty level of about 40%.

Contributions from Soledad are apparently colinear with Cajon and these contributions

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TABLE E. 1. Source Compositions and Uncertainties.

Species SOL CAP TEH EDW,NW EDW,W FOR.NW FOR,W CIHL.NW CHLW

AL 0094 0 1, 0 0171 0145 0037 0392 .0143 0386 0109

0057 0162 0099 0068 0036 0286 0102 0293 0111

SI 0180 0206 0377 0473 ý0145 .1072 0398 0918 0380

0091 0103 04207 0163 .0183 .0779 .0332 0665 0316

S 0915 0735 0653 0812 .0345 0591 .0806 0368 0399

0269 0175 0195 0325 .0333 0257 .0241 .0245 0084

CL 0001 0020 0005 0009 0022 0005 0059 0002 0019

0002 0014 .0014 0008 0020 0010 0059 .0005 0010

K 0073 0049 0107 0094 .0026 0139 0070 0130 0085

0081 0032 0050 0049 0034 0060 .0036 '0080 0041

CA 0040 0042 0061 0099 0017 0232 0082 .0155 0044

0024 0023 0041 0040 0027 0171 .0059 0125 0032

TI '0005 0003 0010 0010 0001 0024 000 .0019 0006

0004 .0003 0006 00U~ .0003 0015 .0007 .0014 0006

v 0001 0001 0004 0003 .0000 0003 .0004 .0003 00

0001 0001 0002 0001 0001 0001 .0003 .0002 0002

CR 0000 0002 0001 0000 .0000 .0001 .0003 .0001 0001

0000 .0001 .0001 .0000 .0001 .0001 .0003 .0001 .0000

MN 0002 0004 0003 0002 '0001 .0005 0008 .0005 .0003

0001 .002 .0002 ý0001 .0002 .0004 .0008 .0004 .0001

FE 0053 .0059 .0098 .0112 .0021 .0206 0068 0182 0060

0034 .0026 .0059 0027 .0028 .0126 .0049 .0130 0054

NI 0001 '0001 .0007 .0005 '0001 .0004 .0002 .0003 0003

0001 0001 0004 .0003 0001 .0004 .0002 0003 0001

CU ý0001 .0005 .0004 0004 0002 .0002 .0010 .0004 0004

0001 0005 .0007 .0003 .0003 .0003 .0013 .0005 0002

ZN 0007 0020 .0008 .0035 .0008 .0019 .0045 .0018 0016

0003 .0014 .0006 0044 .0008 0007 .0042 .0028 '0015

BR .0007 0013 0005 0005 .0004 .0004 '0009 0004 0009

0003 .0009 .000 1 .000 1 .0004 0002 -0006 ý0002 .0002

Pe 0026 0086 0013 0028 M020 ý0013 0033 .0017 .0045

0010 0053 0005 0009 .0019 0010 0026 .0010 ý0012

OC 3304 3981 .3515 3696 .1669 4413 .6127 235 .4229

0624 1409 1307 1682 2755 1231 354 2020 1214

EC 0716 1505 0807 1085 0606 1502 .2185 0470 1295

0272 0672 0504 0274 0733 0875 1202 0809 0371,

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could not be resolved, in the presence of t Cajon contribution, with an uncertainty better

than about 90%.

The conclusions from this analysis are

1. RESOLVE source profiles from the South Coast Air Basin and the San Joaquin

Valley can be used to separate these sources.

2. The key resolving species appear to be lead, vanadium, and nickel.

3. South Coast Air Basin source profiles measured at Cajon and Soledad are too

similar, at least for the species measured in this study, to allow these sources to be

separated.

Local source profiles may still interfere with the source apportionment. The nonwinter

source compositions reported in Table E. 1 for local sources contain higher concentrations for

soil-related species such as Al, Si, and Ca when compared to the regional source profiles

derived from the mountain passes. Winter profiles contain a larger proportion of organic

carbon. The nonwinter profiles also contain from 50 to 75% of the fractional Ni and V found

in the Tehachapi profile, which indicates that these profiles are influenced by aerosol from

the San Joaquin Valley.

These feasibility testing results significantly relax the original conclusions of Trijonis,

et al. (Reference 3), which did not benefit from actual data.

E.4 CHEMICAL MASS BALANCE RESULTS

Table E.2 presents the results of CMB model applications to average receptor

concentrations at Edwards AFB, NWC, and Fort Irwin NTC. The source mnemonics refer to

the source compositions and their uncertainties presented in Table E.1. Source

contributions were calculated by the CMB in several different configurations to estimate

the uncertainty of the source apportionments. These configurations consisted of

* Different averaging periods. Chemical concentration averages were

calculated for the base year and for all samples taken between April 1984 and

August 1985 to determine the effect of different averaging periods on the

source attribution process.

* Different receptor sites. Edwards AFB, NWC, and Fort Irwin NTC

were treated separately to observe differences. Edwards AFB, being closer to

the San Joaquin Valley and Los Angeles air basins, is expected to show a

larger contribution from these regions than are NWC and Fort Irwin NTC.

* Different source profiles included in the CMB. It is possible that

different source profiles will provide equivalent performance measures. These

performance measures are too numerous and complex to report here, but they

were examined and compared for each case to determine which of alternative

mixtures of source profiles could reproduce the annual average chemical

compositions observed at the receptors.

* Different sets of chemical species included in the CMB. The Ni and

Pb used in the tracer solution reported in Section 8.2 were used with the base

year averages to verify the conclusions of Section 8.2. Seventeen species were

156
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included in CMB calculations for base year data and 20 species were included

in the CMB calculations for the averages over all data.

TABLE E.2. Source Contributions and Their Uncertainties

for Different CMB Cases in pg/m3 .

(Uncertainty is shown directly under source contribution.)

Case SOL CAP TEH EDW,NW EDW,W

EDW 30 8 5

Base year 2.4 91

Ni, Pb

65 2.9

30 17

1 8 3.7

1.2 1 8

43 60

2.6 2.4

EDW 10 44 4.0 19

Base year 3.9 2.4 3.0 58

Al, Si, S, K, Ca, Ti,

V, Cr, Mn, Fe, Ni, 43 6.7

Cu, Zn, Br, Pb 2.0 1.9

9.5 15

1.8 38

4.3 60

2.6 2.4

2.9 0.5 7.0

3.1 1.1 2.0

EDW -2.7 14.3 -4.6 138

All samples 9.0 6.5 7.2 120

Al, Si, S, Cl, K, Ca,

Ti, V, Cr, Mn, Fe, 9.9 1 4

Ni, Cu, Zn, Br, Pb, 1.4 30

EC. OC

-6.2 5.0 11.9

5.7 29 3.6

12.5 10

3.1 2.3

112 17 70

40 3.1 73

103 62

2.3 44

157
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TABLE E.2. (Contd.)

Case SOL CAP TEH CHL.NW CHL,W

CHL 31 2.9

Base year 17 10

Ni, Pb

48 3.3

2.3 18

14 3.9

09 1 9

-3 10.2

74 10.3

CHL 3.2 18 4.5 -3

Base year 2.4 2.3 2.1 2.0

Al, Si, S, K, Ca, Ti,

V, Cr, Mn, Fe, Ni, 0.1 79 1.3

Cu. Zno Br, Pb 2.6 19 1.6

8.0 1.3

17 1.1

4.5 44

2.2 2.0

0.2 81 10

2.5 2.4 51

CHL -2.9 4.6 6.0 3 5

All samples 3.1 3.4 2.9 2.4

Al, Si. S, Ci, K, Ca,

Ti, V, Cr, Mn, Fe, 8.3 3.3

Ni, Cu, Zn, Br, Pb, 2.5 19

EC, OC

-1.1 12.9

2.6 3.0

4.3 75

3.0 3.1

1 6 3.4 6.5

1.1 2.7 2.8

100 1.3

20 1.3

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TABLE E.2. (Contd.)

Case SOL CAP TEH FOR,NW FOR,W

FOR 19 22

Base year 2.2 2.1

Ni. Pb

2.8 1.9

1.4 1 1

0.8 2.3

0.6 1 2

-38 114

176 24.2

FOR 1.9 1.3 2.3 02

Base year 11 1.3 1'0 0.5

Al. Si, S, K, Ca, Ti,

V, Cr, Mn, Fe, Ni, 1.0 5.6

Cu,Zn, Br, Pb 1.2 1.4 1.6

3.5 1 3

1 3 0.9

2.2 2.2 09

1.4 1.1 0.7

1 0 0.0 5.6

2.2 0.7 1.5

FOR -0.1 2.1 2.5 1.6

All samples 1 8 2.2 1.6 12

Al, Si, S, Cl, K, Ca.

TiV, Cr. Mn, Fe, 4.7 13

Ni, Cu, Zn, Br, Pb, 1.2 0.8

EC, OC

-1.2 9.8

2.0 2.3

4.3 75

3.0 3.1

0.1 2.8 2.9 10

1.3 1ý8 1.3 0.7

While a wealth of information can be gleaned from Table E.2 and the individual outputs

from the CMB model applications used to produce Table E.2, the important conclusions to

be drawn from these data are the following:

0 Only two source types can be resolved from each other for the annual

average receptor data, regardless of the averaging period or the chemical

species included in the CMB. This statement is consistent with the feasibility

testing, which found that when three source types were tested (with the

species measured in RESOLVE in excess of detection limits), one or more of

the source contributions was associated with uncertainties that far exceeded

159
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the source contribution. In general, the CMB, which included the Tehachapi

source profile with a nonwinter local source profile, returned the best

performance measures and the lowest uncertainties. In every case, however,

the uncertainty-similarity clusters showed that these source profiles are very

similar and that the uncertainty associated with their resolution is large

"(Reference 71). This uncertainty is, however, adequately reflected in Table

E.2.

* For the base year averages, the source contributions from the San

Joaquin Valley calculated by the CMB are generally consistent with those

determined by the Ni and Pb tracer solutions of Section 8.2. The CMB Ni and

Pb solutions yield values toward the low end of the ranges cited in Section 8.2,

but the uncertainties calculated by the CMB cover most of the ranges

specified in the previous section. These uncertainties are large, however,

amounting to more than 50% of the source contribution in most cases. These

large uncertainties are caused by the great similarity among the source

profiles as well as the substantial variability in those profiles as indicated in

Table E. 1.

• The San Joaquin Valley contributions for the base year with 17

species in the CMB are similar to those of the NiIPb solution when a

nonwinter local source is included. When such a source type is excluded,

however, these San Joaquin Valley contributions are much higher and they

exceed the upper bound for the San Joaquin Valley contribution of Section 8.2.

This higher contribution is generally because of the much lower concentration

of geologically-related material in the Tehachapi source profile when

compared to the local nonwinter profiles. These geological species

concentrations are reproduced more accurately with the inclusion of the local

source profile. Unfortunately, local geological profiles are not available. The

geological profiles would substantially improve the results. The conclusion to

be drawn here is that the composition of local soil sources must be included in

the CMB when a large number of species concentrations are used. The

nonwinter local source profile provides the best, though by no means perfect,

representation of this.

* Source contribution estimates for the April 1984 to August 1985

averages and 20 CMB species are also consistent with the other analyses

when a nonwinter local source profile is included.

The overall conclusion of this complementary CMB analysis is that San Joaquin Valley

sources can be resolved from other sources, but only with uncertainties of 50% or more. This

means that the San Joaquin Valley contribution is probably in the range of 25 to 75% of the

fine-particle mass at NWC and Fort Irwin NTC.

160
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Appendix F

DETAILED ANALYSIS OF CASE STUDY DAYS

161
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This appendix presents a detailed description of the 4 case study days. Most of the

discussion centers on wind trajectories and diurnal patterns of particle scattering.

To provide a better understanding of this appendix, we will discuss two issues. One issue

is related to the RESOLVE source sites, and the other to the receptor sites.

Photographs and observations of the regions near Tehachapi and Soledad Passes as well

as the wind fields developed for the trajectory analysis indicate that significant outflow

occurs across whole sections of these mountain ranges, not just through the pass itself. This

outflow occurs because the cross section of the particular pass is not always sufficient for all

of the flow required by the synoptic conditions, even though the pass is at a lower elevaton

than the surrounding mountain range. Thus, both the aerosol and wind measurements at

these two passes can only be used as indicators of the material being transported, not as

complete representations of the flow. In contrast, wind fields and observations indicate that

most of the transport in the Cajon Pass region occurs at the pass. This might be expected,

since the mountains around Cajon Pass are much higher than those surrounding Tehachapi

and Soledad, and the pass is lower.

The RESOLVE desert sites naturally divide into two groups, Edwards AFB and the

other three, because the distance to (and thus transport time from) the pass areas is much

shorter for Edwards AFB than the other sites. Edwards AFB is about 30 miles from

Tehachapi and Soledad Passes. This distance implies an average transport time of about 6

hours when the wind speed is 5 miles per hour, and about 3 hours when the wind speed is 10

miles per hour. However, the three other desert sites are 65 to 110 miles from any of the pass

areas and thus are 13 to 22 hours away at 5 miles per hour, or 6.5 to 11 hours away at 10

miles per hour. The longer transport times make it much more difficult to track air masses

from the passes to the more distant desert sites. Pollutant dilution and mixing also reduce

the potential impact of a particular pass at the more distant sites. Thus, the analysis of

transport to the Edwards AFB site is more straightforward than the analyses for the other

three desert sites.

F.1 4 OCTOBER 1984

Figure F.1 shows the hourly wind speed, wind direction, temperature, relative

humidity, and particle scattering for Edwards AFB on 4 October 1984. Generally, western

flow was established quite early at Edwards AFB. By noon, the flow was southwesterly,

becoming quite strong by 1500 PST. The temperature and relative humidity plots show a

typical diurnal profile, with the temperature increasing and the relative humidity

decreasing during the daylight hours. However, the relative humidity increased quite early

(after about 1400 PST) when air from the coastal region began to arrive. The particle

scattering increased significantly about the same time.

Figure F.2 shows similar plots for particle scattering at the four RESOLVE desert sites.

All four sites showed a dramatic increase in particle scattering in the early to late

afternoon. Figure F.2 also shows similar plots for the three pass sites. Particle scattering at

both Tehachapi and Cajon slowly increased most of the day, while particle scattering at

Soledad increased dramatically in the afternoon. There was outflow (flow into the desert) all

day from all three passes.

Figure F.3 shows some example trajectories for air parcels that arrived at Edwards

AFB, NWC, and Fort Irwin NTC on 4 October. Such trajectories are intended to represent

the general paths of air parcels on their way to the sampling sites. These trajectories were

calculated using surface-wind streamline maps drawn from hourly wind speed and wind

163
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OCT 4, 1984 - 'EDU'

is

S

WD (degrees)

279

ISO

30

29

IS

RH (percent)

7S

so

25

BSP (Mnrl vs hours of the day)

ISO

ISO

so

1 3 4

FIGURE F. 1. Hourly-Averaged Measurements at Edwards AFB on 4 October 1984.

164
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.n . .. . . . . . . . .

no I

.m OCT .

ou~~a . . i . . . Ia. . .

.I . .S . . . . . . . . . . . .

t655
NWC TP 6869

SAN JOAQUIN Nc

VALLEY RIJS

TEHACHAPI PASS AAFASS

ED WAAOS LGN

Areceptor installations

A 3 pass monitoring sites

illou,,tJin ranges

LOSE

SAN JAAQUINS

BA IN . 'z.. '. ..

CHIN

VALEYAHP PASSU

TEHACHAPI PASS,. PLMA

FIGRE Exmpe .3bTajetores f ir arcls rriin aeceFort [rinstlain

Octoberin on 1984. 4as 14 

166ai ane
NWC TP 6869

direction data taken at the RESOLVE sites, air pollution district sites, and NWS stations in

the region. The characteristics of the streamline maps discussed in this section are similar

to the average ones shown in Chapter 4 of References 7 and 35. The trajectory calculation

procedure is similar to that used in the Los Angeles basin by Angell, et al. (Reference 84).

Shorter trajectories are generally more reliable than longer ones because of the variability

of wind speed and direction. Trajectories were not calculated for every hour during the day,

only for the most interesting periods (especially when particle scattering was high or

changing dramatically).

Figure F.4 shows particle scattering and information on trajectories for Edwards AFB

during the day. The 1600 PST trajectory is shown in Figure F.3a. Earlier trajectories had

similar paths. but longer transport times. By 1600 PST, the trajectories were significantly

shorter and traveled along a more direct route from the Los Angeles basin. According to the

1600 PST trajectory, this air parcel was in the San Fernando Valley area most of the

morning before crossing the mountains just northwest of Soledad Canyon and arriving at

Edwards AFB. The air parcel associated with the increase in particle scattering at Edwards

AFB (between 1400 and 1600 PST) had passed into the desert about the same time as a large

increase in particle scattering was measured at the Soledad Canyon site (see Figure F.2).

The Ni/Pb ratio at Edwards AFB on 4 October is 0.12 (see Table F.). This ratio is

slightly lower than the average for the RESOLVE base year at Edwards AFB. Also, it is

slightly higher than the Soledad Pass Ni/Pb, and significantly lower than the Tehachapi

Pass Ni/Pb on 4 October. These data show that even on a day when the trajectories indicate

transport from the Los Angeles basin all day, the 24-hour average sample still shows a

small influence from the San Joaquin Valley.

Figure F.4 also shows particle scattering and trajectory information for NWC during

the day. The winds were light and variable until about 1600 PST, when they increased in

velocity and came from about the south-southwest. Both the 0600 PST and the 1500 PST

trajectories indicate that the air parcels had been within about 15 miles of NWC for the last

day or more. The 2000 PST trajectory (shown in Figure F.3a) indicates that transport from

the Tehachapi Pass region was established, This transport matches a significant increase in

particle scattering that lasted the rest of the day. Note in Figure F.4 that the 2000 PST

trajectory was significantly shorter than the 1900 PST trajectory and thus had traveled

more quickly along a direct route from the San Joaquin Valley.

The Ni/Pb ratio at NWC on 4 October is 0.31 (see Table F. 1). This ratio is slightly higher

than the average for the RESOLVE base year at NWC. Also, the ratio is significantly higher

than the Soledad Pass Ni/Pb and slightly lower than the Tehachapi Pass Ni/Pb on 4 October.

Figure F.4 also presents information for Fort Irwin NTC. The particle-scattering

diurnal pattern is very similar to that at NWC. However, the winds were higher (7 to 16

miles per hour) than at NWC and consistently from the west to southwest all day. Figure

F.3b shows how the trajectories shifted during the day. The 1600 PST trajectory (from

Tehachapi Pass) probably represents most of the day before 1800 PST. However, note the

sharp increase in particle scattering at 1900 PST that occurred just as the transport shifted

to the Soledad Pass region and the transport time decreased. Later trajectories indicate

transport was from Cajon Pass.

The Ni/Pb ratio at Fort Irwin NTC on 4 October is 0.35. This ratio is slightly higher than

the average for the RESOLVE base year at Fort Irwin NTC. Also, the ratio is significantly

higher than the Soledad Ni/Pb and slightly lower than the Tehachapi Ni/Pb on 4 October.

167
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OCT 4 1984 'tOW

2

i,, OCT 4, 19114 "Oft,

1 V . -. -0

asp 41

t *

ap -

: •- a- a

* i 5i o w • C S * C i.•

Is@

Is. -- * iz :

AFNC andFor 9ri NT o Otoe 8. arilesctern

IBct nMmtvru huso h dy oethtPrmd n ati

.. . . . . . . .a .;

(Bs. t in Mmtvru or ftedy oeta ya i n ati

Lakes are 10 to 25 miles west of Soledad Canyon.

168
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TABLE F. 1. NiUPb Ratios on Case Study Days.

Average nickel to average lead

Site For individual For RESOLVE 12/25/84 10/4//84 7/14/85 2/20/85

outflow days base year

Cajon Pass 0-023 ...-- 0.022 0.056 0.061

S6:zdad pass 0.056 --- .-- 0.055 0.074 0.087

Tehachapipass 0.60 --- 0.43 1 14 049

EdwardsAF8 -- 0 16 0003 0.12 021 058

NWC --- 0.18 0.075 0.31 0.53 -

Randsburg Wash -- .--- 0 47 0 52 0,45

Fort Irwin NTC --- 020 00 0.35 052 043

This result for Fort Irwin NTC on 4 October illustrates an interesting phenomenon.

During the worst particle-scattering hours of the day, the trajectories indicate that the Los

Angeles basin was the major contributor to aerosols at Fort Irwin NTC. However, the Ni/Pb

ratio suggests that the major source contributor to the 24-hour aerosol concentrations was

the San Joaquin Valley. Thus, the source contributions during the worst hours of this

particular day seem to be different than the source contribution averaged over the complete

day. This may not be the situation for other days at Fort Irwin NTC, since we have only

looked at this one day. Also the Ni/Pb ratio is not a reliable tracer for individual days (see

Chapter 8).

F.2 14JULY 1985

14 July 1985 is another day of transport from both the San Joaquin Valley and the Los

Angeles basin to the RESOLVE receptor sites. However, this day provides an interesting

contrast to the other transport day, since many of its characteristics are significantly

different than those for 4 October.

Particle scattering and trajectory information are shown for NWC in Figure F.5.

Temperature increased and relative humidity decreased during the daylight hours in a

typical diurnal pattern. Winds were light and variable until almost noon. The winds then

increased and came from the south to the southwest for the rest of the day. The particle

scattering was modest and constant until the wind increased and a large pulse arrived from

the Tehachap: Pass region. Actually, trajectories throughout the day at NWC entered the

desert near Tehachapi Pass.

The NWC trajectories (see Figure F.6) show that the particle-scattering pulse coincided

with a shorter transport time and an air parcel with early morning (0600 to 0900 PST)

concentrations in the southern San Joaquin Valley. In fact, this same pulse also arrived at

Edwards AFB (about 0700 to 1000 PST), and at Randsburg Wash (about 1100 to 1300 PST).

NWC particle scattering dropped back to its prenoon value after 1700 PST, even though

transport was still quick (about 4 hours) and from the Tehachapi Pass region. These air

parcels were in the southern San Joaquin Valley about noon, rather than early morning,

and may not have resided in the San Joaquin Valley as long under conditions of poor

dispersion.

169
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A & los J & *. 'efA~ *t f!

a 1 21 ~~8 2 

IE ~lI

~ au

4 x

2 2

. . . .. . . . .7

.- . . . . . . . .2- . . . . .

FIGURE F.. arile cteigadTaetr nomto odad

FIGttrEn F.5 art)icl Scam-1teriug andr Trathectoay.Ifrai o dad

170
NWC TP 6869

SAN JOAQUIN N..c

VALYRMS

reBURGiia~j~n

\' AKIRSFAELOO

TEHACHAPIPS. o

YE' DWAROS LGN

* .. VON SLSDA~C * PALOALE litnain ranges

~CAJON PASS

BASIN,.- 5-CAESTT

and 4July1985 NC on 

VALLEYRA171
NWC TP 6869

Figure F.5 also shows the particle scattering and trajectory information for Edwards

AFB and Randsburg Wash on 14 July. For Edwards AFB, the particle-scattering pulse

between 0700 and 1000 PST was the one with early-morning San Joaquin Valley

concentrations. Trajectories show that a similar air parcel arrived at Randsburg Wash at

the front end of a particle-scattering pulse (about 1100 to 1300 PST). However, right after

this pulse, air parcels from the Los Angeles basin began arriving at both Edwards AFB and

Rand-burg Wash. Figure F.6 shows two sample Edwards AFB trajectories, one from the

San Joaquin Valley and one from the Los Angeles basin. Both trajectories show that the air

parcels would have included early-morning concentrations.

Particle scattering and trajectory information for Fort Irwin NTC on 14 July is also

shown in Figure F.5. The trajectories show that all air parcels that arrived at Fort Irwin

NTC had originated in the Los Angeles basin. The particle-scattering pulse in the late

afternoon corresponds to a shorter transport time and morning Los Angeles basin

concentrations.

Ni/Pb ratios for all sites on 14 July are shown in Table F.l. All of the receptor sites

show a significant influence from the San Joaquin Valley. The interpretation of the Fort

Irwin NTC data is again confusing (see discussion of 4 October data); the hourly

trajectories indicate transport from the Los Angeles basin all day, while the 24-hour Ni/Pb

ratio suggests significant San Joaquin Valley influence.

Part of the influence on this day was because of extremely large forest fires during two

weeks of mid-July in Southern California. On 14 July, as on surrounding days, the organic

component of the aerosol was high throughout the RESOLVE network.

F.3 25 DECEMBER 1984

In general, 25 December 1984 was a day with mostly local desert effects on the

RESOLVE receptor sites. The wind speeds were light most of the day (and the previous

day) and wind direction was more variable than on other days. Consequently, the

calculated trajectories are less reliable. This could be especially true east of NWC, where

the wind flow can be very complicated. However, the trajectories should still be adequate,

since we have wind data at three locations in the vicinity: NWC, Trona, and Randsburg

Wash.

Particle scattering and trajectory information for NWC are shown in Figure F.7. Air

parcels that arrived at NWC probably spent the last 10 to 20 hours within about 25 miles of

NWC. The trajectories indicate that the air parcels that arrived between about 1500 and

2000 PST probably passed through the Searles Valley before arriving at NWC.

Particle scattering and trajectory information for Edwards AFB are also shown in

Figure F.7. Even on this day, with slow winds and mostly local effects, the situation at

Edwards AFB was complicated, since Edwards AFB is so close to the passes. The morning

trajectories indicated slow transport from the San Joaquin Valley and possibly the Los

Angeles basin. However, the air parcels that arrived in the late afternoon and evening

probably spent at least the last 16 hours in the desert.

Particle scattering and trajectory information for both Randsburg Wash and Fort Irwin

NTC are also shown in Figure F.7. Air parcels that arrived at Randsburg Wash between

about 1300 and 1800 PST came from the northwest and had spent the last 9 to 24 hours

within about 45 miles of the site- 'k . ý of the trajectories indicated that the air parcels

passed through the Searles Valley. Fort Irwin NTC was much cleaner on this day than the

1'12
NWC TP 6869

KC 2S. tee. -- Cli

Dec as. t'9 -

- .. -- . ,,

as-

"/'S " -: -: 'I • 

isas

. . .. ... . . . .

KC 2S. tle. --

OMC 2S1 IS. O ~t Z2 2 a a

I sas

"as -I I I I . .. . . . .

. .. . . . . . . . . .-.

FIGURE F.7. Particle Scattering and Trajectory Information for Edwards

AFB, NWC, Randsburg Wash, and Fort Irwin NTC on 25 December 1984.

Particle scattering (Bscat) in Mm-1 versus hours of the day.

173
NWC TP 6869

other sites. The trajectories before about 1000 PST indicate that the air parcels had spent

at least the last 6 hours north of the sampling sike. The air parcels that arrived around noon

had spent 10 to 16 hours traveling from the south and southwest, possibly passing through

the Barstow area. Late in the day, the air parcels came directly from the east.

F.4 20 FEBRUARY t985

Very high wind speeds and blowing dust were reported at many locations in the high

desert and much of Southern California on 20 February 1985. The synoptic conditions

showed strong winds from the north early in the day (0500 PST). However, surface winds

were from the west in the western end of the high desert (between Edwards AFB and

Palmdale) until early afternoon, even as extremely strong winds from the north covered

the rest of the high desert. Lancaster reported blowing dust at 0600 PST with westerly

winds. On a day such as this, surface winds and trajectories can be misleading about the

origin of particular air parcels. Upper-level winds can carry pollutants long distances, after

which the pollutants can be mixed down into a surface-layer air parcel that is traveling

from the opposite direction. Good examples of this phenomenon are Edwards AFB and Fort

Irwin NTC early in the day.

Particle scattering and trajectory information for the four desert sites is shown in

Figure F.8. The trajectories indicate transport from the upwind basins at Edwards AFB,

Randsburg Wash, and Fort Irwin NTC early in the morning. At all three pass sites, this

corresponds to a large particle scattering peak late on 19 February that decreased to

background by about 0600 PST on 20 February.

Trajectories for NWC all day, and for the other three desert sites after 1000 PST, show

strong winds and transport from the north. The high particle-scattering levels occurring

throughout the day at all of the sites demonstrate the influence of wind-blown dust. The

measurement of the soil component at Edwards AFB and Fort Irwin NTC (measurements

are not available at NWC and Randsburg Wash) are much higher than normal, also

indicating a large wind-blown dust component.

174
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fee 20. SONVS .0.0.

" rn220.0.-* -

_IU N.,961 .0 -

4 Z

"8-"::: - - • ii il I i !

t I I I -

os %

as./ ms.

"" . - -aM. 

9S MS-. S a a

US - . a a - -

*;! * : " " " " aj: " * "" I :

~. .. .......................................................

FIGURE F.8. Particle Scattering and Trajectory Information for Edwards

AFB, NWC, Randsburg Wash, and Fort Irwin NTC on 20 February 1985.

Particle scattering (Bscat) in Mm-' versus hours of the day.

175
NWC TP 6869

REFERENCES

1. Naval Weapons Center. Visibility Monitoring in the Southern California Desert for the

Department of Defense: Research on Operations-Limiting Visual Extinction, by D

Blumenthal and J. Trijonis. China Lake, Calif., NWC, September 1984. (NWC rP

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2. J. Trijonis. RESOLVE Data Analysis Plan, prepared for the Naval Weapons Center,

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3. J. Trijonis, et al. RESOLVE Monitoring Program Plan Evaluation Using Historical

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5. M. McGown, et al. RESOLVE Data Summary, Vol. I: Annual Report, prepared for the

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7. T. Smith, et al. The Impact of Transport from the South Coast Air Basin on Ozone

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8. R. Husar, et al. "Three-Dimensional Distribution of Air Pollutants in the Los Angeles

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14. D. Reible, et al. "Atmospheric Transport of Visibility Degrading Pollutants into the

California Mojave Desert," Atmospheric Environment, 16: 599-613, 1982.

177
NWC TP 6869

15. California Air Resources Board.. Assessment of Aerosol Transport into the Mojave

Desert, by L. Myrup and R. Flocchini. Sacramento, Calif. CARB, 1986. (Report for

contract A1-153-32.)

16. J. Barone, et al. "The Effect of Owens Dry Lake on Air Quality in the Owens Valley

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328-346.

17. D. Chaloud. Standard Operating Procedures and Quality Assurance Manual for

RESOLVE. Lockheed Engineering and Management Services Report to Naval

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18. M. Pitchford. EPA Environmental Monitoring Systems Laboratory, Las Vegas, Nev.

Personal communication, 1986.

19. C. Mathai, et al. "Intercomparison of Ambient Aerosol Samplers Used in Western

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20. A. Waggoner, et al. Waggoner Electronics, Inc., Seattle, Wash. Private

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