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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 AlanWaggoner, Waggoner Electronics

JohnWatson andJudith Chow, Desert Research Institute Robert Flocchini, University ofCalifornia at Davis 

# F)T'

Cfor the 

# D

f'7" R2508 JointPlanning and Policy Board Visibility Protection Program Environmental Division PublicWorksDepartment 

# 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 inthis airspace from adverse impactscaused by degraded visibilitv The RESOLVE study was carried out in three overlapping phases: the design and planning phase from 1983to 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 PublicWorks Department Capt., U S. Navy 30June 1988 Commander
Released forpublication by G. R. SCHIEFER TechnicalDirector

NWCTechnical Publication 6869 Published by ................ Technical Information Department Collation 

### . . . . . . . . . . . . . . . . . . . . . .. . Cover, 92 leaves Firstprinting.................... .......................... .. 400 copiesUNCLASSIFIED SECURITY CLASSiFICATION OF THIS PAGE 1When Data Entered) REPORT DOCUMENTATION PAGE '. REPOR' SECUJRITY CLASSFiCATION to RESTRICTIVE MARKINGS UNCLASSIFIED 21 SECRITY CLASSiFiCATION AUTHORITY 3 ODSTRIaUTIONAVAILABILiTY OF REPORT A statement-public release: 8 October 1987 :.D :EC.ASSIF=CATtON;DOWNGR.AoING SCHEDULE

4 -ERFORMING ORGANIZATION REPORT NUMBER(S) 5 MONITORING ORGANIZATION REPORT NUMBER(S) NWC TP6869 ia NAME OFOERFORMING ORGANIZATION 60 OFFICE SYMBOL 7a NAME OFMONITORING ORGANIZATION 

### "NavalWeapons Center (ifA•p~lcaile) 

### ",c 4.OORESS (Cze, State. ara ZIP Code) 7b, ADDRESS (C:ty,State.andZIPCode) China Lake, CA93555-6001

3a NAME OF FUNOINGSPONSORING ORGANIZATION 8b OFFICE SYMBOL 9 PROCUREMENT NSTRUMENT IOENTIFICATION NUMBER (IfADoucabtIe)
Sc ADDRESS (Cily, State. andZIPCode) t0 SOURCE OF FUNDING NUMBERS PROGRAM PROJECT TASK NORK UNIT ELEMENT NO. NO. NO. NO

11 TITLETncludeSecuntyClasstficatuin) RESOLVE PROJECT FINAL REPORT, VisibilityConditions and Causes ofVisibility Degradation inthe Mojave DesertofCalifornia 12 PERSON-L AUtHOR(S Trijonis, McGown, Pitchford, Blumenthal, Roberts, White, Macias, Weiss, Waggoner, Watson, Chow. Flocchini
13a TYPEOFREPORT 1bTIME COVERED 14.DATE OF REPORT (Year. MOnth. Day) 15. PAGECOUNT 

### Final 1;Fromn 

### 1983 To 

### 1986 1988,July 182 I6 SUPPLEMENTARY NOTATION
17 COSAT?CODES iS. SUBJECT TERMS (Contnue on rvuerse sideifnecessary and ainfo byblocknumber)

-,FIELD GROUP SUB.GROUP Degradation, visibility, aerosol mass, extinction, emission, variations, airparticles,particulate matter,nitrates, organic
19 ABSTRACT (Con•nu oan revers sidectnecessary and Idenifyby 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 ofa DOD program to protect flight and test operations in the Mojave Desert ofCalifornia 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 ofexcellent visibility, dependably good weather, and large areas ofunused land and airspace. Since the 1940s a decrease in visibility in the airspace has been occurring and DOD is concerned about potential adverse effects onoperations from further declines in visibility.Highlighted inthis study are the major conclusions thatpertain directly to the two RESOLVE objectives: documenting baseline visibility conditions in the study region andcharacterizing thecauses and source areas ofvisibility degradation.

jJ OISTRIBUTiON/AVAILABI1ITY OF ABSTRACT 21 ASTRACT SECURITY CLASSIFICATION

I UNCLASSIFIED/UNLIMITED []SAMEAS RPT. C DTIC USERS UNCLASSIFIED 220 NAME OF RESPONSIBLE INDIVIDUAL 22b TELEPHONE (IncludeAmo C'd&) 22c. OFFICE SYMBOL J. O'Gara (619) 939-3•11 x378 Code 266 83 APR edition may be used untOi exhausted SECURITY CLASSIFICATION OF "-IS PAGE DO FORM 1473, 84 MAR All other editions are obsolete UNCLASSIFIEDNWC TP 6869

CONTENTS
1.0 Introduction
............................................................. 3 1.L Background
.......................................................... 3 1.2 StudyPhilosophy
............. ............................ .......... 4 1.3Terminology andBasicConcepts ofVisibility
............................ 5 1.4 Summary ofMajorConclusions
......................................... 6 2.0 Monitoring and Data Overview
............................................ 9 2.1 Monitoring Design Considerations
...................................... 9 2.2Routine Monitoring Network . ............... ........................ 10 2.3Monitoring Schedule and Data Recovery
................................ i 1 2.4Special Studies
....................................................... 15 3.0 Data QualityandRepresentativeness
..................................... 19 3.1 Data-Screening Procedures
........................................... 19 3.2 Precision andAccuracy
....................................... ....... 24 3.3 MassandExtinction Balances
......................................... 28 3.4 Interparameter Correlations
.......................................... 31 3.5 Diurnaland Vertical Representativeness
............................... 33 4.0 Climate andMeteorology
................................................. 35 4.1 Climate ofthe RESOLVE Study Region
................................ 35 4.2RESOLVE Meteorology andComparison to Long-Term Averages
......... 41 4.3 Relationship ofVisibilityto Meteorology
............................... 56 4.4 DefinitionofFlow Regimes ............................................ 65 5.0 Spatial PatternsofVisibility
............................................. 66 5.1 Air-QualityMaps .................................................... 66 5.2Interstation Relationships
............................................ 70
6.0 Temporal Patterns ofVisibility
........................................... 72 

### 6.1 Seasonal Variations............................................... 742 6.2 Diurnal Variations
.................................................. 77 6.3Time-Series Plots
.................................................... 78 7.0 Causes ofLight Extinction
............................................... 84 7.1 Scope ofthe Analysis
................................................. 84 

### 7.2 Extinction Allocation Procedure..................................... 85 7.3 Extinction Budget Resultso
............................................ 90 7.4Uncertainty Estimates ............................................... 93 8.0 Sources ofAtmosDheric Light Extinction ... .......................... 94 8.1 Natural VersusMan-Made Sources
.................................... 94 

### 8.2Tracer-Element Model forSource Areas .............................. 95 8.3Conclusions Regarding Source Areas
................................ 105 8.4Extinction Contributionsfrom Major Anthropogenic Sources
............ 106



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9.0 Illustrative Case Studies
.......................................... 108 9.1 Selection ofCaseStudy Days
.................................... .. 108 9.2 Discussion ofthe Case Study Days
.... .... ........ ................. 109 9.3Conclusions
......... ................................. 113 Appendixes: A. Precision Estimates for RESOLVE Data Sets
......................... 115 B. Estimation ofExtinctionContributions from Major Source Categories ..... 131 C. Uncertainty Analysis forthe Extinction Budget
. ............ ... 139 D.Theory for theNiIPbTracer Model
..................................... 145 E. Chemical Mass Balance Approach to Source Apportionment
.............. 151 F. Detailed AnalysisofCase Study Days
.................................. 161 References ................................................................... 177


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1.0 INTRODUCTION
For several years, the Department ofDefense (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 ofa DOD program to protect flight and testoperations in the desert from adverse impacts caused by degraded visibility. Within the overall program toprotect operations, the purpose ofRESOLVE 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 visibilitydegradation. The RESOLVE study was carried out in three overlapping phases: design, data collection, and data analysis.The design andplanning phase took ayear and a half(1983 to mid-1984), although additions and modifications tothe 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 ayear later.This document is the culmination ofthe data analysis effort.
1.1 BACKGROUND Historically, theMojave 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 ofunused land and airspace. Because ofthese advantages, DOD located anumber ofmajorfacilities in theregion (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 abudget of 1.5 billion dollarsper year. Since the mid-1940s, when thefacilities were established, a decrease in visibility in the R-2508 airspace has beenoccurring. Sometimes visibilitydegradation isnow 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 oftest and training activities conducted at the facilities. However, DOD is concerned about potential adverse effectsonoperations fromfurther declines invisibility. 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 ofthe visibility issue, which, in turn, have led to the formation of a DOD Visibility Protection Program (VPP). The VPP consists offour 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



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### planning study toidentify thebest strategiesforprotecting operations. Asnoted, thisreport presents the resultsofthe second partofthe 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), adesign evaluation basedon 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,
4NWC TP 6869 agriculture, utilities, developers, and local and county governments in the Mojave Desert and in the upwind air basins. The two upwind regions ofmost concern are the Los Angeles basin (the South Coast Air BasinofCalifornia) and the San Joaquin Valley. Because ofthe 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 ofvisual air quality that is not only comprehensive but also accurate. Ifthe results of RESOLVE are biased in any way, DOD will be hampered in its management effort. Inother 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 ofRESOLVE: (1) high-sensitivity X-ray fluorescence IXRF) analysis of RESOLVE particle filters, (2) a study oforganic 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 ofthe RESOLVE Interagency Agreement between EPA and DOD. Thesecond majorarea ofinput was in the review process. To keep other parties abreast of RESOLVE, DOD members participated in SCENES (Subregional Cooperative Electric Utility, Department ofDefense, 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 similarprocess ofreview 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 ofthe RESOLVE approach and, in fact, comprehensive treatment ofuncertainties is one ofthe unique features ofRESOLVE. The precisions ofthe 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 assingle standard errors. 1.3 TERMINOLOGY AND BASIC CONCEPTS OF VISIBILITY Theair quality issue ofinterest in this study is atmospheric visibility. Visibility can be defined quantitatively in terms ofcontrast (the relative brightness ofvarious 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 oflight that is attenuated per unit distance as a light beam traverses the atmosphere). As illustrated inthe followingparagraphs, 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 oflight byboth gases and particles. The extinction coefficient, "B"NWC TP 6869

inconventional units ofMm- 1,is simply the sumofthose four components, B = Bsg

- Bag + Bsp -r Bap. Here, Bsg = light scattering by air molecules (Rayleigh or natural blue-sky scatter). Thisterm is on the orderof 10 to 12Mm

- 1,depending onaltitude. 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 inthe size rangeof0.1 to 1.0micrometers. 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 contrastdetection threshold ofthe observer. A "standard"observer isdefined asone with a2% 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 ofextinction 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 notexactly equal to realparticle scattering (Bsp)because ofmeasurement limitations ofthe nephelometer. As explained in Section 3.3 of this report, adjustments are made for measurement biases when calculatingextinction coefficient orvisual 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 (gaseousNO2isfoundtocontribute 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 ofthese reasons, and because the size and chemical composition ofparticles reflect their origin, many ofthe analyses in this report revolve around the mass, size (fine versus coarse), andchemical composition ofambientparticles. Withrespect tochemical composition, inmost areasonlyfive typesofspeciesaccount for essentially allofparticle mass andassociated lightextinction. These fivetypes are organics, sulfates, nitrates, elemental carbon, and soil dust. Formulas for calculating the mass of thesespecies from the RESOLVE data are given later inthe 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 thecauses and source areas ofvisibility 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 

### "studyregion (Figure 2). This map should provide a reasonable estimate ofaverage visibility at anypointofinterest in the study region. * From lowest visibility to highest visibility, the ranking ofthe 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 foraverage conditions. * At the desert receptor locations, visibility tends to be lowestjust before midnight and highest in the early afternoon. The daytime (0800 to 1600) average for light extinction isabout 10% lowerthan the 24-houraverage.
Causes and Sources ofVisibility Degradation 

### " Averaged over the three receptor sites, the allocation of visibility degradation by species isas follows: organics 26% ± 9%; sulfates 24% ± 5%; elemental carbon 19% ± 6%; soil dust 18% ± 8%; and nitrates plus NO2 13% ± 5%. The extinction budget shows some minor variations from site to site. The main seasonal variation occurs forthe nitrates plus NO2percentage, which becomes as large as the other categories in the winter, but which is especially small in the summer. The extinction budget forworst-case days is almost identical tothe onefor averageconditions. 

### " 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) ofnon-Rayleigh extinction. In the northern majority ofthe studyregion (NWC), the remainder ofnon-Rayleigh extinctioncomes from natural sources. Alongthe southern edge ofthe 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 tobe more important than itis foraverage 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 ofman-made extinction. No other source categories individually account for morethan5% ofman-made extinction inthe region as a whole.


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NOTES: * Circled data points represent RESOLVE monitoring data. Theentire 2 years ofRESOLVE 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 tototal extinction using RESOLVE relationships. 

### "* Other data pointsare based on medianairport datacalibrated to RESOLVE data. 

### "* The dashed linesindicate uncertainty inthe isopleths near Searles Valley 

### "* Isopleths near the border ofthe map are guided by nephelometer data at Death Valley as well as data from otherairports in California and Nevada 

### "* The Beaumont (Banning Pass) value, shown inparentheses, isinconsistent (higher) than surrounding stations This inconsistency may be because of the high altitude of that site (790 meters) or because of uncertainties associated with thelimited visibility markers atthat 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 ofthe military operations sensitive to visibility occur in the southern halfofthe R-2508 restricted airspace (see Figure 1). Aircraft measurements in the region show that visibility degradation isprimarily confined to the lower troposphe:e below the level ofthe 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 withinthemixing layerin the southern R-2508region. Indesigning the RESOLVE monitoring network, theresultsofprior 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. Someofthe critical assumptions affecting the program design were asfollows: 

### " 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 ofinterest should includeorganics, sulfates, elemental carbon, soildust, andnitrates(References 10and 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 roadsin thedesert. 

### " Since, atleast under some circumstances, the aerosol species fromthe Los Angeles basin and the SanJoaquin Valley should have different chemical compositions, it was assumed that a combination ofchemically 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 ofhigh correlations between daytime and 24-hour particle averages, because ofthe similarity between daytime and 24-hour particle composition, and because ofsome 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 forcharacterizing the nature andscope 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 precisionwith lessoperational difficulties andsmaller expenses. 

### " Based on the results ofpreliminary 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 andaccuracy.

All references to altitude in this reportrefer toheight 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 asdailyparticle 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 ateach site is listed in Table 1.The measurements at all sites included continuous light scattering coefficient, continuous meteorological parameters, and daily collection offine (< 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 allstudy days.

TABLE 1. RESOLVE Monitoring Stationsand 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 particlesampler

(4-hour) 1 4 METEOROLOGY Wind speed/direction and

temperature 1 1 1 1 1 1 1 7 Oewpoint I 1 1 1 4 Relativehumidity 1 1 1 3 Radiosonde (upperair

wind, temperature, and RH) IC IC 2 DATA RECORDING Satellitedata system 1 1 1 1 1 1 1 7 Cassette tapedatasystem 1 1 1 1 4 Stripchartrecorders 1 1 1 1 1 1 1 7 '2x4refersto. ,Ilctionintwosize rangesofa totaloffoursimultaneoussamples(1 totai,d < 10 micrometers; 3fine.d < 2.5 micrometers 0Multistage rotating DRUM impactor. Thesesamplers wereusually atthesites indicated,butwere occasionally moved between sites. Notco-locatedwich monitoring site.Operatedby DOD personnel.

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The fine and total particle measurements at all sites were made using the RESOLVE 2x4sampler, whichacquired one total and threefine 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 ofTeflon and provided data on mass, absorption, and elemental composition. The second fine Teflon filter served asa data quality check and asa 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 moredetail inother RESOLVE reports (References 3and 4). For each site/day, all three Teflon filters (one total and two fine) were analyzed for particle massand light absorption. Forevery 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 forelemental 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 ofanalysesforthe 2x4 samples atthe various sites are summarized inTable 2. The existence ofboth 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 ofvalues 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 withthehigher percentage inTable 3). Extensive quality-control procedures were developed and documented for all measurements, andquality-assurance audits were performed semiannually at all sites bya 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 varioussamplers involved inthe SCENES program (Reference 19). In addition, the 2x4 sampler was designed so that one fine filter was redundant. Accordingly, the quality ofthe 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 ofthe 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 ofthe 4 calendar quarters when the particle samplers were fully operational. To avoid seasonal biases, manyoftheanalyses reported

* All references tohoursin this reportrefer to Pacific StandardTime.

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TABLE 2. RESOLVE 2x4 Sampler FilterAnalyses.
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 Allfilters Every third filter Total Teflon (day) plus -40 additionai filters persite.e 2 <2 5 Allfiters Every third filter Selected filters Fine Teflon (day) plus-40 additionai filters persite., 3 <2 5 All filters Fine Teflon 4 <2.5 Every third filter Selected filters Fine quartz (day) plus -40 additional filters persite.e Tehachapi,Soledad Pass,Cajon Pass. Randlsburg Wash 1 <10 All filters Routine plus -4 rotal Teflon additiona) fiters perslte.e 2 <2.5 Allfilters Routine plus

- Selected filters Fine Teflon additional filters persite.e 3 <2.5 All filters Fine Teflon 4 <2.5 Routine plus -40 Selected filters Finequartz additional filters persite.(*

aPIXE

-Elemental analysisby proton-induced X-ray emissiontechnique bOAT -Lightabsorption and elemental carbon byoptical absorption technique. cXRF

-special sensitivity X-rayfluorescence; performed onfine filters only. dOrganic, elemental, and total. e Selected based onthe resultsoftheroutine analyses.

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TABLE 3. Percent ofXRFand 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 18 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 N JA S 0 N D J F M A J AS 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 studywas conducted atEdwards AFB and NWC from November 1984 to August 1985. an insulated nephelometer study wasconducted atEdwards AFB during the fall/winter of 1985-86 and summer of 1986, awinter nitrate study wasconducted at EdwardsAF8 and NWC in January-February 1986. and an organic originsstudy wasconducted at EdwardsAFB and NWC in thewinter and summer of 1986. 

### "* Chart includesall data gapsexceeding 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 canbe easily discounted, the analysis is conducted for the entire 15-to 17-month program, orforthe entire 2years oftheprogram. Figure 3 also illustrates all significant data gaps that occurred during the monitoring program. Thetwo most noteworthy data gaps were 1. In late March 1985, the 2x4 samplers were inoperative for about I to 3 weeks (dependingonthe site)because ofatemporary disruptioninthe supply ofTeflonfilters. 2. Absorption measurements were missing during November and December 1984 because the quality assurance program detected unreliable blank values for that period causedbyadeteriorating 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 ofthe data.*

* Theone 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 thatthe effect ofthe missingabsorption dataonour conclusionswas negligible.

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The period forthe Online Data Analysis Project isindicated on Figure ,eiow the last entry on the chart. The purpose ofthe 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 (seefurtherdiscussion inSection 2.4). Figure 4illustrates the completeness ofthe data for daily averages. We chose to define a daily average as valid or nonmissingifthe 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, andteleradiometers. Withtheexception ofstart-up months and the March 1985 gap for the 2x4 sampler, the monthly data recovery always exceeded 80% and usually exceeded 90% forall four types ofinstrumentation. Considering the entire period after instrument start-up, the average fractions of daily data recovery were as follows: 2x4samplers 90% meterological data 95% nephelometers 92% teleradiometers 95% For the continuous monitors, ifwe examine data completeness on an hourly basis rather thanadaily average basis, the fractionsofdatarecovery were asfollows: meteorological data 95% nephelometers 95% teleradiometers 95% Note the size ofthe routine RESOLVE data set withrespect to the number ofrecorded observations. Forthe2 yearsofcontinuous monitoring, about 115,000hourly 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 filtersandone quartz filterwere exposed inthe 2x4samplers, ledto about 9000 filter mass measurements and 8000 absorption measurements. Analyses by PIXE, high-sensitivity XRF, and the carbon technique were performed on about 800 ofthe 3100 site-days with particle sampling.

2.4 SPECIAL STUDIES Although the routine monitoring network provides most ofthe data needed to attain the RESOLVE objectives, the network does not provide all ofthe measurements required for theprogram goals. The remainder oftherequisite information isfurnished by intensive field experiments and other special studies carried out for limited time periods and/or at a limited number ofsites. In the planning work for RESOLVE, the decision as to whether a certain measurement should be done routinely oraspart ofa special study wasbased onan evaluation ofthe trade-offs between the.needs forcomplete data coverage and the costs of conductingthe measurements.

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100% •"'"•

50%
• 2x4 SAMPLERS

50% MTOROLOGICAL DATA

100% " U111t I.. 50% NEPHELQI4ETERS

TELERAOIOMETERS

A SO0 N DJ FMA MJ J A SO0 ND0J FMA MjJJA 1983 1984 198S FIGURE4. Completeness ofRESOLVE DataCollection.
Some ofthe 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 1984through the middle of 1985. The Online Data Analysis Projectalsoled to midcourse adjustments in theroutine monitoring program (e.g., atimeextension from April 1985 to August 1985, and the addition ofhigh-sensitivity XRF analysisforthe Teflon filters).

16NWC TP 6869

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 ofemission-type datarather 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 supportofRESOLVE. 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 NO2 absorption to the lightextinction 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 anylon 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 massbalance (seeSection 3.3). Thermidograph Nephelometer A cyclone (fine particle) nephelometer that heated atmospheric samples to 350°C was deployed atEdwards AFB in the summerof 1984. This instrument indicated whatfraction offine particle scattering was caused by refractory materials as opposed to evaporable compounds. The data provided an important check on the allocation offine-particle light scattering amongaerosol components (seeSection7.2.3). CycloneNephelometer 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 ofcoarse-particle scattering to the light extinction budget (seeSection 7.2.3) and toevaluate the accuracy ofthe nephelometer as a measure oftotal particle scattering (seeSection 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 aWOGA contract to Global Geochemistry Corp.These samples were analyzed with respect tocarbon-12/carbon-14 isotope ratios and with respect to molecular composition. The results provided information on the origin of theorganic aerosol in terms ofcontemporary versus fossil and in terms ofnatural 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 summerof 1985. Thisdata set was used tohelp determine the relative contributions ofelemental 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 (seeSection 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 totheTeflon absorption measurements (seeSection 3.2.2).
Insulated Nephelometer For 9 months during the fall and winter of 1985 and 1986 and the summerof 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 ofthis intensive study was to investigate whether the 24-hour composition data were representative of shorter time periods (seeSection 3.5).
DustCharacterization A literature review was conducted by AeroVironment, Inc., to characterize the :hemical composition ofdust sources in the RESOLVE study region. The information was needed tohelp evaluate the origins ofsoildustparticles (see Section 8.1).

18NWC TP 6869

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 chemicalcomposition. The results were useful in evaluating the contributions of local desert source categories to extinction (see Section 8.4 and AppendixB)
3.0 DATA QUALITY AND REPRESENTATIVENESS Chapter 2.0 dealt with the scope and quantity of the RESOLVE data. Chapter 3.0 focuseson the quality ofthe data set. Understanding the quality ofthe 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 foraquality 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 screeningprocess toflagerroneous or suspicious values. 3.1 DATA-SCREENING PROCEDURES Once the RESOLVE database wasassembled, thecritical air quality parameters (daily averages ofnephelometer 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 anabsolute necessity with mostair-quality data setsbecause erroneous values dobecome recorded andbecausejustafew suchoutliers 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 ormodels thedata should follow, the screening process was performed before starting any ofthe 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. 1in Table 4 indicates pairs of data-base parameters thatwere essentially duplicate measurements of the same phenomenon. Scrutinizing these duplicate measurements was the most important aspectofthe screening procedure. The screeningprocess includedthe following stepstoidentifyandflag outliers: 1. For each ofthe seven sites individually, special scatterplot outputs were prepared for 24pairsofcloselyassociated parameters. These pairsincluded theduplicate parameters (List No. I ofTable 4)as wellasother closely related parameters (ListNo. 2ofTable 4). The scatterplot outputs contained notonly aroutine scatter diagram ofthe databut a listing of the dateand data values as well asmarkings indicatingthe number ofstandard deviations thateach observation varied fromaleast-squares line fittothedata. 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 andmarked onall scatterplots pertainingto that parameter.
19NWC TP 6869

TABLE 4. Plots Prepared forEach Siteas Part of the Data-Screening Process.
ListNo 1: Scatterplots ofDuplicate Parameters Twoalternate fine masses Each fine massversussum ofchemical speeies (organics +sulfates +elemental carbon + soil) Two alternate fineabsorptions Fineabsorption versuselemental carbon PIXE SversusXRFS PIXE FeversusXRFFe PIXE Ca versusXRFCa PiXE KversusXRFK PIXETiversusXRF Ti PIXE PbversusXRFPb PIXEZn versusXRFZn ListNo.2: Scatterplots ofClosely Related Parameters Total massversuseach finemass Each finemass versusBsp Total massversusBsp Total absorption versuseach fine absorption Each finemassversuseach fineabsorption Total massversustotal absorption Eachfine massversus organics Eachfine massversus sulfur Eachfine massversuselementalcarbon 8spversus organics Dspversussulfur 8spversuselemental carbon Organics versuselemental carbon ListNo. 3: TimeSeriesPlots ofDailyAveraqes Ratioofeachfinemassto8sp Ratioofeach finemasstototal mass Ratioofeachfine absorption to8sp
3. The day in question wasfound on three time-series plots ofdaily "key ratios" (see List No. 3 in Table 4; also see Figure 5 for an example). This information allowed atest to be made as to whether the anomaly occurred just for the date in question (or also for surrounding dates) and whether the anomaly occurredjust at the site in question (or also for surrounding sites). Inthe process ofchecking the scatterplots ofduplicate parameters, the scatterplots ofother 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 wastomake ajudgment whether the data value was erroneous and towritedown thereasons forthe error.A fileofthese reasons wasassembled. 5. The last step was to label certain data values as "definitely" or "probably" erroneous and toflagthem assuch inthe 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 andthe flagged data point was fixed. Forsome cases,a likely cause was found, but no way was found to correct the data. Sometimes, no obvious cause wasidentified.

20NWC TP 6869



### 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 ofthe XRF dataand almost none ofthe nephelometer data appeared tobe erroneous. About 5 to 7% ofthe carbon data were flagged. The thermal separation technique for organic/elemental carbon is an evolving and uncertain approach

21NWC TP 6869

that has not yet been standardized andcarbon measurements tend tobe the least precise of all the RESOLVE data sets. TABLE 5. PercentofDaily Average DataFlagged By the Data Quality Screening Process. Nephelometer Scattering 0 1 F,ne-Particie Mass 22 Total Particle Mass 10 F,ne-Particle Absorption 22 Total Particle Absorotion 13 Fine-Organic Carbon 54 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 ofdaily observations. For the data set used in this report (which exciudes flagged values), the two simultaneous fine mass or absorption measurements were averaged together, butjust the single valid measurement was used ifthe other measurement was missingorerroneous. Figures 6, 7, and 8 illustrate some ofthe flagged data by showing scatterplots (for all sitescombined) ofthe two alternative fine-mass values, thetwo 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 ofall the graphic outputs listed inTable 4.
A.L SITES
I..

4 0 0

see

* + + 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 +).

22NWC TP 6869

%LL SITES

V2.

F U .o. *
. - 4

"F

- o e , .. 4 Fg-a

.. ÷ ,.. 4 * ÷+ *6 ÷ +

FUR 7. Sctepo4 fAlent ie 

### AbsorptionChannels, Showing Flagged Data (+).

ALL. SITES
I I

I.. ÷ . •. .'


### "3. "4

44 . * ".. S..
.

oo0 I I " " I" I l I

.861 4* O3 00 II 3 4 *3 3I 3 ? 5j *6l i FIGURE 8. Scatterplot or"Fine-Absorption Versus 

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

### 23NWC TP 6869

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 ofa 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 ofthe 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 thusrepresents systematicerror.
3.2.1 Precision Estimates Precision is defined here as ± 1 standard error of measurement reproducibility. Precision can beconceptualized asthe standard deviation ofthe data set that would occur if a particular parameter were measured simultaneously by a large number ofequivalent monitors. In practice, precision is often determined byoperating two equivalent monitors together over a large number ofsampling periods. In the latter case, the deviation between the two monitors includes the imprecisionofboth, andthetotal deviation is thus(bysimple error propagation) v'-times the individual imprecision. Thus, for the two-monitor case, precision is estimated asthe standard deviation ofthe difference between thetwo monitors divided by 

### V_27 Appendix A derives precision estimates for the critical air-quality parameters in RESOLVE-particle mass, absorption, scattering,andcomposition (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 ofwhich 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 (ona 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 knownprecisely ona daily basis. To geta better feeling for the precision estimates, refer to the scatterplots in Figures 6, 7, and 8 (neglecting the flagged, erroneous data). The high precision ofparticle 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 ofthe PIXE and XRF analyses for the four elements (S, K, Ca, and Fe) that are nearly always above detection limits forboth methods.

24NWC TP 6869

TABLE 6. Precision ofRESOLVE 24-Hour Data. Average base-year concentrations Elements Average precisiona Edwards AF8 NWC Fort IrwinNTC Scattering and Absorption Particlescattering ± 2% 31.2 Mm- 25.7 Mm- 20 3 Mm

- Fine-particleabsorptionb ±5% 5,2Mm

- 58Mm 4 1Mm

- Totalparticle absorption

. ±7% 6.4 72 51 Coarse-particle absorption

. . (
_ 45%)c 12 1.4 10 Particle Mass Fine-particle massb ......... 5% 83V±g/m 3 9.0Vig/m 3 6.5 pig/mJ Total particlemass ...... ±.. 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/mi 3 1.9Pg/m 3 1 1pg/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 Otherelementsofinterertd XRF V ........ ± 25% .0022Vg/m3 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 aPrecision uncertainty representsasingle standard error. bReported precision pertainstotheaverage ofthetwofine 2x4values(as used inthe RESOLVEDATA ANALYSIS). The imprecision ofasingle finechannelwould begreater byafactor ofV2 cObtained bysubtraction offinefrom total. dTheRESOLVE special experiments forfineparticulate nitrateinvolvedjusta fewweeks ofsampling at EdwardsAF8and NWC.Thedata suggestannual averageNO 3concentrations ofabout 1.2 gg/m3at EdwardsAFSand0.5 pg/M3atNWC(seeSection 7.3).Theprecision ofan individual 24-hour sample is dominated byflow rateuncertainties and isestimated as ±5% (Reference 20).

25NWC TP 6869

$. IFPtee ,.

J C -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 AreHighly Detectable by BothMethods. All ofthe major conclusions ofthedata analysis are based on longer term averages of the RESOLVE data. For a longer term average ofN.data points, the error in the average due to measurement imprecision is reduced by a factor ofý N Because ofthis effect, and because measurement precision is generally quite good, we found that measurement imprecision isanegligibie component ofthe overalluncertainty levelinourconclusions.
3.2.2 Accuracy Considerations In analyzing the RESOLVE data, we adopted the approach of correcting for any inaccuracies thatwere 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 oneofthe mainsources ofuncertainty inthe results. Nevertheless, wedidattempt to make corrections whenever theywere 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

26NWC 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 scatteringfrom coarseparticles. Two methods are used here to estimate an accuracy calibration factor for the Teflon absorption measurements. The first isbased on a comparison ofRESOLVE 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 acorrection factor of0.6for 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 inteipreting 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 thefact thatthe 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 almostalways less than20%. The Insulated Nephelometer Study demonstrated that the RESOLVE nephelometers closely approximate ambient scattering conditions with respect to volatile aerosols (to within 1to 2% on the average). The Heated/Ambient Nephelometer Study explained why thiswas reasonable by showing that water andother easily volatilized species are notvery 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 smalland variable. With respect to the issue ofmissing coarse-particle scattering, the inaccuracy of the RESOLVE nephelometer data is more significant. We estimate that the RESOLVE nephelometers miss about one-half ofcoarse-particle scattering based on Stokes theory calculations ofsampling 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 2.5Mm- Iofcoarse-particle scattering is missing on the average, which is about 10%ofthe three-site mean nephelometer value (25.5 Mm- 1). The corrected three-site mean fortotal-particle scattering would thusbe 28Mm

- 1.

27NWC 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 notbe 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 tobe available on a given day, and (3) significant imprecision would be added to the nephelometer data base because the coarse-particle scattering correction of0.3 m2/g times coarse-particle mass is very imprecise on a daily basis (because ofimprecision in coarse-particle mass data and because ofvariations 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 forcoarse-particle scattering. But, whenever we sum all extinction components or report visual range, the results always include thecorrection for missingcoarse-particle scatter.
3.3 MASS AND EXTINCTION BALANCES One ofthe most important quality checks for the particle and extinction data bases in RESOLVE involves comparing the whole tothe sumoftheparts. Does the sum ofthe 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? Table7presents average fine-particle mass balances forall seven stationsbased on the routine data during the RESOLVE base year (July 1984 toJune 1985). About 10 to 20% of the fine-particle mass isnot accounted forby organics, sulfates, soil, andelemental carbon. Thisremaining mass isbelieved to bemostly 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 onTeflon filters) andthe massofothermiscellaneous elements (about3%), thetotal massaccounted for is 104% at Edwards AFBand 97% at NWC. Thus, the average mass balance for fine particles seems tobequite 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 isfairly good (R = 0.79). Thus,both for the average and individual days,the fine-particle-mass balance isindicative ofgooddata quality. Table 8compares 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 ofextinction components-averaged over the three sites-consists of 24% from Rayleigh scatterby air molecules (assumedto 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 absorptionby elemental

"Several indicationsare presentthat theremaining mass mostlyrepresents nitrates.One important piece of evidence is that nitrates and unaccounted-for mass are the only two aerosol components that show a winter maximum ratherthan asummer maximum (seedata anddiscussion in Section7.3.2).

28NWC TP 6869

TABLE 7. Fine-Particle Mass Balances Calculated for theRESOLVE Base Year.
Elements Mass componentsaivided bytotal measured fine mass bysite EDW CHLJ FOR RAW TEl- SOL CAP Organicsd 32% 37%/ 29% 30% 34% 30% 36% Sulfatesd 30% 24% 28% 32% 26% 32% 27% Elementalcarbon 6% 6% 7% 5% 4% 8% 7% Soils 16% 20% 27% 17% 15% 10% 8% Unaccounted forb 16% 13% 9% 16% 21% 20% 22% Numberofdays( 85 87 80 26 26 30 33

aTo include the mass of associated ions, the following conventions are adopted throughout this report: sulfates = 4 12x S(assuming ammonium sulfate), organics = 1.5xOrganic C(accounting for associated hydrogens and other ions), and soil = 2.14 Si + 1.67 Al + 144 Ca + 140 Fe * 1.66 Mg + 1.22 K + 144 Mn (accounting for common soilcompounds). bThis category represents the fine-particle mass not accounted for by the other four categories. It isthought 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, isapproximately 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 forabout 3% ofthe total at allseven sites. cThistable isrestricted to days in the RESOLVE base year with data availabLe for finemassand all chemical components.

ALL SZTES
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29NWC TP 6869

TABLE 8. Comparison ofTeleradiometer Total Extinction to the Sum ofExtinction Components. Ratioofteleradiometer Correlationofdaily teleradiometer Site andteleradiometer targets tosum ofcomponents* tosum ofcomponents All data Clearskies Alldata Clear skies EdwardsAFB 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) NWCTarget1 78 83 79 (322) 89 (47) "Comparisons arecarried outusing dailyaverages. Thesum ofcomponents iscalculated inMm

-Ias 11+ 

## f(Bca02 4 + .3CP)+ .04 B + B Bap2at 8 Iscat24 4p24 B The first term isRayleigh scatter. The value inparentheses represents particle scatter, adjusted to account for the missing partofcoarse-particle scattering. The second and third terms inthe brackets are NO 2 and particle absorption, respectively. Thesubscript *24" refersto24-hour averages.The last ratio inthe 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.,2 4 isestimated 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(seefootnote toTable 8). As indicated in the left-hand side ofTable 8, teleradiometer extinction is generally about 15 to 25% less than the sum ofcomponents. 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 aline 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 mixinglayer). The right-hand side of Table 8 presents the correlation between daily values for teleradiometer extinction and sum ofcomponents. Most of the correlations range from about 0.75 to 0.9, which is good, especially considering the potential problems with

30NWC 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 forextinction components (i.e., scattering andabsorption).
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 sitesduring 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, wefocus mainlyon the data quality implications. One feature shown inTable 9 isthe 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 offine-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 ofcorrelation 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 ofcorrelation with the others suggests that none ofthese parameters has uniquely poordataquality. 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 a0.6correlation withelemental carbon ispresent. Thereason for this is therelatively lowprecision ofthe elemental carbondata (seeTable 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 isa reflection ofthe good precision inthe PIXE andXRFanalyses. 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 Pbcorrelate ata level of0.25 to0.40. This factshould not represent adata quality problem, because precision is quite good for Ni and Pb (see Table 6). Rather, the explanation forthe lower correlations liesin someofthe findings ofChapter 8. Ni and Pb are usedas tracer elements to investigatecontributions from different source areas (San Joaquin Valley (Ni rich) versus Los Angeles and desert sources (both Pb rich)). Tracer element fraction of fine mass inair leavingthe source areasexhibits a significant

* Thepotential forimprecision in theteleradiometer measurements is suggested by somecorrelation studies with the RESOLVE datasets. Comparing hourly teleradiometer datafor different targetsat the same site yields correlations around 0.65 to 0.80. Comparing hourly teleradiometer data with hourly nephelometer data yields correlations around 0.6to0.8.

31NWC 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 084 Fabs 0.79 So 100 Soil 098 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 006 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: 

### "* Allmeansand standarddeviations areinMm -IorVg/m2 

### "* Data arefor all 24-houraverages duringthe RESOLVE baseyearatthethreereceptor sites.

32NWC TP 6869
degree ofvariability.* In fact, at the passes, the critical tracer elements (Ni at Tehachapi and Pbat Soledad and Cajon) exhibit correlations ofonly 0.3 to0.7 with the other key airquality variables. At the receptor sites, the correlations should be even lower, because Ni (orPb) can further vary independently ofthe other air-quality parameters, depending on therelative contributions ofthe varioussource 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 airquality during daytime hours (when visibility is of special concern), and (2) are the ground-level measurements adequate to characterize visibilityathigher 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 notnegate the use of24-hour particle data tocharacterize daytime air quality. Perhaps the 24-hourparticle data can simply be factored by the diurnal variations in the nephelometer ifdaytime 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 ofdiurnal variations in particle composition, a special field experiment was conducted at Edwards AFB and NWC for 3 weeks during August 1985. Theexperiment involved operating the2x4particle samples on an 8-hour basisrather than a24-hour basis. Table 10 compares average fine-particle composition for the daytime and 24-hourperiods. With onlytwoexceptions-denuder nitrates at Edwards AFB andorganics 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 ofa 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 planningstudy byTrijonis, et al. (Reference 3) alsonoted 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 areadequate forcharacterizing airquality duringdaytime hours.

* Day-to-day fluctuations in the Ni mass fraction atTehachapi 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 offine mass, (2)variation in trajectories(and therefore sourceexposure) within the upwind air basins, and (3) variations insource emissions. ** TheRESOLVE planning studyhas also beenverified in anothersense.The planning study warned ofdata quality and precisionproblems for 8-hoursampling asopposed to24-hoursampling. Duringthe 8-hour samplingof August 1985, it was found that (I) precision was generally a factor of2or 3 worse than for the routine 24-hour sampling; (2) the number ofvalues flagged by the data-screening procedure increased by an order ofmagnitude; and(3) thesampling protocolwasmoreproblematical, leading toseveral questionsregardingdataquality.

33NWC TP 6869

TABLE 10. Fine-Particle Mass Balances,

Daytime (0800-1600) Versus 24-Hour. Masscomponents plustotal measured fine mass bysite and period Edwards AFB NWC Elements Daytime I 24-Hour Daytime 24-Hour Masscomponents + 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. bComputed from particle absorption because of poor-quality, elemental-carbon data during thespecial study. cThis 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% overthe daytimeperiods during thisset ofsampling days. dSample isrestricted toperiods without missing data forall key parameters.
The question ofvertical 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 forthe three sites over the 2-year RESOLVE period (September 1983 toAugust 1985). Over the 2 years, particle scattering at B Mountain and Laurel Mountain averaged 64% and 89%, respectively, ofthat 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 explainedbythe closerproximity ofLaurel Mountaintoflow 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 isthe RESOLVE receptor site that ismost 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 ofonly 0.79 isobtained even when the two mountain sites are compared. Good correlations, despite the horizontal distances, show that (atleaston adaily basis, onan hourly basis, thecorrelations are not as

34NWC TP 6869 good-0.66 between NWC and B Mountain, and 0.61 between NWC and Laurel Mountain) ground-level airquality isnot severely decoupled from elevated air quality.
4.0 CLIMATE AND METEOROLOGY The two major objectives ofRESOLVE 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 ofthe study region. 4.1 CLIMATE OF THE RESOLVE STUDY REGION 4.1.1 Temperature and Moisture The RESOLVE study region encompasses much ofthe California high desert, including the western halfof the Mojave Desert and the southern portion ofthe Owens Valley (see Figure 1). The region includes many desert valleys and dry lakes with elevations of600 to 1000meters and several mountain rangeswith peaksof 1500 to 2500 meters. The climate ofthe study area is strongly influenced by the mountain ranges that border thedeserton the south and west. The Sierra Nevada andTehachapi Mountains on the west and the San Gabriel Mountains on the south intercept the storms that bring rain to the coastal regions ofCalifornia. Air masses crossing these mountains typically drop much of theirmoisture on the mountain barriers and become warmer and drier as they move down the slopesintothe desert. Average rainfall in the desert region ranges from about 10 to 25 centimeters per year (Reference 31). Most oftherain 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 ofonly about 25 days per year havingover. 025centimeter ofprecipitationoccurs atEdwards AFB. As aresult ofthe trapping ofmoisture 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 inTable 11. TABLE 11. Climatic Datafor 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 11 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 monthlyweather summaries (Reference 32)

35NWC 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 flowstend to be from the west ornorthwest most ofthe year (Reference 33), with a shift to southwesterly flows in the summer (Reference 34). The actual flow patterns and wind directions in the lower levelsofthe atmosphere are controlled by the lucations ofhighand low-pressure systems andby thetopography. 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 ofgood ventilation and good visibility, mostofourdiscussion will focus onnonstorm flow patterns. In the absence of storms in the summer, and between storms in the winter, the prevailing flow patterns tend tobe 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 airbasins tothe westand south tends to bestable, 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 forJanuary andJuly in the southern part ofthe 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 areillustrated 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 (seeFigure 11). In the winter, radiative coolingcreates very stable air nearthe surface, with the surface flowsessentially decoupled from the flows aloft during much ofthe 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 byheating inland, push some airinto the desert from the SanJoaquin Valley and theLosAngeles basin (seeFigure 11). An important feature ofthe winter regime in the desert is the relatively light wind at the surface. Calms are reported 26% ofthe 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% ofthe hours at NWC and 10% at Edwards AFB are reported ascalm. The large percentage ofwintercalms allows the potential buildup oflocal emissions from wood burning, motor vehicles, and industrial sources to supplement the pollutants transported into the desert from upwindsource 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- hourday. The wind speeds, however, are typically much higher in the afternoon than in the late night andmorning.

36NWC TP 6869
Figures 12and 14 indicate that the San Joaquin Valley is the primary source ofsummer air for most ofthe study region, with the Los Angeles basin (and possibly Ventura Countvi influencing the southern part ofthe region. The SanJoaquin Valley isthe dominant source ofsummer air at NWC and the primary source at Fort Irwin NTC. The San Joaquin Valley may also be the primary source ofsummer air at Edwards AFB, although Edwards AFB frequently receives air from the LosAngeles basin late in theday. The convergence zone to the east ofFort 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 (southeastofPalm Springs) to impact theFortIrwin NTC region.

"". •0800 PST

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39NWC TP 6869

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FIGURE 14. Calif'ornia Predominant Surface Wind-Flow Patterns for the Summer (JunetoAugust). (Reference 38.)

40NWC 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 ofSO, in the San Joaquin Valley and the South Coast changed somewhat between 1983 andthe 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 ofestimating emissions may not be consistent between districts. Nevertheless, it is clearly shown in Figure 15 that reported emissions ofthe air basins upwind of the RESOLVE region (San Joaquin Valley, Los Angeles, and South Central Coast) are much greater than those ofthe 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 forPM 10. Theemission patterns are reflected inthe spatial distribution ofvisibility illustrated in Figure 16(Reference 40).The median airport visual range fortheupwind air basins is inthe 15- to40-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 isclear from Figures 15and 16 that airmasses transported into the desert from the upwind counties have the potential todegrade visibility inthe desert.
4.2 RESOLVE METEOROLOGY AND COMPARISON TO LONG-TERM AVERAGES In this section we describe the meteorology ofthe RESOLVE studyperiod and compare the RESOLVE period to long-term averages. We made the comparisons using parameters forwhichhistorical dataare readily available at EdwardsAFB and NWC.
4.2.1 Temperature and Moisture Annual and quarterly maps for temperature and relative humidity at the RESOLVE measurement sites forJuly 1984through June 1985are presented in Figures 17 and 18. It is apparent from Figure 17 that the desert sites average 3 to5°C warmer than the pass sites during all seasons. This difference in temperature reflects three factors: (1) Cooler marine airfrom theupwind valleys is transported through the passes, (2) increased solar heating is present inthe desert, and(3) thepasssitesare higher inelevation than thedesert. 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 ofEdwards AFB to the passes and the impactofmoist marine air from the LosAngeles 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 ofdaytime heating and nighttime drainage are both maximized. The impact ofmarine air at Edwards AFB late inthe afternoon is indicated bythe temperature starting todecrease earlier in the day than attheother sites,and by the relative humidity being higher (especially in the late afternoon).

41

### NWC TP 6869

MOMN W&UT 10

270

a. Particulate Matter less than 10 jam (1000 tons/yr)

Wowed miss, 

### 0.6 58

. %11111 ai111f cost 12



### b. Sulfur Oxides (1000 tons/yr)

aii nal "1.1logi IN NOW OWI WLI 

### 2
I"'186 'A 

### SSo 3655 

### C. Nitrogen Oxides (1000 tons/yr) FIGURE 15. 1983 Emissions in the RESOLVE Region and UpwindAirBasins (Reference 39).

42NWC TP 6869

25 45 65

25!

64 3

-m 15 4,0
Caiori1 167 t 197 90a)

436NWC 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 *-aSOLCAP 8 u- - :j
.

8 il, a a! a 

### In -2

,.. 

### T a 22 - ?2 - aaa

2 •, A23 22a 4 4

23 a2 7 a

a~~ 5Z 6 e 7 I ýýLA FIUE 7 An al n urel vrg Tmeatr (T a the REOV Sie fo Jul a94truhJn 95

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 5- 2 30 0 ca 

## T

.

aa 

### 3" t 

### .

## Ta.. 2 aa252a .34

aIUE .AnuladQ arelAeagRetie umdy a% at ah EOV ie o auy18 houhJn 9 45 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 forJuly 1984 throughJune 1985.

45NWC TP 6869

EDW CHL_
... FOR•.. .30

20 -E

b4 

### SSI I !..
..



### Hours (PSI) FIGURE 19. Annual Average Temperature an4 Relative Humidity Diurnal Patterns at Edwards AFB, NWC, and Fort Irwin NTC.

46NWC 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 thanaverage atboth locations. This coolingwas 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 sitebut notthe Edwards AFB site. Upper-air temperatures are an indication ofthe stability ofthe atmosphere. Typically, temperatures above about 20'C at 850 mb (about 1500 m/ms 1)inthe 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 areplotted in Figure 23, along with the long-term averages. (Vandenberg AFB islocated on the coast, west ofthe RESOLVE study area.) Generally, significantly warmer 850 mb temperatures than the rest ofthe year occur from June through September. These months canbe considered a "summer" regime with the greatest potential forphotochemr('al pollutants. For the RESOLVE period, the 850 mb temperatures for most months at both sites were reasonably closeto the long-term averages. However, the October through December 1984 period averaged about 4 degrees cooler than normal, reflecting increased frontal system activity during thatperiod. 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 FlowPatterns The predominant flow patterns for winter and summer are shown in Figures 11 to 14. Annual windroses forthe RESOLVE sitesare shown in Figure 24. Thepositions ofthe roseg onthepage reflect their geographic locations. Thepredominant wind directions inFigure 24 are consistent with the summer flow patterns in Figures 12 and 14, and with the general winterafternoon flowsin 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 and26, respectively. During RESOLVE, the winddirections in all months atboth 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.

47NWC TP 6869

.* 

### 40



### . -a- LongTermAverage

-- 

### RESOLVEYear

## Average /-j 

### 30

cc 20 10 * Edwards AFB S0 1 1 - I 1 I I I I I I 

### Jul. Aug.Sep. Oct. Nov.Dec.Jan.Feb.Mar. Apr. May Jun. a. 

## 40 S -a- LongTermAverage

.- 

### RESOLVE YearAverage 

## 30

,o 10 China Lake NWC 0 1 1

- I I I I I t L I I I I 

### Jul. Aug.Sep. Oct. Nov.Dec. Jan.Feb. Mar. Apr. MayJun. Month FIGURE 20. Comparison of Long-Term and RESOLVE Year Average Daily Maximum Surface Temperatures at Edwards 

### AFB and NWC.

48NWC TP 6869



### 120 100 -W. Long-Term Average 

### S0A-*- RESOLVE Year Aver a 

## EdwardsAFB 60 40 20



## 0* Jul. 

## Aug.Sep. Oct.Nov.Dec.Jan.Feb.Mar.Apr.May Jun. 120 

### 100

80 -a- LongL-TermAverage

..

## China LakeNWC 

# A

- 

### RESOLVE YearAverage .2 60 ~40 

# A 20

0 Jul.Aug.Sep.Oct. Nov.Dec.Jan.Feb.Mar.Apr. MayJun. Month FIGURE 21. Comparison of Long-Term and RESOLVE Year Precipitation atEdwards AFB and NWC.

49NWC TP 6869



### 80 ~70 

## .- A§ Lon .Te Average Is 

### 6 RESO V Ya Aver a 

## EdwardSAFB 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



# $ '



## 40 30 JuL Aug.Sep. Oct Nov.Dec.Jan. Feb. Mar.,Apr. MayJun. Month FIGURE 22. Comparison of Long-Term and RESOLVE Year Relative Hunrdity at Edwards AFB and NWC. Long-term and 

### RESOLVE measurements at NWC are from different measurement stations.

.50NWC TP 6869



## U 30

- Long-Term Averages -0- Resolve YearAverages

2.. 0.

### S10 

### E 

## EdwardsAFB Jul. Aug.Sep. Oct. Nov Dec. Jan. Feb. Mar. Apr. May Jun. 

## U30 -&" Long-Term Averages

-- 

### Resolve YearAverages 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 ofLong-Term and RESOLVE Year 850 mbTemperatures From the Edwards AFB and Vandenberg AFB Soundings (References 32, 42 and43). Errorbars for Vandenberg AFB are I standard deviation of30-yearmonthly averages.

51NWC 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 ofhours 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.

52NWC TP 6869

360 o£ -ow Longj TermAverages

- -e- RESOLVEYew Averages 240

_ _ _ _ v-ariable 180 EdwardsAFB 120

600Jul. Aug. Sep. Oct. Nov.Dec. Jan.Feb. Mar. Apr. MayJun. 6 411 Lana TetrflAveraes__ 5 -4- RESOLVEYew Averages 

# Ga 44 W~ 3 EdwardsAFB
Jul. Aug.Sep.Oct. Nov.Dec.Jan.Feb. Mar. Apr. MayJun. 60 50 Ln emAeae -0 RESOLVE

### YearAverages S40 *30 

### S20

10 Edwards AFB 0 I I I I I
_ I Jul. A4g.Sep. Oct. Nov. Dec.Jan. Feb. Mar. Apr. MayJun. Month FIGURE 25. Comparison of Long-Term and RESOLVE Year Surface Winds at Edwards AFB. Median direction and average speed are shown.

53NWC TP 6869

360 c LonW TtmAvermao , o 315 -e- RESOLVE YearAvwages q;270

- 225

_ _ _ _ _ _ _ _ _ _; 

# Sj180
-e 135 90 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- LongTermAverages 5 -0- RESOLVEYearAverages .4

China Lake NWC
Jul. Aug.Sep. Oct. Nov Dec.Jan. Feb. Mar. Apr. MayJun. Month NOTE: NodataonRESOLVEyearcalmswereeasilyavailable fromNWC. 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 ofcalms 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 ofsubstantial 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 placementhas remained consistent for theperiodofrecord.)
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 areplotted in Figure 27. Monthly averages ofscattering 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% higherthan the long-term mean.The December average during RESOLVE was
54NWC TP 6869
47 Mm- 1compared 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 humidityand fogrecorded that month.

50 -C- '79*85 Averages 440


### ~30 

## :2 20 Values include o 10 Mm-11 lot Rayleigh Scatter 0 0 ' 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 ofClimatological 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 RESOLVEyear 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 toyear, and rain typically falls during short storm periods in any one year. It is not unusual to have the rainfallconcentrated during afewmonths, and summer rains are also notrare. To help assess the representativeness of the study period, it is useful to know the standard deviations ofthe long-term averages. For the parameters discussed in the first three sections ofthis 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, ofthe long-term monthly averages. Thus, except for the heavy rainfall in 2 months, itdoes not appear that the RESOLVE year was an outlier. Fora visibility study, the major climatological norm ofinterest isthe onefor Bscat. The nephelometer monthly averages at NWC were similar to their 7-year averages for all monthsexcept 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.

55NWC 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 foreach day ofthe 2-year RESOLVE nephelometer record. It was found that low visibility in the desert occurs primarily under two types ofsynoptic 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 areonshore flowbelow about 3000 m/ms Iand a deep enough mixing layer to allow marine air to flow through the passes or over the mountains into the desert. Examples ofthe two synoptic conditions, underwhich 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 inFigure 28B typically occurs inother seasons. Another condition thatoccasionally leads todegraded visibility in thedesert isa 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 thedays ofhiFlestBscat (see Figure 29a) show that the winds on the days ofworst 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 thebest visibility days (see Figure 29b)are significantly different, however, and reflect the desert originofthe airon clean days. The highpercentage ofcalms at NWC in Figure 29a is indicative ofa 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 ofthe reduced importance ofthe Los Angeles basinon cleandays atthat site.
4.3.3 Relationship ofDiurnalAir-Quality Variationsto Flow Patterns The diurnal patterns oflight scattering coefficient on best-case and worst-case days at the RESOLVE pass and receptor sites are presented in Figures 30and 31. These figures are consistent with the streamlines in Figures 11 and 12 as well as with the wind roses for the highBscat days inFigure 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 ofthe upwind basins (References 7, 35, and 45). Figure 31 shows that, again on hazydays, 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 pressurecenters T-d6
.... Surfacefronts

- o .... Surfaceisobars (every 4mb)

.... Flowlines at500mrnb 

### FIGURE 28. Example of Synoptic Conditions Leading to Degraded Visibility inthe California Deserts.


### 57NWC TP 6869

RESOLVE 25Z HIGH HOUR dil TO 24,UU RESOLVE 252 LOW HOUR I10l 10 24,91 China Labe Chis Lak.. 

# Sa Joaquin landsburg Sao Joaquin *a5 

### ~ tr Viille•llssisValIley Randsbvjrl 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
4. A. 3sl. ,L. A. leasi <2I 

# I

5 602 j 0 6411 (a) HighestBscat. (b) Lowest Bscat FIGURE 29. Twenty-Four-Hour Surface Wind Roses for the RESOLVE Network for the Daysofthe 25% Highest (a)and Lowest (b) Daily Maximum Bscat Values atEach Site. Average wind speed for eachdirection is indicated bythe bar style, and percent ofhours with wind in the direction is shown by the length ofthe bar.The percentages ofhours withcalm 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) 

### FIGURE30. Diurnal Average Bscat for FIGURE 31.Diurnal Average Bscat for RESOLVE Sampling Period for 25% RESOLVE Sampling Period for 25% Highest and25% Lowest Daily Average Bscat at Highest and25% Lowest Daily Bscat atRESOLVE PassSites. Average RESOLVE ReceptorSites.

59NWC TP 6869

values atthe receptor sites occur duringthe late nightand early morning. This phenomena is consistent both with transport ofthe evening peaks from the upwind passes and with the added early morning contribution oflocal 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 thosedays. 4.3.4 Relationship ofMean Bscat toWind Speed Figures 32 and 33 display the relationship of24-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 4or 5 m/s andthen 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 ofthe LosAngeles basin becomes more apparent. At still higher speeds, the decrease in Bscat is probably due to better ventilation relatedtothehigher winds. A second type ofpattern isadecrease in Bscat as speedincreases fromcalm torelatively 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 dilutionofemissio.-.s from local sources (however, it is notobvious whatthe local sources at Tehachapi are). The subsequent increases with speed might be attributed to transport of pollutants fromotherlocations, andtowind-blown dustathigher speeds. At Fort Irwin NTC, the trend isa steady increase with wind speed suggesting a lack of local sources. 4.3.5 Transport Direction forDays ofHigh and LowBscat Toassess therelative frequency ofoccurrence ofair mass transport into the desertfrom 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 ofthe 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 ofsynoptically driven transport without distortion by afternoon thermally driven flows. The 850mb height waschosen to be above local nighttime drainage flows but still representative ofthe afternoon mixed layer. The frequencies and average speedsofthe 850mb windsforeach direction are shown in Figure 34.It isevident that, on an annual basis, theprevailing boundary layer flow isfrom the southwest through west.However, in the winter months, the flow isbimodal with about equal frequency from thenortheastandwest. Figure 35 presents similar frequency distributions ofthe 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 ofworst visibility at the receptor sites are dominated by flow from the southwest, and the days ofbest 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% ofall days originates in these regions. In contrast, 77% ofthe worstcase daysand only26% ofthe best-case days at Edwards AFB have flow from these regions. The results are similar forthe other receptor sites.
60NWC TP 6869



### 60I 

### Iii Soledad Pas 

# I sCajon 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 1standard deviation ofBscat. Note thatBscat scales vary between plots.


### 61NWC TP 6869



### 50 40 , , 

# IiI~l

40 30

It 

# I .1Edwards caChina Lake

30 2 

### ..... z.... 

### 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 WindSpeed Category at the Receptor Sites. Error bars indicate ±I standard deviation of Bscat. Note that Bscat scales varybetween plots.

62NWC TP 6869
45, April

- September A; 7 ("Sumer" ) 

### 40. c 35, S30. U

2C5 4 3 M 6 2 7--2



# so.c Olober M March 25. 7 Winter*) 

### 303

E - 5



### 70 All Data 60. U0 50.

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 ofEdwards AFB 850 mb Winds for Each Direction for AM Soundings. Data are from August 1983 throughJuly 1985. Note that417 daysofsoundings are available. The missing days include February through April 1984plusother randomly distributed days.

63NWC TP 6869

23.

251 41 ries24hr/~21Ni~s 4N Average 81c~t Oays/ 13 Average 'scat Days a

_ __ _ __ _ _

4 P/7 

# 7 13

25. ' 

## Loes Z4 P/

Aea* 251a 3ighst21 12 AvtraragesciD ay

C2

2 7;__________________________

0~ 

### ~ 45 90 135 180 225 270 315 3600 s 9 13 10 22 27 35 36 Mb insfo Ec Direction for~AWSonings onrethen 25%r~
NWC, and 

### ~Fort IrwinNT.Dtbaeithsmesfo Figur 34.et24h
264NWC TP 6869

Some exceptions exist to these generalizations. In Figure 35, on days ofhigh 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 frontalconditions 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 ofair mass origin for each site. The simplest approach is touse thedirection ofthe upper winds in the region as an indicator ofair mass movement. The most easily accessible and appropriate parameter is the morning 850 mbwind at Edwards AFB. The definitions ofthe source regions andassociated wind sectors are presented inTable 12. At each desert site, the days are divided into five classes, each representing potential transport froma specificsource region. Itis interesting to assessthe percent ofvariance thatcan be explained by the classes for various measures ofaerosol mass, extinction, and composition. In general, the classes are notvery successful at explaining the variance in airquality. About 7to 20% ofthe variance at Edwards AFB andNWC and 20to40% ofthe variance at Fort Irwin NTC isexplained by the classification scheme. This result is not unexpected, however, since the purpose of the scheme isto assess transport directions, notaerosolconcentrations.

TABLE 12. DefinitionofWind-Flow Classes for DesertSites. Wind direction sectors foreach site Class adbr Edwards AFB NWC Randsburg FortIrwin NTC Wash Northern desert sources and local 3150 -67.5° 3150 -45° 315' -22.5' 3150 -450 (includesSearlesValley atRandsburg Wash) Eastern desertsources and local 67 5' - 135' 450

- 1350 22.5'

- 135' 45°

- 180° (includes SearlesValley atNWCand ImperialValley atFort Irwin) Southern Mojave Desert. Los Angeles 135'

- 247 5' 135'

- 225' 135'

- 225' 180'

- 202.5' basin,and upwindcoastal counties (Ventura, Santa Barbara) sources LosAngeles basin, coastal counties. 247 5'

- 270' 225' -270' 225' -270' 247 5'

- 270' San Joaquin Valley, and western desert sources San Joaquin Valley andwestern desert 270' -315' 270' -315' 270' -315' 270' -315' sources

65NWC TP 6869

5.0 SPATIAL PATTERNS OF VISIBILITY The first objective ofthe RESOLVE program is to characterize baseline visibility in the RESOLVE study region. One ofthe most important aspects ofthisobjective is todevelop an understanding ofgeographical air-quality patterns.
5.1 AIR-QUALITY MAPS The most straightforward method ofillustrating the spatial features of the RESOLVE data is to plot averages (or other statistics) for each site on a map ofthe study area. The RESOLVE Annual and Quarterly Data Summary Reports (References 5 and 6) include numerous maps ofthis type. Figure 36 presents some examples- maps ofannual 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 toprepare mapsfor many statistical parameters. Figure 36 does reveal some of the more salient spatial features ofair quality in the RESOLVE study area. For example, it is obvious that the worst visual air quality occurs alongthe southern edgeofthe study area (atthepass sites near the Los Angeles basin), and that the best visual air quality occurs in the northeastern part ofthe study area. However, the maps of Figure 36 are not really satisfactory in terms ofgiving a comprehensive and vividpicture ofspatial air-quality patterns. To obtain a better picture of visual air quality in and around the RESOLVE study region, the RESOLVE datawere combined withdata from other sources. Most ofthe 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 tothe 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 onthe properties ofthe observer, the target, and the illumination conditions. The airport readings generally correspond to an observer contrastthreshold ofat least 5% (i.e., a Kischmeiderconstant at least 1.3 times smaller than the constant fora 2% contrastthreshold). 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 1974to 1976 atthe other California sites, anddo notcorrespond to the RESOLVE study period. The calibration factor forthe airport data was derivedby comparing the RESOLVE data to nearby airport visibilities. We found that median airport visual range should be multiplied bya factor of 1.4 tobe equivalent to a visual range based on average RESOLVE extinction data (sum of Rayleigh scatter, fine scatter, coarse scatter, NO2 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

66NWC TP 6869



### RESOLVE YEAR (7/84 

### to 6/85)
3a *6.4 iT
a33 5. N ^ 0" N ^ 

# r 

# T .39 "
.. 8 30 3 

### " 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 50km , 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 

### 9 11

- a 

### *2. aa W 

## 3.2 3.1 S.a a at 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.

67NWC TP 6869

TABLE 13. ExamplesofTables forStatistical Parameters and Distributions. RESOLVE year (7/84 to 6/85)

- Distribution ofdata. Arithmetic Geometric Values equaled or exceeded bystated percentage ofobservations S,te of Max Min Sdt obs. Mean dev 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 18 141 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 - forRayleigh 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 16 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 Dailytotal 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, and50) suggest that such changes were probably not large. Inessence, Figure 37 isa more detailed, "blown-up" picture ofthe California visibility map presented previously as Figure 16 and is also the same as Figure 2, repeated here for the convenience ofthe reader. The California visibility map shows that two pockets oflow 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 ofthe isop!eths (i.e., the form ofthe 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.

68NWC TP 6869

30 40kg 60, j".km 100 150km

1, RAW

32 -

17 2

NOTES; * Circled data points represent RESOLVE monitoring data. The entire 2years 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 datapoints are based on median airport datacalibrated toRESOLVE data. * Thedashed lines indicate uncertainty in the isopleths nearSearles Valley. * isopleths near the border of the map are guided by nephelometer data at Death Valley aswell as data from other airports inCalifornia and Nevada. * The Beaumont (Banning Pass) value, shown inparentheses, isinconsistent (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 visibilitymarkers 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.

69NWC 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 ofsource/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) existsat apass site). Table 14 presents interstation correlation coefficients for daily averages of particle scattering and particle absorption. All data available from the 2 years ofroutine monitoring are usedto derive the table, sothe 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 ofthe 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 orpass 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

70NWC TP 6869

These two observations indicate that the whole study region, especially the desert part ofthe study region, tends tohave at least some degree ofhomogeneity in terms ofair-quality variations. That the air-quality variations are not perfectly uniform, however, can be appreciated by noting that acorrelation of.80represents 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 finda 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 tendstooccur atboth passes together. Other features are therelatively high correlation between Soledad and Edwards AFB, and the tendency for Cajon to be the pass site least correlated withthe desert sites (except Cajonversus 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 thehighest factor loadings. These results are consistent with thediscussion ofTable 14and reflectthegeneral correlation ofall the sites, especially the desert sites. iThe results for quarters 2 and 3 look very similar to those ofthe annual data set; again allsites fall into 1factor. Quarters I and 4(fall and winter)divide the sitesinto 2factors. In general, factor 1includes thepass sites, and factor 2the desert sites. In quarter 4, Edwards AFB could be grouped with either pass ordesert sites; and Fort Irwin NTC is grouped with thepasssites. Factor analysis performed month by monthprovides 1,2, or 3 factors depending onthe particular month. Site groupings within these factors vary and it is difficult to interpret what the factors actually represent. Table 15 is a summary ofthe monthly factor analysis and shows howoften one site isin the samefactor 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 nothighly correlated with eitherofthe Los Angeles pass sites.All ofthese results seem consistent with theprevious interpretation ofTable 14.

TABLE 15. Summary oftheMonthly FactorAnalyses. Number ofmonthsinwhich sitesoccur inthesame factor(24months 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

71NWC TP 6869

6.0 TEMPORAL PATTERNS OF VISIBILITY
Two temporal patterns ofinterest exist: seasonal variations over the time of year and diurnal variationsover the time ofday.
6.1 SEASONAL VARIATIONS Figure 38presents theseasonal pattern for particle scattering at the three receptor sites ona 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 ofnephelometer monitoring. Incidentally, the observed seasonal patterns in particle scattering are representative of seasonal patterns in total extinction because the seasonal variation inparticle absorption closely resembles thatofparticle 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 forParticle Scattering

At least fourmajorpoints are tobe made with respect toFigure 38. 1. The ranking ofthe three receptor sites with respect to light extinction is Edwards AFB highest, then NWC, then Fort Irwin NTC. Basically, NWC experiences about average
72NWC TP 6869 particle scatteringamong the three receptor sites, with Edwards AFB about 20% worse, and FortIrwin NTC about20% 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 springquarter tending tobe slightlyworse 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. Onthe other hand, Fort Irwin NTC hashigher average visibility than NWC, mostly because Fort Irwin NTC experiences especially goodbest-case conditions. 4. The seasonal pattern is notas constant or strong for the worst-case tenthpercentile 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 islowest duringsummer. 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 ofparticle sampling.Thefigures reveal the followingfeatures: * Particle scattering peaks in the third quarter (Julythrough September). The local maximum in July is due todata fromJuly 1985 when the RESOLVE network was affected byvery severebrush and forest fires inSouthern 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 oftransport for each ofthese three aerosol components. The localized peak fororganics 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 ofcoarse 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 tobe the controlling factor forthe seasonal pattern ofaerosol nitrate.

73NWC 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 isextended to April because offine soil inspring, and to October because ofunaccounted-for fine mass(nitrates) infall. An additional aspect of seasonal patterns is the seasonal variation in the light extinction budget. Thisaspect isdiscussed inSection 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
00 I 2 3 4 S 0 7 a I is II t 2. MONTH 

### FIGURE 39.Monthly Variations in Particle Scattering,Absorption,and Mass AveragedOvertheThreeReceptorSites.

74NWC TP 6869

i|,
S 2.1

4 0 SIs

2 S 41 4. 23.6 2.1

2-S S$ 

# ___ 1r

a.i

w 31 

# u ai

Sl 

# l r "n. 210



## am 

# f-, M 

# M

2. 3. Is 1$ 12?
. O o oo •. MONTH FIGURE 40. Monthly Variations in the Chemical Components of FineParticle Mass Averaged Overthe Three Receptor Sites(All in lag/m3).

75NWC TP 6869

I I I i I I I I | i I 4.a 

# S

0
"3 2.a

264S Is 

## Ovr..Ilfrnnr1D tu13 2

72.0

-

3 4. S, a. 7 a. 9. Is. 1t. 12. MONTH FIGURE 41. Monthly Variations in Meteorological Parameters Averaged OvertheThree ReceptorSites.

76TNWC TP 6869

6.2 DIURNAL VARIATIONS Diurnal variations inair 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, leadingto aminimum inearly afternoon. This factmost likely reflects greater dispersion in the morning because ofthe 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 fromthe 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 6II 2621 20223 9 f a 3 a 7 0 0 ' 6 .3 1.Is s 176 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 toprow ineach figure presents nephelometer particle scattering aswell as the sumofnephelometer particle scattering and particle absorption. The second row pertains to fine-particle mass, and the next five rows give the components offine mass (all in tag/m 3).The last row presents the ratio ofnickel 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 sourceareas (see Section 8.2). Table 16provides a list ofdays when standard visual range was 50 kilometers or lessat anyofthethree RESOLVE receptor sites. Asdiscussed previously (see Figure 38), Edwards AFB has significantly more low-visibility days than NWC or Fort IrwinNTC-the latter two sites being about equivalent in frequency of low-visibility days. It is also apparent that worst-case days can occur inany season. Table 16 and Figures 43 through 45 were used to select episodes forthe illustrative case studies ofChapter 9.

Bscat+1abstI$ Ia o(uM-1) Bscat s* o0

- (ug/m 3) Fine Mass to
0 Or2anics 2 44 Sulfate * 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. Thedashed line inthe firstrow isBscatplus particle absorption. 

### " -Rem," finemass minus thenext4components,shouldqualitatively reflect tinenitrates. FIGURE 43a. Time Series ofDaily Averages at Edwards AFB (April 1984 to September 1984).

78

### NWC TP 6869

Fine . 1 Organic$ I
Sulfate4
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. Thedashed linein the firstrow isBscat plus particle absorption. ' Rem," fine mass minusthe next4components, shouldqualitatively reflectfine nitrates. 

### FIGURE 43b. Time Series of Daily Averages at Edwards AFB (October 1984 to March 1985).

sicat a.(ug/m 3) 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 oreprilNWC TP 6869

s~cat t
N".~ Pass to3 Organics 3 SuIfate n
Soil

4aMl/Pb
Apr. Key Jun. Jul. Aug. Se.. NOTES: 411Bscat represents nephelometer particle scattering without correction for coarse-particle scattering. The dashed line in thefirst rowis Bscatplusparticle absorption. * -Rem," fine massminus the next4components, should qualitatively reflect Fine nitrates. FIGURE 44a. Time Series of Daily Averages at NWC (April 1984 to September 1984).

oscat'.i. 

## 1So13I
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~edashed line in the first rowis Bscat plus particle absorption. * Aem," fine mass minus the next 4components. stiould qualitativeiy reflect tine nitrates. FIGURE 44b. Time Series of Dai[Y Averages at NWC (October 1984 to March 1985). 

## 80NWC TP 6869

sulfate ARM Sole A~s AM'i anna A4R
* C
* j 4L. _LA L i , aAL Xrai cs 2

..... . Ap PlyJn4ul uýSo NOTES "Sl *sa ersnsnpeo ee atcesateigwtotcreto o orepril 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,-, flsh~n.I1 Al0a i Apre Muss 19i Au.Ag NOrgEis:ngR1 Sulscate rpeet ehlmtrpril cteigwtotcreto o orepril scattering.~ 

### ~ ~ ~ ~ ~~~~li Thdahdln3ntefrtrwi aa lspril bopin SaIRm, AAne msmiuthnet4cmoethulqulttelreetneirAtes FIUR 

### 1c TieSre3fDiyAeae a W Arl18 oAgs 1985).

*icai.Bb i* 4* 5A Apr. Ny iuftJul. Ag./Soo NOTES:ii4

" R

m "4 fiem s iu4h et4cm oet.hul L2iai yrfetfn irts FIU REa~45.Tm2eiso al vrge tFr ri A rl'8

to See b 94
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 inthefirst row is Bscat plusparticle absorption. " "Rem," fine mass minus the next4components, shouldqualitatively reflectfine nitrates. 

### FIGUJRE 45b. Time Series ofDaily Averages at Fort Irwin NTC (October 1984to.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. Thedashed line in thefirst row is Bscat plus particle absorption. "* 'Rem," fine mass minus the next4components, should qualitatively reflect fine nitrates. FIGURE 45c. Time Series of

### Daily Averages at Fort Irwin NTC (April 1985 

### toAugust 1985).


### 82NWC TP 6869

TABLE 16. DaysWhen Standard Visual Range* at the RESOLVE Receptor Sites Was50 Kilometers orLess-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 39/8ext. Nmere Bext istotal instrumental extinction Based on the discussions of Sections 322 and 72. total extinction -scalculated as the sum of 11 Mm-1 (Rayleigh scatter), Bext (nephelometer Darticle scattering), 3CP (the sum ofcoarse-Oarticle scattering missed by the nepnetometer), 04 Bext (an estimate of NO2 absorotion), and Bao (total oarticle aosorption) IfCP and/or Sap are missing, total extinction isestimated as 11 + 148scat (seeSecton 72)

83NWC TP 6869


### 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,justfive types ofaerosol (plusgaseous NO2) usually contribute nearly all of ambient light extinction. These five categories are sulfates (from SO, emissions), nitrates plus NO2 (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 (organicsand soil dust)arise from a wide variety ofboth natural and anthropogenic sources. Knowledge ofthe extinction contributions from Rayleigh scatter and these five categories already provide important insights about the nature and origins of visibility degradation inthe 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 1 1 + 28
83 k. Average 46 , 11 + 35

Several aspectsofthe scope ofthischapter canbe explained inrelation toTable 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 20to 30% ofaverage extinction at the receptor sites,the Rayleigh scatter component is natural and constant.
34NWC TP 6869
3. The resultspresented here are for the RESOLVE base year, July 1984 throughJune 1985. The RESOLVE particle samplers operated for 16 months (May 1984 through August 1985), but we haverestricted the data tothe four complete quarters to avoid seasonal bias. 4. The definition of aerosol species deserves a brief explanation. To account for the mass ofassociated ions, thefollowing conventions areadopted: 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 46presents an overview ofthe entire extinction allocation procedure. Total nonRayleigh extinction at the three desert receptor sites, 35 Mm-1, consists ofcontributions 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 thecontributions. The followingthree 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 NO2 is determined from measured NO 2 concentrations by the formula, Bag = .33 [NO2], where the units ofabsorption are Mm-I and the units of [NO2] are ppb2,3,4. Special high-sensitivity NO2data collected for RESOLVE at Edwards AFB and NWC during the summer/fall 1984 indicate that NO2 absorption is about 4% of particle scattering in the Mojave Desert. This result is consistent with an analysis of long-term routine data sets forthe desert byTrijonis, etal. (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 avalue for NO2 absorption ofonly 1Mm- I (seeFigure 46). 7.2.2 Particle Absorption The extinction budget analysis for particle absorption is illustrated on the right-hand side ofFigure 46. Particle absorption measurements with the daily RESOLVE Teflon filters were made at the University ofCalifornia, 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. Thenext 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% ofthe 1Mm -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 arisesalmost entirely fromelemental carbon.

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BNon.Rayie,gh = Sag Bsp + Bap
Routine RESOLVE data. Routine RESOLVE data. Analysis ofexisting NO2 data. RESOLVE special RESOLVE special

RESOLVE special N02 data. experiment experiments. 

# E 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. Literaturereview, data. RESOLVE Mie Theory. Sloane Interactive Theory.

Speciescontributions = 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. ExtinctionAllocation Procedure.



### 36NWC TP 6869

TABLE 18. EstimationofSoil Dust Contribution to CoarseAbsorption. Percentage ofcoarse 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 soilelements (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 accountforentire coarse-soil distribution.

7.2.3 Particle Scattering Particle scattering is the most complex part ofthe extinction budget analysis. The average particle scattering measured by the routine RESOLVE nephelometers atthe 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 ofthe nephelometer for coarse particles, however, indicate that the nephelometer misses one-halfofcoarse-particle scattering (laterestimated tobe 5 Mm- 1).When adjusted forthismissingcoarse-particle scattering,the corrected totalparticle scattering becomes 28 Mm- 1(seeFigure46). Three methods are used here toestimate theportion ofparticle scattering due tocoarse particles. Toderive afactor thatcan be applied to averages ofthe routine RESOLVE coarsemass data, and to provide a common basis for comparison, the results ofall three methods are specified in terms ofa 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 ouropinion, the most relevant result isthat from the first method (RESOLVE Cyclone Nephelometer Study) that was designed specifically to address this issue. As aconcensus finding, we have chosen a pseudo coarseparticle scattering efficiency of0.6 m2/g. Applying the 0.6 m2/gpseudo coarse-scattering efficiency to the average coarse massat 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 engineeringjudgments. 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% ofthe coarse massgreater than 10 micrometers. Second, soil dustcontributions 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 ofPseudo Coarse-Particle Scattering Efficiency. Methodology Pseudocoarse-scattering efficiency* RESOLVE cyclone nepheiometer experiment, with adjustments for 6m2 /g nephelometer collection efficiency and nephelometer measurement efficiency forcoarse particles. Mietheory calculation (Reference 58) basedon RESOLVE DRUM sampler 3-5m2/g data forsoil,with adjustmentstoaccount forentirecoarse-particle size distribution. Regressions basedon teleradiometer dataand fine-/coarse-particle data. Conventional regression (Reference 61) 4m2 /g Zero interception(References 9and61) 7-13 m2 /g "Total coarse-particle scattering/Particle massfrom 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 offine organics, sulfates, elemental carbon, and soil dust. The scattering efficiency of fine nitrates is assumed to be the same as that offine sulfates (References 11, 55, 56, and 57). Note that good agreement is found among the methods, within about ± 1m2/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 basedon 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 budgetare quantified inAppendix A. InTable 20 we chose to include the scattering contribution from aerosol water as apart 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 atEdwards AFB and3% at NWC. To obtain a fine-scattering budget, the consensus scattering efficiencies ofTable 20 are multiplied byaverage concentrations fororganics, sulfates, elemental carbon, and soil dust

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TABLE 20. Determination ofFine-Particle Scattering Efficiencies. Scattering efficiencies (m2 /g) Methodology Organics Sulfates Elemental Soil dust carbon REGRESSIONANALYSIS (basedonroutine RESOLVE data atthethree receptor sites) Conventional regression 3.7 5.0 0 6b 0.4 Structural regressiond 3.8 5.1

- 1.8b 0,5 LITERATURE REVIEW (20studieswith the following adjustments forconsistency) Nephelometer calibration 2-3 3-6 (2-3)c 1-2f Airport contrast =5% Nephelometer A=530nm Organics = 15 OC Relative humidity = 40% MIE THEORY (forMolaveDesertdata) Ouimetteand Flagan (Reference 49) 2.5d 3.2 NA 14 RESOLVEDRUM sampler data NA 3.2 NA 1.4 INTERACTIVE MIE THEORY (basedon Detroitsize distributions, Reference 62) 3.8 4.7 (3.8)c 1.3 CONSENSUS 31 4-J 1- 1

aAs inMadansky (Reference 63) andWhite and Macias (Reference 64). b

Difference from zeronotstatistically significant. cElemental carbongrouped withorganic carbon d

Mietheorybased on volume sizedistribution forall material, notjust organics.
as well as special-study concentrations fordenuder difference nitrates.* The addition ofthe five resulting components yields a scattering level that is 13% greater than measured fine scattering. To achieve the species allocation of the 23 Mm

- Ilisted in Figure 46, the five calculated components are eachdividedby afactor of1.13. * The RESOLVE special studies for fine particulate nitrate using the denuder difference method found the following (samplesizeinparentheses): FineNO 3concentrations, pIg/m 3 Edwards AFB NWC SUMMER 1984Study 5(27 day) .4(25day) 1985Study 11(22day/20 night) W'NTER1986Study 16(12day/9 night) 6 (9day/8 night) Multiplying by 1.29 toaccount forthe cation mass, we conclude that the average fine NH4NO 3 concentrations are approximately 1.55 pig/m3 (11.0lig/rm summer/2.0 pig/mJ winter)at Edwards AFB and .65 pig/m 3 (.5 lg/mi 3 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 AFBboth interms ofdistance from upwind source areas and interms ofpercentage offine mass not accounted for bynon-nitrate components. Scaling the NWC data by fine mass, we estimate that average fine NH 4NO1 concentrationsare approximately .46pg/mr 3(.4 ig/mJsummer, .5pig/m, 3winter)at FortIrwin 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 listedalong the bottom ofFigure 46. (Table 21 is moreprecise than Figure 46 in the sense thata 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 mostimportant,and the nitrates plus NO_ 2 category leastimportant. TABLE 21. Average Extinction Budget Calculated forthe Three RESOLVE Receptor Sites.

TotalNon-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 ofclosing a mass balance in his data). The coarseparticle scattering in the 1979 study may be overestimated because of potential problems with theopticalparticle counter inthecoarse-particle range.
7.3.1 Site-to-Site Variations Table 22 presents extinction budgets for the three sites individually. The values in Table 22were derived byusing the assumptions and parameters ofSections 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) thatis specific toeach 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 Iuefate Elemental Nitrates Rayleigh Organics Sulfates carbn Si dust +N2

total ao o NO 2 Absolute contribution (Mm

- 1) EDW 42 10 10+ 8 6 7 CHL 36 701- 8 7 I 7 3j 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 oforganic, sulfate, and soildust concentrations in summercompared to winter. In contrast, nitrate concentrations are higher in winter than in summer. As a result, the nitrate plus NO2 category, that accounts for only 9% of the total in summer, contributes about 20% in winter.

TABLE 23. Extinction Budgets forSummer VersusWinter.
Non- Elemental Nitrates Rayleigh Organics Sulfates c Soil dust Nia ttlcarbon 

# j÷___ NO 2 total 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

=Inthissection, tomake maximum use ofthe RESOLVE data set, all 17months (6 winterand 11 summer) of particle dataare used insortingby winter/summer orcalendarquarter.

91NWC TP 6869 Complete extinction budgets are not possible on aquarterly basis because ofthe 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 ofparticleextinction and mass (fromthe highest quarter to the lowest quarter) is 3- 2-4-i. The percentage contributions ofabsorption, 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, thesedataagree with the special-study data that show awintertime nitrate peak. TABLE 24. Quarterly Variations inthe Components ofParticle Extinction and Fine Particle Mass. Calendarquarter
January-March April-June July-Seotember October-December Contributions - absolute (/)

Total partic'e extinction 25 Mm

- 36 Mm - 1 44j Mm - 1 30+-Mm

- Particleabsorption 4 (17%) 6- (17%) 7+ (17%) 6 (19%) Coarse-oartile scatter 3j (14%) 7j (21%) 5j (1 30%) 4 (13%) Fne-oart~cIe scatter 17j (69%) 22 (62%) 31j (70%) 20j (68%) Total fine-particle mass 5 7pg/m 3 10 2 Vg/m 3 10.6 pgimJ 6.4 gg/m3 Organics 14 (25%) 3.3 (32%) 40 (38%) 19 (30%) Sulfates 13 (23%) 2.6 (26%) 3 1 (29%) 15 (23%) Elemental carbon 4 (6%) 6 (5%) 7 (7%) 3(5%) Soil dust 14 (25%) 2.7 (27%) 17 (16%) 9 (14%) Unaccounted-for 12 (21%) 10 (10%) 1 1 (10%) 1,8 (28%)

7.3.3 Quartile Variations Although a complete extinction budget sorted by percentiles ofvisibility is not possible because ofthe lack ofdetailed 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% ofdays, all days, and worst25% ofdays. 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 ofminor practical importance. The average ofworst-case days closely resembles the average ofall days in terms ofextinction contributions and is nearly identical in termsoffine-mass contributions. Even examining the highest 10% ofworst-case days, we donot finddramatic shifts in the composition ofparticle extinctionor fine mass.

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TABLE 25. Components ofParticle Extinction and Fine Particle Mass for Best,Average, andWorst Visibility Conditions.* Average ofbest Average ofail Average ofworst

25% ofdays days 25% ofdays 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 4lig/m3 '3.0 pg/ml Organics 11 (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 10 (28) 17 (20) 2.4 (19) Unaccounted-for 4 (11) 1.1 (13) 2.0 (15) 

### "Sortingofdaysat each site isaccording to the sum ofallextinction components atthatsite.Averages are then taken over thethree sitestogether

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% Soildust 18% ± 8% Nitratesplus NO2 13% ± 5% The errors in the species contributions range from 5% to 9% of non-Rayleigh extinction, withthe greatest absolute errorsbeing in theorganics and soil-dustcategories. 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 tobe used with these components. (Note thaterrors introducedby sampling artifacts are included within the uncertainties ofthe scattering efficiencies. See Appendix B for details.) Uncertainty in the accuracy correction factor forTeflon abosrption accounts 'or the major part ofthe error in the elemental-carbon category. Error in the soildustcategory arises essentially all from uncertainty in our estimate oftotal coarse-particle

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scattering. Uncertainty in the nitrate-plus-NO2 category is about one-halfbecause oferror in the scatteringefficiency for nitrates, andone-halfbecause oferror in theconcentration of nitrate particles. Uncertainties related to measurement imprecision and climatological representativeness are quite negligible (except for the sparse nitrate sampling) because of the large numberofobservations in theRESOLVE data set. 8.0 SOURCES OF ATMOSPHERIC LIGHT EXTINCTION Oneofthe main objectives ofthe RESOLVE program istodetermine the sources oflight extinction-to understand the origins, source areas, and source categories of visibility reducingparticles 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 orwithrespect totracer 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 longterm. The development ofsource-oriented models, however, maybe 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 NO2, elemental carbon, organics, and soil dust. By considering the origins ofeach ofthese categories individually, we can derive an overall estimate regarding natural versus man-madecontributions tolight extinctior. Trijonis (Reference 40) evaluated natural versus anthropogenic origins for sulfates, nitrates, andelemental carbon. All three ofthese aerosol categories are dominated by manmade sources. Based on a review ofthat study, a reasonable judgment for the RESOLVE region isthat man-made sources contribute about 85% ± 10% ofcombined extinction from sulfates, nitrates plus NO 2, and elemental carbon. (Note that the ± 10% uncertainty is a judgmental estimate ofasingle 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 andcarried out byGlobal Geochemistry Corp. (GGC). That study included measurements ofcarbon isotope ratios and analyses oforganic molecular composition. Overall, the data from that study suggest that about 80% ± 15% of organic aerosolsarefromman-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 tocharacterize transport and diffusion, and (4) a modelframework capable ofhandling the complex terrain of the studyregion. ** The basis forthis estimate isas follows:The GGC isotope testsconsistently show that about 65% (±5%) of total carbon is fossil.Assuming that 85%ofelemental carbon is fossil (a noncritical assumption), and noting that 80%oftotal carbon isorganic, we find thatan estimated60% oforganic carbon is fossil.All thesefossil organics are assumed to be anthropogenic. In addition, some ofthe 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 fromplant terpenes). Actually, the 60% fossil fractionis representative ofanthropogenic primary organic emissions (Reference 65), soone could argue that the organics are essentially all anthropogenic. However, natural sources are known tobe non-negligible basedboth on a literature review iReference 38) and on obvious wild-fire impacts seen inthe RESOLVE data (e.g., July 1985). Somewhat arbitrarily, we will assume that the 40%contemporary fraction ;-halfman-ma.e ai,a halfnatural. yielding an 80% total for anthropogenic. A high anthropogenic percentage is supported qualitatively by the results ofthe molecular composition studies that suggest thatorganic aerosols aredominated by urbansources.
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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 ofnatural is ambiguous. Should wind-blown dust be considered natural ifit 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 ofemission rates for wind-blown dust (Reference 66). Faced with thesedifficulties, 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 dustdoes notaccount for a large part oflightextinction. 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 ofmanmade contributions to light extinction. Averaged over the three RESOLVE receptor sites, the estimate is that 77% ± 14% ofnon-Rayleigh extinction is from man-made sources. Adding in natural Rayleigh scatter, the fraction oftotal extinction from man-made sources is 59% ± 12%. The above line ufanalysis 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 ofthe 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 ornatural. Because sulfates, nitrates, organics, andelemental 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 ofNO, are dominated by three categories: gasoline-powered vehicles, the petroleum industry, and diesel trucks. Primary organic emissions come from alarge number ofrelatively small source categories. Elemental carboncomes mostly from enginesburningdiesel type fuel.

8.2 TRACER-ELEMENT MODEL FOR SOURCE AREAS
An important aspect ofsource characterization isthe quantification ofthe contributions ofmajor source areastovisibility 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 ofthese 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 ofparticles leaving the Los Angeles basin, while Tehachapi Pass isused to characterize particles from theSan Joaquin Valley (see Figure 1). These elemental signatures are then combined with particle composition measurements in the high desert to estimate Ute contributions ofthe source areas to aerosol loadings at the receptor sites.

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TABLE 26. Emissions Inventories for SO,, NOx, Primary Organics, andElemental 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% Fineorganic 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% ofthe total in either air basin are listed. The gasoline vehicles category includes motorcycles. The off-road equipment category isdominated 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. b1982 TnventorybyGray (Reference 67).

8.2.1 Analysis Overview The basis for distinguishing the contribution ofthe SanJoaquin Valley to the Mojave Desert aerosol is displayed in Figure 47. Fineparticles from the San Joaquin Valley exhibit consistently higher Ni/Pb ratios thando fineparticles from the LosAngeles basin. Theratio ofaverage Ni and Pbcontents measured on days withconcurrent outflow from both source basins was0.64 atTehachapi and 0.062 atSoledad, anorder-of-magnitude difference. The Ni/Pb ratios measured atthe 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) ofthese ratios. These intermediate ratios reflect varying mixes ofNi-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 SanJoaquin Valley, whichistheonlysource ofNi-rich aerosol. Nickel and lead were chosen as the basis for the analysis after screening the compositionprofiles atthe pass sites forelemental indicators ofthesource 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- 0o 00 

# z, 8

S0.4 0 

### 0 

### " 

## 0.2 - 01

0 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 (TehachapiandSoledad)or 12hours(Cajon)ofoutflow. FIGURE 47.Trace-Element Content ofFine-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 ox N. * 0 

### X6 

# 6-
.. 

### '

0 9 0

2 '40 0A 0 0 

### o o

0 30 60 90 

## Pb, ng/m 3 NOTES: * TheNi and Pbscales areinthesameratioasthoseofFigure 47. * The symbols distinguish days on which morning 850 mb winds were from the directionofthe Los Angelesbasin (SE-WSW), theSanJoaquinValley (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, orthe 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). Afinal consideration isthat Ni and Pbcome 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 ofTable 26). Thecorrelation level with extinction and fine mass was not one ofthe criteria for tracer selection. For example, bromine accounts for 55% oftne 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 brominecomposition does notvary greatly betweenthe 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 ofthe Ni/Pb ratio.*Thus, the following analysis does notuse data stratifications basedon synopticregimes.
8.2.2 Source Area Characterization To obtain a representative characterization ofthe Ni and Pb composition ofparticles 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 metthisrequirement were used in Figure 47.Joint outflow days had no hours ofbackflow at any ofthe three passes. Elemental datathat meet both criteria are given inTable 27. Note that the mean composition onjoint outflow days is similar to that onindividual outflow days.
8.2.3 Theory The theory for the method employed here is described in detail in Appendix D. This sectionprovides abriefoverview andexplanation. Elemental concentrations at each receptor site are assumed to be linear combinations ofsource contributions from the SanJoaquin Valley, the Los Angeles basin, and the high desert. The percentage ofNi 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 ofthe sourcebasins.

* In retrospect,thisis notsurprising because air massesfrom boththe LosAngeles basinand theSanJoaquin Valley arrive at EdwardsAFB from thewest. Itisdifficult forthesynoptic regimes to resolve the two source areas under westerly flows.

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TABLE 27. Concurrent Ni and Pb Datafrom Three Pass Sites andOne 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/m3 .±g/m 3 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 ofvariation. 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 SanJoaquin Valley and LosAngeles 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 66and68). The method becomes understandable when you consider the fact that the SanJoaquin Valley is the only source ofhigh 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 ofthe contribution ofthe San Joaquin Valley at a receptor are determined by assuming alternately that only one ofthe two other source areas exists in addition tothe SanJoaquin Valley-either the local high desert or the Los Angeles basin. Figure 49 provides geometric insight into thedependence ofthecalculated SanJoaquin Valley contribution on Ni/Pb ratios from the other source areas. In Figure 49, the point labeled EDWARDS shows the measured concentrations ofNi and Pb at Edwards AFB on 12 July 1985. The solid lines labeled TEHACHAPI, SOLEDAD, and CAJON show the Ni andPbconcentrations corresponding tovarying dilutions ofthe aerosols measured atthese passes on this date. The solid line labeled DESERT shows the Pb concentrations corresponding to varying dilutions ofthe 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 ofthe pass anddesert aerosols are indicated byticks at I pg/m3 intervals. The combiningofaerosols in the atmosphere corresponds to the addition ofcomposition vectors in Figure49 Thedotted and dashed lines indicate two methods ofrepresenting 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 ifthe Ni-poor high desert is taken as the only other source (dashed line) Somewhat less is required ifsome ofthe Ni is contributed by the Los Angeles basin via Soledad (dottedline). 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: 11) 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 morecomplete 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 forexplanationofsymbols.
1. Probably, the bulk ofthe observed variability ofNi and Pb contents in the passes represents real variations in source composition. Measurement precision was good, and averaging over 24 hours ofoutflow should have minimized the impact ofinhomogeneities. 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-toparticleconversion. 2. The characteristics ofthe Ni and Pb fractions offine mass ofan upwind basin are controlled by the within-basin source mix and gas-to-particle conversion, while the

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concentrations ofNi and Pbat a receptor are controlled by interbasin transport factors. In this case, the covariance of source characteristics and receptor concentration can be expected tobe small.
8.2.4 Results At Edwards AFB during the RESOLVE base year, 84 routine samples were analyzed. Average concentrations forthebaseyearwere Ni =3.1 ng/m3,Pb = 19.3 ng/m3, and FM = 8.5 g/rm 3

. A lower bound for the San Joaquin Valley contribution is obtained byattributing 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 Soledadonjoint outflow days, thislower bound for the average cGntribution ofthe San Joaquin Valley primaryfine mass atEdwards AFBis 3.2 (±0.5) Pg/m3,or38% offine mass. The indicated uncertainty in this estimate is the standard deviation attributable to the small sample(n = 61 ofsourcecharacterizations on which this estimate isbased. An upper bound for the SanJoaquin Valley contribution is obtained by attributing all Nitothe SanJoaquin Valley. Based on the observed Ni content atTehachapi on individual outflow days and the average Ni concentration at Edwards AFB, this upper bound for the average contribution ofthe 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 thisestimate isbased. 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 samplingfluctuations. 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 28are consistentwith expectations and addconfidence 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 Valleycontributions estimated forthese conditionsare, in fact, indistinguishable from zero at all three receptors. Conversely, sustained outflowsfromTehachapi should maximize the impact of the San Joaquin Valley on the high desert. The estimated San Joquin Valley contributions forthiscondition 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% ofdays with highest Bscat. As a fraction ofobserved 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 LosAngeles basin andhigh desert.

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TABLE 28. Estimated Contributions oftheSan Joaquin Valley to Fine-Particle Mass Concentrations atEdwards AFB Under Various Conditions.
Estimated contributionsof Number ofdays Measured fine SanJoaquin 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 57 117 39 59 Easterly upper

winds 5 46 0 24 NoTehachapi

outflow 5 60 0 13 48-hour Tehachapi

outflow 24 10 1 66 77 NOTES: * Lower boundsare basedon the data from Tehachapi and Soledad for the 6joint 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 atEdwards AF8 for theindicated subsample. * The source characterization days are not necessarily included among the receptor subsamples, for example, thecategory "No Tehachapi outflow" obviously includesno source-characterization days.

TABLE 29. Estimated Contributions ofthe SanJoaquin Vailey to Fine-Particle Mass Concentrations atThree Receptor Sites.
Estimatedcontributions of Measured fine SanJoaquin Valley, %

mass, Vg/mJ Lower bound Upper bound EdwardsAF8 RESOLVE base year 8.5 38 63 Worstquartie 8sp 13.6 30 51 NWCRESOLVE base year 8.9 36 57 WorstquartileBsp 12,6 42 56 Fort IrwinNTC RESOLVE baseyear 6 5 37 53 Worst quartile 8sp Il8 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 ofthe SanJoaquin Valley contributions atthe receptors. It is appropriate atthis pointto reconsider these assumptions and the consequences ofany 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 SanJoaquin Valley wereconsistently associated with low Ni/Pbratios at the receptors. The conditionsconsidered here include minimal outflowfrom allpasses (2 days), nooutflow fromTehachapi 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 ofthese minimal-transport days were from 3 winter months: December,January, and February. The assumption that the Tehachapi Ni/FM ratios are representative ofthe San Joaquin Valley contributions ismoredifficult totest.At leastfour sources ofbiascan beimagined. 1. San Joaquin Valley aerosol on outflow days might be unrepresentative of San Joaquin Valleyaerosol onother days.The identification ofoutflow days wasbased solelyon winddata, without requiringhigh 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 itselfcould, by decreasing residence times in the San Joaquin Valley, produce unreprenenratively high Ni/FM ratios. 2. San Joaquin Valley aerosol might be modified downwind ofTehachapi by gas-toparticle conversion. Little indication exists in the data of significant gas-to-particle conversion downwindofthe sourcebasins; "primary" aerosols from the San Joaquin Valley and LosAngeles basin together can account formost orall ofthe fine massobserved in the high desert. If some residual conversion exists, its effect is to reduce Ni/FM ratios below thoseobserved atTehachapi. 3. Aerosol leaving the San Joaquin Valley via Tehachapi might be unrepresentative ofaerosol leaving the San Jaquin Valley via other routes. Oil recovery operations, the presumed source ofhigh 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 mightbeless rich inNi. 4. Also, the possibility existsofparticle-to-gas conversion (e.g., nitrates and organics). However, the RESOLVE thermidograph nephelometer experiments suggest that the effect on total fine massshould berather small.

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The first two ofthese sources ofbias imply that the model would underestimate San Joaquin Valley contributions. Thethird source ofbiascouldproduce either an overestimate (ifhypothesized Lake Isabella Pass effects were critical) or an underestimate (ifoverall 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 SanJoaquin 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 descriptionofthis 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" thatidentify source profiles that arevery similar to each other and thereby produce large uncertainties. The CMB application and validation protocol ofPace and Watson (Reference 71) wasfollowed to assess the validity of the input dataand 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 anddifferent numbers ofspecies affected the solution. The firstpart ofthe analysis involved feasibility testing using the methodology from an early RESOLVE design evaluation study (Reference 3).Theconclusions 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 (orvanadium); (3) profiles atthe Soledad and Cajon sites are too similar tobe separated from each other; and '4) the addition ofa third (local) source profile would interfere with the source apportionment. The second, more extensive, part ofthe analysis was to apply the EPA CMB model for various receptor sites, averaging periods, source area profiles, and sets of elemental species. Themajor 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 ofthe Ni/Pb tracermodel. 

### "* The source contributions for the San Joaquin Valley calculated by the EPA CMB model using only Ni and Pb are generally consistent with those ofthe 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 EPACMB modelcover mostofthose ranges. 0 The SanJoaquin Valleycontributions from the EPACMB model foreither 17 or20 elemental species and two averaging times are generally similar to those ofthe Ni/Pb tracer model when a nonwinter, local-source profile isincluded. When such a source profile is excluded, the San Joaquin Valley contributions are much higher, exceeding the upper bound ofthe Ni/Pb tracer model. The uncertainties are large (about 50% of the source contribution), however, because of the great similarity amongthe source profiles used withthe EPACMB model. The overall conclusion ofthis complementary analysis is that the San Joaquin Valley can be resolved from other source areas but only with uncertainties of50% or more. The San Joaquin Valley contribution is probably in the range of25 to 75% offine-particle mass at the receptor sites. Some ofthe conclusions may change ifthe estimates ofthe local source profile are refined and improved. In addition to the uncertainties discussed in this section, the aboveresults are subject toall the limitations described in Section 8.2.5.
8.3 CONLCUSIONS REGARDING SOURCE AREAS The purpose ofthis section isto 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 andconsiderations 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 ofSection 8.2 predicts average fine-mass contributions from the San Joaquin Valley of 36 to 57%. The tracer model, however, includes natural as well asanthropogenic contributions from the SanJoaquin 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 SF6 tracer studies (Reference 14) all indicate thatanthropogenic 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% ofthat at NWC itself (Section 3.5). Among other things, this 64 to 89% may be a reflection of regional sources (basically transport) as opposedto 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 tothe 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 byanthropogenic transport from both the SanJoaquin Valley and the LosAngeles basin. Local emissions data (Reference 68), the calculation methods of Section 8.1, and various Bscat/meteorology relationships inChapter 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 oforganics 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 ofFort Irwin NTC, however, suggest that anthropogenic transport shouldbe 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 to38to 63%atEdwards AFB). For worst-case days at Edwards AFB and Fort Irwin NTC, transport from the Los Angeles basinappears tobemore important than itisforaveragecontributions. 

### "* The Ni/Pb tracer model for Edwards AFB and Fort Irwin NTC indicates fine-mass contributions from the San Joaquin Valley ofabout 40 to 60% on the average, but only30to 50%for the worst-case quartile (seeTable 29). 

### "* The illustrative case studies ofChapter 9, although very limited in scope, feature the importance of transport from Los Angeles under worst-case conditions at Edwards AFB andFort 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 (seeTable 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 asa whole (i.e., to an average ofthe three RESOLVE receptor sites)andto annual meanconditions (ratherthan worst-case days).
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The estimation procedure involvesthree steps: 1. Allocating anthropogenic extinction in the RESOLVE region among three source areas (SanJoaquin, LosAngeles, and localdesert) 2. For each source area, apportioning man-made extinction among the five aerosol components (organics, sulfates, elementalcarbon, soildust, and nitratesplus 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 inpetroleum industry SO, (References 72and 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 excludingplanes/ships/trains), the petroleum industry (mostly from the San Joaquin Valley), and gasoline vehicles (including both exhaust and suspended dust). Together, these sources account forabout 60% ofanthropogenic 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 asawhole. These findings areconsistent with ourdiscussion ofemission inventories in Section 8.1. (There we noted thatdiesels, 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 errorestimates forthe various parametersin 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 shouldbe 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 ofaggregated 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 ofOwens 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 visibilityisopleths around the Searles Valley in Figure 37, the * From 1983 to 1986,petroleum industry emissions forSO, in theSan Joaquin Valley were approximately as follows(rounded to the nearest 5tons per day;: 1983 = 110TID, 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 ofsuspended 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 usedherein. 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 ofthese 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. Adetailed analysis ofthese latter days can lead to abetter understanding of the atmospheric processes that link emissions in the source area to air quality at the receptor site.Thisisthe objective ofillustrative case study analysis. The analyses here will focuson 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 ofdirect evidence ofthe path andtime oftransport 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 ofthe wind trajectories and diurnal airqualitypatterns foreachday.
9.1 SELECTION OF CASE STUDY DAYS The days forthe illustrative case studies were selected by scrutinizing time series plots ofaerosol andextinction data (e.g., Figures 43 through 45) as well asdata 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 ofJuly 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 ofdata 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 ofa particular source area, and (4) time duration ofthe 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 ofthe 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 impactsoccur at allofthe RESOLVE receptor sites on thedays shown.
TABLE 31. TheRESOLVE Case Study Days. Date Characteristics 4October 1984 Transportfrom bothupwind basins intothedesert. All desertsitesaffected. 14July 1985 Transport from both upwind basins into the high desert, with all receptor sites affected.Impacts from Ojai forestfires. 25December 1984 Transport effects at Edwards AFB early in the day. Local and regional impacts at EdwardsAFBthe restoftheday and atother receptorsitesallday. 20 February 1985 Wind-blown dust impact at all receptor sites and the two Los Angeles pass sites. Some transport im pacts atreceptor sitesearlyin theday.

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 ofair parcels on their way to the sampling sites.Thedetails ofthis analysis are presented inAppendix F. 9.2.1 4October 1984 4October 1984 was a dayoftransport tothe RESOLVE receptor sites fromboththe 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 atboth Tehachapi and Cajon Passes slowly increased most ofthe day, while the particle scattering at Soledad Pass increased dramatically in the afternoon. Outflow (flow intothehigh desert)occurred allday fromall three passes. Trajectories for Edwards AFB indicate that morning airparcels took about 6 hours to travel fromthe pass regions. However, by lateafternoon, thetrajectories 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 ofthe morning before crossing the mountains near Castaic Lake(northwest ofSoledad 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 ofthe significance ofthe 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. Thetrajectories 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 therestofthe day. Thisaction isa 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 atNWC and was consistently from the west to southwest all day. Trajectories were from Tehachapi until late in the day, when transport shifted tothe Soledadregion, thetransport timedecreased, anda 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 ofthis particular day seem to be verydifferent 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 inthis manner.
9.2.2 14July 1985 14 July 1985 isanother dayoftransport from boththe 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 ofthe 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 LosAngelesbasin beganarriving atboth Edwards AFB and Randsburg Wash.

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Thetrajectories indicate that all airparcels 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 ofthe 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 ofeffects 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 thiscomparison.
TABLE 32. Percentage ofVariousComponents atthe RESOLVE Sites. Day Component EDW CHL FOR RAW TEH SOL CAP RESOLVE Organics 32 37 29 30 34 30 36 baseyear 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 massless than 1.5 pg/m3

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 toproducing sulfate aerosol on4 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 ofthe day (and the previous day) andwinddirection was morevariable than onother days. Air parcels arriving at NWC apparently spent the last 10 to 20 hours within about 25 miles ofthat site. The trajectories indicate that the air parcels arriving between 1500 and 2000PST 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 spentat leastthe last 16hours in thedesert. Air parcels arriving at Randsburg Wash between about 1300 and 1800 PST had come from the northwest and spentthe last9to 24 hourswithin about 45miles ofthe monitoring site.Twoofthe trajectories indicate thatairparcels passedthrough the SearlesValley. 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 ofthe sampling site.The airparcels that arrived around noon had spent 10 to 16 hours traveling from the south and southwest, possibly passing throughthe Barstow area. Late in theday, the airparcelscame directly fromtheeast. Thechemical components on thisday show an extremely large unmeasured component at Edwards AFB and NWC. Other parts of this report indicate that this unmeasured component maybe an indicatorofnitrate aerosols.
9.2.4 20February 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 strongwinds fromthe 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 ofthe desert. On a day such as this, surface winds and trajectories can be misleading about the origin ofair parcels. Upper-level windscan carry aerosols long distances, after which the aerosols can bemixed down intoasurface layerthathad traveled inthe 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 highparticle scattering levels thatoccurred throughout the day at all ofthe sitesreflecttheinfluence ofwind-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 ofwind-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 winddata suggest impacts from the SanJoaquin Valley.

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9.3 CONCLUSIONS The case study analysis evaluated a number of days where fairly obvious source impactsexisted atthe RESOLVE receptor sites.This information helped toprovide a better understandingofhow visibility impacts occur attheRESOLVE receptor sites. Theanalysis of flow patterns, transport routes and times, and aerosol concentration patterns in the RESOLVE region provided direct evidence oftransport from both the San Joaquin Valley and the LosAngeles basintothe 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 thereceptor sites. Underthese conditions, transport time fromthe 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 6to 8 hours. Particle scattering is generally lower at thereceptor sites when thetransport timeis longer. On the fourcase studydays, NWC was never impacted bytransport ofaerosols fromthe LosAngeles basin. Edwards AFB was often impacted by aerosols transported from the San Joaquin Valley early in the day and byaerosols transported from LosAngeles later in the day. On the case study days, Edwards AFB was never impacted by Los Angeles via Cajon Pass.Randsburg Washwas impacted byboth upwind basins. Since Fort Irwin NTC is sofar 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 duringtransport ofaerosols from LosAngelesvia Soledad PassorCajon Pass. On one case study day, only local impacts were documented atNWC, Randsburg Wash, and Fort Irwin NTC. Some ofthe airparcel trajectories passed through the Searles Valley before they arrived atNWC andRandsburg Wash. Some ofthe 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 ofthe background that day, inturn, become part ofthe nextday's background. Otheraspects ofthis report that support thisview are as follows: (I) the consistent,but weak, average diurnal variations ofaerosol concentrations (Section 6.2); (2) the lack ofa correlation between Ni/Pb ratios andcurrent-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 areaverage conditions (Section 8.2.4).

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AppendixA PRECISION ESTIMATES FOR RESOLVE DATA SETS

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Thepurpose ofthis appendix is toestimate the precision ofvarious RESOLVE data sets. Thediscussion is organized according tofourtypes ofdata: mass/absorption samples, carbon measurements, PIXE/XRF data, and nephelometer measurements. In general, precision refers to the reproducibility of measurements ithe case ofrandom error as opposed tothe case ofsystematic error). Here, precision is defined as one standard error ofrandom deviation away from the stable value (the value with no imprecision). We can estimate from a series ofindependent, simultaneous, duplicate measurements as the standard deviation ofthe difference between thetwo duplicates divided by the square rootof two, thatis
(X

- X )2 (A.1) Precision- 

### -2 N
The square root of2 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 relativeto the stable value.
A-1 MASS AND ABSORPTION DATA The duplicate fine samples taken throughout RESOLVE should represent all three fundamental sources oferror: 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 ofthe 2x4 sampler data formassand absorption, including thefine, coarse, andtotal mass fractions. Figures A.1and 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/m3 (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 ofan individual sample as a function of mass or absorption can be derived fromTable 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 thatthe precision oftotal mass is 1.0 Vg/m 3 (5. 1%). Similarly, at the average ABT level of7.3 Mm-l, the precision fortotal absorption is .49 Mm-1 (6.7%).

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### 53.81" 48.44 43.08; 37.72.
32.35- 3 221 • S I02772 a


### 216.262; 212te1lt I II t710 10 1 I I I dIIt i
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## 'r' .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 theTwoAlternate Channels ofthe RESOLVE 2x4 Sampler.

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17.37 15.65; li l I 

### 13.93; a a 2 r5t 2112 12.21.; 2 sitI 225 I amaII I I z= I tz


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304772t • :6lit 1 .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 ofthe RESOLVE 2x4 Sampler.

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TABLE A. 1. Statistics from Duplicate 2x4 Samples forMass 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 aso/-,/ vvhereo isthe standard deviation of the difference between the two duplicates.

TABLE A.2. Variation ofPrecision 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) 74 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 massand fine absorption data actually used in the RESOLVE data analysis represent averages of two samples, sothat uncertainty isdecreased by a factor of v-from the values listed above.

Actually, the RESOLVE data analysis uses averages ofthe two fine samples for mass and absorption. Thus, for fine mass and fine absorption, the error ofthe data in the analysis is decreased b afactor ofV"2. The precision ofthe data in the RESOLVE analysis is thus .73 pg/rm 3 2- V"= 52pg/m3 (5.2%) for fine mass and .45 Mm-1 I V2 = .32 Mm-1 (5.3%) for fine absorption. Coarse mass and coarse absorption are obtained by subtraction, so the precision isjust the root mean square ofthe fine and total errors. At the average coarse mass of9.6 pg/M3, the coarse mass precision is N/.522 + 1.02 = 1.13 pg/rm 3 (1Z%). At the average coarse absorption of1.3 Mm- 1,the coarse absorption precision is V.322 +

### .492 = .59 Mm-I (45%). To summarize briefly, the average precision ofthe 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 givencan be used toestimatethe precision ofindividual samples atvarious overall loadings.

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A.2 ORGANIC AND ELEMENTAL CARBON This section summarizes the results ofthe 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. FromTable A.3 we see that the precision ofthe GGC organic carbon analysis is. 13 pg/m3 (4%), while the precision ofthe 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 fororganic carbon and .1 pg/m 3 forelemental 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 22 +- .052 = ±24% forelemental 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 oferror (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 ofthe analytical precision for variouselements. Table A.5 summarizes the total uncertainty for elements of greatest interest in the RESOLVE program. The middle column ofthe table denotes the analytical technique (XRF versus PIXE) that provides the greatest percent ofdata 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 ofvanadium, most ofthe random uncertainties in the elemental data range from about 5 to 15%. Several ofthe 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.

[.21NWC TP 6869

P.; 9+

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-- S

= s AA 2+



### o~~ A, ,..l,

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### SB A

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 +

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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 (_ standarddeviation) 3.16 t 190gIg/m3 72 ± 59 Vgim 3 R(R2)for duplicate analysis 995(991) 947(898) Standard errorofanalysis'(%) 127 pg/M3(4.0%) 136 pg/m3(18 9%)
" The standarderror oftheanalysis isdefined aso/V-,where o isthe rootmean square error ofthe difference between thetwo duplcates.

TABLE A.4. Precision ofCarbon Concentrations asaFunction ofLoading. Organic carbon Elemental carbon Average concentration, Precision absolute, Average concentration Precision absolute, gg/mr 3 pg/m3 (%) gg/m 3 Vg/m3(%) 1stquartile 1.24 17 (14) 1st quartile 24 13 (54) 2nd quartile 2.18 22 (10) 2ndquartile .46 17 (38) 3rd quartile 3.47 28 (8) 3rd quartile 77 18 (24) 4thquartile 5.78 35 (6) 4th quartile 140 18 (13)



### TABLE A.5. PrecisionofRESOLVE 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 for24-hour averages are less bya factor ofV-8. Based on the above information, the precision ofhourly and 24-hour nephelometer data canbe expressed inunitsofMm-I as follows: Hourly: PI = V/1.5 + .041lBscat 2 

### (A.3a) 24-hour: P24 = P//'' (A.3b) At the average RESOLVE Bscat level of33 Mm-1, thehourly 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 ofthe RESOLVE monitoring program, quartz filters were exposed on a weekly basis to represent field blanks for the carbon analyses. Ofthese 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 andtoderiveblank adjustment factorsfor theRESOLVE carbon data. Blank values should be independent ofthe 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 theblank data in units ofpig/m 3by normalizing all valuesto a standard daily flowof32 m 3
. 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 arisesisdo the blank values depend on the site and season?. Figure A.5 shows aplotofthe seasonal variations (averagedfor allsites) aswell as aplotofthe 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 forboth organic and elemental carbon are ofmarginal statistical significance (many t-scores on theorder of 1.0to 1.5). It seems reasonable that both seasonal and site variations should be included in the specification ofcarbon blanks. One possible method to include both seasonal and site variations in the specification of carbon blanks isto 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

*Notethat,in the production ofFigure A.5, biases introducedby differentsample sizesforvarious seasonsand siteswere eliminated. For example, the seasonal patterns were computed by firstaveraging within each site and thenaveraging the 7sitevalues eachseason. Also, thesitepatterns were determinedbyfirst averaging within each seasonand thenaveragingthe 4seasonsfor eachsite.

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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
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TABLE A.6. Average Carbon Blank Values by Siteand Quarter. i Allvalues correspond to astandard flow of32 cubic meters per day. Organic carbon (pg/m 3), Elemental carbon (pg/m 3), mean ± standard error ofmean mean - standard error ofmean (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) () (11) (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-±.12 120 ±02 123-±.06 RAW 2 07 26±,06 .19 ± 05 47-± 12 (4) (5) (5) (4) (4) (5) (5) (4) FOR 70± 04 139±.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 ofthe small sample sizes (e.g., third quarter for elemental carbon at Fort Irwin NTC) and substantial discontinuities from quarter to quarter (e.g., organic carbonat Soledad going from .67 11g/m 3 to 1.69 pg/m 3 from 31 March to 1April). 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 ofseasonal factors. This possibility, however, does not appeardefensible because the seasonal patterns are, inactuality, notthatconstant from site tosite (seeFigureA.6). A third possibility (the author's recommended possibility) is the scheme presented in Table A.7. Here, toincrease 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 aretaken asthe average ofthe summer andwinter 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 bycomparing the average blank values for the summers of 1984 and 1985 (Quarters 2 and 3), the first 2 and last2 quarters ofsampling. These values (including standard errors ofthe means)are asfollows: Summer 1984 Summer 1985 Organiccarbon 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 ofCarbon Blank Values forthe RESOLVE Sites.

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TABLE A.7. Average Carbon Blank Valuesby Siteand Seasons. (All valuescorrespond to a standard flowof32 cubic meters per day.) Organic carbon (gg/m3), 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 118 108 1.13 RAW 38 22 30 FOR 80 120 100 FOR .25 .16 21 TEH 81 146 1 14 TEH .35 .31 33 SOL 81 1 70 126 SOL 26 .19 23 CAP .69 125 .97 CAP 34 .26 30 No differences are apparent inthese numbers. A final question is what is the uncertainty in the blank values that should be used in assessing the overall uncertainty ofcarbon concentrations. Apparently, no definitive answer is given by the data.The blank uncertainties are surely not as high as the standard deviations ofindividual blanks (about4 pg/m3fororganic carbon and .2pg/m3 forelemental carbon) because these individual datapoints 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 ajudgment call. The author's judgment isthattheuncertainty approximately corresponds to a .2 pg/m3 standard error for organic carbon anda. 1pg/m3 standard error forelemental 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 ofthe three RESOLVE receptor sites (i.e., to the study region asacomposite)and toannual meanconditions (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 desertregion. Second, foreach ofthese 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 areallocated 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 SanJoaquin Valley. The remaining one-third of anthropogenic extinction is assumed to come from local desert sources and theLos Angeles basin. Although our analyses indicate that both ofthese sources are significant, we do not have an objective method ofapportionment for them. We assume that the remaining one-third ofanthropogenic 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: SanJoaquin Valley = 2/3, local desert sources = 1/6, and LosAngeles 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 NO2 13%, with atotal non-Rayleigh extinction of35 Mm-1

. It is argued in Section 8.1 that 85% ofsulfates/elemental-carbon/nitrates-plus-N02, 80% oforganics, and 50% ofsoil dust are anthropogenic. When we combine these two results, the budget for anthropogenic extinction is organics 27%, sulfates 26%, elemental carbon 21%, soildust 12%, and nitrates plusNO2 14%, with atotalanthropogenic extinction of27Mm- 1

. For the purpose of this appendix, we need extinction budgets for anthropogenic contributions from each ofthe three source areas individually. A reasonable estimate of these extinction budgetscan bemade bynotingthree constraints: 1. The weighted sum of the three source areas must equal the total anthropogenic extinction budget listedat the end ofthe firstparagraph in Section B.2. 2. The coarse soil dust component should be assigned predominantly to local sources because ofthe relatively short atmospheric lifeofcoarse 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
133NWC TP 6869 anthropogenic extinction budgets that meet these constraints. All values in the table are stated tothe nearest 5 or 10% to more appropriately reflectuncertainties. TABLE B.I. Extinction Budgets for Anthropogenic Contributions in the RESOLVE Region. SanJoaquin Valleya LosAngeles basinad Localdesert sources' RESOLVE region Organics 30% 25% 20%c 27% Sulfates 30% 30% 10% 26% Elemental carbon 20%d 30%c 10% 21% Soildust 5%e 0%d 50% 12% Nitrates plusNO2 15% 15% 10% 14% 

### SWith the minorexceptions discussed below, budgets forthese two source areas should be similar because ofsimilar "five-component" Composition atthe pass sites Because together they dominate the total, they each should be similar to the RESOLVE region anthropogenic budget (except for soil dust).

bCoarsesoil dust isbasicallyall assigned tothe local source category IEmission and ambient data for the desert suggest the possibility of relatively high organics comparedtosulfatesand elemental carbon.

dTehachapi passshows relatively less elemental carbon than theLosAngeles passes. e Tehachapi pass shows relatively more soil dust than the Los Angeles passes Also, transport distances areshorter from Tehachapi. afactor thatmay bemore critical for soil components.

B.3 ANTHROPOGENIC EMISSION INVENTORIES The thirdpart ofthe engineering calculation isto assign the anthropogenic components (by source area) to emission source categories. For each source area, man-made sulfates, elemental carbon, andsoil dust will be allocated according to anthropogenic inventories for SOx,elemental carbon, and soil dust, respectively. Based on the assumption that two-thirds ofman-made organics are primary (Reference 65), two-thirds oforganics will be allocated by primary organic aerosol emissions, with one-third apportioned by reactive organic gas (ROG) emissions. It is reasonable to allocate NO2 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 emissioninventories, we will apportion nitrates accordingtoNO, emissions. An initial set ofrough calculations demonstrates that only three source categories contribute significantly to anthropogenic extinction, with any other source category individually accounting for at most about 5% ofman-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.2presents therequired emission data. Asindicated bythe footnotes tothetable, 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 inpetroleum industry SO, (References 72and 73). (See footnote inSection 8.4.)
TABLE B.2. PercentofEmissions fromMajor Source Categories. Organic Elemental Soil ROgaGcSO. NO, 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

aBased 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) To separate out diesel tignt/medium-duty vehicles, it isassumed 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).

bBased on applying the fine-organic carbon and fine-elemental carbon fractions ofGray (Reference 67)to allsource categories in the PM-10 inventory byCARB (Reference 39).

cBasedonthe 1982 inventory by Gray(Reference 67). d Based on the early/mid-1980s inventory by Nero (Reference 68). Vehicular emissions are split between gasolineand 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 isobtained by multiplying the factors ofSections B.1, B.2, and B.3. Forexample, 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 ofSan Joaquin Valley man-made extinction due to elemental carbon) times 66% (portion ofSan 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 ofman-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 offactors discussed in Sections B.1, B.2, and B.3. However, this method would not be strictly correct because some ofthe 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 areadded according toroot mean sums ofsquares, one or twosources oferrorusually 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 viaeach aerosol component Source 

### ~ mnmd category extinction Organics Sulfates EC Soildust 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 33% Gasoline vehicles 15 7% 35% 2.0% 8% 58% 3611,

A review ofpotential errors forthe diesel vehicles/equipment category indicates thatthe 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 resultis notvery sensitive to source area allocation). The relative errorforelemental carbon inthe anthropogenic extinction budget allocation (Section B.2) should be nearly the same as the relative error forelemental carbon intheextinction budget (t 32%, seeAppendix C). For all three source areas, diesels account for about one-half of elemental carbon emissions (Section B.3); therelative errorin thisthird factor isjudged to be ±35%based on experience with emission inventories for elemental carbon (References 67 and 83). Accordingly, the total relative errorfor 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 anoverall absolute error of±7%for thediesel category. For the petroleum industry, the largest error is in the sulfate contribution from the San Joaquin Valley (the estimated contribution for thiscategory is 9.4%). The relative error in the first factor, source area contribution (Section B.1), isjudged to be =25% based on a review ofChapter 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 isjudged to be ±30%. Propagating these errors yields a relative error of ±44%for the San Joaquin Valley petroleum SO, category, oran absolute error of4.2%. Arbitrarily assuming a ±50% relative error for the organics and NOx categories brings the total root mean square absolute error uptoonly 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 ofthe extinction budget. Also,emissions data are fairly well documented for motor vehicles. The one major exception is in the soil dust category. Uncertainty existsregarding the portionofsoil 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 errorof±5%. In summary, the contributions of the three source categories to average, regional anthropogenic extinction are asfollows:

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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 inthe 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., inextinction 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 databased on previous analysis of long-term airquality 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 arepg/m3,Mm- 1 ,and m2/g andthatall uncertainties pertain tolong-term averages. Fortotal non-Rayleigh extinction, we have BNR = BNO2 + Bsp + Bap. The three terms andthe associated uncertainties areasfollows: 1. NO2 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 representsjudgment ofclimatological representativeness (inflated well above simple o/-'Nbecause ofnonindependent 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 (±3Mm- 1). 3. Particle absorption, Bap = 6.2 ± 2 Mm-1. The significant uncertainties here are the accuracy correction factor forTeflonabsorption (±30%)andclimatology (± 10%). Scattering and absorption byparticles are allocated amongaerosol constituents asdescribed inSection 7.2.The uncertainties are quantified in the following paragraphs. Fororganics, the contribution toextinction isBORG = (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/m3, should represent the error in the relative contributionoforganics to fine mass. This error is
141NWC TP 6869 extremely small because ofthe large sample size and because accuracy considerations have been obviated by considering the scattering efficiency as appropriate to wlatever [ORGI represents.The second term iscoarse-particle scattering, with4% ± 3% assumed to be from organics. Total coarse scattering, Bspc = 5.1 ± 3 Mm-1, has a substantial uncertainty because ofthe large potential errorinourestimate ofthisparameter.
For sulfates, we again have fine andcoarse 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% oftotal 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% ofthis assumed to be from elemental carbon. For soildust, 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 coarseabsorption. For nitrates, BNO 3 - (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 representscoarsescattering. Carrying outthe uncertainty analysis usingroutine 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 oferror beingthe 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% Soildust 18% ±8% Nitrates plusNO 2 13% ± 5% It is apparent that the errors for the species contributions range from 5 to 9% oftotal 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 byonlyfour typesofuncertainty as follows:

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1. Uncertainties in the fine-scattering efficiencies for organics, sulfates, and nitrates account for basically all ofthe error in the organic and sulfate categories, and about halfof the error in the nitrate category. (Note that potential errors introduced by sampling artifacts havebeen included within the uncertainty ofthe scattering efficiencies.) 2. Uncertainty in the accuracy correction factor forTeflon absorption accounts for the major partofthe error in the elementalcarbon category. 3. Uncertainty in ourestimate ofcoarse-particle scattering dominates the error in the soil-dust category. 4. Uncertainty in the concentration ofnitrate particles contributes about halfof the errorin the nitrate category. With respect to the extinction budget, the uncertainties related to measurement imprecisionand climatological representativeness are quitenegligible (exceptfor the sparse nitrate sampling)because ofthe large number ofobservations 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 ofminor importance because ofthe small magnitude of mostofthese 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 sourcesas follows: CNi = aNi.SJ sSJ + aNUf.SLAs + aNiIAc sLAc + aNi.HDSHD 

### and (D.1) CPb = aPb.SjsSJ+ apb,LAs SLAs + aPb,LAcSLAc + aPb.HDSHD where
CNiand cpb = elemental concentrations (mass ofelement per volume ofair) atthereceptor. 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, andthe highdesert. aij = content (mass of element per mass of primary aerosol) of element iin the aerosol from sourcej. The coefficients aiSj, aiLAs', and ai.LAc in Equation D.1are supplied by the measurements inthe passes onoutflow days.The coefficients aiWDare available from measurements atthe 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 thetwo equations, the source contributions sj.
D.A BOUNDING CONTRIBUTIONS Although Equation D.1 is underdetermined, admitting an infinite number ofsolutions, ityields useful bounds in the SanJoaquin Valley contribution ssjbecause 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 itcan 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 ofssj 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 valueofsSj isthat forwhich the non-SJV source with the lowest Ni/Pb ratio is the only other contributor. Figure 49 provides geometric insight into this dependence ofthe calculated SJV contribution onother sources' Ni/Pb ratios. For an example ofthe calculations involved we will bound the contribution ofthe San Joaquin Valley to Edwards on 12 July 1985. This was an outflow day at all three passes, withNi/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 ofoutflow 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 ofthe ratios for the Los Angeles basin.

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TABLE D.1 Estimated Contributionsofthe SanJoaquin Valley and LosAnr-%'eF Basin toFine-Particle Mass Concentrations at Edwards AFB on 6Joint Outflow Days. Estimated contributions to Edwards AF8fine mass SanJoaquin Valley LosAngeles basin Inputdata 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 highdeserttozero leaves two equations in twounknowns, easily solved for aNij., pPb P.LAs N.i (D.2) S(lower)= aNi.As aPBa.&

- pbLAs aNi' The upper bound for ssjis provided by the solution toEquation D.1with SLAS = SLAc = 0. Significant uncertainties exist inthe Ni and Pbcontents ofthehigh desertcontribution that were notmeasured directly as those ofthe SanJoaquin 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 ofall Ni. The San Joaquin Valley contribution isthen simply C~. 

### (D.3) ss,(upper)=- The contribution of the San Joaquin Valley at Edwards AFB on 12 July 1985 is thus estimated tohavebeen between 49 and55% oftheobserved fine-particle mass. Thecontributions ofthe 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 12July 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 ofthe non-SJV aerosol to transport through Cajon yields ssj = 12.2 gg/m3,SLAs + SLAc = 2.6 

### pg/m3,andSHD = 0 pg/mr 3



### . Alternatively, attributingall of the non-SJV aerosol tohigh desert aerosol ofthetype 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 of49 and 55% determined earlier, while those for the LosAngeles basin range from 37% down to0%. Even the sum ofall primary contributions is comparatively poorly determined, ranging between 86% down to 57% ofthe observed total.

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This lastobservation isconsistent with the resultsofregression analysis thatshows that Ni and Pbtogether account foronly25% ofthe observed variance in finemass atEdwards AFB.
D.2 AVERAGING CONTRIBUTIONS We want todetermine the SanJoaquin 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 ofthe pass measurements were compromised by backflow. The full set ofreceptor measurements can be utilized only by extracting from the limited source measurements a statistical characterization ofsource composition and itsvariability. Theequations givingrise to thebounds 2and 3 can be expressed in matrix terms as C = AS, where C =cN., A =a vi.s,aNiX, 

### and S =sS, (D.4)
cPb aPbSJ ,a PbX SX where the subscript Xdenotes a non-SJV source. The solution toEquation D.4 then has the simple form S = A-'C (D. 5) We write daily values in terms oftheir 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 Scan be written in terms ofthe means andcovariance ofA-, and Cas 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 ofa source to a receptor can be accurately estimated from mean source characteristicsA-I and mean source receptor concentrations C only ifthe covariance (A-I

- A-I) (C

- C)between source characteristics Ni/FM and Pb/FM ofan 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 anddispersion. Only indirect linkages existbetween these two sets of factors so that there is reason to expect the covariance of source characteristics and receptor concentrations tobe small. The onlydata available forcalculating source-receptor covariance are those from the 6 joint outflow days ofTable D.1, when both source basins were characterized concurrently with measurements at Edwards AFB. The effects ofthe 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 SanJoaquin 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/m3while 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. Sixsamples are toofew toprovide areliable testofindependence, however, and the decision toneglect source-receptor covariance ultimately mustreston the apriori reasoning outlined inthepreceding paragraph. Another point in Equation D.6 concerns the proper averaging ofsource characteristics. One interpretation of the observed variability ofNi/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 lessconstant. This interpretation underlies the "effective variance" method ofWatson, 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 ofthe inverse source composition matrixes as in Equation D.6. The bottom two rows ofTable D.1 compare the source contributions calculated according to the two interpretations. The upper bound for the SanJoaquin Valley contribution again provides a simple illustration ofthe distinction. The inverse mean SanJoaquin Valley Nicontent on the 6joint 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 ofthe observed variability ofNi 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 ofsource variability. (Note, for example, the uniformly low Ni and Pb contents atall three passes on 12July 1985, suggesting synoptic conditions favorable togasto-particle conversion.) The estimates of average source contributions to follow will accordingly becalculated bythe 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.

150NWC TP 6869

Appendix E CHEMICAL MASS BALANCE APPROACH TO SOURCE APPORTIONMENT

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Section 8.2 described atracer 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 toprovide quantitative estimates ofthe uncertainties ofthose 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) toa set of chemical mass balance equations to calculate source contributions and their uncertainties. The software provides several performance measures that indicate the validity ofeach application. The most useful ofthese 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 ofuncertainty. A separate CMB applications and validationprotocol (Reference 71) was followedtoestimate the validity ofthe input data and the compliance with model assumptions. The fundamental assumptions oftheCMB are 1. Compositions of source emissions, as perceived at the receptor, are constant over theperiod ofambientand source sampling. 2. Chemical species concentrations add linearly. 3. All sources with a potential for significantly contributing to the receptor havebeen identified and had theiremissions characterized. 4. Source compositions are linearly independent ofeachother. 5. The number ofsources 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, andmodel testing anduncertainty 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
153NWC TP 6869 days inwhich winddirections pointed from each basinto the desert formore than two-thirds of the 24-hour sampling period. The average fraction of fine mass and the standard deviaiion were caiculated foreach ofthese subsets ofsamples, with the results appearing in the first three columns ofTable 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 thanone-third ofthe 24-hour sampling periodat 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 ofthe average and standard deviation offractional fine-mass composition. Undoubtedly, some contamination of these local profiles exists with contributions fromthe LosAngeles andSanJoaquin Valley airbasins, since there was some outflow from these basins during the sampling periods. However, these are the best nonregional profiles obtainable withcurrent data. Annual averages forthebase 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 ofsamples) was used as an estimate oftheir uncertainty. Cases were eliminated when any single species did not have valid data reported. Another set ofannual 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 includedin each average. The CMB applied tothese two different data sets allows the effectsofdifferent averaging methods and different numbers ofspecies in the solution to bedetermined.
E.3 FEASIBILITY TESTING The source apportionment ofnonlocal 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 tothe actual data taken at Cajon, Soledad, andTehachapi Passes. Coefficients ofvariation 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 offive different sources used in their tests. However, the fractional compositions fortheTehachapi Passsource profile (representing the SanJoaquin 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 tooneor theother oftheremaining twoprofiiles. Random uncertainty proportional tothe coefficients ofvariation was applied to ambient concentrations generated fromthese three source profiles andequal contributions from each source. The procedure ofTrijonis, et al. (Reference 3), was followed to maintain equivalence to the a prioritests made for RESOLVE. The results were that contributions from Tehachapi andCajon couldbe resolved from each otherat an uncertainty level ofabout 40%. Contributions from Soledad are apparently colinear with Cajon and these contributions

154

### NWC TP 6869

TABLE E.1.SourceCompositions and Uncertainties. Species SOL CAP TEH EDW,NW EDW,W FOR.NW FOR,W CIHL.NW CHLW

AL 0094 01,0 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 .0001 .0001 .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,

155NWC TP 6869

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 inTable E. 1forlocal sources containhigherconcentrations 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 oforganic carbon. The nonwinter profiles also contain from 50 to75% ofthe fractional Ni and V found in the Tehachapi profile, which indicates that these profiles are influenced by aerosol from theSan Joaquin Valley. These feasibility testingresults significantly relax the original conclusions ofTrijonis, et al. (Reference 3), which didnot benefitfromactual data.
E.4 CHEMICAL MASS BALANCE RESULTS Table E.2 presents the results of CMB model applications to average receptor concentrations at EdwardsAFB, 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 ofthe source apportionments. Theseconfigurations consistedof * Different averaging periods. Chemical concentration averages were calculated forthe base yearandfor 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 largercontribution fromthese regions than are NWC and FortIrwin NTC. * Different source profiles included in the CMB. It is possible that different source profiles will provide equivalent performance measures. These performance measures are toonumerous andcomplex to report here, butthey were examined and compared for each case to determine which ofalternative mixtures of source profiles could reproduce the annual average chemical compositions observed atthereceptors. * Different setsofchemical species included in the CMB. The Ni and Pb used inthe tracer solution reported in Section 8.2 were used with thebase year averages to verify the conclusions ofSection 8.2. Seventeen species were
156NWC TP 6869

included in CMB calculations for base year data and 20 species were included in the CMB calculations forthe averages over alldata.

TABLE E.2. Source Contributions andTheir Uncertainties for Different CMB Cases in pg/m3

. (Uncertainty isshown directly undersource 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 18 3.7 1.2 18 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 Allsamples 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

157NWC TP 6869

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 19 -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

158NWC TP 6869

TABLE E.2. (Contd.)
Case SOL CAP TEH FOR,NW FOR,W FOR 19 22 Baseyear 2.2 2.1 Ni. Pb 2.8 1.9 1.4 1 1 0.8 2.3 0.6 12 -38 114 176 24.2 FOR 1.9 1.3 2.3 02 Baseyear 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 13 0.9 2.2 2.2 09 1.4 1.1 0.7 10 0.0 5.6 2.2 0.7 1.5 FOR -0.1 2.1 2.5 1.6 All samples 18 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 ofinformation 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 bedrawn from these dataare the following:
0 Only two source types canbe resolved from each other forthe annual average receptor data, regardless of the averaging period or the chemical species included in the CMB. This statement is consistent withthe feasibility testing, which found that when three source types were tested (with the species measured in RESOLVE in excess ofdetection limits), one or more of the source contributions was associated with uncertainties that far exceeded
159NWC TP 6869

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 

### "(Reference71). 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 bythe Ni and Pb tracer solutionsofSection 8.2. The CMB Ni and Pb solutions yieldvalues toward the lowend oftheranges cited inSection 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% ofthe 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 upperboundfor the SanJoaquin Valley contribution ofSection 8.2. Thishigher contribution is generally because ofthemuch 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 ofthe local source profile. Unfortunately, local geological profiles are not available. The geological profiles would substantially improve the results. Theconclusion to bedrawn here isthat thecomposition oflocal 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 ofthis. * 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 isincluded. Theoverall conclusion ofthiscomplementary CMB analysis isthat San Joaquin Valley sources canberesolved from other sources, but only withuncertainties of50% or more. This means thatthe San Joaquin Valley contribution is probably inthe range of25 to75% ofthe fine-particle mass atNWC and FortIrwin NTC.

160NWC TP 6869

Appendix F DETAILED ANALYSIS OF CASE STUDY DAYS

161NWC TP 6869
This appendix presents a detailed description of the 4 case study days. Most of the discussion centers on wind trajectories anddiurnal patterns ofparticle scattering. To provide abetterunderstanding ofthis appendix, we willdiscuss two issues. One issue is related tothe RESOLVE source sites, andthe other to thereceptor sites. Photographs and observations ofthe regions nearTehachapi andSoledad Passes as well as the wind fields developed for the trajectory analysis indicate that significant outflow occursacross whole sections ofthese mountain ranges, notjust through thepass itself.This outflow occurs because the cross section oftheparticular pass isnot always sufficient forall ofthe 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 ofthe material being transported, not as complete representations ofthe flow. In contrast, wind fields and observations indicate that most ofthe transport in the Cajon Pass region occurs at the pass. This might be expected, since the mountains around Cajon Passare much higher than those surrounding Tehachapi andSoledad, andthepass islower. 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 ofabout 6 hours when the wind speedis5 miles per hour, and about 3 hours when the wind speed is 10 miles per hour. However, thethree other desert sitesare 65to 110 milesfrom anyofthepass areas and thus are 13 to 22 hours away at 5 miles per hour, or 6.5 to 11 hours away at 10 milesper 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 threedesert 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 aboutthe sametime. Figure F.2 shows similar plots forparticle 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 forthe three pass sites. Particle scattering at both Tehachapi and Cajon slowly increased most of the day, while particle scattering at Soledad increased dramatically inthe afternoon. There was outflow (flow into the desert)all dayfrom all threepasses. 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 ofair 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

### NWC TP 6869

OCT 4, 1984 - 

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164

### NWC TP 6869

.n . ... . . . . . . .

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### NWC TP 6869


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Exmpe Tajetores f ir arcls rriin aeceFort[rinstlain 14 on 4as Octoberin 1984. 166ai aneNWC TP 6869

direction datataken at the RESOLVE sites, airpollution district sites,and NWS stations in the region. The characteristics ofthe streamline maps discussed in this section are similar to the average ones shown in Chapter 4 ofReferences 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 ofthe variability ofwind speed and direction. Trajectories were not calculated forevery 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 amore 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 mountainsjust northwest ofSoledad Canyon and arriving at Edwards AFB. Theairparcel associated with the increase in particle scattering at Edwards AFB (between 1400and 1600 PST)had passed intothe desert aboutthe same time as a large increase inparticle scattering was measured attheSoledad Canyon site (seeFigure 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 aday when the trajectories indicate transport from the Los Angeles basin all day, the 24-hour average sample still shows a small influencefrom the SanJoaquin 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 airparcelshad beenwithin about 15 miles ofNWC for the last day or more. The 2000 PST trajectory (shown in Figure F.3a) indicates that transport from theTehachapi Pass region was established, Thistransport 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 quicklyalonga directroute from the SanJoaquin Valley. The Ni/Pb ratioatNWC on4 October is0.31 (seeTable F.1). Thisratio is slightly higher thanthe average for the RESOLVE baseyear atNWC. Also, the ratio is significantly higher thanthe Soledad PassNi/Pb and slightly lower thanthe 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 ofthe day before 1800 PST. However, note the sharpincrease in particle scattering at 1900 PST thatoccurredjust as the transport shifted to the Soledad Pass region and the transport time decreased. Later trajectories indicate transport was from Cajon Pass. TheNi/Pb ratio at FortIrwinNTC on4 Octoberis0.35. This ratio isslightly higher than the average forthe RESOLVE base year at Fort Irwin NTC. Also, the ratio is significantly higher thanthe Soledad Ni/Pb and slightly lower than theTehachapi Ni/Pb on4 October.

167NWC TP 6869

OCT 4 1984 'tOW

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168

### Nwc TP 6869

TABLE F. 1. NiUPb Ratios onCase Study Days. Average nickel to average lead Site For individual ForRESOLVE 12/25/84 10/4//84 7/14/85 2/20/85 outflowdays baseyear 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. Duringthe worst particle-scattering hours ofthe day, the trajectories indicate that the Los Angeles basin was the major contributor to aerosols atFort Irwin NTC. However, theNi/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 tobe different than the sourcecontribution 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 14July 1985 is another day oftransport 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 for4 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 ofthe 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 desertnearTehachapi Pass. TheNWC trajectories (see Figure F.6)show that theparticle-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 (about0700 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

### NWC TP 6869

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## VALLEYRA171NWC 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 ofa particle-scattering pulse (about 1100 to 1300 PST). However, right after this pulse, airparcels from the Los Angeles basin began arriving atboth Edwards AFB and Rand-burg Wash. Figure F.6 shows two sample Edwards AFB trajectories, one from the SanJoaquin Valley andone 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. Partofthe influence on thisday wasbecause ofextremely large forest fires during two weeks ofmid-July in Southern California. On 14 July, ason surrounding days, the organic component ofthe 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 ofNWC, 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 thatarrived atNWC probably spentthe last 10 to20 hours withinabout 25 miles of NWC. The trajectories indicate that the airparcels that arrived between about 1500 and 2000 PSTprobably passed through the Searles Valleybefore arrivingatNWC. 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 spentatleastthe last 16 hours in the desert. Particle scattering and trajectory information forboth 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
. ýof the trajectories indicated that the air parcels passed through the Searles Valley. Fort Irwin NTC was much cleaner onthis day than the

1'12NWC TP 6869

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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) inMm-1 versus hours oftheday.

173NWC TP 6869

other sites. The trajectories before about 1000 PST indicate that the air parcels had spent at leastthe last 6 hours northofthe sampling sike. The airparcels that arrived around noon had spent 10 to 16 hours traveling from the south and southwest, possibly passing through the Barstow area. Late inthe day, the air parcelscame directly fromthe 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 ofthe 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 ofparticular airparcels. Upper-level winds cancarry pollutants long distances, after which the pollutants can be mixed down into a surface-layer air parcel that is traveling fromthe opposite direction. Good examples ofthisphenomenon are Edwards AFB and Fort Irwin NTC early inthe 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 about0600 PSTon 20 February. Trajectories forNWC 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 ofthe sites demonstrate the influence of wind-blown dust. The measurement ofthe 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.

174NWC TP 6869

fee 20. SONVS .0.0. rn220.0.-* "

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### FIGURE F.8. Particle Scattering and Trajectory Information for Edwards AFB, NWC, Randsburg Wash, and Fort Irwin NTC on 20 February 1985. Particlescattering (Bscat) in Mm-' versus hours oftheday.

175NWC TP 6869

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182