Haze in the Grand Canyon: An Evaluation of the Winter Haze Intensive Tracer Experiment (1990)

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Appendix 2: Selected Pages from the NPS-WHITEX Report Pagei CHAPTER 1 Overview (1-1) 48 Background (1-1) 48 Study Plan (1-3) 50 Interrelationships Between Aerosol, Optical, and Visibility Apportionment (1-7) 54 Measurement Program (1-7) . 54 Report Outline (1-12) 59 References- (1-13) 60 CHAPTER 9 Introduction (9-1) 63 Climatology of the Area (9-23 64 Light Extinction Budget (9-3) 65 Attribution of Regional Sulfur and Visibility Impairment (9-3) 65 Emissions Analysis (9-4) 66 Trajectory and Streakline Analysis (9-5) 67 Spatial and Temporal Trends in Ambient Concentrations (9-6) 68 Deterministic Wind Field Modeling (9-7) 69 Tracer Mass Balance Regression (TMBR) Analysis (9-7) 69 Differential Mass Balance (DMB) Analysis (9-9) 71 NP~WHITEX page numbers are shown in parentheses. 45

46 · APPENDIX 2 Attribution of Visibility Synthesis (9 9) 71 (9-10) 72 OTHER PERTINENT HPS-WHITEX REPORT PAGES CD4 Tracer Injection (3-2) 75 Sample Collection (3-2) 75 Figure 4.15: CD4 concentrations (ppt) at eight WHITED receptor sites (~20) 76 Table 4.5: Statistical summary of elemental sulfur concentrations (1lg/m5) during the WHITEX study period Table 4.6: Statistical summary of CD4 (ppt) at eight receptor sites using fully scaled CD4 at Page and Hopi Figure 5.4: Time traces of optical data and relative humidity at Hopi Point Figure 5.22: Measured and reconstructed 12-hour averaged extinction coefficients (Km~~) and the fraction due to each component at Hopi Point Table 6.1: Regional emissions in units of tons/day from coal-fired power plants, copper smelters, and large urban areas (4-29) 77 (~29) 77 (5-11) 78 (5-42) 79 (6-1) 80 - Figure 6.10: Calculated gas-phase SO2 omdation rates as a function of time of day and season (6-20) 81 Table 6.10: Estimated age of the Navajo Generating Station plume fin hours) at various locations in the WHITEX study region, January-February 1987 Table 6.43: Time history of measured total sulfur (,ug/m3), NGS total sulfur (pg/m3) and fraction of ambient total sulfur due to NGS based on DMB analysis for Hopi Point Figure 6.71: Time plot of predicted upper limit of NGS contribution to total sulfur at Hopi Point (540 x SCD4) and measured total sulfur Chemical Mass Balance with Unique Tracer (6-33) 82 (6-110) 84 (6-124) (6-124) 85 85 APPENDIX 6B Overview (6B-1) 86 Model Equations (6B-1) 86 Model Calculations and Uncertainties (6B-4) 89 ModelAssumptions (6B-6) 91

~~ ~~ ~~ ~ ~7 Potential D^a[10= Tom ^ss~p[lons of Nonco~t~t Rc~es610n CoeGdonts e MOB Todd ~~ Gas Model Outputs ., (6B-6) (6B-6) (6B-~ (6B-8) ~1 91 ~3

48 · APPENDIX 2 Chapter 1 INTRODUCTION THE DESIGN AND IMPLEMENTATION OF THE WINTER HAZE INTENSIVE TRACER EXPERIMENT- ~THITEX 1.1 Overview Protection of vistas for certain national parks and wilderness areas as provided by the Clean Air Act Amendments of 1977~ has stimulated an interest in visibility research. Methods are being developed and used to characterize atmospheric transparency, to identify the relative importance of the various particulate and gaseous atmospheric materials and to determine the role of man- made emissions. Much of the research has been conducted in the desert Southwest, in particular in northern Arizona and southern Utah. The juxtaposition of energy resources (especially coal) and national parks (including Grand Canyon, Bryce Canyon and Canyonlands) in an area where small changes in aerosol concentration can significantly affect visibility justifies concern by government and private organizations for visibility impacts resulting from industrial emissions. Figure 1.1 is an emission density map showing locations of major SOz sources and national parks on the Colorado Plateau. Accordingly, a cooperative effort, the Subregional Cooperative Electric Utility (comprised of the Electric Power Research Institute, Southern California Edison and the Salt River Project), National Park Service (NPS), Environmental Protection Agency (EPA) and Department of Defense (DOD)) Study, SCENES, is centered in this area. It operates on a five-year plan (1984-1989) involving continual visibility and aerosol measurements at a dozen locations, plus more in-depth intensive and special studies conducted over shorter, seasonally representative periods. One of these, the Winter Haze Intensive Tracer Experiment (WHITEX) was conducted in January and February 1987 in the Colorado River area of the Colorado Plateau. 1.2 Background The Colorado Plateau, with its many associated class I national park areas, was chosen as the location to implement a scoping study designed to evaluate the ability of a variety of receptor modeling approaches to attribute visibility impairment in a number of class I areas to emissions from a single point source, the Navajo Generating Station. The area, shown in Figure 1.3, is by most standards remote, undeveloped and sparsely populated. The nearest large urban areas are over 300 km away. Only a few smaller urban areas or towns are within the area; these include Moab, Utah, Page, Arizona and at the most western end of the study area, Las Vegas, Nevada.

EXCERPTS FROM NPS-WNITEX · 49 \ 1 \ ~ U e g ~ 5 -O i 1 \ ~ e ~ ~ O J ~ v ` L o s ~ n g e I e s \ ~ " ,.~ | ~ ~ Power an~ I L g ~ s ~ _ . ~ 1 S ~ l t L ~ k e Ci tg ~P 0 ~ ~ r pl an t 14 ~ ~ a j 0 Phoen i x S ~ ~ I t e P 0 ~ e ~pl an t e. s · ~ P 0 ~ e r t 1 a n t s Po ne r p I ~ n t s ~_ 1 P ~ s o Figure 1.1: Approximate SOz emissions from major point sources and urban areas in the southwest United States for 1987.

50 · APPENDIX 2 ., There are a few small industrial enterprises in the vicinity such as sawmills, mining and milling Operations and a number of large coal fired electric generating facilities, one of which is the Navajo Generating Station (NGS) located near Page, Arizona. The Navajo Generation Station is a large (2300 MWe) coal-fired power plant without sulfur dioxide control equipment; thus, it is a significant single contributor of sulfur species—sulfur dioxide (S02) and sulfate particles (5O4-)—to the atmosphere of the region. With the control and shutdown of several smelters in the western U.S., NGS has become the largest single S02 emission source in the West.2 The terrain surrounding the lower Colorado River rises to about 800 meters above the water's surface. Wintertime meteorology in the area is characterized by several periods of stagnation, of about one week each. Air pollutants can be trapped by a persistent thermal inversion below the height of the surrounding terrain during the stagnation periods, resulting in a distinct visible surface haze layer. These stagnant periods are interrupted by synoptic-scale fronts with associated strong winds sweeping rapidly through the area.3 The winter haze over the Colorado Plateau area has been routinely documented with photographs since 1978 by the EPA, NPS and BLSI (Bureau of I`and Management). The haze is usually seen as a bright white layer with a distinct upper edge and it occasionally includes one or more perceptible layers.3 4 A number of earlier investigations have been prompted by the need to determine the origins of the haze. NPS sponsored several modeling efforts to evaluate the possibility that the Navajo Generating Station is partially responsible for the haze.5, 6 A wind field model was adapted for the area's terrain and winter meteorology to investigate transport and diffusion. In a separate study, ambient nitrogen chemistry was theoretically simulated to estimate the role of particulate nitrates.7 Though both efforts added to the knowledge of the source of the haze, the uncertainties in modeling this situation led to approaching the question with observational studies. A SCENES special study was previously conducted in 1986 at Glen Canyon to provide informa- tion to aid in planning subsequent observations.8 The primary objectives of this exploratory study were to determine the horizontal and vertical extent of the haze and to identify major constituents of the haze. Aircraft-based measurements confirmed the haze to be more extensive horizontally than just the Glen Canyon area (e.g., extending at least to Bryce and Grand Canyon to the west). This greater horizontal extent enlarges the number of possible emission sources to be considered. This complicates the source attribution because the contribution of any one source may vary con- sideraHy with time and location within the haze. In mapping the vertical extent of the haze with an instrumented aircraft, the air above the inversion layer was found to be essentially particle-free, while below the layer, scattering coefficients varied from two to five times clean air values. Sam- pling at the south end of the lake only, the particles were found to be composed largely of sulfates and organics. Nitrates were found to be primarily gaseous, with about a tenth of the total being particulate nitrate. 1.3 Study Plan Shortly after the completion of the winter 1986 study, the SCENES participants began planning a more comprehensive effort for the winter of 1987 to address persistent questions about the nature and sources of winter haze conditions. The overall study objective was to assess the feasibility of attributing emissions from a single point source to visibility impairment in prespecified geographic regions. Specifically, various receptor and deterministic models were to be evaluated and inter- compared as to their ability to link Navajo Generating Station emissions to visibility impairment at Grand Canyon and Canyonlands National Park and Glen Canyon National Recreation Area. Meeting this objective is a three tier process. First the relative contribution at the receptor site

EXCERPTS FROM NPS-WHITEX · 51 of primary and secondary aerosols associated with the NGS must to be achieved. Secondly, the contribution of these aerosol species to atmospheric optical variables needs to be established, and finally the contribution of optical variables associated with power plant emissions to an incremental decrease in visibility below that which would have existed otherwise needs to be examined. The major focus of the WHITEX study is centered around the evaluation of receptor oriented approaches for linking NGS emissions to aerosol concentrations in Grand Canyon and Canyonlands National Parks and Glen Canyon National Recreation Area. In a receptor type model the atmo- sphere is treated as a black box through which source emissions are transferred to the receptor site. Source receptor relationships are empirically developed using statistical inference techniques. Historically, the chemical mass balance (CMB) formalism has been most often used to link source emissions to aerosol concentrations at a receptor site. The CMB approach essentially uses ratios of trace material associated with different sources in combination with trace material measured at the receptor site to apportion primary (nonconverting) aerosol species. However, CMB has serious short comings in that it is not designed to apportion secondary aerosols, such as ammonium sulfate, to its SO2 source. Other common types of receptor models include principal component analysis (PCA) and multiple linear regression (MLR). Explanations of these models are given by Watson,9 Chow,~° and Hopke.~i Furthermore, Dzubay et al., Lewis and Stevens,~3 and Stevens and Jewish have integrated a number of these approaches into a hybrid receptor model that can be used to apportion secondary aerosols. All these models can be shown to be special cases of a deterministic statement, referred to in this report as the general mass balance model (GMB), of how gases and aerosols are transported and transformed as they pass through the atmosphere. The GMB model is discussed in detail in Appendix 6A. In this report a regressional model, derivable from the GMB equations and referred to as the tracer mass balance regression (TMBR) model, will be used to apportion secondary aerosol species. A full derivation of TMBR can be found in Appendix 6B. TMBR is formulated to apportion sec- ondary aerosols if certain assumptions are met. First, a unique trace material must be associated with a source or group of sources and secondly the atmospheric transfer processes must be ap- proximated be a linear model. If a unique tracer is not available CMB can be used to apportion non-unique tracer species to source types and the analysis can still be carried out. TMBR essen- tially relies on relative changes in secondary aerosol and tracer concentrations over time to yield the desired apportionment. Finally, a differential mass balance model (DAB) having elements of both deterministic and receptor modeling approaches will be used to apportion secondary aerosols. The term differential is derived from the use of unique trace material spatial concentration gradients to calculate atmo- spheric dispersion. Atmospheric deposition, chemical conversion and transport time are calculated from first principles. A full derivation of the DMB model can be found in Appendix 6C. Table 1.1 outlines the different approaches as well as summarizes the major advantages and disadvantages of each technique. For the sake of completeness, advantages and disadvantages of deterministic modeling are also included in Table 1.1. Several less quantitative approaches are also used to gain insight into basic physio-chemical processes at work over the time period for which apportionment estimations are carried out. These include evaluation of the relative emission strengths, plume trajectory and streakline analysis, spatial and temporal trends, analysis of synoptic meteorological conditions, and deterministic wind field modeling on the mesoscale level (<200 km). The focus of attributing NGS emissions to optical variables will be directed toward the re- lationship between various attributed aerosol species and optical extinction. Since a change in atmospheric transmittance (extinction) under a variety of conditions has been shown to be a good approximation to the change in the atmospheric modulation transfer function.~5 The optical ex-

52 · APPENDIX 2 o ._ ~d o U3 ~o cd ~d .ca CO ~ C td ~ ~ ~C _, ~ ~o ~ ~o C oo o C) ._ _ ·— ~V u, c ~ 3 ._ ~ ~ _. e ._ Z C ct: a U~ Z C ,.~,. .\ - C ;~ Ct C C~ (; C: O ~ ~ . ~ -_ E 8~ o ~ ~ I., ~ · _ ° a' ~ C ~ rd ~ ~ O o o ° td ~ ~ ~ ~ ~ ~ e ~ 0 ._ ~ _ _ _ C C V Ce ~ O O O ~ ~ .0 ~ V ~ ~ ~ C, y Y. Y ~ O O ,o ~ ~ ~ ~ = = _ o _ ee · ~ ed CL ~ ~. — ° 47 ~ E.° ° .> ~ ~ ~ ~ 8-V ~ 8~ ~ o o o ~, C C C ~ C .~ `,, ~ ~ ~ ~ ~ ,o Y _ y ~ ° , ~ ~ ~ ~ ~ E ~ o c _ o ~ ~ X ~ ~.= g E == o ~ ~ o _ C o c.o ~ E ° o C g C C ~ o ° ._ . 0 :~ _. ~ :- ° ~ _. o bO ~ C O C C ° ~-- 4} Lc E 13 o cE ~c ,- c · ~ = E C ~ 5 46 ~ C ~ ~ E C c = _ ~ = 0 ~ a-- ~ ~ 0 ~ ~ _ `', m 0 = c~ 8 :~ _ . ~ ~ ~ .~ ~ ·C .o ~ 8 = ~ ~ = ~ ~ , U, o E o v -, E E .= =4 E c ~ c _ e V ~o ~ ~ _ ~o ~ ._ ._ 5 ~; . 3 5 0 E _.= ~ 0 ~ ~ ~ ._ 0 ~ ~ ~ ~ c 0 ~ ~ 0 " 00 5 ~ ~ ~ O ~ K ro · 0 o. . o _ ,C ~) td 4, ~ .., . ;> Ct ~ ~ ~ O ~ C _ ._ O ~ ~ ~ O ~ · a: ~ · _ {c ~ O c~ E .)—o =° ,. C,, _ ~ 4~ u: 46 ~ ~ ~ ~ ' _ ~ E i6 . ~ ~ ~ ~c o.= 46 ~ ~ ~ ~ ^,c ._ _ O ~ ~ c ~ ~ . ~ 0 ~-. C ~ ~ ~ Cl o ~° ~ E 0=

EXCERPTS FROM NPS-WHITEX · 53 tinction associated with particles can be calculated using Mie theory if the particle characteristics are well documented. The size distribution, shape, density and refractive index of the particles are needed for such a calculation. This information is generally either unavailable or available with insufficient detail, so that Mie theory must rely upon a number of assumptions. Mass size distribu- tion data can be obtained using size segregating samplers, however, the capability of such samplers to correctly represent the particulate nitrate or the labile fraction of organic carbon is questionable. For instance, the mass of the most common labile component, water, is not accounted for. A second approach for attributing aerosol species to extinction is a statistical methodology that relies on multilinear regression (MLR) analysis where it is assumed that the relationships between atmospheric extinction, ban, and aerosol species mass concentrations, m', are represented by bar: = Hi <rim,. The measured ml's are assumed to be independent variables, berg is the dependent variable and regression coefficients, ai's, are interpreted as extinction or scattering efficiencies depending on whether bars or b, is used in the analysis. For the relationship between be=' and the ml's to be linear requires many restrictive assump- tions. Assumptions associated with inherent unknown and physical processes are discussed in some detail by White.~9 To minimize uncertainty in estimated extinction efficiency, MLR is compared to efficiencies derived from other studies. Two ways of apportionment of extinction will be examined in this report, first by fine mass: butt = army + a2cc ~ a3[N02] + bRAY (1.1) where my is fine mass associated with scattering, Cc is elemental carbon which is primarily a particle absorption term, [NO21 is nitrogen dioxide concentration, and bRAY is scattering due to atmospheric gases. The second procedure is apportionment by aerosol/chemical species: be =ao+a~s+a2N+a3co+a4cc+a5iNo2]+a6soil+a7R (1.2) where S. 1V, CO, Cc are particulate ammonium sulfate, ammonium nitrate, organic and elemental carbon respectively, soil is the fine mass oxides associated with Ca, Si, Fe, K and Ti, and R refers to mass between 2.5 ~ and 10 ~ . Once source emissions have been attributed to extinction it is possible to estimate whether those emissions will- effectively reduce the ability to see a landscape feature. Two optical variables which effectively characterize the visual effect of atmospheric haze on vistas are the change in contrast of adjacent scenic features or those features against the horizon sky as a function of aerosol concentration and/or composition and the contrast of the haze itself as seen against the sky or terrain background. Calculation of the change in contrast of adjacent scenic features as a function of aerosol concentration in the most general case requires a knowledge of the atmospheric modulation transfer function, M`f,a which, in turn, requires information on inherent scene brightness, path radiance and atmospheric transmittance between scene and observer. However, as stated above, atmospheric modulation transfer can be approximated by atmospheric transmittance under a wide variety of conditions. Under these circumstances, Or =_RdbC,rt r (1.3) where dCr/Cr is the percentage change in apparent contrast of a vista at a distance R and dams is the incremental change in extinction coefficient derived from the extinction attribution portion of the program. Thus, Equation 1.3 can be used to assess the amount of vista contrast reduction associated with NGS emissions. The calculation is straightforward and can be carried out by the interested reader. However, these calculations will not made as part of this report.

54 · APPENDIX 2 1.4 Interrelationships Between Aerosol, Optical and Visibility Apportionment The interrelationship between measurements and apportionment methodologies as designed for the WHITEX program are schematically represented in Figure 1.2. For purposes of understanding the relative accuracy of the various receptor modeling approaches, the study was designed to calculate aerosol and optical apportionment in at least two separate ways. In this way, a relative accuracy of each technique can be estimated. For instance, measurement of be:' and aerosol composition are combined in an MER model to apportion extinction to aerosol species. An independent analysis using a literature review of theoretically derived extinction efficiencies allows for an independent estimation of extinction apportionment. The two estimations can then be intercompared, and dif- ferences, if any, reconciled. Similarly, tracer (CD4) released over time can be used in a TMBR analysis to apportion sulfur and nitrate aerosols to NGS emissions, while DMB analysis will yield an independent estimation of NGS emission contribution to secondary aerosols at the receptor site. The NGS sulfate and nitrate contribution to total particulate matter at the receptor sites is then combined with the extinction apportionment analysis to yield the extinction that can be attributed to NGS. The two techniques can then be intercompared and differences, if any, reconciled. Fi- nally, the extinction apportionment data can be combined with information from radiative transfer calculations and radiance measurement program to apportion visibility impairment. 1-.5 Measurement Program The measurement program consisted of four different types of ground station configurations and one airborne platform. The configurations are classified as major receptor, satellite, gradient, and background sites. Table 1.3 summarizes the variable measured, the methodology used to collect the data and the frequency at which the measurement was made while Table 1.2 summarizes the function of each monitoring site. Figure 1.3 shows the location of each monitoring site. Major receptor (Type A) sites had all those measurements required for aerosol, extinction, and visibility impairment attribution while satellite sites consisted only of trace element, wind speed, and wind direction measurements. Satellite sites were used to characterize air masses flowing into and out!of the study region and were used to explore temporal and spatial trends. At gradient and background sites b,Ca', fine mass, ions, carbon, trace elements, and tracer concentrations and meteorological variables were measured. Gradient sites were also used to examine spatial and temporal trends while the background site helped characterize air masses on a regional scale. A full description of how each parameter was measured is discussed in chapter three. Therefor only a brief description of the measurements will be presented here. Atmospheric extinction was measured with a newly developed long path transmissometer.20 Atmospheric scattering was mea- sured with MRI 1550 integrating nephelometers which were zeroed with clean air every few hours and span calibrated twice during the course of the study.22 Haze, contrast, and Mom, can be calcu- lated from reconstructed radiance fields derived from slides taken during the course of the study.23 The color slides were taken using automatic photographic monitoring instrumentation comprised of 35 mm cameras using 135 mm lenses and loaded with Kodachrome 25 color slide film. Particulate measurements were made by the IMPROVE sampler24 at nine sites and by the stacked filter unit25 (SFU) at three additional sites. At three of the twelve sites, the size-classifying isokinetic sequential aerosol sampler26 (SCISAS) collected fine and total (smaller than 15 ,um samples on four filters. The SCISAS sampler was used primarily to establish the relative accuracy and precision of the various sampling systems.

EXCERPTS FROM NPS-WHITEX _ BENT 1 _ ST^T~S7~C^L , EXT#`JCTlON EF~IC'El~CY 1 ILL CO - WTIaN | Ll7ER^T~L IVES | L ~ 1 11 Ll I t~^T~E EXT'NCTtON E~ IClENCY . arm tar 1 Else . -~! Inch ..- l EXT'~CT'O eL~ \ / J~ , ~ ~ ~ ~~ ~ ~ _ 1= , , ~ _~r ~ ~ ~ 1 1 SON *~0 "T - C DECO C~7~41 MTF AT RE=PTOtR · 55 _ homer Figure 1.2: Flow diagram showing the relationship between measurement and apportionment of risibility impairment.

56 · APPENDIX 2 A "DRUM" sampler was operated to yield size resolved atmospheric element concentrations.29 The version used for this study consisted of six rotating drums of the Mercer impactor type and to low pressure rotating drums. The nominal size ranges (aerodyna~ruc diameter in ~m) for five stages are 0.~1.2, 1.2-2.4, 2.4-4.85, 4.85-9.6, 9.6-16.0 ~m. The optical absorption of particles on the fine tenon filter were measured by a laser integrating plate method (IPM). Light of a 633 nm wavelength from the He(lVe) laser was diffused and colli- mated to provide a uniform beam of around 0.7 cm2 at the sample. The light transmitted through the sample was collected with an ORIEL photodiode detection system.30 The tracer injected into NGS stacks was deuterated, or "heavy", methane, CD4.3~ Though chemically similar to normal methane, heavy methane has a higher molecular weight which allows it to be distinguished by mass spectrometry at very low concentrations (1 part in 107). CD4 was released continuously in proportion to NGS emissions in the time period of study. At receptor sites, 60 liters of air were pumped into large mylar/polyethylene bags and then concentrated under pressure into steel pressurized containers. The containers were shipped to Los Alamos National Laboratory for mass spectrometer analysis. Meteorological variables were gathered using standard sensors, while upper air winds, tem- perature and dew point temperature were gathered using airsondes. The aircraft was equipped with an SO2 mowtor, an integrating nephelometer, a particulate monitor used for NGS plume characterization and bottles used for collecting large volumes of air required for the CD4 analysis. Table 1.2: List of the function associated with each of the monitoring sites. SITE SITE TYPE FUNCTION Glen Canyon National Recreation Area ~ Grand Canyon National Park Receptor site Canyonlands National Park ,, . Lake Mead National Recreation Area Wupatlci National Monument Navajo National Monument Monticello, Utah Ciaco, Utah lIanlts~rille, Utah Calculation of the relative contribution of each aerosol species associated with NGS emissions. Characterize the inflow/outflow Satellite — of tracers olopportunity and spatial and temporal trends Bite, Utah Gradient — Characterize spatial and temporal trends. Bullfrog, Utah Bryce Canyon National Park ~ Background - Used to estimate bad;ground associated with long range transport from distant sources.

EXCERPTS FROM NPS-WHITEX · 57 STUDY AREA KIlam-~-.. - 0 50 100 L Coal Fired Electrical Gen-rator8 · Major Receptor Site8 · Satellite Sil" · Gradient S'tes Background Sete ~" .... ,, ;~\ .: Lat`e Mead ~J Nattonal Recreation Area BrlCe Canyon Nellonal Parit Z I ~1 ID —. lhmaull ~ Canyon )4 I ~ _~ ~ tlonal' g ~ C~ ~ ~ Hopl Polnt D~rt Vt~v · ., - ~ ~h ., .... l Wupatttl Natlona' ~onument ....L L ~7 Gr~n Rlunr ~ ~z~>v- - Cleco~ I ~ ~ I {,._/anyonl nde rk I ~ I ~f Mont cellol Bullfrog ~ Ghn Canyon ~ 5 1 0 Natlond ~ ~ r Recrestlon ~ S~ ~ | O ~' I ~1 Grand Junctlon I ·. ~FY.O lItn Bbaken UTAH \] COLD.] ~as ~ Nevelo · Natlonal ldonument g~ aart~ = — ~ I ~M 1 Z ~ ~ I,.] IJ F`igure 1.3: Map of SCENES WHITEX study area.

58 · APPENDIX 2 Table 1.3: List of the optical variables, aerosol species, meteorological variables, and measurement methodologies used at each of the monitoring sites. Location' Method Substrate Frequency Optical iSCAT A,C Nephelometer NA Continuous best A Transmissometer NA Continuous Miff A,D,E, Cider Mt., Photographic NA Hourly Echo Cliffs, Arches Haze Contract D, Cedar Mt., Photographic NA Hourly Echo Cliffs Particulate Fine Particles Mass A,B,C,D IMPROVE/SCISAS Teflon 12 hr. Ions A,D IMPROVE/SCISAS Teflon 6 tc 12 hr. Nitrate (denudes) A,D IMPROVE/SCISAS Nylasorb 12 hr. Elementd Liz Organic Carbon A,D IMPROVE/SCISAS Quartz 12 hr. Mace Elements A,B,C,D,E IMPROVE/SCISAS/ Teflon 6 tc 12 8: 24 hr. -*SFU ., Size Se'5re'5atin~s Trace Elements A Giant Particles (>10.0 A) Canyonlande ·.DRUM Teflon 6 hr. Large Particles (2.5-10.0 A) Grand Canyon, Bryce, SCISAS Teflon 12 hr. Glen Canyon, Lake Mead G - es SO2 Gas chro~tograph Canyonlants Gas Chromatograph NA 2 tc 4 hr. K2CO3 Impregnated Filter A K2CO3 Imprelsnatet Same 12 hr. Filter CD. Tracer A Grab Sample ~ NA 6 hr. Gas Chromatograph Meteorological WS,WD A,B,C,D — — 10 rnin. Temperature, RH A,B,C,D — — 10 min. Upper Air . Canyonlants, Glen Canyon Airsonde — Twice/Day ~ A = Major receptor 8 = Satellite C = Gradient D = Background E = Airborne Platform ·. IMPROVE, SCISAS, SFU and DRUM refer to types of samplers which are discussed i', crime detail in the text.

EXCERPTS FROM NPS-WHITEX · 59 1.6 Report Outline Chapter 2 examines the climatology of the study area during the time period the WHITEX study was carried out. Chapter 3 presents the measurement program. A description of each sampling procedure and method of establishing accuracy and precision is presented. When a variable was obtained by more than one method an intercomparison of those variables is examined. Finally CD4 injection and measurement is described. Chapter 4 is an overview of the data used when making attribution calculations. First the temporal history of each variable is presented along with pertinent descriptive statistics. Finally relationships among variables is explored with simple scatter plots of one variable plotted as a function of another. Chapter 5 examines the optical characteristics of pertinent aerosol species. Average extinction budgets are calculated for the full WHITEX time period as well as on a daily basis. Chapter 6 is the heart of the WHITEX report. The ability of various receptor modeling techniques to attribute secondary aerosols to their respective sources is explored. Chemical mass balance, tracer mass balance and differential mass balance are quantitative models investigated while several non quantitative approaches are used to either confirm or negate quantitative calculations. The non quantitative procedures were also used to gain insight into physicochemical processes at work during the study period. Appendices present a complete description of each of the receptor modeling techniques. Chapter 7 presents a deterministic calculation of wind fields and transport pathways of conservative tracers during one particular low visibility episode. The possibility of transport into and out of the study area is also explored. Chapter 8 explores the climatology of extreme (high) sulfate episodes and compares the WHITEX climatology to historic records of other sulfate episodes. Finally, Chapter 9 presents an overview of conclusions that can be drawn from the WHITEX study.

60 · APPENDLY 2 References 142 U.S.C. 7475(d)(2), Pub. L. 95-95 Stat. 735. 2Chinkin, L.R., D.A. Latimer, L. Mahoney, "Western States Acid Deposition Project Phase I: Volume 2—A review of emission inventories needed to regulate acid deposition in the western United States,'' SYSAPP-8 7/072. Systems Applications, Inc., San Rafael, CA. 3Chinkin, L.R., D.A. Latimer, H. Hogo, "Layered haze observed at Bryce Canyon National Park: A statistical evaluation of the phenomenon," In: Proceedings of the Air Pollution Control Asso- ciation International Specialty Conference 'Visibility Protection: Research and Policy Aspects', September 8-10, 1986, Jackson Hole, Wyoming. P.S. Bhardwaja, Editor. 1987. Realm, W.C., R. Eldred, T. Cahill, "Visibility and particulate measurements in the western United States," 16: Proceedings of the 78th Annual Meeting and Exhibition of the Air Pollution Control Association, June 1985, Detroit, Michigan. Paper #85-11.3. ., 5R.A. Pielke, UThe use of mesoscale models to assess wind distribution and boundary layer struc- ture in complex terrain," Boundary layer Meteorology, 31:217-231. 6Yu, C.H., R.A. PieLke, UMesoscale air quality under stagnant synoptic cold season conditions in the Lake Powell area," Atmos. Environ., 20:1751.1986. 7Latimer, D.A., M.W. Gery, H. Hogo, "A theoretical evaluation of the role of nighttime nitrate formation in the formation of layered haze," Systems Applications, Inc. Report SYSAPP- 86/167. November 1986. 8Fitz, D.R., P.Bhardwaja, J. Sutherland, G.M. Markowski, "Characterization and extent of winter hazes in the vicinity of Lake Powell," Presented at the 1987 Annual Meeting of the American Association for Aerosol Research, September 1987, Seattle, Washington. 9Watson, G.W., UOverview of receptor model fundamentals," JAPCA, 34:619. 1984. Chow J.C., Development of a composite modeling approach to assess air pollution source/receptor relationships, Doctor of Science Dissertation, Harvard University, Cambridge, MA. ~Hopke, P.K. Receptor Modeling in Environmental Chemistry," Chemical Analysis, 76, John Wiley & Sons, New York, NY. i2Dzubay, T., R.K. Stevens, G.E. Gordon, I. Olmez, A.E. Sheffield, W.J. Courtney, "A composite receptor method applied to Philadelphia aerosol," Environ. Sci. ~ Technol., 22(1). 1988. Lewis, C.W., R.K. Stevens, "Hybrid receptor model for secondary sulfate from an SO2 point source," Atmos. Environ. 19(~):917-924.1985. Stevens, R.K., C.W. Lewis, "Hybrid receptor modeling," At: Extended Abstracts for the Fifth Joint Conference on Applications of Air Pollution Meteorology with APCA, November 18-21, 1986, Chapel Hill, N.C. Published by the American Meteorological Society, Boston, Mass. 35Malm, W.C., R.C. Henry, "Regulatory perspective of visibility research needs,nIn: Proceedings of the 80th Annual Meeting of the Air Pollution Control Association, June 21-26, 1987, New York, NY. 1988.

EXCERPTS FROM fJPS-WHITEX · 61 Henry, R.C., C.W. Lewis, P.K. Hopke, H.J. Williamson, "Review of receptor model fundamen- tals," Atmos. Environ., 18:1507.1984. 7Malm, W.C., M. Pitchford, H.K. Iyer, "Design and implementation of the Winter Haze Intensive Tracer Experiment—WHITEX," presented at the APCA International Specialty Conference "Receptor Models in Air Resource Management," February 1988, San E}ancisco, California. Becalm, W.C., R.C. Henry, "Regulatory perspectives of visibility research needs," 16: Proceedings of the 80th Annual Meeting and Exhibition of the Air Pollution Control Association, June 21-26, 1987, New York, NY, bobwhite, W.R., "On the theoretical and empirical basis for apportioning extinction by aerosols: A critical review," Atmos. Environ, 20:1659. 1986. 20Malm, W.C., G. Persha, R. Tree, R. Stocker, I.Tombach, H. Iyer, "Comparison of atmospheric ex- tinction measurements made by a transmissometer, integrating nephelometer and teleradiometer with natural and artificial black target," 16: Proceedings of the Air Pollution Control Associ- ation International Specialty Conference 'Visibility Protection: Research and Policy Aspects', September 8-10, 1986, Jackson Hole, Wyoming. P.S. Bhardwaja, Editor. 1987. 2~Ma-lm, W.C., "An examination of the ability of various physical indicators to predict judgment of visual air quality," 1~: Proceedings of the 78th Annual Meeting of the Air Pollution Control Association, June 16-21, 1985, Detroit, MI. Paper #85-10.1. 1986. 22Charlson, R.J., H. Horvath, R.F. Pueschel, "The direct measurement of atmospheric light scat- tering coefficient for studies of visibility and pollution," Atmos. Environ., 1:469. 1967. 23Johnson, C.E., J. V. Molenar, J.R. Hein, W.C. Maim, "The use of a scanning densitometer to measure visibility-related parameters from photographic slides," In: Proceedings of the 77th An- nual Meeting and Exhibition of the Air Pollution Control Association, June 1984, San Francisco, CA. Paper #84~1P.1. 1985. 24Eldred, R.A., T.A. Cahill, M. Pitchford, W.C. Maim, "IMPROVE—A new remote area partic- ulate monitoring system for visibility studies," In: Proceedings of. the 81st Annual Meeting of the Air Pollution Control Association, June 19-24,1988, Dallas, TX. Paper #88-54.3. 1989. 25Cahill, T.A., R.A. Eldred, P.J. Feeney, "The stacked filter unit revisited," In: Proceedings of the AWMA/EPA International Specialty Conference 'Visibility and Fine Particles', October 15-19, 1989, Estes Park, CO. 1990. To appear. 26Rogers, C.F., J.G. Watson, C.V. Mathai, UDesign and testing of a new size classifying isol;inetic sequential aerosol sampler," JAPCA, December 1989. To appear. 27Sutherland, J.L., P.S. Bhardwaja, "Composition of the aerosol in northern Arizona and southern Utah,n ~ Proceedings of the Air Pollution Control Association International Specialty Confer- ence 'Visibility Protection: Research and Policy Aspects', September 8-10, 1986, Jackson Hole, Wyoming. P.S. Bhardwaja, Editor. 1987. 28IMPROVE Sampler Manual, Version 2. Robert A. Eldred, Crocker Nuclear Laboratory, University of California, Davis, CA 95616. January 1988.

62 · APPENDIX 2 29Cahill, T.A., P.J. Feeney, R.A. Eldred, W.C. Malm, "Size/time/composition data at Grand Canyon National Park and the role of ultrafine sulfur particles," In: Proceedings of the Air Pol- lution Control Association International Specialty Conference 'Invisibility Protection: Research and Policy Aspects', September 8-10, 1986, Jackson Hole, Wyoming. P.S. Bhardwaja, Editor. 1987. 30Eldred, R.A., P.J. Feeney, T.A. Cahill, W.C. Malm, "Sampling techniques for fine parti- cle/visibility studies in the National Park Service network," In: Proceedings of the Air Pol- lution Control Association International Specialty Conference 'Visibility Protection: Research and Policy Aspects', September 8-10, 1986, Jackson Hole, Wyoming. P.S. Bhardwaja, Editor. 1987. 31Alei, M., J.H. Cappis, M.M. Fowler, D.J. Frank, M. Goldblatt, P.R.Guthals, A.S. Mason, T.R. Mills, E.J. Mroz, T.L. Norris, R.E. Perrin, J. Poths, D.J. Rokop, W.R. Shields, "Determination of deuterated methanes for use as atmospheric tracers," Atmos. Environ., 21:909. 1987. .,

EXCERPTS FROM NPS-WHITEX Chapter 9 Conclusions 9.1 Introduction · 63 The Winter lIaze Intensive Tracer Experiment (WHITEX) was designed to evaluate the feasibility of attributing single point source emissions to visibility impairment in selected geographic regions. Specifically, WHITEX was conducted during January and February 1987 in the vicinity of the Navajo Generating Station (NGS), a large (2250 MWe) coal-fired power plant located relatively close to several national parks including the Grand Canyon, WHITEX, primarily an exploratory scientific study, was prompted by the regulatory program and congressional mandate to protect and improve visibility in those national parks and wilderness areas that have been afforded special visibility protection (mandatory PSD Class I areas). Grand Canyon, Bryce Canyon, Capitol Reef, and Canyonlands National Parks, all within the WHITEX study area, are afforded such visibility protection Because NGS, a power plant without sulfur dioxide (SO2) emission control equipment, ~ , is now estimated to be the largest single 6~2 emission source in the west, its impact on these national parks is of great concern to government, industry, and the public. The WHITEX study area has long been suspected of experiencing regional stagnation in the winter. Light winds~and shallow mixing depths, in conjunction with barriers caused by elevated terrain, suggest the possibility of buildup of NGS emissions over multi-day periods which end when fronts pass through and clean out accumulated pollution. Although ambient aerosol concentrations and light extinction properties of the atmosphere have been measured in the area for several years, and layered haze has been documented photographically, there have not been any measurement studies directed at attributing regional aerosol concentrations and light extinction to NGS and other sources in the area. Deterministic modeling studies have been carried out in the area in an attempt to understand NGS contributions to regional air quality and visibility problems. A primitive-equation wind field model was implemented for the NGS area. NGS emissions were injected into the model's wind field and ambient concentrations were estimated. However, this study was not specifically directed to source attribution. Another deterministic modeling study was done with a Lagrangian regional dis- persion model. Estimates of NGS's contribution to ambient concentrations and to light extinction in the vicinity of NGS (i.e., the WHITEX study area) and the entire Southwest U.S. were made. However, these estimates were deemed uncertain because of (1) uncertain and changing regional emissions (copper smelter emissions have been significantly reduced since the study was carried out in 1985), (2) uncertainties in predicting wintertime stagnation and terrain influenced wind fields, and (3) uncertainties in the 502 oxidation rate. Until WHITEX no data were available to estimate homogeneous (gas-phase) and heterogeneous (liquid-phase) S02 oxidation rates in winter. From

64 · APPENDIX 2 the insight afforded by WHITEX, this latter modeling study apparently underestimated the liquid- phase SO2 oxidation rate and, hence, NGS's primary contribution to regional sulfate aerosol and visibility degradation. The distinguishing feature of WHITEX was the injection of a unique tracer (deuterated methane or CD4) into the NGS stacks at a rate which could be scaled to the known emissions of S02, nitrogen oxides (NO-), and Articulates. Using a variety of statistical and deterministic techniques, measured ambient concentrations of species that interact with light (e.g., sulfate, SO4-) and NGS- specific tracer were analyzed to calculate NGS's contribution to the measured aerosol at various receptors. For example, the total ambient sulfate concentration is the sum of the contributions from NGS and from background sources (both natural and non-NGS man-made): iSO4 ]`otal = [so;~ground ~ [SOT ]NGS, (9.1) where ISO4-]NGS is determined from ambient concentrations of CD4 scaled to NGS sulfur emissions and accounting for oxidation and deposition. Without the tracer unique to NGS, WHITEX measurements of winds, spatial and temporal trends of ambient concentrations, and light extinction alone could be used to assess qualitatively the contribution of NGS. With this tracer data, a quantitative estimate of NGS's contribution was possible. 9.2 Climatology of the Area The synoptic meteorology of the WHITEX region was classified into four synoptic categories based on data from the years 1980 to 1984: (1) warm sector ahead of a cold front, (2) cold sector ahead of a warm front, (3) behind a cold front, and (4) under a polar high. Of the four synoptic categories, category 4 is associated with the most stagnant air masses, because of the light winds and limited mixing heights caused by small pressure gradients and subsiding air. This category was found to occur more often than the other three categories: 65 percent of the time during the winter months (defined here as November through March). The persistence of the stagnant category 4 was analyzed for the WHITEX region. A conservative estimate of persistence was made using the assumption that a category-4 stagnation event ended when any portion of the WHITEX region had non-category-4 meteorology. A nonconservative estimate was made using the assumption that if category-4 conditions existed anywhere in the WHITEX region, the stagnation event was assumed to persist until all parts of the region had non- category~ meteorology. Using the nonconservative and conservative assumptions, respectively, 42~4 percent and 62-66 percent of the all stagnation events are of 3-5 day duration, 43-45 percent and 2~32 percent are of 6-14 days duration, and 12 percent and 4 percent are of greater than 14 days duration (on the average about once per winter). The mean length of a stagnation event is approximately 6 days for the conservative method and 8 days for the nonconservative method. Approximately 45 percent of the wintertime days experience stagnation events of three days or longer based on the conservative estimate; 60 percent for the nonconservative estimate. These statistics suggest that the 9-day stagnation that occurred during the WHITEX study between February 6 and February 14, 1987 and that led to the highest sulfate concentrations in the region, was not anomalous. Persistent stagnation events of this duration or longer are expected 16 percent of the time during the winter months.

EXCERPTS FROM NPS-WHITEX 9.3 Light Extinction Budget 65 The contribution of Various aerosol species to the total light extinction was ascertained on the basis of simultaneous measurements of (1) ambient concentrations of specific species and (2) light extinction coefficients (ban) and light scattering coefficients (boa`). Light extinction budgets were developed using light extinction efficiencies determined from multiple linear regression analysis and from the literature. Light extinction budgets were developed for three WHITEX sites: Glen Canyon National Recreation Area (Page), Grand Canyon National Park (Hopi Point), and Canyonlands National Park. Average total light extinction (including natural blue-sky Rayleigh scattering) during the WHITEX experiment ranged from 0.0161 km~~ at Hopi Point, to 0.0246 km~~ at Canyonlands, to 0.0291 km~~ at Page. These values are 1.69, 2.54, and 2.83 times the natural blue-sky Rayleigh scattering (the light extinction caused solely by the scattering of light by air molecules). Light scattering by fine particles, i.e., sulfates, organics, fine soil, and nitrate was the major contributor, (approximately 75 percent) to the non-Rayleigh light extinction. Most of the remaining non-Rayleigh extinction was light absorption caused by light absorbing carbon. Extinction caused by coarse particles and by NO2 were relatively small, each less than 5 percent of total extinction. The fine-particle light scattering was further subdivided into contributions from fine sulfate, organic carbon, nitrates, and soil components. Sulfate was found to be the largest contributor to fine-particle light scattering. Sulfate was estimated to be 48 to 54 percent of the fine-particle scattering at Page. The first number is based on literature-derived extinction efficiencies and the second- is based on the regression analysis; 58 to 60 percent at Canyonlands; and 62 to 72 percent at Hopi Point. Organic£ were the next largest contributor, estimated to be 33 to 41 percent of fine-particle scattering at Page (In this case, the first number is the regression-derived value, and the second is the literature-derived value.~; 20 to 27 percent at Canyonlands; and 15 to 16 percent at Hopi Point. Nitrate was the third largest contributing component with 6 to 14 percent of the fine-particle scattering at Page (The first value is the literature-derived value and the second is the regression-derived value.~; 9 to 20 percent at Canyonlands; and 5 to 13 percent at Hopi Point. Fine soil contributed the least, with O to 5 percent of the fine-particle scattering at Page (The first value is the regression-derived value, and the second is the literature-derived value.~; O to 6 percent at Canyonlands and O to 17 percent at Hopi Point. - On the average, during the WHITEX program sulfate aerosol (and associated water) was found to contribute about two-thirds of the non-Rayleigh light extinction at Hopi Point, and one-half at Page. However, during sulfate episodes, the fraction contributed by sulfate increased significantly. For example, during the episode on February 12, sulfate caused 84 percent of the non-Rayleigh extinction in the Glen Canyon National Recreation Area (Page) and 97 percent of non-Rayleigh extinction in Grand Canyon National Park (Hopi Point). Because the average relative humidity during WHITEX was relatively high, approximately 60 percent, water associated with sulfate and nitrate doubled the light scattering efficiency of these aerosols, from 2.5 m2/g to 5 m2/g. Only elemental carbon is more efficient in extinguishing light than sulfate. Its extinction efficiency was estimated to be 9 m2/g. Scattering efficiencies for Organics, fine soil, and coarse mass were estimated to be 4, 1.25, and 0.45 m2/g, respectively. 9.4 Attribution of Regional Sulfur and Visibility Impairment The aerosol attribution component of WHITEX was designed to evaluate the feasibility of at- tributing the emissions of a single source (in this case, NGS) to ambient aerosol concentrations

66 ~ APPENDIX 2 at a number of receptor sites. The primary receptor sites were Grand Canyon and Canyonlands National Parks and Glen Canyon National Recreation Area (Page). Several quantitative and qualitative analysis techniques were used to gain insight into the con- tribution of NGS to ambient aerosol and the performance of the individual receptor modeling techniques. These techniques include: · Emissions. The relative source strength of NGS compared to other sources in the region and the location of NGS and other regional emissions relative to key receptor sites. . Trajectory and streamline analysis. The probability was examined that the predicted presence of NGS or other source emissions is coincident with elevated ambient sulfur concentrations or is due to random processes. · Spatial and temporal patterns in visibility-reducing aerosol concentrations. Spatial patterns in aerosol concentrations as a function of time are examined qualitatively and quantitatively through empirical orthogonal function analysis. Synoptic meteorological. Analysis of the synoptic climatology helps to understand the origin of stagnation periods and yields insight into why pollutants were transported along various pathways. Deterministic wind field modeling. Model simulations are used to help understand how pollu- tants can be transported along various pathways and to assist in building conceptual models of physio-chemical processes associated with observed aerosol concentrations and visibility impairment. Tracer mass balance regression. As a special case of the general mass balance (GMB) equation, the variation of sulfur and natural or artificial tracers as a function of time were used to attribute emissions. · Differential mass balance. As a second special case of the GMB formalism, the ambient concentration of a unique tracer, CD4, was used to estimate dispersion and deterministic model;calculations are used to calculate conversion and deposition from estimated plume age. . ., · Chemical mass balance. As a third special case of the GMB, the chemical mass balance formalism was used to estimate source contributions of primary aerosol species and to set an upper bound on NGS contributions. 9.4.1 Emissions Analysis Emission inventories and maps indicate that NGS is the largest single point source of 502 emissions in the vicinity of Grand Canyon National Park. The sources within approximately 300 km of the Grand Canyon in rank order of estimated 1987 502 emissions are NGS, 163 tons/day; San Juan, 116; Four Corners, 106; Mohave, 52; and Cholla, 45; all coal-fired power plants within the Colorado River basin. Also within the Colorado River drainage are other large coal-fired power plants, including the Huntington Canyon, Hunter, Hayden, Craig, Jim Bridger, and Naughton power plants. However, these sources are much more distant than the others. The power plant emissions previously mentioned are dwarfed by the emissions from copper smelters during 1987. The largest copper smelters at that time were San Manuel, 480 tons/day; Nacozari, 380; and Cananea, 240, the first being in southern Arizona and the other two being in northern Mexico. The copper smelters are more distant and are not located within the Colorado

EXCERPTS FROM NPS-WHITEX · 67 River Basin. These sources are south of the Colorado Plateau and the elevated Mogollon Rim, so that elevated terrain would tend to block southerly flows of stable air masses from the copper smelter region. Although the emission inventory alone does not provide quantitative attribution information, one would expect that NGS could contribute significantly to air quality problems in the winter at the Grand Canyon because (1) the magnitude of NGS emissions, (2) the proximity of these emissions to Grand Canyon, (3) the fact that NGS and the Canyon are in the same air basin, and (4) that downslope drainage flows would funnel NGS emissions directly into the Canyon. 9.4.2 Trajectory And Streakline Analysis The poor dispersion resulting from light winds and limited mung heights that is associated with a polar high pressure condition occurred very frequently during the WHITEX experiment. This synoptic meteorological condition occurred on part or all of 41 days out of 49 days of WHITEX. Before and during the worst sulfate episode of the period Julian Days (Days) 36-44], a polar high pressure condition persisted for nine days. This condition persisted for four days at a time on two other occasions (Days 9-12 and 24-27). Sulfate concentrations in the WHITEX study area were also relatively high during these two periods. Wind speeds and directions measured at 300, 600, and 1000 meters above ground level (agl) in Page were used to divide the WHITEX study period into 13 time periods of somewhat similar meteorology. Two of these time periods ended with the passage of major fronts that effectively eliminated the regional sulfate that had accumulated in prior days. These major front passages occurred on Days 28 and 44. Backward air mass trajectories were calculated from the Grand Canyon using National Weather Service upper-air wind data and the ATAD trajectory model. Thus these trajectories were not based on local winds and may not reflect actual transport conditions during the WHITEX experiment. The actual conditions may have been dominated by mesoscale forcing (i.e., drainage, up-slope flows, blocking, and channeling resulting from the complex terrain of the WHITEX region). However, these trajectories were consistent with the meteorological classification of WHITEX time periods .that was based on Page winds and synoptic weather maps. These trajectories also suggest the possibility of long-range transport of copper smelter emissions from southern Arizona and northern Mexico into the WHITEX study region on Days 39-40. Upper-air winds at 300 and 600 m agl, measured three times per day at Page during WHITEX, were used as a basis for estimating the position and age of NGS plume parcels throughout the study region and study period. Although these estimates are uncertain because of the assumption of spatially uniform winds, they suggest that NGS plume material was transported frequently toward the major WHITEX receptor sites at Page and Grand Canyon. Because of its proximity to NGS, Page was estimated to be impacted almost every day. Hopi Point was also estimated to be impacted by NGS emissions quite often. Out of 40 days analyzed during WHITEX, the NGS plume was estimated to be impacting Hopi Point on 29 days, or 71 percent of the time. During some of these periods relatively freshly emitted NGS material was estimated to impact Hopi Point, while during other periods, very aged air masses (as old as 5 days) were estimated to be influencing the Grand Canyon. While the average NGS plume age in Page was estimated to be 17 hours, the average NGS plume age at the Grand Canyon was estimated to be nearly two days (46 hours). Because of their proximity to NGS, the Bullfrog and Hite sites were also estimated to be impacted relatively often. The average NGS plume age at these sites was estimated to be 44 hours. Other WHITEX sites more distant from NGS—Green River, Monticello, and Mexican Hat—were

68 · APPENDIX2 estimated to be impacted much less frequently. The average NGS plume age at these more distant sites was estimated to be 62 to 76 hours. It might be expected that the prediction of plume position and age based on wind measure- ments made three times per day at only one site (Page) would be highly uncertain. However, there is remarkable agreement between impacts predicted based on the NGS plume position and observations of elevated CD4, sulfate, SO2, and nitrate concentrations. The association between NGS plume chits" predicted on the basis of trajectory analysis and elevated concentrations was analyzed using a statistical procedure known as multi-response permutation procedures. It was found that the probability that the association between NGS plume "hits" and elevated sulfate could be due to random processes was less than ~ percent. For SO2 and nitrate the probability of random association was 6 and 10 percent, respectively. Thus, these analyses suggest that the prediction of NGS plume position was not far off target. 9.4.3 Spatial and Temporal Trends in Ambient Concentrations As previously mentioned, there is a statistically significant correlation between sulfate concentra- tions and predicted NGS plume "hits." One can also use spatial and temporal patterns of ambient sulfate concentrations to deduce whether this large, local source is a contributor or whether more distant sources contribute. Over the entire period of the WHITEX study, the average sulfate sulfur concentration was the lowest at Hopi Point (0.17 ~g/m3) and highest at Page (0.33 ~g/m3) and at Green River (0.34 g/m3~. If the sulfate in the region were due to distant sources, one would expect much more uniform concentrations. Instead, average concentrations vary by over a factor of two. Much larger spatial variations occur during certain episodes. It may not be a coincidence that the highest sulfate concentrations occurred at sites relatively close to uncontrolled sources of SO2: NGS is near Page and the uncontrolled units at the Huntington Canyon and Carbon power plants are relatively close to Green River. In general, the spatial variation and history of sulfate episodes during WHITEX strongly suggests impacts due to local sources. A more systematic way of looking at spatial and temporal variation in ambient sulfate concentra- tions than the case studies and averages previously discussed involves the application of Empirical Orthogond1 Function (EOF) analysis. Essentially this technique separates the time/space matrix of ambient concentrations into two sets of matrices, one that is solely a function of space and the other which is solely a [unction of time. The EOF was applied to sites and time periods for which rela- tively complete sulfate data were available. Concentrations for a total of 79 12-hour time periods at the following 11 sites were used in the EOF analysis: Canyonlands, Hopi Point, Bullfrog Marina, Page, Green River, Monticello, Mexican Hat, Hite, Bryce Canyon, Navajo National Monument and Wupatki National Monument. Only two of the unrotated spatial EOF patterns were needed to explain more than 80 percent of the variance in the ambient sulfate data in the WHITEX region. The first, centered on NGS, explains 70 percent of the variance, and the second, with a minimum centered on Bullfrog and Hite and a strong south-to-north gradient, explains 10 percent of the variance. The first pattern (somewhat like a target with NGS as the bulls-eye) is exactly the pattern of sulfate one would expect if NGS were the major contributor during stagnant conditions. Indeed, this EOF is most strongly weighted during the worst sulfate episode (February 11- -14, 1987). This pattern explains 70 percent of the variance in the regional sulfate. The second EOF has three possible explanations. Its strong south-to-north gradient suggests that when this EOF is positively weighted it could represent sulfate transported from the smelter region to the south. When negatively weighted, the EOF has a maximum at Bullfrog and Hite,

EXCERPTS FROM NPS-WHITEX · 69 in the middle of the Lake Powell basin. This relative sulfate maximum could result from NGS emissions transported and converted in southwesterly flow or by emissions from the Huntington Canyon, Hunter, and Carbon plants in southerly flow. These three possible contributors to the second EOF pattern are consistent with meteorological analyses For example, the second EOF is most strongly negatively weighted on February 13, when the emissions from the three northern power plants would most likely be stagnating within the Lake Powell basin. The second EOF is also strongly negatively weighted during the period from January 24 to 28, when NGS emissions are expected to be transported to the northeast. The strongest positive weightings of the EOF occur on January 15 and 16 and February 9 to 11, when transport from the smelter region to the south was identified. 9.4.4 Deterministic Wind Field Modeling A prognostic meteorological model, based on conservation principles of velocity, heat, mass, and moisture, was exercised over the time period from February 11 to 12. The primary purpose of the wind field modelling was to assist in explaining and development of conceptual models of how material emitted by NGS can be transported towards the Grand Canyon and to assess whether emissions from other coal fired power plants can impact the Grand Canyon region. The model showed that thermally- induced winds associated with the Kaibab Plateau (north rim of Grand Canyon) act to transport NGS emissions toward the southwest and Grand Canyon. Furthermore, the modeling effort showed that locally generated emissions (within 200 km) to the southwest and east of Grand Canyon would not contribute to haze in the canyon but sources to the north of page could. However, a transport time of two days or longer are required under these synoptic regimes. 9.4.5 Tracer Mass Balance Regression (TMBR) Analysis It is a simple statement of fact that the total ambient concentration of a given species (such as sulfate) is the sum of the concentrations contributed by NGS and by other sources. Since the deuterated methane tracer (CD4) was unique to NGS, its ambient concentration should correlate with the concentrations of species contributed by NGS. In addition, since the trace metal, arsenic, was below detectable limits in the NGS plume (and presumably from other coal-fired power plants) and arsenic is a known constituent of copper smelter plumes, it was used as a tracer for copper smelters. TMBR analysis was performed to explore the relationship between measured ambient concentrations of sulfur species and CD4. The portion of the ambient sulfur concentration that correlates with tracer is likely to be due to NGS emissions, while the portion that correlates with arsenic is likely to be due to smelters. That portion that is not correlated with either tracer (the intercept term) is interpreted as background from other sources (e.g., other power plants). Another natural tracer, selenium, is associated primarily with coal combustion; therefore, this tracer was used to attribute sulfate to the general category of coal fired power plants. TMBR analysis were carried out separately for tote] sulfur and sulfate sulfur. For some analysis, the relative humidity was factored in to account for sulfur oxidation which is faster in the aqueous phase (which is associated with the high-water content aerosols and fog and cloud droplets that are likely to exist at high relative humidities). All TMBR regressions were carried out using both ordinary least squares (OLS) regression and orthogonal distance regression (ODR). ODR explicitly takes into account the uncertainties in both the dependent and independent variables. ODR gives more weight to samples with small uncertainties. Intercepts were forced to be positive for all ODR regressions.

70 · APPENDIX 2 Since the sulfur to CD4 emission rate at NGS revere not kept constant, the CD4 data was scaled to a constant 2.5 mg/MWe-h rate from the known and time-varying tracer emission rate and power generation at NGS. In addition, the CD4 data was scaled to account for the estimated travel time from NGS to the receptor of interest (e.g., Hopi Point). Thus, if, for a given sample, the plume age is estimated at 48 hours, the tracer emission rate and NGS load for the period 48 hours prior were used for scaling purposes. TMBR regressions were attempted first using only the scaled SCD4 concentration as an inde- pendent variable. However, these regressions explained very little of the sulfate variation at Hopi Point. Additional TMBR regressions were performed multiplying the SCD4 concentration by rel- ative humidity to account for faster aqueous-phase oxidation at higher humidities and by adding arsenic as another independent source variable. By so doing, the variance explained increased to 70 percent and above (R2 > 0.70). Thus, it appears that ambient sulfate concentrations are a strong function of humidity as well as the concentrations of the tracers for NGS and smelters. Best estimates of source attribution of sulfates at Hopi Point for those days that CD4 data were available are: NGS, 70~4 percent; smelters, 3013 percent; and all other sources, 0~1 percent. Re- sults are based of ODR regressions. The quoted uncertainties are one standard error on either side of the mean. The error incorporates measurement uncertainty as well as uncertainty in the regres- sion coefficients. Using all TMBR results with R2 > 0.7 and physically reasonable coefficients and intercepts, the average attribution of ambient sulfate at Hopi Point over all days during WHITEX for which CD4 data are available is as follows: NGS, 62-73 percent; smelters, 23-30 percent; and all other sources, 0-14 percent. When regressions were repeated by substituting selenium (a tracer for all power plants) for CD4 (a tracer for NGS), comparable results were obtained, suggesting that NGS contributes most of the ambient sulfate contributed by all power plants at Hopi Point. Best estimates of coal fired power plants other than NGS contribution to sulfate at Hopi Point is approximately 5 percent. The period of highest smelter contribution was February 9 and 10, which is consistent with the insight gained from both the meteorological analysis and the EOF analysis. Additional TMB~ regressions were attempted for total sulfur, nitrate, organic carbon, and light- absorbing carbon. Variance of total sulfur explained by CD4 was only 30 percent, and variance of nitrate explained was only 20 percent. Organic carbon and light-absorbing carbon were totally uncorrelated with tracer, as one might expect since these species are not emitted from NGS. A similar analysis was carried out for Glen Canyon National Recreation Area (Page, Arizona). At Page the TMBR analysis did not reveal any other sources of sulfate than coal fired power plants, specifically NGS. Furthermore no simple transformation of independent variables involving RH was found that would better account for variance explained than the variable itself. However, the analysis did disclose four data points (JD 42.8-44.3) that fell outside the general relationship between CD4 and sulfate. On JD 42.8-44.3 the sulfate to CD4 ratio was high suggesting either contributions from sulfate sources other than NGS, accelerated sulfur dioxide to sulfate oxidation or an aged NGS air mass. Independent analyses of each of these possibilities suggest that the elevated sulfate is most likely associated with accelerated oxidation of NGS emitted sulfur dioxide. If it is assumed that sulfates on JD 42.8-44.3 are primarily associated with NGS it is estimated that for those days for which there is CD4 data NGS contributed 75 ~ 2 percent of the observed sulfate. On the other hand, if it is assumed that a portion of the observed sulfate (determined by subtracting predicted NGS sulfate from observed sulfate) is associated with other sulfate sources NGS is estimated to contribute 62 ~ 5 percent of the observed sulfate. Relationships between CD and other aerosols was found to be weak or nonexistent implying NTGS did not contribute to their ambient concentrations.

EXCERPTS FROM NPS-~ITEX ~ 71 9.4.6 Differential Mass Balance (DMB) Analysis Differential mass balance (DMB) analysis calculates the fraction of ambient sulfur at a given re- ceptor attributed to NGS by multiplying the measured CD4 concentration by the ratio of sulfur to tracer emissions at the stack, multiplied by a factor that accounts for the amount of sulfur deposited and converted in the estimated travel time from NGS to the given receptor. A literature survey was conducted to determine the rates of so2 and sulfate deposition. Quite a wide range of values was identified. The deposition velocities for SO2 measured in prior studies ranged prom 0.1 to 2.3 cm/s, with a median of 0.7 cm/s. The deposition velocities measured for sulfate ranged from O to 0.9 cm/s, with a median of 0.2 cm/s. A literature survey was also conducted to determine likely SO2 oxidation rates. The survey indicated that gas-phase oxidation in winter is likely to be very slow, less than 0.2 percent per hour. However, aqueous-phase oxidation (in aerosol, fog, and cloud droplets) can be very rapid. Major oxidants in the aqueous phase appear to be hydrogen peroxide, ozone, and oxygen (catalyzed by manganese and iron, both plentiful in power plant plumes). The literature survey definitely confirmed the finding previously mentioned that oxidation rates appear to be a function of relative humidity. There is a clear theoretical and empirical basis for such humidity-dependent oxidation. However, the literature review supported a wide range of plausible oxidation rates as it did for deposition rates. Because the literature survey could not support a single set of deposition and oxidation rates, a sensitivity analysis was performed over the wide range of literature values of deposition and oxid~tion-rates. A total of nearly 4000 different combinations of SO2 deposition, sulfate deposition, and oxidation rates were tested. The variance of ambient sulfate at Hopi Point explained by each combination was tabulated. The combinations of parameters pros icing R2 > 0.7 were deemed to be reasonable; there were a total of more than 400 such combinations. The highest ~2 was achieved with SO2 and sulfate deposition velocities of 0.91 and 0.14 cm/s and an SO2 oxidation rate of 1.7 percent per hour per fractional humidity (i.e., 1.7 %/fur at 10058 RH). The average NGS contribution to ambient sulfate at Hopi Point during WHITEX was calculated to 68 percent based on the parameters with the highest R2. For all combinations of parameters yielding R2 > 0 7, NGS average contribution ranged from 43 to 96 percent. Additional sensitivity analyses were carried out with the optimized deposition and oxidation parameters to test the sensitivity to assumptions regarding the plume age. The use of the average of the lower and upper bound of the estimated plume age for Hopi Point yielded the most physically realistic estimates of the average contribution of NGS to SO2 and sulfate: 83 and 73 percent, respectively. Additional DMB analyses were conducted for Page. The analyses suggested, within the con- siderable uncertainty in estimating plume age at Page, that essentially all of the sulfur in Page is attributable to NGS. The most physically realistic estimates of NGS contributions to SO2 and sulfate in Page were obtained when the average of the lower and upper bound of NGS plume age was used in the DMB calculations. 9.4.7 Attribution of Visibility The light extinction attributable to scattering by NGS sulfate, scattering by other sulfate, extinc- tion by carbonaceous material (scattering by organics plus absorption by light absorbing carbon), scattering by natural particulates (fine soil + coarse mass), and scattering by nitrate was calculated for both Hopi Point and Page. Reconstructed extinction is defined as the sum of these components. The extinction efficiencies for each chemical species were based on consensus literature values, and the portion of the sulfate due to NGS was determined by the results of TMBR. Some of the extinc-

72 · APPENDIX 2 .. lion attributed to carbons and nitrates, may be due to NGS emissions also, however apportionment of these species was not possible with TMBR. Uncertainties in the fractions are one standard devi- ation from the mean based on the measurement uncertainties in the particulate concentrations and relative humidity, the standard errors of the TMBR regression coefficients, and the uncertainties in the extinction efficiencies. The mean non-rayleigh reconstructed extinction at Hopi Point is 0.0121 :E 0.0009 1/km. The mean fraction of this due to NGS sulfate is 42 d: 13 percent. The mean fraction due to sulfate from other sources is 21 ~ 2 percent. The time period with the highest reconstructed extinction (0.073 it 0.002 1/km) at Hopi Point was JD 42.8 when the fraction due to NGS sulfate was 59 18 percent and the fraction due to other sulfate was 35 ~ 27 percent. At Page, NGS sulfate was calculated in TMBR by two different methods. The first method, assumed that all sulfate not associated with the intercept was NGS sulfate. The second method assumed that all sulfate not associated with the regression coefficient for SCD4 was due to other sources. The reconstructed extinction using both methods is 0.0253 1/km. The uncertainty for method 1 is 0.0009 1/km and for method 2 is 0.0011 1/km. Using method 1, the mean fractions of the reconstructed extinction at Page are 38+ 14 percent NGS sulfate and 8 ~ 2 other sulfate. The means for method 2 are 24 ~ 8 percent NGS sulfate and 22 i: 2 percent other sulfate. The time period with the highest extinction at Page was JD 44.3, when the reconstructed extinction by both methods 1 and 2 was 0.082 1/km with the uncertainty for method 1 being 0.007 1/km and for method 2 being 0.009 1/km. Using method 1 the attribution of non-Rayleigh light extinction was 62 ~ 9 percent NGS sulfate and 4 it 4 percent other sulfate. Using method 2 the attribution was 34 ~ 5 percent NGS sulfate and 33 :E 11 percent other sulfate. It should be noted that much of the uncertainty in the sulfate and nitrate portions of the light extinction budgets is due to uncertainty in the relative humidity measurements. Therefore, the uncertainties in the fractions of extinction due to these components are not fully independent. For example if the RH value were underestimated, then the extinction due to NGS sulfate, other sulfate, and nitrate would all be underestimated. 9.4.8 Synthesis Differentiai Mass Balance and Tracer Mass Balance Regression are the two receptor oriented mod- eling approaches that were successfully exercised to yield quantitative attribution of sulfate aerosol concentrations. Chemical Mass Balance was successfully used to attribute primary aerosols to re- spective sources. Empirical orthogonal function analysis, although quantitative in nature, does not explicitly quantify the contribution of a source to aerosol concentrations to a source in its present formulation. It is primarily used to corroborate and interpret DMB and TMBR results. Likewise, trajectory and streakline analysis are used to evaluate whether or not the DMB and TMBR results are reasonable and to yield insight into the physical and chemical mechanisms that are associated with spatial and temporal patterns of sulfate concentrations. Figure 9.1 shows a scatter plot of NGS sulfate concentrations at Hopi Point that are predicted by the TMBR and DMB model. Estimated uncertainties are also shown on the graph. The orthogonal departure regression (ODR) calculation yields a slope of 0.99 and an intercept of -0.001 with an R2=o.90. The agreement between these two independent modeling approaches is better than might be expected. The TMBR approach implicitly assumes that transport times, deposition rates are constant and that SO2 to 5O4 oxidation is proportional to RH while the DMB calculation explicitly accounts for all these factors. In both modeling approaches dispersion is accounted for by use of ambient CD4 concentrations. Apparently the variation in sulfate concentration resulting

EXCERPTS FROM NPS-WHITEX · 73 T R -. O— . ~ _ _ - _ ~1 _ 1 -' I ' 1 ' ~ ' 1 ' 0 0.1 0.2 0.3 0.4 0.5 0.6 DMB REGRESSION: SLOPE — 0. 987 Y INT - -0.001 Figure 9.~: Scatter plot of sulfate sulfur (pg/m3) due to .N'GS at Hopi Point as calculated b! T}IBR and DMB. The lines shown are the 1:1 line and the ODR regression line. from the RH dependent SO2 to sod oxidation dominates any uncertain", associated with imprecise knowledge of deposition rates or transport times. TMBR and DRIB attribution results are very similar. For those days that CD4 data were Available the T1dBR best estimate of JUGS average contribution to sulfate at Hopi Point is 73 ~ 4 percent while the DMB calculation yielded 68 ~ 3.5 percent. The quoted uncertainties are one standard error on either side of the mean. For the TMBR analysis the standard error incorporates uncertainty in the regression coefficient as well as measurement error while the DRIB uncertainty is only the result of measurement uncertain",. The best estimate of uncertainty in the DRIB calculation associated with imprecise knowledge of variables such as deposition, conversion and travel time is 68 ~ 28 percent. At Page the results from the too techniques are again quite similar. Best estimate of NGS contribution to sulfate are 75 ~ 2 percent while the DRIB calculation suggests NGS contribution to sulfate is between 60 and 100 percent depending on whether the average minimum air mass age is assumed to be 6 or 12 hours. It is emphasized that quoted uncertainties are averages of uncertainties associated with each sampling period. Uncertainties for any giving sampling period can be quite bigly. Furthermore, only uncertainties associated with measurement error and imprecise l;nouledge of physical variables is addressed. Uncertainty as to the appropriateness of the model used was not addressed. Both DMB and T}IBR suggest that attribution of a seconder, aerosol, in this case sulfate, to a specific source (FIGS) can be done u ith a [air amount of certainty. The more qualitative analysis techniques are supportive of this presumption. The exercising of the CRIB equations visas not

74 · APPENDIX 2 successful in directly attributing sulfate, however, results were consistent with TMBR and DMB. CMB analysis suggests that the only two sources associated with SOW emissions were coal fired power plants and copper smelters. Furthermore the time periods where CMB analysis predicted copper smelter and power plant contributions to primary aerosols were the same time periods that TMBR attributed secondary sulfate to these two sources. A cursory examination of emission strength as a function of distance from Grand Canyon show that NGS is the largest coal fired power plant within hundreds of kilometers of Grand Canyon with copper smelters being a significant S02 source that is approximately 300 km to south of Grand Canvon. Based lust on emission strengths alone one might expect a large NGS contribution to 't .^ . . ~ . ~ m1 ~~ ___1 ~ .: ~A__+A~ +;ql -~ t^~n^^rO1 trDn~C chow sulfate at Grand Canyon. Lee flub analysis wmcn Incorporates spaclm ally L~1~ ~l=~`u~ OllVW that a sulfate concentration field with highest sulfate concentrations found at NGS and decreas- ing as one moves radially out from NGS explains 70 percent of the variance in the concentration field. This spatial concentration gradient is predominant under stagnant meteorological conditions. Concentration fields of this nature under stable meteorology are suggestive of NGS emissions as be- ing a significant contributor to ambient sulfate concentrations. Furthermore the strong correlation between predicted "hits" of the NGS plume and all time periods with elevated sulfate suggest a sig- nificant contribution to sulfate concentrations by NGS. Finally, deterministic wind field modelling tended to confirm conceptual models suggested by TMBR and DMB models and especially ideas developed from the EOF analysis. Release of particles at NGS plume height into modeled wind fields on February 11 and 12 resulted in transport of those particles into Grand Canyon region. Finally, based on the results of TMBR, the fraction of the mean non-Rayleigh light extinction at Hopi Point due to sulfate from NGS was 42 ~ 13 percent. For the time period with the highest light extinction the fraction due to NGS was 59 ~ 18 percent. .

EXCERPTS FROM NPS-WHITEX · 75 . · fine particles: Fine particles (smaller than 2.5 ~m) were collected on Teflon, nylon, and quartz filters by IMPROVE,2 SFU,4 and SCISAS samplers. These filters were analyzed by a variety of methods: gra~imetric analysis, particle induced x-ray emission (PIXE), x-ray fluorescence (XRF), proton elastic scattering, ion chromatography, and thermal carbon methods. Con- centrations were determined for mass, major and trace elements, hydrogen, sulfate, nitrate, nitrate plus nitric acid vapors, and organic and elemental carbon. Comparisons were made between IMPROVE Ad SCISAS samples at Page. The model analyses were based primarily on the measurements of the IMPROVE sampler. coarse particles: Coarse particles (2.5 Am to 15 ~m) were collected on Nuclepore filters by SFU samplers at four sites and analyzed by gravimetric analysis. Total particles (smaller than 15 ~m) were collected on Teflon filters by SCISAS samplers at four other sites and analyzed by gravimetric analysis; the coarse mass was estimated by subtraction. · particles in multiple size ranges: Particles in nine size ranges were collected by the DRUM (Davis Rotating Unit for Monitoring) sampler and analyzed by PIXE.6 · SO2: SO2 was measured by the IMPROVE sampler using the impregnated-filter method; in this method, SO2 was converted to sulfate on 1~2CO3-impregnated filters and analyzed for sulfate by ion chromatography. Results were compared with measurements on impregnated- filter and annular denuder samplers operated at Page by Brigham Young University (BYU).7 ·~ CD4: CD4 was released from the Navajo Generating Station. Air samples were collected in 60 liter bottles at the sampling sites and analyzed for CD4 by gas chromatography/mass spectrometry.8 3.2 CD4 Tracer Injection During the WHITEX period, 4.9 kg of CD4 was released through the stacks of the Navajo Gener- ating Station. A manifold and valve system permitted the release through any of the three stacks. Preliminary experiments determined that the CD4 was well-mixed and conserved in the stack. The release rate was based on the power output of the station. From January 7 to 30, the ratio of tracer released to the power output was held between 2.0 to 2.5 mg CD4/MW. Beginning on January 30, the rate was increased to approximately 3.5 mg CD4/MW. However, between February 6 and 10, the ratio rose because of an unanticipated outage (to nearly 5 mg CD4/MW), and on February 13, the ratio decreased to 2.5 mg CD4/MW, because of an unanticipated early restart. The details of the injection are discussed in Appendix 3B. The analysis of the samples is discussed in section 3.5 and in Appendices 3A and 3B. 3.3 Sample Collection A complete suite of measurements were made at the three receptor sites: Canyonlands National Park, Hopi Point (Grand Canyon National Park), and Page (Glen Canyon National Recreation Area). Each site had (1) a multimodule version of the IMPROVE sampler that collected fine particles on 6 filters and SO2 on another filter, with durations ranging from 6 to 24 hours; (2) , ~ , a DRUM sampler that collected particles in nine size ranges; (3) a system to collect 6-hour air samples for CD4 analysis; and (4) a transmissometer to measure beats an integrating nephelometer to measure bscat, and a meterological package. SCISAS samplers operated at Hopi Point and Page as part of the SCENES network, with the sampler at Page following the time schedule of the

76 · APPENDIX 2 WHITEX CD4 7 :———-—·—-—-—-~_.— —_. 0.0500~ PAGE i , , . i , ~ i , , , ~ li ~ 2 i 0.025~ _ . ..~ — .—— —T--—·-'-—— ·——t ~r-~l .~ ~ . 0 000 °' ~ . i i I. i i , , i i 0.00601 ~_~r.r.T_T..~_~._.~. - r r t ~ ~r t~ t-' t r 7 ~ =~;~ ~l s -r . ~ 0 . 0030- B UL~,F~oG . ~ 2 . . . . ! i . ,~ O OO~i~ '3 I I ~; ~ · : · · I . · . . i ._. . i . I . . O~OO1~i · i—~r~~-!—r~t-rT ~. s-r-T- I r—. -T-s--~.~s~~t~7i—~~~r j~r~~~~.~~!~- ~:~-r t~~ O. OOO{L 1 ; ~ ; ., ., ',,,, s~L ~ ~ . ~ ~ ~ 0 003~ ~ ~- i~r ~ r~ T l ~r rr s ~ ~~~t~~r~ r~t ~~~~r~l -s r t T~ r l T r . 1~t~r~ l t 0~001~ ~ T——~ . ~ ~—r ~ r~ ~. !~~~—r ~.~ T'-t- '—t —-.—T-~1' T—l—T ~~r~~~ ~~ ~-r~~l~~- 0~000~ ~ ~ ~ ~ ~ ~ t; ~ t~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ l ~ ~ l ~ ~ l l ~ 1~, ~ ....... 0.003~ -~I`EE— rs-~- r - --~' .~ rr T 1 . s =. r T . T t . t s .. .. o.ool~ ~ . . _r~—~—T— -~t~-i ——t- i 7t t s ~.~ .~. ., ~ ~ · ~ i ~ ' ' a89 . 2 O.000Q I ~ t~ls.~ ~ ! l l ~~'s 's_.s. ~ ~.~l...~' ~ ~ ~ I ~ ~ ~ ~ I s ~ ~ ~ I ~ ~ ~ ~ ~ I.~ ~ ~ 0.0030- .l~ONTICi8LLO s . i s . j s .t t 0 ~ 00 15 —t~i —r ~ S + t T—s ~ t r ~ t— — t 7 s i—r— t ~ t r r i r — S S . . S S . O.OOOQ ..... ..... ................. _ ~- - —. - .~ - - -- r s - s ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.0030~ GREEN RIV ;= g . s s g g n nnl~ g g l ~ ... ~.~ .~ ~ ' ~ i , ~ _ - . w" ~ _.___~. -——~ ~—..~ ~ ~ ^. ~ . . . ! g ~ . ~ O OOOtL · .; ..;,,,, . ~, ................................ L ~ 7 10 13 16 19 22( 25 28 31) 34 37 40 43 46 49 Figure 4.lS: CD4 concentrations (ppt) at eight ~ lIITE.X receptor sites.

EXCERPTS FROM NPS-WHITEX · 77 Table 4.5: Statistical summary of elemental sulfur concentrations (pg/m3) during the NVHITEX study period. ELEMENTAL SIJLFIJR {`ln/~31 NO. of STD 90th SITE OBS DEV MAX PRCNTL 75th PRCNTL MEAN MED MIN GREEN RIVER PAGE CISCO MEXICAN HAT BITE BULLFROG CANYON LANDS MONTICELLO WUPATKI NAVAJO HOPI POINT MEADVIEW BRYCE CANYON _ . . 81 0.1913 133 0.2723 44 0.1243 71 0.1421 80 0.1840 166 0.2034 163 0.1215 72 0.1559 43 0.1331 42 0.0981 167 0.1341 16 0.0860 39 0.0699 1.0960 0.5786 4.2724 0.7403 0.5759 0.4868 0.6048 0.4602 1.0732 0.4655 4.7449 0.4308 1.8883 0.4225 0.8355 0.3870 0.7264 0.3779 0.4774 0.3025 1.9556 0.3498 0.3067 0.2815 0.2805 0.2276 0.4217 0.3451 0.3801 0.3320 0.3811 0.3031 0.3732 0.2920 0.3676 0.2826 0.3382 0.2656 0.3149 0.2424 0.3274 0.2421 0.2792 0.2238 0.2360 0.1662 0.2374 0.1652 0.2466 0.1589 0.1592 0.1125 1 0.3141 0.0398 0.2852 0.0157 0.2847 0.0297 0.3016 0.0169 0.2599 0.0216 0.2230 0.0105 0.2278 0.0142 0.2020 0.0336 0.2343 o.oooo 0.1416 0.0197 0.1212 -.0032 0.1461 0.0405 0.1061 0.0053 Table 4.6: Statistical summary of CD4 (ppt) at eight receptor sites using fully scaled CD4 at Page and Hopi. CD4 (pot) NO. of STD 90th 75th SITE OBS DEV MAX PRCNTL PRCNTL MEAN MED MIN PAGE HOPI POINT BITE BULLFROG GREEN RIVER CANYON LANDS MEXICAN HAT MONTICELLO 32 .00803 .04328 36 .00192 .01051 2 .00171 .00279 20 .00102 .00288 3 .00040 .00093 20 .00057 .00253 4 .00040 .00095 3 .00020 .00049 .0 1 066 .0078 1 .00587 .0042 1 .00470 .00388 .00293 .00285 .00279 .00279 .00158 .00158 .00275 .00207 .00101 .00077 .00093 .00093 .00053 .00053 .00 1 1 1 .00050 .00045 .00023 .00095 .00087 .00046 .0004 1 .00049 .00049 .00009 .00028 .00000 .00034 .00037 .00009 .00013 .00009 .00008 .00009

78 ~ ~PE~ 2 o.o40o 1 0.020Q o.ooo~ I I I I t I I I I I I I 1 1 1 1 1 1 1 1 I t I I I l I I I l l I I I I I I I I I I I I I I I I I I o . o~oo- . ..... . . ,. ....... .. , .. .. . ..................... % ........ . Bscat (l/I;m) L 0.020~ 1IAL O.OOOQ I I I I I I I I I I I I I I I I I I I 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 00 . 000( 7 ........................ .............................................................. , ~ ,~ ; T]~PI Bext (l/lim) ....... ~ l.,. . . ...... ,~ ,~ . . . . . . . .. ..~. ~ r ~ . ~ n . . . . . . . . . . . . . . . . . i , ~r ~ ~- ~ k . . ~ . . , : — ~ - : .:: ...... :: ~ ~ . . . 50.OOOQ - o.ooom I I I I l I I I I I t I I I I I I I I I I I I I t I I l l I I ! ! I I ! I I ! ! I ! l I ! ! I l ! 0 . 0 1 0~ . . . s . ........... . . . ........ ... Bext - Bscat (i/Rm) RH(07) O.OOOQ . . . . . . . 2T.~- : ~1 -0.010~ I I 1 1 1 1 1 1 1 1 1 1 1 i'I I I I I I I l I ~ I ~ I I I I I I I I I I I I I ' i ! I I I ! I 2.0000- . ; . .. . . .. . . ; P'ext/Becat ~ , .,.~, T[,~,~V:,,,:~,'T.J,N,: : —! 1, 1 1 ~ 1 1 llU1 1 l1.~1 11161 1119 1221 125' 12!81 1;111 t34l 1371 41U 143 1416 14191 TIME(JULIAtI DA\) 1 .Of)t3Q 0.000(~ . . . . Figure 5.4: Time traces of optical data and relative humidity at Hopi Point.

EXCERPTS FROM NPS-VENITEX · 79 0 (~rr }lEASt1RED Bext (1, lLm) n nl1n . (, n()oQ U ()83~ RECONSTR UCTED Bex-t ( l/ Em) O.04 1 Ql . - .. - ' - . - 2 .... ; .. : ... .. .............. ~.t 1. ................... O. OOOQ ~_ . ~1 : n r~ n ~ L J ·Ol)()Cn T>~` 7 F,rw C:rArrT=.OT~r- ; I ' '~ ' ^ ~ ~ ~ & ~ ~ ~ ~ ~ ~ ~ ~ 1 J · ~ _ _ ~ · I 1~1 -,h,_-,, (, , ,Ir,,,.,,,, ,U,,,,, ,,~' ,F - g ~,,,,,,,,W, , ~.......... .JU n . nnnn L n . .~ooN ~ ~ ~' ' LI 1 ·.)()ocr ·'t:)ARSE S(:ATTERING sr~r~o . ...... .. r) onnn t?~(~ LIGHTABSORBING CARBON : ; . sor)e : .. . .. . O. OOOQ . . . . - . . . J .~)000~ f;L7LFATES n.~or)Q . . . n nnnn r- . . . ~ ~ ~ J~ ~_~~ ' I-L, ; ... ~_.,_~., , PL m]. ..... n r'r~r~n I 1 .000m r)RGANlCS {~. r{JOrL J or1r)~ ]lITRATES o sn~Q .... n . ()orJ(L . . . . .. .~)Or)m FINE SOIL ~ '()~.1Q . : o. r'~nrL 1 7 10 13 16 19 22 25 28 31 :34 37 40 43 46 49 TIME(JULIAN DA1~) Figure 5.22: Measured and reconstructed 12-hour averaged extinction coefficients (k m~~) and the fraction due to each component at Hopi Point. Extinction includes Rayleigh scattering.

80 · APPENDIX 2 Table 6.1: Regional emissions in units of tons/day from coal fired power plants, copper smelter, and large urban areas. These values are based on annual data for 1987. SITE .. LOCATION Apache Coronado NGS Springerville Cholla Cameo Craig Hayden Escalante Four Corners San Juan North Valmy Mohave Carbon Hunter Huntington Bridger Naughton Cohise, AZ St. Johns, AZ Page, AZ Springerville, AZ Joseph City, AZ Grand Junction, CO Craig, CO Hayden, CO Preuritt, NM Fruitland, NM Waterflow, NM Valmy, NV Mohave, NV Castledale, UT Castledale, UT Huntington, UT Point of Rocks, WY Kemmerer, WY Asarco-Hayden Hayden, AZ Inspiration Miami, AZ Magma San Manuel, AZ Nacozari Nacozari, Sonora Cananea Cananea, Sonora LA/Southern CA Phoenix, AZ Las Vegas, NV El Paso, TX Salt Lake City,UT SO2 IVOz PART 5.5 17.5 163 13.2 44.7 8.2 9.6 24.4 38.4 40.5 23.0 2.7 7.9 105.5 227.7 115.6 93.2 11.5 15.9 51.5 42.2 15.6 12.0 15.9 53.4 32.6 58.1 145.9 90.1 41.4 40.3 10.1 92 * * 54 * * 480 * * 380 * * 240 * * * * * * * * * * * * * * * * * 7.4 3.8 19.2 .27 73 6.4 .5 35.9 4.4 * * .4 2.8 3.3 * * * 12.3

EXCERPTS FROM NPS-WHITEX · 81 vapor concentrations or ozone concentrations were less than assumed actual rates would be less than these calculations. 21 ^ 1.S c ~ 1 - .5 .,dl it\ i Alit 3 .. 9 argon 3 -- Tle. ·, .~, P6e 9 ..eA29~t Figure 6.10: Calculated gas-phase SO2 oxidation rates as a function of time of day and season (Source: Latimer et al., 1985~. Perhaps the most important conclusion from Figure 6.10 is that gas-phase reactions simply are not fast enough to explain the SO2 oxidation rates on the order of 1 percent per hour suggested by WHITEX data-If one computes the 24-hour average SO2 oxidation rate from Figure 6.10 for winter, one obtains a value of approximately 0.03 percent per hour, a factor of 30 too low. Even the maximum gas-phase oxidation rate of 0.2 percent per hour is too low by a factor of 5. Thus, these high oxidation rates can only be explained by aqueous-phase oxidation: reactions that occur within aerosols, or fog and cloud droplets. Theoretical calculations suggest that the higher rates observed during WHITEX are indeed plausible. Figure 6.11 shows the sulfur oxidation rates as a function of droplet pH as a result of reactions with ozone (O3), hydrogen peroxide (H202), iron (Fe>, manganese (Mn), carbon (C), nitrous acid (HNO2), and nitrate nitrogen dioxide (~02) All reactions, except the one with hydrogen peroxide, are strong functions of pH. As the droplet becomes more acidic (from production of sulfuric acid), oxidation is dramatically slowed. However, the reaction with H202 remains rapid. The reaction rates shown in Figure 6.11 are based on an assumed 112O2 concentration of 1 ppb. Although 82O2 concentrations were not measured during WHITEX, a series of measurements were made by Van Valiant during February 1987 along the 91.5 degree meridian from Iowa to the Gulf of Mexico. They found 82O2 concentrations varied inversely with latitude, with values in the range from <0.1 to 1.0 ppb. At the latitude of the Grand Canyon (36 degrees), they found H2O2 concentrations in the range from 0.1 to 0.6, centered on 0.3 ppb. They also found that B2O2

82 · APPENDIX2 Table 6.10: Estimated age of the Navajo Generating Station plume (in hours) at various locations in the WHITEX study region, January-February 1987 Notes 1. If numbers are not presented for given time and place, the NGS plume is not expected to be in the area. 2. ~ numbers are presented in parentheses (), the NGS plume would be present with minor modifications of winds. WHITEX Study Period in 1987 Dater Hour Navajo GeDeratis~ Station Plume Age (hours) Canyonlu~~ Bull Marina Page, Arizona Hopi Point ~ Monticello tic Hitc, Utah Gwen River, Utah Mexican Hat, Utah .. 9 6 6-12 9 17 6-12 12 10 6 6-12 10 17 6-36 11 6 6~12 3608 11 17 6-12 58 12 6 6-12 (6-72) 12 17 012 (24) 13 6 6-12 (48) 13 17 6-12 (~48) 6-36 14 6 6-24 (~48) 14 17 6-12 (2400) 15 6 6-12 (12-2 15 17 6-12 24. 16 6 012 25. 16 17 012 60. 17 6 6-12 (36) 17 17 6-12 (36) 18 7 6-12 (6) 18 17 012 ~12 12 12 19 17 012 e 18 (24) 20 6 6-12 6 20 - 17 6-12 10-18 21 i- 6 6-24 24-30 21 17 6-12 4~8 24-36 22 6 6-24 22 17 6-12 10-36 23 6 6-12 84 23 17 6-12 96 120 24 7 6 60 (24) 48 48 96 96 24 17 6-12 (36) 120 6 25 7 6-48 18 24 (72) 25 17 6-12 (24) 24-36 (84) 10 26 6 6-24 12 48 (96) 26 17 ~84 72 120 48 27 6 6-1- 72 12-(96) 4-120 48 27 17 6-12 60 120 2~120 48 28 6 6-(36) 96 72-120 132 (96) 'Bawd on Surface Winds

EXCERPTS FROM NPS-WHITEX Table 6.10: cont. · 83 WHITEX Study Navajo Generating Station Plume A'sc (hours) Period in 1987 CanyonlAnds Bullfrog Manna Dav Hour Page, Arizona Hopi Print ~ Monticello ~ Hite, Utah Green River, Utah Mexican Hat, Utah 28 17 6-12 (36) 29 6 6-12 (12) 48 (48) (48) 29 17 6-(,,2) 6-(54) 48-(120) 120 30 6 6-(1:20) 12-96 30 17 6-12 60-120 31 6 6-12 31 17 6-12 (12) 32 6 6-1~ (132) 32 17 6-(24) (30) 12 24 33 6 6-(48) (132) 40 6 24-30 48 33 17 6-(48) (48-72) 48 48 48 60 34 6 6 60 84 60 72 20-48 `2 34 17 6-(3'`6) 96 72 24-(36) 30-(60) R4 35 6 6-~48) (24) 60-72 48-60 (60- ~ 2) 90 35 17 6~;0 10-(60) 96 72 (72) 96 36 6 6-~.6 96 120 36 17 ~(1~0) 6-120 37 6 6-(1260) 6-120 37 17 6-12 (12-36) 38 ~ 6 0~24 ) 48-( 132) 38 17 6-(12) 3~60 39 6 6-12 36-(48) 39 17 6-12 (fi) 40 6 6-12 (24) 40 17 6-12 (24-36) 41 6 6-12 41 17 6-12 42 6 6-12 (18) 42 17 6-12 (12-30 43 6 6-12 12 43 17 6, (24) 3~(48) 44 6 '~( 120) (96) 120 12-(96) (132) 12-(96) 44 17 ~ ' 6-(120) (48) 24-(96) 10-(120) (132) (96) 45 ~ 11 6--12 4.5 17 6-12 46 7 6-12 4fi 18 6-12 (1~-24) 4 ~ 6 6-12 47 17 6-12 6-12 48 6 6-12 4~R 1 7 6-24 49 6 6-12 24-(30)

84 · APPENDIX 2 Table 6.43: Time history of measured total sulfur (pg/m3), NGS total sulfur (pg/m3) and fraction of ambient total sulfur due to NGS based on DMB analysis for Hopi Point. Ambient Julian S Day Concent Total Measured 5 Uncertainties NGS due to due to S K's Measmt Concent NGS Contribution to S Uncertainties due to due to K's Measmt Fraction Due to GIGS Uncertainties Ratio due to due to K's Measmt 13.8 0.25 0.04 0.04 1.40 0.47 0.18 5.63 1.90 1.28 14.3 1.50 0.11 0.11 3.31 1.12 0.31 2.21 0.75 0.24 14.8 0.71 0.05 0.05 0.66 0.38 0.11 0.93 0.53 0.17 15.3 0.38 0.05 0.05 1.39 0.44 0.13 3.65 1.16 0.66 15.8 0.33 0.04 0.04 0.76 0.38 0.28 2.34 1.18 0.93 16.2 0.59 0.04 0.04 0.77 0.42 0.11 1.30 0.71 0.20 16.7 0.78 0.05 0.05 0.57 0.37 0.04 0.72 0.47 0.06 17.2 0.57 0.04 0.04 0.57 0.47 0.17 1.01 0.83 0.32 17.7 0.34 0.04 0.04 0.40 0.37 0.14 1.20 1.11 0.47 18.2 0.20 0.04 0.04 1.23 0.42 0.24 6.19 2.12 1.90 32.2 0.08 0.03 0.03 0.06 0.10 0.01 0.78 1.26 0.76 32.7 0.07 0.03 0.03 0.22 0.17 0.09 2.90 2.23 2.52 33.2 0.09 0.03 0.03 0.05 0.10 0.01 0.57 1.10 0.38 33.7 0.12 0.03 0.03 0.15 0.16 0.10 1.24 1.36 0.89 34.2 1.01 0.06 0.06 0.09 0.11 0.02 0.09 0.11 0.02 34.7 0.50 0.04 0.04 0.12 0.06 0.09 0.24 0.11 0.19 35.2 4.42 0.33 0.33 0.59 0.29 0.10 0.13 0.06 0.03 35.7 0.61 0.05 0.05 1.24 0.39 0.29 2.05 0.64 0.53 36.2 0.91 0.07 0.07 0.16 0.23 0.01 0.17 0.26 0.02 36.7 1.33 0.10 0.10 1.66 0.57 0.46 1.25 0.43 0.36 37.2 0.75 0.06 0.06 1.08 0.37 0.11 1.43 0.49 0.19 37.7 0.71 0.06 0.06 0.72 0.24 0.13 1.02 0.35 0.22 38.2 0.55 0.05 0.05 0.24 0.31 0.02 0.43 0.56 0.06 38.7 0.24 0.03 0.03 0.26 0.24 0.07 1.10 0.98 0.35 39.2 ~ 0.34 0.04 0.04 0.11 0.11 0.07 0.31 0.31 0.22 39.7 0.55 0.04 0.04 0.19 0.07 0.24 0.35 0.12 0.43 40.2 0.79 0.06 0.06 0.10 0.07 0.04 0.13 0.09 0.06 40.7 0.65 0.05 0.05 0.10 0.06 0.08 0.16 0.08 0.13 41.2 0.49 0.04 0.04 0.20 0.06 0.14 0.42 0.13 0.30 41.7 0.27 0.04 0.04 0.69 0.21 0.41 2.60 0.78 1.67 42.2 0.54 0.04 0.04 0.93 0.39 0.14 1.71 0.71 0.29 42.7 0.46 0.04 0.04 0.98 0.29 0.33 2.12 0.63 0.79 43.2 0.40 0.04 0.04 1.22 0.38 0.12 3.05 0.96 0.41 43.7 0.25 0.04 0.04 0.46 0.27 0.21 1.79 1.06 0.90 44.2 0.16 0.03 0.03 0.02 0.01 0.03 0.12 0.09 0.21 44.7 0.15 0.03 0.03 0.17 0.10 0.05 1.14 0.70 0.50

fi.800 2.900(L 5.800(T 2. 900Q 0.000a EXCERPTS FROM NPS-WHITEX · 85 Hopi Point 0.000Q'' ~ ~ I I I ~ I I I t t t t I I I t I I I t I I I I I I I I t I ~ ~ t I t 1~1 1 I~ t t I I I I Meedurea Total. Sulfur . . . : ~ : : . ~ ~ _ ~ — _ ~ - I t ~ ~ ~ l I I I ~ , ~ I I ~ I I I I ,, I, I `,, I',, , ~ ., . L ~ ~ t 10 13 116 ~19t 132 25 28 3~1 44 :47 40 43 46 49 TIME(JULIAN DAY) Figure 6.71: Time plot of predicted upper limit of NGS contribution to total sulfur at Hopi Point (540 x SCD4) and measured total sulfur. . When additional source profile development has been completed, the CMB analyses should be repeated at these two sites and at the remaining WHITEX core and gradient sampling sites. Chemical Mass Balance with Unique Tracer When there is a unique tracer associated with a source and when ratios of tracer to other emissions are known, Equation 6.9 can be used to estimate the upper limit of contributions to ambient aerosol species. For instance, ambient concentrations of total sulfur associated with NGS emissions can be calculated using ST,a = IST/CD4]P X CD4,a (6.9) where the subscripts a and p refer to ambient and in-plume concentrations and ST = (SO2/2 + SO4/3) is the total sulfur. For purposes of this study ambient CD4 concentrations were scaled to an equivalent ST/CD. in-plume ratio of 540 ~g/m3ppt. Thus ST'a = t540]SCD4,a (6.10) can be used to estimate the upper bounds of NGS contributions at any site for which there are ambient CD4 data. Figure 6.71 is a time plot of predicted and measured total sulfur as a function of time at Hopi Point. In almost all cases the upper limit of the NGS contribution is considerably greater than that which was measured. The average upper limit of total sulfur calculated using Equation 6.10 is 1.58 d: 0.08 ~g/m3 while the average measured total sulfur at Hopi Point is 0.61 0.01 ~g/m3. The upper limit is approximately 3 times higher than the ambient levels. A similar plot for Page is shown in Figure 6.72 where the average upper limit of the NGS contribution is 3.16 ~ 0.15 ~g/m3 while the average measured total sulfur is 1.75 :E 0.04 ~g/m3. The mean upper limit at Page is nearly 2 times higher than the mean measured total sulfur.

86 ~ APPENDS 2 APPENDIX 6B: Tracer Mass Balance I\Iode! Regression (T>fBR) Itio(lel and Tracer Mass Balance (TAB) Lioclel Overview The Tracer Mass Balance Regression Model is a multiple regression based model which may be used to apportion an aerosol species of interest measured at a receptor site to the various contributing sources. It has been shown to be a special case of the General Mass Balance (GMB) Model. The actual regression analysis may be performed using the method of least squares. However, since the independent variables in this model are ambient concentrations of various aerosol components which are measured levity error? the method of Orthogonal Distance Regression (ODR) is expected to give better estimates of else source contributions. A detailed discussion of the method of ODR may be found in the boor; by Fuller(1987). Model Equations The basic equation for TLIBR model equation is: . where: h Cik = yo + ~ Ink u=1 (1) Cik = concentration of species i at the receptor for time period k^. In the current application i refers to Sulfate Sulfur or S02 sulfur. Ciuk = concentration of trace element iu which serves as a tracer for a group of ogle or more sources, for time period h. Aid = regression coefficient for trace element in ~vl~icl~ acts as a tracer for a group of one or more sources. Do = intercept representing the mean background concentrations of tl~e species of interest, at the receptor.

EXCERPTS FROM NPS-WHITEX · 87 = number of groups of sources, each group being represented by a particular aerosol species which acts as a tracer for that group of sources. (ok = a factor which is a function of field measurements, sampling period and possibly source type, chosen in such a way that the ~ coefficients in the model (1) are invariant with respect to the sampling period. The model is known as the tracer mass balance (TMB) model when only a single trace element is used as a tracer for a particular source and all the remaining sources are accounted for by the intercept term in the model. When several trace elements are used in addition to the tracer for the distinguished source of interest, then the model is referred to as tracer mass balance regression (TMBR) model. The simplest versions of the TMBR model and the TMB model use Wok = 1 for all time periods and source groups. In the current application we have used Wok = 1 as weD as Auk = RHk where RHk is the relative humidity at the receptor during sampling period k. The use of RlIk as a linear factor in the above model was motivated by the following consider- ations. In apportioning a secondary aerosol, the constant ,Biu~k derived from the GLIB model had the form pi jk = ~ C2 jk riUjk CiUjk with and (2) 4, ~c(i*,i'L') r,. = Y It c(i*, j, k) + K~(i*, j, k-) - Iid(i, j,k) {exp(-Xd(i, j, k)tjk) - exp(-[Ac(i*, j, k) + Ite(~*, j, ~')]tjk)} (3) riUjk = exp(—(Itc(iu~i, k^) + It`(it,, j, (^))tjk) (4) If the species in does not convert and its deposition rate is the same as that of the secondary aerosol species i being apportioned, then . riujk = exp(-K`(i, j,k)t't) so that the ratio r;jk/riujk reduces to KC(i*, j, k)tjk after using the approximation exp(~) ~ 1 + ~ (when x is sufficiently small). Recall that the full infinite series expansion for exp(~) is given by exp(~) = 1 + ~ + 2! + 3! (5) (6) and we have used a first order approximation in (6). It is possible to use hillier order approximations of e~p(~) in these derivations but this is not pursued here.

88 · APPENDIX2 Assuming that KC(i*, j, k) is proportional to RHk with proportionality constant B. we obtain that the ratio r;jk/riujk is equal to BtjkRHk which gives Irk—BtjkRHi j - Defining Mink = piUk/¢uk (a) where Ilk = RHk and assuming that disk are constant for an sampling periods rather than the quantities §.uk suggests the use of RHk as a linear factor in the T)IBR model equation (1). For purposes of attributing total sulfur or sulfate sulfur to NGS, SCD4 is a unique NGS tracer. Furthermore, As was found to be below the detectible limit in samples gathered from within the NGS pluem (refer to Table ??. Therefore, As is considered to be a unique tracer for emissions other than NGS and most probably associated with copper smelter emissions. Therefore, in actual application of the TLIBR model to WHITEX data, we have grouped the sources into 3 categories: (7) · NGS with CD4 serving as the tracer · Sources with Arsenic (As) serving as a tracer, and, · all remaining sources, if any. - In the application of the TMB model, there are only two categories, viz, NGS with CD4 as a tracer and all remaining sources. The TMBR Model equations used in the application are: h Ck = To + ~ ~i~ci~k~uk u=1 where: C,` = concentration of sulfate sulfur or total sulfur for time period k Ci,,k = concentration of trace element in for time period k yin = regression coefficient associated with trace element is (9) ,0 = intercept representing the mean background concentration of the species being apportioned, due to all sources not accounted for explicitly. Auk = RHk, the relative humidity at the receptor during sampling period k, or 1, depending on the particular application. All of the cases considered may be written in the form 2 Ck = To + ~ hijack u=1 where: (10) I

EXCERPTS FROM NPS-WNITE:X · 89 AUk = concentration of trace element in for time period k or concentration of trace element i', multiplied by RHk- The model is known as the tracer mass balance (TAB) model when the only trace element used is CD4 or scaled CD4 (SCD4). When other trace elements are used in addition to CD4 then the model is referred to as tracer mass balance regression (TAlBR) model. Multiplication by RlI, when included, is a surrogate for the RH dependent oxidation rate of S02 to S04. Model Calculations and Uncertainties The concentrations CUk of sulfate sulfur or total sulfur associated with each trace element in for each time period are calculated by multiplying the measured values of AUk for each trace element by the respective regression coefficients as follows. Cok would just be the intercept representing the contribution from all sources not explicitly accounted for by any of the reference species used in the TMBR model. CUk = Mu X Auk The uncertainties for each of these concentrations are calculated by: aCU': = N/AUkC~,u + 72'UaAUk + arid aAuk (11) ( 12) The quantities ~Au,, are part of the ~7HITEX data base. The quantities yi', are obtained as outputs from the regression packages that were used. Errors in AUk and the estimated regression coefficients have been assumed to be uncorrelated. The total calculated sulfur Ck for each time period is the sum of the CUk'S summed over all the reference aerosol species in and the intercept. h Ck = Co + it, Cuk u=1 (13) The uncertainty associated with the total calculated sulfur concentration for each time period h ACE = ~ Deco + ~ ~Cuk u=1 (14) assuming the covariance terms arising in the derivation are negligible. The sources assumed to be associated with each trace element are: · CD4 or SCD4 - Navajo Generating Station (N'GS) · Selenium (Se) - All power plants including NGS · Arsenic (As) - Copper smelters

90 · APPENDIX 2 · Intercept- Mean background concentration The estimated fraction of sulfur from each source for any given time period is equal to the sulfur associated with the trace element divided by the total calculated sulfur concentration: FUk = k ( 1 5 ) Ok The uncertainty for each of these fractions is: aFUk = i c2 ac C4 k (16) The mean fraction Fu of the sulfur attributed to each source is estimated by the mean sulfur concentration Cu for that source divided by the mean total calculated sulfur C. r ~ flu r — _ an— r v where l and and ( 17) 1 ~ = —~ Cork k=1 ( 18) C = —~ Ck (19) k=1 The uncertainties for Cu and C are calculated by: ecu = .. '`I ~ aC,\ ... aC = I, ~ ~ aC k=l The uncertainties associated with the mean fractions are calculated by ARC -W c2 C4 (20) (21) (22) The uncertainty formulas are all derived using propagation of error methods and assuming the covariances between various terms occurring in the derivation are negligible.

EXCERPTS FROM NPS-WHITE:X · 91 Model Assumptions. The regression coefficients, including the intercept term, in the model have been assumed to be time independent. The aerosol species used in the model are assumed to be tracers for nono~erlapping groups of sources. In particular, none of the species other than the tracer associated with the source of interest can be emitted by that source unless there is an independent method such as CMB modeling to partition the ambient species concentrations into components attributable to the various groups of sources. Potential Deviations from Assumptions. It is highly unlil;ely that the regression coefficients are constant for all sampling periods. This will inflate the uncertainty in the final apportionments but the extent to which this inflation occurs will depend on how variable the regression coefficients are. eve investigate below the possible effects of nonconstant regression coefficients in the TAB model. A similar instigation may be carried out for the more general T\IBR model but the derivations are rather cumbersome and details are omitted here. For reasons of convenience, the notation in the subsequent subsection is entirely independent of the rest of the appendix but this need not cause any confusion. Effect of nonconstant regression coefficients in the TMB model. Suppose y' = pollutant concentration at the receptor at time t. z, = concentration of tracer at the receptor at time t. u'' = pollutant concentration at the receptor attributable to the source under study. z' _ pollutant concentration at the receptor attributable to other sources. Then We define so that Y' = wt + z`. me = ut`|~t y'= m`~+z~ t23) (24) (25) It may be desirable to account for the fact that the actual measurements of {y`), {x`) involve measurement errors. Suppose the observed quantities are {id), {X'] Allege Y' = y' + St Xt = =t + Et, (26) (S`), (E`) being the independent set of measurement errors with means equal to O and lanolin standard deviations equal to aS, aE, respectively. An estimate of the a`,erage contribution of the

92 · APPENDIX 2 pollutant by the source under study is given by _ NGS = ~x, (27) where l] is the slope of a structural regression line fit obtained by regressing (Y'3 on {X'}, while the estimated average fractional contribution, f, of the source to the receptor, for the duration of the study, is (28) We now investigate how the estimated average pollutant concentrations due to NGS can differ from the actual value for the time period in question. In the following discussion, a quantity such as ,6{y, x) will refer to the slope of the least squares line fitted to ((y`, z') I t = 1, ..., n), with {y`) as observations on a dependent variable and {Gil as observations on an independent variable. A quantity such as x will represent 1 Ad=' x' and ~2 will represent L ~~=~ ~2 ~ _ r )2. The true average contribution of the pollutant from NGS to the receptor site is u' = -God. The estimated average contribution is ,Bx, where l] is the slope of the regression line fitted to the data ((Y`,X`) I t _ 1,...,n). At first we will consider else situation when ,l3 is Else least squares estimate in which case we write pts. It is easily verified that = ELSE —W = (z Jr Eat, 2) ~ p{Z, A} + p{S, A} + {I, E} ~ A§{Z, E} + A'{S, E}) _ W 1 + 24{E, x} + ~ (29) where ~ = aE/a2 and /` is the difference between the estimated average NGS contribution and the true average FIGS contribution. It seems reasonable to assume that the quantities E, HIS, by, §{w, E), §(z, E), §{S, E), '{E, ~) (3o) are all nearly zero because we expect measurement errors Ed averaged over n time periods to be nearly zero and because we expect measurement errors to be uncorrelated with the true values x, z and w. To this degree of approximation, ~ ~ x{, a}—w + z'{z, x)—tow 1 + ~ (31) If,5 is the estimate obtained using structural regression (or Orthogonal Distance Regression (ODR)), denoted by pODR, ``e ~ ould obtain /\ ~ (I, x) - w) + Waltz, x} (32) since TODD ~ pr5(1 + a), where ~ is the difference between estimated and actual average NGS contribution during the time period under study.

EXCERPTS FROM NPS-WHITE:X 93 The quantity :z,l~{w, z} - ~ is zero if we/ is constant and will differ from zero if the least squares line fitted to the points {(u'`, at) I t = 1, ..., n} has a nonzero intercept. On the other hand, the quantity ,B{z,x) is zero or nonzero depending on whether the least squares line fitted to Else points {(z`,~) I t = 1,..., n}hasazeroslopeornot,i.e.,whetheror not zig end x' are "correlated". Ideally, if there was a constant background pollutant concentration zig _ z and if the tracer release was directly proportional to emissions, and emissions revere conservative, so that m'—ale, lee would have ~ ~ O and the reported estimated average NGS contribution should be a reliable estimate of the actual value for the time period in question. Model Inputs. The model requires the following quantities as inputs: · The ambient concentrations of the aerosol species being apportioned, which is S04 in our application. · The ambient concentrations of the reference or tracer species, CD4 and As. · Relative humidity at the receptor for each of the sampling periods, when jUk = RHk is used in the model rather than jUk = 1. · The uncertainties in else above quantities, when ODR is used to estimate the ~ coefficients, rather than OLS. Model Outputs. The model outputs include: · Estimates of the actual amount of the contribution and the fractional contribution of the aerosol species of interest by the source or source type of interest to the receptor, along with the associated uncertainty estimates. Estimates of the average amount and the average fractional amount of the aerosol species of interest contributed by each source or source type of interest along with the associated uncertainty estimates.