ARTICLE IN PRESS

1352-2310/$ - se

doi:10.1016/j.at

�Correspondfax: +1530 75

E-mail addr

Atmospheric Environment 41 (2007) 1119–1127

www.elsevier.com/locate/atmosenv

Comparison of multi-receptor and single-receptor trajectorysource apportionment (TSA) methods using artificial sources

Stephanie Lee�, Lowell Ashbaugh

Crocker Nuclear Laboratory, University of California, Davis, CA 95616, USA

Received 28 April 2006; received in revised form 9 August 2006; accepted 5 October 2006

Abstract

Two trajectory source apportionment methods were tested using an artificially generated data set to determine their

ability to detect the known sources. The residence time or conditional probability method developed by Ashbaugh et al.

[1985. A residence time probability analysis of sulfur concentrations at Grand Canyon National Park. Atmospheric

Environment 19(8), 1263–1270] uses a single receptor at a time, whereas the new multi-receptor (MURA) method

developed here uses several receptors at once in an attempt to detect the sources with higher accuracy. The methods were

first tested using a simulation with a single source and then with another simulation using four sources. The ability of the

methods to detect the sources was quantified for each simulation. The MURA trajectory method proved to be superior at

identifying sources for these simulations.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Residence time analysis; Trajectory analysis; HYSPLIT; Source apportionment

1. Introduction

The 1977 Clean Air Act included a national goalto protect visibility in sensitive areas. It required the‘‘prevention of any future, and the remedying of anyexisting, impairment of visibility in mandatoryClass I federal areas, which impairment resultsfrom man-made air pollution’’ (EPA, 2003). The actrequired the federal EPA to create regulations thatmake ‘‘reasonable progress’’ (EPA, 2003) towardmeeting this visibility goal.

The Clean Air Act Amendments of 1990 requiredthe EPA to make regional haze a higher priority.

e front matter r 2006 Elsevier Ltd. All rights reserved

mosenv.2006.10.019

ing author. Tel.: +1916 421 6482;

2 4107.

ess: sjhuber@ucdavis.edu (S. Lee).

Toward this end, the EPA established the GrandCanyon Visibility Transport Commission(GCVTC), which concluded its work in 1996 withtechnical analysis and strategies to improve visibi-lity in the Class I areas on the Colorado Plateau. Inthe early 1990s, a National Academy of SciencesCommittee reviewed the state of visibility scienceand found that the knowledge was adequate andcontrol techniques existed to justify regulatoryaction to improve and protect visibility. So in1999 the EPA issued the Regional Haze Rule. Therule uses a visibility metric based on measurementsof coarse mass, nitrate, sulfate, fine soil, andelemental and organic carbon.

The main purposes of the EPA regulations were:(1) to establish current visual air quality conditions,(2) to identify sources of man-made visibility

.

ARTICLE IN PRESSS. Lee, L. Ashbaugh / Atmospheric Environment 41 (2007) 1119–11271120

impairment, and (3) to document long-term spatialand temporal trends to track progress towardmeeting the long-term goal of no man-madevisibility impairment of protected areas. The EPAmonitors protected environments through the IM-PROVE network (Malm et al., 1994). This networkis responsible for tracking visibility at Class I federalareas. Data from 2000 to 2004 will be used tocalculate the baseline visual air quality which iscompared to natural conditions for each IM-PROVE site. The difference between the baselineand natural conditions then defines how muchimprovement must occur to meet the goal of noman-made visibility impairment in 50–60 yr.

An important aspect of meeting the goal of noman-made visibility impairment is to identify thesources that contribute to it. Trajectory sourceapportionment (TSA) methods have been usedextensively in this manner. Ashbaugh et al. (1985)was one of the first to use back trajectory analysis toidentify source regions. Using computed backtrajectories to indicate the history of air sampledat Grand Canyon National Park, they mapped thespatial distribution of aggregate upwind residencetime to identify the regions from which air masseswere most likely to arrive. They then used monitor-ing records to identify, by similar means, thoseregions from which high concentrations were mostlikely to arrive. By comparing these residence-timedistributions for the special (high concentration)and general cases, they identified statistical associa-tions between air masses passing over certainregions and their arriving with above-averageconcentrations. In particular, they calculated theconditional probability (CP) that air would arrivewith elevated concentrations given that it hadresided in an upwind map grid. A map of this CPdistribution could then be examined for clues to thelocation of emissions sources.

Much work has been done to improve on thisbasic method. Vasconcelos et al. (1996a, b) exam-ined the underlying statistics to determine theresolution of the residence time analysis describedby Ashbaugh et al. (1985). They found that themethod correctly identified the direction of thesource but offered less resolution radially Vascon-celos et al. (1996a, b). Potential source contributionfunction (PSCF), a method based on Ashbaughet al.’s (1985) CP, has been used extensively (Luiet al., 2003; Yli-Tuomi et al., 2003). Other methodshave been developed based on the principles ofAshbaugh et al.’s (1985) work such as quantitative

transport bias analysis (QTBA) (Keeler and Sam-son, 1989), concentration weighted field (CWT)(Seibert et al., 1994), and residence time weightedconcentration (RTWC) (Stohl, 1996). Hsu et al.(2003b) compared PSCF, CWT, and RTWC andfound that they identified similar source areas. Zhouet al. (2004) compared RTWC and a simplifiedQTBA and found that they gave similar results forlarge sources but were less consistent for smallerand multiple sources.

Many authors have suggested that using multiplereceptors in back trajectory analysis would increasethe resolution in source identification. Keeler andSamson (1989) suggested overlaying QTBA fieldsfor each receptor to illuminate patterns of transportof higher concentrations from source areas. Hsuet al. (2003a, b) suggested that using multiplereceptors would give more accurate results in sourceidentification. Stohl (1996) used multiple receptorsto redistribute concentrations along trajectories toidentify sources. Zeng and Hopke (1989) multipliedPSCF fields for two receptors to identify commonsources. This work focuses on developing a newmulti-receptor (MURA) TSA method using trajec-tories from multiple receptors simultaneously toidentify source regions. This new method will betested against Ashbaugh et al.’s (1985) original CPmethod using an artificially created concentrationfield. Because there are several variants of CPanalysis, the methods tested here will be referred toas the MURA TSA and the CP single-receptor CPTSA method.

2. Method

2.1. Data

Daily artificial SO2 concentrations were generatedfor the year 2002 using the Hybrid Single ParticleLagrangian Integrated Trajectories model version 4(HYSPLIT_4, 2003) with wind fields from theEDAS model (ARL, 2004). The model was usedin the puff mode and incorporates dry depositionbut does not account for wet deposition or chemicaltransformation. In the puff mode, puffs of pollutionreleased from a designated source expand until theyexceed the grid cell and then the puff splits intomultiple puffs each with their own share of thepollution (HYSPLIT_4, 2003). Multiple sourceswith unique emission rates and stack heights canbe designated. Plume rise is not taken into account.The concentration field, then, represents an inert

ARTICLE IN PRESS

Fig. 1. Simplified diagram of how several receptors can pinpoint

a source of pollution.

S. Lee, L. Ashbaugh / Atmospheric Environment 41 (2007) 1119–1127 1121

tracer with only dry deposition for removal. Thisselection was made deliberately to minimize theeffects of chemical transformations. Two simula-tions were run to test the robustness of eachmethod. Both simulations used actual source loca-tions and emissions (Airdata, 2005). Daily concen-trations were obtained at each IMPROVE site fromthe calculated concentration field, and the contribu-tion from each source was likewise obtained. Forsimulations with multiple sources, the concentra-tions at each site were assessed to determine howmuch was contributed by each source.

HYSPLIT_4 (2003) was then used to computeback trajectories from each IMPROVE site usingdata from the EDAS meteorological model (ARL,2004). Trajectory calculations differ from thedispersion calculation. In the trajectory mode themodel follows a single air parcel backwards in timewhereas the dispersion model simulates puffs thatexpand with time (HYSPLIT_4, 2003). Verticalmotion was used in the default mode. Trajectorieswere initiated every 6 h at a height of 10m and runbackwards 5 d. Though Gebhart et al. (2005)showed that trajectories starting higher above thesurface tend to be faster and may identify sourcesfarther away than those started at lower heights, the10m starting height was to ensure that thetrajectory started close to the sampler. IMPROVEconcentrations are typically measured at 2m aboveground. Trajectories that arrived at the receptorduring a measurement period at or above the 80thpercentile are designated high-incident trajectories.

The same meteorological files were used in boththe forward and backward direction to minimizebiases created by the wind fields and allow the twoTSA methods to be more fully evaluated. Somebiases remain because the calculations for theforward and backward modes are different.

2.2. TSA methods

2.2.1. Single-receptor CP TSA

CP is the statistical association between airmasses currently residing in certain regions andtheir arriving with a high concentration at thereceptor site (Ashbaugh et al., 1985). A high CPindicates a higher probability that that locationcontains a source. To calculate the single-receptorCP, computed back trajectories are divided into 1 hsegments for both the sample and high incidentdays. A grid (the size is determined by user) is laidover the area of interest and the number of

trajectory segment points located in each grid cellis then counted for both sample days and highincident days. Then the CP is calculated as

CP ¼ Aij=Bij,

where Aij, number of high incident trajectory (HIT)segments counted in the grid cell (i, j); Bij, totalnumber of trajectory segments counted in the gridcell.

In this work, the results are filtered for signifi-cance using a binomial test with a 99% confidencelevel. Those CP levels not significantly exceeding20% are set to zero. This value was chosen becausethe high incident days are defined as those days at orabove the 80th percentile, which in turn means thatwe are looking at those concentration values thatreside in the highest 20% of all measurements. Thisfilter eliminates those grid cells whose CP values arenot statistically significant.

Vasconcelos et al. (1996a, b) showed in a series ofpapers that the CP function worked well foridentifying the direction of the source. They showedthat the method was less accurate for determiningexactly where in that pathway the source wasactually located.

Fig. 1 shows a simple diagram that illustrates theconcept of using multiple receptors as we apply it.The stylized CP pattern for each receptor includesthe pollution source, but does not fully identify it.But if the right combination of receptors is used,their transport pathways cross (for different days)pinpointing the source area.

2.3. MURA TSA

The MURA method consists of two major parts.The first part identifies potential source regions thatcontribute to high concentrations at sampling sites

ARTICLE IN PRESSS. Lee, L. Ashbaugh / Atmospheric Environment 41 (2007) 1119–11271122

(receptors) at the 80th percentile or greater asexpected from the definition of high incident days.To make this identification we first count the totalnumber of trajectories that pass through each gridcell for all the receptors during the sampling period.We then count the number of HITs for all receptorsthat pass through each grid cell. These two numbersare used to perform the HITs calculation for eachgrid cell. This is done grid cell by grid cell bydividing the number of HITs by the total number oftrajectories for all receptors that pass through it.Note that high incident days may be different atdifferent receptors.

HITij ¼ Hij=Tij ,

where Hij, number of HITs passing through the gridcell; Tij, total number of trajectories passing throughthe grid cell; i, j, cell designation (coordinates).

The HIT parameter is filtered for significanceusing a binomial test with a 99% confidence level.This is the same filter used on the CP values in theCP method. The filter eliminates those grid cellswhose HIT values are not statistically significant.The remaining cells are contoured. These contouredareas are designated potential source regions, orPSRs. Contoured areas can be left as they are anddesignated as one PSR or may be divided intomultiple PSRs using saddle areas as boundaries. Wehave found that splitting the larger PSRs intomultiple PSRs may affect the accuracy and shouldbe explored on a case by case basis.

The second part of the MURAmethod identifies therelationship between each combination of PSR andreceptor by calculating the fraction of each receptor’strajectories that pass through each PSR for both thesample days and high incident days. For the sampledays the fraction of trajectories passing through PSR S

and terminating at receptor R is given by

Rrs ¼ Srs=Sr,

where Srs, number of sample day trajectories endingat receptor R that passed through PSR S; Sr, totalnumber of Sample day trajectories ending atreceptor R.

For high incident days, the fraction of trajectoriespassing through PSR S and terminating at receptorR is given by

R�rs ¼ Hrs=Hr,

where Hrs, number of HITs ending at receptor R

that passed through PSR S; Hr, total number ofHITs ending at receptor R; A high R�rs value

suggests that PSR S may be an important sourceto receptor R.

The MURA method and the CP method aresimilar, but the MURA method counts trajectoriesfor each grid cell where the CP method countstrajectory segments. Because each trajectory repre-sents a fixed time at the receptor, the MURAmethod reflects the fraction of time that air massesfrom upwind sources spend at the receptor. Thischoice may affect the accuracy of identifyingsources because trajectories are known to havebiases (Gebhart et al., 2005). This topic will beexplored in future work. The second difference isthat the MURA method assigns PSRs to thecontoured HIT areas and then determines whichPSRs are more important to each receptor. This caneliminate areas that do not actually contain sourcesthat affect the receptors.

3. Results

The geographical area of the continental UnitedStates, southern Canada, and Northern Mexico wasdivided into 0.5� 0.51 grid cells. Only trajectoriesresiding in the area bounded by 15–601N latitudeand 30–1501W longitude were considered in thisanalysis.

Gebhart et al. (2005) tested back trajectoriesusing different models and meteorological inputdata files. They found that there were directionalbiases depending on the model and meteorologicalinput data but that no model/meteorologicaldata file combination was superior to the others(Gebhart et al., 2005). Because of theseknown biases, each method was tested with andwithout a one-degree buffer around their HIT or CPvalues.

3.1. Simulation one

The first simulation tested both methods todetermine how effectively they can locate a singlesource. The Mohave power plant’s location andemissions (Airdata, 2005) were used to create theartificial SO2 concentration field for 2002. SixIMPROVE receptors (Table 1) were chosen foranalysis based on the highest average 80th percentilevalues in the artificial data. Each method wasapplied to the six IMPROVE sampling receptors.Trajectories associated with high SO2 concentra-tions were identified for each receptor and the HITand CP parameters were calculated. The HIT value

ARTICLE IN PRESS

Table 1

Receptors used in simulation one

Site code Location Latitude Longitude

BRCA Bryce Canyon, UT 37.6184 �112.1736

CANY Canyonlands, UT 38.45873 �109.8209

GRBA Great Basin, NV 39.00518 �114.2161

GRCA Grand Canyon, AR 35.97311 �111.9841

PEFO Petrified Forest, AR 35.078 �109.7683

SAGO San Gorgonio, CA 34.1924 �116.9013

Fig. 2. Contoured HIT values, PSRs, source, and receptor

locations for simulation one. The source resides in PSR 3.

Table 2

Rrs and R�rs values for simulation one

PSR BRCA CANY GRBA GRCA PEFO SAGO

Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs

1 0.05 0.05 0.39 0.77 0 0 0.14 0.10 0.28 0.21 0 0

2 0.03 0.03 0.14 0.24 0 0 0.11 0.07 0.54 0.76 0.03 0.03

3 0.59 0.94 0.34 0.63 0.57 0.95 0.86 1.00 0.42 0.60 0.50 0.79

4 0.22 0.51 0.19 0.32 0.11 0.35 0.32 0.45 0.38 0.55 0.10 0.12

Bold face indicates ‘‘primary’’ PSRs for each receptor.

S. Lee, L. Ashbaugh / Atmospheric Environment 41 (2007) 1119–1127 1123

was calculated for all receptors simultaneouslyusing trajectories while the CP was calculated foreach receptor individually using 1 h trajectorysegments. At this point the methods diverged. Inthe CP analysis, no further calculations wereperformed. The CP values were mapped andexamined for peaks that might indicate sourceareas. For the MURA TSA method contouredHIT values were designated as PSRs and these PSRswere used in the Rrs and R�rs calculations todetermine how often receptor trajectories traverseeach PSR for each receptor on high incident daysand throughout the year. This indicates howimportant each PSR is to each receptor.

Fig. 2 shows four PSRs identified by the multi-receptor TSA method that had at least 10% of somereceptor’s HITs passing through them. Since PSRsare created from all the receptors, some receptorswill have more HITs passing through an individualPSR than another receptor and some PSRs willhave only a only a few trajectories traversing it fromeach receptor, allowing it to pass the binomial test.Thus we found empirically that if a PSR did nothave at least 10% of at least one receptor’s HITstraversing it, it was unimportant in terms of locatingsources. Other areas are also shown in Fig. 2 that donot meet the 10% threshold for contributing highincident trajectories to receptors.

We designated these four PSRs as ‘‘primary’’PSRs (see Table 2), i.e. they have the majority of areceptor’s high incident trajectories passing throughthem. A ‘‘saddle’’ in the contoured HIT distributionseparates PSRs 3 and 4, with the source residing inPSR 3. With or without the one-degree buffer, allsix receptors identified the source using this method.

Fig. 3 shows the CP distribution for BRCA(Fig. 3a) and PEFO (Fig. 3b) using the single-receptor CP TSA method. With the one-degreebuffer, the CP method identified the source for all

six receptors, but just missed it for PEFO withoutthe buffer. The source lies just outside the CP areafor PEFO.

In this first test, both methods identified thesource when the one-degree buffer was included.Without the buffer, the MURA method performedslightly better. It identified the source for allreceptors whereas the CP method missed the sourcefor PEFO.

3.2. Simulation two

In simulation two, four sources were used tocreate an artificial SO2 concentration field for 2002based on the location and SO2 emission values(Airdata, 2005) of Mohave, Navajo GeneratingStation, Phelps Dodge Copper Smelter, and CarbonI and II power plants. The amount of SO2 at each

ARTICLE IN PRESS

Fig. 3. Single-receptor CP maps for simulation one for: (a) BRCA and (b) PEFO. The CP distribution for BRCA identifies the source

without the buffer, but the distribution for PEFO identifies it only with the buffer.

Table 3

Receptors used in simulation two

Site code Location Latitude Longitude

BADL Badlands, SD 43.7435 �101.9412

BAND Bandelier, NM 35.77967 �106.2664

BIBE Big bend, TX 29.30277 �103.178

BRCA Bryce Canyon, UT 37.6184 �112.1736

CANY Canyonlands, UT 38.45873 �109.8209

CHIR Chiricahua, AZ 32.00893 �109.3891

GICL Gila, NM 33.22043 �108.2351

GRBA Great Basin, NV 39.00518 �114.2161

GRCA Grand Canyon, AR 35.97311 �111.9841

GRSA Great Sand Dunes, CO 37.72491 �105.5186

GUMO Guadalupe Mountains, TX 31.83301 �104.8094

MEVE Mesa Verde, CO 37.19842 �108.4907

MOZI Mount Zirkel, CO 40.53833 �106.6765

PEFO Petrified Forest, AZ 35.078 �109.7683

ROMO Rocky Mountain, CO 40.27833 �105.5457

SAGO San Gorgonio, CA 34.1924 �116.9013

TONT Tonto, AZ 33.64935 �111.1088

UPBU Upper Buffalo, AR 35.82587 �93.20291

WEMI Weminuche, CO 37.65935 �107.7998

S. Lee, L. Ashbaugh / Atmospheric Environment 41 (2007) 1119–11271124

receptor was tracked separately for each source forcomparison to the TSA methods.

Table 3 shows the 19 IMPROVE samplingreceptors selected for analysis based on the highestaverage 80th percentile values. Table 4 shows theeight PSRs identified by the multi-receptor methodthat ‘‘might be important’’ and the five PSRsidentified as ‘‘primary’’ (in bold type). They arealso shown in Fig. 4, where PSR 1 contains Mohaveand Navajo, PSR 2 contains Phelps Dodge CopperSmelter, and Carbon I and II resides 11 to thesouthwest of PSR 4. The remaining five PSRs donot contain sources. GRCA and MEVE are locatedwithin PSR 1. Because all trajectories pass throughthe cells that contain the receptors, the grid cellscontaining these receptors were not used in theanalysis of this simulation.

The artificially generated concentration field wasexamined to identify the impact of each source oneach receptor. Table 5 lists the modeled contribu-tion of each source to each receptor. The perfor-mance of each TSA method was evaluated bycomparing its results to this concentration assess-ment for each receptor. Each method was consid-ered successful if it identified all the sourcescontributing 10% or more to each receptor’s totalconcentration for 2002 in the correct order. Sinceboth methods infrequently pick up the smallersources (o10%), neither method was penalized ifit failed to identify these small sources. The MURA

method correctly identifies the sources for 16 of the19 receptors. The CP method correctly identifies thesources for three receptors.

3.2.1. MURA TSA method

In the MURA method the one-degree buffermakes a slight difference in the accuracy. With the

ARTICLE IN PRESS

Table 4

Rrs and R*rs values for simulation two

PSR BADL BAND BIBE BRCA CANY CHIR GICL

Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs

1 0.04 0.13 0.69 0.75 0.15 0.05 0.75 0.97 0.56 0.95 0.51 0.41 0.62 0.51

2 0 0.01 0.19 0.21 0.03 0.03 0.02 0.01 0.07 0.09 0.35 0.51 0.40 0.34

3 0.08 0.26 0.04 0.03 0.12 0.15 0 0 0.01 0.01 0.03 0.06 0.03 0.04

4 0.06 0.22 0.16 0.28 0.81 0.91 0 0.01 0.01 0.01 0.13 0.36 0.16 0.42

5 0.01 0.03 0.03 0.11 0.20 0.25 0 0 0 0 0.01 0.02 0.02 0.07

6 0.01 0.02 0.01 0.04 0.11 0.14 0 0 0 0 0 0.01 0.01 0.02

7 0.01 0.02 0 0 0.02 0.08 0 0 0 0 0 0.02 0 0.01

8 0.05 0.16 0.01 0 0.02 0.05 0 0 0 0 0 0.01 0 0

PSR GRBA GRCA GRSA GUMO MEVE MOZI PEFO

Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs

1 0.58 0.98 1 1 0.71 0.93 0.28 0.11 0.98 1 0.37 0.75 0.88 0.98

2 0.01 0.03 0.06 0.08 0.14 0.10 0.09 0.06 0.16 0.25 0.03 0.05 0.28 0.11

3 0 0 0 0 0.04 0.01 0.16 0.31 0.02 0 0.02 0 0 0

4 0 0.01 0.01 0.01 0.08 0.14 0.57 0.90 0.02 0.01 0.02 0.01 0.04 0.05

5 0 0 0 0 0.02 0.05 0.08 0.24 0 0 0 0 0.01 0.02

6 0 0 0 0 0.01 0.04 0.06 0.14 0 0 0 0 0 0

7 0 0 0 0 0 0 0.02 0.05 0 0 0 0 0 0

8 0 0 0 0 0 0 0.04 0.08 0 0 0 0 0 0

PSR ROMO SAGO TONT UPBU WEMI

Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs Rrs R�rs

1 0.35 0.64 0.48 0.79 0.85 0.79 0.02 0.05 0.79 0.99

2 0.02 0.02 0.01 0.04 0.54 0.88 0 0.01 0.14 0.18

3 0.05 0.04 0 0 0 0 0.16 0.28 0.03 0

4 0.04 0.02 0 0 0.03 0.01 0.12 0.26 0.04 0.02

5 0.01 0 0 0 0.01 0 0.05 0.08 0 0

6 0 0 0 0 0 0 0.03 0.01 0 0

7 0 0 0 0 0 0 0.11 0.16 0 0

8 0.01 0 0 0 0 0 0.24 0.33 0 0

Bold face indicates ‘‘primary’’ PSRs for each receptor.

S. Lee, L. Ashbaugh / Atmospheric Environment 41 (2007) 1119–1127 1125

buffer this method correctly identifies the sourcesfor 16 of the 19 receptors. Without the buffer, thenumber of correct identifications drops to 13because the Carbon power plants are offset fromPSR 4 by 11 to the southwest. Thus three receptors,BIBE, GICL, and GUMO, do not identify theCarbon power plants without the buffer. TONTpresents an interesting case and illustrates a short-coming of this method. The majority of TONT’shigh incident trajectories pass through PSR 1 (79%)and PSR 2 (88%). Taking these numbers literally,both PSRs are equally important to TONT.However, the concentration assessment says PSR2, which contains Phelps Dodge, contributes to 97%

of TONT’s artificial high SO2 concentrations. ThusPSR 2 should be the dominant PSR. This methoddoes not detect that the majority of TONT’s highincident trajectories that pass through PSR 2 alsopass through PSR 1. Thus, PSR 1 is detected as a‘‘false positive’’. A decomposition of PSR andtrajectories might be able to unravel this relation-ship.

3.2.2. Single-receptor CP TSA method

The CP method performs equally well with andwithout the buffer. Fig. 5 shows the CP distribu-tions for CANY (Fig. 5a) and BAND (Fig. 5b). Thedistribution for CANY identifies the sources with or

ARTICLE IN PRESS

Fig. 4. Contoured HIT values, PSR, source, and receptor

locations for simulation two. PSR 1 contains Mohave and

Navajo Power Plants, PSR 2 contains the Phelps Dodge copper

smelter, and the Carbon I and II power plants lie 11 to the

southwest of PSR 4.

Table 5

Percent contribution of each source to each receptor for

simulation two

Site Percent contribution

Mohave Navajo Phelps dodge Carbon I and II

BADL 22 48 0 30

BAND 15 72 4 9

BIBE 0 1 0 99

BRCA 27 71 0 2

CANY 17 81 0 2

CHIR 13 24 19 44

GICL 14 50 5 31

GRBA 84 14 0 2

GRCA 8 92 0 0

GRSA 14 76 2 8

GUMO 6 12 3 79

MEVE 10 85 1 4

MOZI 26 66 1 7

PEFO 10 87 1 2

ROMO 23 66 2 9

SAGO 77 18 1 4

TONT 0 3 97 0

UPBU 6 13 2 79

WEMI 13 78 1 8

S. Lee, L. Ashbaugh / Atmospheric Environment 41 (2007) 1119–11271126

without the buffer, but the distribution for BANDdoes not identify the sources even with the buffer. Ineither case the method correctly identified thesources for three of the 19 receptors. In the effortto quantify how well this method identified theartificial sources as compared to the MURA

method, individual CP values were used to rankthe sources in order of importance. This is not donein general as Vasconcelos et al. (1996b) showed thatthis method could identify the direction of thesource but not where on that pathway the sourceexisted. However, the underlying principles of thismethod state that a higher CP value indicates ahigher probability that a trajectory passing throughthat grid cell will arrive at the receptor with a highconcentration (Ashbaugh et al., 1985). And thus, itcan be inferred that a higher CP value wouldindicate a more important source.

4. Conclusions

The first simulation showed that both methodswere able to identify the single source when the one-degree buffer is included. However, without thatbuffer, the single-receptor method failed to identifythe source for one receptor, PEFO.

Simulation two reinforced the improved perfor-mance of the MURA method over the single-receptor method. The MURA method identifiedthe correct sources for 16 of the 19 receptors withthe buffer. The method identified all the sourcesfor one additional receptor, CHIR, but in thewrong order. Without the buffer, the multi-receptor method identified sources correctly for 13of the 19 receptors. However, these resultswere both improvements on the single-receptormethod, which identified sources correctly for onlythree of the 19 receptors with and without thebuffer.

There are several differences between these twomethods that could account for the improvedperformance of the MURA TSA method over thesingle-receptor CP TSA method. The most obviousis that the MURA method uses two or morereceptors simultaneously. Perhaps more impor-tantly, though, the MURA method designates PSRsand then examines them to see how often each PSRaffects each receptor. This additional step also onlyuses trajectories, not trajectory segments. Identify-ing PSRs and then examining them to see how oftenthey affect the receptor could be applied to thesingle-receptor CP TSA method, and might increasethe resolution and accuracy of this method so that itapproaches or exceeds the accuracy and resolutionof the MURA method. Additional research isplanned to apply this additional step to the single-receptor CP TSA method.

ARTICLE IN PRESS

Fig. 5. Single-receptor CP maps for simulation two for: (a) CANY and (b) BAND. The distribution for CANY identifies the sources

without the buffer, but the distribution for BAND does not identify the sources even with the buffer.

S. Lee, L. Ashbaugh / Atmospheric Environment 41 (2007) 1119–1127 1127

Acknowledgements

The authors would like to thank Mark Green atthe Desert Research Institute for his invaluable helpin getting HYSPLIT_4 up and running. This workwas supported by the Crocker Nuclear Laboratory.

References

AirData: Access to Air Pollution Data, /http://www.epa.gov/

air/data/S (accessed June 2005).

ARL Gridded Meteorological Data Archives, /http://www.arl.

noaa.gov/ss/transport/archives.htmlS (accessed 2004).

Ashbaugh, L.L., Malm, W.C., Sadeh, W.Z., 1985. A residence

time probability analysis of sulfur concentrations at Grand

Canyon National Park. Atmospheric Environment 19 (8),

1263–1270.

EPA Clean Air Act, /http://www.epa.gov/oar/oaq_caa.htmlS(accessed 2003).

Gebhart, K.A., Schichtel, B.A., Barna, M.G., 2005. Directional biases

in back trajectories caused by model and input data. Journal of

Air and Waste Management Association 55, 1649–1662.

Hsu, Y., Holsen, T., Hopke, P.K., 2003a. Locating and quantify-

ing sources in chicago: receptor modeling and field sampling.

Environmental Science and Technology 37 (4), 681–690.

Hsu, Y., Holsen, T., Hopke, P.K., 2003b. Comparison of hybrid

receptor models to locate PCB source in Chicago. Atmo-

spheric Environment 37, 545–562.

HYSPLIT_4, 2003. NOAA ARL HYSPLIT Model, /http://

www.arl.noaa.gov/ready/hysplit4.htmlS (accessed 2003).

Keeler, G.J., Samson, P.J., 1989. Spatial representativeness of

trace element ratios. Environmental Science and Technology

23 (11), 1358–1364.

Lui, W., Hopke, P.K., Han, Y., Yi, S., Holsen, T.M., Cybart, S.,

Kozlowski, K., Milligan, M., 2003. Application of receptor

modeling to atmospheric constituents at Potsdam and Stock-

ton, NY. Atmospheric Environment 37, 4997–5007.

Malm, W.C., Sisler, J.F., Huffman, D., Eldred, R.A., Cahill,

T.A., 1994. Spatial and seasonal trends in particle concentra-

tion and optical extinction in the United States. Journal of

Geophysical Research 99 (D1), 1347–1370.

Seibert, P., Kromp-Kolb, H., Baltensperger, U., Jost, D.T.,

Schwikowski, M., Kasper, A., Puxbaum, H., 1994.

Trajectory analysis of aerosol measurements at high alpine

sites. In: Borrell, P.M., Borrell, P., Cvitas, T., Seiler, W.

(Eds.), Transport and Transformation of Pollutants in

the Troposphere. Academic Publishing, Den Haag,

pp. 689–693.

Stohl, A., 1996. Trajectory statistics—a new method to establish

source-receptor relationships of air pollutants and its

application to the transport of particulate sulfate if Europe.

Atmospheric Environment 30 (4), 579–587.

Vasconcelos, L.A.d., Kahl, J.D.W., Liu, D., Marcias, E.S.,

White, W.H., 1996a. Spatial resolution of a transport

inversion technique. Journal of Geophysical Research 101

(D14), 19337–19342.

Vasconcelos, L.A.d., Kahl, J.D.W., Liu, D., Marcias, E.S.,

White, W.H., 1996b. A tracer calibration of back trajectory

analysis at the Grand Canyon. Journal of Geophysical

Research 101 (D14), 19329–19335.

Yli-Tuomi, T., Hopke, P.K., Paatero, P., Basunia, M.S., Land-

sberger, S., Viisanen, Y., Paatero, J., 2003. Atmospheric

aerosol over finnish artic: source analysis by the multilinear

engine and the potential source contribution function.

Atmospheric Environment 37, 4381–4392.

Zeng, Y., Hopke, P.P., 1989. A study of the sources of acid

precipitation in Ontario, Canada. Atmospheric Environment

23, 1499–1509.

Zhou, L., Hopke, P.K., Liu, W., 2004. Comparison of two

trajectory bases models for locating particle sources for two

rural New York Sites. Atmospheric Environment 38,

1955–1963.