Prediction of Soil Lead Recontamination Trends with Decreasing Atmospheric Deposition

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  • This article was downloaded by: [Northeastern University]On: 01 November 2014, At: 20:26Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

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    Prediction of Soil Lead RecontaminationTrends with Decreasing AtmosphericDepositionTeresa Bowers a , Peter Drivas a & Rosemary Mattuck aa Gradient , Cambridge , Massachusetts , USAAccepted author version posted online: 18 Nov 2013.Publishedonline: 05 Feb 2014.

    To cite this article: Teresa Bowers , Peter Drivas & Rosemary Mattuck (2014) Prediction of Soil LeadRecontamination Trends with Decreasing Atmospheric Deposition, Soil and Sediment Contamination:An International Journal, 23:6, 691-702, DOI: 10.1080/15320383.2013.857294

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  • Soil and Sediment Contamination, 23:691702, 2014Copyright 2013 GradientISSN: 1532-0383 print / 1549-7887 onlineDOI: 10.1080/15320383.2013.857294

    Prediction of Soil Lead Recontamination Trendswith Decreasing Atmospheric Deposition

    TERESA BOWERS, PETER DRIVAS,AND ROSEMARY MATTUCK

    Gradient, Cambridge, Massachusetts, USA

    The mathematical model of soil mixing after atmospheric surface deposition developedin Drivas et al. (2011) is expanded here and applied to a case study of soil recontamina-tion in areas near a lead smelter in Herculaneum, Missouri. Soil lead samples collectedfrom the yards of several residences in Herculaneum between 2001 and 2009 show thatrecontamination of previously remediated yards has taken place. The model is used topredict a relative soil lead recontamination trend with time, based on the remediationdate and decreasing smelter emissions over time. An average scaling factor betweenrelative and absolute soil lead levels is derived based on over 1600 data points from 24properties, using modeled air lead levels and the remediation date for each property. Thescaling factor was used to predict soil lead recontamination trends at an additional sixproperties that were remediated in the mid-1990s. The predicted soil lead concentrationvs. time curves match the time-trends in the soil data, explaining the observations thatsoil lead levels increased during the 2000s for properties remediated in 20012002, butdecreased during the same time frame for properties remediated in the 1990s. The modelcan be used to predict expected recontamination trends under differing air depositionscenarios and to extrapolate expected recontamination trends into the future.

    Keywords Atmospheric deposition, mathematical modeling, soil contamination, lead

    Introduction

    This analysis provides an evaluation of soil lead recontamination data collected in Hercu-laneum, Missouri, using the soil mixing mathematical model developed by Drivas et al.(2011), which describes the time behavior of the soil mixing of a chemical after atmosphericdeposition onto the soil surface. The aim of the analysis presented here is to determine theextent to which the model proposed by Drivas et al. (2011) can explain observed soillead recontamination trends. The Drivas et al. (2011) model makes use of an effectivediffusion coefficient to model the combined physical, chemical, and biological processesthat result in downward mixing of atmospherically deposited chemicals in the soil column.The effective diffusion coefficient is derived from fitting the model to observations of thedistribution of lead, cesium, and dioxins in soil from several locations (Fernandez et al.,2008; Doering et al., 2006; Rosen et al., 1999; VandenBygaart et al., 1999; He and Walling,1997; Brzuzy and Hites, 1995). The model developed by Drivas et al. (2011) can be used to

    Address correspondence to Teresa Bowers, Gradient, 20 University Road, Cambridge, MA02138, USA. E-mail: tbowers@gradientcorp.com

    Color versions of one or more of the figures in the article can be found online atwww.tandfonline.com/bssc.

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    estimate chemical concentrations in soil as a result of either a one-time (i.e., instantaneous)or continuous deposition to the surface soil. The model also can be used to estimate theeffect of changing, in this case decreasing, deposition to the surface soil resulting from theserial implementation of various air pollution controls.

    In Herculaneum, air lead emissions from the lead smelter result in deposition of leadfrom the air onto soil over time, resulting in a build-up of lead in soil and a measureableincrease in surface soil lead concentrations. A significant data collection effort took placebetween 2001 and 2009 for a group of properties that were remediated in 2001 or early2002. In addition, some properties that were remediated in the mid-1990s were sampledsubsequently, allowing an examination of recontamination trends from the mid-1990s tothe present. Several emission controls at the lead smelter were implemented over this timeperiod. As a result, total lead emissions from the facility have varied over this time period,and have generally decreased, with corresponding decreases in local air lead levels. Themodel presented in Drivas et al. (2011) is combined with information on lead emissionsfrom the smelter and air lead levels in the community in order to estimate, based on thelocation of each property relative to the facility, the expected trend of surface soil leadconcentrations. These expected trends are compared to measured soil lead data.

    Model Development

    Drivas et al. (2011) presented mathematical equations to calculate the impact on concen-trations in soil of either an instantaneous or continuous source of atmospheric surfacedeposition of a contaminant. Here we expand this analysis to calculate the impact of a con-tinuous source that, through the implementation of air pollution controls at specific points intime, results in decreasing emissions and surface deposition with time. The equations usedhere are a superposition of Eqs. (9) and (13) in Drivas et al. (2011) for the depth-averagedsoil concentration from continuous deposition and the depth-averaged soil concentrationafter a finite period of continuous deposition, respectively. These two equations for a soilconcentration averaged from the surface to a depth L are:

    Cs,ave = Q tL

    [(L

    Deff t

    )exp

    ( L24Deff t

    )+ erf

    (L

    2Deff t

    )

    (

    L2

    2Deff t

    )erf c

    (L

    2Deff t

    )](1)

    Cs, ave(t) = Q L2 Deff

    [exp

    (s2L) sL +

    (1 + 1

    2 s2L

    ) erf (sL)

    exp(s2U ) sU

    (1 + 1

    2 s2U

    ) erf (sU )

    ](2)

    with sL = L2Deff t

    and sU = L2Deff (t T )

    ,

    where:

    Cs,ave = average soil concentration (g/cm3)Q = continuous surface deposition rate per unit area (g/yr-cm2)Deff = effective diffusion coefficient (cm2/yr)

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  • Soil Lead Recontamination Trends 693

    L = soil depth (cm)t = time (yr)T = deposition time (yr).

    Eq. (1) applies for t T (i.e., during the deposition period), and Eq. (2) applies for t >T (i.e., after the deposition has ended). These equations can be superimposed to represent acase of decreasing atmospheric deposition with time. For example, lets consider the case ofan initial atmospheric deposition rate (Q0) from an industrial source that decreases by 50%after a given time period T0 (i.e., Q = 1/2 Q0 for times t > T0). For the initial time periodt T0, the continuous deposition solution in Eq. (1) applies with Q = Q0. For the timeperiod t > T0, continuous deposition is reduced by half and modeled with Q = 1/2 Q0 in Eq.(1). Eq. (2) with Q = 1/2 Q0 models the impact of the half of the previous deposition thatis no longer occurring after T0. The soil concentrations in Eqs. (1) and (2) are summed torepresent the changes in average soil concentration due to the reduced atmospheric surfacedeposition after t > T0. Similar superposition of the solutions in Eqs. (1) and (2) can beused to model several sequential emission decreases over specified time periods.

    Summary of Available Data

    Soil Lead Concentrations

    Soil lead concentrations are significantly elevated in this community. Many properties havebeen remediated since the early 1990s, some of them more than once. Other propertieswere acquired by the facility and have not been remediated. In addition, some owners haverefused remediation. As a result, there is no uniform soil lead pattern consistent with adeposition source at this time in the community. The amount of data available varies byproperty, as some properties have been sampled numerous times and others only rarely ornot at all. This disparity largely reflects the willingness of property owners to be includedin various sampling plans.

    In this analysis, we examine 24 properties from which the U.S. EPA collected soilsamples for lead analysis on a monthly or quarterly basis beginning in 2001. This data set isreferred to below as the EPA soil lead data set. These properties were all remediated in the20012002 time frame by excavation of 12 to 24 inches of soil and replacement with cleansoil. The original goal of this sampling was to assess the extent to which recontaminationoccurred. A single composite soil sample was collected from each of four quadrants (frontright and left, back right and left) of each property included in the study. Analyses wereconducted in the field with a portable X-ray fluorescence machine (XRF). Concentrationsabove approximately 20 mg/kg were detectable. Samples were collected from a shallowdepth of approximately 0.6 cm. Not all properties have a complete data set, as someproperties were added or dropped during the course of the EPA study.

    Initial sample results shortly after property remediation indicated soil lead concen-trations approximating natural background levels, in the range of 25 to 100 mg/kg. Withtime, soil lead concentrations on most properties increased, although there was significantvariability in the concentrations, likely reflecting both soil heterogeneity and analyticaluncertainty. Concentrations on some properties increased to levels above the EPAs currentsoil lead screening level of 400 mg/kg. In addition to the EPA soil lead data set, we also ex-amined data from a few properties in Herculaneum that were remediated in the mid-1990s,have not been remediated since, and have multiple soil lead sampling events. Soil samplesfrom these properties were taken from the surface to a depth of 2.5 cm. Samples were

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    Figure 1. Map showing locations of emissions source, properties, air monitors, and air dispersionmodel lead concentration isopleths. Properties remediated in the mid-1990s include C, D, K, L, Mand T. All other properties (the EPA soil lead data set) were remediated in 2001 or 2002.

    composites of either the front or back yard in the 1990s, while samples taken after 2000followed the quadrant sampling scheme described above. All analyses were by portableXRF. Soil lead concentrations on these properties, although initially low after remediation,increased in several cases to above 1000 mg/kg by several years after the remediation. Thelocations of properties in both data sets are shown in Figure 1. All soil lead data wereprovided by personal communication from the Doe Run Company.

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  • Soil Lead Recontamination Trends 695

    Table 1Annual lead emissions (data provided by personal com-

    munication from the Doe Run Company)

    Year Annual Lead Emissions (tons)

    1997 101.31998 96.21999 139.52000 139.82001 113.52002 58.82003 25.12004 26.02005 28.12006 28.42007 21.32008 19.3

    Air Lead Emissions

    The lead smelter has been a source of lead emissions and lead deposition to soils inHerculaneum throughout its operational history. However, emissions have varied withtime. Table 1 gives annual lead emissions reported by the Doe Run Company in AnnualEmissions Inventory Questionnaires filed with the U.S. EPA (data provided by personalcommunication from the Doe Run Company). Decreases in emissions shown in this tableare largely a result of the implementation of various emission controls that reduced bothstack and fugitive emissions, primarily implemented between mid-2001 to mid-2002 andtowards the end of 2008. Table 1 shows that annual lead emissions decreased from 2001 to2002, and decreased again in 2003, after which they remained relatively constant through2008.

    Air Lead Concentrations

    Air lead concentrations are measured at several high-volume TSP (total suspended par-ticulate) monitors in the community. Concentrations were always above detection limits.Table 2 summarizes annual air lead levels measured at four monitors near the facility.Annual air lead levels at these monitors showed a decrease between 2001 and 2002, andthen stayed relatively constant until apparently decreasing further in early 2009. The loca-tions of the monitors are shown in Figure 1. All air lead data were provided by personalcommunication from the Doe Run Company.

    Model Application

    Model Parameterization

    A model of soil lead concentration as a function of soil depth and time was constructedbased on the information given in Tables 1 and 2 to describe deposition rates to the surfacesoil. The model uses the relative lead deposition timeline shown in Figure 2, which assumes

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    Table 2Annual average air lead concentration (g/m3) (data provided by personal communication

    from the Doe Run Company)

    Year Broad Street High School Bluff City Hall

    2000 4.70 1.17 0.842001 5.36 1.87 1.402002 1.26 0.38 0.542003 1.30 0.35 0.462004 1.41 0.48 0.66 1.242005 1.62 0.23 0.26 0.902006 1.57 0.32 0.37 1.392007 1.52 0.35 0.48 1.162008 1.57 0.32 0.35 1.162009 0.82 0.26 0.22 0.85

    a constant initial value of lead deposition for 2001 through July 2002, decreasing to 50% ofthe initial value for the time frame from August 2002 through 2008, and decreasing to 25%of the initial value in 2009. This timeline relies primarily on the air lead levels rather thanthe emissions estimates as a proxy of air lead deposition rates as a function of time. Forexample, Table 2 shows that the average air lead level in 20022008 at the Bluff St. monitordecreased by approximately 40% compared to the average air lead level in 20002001,

    Figure 2. Modeled relative deposition of lead as a function of time. Decreases in deposition corre-spond to time period where additional air pollution controls were implemented.

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  • Soil Lead Recontamination Trends 697

    while the average air lead level in 2009 decreased by approximately 50% compared to theaverage air lead level in 20022008.

    Review of air lead levels at the monitor closest to the facility through the 1990s suggeststhat air lead levels in the area very close to the facility decreased more dramatically as a resultof implementation of the 2002 emission controls. This may relate to the variable impactwith distance from the facility of controls aimed at reducing stack emissions vs. fugitivesources. For example, the Broad St. monitor showed yearly average air lead concentrationsof 4.7 and 5.4 g/m3 in 2000 and 2001, vs. 1.3 g/m3 in 2002 and 2003, a decrease byapproximately a factor of 4. The properties that were remediated in the early 1990s fallwithin the area closest to the facility. Therefore, a second lead deposition timeline was usedfor these nearby properties, where the deposition of lead was assumed constant from theearly 1990s through July 2002, decreasing to 25% of the initial value in mid-2002, and to12.5% of the initial value in 2009.

    The model requires an effective diffusion coefficient, which was set at 0.75 cm2/yrbased on the analysis presented in Drivas et al. (2011). Drivas et al. (2011) showed thatthe diffusion coefficient varied from 0.5 to 2.0 cm2/yr for a range of soil conditions. Weselected 0.75 cm2/yr for this analysis from the lower end of the range in recognition ofthe largely clay soils in Missouri and from a desire to not overestimate the movement oflead to depth. No soil samples were collected at depth from the properties evaluated here,thus we only model soil lead concentrations in the surface intervals that correspond to thesample depths. The lack of data at lower depth intervals limits our ability to evaluate bestestimates of the effective diffusion coefficient, and yet it remains necessary to model themixing of chemicals with depth because the movement of chemicals to depth impacts theconcentrations remaining in the surface interval. Further, use of the Drivas et al. (2011)model allows us to estimate appropriate depth-averaged concentrations for the two differentsurface intervals for which samples were collected.

    The model predicts the average relative soil lead concentration in the top 2.5 cm ortop 0.6 cm as a function of time for property remediation dates in 1992 and 2001/2002.Figure 3 shows the predicted relative soil lead concentration trend for a 2.5 cm averageconcentration for a property remediated in 1992, and a 0.6 cm average concentrationfor a property remediated in 2002. The vertical axis corresponds to relative soil leadconcentration, and the curves indicate the expected shape of the recontamination trendin surface soils without regard to the absolute concentrations.

    The inflection points in the curves in Figure 3 correspond to the times that depositionis modeled to decrease; i.e., the time of implementation of additional air pollution controls.The predicted curvature of the trends between inflection points reflects the fact that therecontamination rate is not constant with time, even during periods when emissions (andthus deposition) are assumed constant. This is because mixing in the soil column is occurringdue to physical, chemical, and biological processes, removing lead from the surface andtransferring it to depth. Note that once emissions decrease sufficiently, soil that is highlycontaminated begins to recover; that is, the lead concentrations at the surface begin todecrease. This is because deposition adds less lead to the surface than mixing in the soilcolumn removes from the surface.

    Scaling of Model Results to Property Data

    The depth-averaged relative soil lead concentrations shown in Figure 3 show the relativetrend expected for the change in concentrations with time in the community, but the modeldoes not predict absolute concentration levels. Therefore, we develop a scaling approach in

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    Figure 3. Modeled depth-averaged relative soil lead concentrations as a function of time. The curvesdescribe two scenarios: 1) a property remediated in 1992 that is very near the facility, with reductionsin deposition by a factor of 4 in 2002 and a factor of 2 in 2009 for a 2.5 cm depth (solid line); 2) aproperty remediated in 2002 that is more distant from the facility, with reductions in deposition by afactor of 2 in both 2002 and 2009 for a 0.6 cm depth (dashed line).

    order to compare the predicted concentration trends with those observed in the community.An average scaling factor was developed based on the EPA soil lead data set consisting of24 properties and 1609 data points where all data were collected from approximately thesame depth interval. The scaling factor is used to translate the relative concentrations shownin Figure 3 to absolute soil lead concentrations for comparison with the measured data.

    Air lead levels decrease with distance from the facility, and this would be expectedto affect the rate of particulate deposition and recontamination of soil. In order to developa scaling approach that incorporates this concept, an estimated air lead concentration for2002 was assigned to each property in the data set to serve as a proxy for the effect ofdistance from the facility on deposition of lead. The 2002 date used in this analysis is notimportant; it is merely a method to assess the relative effects of location. The estimated 2002air lead concentrations were developed with the ISCST3 air dispersion model (not part ofthis study) used to support the development of air pollution controls. The resulting annualaverage air lead concentration isopleths are shown in Figure 1. The air lead level assignedto each property for the scaling analysis varied from 0.25 to 0.61 g/m3 and representstotal suspended particulate. Note that an alternative approach would be to use the property-specific soil lead concentration prior to remediation as the basis for this adjustment, butthese data were not uniformly available for all properties.

    We then used the following equation to translate the predicted relative concentrationcurves into predicted absolute concentrations:

    Cp = Cunit S Pbair + C0 (3)

    where

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  • Soil Lead Recontamination Trends 699

    Cp = Scaled predicted (absolute) soil lead concentration (mg/kg)Cunit = Unit predicted (relative) soil lead concentration (no units) (see Figure 3)S = Scaling factor (mg/kg per g/m3)Pbair = Estimated air lead concentration (g/m3) (see Figure 1)C0 = Concentration of lead in background soil (30 mg/kg).

    The value for S is selected by optimizing the fit between Cp and the observed data. Thisis done by minimizing the sum of the differences between Cp and the observed 1609 datapoints simultaneously at all sampling dates for all 24 properties in the EPA soil lead dataset. In other words, the scaling and optimization step is not property-specific; it is done onceand applied to all properties and data in the EPA soil lead data set. The resulting value ofS is 363 mg/kg per g/m3. Other functional relationships between Cunit and Pbair were notexplored because the large scatter in the soil lead data set and the relatively close proximityof the properties to each other made it unlikely that a better fit could be easily distinguished.Although this approach to scaling the predicted recontamination trend to the observed datais arbitrary, the use of a consistent scaling approach for all properties, varying only by theproperty-specific air lead concentrations, allows for comparison of the time-trend predictedby the theoretical model with the data, including consideration of distance from the facility.Data from properties remediated in the 1990s were not included in the optimization toderive S. Note that, because of the variability in the data itself, we are not interested in howwell the model predicts any one data point, but rather the extent to which the time-trendpredicted by the model approximates the time-trend observed in the data.

    Discussion

    Comparison figures of observed and modeled recontamination trends for all propertiesin the EPA data set were developed, from which a representative subset are shown here.Figures 4 and 5 show the comparison for eight properties at between 0.3 and 1.3 kilometersfrom the facility. Properties were selected for inclusion in the figures from those with themost complete data sets. Data from each of the four sampled quadrants of the yard areshown. The data vary by quadrant and from one sampling period to the next. The variabilitylikely results from a combination of soil heterogeneity, sample heterogeneity, and analyticaluncertainty. Differences between quadrants may be a result of shielding of some areas fromdeposition by trees or structures.

    The properties shown in Figure 4 are west of the facility, at distances ranging from 0.3to 0.8 kilometers. All properties were remediated a few months prior to final implementationof the 20012002 air pollution controls. The modeled recontamination trends fall withinthe range of the quadrant data, and have similar slopes, corresponding to the increase inconcentrations with time.

    The properties shown in Figure 5 are north and northwest of the facility, at distancesranging from 0.7 to 1.3 kilometers. All properties were remediated within a few monthsprior to final implementation of the 20012002 air pollution controls. For these properties,the modeled recontamination trends correspond to higher soil lead levels than are observedwith few exceptions, although the predicted slopes of the time-trend are similar to thoseobserved in the data. Properties where the soil concentrations are overestimated are all morethan 0.7 kilometers from and to the north and northwest of the facility. This observationmay result from using modeled air lead levels as a proxy for location when soil lead levelsare more closely related to air lead deposition. Air lead deposition, relative to air lead levels,is expected to be higher closer to the facility as a result of the deposition of heavier particles

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    Figure 4. Modeled and observed recontamination trends for properties A, E, H, and J. These prop-erties were remediated in late 2001 or 2002, and are located 0.4, 0.8, 0.4, and 0.3 kilometers westof the facility, respectively. Data collected from four quadrants of the yards are represented by thecolored symbols. The green curves correspond to the scaled model predictions.

    near the source. Therefore, we expect the model will over-predict soil lead concentrationsmore distant from the facility, since the scaling factor is based on air lead concentrationsrather than lead surface deposition rate.

    The additional data set of six properties remediated in the early 1990s was not usedin the development of the scaling factor because the data set is much more limited. Theseproperties are within 300 meters of the facility, and the recontamination trend was modeledassuming that deposition decreased by a factor of 4 in 2002. The same scaling factordeveloped for the EPA soil lead data set was applied here. Note that the scaling factor wasdeveloped on the basis of 0.6 cm depth samples, while these data are 2.5 cm depth samples.

    Figure 6 shows the comparison of the data and modeled recontamination trend forproperties D and K. The modeled recontamination trends show that surface soil leadconcentrations are expected to increase from the remediation date to the time of the im-plementation of the 2002 air pollution controls, and then begin to decrease as a resultof the decreased air lead deposition. The limited available sampling data show soil leadconcentrations both higher and lower than the modeled trend, but the time-trend of the datashows the predicted decreases in concentration after 2002. Note that while surface soil leadconcentrations on the properties shown in Figures 4 and 5 increase between 2002 and 2009,the concentrations on property D and K in Figure 6 decrease during this time period. Thisobservation is correctly predicted by the model.

    In addition to uncertainty in the parameterization of the model, uncertainties also existin the sources and mechanisms leading to recontamination of soils in the community. Thepotential impact of trees and structures that can provide shielding from deposition to some

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  • Soil Lead Recontamination Trends 701

    Figure 5. Modeled and observed recontamination trends for properties B, O, Y, and BB. Theseproperties were remediated in late 2001 or 2002, and are located 1.2, 0.7, 1.3, and 0.7 kilometersnorth northwest of the facility, respectively. Data collected from four quadrants of the yards arerepresented by the colored symbols. The green curves correspond to the scaled model predictions.

    areas is mentioned above. In addition, because only certain areas of this community havebeen remediated, there is also the potential for recontamination of soils to occur from wind-blown or tracked dust from unremediated parts of the community. These factors likelyalso contribute to some of the variability in the results. Nevertheless, the model predicts

    Figure 6. Modeled and observed recontamination trends for properties D and K, remediated in 1992,and located 0.3 kilometers northwest of the facility. The symbols for 1996 correspond to front andback yard composite sample results (symbols overlap). Data collected from four quadrants of theyards after 2001 are represented by the additional colored symbols. The green curves correspond tothe scaled model predictions.

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    a time-trend for recontamination that is similar to that seen in the community, suggestingthat air deposition is the primary source of recontamination.

    In summary, this model can be used to predict the magnitude of expected soil recon-tamination as a function of time given known or modeled emissions or deposition estimates,and to predict the geographic boundary beyond which soil recontamination levels wouldnot be expected to increase above certain specified levels (e.g., a health-based screeninglevel), in the face of emissions that remain constant or declining. This approach may behelpful in defining the geographic boundaries of areas requiring remediation surroundingoperating facilities with known air releases.

    Acknowledgments

    We thank the Doe Run Company for providing financial support for portions of this work.

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