A New Metric for Long-Range Transport Potential of Chemicals

Center for Research Development, Research Institute for Humanity and Nature, Kyoto 603-8047, Japan. Environ. Sci. Technol. , 2014, 48 (6), pp 3245–3...
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A New Metric for Long-Range Transport Potential of Chemicals Toru Kawai,*,† Karolina Jagiello,‡ Anita Sosnowska,‡ Katarzyna Odziomek,‡ Agnieszka Gajewicz,‡ Itsuki C. Handoh,§ Tomasz Puzyn,‡ and Noriyuki Suzuki† †

Center for Environmental Risk Research, National Institute for Environmental Studies, Tsukuba 305-8506, Japan Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdansk, Poland § Center for Research Development, Research Institute for Humanity and Nature, Kyoto 603-8047, Japan ‡

S Supporting Information *

ABSTRACT: We propose a new metric for long-range transport potential (LRTP), GIF, based on source−receptor analyses and evaluate the LRTP and persistence of a wide variety of chlorinated and brominated organic compounds using GIF and overall persistence (POV), respectively. We calculated GIF and POV using our global 3D dynamic multimedia model (FATE). Physicochemical properties were obtained from quantitative structure−property relationship (QSPR) models. The FATE−QSPR combined model enabled us to systematically investigate the LRTP and persistence of a wide variety of chemical substances. On average, the estimated GIF and POV for chlorinated compounds were larger than those for their brominated counterparts, with the largest and smallest values found for polychlorinated biphenyls and polybrominated dibenzodioxins, respectively. We also compared GIF with four differently defined LRTP metrics and two LRTP metrics obtained from a simple model. The results of our analyses indicate that the LRTP ranks can differ considerably among LRTP metrics, the differences being dependent on the governing environmental processes, relevant physicochemical properties, and multimedia model.

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al.,2 LRTP metrics can be classified into “target-oriented” and “transport-oriented” metrics. Target-oriented metrics are defined as percentages of emitted substances that migrate to surface media in selected target regions as a consequence of transport in the mobile media and subsequent deposition. Transport-oriented metrics denote the potential for transport in mobile media with simultaneous exchange with surface media. In both definitions, however, the results are dependent to some degree on the selection of a target remote region or the location of a point emission source unless the simplest unit-world models are used. In this paper, we propose a new LRTP metric, GIF, based on source−receptor analyses and assess the LRTP of chlorinated and brominated organic compounds (hereafter called Cl and Br compounds) using our global 3D dynamic MM, named the finely advanced transboundary environmental (FATE) model. This is the first attempt to assess the LRTP of a large number of organic compunds using a computationally expensive 3D dynamic model. The physicochemical properties of the analyzed substances [polychlorinated biphenyls (PCBs), polychlorinated naphthalenes (PCNs), polychlorinated diben-

INTRODUCTION The long-range transport potential (LRTP) and persistence (P) of hazardous chemicals are predictors of their fate in and threat to the environment. LRTP and P can be quantified by any of several metrics.1 Because LRTP and P are not determined by any single physicochemical property but rather by complex multimedia processes, metrics for LRTP and P are usually estimated by using multimedia environmental fate models (MMs).2,3 Over the past few decades, a number of MMs have been developed,4,5 ranging from the simplest unit-world MMs, which assume steady-state equilibrium partitioning, to spatially explicit segmented MMs, to more complicated three-dimensional (3D) dynamic MMs that have been developed on the basis of atmospheric transport or general circulation models. Because there are a vast number of candidate substances, computationally cheap, simple models such as the OECD POV and LRTP screening tool6 are convenient in screening assessments. In contrast, these models necessarily assume rather simplified transport processes because of their limited spatial and temporal resolutions. To date, the applications of 3D dynamic MMs have been limited to a small number of important substances such as persistent organic pollutants (POPs). Another issue is that metrics for LRTP have a plethora of ambiguous definitions because there are no consistent metrics acceptable to all interested researchers. According to Fenner et © 2014 American Chemical Society

Received: Revised: Accepted: Published: 3245

June 12, 2013 February 27, 2014 February 28, 2014 February 28, 2014 dx.doi.org/10.1021/es4026003 | Environ. Sci. Technol. 2014, 48, 3245−3252

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vertical carbon cycle were modeled by synthesizing satellitebased empirical models.16−18 For more details of the parametrizations, see the respective references. Degradations, which are definitely predominant processes in POV estimates, were formulated using a second-order equation with OH radical concentration ([OH]) for the atmosphere and firstorder equations for the other compartments. Spatial and temporal variations of [OH] were taken into account as described later. Substances and Physicochemical Properties. The Cl and Br compounds analyzed in this study were PCBs, PCNs, PCDDs, PCDEs, PCDFs, PBBs, PBNs, PBDDs, PBDEs, and PBDFs. These groups include well-known legacy POPs such as PCBs and PCDD/Fs and emerging POPs such as PBDEs. It should be noted that although these compunds cover a range of distribution and degradation behaviors, they still cover only a fraction of the chemical species for which LRTP metrics have previously been assessed.2 From a total of 1411 congeners/ homologues, 300 representative substances were selected. These representative substances were selected randomly on the basis of their partitioning properties (i.e., log KOW, log KOA, log KAW) by applying the Kennard−Stone algorithm.19 The physicochemical properties used in FATE are partitioning coefficients (KOW, KOA, KAW, KOC), liquid-phase vapor pressures (PL), internal energies for phase transfers (ΔUOW, ΔUOA, ΔUAW), degradation half-lives in all phases in air, soil, and water (t1/2,air, t1/2,soil, t1/2,water), and sorption coefficients on water and snow surfaces. These properties were estimated by QSPR and linear free energy relationship (LFER) models. Specifically, KOW, KOA, and KAW were obtained from Puzyn et al.;20 KOC was taken from Jagiello et al.;21 PL was obtained from Gajewicz et al.;22 internal energy of vaporization (ΔUA) was taken from Sosnowska et al.;23 degradation halflives were obtained from Puzyn et al.;24 and sorption coefficients were taken from Lei and Wania.13 Internal energies of phase transfers, which are used to take account of the temperature dependences of partitioning coefficients,25 were estimated from ΔUA and constant ΔUW (=20 kJ mol−1) and ΔUO (=0 kJ mol−1).26 The degradation rates in air used in the second-order equation with [OH] were estimated from t1/2,air and the annual mean of the global average [OH] (1.16 × 106 molecules cm−3).30 Simulation Design and Data Used. Simulations were performed for 10 years (1998−2007), and the results for the last year were analyzed. We performed supplementary analyses and confirmed that the 10-year simulation was sufficiently long for our discussion (the rank correlation coefficients of the estimated LRTP metrics and POV between the 10-year simulation and the 9-year simulation were greater than 0.99; data not shown). The climate forcing data were obtained from the National Centers for Environmental Prediction (NCEP)/ National Center for Atmospheric Research (NCAR) Reanalysis 127 and from Geophysical Fluid Dynamics Laboratory ocean data assimilation (ODA) experiments.28 The satellite data used to estimate POC biomass and vertical carbon cycle in the ocean were obtained from the sea-viewing wide field-of-view sensor (SeaWiFS).29 Spatial and temporal variations of atmospheric [OH] were estimated by interpolating 3D monthly climatological data from Spivakovsky et al.30 onto each model grid in the atmosphere. For emission, a hypothetical scenario was used because it was impractical to prepare emission inventories for all of the selected substances. This emission scenario was developed on the basis of the assumptions that the selected

zodioxins (PCDDs), polychlorinated diphenyl ethers (PCDEs), polychlorinated dibenzofurans (PCDFs), polybrominated biphenyls (PBBs), polybrominated naphthalenes (PBNs), polybrominated dibenzodioxins (PBDDs), polybrominated diphenyl ethers (PBDEs), and polybrominated dibenzofurans (PBDFs)] were parametrized by using quantitative structure− property relationship (QSPR) models developed by our research group. This FATE−QSPR combined model enabled us to assess all of the theoretically possible congeners/ homologues of target substance groups. The objectives of this paper are to (1) describe the FATE−QSPR combined model, (2) propose GIF as a new LRTP metric, (3) assess the LRTP of Cl and Br compounds using the new metric calculated from the FATE−QSPR combined model, and (4) evaluate the new metric by comparisons with other available LRTP metrics. Although our major focus was on LRTP, we also evaluated overall persistence (POV) because it is commonly used in conjuction with LRTP metrics in screening assessments.

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METHODS Multimedia Model. FATE7 was used to estimate LRTP metrics and POV. FATE is the most recent 3D dynamic MM developed by the authors and is capable of predicting the global fate of organic substances in and across the atmosphere, ocean, soil, vegetation, and cryosphere (permanent snow and seasonal snowpack8). The model consists of a coupled atmosphere− ocean transport submodel as its dynamical core. Advection and diffusion are computed both in the atmosphere and in the oceans with spatial resolutions of 2.5° × 2.5° × 20 σ layers (1− 0.01) in the atmosphere and 1.0° × 1.0° × 50 layers (0−5500 m) in the ocean. In addition to unit processes common for multimedia modeling, such as dry and wet depositions, intercompartment diffusive exchange of gaseous substances, degradations, phase partitions, and 1D transport across the vegetation−soil boundary (i.e., defoliation, infiltration, and diffusion), the model considers bioconcentrations in lowerorder organisms in the ocean (i.e., phytoplankton and zooplankton) and biologically driven vertical cycles in the ocean interior. The major parametrizations used in physical transports, depositions, and biological processes in the ocean are summarized in Table S1 in the Supporting Information. Advection in both the atmosphere and the ocean was explicitly solved using a modified Bott’s advection scheme with fourthorder accuracy.9 Vertical mixing by turbulent diffusion in the atmosphere was parametrized using the Yonsei University planetary boundary layer model (YSU-PBL10) and that in the ocean using the K profile parametrization (KPP11). The aerosol size resolving model of Giorgi12 was used to parametrize the dry deposition velocity, and in wet deposition, both partition to and sorption on raindrops and snow were taken into account by using the model of Lei and Wania.13 Near-surface diffusive transport of gaseous substances was formulated by the conventional bulk formula. The bulk transfer coefficient (or transfer velocity) of the atmospheric side was parametrized with stability correction by the Monin−Obukov similarity theory.14 The roughness length for scalar substances, which is a key aerodynamic land surface parameter in this formulation, was estimated using the theoretical formula of Brutsaert. 15 Bioconcentration in the ocean was calculated from the organic carbon−water partitioning coefficient (KOC) assuming steadystate partitioning between the dissolved-phase substance and the particle organic carbon (POC). POC biomass and the 3246

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Figure 1. (a) GDPs for the year 2005 used as surrogate data for spatial distributions of emission. GDPs from the International Monetary Fund were interpolated onto 2.5′ grids using population density data (GPWv3). (b) Geographical distribution of source−receptor regions. Values shown in parentheses are the GDPs for the year 2005 in units of trillions of U.S. dollars.

POV, three target-oriented LRTP metrics [two Arctic contamination potentials (ACP and eACP) and the transport efficiency (TE)], and two transport-oriented metrics [TD and characteristic travel distance (CTD)] were also calculated for comparative purposes. POV is defined as follows:

substances were of industrial origin and the emissions were proportional to gross domestic products (GDPs) (Figure 1). We employed annual GDP data from the International Monetary Fund that was interpolated onto each model grid using population density data [Gridded Population of the World (GPW), v3]. Annual GDP data were estimated by linearly interpolating the original 5-year data. An assumed annual global emission of 1 Mg entirely to the atmosphere was applied throughout the simulation period. It should be noted, however, that the magnitude of emission is less important to the present analyses because LRTP metrics and POV do not have a mass dimension. Theoretical Basis of the LRTP Metrics. We propose a new LRTP metric, GIF, based on the source−receptor analyses. GIF is a global average of imported fractions in receptor regions weighted by the contents: imax

GIF =

POV = CG/SGH

where CG represents the contents in the model domain for the analyzed year (i.e., 2007) and SGH is the global historical sink (i.e., the sum of the degradative sinks and removals from the model domain during the simulation period). The values of POV calculated from eq 2 have the same units as the simulation period (i.e., 10 years in this study). In the standard definition of POV, advective and diffusive removals from the model domain are not taken into account in the denominator in eq 2. The POV discussed in this study therefore represents the overall residence time, although we call this metric the overall persistence throughout the text. ACP, eACP, and TD were calculated using FATE, whereas CTD and TE were obtained from the OECD POV and LRTP screening tool, version 2.2 (hereafter called the OECD tool).6 ACP and eACP are defined in the same way as in Wania31 and Fenner et al.,2 respectively:

imax

∑ IF(i)·C(i)/∑ C(i), i=1

i=1

(2)

(1)

where IF(i) is the imported fraction in region i, C(i) is the content in the atmospheric boundary layer in region i, and imax is the number of source and receptor regions. The imported fraction is defined as the fraction of the contribution from regions other than a receptor region of interest, and thus, it takes a value from 0 to 1. This new LRTP metric is based on the inverse of the transport distance (TD) idea. That is, a substance with a higher LRTP must be more mixed in the atmosphere as a result of long-range transport. We stress that in GIF calculations we can use a realistic emission scenario if one is available, unlike TD calculations, where a point emission souce must be assumed. IF(i) values were estimated from source−receptor analyses. In the source−receptor analyses, we adopted the emission sensitivity method (see, e.g., HTAP20105 for the details of the method) and defined the following source regions based on the geographical regions and composition adopted by the United Nations (ref 34; see Figure 1b): (i) Africa (AF); (ii) Latin America and the Caribbean (LA); (iii) North America (N-AM); (iv) Central, Southern, and Western Asia (CSW-AS); (v) Eastern and Southeastern Asia (ESE-AS); (vi) Eastern Europe (E-EU); (vii) Northern, Southern, and Western Europe (NSW-EU); and (viii) Oceania (OC). The fact that these eight regions cover all of the land surfaces of the globe except for Antarctica enabled us to perform closed source−receptor analyses. The receptors were defined as the atmospheric boundary layers of the same eight source regions.

ACP = CAr /CG

(3)

and eACP = CAr /EGH

(4)

where CAr represents the contents in the Arctic surface media and EGH is the global historical emission. TD is defined in the same way as used in the MSCE-POP in Hollander et al.,3 namely, as the average distance from the source at which the mean annual atmospheric concentration of a substance is 1000 times lower than the concentration near the point source. The arctic surface media used in the ACP and eACP calculations are defined as the polar climate zones32 and the Arctic Ocean.33 A point emission source for TD calculations was applied to a grid with the maximum GDP (i.e., a grid with Tokyo, Japan). We also calculated additional LRTP metrics (i.e., TD100, TD10000, TDNP, TDEQ, eAnCP, AnCP, GIFland, and TEsoil) to facilitate sensitivity analyses and supplementary discussions. Definitions of these metrics are given where they appear in the text.

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RESULTS AND DISCUSSION Results for GIF and POV. The POV−GIF correlation diagram is shown in Figure 2, and average, maximum, and minimum values of the estimated GIF and POV are listed in 3247

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values for PCBs and PCDEs increased and then decreased with increasing NCl, with the maximum values found around NCl = 3, but no clear trend was evident for the other substance groups (i.e., PCNs, PCDDs, and PCDFs). In contrast, the GIF values of the Br compounds simply decreased with increasing NBr, and the interhomologue variabilities were much smaller than those for the Cl compounds (Figure 3b). The log POV values for the Cl compounds increased slightly and then decreased with increasing NCl, with the maximum values around NCl = 6 (Figure 3c). The log POV values for the Br compounds showed two distinct modes, with the maximum values also found around NCl = 6 in one of the two modes (Figure 3d). The foregoing results are partly explained by considering the governing physicochemical properties or related environmental processes of GIF and POV. The partitioning coefficients used in our analyses are approximated by a linear increase or decrease with increasing NCl and NBr (Figure S1), and the degradation half-lives (t1/2,air, t1/2,soil, and t1/2,water) are sorted into 3−5 discrete classes (Figure S2). As discussed later, degradations, especially in soil, are the predominant processes in POV. Thus, the overall trends of log POV and log t1/2,soil with respect to NCl and NBr are similar (compare Figure 3c, Figure 3d, and Figure S2). In contrast, GIF is less correlated with log t1/2,soil than with POV but more positively correlated with KAW (see later discussion for Figure 6). Therefore, the values of NCl and NBr with the largest GIF are smaller compared with the cases for log POV. The log t1/2,soil and log t1/2,water values for the Cl and Br compounds generally increase and decrease, respectively, with increasing NCl and NBr (Figure S2). This difference probably produced the different GIF trends for the Cl and Br compounds. Next we discuss the estimated values of GIF and the corresponding global distributions of two substance groups in the atmosphere. We compared substance groups with the largest (PCBs; GIF = 0.31) and smallest (PBDDs; GIF = 0.15) values of GIF. It should be noted that because we used a hypothetical emission scenario, the predicted concentrations and distributions of PCBs and PBDDs are not necessarily those in the environment. However, we believe that the estimated sources and spatial distributions do not differ significantly from the actual cases because the surrogate data used in the spatial distribution (i.e., GDP) are just as plausible with the limited information available. We found distinct differences in the predicted distributions of ∑PCBs and ∑PBDDs in the lowermost layer in the

Figure 2. log POV−GIF correlation diagram for 300 selected chlorinated and brominated organic compounds.

Table 1. GIF ranged from a minimum of 0.08 (PBDE209) to a maximum of 0.5 (PCB36). The GIF value of 0.5 for PCB36 means that, on a global average, domestic contributions explain only half of the PCB36 in the atmospheric boundary layer. On average, PCBs and PBDDs showed the largest and smallest GIF and POV values, respectively (Table 1). It should be noted that this result does not indicate that PBDDs are non-POPs but rather that they are the least POP-like of the substances analyzed in this study. Intercongener/homologue variabilities (e.g., for PCBs, 0.31 for GIF and 1.9 for log POV) were more significant than the variabilities between substance groups (0.16 for GIF and 1.08 for log POV). This result indicates that congener/homologue-based assessments are also necessary in addition to assessments between substance groups. Overall, the values of GIF and log POV for chlorinated compounds were larger than those for their brominated counterparts. We plotted GIF and log POV against the numbers of chlorine and bromine atoms (NCl and NBr) (Figure 3) to investigate the variabilities of GIF and log POV in each substance group and between substance groups. We discuss the results in this manner because the major physicochemical properties used in the analyses primarily vary with NCl or NBr (Figures S1 and S2 in the Supporting Information). We found significant interhomologue variabilities (i.e., NCl is the same but the substitution pattern is different) in the GIF versus NCl plot (Figure 3a), especially for substances with NCl = 2−6. The GIF

Table 1. Average, Maximum, and Minimum Values of Estimated GIF and POVa chlorinated compounds (127) PCBs (42)

PCNs (16)

PCDDs (5)

PCDEs (51)

brominated compounds (173) PCDFs (13)

PBBs (49)

PBNs (24)

PBDDs (20)

PBDEs (49)

PBDFs (31)

−1.44 −0.99 −2.35

−1.35 −1.02 −2.36

−1.54 −1.95 −1.39 −2.22

−1.62 −1.01 −2.35

−1.43 −1.01 −2.36

0.15 0.20 0.08

0.17 0.21 0.11

log POV Cl/Br ave ave max min

−0.87 −0.43 −2.33

Cl/Br ave ave max min

0.31 0.50 0.19

−1.81 1.11 −2.34

0.21 0.29 0.13

−1.14 −1.41 −1.01 −1.77 0.25 0.20 0.22 0.17

−1.01 −0.47 −2.34

−1.59 −1.23 −2.32 GIF

0.23 0.48 0.10

0.18 0.27 0.13

0.19 0.30 0.08

0.18 0.26 0.12

0.17 0.15 0.21 0.09

a

Average values for chlorinated compounds (Cl ave) and brominated compounds (Br ave) and average (ave), maximum (max), and minimum (min) values for each substance group are shown. Values in parentheses are the numbers of substances analyzed. 3248

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Figure 3. (a) Relationships between the number of atoms in the chlorinated and brominated compounds and the metrics GIF and POV: (a) GIF vs NCl; (b) GIF vs NBr; (c) log POV vs NCl; (d) log POV vs NBr.

Figure 4. Predicted distributions of substance groups with (a) maximum (PCBs; GIF = 0.31) and (b) minimum (PBDDs; GIF = 0.15) values of GIF in the lowermost layer of the atmosphere (σ = 1−0.982). Annual mean concentrations for the year 2007 are shown.

∑PCBs in all receptor regions. This is clear evidence that the LRTP of ∑PBDDs is smaller than that of ∑PCBs. Comparison of POV and LRTP Metrics. We used rank correlation coefficients (RCCs)2 to compare POV and LRTP metrics with different definitions. We initially compared the results for POV, eACP, ACP, TD, and GIF (i.e., the metrics calculated using FATE; Figure 5). POV is most correlated with eACP (RCC = 0.7) and least correlated with ACP (RCC =

atmosphere (Figure 4). As expected from the estimated GIF values, ∑PCBs (Figure 4a) are more mixed in the atmosphere than ∑PBDDs (Figure 4b) because of long-range transport. Significant amounts of ∑PCBs are transported to remote regions in the northern hemisphere such as the Arctic, leading to ∑PCBs concentrations in the Arctic that are approximately 1−2 times lower than those near major emission sources (i.e., North America, Europe, and East Asia). By contrast, elevated ∑PBDDs concentrations are found primarily near industrialized countries or cities. The concentrations of both ∑PCBs and ∑PBDDs are much lower in the southern hemisphere than in the northern hemisphere because (i) GDPs in the southern hemisphere are approximately 15 times lower than those in the northern hemisphere and (ii) in the atmosphere interhemispheric transport is less important than intrahemispheric transport.35 The predicted relative contributions from source regions to the amounts of ∑PCBs and ∑PBDDs in the atmospheric boundary layer in receptor regions are given in the Figure S3 in the Supporting Information. The results show that the imported fractions of ∑PBDDs are smaller than those for

Figure 5. Rank correlations between POV, eACP, ACP, TD, and GIF. 3249

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0.27). The relatively high correlation between POV and eACP can be explained by noting that the global historical emission is used as a normalization variable in the definition of eACP (eq 4). Therefore, global historical sinks and removals are implicitly taken into account in addition to the long-range transport process itself. As expected, the RCCs between LRTP metrics are generally higher than those between POV and LRTPs. The highest correlation is between GIF and TD (RCC = 0.94). GIF and TD are not correlated significantly with target-oriented metrics (i.e., eACP and ACP; the RCCs ranged from 0.64 to 0.78). Here we note that RCCs between GIF and eACP, GIF and ACP, and GIF and TD showed different trends when we calculated GIF values based on receptors defined with respect to land compartments (i.e., surface soil, vegetation, and the cryosphere) instead of with respect to the atmospheric boundary layer (GIFland). That is, compared with GIF, GIFland showed higher rank correlations with target-oriented metrics (the RCCs between GIFland and eACP and between GIFland and ACP were 0.85 and 0.96, respectively) and lower rank correlations with POV and transport-oriented metrics (the RCCs between GIFland and POV and between GIFland and TD were 0.37 and 0.81, respectively). The differences in RCCs described above are entirely induced by the different governing environmental processes or the related physicochemical properties for LRTP metrics and POV, because an identical model was used in all of the analyses in this study. To discuss this issue, we performed linear regression analyses between the common logarithms of the LRTP metrics and POV and the common logarithms of the major physicochemical properties (i.e., log KOW, log KOA, log KAW, log t1/2,air, log t1/2,soil, and log t1/2,water). We used the coefficient of determination (R2) in each regression analysis to determine the degree of relevance between the LRTP metric or POV and the individual physicochemical property (Figure 6). All of the analyzed metrics were positively correlated with log KAW, log t1/2,soil, and log t1/2,water and negatively correlated with log KOW and log KOA. The R2 statistics with log t1/2,air were generally low. POV was most correlated with log t1/2,soil (RCC =

0.88) and poorly correlated with the partitioning coefficients. This result is reasonable because degradations are considered to be the predominant processes in POV estimates and most of the analyzed substances are prone to be partitioned into soil in the global environment. eACP was also highly correlated with log t1/2,soil and log t1/2,water, which resulted in a relatively high correlation between POV and eACP (RCC = 0.7). In contrast, ACP was highly correlated with the partitioning coefficients, especially (and positively) with log KAW (R2 = 0.77). In FATE, KAW is used in wet deposition and air−sea diffusive exchange of gas-phase substances. The strong correlations between ACP and the partitioning coefficients indicates that one or both of these processes are dominant in the determination of ACP. As expected from their high RCCs, the R2 values for TD and GIF did not differ by much; the R2 for TD was slightly higher than that for GIF. GIF was not significantly correlated with any single physicochemical property. This result may imply that GIF may be determined by more complex, multiple environmental processes. As we discussed above, GIF and TD showed similar results in terms of both RCC and R2. However, in GIF estimates, we can use a realistic emission scenario if one is available. This is an important advantage of GIF over TD. To evaluate this advantage, we performed sensitivity analyses and explored how sensitive the results were to the location of a point source in the TD calculation (see Figure S4 in the Supporting Information for the RCC values calculated on the basis of these analyses). Two transport distances, TDNP and TDEQ, were chosen for this analysis. They are defined in the same way as TD except that the locations of the point sources were replaced by grids closest to the North Pole and the equator, respectively. We also calculated two target-oriented metrics, eAnCP and AnCP, to investigate the sensitivity of the results to the selection of a remote target region. eAnCP and AnCP were defined in the same way as eACP and ACP, respectively, except that a remote target region was replaced by the Antarctic surface medium, defined as the Antarctic Continent and the Antarctic Ocean.33 TD was not highly correlated (rank correlation) with TDNP and TDEQ, but the RCCs were 0.7 and 0.77, respectively. The governing environmental processes for these metrics also differed to some degree (see the R2 values for TDNP and TDEQ in Figure S6a in the Supporting Information). That is, compared with TD, t1/2,soil is less important in TDNP and TDEQ because the point souces in TDNP and TDEQ calculations are surrounded by permanent snow/ice or seasonal snowpack and the ocean, respectively. Although we chose two extreme locations for the point sources in this analysis, this result indicates that TD is sensitive to the location of the point source, which was arbitrarily defined. It should be noted that TD is also sensitive on the basis of its definition. For example, when “1000 times” in the TD definition adopted in this study was changed to “100 times” (TD100) or “10000 times” (TD10000), the results changed significantly (the RCCs between TD and TD100 and TD10000 became 0.81 and 0.58, respectively). In contrast, eACP and eAnCP as well as ACP and AnCP showed relatively high rank correlations (RCC = 0.95 and 0.91, respectively). The R2 values of these metrics also did not differ by much (see Figures S6b and S6c). This result may indicate that the results of targetoriented metrics are not so sensitive to selection of a remote target region. Next, we compared the eACP, ACP, TD, GIF, and GIFland values calculated using FATE with the CTD and TE values

Figure 6. Coefficients of determination (R2) of linear regression analyses between the common logarithms of the partitioning coefficients (log KOW, log KOA, and log KAW) and the degradation half-lives (log t1/2,air, log t1/2,soil, and log t1/2,water for t1/2 in units of hours) and the common logarithms of the LRTP metrics and POV (log eACP, log ACP, log TD, log GIF, and log POV). Bars with diagonal lines indicate that the two variables of interest are negatively correlated. 3250

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obtained with the OECD tool, which is used as an international standard for evaluating POV and LRTP (see Figure S5 in Supporting Information for the RCC values calculated on the basis of this analysis). In this comparison, we also calculated an additional LRTP metric, TEsoil. This metric is defined in the same way as TE except that deposition to water is eliminated from the TE calculation. A comparison of the results for CTD, TE, and TEsoil showed that CTD was in general more highly correlated with the LRTP values calculated using FATE; the highest rank correlation was found between CTD and GIF (RCC = 0.74). The RCCs for TE and TEsoil were all low (RCC = −0.15 to 0.41 for TE and −0.49 to 0.1 for TEsoil). Such low correlations were found even between metrics that are similar from a process point of view, for example, TE and eACP (RCC = 0.3), TE and ACP (RCC = −0.15), and TEsoil and GIFland (RCC = −0.33). On the other hand, comparison of the results for eACP, ACP, TD, GIF, and GIFland revealed that the GIF values were most highly correlated with all of the LRTP metrics obtained with the OECD tool. We note that the rank correlation between the POV values calculated using FATE and the POV values obtained with the OECD tool was high (RCC = 0.93). It is not straightforward to interpret the results described above, especially the low rank correlations found in the results for TE and TEsoil, because the FATE and OECD tools are very different in terms of their spatial and temporal resolutions as well as with respect to their model internal processes. However, the results we were able to obtain implied that CTD better represents LRTP metrics calculated using the 3D dynamic model than does TE, regardless of the conceptual basis of the metric. Although there is a great advantage to the use of simple models in screening assessments of a vast number of chemicals, it is apparent that ancillary LRTP assessments using 3D dynamic models are desirable, as the highest rank correlation between GIF and CTD was not very high. The results of our analyses indicate that the LRTP ranks may differ considerably among differently defined LRTP metrics as a result of differences in the governing environmental processes or relevant physicochemical properties. Also, we found that LRTP metrics obtained from unit-world, steady-state models such as the OECD tool are substantially different in their ranks from those calculated using 3D dynamic models such as FATE. Although the present analysis did not allow us to conclude which LRTP metric or model is the most appropriate, we believe that GIF will contribute to the improvement of the methodology of LRTP assessment of chemicals because (1) GIF is based on a new concept that is very different from those of other target-oriented and transport-oriented metrics and (2) we can use realistic emission scenarios (if they are available) in GIF estimates, unlike transport-oriented metrics such as TD. With limited information on emission inventory available, we used the most plausible hypothetical emission scenarios. The results of our analyses for a large number of chlorinated and brominated organic compounds (available as Supporting Information) may therefore serve as reference data for future assessments of these substances.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]; phone: +81-29-850-2888; fax: +81-29-850-2920. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was financially supported by the Japan Society for the Promotion of Science (JSPS) and the Polish Academy of Science (PAN) under the Bilateral Joint Research Project and by JSPS Grants-in-Aid for Young Scientists (B) (25871087 and 24710037). The model calculations were performed on the supercomputer system (NEC SX-8R and SX-9/A) of the National Institute for Environmental Studies, Japan.

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ASSOCIATED CONTENT

S Supporting Information *

Physicochemical properties used in the analyses and estimated POV and LRTP metrics. This material is available free of charge via the Internet at http://pubs.acs.org. 3251

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