Environ. Sci. Technol. 2005, 39, 6749-6756
Assessing the Influence of Climate Variability on Atmospheric Concentrations of Polychlorinated Biphenyls Using a Global-Scale Mass Balance Model (BETR-Global) M A T T H E W M A C L E O D , * ,†,‡ WILLIAM J. RILEY,§ AND T H O M A S E . M C K O N E †,| Environmental Energy Technologies Division and Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, ETH Ho¨nggerberg, HCI G129, CH-8093 Zu ¨ rich, Switzerland, and School of Public Health, University of California, Berkeley, California, 94720
We introduce a new global-scale multimedia contaminant fate model (the Berkeley-Trent Global Model; BETRGlobal) that integrates global climate data from the National Centers for Environmental Prediction (NCEP). BETRGlobal represents the global environment as a connected set of 288 multimedia regions on a 15° grid. We evaluate the model by simulating the global fate and transport of seven PCB congeners over a 70 year period and find satisfactory agreement between model output and observations of atmospheric PCB concentrations at 11 longterm monitoring stations in the Northern Hemisphere. We demonstrate the use of the model as a tool for understanding global pollutant dynamics by examining the hypothesis that variability in global-scale climate conditions, as reflected by the North Atlantic Oscillation (NAO), influences atmospheric PCB concentrations in the Northern Hemisphere. We estimate that the maximum variability in atmospheric PCB concentrations attributable to NAO variability is approximately a factor of 2. The influence of variability in the NAO on PCB concentrations in air is most likely to be observed in the winter and spring at monitoring sites in Northern Europe and the Arctic. Analysis of long-term monitoring data from 11 sites shows some statistically significant relationships between NAO indices and atmospheric PCB concentrations during the winter and spring. Giving consideration to competing factors that influence atmospheric PCB concentrations, longer time series of monitoring data are required to fully evaluate the modeling results and to improve our understanding of the role of climate variability on the long-term fate of persistent semivolatile pollutants.
* Corresponding author phone: +41 44 632 3171; fax: +41 44 632 1189; e-mail:
[email protected]. † Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory. ‡ Swiss Federal Institute of Technology. § Earth Sciences Division, Lawrence Berkeley National Laboratory. | University of California. 10.1021/es048426r CCC: $30.25 Published on Web 07/06/2005
2005 American Chemical Society
Introduction Persistent organic pollutants (POPs) are chemicals resistant to degradation in the environment that partition between environmental media such as air, water, soil, and vegetation. Over the past decade, POPs have generated a great deal of political concern and have been the subject of extensive scientific research. POPs may be subject to long-range transport in air and/or water and can accumulate in and impact ecosystems, such as the Arctic, far from the point of use and release (1). Our current understanding of the behavior of POPs in the global environment is based on four categories of information. First is information about the substances’ physicochemical properties and susceptibility to degradation in environmental media. Second are estimates of the global usage history and emissions of POP chemicals. These estimates include amounts used or released, whether releases were to air, water, or soils, and the geographical location where the release took place. Third are field measurements of POP concentrations in air, water, soil, and biota from around the globe. Fourth are models of chemical fate and transport in the global environment. The models make a quantitative link between physicochemical properties, emission estimates, and measured concentrations and thus provide insight into the chemical, biological, and physical factors that determine how pollutants behave in the environment. Most established POP fate models have evolved from multimedia mass balance models that represent the environment as a set of connected compartments including air, water, and soil and, depending on the application, a variety of other media. A review of global-scale multimedia models and their application in understanding the fate and transport of POPs has been compiled by Scheringer and Wania (2). Compartment-based multimedia models are generally viewed as being appropriate for (i) explaining differences in environmental fate and transport between chemicals in a defined environment and (ii) exploring the influence of variable environmental factors on chemical fate and transport. A second category of global contaminant fate models based on general circulation models (GCMs) is becoming more prominent for describing the fate of POPs. GCMs simulate climate using mass, energy, and momentum conservation equations that are discretized over connected atmospheric, land, and ocean control volumes. GCMs typically have high temporal and spatial resolution and can be structured to fit detailed air and water flow patterns. GCMs can be adapted to simulate the fate and transport of POPs by adding equations to describe degradation, deposition, revolatilization from surfaces, and partitioning to sorbed phases (3). However, they are mathematically complex and computationally expensive, demand detailed model inputs, and produce results on spatial and temporal resolutions that exceed those of any current or proposed POPs monitoring strategy. The high demand for input data and limited information about how errors propagate through the model raise questions about the transparency of chemical fate models based on GCMs, may present a barrier to developing confidence in model results, and may reduce their utility for evaluating observations (4). All models of POP fate and transport face a severe limitation in that they provide an incomplete description of a complex system and thus cannot be generally validated (4). Nevertheless, important and useful insights both for policymaking and for advancing scientific understanding are VOL. 39, NO. 17, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Regional segmentation of the BETR-Global model showing numbers used to identify regions referenced in Tables 1 and 2. possible once the capabilities and limitations of the models are given careful consideration. Model credibility can be established through quality control and demonstration that the description of fate and transport of POPs is consistent with (i) available data on emissions and environmental concentrations and (ii) scientific understanding of mechanisms that control POP transformation and transport in the environment. In this paper, we describe the development of the Berkeley-Trent Global model (BETR-Global) and evaluate its performance in describing atmospheric concentrations of individual congeners of polychlorinated biphenyls (PCBs) and the dependence of these concentrations on large-scale climate variability. BETR-Global is an intermediate between multimedia mass balance models and GCM-based models for addressing global-scale pollution issues. It provides more detail than extant multimedia fate models by incorporating some features of GCMs but still retains the transparency and characteristic structure of multimedia mass balance models. The BETR-Global model consists of 288 linked multimedia regions coupled to monthly averaged climate data from a GCM. By using averages of input data, the model can also calculate pseudo steady-state solutions for constant emission scenarios. Here, we evaluate the model by applying it to describe the global fate and transport of seven individual PCB congeners during their 70 year use history. We compare atmospheric concentrations calculated by BETR-Global with observations at 11 long-term monitoring stations in the Northern Hemisphere. We then evaluate the influence of interannual variability in the North Atlantic Oscillation (NAO) on atmospheric concentrations of PCBs. This evaluation illustrates an aspect of POPs fate and transport that cannot be addressed by existing compartment-based multimedia models. The NAO is a climate indicator determined by the sea level atmospheric pressure field over the Atlantic Ocean between Iceland and the Azores (5). Positive NAO index conditions are associated with (i) a stronger than normal high-pressure center over the Azores; (ii) low pressure throughout the Arctic and subArctic regions; (iii) shifting of storm paths across the Atlantic onto a more northerly course; (iv) warming over Europe, Eurasia, and to a lesser extent, North America; and (v) cooling in North Africa and the Middle East (6). The decade from 1990-2000 was a period with unusually high, positive values of the NAO index (5). Climate models have been used to demonstrate that increases in greenhouse 6750
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gas concentrations can lead to sea level pressure trends that correspond to positive NAO conditions (7). Therefore, examining differences between periods with positive and negative NAO indexes offers potential insight into the effects of climate change on fate and transport of POPs. Macdonald et al. (8) have hypothesized that increasing positive NAO strength associated with climate change could enhance the atmospheric transport of POPs to the Arctic, and Ma et al. (9) have reported correlations between atmospheric concentrations of persistent pollutants in the Great Lakes region and the NAO index. After describing and evaluating the BETRGlobal model, we use it to interpret monitoring data and model results to critically examine the hypothesis that the NAO influences the global fate of PCBs.
Materials and Methods The BETR-Global model is based on the Berkeley-Trent contaminant fate modeling framework (10) that has been successfully applied at the North American scale to describe the fate of toxaphene in 24 ecoregions (11) and to analyze pathways for atmospheric transport and deposition of organic contaminants to the Great Lakes (12). The BETR framework has also been applied to describe contaminant fate in continental Europe using a grid of 54 regions (13) and in the global environment on a coarse scale using 25 regions representing land and oceanic environments (14). All previous applications of the BETR model framework used a single set of parameters to describe the long-term average transfer rates of air and water between the regions within the model domain. As a result, temporal resolution has been limited to describing trends in contaminant concentrations that take place over several years. In the current application, we use a monthly time scale to specify atmospheric conditions and other selected parameters, and a 15° × 15° grid coverage of the globe, resulting in 288 multimedia regions (Figure 1). This approach significantly increases both spatial and temporal resolution as compared to previous multimedia models based on the BETR framework but is still substantially less temporally and spatially resolved than GCM-based models. We use information from the National Centers for Environmental Prediction (NCEP) reanalysis of global climate (15) to describe the atmosphere in BETR-Global. As described next, implementing the monthly resolved time scale and 15° × 15° grid coverage required adaptations of the multimedia model framework and averaging of the NCEP data.
Modifications to the BETR Framework The BETR model framework describes the movement of air and water between regional environments using matrixes of average flow rates (10). There are a total of five matrixes, specifying flow rates of air in the upper and lower troposphere, water between adjacent freshwater and ocean water compartments, and runoff of freshwater from one region into the ocean water of an adjacent region. Monthly time resolution is achieved in BETR-Global by replacing each of the yearly averaged values in these matrixes with a set of 12 values that represent monthly average conditions for a specified year. During a dynamic simulation, the values in the flow matrixes are adjusted every 30.417 ()365/12) days of model time. Simulations begin on day 1 of the year, and if they continue for more than 365 days, the cycle of monthly data is repeated. Using a similar approach, we also incorporated monthly resolved datasets for temperature and rainfall rate into the BETR framework. We model the episodic nature of rainfall events using the approximation method described by Jolliet et al. (16), with the number of rainfall events per month in each region estimated from data provided by the International Research Institute for Climate Prediction. Ocean water compartments have a 100 m depth that represents the surface mixed layer. The transfer of contaminants to the deep oceans by particle settling and mixing of bulk water across the interface between the mixed layer and the deep ocean is included as an irreversible loss process. Particle settling rates in the oceans are assigned to each region based on the data reported by Dachs et al. (17), and mixing from the surface layer to deep ocean water is modeled using an assumed vertical mass transfer coefficient of 0.55 cm day-1 (18). As a model designed to bridge the gap between multimedia fate models and GCMs, the BETR-Global model includes the option to solve the contaminant mass balance equations for pseudo-steady-state conditions. Multimedia models using a steady-state description of contaminant fate have proven to be powerful tools for sorting out environmental contaminants based on their fate and transport potential. Descriptors of chemical fate such as overall persistence (19) and characteristic travel distance (20, 21) are derived from steady-state multimedia models. GCM global contaminant fate models cannot produce steady-state solutions because their description of environmental conditions is temporally variable. In the BETR-Global model, we calculate pseudo-steady-state solutions by averaging the monthly resolved environmental conditions over a year in each of the 288 model regions. An advantage of the pseudo-steady-state solutions is a relatively short computing time, which makes feasible model sensitivity and uncertainty analyses.
Atmosphere and Surface Characterization The U.S. Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) have developed a retrospective (1948 to the present) record of global atmosphere, land, and ocean surface conditions, as well as energy and water fluxes between the surface and the atmosphere (15). This reanalysis product combines observations collected from a variety of sources with GCM analyses (22). Global climate data available from the reanalysis include atmospheric temperature and wind velocities at 28 pressure levels and geo-referenced precipitation rates. We used the monthly averaged reanalysis datasets of precipitation rates, air temperature, and atmospheric profiles of wind velocities to create a database of environmental conditions for the BETR-Global model. These data are compiled in the reanalysis on a 2.5° grid that we translated to a 15° grid by averaging values for air temperature and
precipitation rate within the larger grid area. To estimate the monthly average volumetric air flows in both directions across the boundaries of each grid cell, we multiplied the average of the 2.5° wind velocities resolved into scalar vectors perpendicular to the grid cell boundaries by the estimated height of each atmospheric layer and the length of the boundary between adjacent regions. Temperature and atmospheric circulation are modeled in the BETR-Global model in two separate atmospheric layers, parametrized using reanalysis data at the 950 and 500 hPa pressure levels. As a result, the heights of the air compartments vary among regions. The lower atmosphere layer, representing the planetary boundary layer, is typically ∼800 m high. The upper atmosphere compartment extends from the top of the lower compartment to approximately 6 km and represents the free troposphere above the planetary boundary layer. The BETR-Global model environmental database includes atmospheric data representing 40 individual years (19601999) drawn from the reanalysis dataset. In addition, the database includes a generic year in which temperatures, mixing heights, precipitation rates, and atmospheric flow rates are calculated as monthly geometric means over the 40 years. Land surface characteristics are parametrized using global 15° datasets of leaf area index (LAI), percent vegetation cover, vegetation height, soil type, soil organic carbon content, river runoff, and percent of surface covered by fresh and ocean water based on NCAR’s global common land model datasets (23). Annual changes in LAI are a characteristic of many plants, and these changes may affect the dynamics of POPs. However, for this first model application, we represent the global LAI distribution as constant throughout the year. We include a simple representation of ocean water circulation in the surface layer by assuming a uniform bulk horizontal mixing coefficient of 107 m2 h-1 (18).
Model Performance Evaluation The BETR-Global model is designed to capture important aspects of a highly complex system. We do not suggest that it is a true representation of how chemicals behave in the global environment. However, it becomes useful as a descriptive tool through a process of confidence building that includes testing of input parameters, verification of algorithms (i.e., testing of submodel structure, parametrization, and model implementation), and evaluation of model results against measurements. The goal of this section is to explain the processes we used for (i) quality assurance of atmospheric inputs, (ii) verification of the algorithm implementation, and (iii) evaluation of the description of the fate and transport of PCBs provided by the model. Quality Assurance of Atmospheric Inputs. The first step in our model evaluation process is to ensure that the atmospheric inputs used in the BETR-Global model accurately represent the NCEP reanalysis climate data. The Supporting Information includes maps of the 40 year averages of wind speeds and temperatures that are used as model inputs. We used maps of this type to compare model inputs to visualizations of the NCEP data to verify that they accurately represent the source data. The Supporting Information also includes maps showing statistical relationships between NAO index and (i) surface temperature and (ii) wind speeds during 1960-1999. Figure 2 shows an example of the relationship between surface temperature and the NAO index during the first quarter of the calendar year (Q1: January, February, and March). The relationships evident in the model inputs are consistent with the influence of NAO on temperature and air circulation as summarized by Hurrell (5). As shown in Figure 2 and Figures S4-S6 in the Supporting Information, positive NAO index conditions are correlated at some times VOL. 39, NO. 17, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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information on three different levels: for each environmental compartment, each region, and the entire global system. Any errors in the solutions of the mass balance equations are evident in at least one of these levels of mass balance accounting. Samples of model outputs illustrating the mass balances for a 70 year dynamic calculation for PCB153 are provided in the Supporting Information. Model Performance Evaluation. We evaluate the performance of the model as a descriptor of global-scale chemical fate by comparing model results with measured concentrations of PCBs at 11 monitoring stations located in seven different model regions. Data sources, monitoring locations, and time periods for the 11 sites are reported in Table 1. We calculated the corresponding atmospheric concentrations of the PCB congeners with the model using the maximum and default emission scenarios derived by Breivik et al. (24, 25) for the 70 year period 1930-2000. These calculations used the monthly resolved generic environmental properties (i.e., the model parametrization is representative of average conditions between 1960 and 1999). We used physicochemical properties and internal energies of phase change for the PCB congeners recommended by Li et al. (26) and estimated pseudo-first-order degradation rate constants for each environmental compartment from the generic half-life classes recommended by Mackay et al. (27). We modeled the temperature dependence of degradation reactions in all compartments by assuming an activation energy of 20 kJ/ mol, which corresponds approximately to reducing the degradation rate constant by a factor of 1.4 for every 10 °C reduction in temperature in the range between 25 and -25 °C. Figure 3 compares modeled and measured yearly average concentrations of the seven selected PCB congeners in air at the 11 monitoring sites for the two emission scenarios. We provide the diagonal lines for comparison. These lines represent perfect agreement between model and measurements, agreement within a factor of 3.16 ()100.5), and agreement within a factor of 10. Correlation coefficients between observed and modeled concentrations are statistically significant. Inspection of Figure 3 indicates that concentrations are generally under-predicted using the default emission scenario and slightly over-predicted with the maximum scenario. When we use the maximum scenario, 59% of the 479 modeled concentrations are within a factor of 3.16 of the measured concentration, and 96% are within a factor of 10. Therefore, the variability left unexplained by a 1:1 relationship between modeled and measured concentrations (i.e., the residual error) is less than 1 order of magnitude for 96% of the data. Agreement between modeled and measured PCB concentrations in air is satisfactory and demonstrates that the model is providing a verifiable
FIGURE 2. Relationship between NAO index and average surface temperature in Q1 (January, February, and March) for the years 1960-1999. Negative relationships are indicated in blue and positive relationships in red. of the year with (i) higher temperatures in Europe and central Asia, (ii) lower temperatures centered over southern Greenland and Labrador, (iii) higher west-to-east wind speeds across the Atlantic at mid-latitudes and, (iv) enhancement of south-to-north winds over western Europe and central Asia. Verification of Algorithm Implementation. In the second stage of our model evaluation process, we applied code verification tests to ensure that the equations and algorithms used in the model are implemented without error. Since it is based on the BETR contaminant fate modeling framework (10), many of the equations and algorithms used in the BETRGlobal model have been verified as correctly implemented in previous applications and model evaluation activities (11-14). Furthermore, for both steady state and dynamic calculations, the model output provides mass balance
TABLE 1. Sources for Long-term Monitoring Data of Atmospheric PCB Concentrations abbreviation
site name and location
latitude, longitude
BETR-Global model region
data period used in analysis
data availability
Zeppelin IVL Pallas IADN EGH IADN BNT Hazelrigg IVL Rorvik IVL Aspvreten CZK IADN SBD IADN STP IADN PPT
Zeppelin Station, Svalbard, Norway Pallas, Sweden Eagle Harbor, MI Burnt Island, Ontario, Canada Hazelrigg Station, Lancaster, England Rorvik, Sweden Aspvreten, Sweden Kosetice Observatory, Czech Republic Sleeping Bear Dunes, MI Sturgeon Point, NY Point Petre, Ontario, Canada
78°54′N, 11°53′E 67°58′N, 24°07′E 47°27′N, 88°08′W 45°49′N, 82°56′W 54°01′N, 02°46′W 57°25′N, 11°56′E 58°48′N, 17°23′E 49°35′N, 15°05′E 44°45′N, 86°03′W 42°41′N, 79°03′W 43°50′N, 77°09′W
13 38 55 55 60 61 62 62 79 79 79
1998-2003 1996-2002 1991-2000 1993-2000 1994-2003 1995-2001 1996-2002 1999-2002 1992-2000 1992-2000 1992-2000
a b c c d b b a c c c
a By request from Dr. Weche Aas, Norwegian Institute for Air Research (NILU). b Available for download from IVL Swedish Environmental Research Institute, http:// www.ivl.se/miljo/db/. c By request from Environment Canada. d By request from Dr. Robert G. M. Lee, Institute of Environmental and Natural Sciences, Lancaster University.
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FIGURE 3. Comparison of yearly averaged measured and modeled atmospheric PCB congener concentrations for the default and maximum emission scenarios reported by Breivik et al. (25). Measurement data are from 11 long-term monitoring stations located in seven different model regions (Table 1). description of PCB concentrations in the atmosphere of the Northern Hemisphere.
Model ApplicationsAnalysis of the Influence of the NAO on PCB Concentrations in Air The NAO corresponds with large-scale climate variability and is strongly correlated with the Arctic Oscillation (AO), a large-scale atmospheric phenomenon that impacts climate conditions in the Northern Hemisphere (8). Its influence is most clearly observed in monthly or seasonally averaged weather and climate data since transient and local influences often average out. As described previously, the BETR-Global Model uses monthly-resolved atmospheric circulation data corresponding to the years 1960-1999. Monthly NAO indices for this period were obtained from the NOAA Climate Prediction Center website (http://www.cpc.ncep.noaa.gov/products/ precip/CWlink/pna/nao_index.html). Our goal is to apply the BETR-Global model to estimate the impacts of NAO conditions on PCB concentrations in the atmosphere and to identify regions where these impacts might be observed. Therefore, we designed modeling experiments as a sensitivity analysis that compares simulated air concentrations under different NAO conditions. We carried
out these experiments using a monthly resolved database of environmental conditions for the years 1960-1999 that included NAO indexes, volumetric flow rates of air in the upper and lower atmosphere, heights of the planetary boundary layer, precipitation rates, and temperatures. We specified initial conditions for model simulations as the pseudo-steady-state global PCB inventories calculated using averages of the 1960-1999 environmental conditions. We used generic steady-state inventories as initial conditions to provide a consistent baseline for assessing variability in calculated concentrations due to environmental conditions. We then ran the model, using environmental conditions for each individual year, for two consecutive years of model time and emissions to the lower air compartment distributed according to the geographical apportionment of the total emission inventory for PCBs in the default emission scenario proposed by Breivik et al. (25). We recorded PCB concentrations in the lower atmosphere compartment at the end of each month during the second model year and converted these concentrations to quarterly averages. Relationships between quarterly averaged modeled PCB concentrations in air and quarterly averaged NAO indices were quantified by linear regression. Variability in calculated concentrations is the key parameter in evaluating the influence of the NAO on atmospheric PCB concentrations. We therefore adopted the default emission scenario rather than the maximum scenario because it gave a slightly higher correlation coefficient (0.67 vs 0.66) and a slope closer to one (0.80 vs 0.64) in linear regressions of the logarithms of monitored and modeled concentrations in our evaluation exercise (Figure 3). We used two emission scenarios and two representative PCB congeners to explore the possible range of chemical properties and contemporary emissions. The model was run for two PCBs representing low and high chlorination levels, PCB28 and PCB153, and two emission scenarios, a primary emission scenario in which there were continued constant emissions to the lower air compartment during the dynamic calculation and a secondary emission scenario in which primary emissions were set to zero during the dynamic modeling phase and the only source of PCB to the atmosphere was revolatilization from surface compartments. For comparison against modeled results, we analyzed quarterly averaged monitoring data for seven PCB congeners by performing regression analyses on quarterly averaged data from each of the 11 long-term monitoring sites against quarterly averaged NAO index. Quarterly averages were used because monitoring data from the Hazelrigg station is reported as quarterly bulked samples.
Results We evaluated potential relationships among the NAO index and PCB concentrations using both modeled PCB concentrations and measured concentrations. In both cases, we use regression analyses to test for significant relationships between variations in PCB concentrations and variations in the NAO index over the same period. Thus, we apply the same process to evaluate variability in both model output and observed concentrations with respect to variations in NAO index. Relationships in the monitoring data were evaluated using Spearman rank correlation coefficients to minimize the influence of outliers in the small data sets, which is consistent with the analysis by Ma et al. (9). Relationships between modeled PCB concentrations in air and NAO index were evaluated using Pearson’s correlation coefficients. Table 2 presents a summary of statistically significant relationships between NAO indices and modeled PCB concentrations in regions where long-term monitoring stations are located. Significance corresponds to a probability VOL. 39, NO. 17, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Normalized Slope of Statistically Significant (p < 0.05) Modeled Relationships between NAO Index and Modeled Atmospheric PCB Concentrations at Locations of Long-Term Monitoring Stations for the Primary and Secondary Emission Scenarios (% Change in PCB Concentration/Unit NAO Index) Primary Emissions Q1 (Jan., Feb., Mar.) Q2 (Apr., May, Jun.)
Q3 (Jul., Aug., Sep.)
Q4 (Oct., Nov., Dec.)
model region
monitoring stations
PCB28
PCB153
PCB28
PCB153
PCB28
PCB153
PCB28
PCB153
13 38 55 60 61 62 79
Zeppelin IVL Pallas IADN BNT and EGH Hazelrigg IVL Rorvik CZK and Aspvreten IADN SBD, STP, and PPT
nsa ns ns -10% -11% ns -3%
ns ns -2% -10% -5% +6% ns
ns +16% ns ns ns -16% ns
+19% +11% ns ns ns ns ns
ns ns ns ns ns ns ns
ns ns ns ns ns ns ns
+18% ns -3% -6% -4% ns ns
+11% ns ns -9% ns ns ns
Secondary Emissions Q1 Q2
Q3
Q4
model region
monitoring stations
PCB28
PCB153
PCB28
PCB153
PCB28
PCB153
PCB28
PCB153
13 38 55 60 61 62 79
Zeppelin IVL Pallas IADN BNT and EGH Hazelrigg IVL Rorvik CZK and Aspvreten IADN SBD, STP, and PPT
ns +8% ns -5% -4% +5% ns
ns ns +4% ns ns ns ns
ns ns ns ns ns -8% ns
ns ns ns ns ns -13% ns
ns ns -2% ns ns ns ns
ns ns ns ns ns ns ns
+13% ns ns ns ns ns ns
ns ns ns ns ns ns ns
a
ns: relationship not significant at p < 0.05.
TABLE 3. Results of Quarterly Averaged Monitored PCB Concentration Regressed Against NAO Index for PCB28, PCB52, PCB101, PCB118, PCB138, PCB153, and PCB180a number of significant regressions station name
N
Q1
Q2
Q3
Q4
TOTAL
F
24b
0 0 0 4 1 1 0 0 0 0 0 6
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 2 0 1 1 1 2 0 0 7
0 0 0 6 1 2 1 1 2 0 0 13
0 0 0 0.250 0.042 0.071 0.036 0.036 0.071 0 0 0.045
IADN SBD IADN STP 24b IADN EGH 24b IADN PPT 24b IADN BNT 24b Hazelrigg 28 Zeppelin 28 CZK 28 IVL Rorvik 28 IVL Aspvreten 28 IVL Pallas 28 total 288
a N ) number of regressions attempted and F ) fraction of attempted regressions that produced a significant relationship between PCB concentration and NAO index at p < 0.05. b Data for PCB153 are not available for STP, SBD, and EGH. Data for PCB118 are not available for PPT and BNT. Therefore, only six regressions per quarter were attempted for these sites.
FIGURE 4. Modeled relationship between average concentration of PCB28 in lower air and NAO index during Q1 (January, February, and March) under the primary emission scenario. of the null hypothesis less than 0.05 (p < 0.05). Complete results of the four modeling scenarios showing the slope and level of statistical significance of the quarterly relationships between atmospheric PCB concentration and NAO index in all 288 regions are included in the Supporting Information (Figures S9-S12). Figure 4 shows sample results for PCB28 under the primary emission scenario in Q1, when the effect of the NAO is pronounced. 6754
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As was done for the modeled results, we also looked for significant relationships among measured PCB concentrations and the NAO index. We regressed the quarterly averaged monitored PCB concentration against the NAO index for PCB28, PCB52, PCB101, PCB118, PCB138, PCB153, and PCB180. Table 3 shows a summary of the number of significant relationships between measured PCB concentration and NAO index obtained by analysis of the long-term monitoring data.
Discussion Generally, the relationships between modeled atmospheric PCB concentrations and NAO index can be explained by referring to the relationships between environmental conditions and NAO index. For example, in Q1 under both the primary and the secondary emission scenarios, there are positive relationships between NAO index and calculated
PCB28 concentrations over Eastern Europe and Russia (Figure 4 and Figures S9 and S10 in the Supporting Information). At this time of the year in these regions, there is also a positive correlation between surface temperature and NAO index, and we attribute the higher PCB concentrations to enhanced revolatilization from the terrestrial surface during relatively warm, positive NAO index years. The strongest modeled relationships between NAO index and PCB concentrations are characterized by approximately a 20% change in PCB concentration per unit of NAO index. Strong and significant relationships are predicted more often for PCB28 than for PCB153 and are more evident in Q1 and Q4 than in Q2 and Q3. This is consistent with the effects of the NAO being most pronounced during the winter and early spring. Given that the historical yearly variability in the NAO index is typically less than 4 units, the maximum variability in atmospheric PCB concentrations in response to NAO variability is predicted to be of the order of a factor of 2 and substantially less at most locations and times. The maximum magnitude of the relationship between PCB concentrations in air and NAO index derived from BETRGlobal is similar to that reported by Eckhardt et al. (28), who used a GCM to examine the influence of the NAO on transport of volatile chemicals to the Arctic in winter. Furthermore, the spatial patterns of positive and negative slopes of concentration in air as a function of NAO index reported by Eckhardt et al. (28) for volatile chemicals are very similar to our results for PCB28 under the primary emission scenario in Q1 (Figure 4). This is further evidence that the BETRGlobal model is providing results that are consistent with GCMs. In our opinion, a forcing due to the influence of the NAO that produces variability of less than a factor of 2 in concentrations would not necessarily be observable by analysis of the relatively short time series of PCB concentration data available from monitoring stations. Although a factor of 2 differences would be easily detectible using modern analytical techniques, there are several competing influences that could obscure the variability due to the NAO in the timeseries of monitoring data. For example, fluctuations in local PCB source strength to the atmosphere, which are ignored in the model but do exist in the real world, could conceivably overwhelm the effect of climate variability on atmospheric PCB concentrations at any location. Indeed, as shown in Table 3, our analysis of monitoring data from the 11 sites for all four quarters yielded only as many statistically significant relationships between atmospheric PCB concentration and NAO index as would be expected by random chance when p < 0.05. It is therefore difficult to make detailed comparisons between the modeled influence of the NAO on atmospheric PCB concentrations and data from the monitoring stations. It is noteworthy that the lack of statistically significant relationships in the monitoring data during Q2 and Q3 is consistent with the influence of the NAO being strongest in winter and spring (5). Our analysis of monitoring data is consonant with the positive correlations between concentrations of PCBs in air in the Great Lakes and NAO index reported by Ma et al. (9). We find a relatively high number of correlations between NAO index and monitoring data from the Point Petre IADN station (Table 3), and statistically significant correlations are only found in data from Q4 and Q1. Our model results do not indicate a strong dependence of atmospheric PCB concentrations on NAO index in the Great Lakes region. However, the model does predict positive correlations east of the Great Lakes, centered over the Maritime Provinces of Canada and Newfoundland (Figure 4 and Figures S9-S12 in the Supporting Information). Point Petre, as the most easterly of the IADN sites, would be the monitoring station most likely to be influenced by this effect.
Our model results suggest that relationships between atmospheric PCB concentrations and NAO index are most likely to be evident in data from the Zeppelin monitoring station on Svalbard, the Hazelrigg station in the UK, and the Rorvik station in Sweden. At Zeppelin, the model predicts relatively strong and positive relationships between atmospheric PCB concentration and NAO index during Q4 and Q2 under the primary emission scenario. We found a significant (p < 0.05) positive correlation between PCB52 concentration and NAO index in our analysis of monitoring data from Zeppelin. Ma et al. (9) reported positive correlations between NAO index and concentrations of PCB31, PCB44, and PCB138 at the Alert monitoring station (82°28′N, 62°30′W) in the high Arctic during March, April, and May, which approximately corresponds to Q2. At Hazelrigg and Rorvik, the model predicts statistically significant negative relationships between PCB concentration in air and NAO index in Q4 and Q1 under the primary emissions scenario. We found significant (p < 0.05) negative correlations between concentrations of PCB153 and PCB180 at Rorvik in Q4, which is consistent with the model results but positive correlations at Hazelrigg in Q1 for PCB52 and in Q4 for PCB28, which is the opposite of the model’s prediction. Hazelrigg and Rorvik are located in regions where the temperature is positively correlated with NAO index, but atmospheric PCB concentrations are predicted to be negatively correlated (see Figures 2 and 4 and the Supporting Information). Results from the Hazelrigg site may therefore be attributable to local sources of PCBs, which are more readily volatilized at warmer temperatures. Although the historical range of NAO variability implies a maximum influence on atmospheric concentrations of PCBs of approximately a factor of 2, if greenhouse gas forcing leads to wider fluctuations in the NAO, the impact will be more pronounced. We look forward to continued data collection from all long-term monitoring stations, which will increase the statistical power of the regression analysis and allow more meaningful comparisons against model predictions. The BETR-Global model illustrates the feasibility of incorporating complex and highly resolved information on global climate patterns into a compartment-based mass balance model framework. As a hybrid of the compartmentbased and GCM modeling philosophies, the BETR-Global model has merged some of the intuitiveness of conventional mass balance models and some of the spatial and temporal resolution of GCM-based POP fate calculations. It is desirable to continue developing, refining, and evaluating models of both types and to attempt to rationalize results and insights across the two modeling philosophies. This situation is advantageous since it encourages critical assessment of the models. We believe that BETR-Global will provide a valuable bridge between these two modeling philosophies, as well as be a useful tool for investigating and understanding the processes that control the global distribution of persistent chemicals.
Acknowledgments This work was supported by a Laboratory Directed Research and Development Program grant from the Lawrence Berkeley National Laboratory, by the U.S. Environmental Protection Agency National Exposure Research Laboratory through Interagency Agreement DW-988-38190-01-0, and by the U.S. Environmental Protection Agency Great Lakes National Program Office through Interagency Agreement DW-8994807501 and was carried out at the Lawrence Berkeley National Laboratory through the U.S. Department of Energy under Contract DE-AC03-76SF00098. Dr. MacLeod’s postdoctoral fellowship was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors thank Dr. Martin Scheringer (Swiss VOL. 39, NO. 17, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Federal Institute of Technology), Dr. Hayley Hung (Meteorological Service of Canada), and three anonymous reviewers for helpful comments and criticisms.
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Supporting Information Available Figures illustrating spatially resolved input data for the BETRGlobal model and relationships between selected input parameters and the NAO index for the years 1960-1999. Sample model output for the dynamic mass budget of PCB153 between 1930 and 2000 under the default emission scenario reported by Breivik et al. (25). Figures showing modeled relationships between atmospheric concentrations of PCB28 and NAO index, and PCB153 and NAO index, for the primary and secondary emission scenarios for all four quarters. This material is available free of charge via the Internet at http:// pubs.acs.org.
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Received for review October 6, 2004. Revised manuscript received June 3, 2005. Accepted June 7, 2005. ES048426R
