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Polychlorinated biphenyls (PCB) and dichlorodiphenyltrichloroethane (DDE) air concentrations in the Lake Ontario region: Trends and potential sources
Atmospheric Environment 44 (2010) 3173e3178
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Polychlorinated biphenyls (PCB) and dichlorodiphenyltrichloroethane (DDE) air concentrations in the Lake Ontario region: Trends and potential sources Hyun-Deok Choi a, James J. Pagano b, Michael S. Milligan c, Philip K. Hopke d, Steven Skubis e, Thomas M. Holsen a, * a
Department of Civil and Environmental Engineering, Clarkson University, Potsdam, NY 13699-5710, USA Environmental Research Center, Department of Chemistry, 310D Piez Hall, Takamine St., SUNY Oswego, Oswego, NY 13126, USA Department of Chemistry, 218 Houghton Hall, 280 Central Avenue, SUNY Fredonia, Fredonia, NY 14063, USA d Department of Chemical and Biomolecular Engineering, Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699-5708, USA e Department of Earth Sciences, SUNY Oswego, Oswego, NY 13126, USA b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 3 February 2010 Received in revised form 14 May 2010 Accepted 14 May 2010
Airborne particle and gas samples were collected approx every 12 days from April 2002 to June 2006 at the Sterling Nature Center located near the southeast corner of Lake Ontario. These samples were analyzed for polychlorinated biphenyls (PCBs) and dichlorodiphenyltrichloroethane (DDE). ClausiusClapeyron (CeC) regression analyses of PCBs and DDE yielded moderate correlations (r2 ¼ 0.54, p < 0.001; r2 ¼ 0.74, p < 0.001, respectively) indicating that much of the variations in concentrations can be explained by temperature. Back trajectory analysis indicated that the most important factors driving unusually high PCB partial pressures relative to those predicted by the CeC regression were slow wind speeds and winds generally from the southwest. This combination, which occurred frequently in 2004, increased contact of the air with contaminated upwind surfaces with minimum dilution. Hybrid receptor modeling (Potential Source Contribution Function (PSCF)) results for the total PCBs identified the midwestern US region that contains the urban areas of southern Indiana (IN), southwestern Ohio (OH), and northern Kentucky (KY) having the highest PSCF values. In general urban areas like Chicago (IL), Detroit (MI), Cleveland (OH), St. Louis (MO), and Nashville (TN) also had significant possibilities. In contrast, the PSCF modeling for DDE identified northern Alabama as the area with the highest probability where DDT was applied to cotton fields. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Polychlorinated biphenyls (PCB) Dichlorodiphenyltrichloroethane (DDE) Potential source contribution function (PSCF) Lake Ontario
1. Introduction Polychlorinated biphenyls (PCBs) and dichlorodiphenyltrichloroethane (DDE) are persistent, bioaccumulative, and toxic (PBT) organic pollutants. PCBs are classified as probable carcinogens by the U.S. Environmental Protection Agency (EPA) and their production was banned in 1977 (ATSDR, 2000). DDE is listed as a pollutant of concern by the U.S. EPA’s Great Waters Program because of its persistence in the environment, potential to bioaccumulate, and toxicity (EPA, 1994). DDE is found in the environment as a result of the decomposition of DDT, which was widely used for agricultural and commercially from 1945 to 1972 when its use was restricted or banned (EPA, 1975). PCBs are released to the environment from hazardous waste sites; illegal or improper disposal of industrial wastes and consumer products; leaks from old electrical
* Corresponding author. E-mail address: [email protected] (T.M. Holsen). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.05.031
transformers containing PCBs; landfills and burning of some wastes in incinerators (ATSDR, 2000). These activities occur primarily in urban areas suggesting that these areas are likely to be significant sources of PCBs (Hafner and Hites, 2003 and Yi et al., 2008). DDT has been widely used in agriculture to control insects on cotton, fruit, and corn. In 1972, 67 to 90% of the total United States consumption of DDT was applied to cotton. The remainder was primarily used on peanuts and soybeans (ATSDR, 2002). DDE, a major breakdown product of DDT, along with DDT is now commonly found in the environment. Since the production of these chemicals has been phased out, volatilization of previously deposited pollutants from terrestrial and aquatic sources is often the major source of PCBs and DDE measured in ambient air particularly in non-urban areas (Cortes et al., 1999, Simcik et al., 1999, Yi et al., 2008). Since volatilization from surfaces is an important source of both PCBs and DDE to the atmosphere, their atmospheric partial pressures are strongly correlated with ambient temperature (Cortes et al., 1998, Simcik et al., 1999). This relationship can be described using the Clausius-Clapeyron
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(CeC) equation: ln P ¼ (DH/R)(1/T) þ const. (where P is the partial pressure in atm, T is the ambient temperature in K, DH is the heat of vaporization in kJ mol1, and R is the gas constant). This equation relates the increase in natural logarithm vapor pressure to increasing temperature and predicts that these compounds can revolatilize from terrestrial and aquatic surfaces more readily as the temperature increases (Buehler et al., 2001, Su et al., 2007). In this study, PCBs and DDE air concentrations were measured at Sterling NY near Lake Ontario between 2002 and 2006. The objective of this study was to determine trends in PCB and DDE concentrations, to investigate the relationship between pollutant concentrations and meteorology using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and to indentify source regions for each pollutant contributing to the receptor site using a hybrid receptor model (potential source contribution function (PSCF)). 2. Experiments 2.1. Sampling site The sampling site was located at the Sterling Nature Center (43.34 N, 76.66 W), a 1400 acre nature preserve located near the southeast corner of Lake Ontario about 21 km southwest of Oswego, NY (Supporting information (SI) e 1) with approximately 3.2 km of undeveloped shoreline. The monitoring site is primarily forest and naturally reclaimed farmland and includes several dissected drumlins with vertical relief of 30 m or more above the lake. 2.2. Sampling methods and analytical methods Twenty-four hour air samples (n ¼ 132) were collected utilizing Tisch Environmental (Cleves, OH) TE-5100/TE PUF high volume air samplers between April 2002 and June 2006. The sampling schedule was based on the Integrated Atmospheric Deposition Network (IADN) sampling schedule and also included every six days sampling for the first year and three intensive 10day campaigns (SI-2). Flow rates of 0.85 m3 min1 resulted in total sample volumes of approximately 1200 m3 for each sample. Glass fiber filters (Whatman EPM 2000) were baked at 340 C overnight before use. Prior to their use, the polyurethane foam plugs (PUFs) were extracted in a Soxhlet apparatus for 24 h with dichloromethane, followed by hexane, and dried in a vacuum chamber. Analytical methods (congener specific capillary column ECD) were based on methods and standard operating procedures developed at the SUNY Oswego Environmental Research Center, the Lake Michigan Mass Balance Study, and the Integrated Atmospheric Deposition Network (IADN) as described in SI-3 and SI-4. 2.3. QA/QC Laboratory Quality Assurance/Quality Control was based on EPA protocols (EPA, 1997). The program consists of replicate analyses, surrogate analyte recoveries (IUPAC 14, 65, 166, 209, and PCT3 e F2), matrix spikes/matrix spike duplicates and, method, reagent and system blanks at prescribed intervals. Percent surrogate recoveries for air samples averaged (standard deviation (SD)): 98.5 11.9, 84.3 17.4, 102 13.7, 104 12.8, and 88.8 11.7 for PCB14, PCB65, PCB166, PCB209, and PCT3, respectively. PUF method blanks (n ¼ 25) averaged (SD) 0.030 0.014 ng m3. Field blanks (n ¼ 13) averaged (SD) 0.021 0.015 ng m3. A summary of all QC requirements and sample acceptance criteria for PCB analyses can be found in the project Quality Assurance Project Plans (Holsen, 2002; Pagano, 2005).
2.4. Hybrid Single-particle Lagrangian Integrated Trajectory (HYSPLIT) trajectory model Back trajectories were calculated with Eta Data Assimilation System (EDAS) (40 km grid) obtained from Gridded Meteorological Data Archives by the National Oceanic and Atmospheric Administration-Air Resources Laboratory (NOAA-ARL). The trajectories were calculated using the HYSPLIT Model Version 4.8 (three trajectories: starting at 0, 8, and 16 h of each sampling day). Based on a previous study (Hafner and Hites, 2003), 96 h back trajectories were used in this study. Several arrival heights (100, 500, 1000 m) above the local ground level were calculated. Similar to a previous study (Hsu et al., 2003), the trajectories starting at different heights had very similar trajectories. However, since 100 m arrival height trajectories often intersected the ground close to the sampling site and 1000 m arrival height results in greater trailing effects in PSCF modeling (discussed below), the trajectories with a 500 m arrival height were primarily used in this study. The trajectories paired with their concentration were divided into three categories based on the difference between measured and predicted partial pressures (taken as natural logarithm); then each sampling event was characterized in terms of temperature, precipitation, wind direction and wind speed creating two groups per category. The importance of each category was then evaluated. 2.5. Potential source contribution function (PSCF) PSCF is the probability that an air mass with specified pollutant concentrations arrives at a receptor site after having been observed to reside in a specific geographical cell. Cells containing pollutant sources have high conditional probabilities, so the conditional probability function can identify those source areas that have a potential to contribute to the high concentrations of contaminants observed at the receptor site (Malm et al., 1986, Zeng and Hopke, 1989). The algorithm of the PSCF model counts each trajectory segment endpoint that terminates within that grid cell. A detailed description of this approach can be found in Zeng and Hopke (Zeng and Hopke, 1989) and Choi et al. (Choi et al., 2008). An arbitrary weighting function (Wij) was applied to the PSCF values to reduce the uncertainty in a grid cell with a small number of endpoints since a small number of endpoints in a grid cell (nij) results in PSCF values with high uncertainty (Zeng and Hopke, 1989; Polissar et al., 2001). The function used was:
W nij ¼
8 1:00 > > < 0:75 0:50 > > : 0:15
2 ave < nij ave < nij 2 ave 0:5 ave < nij ave nij 0:5 ave
where “ave” is the average number of endpoints per each cell. A grid cell of 1 by 1 latitude and longitude was used. Generally, PSCF results show possible sources areas instead of indicating individual sources due to the trailing effect, plume dispersion, and inherent trajectory inaccuracies. The trailing effect occurs because PSCF gives a constant weight along the path of the trajectories. Therefore, areas upwind and downwind of actual sources may be identified as possible source areas, especially under low wind speed conditions. 3. Results and discussion 3.1. Overall concentrations of PCB and DDE The seasonal average concentrations SD (spring, summer, fall, and winter) of total (gas þ particle) PCBs and DDE were 354 319
H.-D. Choi et al. / Atmospheric Environment 44 (2010) 3173e3178
(n ¼ 36), 1105 961 (n ¼ 40), 638 868 (n ¼ 33), and 127 89 pg m3 (n ¼ 23) and 20.2 22.0 (n ¼ 36), 38.5 32.6 (n ¼ 40), 14.8 14.6 (n ¼ 33), and 2.9 2.6 pg m3 (n ¼ 23), respectively from April 2002 to June 2006 (Fig. 1). These concentrations are within the range of values reported for gas-phase concentrations near the Great Lakes (Hillery et al., 1997, Cortes et al., 1998, Cortes et al., 1999, Carlson et al., 2004, Sun et al., 2006) except for the PCB concentrations in the summer and fall of 2004. It should be noted that particle phase concentrations generally account for only 5e10% of the total PCBs (Chiarenzelli et al., 2001) and <10% of the total DDE (Hermanson et al., 2007). Previous work has shown that gas-phase concentrations of legacy semi-volatile organic compounds are strongly correlated to atmospheric temperature resulting in higher concentrations in warmer seasons and lower concentrations in cold seasons (Hillery et al., 1997, Cortes et al., 1998, Wania et al., 1998, Cortes et al., 1999, Carlson et al., 2004). Seasonal trends in total (gas þ particle) PCBs and DDE concentrations measured in this study generally agree with this finding (Fig. 1). It should be noted that the number of samples and the season in which they were collected were not the same in all years which may influence these results. Interestingly high PCB concentrations (averaged 1.6 ng m3) were measured in 2004. This value is much higher than other sampling years (averaged 0.4 ng m3). Nearly all of the PCB partial pressure data points in 2004 were distributed above the 95%
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prediction interval of the CeC regression (SI-5) (Note: the use of natural logarithm partial pressure (ln P) in these figures is not strictly correct since the measured concentrations included the particle phase). The concentrations measured each year were found to be significantly different from other years using a post-hock ANOVA test. To determine if these unusual PCB concentrations in 2004 occurred over a larger area, these data were compared to data from the Integrated Atmospheric Deposition Network (IADN) sites at Sturgeon Point and Point Petre (Hites, 2009). The Sturgeon Point site is located at about 225 km west-southwest of Sterling and the Point Petre site is located at about 72 km northwest of the Sterling site across Lake Ontario (SI-1). Data from Point Petre did not show the abnormally high PCB concentrations in the summer of 2004 as was seen at Sterling (SI-6). Unfortunately, the summer of 2004 data from Sturgeon point site are not available. The unusual increases of PCB concentrations at Sterling in 2004 will be discussed in more detail below. In contrast the DDE partial pressure data points were all distributed within the 95% confidence interval of the regression (SI-5). 3.2. HYSPLIT trajectory modeling The differences between the measured natural logarithm partial pressure and the natural logarithm partial pressure predicted by the CeC regression equations (SI-5) were used to categorize the samples into high, middle and low relative natural logarithm partial pressure (RP). RP was defined as the difference between the actual natural logarithm P and the natural logarithm P predicted by the CeC regression line. The category boundaries (high, medium and low) were determined using half the value of the RP standard deviation. The number of samples in each category for PCBs (high, medium, and low) was 26, 78, and 28 and for DDE was 27, 84 and 22 (Table 1). To help determine the conditions responsible for a sample being in a particular category, back trajectories were calculated for all of the samples using HYSPLIT. Precipitation or no precipitation was determined using the precipitation record at the sampling site. If the precipitation depth was less than 1.0 mm per day, it was regarded as a non-precipitation sample. The criterion to delineate between high and low temperature was the average surface temperature (15 C). Transport speed and wind direction were determined using 96 h back-trajectory maps. If the total travel distance over 96 h was more than approx. 2000 km (about 5.8 m s1: the average speed of all the back trajectories), it was categorized into the fast transport category, and if it was less than this, the sample was categorized into the slow transport category. The wind direction was determined using the primary orientation of the back trajectory. Trajectory flow directions were partitioned into two 180 sectors on the basis of source locations upwind from
Table 1 The number of event that occurred at each criterion of temperature, precipitation, wind direction, and wind speed for PCBs and DDE. The number in the parenthesis is the number of samples in each category. (Unit:event)
Temperature Precipitation
Fig. 1. Seasonal average concentrations and standard deviations (SD) (left) and yearly average concentrations and SD (right) of (a) PCBs and (b) DDE.
Wind Direction Wind Speed
High (68) Low (64) Yes (53) No (79) Northeast (42) Southwest (90) Fast (82) Slow (50)
Relative PCB partial pressure
Relative DDE partial pressure
High (26)
Middle (78)
Low (28)
High (27)
Middle (83)
Low (22)
15 11 7 19 2 24 8 18
36 42 34 44 24 54 52 26
17 11 12 16 16 12 22 6
14 13 14 13 1 26 13 14
40 43 30 53 29 54 52 31
14 8 9 13 12 10 17 5
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the sample site: northwest to southwest to southeast and northwest to northeast to southeast are designated the southwest and northeast sectors, respectively. A high PCB RP was found when winds came from the southwest sector (24 out of 26), transport speeds were slow (18 out of 26), and there was no precipitation (19 out of 26) (Table 1). (Note: winds were mostly confined to the southwest sector (90 out of 132) during sampling events.) A low PCB RP was related to wind coming from the northeast (16 out of 28) and high wind speed (22 out of 28). Low PCB RP generally occurred when the temperature was high (17 out of 28) and there was no precipitation (16 out of 28). Based on this analysis, the most important factors related to the abnormally high RP were low wind speeds and wind flow within the southwest sector where significant PCB sources are located (primarily urban areas; see the modeling section below). These conditions occurred most often when the temperature was higher than a criterion of 15 C, there was no rain, and most commonly in 2004. This combination of factors resulted in elevated volatilization of PCBs from surfaces with less dilution with cleaner air. These samples are to the right (lower temperature) of where the CeC equation would predict suggesting that the PCBs measured at these times were not controlled by local air temperatures. Abnormally low RP occurred primarily when winds were from the northeast and wind speeds were elevated. These conditions would bring air from relatively unpolluted regions with significant dilution to the sampling site. These samples are to the left of the position predicted by the CC regression, showing lower than expected PCB partial pressures at the given temperatures.
The DDE results indicate that high DDE RP values were strongly related to wind flow within the southwest sector (26 out of 27) (Table 1). High temperature, no precipitation, and low wind speed had minor or no effects (14 out of 27, 13 out of 27, and 14 out of 27). Similar to PCBs, low DDE RP were related to high wind speeds (17 out of 22) and wind direction from northeast (12 out of 22). In order to confirm that prevailing wind directions and the residence time during transport were related to the high PCB RP measured in 2004, the total number of endpoints in each grid were plotted for 2004 and the other years (SI-7). The difference in endpoint locations shows a greater percentage of endpoints occurred to the southwest and northwest of the sampling site in 2004 than in the other years (SI-7). To further analyze the PCB data they were categorized into 4 groups: 1) low concentration and low RP (n ¼ 81), 2) low concentration and high RP (n ¼ 17), 3) high concentration and low RP (n ¼ 5), and 4) high concentration and high RP (n ¼ 31) (SI-8). The arithmetic mean concentration was used to distinguish between high and low PCB concentrations and the regression line in PCB CeC plots was used to distinguish between high and low PCB RP. Back trajectory analysis indicated most of the Group 1 samples were obtained on the days of northeast wind direction, fast wind speed, or precipitation. In general back trajectories in Groups 2, 3, and 4 passed slowly through urban areas during periods of no precipitation. Of particular interest are samples in Group 2 which had low concentrations but high RP. All of these samples had back trajectories that passed through urban areas (Fig. 2). The samples farthest from the CeC regression line (most impacted) passed
Fig. 2. 4 day back trajectories of sampling events coordinating with low concentrations (below mean concentration) but high relative partial pressure (above the regression line of CeC plots) of PCBs shown in order of distance between regression line and partial pressure. (a) 12/15/2004, (b) 12/6/2004, (c) 12/5/2004, (d) 4/9/2004, (e) 4/8/2002, (f) 12/12/05, (g) 4/14/ 2002, (h) 1/5/2006, and (i) 5/3/2004. The urbanized area shown in grey (US only) is based on the Census 2000 Urbanized Areas, a population of at least 50,000 people.
H.-D. Choi et al. / Atmospheric Environment 44 (2010) 3173e3178
through the Toronto urban area, the closest large urban area to the sampling site. Interestingly of these nine Group 2 samples, five occurred in 2004. 3.3. PSCF modeling The CeC regression lines for both PCBs and DDE (SI-5) were used as criteria to distinguish between high and low RP. PSCF results for PCBs identified the Midwestern US that contains the urban areas of southern IN, southwestern OH, and northern KY with the highest PSCF values (Fig. 3a). In general, urban areas like Chicago (IL), Detroit (MI), Cleveland (OH), St. Louis (MO), Nashville (TN), Birmingham (AL), and Winnipeg (Canada) had significant conditional probabilities (Fig. 3a). These identified areas agree with results of previous studies (Hafner and Hites, 2003; Breivik et al., 2007). However, Birmingham (AL), Madison (WI), Winnipeg (Canada), and Thunder Bay (Canada) may be affected by the trailing effect because those locations are upwind of locations identified as significant source areas (white square in Fig. 3-a). The PSCF model results for DDE are not as complicated as the model result for PCBs probably because DDE was primarily used in a smaller geographic region. Northern Alabama was the only area with the highest and most significant probability (Fig. 3b).
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Historically, over 80% of the DDT produced in 1970e72 was applied to cotton fields located in mostly southern United States (EPA, 1975). In addition, between 1947 and 1970, the Olin Chemical Company discharged as much as an estimated 8000 tons of DDT residues (DDT, DDD and DDE) into Huntsville Spring Branch and Indian Creek in Alabama shown in white circle in Fig. 3b (Reich et al., 1986, EPA, 2000). Acknowledgements This project was funded by the U.S. Environmental Protection Agency Region 2 and 5 (Project Numbers XA-97298703, X98248702-1, X98248701-0), NEIWPCCF (Project Number M-001991-02), and Great Lakes National Program Office (GLNPO). The authors acknowledge the many dedicated hours of field sampling and sample preparation by Lauren Falanga and Greg Sumner at the SUNY Oswego ERC. We also thank the Jim D’Angelo and the entire staff of the Sterling Nature Center, Cayuga County Parks and Recreation for the many years of assisting our project. This is the Clarkson Center for the Environment publication No. 211. Appendix. Supplementary information Supplementary information associated with this paper can be found, in the online version, at doi:10.1016/j.atmosenv.2010.05.031 References
Fig. 3. PSCF results of (a) PCBs and (b) DDE with 4 day back trajectories from 2002 to 2006. White dots are urban cities in (a). The Kriging method was applied to interpolate between geographic locations.
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Hillery, B.R., Basu, I., Sweet, C.W., Hites, R.A., 1997. Temporal and spatial trends in a long-term study of gas-phase PCB concentrations near the Great Lakes. Environmental Science & Technology 31, 1811e1816. Hites, R.A., 2009. Personnel communication. Holsen, T.M., 2002. Quality Assurance Project Plan (QAPP) Lake Ontario Atmospheric Deposition Study (LOADS), U.S. EPA Region 2, New York, NY. Hsu, Y.-K., Holsen, T.M., Hopke, P.K., 2003. Comparison of hybrid receptor models to locate PCB sources in Chicago. Atmospheric Environment 37, 545e562. Malm, W.C., Johnson, C.E., Bresch, J.F., 1986. Application of Principal Component Analysis for Purposes of Identifying SourceeReceptor Relationships in Receptor Methods for Source Apportionment. Air Pollution Control Association, Pittsburgh, PA, pp. 127e148. Pagano, J.J., 2005. Deposition and Ambient Concentrations of PBTs (PCBs, OCs, PBDEs, and Dioxins/Furans) in Support of the Lake Ontario Air Deposition Study (LOADS), Quality Assurance Project Plan (QAPP), Great Lakes Commission-Great Lakes Air Deposition (GLAD) Program, Ann Arbor, MI. Polissar, A.V., Hopke, P.K., Harris, J.M., 2001. Source regions for atmospheric aerosol measured at Barrow, Alaska. Environmental Science & Technology 35, 4214e4226.
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Polychlorinated biphenyls (PCB) and dichlorodiphenyltrichloroethane (DDE) air concentrations in the Lake Ontario region: Trends and potential sources Hyun-Deok Choi a, James J. Pagano b, Michael S. Milligan c, Philip K. Hopke d, Steven Skubis e, Thomas M. Holsen a, * a
Department of Civil and Environmental Engineering, Clarkson University, Potsdam, NY 13699-5710, USA Environmental Research Center, Department of Chemistry, 310D Piez Hall, Takamine St., SUNY Oswego, Oswego, NY 13126, USA Department of Chemistry, 218 Houghton Hall, 280 Central Avenue, SUNY Fredonia, Fredonia, NY 14063, USA d Department of Chemical and Biomolecular Engineering, Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699-5708, USA e Department of Earth Sciences, SUNY Oswego, Oswego, NY 13126, USA b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 3 February 2010 Received in revised form 14 May 2010 Accepted 14 May 2010
Airborne particle and gas samples were collected approx every 12 days from April 2002 to June 2006 at the Sterling Nature Center located near the southeast corner of Lake Ontario. These samples were analyzed for polychlorinated biphenyls (PCBs) and dichlorodiphenyltrichloroethane (DDE). ClausiusClapeyron (CeC) regression analyses of PCBs and DDE yielded moderate correlations (r2 ¼ 0.54, p < 0.001; r2 ¼ 0.74, p < 0.001, respectively) indicating that much of the variations in concentrations can be explained by temperature. Back trajectory analysis indicated that the most important factors driving unusually high PCB partial pressures relative to those predicted by the CeC regression were slow wind speeds and winds generally from the southwest. This combination, which occurred frequently in 2004, increased contact of the air with contaminated upwind surfaces with minimum dilution. Hybrid receptor modeling (Potential Source Contribution Function (PSCF)) results for the total PCBs identified the midwestern US region that contains the urban areas of southern Indiana (IN), southwestern Ohio (OH), and northern Kentucky (KY) having the highest PSCF values. In general urban areas like Chicago (IL), Detroit (MI), Cleveland (OH), St. Louis (MO), and Nashville (TN) also had significant possibilities. In contrast, the PSCF modeling for DDE identified northern Alabama as the area with the highest probability where DDT was applied to cotton fields. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Polychlorinated biphenyls (PCB) Dichlorodiphenyltrichloroethane (DDE) Potential source contribution function (PSCF) Lake Ontario
1. Introduction Polychlorinated biphenyls (PCBs) and dichlorodiphenyltrichloroethane (DDE) are persistent, bioaccumulative, and toxic (PBT) organic pollutants. PCBs are classified as probable carcinogens by the U.S. Environmental Protection Agency (EPA) and their production was banned in 1977 (ATSDR, 2000). DDE is listed as a pollutant of concern by the U.S. EPA’s Great Waters Program because of its persistence in the environment, potential to bioaccumulate, and toxicity (EPA, 1994). DDE is found in the environment as a result of the decomposition of DDT, which was widely used for agricultural and commercially from 1945 to 1972 when its use was restricted or banned (EPA, 1975). PCBs are released to the environment from hazardous waste sites; illegal or improper disposal of industrial wastes and consumer products; leaks from old electrical
* Corresponding author. E-mail address: [email protected] (T.M. Holsen). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.05.031
transformers containing PCBs; landfills and burning of some wastes in incinerators (ATSDR, 2000). These activities occur primarily in urban areas suggesting that these areas are likely to be significant sources of PCBs (Hafner and Hites, 2003 and Yi et al., 2008). DDT has been widely used in agriculture to control insects on cotton, fruit, and corn. In 1972, 67 to 90% of the total United States consumption of DDT was applied to cotton. The remainder was primarily used on peanuts and soybeans (ATSDR, 2002). DDE, a major breakdown product of DDT, along with DDT is now commonly found in the environment. Since the production of these chemicals has been phased out, volatilization of previously deposited pollutants from terrestrial and aquatic sources is often the major source of PCBs and DDE measured in ambient air particularly in non-urban areas (Cortes et al., 1999, Simcik et al., 1999, Yi et al., 2008). Since volatilization from surfaces is an important source of both PCBs and DDE to the atmosphere, their atmospheric partial pressures are strongly correlated with ambient temperature (Cortes et al., 1998, Simcik et al., 1999). This relationship can be described using the Clausius-Clapeyron
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(CeC) equation: ln P ¼ (DH/R)(1/T) þ const. (where P is the partial pressure in atm, T is the ambient temperature in K, DH is the heat of vaporization in kJ mol1, and R is the gas constant). This equation relates the increase in natural logarithm vapor pressure to increasing temperature and predicts that these compounds can revolatilize from terrestrial and aquatic surfaces more readily as the temperature increases (Buehler et al., 2001, Su et al., 2007). In this study, PCBs and DDE air concentrations were measured at Sterling NY near Lake Ontario between 2002 and 2006. The objective of this study was to determine trends in PCB and DDE concentrations, to investigate the relationship between pollutant concentrations and meteorology using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and to indentify source regions for each pollutant contributing to the receptor site using a hybrid receptor model (potential source contribution function (PSCF)). 2. Experiments 2.1. Sampling site The sampling site was located at the Sterling Nature Center (43.34 N, 76.66 W), a 1400 acre nature preserve located near the southeast corner of Lake Ontario about 21 km southwest of Oswego, NY (Supporting information (SI) e 1) with approximately 3.2 km of undeveloped shoreline. The monitoring site is primarily forest and naturally reclaimed farmland and includes several dissected drumlins with vertical relief of 30 m or more above the lake. 2.2. Sampling methods and analytical methods Twenty-four hour air samples (n ¼ 132) were collected utilizing Tisch Environmental (Cleves, OH) TE-5100/TE PUF high volume air samplers between April 2002 and June 2006. The sampling schedule was based on the Integrated Atmospheric Deposition Network (IADN) sampling schedule and also included every six days sampling for the first year and three intensive 10day campaigns (SI-2). Flow rates of 0.85 m3 min1 resulted in total sample volumes of approximately 1200 m3 for each sample. Glass fiber filters (Whatman EPM 2000) were baked at 340 C overnight before use. Prior to their use, the polyurethane foam plugs (PUFs) were extracted in a Soxhlet apparatus for 24 h with dichloromethane, followed by hexane, and dried in a vacuum chamber. Analytical methods (congener specific capillary column ECD) were based on methods and standard operating procedures developed at the SUNY Oswego Environmental Research Center, the Lake Michigan Mass Balance Study, and the Integrated Atmospheric Deposition Network (IADN) as described in SI-3 and SI-4. 2.3. QA/QC Laboratory Quality Assurance/Quality Control was based on EPA protocols (EPA, 1997). The program consists of replicate analyses, surrogate analyte recoveries (IUPAC 14, 65, 166, 209, and PCT3 e F2), matrix spikes/matrix spike duplicates and, method, reagent and system blanks at prescribed intervals. Percent surrogate recoveries for air samples averaged (standard deviation (SD)): 98.5 11.9, 84.3 17.4, 102 13.7, 104 12.8, and 88.8 11.7 for PCB14, PCB65, PCB166, PCB209, and PCT3, respectively. PUF method blanks (n ¼ 25) averaged (SD) 0.030 0.014 ng m3. Field blanks (n ¼ 13) averaged (SD) 0.021 0.015 ng m3. A summary of all QC requirements and sample acceptance criteria for PCB analyses can be found in the project Quality Assurance Project Plans (Holsen, 2002; Pagano, 2005).
2.4. Hybrid Single-particle Lagrangian Integrated Trajectory (HYSPLIT) trajectory model Back trajectories were calculated with Eta Data Assimilation System (EDAS) (40 km grid) obtained from Gridded Meteorological Data Archives by the National Oceanic and Atmospheric Administration-Air Resources Laboratory (NOAA-ARL). The trajectories were calculated using the HYSPLIT Model Version 4.8 (three trajectories: starting at 0, 8, and 16 h of each sampling day). Based on a previous study (Hafner and Hites, 2003), 96 h back trajectories were used in this study. Several arrival heights (100, 500, 1000 m) above the local ground level were calculated. Similar to a previous study (Hsu et al., 2003), the trajectories starting at different heights had very similar trajectories. However, since 100 m arrival height trajectories often intersected the ground close to the sampling site and 1000 m arrival height results in greater trailing effects in PSCF modeling (discussed below), the trajectories with a 500 m arrival height were primarily used in this study. The trajectories paired with their concentration were divided into three categories based on the difference between measured and predicted partial pressures (taken as natural logarithm); then each sampling event was characterized in terms of temperature, precipitation, wind direction and wind speed creating two groups per category. The importance of each category was then evaluated. 2.5. Potential source contribution function (PSCF) PSCF is the probability that an air mass with specified pollutant concentrations arrives at a receptor site after having been observed to reside in a specific geographical cell. Cells containing pollutant sources have high conditional probabilities, so the conditional probability function can identify those source areas that have a potential to contribute to the high concentrations of contaminants observed at the receptor site (Malm et al., 1986, Zeng and Hopke, 1989). The algorithm of the PSCF model counts each trajectory segment endpoint that terminates within that grid cell. A detailed description of this approach can be found in Zeng and Hopke (Zeng and Hopke, 1989) and Choi et al. (Choi et al., 2008). An arbitrary weighting function (Wij) was applied to the PSCF values to reduce the uncertainty in a grid cell with a small number of endpoints since a small number of endpoints in a grid cell (nij) results in PSCF values with high uncertainty (Zeng and Hopke, 1989; Polissar et al., 2001). The function used was:
W nij ¼
8 1:00 > > < 0:75 0:50 > > : 0:15
2 ave < nij ave < nij 2 ave 0:5 ave < nij ave nij 0:5 ave
where “ave” is the average number of endpoints per each cell. A grid cell of 1 by 1 latitude and longitude was used. Generally, PSCF results show possible sources areas instead of indicating individual sources due to the trailing effect, plume dispersion, and inherent trajectory inaccuracies. The trailing effect occurs because PSCF gives a constant weight along the path of the trajectories. Therefore, areas upwind and downwind of actual sources may be identified as possible source areas, especially under low wind speed conditions. 3. Results and discussion 3.1. Overall concentrations of PCB and DDE The seasonal average concentrations SD (spring, summer, fall, and winter) of total (gas þ particle) PCBs and DDE were 354 319
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(n ¼ 36), 1105 961 (n ¼ 40), 638 868 (n ¼ 33), and 127 89 pg m3 (n ¼ 23) and 20.2 22.0 (n ¼ 36), 38.5 32.6 (n ¼ 40), 14.8 14.6 (n ¼ 33), and 2.9 2.6 pg m3 (n ¼ 23), respectively from April 2002 to June 2006 (Fig. 1). These concentrations are within the range of values reported for gas-phase concentrations near the Great Lakes (Hillery et al., 1997, Cortes et al., 1998, Cortes et al., 1999, Carlson et al., 2004, Sun et al., 2006) except for the PCB concentrations in the summer and fall of 2004. It should be noted that particle phase concentrations generally account for only 5e10% of the total PCBs (Chiarenzelli et al., 2001) and <10% of the total DDE (Hermanson et al., 2007). Previous work has shown that gas-phase concentrations of legacy semi-volatile organic compounds are strongly correlated to atmospheric temperature resulting in higher concentrations in warmer seasons and lower concentrations in cold seasons (Hillery et al., 1997, Cortes et al., 1998, Wania et al., 1998, Cortes et al., 1999, Carlson et al., 2004). Seasonal trends in total (gas þ particle) PCBs and DDE concentrations measured in this study generally agree with this finding (Fig. 1). It should be noted that the number of samples and the season in which they were collected were not the same in all years which may influence these results. Interestingly high PCB concentrations (averaged 1.6 ng m3) were measured in 2004. This value is much higher than other sampling years (averaged 0.4 ng m3). Nearly all of the PCB partial pressure data points in 2004 were distributed above the 95%
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prediction interval of the CeC regression (SI-5) (Note: the use of natural logarithm partial pressure (ln P) in these figures is not strictly correct since the measured concentrations included the particle phase). The concentrations measured each year were found to be significantly different from other years using a post-hock ANOVA test. To determine if these unusual PCB concentrations in 2004 occurred over a larger area, these data were compared to data from the Integrated Atmospheric Deposition Network (IADN) sites at Sturgeon Point and Point Petre (Hites, 2009). The Sturgeon Point site is located at about 225 km west-southwest of Sterling and the Point Petre site is located at about 72 km northwest of the Sterling site across Lake Ontario (SI-1). Data from Point Petre did not show the abnormally high PCB concentrations in the summer of 2004 as was seen at Sterling (SI-6). Unfortunately, the summer of 2004 data from Sturgeon point site are not available. The unusual increases of PCB concentrations at Sterling in 2004 will be discussed in more detail below. In contrast the DDE partial pressure data points were all distributed within the 95% confidence interval of the regression (SI-5). 3.2. HYSPLIT trajectory modeling The differences between the measured natural logarithm partial pressure and the natural logarithm partial pressure predicted by the CeC regression equations (SI-5) were used to categorize the samples into high, middle and low relative natural logarithm partial pressure (RP). RP was defined as the difference between the actual natural logarithm P and the natural logarithm P predicted by the CeC regression line. The category boundaries (high, medium and low) were determined using half the value of the RP standard deviation. The number of samples in each category for PCBs (high, medium, and low) was 26, 78, and 28 and for DDE was 27, 84 and 22 (Table 1). To help determine the conditions responsible for a sample being in a particular category, back trajectories were calculated for all of the samples using HYSPLIT. Precipitation or no precipitation was determined using the precipitation record at the sampling site. If the precipitation depth was less than 1.0 mm per day, it was regarded as a non-precipitation sample. The criterion to delineate between high and low temperature was the average surface temperature (15 C). Transport speed and wind direction were determined using 96 h back-trajectory maps. If the total travel distance over 96 h was more than approx. 2000 km (about 5.8 m s1: the average speed of all the back trajectories), it was categorized into the fast transport category, and if it was less than this, the sample was categorized into the slow transport category. The wind direction was determined using the primary orientation of the back trajectory. Trajectory flow directions were partitioned into two 180 sectors on the basis of source locations upwind from
Table 1 The number of event that occurred at each criterion of temperature, precipitation, wind direction, and wind speed for PCBs and DDE. The number in the parenthesis is the number of samples in each category. (Unit:event)
Temperature Precipitation
Fig. 1. Seasonal average concentrations and standard deviations (SD) (left) and yearly average concentrations and SD (right) of (a) PCBs and (b) DDE.
Wind Direction Wind Speed
High (68) Low (64) Yes (53) No (79) Northeast (42) Southwest (90) Fast (82) Slow (50)
Relative PCB partial pressure
Relative DDE partial pressure
High (26)
Middle (78)
Low (28)
High (27)
Middle (83)
Low (22)
15 11 7 19 2 24 8 18
36 42 34 44 24 54 52 26
17 11 12 16 16 12 22 6
14 13 14 13 1 26 13 14
40 43 30 53 29 54 52 31
14 8 9 13 12 10 17 5
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the sample site: northwest to southwest to southeast and northwest to northeast to southeast are designated the southwest and northeast sectors, respectively. A high PCB RP was found when winds came from the southwest sector (24 out of 26), transport speeds were slow (18 out of 26), and there was no precipitation (19 out of 26) (Table 1). (Note: winds were mostly confined to the southwest sector (90 out of 132) during sampling events.) A low PCB RP was related to wind coming from the northeast (16 out of 28) and high wind speed (22 out of 28). Low PCB RP generally occurred when the temperature was high (17 out of 28) and there was no precipitation (16 out of 28). Based on this analysis, the most important factors related to the abnormally high RP were low wind speeds and wind flow within the southwest sector where significant PCB sources are located (primarily urban areas; see the modeling section below). These conditions occurred most often when the temperature was higher than a criterion of 15 C, there was no rain, and most commonly in 2004. This combination of factors resulted in elevated volatilization of PCBs from surfaces with less dilution with cleaner air. These samples are to the right (lower temperature) of where the CeC equation would predict suggesting that the PCBs measured at these times were not controlled by local air temperatures. Abnormally low RP occurred primarily when winds were from the northeast and wind speeds were elevated. These conditions would bring air from relatively unpolluted regions with significant dilution to the sampling site. These samples are to the left of the position predicted by the CC regression, showing lower than expected PCB partial pressures at the given temperatures.
The DDE results indicate that high DDE RP values were strongly related to wind flow within the southwest sector (26 out of 27) (Table 1). High temperature, no precipitation, and low wind speed had minor or no effects (14 out of 27, 13 out of 27, and 14 out of 27). Similar to PCBs, low DDE RP were related to high wind speeds (17 out of 22) and wind direction from northeast (12 out of 22). In order to confirm that prevailing wind directions and the residence time during transport were related to the high PCB RP measured in 2004, the total number of endpoints in each grid were plotted for 2004 and the other years (SI-7). The difference in endpoint locations shows a greater percentage of endpoints occurred to the southwest and northwest of the sampling site in 2004 than in the other years (SI-7). To further analyze the PCB data they were categorized into 4 groups: 1) low concentration and low RP (n ¼ 81), 2) low concentration and high RP (n ¼ 17), 3) high concentration and low RP (n ¼ 5), and 4) high concentration and high RP (n ¼ 31) (SI-8). The arithmetic mean concentration was used to distinguish between high and low PCB concentrations and the regression line in PCB CeC plots was used to distinguish between high and low PCB RP. Back trajectory analysis indicated most of the Group 1 samples were obtained on the days of northeast wind direction, fast wind speed, or precipitation. In general back trajectories in Groups 2, 3, and 4 passed slowly through urban areas during periods of no precipitation. Of particular interest are samples in Group 2 which had low concentrations but high RP. All of these samples had back trajectories that passed through urban areas (Fig. 2). The samples farthest from the CeC regression line (most impacted) passed
Fig. 2. 4 day back trajectories of sampling events coordinating with low concentrations (below mean concentration) but high relative partial pressure (above the regression line of CeC plots) of PCBs shown in order of distance between regression line and partial pressure. (a) 12/15/2004, (b) 12/6/2004, (c) 12/5/2004, (d) 4/9/2004, (e) 4/8/2002, (f) 12/12/05, (g) 4/14/ 2002, (h) 1/5/2006, and (i) 5/3/2004. The urbanized area shown in grey (US only) is based on the Census 2000 Urbanized Areas, a population of at least 50,000 people.
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through the Toronto urban area, the closest large urban area to the sampling site. Interestingly of these nine Group 2 samples, five occurred in 2004. 3.3. PSCF modeling The CeC regression lines for both PCBs and DDE (SI-5) were used as criteria to distinguish between high and low RP. PSCF results for PCBs identified the Midwestern US that contains the urban areas of southern IN, southwestern OH, and northern KY with the highest PSCF values (Fig. 3a). In general, urban areas like Chicago (IL), Detroit (MI), Cleveland (OH), St. Louis (MO), Nashville (TN), Birmingham (AL), and Winnipeg (Canada) had significant conditional probabilities (Fig. 3a). These identified areas agree with results of previous studies (Hafner and Hites, 2003; Breivik et al., 2007). However, Birmingham (AL), Madison (WI), Winnipeg (Canada), and Thunder Bay (Canada) may be affected by the trailing effect because those locations are upwind of locations identified as significant source areas (white square in Fig. 3-a). The PSCF model results for DDE are not as complicated as the model result for PCBs probably because DDE was primarily used in a smaller geographic region. Northern Alabama was the only area with the highest and most significant probability (Fig. 3b).
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Historically, over 80% of the DDT produced in 1970e72 was applied to cotton fields located in mostly southern United States (EPA, 1975). In addition, between 1947 and 1970, the Olin Chemical Company discharged as much as an estimated 8000 tons of DDT residues (DDT, DDD and DDE) into Huntsville Spring Branch and Indian Creek in Alabama shown in white circle in Fig. 3b (Reich et al., 1986, EPA, 2000). Acknowledgements This project was funded by the U.S. Environmental Protection Agency Region 2 and 5 (Project Numbers XA-97298703, X98248702-1, X98248701-0), NEIWPCCF (Project Number M-001991-02), and Great Lakes National Program Office (GLNPO). The authors acknowledge the many dedicated hours of field sampling and sample preparation by Lauren Falanga and Greg Sumner at the SUNY Oswego ERC. We also thank the Jim D’Angelo and the entire staff of the Sterling Nature Center, Cayuga County Parks and Recreation for the many years of assisting our project. This is the Clarkson Center for the Environment publication No. 211. Appendix. Supplementary information Supplementary information associated with this paper can be found, in the online version, at doi:10.1016/j.atmosenv.2010.05.031 References
Fig. 3. PSCF results of (a) PCBs and (b) DDE with 4 day back trajectories from 2002 to 2006. White dots are urban cities in (a). The Kriging method was applied to interpolate between geographic locations.
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