Temporal Trends and Controlling Factors for Polychlorinated

The Toxic Organic Micro-Pollutants (TOMPS) program in the United Kingdom started in 1991, generating a data set for polychlorinated biphenyls (PCBs)...
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Environ. Sci. Technol. 2010, 44, 8068–8074

Temporal Trends and Controlling Factors for Polychlorinated Biphenyls in the UK Atmosphere (1991-2008) JASMIN K. SCHUSTER, ROSALINDA GIOIA, ANDREW J. SWEETMAN, AND KEVIN C. JONES* Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, U.K.

Received June 24, 2010. Revised manuscript received August 27, 2010. Accepted September 7, 2010.

Long-term air monitoring data sets are needed for persistent organic pollutants (POPs), to assess the effectiveness of source abatement measures and the factors controlling ambient levels. The Toxic Organic Micro-Pollutants (TOMPS) program in the United Kingdom started in 1991, generating a data set for polychlorinated biphenyls (PCBs). The history and volumes of production, usage, and subsequent restrictions on PCBs in the UK are well-characterized relative to many countries, providing a valuable case study on the effectiveness of controls and the factors influencing ambient levels and trends of these “model POPs”. PCB air concentrations (congeners PCB 28, 52, 90/ 101, 118, 138, 153, and 180) from six rural and urban monitoring sites are presented. Most show a statistically significant decrease in PCBs levels over time, consistent with estimates of emissions, helping to validate emissions inventories. Times for a 50% decline in concentrations (sometimes called clearance rates) averaged 4.7 ( 1.6 years for all congeners at all sites. The trends at different sites and for different congeners were not statistically different from each other. Concentration differences between sites are correlated with local population density (i.e., the degree of urbanization), which supports approaches to modeling of primary emissions on the national and regional scale. The data set indicates that ambient levels and underlying trends of PCBs continue to reflect the controlling influence of diffuse primary sources from the ongoing stock of PCBs in urban environments. Production and use restrictions cameintoforceintheUKover40yearsago;trendssincemonitoring began in the early 1990s should be seen as part of a continuing decline in ambient levels since that time.

Introduction Despite the production and use of polychlorinated biphenyls (PCBs) being banned in many countries decades ago, they continue to generate interest in the scientific literature and among regulators. A recent special issue of Environmental Science and Technology focused on PCBs, highlighting that there are still questions about their global fate and behavior and potential toxicological significance (1). An interesting aspect of PCBs is thatsin comparison with some other persistent organic pollutants (POPs)sthere are reasonably * Corresponding author e-mail: [email protected]. 8068

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good estimates of global production and use, so they have been used as “model compounds” in regional and global monitoring and modeling work (2-5). As a class of compounds with a range of well-characterized physical and chemical properties, they are also key to testing ideas about global fractionation, cold condensation, “grasshopping” (6, 7), and the relative importance of primary versus secondary sources of banned or restricted substances, a key issue for regulators and policy makers. These issues require good longterm and spatially resolved monitoring data. In this paper we present some of the longest trends in ambient levels of PCBs available anywhere, from the UK Toxic Organic Micro-Pollutants (TOMPS) program, funded by the UK Department of the Environment, Food, and Rural Affairs (DEFRA) since 1991. Other long-term data sets for polynuclear aromatic hydrocarbons (PAHs) and polychlorinated dibenzop-dioxins and -furans (PCDD/Fs) from this program have also been reported recently (8, 9). Compared to many other countries, good-quality information is available on the amounts and timing of the production and use of PCBs in the UK. Emissions inventories have been conducted (10-13), together with monitoring of spatial and temporal trends in air and a range of other media (14-20). PCBs are classified as POPs under the 1998 UNECE Protocol and the 2001 Stockholm UNEP Convention. They are therefore subject to international restrictions on production and use and to efforts to identify and reduce ongoing sources. However, they have been the subject of restrictions in the UK for much longer. A voluntary ban on production was agreed with manufacturers in the late 1960s/early 1970s. Limits on emissions from incinerators and the handling of PCB-containing wastes were applied from the late 1970/80s onward. These efforts began before reliable routine environmental monitoring was possible. An important issue, which relates to whether possible further source reductions can be made, is whether primary or secondary sources control ambient levels now, some 40 years after production and fresh use in the UK ceased. Estimates have been made of the burdens of PCBs in the UK environment (21, 22). It is therefore necessary to consider the burden of previously emitted PCBs in surface soils and sediments (potential secondary sources to the atmosphere if they volatilize) (14, 23, 24) and the PCB stocks from past use in transformers, capacitors, buildings/sealants, etc. (all potential diffusive primary sources to the atmosphere if they volatilize) (21-23). In this paper we present the ambient data sets collected in the TOMPS program and examine the levels and trends. Time trend data are a key requirement to measure the effectiveness of source reduction measures and regulatory controls. These are set in context by comparisons with other international monitoring data and extrapolations to likely pre-1991 (i.e., preambient monitoring) trends for the UK. We explore relationships between urban/rural sites, the measured ambient trends, and estimates of atmospheric emissions over time. Finally, we consider the impacts of various national and international controls on PCBs and their release to the environment.

Methodology Section SI-1 (Supporting Information) summarizes the sites and the duration of the sampling campaign. Six sampling sites (see Figure 1) have been maintained, three urban [central London (LON), central Manchester (MAN), Middlesbrough (MD)], one semirural [Hazelrigg (HZ), near Lancaster], and 10.1021/es102134d

 2010 American Chemical Society

Published on Web 09/30/2010

FIGURE 1. TOMPS sampling sites LON, MAN, MD, HZ, HM, and SF.

TABLE 1. Overview of the Occurrence of Selected PCBs in the UK Atmosphere (pg/m3) LON n ) 48 PCB median 28 52 101 118 138 153 180 a

47 52 39 16 16 17 6

max 1000 1400 250 120 83 95 33

MAN n ) 51 min 2.4 3.3 1.1 1.1 0.58 0.66 0.56

median max 49 45 34 11 16 17 6.2

230 200 160 56 64 69 51

MD n ) 46 min 7.3 4.0 1.4 1.3 2.3 3.6 0.26

median max 25 12 6.5 2.1 2.5 3.1 0.71

210 110 29 14 72 140 39

HZ n ) 46 min 5.6 4.2 0.60 0.34 0.25 0.60 0.20

median max 20 10 4.4 1.5 1.7 2.4 0.60

90 70 48 20 22 33 27

HM n ) 31 min 0.076 0.70 nd nd nd nd nd

median max 9.1 6.1 2.3 0.80 0.93 1.3 0.31

49 39 7.7 3.4 46 3.6 2.2

SF n ) 28 min a

nd 0.10 nd nd nd nd nd

median max 4.7 2.9 2.1 0.68 1.1 1.1 0.44

18 34 59 56 61 37 6.5

min 1.7 0.011 MAN > MD > HZ > HM > SF - in other words, urban > rural. This trend has been reported previously (25). Concentrations have declined with time. During the early 1990s, urban concentrations were generally several hundred pg/m3 for ∑PCB (28, 52, 101, 118, 138, 153, 180), with a

maximum of a few thousand pg/m3 for LON. Concentrations were lower at semirural HZ, but exceeded 100 pg/m3 for some quarters. During the most recent years, concentrations have decreased to a few hundred pg/m3 for LON and MAN, with rural areas HM and SF in the low tens of pg/m3. The lighter homologues tri- and tetra-CBs make up the biggest fraction of the mix, consistent with the composition of technical mixtures and the predicted emission fingerprint (11). The fingerprint or mixture in the TOMPS data did not change over time. This is an indication that primary sources are still dominant (see also below). Seasonality in PCB air concentrations (summer > winter) has been reported previously, with temperature a controlling factor (26). Studies showing the clearest effects of temperature take short-term samples, over hours or days, whereas the data presented here is for 3 month averages. Effects of temperature are less clear in longer-term seasonally averaged data, because of confounding influences of meteorology and the averaging of the data for quarters. In this study, seasonality was only detected in the urban sites (LON, MAN). Rural and Urban Differences and the Relationship to Population Density. As noted above, this study [in line with others (27)] reports urban > rural PCB air concentrations. Efforts to model PCB emissions and air concentrations have VOL. 44, NO. 21, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. PCB 28 (a), 52 (b), 101 (c), 138 (d), 153 (e), 180 (f) at all TOMPS sampling sites [concentrations (pg/m3) in ln plotted against sampling year]. used population or population density as a surrogate (12, 13), although it is not clear which approach is most satisfactory for modeling pollutants such as PCBs, which may be emitted largely from many diffusive sources. Here we explore that relationship between local PCB concentration and population density and thereby investigate the “sphere of influence” around a monitoring site, to yield information that may be helpful for future modeling work. UK population census data are available on a 200 m × 200 m grid for 2001 (28). We tested different approaches by plotting the weighted population densities for each site against the atmospheric PCB concentrations (as average ratios between concentrations at all TOMPS sites). In approach A, population densities (popi) from every grid of the UK census data were divided by the squared distance to the sampling site (di), to gain a weighted influence of the distance from the sample site. A)

∑

popi di2

Approach B summed up the population in a radius (r) of the site and divided it by the area: 8070

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B)

∑ pop

i

ir

popi di2

Correlations between A, B, and C and the median of the atmospheric PCB concentrations at each site were determined. Different “spheres of influence” around the monitoring stations were investigated, by varying the radius r from 1 to 5, 15, 30, and 50 km. The results and correlation parameters are listed in the Supporting Information. Significant correlations were obtained for all the approaches, except A and B1 (i.e., B using 1 km radius). For approach A the values at MAN are very low, because the sampling site is in the city center surrounded by office buildings that were not covered in the census data. For C1, the population values in a radius of 1 km around the sampling site are 0 for the

FIGURE 3. ∑PCB (median of PCB 28, 52, 101, 118, 138, 153, and 180) for all sites plotted against the relative population density for each site for scenario C30. rural sites (HZ, HM and SF). The highest correlations obtained were for approach B50 and C30 with R2 ) 0.94 for each of them. The correlation between the median of the PCB concentrations and the data from C30 can be observed in Figure 3. For the correlation data of approach B, R2 seems to increase with the area with the best correlation observed for the highest radius (i.e., 50 km). However, the values of R2 do not correlate solely with increasing radius. Assuming a radius of 100 km would provide the rural sites HZ and HM with a higher population value than the urban site MD, which does not correlate with the observed concentrations. Approach C is recommended; the sphere of influence around the sampling site of 30 km gave good correlation, while still taking sources outside this radius in account. Overall these data indicate that population density can be used as a good surrogate to derive an estimate of PCB air concentrations, at least in a country like the UK, where there is a sharp gradient in population density. But it should be noted that a significant difference between sampling sites is necessary to obtain valid results with these simple correlations. If only remote/rural sites are considered, the differences between the population density values would be rather low and other factors (e.g., wind direction) might gain in influence. Time Trends and Calculation of Half-Lives. Time trends were assessed for the whole data set and for the time period for which data is available at all six sites simultaneously. The pattern of decline was exponential and therefore treated as a process following first-order kinetics PCBi ) PCB0e-λti where PCBi is the observed concentration at time ti, λ is the decline constant, and PCB0 is the virtual original PCB concentration. The time period necessary for PCB concentrations to decline by half was therefore calculated as t1/2 )

ln 2 λ

These were calculated for individual congeners and ∑PCB at each sampling site (i.e., a total of 48 linear regressions). Of these, 44 were statistically significant. The four that were not were PCBs 28, 153, and 180 at HM and PCB 153 at SF.

The temporal trends for the PCB congeners 28, 52, 101, 138, 153, and 180 for all sites can be observed in Figure 2. The 44 significant half-life values were tested for outliers at the different sites and for each of the six congeners as well as ∑PCB. The values for the individual congeners and the respective p-values can be found in the Supporting Information. The average half-life for PCBs at the TOMPS sites between 1990 and 2008 is 4.7 years, with a standard deviation of 1.6 years and values spanning from 2.3 to 8.9 years. The minimum and maximum values for the borders of a 95% confidence level ranged from 1.3 to 22.6 years. There are wide confidence intervals around the half-life values derived from such monitoring studies, which means that great caution is needed in assessing whether trends in regional/global POPs contaminants in air between sites and compounds are truly different from each other and in considering whether any “apparent” differences in half-lives between them are “real”. The key finding from this section is that no significant differences in the rates of decline in PCB air concentrations were detectable between sites and compounds. This observation is important, because it indicates that primary diffusive sources in urban areas are controlling the underlying trends in ambient levels. The sources in urban areas supply the surrounding rural areas. Comparisons with Other Ambient Time Trend Data. As mentioned above, wide confidence intervals for half-life values derived from such monitoring studies are an impediment for clear conclusions about differences between the observed values. This is important when considering regional and global scale processes. The cold condensation hypothesis proposes that rates of decline would be slower in remote polar regions than temperate source regions. Other key long-term monitoring projects for PCBs in the atmosphere are being conducted by the European Monitoring and Evaluation Programme (EMEP) for Europe (29) and the Integrated Atmospheric Deposition Network (IADN) for the Great Lakes area of North America (30). EMEP data are available via their Web site for nine sites covering a similar timeperiod[SwedishsitesSE02(1994-2002),SE12(1995-2008), SE14 (2002-2008); Norwegian sites NO01 (2004-2009) and NO42 (1998-2008); Finnish sites FI36 (1996-2008) and FI96 (1996-2007); Icelandic site IS91 (1996-2008); and the Czech site CZ 03 (1999-2008)]. We calculated half-lives for these sites and found that (with the exception of the Czech site) VOL. 44, NO. 21, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. Correlation of observed PCB air concentrations (pg/m3) to emission estimates (tonnes) for the sampling areas of LON, MAN, MD, and HZ [Breivik et al. (13)] (2) (sum of PCB congeners 28, 52, 90/101, 118, 138, 153/132, 180) and to emission inventory estimates (tonnes) by the National Atmospheric Emissions Inventory [NAEI (10)] for the whole of the UK (b) (total sum of PCB congeners). they were not significantly different from the TOMPS data, giving an average half-life of 8.1 years with a standard deviation of 2.5 years. The calculated half-life for CZ03 was 2.0 years and a standard deviation of 0.3 years. Buehler et al. (31) report PCB data for the IADN sites for the period 1990-2001. Half-lives derived for sites on Lake Superior, Lake Michigan, and Lake Erie were in the range of 18.0 ( 7.1, 8.3 ( 1.5, and 9.1 ( 1.4 years, respectively. The higher half-lives at Lake Superior for PCBs and other monitored compounds were explained as being due to a buffering effect of the lake. Sun et al (30) reported values of 7.7 ( 1.1 years for Chicago air and 6.8 ( 3.1 years for precipitation for the monitored time period of 1996-2003. Half-lives have been reported by Hung et al. (32) for the Canadian Arctic with a range from 3.6 to 20 years at Alert (monitored 1993-2001) and for the Norwegian Arctic with 3.2-9.6 years at Zeppelin (monitored 1998-2006). The half-lives in these studies were calculated using different approaches. For IADN the data were normalized to a constant temperature. This was not done for the TOMPS data due to the integrated sample quarters and the lack of seasonality for PCB concentrations. The PCB half-lives for the Arctic were obtained by applying a digital filtration trend development technique to the data. In spite of the different methods, the obtained values for these studies are rather close. PCBs have been monitored in various biological samples (see Supporting Information Table 3 for a summary) in which similar trends can be observed. 8072

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Trends in Emissions Estimates and the Measured Air Concentrations. A valuable aspect of having ambient trend data is to evaluate emissions inventories derived from production and use estimates. Breivik et al (12, 13). have made global estimates of PCB production, use, and atmospheric emissions and include UK data. They estimate a decline in emissions for 1990-2000 with a half-life of 4.2 ( 0.6 years. The UK’s National Atmospheric Emissions Inventory (NAEI) estimates PCB emissions declined for 1990-2007, with an average half-life of 5 years (10). Both inventory trend estimates are therefore strongly supported by the TOMPS ambient data. As an additional exercise, we estimated emissions over time around the four TOMPS urban/semirural sites (LON, MAN, MD, and HZ) from the Breivik et al. inventory (13). In this the total emission for a given country are distributed on a longitude-latitude grid based on population density within the grid square. The area described by each grid square varies from 7300 to 7700 km2 for the areas surrounding the TOMPS sites. For the correlations presented in Figure 4, the grid square and the subsequent emission data were chosen closest to the geographical position of the sampling site. The selected emission data from Breivik et al. (13) as well as the NAEI data (10) can be found in the Supporting Information. Figure 4 shows that both emission inventories mirror the decline rates observed for the TOMPS data very closely. The Longer-Term Perspective of PCB Trends in the UK. There are no direct measurements of atmospheric PCB trends in the UK before 1990. However, annual archived herbage

FIGURE 5. ∑PCB for herbage samples from Rothamsted (1965-1989) [herbage in ng/g dry weight for ∑PCB (28, 52, 101, 118, 138, 153)] (17) and ∑PCB for atmospheric samples from LON and MAN (1991-2008) [air in ng/m3 for ∑PCB (28, 52, 101, 118, 138, 153, 180)]. samples collected between 1965 and 1989 from Rothamsted Experimental Station have been analyzed (17). Herbage PCB concentrations are believed to be controlled by air-leaf gas exchange (33). In other words, herbage can broadly act as a passive air sampler and reflect ambient levels. Figure 5 shows trends from these samples and the TOMPS 1991-2008 samples. The rates of decline in herbage and air both have half-lives of ∼4 years. It is clear that ambient PCB levels in the UK have undergone a sustained and consistent decline over the last 40 years. There are several very important aspects to this observation: (1) Declines started around the time of voluntary restrictions in PCB production and use, long before regulations and routine monitoring came into force. These voluntary restrictions happened because high levels of PCBs had been observed in birds of prey in the 1960s, causing concerns (34). Pressure applied as a direct result of this biological monitoring work resulted in a timely intervention on production and highlights the critical role that prospective biomonitoring has had in alerting regulators and industry to potential problems (35). (2) The steady rate of decline since that time suggests that no specific regulatory action or interventionsapart from the ban in production and useshas helped dissipate PCBs from the environment any more quickly over time. Measures which might have been expected to hasten rates of decline include destruction of PCB stocks via high-temperature incineration, particularly through the 1980s; limits on disposal to landfills; and the international agreements that are designed to identify sources and reduce them. However, their apparent lack of influence on trends suggests that slow volatilization releases from the large stocks of PCBs which already existed by the 1970s in widely dissipated source materials (e.g., building materials, window sealants, capacitors and other electrical equipment) and “reservoirs” have controlled levels and trends over the last 40 years. (3) Production and use of a chemical over 40 years ago has resulted in huge expenditure on monitoring programmes, efforts at source reduction, national measures and regulations, and, ultimately, international bans. PCBs therefore truly are “persistent pollutants” with a long legacy. The lesson is

surely that it takes a very long time for the stock of such chemicals to be removed from the environment, despite many well-intentioned efforts to hasten the process. The key challenge for scientists and regulators is therefore to ensure that substances with properties that are deemed undesirable do not reach the marketplace and that property evaluation and testing is sufficiently rigorous and reliable to prevent the design and use of new problem chemicals in the future. However, there needs to be the “safety net” of long-term sampling (ambient and biological) to alert us to future chemicals of concern.

Acknowledgments The UK Department for Environment, Food and Rural Affairs (Welsh Assembly Government (WAG), the Northern Ireland Executive, represented by the Department of the Environment in Northern Ireland (DOE), and the Scottish Government), provide financial support for the Toxic Organic MicroPollutants Program. Further information is available at http://www.airquality.co.uk and http://www.lec.lancs.ac.uk/ research/chemicals_management/tomps.php. The authors also thank AEA Energy & Environment, Dr. Robert Lee, Vicky Burnett, Danielle Lock, Susan Hodson, and David Hughes for help with the collection and analysis of the air samples as well as Matthew MacLeod and Martin Scheringer from ETH Zu ¨ rich for valuable discussion of the data.

Supporting Information Available Details about temperature conditions, sample processing and analysis, full data sets for the discussed compounds for air concentrations, emission data, information on half-lives (TOMPS air, other media), and correlation data for the relative population density. This material is available free of charge via the Internet at http://pubs.acs.org.

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