Environ. Sci. Technol. 2004, 38, 5290-5296
Annual Variations of Pesticide Concentrations in Great Lakes Precipitation DANIEL L. CARLSON, ILORA BASU, AND RONALD A. HITES* School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana 47405
Twenty pesticides and related analytes were measured in 28-day integrated precipitation samples from five U.S. sites in the Integrated Atmospheric Deposition Network (IADN) between 1997 and 2002. Consistent, significant decreases in concentration as a function of time were observed only for p,p′-DDE and p,p′-DDD, while increases in β-HCH were observed at all sites. Significant annual variations were observed for most analytes at each site with higher concentrations in the summer for current-use pesticides (endosulfan and γ-HCH) and peaks in the winter for most others. The increased concentrations in the winter are likely the result of the increased scavenging efficiency of snow compared to rain and, for some analytes, higher concentrations in the particulate phase during winter. These seasonal differences appear to account for a large portion of the observed variability in pesticide concentrations in precipitation samples.
Introduction The Integrated Atmospheric Deposition Network (IADN), operating in the Great Lakes region of North America, is a joint program between the United States and Canada intended to measure the atmospheric input of toxic organic compounds to the ecologically and economically important Great Lakes system (1). This long-term program, in operation since 1990, includes analyzing precipitation samples for a variety of pesticides at 28-day intervals. Precipitation is an important source of semi-volatile organic compounds, including pesticides, to the Great Lakes (2). Previous studies using data from IADN or sites that would later be included in the IADN program have shown that substantial contamination is delivered to the Lakes by way of precipitation (25). All of these previous reports have discussed long-term trends and yearly averages of pesticide concentrations in precipitation; in addition, one study found that current-use pesticides generally had higher concentrations following their application (4). In the research reported here, we discuss the most recent precipitation data from the U.S. IADN sites, and we analyze the sources of the annual variations for most pesticides. Those pesticides in current use show annual peak concentrations in the summer, while banned pesticides generally show peak concentrations in the winter.
Experimental Section Samples were collected from five U.S. IADN sites: Brule River and Eagle Harbor, near Lake Superior; Sleeping Bear Dunes * Corresponding author e-mail:
[email protected]. 5290
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 38, NO. 20, 2004
and Chicago, near Lake Michigan; and Sturgeon Point, near Lake Erie. Details of these sites can be found on the IADN website (www.smc-msc.ec.gc.ca/iadn/index.html). Integrated precipitation samples were collected over 28-day periods (although some periods were slightly longer or shorter), giving 13 samples per year. [For simplicity, these are referred to here as monthly samples, although this is not precisely correct.] Precipitation is sampled using MIC automated wet-only samplers (MIC Co., Thornhill, ON). Each sampler consists of a 46 × 46 cm shallow funnel connected to a 30 cm long by 1.5 cm i.d. glass column (ACE Glass, Vineland, NJ) packed with XAD-2 resin. The sampler is normally covered, but it is opened automatically during a precipitation event, which is sensed by a conductivity grid. This grid is heated to prevent condensation, ice build-up, and prolonged sampling after the end of a precipitation event. The funnel and the interior of the sampler are also heated to melt any snow that falls into the sampler and to keep the XAD-2 column from freezing. The precipitation flows by gravity from the funnel through the XAD-2 filled column and into a large carboy where the total precipitation volume is measured. Because there is no filter in the system, the XAD-2 column collects both particulate and dissolved phase contaminants. Contaminants from the precipitation are integrated for 28 days regardless of the amount of precipitation that occurred during that time period. Gas and particle phase samples were also collected on XAD cartridges and quartz fiber filters, respectively, at the IADN sites for 24 h every 12 days using modified Anderson high-volume air samplers. The precipitation XAD cartridges were prepared at the Indiana University laboratory and sent to the sites for sample collection. After sampling, the cartridges were sent back to Indiana University and emptied, and the XAD was extracted in a Soxhlet apparatus with 1:1 acetone/hexane (Omnisolv, EM Science, Gibbstown, NJ). This extract was then evaporated and exchanged to hexane. The sample was cleaned by silica (Aldrich, silica gel, Davisil grade 634) column chromatography after separation of the water layer; the first eluent, hexane, contained p,p′-DDE, o,p′-DDT, and hexachlorobenzene (HCB), and the second eluent, 1:1 hexane/dichloromethane, contained the remaining pesticides. More details, including information on surrogates, internal standards, recoveries, and the collection of the vapor and particle phase samples can be found elsewhere (2). Because pesticide concentrations were very low in the field and laboratory blanks, the concentrations reported here have not been blank-corrected. No samples were excluded based on low measurements; rather, they were considered to be placeholders representing the maximum possible concentration. Annual variations may, therefore, be more extreme than depicted here. The only analyte routinely close to field blank levels was γ-chlordane. When method detection limits were calculated as three standard deviations greater than the mean of the field blanks, summertime concentrations of γ-chlordane were often below these levels. Although samples had been collected from several sites since 1991, only those collected after March 1997 are presented here. An analytical method change was introduced at that time that resulted in much better reproducibility. After March 1997, 25 mL of HPLC-grade water was added to the Soxhlet extract immediately after completion of the extraction but before separation of the phases by a separatory funnel. This procedure reduced the formation of an oily, turbid layer, facilitated the separation of the aqueous and nonaqueous layers, and gave an extract with fewer gas chromatographic interferences. 10.1021/es049751h CCC: $27.50
2004 American Chemical Society Published on Web 09/17/2004
Originally, wet glass fiber filters were used to wipe off the residual particles sticking to the stainless steel collection funnels of the MIC samplers. These wipes were considered to be part of the precipitation samples and were extracted together with the precipitation XAD. After June 1999, the extraction of the wipes was discontinued because they contained water which interfered with the recovery of the hexachlorocyclohexanes (HCHs). For example, the recovery of the surrogate standard, δ-HCH, improved from 14% to 80% after the wipes were excluded from the extraction procedure. Endosulfan concentrations were also affected. For these two classes of pesticides, only samples collected after June 1999 are discussed here. We do not believe that discontinuing the wipes resulted in significant underreporting of the concentrations of any pesticides. Analysis of wipes alone resulted in nondetects 68% of the time, with detectable levels in the same range as blanks (typically 0.05) but are included to fully describe the sine curve; all other numbers are significant at P < 0.05; bold numbers are significant at P < 0.01; ns ) not significant. Results for the other sampling sites can be found in the Supporting Information. b In cases of significant change over time, the geometric means of the first and last years are given. c t-nonachlor plus R- and γ-chlordane.
β-HCH is less likely to volatilize than R-HCH and would therefore have a greater lag from the time of use to the time when emissions from the soil cease (10). It seems unlikely, however, that atmospheric concentrations of β-HCH were still increasing in the past few years because of an increase in the use of technical HCH in the 1970s. Another possible explanation is that the observed increase in the precipitation and atmospheric concentrations of β-HCH is a brief exception to a general decrease that would be evident over a longer period of time. The concentrations of p,p′-DDT and its degradation products, p,p′-DDE and p,p′-DDD, also show some interesting relationships. Figure 1 shows that p,p′-DDE concentrations decreased significantly in precipitation at all sites, except Chicago; p,p′-DDD had significant decreases at three of the five sites (see Table S1). The other analytes in the DDT family did not show a significant decrease. On average, the concentrations were p,p′-DDT > p,p′-DDE > p,p′-DDD (see Tables 1 and S1). Similar relationships between DDT and its degradation products have been seen in both the dissolved and particulate phases of other precipitation samples from several places around the world (11-13). The observation of greater concentrations of p,p′-DDT than its degradation products does not necessarily mean that there is a “fresh” source of DDT nearby; rather, it could reflect the different properties of these compounds. In one case, in Malawi (Africa), p,p′-DDT had the highest concentration in the air as well as in the precipitation (12). In Texas (North America), however, the concentration of p,p′-DDE was higher than that of p,p′-DDT in the vapor phase, while in the atmospheric particulate phase and the precipitation, the concentration of p,p′-DDT was higher than that of p,p′-DDE (12). Concentrations of p,p′-DDE are generally higher than those of p,p′-DDT in the vapor phase at IADN sites as well. Annual Variability. Aside from those with no periodicity, pesticides had maximum concentrations either in the summer or winter (although those in the DDT family tended to have maxima more toward spring; see Table S1). Representative plots of all three types of datasets are shown in Figure
FIGURE 2. Concentrations of HCH isomers in precipitation at Eagle Harbor and corresponding autocorrelation plots. Three distinct types of behavior are observed: no significant annual variation (r-HCH), a wintertime peak in concentration (β-HCH, modeled using eq 1), and a primary summertime peak in concentration following agricultural application (γ-HCH, modeled using eq 3). 2. Autocorrelation plots are also included in Figure 2, which show the correlation of concentrations at a given time relative to the concentrations at some time (lag number) in the past. In our case, because we had 13 samples per year, a lag number of 13 corresponds to one year; if the autocorrelation function at a lag number of 13 were 1.0, this would mean that the concentrations were exactly the same at the same time every year. If this were the case, for a lag number of 6, we would expect a value near -1.0. Hence, the positive autocorrelation function seen at lag numbers of 13, 26, and 39 for β- and γ-HCH, and the negative autocorrelation function for lag numbers between these values, indicates a repeating annual pattern in the pesticide concentrations. VOL. 38, NO. 20, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
5293
Summertime Concentration Peaks. At the time of sampling, both γ-HCH (lindane) and endosulfan were being used for agriculture (these were the so-called “in use” pesticides). Both of these analytes show peak concentrations in precipitation during the summer months following application. For endosulfans, precipitation concentrations were generally higher at Sturgeon Point, which is nearest to areas with potential endosulfan sources (14). Sturgeon Point also shows the greatest variation in endosulfan concentrations (see Table S1) and appears to have the strongest pattern of annual variation (see Figure 1). Likewise, γ-HCH, which is used mainly in central Canada on canola (15), shows the greatest variation at sites nearest the region of use (Eagle Harbor and Brule River), with generally higher concentrations as well. Increasing concentrations of γ-HCH and endosulfans in the precipitation following their agricultural application has also been observed in the Great Lakes region between the years 1986-1991 (4) and in the mountains of Europe between 1996 and 1998 (16, 17). The annual variations of γ-HCH concentrations in precipitation did not closely correspond to a sine wave; this can be seen in the odd pattern of the autocorrelation function for γ-HCH in Figure 2. Thus, a Gaussian peak was used to model the agricultural input
AgInput )
[( (
) )]
(t - b2) MOD 365 - 182.5 b1 exp -0.5 b3
(3)
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 38, NO. 20, 2004
∆Hvap + constant RT
(4)
and the aqueous activity coefficient, γWsat, as a function of temperature is given by
ln γWsat ) +
∆Hes + constant RT
(5)
Hence
KH )
(2)
The main peak occurs in the late spring, but a second, smaller peak is also apparent in the late fall. This second peak is more apparent at Eagle Harbor and Brule River, the two sites closest to the region of application of γ-HCH in the Canadian prairie provinces (18). This second peak represents ∼5-10% of the mass of γ-HCH applied in the spring planting season. We suspect this fall peak may be the result of a small winter canola crop, planted in the fall, although we can find no reliable data on the extent of the fall planting during the relevant years. Wintertime Concentration Peaks. The observed concentration peaks in the winter for most of the pesticides not in current use was surprising. The vapor phase concentrations decrease in the winter for all of the analytes as the precipitation concentrations increase, and no significant periodicity in the vapor phase concentrations is observed after correcting for atmospheric temperature (19). The wintertime peaks cannot be accounted for by either potential errors in precipitation sampling (which would tend to produce lower winter-time concentrations) or the meteorology of the region (which is similar in amount, frequency, and intensity throughout the year). Changing characteristics of atmospheric particles, and associated changing sorption and scavenging properties, may account for some of this variability, but for the purposes of the following discussion we will assume that particle concentrations and characteristics are not seasonally variable. An increase in the concentration of chlorophenols in precipitation from Zagreb, Croatia was also observed with 5294
ln P0 ) -
2
where t is in days, b1 is the peak height, and b3 is related to the peak width or the duration of the agricultural input. The equation is modified to peak at midyear with an offset b2 in the modulus function to avoid discontinuities between day 0 and day 365. Using a model with one agricultural input function was much better than a model with a sine function for the concentrations of γ-HCH, but, in all cases, a model with two agricultural input functions had an even better fit; see Figure 2.
ln(Cp) ) a0 + a1t + AgInput1 + AgInput2
decreasing atmospheric temperature (11). These authors suggested the increase was due to the decrease of the Henry’s law constant (KH) with decreasing temperature. Ignoring (for the moment) scavenging of particles, however, a decrease in temperature should not theoretically result in an increase in aqueous-phase concentrations in equilibrium with the atmosphere. The temperature dependence of KH can be expressed by the following equations if we assume that (a) KH can be approximated by the vapor pressure (P0) of a liquid divided by the aqueous solubility (CWsat), (b) CWsat ≈ 1/(VW‚γWsat), and (c) the molar volume (VW) is reasonably independent of temperature (20). P0 as a function of temperature is given by
P0 ) P0VWγWsat CWsat
(6)
and
ln KH ) -
[
]
∆Hvap - ∆Hes + constant RT
(7)
Equation 4 says that the vapor pressure of an analyte decreases with temperature, as observed at the IADN sites (19) among many others. If the excess heat of solution (∆Hes ) were small relative to the heat of vaporization (∆Hvap), eq 7 indicates that KH would change with temperature in the same manner as the vapor phase concentration (20). In that case, the change in the Henry’s law constant would cancel out the change in atmospheric concentration, and the concentration in an aqueous phase (precipitation) in equilibrium with the atmosphere would not change as a function of temperature. However, because (∆Hes ) increases with molecular size (20), large hydrophobic pesticides should have a positive excess heat of solution. In this case, the temperature dependence of KH will not completely cancel out the temperature dependence of the vapor phase concentration, and the concentration of the pesticide in precipitation should actually decrease with decreasing temperature along with vapor phase concentrations, although not nearly as much. Phase change considerations also should be considered, because pesticides generally have high melting temperatures. The vapor pressure would be a function of the heat of fusion as well, but phase change costs would cancel each other out for KH. Therefore, vapor phase concentrations would actually decrease even more rapidly with decreasing temperature than KH, resulting in a further decrease in precipitation concentrations. Whether this ideal behavior actually occurs in the environment is questionable, because the temperature dependence of organic compound concentrations in atmospheric samples is typically less than the heat of vaporization measured in the laboratory (21). Nonetheless, the increase of pesticide concentrations in precipitation with decreasing atmospheric temperature cannot be explained theoretically by the temperature dependence of the Henry’s law constant. Another factor also varies with atmospheric temperature: the form of the precipitation changes from rain to snow near 0 °C. Also, scavenging of particles is likely to become
a very important process. Experimental evidence from Minnesota shows that snow is much better than rain at scavenging polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls from either the vapor or particulate phases in the atmosphere (22, 23). The data from one winter rain event and three winter snow events (22) showed that snow is 25-900 times better than rain at scavenging those PAHs that were generally >98% particle-associated in the atmosphere. Snow is only 1.3-180 times better than rain at scavenging PAHs that were
