Contributions of Long-Range and Regional Atmospheric Transport on

Dec 19, 2012 - were located along a northwest-to-southeast transect of the island (Figure 1c). The transect crossed the Southern Alps, which run the l...
0 downloads 0 Views 1MB Size
Download PDF
Article pubs.acs.org/est

Contributions of Long-Range and Regional Atmospheric Transport on Pesticide Concentrations along a Transect Crossing a Mountain Divide Karen S. Lavin and Kimberly J. Hageman* Department of Chemistry, University of Otago, Dunedin 9016, New Zealand S Supporting Information *

ABSTRACT: Twenty-one halogenated legacy and current-use pesticides and pesticide degradation products were measured in pine needles along a coast-to-coast transect that crossed the Southern Alps of New Zealand. Concentration profiles of nine pesticides were used to determine the influence of geographic sources on the atmospheric pesticide burden at the mountain sites. Pesticide concentration profiles were calculated for each source and mountain site by normalizing concentrations (adjusted for temperature at the site and air−needle partitioning) to the sum of all pesticide concentrations at the site. Each mountain site profile was compared to varying mixtures of the potential source profiles to determine the percent contribution of each source. The highest elevation mountain sites were primarily influenced by long-range, synoptic-scale northwesterly winds. Westerly upslope winds had little influence on any of the mountain sites. Easterly upslope winds from the Canterbury Plains, an agricultural region, strongly influenced the mountain sites within close proximity and had progressively less influence with distance.

■

INTRODUCTION Semivolatile organic contaminants (SOCs), including legacy and current-use pesticides, have been detected in many remote alpine ecosystems around the world.1 These reports have generated much interest because they challenge our perceptions about the pristine nature of alpine ecosystems, alert us to contaminant exposure risks that alpine organisms might face, and introduce many intriguing questions about the transport and behavior of chemicals in the environment.2 Several studies have been conducted that investigate the geographic sources of SOCs in mountains3−8 and their distribution patterns, especially along elevational gradients.1,9−13 Although the degree to which SOCs undergo wet or dry deposition in alpine ecosystems is clearly influenced by temperature;12 the location and proximity of SOC source regions14,15 and the behavior of mountain winds4 also have important effects on SOC concentrations and distributions in mountains. A number of studies have shown that the presence of SOCs in mountains can be attributed to a combination of long-range atmospheric transport (i.e., by synoptic-scale winds containing SOCs from distant sources) and regional-scale atmospheric transport (i.e., by diurnal wind systems that carry SOCs to mountains from nearby plains or valleys).3,6−8 However, little is known about the degree to which different source regions contribute to SOC concentrations at different elevations along mountain slopes nor how the interplay between different wind systems affects concentration trends along windward versus leeward slopes of mountains. The © 2012 American Chemical Society

degree to which regional SOC sources located on the leeward sides of mountains influence their concentrations in mountains is of particular interest because upslope winds, which may flow in the opposite direction to synoptic-scale winds,16 are often ignored and are usually not detected in air-mass trajectory modeling.4 Likewise, it has been proposed that high mountains can act as barriers to the atmospheric transport of SOCs but this hypothesis has not been investigated directly.2 The primary objective of this study was to quantify the contributions of long-range and regional atmospheric transport on pesticide concentrations in air at different elevations on the windward and leeward sides of a mountain range, enabling us to test a number of hypotheses regarding how wind patterns affect contaminant distributions in mountains. To this end, samples of first- and second-year Pinus radiata needles, which were used as natural passive air samples,17−19 were collected at 16 sites of differing elevation along a coast-to-coast transect crossing from the windward (west) to the leeward (east) side of the Southern Alps, located on the South Island of New Zealand (Figure 1). Pine needles were selected rather than manufactured passive samplers because they did not require sampler deployment and the pesticides of interest have better uptake into needles.19 The concentrations of 21 halogenated legacy and current-use Received: Revised: Accepted: Published: 1390

November 6, 2012 December 15, 2012 December 19, 2012 December 19, 2012 dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398

Environmental Science & Technology

Article

Figure 1. Map of (a) New Zealand, (b) Australia and New Zealand, and (c) the central section of the South Island of New Zealand, showing the 16 sampling sites.

these sites are provided in the Supporting Information, Table S1. Sample Collection. Needles from Pinus radiata trees were handpicked between November 12 and 15, 2009. To obtain representative samples, needles were collected from five trees at each site if possible; the only exceptions were Otira and Avoca, where only three and two trees were sampled, respectively, due to the limited number of Pinus radiata trees at those sites. Firstand second-year needles were identified using branch bud scars and stored separately. The concentrations of pesticides in second-year pine needle samples were used for most interpretive analyses; the concentrations in the first-year samples were used only to determine air-to-needle uptake processes. All samples were stored in aluminum foil packets that had been baked at 565 °C for 90 min and were transported on ice (to avoid potential losses due to volatilization) to the laboratory, where they were stored at −20 °C until analysis. Sample Preparation, Extraction, and Analysis. The legacy pesticides (pesticides used historically in Australia and New Zealand) and degradation products targeted for analysis were aldrin, cis-chlordane, trans-chlordane, dieldrin, endrin, endrin aldehyde, alpha-hexachlorocyclohexane (HCH), betaHCH, delta-HCH, heptachlor, heptachlor epoxide, cis-nonachlor, and trans-nonachlor. The current-use pesticides (pesticides used in Australia and New Zealand at the time of this study) targeted for analysis were chlorpyrifos, dacthal, triallate, and trifluralin. Pesticides that have recently been phased out in New Zealand and Australia included endosulfan

pesticides and pesticide degradation products were measured in pine needles. The air-to-needle uptake process for each individual pesticide was determined and theoretical pesticide concentrations in air at each site were calculated. Pesticide profiles, based on theoretical air concentrations, at the mountain sites were compared to those from the source regions on the west (the West Coast) and the east (the Canterbury Plains) of the mountains to determine their relative influences. By adapting the Fingerprint Analysis of Leachate Contaminants (FALCON)20 approach to our purpose, percent contributions from each source region were calculated.

■

EXPERIMENTAL SECTION Sample Sites. Sixteen sample sites were selected on the South Island of New Zealand (Figure 1a−b). The sample sites were located along a northwest-to-southeast transect of the island (Figure 1c). The transect crossed the Southern Alps, which run the length of the island, in a region where the highest peaks are 1700−2400 m asl. The selected sample sites were on organic farms, on sites where pesticides had not recently been sprayed (as confirmed via personal communication with councils and land owners), or at locations where pesticide use was very unlikely due to lack of nearby cropland. Four sites were located west of the Southern Alps, in the West Coast Region; six were located in the mountains at various elevations; and six were located east of Southern Alps, five in the Canterbury Plains Region and one on the Banks Peninsula (Figure 1c). The exact locations, elevations and descriptions of 1391

dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398

Environmental Science & Technology

Article

I, endosulfan II, and gamma-HCH. Endosulfan sulfate, the degradation product of endosulfan I and II, was also on the target analyte list. For each pine needle sample, needles of the same age from each of the five trees at the site were pooled. Each sample was frozen in liquid nitrogen and then homogenized using a stainless steel blender (Waring Commercial, Torrington, CT). Each sample was split into three subsamples, which were analyzed separately, to account for variance due to the analytical method. A laboratory blank sample was analyzed with each set of three subsamples. The homogenized samples were extracted using a selective pressurized liquid extraction method with Florisil as the adsorbent; the method is described in detail elsewhere.21 All samples and laboratory blanks were spiked with a mixture of isotopically labeled standards (d14-trifluralin, d6alpha-HCH, d10 -chlorpyrifos, d4-endosulfan I) prior to extraction to correct for recovery. Calibration curves were prepared from the ratio of the target analyte peak area to the corresponding surrogate peak area. A separate recovery analysis was performed by spiking the target analytes; the mean target recovery was 71% for current-use pesticides and 72% for legacy pesticides. Sources of chemicals and further information about the extraction method are provided in the Supporting Information. All sample extracts were analyzed using an Agilent (Santa Clara, CA) 6890N gas chromatograph equipped with an Agilent 5975B mass selective detector in selective ion monitoring (SIM) mode with chemical ionization and methane reagent gas. The pesticides were separated using a DB-5MS (60 m × 0.25 mm i.d. × 0.25 μm film thickness) fused silica capillary column (Phenomenex, Torrance, CA) equipped with a 5-m deactivated guard column (Grace, Deerfield, IL). The instrument quantification limits ranged from 0.005 to 1 pg μL−1 for the pesticides. The SIM program, ions monitored, analytespecific instrument quantification limits, and other method details are described elsewhere.4 Details regarding quality assurance procedures are provided in the Supporting Information. Moisture and Lipid Content. The moisture and extractable lipid contents of each pine needle sample were determined gravimetrically. The moisture content was determined by drying a 3-g subsample at 105 °C for 24 h, at which time the sample weight remained constant. The extractable lipid content was determined by taking a 15-mL aliquot from a 240-mL extract (extracted in the absence of Florisil and using the same ASE parameters for sample extractions) and evaporating to dryness. The mean moisture content was 49% for first-year needles and 52% for second-year needles. The mean lipid content was 0.95% for first-year needles and 1.6% for second-year needles. Mean Site Temperatures during Exposure Periods. Mean monthly temperature data were obtained from CliFlo (Table S2).22 The closest weather station with full temperature data from November 2007 to November 2009 was selected for each site. Mean atmospheric temperatures were determined for the different exposure periods of first- and second-year needles. Temperatures were averaged from November 2008 to November 2009 for first-year needles and November 2007 to November 2009 for second-year needles. Determining Uptake Stage and Calculating Theoretical Air Concentrations. Chemical profiles in pine needles can be expected to differ from those in air due to differential partitioning of individual chemicals into needles. Additionally,

temperature may affect chemical profiles due to its effect on partitioning processes. To ensure that our source apportionment analysis was not affected by differential uptake processes or temperature effects, we calculated theoretical concentrations of SOCs in air from the measured concentrations in needles and conducted source apportionment using these theoretical air concentrations. Because SOC uptake into vegetation is not affected by wet deposition, precipitation differences among sites also did not affect our analyses and therefore, we were able to focus on the effect that wind patterns have on SOC distributions. To calculate theoretical air concentrations, it is necessary to know if a pesticide is in equilibrium between the needle and air or if uptake is kinetically controlled. The uptake stage was determined by comparing concentrations of pesticides in firstand second-year needles. Comparisons were conducted with concentrations of pesticides in first- and second-year pine needles normalized to the lipid content (i.e., pg g−1 lipid) as well as to the dry mass content (i.e., pg g−1 dry mass). The mean concentrations of nine selected pesticides in the first- and second-year needles were compared using Welch’s t-tests at 11 of the 16 sites. In situations where either or both of the firstand second-year needle concentrations were below the instrument quantification limit, the data were not considered in the analysis. Theoretical air concentrations were calculated based on their concentrations in second-year pine needles and assuming equilibrium was established between pine needles and air (see supporting evidence in the Results Section, First- versus Second-Year Pine Needle Concentrations). These calculations required pine needle−air partition coefficients (KPA) and pine needle−air energy of phase change (ΔUPA°) values. Because measured KPA and ΔUPA° values for the pesticides of interest were not readily available in the literature, these values were approximated with octanol−air partition coefficients (KOA) and octanol−air energy of phase change (ΔUOA°) values. The Supporting Information includes a detailed description of the procedure for calculating theoretical air concentrations as well as the site-specific (temperature-dependent) log KOA values (Table S2). Profile Comparison. Pesticide concentration profiles were constructed using the theoretical air concentrations (not the direct pine needle concentrations) of nine of the most frequently detected pesticides. Pesticide concentration profiles were constructed for each subsample from each site by first replacing all nondetect data points with one-quarter of the instrument quantification limit. Then the concentrations for each individual pesticide were divided by the total pesticide concentration (the sum of the nine pesticide concentrations in that subsample) to produce a normalized concentration profile. The normalized pesticide concentration profile for each site was obtained by calculating the mean of the normalized concentrations for each pesticide in the subsample profiles. Similarly, pesticide concentration profiles indicative of specific source regions were calculated by taking the mean of the appropriate subsamples. In other words, the Tasman Sea profile was the mean of the subsample profiles from Awatuna and Kumara, the West Coast profile was that from Greymouth and Lake Brunner, and the Canterbury Plains profile was that from Methven, Oxford, Darfield, West Melton, and Leeston (Figure 1c). Pigeon Bay was not included in the source profile analysis (nor were the other sites in Figure 1c since they were mountain sites). Deposition and atmospheric degradation were assumed 1392

dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398

Environmental Science & Technology

Article

or more gamma-HCH25 and was banned in New Zealand in 1990.24 Technical endosulfan contains ∼66% endosulfan I and ∼33% endosulfan II.26 It was banned in New Zealand on January 16, 200927 and in Australia on October 12, 2012.28 Thus, endosulfan was still in use in New Zealand during the first year of exposure for the second-year needles but not for the second year of exposure or any of the exposure period for the first-year needles. Endosulfan sulfate is the degradation product of endosulfan I and II. Chlorpyrifos, dacthal, triallate, and trifluralin are registered for use in New Zealand.29 Chlorpyrifos is used as a broad-spectrum insecticide, and dacthal, triallate, and trifluralin are used as broad-spectrum herbicides. The concentrations of the individual HCHs were not significantly different on the West Coast, Canterbury Plains and in the mountains, except for beta-HCH, which was higher in the Canterbury Plains compared to the mountains (p = 0.029) (Figure S2a−d). Of the HCH isomers, beta-HCH tended to have the highest mean concentration, most likely due to its persistence in the environment, resistance to biodegradation,25 and preferential sorption to pine needles.30 There was no difference in the mean concentrations of endosulfan I on the West Coast compared to the Canterbury Plains (Figure S2e) (p = 1.0). This indicates that either the overall use of technical endosulfan was similar in both regions or that similar long-range sources influenced both West Coast and Canterbury Plains sites. Of the detected pesticides, chlorpyrifos had the highest concentrations (Figure S2f). Chlorpyrifos concentrations were not significantly different on the Canterbury Plains and the West Coast (p = 1.0) due to the high variation in chlorpyrifos concentrations measured at the different sites. However, chlorpyrifos concentrations were higher on the Canterbury Plains than at the mountain sites (p = 0.0026). This is not surprising given that chlorpyrifos is used extensively in New Zealand’s agricultural areas, including the Canterbury Plains. On the leeward (eastern) side, chlorpyrifos concentrations decreased with increasing elevation and distance from the Canterbury Plains (Figure S2f), suggesting that chlorpyrifos concentrations are primarily controlled by proximity to the Canterbury Plains as a source,4 rather than by mountain cold trapping. Triallate and trifluralin mean concentrations were not significantly different on the Canterbury Plains and the West Coast (p = 0.81 and 0.87, respectively) (Figure S2h−i). However, both triallate and trifluralin had higher concentrations at the Canterbury Plains sites compared to the mountain sites (p < 0.001 for both pesticides). Conversely, mean dacthal concentrations were not statistically different at the West Coast, Canterbury Plains, and mountain sites (p > 0.05), suggesting that dacthal concentrations were not influenced by regional usage. First- versus Second-Year Pine Needle Concentrations. The primary uptake route of lipophilic SOCs from the atmosphere into pine needles is dry gaseous deposition.31−33 However, SOCs in pine needles may or may not be in equilibrium with those in the atmosphere. If not in equilibrium, the uptake may be kinetically controlled. To calculate the theoretical air concentration of any individual pesticide, it is necessary to know if it is in equilibrium. McLachlan outlined a theoretical framework for determining the uptake process of SOCs based on log KOA values.32 According to this framework, more volatile SOCs (log KOA < 8.5) quickly reach equilibrium

to be similar for measured pesticides and therefore were not considered in calculating profiles. Once the mean pesticide profiles for the mountain sites and source regions were obtained, the profiles were quantitatively compared to determine source contributions using Fingerprint Analysis of Leachate Contaminants (FALCON), an empirical multivariant fingerprinting approach.20 With the FALCON approach, two chemical profiles are compared by conducting linear regression between the normalized concentrations of the chemicals of interest. With this method, the Pearson correlation coefficient (r) indicates the degree of similarity between two profiles, such that two identical profiles have an r-value of 1.0. The lower the r-value, the less similar are the two profiles being compared. The FALCON approach was particularly useful in our study because site profiles could be compared to profiles produced from a mixture of sources, enabling quantitative source apportionment. Principal components analysis (PCA) was also conducted as a means of qualitatively comparing profiles; however, it cannot provide a robust analysis in this study due to the low number of observations (i.e., detected pesticides).23 In our analysis, the source contributions at the two highest elevation sites, Cora Lynn and Cass, were first determined. The Cora Lynn and Cass profiles were compared with the three source region profiles (Tasman Sea, West Coast and Canterbury Plains) and then with two distinct series of “mixed-source” profiles to determine the percent contribution of each source. The mixed-source profiles were generated by linearly mixing the Tasman Sea and West Coast profiles and the Tasman Sea and Canterbury Plains profiles in varying ratios. Next, the western mountain site, Otira, was compared to mixed-source profiles of Tasman Sea and West Coast and the eastern mountain sites were compared to mixed-source profiles of Tasman Sea and Canterbury Plains. For each mountain site, a series of r-values were generated (one r-value for each individual comparison). The mixed-source profile with the highest (significant) r-value was identified and the ratio of the source contributions in that mixed-source profile was used as an indicator of source contributions at the site. Statistical Analysis. Statistical analyses, including Pearson’s correlation, Welch’s t-tests (assuming unequal variance) and paired t-tests, were conducted with Microsoft Excel (Reading, Berkshire, UK) and SigmaPlot (Chicago, IL) Version 11.0. Pearson’s correlation was used to compare profiles. Welch’s ttests were used to compare mean concentrations (pesticide-topesticide and site-to-site). PCA was conducted with Matlab R2011b (Natick, MA).

■

RESULTS AND DISCUSSION Pesticide Concentrations in Second-Year Pine Needles. In the second-year needle samples, 20 of the 21 pesticides targeted for analysis were detected (Figure S1). Of these, ten were detected in more than 50% of the samples. These included six legacy pesticides and their degradation products (alpha-HCH, beta-HCH, gamma-HCH, delta-HCH, endosulfan I, and endosulfan sulfate) and four current-use pesticides (chlorpyrifos, dacthal, triallate, and trifluralin). Although endosulfan sulfate was frequently detected, it was not used in the source apportionment analysis due to lack of reliable data for calculating its theoretical air concentrations; thus, nine pesticides are considered in subsequent sections. Technical HCH was banned in New Zealand in 1962,24 at which point it was replaced by lindane. Lindane contains 90% 1393

dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398

Environmental Science & Technology

Article

To calculate theoretical air concentrations, KPA and ΔUPA° values need to be known. Because these values are not readily available in the literature, two options were considered for approximating them. First, the KPA value could have been assumed to be linearly proportional to KOA and an empirical equation relating KPA and KOA from the literature could have been used.17,38 Second, the partitioning of the pesticide between air and octanol could have been assumed to be similar to that between air and the epicuticular wax of pine needles and thus KOA and ΔUOA° values could have been used to approximate KPA and ΔUPA° values.19,39 Although the first option has the potential to provide a more accurate approximation of KPA, the available equations relating KPA and KOA in the literature are not reliable in our case given that they were developed from field measurements of a small set of SOCs (with a small range of KOA values) that did not include any pesticides. For this reason, the second option was selected for this study. Measured KOA and ΔUOA° values were obtained from the literature for all pesticides used in the analysis except endosulfan sulfate for which reliable data were not available. We chose not to use modeling programs to estimate KOA and ΔUOA° values for endosulfan sulfate because we found that modeled values (obtained with EpiSuite)40 for endosulfan I and II were very different from their measured literature values and thus could not assume that the modeled data for endosulfan sulfate would be accurate. The trends in individual pesticide concentrations were different for pine needle concentrations compared to theoretical air concentrations. This was especially true for the HCHs (Figure S6). Alpha-HCH tended to have the lowest concentrations in pine needles compared to the other HCH isomers, yet the highest theoretical air concentrations. This difference is due to the different partitioning properties of the HCH isomers; alpha-HCH is the most volatile isomer (PL = 0.003 Pa)30 and the isomer that least favorably partitions into lipid (or octanol, log KOA = 7.26).41 This result is particularly important because the ratios of HCH isomers in air are used to indicate the predominance of different use patterns, especially historic technical HCH versus lindane use.42 In our study, alpha-/gamma-HCH calculated from theoretical air concentrations ranged from 0.9 to 65. At most sites, this ratio was greater than 4 suggesting historic use of lindane was not common in these areas. The exception was Otira, where the ratio was 0.9, suggesting lindane was used historically at this site. Alpha-/gamma-HCH ratios calculated from second-year pine needle concentrations were different from those calculated from theoretical air concentrations. For example, the ratio for the Springfield site was 0.4 when calculated from pine needle concentrations and 1.6 when calculated from theoretical air concentrations. These results show that the effects of partitioning properties need to be considered to accurately use these ratios when vegetation is used as a biomonitor of SOCs in the atmosphere. Pesticide Concentration Profiles. Normalized pesticide concentration profiles were constructed from the theoretical air concentrations for each sample site (Figures S7−9). Theoretical air concentrations were used, instead of direct pine needle concentrations, to ensure that the subsequent source apportionment analysis was not affected by differences in air−needle partitioning for different pesticides or by temperature. The similarity between profiles was quantitatively assessed using the FALCON method. Sites with similar profiles were

whereas comparatively less volatile SOCs (8.5 < log KOA < 11) are kinetically controlled. The range of the log KOA values (7.82−9.65) for the pesticides of interest in our study closely spanned the threshold value suggested by McLachlan; therefore, we chose to determine the uptake process for each pesticide by comparing pesticide concentrations in first- and second-year needles rather than using McLachlan’s simpler threshold-based framework. The theory behind this approach is that if the uptake of a pesticide from the atmosphere into pine needles is kinetically controlled, the first-year needles will have lower concentrations than the second-year needles due to the shorter exposure time. On the other hand, if pesticides in the atmosphere are in equilibrium with those in pine needles, the first- and secondyear needles will have similar concentrations. Several previous observation-based studies that used different aged needles to determine the uptake stage of pesticides with a large range of KOA values reported higher pesticide concentrations in older pine needles, suggesting kinetically controlled uptake.34−36 These studies compared SOC concentrations normalized to the dry or wet weight of the samples (i.e., g g−1 dry or wet weight). In our study, we found that second-year needles had similar moisture contents (paired t-test, p = 0.206) but higher lipid contents (paired t-test, p = 0.002) than firstyear needles. Given that these pesticides accumulate in the lipid of pine needles,37 the higher concentrations observed in second-year needles compared to first-year needles reported in previous studies may have been due to higher lipid contents in the second-year needles instead of the longer exposure times. For this reason, we opted to evaluate both lipid-normalized pesticide concentrations and dry mass-normalized concentrations to determine uptake stages. Out of 77 individual comparisons of pesticide concentrations in first-year versus second-year needles (11 sites × 9 chemicals minus 22 nondetects), significant differences were not observed in 88% and 79% of the cases in which dry-mass normalized concentrations and lipid-normalized concentrations were compared, respectively (Figures S3−4). Note that although endosulfan registration in New Zealand changed during our study, this did not complicate the interpretation since its concentrations were not significantly different in first- and second-year needles. There were a handful of individual cases in which concentrations were higher in second-year needles; however, this was never true for more than 30% of the comparisons for an individual pesticide. Thus, we concluded that all of the pesticides of interest in this study were in the equilibrium state. Interestingly, 45% of the comparisons for beta-HCH were higher in first-year needles when lipid-normalized concentrations were compared but this was true for only 9% of the cases when dry-mass concentrations were compared (Figures S3−4). This could indicate that beta-HCH was in the equilibrium state and had sorbed into a larger portion of the needle than just the lipid component (i.e., there was an equal mass of pesticide in first- and second-year needles but the lipidnormalized concentrations were higher for first-year needles due to lower lipid content). There was no evidence of this behavior for any of the other pesticides. Theoretical Air Concentrations. Theoretical air concentrations were calculated for nine pesticides (including alphaHCH, beta-HCH, gamma-HCH, delta-HCH, endosulfan I, chlorpyrifos, dacthal, triallate, and trifluralin) (Figure S5). 1394

dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398

Environmental Science & Technology

Article

Kumara to produce a profile representing the West Coast pesticide profile (Figure 2). The profiles calculated for the Canterbury Plains sites were similar to each other (comparison of five Canterbury Plains sites, r = 0.765 − 0.996, p = < 0.001 − 0.016) and characterized by high chlorpyrifos; chlorpyrifos accounted for more than 40% of the total atmospheric pesticide burden (Figure S8). The Canterbury Plains profile was therefore constructed by averaging the profiles of the five Canterbury Plains sites (Methven, Oxford, Darfield, West Melton, and Leeston) (Figure 2). The other east coast site, Pigeon Bay, was located on the Banks Peninsula and had a different profile from the other east coast sites so was not included in the Canterbury Plains profile. PCA also provided evidence that the Tasman Sea, West Coast, and Canterbury Plains source regions had significantly distinct chemical profiles (Figure S10). Using Pesticide Concentration Profiles for Source Apportionment. Three main atmospheric transport pathways for SOCs to the Southern Alps were considered. The first pathway was that of the northwesterly global-scale winds, which contains SOCs from Australian and Southern Hemisphere background air. The Tasman Sea profile was assumed to represent this pathway. As air coming from across the Tasman Sea moves inland, the pesticide profile in the air close to the ground is expected to change due to the volatilization of pesticides, present due to current or historic uses, from the local area. However, the air high in the atmosphere is not expected to be influenced by local pesticide sources, thus maintaining the Tasman Sea profile. The other transport pathways considered were those of the westerly and easterly upslope mountain winds. These upslope winds are likely to have profiles indicative of current and historic pesticide use on the West Coast (the West Coast profile) and on the Canterbury Plains (the Canterbury Plains profile), respectively. The first step in the source apportionment was to determine if the westerly and easterly upslope winds crossed the main divide and influenced mountain sites on the other side. The profiles at the two highest elevation sites, Cora Lynn and Cass (Figure S9), closely matched the Tasman Sea profile (r = 0.974 and 0.996; p < 0.001). On comparison with the mixed-source profiles, Cora Lynn was best matched to 87% Tasman Sea profile and 13% West Coast profile (r = 0.978, p < 0.001), and Cass was best matched to the 100% Tasman Sea profile with no contribution from the West Coast and Canterbury Plains profiles (r = 0.996, p < 0.001) (Figure 3 and Figure S11). PCA also provided evidence that the Cora Lynn and Cass profiles were strongly influenced by the Tasman Sea profile (Figure S10). These results suggest that the high-elevation sites were largely influenced by northwesterly global-scale air flow and not by westerly and easterly upslope winds. This indicates that there was minimal mixing of the small-scale air masses, i.e. the localscale westerly upslope winds had little influence on mountain sites on the eastern side and vice versa. This finding formed the basis for further comparisons with mixed-source profiles. Based on the evidence that there was little mixing of the small-scale air masses, the profile from the one western mountain site (Otira) was compared to a series of mixed-source profiles generated by mixing the Tasman Sea and West Coast profiles (Figure S12). The Pearson correlation coefficient was maximized when the Tasman Sea profile contributed 48% and the West Coast profile 52% of the Otira profile (r = 0.507, p = 0.16). However, the maximum Pearson correlation coefficient did not exceed the r-value required for this result to be

assumed to be influenced by the same sources. Two distinct profiles were observed at the West Coast sites (Figure S7). The Awatuna and Kumara sites had profiles similar to each other (r = 0.835, p = 0.0046, based on Pearson’s correlation analysis). Both sites were located within 8 km of the Tasman Sea coast (Figure 1c) so were likely influenced by air crossing the Tasman Sea. Thus, the profiles of Awatuna and Kumara were averaged (by taking the mean of the normalized concentrations in the three Awatuna and three Kumara subsamples) to produce the Tasman Sea profile (Figure 2). The profiles at the

Figure 2. Pesticide concentration profiles (based on theoretical air concentrations) for the three source regions: (a) the Tasman Sea, (b) the West Coast, and (c) the Canterbury Plains. Bars represent the mean fraction of the total concentration (i.e., the sum of the concentrations of the nine selected pesticides); error bars represent standard deviations of fractions in subsamples (n = 6 for Tasman Sea and West Coast, and n = 15 for Canterbury Plains).

other two western sites, Greymouth and Lake Brunner, were also similar (r = 0.941, p < 0.001) but were distinct from the Tasman Sea profile with the key difference being the higher fraction of chlorpyrifos (Figure S7). Chlorpyrifos is widely used in New Zealand, so the higher chlorpyrifos fraction suggests that the Greymouth and Lake Brunner sites were influenced by local uses on the West Coast. The Greymouth and Lake Brunner profiles were averaged separately from Awatuna and 1395

dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398

Environmental Science & Technology

Article

Figure 3. Pesticide concentration profiles (based on theoretical air concentrations) for source regions and mountain sites, along a transect crossing the Southern Alps. Bars represent the mean fraction the total concentration (i.e., the sum of the concentrations of the nine selected pesticides); error bars represent standard deviations of fractions in subsamples (n = 6 for Tasman Sea and West Coast, n = 15 for Canterbury Plains, n = 2 for Lake Coleridge, and n = 3 for all other sites).

statistically significantly (i.e., p < 0.05), suggesting that local spraying or other sources may have influenced the pesticide burden at Otira. PCA also provided evidence that the Otira site was not strongly influenced by the Tasman Sea or West Coast source regions (Figure S10). A mixing model of the Tasman Sea and Canterbury Plains profiles was constructed in a similar way and used for the mountain sites to the east of the main divide (i.e., Avoca, Springfield, and Lake Coleridge). The Avoca profile was best matched to a mixture of 73% Tasman Sea and 27% Canterbury Plains (r = 0.783, p = 0.013) (Figure 3, Figure S13), indicating that both global-scale northwesterly and easterly upslope winds interacted at this site. The Springfield and Lake Coleridge sites showed more influence from the Canterbury Plains profile, which accounted for 88% and 100% of their profiles, respectively (r = 0.959 and 0.996; p < 0.001) (Figure 3, Figure S13). Although the Springfield and Avoca sites are located at similar elevations, the Avoca site is more shielded from the Canterbury Plains by mountains (the Torlesse and Puketeraki Ranges). This is most likely the reason why these two sites experienced different source influences. PCA also provided evidence that the Canterbury Plains source region had a systematically decreasing influence on the contaminant profiles as the mountain sites were located further away (Figure S10). In summary, synoptic-scale and regional winds are known to undergo complex mixing in mountains but until now, little has been known about how this mixing influences SOC distributions in mountains. The results of this study indicated that, in our model mountain system, (a) pesticide profiles at the highest mountain sites were mainly influenced by atmospheric transport via the northwesterly synoptic-scale air flow, (b) neither easterly nor westerly local-scale upslope winds resulted

in the transport of pesticides to the other side of the mountain divide, and (c) the interplay between northwesterly synopticscale winds and easterly upslope winds resulted in a decreasing influence of the east coast source region with increasing elevation on the eastern slope. Although wind patterns differ among different mountain systems, these results provide important insights about how the atmospheric transport of SOCs in mountains can be affected by synoptic and regionalscale winds. We have also demonstrated a useful approach for quantifying the contributions of different source regions on contaminant burdens in mountains that could easily be applied in other parts of the world and we have reported SOC concentrations for an area of the world where few such data are currently available.

■

ASSOCIATED CONTENT

S Supporting Information *

Additional text, tables, and figures as noted in the text. This material is available free of charge via the Internet at http:// pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]; phone: +64-3-4795214; fax: +64-3-479-7906. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was funded by the University of Otago. We especially thank Duncan Tait for field assistance, Benjamin 1396

dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398

Environmental Science & Technology

Article

(18) Eriksson, G.; Jensen, S.; Kylin, H.; Strachan, W. The pine needle as a monitor of atmospheric pollution. Nature 1989, 341 (6237), 42− 44. (19) Schrlau, J. E.; Geiser, L.; Hageman, K. J.; Landers, D. H.; Simonich, S. L. Comparison of lichen, conifer needles, passive air sampling devices, and snowpack as passive sampling media to measure semi-volatile organic compounds in remote atmospheres. Environ. Sci. Technol. 2011, 45 (24), 10354−10361. (20) Plumb, R. H. Fingerprint Analysis of Contaminant Data: A Forensic Tool for Evaluating Environmental Contamination; EPA/600/504/054; United States Environmental Protection Agency: Washington, DC, 2004. (21) Lavin, K. S.; Hageman, K. J. Selective pressurized liquid extraction of halogenated pesticides and polychlorinated biphenyls from pine needles. J. Chromatogr. A 2012, 1258, 30−36. (22) New Zealand National Institute of Water and Atmospheric Research. The National Climate Database. http://cliflo.niwa.co.nz/ (accessed 24 May 2010). (23) Hair, J. F.; Anderson, R. E.; Tatham, R. L.; Black, W. C. Multivariate Data Analysis, 5th ed.; Prentice-Hall International, Inc.: Upper Saddle River, NJ, 1998. (24) New Zealand Ministry for the Environment. Organochlorines in New Zealand: Ambient Concentrations of Selected Organochlorines in Soils; Wellington, 1998. (25) Li, Y. F. Global technical hexachlorocyclohexane usage and its contamination consequences in the environment: From 1948 to 1997. Sci. Total Environ. 1999, 232 (3), 121−158. (26) Australian Pesticides and Veterinary Medicines Authority. The National Registration Authority Review of Endosulfan; Kingston, 1998. (27) New Zealand Environmental Risk Management Authority. Hazardous Substances (Endosulfan Direction Prohibiting Use and Storage Disposal Control) Notice 9691; New Zealand Gazette, 2008. (28) Australian Pesticides and Veterinary Medicines Authority. Cancellation of Endosulfan Active Constituent Approvals, Products Containing Endosulfan and Label Approvals of Products Containing Endosulfan; Commonwealth of Australia Gazette, 2010. (29) New Zealand Environmental Protection Authority. New Zealand Inventory of Chemicals. http://www.epa.govt.nz/search-databases/ Pages/nzioc-search.aspx (accessed February 2, 2012). (30) Mackay, D.; Shiu, W. Y.; Ma, K.-C.; Lee, S. C. Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals, 2nd ed.; CRC Press, Taylor & Francis Group: Boca Raton, FL, 2006; Vols. I−IV. (31) Simonich, S. L. Organic pollutant accumulation in vegetation. Environ. Sci. Technol. 1995, 29 (12), 2905−2914. (32) McLachlan, M. S. Framework for the interpretation of measurements of SOCs in plants. Environ. Sci. Technol. 1999, 33 (11), 1799−1804. (33) Collins, C.; Fryer, M.; Grosso, A. Plant uptake of non-ionic organic chemicals. Environ. Sci. Technol. 2006, 40 (1), 45−52. (34) Gaggi, C.; Bacci, E. Accumulation of chlorinated hydrocarbon vapours in pine needles. Chemosphere 1985, 14 (5), 451−456. (35) Herrmann, R.; Baumgartner, I. Regional variation of selected polyaromatic and chlorinated hydrocarbons over the South Island of New Zealand, as indicated by their content in Pinus radiata needles. Environ. Pollut. 1987, 46 (1), 63−72. (36) Strachan, W. M. J.; Eriksson, G.; Kylin, H.; Jensen, S. Organochlorine compounds in pine needles: Methods and trends. Environ. Toxicol. Chem. 1994, 13 (3), 443−451. (37) Simonich, S. L.; Hites, R. A. Global distribution of persistent organochlorine compounds. Science 1995, 269 (5232), 1851−1854. (38) Nizzetto, L.; Jones, K. C.; Gramatica, P.; Papa, E.; Cerabolini, B.; Di Guardo, A. Accumulation of persistent organic pollutants in canopies of different forest types: Role of species composition and altitudinal-temperature gradient. Environ. Sci. Technol. 2006, 40 (21), 6580−6586. (39) Paterson, S.; Mackay, D.; Bacci, E.; Calamari, D. Correlation of the equilibrium and kinetics of leaf-air exchange of hydrophobic organic chemicals. Environ. Sci. Technol. 1991, 25 (5), 866−871.

Durrant for laboratory assistance, and Sebastian Schenker and Stravos Papadokonstantakis for discussions about the statistical approach.

■

REFERENCES

(1) Daly, G. L.; Wania, F. Organic contaminants in mountains. Environ. Sci. Technol. 2005, 39 (2), 385−398. (2) Vighi, M. SETAC Europe Workshop. The role of high mountains in the global transport of persistent organic pollutants. Ecotoxicol. Environ. Saf. 2006, 63 (1), 108−112. (3) Hageman, K. J.; Hafner, W. D.; Campbell, D. H.; Jaffe, D. A.; Landers, D. H.; Simonich, S. L. M. Variability in pesticide deposition and source contributions to snowpack in western U.S. national parks. Environ. Sci. Technol. 2010, 44 (12), 4452−4458. (4) Lavin, K. S.; Hageman, K. J.; Marx, S. K.; Dillingham, P. W.; Kamber, B. S. Using trace elements in particulate matter to identify the sources of semi-volatile organic contaminants in air at an alpine site. Environ. Sci. Technol. 2012, 46 (1), 268−276. (5) Usenko, S.; Landers, D. H.; Appleby, P. G.; Simonich, S. L. Current and historical deposition of PBDEs, pesticides, PCBs, and PAHs to Rocky Mountain National Park. Environ. Sci. Technol. 2007, 41 (21), 7235−7241. (6) Primbs, T.; Wilson, G.; Schmedding, D.; Higginbotham, C.; Simonich, S. M. Influence of Asian and Western United States agricultural areas and fires on the atmospheric transport of pesticides in the Western United States. Environ. Sci. Technol. 2008, 42 (17), 6519−6525. (7) Estellano, V. H.; Pozo, K.; Harner, T.; Franken, M.; Zaballa, M. Altitudinal and seasonal variations of persistent organic pollutants in the Bolivian Andes mountains. Environ. Sci. Technol. 2008, 42 (7), 2528−2534. (8) Primbs, T.; Piekarz, A.; Wilson, G.; Schmedding, D.; Higginbotham, C.; Field, J.; Simonich, S. M. Influence of Asian and Western United States urban areas and fires on the atmospheric transport of polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and fluorotelomer alcohols in the Western United States. Environ. Sci. Technol. 2008, 42 (17), 6385−6391. (9) Davidson, D. A.; Wilkinson, A. C.; Kimpe, L. E.; Blais, J. M. Persistent organic pollutants in air and vegetation from the Canadian Rocky Mountains. Environ. Toxicol. Chem. 2004, 23 (3), 540−549. (10) Grimalt, J. O.; Borghini, F.; Sanchez-Hernandez, J. C.; Barra, R.; Torres García, C. J.; Focardi, S. Temperature dependence of the distribution of organochlorine compounds in the mosses of the Andean mountains. Environ. Sci. Technol. 2004, 38 (20), 5386−5392. (11) Mast, M. A.; Alvarez, D. A.; Zaugg, S. D. Deposition and accumulation of airborne organic contaminants in Yosemite National Park, California. Environ. Toxicol. Chem. 2012, 31 (3), 524−533. (12) Wania, F.; Westgate, J. N. On the mechanism of mountain coldtrapping of organic chemicals. Environ. Sci. Technol. 2008, 42 (24), 9092−9098. (13) Blais, J. M.; Schindler, D. W.; Muir, D. C. G.; Kimpe, L. E.; Donald, D. B.; Rosenberg, B. Accumulation of persistent organochlorine compounds in mountains of western Canada. Nature 1998, 395 (6702), 585−588. (14) Choi, S. D.; Shunthirasingham, C.; Daly, G. L.; Xiao, H.; Lei, Y. D.; Wania, F. Levels of polycyclic aromatic hydrocarbons in Canadian mountain air and soil are controlled by proximity to roads. Environ. Pollut. 2009, 157 (12), 3199−3206. (15) McConnell, L. L.; LeNoir, J. S.; Datta, S.; Seiber, J. N. Wet deposition of current-use pesticides in the Sierra Nevada Mountain Range, California, USA. Environ. Toxicol. Chem. 1998, 17 (10), 1908− 1916. (16) Whiteman, C. D. Mountain Meteorology: Fundamentals and Applications; Oxford University Press: New York, 2000. (17) Ockenden, W. A.; Steinnes, E.; Parker, C.; Jones, K. C. Observations on persistent organic pollutants in plants: Implications for their use as passive air samplers and for POP cycling. Environ. Sci. Technol. 1998, 32 (18), 2721−2726. 1397

dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398

Environmental Science & Technology

Article

(40) United States Environmental Protection Agency. Estimation Program Interface (EPI) Suite. http://www.epa.gov/opptintr/ exposure/pubs/episuite.htm. (41) Beyer, A.; Wania, F.; Gouin, T.; Mackay, D.; Matthies, M. Selecting internally consistent physicochemical properties of organic compounds. Environ. Toxicol. Chem. 2002, 21 (5), 941−953. (42) Willett, K. L.; Ulrich, E. M.; Hites, R. A. Differential toxicity and environmental fates of hexachlorocyclohexane isomers. Environ. Sci. Technol. 1998, 32 (15), 2197−2207.

1398

dx.doi.org/10.1021/es304497e | Environ. Sci. Technol. 2013, 47, 1390−1398