Understanding and predicting the fate of semi- volatile organic

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Understanding and predicting the fate of semi-volatile organic pesticides in a glacier-fed lake using a multimedia chemical fate model Xiaolin Wu, Cleo Lisa Davie-Martin, Christine Steinlin, Kimberly J. Hageman, Nicolas J. Cullen, and Christian Bogdal Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03483 • Publication Date (Web): 19 Sep 2017 Downloaded from http://pubs.acs.org on September 22, 2017

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Environmental Science & Technology

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Understanding and predicting the fate of semivolatile organic pesticides in a glacier-fed lake using a multimedia chemical fate model

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Xiaolin Wu,1 Cleo L. Davie-Martin,1‡ Christine Steinlin,2 Kimberly J. Hageman,1* Nicolas J. Cullen,3and Christian Bogdal2

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2

‡

Department of Chemistry, University of Otago, Dunedin 9016, New Zealand Institute for Chemical and Bioengineering, ETH Zürich, CH-8093 Zürich, Switzerland 3 Department of Geography, University of Otago, Dunedin 9016, New Zealand

Present Addresses Department of Microbiology, Oregon State University, Corvallis, Oregon 97331, United States

*

Corresponding Author To whom correspondence should be addressed E-mail: [email protected]

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1 ACS Paragon Plus Environment


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GRAPHICAL ABSTRACT

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ABSTRACT

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Melting glaciers release previously ice-entrapped chemicals to the surrounding environment.

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As glacier melting accelerates under future climate warming, chemical release may also

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increase. This study investigated the behavior of semi-volatile pesticides over the course of

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one year and predicted their behavior under two future climate change scenarios. Pesticides

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were quantified in air, lake water, glacial melt water, and stream water in the catchment of

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Lake Brewster, an alpine glacier-fed lake located in the Southern Alps of New Zealand. Two

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historic-use pesticides (endosulfan I and hexachlorobenzene) and three current-use pesticides

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(dacthal, triallate, and chlorpyrifos) were frequently found in both air and water samples from

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the catchment. Regression analysis indicated that the pesticide concentrations in glacial melt

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water and lake water were strongly correlated. A multimedia environmental fate model was

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developed for these five chemicals in Lake Brewster. Modelling results indicated that

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seasonal lake ice cover melt, and varying contributions of input from glacier melt and stream


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water, created pulses in pesticide concentrations in lake water. Under future climate scenarios,

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the concentration pulse was altered and glacial melt made a greater contribution (as mass flux)

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to pesticide input in the lake water.

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INTRODUCTION

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Climate change has the potential to affect significantly the behavior and distribution

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of semi-volatile organic contaminants (SVOCs) in the environment.1-7 One reason is that

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degradation rates and equilibrium partition coefficients are strongly influenced by

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environmental temperature. However, because persistent SVOCs undergo atmospheric

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transport and accumulate in cold ecosystems,6 their fates are also intimately linked to those of

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the snowpack and glaciers into which they become trapped. As glacial melting occurs,8

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SVOCs that have been stored in glaciers for many decades may undergo release to the

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environment. Studies conducted in the Western Antarctic marine environment9 and in lakes

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in Canada10 and the European Alps11-13 have provided increasing evidence that glaciers are

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acting as secondary sources of legacy SVOCs, such as polychlorinated biphenyls (PCBs) and

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organochlorine pesticides. A number of current-use pesticides are known to undergo

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atmospheric transport to remote alpine ecosystems;14-17 however, their potential incorporation

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into glaciers, or release into glacier meltwater, has not yet been investigated. Glacial melting

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in cold ecosystems is projected to accelerate due to increases in mean atmospheric

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temperatures8 and we therefore hypothesize enhanced release of SVOCs from glaciers to the

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surrounding environment.10, 18, 19 Nonetheless, the release of SVOCs from melting glaciers is

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complex, being affected by melting dynamics as well as SVOC accumulation history in the

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glacier, and questions about the relative importance of glacier meltwater as a source of

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SVOCs to alpine lakes remain.



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Multimedia environmental fate models20, 21 are useful tools for understanding the fate

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and transport of SVOCs in the environment. Recently, such models have been used to predict

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the influences of climate change on SVOC fate, for example in the Adriatic Sea,22 in the

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global atmosphere,23 in a lagoon,24 and in a river catchment.25 Steinlin et al. used chemical

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fate modelling to investigate the deposition and incorporation of PCBs in cold26 and

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temperate glaciers.27 Steinlin et al. also studied the release of PCBs from melting glaciers28

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under future climate change scenarios. However, this previous work did not include the

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measurement of SVOC concentrations in air in the lake catchment, and their catchment did

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not include a significant non-glacier fed stream. Thus, more work is needed to understand the

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relative contributions of all potential pathways of SVOC accumulation in glacier-fed alpine

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lakes, and how that might change under future climate change scenarios.

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This study was conducted at Brewster Lake, a high-alpine lake in the Southern Alps

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of New Zealand that is mainly fed by the melt water from Brewster Glacier. We first

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quantified current-use and historic-use semi-volatile pesticides in air, glacier melt water,

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stream water, and lake water over the course of one year (2014/2015). We next developed a

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multimedia environmental chemical fate model and used it to understand better the processes

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and effects of each input parameter on chemical concentrations and fates. Finally,

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concentrations of semi-volatile pesticides in Brewster Lake were predicted under two specific

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future climate scenarios expected for New Zealand and the Brewster Glacier catchment. To

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the best of our knowledge, this is the first study to show that glacier melt water can be an

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important source of current-use pesticides to alpine lakes.

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METHODS

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Study Site. Brewster Lake (Figure 1) is the terminal lake at the base of Brewster Glacier.

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Brewster Glacier is a relatively small temperate-climate alpine glacier located west of the


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Figure 1. (a) Location of Brewster Glacier and Lake, on the South Island of New Zealand. (b) The relative locations of the glacier and lake. The glacier margin is shown by the blue line, which was digitized from a Quickbird image collected in February 2011. The aerial photograph was collected in March 2011 (i.e. late austral summer).

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main divide of the Southern Alps in Mount Aspiring National Park, New Zealand (44.08 ºS,

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169.43 ºE). Brewster Glacier covers an area of 2.03 km2 and ranges in elevation from 1700 to

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2400 m a.s.l.29 Brewster Lake has a surface area of ~0.02 km2 and maximum depth of ~4 m.

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Due to spring turnover caused by ice melt, combined with the lake being shallow and in a

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windy location, stratification is not expected. The main hydrological inputs to the lake are

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precipitation, annual snowmelt, and glacial melt from Brewster Glacier.30 The meteorological

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station at Brewster Lake indicates that both westerly and easterly winds are equally common

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at this site. Since this is a high-elevation site located in a national park, there is no local

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pesticide use. However, Lavin et al.17, 31 previously showed that semi-volatile pesticides from

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agricultural areas on the east and west coasts of New Zealand’s South Island, as well as from



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Australia (located ~2,000 km to the west), can undergo atmospheric transport to New

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Zealand alpine sites.

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Chemicals, Sampling, Quantification, and Quality Assurance.

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chemicals and materials, sampling strategies, extraction techniques, instrumental analysis,

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and quality assurance can be found in Section 1 of the Supporting Information (SI); a brief

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overview is provided here. The target analyte list included 11 semi-volatile current-use

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pesticides, as well as 28 historic-use pesticides and selected degradation products. A suite of

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PCBs were also targeted; however, their concentrations rarely exceeded those in field blanks

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so they will not be discussed further. Time-weighted average pesticide concentrations in air

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were obtained with diffusion-based passive air samplers (Figure S1) using the approach

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previously described by Shoeib et al.32 and Pozo et al;33 passive air sampling was conducted

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over ~3-month periods between March 2014 and April 2015 (see Table S1 for sampling

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dates). Pesticide concentrations in the particulate and dissolved phase of water (lake, glacier

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melt, and stream) samples were obtained via battery-operated high-volume water samplers

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(Figure S2) using an approach similar to that of Bogdal et al.34 Water samples were collected

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on three occasions between March 2014 and April 2015 (see Table S2&3 for sampling dates

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and sample durations and volumes). Air and water sampling media was returned to the

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laboratory and pesticides were extracted using Accelerated Solvent Extraction. Pesticide

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quantification was conducted with gas chromatography and mass selective detection, using

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internal calibration curves. Quality assurance was ensured by regularly analyzing instrument

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performance standards, laboratory and field blanks, and breakthrough samples (water

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sampling).

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Details regarding

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MODELLING

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Model description. The multimedia chemical fate model was developed in Matlab

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7.6 using the approach described by Steinlin et al.27 and the concept of fugacity to describe

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the rate at which chemicals transfer between each phase. z- and D-values were used to

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describe partitioning and transport processes,21 respectively. The model contained air (air and

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particles), lake water (water and suspended particles), and surface sediment (sediment solids

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and water) compartments (Figure 2). Monthly resolution was used, with the austral summer

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being defined as December to February, autumn as March to May, winter as June to August,

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and spring as September to November.

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Figure 2a shows the modelled processes when there was no ice cover. Processes

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modelled in the atmosphere included advective in-/outflow, degradation, and dry and wet

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gaseous and particle deposition. Wet particle deposition (i.e. the scavenging of particle-bound

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pesticides from air) and wet gaseous deposition (i.e. the scavenging of gas-phase pesticides

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from air) could both occur via rain and snow. Processes modelled in the lake water

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compartment included advective inflow from the glacial melt water and the catchment,

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advective outflow, and water-air diffusion (re-volatilization). Processes modelled in the

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sediment included diffusion between the sediment and water compartments, sedimentation,

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resuspension, and burial. We assumed limited biological activity in the lake because of the

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cold temperatures and because the catchment is mainly exposed bedrock, with negligible

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organic material; thus, degradation in water and sediment were not included in the model.

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An ice compartment was added to the surface of the lake water in the months when

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the air temperature (Tair) was below 0 °C (July - November) (Figure 2b). The ice

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compartment melted over the course of three months (December – February), starting when

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the air temperature first rose above 0 °C. The ice cover was assumed homogeneous and when

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present, it covered the entire lake and prevented air-lake exchange of pesticides. Deposition



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from air to ice occurred via dry and wet gaseous and particle deposition. Transfer from ice to

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air occurred via ice-air diffusion (i.e. re-volatilization). During the ice melt period, some

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transfer of pesticides from ice to lake water occurred via ‘ice cover melt water runoff’;35

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however, transfer directly from the lake water to air was prevented by the ice cover. At the

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end of the ice-melt period (i.e. in March), all pesticide mass remaining in the ice cover

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(including pesticides bound to atmospheric particulate matter that deposited in the ice over

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winter) was transferred to lake water; this process was defined as ‘end-of-season transfer.’

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Although ‘end-of-season transfer’ may happen more quickly in reality, it occurs during a one-

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month period in our model due to the model’s resolution. Neither degradation nor diffusion

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between ice cover and water were included in the model.


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Figure 2. Diagrams of the modelled air, lake water and sediment compartments and processes when there is (a) no ice cover and (b) when there is an ice cover over the lake (air-lake water exchange was shut off; melt water runoff and ‘end-of-season’ transfer from ice cover to lake water exist). Processes in blue, red, and purple represent pesticide inflow into the system, outflow from the system, and transfers between different compartments inside the system, respectively.

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Model Parameters. All environmental parameters and chemical properties are listed

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in Tables S6-10. Time-varied parameters included air temperature, precipitation rate, and

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runoff from glacier and catchment. The input air temperatures were the mean monthly values

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obtained by averaging the hourly measurements collected from the automatic weather station

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placed adjacent to the lake at 1650 m a.s.l.36

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The pesticide concentrations we measured in air, glacial melt water, and stream water

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were used as inputs to the model. The pesticide concentrations obtained with the passive air

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samplers (time-averaged), and those measured in the stream and glacial melt water, were

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used as constant concentrations during the three-month periods represented by each sampling

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event. We assumed that pesticide concentrations in all streams in the catchment were

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identical to those measured in the one stream we sampled. Equations used in the model and

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other details are provided in Section 2 of the SI.

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Sensitivity and Uncertainty Analysis. Detailed investigations into the sensitivity of

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the model to each input parameter, and contributions to uncertainty, were conducted. Method

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details can be found Section 2 whereas results and discussion can be found in Section 3 of the

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SI.

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Modelling Pesticide Behavior under Future Climate Scenarios. The air

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temperature and precipitation changes used in our future climate scenarios are shown in

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Table 1. The temperature predictions were obtained from the original Intergovernmental

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Panel on Climate Change8 A1B scenarios downscaled for New Zealand from the 4th

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Assessment report. According to the model developed by Anderson et al.,30 a 1°C increase in

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air temperature in New Zealand corresponds to a 60% increase in run-off from Brewster

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Glacier whereas a 1°C decrease in air temperature corresponds to a 20% decrease in glacier

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run-off. For our scenario 1 analysis (Table 1), we therefore used a 60% increase in glacier

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run-off. For scenario 2, we estimated that a 2.1 °C increase in air temperature would


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correspond to a 172% increase in glacier run-off. The % change in catchment run-off was

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assumed to be the same as the % change in precipitation. For this modelling exercise, we

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assumed that the pesticide concentrations we measured in all media remain constant in the

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future; this assumption is not likely completely accurate; however, it allowed us to investigate

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the effects of changing climate-related parameters on pesticide concentrations and behaviors

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in the investigated system. More details about future scenarios are provided in Section 2 of

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the SI.

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Table 1. Parameters used to model pesticide behavior under future climate change scenarios. Values shown are changes, or percent changes, relative to current values (for specific air temperature by month, see Table S12). The modelled year starts in March and extents to April of the next year, in accordance with our collected data.

Scenario 1 (2040)

Air Temperature Precipitation Glacial melt water run-off

Scenario 2 Air Temperature (2090) Precipitation Glacial melt water run-off

Mar-May +1.0 °C +3%

Jun-Aug +1.0 °C +11%

Sep-Nov +0.9 °C +5%

Dec-Feb +0.7 °C 0%

Mar-Apr +1.0 °C +5%

+60% +2.1 °C +3%

+2.1 °C +21%

+1.7 °C +8%

+2.2 °C +3%

+2.1 °C -1%

+172%

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RESULTS AND DISCUSSION

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Detected chemicals and measured concentrations

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Air. The six most commonly detected pesticides in air were endosulfan I, HCB,

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dacthal, triallate, chlorpyrifos, and trifluralin. The first five of these were detected in 100% of

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air samples and trifluralin was detected in 58% (7 of 12) of air samples. None of the other

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target compounds were detected in air samples. Technical endosulfan (containing both

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endosulfan I and II with the ratio at 7:3)37 was a widely applied insecticide in New Zealand

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and Australia until its ban in New Zealand38 on 16 January 2009 and in Australia39 on 11

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October 2010. HCB was used as a seed-dressing fungicide for cereal grain in New Zealand



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between 1970 and 1972.40 In Australia, HCB was de-registered as a pesticide by 1987 and

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was phased out by 1997 for use as an industrial chemical;41 however, it may still be produced

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as a by-product in chemical manufacturing.42 Triallate, dacthal, chlorpyrifos, and trifluralin

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are currently used in New Zealand43 and Australia.44 Triallate, dacthal, and trifluralin are pre-

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emergent herbicides whereas chlorpyrifos is a broad-spectrum organophosphate insecticide;

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all are used on a wide variety of crops. Information about spatial and temporal pesticide use

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trends in New Zealand is not available.

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Pesticide concentrations in air were measured previously in New Zealand during 2009

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at a high-elevation site17 at Temple Basin (in Arthur’s Pass National Park, elevation 1320 m

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a.s.l., ~200 km north of Brewster Glacier), and along a transect16 crossing the alpine divide at

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Arthur’s Pass National Park. Although the target analyte lists were nearly identical (with the

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exception being that HCB was not included in either or the previous studies16,

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endosulfan I and chlorpyrifos were detected in both the Temple Basin and current study. On

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the other hand, endosulfan I, triallate, dacthal, chlorpyrifos, and trifluralin were among the

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most commonly detected pesticides in both the alpine transect and the current study.

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), only

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The measured concentrations of each of the six most commonly detected pesticides in

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air are listed in Table S13 and concentrations for all except trifluralin (which was not

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detected in water samples and therefore not considered beyond this section) are also plotted

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in Figure 3. No consistent seasonal patterns were observed for the current-use pesticides

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(chlorpyrifos, dacthal and triallate). On the other hand, a similar seasonal trend, in which the

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highest concentrations were found in the two autumn samples, was observed for both of the

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historic-use pesticides (endosulfan I and HCB).

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Among the three endosulfan-related chemicals on our target analyte list, only

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endosulfan I was detected in air samples despite detection limits of the three chemicals being

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identical (Table S6). Endosulfan I was also the dominant isomer observed in air samples


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collected from the Arthur’s Pass region in New Zealand,17 in Australia,45 and at several

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Antarctic sites.46,

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because endosulfan II can be significantly converted48-51 to endosulfan I and both endosulfan

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II and endosulfan sulfate are readily removed from the air by precipitation.52 Table S14

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compares the pesticide concentrations measured in air in this study to those measured at

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several other Southern Hemisphere locations; the ranges at the different sites are roughly

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similar, although further generalizations cannot be made due to limited data.

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Endosulfan II and endosulfan sulfate were likely not detected in air

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Water. None of the target analytes were detected in the particulate phase, most likely

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because the amount of particulate matter in the water was very low. The most frequently

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detected pesticides in the dissolved-phase of water samples were endosulfan I (81%

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frequency), endosulfan II (89%), endosulfan sulfate (96%), HCB (89%), dacthal (100%),

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triallate (100%), chlorpyrifos (67%), and dieldrin (63%). All other target analytes were

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detected in 0.05), or for

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lake water vs. stream water (p = 0.06) (Figure S4-5). Overall, these results suggest that the

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glacial melt water was an important source of pesticides to the lake. Pesticide concentrations

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exhibited a general trend: lake water > glacial melt water > stream water for all compounds

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except HCB. It is also notable that the triallate concentrations were very high in the autumn

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(March – May 2014) air sample but were highest in the winter (June 2014) lake sample,



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Figure 3. Pesticide concentrations in lake water, glacial melt water, stream water and air. Seasons are indicated as autumn (AUT, white shading), winter (WIN, pink shading), spring (SPR, purple shading), and summer (SUM, green shading). Glacial melt and stream water were not collected during the winter or spring periods due to the lack of flow and lake water was not collected during the spring period due to ice cover; these periods are marked by x. Data was excluded in some cases due to field blank contamination (o), breakthrough (v), or lab blank contamination (+). Error bars for water concentrations indicate one standard deviation (n = 2-3 samples, see Table S13 and 15 for details). For simplicity, error bars are not shown for the air concentrations; however, %RSD values are provided in Table S13.

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suggesting that a spray event in the region (not confirmed) took place during the latter stages

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of the passive air sampling period. A high triallate concentration was not detected in air

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during the second autumn sample (Figure 3), suggesting inconsistent triallate use patterns

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between years. Changing wind patterns could also result in large variations in the amount of

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atmospheric transport of pesticides to Brewster Lake; however our meteorological station

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located at Brewster Lake showed consistent wind pattern trends during the two autumn

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sampling periods.

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Modelled concentrations in lake water

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Chlorpyrifos, dacthal, and triallate. The trends in modelled concentrations for

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dacthal, triallate, and chlorpyrifos in lake water were similar (Figure 4a), so will be discussed

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first. Ultimately, the similarities in behavior for these current-use pesticides can be explained

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by their similar partition coefficients (air-water, octanol-water, octanol-air, and hexadecane-

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air, Table S11). In all three cases, a concentration peak was first observed in late autumn or

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winter (July for dacthal, May for triallate, and June/July for chlorpyrifos). This peak was

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followed by decreasing concentrations, with the minimum concentration observed in late

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spring, directly before lake ice cover melt. A second concentration peak was then observed in

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early summer (December) for all three compounds, followed by a decrease to concentrations

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similar to those before ice melt.

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The modelled concentration patterns for these pesticides was controlled by a

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combination of varying air temperatures, the cycling of ice cover on the lake, and changes in

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glacier and catchment runoff during the seasons (Figure 4b). The concentration peaks

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observed in mid-winter for dacthal and chlorpyrifos were mainly due to lowering air

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temperatures, which drove the pesticides out of the air and into the lake water, mainly via wet

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gaseous deposition. In addition, the measured dacthal concentrations in air increased in

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winter (Figure 3), also leading to higher deposition. The triallate concentration peaked a few



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months earlier than dacthal and chlorpyrifos because of its particularly high measured

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concentration in the Mar-May air sample and lower concentration in the June-August sample

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(Figure 3). When ice cover formed on the lake in mid-winter, the concentrations in lake water

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for all three pesticides decreased because air-water exchange was shut off (Figure 2b). In

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addition, the glacier and catchment runoff decreased sharply from June and stayed low until

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November (Figure 4b). Thus, the chemical input to the lake from these processes (the product

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of runoff and chemical concentration) was low from June to November. Pesticide

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concentrations peaked again in early summer (December) when the ice cover melted and the

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ice-entrapped chemicals were quickly released to the lake water, mainly via ice cover melt

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runoff.

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Endosulfan I. The modeled concentration pattern for endosulfan I in the lake water

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differed from those observed for dacthal, triallate, and chlorpyrifos in that it did not show a

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winter peak and the main summer peak occurred two months later, i.e. in February instead of

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December. The absence of the winter peak can be explained because the endosulfan I

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concentration was relatively high in glacial melt water in autumn (Figure 2), which led to a

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relatively high and constant modelled endosulfan I concentration in lake water throughout

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autumn and winter (Figure 4a). This meant that potential changes in lake water

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concentrations due to increased atmospheric deposition associated with winter temperatures

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(as was observed for dacthal, triallate, and chlorpyrifos) were not observed. There was also a

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particularly large decrease in measured concentrations of endosulfan I in air between autumn

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and winter (Figure 3), meaning less endosulfan I available for atmospheric deposition in

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winter. The concentrations in lake water did, however, decrease in response to lowering

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inputs from glacier and catchment runoff from mid-winter onwards.

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The summer peak for endosulfan I was delayed compared to that of dacthal, triallate,

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and chlorpyrifos (February instead of December) due to its stronger association with


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atmospheric particles deposited in the ice, caused by its higher log KOW (4.94) (Table S11).

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Both Schöndorf and Herrmann53 and Meyer and Wania54 reported in their laboratory-based

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experiments that the more hydrophobic compounds bound to particulate matter in snow

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eluted with the last portion of meltwater. The delay of the endosulfan I concentration peak is

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in agreement with their findings. Thus, rather than being transferred to the lake with the melt

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water runoff during the initial month of melting (December), endosulfan I was mostly

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transferred to lake during the ‘end-of-season transfer’ in February.

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Figure 4. (a) Modelled and measured concentrations in lake water. Error bars for water concentrations indicate one standard deviation (n = 2-3 samples, see Table S13 and S15 for details); in some cases, error bars are too small to see. (b) Selected time-varied parameters used in the model. Seasons are indicated as autumn (AUT, white shading), winter (WIN, pink shading), spring (SPR, purple shading), and summer (SUM, green shading).

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HCB. The modelled concentration pattern for HCB was distinct from that of the other

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four pesticides (Figure 4a). First, the modelled concentrations for HCB were relatively

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constant (in the range of 2.0-3.5 pg L-1) compared to those of the other pesticides. This is

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because HCB has a relatively high log KAW (-1.51) compared to the others (-4.04 to -3.55)

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(Table s11) and therefore had a stronger tendency to stay in the air. Moreover, mass transfer

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from ice cover to the lake water did not occur in summer, as observed for other pesticides (i.e.

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no peak in December) because little HCB deposited into the ice cover.

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Comparison of measured & modelled concentration patterns in lake water

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The modelled concentrations of pesticides in lake water agreed well with the

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measured concentrations, with 15 of 20 measured concentrations falling within a factor of

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two of the modelled concentrations (Figure 4a). Unfortunately, water samples were not

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collected in December due to poor weather conditions and therefore, we were unable to

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verify whether the modelled concentration spikes for chlorpyrifos, dacthal, and triallate could

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be measured in Brewster Lake. However, dacthal showed a similar measured concentration in

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January to its modelled concentration spike in December. Also, although we do not have

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measurements available for February 2015, the detected concentration peak of endosulfan I in

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March 2014 could be due to a concentration peak in late summer, resulting from ‘end-of-

374

season transfer’ from the previous summer. Regarding triallate, the measured concentration

375

in March was lower and in June was higher than the modelled concentrations. This can be

376

explained by the fact that the concentration we measured for each sampling period from the

377

passive air samplers was the time-averaged concentration for those three months; thus, it was

378

assumed to represent the monthly means for all three months in the model even though

379

concentration spikes may have occurred during that period.

380 381



382

Processes in lake water

383

Figure 5a (upper panel) shows how the various sources and input processes

384

contributed to the total pesticide input into the lake water over the entire sampling period.

385

Input directly from air (via a combination of all four input processes) was significant (20%-

386

65%) for all pesticides except HCB, whereas the contribution from glacier melt was

387

significant (30%-45%) for endosulfan I, HCB, and dacthal. For endosulfan I, HCB and

388

dacthal, the contribution from the glacier meltwater outweighed that from air. Also of note

389

were the significant contributions from ice ‘end of season transfer’ for endosulfan I and from

390

ice cover melt water transfer for dacthal, triallate, and chlorpyrifos. The three current-use

391

pesticides (dacthal, triallate, and chlorpyrifos) have similar physical properties (Table S11)

392

and therefore, the differences observed for them were mainly due to variations in the

393

measured concentrations (Figure 3), which were used as input parameters in the model (e.g.

394

the higher contribution from air for triallate was due to its relatively high measured

395

concentration in air). On the other hand, the observed differences between endosulfan I and

396

HCB were due mainly to differences in physical properties, e.g. endosulfan I has a much

397

lower log KAW (-3.55) than HCB (-1.51) (Table S11). Thus, even though the concentrations

398

of HCB in air were higher than endosulfan I (Figure 3), there was more direct deposition of

399

endosulfan I from air into the lake and it had more accumulation in the ice, resulting in its

400

eventual transfer to the lake via ‘end of season’ transfer. As discussed above, the transfer of

401

endosulfan I via ‘end of season’ transfer, rather than via ice cover melt (which occurred for

402

the three current-use pesticides), was due to its higher KOW (4.94) (Table S11).

403

Figure 5a (lower panel) shows the modelled relative contributions of the specific

404

processes involved in chemical transfer from air to water and from sediment to water over the

405

sampling period. Air-to-water transfer included dry and wet particle deposition, wet gaseous

406

deposition, air-water diffusion; sediment-to-water transfer includes sediment-water


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407

resuspension and sediment-water diffusion. Endosulfan I and the three current-use pesticides

408

mainly entered the lake from the atmosphere by wet gaseous deposition (>90%) due to low

409

KAW values (Table S11). MacLeod et al55 also observed that wet gaseous deposition is

410

important for chemicals that are normally present in the gas phase but that can be scavenged

411

by rain drops and/or snowflakes, that is, chemicals that have large water–air and/or

412

snowflake–air partition coefficients. In contrast, air-water diffusion was the main pathway

413

(95%) for HCB entering the lake. Sediment and lake water had little chemical exchange,

414

which could be attributed to the pesticides’ short residence time (18 days) in the lake water.

415

The model showed that 100% of the output loss for all pesticides except HCB was

416

through advective loss via the output stream (Figure S6). For HCB, 90% of the loss was

417

through advection and the remainder was due to volatilization.

418

Predictions under future climate scenarios

419

Figures 5b and 5c show how the various sources and input processes contributed to

420

the total pesticide input to the lake water under the future scenarios. An important

421

observation is that the contribution from glacial melt water increased for all pesticides. This

422

occurred because glacial melt water runoff increased with increasing air temperature (Table 1)

423

and therefore, the pesticide flux into the lake water increased (because flux is the product of

424

water runoff and chemical concentration). Under future scenario 1 (Figure 5b), the

425

contributions from the two ice melting processes were lower than in the current scenario

426

because the pesticide mass deposited to the ice cover decreased. This is due both to decreased

427

deposition from the air to the ice cover when the air temperature is higher and to the ice cover

428

being present for a shorter time period. Under scenario 2 (Figure 5c), no ice cover was

429

formed so there was no transfer of pesticides from the ice cover to the lake water. Wet

430

gaseous deposition was still the dominant process contributing endosulfan I, chlorpyrifos,

431

dacthal and triallate into lake water in both future scenarios (Figures 5b and 5c, lower panels).



432

Interestingly, the contribution of pesticides to the lake water from sediment-to-water

433

diffusion increased under the future scenarios for all pesticides except HCB, suggesting that

434

sediment may also become a secondary source for these pesticides in the future.

435

In the future scenarios, advection via the output stream remained the primary source

436

of pesticide loss from the lake, although net volatilization of HCB increased with increasing

437

air temperatures (Figure S6). Figure S7 compares the modelled pesticide concentrations in

438

lake water by month under the three scenarios. Under future scenario 1, the main

439

concentration peaks for chlorpyrifos, dacthal and triallate occurred significantly earlier

440

relative to those in the current scenarios (i.e. the peaks shifted from December to September)

441

because the ice cover melted earlier. Similarly, for endosulfan I, the concentration peak


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Figure 5. (Upper panels) Modelled percent contributions to the lake water compartment for each source and input process during the entire sampling period for (a) the current scenario, (b) future climate scenario 1, and (c) future climate scenario 2. Air = dry+wet and gaseous+particle deposition; glacier = glacier melt water; stream = stream water input; sediment = sediment resuspension plus sediment-water diffusion. All values are in percent of total input into the lake water. (Lower panels) Modelled percent contributions from processes associated with air-to-water and sediment-to-water diffusion for the three scenarios; all values are in percent of the total input from air plus sediment into the lake water. 442

caused by ‘end-of-season transfer’ shifted from February to November. Under scenario 2, the

443

air temperature was always above 0 °C (Table S12) and no ice cover was formed over the

444

lake during the winter; thus, no concentration peaks associated with ice melt occurred.

445

However, all pesticides had at least a small concentration peak in August when the air-to-lake

446

transfer increased due to lower winter temperatures; because no ice cover was formed,

447

deposition from air to lake water was not hindered and chemicals were constantly transferred

448

to the lake water from the air.



Page 24 of 28

449

In summary, this study has shown that glacial melt water is an important source of

450

pesticides to Brewster Lake. Moreover, the model indicates that a pulse of pesticide

451

concentrations occurs in the spring and that the magnitude and timing of this pulse varies

452

under different climate change scenarios. A potential environmental risk exists because some

453

organisms are very sensitive to pulse exposures of contaminants, especially in the early

454

spring when organisms are at a vulnerable stage of their life.56,

455

combination of changes in temperature, glacier melt run-off, and precipitation that are

456

expected to occur under future climate change scenarios were tested. However, it is also

457

possible that the pattern of release of contaminants from Brewster Glacier will change in the

458

future. If more information about the historical deposition of contaminants in Brewster

459

Glacier, and its melting dynamics were known, better predictions could be made. However,

460

this would require obtaining and analyzing an ice core and unfortunately, ice core collection

461

in New Zealand is particularly challenging due to glaciers being relatively warm.

462 463 464 465 466 467 468 469 470 471 472

57

In this study, the

ASSOCIATED CONTENT Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org. ACKNOWLEDGMENT This work was funded by an Otago University Research Grant. We especially thank Garth Tyrrell for building field equipment and Robert Alumbaugh, Sigurd Wilbanks, and Anna Murdoch for assistance with sample collection. The field research conducted at Brewster Glacier was supported by the New Zealand Department of Conservation under the concession OT-32299-OTH.

473

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