Marine Primary Productivity as a Potential Indirect Source of

Article pubs.acs.org/est

Marine Primary Productivity as a Potential Indirect Source of Selenium and Other Trace Elements in Atmospheric Deposition Tim Blazina,†,‡ Alexander Lad̈ erach,§ Gerrad D. Jones,† Harald Sodemann,∥,§ Heini Wernli,§ James W. Kirchner,⊥,#,∇ and Lenny H. E. Winkel*,†,‡ †

Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland Institute of Biogeochemistry and Pollutant Dynamics, §Institute for Atmospheric and Climate Science, and ⊥Institute of Terrestrial Ecosystems, ETH Zurich, CH-8092 Zurich, Switzerland ∥ Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, NO-5020 Bergen, Norway # Swiss Federal Research Institute WSL, CH-8903 Birmensdorf, Switzerland ∇ Department of Earth and Planetary Science, University of California, Berkeley, California 94720-4767, United States ‡

S Supporting Information *

ABSTRACT: Atmospheric processes play an important role in the supply of the trace element selenium (Se) as well as other essential trace elements to terrestrial environments, mainly via wet deposition. Here we investigate whether the marine biosphere can be identified as a source of Se and of other trace elements in precipitation samples. We used artificial neural network (ANN) modeling and other statistical methods to analyze relationships between a high-resolution atmospheric deposition chemistry time series (March 2007−January 2009) from Plynlimon (UK) and exposure of air masses to marine chlorophyll a and to other source proxies. Using ANN sensitivity analyses, we found that higher air mass exposure to marine productivity leads to higher concentrations of dissolved organic carbon (DOC) in rainfall. Furthermore, marine productivity was found to be an important but indirect factor in controlling Se as well as vanadium (V), cobalt (Co), nickel (Ni), zinc (Zn), and aluminum (Al) concentrations in atmospheric deposition, likely via scavenging by organic compounds derived from marine organisms. Marine organisms may thus play an indirect but important role in the delivery of trace elements to terrestrial environments and food chains.

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estimated ∼13−19 gigagrams (Gg) of Se being cycled through the troposphere annually.13 The estimated atmospheric dry deposition of Se is relatively small (1.1−5.0 Gg Se yr−1)14 compared to wet deposition (7.4−20.0 Gg Se yr−1),14 which has been suggested to account for 80% of total atmospheric Se deposition.13 Selenium in the atmosphere has both natural and anthropogenic sources. Estimates from the 1980s indicated that anthropogenic emissions of Se accounted for 37−41% of total annual emissions.13,15−17 Natural sources, including volcanic

INTRODUCTION The trace element selenium (Se) is an essential micronutrient for human health.1,2 However, the recommended dietary intake range of Se is narrow (i.e., between 30 and 900 μg day−13), and Se intake levels outside this range can lead to various health problems such as reduced immune system function4 in the case of deficient intake levels and selenosis (i.e., Se toxicity5) in the case of oversupply. As Se largely enters the food chain via plants, there has been great interest in studying the sources and mechanisms affecting the input and mobility of Se in agricultural soils (e.g., refs 6 and 7). Marine Biogenic Sources of Se. Atmospheric deposition is a potentially important source of Se to agricultural soils.8−12 The atmosphere is an important reservoir of Se with an © XXXX American Chemical Society

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June 19, 2016 November 24, 2016 November 27, 2016 November 27, 2016 DOI: 10.1021/acs.est.6b03063 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology activity,18 sea salt spray,17,18 and terrestrial emissions,19 have been estimated to make up a larger share of atmospheric Se emissions, accounting for 59−63% of total annual emissions.13 However, the largest estimated source of atmospheric Se is marine biogenic emissions, probably in the form of volatile organic species, e.g., dimethyl selenide (DMSe) and dimethyl diselenide (DMDSe).20 These volatile species have been hypothesized to be either directly emitted by marine phytoplankton or to be released during microbial decomposition of dead phytoplankton species21,22 and free Se-amino acids during phytoplankton blooms.23,24 Furthermore, a number of studies have linked Se in aerosols to emissions of dimethylsulfide (DMS)25−27 based on a relationship between Se and methanesulfonic acid (MSA),27−29 an oxidation product of DMS. In addition, reduced forms of Se (i.e., Se0, Se(−II)) were observed in a number of aerosol samples, which may be explained by condensation of biogenically derived volatile organic Se compounds after atmospheric oxidation.30 Volatile forms of Se emitted from the ocean are likely rapidly oxidized and transformed to nonvolatile forms. For example, in the troposphere, DMSe undergoes hydrolysis, wet and dry depositions, and gas-phase reactions with hydroxyl (OH) radical, nitrate (NO3) radical, ozone (O3), and halogen atoms,31 leading to lifetimes of ∼3 h (OH radicals), 5 min (NO3 radicals) and ∼6 h (O3), respectively.32 Eventually selenious acid (H2SeO3) is formed, which can be further oxidized to selenic acid (H2SeO4).13 Selenium has been reported to be mainly present in oxyanionic form in rainwater,33 but reports on Se speciation in rainfall are scarce. Association between Dissolved Organic Carbon and Trace Elements. In marine waters, it can be assumed that dissolved Se compounds derived from marine organisms are part of the total dissolved organic carbon (DOC) pool. The composition of this DOC pool strongly controls the composition of marine organic aerosols,34 which have been shown to originate mainly from marine organisms during periods of high marine productivity.35−37 For example, longterm measurements of marine organic aerosols at coastal sites have documented a strong seasonal cycle, with aerosol concentration peaks coinciding with peaks in marine biological productivity in spring/summer.38−41 It has been shown that trace elements (especially trace metals) may be associated with DOC in rainwater. For example, iron (Fe) can form Fe(III)oxalate complexes,42 and trace metals, i.e., cobalt (Co), nickel (Ni), copper (Cu), lead (Pb), and cadmium (Cd), were found to be associated with organic complexing ligands in coastal, marine, and urban rain waters.43−45 Although many trace metals largely have anthropogenic sources,19,46 association with DOC likely plays an important role in aerosol properties and atmospheric transport pathways.45 Relationship between Se Concentrations in Rainfall and Marine Productivity? Many past studies have revealed relationships between Se and other trace elements in aerosols and in anthropogenic emission sources, e.g., coal combustion and metal smelting.14,47−53 However, due to more stringent industrial emission controls (e.g. refs 54 and 55) over the past two decades, concentrations of trace elements in rainfall have decreased in Europe and North America. For example, concentrations of nonsea-salt sulfate (nss-SO4), Se, arsenic (As), and vanadium (V) in rainfall at Plynlimon (Wales, United Kingdom) have decreased by around 40−60%, over the period 1999−2010 (see Figure S1). In areas where anthropogenic emissions decrease, the relative contribution of natural

atmospheric sources of trace elements, including marine biogenic emissions, could increase. Therefore, it is important to study relationships between trace elements in atmospheric deposition and marine primary productivity. Here, we use artificial neural network (ANN) modeling to investigate whether marine productivity can be identified as a potential source of Se to terrestrial environments. Specifically, our aim is to determine whether air exposure to marine chlorophyll a could be used to model total concentrations of Se in a two-year high-frequency rainwater chemistry record collected between March 2007 and January 2009 at Plynlimon (UK). Furthermore, we investigate the effect of wind speed (proxy for sea spray) and air exposure to potential anthropogenic emissions sources on concentrations of Se and other trace elements and compounds (As, nitrate [NO3], nss-SO4, chlorine [Cl], V, Co, Ni, zinc [Zn], aluminum [Al]) from the same record, as it is expected that the rainwater chemistry is partially controlled by the input of anthropogenic source emissions.

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METHODS AND MATERIALS Rainwater Chemistry Data Set from Plynlimon, UK. The rainwater chemistry data analyzed in our study comes from an intensive rainwater sampling campaign, which took place between March 6, 2007 and January 27, 2009 at the Plynlimon research catchments (52°26′N, 3°44′W).56 The study site is located 20 km from the coast and receives approximately 2500 mm of rainfall annually.57 The publicly available data set consists of 52 chemical elements and compounds, and a detailed description of the sampling and analytical methods and quality control procedures can be found in ref 56 and at https://catalogue.ceh.ac.uk/documents/551a10ae-b8ed-4ebdab38-033dd597a374. Briefly, rainfall was collected using a continuously open 57.5 cm diameter rain collector positioned 42 cm above the ground. Rainfall volumes were measured with a 12.5 cm diameter ground-level rain gauge located nearby. Sample bottles were automatically switched every 7 h and were collected once a week. Samples were returned to the lab where they were filtered (0.45 μm membrane filters for cations and metals and GF/C glass fiber filters for other analytes). Samples were analyzed for total concentrations by the following methods: ICP-OES (for major cations, S, B, and Si), ICP-MS (for trace elements), IC (for major and minor anions), and Shimadzu analyzers (for DOC and total dissolved nitrogen). Quality controls were performed using materials provided by the Aquacheck LGC Interlaboratory Proficiency Testing Scheme (https://www.lgcpt.com/Default_eng.aspx). To account for potential rainfall volume effects on concentrations (i.e., higher concentrations in low-volume rains and lower concentrations in high-volume rains) we have calculated volume weighted means (VWM) as VWM = Σ(C i*vi)/Σvi

(1)

where Ci is the measured rainwater concentration of a constituent in sample i, and vi is the rainfall volume of that sample.58,59 When an analyte was not detected for a specific rain event (i.e., value below the lowest quantitatively determined concentration), the analyte concentration was treated as a missing value. A scatter plot of the analyzed Se data versus the data in which values below the detection limit are included (but excluding negative values) is illustrated in Figure S2. Origin of Air Masses. The Lagrangian particle dispersion model FLEXPART60 was used to identify daily air source B

DOI: 10.1021/acs.est.6b03063 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

chlorophyll a exposure. Therefore, air mass exposure to chlorophyll a was only calculated for back trajectory points located in the marine boundary layer. Air mass chlorophyll a exposure for trajectory points that were within a grid cell with cloud cover were treated as missing values. The corresponding 8-day composite satellite image was used for the specific date associated with the trajectory points. Proxy of Sea Spray. Because Plynlimon is located 20 km from the coast, sea spray could have a large influence on concentrations of rainwater constituents. Sea spray to the atmosphere is highly dependent upon wind speed;66 therefore, mean daily wind speed was used as a sea spray proxy and was calculated by determining the spherical distance along consecutive 3-hourly trajectory positions. Exposure of Air Masses to Anthropogenic Emission Proxies. It is expected that the rainwater chemistry is partially controlled by the input of anthropogenic source emissions. Therefore, FLEXPART trajectories were also used to calculate air mass exposure to three possible anthropogenic emissions sources: urban land cover, sulfur dioxide (SO2) emissions, and marine shipping emissions. The data sources for the anthropogenic emission proxies with their respective temporal and gridded spatial resolutions are included in Table S1. Daily mean air mass exposure to urban land cover, SO2, and shipping emissions was calculated as the daily mean value of these sources in all grid cells which contained at least one trajectory point with an altitude less than the boundary layer height. Urban Land Cover. Gridded land cover data (300 m × 300 m) was obtained from the European Space Agency (ESA LC CCI).67 The data set includes 22 land classes, one of which is urban land cover. This land cover type represents artificial surfaces built by humans, and a grid cell is classified as urban if >50% of the land cover is artificial. The data set comes from two time periods: 2003−2007 and 2008−2012. The values from the first time period were used to make comparisons to the 2007 data from the rainwater chemistry data set, and values from the second time period were used for 2008 and 2009. The land cover data were recalculated on a 0.1° × 0.1° grid such that the value on this coarser grid corresponds to the percentage of 300 m × 300 m urban cells in the grid cell. SO2 Emissions. Annual 0.5° × 0.5° SO2 emissions for Europe from 2007 to 2009 were obtained from the Centre on Emissions Inventories and Projections (European Monitoring and Evaluation Programme; EMEP-CEIP)68 and contain point and diffuse SO2 emission estimates (in Mg). Shipping Emissions. As the study site is located near an oceanic region with heavy shipping traffic, we investigated the potential for shipping emissions to determine rainwater composition. Gridded global shipping emissions from Wang et al.69 were used to calculate air mass exposure to this potential source. This 0.1° × 0.1° gridded data set was produced using reported ship position data from the years 2000−2002 and by weighting each ship position by the installed power of the ship, which is assumed to relate to its emissions. Monthly ship position data from 2000 to 2002 was used in this study because it was the most recent publicly available data on shipping traffic patterns. The grid cell values represent the local fraction of global ship emissions (expressed in parts per million of the global total). Multivariate Modeling. We used ANN models in order to describe how predictor variables governed analyte concentrations in rainfall. ANN models are advantageous over other statistical techniques because they make no assumptions

regions for Plynlimon. For all days with precipitation at Plynlimon, trajectories from a global air mass transport climatology (for details see ref 61) were analyzed 72 h backward. The 6-hourly ERA-Interim reanalyses have been supplemented by 3-h forecasts to improve the time resolution and thus the trajectory accuracy.62,63 Every 3 h, the FLEXPART output consists of the spatial location (latitude, longitude, altitude), specific humidity (g kg−1), relative humidity (%), and boundary layer height (m) interpolated to the location of air parcels. To quantify the daily mean air mass origin, we have calculated a daily weighted vector average from the gridded FLEXPART data as follows x ̅ (t ) = Σ(w(t )*d(t )*cos Θ(t ))/Σ(w(t ))

(2)

y ̅ (t ) = Σ(w(t )*d(t )*sin Θ(t ))/Σ(w(t ))

(3)

where (x(t), y(t)) are the daily averaged horizontal coordinates ̅ ̅ of the air parcel relative to Plynlimon. The sums are calculated over all trajectory positions at a specific time, t, for all trajectories that reach Plynlimon on the same day. For every trajectory, Θ(t) is the angle (degrees) of the air parcel position relative to Plynlimon, d(t) is its distance (km) from Plynlimon, and w(t) is the mass (kg) of the air parcel. The average angle and distance at this time for all trajectories reaching Plynlimon on the considered day are then given by θ ̅ = tan−1(x ̅ /y ̅ ) d̅ =

x̅ 2 + y ̅ 2

(4) (5)

Because we are investigating sources of elements in rainwater, we are particularly interested in knowing when the last rainfall event occurred along a trajectory. Assuming that aerosols in an air mass act as condensation nuclei and lead to the formation of raindrops, a rainfall event occurring along a trajectory will cleanse the air mass and potentially remove evidence of previous source exposure. Therefore, the back trajectory data were filtered to remove trajectory points prior to the last rain event, i.e. only the trajectory segment from the last rain event to the arrival of the air mass at Plynlimon was kept. This was done by assuming that a rainfall event occurred if specific humidity decreased by more than 0.1 g kg−1 along a 6-h trajectory interval and the relative humidity at the beginning of this interval was ≥80%.64 All subsequent analyses that use the back trajectory data were done using this filtered back trajectory data set. Exposure of Air Masses to Marine Chlorophyll a. To test if increased air mass exposure to marine biological productivity influences trace element concentrations in the rainfall, we calculated daily air mass exposure to oceanic regions with algal blooms. Gridded (0.25° × 0.25°) 8-day composite satellite derived chlorophyll a concentration data from 2007 to 2009 from the European Space Agency GlobColour Project was used to calculate air mass chlorophyll a exposure. The 8day composite data were used instead of daily chlorophyll a data to minimize the number of missing values due to cloud cover over the North Atlantic, which prevents the satellite from estimating marine chlorophyll a. A detailed description of the GlobColour data set can be found at http://www.globcolour. info/CDR_Docs/GlobCOLOUR_PUG.pdf. Similar to Park et al.,65 we assumed that an air mass could transport marine biogenic chemical compounds only if the air mass was within the marine boundary layer at the time of C


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• Higher air exposure to chlorophyll a results in increased DOC concentrations in rain, given that primary productivity within the ocean produces organic aerosols.35−37 If the ANN model was able to detect these known trends, we presumed the model was accurately describing the dominant system processes. All ANN models were developed using the “multilayer perceptron” procedure in SPSS.74 For each ANN model, the number of hidden layers was restricted to one, and the number of units within the hidden layer was automatically computed. The hyperbolic tangent and identity activation functions were used to determine the neuron output for the hidden and output layers, respectively. The following training criteria were used: online training, gradient descent optimization algorithm, initial learning rate = 0.4, lower boundary of learning rate = 0.001, learning rate reduction (in epochs) = 10, momentum = 0.9, interval center = 0, interval offset ± 0.5, minimum change in training error = 0.0001.

regarding data distributions, can account for interactions between predictor variables, and can detect complex nonlinear patterns between predictor and dependent variables.70 Prior to ANN model development, all predictor variables (5 source proxies and precipitation) were scanned for multicollinearity using the variance inflation factor (VIF), which was low (≤1.25) for all pairwise combinations of variables, suggesting that multicollinearity was not an important issue. As a result, all predictor variables were retained for ANN analysis. In order to determine if the ANN model was overfitting the data, we performed the following cross-validation procedure. For each analyte, 90% and 10% of the data were used for ANN model training and testing, respectively. The training and crossvalidation data were chosen at random. The model was iterated 1,000 times, and the training and testing R2 values were recorded for each iteration. The testing R2 represents an unbiased estimator of the model performance, and a large drop in the testing R2 compared to the training R2 is an indication that the ANN model has over fit the data. For each model iteration, a concurrent sensitivity analysis was made, and each model iteration was used to predict the results of a hypothetical data set to evaluate the independent effects of the predictor variables on each analyte. Based on the values within the 2-year data set, we allowed a single predictor variable to vary between its minimum and maximum while holding all other predictor variables constant at their average value. For each analyte, the predictions were averaged (n = 1,000) and normalized by the maximum value (i.e., C/Cmax) such that the concentrations ranged from 0 to 1. For a given analyte, predictor variables that resulted in C/Cmax = 1 were considered a dominant factor. Each analyte was then plotted to explain the trends within the sensitivity analysis. We assumed that given increases in proxy exposure, analyte concentrations were expected to change monotonically (e.g., nss-SO4 deposition is expected to continually increase with increased exposure to SO2 emissions). Any patterns that were not monotonic (e.g., parabolic) were deemed unreliable and were removed. Furthermore, the precision of the modeled estimate was considered when making inferences about the sensitivity analysis. If the ratio of standard deviation:average of the predictions was >0.6, the individual prediction was deleted from the analysis. If more than 2 points were deleted, the sensitivity analysis for a particular analyte was deemed unreliable and was removed from further analyses. Each of the used source proxies have been previously hypothesized to drive concentrations of trace elements and/or major compounds in rainfall. Based on known processes from the literature, it can be expected that certain elements and compounds will behave in a specific manner. Therefore, we tested the following hypotheses: • Increased wind speed results in increased Cl concentrations in rain, given that sea spray is highly dependent upon wind speed.66 • Higher air exposure to SO2 emission sources result in increased nss-SO4 concentrations in rain, given that SO2 is a major source of sulfate in rainfall. • Higher air exposure to urban areas results in increased trace metal concentrations in rain, given that urban emissions are a source of many metals. • Higher air exposure to shipping emissions results in increased Ni and V concentrations in rain, given that shipping emissions are a source of Ni and V.71−73

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RESULTS AND DISCUSSION Correlations between Analyte Concentrations in Rainwater. Over the two-year sampling period, a total of 1116 rainwater samples were collected and analyzed. Selenium was positively detected (i.e., concentrations >0.03 μg L−1) in 759 of these samples at concentrations ranging from 0.31 to 2.83 μg L−1 (Figure S3). Data analyses started by analyzing statistical correlations between concentrations of rainwater analytes. Concentrations of all analytes were negatively correlated with rainfall volume based on Spearman’s correlations (Figure S4) indicating that concentrations were lower when rainfall volumes were higher, illustrating the rainout dilution effect that is commonly observed in rainwater chemistry studies.75−77 Furthermore, most analytes in the rainwater are positively correlated with each other, which could be related to variations in rainfall volumes. In order to control for the potential confounding effects of rainfall volume, partial correlations (ρp) were used. When controlling for rainfall volume, partial correlations between analytes in rainwater (Figure S4) are still statistically significant (p < 0.05) and positive indicating that the positive relationships between analytes are not only due to variations in rainfall volumes alone. Furthermore, when controlling for rainfall volume, Se was positively correlated with the marine-derived elements Cl (ρp = 0.64, P < 0.05), K (ρp = 0.62, P < 0.05), Mg (ρp = 0.55, P < 0.05), and Na (ρp = 0.46, P < 0.05) (Figure S4). As Plynlimon is located only 20 km from the coast, it can be anticipated that the elements Cl, K, Mg, and Na are mainly derived from sea spray. The slope of the Na−Cl relationship (0.52 mg/mg) is very similar to the Na−Cl mass ratio of seawater (0.5678), as has also been observed in a 30-year rainfall chemistry record from the same site.57 Therefore, the positive correlations between Se, Cl, and Na imply that sea spray aerosols are an important source of Se to Plynlimon, especially in the winter months (see Figure S5). However, the Se/Cl ratio in rainwater indicates that Se is enriched by a factor of ∼70−13000 (average = 1719, Figure S6) relative to the Se/Cl ratio in seawater,79,80 implying that aerosols originating from sea spray likely function as a transporter rather than a source of Se, potentially by scavenging Se that is derived from other sources. Other trace elements are also substantially enriched (Figure S6) in rainwater relative to the average seawater composition.79,80 For example, V and Zn are enriched on average by factors of ∼800 and ∼7300, respectively. Also, over the entire analyzed D


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Figure 1. May and June air mass source regions for Plynlimon calculated using FLEXPART.

Table 1. Partial Correlations (ρp) between Air Mass Chlorophyll a Exposure and Daily VWM Concentrations of DOC and Trace Elements in Rainfall in April, May, and Junea April−June

April

chlorophyll a DOC Se As V Ni Co Zn Cl

0.42; P < 0.001 0.41; P = 0.001 0.37; P = 0.003 0.49; P < 0.001 0.04; P = 0.78 0.35; P = 0.005 0.31; P = 0.01 0.12; P = 0.33

DOC 0.61; 0.63; 0.75; 0.28; 0.63; 0.71; 0.63;

P P P P P P P

< < < = < <
0.6, the data point was deleted. If > 2 data points for a given analyte were deleted, the analyte was completely removed from the analysis.

shipping emissions have relatively weak effects on solute concentrations in precipitation (Figure 3d) and cannot be identified as a main source of any of the studied analytes (which would be indicated by C/Cmax = 1). The lack of a strong relationship may arise because the shipping emissions data are not for the analyzed time period but for another time period (2000−2002), and because the shipping track data have a coarse one-month time resolution. Marine Productivity as a Source of DOC and Trace Elements. Marine productivity (air exposure to chlorophyll a) was found to be the most important controlling factor of rainfall DOC concentrations (Table 2 and Figure 3), followed by air exposure to SO2 sources. As discussed above, previous studies have concluded that DOC in rainwater can be largely derived from biogenic marine organic sources,36,37 e.g. via scavenging of surface-active organic matter and other material (e.g., bacteria, viruses, detritus) by bubble bursting.41,85 As with all source proxies, we assumed that marine productivity can only be a source of a specific analyte if chlorophyll a has an overall positive effect on its concentration. ANN sensitivity analyses show that increases in marine productivity have a positive effect not only on modeled Se concentrations (Figure 3a) but also on all other analytes, except Cl and NO3. In fact for all studied trace metals (V, Co, Ni, Zn, Al), modeled concentrations are most sensitive to changes in chlorophyll a, indicated by C/Cmax = 1 at highest modeled chlorophyll a concentrations, which identifies marine productivity as a main controlling factor of these elements. However, over the whole analyzed period, air exposure to urban areas is more important than air exposure over chlorophyll a, probably due to the fact that chlorophyll a concentrations are mainly high in summer months (Table 2, Figure S7).

Cl concentrations were most sensitive to changes in wind speed (see Table 2), which shows that the model describes the expected trend. Also changes in Se were highly sensitive to wind speed (see Table 2). However, wind speed has little effect on Cl and Se concentrations until a certain threshold is reached, after which concentrations increase (Figure 3f). This agrees with previous findings indicating that sea spray is mostly produced at high wind speeds.84 The opposite trend is observed for Ni and, to a lesser extent, DOC with the highest concentrations at low wind speeds and a drop to relatively constant values at intermediate and higher wind speeds. These results suggest that the sources of Ni and DOC are in relatively close proximity to Plynlimon, and strong winds coming from further distances dilutes their signature. Also air exposure to SO2, to urban areas, and to shipping emissions are likely sources of one or more analytes in rainfall (Figure 3a-d). For instance, air exposure to SO2 emissions mainly leads not only to increases in concentrations of nss-SO4, as was expected, but also to increases of NO3 and DOC in rainfall (Figure 3c). This finding may suggest that source areas of SO2 emissions are also source areas of precursors of NO3 and DOC in rainwater, although potentially these relationships are explained by atmospheric cotransport and processing rather than by the same emission sources. Furthermore, sensitivity analyses show that not only air exposure to urban land cover was an important factor in explaining concentrations of all trace metals but also Se and NO3 are sensitive to increases in air exposure to urban areas, identifying these as likely sources of these analytes (Figure 3b). As expected, air exposure to shipping emissions leads to increased Ni but also NO3 concentrations in rainfall at Plynlimon, as well as smaller increases in V and nss-SO4 (see Figure 3d). However, here G


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Figure 4. ANN sensitivity analysis of the effect of air exposure to chlorophyll a on Se (a), As (b), and Zn (c) concentrations while keeping other source proxies and DOC constant. (d) ANN sensitivity analysis of the effect of air exposure to chlorophyll a on DOC concentrations while keeping other source proxies and Se, As, Zn concentrations constant.

Potential Pathways Linking Marine Productivity to DOC and Trace Elements in Rainfall. The link between marine productivity, DOC, and other trace elements in rainfall could be explained by three different pathways. Two pathways involve the release of trace elements from the marine organism to the atmosphere. The third pathway involves the formation of marine organic aerosols that can scavenge trace elements in the atmosphere. The first pathway involves direct emissions of volatile compounds (e.g., for Se, DMSe, DMDSe) by living organisms. Previously, a statistically positive correlation (R2 = 0.59) between coccolithophorid derived C and DMSe in surface waters was found in the North Atlantic during June,20 indicating that this organic Se compound is likely present in the seawater during this month. However, the exact mechanism of atmospheric Se release needs to be further investigated. The second pathway involves the atmospheric injection of trace elements present in nonvolatile organic compounds derived from the decomposition of dead organisms, e.g., via bubble bursting. Sensitivity analyses indicate that in addition to Se, other elements, including As, V and Co, may also be derived from marine productivity. These elements have been found to be enriched in biogenically derived organic compounds in marine surface waters.86−94 For example, Klein et al.95 found a positive correlation between intracellular V concentration and chlorophyll a (R2 = 0.49, P < 0.05) in marine surface waters during an algal bloom in the eastern North Atlantic, indicating that marine organisms are a potential primary source of atmospheric V. Also for As there is a clear link with marine organisms. The formation and excretion of organic As compounds such as monomethylarsenic (MMAs) and dimethylarsenic (DMAs) by marine organisms is relatively well-known (e.g., refs 91, 92, 94, and 96). The third potential pathway leading to enrichment of trace elements in rainfall is atmospheric scavenging by marine organic aerosols derived from marine organisms. As mentioned in the Introduction, complexation of trace elements (mainly

trace metals) to atmospheric DOC is a known process. Scavenging by marine organic aerosols may be analogous to the way that sea salt particles can scavenge atmospheric trace compounds.97 Marine productivity may thus influence transport of trace elements, via formation of organic aerosols that subsequently scavenge trace elements and compounds from the atmosphere. Effect of DOC on Trace Elements in Rainfall. To test if marine productivity is likely a direct or indirect source of trace elements, we reran the ANN model using the original 5 source proxies and precipitation in addition to DOC. In subsequent sensitivity analyses, DOC was held constant. If marine productivity affects rainwater chemistry indirectly, e.g., via the formation of DOC that subsequently scavenges the element/ compound in the ocean or in the atmosphere, then when DOC is kept constant, increases in air exposure to chlorophyll a should not result in increases in analyte concentrations. On the other hand, the relationship between chlorophyll a and DOC should be independent of other analytes (e.g., Se should not affect this relationship). Therefore, additionally, the effect of chlorophyll a on DOC was modeled by allowing each proxy to vary over 10 equal interval values between its minimum and maximum while holding the concentrations of all other independent proxies and selected trace elements constant in the sensitivity analysis (at the average of the 2-year data set). The effects of chlorophyll a and urban exposure on Se, As, and Zn rainfall concentrations while controlling for DOC are included in Figures 4a−c. The effects of chlorophyll a and urban exposure on V, Co, Al, Ni, and nss-SO4 rainfall concentrations while controlling for DOC and Cl are included in Figure S8. These results indicated that no positive effect exists between increased air exposure to chlorophyll a on trace element and nss-SO4 concentrations when controlling for DOC. In contrast, the effect of chlorophyll a exposure on DOC concentrations when controlling for Se, As and Zn was negligible (Figure 4d). H


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Environmental Science & Technology This finding confirms that marine productivity is an important direct source of DOC in Plynlimon rainfall, agreeing with previous findings.39,98−100 Although marine productivity is also an important controlling factor of trace elements, it rather functions indirectly, likely via formation of organic compounds, subsequent atmospheric injection and scavenging of trace elements or via atmospheric injection of organic compounds previously enriched in trace elements. Direct emissions of volatile organic trace element compounds by marine organisms cannot be excluded, but there was no evidence to support this hypothesis within this study. Our study suggests that marine productivity functions as an indirect source of trace elements to terrestrial environments, but the precise mechanism linking marine productivity and trace elements in rainfall as well as the importance of this source in more continental locations needs more detailed investigation. Changes in marine productivity due to 21st century climatic changes101−104 and further changes in industrial emissions could thus affect the supply of essential trace elements to terrestrial food chains. To predict the effect of changes in primary and secondary emission sources on the delivery of trace elements to terrestrial environments, it is important to better understand the mechanisms and dynamics behind atmospheric transport and transformations of trace elements.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b03063. Table S1 and Figures S1−S8 (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

James W. Kirchner: 0000-0001-6577-3619 Lenny H. E. Winkel: 0000-0001-7586-7256 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Funding for this work was provided by the Swiss National Science Foundation (SNF PP00P2_133619; PP00P2_163747). REFERENCES

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