Atmospheric Elemental Analysis of Trace MetalDeposition in Conifer Pine Needles at Varying

ElevationsGraham Brown∗,1 and Isabella Luu∗,1

∗Department of Earth and Environmental Sciences, Mount Royal University

ABSTRACT Conifer needles bioaccumulate atmospheric pollutants, including trace metals, and may be used to monitorvariation in atmospheric concentration. Needles were analyzed to determine whether a correlation exists betweenelevation and trace metal concentrations in proximity to roadways and other non-point sources. Composite samples ofwhite spruce (Picea glauca) and balsam fir (Abies balsamea) needles were collected along hillsides in eastern and westernCalgary, respectively. A combined total of 11 sites were sampled along two transects of increasing elevation. Needleswere prepared for analysis by drying, ashing and digestion in nitric acid. Qualitative and quantitative analysis of tracemetal concentrations was completed with Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and synthesizedusing regression analysis. The concentrations of Co, Ni and Ca in the samples were found to exhibit a relationship withrespect to altitude (P < 0.05). Identification of sources could not be significantly determined due to the abundance ofnon-point sources in both sampling locations.

KEYWORDS biomonitor; elevation; non-point source; trace metal; deposition

Introduction

Tree species are able to uptake polluting particles in the atmo-sphere due to their distinct biological characteristics; hence, itregulates the amount of pollutants present in the atmosphereand is suitable in monitoring trace metals (Beckett et al. 2003;Forbes 2015). The use of conifer needles in monitoring metal con-centrations has been shown in growing research to be an effec-tive biomonitoring technique of atmospheric pollution (Forbes2015).

Previous studies indicate that pine needles can accumulateelevated levels of trace metals beyond to the concentrations thatare crucial for biological growth (Forbes 2015). Studies haveshown that conifer needles have additional resin channels en-abling the uptake of metals and can be more effective in accumu-lating compounds relative to broad-leafed species(Guardo et al.2003). The use of spatial and temporal trends in atmosphericuptake has been used to establish concentrations gradients withrespect to the distance from point sources (Guardo et al. 2003).

Non-point sources, such as traffic, contribute to the variationin trace metal concentrations found in conifer needles (Kordet al. 2010). External sources such as emissions and soil dustscan significantly affect the metal concentration accumulation on

pine needle surfaces (Rautio and Huttunen 2003). Furthermore,infrastructure, such as roads and buildings often increase thewind turbulence that may enhance the capture of particulates inthe atmosphere (Beckett et al. 2000).

Numerous studies have demonstrated variance in trace metaluptake by species necessitating the need for uniform sampling ofspecies (Aboal et al. 2004; Guardo et al. 2003; Gandois and Probst2012; Ronggui et al. 2014). Reference to past studies is unfeasiblewith regards to biomonitoring capabilities of P. glauca due to thelack of literature; whereas, A. balsamea has been determined tobe an effective species for determining atmospheric concentra-tions of trace metals through needle analysis (Lin and Schuepp1995). A study performed by Beckett et al. (2000) indicated anincrease in capture efficiency of atmospheric pollutants withintricate branch compositions and the presence of smaller morenumerous needles. Moreover, atmospheric pollutant trends canbe determined as bioaccumulation is shown to be highest nearthe point source and gradually declines downwind (Guardo et al.2003). Previous studies concerning elevation have been focusedin alpine environments at elevations ranging from 1300 m to1800 m where positive correlations were observed (Chropenovaet al. 2016).

1

Spruce and fir needles were analyzed to determine whethera correlation exists between elevation and trace metal concentra-tion in proximity to non-point sources. The aim of this study wasto identify and quantify metal uptake with respect to varyingelevations of P. glauca and A. balsamea planted in urban environ-ments. Quantification of trace metals in the samples will allowfor association with natural and anthropogenic sources.

Locations and sampling methods

Study AreaTwo experimental locations with two bioindicator species wereselected for our study: Mayland Heights (location 1) and BowRiver pathway in Valley Ridge (location 2) in the eastern andwestern areas of Calgary, Alberta, respectively. Site locationswere justified based on (1) consistent coniferous species withrespect to its altitudes, (2) influence by similar non-point sourcessuch as high traffic roadways, and (3) accessibility with respectto time constraints.

Location 1Mayland Heights is situated northeast of a major city trafficsource, Highway No. 2 (Deerfoot Trail). This expressway isheavily congested during rush hours, typically in the morningand late afternoon, which is in part due to commuters headingboth Northbound and Southbound through the city. Lowertraffic roadways to the SE and N of the sampling locations arepresent at higher elevations, while Highway 2 adjoins with thelowest elevation sampling point. The samples were gatheredfrom six sites ranging from altitudes of 1037 m to 1066 m, andlocated downwind of the northeasterly winds relative to location2.

Figure 1 Location of the sites at Mayland Heights (East of Cal-gary). The altitude and distance (respectively) from the sitesto the expressway are (GPS coordinates in brackets): site 1:1037 m, 200 m (51°2’46”N 114°0’44”W), site 2: 1047 m, 250m (51°2’41”N 114°0’28”W), site 3: 1050 m, 350 m (51°2’38”N114°0’22”W), site 4: 1043 m, 150 m (51°2’34”N 114°0’23”W),site 5: 1058 m, 500 m (51°2’17”N 114°0’1”W), and site 6: 1066m, 530 m (51°2’16”N 113°59’55”W).

Location 2The Bow River pathway in Valley Ridge is situated on the west-ern edge of the city limits, south of the Bow River and northof the Trans-Canada Highway. The Trans-Canada Highway ex-hibits persistent traffic throughout the day, as it one of the dom-inant longitudinal transit ways through the city. Highway 201(Stoney Trail NW) is situated east of the study area and exhibitssimilar traffic volumes to Highway 1. The highest site altitudeadjoins Highway 201, and each site was located at decreasingelevations relative to this expressway.

Figure 2 Location of the sites at Valley Ridge - Bow River path-way. The samples were gathered from five sites North of High-way 1 and the altitudes are (GPS coordinates in brackets): site1: 1095 m (51°5’49”N 114°14’1”W), site 2: 1077 m (51°5’49”N114°14’7”W), site 3: 1097 m (51°5’47”N 114°14’0”W), site 4:1100 m (51°5’47”N 114°14’4”W), and site 5: 1116 m (51°5’45”N114°14’3”W).

Sample CollectionNeedles were collected from all cardinal directions 1-2 metersabove the ground of mature healthy trees displaying minimalfoliage browning and signs of disease. Composite samples werecollected by randomly selecting 2-3 individual trees based onavailability at each site, and was assumed to be representative ofthe trees sampled. To test this assumption, a composite sampleof needles at site 1 was taken along with three additional samplesfrom the individual trees.

Each composite sample contained 10 grams of needles col-lected by peeling them from the branches while removing thecuticle, and transferring into a clean plastic bag. Each bag waslabeled on-site with the location, elevation, and GPS coordinates.Gloves were replaced between each site to minimize the contactwith sources not related to needle collection. Needle collectionwas split into two days: February 24th 2016 for location 1, andFebruary 26th 2016 for location 2. Sampling occurred on sep-arate days to ensure exposure time prior to preservation wasconsistent between locations. The needle samples were stored ina freezer at -3°C to prevent biological decomposition until quan-titative and elemental analysis was performed in the laboratory.

Needle chemical analysisNeedles were prepared for analysis by mixing the needles intheir respective bags prior to stripping the remaining cuticles.

2 Graham Brown and Isabella Luu

Table 1 Concentration in ppm of select non-point source metals from chemical analysis.

Na Mg K Ca V Cr Mn Fe Co Ni Cu Zn Mo Sb Ba Pb

Location 1

1037 m 940.62 1430.10 4241.95 9844.26 0.52 0.99 31.29 132.86 0.24 1.00 3.73 82.13 0.69 0.10 68.11 0.94

1043 m 1127.02 1615.82 4837.07 8877.13 0.50 0.91 24.41 122.56 0.21 0.92 3.28 22.48 1.18 0.09 81.79 0.90

1047 m 723.13 1087.09 6494.74 8423.16 0.51 1.00 38.75 132.11 0.21 0.80 5.51 64.17 0.73 0.07 72.45 0.84

1050 m 1760.31 1376.94 7218.45 9911.03 0.57 0.99 17.24 156.94 0.22 0.93 5.31 23.97 1.73 0.12 56.81 1.25

1058 m 935.80 1288.87 6200.69 9406.87 0.55 1.02 33.83 136.54 0.20 0.91 3.42 66.64 0.89 0.13 104.81 1.16

1066 m 1803.21 1316.05 5459.41 5980.24 0.41 0.74 24.83 105.28 0.18 0.68 4.49 58.36 1.69 0.18 38.11 0.81

Location 2

1077 m 256.37 1481.40 4729.32 4959.61 0.18 0.27 17.95 50.33 0.09 0.57 2.83 51.79 0.51 0.01 22.05 0.38

1095 m 375.17 1682.16 4378.83 9760.08 0.23 0.55 31.58 65.54 0.15 0.96 2.41 67.91 0.56 0.02 29.11 0.57

1097 m 678.85 1039.82 4881.74 6502.92 0.45 0.66 21.14 98.99 0.30 0.75 3.26 41.11 0.50 0.03 65.31 0.48

1100 m 510.97 1163.19 2949.69 9912.12 0.30 0.56 82.20 74.07 0.16 0.90 1.89 24.58 0.18 0.01 41.78 0.46

1116 m 138.91 1037.00 3491.81 13770.55 0.29 0.38 22.02 79.15 0.18 1.06 2.34 40.19 0.52 0.03 123.57 0.42

2-grams of air-dried needles were placed into a plastic weighboat for the purposes of normalizing the data for dry-weight. Toensure quality control prior to transferring the needles into glass-ware, the Pyrex beakers were rinsed with distilled water anddilute acid to ensure contamination was not transferred from theglassware to the needles. The beakers with the air-dried needlesamples, along with an empty beaker were transferred into anoven and heated at 100°C for 16 hours. The dried samples werethen transferred into the Thermolyne F6000, Thermo Fisher Sci-entific (NC, USA) furnace for dry-ashing to occur at 6.6°C/minto a final temperature of 400°C held with a dwell of 24 hours toremove the moisture content.

The samples were acidified using 10 mL of ultrapure (2%)nitric acid. 10 ul of hydrogen peroxide was added before beingagitated and left to digest overnight in a refrigerated environ-ment. Quality control was carried out by a ‘blank’ and ‘tripblank’ that was carried throughout the drying process alongwith the 14 samples to be assessed to account for cross contami-nation and minimize data error.

Qualitative and quantitative analysis of trace metal concen-trations was completed with Inductively Coupled Plasma-MassSpectrometry (ICP-MS) for the 14 samples. The ICP-MS was per-formed with an X Series 2 Thermo Fisher Scientific (MA, USA).The ICP-MS was calibrated using stock solution (Environmentalcalibration standard, 1000 ppm each of Fe, K, Ca, Na, Mg, 10ppm each of Ag, Al, As, Ba, Be, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb,Sb, Se, Tl, V, Zn, Th, U in 10% HNO3, Agilent) and an internalstandard (Single element standard, 10 µg/mL Rh in 2% HCl,Agilent).

Statistical Analysis

Statistical analysis of elemental concentrations contained in theneedle samples was completed using regression analysis andrepeated measures of variance (ANOVA) with respect to thesamples collected at both locations. Due to the small samplesize, pairwise comparisons were not performed. To account forfield error between individual trees and aggregate samples of thetrees, outliers observed within the dataset were discarded with98% confidence using the t-test. Pearson’s correlation coefficients(r) was used to establish a relationship between elevation andtrace metal concentration.

Results and Discussion

Trace metal bio-concentration and site distributionThe concentration of the trace metals studied in the needlesof P. glauca and A. balsamea conifer species are summarized inTable:1. Data was obtained for 17/28 trace metals analyzed inthe needles.

Higher concentrations were exhibited at location 1 (Table:1).Variation between the two locations is likely due to differencesin traffic congestion. Location 1 was surrounded by major road-ways with daily traffic flows of 169,000 and 79,000 vehicles rel-ative to the 51,000 adjacent to location 2 (City of Calgary 2015).Possible compounding factors include the northeasterly windsthat pass through the city and their ability to carry certain tracemetals distances up to 200 km (Dunlap et al. 1999), thereby in-creasing contaminant concentrations downwind of the source(Guardo et al. 2003; Viard et al. 2004).

CorrelationsThree of the 28 trace metals analyzed displayed statistical sig-nificance with regards to a correlation between elevation andconcentration (P < 0.05), indicated by Pearson’s correlation coef-ficients. The analysis of variance of Ca, Ni, and Co are shown inTable:2.

Table 2 Pearson’s correlation coefficients (r) and p-values fortrace metal concentrations relative to elevation.

Location 1 Location 2 p-value

Ca 0.908 0.0331

Ni 0.887 0.0448

Co -0.890 0.0175

CobaltMinimum Co concentrations of 1.79E-1 ppm were observed atthe highest altitude of 1066 m and the maximum of 2.44E-01ppm at the lowest altitude of 1037 m at location 1 (Figure:3).A negative correlation between Co concentration with respect

Journal of ENVS-4405 3

to altitude (P < 0.05, R2 = 0.79184) was observed mirroring thetrend identified by Lin and Schuepp (1995) in A. balsamea (Fig-ure:3). The linear relationship between altitude and trace-metalconcentration suggests the highest concentrations are directlycorrelated to the increased traffic volume associated with Deer-foot Trail relative to surrounding non-point sources.

Figure 3 Concentrations obtained for cobalt at location 1 (May-land Heights) are statistically significant with respect to al-titude (P = 0.0176). Approximately 79% of the variation ofthe concentration of cobalt is explained by the altitude (R2 =79.184%).

Major anthropogenic sources of Co in the environment aretypically associated with industry and mining operations (Umaret al. 2015). Location 1 is upwind of the major industrial sector ineastern Calgary, and there are no Co mines present in proximityto Calgary; however, low amounts of Co are present in vehicleemissions and attributed to the combustion of fossil fuels (Umaret al. 2015).

Preceding studies have shown that background levels of Coin plant tissue near roadways are generally < 1 ppm and arelargely attributed to metabolic processes in organisms at lowconcentrations (Waldron 1980; Gandois and Probst 2012), whileuncontaminated areas range from 0.4-0.8 ppt (Haneke, 2002).The dominant intake method of Co is in conflict in the literaturewhere introduction through the root system is both favored(Adriano 2001; Ronggui et al. 2014) and contested due to lowtransfer potential attributed to minimal bioavailability of Co(Gandois and Probst 2012). Determination of the extent of Cocontamination attributed to vehicle use is hindered by the lackof local background data for the study location.

Nickel

Ni displayed a positive correlation (P < 0.05, R2 = 0.78684) be-tween concentration with respect to altitude at location 2 (Fig-ure:4). Nickel is a common exhaust emission with concentrationsdecreasing with distances from roadways (Ndiokwere 1984; Ballet al. 1991; Viskara and Karenlampi 1999; Kord et al. 2010; Tomase-vic et al. 2011), additional sources are linked to the tire-wear anddeterioration of brake linings on vehicles (Ball et al. 1991). Under-crown snowmelt has been correlated with increased nickel con-centrations in proximity to point-source pollution (Ershov et al.2016). With exposure to the roadway increasing with eleva-tion due to decreased foliage density, sites at higher elevationswould have snow piles more exposed to vehicle emissions thatconsequently melt and enter the soil profile and root system.

Baseline studies in other regions have reported concentra-tions not exceeding 5 ppm (Adriano 2001), with toxicity occur-

ring at 10-15 ppm (Marschner 1995). While lower concentrationsare essential for cell functionality (Salisbury and Roww 1992),a peak Ni concentration of 1.06 ppm at 1116 m questions thecontribution from constant traffic emissions. As Ni is highlybioavailable, the observed concentrations may better reflect thelevels in the soil, as foliage is a sink from uptake in the rootsystems (Adriano 2001).

Figure 4 Concentrations obtained for nickel at location 2 (Val-ley Ridge – Bow River pathway) are statistically significantwith respect to altitude (P = 0.04479). Approximately 79% ofthe variation of the concentration of nickel is explained by thealtitude (R2 = 78.684%).

Calcium

Ca displayed a positive correlation (P < 0.05, R2 = 0.8243) atlocation 2 (Figure:5). Ca is associated with traffic sources, withconcentration expected to decrease sharply to background lev-els as distance increases from the roadway (Ndiokwere 1984;Ball et al. 1991; Viskara and Karenlampi 1999; Kord et al. 2010;Tomasevic et al. 2011). Determination of the impact of the trafficsources is hindered by the lack of background data specific forthe sampling region. Previous bioindicator studies have iden-tified elevated concentrations of Ca in pine needles due to theuse of road salts for de-icing during winter conditions (Viskaraand Karenlampi 1999) which correlates to the sodium chloride(NaCl) and calcium chloride (CaCl2) used by the City of Calgary(City of Calgary 2016).

Figure 5 Results obtained for calcium at location 2 (ValleyRidge – Bow River pathway) is statistically significant withrespect to altitude (P = 0.0331). Approximately 82% of thevariation of the concentration of calcium is explained by thealtitude (R2 = 82.434%).

The dominant source of Ca is linked to calcium carbonate(CaCO3) in atmospheric dust particles and biogenic origins

4 Graham Brown and Isabella Luu

(Lehndorff et al. 2006), with conifer species accumulating largerconcentrations relative to other higher vegetation (Franceschiand Nakata 2006). Ca is naturally present in conifer speciesas it acts as a regulator of stresses such as freezing conditions(DeHayes et al. 1999).

Limitations

The high tree and foliage density that was exhibited at location2 may minimize the effects of wind dispersal and localize itseffects to the upper branches and needles, as well as individualsnear and along the perimeter. With tree heights at location 2ranging from 15-25 m, the samples collected at 1-2 m from theground may not be representative of the tree cluster. In contrast,tree stands at location 1 were less dense and limited to 2-5 treesper site. Low density at location 1 would allow increased airflowa account for the wide variation in trace metal concentrationsobserved relative to elevation. This is due to deposition of par-ticles fluctuating based on regional topography and structuressuch as roadways and other vegetation (Promeyrat 2001).

Sampling occurred without knowledge of the age of sampledneedles for each species. Mature needles are found to display astrong relationship with the soil chemistry relative to youngerneedles correlating to atmospheric deposition (Lin and Schuepp1995). The difference in the mobility of various trace metalscould further affect the concentrations present in the needles, asthis may affect the rate of reabsorption (Rautio and Huttunen2003). Collection of needles of various ages may have introducedsignificant variance into the data confounding any relationshipsto elevation that may exist.

Due time constraints, the location of a site with a significantelevation difference with a single emission source could not belocated. This would reduce the number of variables and allowfor a more direct correlation between concentrations and eleva-tion using trace metals released from the single emission sourceas identification markers. Furthermore, the lack of backgrounddata makes attribution of emissions sources unfeasible.

This could be partially remedied by using a two factor studyincorporating an additional bioindicator suited for atmosphericdeposition such as a species of moss. Being able to differentiatebetween atmospheric deposition and uptake through the rootsystem would allow for accurate analysis to be conducted. Mossmay provide more accurate atmospheric data as many coniferneedles contain a waxy outer layer that can be impermeableto trace metals (Aboal et al. 2004). This can result in 30 folddifference between concentrations in the wax and inner needle(Rautio and Huttunen 2003)

The statistical significance of the composite sampling methodused was not determined to be a valid method of obtainingrepresentative data. From the three individual trees sampledof the respective composite sampling site (location 1, site 1),one of three trees exhibited significantly higher concentrationsfor the majority of elements analyzed, including Ca, Co, andNi. The removal of this data point by the t-test still producedcorrelations that further failed the F-test, suggesting that the con-centrations of each tree at a site are susceptible to considerablevariance despite remaining in close proximity. Alternatively,multiple non-point emission sources at that specific site may notreflect the representativeness of the other sites in location 1 and2. Future analysis should incorporate a baseline sample at anundisturbed location with similar topographical and elevationcharacteristics.

Conclusions

With regards to elevation, our study suggests that there are acombination of factors affecting trace metal bioaccumulationon conifer needles. Overall, our results highlight the need toundertake future analysis that would incorporate backgroundconcentrations at a location with minimal unique non-pointemission sources and the ability to differentiate between atmo-spheric deposition and uptake from the soil.

Variability between the two study locations was evident dueto the proximity to roadways and varied daily traffic volumes.Furthermore, reducing the impacts associated with air pollutantsis important in sustaining vital environments; hence, spatialcharacteristics are important criterion to consider as depositionof atmospheric particles could be due to tree spacing, surfacearea, and other vegetation present (Beckett et al. 2000).

Overall, this study verifies the ability of trees to be an effectiveand low cost biomonitoring species in determining accumulatedmetal concentrations. However, other sampling points such assoil chemistry are required to draw further conclusions fromthe data. Due to time constraints and the time-consuming na-ture of this study, locations were chosen based on accessibilityto locations and sites. While limitations existed, useful rela-tive comparisons of the sampling sites, elevation, and locationswere addressed where appropriate in this study. Much detailedresearch is needed to optimize tree planting schemes and itsrelationship with altitudes in urban environments.

Acknowledgements

The main supervisor, Dr. Gwen O’Sullivan and correspondinglab support personnel’s in the Department of Earth and Environ-mental Sciences at Mount Royal University assisted in variousaspects of the laboratory and data output stages. This study wasnot funded as it was carried out as part of the requirements ofthe ENVS 4405 Air Quality class at Mount Royal University.

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