Final Research Paper 2015pdf

Running head: ARSENIC, LEAD, MANGANESE, AND NICKEL IN POSSESSION SOUND 1

Distribution of Arsenic, Lead, Manganese and Nickel in Possession Sound with Relation to

Snohomish River Discharge

Laura Glastra

Ocean Research College Academy,

EvCC

Spring 2015

ARSENIC, LEAD, MANGANESE, AND NICKEL IN POSSESSION SOUND 2

Abstract

The Snohomish River estuary system is influenced by fresh water sources as well as

exchange with coastal waters. There are many anthropogenic and natural activities in the estuary

that may lead to input of heavy metals such as arsenic, lead, manganese, and zinc. Depending on

the concentration, heavy metals may pose health risks to marine organisms as well as humans

exposed to the metals. Samples of sediments from two locations in Possession Sound were

expected to show variation due to different sediment characteristics, depth, and proximity from

the Snohomish River. It was hypothesized that with increasing seasonal river discharge, there

would be corresponding increases of heavy metal concentration. Statistical analysis comparing

metal concentration and river discharge did not show a strong linear correlation. This suggests

that other internal processes are occurring within the estuary. Continued monthly sampling

cruises, and future research comparing other chemical processes should provide insight on

influences of metal mobility.


Distribution of Arsenic, Lead, Manganese and Nickel in Possession Sound with Relation to

Snohomish River Discharge

Heavy metals exist in both particulate and dissolved phases in estuarine systems

(Fukunaga & Anderson, 2011). Fluvial and riverine environments such as estuaries lead to the

dispersal of trace elements such as heavy metals, which makes it increasingly difficult to track

the sources of the metals (Bird, 2011). Tracing the source of contamination is further

complicated in urban environments due to the fact that heavy metals are continuously

accumulating because of anthropogenic activities (Luo, 2015). Various degrees of absorption

efficiency lead to bioaccumulation of heavy metals in organisms that inhabit aquatic

environments (Fukunaga & Anderson, 2011). Furthermore, pollution of heavy metals can

influence vegetative assemblage structure as well as plant productivity. Soils play a crucial role

in supporting estuarine systems, and affect the degree to which plants and animals may be

contaminated. More specifically, soils are involved in biochemical transformations, the cycling

of elements, filtration of water, and supporting plants and infrastructure of the ecosystem (Luo,

2012). The purpose of this study is to outline any spatial trends that may exist in heavy metal

concentration within the Puget Sound estuarine system. Data will be analyzed at two sites that lie

in a smaller body of water, Possession Sound, which is within the greater Puget Sound (Figures 1

and 2). These heavy metals may affect not only marine organisms and the local estuary, but also

be related to the health of humans and other organisms. It is hypothesized that heavy metal

concentrations will be higher at the Buoy site than at Mukilteo. This is hypothesized because

sediment is shallower at Buoy, which allows for more deposition of heavy metals. It is also


predicted that heavy metal concentrations at each location will increase correspondingly with

increases in seasonal river discharge.

Sources of Influence

There are various anthropogenic sources contributing to heavy metal contamination in

estuaries and marine ecosystems. Sources of metals can be traced back to long-term

industrialization and rapid urbanization. Long-term industrialization is defined by mining,

metallurgy, and fossil fuel combustion, while rapid urbanization consists of traffic and municipal

solid waste (Luo, 2015). In British Colombia long-term industrialization of leaded gasoline led to

heavy metal contamination of lead, zinc, copper, and cadmium. The study concluded that the

source of contamination was lead because the chronology of its isotope in sediment samples

correlated with gasoline consumption around the Strait of Georgia Basin (Macdonald,

Macdonald, O’Brien, & Gobeil, 1991). Another long-term industrialization source leading to

heavy metal contamination was activity at the Tacoma Copper Smelter throughout the 19th and

20th century (Kuo, Louchouarn, Herbert, Brandenberger, Wade, & Crecelius, 2011). In Pakistan,

rapid urbanization is thought to have led to wastewater contamination. One of these sources of

heavy metals in estuaries is thought to be wastewater contamination from various anthropogenic

causes as well as natural sources. Some of the natural contributors are from the erosion and

weathering of ore deposits and bedrock materials. The noted anthropogenic sources contributing

to heavy metal accumulation are various industrial processes, waste disposal, agricultural

practices, and mining. Additionally, emissions from vehicular traffic are thought to have led to

the atmospheric deposition and emissions of heavy metals that have been transported with rain


and storm water. If this water is used for irrigating crops in the area, it poses a potential threat to

humans unless it has been treated prior to irrigation (Khan, Malik, & Muhammad, 2013).

Sources, Movement, and Affects of Specific Heavy Metals

Arsenic. There are various sources that may contribute to arsenic in the environment.

While arsenic can be used in lead-acid batteries for automobiles and in its organic form as a

pesticide in some animal feeds, these sources do not greatly contribute to anthropogenic levels in

the environment. It is also used as a preservative on pressure-treated wood in the form of copper

chromated arsenate (CCA), but this only contributes to low levels of arsenic exposure. The

smelting and mining of copper and lead ores contribute more heavily to arsenic emissions.

Arsenic can also be released into the atmosphere by coal-fired power plants and incinerators

(Toxicological profile for arsenic, 2007).

Once released into the environment, arsenic cannot be destroyed. It can only change form

by becoming attached to or separated from other particles. Smaller particles that may come from

power plants and combustion processes are capable of remaining suspended in air for longer

durations. These particles may be removed from the air by rain, snow, or falling. Sticking to

other particles in water or sediment then transports them. Many arsenic compounds dissolve in

water. Yet, the majority of them remain located in soil/sediment. This is where the larger

particles of arsenic are generally located. Arsenic in aquatic ecosystems is generally so tightly

bonded with other particles and materials that plants and animals are unable to take it in.

However, various species of fish and shellfish can take in an organic arsenic called arsenobetaine.

The arsenobetaine accumulates in tissues, but has not been found to be harmful (Toxicological


profile for arsenic, 2007). But, seafood is listed as the primary contributor of arsenic exposure

through food in the Toxicological Profile for Arsenic.

In an attempt to trace arsenic transport routes in estuarine systems, a research team

collected data from 11 different sites during seven sampling cruises from 1997-2001. Because

the Scheldt estuary is well mixed, only surface samples of water were taken to analyze for

suspended and particulate arsenic particles. The research showed that increases of arsenic

corresponded with increases of river discharge, especially during flood times in the winter. In

summer, as river discharge decreased during dry periods, the arsenic values decreased as well. It

was also found that movement of arsenic particulates correlated with the movement of suspended

particulate matter. Therefore, suspended particulate matter could then be used to trace arsenic for

future studies. Another finding from the research showed that arsenic tended to settle in lower

salinity areas that had greater fluxes of sedimentation (De Gieter, M., Elskens, M., & Baeyens,

W, 2005).

The Handbook of Arsenic Toxicology has stated that arsenic exposure to humans is a

worldwide concern. Exposure to humans can occur through drinking, eating, and inhalation

(Ramasamy & Lee, 2015). Exposure during pregnancy may lead to impaired growth or fetal

death as it crosses the placenta. Arsenic exposure at this stage of development correlates with

lower scores on tests of cognitive function (Liua, McDermottb, Lawsona, & Aelion, 2010). Even

in stages of adulthood, arsenic may affect neurological function and reproductive health

(Ramasamy & Lee, 2015). Inorganic arsenic may lead to skin lesions, skin cancer, and

differences in patterns of the skin if exposed for long periods (Toxicological profile for arsenic,


2007). Circulatory and peripheral nervous disorders may also develop if exposed at low

concentrations over a long period of time. The Department of Health and Human Services

(DHHS), International Agency for Research of Cancer (IARC), and Environmental Protection

Agency (EPA) have all declared inorganic arsenic to be a human carcinogen. It has been linked

to increased risk of cancer in the liver, bladder and lungs. If inorganic arsenic is swallowed, it

may cause decreased red and white blood cell production, abnormal heart rhythms, and blood

vessel damage. These results may then lead to symptoms such as fatigue, bruising, and nerve

damage. If taken orally at a high enough dose, arsenic poisoning can be lethal (Toxicological

profile for arsenic, 2007).

Lead. The United States (U.S.) previously used tetraethyl and tetramethyl lead to

increase the octane rating of gasoline. Tetraethyl lead is still in use today for off-road vehicles

and airplanes. Presently, the largest use of lead is for storage batteries that are used in vehicles.

Pipes, weights, shot and ammunition, cable covers, and sheets used to shield humans from

radiation have also been known to contain lead. Lead is still mined in the U.S., though primarily

in Alaska and Missouri. Mining and other industries are contributors to the amount of

anthropogenic lead in the environment. Some industries such as lead-acid-battery manufacturing

and brass and bronze foundries release lead into the air. It can also be released into the air by

burning solid waste, coal, or oil containing lead. Furthermore, exhaust from workroom air,

degradation of lead-painted surfaces, fumes and exhaust from lead gas, and volcanoes and

cigarette smoke may release lead into the atmosphere. Lead is removed from the air by rain,

snow, or its particles eventually falling on land and/or surface waters. Another way lead can

enter aquatic systems is through wastewater from iron, steel, and lead producing industries,


urban runoff, and piles. This may affect humans if water that has been untreated is being

consumed or used to water crops. Humans can also be exposed to lead if they work at a lead

smelter, refineries, rubber products and plastic industries, soldering, steel welding and cutting

operations, battery manufacturing plants, lead compound manufacturing industries, or if they are

a construction and/or demolition worker (Toxicological profile for lead, 2007).

Metallic lead can only change its state or form because it is resistant to corrosion. If water

is acidic, there is the risk that it may result in lead pipes or solder releasing lead into the

environment. This is problematic because lead released into the environment may remain stuck

to particles in water or soil for many years. Lead that sticks to particles in soil will persist and

generally remain on the upper layer of soil (Toxicological profile for lead, 2007). The chemical

forms of lead were studied in the Pearl River Estuary using sequential chemical extraction

methods from samples of sediment cores. These allowed researchers to compare isotopes and

trace whether they were natural or anthropogenic. Researchers on this team compared the metals

of study to five geochemical terms that may have influenced the movement of lead in the estuary.

These terms were: exchangeable, bound to carbonate phase, bound to iron-manganese oxides,

bound to organic matter, and residual metal phase. In the estuary, the chemical form of the metal

easily influences lead mobility and solubility. It was found that lead increased toward the upper

regions of sediment, which were associated with iron-manganese oxides, residuals, and organic

fractions of the cores. These recent fractions suggest that the abundance of lead in the estuary is

due to rapid urbanization and industrialization (Li, X., Shen, Z., Wai, W.H., & Li, Y-S., 2001).


Most lead that plants and animals ingest through the air, water, and soil, will pass through

their systems (Toxicological profile for lead, 2007).

Lead can accumulate in fetal tissues beginning at 12 weeks of pregnancy. While the

general adult population is thought to absorb only 1-20 percent of ingested lead, pregnant women

are believed to absorb up to 70 percent of ingested lead (Toxicological profile for lead, 2007;

Liua et al., 2010). Exposure to a fetus leads to the risks of miscarriage, premature birth, and

lower average birth weights (Toxicological profile for lead, 2007). Young children are still more

vulnerable to lead absorption than adults with approximately 32 percent leaving their bodies as

waste (Toxicological profile for lead, 2007). It is thought that low-level lead exposure at a young

age may lead to neuro-developmental problems such as mental retardation and developmental

delays (Liua et al., 2010; Toxicological profile for lead, 2007). It may also affect a child’s

physical growth. Slower mental development due to exposure in the womb, infancy, or early

childhood correlates with lower intelligence later on in childhood. Furthermore, research

suggests that these effects persist beyond the stages of childhood. If swallowed at slightly higher

amounts, children may experience effects on their blood, development, and behavior. Finally, if

swallowed in large amounts, children may experience anemia, kidney damage, colic (severe

stomach pain), muscle weakness, and brain damage. These results may ultimately lead to cause

of death (Toxicological profile for lead, 2007).

It has been shown that lead exposure primarily targets the nervous system in both

children and adults. In adults, high levels of exposure may lead to severe and potentially lethal

damage of the brain and/or kidneys. High-level exposure in men specifically may damage organs


related to the production of sperm. It may also contribute to weakness of the fingers, wrists, or

ankles. Smaller exposure is linked to increases in blood pressure and anemia. There is, however,

no conclusive proof that exposure to lead is carcinogenic to humans (Toxicological profile for

lead, 2007).

Manganese. Manganese is released into the environment during manufacturing processes,

through the use and disposal of manganese based products, due to the actions of industries, and

mining. It can also be released into the air through automobile exhaust since some gasoline

contains manganese additives. Some gases that easily degrade may release manganese into the

environment when exposed to sunlight. Humans may also be exposed to manganese if they are

working or welding in a factory where steel is produced. However, the primary source of

exposure is through foods such as grains, beans and nuts, and to heavy tea drinkers. Once

manganese is released into the environment it cannot be broken down. It can only change form,

or become attached to or separated from other particles. Once in water, it tends to attach itself to

water particles or settle in sediment. The type of soil and chemical state of manganese then

determines the movement of manganese in soil (Manganese, 2008).

From January to December in 1999, researchers sampled for manganese and barium in

the Tillamook Bay Estuary in the Pacific North West. Sampling methods were conducted using a

Niskin bottle deployed to a depth of one meter for dissolved elemental samples. These were later

used to calculate suspended particulate matter, which may influence movement of the manganese

particulates. Surface sediments were collected using a surface sediment grab sampler. Samples

were later frozen and homogenized prior to testing for metal concentration. To analyze input and


exchange from rivers and coastal waters, a box model was utilized as a visual representation. The

research showed that as the metal concentrations varied seasonally, they correlated with

suspended particulate materials. It was concluded that adsorption and desorption reactions of

suspended particulates determined seasonal variance of the manganese. Seasonal variance of

manganese was also influenced by benthic sources. Furthermore, the dissolved manganese

values correlated with river discharge rates during winter. This was not true for the other seasons.

It was hypothesized that the lack of correlation could be attributed to internal estuarine processes

such as transport across a sediment-water interface (Colbert, D. & McManus, J., 2005).

Manganese may enter the body through inhalation and ingestion. Miniscule amounts may

enter the body through dermal contact. Of the manganese that enters the body, most will leave

through feces within several days. If a large amount is inhaled, it can lead to lung irritation and

potentially cause pneumonia. Humans may be exposed to manganese if they ingest fish or

shellfish. It has been reported in the journal of Food and Chemical Toxicology that chronic

exposure to manganese through these foods can lead to psychological and neurologic effects that

resemble Parkinson’s disease (Olmedo, Hernandez, Pla, Femia, Navas-Acien, & Gil, 2013). High

exposure to children may affect the brain and lead to behavioral changes, which can decrease

learning capabilities. It is not known whether these changes are permanent in children, nor if

children are more sensitive to manganese exposure than adults. High-level exposure in adults

primarily affects the nervous system. This can lead to behavioral changes and slow/clumsy

movements. Nervous system affects generally occur in workers who are exposed to levels about

one million times higher than those found in the environment. High exposure to men can result

in a loss of sex drive and damage to sperm. Lower levels of exposure may result in slower hand


movements. The EPA has not yet been able to conclude whether excess manganese is

carcinogenic to humans (Manganese, 2008).

Nickel. There are no nickel mining operations presently occurring in the U.S. Most nickel

used in the U.S. is imported from Canada or Russia. Nickel compounds may be used for nickel-

plating, catalysts, color for ceramics, and in the production of some batteries. It may be released

into the atmosphere by mining, oil and coal burning power plants, and trash incinerators. It may

also be released by industries since some discharge wastewater that may contain nickel

(Toxicological profile for nickel, 2005). Nickel released into water can be in its dissolved form

or attach to suspended material in the water. It may be released through pipes into groundwater

under more acidic conditions that can increase the mobility of nickel. The nickel released from

power plants may attach to small particles that can settle as dust or be washed out of the air by

rain and/or snow (Toxicological profile for nickel, 2005).

Nickel distribution, speciation, and particle-water interactions were monitored through

the uptake and release of a beta-emitting nuclide, 63Ni, through the use of suspended estuarine

particles. The data show that there were seasonal variations in nickel concentration due to both

natural and anthropogenic activities, which were sources of nickel. River flow was also found to

contribute to seasonal variation of nickel in the estuary. With increased salinity, it was found that

there was also increased particulate reactivity. Salinity, in addition to suspended particles,

influences the behavior of nickel in estuarine systems. In conditions of higher salinity, nickel

located in sediment had little tendency to desorb, or be released (Martino, Turner, & Nimmo,

2004).


Fish are not known to accumulate nickel in their bodies, but some plants may absorb the

metal (Toxicological profile for nickel, 2005). The major contributor to nickel exposure in

humans is food. Humans can also be exposed to it through dermal contact with water, soil, and

other metals containing nickel as well as by breathing air, drinking water, and smoking tobacco.

If water containing greater than 250 parts per million (ppm) of nickel is ingested, humans may

experience stomach aches, increased counts of red blood cells, and increased protein in their

urine. Welding workers and other workers in some industries may inhale air containing high

amounts of nickel – this is when the most serious effects occur. Inhalation of nickel has been

known to cause chronic bronchitis, reduced lung function, and cancer of the lungs and nasal

sinus. The EPA has stated that nickel refinery dust and nickel subsulfides (the type generally

inhaled by workers) are carcinogenic to humans. Once nickel enters the human body, it tends to

accumulate in the kidneys. But, after entering the body it can make its way to any organ. Nickel

that accumulates in a female’s body be transferred to infants through breast milk and across the

placenta. The levels of nickel found in breast milk are similar to those found in cow’s milk and

soy based formulas. The DHHS has declared nickel metals to possibly be carcinogenic, and

nickel compounds to be a known human carcinogen. The IARC has declared metallic nickel to

possibly be a carcinogen, and some nickel compounds to definitely be human carcinogens

(Toxicological profile for nickel, 2005; Klein & Costa, 2015).

Methods

Field Methods


Underwater sediment samples were collected from June 2009 to March 2015 at site 1.) MUK,

just north of the Mukilteo ferry terminal and inactive fuel docks, and 2.) BUOY, to the southwest

of Everett. Each of these sites has been chosen because of their proximity to anthropogenic

activity. The heavy metals are found in sediments that are collected with a Ponar style grab at the

surface of the substrate. The trends of the substrate and type of sediment are later noted for each

sample in data collection logs. Heavy metal data was then compared to river discharge in ft3/s

through the database provided by USGS (United States Geological Survey).

Lab Methods

The Everett Environmental Lab analyzed samples for the presence of heavy metals including As,

Pb, Mn, and Ni. This process used modified Environmental Protection Agency (EPA) methods

6020 and 7471. Samples are analyzed through the use of a quadrupole inductively coupled

plasma mass spectrometer.

Results Arsenic

The average concentration of arsenic found at Buoy from 2009-2015 was 9.23 ppm. The

average value from the Mukilteo site was lower at 5.39 ppm (Figure 11). At Buoy, average

arsenic levels were highest during spring (10.12 ppm) and lowest during the fall (8.64 ppm)

(Figure 3). The values at Buoy averaged to 9.04 ppm during summer and 8.67 ppm in winter

(Figure 3). At Mukilteo, the highest average arsenic concentration was found in winter at 6.54

ppm with the lowest average value (3.19 ppm) being found in the summer (Figure 4). The second

highest average value of 5.41 ppm in Mukilteo was found during the spring, which was followed


by 5.27 ppm in the fall (Figure 4). Average trend values did not correlate between seasons at

Buoy and Mukilteo, but arsenic concentrations remained greater throughout the year at the Buoy

site than in Mukilteo.

Lead

From 2009-2015, the average concentration of lead at Buoy was 7.20 ppm, and 6.06 ppm

at Mukilteo (Figure 11). Values were highest on average at Buoy during the fall (7.83 ppm) and

lowest during the summer (6.68 ppm) (Figure 5). The next highest values at Buoy were 7.36 ppm

in the spring and 6.86 ppm in the winter (Figure 5). At Mukilteo, the highest average value

occurred in winter (9.93 ppm), and the lowest in summer (1.14 ppm) (Figure 6). Average values

in spring reached 5.79 ppm, which was followed by an average of 4.34 ppm in the winter (Figure

6). Trends for average high and low values did not correlate for lead concentrations between the

two sites. Average values per season remained consistently higher at Buoy than Mukilteo

throughout the year with the exception of values recorded in winter.

Manganese

Manganese values were an average of 268.16 ppm and 179.78 ppm at Buoy and Mukilteo

respectively from 2009-2015 (Figure 11). At Buoy, the average values were highest in spring

(293.87 ppm) and fall (260.15 ppm), and lowest in winter (259.97 ppm) and summer (235.24

ppm) (Figure 7). At Mukilteo, the highest average value of manganese (212.64 ppm) also

occurred during spring (Figure 8). Additionally, the lowest average manganese value (74.52

ppm) also occurred during summer at Mukilteo (Figure 8). The second highest average value at

Mukilteo was during the winter at 195.40 ppm, which was followed by 185.98 ppm in the fall


(Figure 8). Both locations had average highs during spring; 293.87 ppm at Buoy and 212.64 ppm

Mukilteo; and average lows during summer, 235.24 ppm at Buoy and 74.52 ppm at Mukilteo

(Figures 7 and 8). Average values at Buoy remained continually greater than those at Mukilteo

throughout the year.

Nickel

The average concentrations of nickel at Buoy and Mukilteo from 2009-2015 were 32.98

ppm and 24.57 ppm respectively (Figure 11). The highest average concentration of nickel, 35.73

ppm, occurred during spring (Figure 9). This was followed by 35.23 ppm in fall, and 30.12 ppm

in winter (Figure 9). The lowest nickel concentrations at Buoy occurred during summer and

averaged to 29.22 ppm (Figure 9). The same trends occurred for nickel at the Mukilteo site

(Figure 10). Mukilteo had an average of 29.22 ppm in spring, 27.51 ppm in winter, 28.71 ppm in

fall, and a low of 3.89 ppm in the summer (Figure 10). The lowest average nickel concentration

at Buoy (29.22 ppm in summer) was the same as the highest average concentration at Mukilteo

(29.22 ppm in spring) (Figures 9 and 10). Data of nickel concentrations from other seasons were

all greater at Buoy than at Mukilteo.

River Discharge

Snohomish River discharge from 2009-2015 averaged 11,794 ft3/s. Throughout those six

years, the highest recorded discharge from the river occurred during the winter of 2011 at 23,360

ft3/s (United States Geological Survey). Spring of 2010 held the lowest discharge rate of 4,996

ft3/s. The Pearson Coefficient, or correlation coefficient, measures the correlation between to

variables in a single number (r) ranging from positive to negative one. At Buoy, arsenic and


manganese had r-values of 0.188 and 0.134 respectively (Figures 12 and 16). Lead and nickel

had negative r-values at Buoy of -0.144 and -0.260 respectively (Figures 14 and 18). At the

Mukilteo site, all r-values were positive. The r-value of arsenic was 0.442, while the r-value of

manganese was 0.222 (Figures 13 and 17). Lead had an r-value of 0.438, and nickel had an r-

value of 0.034 (Figures 15 and 19). At the Buoy site, both the r-values for arsenic and manganese

were lower than those recorded at the Mukilteo site. The same trend was recorded for r-values

pertaining to lead and nickel.

Discussion

Arsenic

Arsenic concentrations were greater at Buoy than Mukilteo when comparing overall

average values from 2009-2015, and the maximum and minimum values at each site within this

time. The same trend was true when comparing the average concentrations of arsenic per season

at each location. However, maximum and minimum concentrations were not recorded during the

same seasons at both sites. Ergo, river discharge seemed to affect the sites differently, or had

little affect on the arsenic concentrations. When analyzing the Pearson Coefficient, Buoy had a

lower positive value than Mukilteo. This greater positive relationship at Mukilteo represents that

there was a higher correlation between increased river discharge and arsenic concentration than

there was at Buoy. Yet, Buoy still maintained greater arsenic concentrations than those measured

at the Mukilteo site. It is possible that other estuarine processes occurred internally. As well as

that suspended particulate matter may have influenced movement of arsenic in addition to the

river discharge (De Gieter, Elskens, & Baeyens, 2005). Suspended particulate matter was not a

part of the methods in this study.


Lead

Lead concentrations remained greater at Buoy than Mukilteo when comparing overall

averages throughout the time from 2009-2015. The trend continued for average seasonal values

in spring, fall, and summer. However, during the winter, Mukilteo had a greater average lead

concentration that Buoy. This is most likely attributed to the outlier of 35.23 ppm measured

during the winter of 2011 at Mukilteo. Such an outlier could have come up if the Ponar grab hit a

leaded weight, or other source of lead from industry and fishing in the area, during sample

collection. Because of this outlier, it makes it difficult to analyze exactly the trends that occurred.

Including it in the data set, the data show that average maximum and minimum values did not

occur during the same seasons at Buoy and Mukilteo. This shows that river discharge did not

have the same influence on lead concentrations at the two sites. When looking to the Pearson

Coefficient, it can be seen that Mukilteo had a greater positive r-value than the Buoy site, which

had a negative r-value. This is the same trend that was seen with arsenic concentrations. The

greater positive value signifies that there was a stronger correlation between increased river

discharge and increased lead concentrations at Mukilteo than at Buoy. It is important to note with

such an outlier that correlation does not necessarily equate to causation. The negative r-value

recorded at Buoy shows that there was a small correlation between increased river discharge and

decreased lead deposition. It is possible that anthropogenic activity has more of an influence than

river discharge in determining deposition of lead in estuaries, which has been previously studied

using core samples from sediments (Li, Shen, Wai, & Li, 2001). This method was not available

during the time of this research.


Manganese

Concentrations of manganese were greater at Buoy than Mukilteo when comparing

overall averages from 2009-2015 as well as average seasonal trends. Mukilteo and Buoy had the

same seasonal maximum and minimum values for samples of manganese. When analyzing the

Pearson Coefficient, both r-values for Mukilteo and Buoy were rather low positive values. This

means that the increased river discharge and increased manganese deposition did not have a

strong correlation. As with the other metals, though, Mukilteo had a stronger correlation than

Buoy did when compared to river discharge. The correlation at both sites was minimal, and does

not represent a strong connection between river discharge and manganese deposition. Previous

studies have analyzed suspended particulate matter, in addition to river discharge, and found that

adsorption and desorption reactions determined the seasonal variance of manganese

concentrations. The same study also found that benthic sources of manganese contributed to the

seasonal variance (Colbert & McManus, 2005). This research did not take into account

suspended particulate matter nor potential natural benthic sources of manganese along the

Snohomish River.

Nickel

As with the other three metals, the average concentration of nickel was greater at Buoy

than at Mukilteo. This was also true throughout each season. Maximum and minimum values

corresponded for each seasons between the two sites, which suggests similar influence from river

discharge. The Pearson Coefficient shows that there was a positive correlation between river

discharge and nickel deposition at Mukilteo, but a negative relationship between the two factors

at Buoy. Both r-values were the lowest of all metals for both sites. The low positive value at


Mukilteo means there was the least amount of correlation between river discharge and nickel

deposition than any other metal at the site. The lowest negative value at Buoy shows there was

the strongest correlation between increased river discharge and decreased nickel deposition than

any other metal at Buoy. Studies have previously shown that seasonal variations in nickel

concentration can be attributed to natural and anthropogenic activities that are nickel sources.

But, this study also found that river flow contributed to seasonal variation. Additionally, salinity

and suspended particles seemed to influence nickel activity. In higher salinities, nickel in

sediment had little tendency to release, so higher concentrations would be located in sediment of

higher salinity areas (Martino, Turner, & Nimmo, 2004). This study did not assess salinity as an

influence to nickel mobility, nor suspended matter.

Limitations of Methods and Data

Data was collected during various sampling cruises that attempted to portray a seasonal

spread of the data. However, due to weather, some cruises in the past had to be cancelled as a

safety precaution. This results in there being more data for some seasons than others, which

means that not all seasons have an equal portrayal of data averages. Some seasons, such as

summer, may portray a more accurate trend than others. Furthermore, sediment consistency at

Mukilteo often complicated the sampling process that used the Ponar style grab. The result is that

there is much more data, from all seasons, at Buoy than there is at the Mukilteo site.

Once samples were collected, standard laboratory methods were followed, and samples

were sent to the Everett Environmental Lab. Dates recorded portray when samples were analyzed

for metal concentration at the Environmental Lab, not when samples were originally taken. The


gap between these two dates typically spans about one month. Because samples were compared

to river discharge that is dependent on date, the samples were compared to average river

discharges the month prior to the date recorded at the lab. This was in an attempt to align the

river discharge average of the month to the time sediments were collected on sampling cruises.

This approximation decreases the level of accuracy in displaying the correlation between river

discharge and metal concentration.

Conclusion

The hypothesis that arsenic concentrations would be greater at Buoy than Mukilteo was

supported when comparing overall average values from 2009-2015, and the maximum and

minimum values at each site within this time. This hypothesis was also supported when

comparing the average concentrations of arsenic per season at each location. The same

hypothesis was supported for lead concentrations between the two sites, with the exception of

Mukilteo in the winter. For both manganese and nickel, the hypothesis was also supported.

Higher average value trends continued for both of these two metals at Buoy throughout each

season individually as well.

The second hypothesis that metal concentrations would increase with increased river

discharge was not strongly supported. To show the strength of linear correlation between the two

variables, the Pearson Coefficient was used. R-values varied for each metal, with arsenic at

Mukilteo showing the strongest correlation. However, all values calculated for both sites were


not significant enough to show a strong, definite connection between increased river discharge

and increased metal concentration. In fact, some metals at Buoy had negative r-values. A

negative value represents the opposite of the original hypothesis, and shows that increased river

discharge in fact led to decreased metal concentration. Lead and nickel both reported negative r-

values at Buoy, while manganese and arsenic had weak linear correlations. Therefore, the

hypothesis that increased seasonal variance of river discharge would correlate with increased

seasonal variance of heavy metal concentrations was not supported. Further research would need

to be conducted in order to determine the influences of heavy metals in the Snohomish River

estuarine system. Some studies suggest that salinity may affect the mobility and suspension of

metals (Martino, Turner, & Nimmo, 2004). Ergo, this would be another factor to take into

account when analyzing metal concentrations in forthcoming studies. Furthermore, some studies

suggest that heavy metal variance in estuaries can be attributed to anthropogenic activities rather

than natural sources. This could potentially be tested through the use of isotopes and core

sampling, especially for lead (Li, Shen, Wai, & Li, 2001). Various studies also suggest that

internal estuarine processes such as suspended particulate matter greatly influence the movement

and processes of many heavy metals (De Gieter, Elskens, & Baeyens, 2005; Colbert & McManus,

2005; Martino, Turner, & Nimmo, 2004). Analysis of salinity and suspended particulate matter

are two prospective factors that could be duplicated for upcoming studies of heavy metal

mobility in estuaries.


Appendix

Figure 1: Puget Sound

Figure 2: Possession Sound

Buoy

Mukilteo


Figure 3

Figure 4


Figure 6

Figure 5


Figure 7

Figure 8


Figure 9

Figure 10


Average concentration 2009-2015

Buoy Mukilteo

As 9.22695 5.38847368

Pb 7.19985 6.05721053

Mn 268.161 179.777158

Ni 32.97585 24.5731053

Figure 11

0

2

4

6

8

10

12

0 5,000 10,000 15,000 20,000 25,000

Concentration(mg/Kg=ppm)

RiverDischarge

ArsenicatBuoy

Figure 12: Pearson Coefficient showing strength of linear correlation between arsenic concentration and river discharge at Buoy.


0

2

4

6

8

10

12

0 5,000 10,000 15,000 20,000 25,000

Concentration(mg/Kg=ppm)

RiverDischarge

ArsenicatMukilteo

Figure 13: Pearson Coefficient showing strength of linear correlation between arsenic concentration and river discharge at the Mukilteo site.

0510152025303540

0 5,000 10,000 15,000 20,000 25,000Concentration(mg/Kg=ppm)

RiverDischarge

LeadatBuoy

Figure 14: Pearson Coefficient showing strength of linear correlation between lead concentration and river discharge at Buoy.


0510152025303540


RiverDischarge

LeadatMukilteo

Figure 15: Pearson Coefficient showing strength of linear correlation between lead concentration and river discharge at Mukilteo location.

050100150200250300350400


RiverDischarge

ManganeseatBuoy

Figure 16: Pearson Coefficient showing strength of linear correlation between manganese concentration and river discharge at the Buoy site.


050100150200250300350400


RiverDischarge

ManganeseatMukilteo

Figure 17: Pearson Coefficient showing strength of linear correlation between manganese concentration and river discharge at Mukilteo location.

010203040506070


RiverDischarge

NickelatBuoy

Figure 18: Pearson Coefficient showing strength of linear correlation between nickel concentration and river discharge at Buoy.


010203040506070


RiverDischarge

NickelatMukilteo

Figure 19: Pearson Coefficient showing strength of linear correlation between nickel concentration and river discharge at Mukilteo.


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