Correlations Between Water Quality Parameters and Levels of 4-NP in Water and Sediment of Stroubles Creek Watershed

Aaron Bradner

Project Report Submitted to the Faculty of the Virginia Polytechnic Institute and State University

In Partial Fulfillment of the Requirements for the Degree of

Master of Science In

Crop and Soil Environmental Sciences

Dr. Kang Xia, Co-chair Dr. Vinod Lohani, Co-chair

Dr. Carl Zipper, Committee Member

February 2013 Blacksburg, VA

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ABSTRACT

This observational study examined the potential correlation of different water quality

parameters with concentration of the organic contaminant 4-nonylphenol (4-NP) in the bed sediment

and water column of Stroubles Creek, a tributary of the New River in southwest Virginia. Weekly sample

collection occurred over the course of approximately four months (Jun 25-Oct 15, 2012). On Monday

mornings two water samples and a sediment sample were collected from threes sites along Stroubles

Creek to be analyzed for 4-NP. Three water quality sondes maintained by Virginia Tech’s Biological

Systems Engineering Department on lower Stroubles were deployed continuously and recorded five

parameters (dissolved oxygen, pH, conductivity, turbidity, and temperature) every 15 minutes a few feet

upstream of the 4-NP sample collection sites. These data were compiled into a weighted weekly average

using an exponential decay curve, giving more weight to the most recent days before sample collection.

Analyzed along with the weekly average were the weekly minimum (min) and weekly maximum (max) of

each parameter. These max, min, and average values for all 5 parameters at each of the three sites were

compared to the concentration of 4-NP detected in the soil and water samples collected weekly from

each site to determine if there was any significant correlation (r > .30) between these parameters with

4-NP concentration.

The following observations were made for ln(4-NP water concentration): temperature

(maximum; positive correlation), turbidity (maximum and weighted average; both positive correlation),

and dissolved oxygen (minimum % saturation, weighted average % saturation, weekly minimum, weekly

weighted average; all negative correlation) were significantly correlated.

The following observations were made for ln(4-NP sediment concentration): temperature

(minimum, maximum, weekly average, weighted average; all positively correlated), turbidity (maximum;

positively correlated), and dissolved oxygen (minimum % saturation, weekly minimum, weekly

maximum, weekly average; all negatively correlated) were significantly correlated.

KEY WORDS

4-NP, water parameters, stream, dissolved oxygen, conductivity, turbidity, pH, temperature

INTRODUCTION

Stroubles Creek is a tributary of the New River and collects runoff from a large area of

Montgomery County, with a watershed area of approximately 58 km2 (Younos et al., 2010) to the point

at which it flows into the New River. The Stream Restoration, Education, and Management (StREAM) Lab

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in Virginia Tech’s Biological Systems Engineering (BSE) department began monitoring lower Stroubles in

late 2010 with multiparameter water quality sondes YSI 6920 V2 at three sites downstream of the Duck

Pond at Virginia Tech (Figure 1). Sondes at the StREAM Lab locations record dissolved oxygen, pH,

turbidity, conductivity, and temperature at 15-minute intervals. The sondes, located in a section of

Stroubles that flows through agricultural fields, are calibrated every two weeks by BSE students.

The compound 4-nonylphenol (4-NP) is an organic contaminant that results from the photolytic

and microbial breakdown of NP ethoxylate, a compound ubiquitous in soaps, detergents, and

surfactants. The compound 4-NP has been detected in a wide range of environmental samples (Shang

et. Al 1999, Dachs et al., 1999, Sole et al., 2000; Kolpin et al., 2002) and has been found in low

concentrations in Stroubles Creek water and sediment samples by Dr. Xia’s research group.

The length of time over which data were collected in this study (five months) is much longer

than the studies covered in the literature review, in which the data duration typically was on the order

of days-weeks rather than months. By covering a significantly longer amount of time, this should give a

different and interesting approach to how such observational studies are conducted.

LITERATURE REVIEW

Introduction

Alkylphenol ethoxylates are an industrially important class of compounds used as industrial

detergents, emulsifiers, wetting agents, and dispersing agents (Maguire, 1999; Thiele et al., 1997). These

compounds can break down over time through microbial or photolytic degradation into a number of

degradation products. One of those degradation products is 4-NP, an estrogenic endocrine disruptor

(McCormick et al., 2005; Miles-Richardson et al., 1999). Though little is known about the extent of

environmental occurrence of 4-NP due to lack of analytical methods capable of detecting such

compounds at low levels, it poses such threats by causing abnormal physiological processes and

reproductive impairment (Kolpin et al. 2002).

As an organic contaminant and an endocrine disruptor 4-NP can have a negative impact on

aquatic biota in lotic systems, and may linger in sediment for some time. A study (Ekelund et al., 1993)

found that 4-NP degraded at the rate of 1.2% daily in the presence of sediment. Many other studies

documenting microbial degradation in river sediment (Yuan et al., 2003), photocatalytic degradation

(Pellizetti et al., 1989; Turchi and Ollis, 1989; Xia and Jeong, 2004), temperature-dependent sediment

adsorption (Cornellisen et al., 1997), physical properties such as hydrophobicity (Ahel and Giger, 1993),

and bioavailability (Miles-Richardson et al., 1999) of 4-NP exist and will be discussed in further detail in

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subsequent sections. The compound 4-nonylphenol is considered toxic to many organisms at low

concentrations. Leblanc et al. (2000) found that Daphnia magna suffered developmental effects, such as

curved spine and underdeveloped antennae, at concentrations as low as .45 uM. It can also cause

expression of female sex characteristics: one study observed male rainbow trout hepatocytes produce

vitellogenin (egg yolk protein) in the presence of 1.64 mg/L NP over seven days (Xie et al. 2005).

The compound 4-NP could originate from a number of places at these sites. Other studies have

found that prominent sources of 4-NP include biolsolids (Xia and Jeong, 2004), as well municipal sewage

treatment plants, textile manufacturing, pulp and paper production and recycling, and some pesticide

formulations (Topp and Starratt, 2009). Given the lack of textile and paper production in the Stroubles

Creek watershed, and the location of the collection sites in an agricultural field, it is surmised that the

source of 4-NP at the sites originates from pesticide or biolsolids application to crops.

Although there are many published studies on the environmental behaviors of 4-NP, there is

limited information on the effects of water quality parameters on 4-NP environmental behaviors. This

paper will review studies of similar compounds, specifically focusing on factors that have been found to

affect the environmental partitioning, transport, and fate of 4-NP.

Organic carbon content

The partitioning and transport of 4-NP in lotic aqueous systems is an important topic that has

not been widely researched; however, there are a number of studies that have monitored similar

compounds and contaminants. A study investigating the occurrence and accumulation of more than 30

different organic contaminants, including polycyclic aromatic hydrocarbons as well as DDT and its

derivatives, was performed on the San Joaquin River and its tributaries in California (Pereira et al.,

1996). This study measured the concentration of each of these contaminants separately in water and

sediment samples. It found significant concentrations of hydrophobic compounds with similar chemical

properties to 4-NP in downstream sediment. Specifically, the compounds observed were found in a

higher concentration in sediment than the water column. It is important to note that this study was

conducted during a month of little rainfall and therefore low flow which may affect contaminant

partitioning. Pereira et al. (1996) also took samples of suspended sediment during high and low flow to

analyze for contamination and found that concentrations were higher in suspended sediment than bed

sediment due to higher organic carbon content in the suspended sediment.

A second study (Gao et al., 1998) conducted in Germany investigated the effects of organic

carbon on the sorption kinetics of seven different organic contaminants, including Bifenox and

Triadimenol, from agricultural runoff. In addition to the study performed by Pereira et al. (1996), Gao et

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al. (1998) also noted a positive correlation between organic contaminant and organic carbon content,

indicating that organic contaminants had a higher affinity for sediment organic carbon. Because of the

high hydrophobicity of 4-NP (log Kow=4.48) (Ahel and Giger, 1993), it is expected to partition more

readily to sediment than water where organic carbon content is high.

pH

Multiple studies have investigated the effect of pH on the adsorption of various organic

contaminants to sediment. The previously mentioned study by Gao et al. (1998) found that an increase

in water pH leads to an increase in organic contaminant desorption from sediment, in turn leading to an

increase in organic contaminant in aqueous phase. A review of the chemical properties of 4-NP

(Vazquez-Duhalt et al., 2005) noted that its solubility can vary with pH as well as temperature, though

no graphs or data were shown with this claim except for a standard value (6350 ug/L at 25°C and pH 5).

Yet another study (Pionke and Chesters 1973) reviewed a number of contaminants, including Parathion,

DDT, and Barban, testing their concentrations in solution at different pH levels. The authors observed

that the highest adsorption to sediment occurs when the solution pH approaches the pKa for the

observed contaminants. Finally, another study (Roy and Krapac 1994) found that the adsorption of

atrazine (pKa 1.7) to suspended particles decreased steadily as the pH of solution moved away from the

pKa, increasing from 2 to 8. The pKa of a given compound is defined as log base 10 of the acid

dissociation constant of said compound, which in turn is the measure of the compound’s acidic strength.

The pKa of 4-NP has an estimated pKa of around 10.7 ± 1(Vazquez-Duhault et al., 2005). At a pH below

this value it should be predominantly protonated and neutral, making it more likely to bind to organic

sediment. Above this value it will become deprotonated and charged, making it more likely to partition

into water.

Turbidity

Studies have also been conducted to determine the effect of dissolved and suspended solids

present in bodies of water and the tendency of hydrophobic compounds like 4-NP to adsorb to them

(Voice and Weber, 1993; Vinten et al., 1983). Both of these studies found that with suspended

sediment, the content of organic carbon present in the solids is highly important. Vinten et al. (1993)

showed this using different soil types of different particle size, allowing time for the contaminants to

permeate the soil, and analyzing concentration at varying depths of 1, 2, 5, 8, and 12cm. Voice and

Weber (1993) also showed an inverse relationship between sorption and particle size; however, it was

noted that this could be due to the fact that smaller particles were typically organic in origin, and

therefore were able to more readily bind the hydrophobic compounds being observed. Gao et al. (1997)

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noted that a significant amount of contaminant may be lost during rain events due to sorption of the

contaminant to very small particles that do not settle out quickly but rather remain suspended and are

washed away in the storm runoff to settle out elsewhere. However, both did note that suspended

sediment plays a role in the transport and partitioning of organic contaminants.

Temperature

Temperature-dependent solubility has been observed as well (Gerard et al., 1997). In this study

increases in temperature were found to have a positive correlation with desorption of organic

contaminants from sediment; specifically, the percent of slow-rate desorption decreased as

temperature was increased from 5°C to 20°C to 60°C. These tests were run for extended lengths of time

varying from days to months. However, the greatest change in rate of desorption occurred very rapidly,

with most of the organic material desorbing into solution within the first few minutes-hours. After the

first approximately 10 hours of exposure of contaminated sediment to water, desorption of the

contaminant leveled off rapidly and linearized. One previously cited paper (Pionke and Chesters 1973)

also noted that post-application transport is likely dependent on relative humidity as well as soil and air

temperature, which can have a direct effect on water temperature.

Conductivity

It has been found that total dissolved solids played an important role in the transport as well

(Vinten et al., 1983), limiting the movement of organic contaminants within soil. Of the two

contaminants observed, Paraquat and DDT, DDT most resembles 4-NP with a log Kow in the same order

of magnitude (~4.9). Suspended in distilled water, the contaminants were able to penetrate soil to a

depth of 12cm; however, in the presence of very dilute calcium chloride, the contaminants were found

no further than 1cm in the soil. This was found to be due to flocculation to the soil used in the study; the

calcium bound the contaminants, and then bound to the soil, abating transport. However, when

suspended solids were absent and the organic contaminant was not adsorbed to suspended sediment,

the calcium did not bind to the contaminant and it moved freely into the soil.

Dissolved oxygen

One study (Pellizetti et al., 1989) was found that does not specifically address the effect of

dissolved oxygen levels on 4-NP breakdown but notes that under aerobic conditions, 4-NP can degrade

rapidly from sensitized photolysis by dissolved organic matter. Due to the requirement of oxygen for

photooxidation to occur (Faust and Holgne, 1987) poorly oxygenated waters may slow the rate of

breakdown. Conversely, high DO levels may assist in the rapid breakdown of 4-NP. This transformation

can be rapid, as the study by Pelizetti et al. (1989) was able to completely degrade 4-NP through a

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photocatalytic process over the course of one hour. For this reason DO is expected to have a negative

correlation with 4-NP concentrations.

Aerobic microbial degradation of 4-NP is also possible (Yuan et al., 2003). While 4-NP is not

readily broken down anaerobically, in the presence of higher concentrations of DO breakdown proceeds

more quickly. According to Yuan et al. (2003) 4-NP was found to be completely degraded after 70 days in

sediment without acclimated microbes, and just 28 days with NP-acclimated microbes indicating that

aerobic degradation was responsible for breakdown. This would indicate that higher DO would favor

aerobic degradation, and low DO would decrease microbial aerobic degradation.

Anthropogenic effects

It is important to note that the prevalence of organic contaminants is heavily affected by the

location to their application in the case of anthropogenic sources (Weston et al., 2004). It was found

that in the central valley of California, a highly agricultural region, that different bodies of water would

have different concentrations and so could not be directly compared to each other; e.g. irrigation canals

located adjacent to orchards that were treated with pesticides would have a greater degree of

contamination than upstream or downstream influent and effluent, particularly with regards to streams

of differing sizes. The concentration of organic contaminants in the water is also, obviously, affected by

the rate of application (Wauchope, 1978), as higher application rates can increase contamination.

Land application of biosolids is another means of introduction to the environment (Xia and

Jeong, 2004). Several million tonnes of biosolids are land applied every year in the United States and due

to the high hydrophobicity of 4-NP (log Kow=4.48) (Ahel and Giger, 1993), large quantities of 4-NP are

found in biosolids. Even years after application has ceased, 4-NP can be found at high levels in the soil.

Summary

Many of the water parameters monitored have been shown in previously published literature to

have an effect on the concentration of different organic substances such as pesticides, in the water

column and sediment of affected waterways. These support the reasoning behind the monitoring of

water parameters with regards to the concentration, and although 4-NP is not one of the compounds in

these papers that have been reviewed, it is expected that 4-NP will follow trends similar to other organic

compounds of its class that have similar characteristics.

PROJECT OBJECTIVE

The primary objective of this project was to explore the possible correlation between five water

quality parameters (dissolved oxygen, pH, conductivity, turbidity, and temperature) and the

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concentration of 4-NP in the water column and bed sediment at a monitoring site of the StREAM Lab in

Stroubles Creek.

METHODS

Study site

The particular sites used in this observational study were chosen for their scientific interest and

ease of observation. The contaminant 4-NP had already been found at the three sites by Dr. Xia’s

research group and samples were already being taken. Sondes had already been deployed and

maintained by the BSE department some time before, making data collection significantly less

expensive, difficult, and time-consuming. These sites run along an approximately half-mile stretch

through the experimental agriculture fields to the west of campus near the Foxridge apartment

complex. The sites are only accessible by dirt road and are relatively remote with regards to human

access (Figure 1). Bridge 1 is .34 miles upstream of Bridge 2, which is .20 miles upstream of Bridge 3.

Fig 1. Location of 4-NP sampling sites and water quality monitoring stations in Stroubles Creek. Source: Google Earth.

Data collection

Data were collected and analyzed by Dr. Xia’s research group. The data collected in this project covered

two major points:

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1. Parameter data collection: water quality parameter data was collected with sondes every 15 minutes

at three points along Stroubles Creek over the course of the study.

2. 4-NP sample collection: Stream sediment and water samples were collected from the water quality

parameter measurement points along Stroubles each Monday.

Sonde

The multiparameter water quality sondes YSI 6920 V2 at the StREAM Lab sites in lower Stroubles

were deployed continuously 7 days a week and set to record data at 15 minute intervals. These sondes

are maintained by the BSE department and are calibrated every two weeks by BSE students. The sondes

are installed at three bridges: bridge 1 is located at 37°12’36.37’’N 80°26’42.32’’W; bridge 2 at

37°12’23.16’’N 80°26’50.83’’W; and bridge 3 at 37°12’13.96’’N 80°26’46.75’’W. Below is an example

table of how the raw data appeared as it was obtained from the sonde for the first six hours on the first

day of deployment on the most downstream site.

Site DateTime Temp_degC SpCond_mS/cm Turb_NTU DO_% DO_mg/L Batt_V pH [H+]

3 6/18/2012 12:00 22.86 0.41 8.9 97.5 8.38 13.1 7.73 1.86209E-08

3 6/18/2012 12:15 23.1 0.407 8.5 99.7 8.52 13.2 7.75 1.77828E-08

3 6/18/2012 12:30 23.24 0.403 10.4 100.5 8.57 13 7.76 1.7378E-08

3 6/18/2012 12:45 23.51 0.401 7.1 102.3 8.68 13.1 7.76 1.7378E-08

3 6/18/2012 13:00 23.3 0.4 7.1 98.2 8.37 13 7.73 1.86209E-08

3 6/18/2012 13:15 23.37 0.4 6.6 98.4 8.38 13.1 7.74 1.8197E-08

3 6/18/2012 13:30 23.42 0.4 6.4 99.2 8.43 13.1 7.73 1.86209E-08

3 6/18/2012 13:45 23.38 0.4 6.1 98.7 8.4 13.1 7.74 1.8197E-08

3 6/18/2012 14:00 23.25 0.4 6.1 97.9 8.35 13.1 7.73 1.86209E-08

3 6/18/2012 14:15 23.24 0.4 5.4 97.6 8.32 13.3 7.73 1.86209E-08

3 6/18/2012 14:30 23.26 0.4 5.3 99.9 8.52 13.1 7.73 1.86209E-08

3 6/18/2012 14:45 23.25 0.4 5.6 100.8 8.6 13.3 7.73 1.86209E-08

3 6/18/2012 15:00 23.24 0.4 5.5 101.3 8.64 13.1 7.74 1.8197E-08

3 6/18/2012 15:15 23.3 0.399 5 101.3 8.64 13.1 7.74 1.8197E-08

3 6/18/2012 15:30 23.63 0.399 4.7 105.2 8.91 13.1 7.75 1.77828E-08

3 6/18/2012 15:45 24.05 0.399 4.6 107.8 9.05999 13.1 7.78 1.65959E-08

3 6/18/2012 16:00 24.07 0.399 4.3 109.4 9.18 13 7.78 1.65959E-08

3 6/18/2012 16:15 24.07 0.399 4.7 108.4 9.1 13 7.79 1.62181E-08

3 6/18/2012 16:30 24.33 0.399 6.6 109.6 9.16 13.1 7.8 1.58489E-08

3 6/18/2012 16:45 24.6 0.399 4.9 111.5 9.28 13.2 7.81 1.54882E-08

3 6/18/2012 17:00 24.68 0.399 4.7 111.2 9.24 13 7.8 1.58489E-08

3 6/18/2012 17:15 24.65 0.401 5 109.9 9.13 13.1 7.8 1.58489E-08

3 6/18/2012 17:30 24.6 0.403 4.6 109.2 9.08 13 7.79 1.62181E-08

3 6/18/2012 17:45 24.54 0.406 4.7 108 8.99 13.1 7.78 1.65959E-08

3 6/18/2012 18:00 24.39 0.409 4.7 104.7 8.74 13 7.78 1.65959E-08

Fig 2. Example table of data downloaded from sonde at site 3 on first day of deployment.

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4-NP sample collection

Over the course of the summer and fall semester, two students in Dr. Xia’s lab drove through

the agriculture fields to the furthest sampling point downstream and collect two water samples

(~500mL) and one sediment sample (~500mL). The sediment samples consisted of finer organic

sediment near to the bank rather than gravelly sediment that clearly washed off the road. These

samples were stored in glass Mason jars with 5 drops of hydrochloric acid and stored on ice. The student

moved upstream to the next collection site, following the same sampling procedure, until all sampling

sites had been visited.

Prior to sampling, one student gathered 10 pre-cleaned Mason jars (2 water samples and 1

sediment sample for each of the 3 sites, then one overall field blank). Labels were prepared in lab and

applied to the jars. A second student collected ice from Latham hall and fill a cooler about 1/3 full with

ice. A small kit with necessary equipment (a trowel for collecting sediment samples, paper towels, a

solution of 50:50 methanol/water for decontaminating the trowel between samples, a small dropper-

topped bottle containing concentrated HCl for fixing the samples, and Nitrile gloves) was also taken to

the sampling site. The students then proceeded to Site 3, the furthest site downstream so as to prevent

false readings from disturbing upstream sediment.

At the site, three Mason jars and the kit of supplies were taken to the site from the truck, which

was parked along the dirt road through the agriculture field. One student took water samples from the

center of the creek. The samples were preserved immediately after acquisition by placing 5 drops of

concentrated HCl into each sample. A second student took sediment samples from the edge of the

stream bed but still under the water. This sample was also fixed with 5 drops of concentrated HCl. The

equipment and samples were carried back to the truck and the samples were put on ice in the cooler.

The trowel used for sediment sample collection was washed with the Methanol/Water solution to

remove residual debris. The students then proceeded to the next site and repeat the same method for

the three remaining sites. Once all samples were collected, the students returned to lab and stored the

samples in a freezer to prevent degradation before analysis.

4-NP Water Sample Extraction

Samples were thawed and analyzed within 48 hours to prevent degradation of analytes. To

extract 4-NP from the water samples, 200mL of each sample was measured in a graduated cylinder

washed with ultrapure water. The sample container was then washed out with ultrapure water and the

measured sample was poured from the graduated cylinder back into the rinsed container. The

graduated cylinder was washed again with ultrapure water and 200mL of the next sample was

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measured. The second container was washed with ultrapure water and the sample was poured back

from the graduated cylinder into the rinsed container. This was repeated until 200mL of all the samples

had been measured.

The solid-phase extraction (SPE) filter was washed before filtering of samples with 3mL of 10%

methanol (MeOH) solution. Once the MeOH had run through the filter 3mL ultrapure water was run

through the filters. The SPE tubes were filled with 3mL of sample at a time and gravity filtered until the

rate of filtration slowed down enough to warrant using the vacuum filter (usually around 100mL). The

vacuum was turned on to filter the remainder of the sample for the sake of saving time.

Once the samples were completely filtered, the filters were rinsed again with 6mL of a 5%

MeOH solution and 6mL of ultrapure water. They were then dried with the vacuum on for 30 min. Once

dry, test tubes were placed under the filtration tubes and the filters were washed with a 70

dichloromethane: 30 acetone solution to wash the 4-NP in the filter into the tests tubes.

The eluted 4-NP was placed under a flow of pure nitrogen. This allowed for evaporation of liquid

while keeping the 4-NP in the test tube. Once dry, 500uL of MeOH was added and the tubes were

vortexed to dissolve the dry 4-NP in the MeOH. This mixture was placed in the gas chromatograph.

4-NP Sediment Sample Extraction

An arbitrary amount (>4g) of each sediment sample was placed in a vial and dried under vacuum

for 4 days. Once dry, 2 grams of each sample were weighed out and placed in a clean vial. 10 mL of a 1:1

hexane/acetone mixture was added to each sample and the vials were sonicated for 20 minutes.

Meanwhile, silica gel for dehydrating the samples was prepared by measuring 1g SiO2 and 1g Na2SO4 and

combining in one vial.

Once sonication was complete, the vials were centrifuged for 10 minutes at 3500 rpm and 25C.

At the end of centrifugation, the liquid in each vial was pipetted off the top and was processed through

the SPE. The same process for cleaning and filtering the water sample was followed for the sediment

sample.

Gas Chromatograph Analysis

Gas chromatograph-mass spectrometry analysis was performed using an Agilent 790A gas

chromatograph along with an Agilent 7000 series triple quadrupole GC/MS detector and an Agilent 7693

autosampler. The initial column of the GC/MS/MS was held at 60oC for 0.5 min, increased to 100oC at

15oC per minute, further increased to 200oC at 5oC per minute, and finally increased to 280oC at 25oC per

minute. The backflush time is 3.0188min making the whole time for a cycle 26.867min. The inlet

temperature was set at 200oC, transfer line temperature was set at 250oC, and the ion source

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temperature was set at 200oC. The analytical column is HP-5MS (30 m × 0.250 mm i.d., 0.25um film

thickness, Agilent, USA) and with a backflush column of Agilent res. (0.78m×150 um ×0 um). Splitless

mode was used at a Helium gas flow rate of 2.25 mL per minute. The injection volume was 1 uL and the

collision gas used was nitrogen. The x compound was qualified by electron impact at 70 eV using

multiple reaction monitoring (MRM) mode. The MS/MS quantification and confirmation ions are m/z

(107+121+135+149). According to the elute pattern of the isomers, four time segments were set in the

MRM method.

The results from the GC-MS are displayed as chromatograms. Technical grade 4-NP was used as

external standard for the qualification and the quantification of total 4-NP. The graph is created as

counts vs acquisition time in minutes. In order to quantify the concentration of the 4-NP in each sample,

the peak area is determined using the program from 19 to 21.5 minutes. In figure 2, this is shown by the

shaded area under the peaks within this range. Since 4-NP has many isomers, the different peaks

represent the different isomers all of which are important to include when determining the

concentration of 4-NP in the samples.

The area of the peaks is used in conjunction with the calibration curve to determine the 4-NP

concentration in the final extract. The concentration of the final extractant in the 2mL GC vial multiplies

the extraction concentration factor of the corresponding result in 4-NP concentration in the water or

sediment sample. (Moutinho, 2012)

Fig 3. Chromatogram of the 400 μg/L standard run on July 3, 2012. Source: Moutinho, 2012

Data processing

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Data was processed using Microsoft Excel and JMP. The water concentration data from each site

was averaged by site; the sediment concentration was not averaged as only one sample was collected.

The weekly parameter data from the three sites was averaged from one Monday through the following

Monday. The minimum, maximum, and weighted average was determined for each week as well in

order to coincide with the 4-NP sampling dates, which occurred on Monday mornings. Sonde data was

separated by site and week so that one sheet of data would contain one week (12PM Monday-11:45AM

the following Monday) of data from any given site (one, two, or three). This allowed for faster

processing of weekly average, weighted weekly average, weekly minimum, and weekly maximum.

Finally, the concentration data from each site was compared to the parameter data from each site.

The generation of a model for exponential decay was discussed with LISA so that each point

would have an increasingly greater factor in determining weekly averages, giving a total weight of one

for all points. The model that was produced was (y=9x10-6ex). This exponential decay, based on data

from Topp and Starratt (1999), gives a total weight of 1 to stream parameters collected over the entire

week while giving more weight parameters measured closer to the collection dates. Because 4-NP is

removed quite quickly from the stream in a matter of days, any parameters being compared to its

concentration were not expected to have as significant an impact as more recent parameter

measurements and were therefore weighted accordingly in the average. The minimum and maximum

values were also included to determine threshold values for partitioning and solubility.

Due to the strongly negative skewness of the raw data samples, a ln-normal plot was used to

provide a more normalized distribution for parametric analysis. Using JMP, the 4-NP concentration data

was converted into a ln-normal plot for both the water column and the bed sediment. Each value (min,

max, average, weighted average) of each parameter (dissolved oxygen, pH, conductivity, turbidity,

temperature) was plotted against the ln of both the water and sediment concentrations. Residuals were

also calculated.

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Figure 4. Weighted average curve developed by to give more weight to more recently collected parameters, based off data from previous literature (Topp and Starratt 1999).

STATISTICAL ANALYSIS

Weekly analysis comparison: With the assistance of statistical collaborators from LISA

(Laboratory for Interdisciplinary Statistical Analysis), a program that assists students on the Virginia Tech

campus with statistical analysis of project data, the weekly sonde parameter weighted averages were

compared to the weekly 4-NP sample concentration and analyzed for correlation between any of the

parameters and 4-NP concentration.

Each measurement (maximum, minimum, average, and weighted average) of each parameter

(DO, % saturation DO, turbidity, conductivity, pH, and temperature) was then analyzed for correlation in

JMP using a scatterplot matrix. Correlation coefficient values greater than .30 were considered slightly

correlated, while any values greater than .50 were considered strongly correlated (personal

communication, LISA and Dr. Holtzman). This process was used as a preliminary test to determine which

parameters had at least some degree of correlation.

These parameters found to have a correlation coefficient greater than .30 were then analyzed

non-parametrically using Spearman’s rank correlation coefficient. Spearman’s coefficient was chosen to

analyze the correlation between ln(concentration) and parameter values for its ability to measure

statistical dependence between two continuous variables in addition to being less sensitive to strong

outliers that were prevalent in this study. These Spearman rho values were used to verify correlation.

Once non-parametric correlation between parameters and ln(concentration) had been verified, the

residual error of the linear correlation was plotted to show that it was evenly distributed against the ln-

normal data.

14

RESULTS

Distribution

Figure 5. Sediment 4-NP concentration (ug/kg) distribution.

Figure 6. Water 4-NP concentration (ug/L) distribution.

15

Figure 7. Normalized ln(4-NP sediment concentration) distribution.

Figure 8. Normalized ln(4-NP water concentration) distribution.

The distribution indicates that low concentrations are fairly common, with higher

concentrations becoming less concentration as concentrations increase. The ln-normal histograph

indicates that the ln of the concentration is slightly more normally distributed.

Below is an example of the graphs that were created during statistical analysis using the

comparison of ln(water concentration) vs. max temperature. Similar tests were run on all parameters;

following is a table of values for all parameters found to be significantly correlated in the scatterplot

matrix.

16

Figure 9. Scatterplot matrix correlation plot of ln(water conc) vs. temp max.

Figure 10. Plotted residuals of temp max vs. water concentration, showing normalized distribution. Mean residual error =1.015e-16.

17

Figure 11. Plot of ln(water conc) vs. temp max. The equation was found to be: ln (water conc)=-3.108683 + 0.0745811*tempMax

Figure 12. Correlation values, residual values, and equations for significantly correlated ln(water conc) parameters. Note that values determined to be not significant are not included in this table.

Figure 13. Correlation values, residual values, and equations for significantly correlated ln(sediment conc) parameters. Note that values determined to not be significant are not included in this table.

The weekly measured water parameters that were found to be correlated (r > .30) with ln(4-NP

water concentration) were maximum temperature (positive), maximum turbidity (positive), weighted

average turbidity (positive), minimum percent dissolved oxygen (negative), weighted average percent

Scatterplot correlation (p=.10) Spearman's rho Mean residual error Equation

Temp max 0.3158 0.7278 1.02E-16 ln (water conc)=-3.108683 + 0.0745811*tempMax

Turbidity max 0.3362 0.8319 1.27E-17 ln (water conc)= -1.314593 + 0.0005758*TurbMax

Turb weighted avg 0.3117 0.8161 -1.78E-16 ln (water conc)= -1.250195 + 0.0062698*TurbWAvg

DO % min -0.3909 -0.7702 -3.24E-16 ln (water conc)= 1.5642206 - 0.0431757*DO%Min

DO % weighted avg -0.4502 -0.7363 2.73E-16 ln (water conc)= 4.080151 - 0.0620596*DO%Wavg

DO min -0.3057 -0.7456 -2.98E-16 ln (water conc)= 0.3399041 - 0.2616293*DOMin

DO weighted avg -0.4129 -0.4232 -1.11 ln (water conc)= 1.5516164 - 0.3481977*DOWAvg

ln(water 4-NP concentration)

Scatterplot correlation (p=.10) Spearman's rho Mean residual error Equation

Temp min 0.3594 0.7105 -1.39E-15 ln (sed conc)= 4.6388001 + 0.1050106*tempMin

Temp max 0.4953 0.8706 -1.09E-15 ln (sed conc)= 0.896712 + 0.1982751*tempMax

Temp avg 0.476 0.8444 0.0808 ln (sed conc)= 1.9186665 + 0.2072973*tempAvg

Temp weighted avg 0.4731 0.8331 7.43E-02 ln (sed conc)= 2.869848 + 0.1666804*tempWAvg

Turbidity max 0.3742 0.7948 -2.31E-15 ln (sed conc)= 5.9375285 + 0.0010626*TurbMax

DO % min -0.6032 -0.8887 -3.85E-15 ln (sed conc)= 12.683851 - 0.1054644*DO%Min

DO min -0.5652 -0.8903 -4.38E-15 ln (sed conc)= 10.564311 - 0.7901679*DOMin

DO max -0.3915 -0.7509 -3.34E-15 ln (sed conc)= 13.093933 - 0.5637333*DOMax

DO avg -0.473 -0.8597 -1.93E-15 ln (sed conc)= 13.483401 - 0.9288989*DOAvg

ln(sediment 4-NP concentration)

18

dissolved oxygen (negative), minimum dissolved oxygen (negative), dissolved oxygen weighted average

(negative), and total rainfall (positive).

The weekly measured water parameters found to be correlated with ln(4-NP sediment

concentration) were minimum temperature (positive), maximum temperature (positive), average

temperature (positive), weighted average temperature (positive), maximum turbidity (positive),

minimum percent dissolved oxygen (negative), minimum dissolved oxygen (negative), maximum

dissolved oxygen (negative), and average dissolved oxygen (negative).

DISCUSSION

From reviewing previously published literature the observed correlations of ln(4-NP water

concentration)with temperature (positive), turbidity (positive), and rainfall (positive) were expected.

The increase in temperature would allow for better mixing between a lipophilic chemical and water

(Cornellisen et al., 1997), while greater rainfall would transport more 4-NP from its source into the

stream through surface runoff (Sole et al., 2000) and increased turbidity would allow for desorption of 4-

NP from fine sediment (Gao et al., 1998; Liber et al., 1999). The overall negative correlation with

dissolved oxygen was also expected as well based on the work of Faust and Holgne (1987). However, DO

is affected by many other stream parameters such as temperature and rainfall. Further testing should be

performed to rule out confounding variables.

Based on the aforementioned dissolved oxygen studies (Pellizetti et al., 1989; Faust and Holgne,

1987; Yu et al., 2003), the observed negative correlation with ln(4-NP sediment concentration) was

expected. However, the other significantly correlated parameters did not correlate with ln(4-NP

sediment concentration) in the expected manner. Temperature and turbidity both showed a positive

correlation with ln(4-NP sediment concentration), the same as ln(4-NP water concentration). This would

indicate that as temperature and turbidity increase 4-NP increases in both sediment and water, or

causes an overall net gain in the stream rather than partitioning from one matrix to another. From

previously published literature it would have been expected that as water temperatures increased, 4-NP

would desorb from fine particulate sediment more readily and decrease concentration within sediment

(Cornellisen et al., 1997). However, each temperature value measured indicated that increasing

temperature increased 4-NP concentration in sediment. Although this correlation may be caused by a

confounding extraneous variable such as runoff caused by rainfall, further research is suggested.

The positive correlation with turbidity may be explicable by surface runoff of 4-NP from its

source during a rain event (Sole et al., 2000) which would also coincide with increases in turbidity.

19

Although a source has yet to be determined for Stroubles Creek, it is safe to say that Stroubles Creek

itself does not produce synthetic chemicals like 4-NP on its own. Rather rain events cause washing from

the as-yet undetermined source into Stroubles, increasing overall net 4-NP concentration in the stream.

However, again further research would be suggested.

It was expected that decreases in pH during rain events would have a positive correlation with

water 4-NP concentration and a negative correlation with sediment 4-NP concentration. However, due

to the brevity of the changes (typically a .2 unit decrease over <2 hours before recovering) with respect

to the amount of time the stream was monitored (168 hours in a week), it is reasonable that no

correlation would have been noticed. Additionally, because no samples were taken during a rain event

but rather after the stream pH had sufficient time to return to its normal pH, it is reasonable that pH

would not appear to correlate. Finally, because the stream pH is constantly below the pKa of 4-NP, there

would not be much of a change in chemical properties between base flow and rain events. Regardless,

in future studies samples should be collected on shorter time scales, specifically during rain events, to

ascertain the effect acute decreases in pH may have on concentration.

Although runoff would bring more suspended sediment and higher DO, the correlation between

weekly averages of both at all three sites is negative. At high flow, DO is higher and turbidity is lower.

This may be due to the time of year during which data was collected: warm rain events would raise the

temperature from suburban runoff into the stream, decreasing DO rapidly.

Figure 14. A comparison of DO and turbidity shows a negative correlation, with higher DO during low flows rather than high as would normally be expected. This may be due to spikes in temperature during rain events in the summer months during which data were collected.

Concentration over course of collection did not appear to show any significant trends. There are

a number of outliers that could potentially be linked to rain events; however, due to lack of complete

20

rainfall data, this could not be determined, The outliers show no relevant correlation to other

parameters and may be caused by leaching from the source. Further investigation into the source of the

4-NP found in the stream would be informative.

Figure 15. Concentration of 4-NP in sediment samples over duration of sample collection.

Figure 16. Concentration of 4-NP in water samples over duration of sample collection.

Future Work

Because of a restriction of resources, samples were only collected on a weekly basis to ease

scheduling and reduce the use of resources. Future studies should include sampling during high flows to

determine short-term, acute effects as opposed to just chronic, week-long effects. Because all of the

parameters change to some degree for such a short time during rain events they have little impact on

the weekly average which may affect the correlation of some of the data. For example, pH may show

21

strong correlation during a rain event due to sharp decreases during rain events; because no samples

were collected during rain events, any correlation (or lack thereof) could not be observed. DO showed

significant negative correlation in both water and sediment samples; it may show stronger negative

correlation during cold winter rain events that raise DO, or weaker correlation during hot summer rain

events that lower DO. Although this is only conjecture it could be concluded more definitely in a future,

more thorough study.

This observational study occurred over the course of two different seasons (begin early summer,

end mid fall), which is significantly longer than any studies performed in the literature review. These

seasonal changes were unaccounted for in the final analysis that covered the entire study. These

seasonal changes can cause unaccounted variations such as: organic carbon input, water temperature,

rainfall amount, and DO, among others. Accounting for these seasonal differences would require an

increase in resources such as the ability to measure organic carbon. No calendar season was recorded in

entirety, which would make comparing one season to another inaccurate at best without a complete set

of coinciding data.

Rainfall data was obtained from Virginia Tech’s BSE department, but only ran for one month of

data collection and so was not analyzed. Few rain events occurred during the short period of available

rainfall data, which makes determination of correlation difficult. However, there are a number of

outliers present in the data that may well be due to rain events. At the collection points in the stream

rain events are the only likely cause for changes in parameters. In future endeavours it would be worth

obtaining continuous rainfall data, perhaps from the town of Blacksburg which would be more complete

than the one rain gauge at BSE site two.

There were a number of possible factors that could affect the concentration of 4-NP that were

not monitored in this study. As discussed in the literature review, organic carbon concentration in the

sediment was not accounted for nor was is standardized among the sites. The precipitation temperature

was also not recorded for the duration of this study; however, the temperature of the rain may

contribute to the transport of 4-NP from its source to the stream and therefore should be monitored in

the future.

It is possible that some equipment utilized in the acquisition of parameter data was not

functioning properly: for example, the BSE sondes located along the stream indicated a conductivity of

400 ms at the first site, a sudden drop to 0 ms at the second site, and an increase back to 390 ms at the

third site. This happened every week over the course of the study, at random intervals each week lasting

from a few hours to a few days. These readings were consistent, and the second site conductivity data

22

was not included in the final analysis. Additionally, one sonde reported an average turbidity of 200 NTU

which is unlikely except in the event of a weeklong storm, or with multiple readings of very high

turbidity. The rain data indicated there was only 13mm of rain during the week this average was

recorded, implying the possibility of obstructed equipment which is a possibility. These data points were

not excluded during analysis because they were infrequent, and multiple communications with BSE

assured that the equipment is maintained and calibrated regularly.

ACKNOWLEDGMENTS

I thank my committee members Dr. Xia, Dr. Lohani, and Dr. Zipper for their support and

guidance through this project, and for taking the time to read revisions and offer suggestions to help

improve this report. I also thank the members of Dr. Xia’s lab: Terri Sosienski, Paul Parker, Christiana,

and Jennifer Moutinho for performing the sediment and water sample collection and analysis. I thank

my statistics team Andy Hoegh and Amy Till for their patience with me in explaining statistics and their

invaluable assistance with the data analysis. Thanks to Dr. Ingrid Lee for assistance with my literature

review and Dr. Mary Lee for providing suggestions on statistical analysis. Finally thanks to the BSE

department, in particular Dr. Cully Hession, Siavash Hoomehr, and the StREAM Lab for being willing to

work with me and for providing the water parameter data that was used in this observational study.

23

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APPENDIX

Figure A. Spearman rank correlation coefficient and associated p-values values for all ln(4-NP water conc) parameter correlations.

Figure B. Spearman rank correlation coefficient and associated p-values values for all ln(4-NP sediment conc) parameter correlations.

27

Figure C. Pearson rank correlation coefficient values for all parameters.

Figure D. Associated p-values of Pearson rank correlation coefficients for all parameters.

DO%min DO%max DO%avg DO%wavg DOMin DOMax DOAvg DOWavg phMin phMax phAvg phWavg

ln(4-NP water conc) 0.0184 0.0876 0.6573 0.0059 0.0697 0.3928 0.705 0.0123 0.7602 0.6145 0.3936 0.2314

ln(4-NP sediment conc) <.0001 0.5658 0.0749 0.5866 <.0001 0.0078 0.001 0.0464 0.7229 0.93 0.5878 0.3576

TempMin TempMax TempAvg TempWavg CondMin CondMax CondAvg CondWavg TurbMin TurbMax TurbAvg TurbWavg

ln(4-NP water conc) 0.6673 0.0606 0.3949 0.108 0.4944 0.4609 0.555 0.7154 0.6177 0.045 0.4611 0.0642

ln(4-NP sediment conc) 0.0153 0.0005 0.0009 0.001 0.5679 0.3773 0.6075 0.3237 0.2424 0.0113 0.1264 0.1171