ABSTRACT RUDD, HAYDEN. Assessing the Vulnerability Of

Home , Chalcone, Cyclohexylbenzene, Dibenzothiophene, Heneicosane, Hentriacontane, Indene

ABSTRACT

RUDD, HAYDEN. Assessing the Vulnerability of Coastal Plain Groundwater to Flood Water Intrusion using High Resolution Mass Spectrometry. (Under the direction of Dr. Elizabeth Guthrie Nichols).

Communities in the North Carolina Coastal Plain (NCCP) depend on safe and reliable groundwater for private well use, agriculture, industry, and livelihoods. Although storm intensity and frequency are predicted to increase in coastal areas, the risk of surficial and confined aquifer contamination from extreme storms is not understood. In September 2018, Hurricane Florence caused extensive flooding across the NCCP for several weeks. The North Carolina Department of Environmental Quality (NCDEQ) Groundwater Management Branch had just completed sampling of some wells in their monitoring network when Hurricane Florence made landfall. NCDEQ returned to these wells, particularly those flooded by Hurricane Florence, for post-flood sampling. These groundwater samples were analyzed by NCDEQ for regulated semi-volatile organics with few to any detections of regulated organic contaminants. NCDEQ provided NC State the same sample extracts for analysis by high resolution mass spectrometry (HRMS). This research reports on the non-targeted and suspect-screening HRMS analyses of groundwater from nested monitoring wells. Some monitoring well sites experienced flooding during the study period, and some did not. The goal of this research was to advance our understanding of coastal aquifer susceptibility to flooding by producing the first comprehensive organic chemical profiles of coastal aquifers and by determining if aquifers have distinct organic chemical profiles that change after flooding events. This study used HRMS analyses to produce the first comprehensive organic chemical profiles of 11 aquifers in the coastal plain. The mean total chemical feature count per groundwater sample was 5,207 (±1,088). Across all groundwater samples, a total of 396 unique chemicals were tentatively identified using the NIST 20 mass spectral database (M1). The deepest confined aquifer, Lower Cape Fear, had significantly more chemicals of environmental concern (ToxCast) than the Surficial aquifer, and eight ToxCast chemicals had higher detection frequencies in deeper, confined aquifers than more shallow or surficial aquifers. Detected ToxCast chemicals ranged from very water soluble to very water insoluble. Finally, chemical profiles of flooded wells, in confined and surficial aquifers, had several regulated organic compounds that were not detected prior to flooding. The mechanisms by which organic chemicals of variable solubilities transport to confined aquifers merits further research. Overall, HRMS analyses suggest the intrusion of new water to confined aquifers as indicated by the detection of anthropogenic chemicals.

by Hayden Rudd

A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Master of Science

Natural Resources

Raleigh, North Carolina 2021

APPROVED BY:

______Dr. Elizabeth Guthrie Nichols Dr. Damian Shea Committee Chair

______Dr. David P. Genereux

BIOGRAPHY

Guided by a childhood filled with outdoor activities and a love of science, I chose to study chemistry and environmental studies at Furman University. As an undergraduate Beckman Scholar, I worked in Dr. Brian Goess’s organic synthesis laboratory developing a novel synthesis pathway of hibiscone B – a natural product with chemotherapeutic properties. Working in Dr. Goess’s laboratory reaffirmed my interest in career as a research scientist as well as my desire to pivot my research efforts towards environmental challenges. This realization led me to the AmeriCorps CivicSpark program after college. CivicSpark is a national service program aimed at building the ability of local governments to address resiliency and sustainability issues by placing Fellows with local government organizations. I was stationed with the San Luis Obispo (SLO) County Water Resources Division and tasked with helping the Division address severe drought conditions and depleting groundwater supplies. Through my time serving in AmeriCorps, I witnessed the challenging issues that arise from the essential but often underappreciated relationship between humans and water resources; I decided to focus my future career efforts in hydrological research that would inform and improve water resource management. This led me to my pursuit of a M.Sc. in Natural Resources with a focus in Hydrology under the advisement of Dr. Elizabeth Guthrie Nichols, who shares my interest in research that advances sustainable management of local groundwater resources.

ACKNOWLEDGMENTS

I would like to thank the North Carolina Department of Environmental Quality Groundwater Management Branch for collecting, extracting, and sharing the groundwater samples used in this study as well as for their general assistance on this project. I would also like to thank Statera Environmental, Inc. for allowing the use of their laboratory and for their assistance on this project. Finally, I would like to thank my committee members for their support in the execution of this project and thesis. This study was funded by the United State Department of Agriculture Exploratory Research Grant (grant number #12762841).

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TABLE OF CONTENTS

LIST OF TABLES ...... v LIST OF FIGURES ...... vi Chapter 1: Introduction ...... 1 Chapter 2: Methods ...... 5 2.1 Sample Sites ...... 5 2.2 Sample Collection, Extraction, and Analysis ...... 5 2.3 Data Analysis ...... 7 2.4 Quality Control ...... 8 Chapter 3: Results ...... 10 3.1 Chemical Counts by Aquifer ...... 10 3.2 Chemical Features, TICs, and ToxCast Chemicals by Depth ...... 13 3.3 Tentatively Identified Chemicals by NIST 20 Mass Spectral M1 ...... 15 3.4 NIST 20 TICs Matched to USEPA’s ToxCast Chemical Database ...... 17 3.5 TICs and ToxCast Chemicals in Flooded Wells ...... 20 Chapter 4: Discussion ...... 29 4.1 HRMS Analyses of Groundwater ...... 29 4.2 Chemicals of Emerging Concern as Co-tracers ...... 30 4.3 Chemical Fingerprinting ...... 32 4.4 Groundwater Vulnerability to Extreme Storms ...... 33 4.5 Limitations and Advancement ...... 34 Chapter 5: Conclusion ...... 35 References ...... 37 Appendix ...... 42 Appendix A: Supplemental Tables and Figures ...... 43

LIST OF TABLES

Table 1 Mean counts for chemical features, TICs, and ToxCast chemicals (+ one standard deviation) by aquifer...... 11

Table 2 HRMS detections of compounds regulated by NC groundwater standards rule 15A NCAC 02L .0202 ...... 12

Table 3 HRMS detections of regulated compounds (NC groundwater standards rule 15A NCAC 02L .0202) in each aquifer...... 12

Table 4 The 15 most prevalent TICs (90% match factor) across all samples and number of well extracts in which the TIC was detected...... 16

Table 5 ToxCast 90 results for groundwater samples from the Snow Hill nested well site. . 21

Table 6 ToxCast 90 results for groundwater samples from the Falkland nested well site .... 22

Table 7 Chemical counts for Reference Wells, Falkland, and Snow Hill...... 25

Table 8 ToxCast 90 chemicals present in the Snow Hill Well 4 samples and Reference Well Saulston 3 samples...... 26

Table 9 ToxCast 90 chemicals present in the Falkland Well 5 samples and Reference Well NPHS 7 samples...... 27

LIST OF FIGURES

Figure 1 Map of nested well locations in the NCDEQ monitoring well network that were sampled during the study...... 6

Figure 2 Workflow diagram for GC-MS sample analysis. Boxes in gray are steps completed by NCDEQ...... 7

Figure 3 Total chemical features and total ToxCast-matched chemicals (90% match factor) for 150 groundwater samples plotted against the depth to well screen ...... 14

Figure 4 Box plots of chemical counts across samples grouped by well screen depths as shallow (<33 m), middle (33-110 m), and deep (>110 m)...... 15

Figure 5 The number of groundwater samples in flooded and non-flooded wells in which the 16 most frequently detected ToxCast 90 chemicals were identified...... 17

Figure 6 Heat map of 40 ToxCast 90 chemicals and their detection frequency by aquifer. ... 19

Chapter 1: Introduction

Worldwide, intrusion of young water into deep, confined or “fossil” aquifers indicates that deep groundwater is more susceptible to surface interactions and inter-aquifer mixing than previously understood (Jasechko et al., 2017). Impaired or abandoned boreholes, excessive groundwater withdrawals, aquifer heterogeneity, and inter-aquifer mixing represent mechanisms by which young water moves into and between deep aquifers. Extreme rainfall events significantly increase groundwater infiltration rates and macropore processes (Jasechko and Taylor, 2015; Li and Tsai, 2020; Sawyer et al., 2014; Vittecoq et al., 2020). These dynamics make young water intrusion a particular concern for coastal landscapes as storm frequency and rainfall intensity increase with climate change (Kunkel, et al., 2020). In North Carolina (NC), climate change impacts are a particular concern for the NC Coastal Plain (NCCP), which is the primary region for food and fiber production in the state. This area has endured historic flood events from hurricanes, and recently, in 2018, Hurricane Florence devastated this area with catastrophic floods due to rainfall volumes totaling an estimated eight trillion gallons (Moody et al., 2018). The NCCP relies predominantly on aquifers to provide potable water to household wells, municipal drinking water systems, crop irrigation, livestock CAFOs, and food/fiber supply chains (Dieter et al., 2018). As global temperatures rise, the amount and frequency of rainfall from severe storm events will continue to increase in this region; rainfall intensity and amounts have already increased 27% since 1958 (USEPA, 2016). The vulnerability of groundwater in this region to storm water intrusion from extreme storms has not been evaluated. Currently, the North Carolina Department of Environmental Quality (NCDEQ) uses conventional standard-targeted mass spectrometry to quantify a select and small number of known organic chemicals (~200 organic chemicals and pesticides) in groundwater samples from its monitoring well network. Applications of nascent non-targeted screening (NT) and suspect- screening (SS) high resolution mass spectrometry (HRMS) to understand aquifer dynamics and the impact of extreme rainfall events on deep wells are non-existent. NT-HRMS produces a full chemical profile of thousands of organic chemical features of which a small subset (hundreds) is identified using complex workflows and mass spectral databases (Schymanski et al., 2014). NT- HRMS is an advanced approach for complex water quality characterization and recognized for its ability to expand environmental and geological characterization (Hollender et al., 2017). No

one has applied NT-HRMS to compare surficial and confined aquifer profiles in response to extreme storms nor utilized the plethora of chemical features (known and unknown) to characterize commonalities and differences between overlying aquifers. NT-HRMS data can be processed through chemical databases to match mass spectral results to known spectral data of registered chemicals (Schymanski et al., 2014); this process is defined as suspect-screening HRMS (SS HRMS). While NT- and SS-HRMS analyses have been used to study groundwater, their applications have been limited. Several studies have used SS- HRMS to evaluate the presence of chemicals of emerging concern (CECs) including high production volume anthropogenic chemicals (Sjerps et al., 2016), pesticides (Kiefer et al, 2019; Pinasseau et al., 2019; Soulier et al., 2016), pharmaceuticals (Gray et al., 2020; Pinasseau et al., 2019, Soulier et al., 2016), personal care products (Hedgespeth et al., 2019; Soulier et al., 2016), and industrial chemicals (Mohler et al., 2013; Soulier et al., 2016) in groundwater. However, many of these studies are limited by their use of specific mass spectral databases that are limited to a few hundred or a few thousand chemicals from certain classes (Kiefer et al., 2019; Pinasseau et al., 2019; Soulier et al., 2016). The use of a more comprehensive mass spectral database, such as the NIST database with mass spectral data for hundreds of thousands of chemicals, has been used in just a few studies of groundwater contaminated by specific sources (Hedgespeth et al., 2019; Mohler et al.; 2013). NT- and SS-HRMS approaches have been used in two cases for chemical fingerprinting of groundwater (Sjerps et al., 2016; Soulier et al., 2016). Both studies were conducted in Europe and used a liquid chromatography quadrupole time of flight mass spectrometer (LC-QTOF-MS) for HRMS analysis. These studies distinguished groundwater from other water sources such as waste water and surface water (Sjerps et al., 2016), and characterized two surficial aquifers in France (Soulier et al., 2016). HRMS analyses have not been used for fingerprinting of confined aquifers. Two recent studies used NT- and SS-HRMS analysis to examine stormwater impacts on groundwater (Hedgespeth et al., 2021; Pinasseau et al., 2019). One study evaluated the impact of extreme rainfall and flooding caused by Hurricane Florence on groundwater, wastewater, and surface water at a land treatment site, where secondary treated wastewater is applied to forest land, in coastal NC (Hedgespeth et al., 2021). The other study, conducted in France, used SS HRMS to examine the impact of infiltration via constructed stormwater infiltration sites on

surficial groundwater quality (Pinasseau et al., 2019). These studies were limited in their investigation of surficial groundwater from sites with specific land use types and did not involve confined aquifer assessments. The potential use of HRMS analyses to assess the impact of extreme storms on groundwater quality builds on recent isotopic tracer studies of surficial and confined aquifer vulnerability to surface phenomenon (Jasechko et al., 2017; Kingsbury et al., 2017; Kuroda et al., 2014). In tropical climates, stable isotope ratios of O and H were used to determine that extreme rainfalls contributed a greater proportion of recharge to groundwater than less intense storms due to macropore processes (Jasechko and Taylor, 2015). Age-dating tracers, such as radiocarbon, tritium, and sulfur-hexafluoride, have been used to determine young water ratios in groundwater. Global and U.S. research have observed young water tracer signatures in deep, confined wells which suggests aquifer vulnerability to modern contaminants (Jasechko et al., 2017; Kingsbury et al., 2017). Compromised boreholes, infiltration through preferential flow paths, and inter- aquifer leakage from multi-layer aquifer extraction appear to play significant roles in the unexpected presence of young water (after 1960s) in deep, confined aquifers (Jasechko et al., 2017). Other studies have used targeted chemical analyses of known organic CECs as co-tracers that can also age-date water and delineate groundwater contamination sources, recharge, and residence times (Erostate et al., 2019; Kuroda et al., 2014; McCance et al., 2018). Commonly used CEC co-tracers have been frequently detected and persistent in groundwater, such as the pharmaceuticals carbamazepine and crotamiton, the pesticide atrazine, and PFAS surfactants (McCance et al., 2018). Higher concentrations and occurrences of organic CECs in groundwater correlate with increased presence of young water (Moreau et al., 2019). CECs also provide distinct information compared to isotopic tracers; CEC use, source, and reactivity information can be used to investigate the time of recharge and sources of contamination (Erostate et al., 2019; Kuroda et al., 2014). To our knowledge, NT- and SS-HRMS analyses have not been used to investigate deep groundwater vulnerability and identify CEC co-tracers. HRMS analyses provide more detailed organic chemical profiles than current target MS methods by capturing all chromatographic mass features in an environmental sample. These chromatographic features can be further analyzed through recently developed software and USEPA databases to identify regulated chemicals, known non-regulated chemicals, and unknown chemicals with varying

degrees of certainty (Hernandez et al., 2015; Hollender et al., 2017). HRMS not only provides more detailed organic chemical profiles than target MS methods, but the HRMS chromatogram can be archived and re-processed through updated mass spectral chemical libraries as new chemicals are identified. The growing list of regulated chemicals and unregulated chemicals in detected in U.S. waters reinforces the utility of CECs as co-tracers (Bradley et al., 2018; Rager et al., 2016). Using current chemical databases, such as the 720,000+ chemical EPA CompTox Chemistry Dashboard, HRMS chemical features can be categorized as geogenic, biogenic, and anthropogenic. Compared with standard targeted analysis, HRMS analyses can determine additional chemical signatures, which could be used for evaluating young water intrusion into groundwater. This study’s application of NT- and SS-HRMS can address knowledge gaps regarding the presence of CECs in NC coastal aquifers and the impact of flood water intrusion on unconfined and confined groundwater quality. The goal of this research was to advance our understanding of the susceptibility of coastal aquifers to storm events and to generate useful information for municipal, state, and federal water resource managers, private well owners, and the scientific community. The first objective of this research was to use both NT- and SS-HRMS to produce the first comprehensive organic chemical profiles of NCCP aquifers. A second objective was to analyze groundwater samples from known flooded wells in the NCCP to evaluate coastal aquifer vulnerability to extreme storm events and floods. Several research questions framed this study. First, would aquifers have distinct organic chemical profiles? Secondly, would organic chemical profiles differ between pre-flood and post-flood groundwater samples if a particular well experienced flooding? One initial prediction was that surficial wells, due to their direct connectivity to surface dynamics, would have a greater number of organic CECs than wells in confined aquifers. A second prediction was that the number of CECs would be greater in groundwater from a well after flooding than before flooding.

Chapter 2: Methods

2.1 Sample Sites From August 2018 to March 2021, 150 groundwater samples were collected from 50 locations of nested wells that are part of a well network managed and monitored by the North Carolina Department of Environmental Quality Groundwater Management Branch (NCDEQ) (Figure 1). The sampling campaign included 2 reference nested well locations that were sampled more than once to understand variability without flooding, 13 well locations that were flooded in the last two years, and 35 well locations that did not experience flooding for the last two years but may have historically flooded. The reference wells were sites that had not experienced flooding in recent history nor during the study period; they were sampled to provide a basis of comparison for nearby flooded well sites, which presumably experienced similar environmental effects. For this study, a "flooded well" refers to a well that had standing water around the well casing at least once during the study; extreme storm events, such as Hurricane Florence (2018) may have created flood levels that exceeded the tops of some well casings. All sampled wells had upright metal casings. 2.2 Sample Collection, Extraction, and Analysis NCDEQ collected groundwater samples in adherence to the NC Division of Water Quality Aquifer Protection Section Quality Assurance/Quality Control and Standard Operative Procedures Manual for Sample Collection (2006). Briefly, after purging three well volumes, groundwater samples were collected in 4-L pre-cleaned amber glass bottles, kept on ice in the field, and stored at <6 °C for no more than 7 days. A field blank accompanied each sampling trip and field duplicates were collected per every 10 wells sampled. Prior to liquid/liquid solvent extraction with dichloromethane, isotopically-labeled surrogate internal recovery standards [2- fluorophenol, phenol-d5, nitrobenzene-d5, 2-fluorobiphenyl, 2,4,6-tribromophenol, and p- terphenyl-d14] were added to each groundwater sample. Samples were extracted via liquid/liquid solvent extraction and reduced to 1 mL in volume. The following internal standards, 1,4- dichlorobenzene-d4, naphthalene-d8, acenapthene-d10, phenanthrene-d10, chrysene-d12, and perylene-d12, were added prior to analysis by Agilent 7890B GC system coupled with 5977A MSD in full scan mode as prescribed by USEPA Method 625/8270D (USEPA, 2016). The same extracts were sealed and stored at -20 °C prior to HRMS analyses at Statera Environmental, Inc, using a GC-Quadrupole Time of Flight-MS (QTOF-GC/MS). Figure 2 shows the workflows for

Figure 1. Map of nested well locations in the NCDEQ monitoring well network that were sampled during the study.

targeted analyses by NCDEQ using USEPA Method 625/8270D and HRMS analyses performed at Statera Environmental Inc. NT-HRMS analyses were performed using an Agilent 7890A GC system coupled with an Agilent 7200 QTOF GC/MS. The Agilent DB-17ms column (30 m x 0.25 mm x 25 µm) was used with helium carrier gas and 2 µL splitless injection. The column flow rate was 1 mL/min and the initial pressure was 10 psi. An initial oven temperature of 85 °C was held for 0.5 min then ramped at 10 °C/min to 320 °C and held for 4 min. Mass spectra were created using an electron ionization (EI) mode and a source temperature of 230 °C. The mass range was 40 to 700 amu, and the spectral acquisition rate was 5 Hz.

1 Groundwater sample taken

EPA Method 625 (LLE)

GC x MS GC-QTOF-MS

Targeted screening MassHunter Unknown Analysis used for regulated for data processing organic chemicals

Select unique tentative IDs from NIST 20 database

Comparison against ToxCast Library

# of ToxCast Hits # of TICs # of Targeted # of Total Chemical Features Chemicals Figure 2. Workflow diagram for GC-MS sample analysis. Boxes in gray are steps completed by NCDEQ. Boxes in white were conducted at Statera Environmental, Inc. 2.3 Data Analysis Agilent MassHunter Unknown Analysis (v 10.0) was used to process the MS data. Sample files were first converted to SureMass format. Peak detections were based on a minimum signal-to-noise ratio of 10 and an absolute area minimum of 1,000 counts. Deconvolution was

conducted using an ion peak range of 3-10. Analytical blanks and field blanks were run with each sample batch and subtracted from the spectra. The total number of chemical features detected was used as the NT chemical feature count. A comparison to the NIST 20 mass spectral database (M1) was conducted with a match factor threshold of 50. The NIST 20 database includes EI MS spectra for 306,869 chemical compounds; it allows for identification of a broad range of chemicals as it includes spectra for human and plant metabolites, pharmaceuticals, pesticides, lipids, environmental contaminants, industrial chemicals, and more. The SS analysis using the NIST database produced the list of tentatively identified chemicals (TICs). The TICs were compared to the EPA Phase III ToxCast chemical database to develop the ToxCast chemical list for each sample (USEPA, 2017; Richard et al., 2016). Physical and chemical properties of TICs were identified using ChemSpider; chemical sources were identified using PubChem. The earliest dates of first use were determined for tentatively identified anthropogenic chemicals based on internet and literature searches. This information was used in the comparison of chemical profiles across sample types and to identify possible CEC co-tracers of young water, or groundwater recharged after 1953. Identification of anthropogenic chemicals produced and widely used only after 1953 was used to determine young water presence, since groundwater recharged before 1953 and isolated since then should in theory not contain such anthropogenic chemicals. During analyses of results, the TICs and ToxCast chemicals were filtered using various NIST match factor thresholds (i.e., confidence levels for identification). The number listed after TIC or ToxCast indicates the minimum match factor included in that subset of results; for instance, ToxCast 90 results include only ToxCast chemicals with a match factor of 90 or higher. R studio was used for statistical analyses of mean counts of HRMS data such as chemical features, TICs, and ToxCast chemicals. Chemical count data by aquifer were first analyzed for normality using the Shapiro-Wilk test. Because chemical counts were not normally distributed for some aquifers, all chemical data were analyzed using a Kruskal Wallis test for significance among groups and a post hoc Dunn’s test to determine which groups were significantly different. Significance is reported at α < 0.05. 2.4 Quality Control NCDEQ uses USEPA Method 625 with USEPA Method 8270D to achieve more stringent quality control for precision and accuracy. NDCEQ performance metrics are precision

of < 20% relative standard deviations and accuracy of < 30% percent recovery. NCDEQ calculates the average recovery at three times the standard deviation of laboratory control samples over a 6-month to 12-month period to meet expected upper and lower recovery limits of < 150% and > 50%, respectively. For 2020, reported upper and lower recovery limits (averaged across chemicals) were 47% and 84%, respectively, for laboratory control samples. Reported upper and lower recovery limits for matrix spike samples were 49% and 88%, respectively. Limits for surrogate recoveries were 46% and 92%. Relative percent differences between initial calibration and calibration verification were less than < 20%. For detailed recovery limits by chemical see Table S1 in Appendix A. Using field duplicate samples from the same well, field precision for total NT-HRMS chemical features, TICs, and ToxCast chemical counts ranged from 5% to 33% for relative percent deviations. HRMS analysis was duplicated for 32 of the samples, over 20% of all samples; the mean relative percent differences for duplicate analyses were 16% ±12%, 30% ±18%, and 26% ±21% for total chemical features, TIC 50, and ToxCast 50 counts, respectively. Some of the early NCDEQ extracts were archived frozen for 150 days prior to HRMS analysis which exceeds normal extract holding times of 40 days; the majority of extracts were analyzed by HRMS within 40 days of extraction.

Chapter 3: Results

In total, 150 extracts of groundwater were collected across the NC Coastal Plain and provided by NC DEQ for HRMS analyses. These samples represent 11 aquifers and 139 distinct wells with screen depths of 1 meter to 315 meters below surface. The extracts include 14 samples from reference wells in close proximity to flooded wells, 38 samples from wells that were flooded during the study period, and 98 samples from wells that did not experience flooding during the study period but may have flooded in the past. The total number of chemical features, tentatively identified chemicals (TICs) using the NIST 20 mass spectral database (M1), and NIST 20 M1 TICs matched to USEPA’s ToxCast database are provided in Table S2 for each groundwater extract. It should be noted that the total number of chemical features is not a count of the number of chemicals in the sample. Due to fragmentation from electron ionization, multiple unidentified mass spectral features may correspond to a single compound. The NT chemical features which were not identified during SS analysis remain unknown; therefore, the extent of overlap in total chemical features across samples could not be determined. Table 1 shows a summary of mean values for chemical features, TICs, and ToxCast-matched chemicals by aquifer for the 150 extracts in Table S2. 3.1 Chemical Counts by Aquifer Because chemical counts in some aquifers failed normality tests, a Kruskal Wallis test with post-hoc Dunn’s test (p<0.05) evaluated if counts of chemical features, TICs, and ToxCast chemicals differed significantly between aquifers. Mean count values were not different for total chemical features, TIC 50 (50 is the threshold match factor), TIC 90, and ToxCast 50 chemical counts despite the varied range of screen depths across and within aquifers. There was a significant difference (p < 0.05) between two aquifers for mean counts of ToxCast chemicals using a threshold match factor of 90. The Lower Cape Fear aquifer had significantly higher mean ToxCast 90 counts (Dunn’s test, p = 0.018) than the Surficial aquifer. The Lower Cape Fear aquifer is the deepest aquifer in NC and had a mean ToxCast 90 count of 12 (± 4.0); the Surficial aquifer had a lower mean count of 7.3 (± 3.5). NCDEQ extracts were analyzed for 3 and 2 reference wells, 11 and 3 flooded wells, and 22 and 4 non-flooded wells for the Surficial and Lower Cape Fear aquifers, respectively. Additional analyses of the Lower Cape Fear might alter significance testing given that there were more samples from the Surficial aquifer.

Table 1. Mean counts for chemical features, TICs, and ToxCast chemicals (+ one standard deviation) by aquifer. Average Count (± Standard Deviation) Sample Depth Total Chemical ToxCast+ ToxCast+ Aquifer TIC* 50 TIC* 90 Number Range (m) Features 50 90 Surficial 36 1-32 5,146 (± 1,177) 1504 (± 410) 24 (± 8.4) 104 (± 24) 7.3 (± 3.5) Basement Rock 7 6-60 4,289 (± 829) 1,158 (± 175) 22 (± 8.1) 88 (± 12) 8.3 (± 5.4) Yorktown 13 7-62 5,435 (±1,339) 1,631 (± 535) 26 (± 6.9) 114 (± 27) 9.1 (± 3.8) Basement Saprolite 1 155 4,040 1,285 21 84 8 Castle Hayne 26 4-198 5,500 (± 844) 1,529 (± 250) 27 (± 8.9) 109 (± 19) 8.8 (± 3.5) Peedee 11 20-292 5,408 (± 1,270) 1,520 (± 434) 29 (± 12) 102 (± 30) 9.0 (± 4.6) Upper Black Creek 6 28-119 5,177 (± 1,327) 1,289 (± 217) 20 (± 3.6) 99 (± 16) 6.8 (± 2.2) Black Creek 17 22-261 5,005 (± 922) 1,583 (± 386) 27 (± 6.8) 111 (± 21) 10 (± 3.5) Beaufort 7 30-257 5,736 (± 1,185) 1,534 (± 406) 27 (± 5.1) 112 (± 19) 8.9 (± 1.8) Upper Cape Fear 17 12-315 5,138 (± 1,012) 1,563 (± 463) 27 (± 5.2) 112 (± 22) 10 (± 4.1) Lower Cape Fear 9 115-250 4,993 (± 946) 1,585 (± 515) 31 (± 6.8) 114 (± 26) 12 (± 4.0) Overall 150 1-315 5,207 (± 1,088) 1,516 (± 399) 26 (± 8.0) 107 (± 23) 8.8 (± 3.8) *Tentatively identified chemicals based on comparison to NIST 20 mass spectral databse (M1) + USEPA ToxCast Phase III Chemicals: https://comptox.epa.gov/dashboard/chemical_lists/toxcast_phaseiii

Table 2. HRMS detections of compounds regulated by NC groundwater standards rule 15A NCAC 02L .0202. Detection Count Total Flooded Not Flooded Reference Well

Compound Class log Kow NC Standard CAS n=150 n=38 n=98 n=14 Diethyl Phthalate Phthalate 2.42 6 mg/L 84-66-2 81 18 54 9 Acenaphthene PAH 3.92 80 μg/L 83-32-9 51 8 42 1 Dibutyl phthalate Phthalate 4.5 700 μg/L 84-74-2 46 9 28 9 Benzoic acid Benzoic acid 1.87 30 mg/L 65-85-0 16 10 3 3 Bis(2-ethylhexyl) phthalate Phthalate 7.6 3 μg/L 117-81-7 16 7 5 4 Pyrene PAH 4.88 200 μg/L 129-00-0 13 7 6 0 1,2,4-Trimethylbenzene Benzene 3.63 400 μg/L 95-63-6 11 3 6 2 Fluoranthene PAH 5.16 300 μg/L 206-44-0 6 4 2 0 Atrazine Triazine 2.61 3 μg/L 1912-24-9 6 2 4 0 Biphenyl Benzene 4.01 400 μg/L 92-52-4 3 2 1 0 Phenanthrene PAH 4.46 200 μg/L 85-01-8 3 0 3 0 1,2-Dichlorobenzene Chlorobenzene 3.43 20 μg/L 95-50-1 1 0 1 0 Table 3. HRMS detections of regulated compounds (NC groundwater standards rule 15A NCAC 02L .0202) in each aquifer.

Detection Count Bis(2- 1,2,4- 1,2- Diethyl Acena- Dibutyl Benzoic ethyl- Fluor- Phen- Pyrene Trimethyl- Atrazine Biphenyl Dichloro- Sample Depth Phthalate phthene phthalate acid hexyl) anthene anthrene benzene benzene Aquifer Number Range (m) phthalate Surficial 36 1-32 17 10 6 4 6 4 3 2 1 1 0 0 Basement Rock 7 6-60 5 2 2 1 1 0 0 0 0 0 0 0 Yorktown 13 7-62 6 3 6 0 0 0 0 0 0 0 0 0 Basement Saprolite 1 155 1 0 0 0 0 0 0 0 0 0 0 0 Castle Hayne 26 4-198 17 11 7 2 2 4 2 1 0 0 2 1 Peedee 11 20-292 4 4 6 1 0 1 0 1 0 0 0 0 Upper Black Creek 6 28-119 1 2 2 0 0 0 0 1 0 0 1 0 Black Creek 17 22-261 9 6 7 3 2 1 1 0 1 0 0 0 Beaufort 7 30-257 5 1 4 0 0 0 2 0 0 1 0 0 Upper Cape Fear 17 12-315 10 7 5 4 1 3 1 1 2 1 0 0 Lower Cape Fear 9 115-250 6 5 1 1 4 0 2 0 2 0 0 0

The targeted analysis for regulated semi-volatile organics and pesticides completed by NCDEQ using USEPA Method 625 resulted in only one detection; bis(2-ethylhexyl) phthalate was detected in a groundwater sample from the Surficial aquifer at a concentration of 36 μg/L. In contrast, Table 2 shows the tabulated summary of regulated organic chemicals detected by HRMS. The 12 regulated organics detected by HRMS represent mostly plasticizers, surfactants, polycyclic aromatic hydrocarbons (PAHs), and herbicides/pesticides. Table 3 lists the HRMS detections of regulated chemicals by aquifer. 3.2 Chemical Features, TICs, and ToxCast Chemicals by Depth Figure 3 depicts the depth to top of well screen versus total chemical features and total ToxCast chemicals, 90% match factor, for all 150 groundwater extracts. Total chemical feature counts at depths >50 m (n=70) ranged from 3,850 to 7,263 while total chemical features at depths <50 m (n=80) ranged from 2,988 to 8,209. The smallest number of total chemical features (2,988 features) and largest number of chemical features (8,209 features) occurred at shallow screen depths of 26 m and 5 m, respectively. Both flooded and non-flooded wells had equivalent ranges of total chemical feature counts to screen depths of 200 meters. The range of ToxCast chemical counts, 1 to 19, was more consistent across aquifers and screen depth. Wells with screened depths between 200-315 m had a ToxCast chemical range of 3 to 19, which was slightly narrower than the range (1 to 19) for shallower screened wells from the Black Creek, Lower Cape Fear, Upper Cape Fear, Beaufort, and Peedee aquifers. Both flooded and non- flooded wells, to depths of 200 m, had similar ranges of ToxCast chemical counts. The distribution of chemical features, TICs, and ToxCast counts for flooded (n=38) and non-flooded wells (n=98) were assigned to one of three categories for screen depth – shallow (< 33 m), middle (33 to 110 m), or deep (> 110 m) – and visualized using box plots (Figure 4). Shallow screen depths had the widest range of counts, the most outliers, and lowest median values of chemical features, TICs, and ToxCast chemicals. Screen depths greater than 33 m had larger median values but less variability of total chemical feature counts, TICs, and ToxCast chemical counts. The general upward trend of total chemical counts, TICs, and ToxCast chemicals at greater screen depths was not expected, as discussed below.

Total Features ToxCast 90%

0 0

100 100

Depth to Screen (m) 200 200

300 300

3000 4000 5000 6000 7000 8000 5 10 15 Chemical Count

Flooded Not flooded Reference Well Basement Rock Black Creek Peedee Upper Cape Fear

Aquifer Basement Saprolite Castle Hayne Surficial Yorktown

Beaufort Lower Cape Fear Upper Black Creek

Figure 3. Total chemical features, left, and total ToxCast-matched chemicals (90% match factor), right, for 150 groundwater samples plotted against the depth to well screen. Color of dots indicates whether the groundwater sample is from a well that flooded (blue), well that was not flooded during the study (orange), or reference well (black). The aquifer from which the sample was taken is indicated by marker shape.

Total Features TIC 80% TIC 90% ToxCast 80% ToxCast 90%

50 200 8000 50

7000 40 15 40 150

6000 30 30 10

Chemical Count 5000 100

20 20 5 4000

50 10 10 3000

Depth Shallow (<33 m) Middle (33−110 m) Deep (>110 m)

Figure 4. Box plots of chemical counts across samples grouped by well screen depths as shallow (<33 m), middle (33-110 m), and deep (>110 m). Data from 136 groundwater samples; chemical counts from reference wells are not included. The uppermost and lowermost extent of vertical lines indicate the minimum and maximum count values, respectively. The bottom and top of each box represent the first and third quartiles, respectively. The line across the box represents the median, and dots represent outliers. 3.3 Tentatively Identified Chemicals by NIST 20 Mass Spectral Database M1 Across all groundwater sample extracts, including reference well samples, 396 TICs were matched to the NIST 20 database at a match factor, i.e., a confidence level, of 90. The NIST 20 database includes EI MS spectra for 306,869 chemical compounds; it allows for identification of a broad range of chemicals as it includes spectra for human and plant metabolites, pharmaceuticals, pesticides, lipids, environmental contaminants, industrial chemicals, and more. TICs provide a general chemical profile, while ToxCast chemicals are a subset of TICs (specifically, compounds of regulatory interest included in the EPA’s ToxCast Phase III list). There were 68 TICs detected in at least 10% of all samples, and 151 TICs that were detected in 0.67% of all samples (1 sample out of 150). The average TIC count per sample was 26 (± 8.0).

Table 4. The 15 most prevalent TICs (90% match factor) across all samples and number of well extracts in which the TIC was detected. Detection TIC Class log Kow CAS Count 1 Diethyltoluamide, DEET Amide 2.18 134-62-3 143 2 Cyclomethicone 6 Organosiloxane 6.33 540-97-6 140 3 Cyclomethicone 7 Organosiloxane - 107-50-6 134 4 Octadeamethyl-cyclononasiloxane Organosiloxane - 556-71-8 95 5 Hexadecamethyl-cyclooctasiloxane Organosiloxane - 556-68-3 92 6 Triphenyl phosphate Organophosphate 4.59 115-86-6 89 7 4-Fluorobiphenyl Benzene 3.96 324-74-3 82 8 Diethyl phthalate Phthalate 2.42 84-66-2 82 9 Benzothiazole Thiazole 2.01 95-16-9 74 10 2-Methylnaphthalene PAH 3.86 91-57-6 64 11 Eicosamethyl-cyclodecasiloxane Organosiloxane - 18772-36-6 63 12 Ethyl 4-ethoxybenzoate Benzoate 3.33 23676-09-7 59 13 Dibutyl terephthalate Phthalate 4.61 1962-75-0 58 14 Octanoic acid Carboxylic acid 3.03 124-07-2 58 15 Butylated hydroxytoluene Phenol 5.10 128-37-0 57 Table 4 shows the 15 most frequently detected TICs across all sample extracts. Diethyltoluamide (DEET) is the active ingredient in insect repellents, and cyclomethicone 6 (D6) and 7 (D7) are cyclic volatile methylsiloxane oils commonly used in personal care products. DEET, D6, and D7 are all anthropogenic chemicals. These chemicals were identified in more than 134 wells; although, their log Kow and water solubility differ by 3 to 4 orders of magnitude. DEET was first developed by the US Army in 1946 and made available for public use in 1957 (https://www.epa.gov/insect-repellents/deet), and it is commonly detected in surficial and confined aquifers in the US and globally (Barnes et al., 2008; Kuroda et al., 2012; Pinasseau et al., 2019). However, the common detection of DEET in water has been questioned as an over- estimation caused in part by interference from potential analytical mimics (Merel et al., 2014; Merel et al., 2015); this issue is discussed below. D6 was first used in personal care in the 1940s but widespread use in the U.S. began in the 1970s (Dudzina et al., 2014). With over 1 million pounds produced annually, D6 is classified as a high production volume chemical in the US (USEPA, 2021). The other 13 commonly detected TICs include a pesticide, PAHs, plasticizers, and surfactants (Table 4). One surprising aspect of these chemicals is their moderate to high log

Kow values. Table S3 includes the complete list of TICs (with threshold match factor of 90) detected across all samples.

3.4 NIST 20 TICs Matched to USEPA’s ToxCast Chemical Database NIST 20 TICs were matched to USEPA’s ToxCast Phase III Chemicals to identify chemicals of environmental and regulatory interest to the USEPA (USEPA, 2017). A total of 103 unique ToxCast chemicals (with threshold match factor of 90) were identified across all samples including reference wells. Figure 5 summarizes the 16 most frequently detected ToxCast 90 chemicals for the number of flooded and not flooded samples analyzed and denotes their source as anthropogenic, biogenic, or both. Of the 16 commonly detected ToxCast 90 chemicals, seven chemicals were anthropogenic. The proportion of flooded sample detections to total detections varies across these 16 ToxCast chemicals. Notably, hexanoic acid was detected in more flooded well samples than non-flooded wells during the study period. Other ToxCast chemicals detected twice as often in flooded wells than non-flooded wells include nonanoic acid, diethylene glycol

Figure 5. The number of groundwater samples in flooded and non-flooded wells in which the 16 most frequently detected ToxCast 90 chemicals were identified. Superscripts denote the source types of ToxCast chemicals with a for anthropogenic sources, b for biogenic, and g for geogenic.

17 dibenzoate, n-hexadecanoic acid, and 1,2,3-trichlorobenzene. Two of these chemicals, diethylene glycol dibenzoate and 1,2,3-trichlorobenzene, have only anthropogenic sources. Diethylene glycol dibenzoate is a plasticizer and had an annual production volume of over 10 million pounds in the US in 2016 (UESPA, 2021). 1,2,3-Trichlorobenzene is an industrial solvent and has been used for termite control. Hexanoic acid, nonanoic acid, and n-hexadecanoic acid have anthropogenic and biogenic sources; their biogenic sources include apples, grapes, animal fats, and common trees. In Figure 6, a heat map shows detection frequency of 40 ToxCast chemicals by aquifer; reference well data is not included in this figure (see Figure S1 for full heat map with all 103 ToxCast chemicals). DEET, triphenyl phosphate, benzothiazole, butylated hydroxytoluene, hexadecane, acenaphthene, and diethylene glycol dibenzoate were found in at least one groundwater sample from every aquifer; however, the frequency of detection for these seven chemicals varied across aquifers. 2-(Methylmercapto)benzothiazole, diphenylamine, 1,2,4- trimethylbenzene, triethylene glycol di(2-ethylhexoate), gamma-dodecalactone, benzothiazolone, and 2-ethylhexanoic acid had higher detection frequencies in deeper, confined aquifers than surficial or shallow confined aquifers. While several of these ToxCast chemicals can occur naturally, triethylene glycol di(2-ethylhexoate), benzothiazolone, and 2-ethylhexanoic acid derive solely from anthropogenic sources and were each detected in the Black Creek and Upper Cape Fear aquifers. The deepest confined aquifer is the Lower Cape Fear aquifer; seven of the 10 most prevalent ToxCast 90 chemicals were frequently detected (>50% of samples) in groundwater samples from this aquifer. The log Kow values for these seven chemicals have a wide range of 2.01 to 8.20, and four of these frequently detected chemicals – DEET, triphenyl phosphate, diethyl phthalate, and diphenylethanedione – have solely anthropogenic sources.

Basement Upper Black Upper Cape Lower Cape Aquifer Surficial Yorktown Castle Hayne Beaufort Peedee Black Creek Rock Creek Fear Fear Depth Range (m) 1-32 6-60 7-62 4-198 30-257 20-292 28-119 22-261 115-250 12-315 Sample Number 33 7 13 26 7 11 3 14 14 7

Name Class log Kow CAS a Diethyltoluamide, DEET Amide 2.18 134-62-3 88% 86% 92% 100% 100% 91% 100% 93% 100% 100% a Triphenyl phosphate Organophosphate 4.59 115-86-6 27% 86% 62% 62% 71% 73% 100% 64% 79% 71% a Diethyl phthalate Phthalate 2.42 84-66-2 45% 71% 46% 65% 71% 36% 0% 50% 57% 57% a,b Benzothiazole Thiazole 2.01 95-16-9 39% 29% 38% 62% 100% 36% 33% 71% 57% 43% a,b Butylated hydroxytoluene Phenol 5.10 128-37-0 6% 43% 38% 54% 57% 27% 100% 50% 50% 71% a,b,g Hexadecane Alkane 8.20 544-76-3 42% 57% 46% 19% 29% 36% 33% 21% 43% 71% a,g Acenaphthene PAH 3.92 83-32-9 30% 29% 23% 42% 14% 36% 33% 43% 50% 71% a,b Nonanoic acid Carboxylic acid 3.42 112-05-0 42% 29% 31% 23% 14% 36% 0% 21% 29% 29% a Diphenylethanedione Phenol 3.38 134-81-6 0% 43% 46% 42% 71% 27% 33% 43% 43% 57% a,b Dibutyl phthalate Phthalate 4.50 84-74-2 12% 29% 46% 27% 57% 55% 33% 29% 21% 0% a Diethylene glycol dibenzoate Benzoate 3.00 120-55-8 21% 29% 31% 31% 29% 36% 33% 7% 43% 29% n-Hexadecanoic acida,b Carboxylic acid 7.17 57-10-3 30% 14% 15% 35% 14% 9% 0% 29% 29% 14% a,b Benzophenone Ketone 3.18 119-61-9 30% 0% 38% 27% 29% 27% 0% 36% 21% 29% a,b Hexanoic acid Carboxylic acid 1.92 142-62-1 24% 14% 15% 8% 0% 36% 0% 14% 21% 29% a 3,4-Dimethylbenzaldehyde Benzene 2.51 5973-71-7 24% 43% 8% 15% 14% 27% 33% 0% 29% 29% a 1,2,3-Trichlorobenzene Chlorobenzene 4.05 87-61-6 15% 29% 23% 15% 0% 27% 0% 14% 29% 43% a Bis(2-ethylhexyl) terephthalate Phthalic acid 8.14 6422-86-2 18% 14% 8% 8% 14% 27% 0% 21% 21% 0% a,b n-Decanoic acid Carboxylic acid 4.09 334-48-5 15% 0% 0% 8% 14% 27% 0% 7% 14% 14% a,b Benzoic acid Benzoic acid 1.87 65-85-0 12% 14% 0% 8% 0% 9% 0% 14% 21% 0% a Bis(2-ethylhexyl) phthalate Phthalate 7.60 117-81-7 15% 14% 0% 8% 0% 0% 0% 7% 7% 29% a,b Tetradecanoic acid Carboxylic acid 6.11 544-63-8 6% 14% 8% 12% 0% 9% 0% 7% 14% 29% a,b Longifolene Sesquiterpene 5.41 475-20-7 12% 14% 23% 15% 14% 9% 33% 0% 0% 0% a,g Pyrene PAH 4.88 129-00-0 12% 0% 0% 15% 0% 9% 0% 7% 21% 0% a Diisobutyl phthalate Phthalic acid 4.11 84-69-5 15% 0% 0% 4% 14% 0% 0% 14% 0% 14% a,b Vanillin Benzene 1.21 121-33-5 12% 14% 8% 0% 0% 9% 0% 14% 7% 0% a,b 2,4-Dibromophenol Phenol 3.22 615-58-7 3% 0% 23% 12% 0% 0% 0% 7% 0% 29% a,b 2-(Methylmercapto) benzothiazole Thiazole 3.08 615-22-5 6% 0% 0% 0% 14% 9% 67% 21% 14% 14% a,b Diphenylamine Amine 3.50 122-39-4 3% 0% 15% 4% 29% 9% 0% 14% 7% 0% a,g 1,2,4-Trimethylbenzene Benzene 3.63 95-63-6 6% 0% 0% 8% 29% 0% 0% 7% 7% 14% a Triethylene glycol di(2-ethylhexoate) Ester 5.15 94-28-0 0% 0% 0% 8% 0% 9% 0% 14% 29% 29% a,b Triethyl citrate Carboxylic acid 0.97 77-93-0 0% 14% 8% 4% 29% 0% 0% 14% 7% 29% Octadecanoic acida,b Carboxylic acid 8.23 57-11-4 9% 0% 8% 12% 0% 0% 0% 0% 0% 14% a,b gamma-Dodecalactone Lactone 3.53 2305-05-7 0% 0% 8% 0% 0% 0% 0% 14% 14% 29% a Benzothiazolone Thiazole 1.78 934-34-9 0% 0% 0% 8% 0% 0% 0% 29% 7% 0% a Triphenylphosphine oxide Organophosphorus 2.83 791-28-6 12% 0% 8% 0% 0% 9% 0% 7% 0% 0% a Tetraethylene glycol di(2-ethylhexanoate) Ester 4.82 18268-70-7 0% 29% 0% 4% 14% 0% 0% 7% 7% 14% a 3-Methylphenanthrene PAH 5.15 832-71-3 6% 0% 8% 8% 0% 9% 0% 7% 0% 0% a 2-Ethylhexanoic acid Carboxylic acid 2.64 149-57-5 3% 0% 0% 4% 0% 0% 0% 7% 14% 14% a Cyclododecanol Alcohol 4.58 1724-39-6 3% 0% 8% 4% 0% 0% 0% 7% 0% 14% a,b,g Fluoranthene PAH 5.16 206-44-0 6% 0% 0% 4% 0% 9% 33% 0% 7% 0% Figure 6. Heat map of 40 ToxCast 90 chemicals and their detection frequency by aquifer. Percentage values indicate the percentage of samples from a given aquifer in which the ToxCast chemical was detected. Reference well samples are not included in this heat map. Darker cell colors correspond to greater percentages. Superscripts indicate the possible sources of the chemical: a for anthropogenic, b for biogenic, and g for geogenic sources. Compound names in orange signify the chemical has only anthropogenic sources.

3.5 TICs and ToxCast Chemicals in Flooded Wells Two nested well sites, Snow Hill and Falkland, experienced flooding during this study and were sampled before as well as after flooding events to compare chemical features, TICs, and ToxCast chemicals to pre-flood samples. Snow Hill has wells with screens in the Surficial, Upper Cape Fear, and Lower Cape Fear aquifers; Falkland has wells with screens in the Surficial, Yorktown, Black Creek, Upper Cape Fear, and Lower Cape Fear aquifers. Both nested well locations are in areas of low populations, 1,800 residents in Snow Hill and 80 residents in Falkland; land use is predominantly forested and agricultural. The Snow Hill well site is located in the town center along the south bank of the Contentnea Creek. Snow Hill was sampled September 5th and 11th, 2018 just prior to Hurricane Florence on September 18th, 2018 which caused extreme flooding across much of North Carolina. Snow Hill was sampled again August 15th, 2019 and again in mid-June 2020 after another flood event, and finally, August 12th, 2020. The Falkland well site is located along the bank of the Tar River near a boat ramp. The surficial well was first sampled August 16th, 2018 prior to Hurricane Florence. This site experienced flooding in mid-June of 2020, and samples were collected from all five Falkland wells August 27th, 2020. The mean total feature count for the Snow Hill wells increased from 6,058 in 2018 to 6,263 in 2019 (after flooding during Hurricane Florence). However, the mean total feature count for the four Snow Hill wells had a sharp decrease in 2020, dropping to 4,398. At Falkland, the total feature count for Well 5 was similar pre- and post-flood with counts of 4,458 and 4,324, respectively. The mean TIC 90 counts at Snow Hill were higher in both 2019 and 2020 (34 and 27, respectively) than in 2018 (26) before the observed flood events. While each Snow Hill well experienced an increase in TIC 90 count in 2019 relative to 2018, the only well that experienced an increase in TIC 90 count in 2020 (relative to 2019) was Well 3 in the Lower Cape Fear aquifer. The TIC 90 count for Falkland Well 5 in the Surficial aquifer more than doubled post- flood going from 14 TICs in 2018 to 33 TICs in 2020.

Table 5. ToxCast 90 results for groundwater samples from the Snow Hill nested well site. An ‘x’ denotes the detection of a ToxCast chemical. Sample dates shaded in blue indicate samples were taken after a flood event. Superscripts after compound names indicate the possible sources of the chemical: a for anthropogenic sources, b for biogenic sources, g for geogenic sources. Compound names in orange signify the chemical has only anthropogenic sources.

Well 6 5 4 3 Aquifer Surficial Surficial Upper Cape Fear Lower Cape Fear Depth (m) 3.0 20.1 68.3 121.9 Sep. Aug. Aug. Sep. Aug. Aug. Sep. Aug. Aug. Sep. Aug. Aug. Sample Date 5, 15, 12, 5, 15, 12, 11, 15, 12, 11, 15, 12, 2018 2019 2020 2018 2019 2020 2018 2019 2020 2018 2019 2020

Compound Class log Kow CAS Diethyltoluamide, DEETa Amide 2.18 134-62-3 x x x x x x x x x x x Hexadecanea,b,g Alkane 8.20 544-76-3 x x x x x x x x x x x Diethylene glycol dibenzoatea Benzoate 3.00 120-55-8 x x x x x x x x Nonanoic acida,b Carboxylic acid 3.42 112-05-0 x x x x x x x Benzothiazolea,b Thiazole 2.01 95-16-9 x x x x x 3,4-Dimethylbenzaldehydea Benzene 2.51 5973-71-7 x x x x n-Hexadecanoic acida,b Carboxylic acid 7.17 57-10-3 x x x x Hexanoic acida,b Carboxylic acid 1.92 142-62-1 x x x x Bis(2-ethylhexyl) phthalatea Phthalate 7.60 117-81-7 x x x x 2-Ethyltoluenea Benzene 3.67 611-14-3 x x x x Acenaphthenea,g PAH 3.92 83-32-9 x x x x Butylated hydroxytoluenea,b Phenol 5.10 128-37-0 x x x x Bis(2-ethylhexyl) terephthalatea Phthalic acid 8.14 6422-86-2 x x x Pyrenea,g PAH 4.88 129-00-0 x x x Diethyl phthalatea Phthalate 2.42 84-66-2 x x x Atrazinea Triazine 2.61 1912-24-9 x x x Diphenylethanedionea Phenol 3.38 134-81-6 x x x Triphenyl phosphatea Organophosphate 4.59 115-86-6 x x x Benzoic acida,b Benzoic acid 1.87 65-85-0 x x Diisobutyl phthalatea Phthalic acid 4.11 84-69-5 x x 1,2,3-Trichlorobenzenea Chlorobenzene 4.05 87-61-6 x x n-Decanoic acida,b Carboxylic acid 4.09 334-48-5 x x Tetradecanoic acida,b Carboxylic acid 6.11 544-63-8 x x gamma-Dodecalactonea,b Lactone 3.53 2305-05-7 x x Triethylene glycol di(2-ethylhexoate)a Ester 5.15 94-28-0 x x 1,2,4-Trimethylbenzenea,g Benzene 3.63 95-63-6 x x 2,4-Dibromophenola,b Phenol 3.22 615-58-7 x x 1,2-Dimethylnaphthalenea,b PAH 4.31 573-98-8 x 2,6-Dimethylnaphthalenea,g PAH 4.31 581-42-0 x Tris(2-chloropropyl) phosphatea Organophosphate 2.89 6145-73-9 x Fluoranthenea,b PAH 5.16 206-44-0 x Triphenylphosphine oxidea Organophosphorus 2.83 791-28-6 x Biphenyla,g Benzene 4.01 92-52-4 x 2-Phenylphenola,b Phenol 3.09 90-43-7 x 2-Ethylhexanoic acida Carboxylic acid 2.64 149-57-5 x Benzophenonea,b Ketone 3.18 119-61-9 x Mesitylenea,g Benzene 3.42 108-67-8 x Sulfentrazonea Sulfur compound 0.99 122836-35-5 x Tetraethylene glycol di(2-ethylhexanoate)a Ester 4.82 18268-70-7 x 3,5,6-Trichloro-2-pyridonea Pyridone 1.22 6515-38-4 x Triethyl citratea,b Carboxylic acid 0.97 77-93-0 x Methyl palmitatea,b Ester 7.38 112-39-0 x Ronnela Organophosphorus 4.88 299-84-3 x 1,2,3-Trimethylbenzenea,b,g Benzene 3.63 526-73-8 x Octadecanoic acida,b Carboxylic acid 8.23 57-11-4 x Number of Compounds per Sample 5 9 9 4 12 6 7 12 19 10 14 17

Table 6. ToxCast 90 results for groundwater samples from the Falkland nested well site. Sample results are grouped by sample date. An ‘x’ denotes the detection of a ToxCast chemical. The sample date shaded in blue indicates that samples were collected after a flood event. Superscripts indicate the possible sources of the chemical: a for anthropogenic sources, b for biogenic sources, g for geogenic sources. Compound names in orange signify the chemical has only anthropogenic sources.

Aug. Sample Date 16, Aug. 27, 2020 2018 Well 5 5 4 3 2 1 Upper Lower Sur- Sur- York- Black Aquifer Cape Cape ficial ficial town Creek Fear Fear Depth to Screen 1.5 1.5 11.3 22.2 55.8 133.8 (m)

Compound Class log Kow CAS Diethyltoluamide, DEETa Amide 2.18 134-62-3 x x x x x x Diethyl phthalatea Phthalate 2.42 84-66-2 x x x x x Nonanoic acida,b Carboxylic acid 3.42 112-05-0 x x x x x Hexanoic acida,b Carboxylic acid 1.92 142-62-1 x x x x x n-Hexadecanoic acida,b Carboxylic acid 7.17 57-10-3 x x x x Cyclododecanola Alcohol 4.58 1724-39-6 x x x x 2-Ethylhexanoic acida Carboxylic acid 2.64 149-57-5 x x x x Triphenyl phosphatea Organophosphate 4.59 115-86-6 x x x x Benzoic acida,b Benzoic acid 1.87 65-85-0 x x x Bis(2-ethylhexyl) phthalatea Phthalate 7.60 117-81-7 x x x Benzothiazolea,b Thiazole 2.01 95-16-9 x x Diisobutyl phthalatea Phthalic acid 4.11 84-69-5 x x Hexadecanea,b,g Alkane 8.20 544-76-3 x x Diethylene glycol dibenzoatea Benzoate 3.00 120-55-8 x x Butylated hydroxytoluenea,b Phenol 5.10 128-37-0 x x Acenaphthenea,g PAH 3.92 83-32-9 x x 1,2,3-Trichlorobenzenea Chlorobenzene 4.05 87-61-6 x x Longifolenea,b Sesquiterpene 5.41 475-20-7 x Benzophenonea,b Ketone 3.18 119-61-9 x Dibutyl phthalatea,b Phthalate 4.50 84-74-2 x gamma-Dodecalactonea,b Lactone 3.53 2305-05-7 x Atrazinea Triazine 2.61 1912-24-9 x 1,2,4-Trichlorobenzenea Chlorobenzene 4.02 120-82-1 x Metolachlora Benzene 3.13 51218-45-2 x Tetradecanoic acida,b Carboxylic acid 6.11 544-63-8 x Triethyl citratea,b Carboxylic acid 0.97 77-93-0 x Number of Compounds per Sample 4 12 9 13 15 13

The ToxCast 90 results for the four Snow Hill wells are summarized in Table 5. A total of 45 unique ToxCast chemicals were identified across the three groundwater samples from each of the four wells. Wells 3 and 4, which are screened in the Lower Cape Fear and Upper Cape Fear aquifers, respectively, had more ToxCast chemicals detected on average than samples from wells 5 and 6, which are screened in the Surficial aquifer. The total number of unique ToxCast chemicals tentatively identified across all four wells increases after each flood event from 13 unique ToxCast chemicals in August 2018 to 23 in 2019 then to 29 in 2020. The number of ToxCast chemicals shared across wells increases after the first flood event. In 2018, a total of 7 ToxCast chemicals are detected in two or more Snow Hill wells. In both 2019 and 2020, 11 ToxCast chemicals were detected in two or more Snow Hill wells. Post-flood samples from each well had several chemicals that were previously undetected. These newly detected post-flood chemicals were found more frequently in confined aquifer wells. Wells 3 and 4 each had 22 newly identified ToxCast chemicals between the 2019 and 2020 samples, while Wells 5 and 6 had 14 and 11 newly identified chemicals from 2019 and 2020 samples, respectively. Nine of the 22 newly detected ToxCast chemicals in the Upper Caper Fear aquifer derive from only anthropogenic sources, and 12 of the 22 in the Lower Caper Fear aquifer derive from only anthropogenic sources, and 12 of the 22 in the Lower Caper Fear aquifer derive from only anthropogenic sources. The log Kow values of new ToxCast chemicals range from 0.97 to 8.23. Several of the new ToxCast chemicals have high log Kow values, such as methyl palmitate (7.38) and octadecanoic acid (8.23). Across the six samples taken from Falkland wells, there were 26 unique ToxCast 90 chemicals identified (Table 6). Only the well in the Surficial aquifer at Falkland was sampled and analyzed before the flood of June 2020. Comparison of GC-QTOF-MS results from Falkland Well 5 before and after the flood showed similar patterns as observed at Snow Hill, specifically, the presence of multiple newly identified ToxCast chemicals and an increase in the number of ToxCast chemicals after the flood event. For the Falkland Well 5 sample taken in 2018, only four ToxCast 90 chemicals were identified, but the sample taken in 2020 had 12 ToxCast 90 chemicals present. Of the 12 ToxCast chemicals in the post-flood sample, three were also identified in the pre-flood sample and nine were newly identified. Similar to Snow Hill, the newly identified ToxCast chemicals included some with only anthropogenic sources (four of the nine). After the flood event, there were 16 ToxCast chemicals found in two or more wells, which

23 is more than the 11 ToxCast chemicals found in two or more wells in 2019 and 2020 samples at Snow Hill. Table 7 summarizes the chemical counts for reference well samples as well as Snow Hill and Falkland samples from the same aquifers. At Snow Hill and Falkland, the TIC and ToxCast counts were higher than previous samples for 67% and 78% of post-flood samples, respectively. In contrast, only 20% of Group II samples from reference wells experienced an increase in TIC and ToxCast counts. The TIC 90 and ToxCast 90 chemical counts for samples from NPHS Wells 3 and 7 was greater for samples from June 2020 than samples from September 2020. At Saulston, only one of the three wells that were sampled twice had greater TIC 90 and ToxCast 90 chemical counts in 2020 than in 2019. To further explore the relationship between flooded wells and reference wells, a comparison was made between the ToxCast 90 chemicals identified in Snow Hill and Saulston samples (Table 8) as well as Falkland and NPHS samples (Table 9). Snow Hill and Saulston both have wells in the Upper Cape Fear aquifer that were sampled more than once. Both sites had an increase in the ToxCast 90 counts over time, but Snow Hill had more newly identified ToxCast chemicals when resampled. Saulston 3 had seven newly identified ToxCast 90 chemicals when resampled in 2020 (no flooding since last sample); Snow Hill 4 had nine newly identified ToxCast 90 chemicals in 2019 (one year after Hurricane Florence) and 13 in 2020 (two months after the June 2020 flood). Two of the newly identified ToxCast chemicals from the Snow Hill 2020 sample – diethyl phthalate and benzophenone – were also newly identified chemicals in the second Saulston 3 sample. Snow Hill 4 also had six chemicals detected in both the 2019 sample and the 2020 sample. Of these six ToxCast 90 chemicals found in both post-flood samples, three chemicals – nonanoic acid, triphenyl phosphate, and butylated hydroxytoluene – were not detected in either Saulston sample. Nonanoic acid and butylated hydroxytoluene both have biogenic sources, but triphenyl phosphate is a plasticizer that originates solely from anthropogenic sources. While only five of the 22 newly identified ToxCast chemicals from Snow Hill post-flood samples were detected in one or more Saulston 3 samples, half of the Saulston 3 newly identified ToxCast chemicals were detected in at least one of the Snow Hill 4 samples.

Table 7. Chemical counts for Reference Wells, Falkland, and Snow Hill. Rows in gray are for reference wells. Three different chemical count types are included as indicated by bolded column headers. Sample groups represent the different sample dates for each site in chronological order (i.e., Sample Group I is the earliest sample date from that nested well location). For Falkland and Snow Hill, Sample Group I is before flooding and Sample Groups II and III are after flood events. Bolded count numbers indicate counts that are higher than the previous sample’s count from that same well.

Total Features TIC 90 ToxCast 90 Sample Group Sample Group Sample Group Location Well Aquifer Depth (m) I II III I II III I II III 7 Surficial 3.0 6,368 4,357 26 20 9 6 Reference Well 2 Upper Black Creek 29.9 4,579 20 8 North Pitt 6 Black Creek 59.4 4,468 19 5 High School 5 Upper Cape Fear 105.8 4,464 17 8 3 Lower Cape Fear 167.6 4,481 4,538 38 35 15 13 Falkland 5 Surficial 1.5 4,458 4,324 14 33 4 12 4 Surficial 3.4 4,534 18 6 Reference Well 1 Upper Black Creek 12.2 7,334 4,171 24 17 6 4 Saulston 2 Black Creek 26.2 5,459 4,171 31 21 12 10 3 Upper Cape Fear 50.3 7,167 6,139 27 34 8 11 6 Surficial 3.0 6,418 6,368 4,737 22 26 25 5 9 9 5 Surficial 20.1 5,949 5,746 3,972 24 42 17 4 12 6 Snow Hill 4 Upper Cape Fear 68.3 5,853 5,812 4,327 29 36 32 7 12 19 3 Lower Cape Fear 121.9 6,013 7,124 4,556 30 32 33 10 14 17

Table 8. ToxCast 90 chemicals present in the Snow Hill Well 4 samples and Reference Well Saulston 3 samples. An ‘x’ denotes the detection of a ToxCast chemical. Sample dates shaded in blue indicate samples were taken after a flood event. Superscripts indicate the possible sources of the chemical: a for anthropogenic sources, b for biogenic sources, g for geogenic sources. Compound names in orange signify the chemical has only anthropogenic sources.

Reference Well Well Snow Hill 4 Saulston 3 Aquifer Upper Cape Fear Upper Cape Fear Depth (m) 68.3 50.3 Sep. Aug. Aug. Feb. Sep. Sample Date 11, 15, 12, 2019 2020 2018 2019 2020

Compound Class log Kow CAS Diethyltoluamide, DEETa Amide 2.18 134-62-3 x x x x x Hexadecanea,b,g Alkane 8.20 544-76-3 x x x Diethylene glycol dibenzoatea Benzoate 3.00 120-55-8 x x x Diphenylethanedionea Phenol 3.38 134-81-6 x x x Benzothiazolea,b Thiazole 2.01 95-16-9 x x x n-Decanoic acida,b Carboxylic acid 4.09 334-48-5 x x Nonanoic acida,b Carboxylic acid 3.42 112-05-0 x x Triphenyl phosphatea Organophosphate 4.59 115-86-6 x x Butylated hydroxytoluenea,b Phenol 5.10 128-37-0 x x Bis(2-ethylhexyl) terephthalatea Phthalic acid 8.14 6422-86-2 x x n-Hexadecanoic acida,b Carboxylic acid 7.17 57-10-3 x x Diethyl phthalatea Phthalate 2.42 84-66-2 x x Benzophenonea,b Ketone 3.18 119-61-9 x x Dibutyl phthalatea,b Phthalate 4.50 84-74-2 x x Benzeneacetic acida,b Benzene 1.41 103-82-2 x x Acenaphthenea,g PAH 3.92 83-32-9 x 1,2,3-Trichlorobenzenea Chlorobenzene 4.05 87-61-6 x Atrazinea Triazine 2.61 1912-24-9 x 3,4-Dimethylbenzaldehydea Benzene 2.51 5973-71-7 x Pyrenea,g PAH 4.88 129-00-0 x o-Phenylphenola,b Phenol 3.09 90-43-7 x Hexanoic acida,b Carboxylic acid 1.92 142-62-1 x Bis(2-ethylhexyl) phthalatea Phthalate 7.60 117-81-7 x 2-Ethyltoluenea Benzene 3.67 611-14-3 x Benzoic acida,b Benzoic acid 1.87 65-85-0 x Tetradecanoic acida,b Carboxylic acid 6.11 544-63-8 x 1,2,4-Trimethylbenzenea,g Benzene 3.63 95-63-6 x Triethylene glycol di(2-ethylhexoate)a Ester 5.15 94-28-0 x gamma-Dodecalactonea,b Lactone 3.53 2305-05-7 x 2-Ethylhexanoic acida Carboxylic acid 2.64 149-57-5 x Mesitylenea,g Benzene 3.42 108-67-8 x Methyl formatea,b Ester 0.03 107-31-3 x Sulfentrazonea Sulfur compound 0.99 122836-35-5 x Vanillina,b Benzene 1.21 121-33-5 x Tris(2-ethylhexyl) trimellitatea Benzoate 5.94 3319-31-1 x Number of Compounds per Sample 7 12 19 8 11

Table 9. ToxCast 90 chemicals present in the Falkland Well 5 samples and Reference Well NPHS 7 samples. An ‘x’ denotes the detection of a ToxCast chemical. Sample dates shaded in blue indicate samples were taken after a flood event. Superscripts indicate the possible sources of the chemical: a for anthropogenic sources, b for biogenic sources, g for geogenic sources. Compound names in orange signify the chemical has only anthropogenic sources. Reference Well Well Falkland 5 North Pitt HS 7 Aquifer Surficial Surficial Depth (m) 1.5 3.0 Sample Aug. 16, Aug. 27, June 24, Sep. 8, Date 2018 2020 2020 2020

Compound Class log Kow CAS Diethyltoluamide, DEETa Amide 2.18 134-62-3 x x x x Diethyl phthalatea Phthalate 2.42 84-66-2 x x x Benzothiazolea,b Thiazole 2.01 95-16-9 x x Nonanoic acida,b Carboxylic acid 3.42 112-05-0 x x n-Hexadecanoic acida,b Carboxylic acid 7.17 57-10-3 x x Bis(2-ethylhexyl) phthalatea Phthalate 7.60 117-81-7 x x Diisobutyl phthalatea Phthalic acid 4.11 84-69-5 x x Hexanoic acida,b Carboxylic acid 1.92 142-62-1 x x Longifolenea,b Sesquiterpene 5.41 475-20-7 x Benzophenonea,b Ketone 3.18 119-61-9 x Benzoic acida,b Benzoic acid 1.87 65-85-0 x 2-Ethylhexanoic acida Carboxylic acid 2.64 149-57-5 x Cyclododecanola Alcohol 4.58 1724-39-6 x Tetradecanoic acida,b Carboxylic acid 6.11 544-63-8 x Vanillina,b Benzene 1.21 121-33-5 x 2,4-Dibromophenola,b Phenol 3.22 615-58-7 x Octadecanoic acida,b Carboxylic acid 8.23 57-11-4 x 2-Phenylphenola,b Phenol 3.09 90-43-7 x Triphenyl phosphatea Organophosphate 4.59 115-86-6 x Hexadecanea,b,g Alkane 8.20 544-76-3 x Dibutyl phthalatea,b Phthalate 4.50 84-74-2 x 1,2,4-Trimethylbenzenea,g Benzene 3.63 95-63-6 x Number of Compounds per Sample 4 12 11 6

Several notable differences exist between the ToxCast 90 results for Falkland Well 5 and NPHS Well 7 (Table 9). Both wells are in the Surficial aquifer, and the well sites are in the same county and located in small, low-development towns. While three ToxCast 90 chemicals were detected in both Falkland 5 samples; DEET was the only chemical identified in both samples from NPHS 7. More ToxCast 90 chemicals were shared between the post-flood Falkland sample and the June 2020 NPHS sample (six shared chemicals) than were shared between either pairs of samples from the same well. Only 2 ToxCast chemicals, DEET and hexanoic acid, were shared between the post-flood Falkland sample and September 2020 NPHS sample; hexanoic acid was a newly identified chemical for both wells. Another distinction between the two wells was the increase in the number of ToxCast 90 chemicals identified in the second sample at Falkland 5. While the Falkland 5 second sample (taken two months after the June 2020 flood) had eight more ToxCast chemicals than the pre-flood sample, NPHS 7 had five fewer ToxCast 90 chemicals in its second sample. However, the newly identified ToxCast 90 chemicals in both post-flood samples cover a wide range of log Kow values, 1.87-7.60 at Falkland 5 and 1.92-8.60 at NPHS 7.

Chapter 4: Discussion

4.1 HRMS Analyses of Groundwater HRMS analyses resulted in 253 tentative identifications of regulated organic chemicals versus one regulated organic chemical detection by standard targeted analyses of the same groundwater extracts. For wells that were sampled before and after flooding events, TIC and ToxCast chemical counts increased by 67% and 78%, respectively, in post-flood samples. Subsequent sampling of reference wells increased TIC and ToxCast chemical counts by 20%. Storm events and flooding liberate chemical reservoirs for transport to surface waters (Noyes et al., 2009; Fisher et al., 2016). Surficial aquifers should be vulnerable to chemical infiltration because these unconfined aquifers interact directly with surface phenomena and receive direct infiltration and groundwater recharge (Bonneau et al., 2017; Jasechko and Taylor, 2015; Sawyer et al., 2014). However, in this study, mean counts of total chemical features and TICs did not differ significantly across aquifers, and ToxCast chemicals were significantly lower for the surficial aquifer than the confined Lower Cape Fear aquifer (Table 1). These results support prior tracer studies acknowledging that confined aquifers are vulnerable to contamination (Jasecko et al., 2017; Kuroda et al., 2014) and suggest that storm events and flooding can increase anthropogenic chemicals in flooded wells for surficial or confined systems. To our knowledge, this study is the first non-targeted high-resolution GC-MS assessment of chemical profiles comparing flooded and non-flooded groundwater wells. Mohler et al. (2013) did use GC-HRMS and suspect screening to analyze groundwater around a fuel site, but this study limited TICs analysis to petroleum chemicals and by products, despite the tentative identification of phthalates and pesticides. In this study, analysis of the full TIC profiles identified various chemical use classes including pesticides, phthalates, personal care products, PAHs, and siloxanes; detection frequency was determined for all TIC 90 and ToxCast 90 chemicals. Recent NT and SS HRMS studies of groundwater have utilized liquid chromatography (LC)-HRMS instrumentation with limited mass spectral databases for tentative identification of chemical features (Gray et al., 2020; Hedgespeth et al., 2021; Kiefer et al., 2019; Pinasseau et al., 2019; Sjerps et al., 2016; Soulier et al., 2016). For suspect screening, Kiefer et al. (2019) used a limited database of 396 pesticides and 1,120 transformation products for tentative identification. Soulier et al. (2016) used a database of only 280 pesticides, pharmaceuticals, personal care

29 products, and industrial compounds, and Pinasseau et al. (2019) used a TIC database of 2,500 pesticides and pharmaceuticals for groundwater suspect-screening analyses. In this study, the number of compounds tentatively identified in at least one sample during SS (total TIC 90 count, n=396) was greater than previous SS-HRMS screening of groundwater as reported by Kiefer et al. (2019; n=27), Soulier et al. (2016; n= 41), and Pinasseau et al. (2019; n=30). This difference could be attributed to the use of GC-HRMS opposed to LC- HRMS used in the listed studies or the use of a more comprehensive chemical database for SS analysis. Use of the NIST 20 mass spectral database and the ToxCast Phase III list provides a more complete analysis of chemicals present in groundwater extracts. And, in contrast to past LC-HRMS studies, in this study pharmaceuticals comprised a relatively small percentage of TICs for reference, flooded, and non-flooded wells. Fewer tentative identifications of pharmaceuticals may reflect differences of LC versus GC separations modules and mass spectrometer ionization efficiencies. The published literature on tentative identification of chemicals of emerging concern continues to grow for groundwater assessment. This study applied GC-HRMS to fingerprint groundwater to evaluate commonalities and dissimilarities between aquifers and within aquifers before and after flooding. Recent groundwater fingerprinting efforts in Europe have characterized anthropogenic chemical profiles of different water types, i.e., surface-, ground-, and drinking water (Sjerps et al., 2016) and provided molecular fingerprints of aquifers (Soulier et al., 2016). Like Sjerps et al. (2016), this study focused on a subset of chemicals, ToxCast chemicals, to compare aquifers and assess aquifer response to flooding. Unlike other recent studies that used HRMS to assess stormwater impact on groundwater at the catchment (Pinasseau et al. 2019) or watershed scale (Hedgespeth et al., 2021), this study applied HRMS across a multi-basin region of the NCCP. 4.2 Chemicals of Emerging Concern as Co-tracers To our knowledge, this study is the first effort to use NT and SS HRMS to evaluate potential storm water intrusion in deep confined aquifers. CECs have been used as co-tracers in targeted studies to evaluate contamination sources, plumes, recharge, and young water presence (Erostate et al., 2019; Kuroda et al., 2014; McCance et al., 2018). Specifically, frequently detected and persistent PPCPs such as DEET, crotamiton, and carbamazepine have been used to detect and trace impacts from wastewater treatment plants within aquifers as well as determine

30 recharge pathways (Kuroda et al., 2012; Lapworth et al., 2018; McCance et al., 2018). Pesticides, herbicides, and their transformation products – including atrazine, simazine, phenoxyacetic acid, and chlorpyrifos – can be used as co-tracers to evaluate agricultural impacts on groundwater (Lapworth et al., 2018; McCance et al., 2018). Perfluoroalkyl acids have been used as co-tracers for determining the relative age of groundwater and recharge pathways (Kuroda et al., 2014). Lastly, aggregate CEC detections and concentrations have been used as an indicator of groundwater age (Moreau et al., 2019). This study used tentatively identified CECs as indicators of the presence of relatively young water in wells. Specifically, the detection of the common pesticide atrazine in wells in three confined aquifers (Black Creek, Upper Cape Fear, and Lower Cape Fear) indicates the presence of agricultural impacts in deep wells. The presence of DEET in 91% to 100% of samples from each confined aquifer in this study indicates the nearly ubiquitous presence of new water in all aquifers as DEET is solely anthropogenic in source and has a short half-life (days or weeks). This conclusion must be qualified by noting that widespread detection of DEET in water via GC-MS and LC-MS has been investigated and possible analytical interferences have been observed, such as analytical mimics and solvent interferences, which can cause over-estimation of DEET occurrence (Merel et al., 2015; Merel and Snyder, 2016). However, Morel et al. (2015) investigated five of the closest DEET mimics using GC-MS and the NIST database for identification, and none of the mimics were falsely identified as DEET. This finding suggests the tentative identification of DEET in this study is accurate, but targeted analysis with analytical standards is needed to confirm these results. The detection of three phthalate plasticizers with solely anthropogenic sources (diethyl phthalate, bis(2-ethylhexyl) phthalate, and diisobutyl phthalate) in the Black Creek and Upper Cape Fear aquifers further indicates the presence of new water in these deep, confined aquifers. This study observed significantly greater mean ToxCast 90 chemical counts in the Lower Cape Fear aquifer relative to the Surficial aquifer (Table 1). Similarly, Lapworth et al. (2018) reported greater CEC concentrations in groundwater from deep, confined aquifers compared to groundwater from more intermediate or shallow depths. Young water presence and anthropogenic contamination in the deep, confined aquifer possibly reflects vertical migration of water within aquifer systems (especially through vertical fractures or other preferential flow pathways), poor well construction that allows surface water intrusion to

31 confined aquifers, or high pumping rates in deeper aquifers that draw young water downward from above (Lapworth et al. 2018; Jasechko et al. 2017). 4.3 Chemical Fingerprinting TIC and ToxCast chemicals for flooded well samples and reference wells were used to identify distinguishing features of flooded and non-flooded wells. Seven TICs had a detection frequency that was at least 15% higher in flooded samples than in reference well samples, and two of these TICs, octadecyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionate and hentriacontane, had exceptionally high log Kow values (13.93 and 15.57, respectively). The presence of these low solubility chemicals in post-flood groundwater samples could result from rapid infiltration of water containing organic matter through existing macropores or through fractures that are unclogged as a result of increased water pressure from extreme rainfall events (Jasechko and Taylor, 2015; Vittecoq et al., 2020). Four regulated organic compounds (pyrene, fluoranthene, atrazine, and biphenyl) were detected in two or more flooded samples but not reference well samples, which suggests these chemicals may indicate flood water intrusion. Pyrene, fluoranthene, and biphenyl are all fossil fuel components, which could indicate fuel runoff or leakage from underground storage tanks are more readily transmitted into aquifers during flood events. Conversely, three regulated phthalates, dibutyl phthalate, diethyl phthalate, and bis(2-ethylhexyl) phthalate, had higher detection frequencies in reference well samples than flooded samples. The utility of regulated chemicals to improve our understanding of aquifer dynamics merits further study relative to nearby sources and mechanisms of infiltration. TICs and ToxCast results were analyzed by aquifer to query potential development of aquifer fingerprints (Figure 6). Of the 10 most frequently detected ToxCast chemicals, 4 were detected in all but one aquifer (not including reference well detections); these absences are described as follows. Neither the regulated plasticizer diethyl phthalate nor the common food additive nonanoic acid were detected in the Upper Black Creek aquifer; however, there were only three samples from this aquifer (not including reference wells). The Surficial aquifer was the only aquifer in which diphenylethanedione was not detected. Because of the larger sample size of the Surficial aquifer (n=33), the absence of this chemical may be a distinguishing feature of Surficial aquifer groundwater. The regulated contaminant dibutyl phthalate was not detected in any samples from the Lower Cape Fear aquifer; hence, future detection of dibutyl phthalate in this aquifer could signify the presence of new water.

Other notable aquifer signatures were benzothiazole in 100% of samples from the Beaufort aquifer and butylated hydroxytoluene in 100% of samples from the Upper Black Creek aquifer. Detection frequency of benzothiazole in other aquifers ranged from 29% to 71%, and detection frequency of butylated hydroxytoluene in other aquifers ranged from 6% to 71%. The regulated pesticide atrazine was detected in the Surficial aquifer and the three deepest aquifers - Black Creek, Upper Cape Fear, and Lower Cape Fear. A powerful advantage of HRMS data is the ability to retrospectively reanalyze mass spectral scans. Hence, the archived mass spectra from this study can be reanalyzed with new sample scans to further assess changes in chemical fingerprints that indicate the emergence of organic chemicals in NC coastal aquifer systems. 4.4 Groundwater Vulnerability to Extreme Storms Previous studies have demonstrated that hurricanes and intense rainfall result in increased infiltration rates making groundwater more susceptible to contamination (Jasechko and Taylor, 2015; Li and Tsai, al., 2020; Sawyer et al., 2014; Vittecoq et al., 2020). Extreme storm events in the Southeastern U.S. are projected to increase in intensity and frequency amplifying the risk of contamination of NC coastal aquifers (Knight et al., 2009; Kossin et al., 2017). Evaluation of HRMS data for wells before and after flooding at Snow Hill provides insight to the vulnerability of these aquifers. Before Hurricane Florence, only two regulated organic compounds were detected at Snow Hill: atrazine at Wells 3 and 4, and acenaphthene at Wells 3, 4, and 5. After each flood event at the Snow Hill site, several new regulated compounds were detected (Table 5). After Hurricane Florence, newly detected regulated contaminants were found in the Surficial aquifer Well 6 (pyrene) and Well 5 (pyrene, diethyl phthalate, and atrazine) and in the Upper Cape Fear Well 4 (pyrene). After the flood in 2020, regulated compounds were detected in the Surficial Well 6 [bis(2-ethylhexyl) phthalate and benzoic acid], Surficial Well 5 [bis(2- ethylhexyl) phthalate and biphenyl], Upper Cape Fear Well 4 [bis(2-ethylhexyl) phthalate, benzoic acid, diethyl phthalate, and 1,2,4-trimethylbenzene], and Lower Cape Fear Well 3 [acenaphthene, atrazine, bis(2-ethylhexyl) phthalate, diethyl phthalate, and 1,2,4- trimethylbenzene]. Comparison of results from Snow Hill Well 4 with reference well Saulston 3, both in the Upper Cape Fear aquifer, show that the reference well did not experience similar detections of regulated organics (Table 8). Two regulated phthalates were detected at Saulston 3, diethyl phthalate in 2020 and dibutyl phthalate in both samples. However, diethyl phthalate was also

33 detected in the 2020 Snow Hill 4 sample. Additionally, pyrene (2019), bis(2-ethylhexyl) phthalate (2020), benzoic acid (2020), and 1,2,4-trimethylbenzene (2020) were each detected in a post-flood Snow Hill 4 sample (sample date of detection in parenthesis) but in neither Saulston 3 samples. The increased presence of regulated organics in the Upper Cape Fear confined aquifer at the Snow Hill site relative to the Saulston reference well site reflects the vulnerability of deep NC aquifers to the impacts of flood events. 4.5 Limitations and Advancement In future efforts it could be beneficial if sampling of wells could be conducted more frequently and closer to the dates of storm events. In addition to increased sampling frequency, the use of passive samplers along with grab samples might allow for more comprehensive analysis of the groundwater chemical profiles (Soulier et al., 2016; Pinasseau et al., 2019). Passive samplers can be deployed for days to months and account for temporal variations in chemical profiles (Soulier et al., 2016; Pinasseau et al., 2019); they also allow for detection of chemicals at lower concentrations than grab samples (Soulier et al., 2016). Additional statistical analysis of existing results, such as mixed model and cluster analysis, could be used to further compare TIC and ToxCast chemical counts and profiles between flooded, not flooded, and reference wells. Sample analysis could be improved by using both LC- and GC-HRMS; paired analysis could expand the chemical profile of each sample and increase identification confidence for TICs detected by both instruments (Huo et al., 2020). Confidence in chemical identification could also be enhanced by targeted analysis of tentatively identified chemicals with available analytical standards using the GC-HRMS, which has higher sensitivity than the GC-MS used by NCDEQ. Lastly, the relationship between land use around well sites and TICs should be investigated to determine possible chemical source patterns.

Chapter 5: Conclusion

NT- and SS-HRMS analyses were used to produce the first comprehensive organic chemical profiles for 11 aquifers in the NCCP. These aquifer profiles include the first organic chemical fingerprints of confined aquifers using HRMS. Contrary to assumptions, the direct connectivity of surficial aquifers to the land surface did not result in significantly higher chemical counts. The only significant difference in chemical counts between aquifers was the significantly higher mean ToxCast 90 chemical count in the Lower Cape Fear aquifer, which is the deepest confined aquifer in the study, relative to the mean count in the Surficial aquifer. This finding supports other research highlighting the vulnerability of confined aquifers to modern contaminants. Comparison of chemical detection frequency by aquifer proved more useful in distinguishing aquifers, as described in the discussion of Figure 6. Despite the differences in chemical detections by aquifer, the range of log Kow values for ToxCast chemicals was similar across all aquifers and variable from anticipated low values to unexpected high values, such as that of hexadecane (log Kow = 8.20), which was detected in every aquifer. Application of NT- and SS-HRMS analyses to evaluate confined and unconfined aquifer vulnerability to flood water impacts is novel. The number of ToxCast 90 chemicals increased more frequently for wells resampled after flood events than for subsequent samples from reference wells that did not flood. Additionally, flooded wells had several regulated organic compounds that were not detected prior to flooding. In contrast, reference well sites had, at most, one regulated organic chemical detected in their second samples but not in the first sampling event. New detections of regulated organics after flooding occurred in unconfined and confined aquifers; some new detected chemicals, post-flood, had exceptionally high log Kow values >13.

The mechanisms by which organic chemicals of variable log Kow values contaminate wells merit further research. This study demonstrates the utility of NT- and SS-HRMS analyses as a nascent analytical tool to advance knowledge of aquifer dynamics and vulnerability to flood events. The plethora of organic chemical features detected by HRMS in comparison to a single detection of a semi- volatile regulated organic chemical by standard targeted methods illuminates the potential of HRMS to expand understanding of organic chemicals in groundwater whether from anthropogenic, biogenic, or geogenic sources. The HRMS findings from this exploratory study can be used in the future to evaluate changes in aquifer organic chemical profiles from variable

35 perturbances such as flooding, withdrawal rates, and changing land use dynamics. This study focused on SS-HRMS analyses of CECs to investigate young water presence in aquifers before and after flooding events. More frequent groundwater sampling and further statistical analysis would improve future research efforts in this area by improving knowledge of temporal variations in groundwater chemical profiles and enhancing comparison of HRMS sample results. GC-HRMS analyses were appropriate for this study given NCDEQ sample preparation for semi- volatile targeted analyses by GC-MS. However, further research utilizing HRMS to understand aquifer connectivity and response to surface disturbances would benefit from the complementary use of LC-HRMS and targeted HRMS analyses.

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APPENDIX

Appendix A: Supplemental Tables and Figures

Table S1. Standard recovery upper and lower limits from NCDEQ targeted analysis in 2020. LCL is lower recovery limit. UCL is upper recovery limit. RPD is relative percent difference.

LCL UCL RPD Surrogate Standards 2-Fluorophenol 31% 69% Phenol-d5 22% 46% Nitrobenzene-d5 56% 106% 2-Fluorobiphenyl 56% 104% 2,4,6-Tribromophenol 50% 112% Terphenyl-d14 62% 117% Average 37% 92% Laboratory Control Standards Phenol 21% 44% 2-Chlorophenol 49% 92% 1,4-Dichlorobenzene 30% 85% n-Nitroso-di-n-propylamine 69% 95% 1,2,4-Trichlorobenzene 38% 88% 4-Chloro-3-methylphenol 60% 95% Acenaphthene 64% 91% 2,4-Dinitrotoluene 67% 89% 4-Nitrophenol 10% 53% Pentachlorophenol 38% 93% Pyrene 69% 101% Average 47% 84% Matrix Spike Standards Phenol 21% 46% 23% 2-Chlorophenol 53% 91% 26% 1,4-Dichlorobenzene 54% 88% 27% n-Nitroso-di-n-propylamine 61% 104% 27% 1,2,4-Trichlorobenzene 56% 93% 28% 4-Chloro-3-methylphenol 60% 97% 28% Acenaphthene 62% 100% 23% 2,4-Dinitrotoluene 60% 96% 26% 4-Nitrophenol 10% 56% 37% Pentachlorophenol 39% 95% 34% Pyrene 65% 104% 24% Average 49% 88% 28%

Table S2. Summary of HRMS chemical counts for all 150 groundwater samples. Sample types are abbreviated as follows: F for samples taken after a well flooded, NF for samples taken from wells that had not experienced flooding during the sample period, and RW for reference wells. Well ID is the NCDEQ Well Quad. Depth indicates depth to top of well screen. Percentages for TIC and ToxCast counts indicate the threshold match factor (confidence level) used.

Sample Depth Total TIC ToxCast TIC ToxCast TIC ToxCast TIC ToxCast Sample ID Location Well ID Aquifer Type Sample Date (m) Features (50%) (50%) (75%) (75%) (80%) (80%) (90%) (90%) AC53871 Falkland L 25P5 Surficial NF 8/16/18 1.5 4,458 1,186 91 140 35 83 23 14 4 AC53942 Cleveland M 38Q1 Basement Rock NF 8/22/18 25.9 2,988 788 64 67 13 33 9 6 3 AC54693 Snow Hill O 28K5 Surficial NF 9/5/18 20.1 5,949 1,472 100 163 28 106 22 24 4 AC54695 Snow Hill O 28K6 Surficial NF 9/5/18 3.0 6,418 1,833 119 202 37 114 24 22 5 AC54714 Snow Hill O 28K3 Lower Cape Fear NF 9/11/18 121.9 6,013 2,009 132 196 46 123 36 30 10 AC54716 Snow Hill O 28K4 Upper Cape Fear NF 9/11/18 68.3 5,853 1,794 115 173 37 102 29 29 7 AC55798 Farmville M 27U7 Yorktown NF 10/4/18 15.2 4,416 1,390 95 134 28 66 15 16 2 AC55800 Farmville M 27U8 Black Creek NF 10/4/18 27.4 3,787 906 71 101 21 61 10 13 1 AC55866 Chicod O 23L2 Surficial NF 10/10/18 2.1 4,126 1,115 62 137 22 74 11 16 3 AC55868 Chicod O 23L6 Castle Hayne NF 10/10/18 21.9 3,878 862 49 91 15 48 8 11 3 AC55870 Chicod O 23L7 Peedee NF 10/10/18 48.8 3,939 880 44 87 12 42 8 12 3 AC56708 Bonnerton P 18V3 Surficial NF 10/16/18 6.1 3,917 1,105 100 148 33 88 23 19 3 AC56710 Bonnerton P 18V4 Castle Hayne NF 10/16/18 97.5 4,546 1,377 91 148 29 84 17 16 4 AC56712 Bonnerton P 18V5 Castle Hayne NF 10/16/18 51.5 5,746 1,782 130 209 41 125 31 42 12 AC56714 Bonnerton P 18V6 Yorktown NF 10/16/18 23.2 4,492 1,127 80 119 21 78 15 20 3 AC56716 Bonnerton P 18V8 Beaufort NF 10/17/18 136.2 4,221 1,302 105 150 40 79 28 26 9 AC56796 Bonnerton P 18V7 Black Creek NF 10/25/18 231.0 5,056 1,987 149 245 62 151 46 36 19 AC56798 Cox Crossroads P 19M2 Yorktown NF 10/24/18 7.0 6,013 2,009 132 196 46 123 36 30 10 AC56800 Cox Crossroads P 19M4 Black Creek NF 10/24/18 174.0 4,962 1,797 105 202 36 127 24 33 8 AC57001 West Research Campus M 25F4 Upper Cape Fear NF 11/9/18 81.4 4,726 1,067 81 132 25 87 21 23 7 AC57868 Chicod O 23L8 Lower Cape Fear NF 12/4/18 250.2 4,313 1,003 81 118 30 79 23 15 3 AC57870 Chicod O 23L3 Upper Cape Fear NF 12/4/18 167.3 3,933 1,122 79 141 26 76 18 21 6 AC57873 Chicod O 23L4 Black Creek NF 12/5/18 131.1 4,123 1,236 99 157 34 94 24 23 9 AC57875 Chicod O 23L5 Upper Black Creek NF 12/5/18 92.7 4,281 1,017 79 111 24 68 17 16 5 AC57922 New Lake M 12L1 Castle Hayne NF 12/12/18 134.1 5,307 1,410 92 150 39 89 24 19 6 AC57924 New Lake M 12L2 Surficial NF 12/12/18 2.7 5,932 1,936 110 198 29 103 19 30 7 AC57926 New Lake M 12L4 Castle Hayne NF 12/12/18 198.1 5,395 1,398 123 172 37 113 30 28 7 AC57928 New Lake M 12L5 Yorktown NF 12/12/18 19.2 7,611 1,621 109 152 30 84 16 24 6 AC58507 New Lake M 12L3 Beaufort NF 12/18/18 257.2 7,263 1,413 104 169 37 106 27 35 8 AC58509 New Lake M 12L6 Yorktown NF 12/18/18 61.9 7,188 2,345 143 230 58 132 38 27 11 AC58511 Bear Grass School K 21R2 Beaufort NF 12/19/18 29.6 5,591 1,357 108 151 39 89 30 25 11 AC58571 Godley Q 16G3 Castle Hayne NF 1/8/19 70.1 5,466 1,424 112 166 39 110 32 26 9 AC58573 Godley Q 16G4 Castle Hayne NF 1/8/19 123.7 6,679 1,638 125 213 48 137 42 38 12 AC58575 Godley Q 16G5 Yorktown NF 1/8/19 10.7 5,598 1,447 115 153 42 99 32 28 11 AC58577 Godley Q 16G6 Surficial NF 1/8/19 3.4 5,390 1,325 108 149 32 88 24 25 8 AC58942 Godley Q 16G8 Castle Hayne NF 1/16/19 108.2 5,741 1,609 100 168 37 101 26 17 8 AC59015 Wilmar P 21K5 Black Creek NF 1/23/19 260.6 5,949 1,887 144 208 49 119 33 28 9 AC59017 Wilmar P 21K6 Castle Hayne NF 1/23/19 21.3 6,359 1,678 115 195 39 124 33 34 11 AC59019 Wilmar P 21K7 Beaufort NF 1/23/19 88.4 6,048 1,868 126 225 51 135 39 30 9 AC59729 Wilmar P 21K3 Surficial NF 1/29/19 7.0 6,468 2,086 160 308 65 204 52 50 13 AC59731 Wilmar P 21K9 Black Creek NF 1/29/19 211.8 6,953 2,246 146 224 55 134 37 36 11 AC60673 Saulston O 30J1 Upper Black Creek RW 2/28/19 12.2 7,334 1,492 114 198 40 112 29 24 6 AC60675 Saulston O 30J2 Black Creek RW 2/28/19 26.2 5,459 1,469 114 188 39 123 26 31 12

Sample Depth Total TIC ToxCast TIC ToxCast TIC ToxCast TIC ToxCast Sample ID Location Well ID Aquifer Type Sample Date (m) Features (50%) (50%) (75%) (75%) (80%) (80%) (90%) (90%) AC60677 Saulston O 30J3 Upper Cape Fear RW 2/28/19 50.3 7,167 2,376 144 240 53 140 36 27 8 AC60794 Whitley Farms P 17E1 Castle Hayne NF 3/19/19 40.5 5,980 1,884 129 230 46 143 35 47 12 AC60796 Whitley Farms P 17E2 Castle Hayne NF 3/19/19 82.3 5,408 1,479 116 159 50 101 38 32 12 AC60798 Whitley Farms P 17E3 Yorktown NF 3/19/19 13.4 5,571 1,590 112 197 44 128 32 36 13 AC60800 Whitley Farms P 17E5 Surficial NF 3/19/19 6.1 4,979 1,298 89 160 28 106 23 29 7 AC61586 Hydeland O 10W3 Castle Hayne F 3/28/19 195.1 5,084 1,846 142 233 51 148 39 26 7 AC61588 Hydeland O 10W6 Surficial F 3/28/19 2.1 5,484 1,875 109 149 32 87 24 21 4 AC61631 T.L. Harris L 16A1 Castle Hayne NF 4/3/19 4.3 5,904 1,792 110 177 38 105 29 24 10 AC61645 Hobucken Q 15U3 Castle Hayne NF 4/4/19 110.3 5,685 1,617 121 182 44 114 33 22 7 AC61649 Hobucken Q 15U5 Castle Hayne NF 4/4/19 182.9 5,651 1,441 105 181 37 111 24 29 7 AC61651 Hobucken Q 15U6 Yorktown NF 4/4/19 27.4 7,124 2,736 169 244 53 133 39 32 14 AC61653 Hobucken Q 15U7 Peedee NF 4/4/19 292.0 5,747 1,151 94 145 34 95 26 30 5 AC61729 Comfort U 26J1 Castle Hayne NF 4/11/19 8.2 5,853 1,794 115 173 37 102 29 29 7 AC61731 Comfort U 26J3 Surficial NF 4/11/19 2.7 4,240 1,190 75 147 23 89 15 22 3 AC61733 Comfort U 26J9 Beaufort NF 4/11/19 35.4 4,243 1,023 89 123 34 77 24 21 6 AC61735 Comfort U 26J10 Upper Cape Fear NF 4/11/19 243.8 4,572 1,103 106 155 42 100 23 25 3 AC62784 Comfort U 26J4 Black Creek NF 5/1/19 154.2 5,033 1,599 106 223 45 137 36 39 9 AC62786 Comfort U 26J5 Peedee NF 5/1/19 83.5 5,396 1,837 133 272 57 171 45 47 12 AC62790 Highway 102 O 21Q1 Castle Hayne NF 5/2/19 21.9 5,392 1,745 122 189 45 108 33 33 14 AC62792 Highway 102 O 21Q2 Surficial NF 5/2/19 2.1 6,478 1,882 138 215 49 130 34 25 6 AC62794 Highway 102 O 21Q3 Yorktown NF 5/2/19 10.7 6,337 2,214 138 252 52 150 35 34 13 AC62800 Wilmar Fire Tower P 21G1 Castle Hayne NF 5/8/19 27.4 7,554 1,753 125 227 53 135 37 30 7 AC63004 Blackjack N 22Y1 Yorktown F 5/16/19 9.1 4,274 1,328 117 168 45 106 35 26 9 AC63006 Winterville N 25Q2 Peedee NF 5/16/19 22.6 7,412 1,768 115 199 41 129 27 45 11 AC63081 Clinton U 35I1 Surficial NF 5/29/19 9.1 4,547 1,176 100 176 39 108 30 29 10 AC63083 Clinton U 35I2 Lower Cape Fear NF 5/29/19 143.2 4,606 1,300 97 167 32 110 25 29 13 AC63085 Clinton U 35I6 Black Creek NF 5/29/19 53.3 4,683 1,306 108 182 39 111 32 26 10 AC63904 Clinton U 35I3 Lower Cape Fear NF 6/4/19 114.6 4,843 1,692 112 211 38 133 27 37 11 AC63906 Clinton U 35I4 Upper Black Creek NF 6/4/19 28.3 4,368 1,147 108 152 37 100 29 24 10 AC63908 Clinton U 35I5 Upper Cape Fear NF 6/4/19 83.5 4,410 1,277 98 176 34 107 24 22 7 AC65020 Southside Ferry P 16O2 Yorktown F 6/26/19 11.3 3,666 1,015 75 113 25 69 19 14 6 AC65022 Southside Ferry P 16O3 Castle Hayne F 6/26/19 85.3 3,852 1,056 90 122 33 76 25 11 5 AC65024 Southside Ferry P 16O4 Castle Hayne F 6/26/19 53.9 4,087 1,150 97 128 24 77 16 13 3 AC66348 D Canal Road L 15T3 Surficial NF 8/1/19 3.0 6,962 2,011 110 221 38 126 26 25 7 AC66350 D Canal Road L 15T4 Castle Hayne NF 8/1/19 104.8 6,019 1,538 98 175 33 104 27 26 7 AC66352 D Canal Road L 15T5 Castle Hayne NF 8/1/19 85.3 6,119 1,766 111 172 36 96 28 28 9 AC66355 D Canal Road L 15T1 Surficial NF 8/1/19 23.2 5,982 1,876 133 197 43 118 32 20 6 AC66357 D Canal Road L 15T2 Beaufort NF 8/1/19 168.5 5,852 1,524 104 170 41 103 29 23 8 AC66616 Snow Hill O 28K3 Lower Cape Fear F 8/15/19 121.9 7,124 2,736 169 244 53 133 39 32 14 AC66618 Snow Hill O 28K4 Upper Cape Fear F 8/15/19 68.3 5,812 2,016 133 234 51 141 35 36 12 AC66620 Snow Hill O 28K5 Surficial F 8/15/19 20.1 5,746 1,782 130 209 41 125 31 42 12 AC66622 Snow Hill O 28K6 Surficial F 8/15/19 3.0 6,368 2,234 129 200 37 114 26 26 9 AC66655 Vaughn Elementary School C 31Y1 Basement Rock NF 8/21/19 18.3 4,231 1,156 97 153 34 96 26 25 8

Sample Depth Total TIC ToxCast TIC ToxCast TIC ToxCast TIC ToxCast Sample ID Location Well ID Aquifer Type Sample Date (m) Features (50%) (50%) (75%) (75%) (80%) (80%) (90%) (90%) AC67577 NC Zoo M 53L1 Basement Rock NF 8/29/19 6.4 4,359 1,284 97 160 36 101 28 30 14 AC67624 Robeson Correctional Center Y 44O6 Upper Cape Fear F 9/4/19 139.9 4,154 1,418 107 192 37 115 29 31 14 AC69729 Bushy Lake V 39O1 Upper Cape Fear NF 10/31/19 53.3 4,078 1,056 92 128 37 85 25 28 11 AC69731 Bushy Lake V 39O2 Surficial NF 10/31/19 6.7 4,098 960 88 127 27 79 21 19 3 AC69733 Bushy Lake V 39O3 Surficial NF 10/31/19 32.0 3,542 932 90 143 31 86 24 20 6 AC69854 Seabrook School U 41A1 Surficial NF 11/12/19 7.6 7,205 2,368 149 238 41 140 33 28 12 AC69857 Cedar Creek Fire Tower U 40Y1 Upper Cape Fear NF 11/13/19 56.4 5,944 1,623 116 150 34 91 24 23 9 AC69860 Southern Pines Water Plant R 48G2 Surficial NF 11/14/19 6.1 5,953 1,845 134 202 43 107 27 30 11 AC70647 Clarks S 22J5 Castle Hayne NF 11/19/19 16.8 6,350 1,475 92 131 25 77 18 21 7 AC70649 Clarks S 22J10 Black Creek NF 11/19/19 218.2 6,074 1,781 116 180 39 96 27 28 10 AC70651 Clarks S 22J12 Upper Cape Fear NF 11/19/19 314.5 6,461 1,894 139 197 30 119 24 27 5 AC72679 Maco CC 33O2 Peedee NF 2/4/20 6.1 6,418 1,833 119 202 37 114 24 22 5 AC72681 Maco CC 33O5 Surficial NF 2/4/20 1.2 3,452 944 59 110 20 64 13 17 4 AC72684 Clarks S 22J8 Beaufort NF 2/5/20 100.0 6,933 2,254 147 197 50 109 38 32 11 AC72686 Clarks S 22J9 Peedee NF 2/5/20 157.3 6,382 2,082 118 217 42 135 32 38 17 AC73482 Boiling Springs RS 1 FF 33S1 Surficial F 2/19/20 18.9 3,869 1,143 84 120 28 76 22 16 4 AC73484 Boiling Springs RS 2 FF 32Y1 Peedee F 2/19/20 20.4 3,760 1,048 66 107 21 66 13 14 3 AC73486 Boiling Springs RS 2 FF 32Y2 Surficial F 2/19/20 2.7 3,829 1,166 73 120 22 70 14 11 4 AC73513 Bunn I 35K2 Basement Rock NF 2/26/20 15.5 5,785 1,298 97 136 36 85 24 21 7 AC73516 Nash County Well No. 3 I 31M1 Basement Rock NF 2/27/20 60.0 4,472 1,255 88 151 30 100 23 21 6 AC73616 Gibsonville G 50W2 Basement Rock NF 3/10/20 10.1 4,287 1,201 82 142 19 89 15 21 3 AC73618 Chi Psi Fraternity, UNC J 44D1 Basement Saprolite NF 3/10/20 154.8 4,040 1,285 84 168 29 93 20 21 8 AC73657 Burgaw Y 30S3 Peedee F 3/17/20 36.6 3,844 1,093 68 124 24 71 18 17 7 AC73659 Burgaw Y 30S7 Black Creek F 3/17/20 112.8 3,850 1,418 107 169 33 93 29 17 8 AC74252 Holly Shelter Z 29N1 Surficial F 5/26/20 9.8 4,190 1,428 99 170 38 101 30 28 10 AC74254 Holly Shelter Z 29N2 Upper Cape Fear F 5/26/20 205.7 5,515 2,358 136 246 54 131 36 33 12 AC74256 Holly Shelter Z 29N3 Peedee F 5/26/20 41.1 5,812 2,016 133 234 51 141 35 36 12 AC74258 Holly Shelter Z 29N4 Black Creek F 5/26/20 131.1 4,546 1,855 101 190 33 106 23 22 10 AC74369 Long Creek AA 32R1 Surficial F 6/3/20 4.9 8,209 1,545 100 184 41 114 27 30 7 AC74371 Long Creek AA 32R3 Peedee F 6/3/20 67.1 6,518 1,743 128 181 47 115 35 25 11 AC74373 Long Creek AA 32R4 Upper Black Creek F 6/3/20 118.9 6,326 1,561 101 159 41 102 28 17 8 AC74489 Long Creek AA 32R2 Black Creek F 6/9/20 141.7 5,421 1,529 98 154 38 97 28 25 7 AC75011 North Pitt High School L 24B7 Surficial RW 6/24/20 3.0 6,368 2,234 129 200 37 114 26 26 9 AC75014 North Pitt High School L 24B3 Lower Cape Fear RW 6/24/20 167.6 4,481 1,390 124 191 49 122 40 38 15 AC75135 Purser P 21N1 Castle Hayne NF 7/7/20 15.8 4,755 1,382 117 156 46 95 38 30 16 AC75137 Purser P 21N2 Yorktown NF 7/7/20 10.7 4,452 1,288 103 154 35 94 28 31 11 AC75139 Purser P 21N3 Surficial NF 7/7/20 4.0 4,870 1,479 135 181 48 111 34 41 19 AC75312 Sladesville O 13F1 Castle Hayne F 7/16/20 104.2 5,004 1,422 96 157 35 99 24 28 12 AC75579 Ivanhoe Y 34P2 Black Creek F 7/22/20 55.2 4,355 1,165 92 160 33 102 26 29 12 AC75581 Ivanhoe Y 34P3 Surficial F 7/22/20 8.5 4,200 1,140 71 133 24 82 19 17 8 AC75583 Ivanhoe Y 34P7 Peedee F 7/22/20 33.5 4,260 1,270 100 163 41 108 31 33 13 AC75585 Ivanhoe Y 34P9 Upper Cape Fear F 7/22/20 134.1 3,902 1,151 98 162 40 100 28 25 13 AC75760 I-40/I-95 P 38I1 Basement Rock NF 7/30/20 49.7 3,898 1,124 90 149 41 101 32 30 17

Sample Depth Total TIC ToxCast TIC ToxCast TIC ToxCast TIC ToxCast Sample ID Location Well ID Aquifer Type Sample Date (m) Features (50%) (50%) (75%) (75%) (80%) (80%) (90%) (90%) AC75910 Bay City R 17I1 Surficial NF 8/5/20 20.1 5,756 1,826 94 139 19 77 16 12 6 AC75912 Bay City R 17I2 Castle Hayne NF 8/5/20 111.6 5,174 1,435 105 160 39 107 34 34 14 AC76047 Snow Hill O 28K3 Lower Cape Fear F 8/12/20 121.9 4,556 1,334 112 184 49 123 40 33 17 AC76049 Snow Hill O 28K4 Upper Cape Fear F 8/12/20 68.3 4,327 1,096 90 137 42 89 35 32 19 AC76051 Snow Hill O 28K5 Surficial F 8/12/20 20.1 3,972 942 73 124 31 77 21 17 6 AC76053 Snow Hill O 28K6 Surficial F 8/12/20 3.0 4,737 1,430 93 174 36 107 24 25 9 AC76751 Falkland L 25P1 Lower Cape Fear F 8/27/20 133.8 4,459 1,322 92 147 29 94 24 29 13 AC76753 Falkland L 25P2 Upper Cape Fear F 8/27/20 55.8 5,889 1,988 142 184 41 110 32 31 15 AC76755 Falkland L 25P3 Black Creek F 8/27/20 22.2 4,302 1,204 98 142 29 88 25 28 13 AC76757 Falkland L 25P4 Yorktown F 8/27/20 11.3 3,919 1,099 93 124 34 69 22 20 9 AC76759 Falkland L 25P5 Surficial F 8/27/20 1.5 4,324 1,240 108 164 36 92 27 33 12 AC76821 North Pitt High School L 24B2 Upper Black Creek RW 9/8/20 29.9 4,579 1,369 112 170 38 94 31 20 8 AC76823 North Pitt High School L 24B3 Lower Cape Fear RW 9/8/20 167.6 4,538 1,477 109 189 43 118 29 35 13 AC76825 North Pitt High School L 24B5 Upper Cape Fear RW 9/8/20 105.8 4,464 1,313 93 140 31 80 23 17 8 AC76827 North Pitt High School L 24B6 Black Creek RW 9/8/20 59.4 6,368 2,234 129 200 37 114 26 26 9 AC76829 North Pitt High School L 24B7 Surficial RW 9/8/20 3.0 4,357 1,306 101 152 37 79 20 20 6 AC77629 Saulston O 30J1 Upper Black Creek RW 9/30/20 12.2 4,171 1,145 79 117 16 59 11 17 4 AC77631 Saulston O 30J2 Black Creek RW 9/30/20 26.2 4,171 1,295 112 150 35 100 30 21 10 AC77633 Saulston O 30J3 Upper Cape Fear RW 9/30/20 50.3 6,139 1,914 130 204 45 103 31 34 11 AC77635 Saulston O 30J4 Surficial RW 9/30/20 3.4 4,534 1,505 99 163 21 97 17 18 6 AC77713 Fort Fisher GG 31J1 Surficial NF 10/7/20 1.8 4,336 1,330 95 174 32 107 23 31 10

Table S3. Complete list of TICs detected across all 150 samples using a match factor threshold of 90.

Sample Compound CAS Count 1 Diethyltoluamide 134-62-3 143 2 Cyclomethicone 6 540-97-6 140 3 Cyclomethicone 7 107-50-6 134 4 Octadeamethyl-cyclononasiloxane 556-71-8 95 5 Hexadecamethyl-cyclooctasioxane 556-68-3 92 6 Triphenyl phosphate 115-86-6 89 7 Diethyl phthalate 84-66-2 82 8 4-Fluorobiphenyl 324-74-3 82 9 Benzothiazole 95-16-9 74 10 2-Methylnaphthalene 91-57-6 64 11 Eicosamethyl-cyclodecasiloxane 18772-36-6 63 12 Ethyl 4-ethoxybenzoate 23676-09-7 59 13 Octanoic acid 124-07-2 58 14 Dibutyl terephthalate 1962-75-0 58 15 Butylated hydroxytoluene 128-37-0 57 16 Phthalic acid, octyl 2-propylpentyl ester 998377-92-4 56 17 Hexadecane 544-76-3 56 18 Dodecanoic acid 143-07-7 56 19 Heptacosane 593-49-7 51 20 Acenaphthene 83-32-9 51 21 Nonanoic acid 112-05-0 50 22 Phthalic acid, di(oct-3-yl) ester 998377-72-3 49 23 Diphenylethanedione 134-81-6 49 24 Phthalic acid, di(2-propylpentyl) ester 998377-93-5 47 25 Octadecanoic acid, butyl ester 123-95-5 46 26 Dibutyl phthalate 84-74-2 46 27 Hexathiane 13798-23-7 45 28 Diethylene glycol dibenzoate 120-55-8 43 29 7,9-Di-tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione 82304-66-3 42 30 1,2,3,4-Tetrahydronaphthalene-d12 75840-23-2 40 31 n-Hexadecanoic acid 57-10-3 38 32 Benzophenone 119-61-9 38 33 Docosamethyldecasiloxane 556-70-7 35 34 Benzene, 1,2,3,5-tetrachloro- 634-90-2 34 35 Hexanoic acid 142-62-1 33 36 Benzene, 1,3,5-tribromo-2-methoxy- 607-99-8 31 37 Hexasiloxane, tetradecamethyl- 107-52-8 30 38 Ethanone, 2-(formyloxy)-1-phenyl- 55153-12-3 29 39 3,4-Dimethylbenzaldehyde 5973-71-7 28 40 Benzene, (1-methyldecyl)- 4536-88-3 27 41 1,2,3-Trichlorobenzene 87-61-6 26 42 Benzene, 1,3,5-trichloro- 108-70-3 25 43 Octadecamethyloctasiloxane 556-69-4 23 44 cis-9-Tetradecen-1-ol 35153-15-2 23 45 Benzenecarbothioic acid, 2,6-dichloro-, S-methyl ester 68504-39-2 22 46 Hexadecanamide 629-54-9 21 47 Bis(2-ethylhexyl) terephthalate 6422-86-2 21 48 Phenacylidene diacetate 5062-30-6 20 49 Octadecyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionate 2082-79-3 20 50 Heneicosane 629-94-7 20

Sample Compound CAS Count 51 Hentriacontane 630-04-6 19 52 Cyclotrisiloxane, 2,4-dimethyl-2,4,6,6-tetrahenyl- 17210-14-9 19 53 n-Decanoic acid 334-48-5 18 54 9-Octadecenamide, (Z)- 301-02-0 18 55 Pentasiloxane, dodecamethyl- 141-63-9 17 56 (3R,4R)-2,5-Dioxotetrahydrofuran-3,4-diyl dibenzoate 64339-95-3 17 57 Formamide, N-methyl- 123-39-7 16 58 Bis (2-ethylhexyl) phthalate, DEHP 117-81-7 16 59 Benzoic acid, 2,5-dichloro-, methyl ester 2905-69-3 16 60 Benzoic acid 65-85-0 16 61 1,14-Tetradecanediol 19812-64-7 16 62 Tetradecanoic acid 544-63-8 15 63 Methanol, oxo-, benzoate 78823-32-2 15 64 Longifolene 475-20-7 15 65 Eicosamethylnonasiloxane 2652-13-3 15 66 Dodecyl acrylate 2156-97-0 15 67 Anthracene, 2-methyl- 613-12-7 15 68 1-Pentadecyne 765-13-9 15 69 3,5-di-tert-Butyl-4-hydroxybenzaldehyde 1620-98-0 14 70 Vanillin 121-33-5 13 71 Pyrene 129-00-0 13 72 Dioctyl ether 629-82-3 13 73 Diisobutyl phthalate 84-69-5 13 74 Benzene, (1-propylnonyl)- 2719-64-4 13 75 Benzene, (1-pentylheptyl)- 2719-62-2 13 76 Phenol, 2,6-dibromo-4-methyl- 2432-14-6 12 77 Diphenylamine 122-39-4 12 78 2,4-Dimethylbenzaldehyde 15764-16-6 12 79 2,4-Dibromophenol 615-58-7 12 80 2-(Methylmercapto)benzothiazole 615-22-5 12 81 Triethylene glycol di(2-ethylhexoate) 94-28-0 11 82 Tetrathiane 290-81-3 11 83 Nonadecane 629-92-5 11 84 Benzene, (1-propyloctyl)- 4536-86-1 11 85 Benzene, (1-methylundecyl)- 2719-61-1 11 86 1,2,4-Trimethylbenzene 95-63-6 11 87 Triethyl citrate 77-93-0 10 88 Squalene 111-02-4 10 89 Octadecanoic acid 57-11-4 10 90 Di-p-tolyl sulfone 599-66-6 10 91 Bis(2-ethylhexyl) isophthalate 137-89-3 10 92 Benzene, (1-butyloctyl)- 2719-63-3 10 93 Naphthalene, 1,6,7-trimethyl- 2245-38-7 9 94 Naphthalene, 1,6-dimethyl- 575-43-9 9 95 Benzene, (1-butylheptyl)- 4537-15-9 9 96 9-Methylene-9H-fluorene 4425-82-5 9 97 2-Ethylhexyl methyl isophthalate 2135327-80-7 9 98 1,3-Dimethyl-5-pylazolyl-2,4-dichlorobenzoate 998425-21-6 9 99 Tetradecanamide 638-58-4 8 100 Phenanthrene, 2,5-dimethyl- 3674-66-6 8

Sample Compound CAS Count 101 gamma-Dodecalactone 2305-05-7 8 102 Benzamide, N,N,3-trimethyl- 6935-65-5 8 103 Benzothiazolone 934-34-9 8 104 2,3,5-Tribromophenol 57383-81-0 8 105 Vinyl benzoate 769-78-8 7 106 Triphenylphosphine oxide 791-28-6 7 107 Tetraethylene glycol di(2-ethylhexanoate) 18268-70-7 7 108 Phenanthrene, 3,6-dimethyl- 1576-67-6 7 109 4-Ethyltoluene 622-96-8 7 110 3-Methylphenanthrene 832-71-3 7 111 2-Chloroethyl benzoate 939-55-9 7 112 13-Docosenamide, (Z)- 112-84-5 7 113 3,4-Dibromophenol 615-56-5 7 114 Triallyl isocyanurate 1025-15-6 6 115 Phytane 638-36-8 6 116 Phthalic acid, 4-fluoro-2-nitrophenyl methyl ester 998315-63-4 6 117 Phenol, 2,6-dibromo- 608-33-3 6 118 Oxygen difluoride 7783-41-7 6 119 Hexanoic acid, 2-ethyl- 149-57-5 6 120 Fluoranthene 206-44-0 6 121 Cyclododecanol 1724-39-6 6 122 Chalcone 94-41-7 6 123 Atrazine 1912-24-9 6 124 Nitrogen fluoride oxide 13847-65-9 5 125 2,3-Dimethylphenanthrene 3674-65-5 5 126 1,3-Diisopropenylbenzene 3748-13-8 5 127 Decamethylcyclopentasiloxane 541-02-6 5 128 Tri(2-chloroethyl) phosphate 115-96-8 5 129 Propanoic acid, anhydride 123-62-6 5 130 Methyl stearate 112-61-8 5 131 Benzo[a]pyrene-d12 63466-71-7 5 132 Benzeneacetic acid 103-82-2 5 133 11-Hexadecen-1-ol, (Z)- 56683-54-6 5 134 1,4-Dimethylnaphthalene 571-58-4 5 135 1,2,3-Trithiolane 6669-39-2 5 136 1,15-Pentadecanediol 14722-40-8 5 137 1-Propanone, 3-chloro-1-phenyl- 936-59-4 5 138 1-Ethyl-2-methyl-benzene 611-14-3 5 139 4-Ethenyl-1,2-dimethylbenzene 27831-13-6 5 140 1,2-Dibenzoylethane 495-71-6 4 141 Cholesta-3,5-diene 747-90-0 4 142 Dodecyl vinyl ether 765-14-0 4 143 Cyclohexylbenzene 827-52-1 4 144 Hex-1-enylbenzene 828-15-9 4 145 Pyrolo[3,2-d]pyrimidin-2,4(1H,3H)-dione 65996-50-1 4 146 Phthalic acid, methyl 2-pentyl ester 998315-55-1 4 147 Palmitoleamide 106010-22-4 4 148 Drometrizole 2440-22-4 4 149 Dimethyl phthalate 131-11-3 4 150 Cyanogen chloride 506-77-4 4

Sample Compound CAS Count 151 cis-11-Tetradecen-1-ol 34010-15-6 4 152 Benzene, (1-ethyldecyl)- 2400-00-2 4 153 3-tert-Butylbenzoic acid 7498-54-6 4 154 3-Nonen-1-ol, (Z)- 10340-23-5 4 155 3-Hydroxyphenyl benzoate 136-36-7 4 156 2-Isopropyl-10-methylphenanthrene 66552-97-4 4 157 1,2-Benzenedicarboxylic acid, dinonyl ester 84-76-4 4 158 (+)-2-Bornanone 464-49-3 4 159 N,N-Diethyl-p-toluamide 2728-05-4 3 160 2,6-Dimethyl-3-isopentylpyrazine 111150-30-2 3 161 m-Isopropenyltoluene 1124-20-5 3 162 Bis(4-(2,4,4-trimethylpentan-2-yl)phenyl)amine 15721-78-5 3 163 1,4-Diisopropenylbenzene 1605-18-1 3 164 Docosyl acrylate 18299-85-9 3 165 2-(Decanoyloxy)propane-1,3-diyl dioctanoate 33368-87-5 3 166 2,6,10-Trimethyldodecane 3891-98-3 3 167 2,7,10-Trimethyldodecane 74645-98-0 3 168 Succinic acid, 3-methylbut-2-en-1-yl diphenylmethyl ester 998390-16-7 3 169 Sulfentrazone 122836-35-5 3 170 Phenanthrene 85-01-8 3 171 Pentacosane 629-99-2 3 172 Naphthalene, 2,3,6-trimethyl- 829-26-5 3 173 Naphthalene, 1,6-dimethyl-4-(1-methylethyl)- 483-78-3 3 174 Naphthalene, 1,3-dimethyl- 575-41-7 3 175 Methyl palmitate 112-39-0 3 176 hexadecyl acrylate 13402-02-3 3 177 Heptadecane 629-78-7 3 178 Ethane, pentachloro- 76-01-7 3 179 Biphenyl 92-52-4 3 180 3H-1,2-Dithiole 288-26-6 3 181 3,7-Dimethyldibenzothiophene 1136-85-2 3 182 2,6-Diisopropylphenyl isocyanate 28178-42-9 3 183 2-Mercaptobenzothiazole 149-30-4 3 184 2-Cyclohexen-1-one 930-68-7 3 185 1H-Indene, 1-(phenylmethylene)- 5394-86-5 3 186 Naphthalene-d8 1146-65-2 2 187 2,2,4-Trimethyl-1,2-dihydroquinoline 147-47-7 2 188 p-Terphenyl-d14 1718-51-0 2 189 2,5-dimethyl-3-(3-methylbutyl)pyrazine 18433-98-2 2 190 1-Cyclohexene-3-thione 201139-65-3 2 191 (Benzhydryloxy)acetic acid 21409-25-6 2 192 2,6-Diisopropylaniline 24544-04-5 2 193 2-Methylphenanthrene 2531-84-2 2 194 4H-1,2,3-Trithiine 290-30-2 2 195 Methyl 2,3-dichlorobenzoate 2905-54-6 2 196 2,7-Dimethyldibenzothiophene 31317-19-8 2 197 3,3-Diphenylacrylonitrile 3531-24-6 2 198 2,2',3,5,5',6-Hexachlorobiphenyl 52663-63-5 2 199 O-Cymene 527-84-4 2 200 3-Methyl-3-phenylazetidine 5961-33-1 2

Sample Compound CAS Count 201 3,5-Dimethylhexanoic acid 60308-87-4 2 202 4-Methylindoline 62108-16-1 2 203 Docosanol 661-19-8 2 204 1-Phenyl-1-cyclohexene 771-98-2 2 205 1-Chloronaphthalene 90-13-1 2 206 2-Phenylpropene 98-83-9 2 207 Undecane, 4,7-dimethyl- 17301-32-5 2 208 Undecane, 3,8-dimethyl- 17301-30-3 2 209 Tetramethylthiourea 2782-91-4 2 210 Tetradecane 629-59-4 2 211 Tetracosane 646-31-1 2 212 Pyrene, 1-methyl- 2381-21-7 2 213 Phenanthrene, 2,3,5-trimethyl- 3674-73-5 2 214 Octacosane, 2-methyl- 1560-98-1 2 215 O-Toluenesulfonamide 88-19-7 2 216 o-Hydroxybiphenyl 90-43-7 2 217 Naphthalene, 1,5-dimethyl- 571-61-9 2 218 N-Cyclohexyl-N'-methylurea, N'-methyl 31468-12-9 2 219 N-Butylbenzenesulfonamide 3622-84-2 2 220 Methyl salicylate 119-36-8 2 221 Methyl formate 107-31-3 2 222 L-Fenchone 7787-20-4 2 223 Hydroxymethyl 2-hydroxy-2-methylpropionate 998289-09-5 2 224 Hexadecanoic acid, butyl ester 111-06-8 2 225 Heptasiloxane, hexadecamethyl- 541-01-5 2 226 Diphenylacetylene 501-65-5 2 227 Di-isononyl phthlate 20548-62-3 2 228 Bisphenol A 80-05-7 2 229 Benzeneacetic acid, .alpha.-oxo-, methyl ester 15206-55-0 2 230 Benzene, 1,4-dimethoxy-2-methyl-5-isopropyl- 14753-08-3 2 231 Benzene, 1,3,5-trimethyl-2-(1-methylethenyl)- 14679-13-1 2 232 Benzaldehyde, 3-hydroxy-4-methoxy- 621-59-0 2 233 9H-Fluorene, 9-methyl- 2523-37-7 2 234 2,6,6-Trimethyl-2-cyclohexene-1,4-dione 1125-21-9 2 235 2,2'-Bifuran, 2,2',5,5'-tetrahydro- 98869-92-2 2 236 1,2,3-Trimethylbenzene 526-73-8 2 237 1,2-Diphenoxyethane 104-66-5 2 238 1,2-Dimethylnaphthalene 573-98-8 2 239 1-Tetradecyne 765-10-6 2 240 1-Octanol, 2-butyl- 3913-02-8 2 241 1-Octadecyne 629-89-0 2 242 1-Methylphenanthrene 832-69-9 2 243 1-Methyldibenzothiophene 31317-07-4 2 244 1-Chloro-3-methylbenzene 108-41-8 2 245 .alpha.-Calacorene 21391-99-1 2 246 5-Acetylindane 4228-10-8 1 247 2-(4-Methylphenyl)pyridine 4467-06-5 1 248 4-Phenyldecane 4537-12-6 1 249 3,4-Dichlorophenyl isocyanate 102-36-3 1 250 1,4-Dicyclohexylbenzene 1087-02-1 1

Sample Compound CAS Count 251 Dimethoxymethane 109-87-5 1 252 1,10-Decanediol 112-47-0 1 253 Oleonitrile 112-91-4 1 254 2,6-Dimethylbenzaldehyde 1123-56-4 1 255 1,4-Dimethylazulene 1127-69-1 1 256 1-Ethylnaphthalene 1127-76-0 1 257 2,4-Dimethylquinoline 1198-37-4 1 258 Corodane 13380-94-4 1 259 (17alpha)-A'-neogammacerane 13849-96-2 1 260 Tetratriacontane 14167-59-0 1 261 8-Oxotricyclo[5.2.1.0(2,6)]dec-4-ene 14888-58-5 1 262 3-Methyldibenzothiophene 16587-52-3 1 263 3,5-Dimethylundecane 17312-81-1 1 264 (E)-Vinyl cinnamate 17719-70-9 1 265 2-Tert-Butyl-1H-indole 1805-65-8 1 266 Methoxyacetic anhydride 19500-95-9 1 267 1-Heptacosanol 2004-39-9 1 268 Fenozan 20170-32-5 1 269 2-Hydroxy-3,4-dimethylcyclopent-2-en-1-one 21835-00-7 1 270 4-Hexen-2-one 25659-22-7 1 271 2,3,5,6-Tetramethylbenzoic acid 2604-45-7 1 272 1-Heptadecyne 26186-00-5 1 273 1,2-Benzisothiazole 272-16-2 1 274 Didecan-2-yl phthalate 28029-89-2 1 275 Cyclotetradecane 295-17-0 1 276 Ronnel 299-84-3 1 277 (R)-(-)-(Z)-14-Methyl-8-hexadecen-1-ol 30689-78-2 1 278 3,3-Dimethylpentanoic acid 3177-74-0 1 279 Tris(2-ethylhexyl) trimellitate 3319-31-1 1 280 2-(Octanoyloxy)propane-1,3-diyl bis(decanoate) 33368-86-4 1 281 2,2',4,4',5,5'-Hexachlorobiphenyl 35065-27-1 1 282 11-Dodecen-1-ol 35289-31-7 1 283 Tolycaine 3686-58-6 1 284 2,2,4-Trimethyl-1,2,3,4-tetrahydroquinoline 4497-58-9 1 285 2-Phenyldecane 4537-13-7 1 286 9,9-Dimethyl-9H-fluorene 4569-45-3 1 287 Stigmasterol acetate 4651-48-3 1 288 16-Hentriacontanone 502-73-8 1 289 Azetidine 503-29-7 1 290 2,2',3,4,4',5',6-Heptachlorobiphenyl 52663-69-1 1 291 17.alpha.(H),21.beta.(H)-Homohopane 53584-61-5 1 292 Aminoacetonitrile 540-61-4 1 293 3,3,6,8-Tetramethyl-1-tetralone 5409-55-2 1 294 1,8-Dimethylnaphthalene 569-41-5 1 295 1,2,2,4-Tetramethyl-1,2,3,4-tetrahydroquinoline 5855-26-5 1 296 9-Ethylanthracene 605-83-4 1 297 1-Methyl-1,2,4-triazole 6086-21-1 1 298 Chalcone 614-47-1 1 299 1-Pentyl-2-propylcyclopentane 62199-51-3 1 300 3,5,6-Trichloro-2-pyridinol 6515-38-4 1

Sample Compound CAS Count 301 1,1,2,3-Tetramethylcyclopropane 74752-93-5 1 302 Diiodomethane 75-11-6 1 303 2-Methyl-1-phenylpropene 768-49-0 1 304 Nitrogen trifluoride 7783-54-2 1 305 Phosphorus trifluoride 7783-55-3 1 306 Ethyl p-toluenesulfonate 80-40-0 1 307 Glyceryl 1-caprylate dicaprate 82426-88-8 1 308 Methyl nonyl phthalate 91485-82-4 1 309 1,2-Benzenedicarboxylic acid, methyl octyl ester 91485-83-5 1 310 beta-Sitosterol acetate 915-05-9 1 311 2-Bromotoluene 95-46-5 1 312 4-Methylbenzoic acid 99-94-5 1 313 2-Ethylhexyl isohexyl ester phthalic acid 998308-98-5 1 314 Hexyl pentadecyl ester sulfurous acid 998309-13-7 1 315 1,2-Benzenediol, O-(1-naphthoyl)- 998325-94-2 1 316 Methyl 8-(5-hexyl-2-furyl)-octanoate 998336-35-9 1 317 Acetate (-)-Isolongifolol 998352-28-0 1 318 Phthalic acid, hept-4-yl isobutyl ester 998356-78-3 1 319 Phthalic acid, hept-3-yl undecyl ester 998356-99-5 1 320 Methyl pentyl phthalate 998373-89-5 1 321 Carbonic acid, tetradecyl vinyl ester 998382-54-5 1 322 Chlorodifluoroacetate cholesterol 998394-58-1 1 323 Decyl octyl ether 998406-38-3 1 324 3-Methoxy-4-hydroxy mandelonitrile 998425-21-5 1 325 Amberonne (isomer 2) 998470-69-8 1 326 Urea, tetramethyl- 632-22-4 1 327 Tris(2-chloropropyl) phosphate 6145-73-9 1 328 Tris(2-butoxyethyl) phosphate 78-51-3 1 329 Sedoheptulosan tetrabenzoate 998130-06-2 1 330 Retene 483-65-8 1 331 Quinoline, 2,7-dimethyl- 93-37-8 1 332 Phthalide 87-41-2 1 333 Phthalic acid, hex-3-yl nonyl ester 998356-96-0 1 334 Phthalic acid, hept-4-yl nonyl ester 998356-79-0 1 335 Phthalic acid, 2-ethylbutyl nonyl ester 998356-89-4 1 336 Phenol, 4-(1,1-dimethylpropyl)- 80-46-6 1 337 Pentatriacontane 630-07-9 1 338 Pentadecane 629-62-9 1 339 p-Anisic acid, 4-cyanophenyl ester 998307-63-7 1 340 p-Aminotoluene 106-49-0 1 341 Oxybis(propane-1,2-diyl) dibenzoate 94-03-1 1 342 Octacosanol 557-61-9 1 343 Octacosane 630-02-4 1 344 Naphthalene, 2-ethenyl- 827-54-3 1 345 Naphthalene, 2-(1-methylethyl)- 2027-17-0 1 346 Naphthalene, 1,3,5,7-tetrachloro- 53555-64-9 1 347 Naphthacene 92-24-0 1 348 N-Butyl-p-toluenesulfonamide 1907-65-9 1 349 Metolachlor 51218-45-2 1 350 Methyl tetradecanoate 124-10-7 1

Sample Compound CAS Count 351 Methyl dodecanoate 111-82-0 1 352 Mesitylene 108-67-8 1 353 Iodoform 75-47-8 1 354 Dotriacontane, 1-iodo- 998406-32-4 1 355 Docosane, 1-iodo- 998406-31-9 1 356 Diheptyl phthalate 3648-21-3 1 357 Dibenzothiophene 132-65-0 1 358 Deethylatrazine 6190-65-4 1 359 Decane, 3,7-dimethyl- 17312-54-8 1 360 Carbonyl sulfide 463-58-1 1 361 Caffeine 58-08-2 1 362 Bicyclo[3.1.1]hept-3-en-2-one, 4,6,6-trimethyl-, (1S)- 1196-01-6 1 363 Benzyl Benzoate 120-51-4 1 364 Benzothiazole, 2-methyl- 120-75-2 1 365 Benzene, 1,2,4-trichloro- 120-82-1 1 366 Benzene, 1,1'-methylenebis[4-isocyanato- 101-68-8 1 367 Benzene, (1-ethylnonyl)- 4536-87-2 1 368 A'-Neogammacer-22(29)-ene 1615-91-4 1 369 7H-Benzo[c]fluorene 205-12-9 1 370 4,4'-Dimethylbiphenyl 613-33-2 1 371 4-tert-Butylstyrene 1746-23-2 1 372 4-Methylbenzenethiol 106-45-6 1 373 4-Methylbenzenesulfonamide 70-55-3 1 374 4-Ethylbenzoic acid, 4-nitrophenyl ester 998307-12-7 1 375 4-Chloroaniline 106-47-8 1 376 3-Methylbenzoic acid 99-04-7 1 377 2,6-Dimethylnaphthalene 581-42-0 1 378 2,4,6-Trimethylbenzoic acid 480-63-7 1 379 2-Propenal 107-02-8 1 380 2-Methyldibenzothiophene 998383-55-1 1 381 2-Isopropyl-5-methylanisole 1076-56-8 1 382 2-Ethylhexyl salicylate 118-60-5 1 383 2-Acetyl-5-methylfuran 1193-79-9 1 384 2-Acetyl-2-methyltetrahydrofuran 32318-87-9 1 385 2-[(Chloromethyl)sulfanyl]-1,3-benzothiazole 28908-00-1 1 386 1,4,5,8-Tetramethylnaphthalene 2717-39-7 1 387 1,3-Benzenediol, o-(2-methoxybenzoyl)-o'-ethoxycarbonyl- 998330-77-1 1 388 1,2,4,5-Tetrachlorobenzene 95-94-3 1 389 Longicyclene 1137-12-8 1 390 1,2-Dichlorobenzene 95-50-1 1 391 1,16-Hexadecanediol 7735-42-4 1 392 1,13-Tetradecadiene 21964-49-8 1 393 1,12-Dodecanediol 5675-51-4 1 394 1,1'-Biphenyl, 4-methyl- 644-08-6 1 395 (Z)-Docos-9-enenitrile 998465-48-0 1 396 (Z)-9-Octadecen-1-ol 143-28-2 1

Figure S1. Complete heat map of ToxCast 90 chemicals and their detection frequency by aquifer. Percentage values indicate the percentage of samples from a given aquifer in which the ToxCast chemical was detected. Reference well samples are not included in this heat map. Darker cell colors correspond to greater percentages. Superscripts indicate the possible sources of the chemical: a for anthropogenic sources, b for biogenic sources, g for geogenic sources. Compound names in orange signify the chemical has only anthropogenic sources.

Upper Basement Castle Black Upper Lower Aquifer Surficial Yorktown Beaufort Peedee Black Rock Hayne Creek Cape Fear Cape Fear Creek Depth Range (m) 1-32 6-60 7-62 4-198 30-257 20-292 28-119 22-261 115-250 12-315 Sample Number 33 7 13 26 7 11 3 14 14 7

Name Class log Kow CAS a Tetraethylene glycol di(2-ethylhexanoate) Ester 4.82 18268-70-7 0% 29% 0% 4% 14% 0% 0% 7% 7% 14% a 3-Methylphenanthrene PAH 5.15 832-71-3 6% 0% 8% 8% 0% 9% 0% 7% 0% 0% a 2-Ethylhexanoic acid Carboxylic acid 2.64 149-57-5 3% 0% 0% 4% 0% 0% 0% 7% 14% 14% a Cyclododecanol Alcohol 4.58 1724-39-6 3% 0% 8% 4% 0% 0% 0% 7% 0% 14% a,b,g Fluoranthene PAH 5.16 206-44-0 6% 0% 0% 4% 0% 9% 33% 0% 7% 0% a Atrazine Triazine 2.61 1912-24-9 3% 0% 0% 0% 0% 0% 0% 7% 14% 29% 2-Ethyltoluenea Benzene 3.67 611-14-3 6% 0% 0% 4% 0% 0% 0% 0% 7% 14% a,b 1,4-Dimethylnaphthalene PAH 4.37 571-58-4 3% 0% 8% 0% 0% 0% 0% 14% 0% 0% a Tris(2-chloroethyl) phosphate Organophosphate 1.44 115-96-8 0% 14% 8% 4% 14% 0% 0% 0% 0% 0% Benzeneacetic acida,b Benzene 1.41 103-82-2 6% 0% 8% 0% 0% 0% 0% 0% 0% 0% a,b (+)-Camphor Ketone 2.51 464-49-3 3% 0% 0% 0% 0% 0% 0% 14% 7% 0% Drometrizolea PAH 3.86 2440-22-4 3% 0% 0% 0% 0% 9% 0% 7% 0% 0% a 3-Hydroxyphenyl benzoate Phenol 2.85 136-36-7 6% 0% 0% 4% 0% 0% 0% 0% 0% 0% a 2-Mercaptobenzothiazole Thiazole 2.51 149-30-4 0% 0% 0% 0% 0% 0% 0% 14% 7% 0% a 2,6-Diisopropylphenyl isocyanate Benzene 4.58 28178-42-9 0% 0% 0% 4% 14% 0% 0% 0% 7% 0% Biphenyla,g Benzene 4.01 92-52-4 3% 0% 0% 0% 14% 0% 0% 0% 7% 0% Methyl palmitatea,b Ester 7.38 112-39-0 0% 14% 0% 0% 0% 9% 0% 0% 0% 14% Sulfentrazonea Sulfur compound 0.99 122836-35-5 0% 0% 0% 0% 0% 0% 0% 0% 0% 14% a,b,g Phenanthrene PAH 4.46 85-01-8 0% 0% 0% 8% 0% 0% 33% 0% 0% 0% a,b Heptadecane Alkane 9.06 629-78-7 3% 0% 0% 4% 0% 0% 0% 0% 0% 0% a,b 2-Butyl-1-octanol Alcohol 4.65 3913-02-8 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% a Tetramethylthiourea Sulfur compound 0.49 2782-91-4 0% 0% 0% 0% 0% 0% 0% 14% 0% 0% a Bisphenol A Phenol 3.32 80-05-7 0% 0% 0% 4% 0% 0% 0% 7% 0% 0% a,b 2-Phenylphenol Phenol 3.09 90-43-7 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% 1,2,3-Trimethylbenzenea,b,g Benzene 3.63 526-73-8 3% 0% 0% 0% 0% 0% 0% 0% 0% 14% a,b Methyl salicylate Benzoate 2.55 119-36-8 0% 0% 0% 4% 0% 0% 0% 0% 0% 14% a,b N-Butylbenzenesulfonamide Sulfone 2.18 3622-84-2 0% 0% 0% 0% 0% 9% 0% 7% 0% 0% a,g 1-Methylphenanthrene PAH 5.08 832-69-9 0% 0% 0% 4% 0% 0% 0% 7% 0% 0% a 1,2-Diphenoxyethane Benzene 3.81 104-66-5 3% 0% 0% 0% 0% 9% 0% 0% 0% 0% 1,2-Dimethylnaphthalenea,b PAH 4.31 573-98-8 3% 0% 8% 0% 0% 0% 0% 0% 0% 0% a,b Tetradecane Alkane 7.60 629-59-4 0% 0% 8% 0% 0% 9% 0% 0% 0% 0% a 3-Chlorotoluene Chlorobenzene 3.28 108-41-8 0% 0% 0% 8% 0% 0% 0% 0% 0% 0% Methyl formatea,b Ester 0.03 107-31-3 0% 14% 0% 0% 0% 0% 0% 0% 0% 0% a o-Toluenesulfonamide Sulfone 0.84 88-19-7 0% 0% 8% 4% 0% 0% 0% 0% 0% 0% a,b Oleyl alcohol Alcohol 7.60 143-28-2 0% 0% 0% 0% 0% 9% 0% 0% 0% 0%

Name Class log Kow CAS a 1,2,4-Trichlorobenzene Chlorobenzene 4.02 120-82-1 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% a Metolachlor Benzene 3.13 51218-45-2 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% a,b Benzyl benzoate Benzoate 3.97 120-51-4 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% a 4-tert-Amylphenol Phenol 3.91 80-46-6 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% a,b Thymol methyl ether Benzene 4.04 1076-56-8 0% 0% 0% 0% 0% 0% 33% 0% 0% 0% a 1,2,4,5-Tetrachlorobenzene Chlorobenzene 4.62 95-94-3 0% 0% 0% 0% 0% 0% 33% 0% 0% 0% 2,6-Dimethylnaphthalenea,g PAH 4.31 581-42-0 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% Ronnela Organophosphate 4.88 299-84-3 0% 0% 0% 0% 0% 0% 0% 0% 0% 14% Mesitylenea,g Benzene 3.42 108-67-8 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% Tris(2-chloropropyl) phosphatea Organophosphate 2.89 6145-73-9 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3,5,6-Trichloro-2-pyridonea Pyridone 3.21 6515-38-4 0% 0% 0% 0% 0% 0% 0% 0% 0% 14% a,b p-Toluidine Amine 1.39 106-49-0 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% a 4-Chloroaniline Amine 1.83 106-47-8 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% a,b Iodoform Organoiodine 3.08 75-47-8 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% a Deethylatrazine Triazine 1.51 6190-65-4 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% a,b Phthalide Benzofuran 0.80 87-41-2 0% 0% 0% 0% 0% 9% 0% 0% 0% 0% a,g Dibenzothiophene PAH 4.44 132-65-0 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% a 4-tert-Butylstyrene Benzene 4.43 1746-23-2 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% a 2,4,6-Trimethylbenzoic acid Benzoate 2.89 480-63-7 0% 0% 8% 0% 0% 0% 0% 0% 0% 0% a 2-Ethylhexyl salicylate Phenol 5.29 118-60-5 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% a 1,2-Dichlorobenzene Chlorobenzene 3.43 95-50-1 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% a Methyl dodecanoate Ester 5.41 111-82-0 0% 14% 0% 0% 0% 0% 0% 0% 0% 0% a Corodane Cycloalkane 2.55 13380-94-4 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% a,b Caffeine Xanthine -0.07 58-08-2 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% Tris(2-ethylhexyl) trimellitatea Benzoate 5.94 3319-31-1 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% a,b,g Retene PAH 6.08 483-65-8 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% a 4-Methylbenzenethiol Sulfur compound 2.82 106-45-6 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% a p-Toluenesulfonamide Sulfone 0.82 70-55-3 0% 0% 8% 0% 0% 0% 0% 0% 0% 0% a Tris(2-butoxyethyl) phosphate Organophosphate 3.75 78-51-3 0% 0% 8% 0% 0% 0% 0% 0% 0% 0% a,b,g Pentadecane Alkane 8.04 629-62-9 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% a Diheptyl phthalate Phthalic acid 7.49 3648-21-3 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% a,b m-Toluic acid Benzoate 2.37 99-04-7 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% a N-Butyl-p-toluenesulfonamide Sulfone 2.75 1907-65-9 0% 14% 0% 0% 0% 0% 0% 0% 0% 0%