Bacterial quality of groundwater downgradient of onsite wastewater disposal systems and the influence on eastern Long Island's embayments

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Bacterial quality of groundwater downgradient of onsite wastewater disposal systems and the influence on eastern Long Island's embayments Michael E. Kauscha,b,1, Shawn C. Fisherb, Irene J. Fisherb, Patrick J. Phillipsc, Gregory D. O'Mullana,∗ a

School of Earth and Environmental Sciences, Queens College, City University of New York, 65-30, Kissena Boulevard, Queens, New York, USA U.S. Geological Survey, New York Water Science Center, 2045 Route 112, Building 4, Coram, NY, USA c U.S. Geological Survey, New York Water Science Center, 425 Jordan Road, Troy, NY, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: Fecal indicator bacteria Antibiotic resistance Coastal water quality Groundwater

Onsite wastewater disposal systems (OWDS) can introduce bacterial and chemical contaminants, via groundwater, into aquifers and adjacent waterways. We evaluated the concentration of fecal indicator bacteria (FIB) and antibiotic resistant bacteria (ARB) in the shallow groundwater of Eastern Long Island, New York, downgradient of OWDS using cultivation approaches and analysis of 16 S rRNA genes. While FIB and ARB were detected in 80% and 67% of groundwater samples, respectively, concentrations were low, suggesting that, at least at the time of sampling, groundwater was not a large-scale source of fecal bacterial contamination to adjacent embayments. ARB isolates did not include common fecal associated genera and the concentration of FIB and ARB did not correlate well with the concentration of pharmaceutical contaminants, suggesting that bacterial contaminants were poorly linked to OWDS discharge. Concentrations of FIB in the studied embayments were significantly greater in nearshore compared to mid-channel environments, suggesting that land-based sources are likely to be the major contributors of bacterial contamination.

1. Introduction Coastal ecosystems are prone to anthropogenic alteration and degradation through the delivery of nutrients (Howarth et al., 2003; Kirby and Miller, 2005; U.S. EPA, 2015), pharmaceuticals (Cantwell et al., 2017), industrial pollutants (Islam et al., 2015; Phillips et al., 2016), sewage, and fecal contaminants (WHO, 2003; Sauer et al., 2011; U.S. EPA, 2012). Fecal bacteria are among the most commonly evaluated surface water contaminants with thousands of beaches monitored at least weekly across the United States for fecal indicator bacteria (FIB) including fecal coliforms, Escherichia coli, and enterococci. In 2013, approximately 10% of all (13% for New York) coastal beach samples collected nationwide exceeded the U.S. Environmental Protection Agency (EPA) Beach Action Value (190 Colony Forming Units/100 ml for E. coli and 60 Colony Forming Units/100 ml for enterococci; Dorfman and Haren, 2014), suggesting an elevated risk of illness. The concentration of FIB has been found to positively correlate with the risk of infection from contact with recreational water (Cabelli et al., 1982; Prüss, 1998; Yau et al., 2009), especially in children (Arnold et al., 2016). The number of infections and waterborne disease outbreaks

resulting from recreational contact with contaminated waters has steadily increased in the United States in recent decades (Yoder et al., 2008; Dorfman and Haren, 2014). Nationwide, there is growing concern about the cumulative impact of increasing population density in coastal areas (Creel, 2003; Crosset et al., 2005) and aging sewage infrastructure on the water quality of coastal beaches and waterways used for recreation. The water quality of coastal environments can be influenced by both surface and subsurface transport of anthropogenic contaminants (Howarth et al., 2002; Boehm et al., 2004; Slomp and Van Cappellen, 2004; Ahn et al., 2005; Fisher et al., 2016). Groundwater contamination has been associated with the degradation of coastal water quality through delivery of FIB (Weiskel et al., 1996; Boehm et al., 2004), nutrient enrichment (Slomp and Van Cappellen, 2004; Kroeger et al., 2006), and expansion of harmful algal blooms (LaRoche et al., 1997; Cerrato et al., 2004). Contaminant transport through shallow subsurface systems is determined by factors including concentration and properties of the contaminant, hydrodynamic dispersion, and adsorption. Transport rate differs among dissolved and suspended materials with subsurface bacterial transport constrained by pore size distribution

∗

Corresponding author. E-mail address: [email protected] (G.D. O'Mullan). 1 Present address: Louis Calder Center - Biological Field Station, Fordham University, 31 Whippoorwill Road, Armonk, New York, USA. https://doi.org/10.1016/j.marpolbul.2019.110598 Received 12 October 2018; Received in revised form 10 September 2019; Accepted 11 September 2019 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Michael E. Kausch, et al., Marine Pollution Bulletin, https://doi.org/10.1016/j.marpolbul.2019.110598

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embayment surface water. We hypothesized that elevated FIB and ARB assemblages would be detected at high concentrations downgradient of communities that utilize OWDS, acting as a potentially significant source of microbial contamination to adjacent coastal embayments. Additionally, we predicted that ARB abundance would be positively correlated with FIB and chemical contaminants. We accomplished our objectives by analyzing bacterial communities in samples collected from shallow groundwater downgradient of OWDS (n = 15) and by analyzing patterns in publicly available coastal water-quality monitoring FIB data from embayment sites in the vicinity of the groundwater monitoring locations. These analyses could expand our understanding of groundwater-surface water interaction in coastal areas and aid managers in better understanding the consequences of sewage discharge from OWDS on adjacent coastal waterways.

and particle surface adsorption (Lindgvist and Enfield, 1992; Bellin and Rao, 1993). Onsite wastewater disposal systems (OWDS), such as cesspools, do not employ treatment methods and are used by approximately 380,000 residences in Suffolk County (Fisher et al., 2016). Discharge to the vadose (unsaturated) zone can be a source of shallow groundwater contamination, particularly in areas with high population densities and permeable soils. Well-sorted sandy systems, such as Long Island's shallow aquifer, would be expected to have fairly efficient transport of sewage associated contaminants, but perhaps greater chemical than microbiological transport. In addition to the delivery of FIB and nutrients, OWDS can also introduce chemical contaminants of emerging concern, including antibiotics and other pharmaceutical compounds, to shallow groundwater (Conn et al. 2006, 2010; Godfrey et al., 2007; Underwood et al., 2011; Haack et al., 2012; Fisher et al., 2016). Exposure of groundwater bacteria to antibiotics, even at trace concentrations, can alter biogeochemical activity and encourage evolution of antibiotic resistance (Segura et al., 2009; Underwood et al., 2011; Haack et al., 2012). The widespread development of resistant infections attributed to the overuse and misuse of antibiotics in agricultural and clinical applications has become a serious public health concern (Wise et al., 1998; Morris and Masterton, 2002; Wise et al., 2011; WHO, 2014). Antibiotic resistant bacteria (ARB) abundance has been linked to sewage discharges in aquatic ecosystems, including detection of resistant organisms associated with the human gut microbiome (Baquero et al., 2008; Young et al., 2013). Even relatively pristine natural environments can harbor diverse populations of ARB (Martínez, 2008; Allen et al., 2010), however, the taxonomic identity of resistant bacteria would be expected to differ as compared to those in sewage impacted environments. The combined introduction of allochthonous microbes, nutrients, and pharmaceuticals can stimulate microbial succession in the subsurface environment and promote acquisition of antibiotic resistance genes through horizontal gene transfer (Segura et al., 2009), an important pathway by which resistance genes are spread through a microbial community (Bennett, 2008; Norman et al., 2009; Davies and Davies, 2010). Therefore, changes in groundwater bacterial resistance can occur from either transport of allochthonous bacteria from a concentrated pollution source or via selection in autochthonous microbes as a consequence of pre-existing bacteria being exposed to antibiotics or genetic elements promoting resistance (Gillings, 2018). Inadequate onsite wastewater disposal has been listed as one of the top ten sources of water quality impairment in New York State (N.Y. DEC, 2016). Recent studies in Long Island, New York, have highlighted the importance of nutrient and pharmaceutical discharges from OWDS to the shallow subsurface medium (Zhao et al., 2011; Phillips et al., 2015; Fisher et al., 2016). With a population of approximately 1.5 million residents (https://www.census.gov/quickfacts/fact/table/ suffolkcountynewyork), greater than 50 marine swimming beaches, and the high frequency of OWDS in Suffolk County, an understanding of bacterial contaminants present in shallow groundwater and interaction with surface water has become a management priority. Despite this, while FIB have been monitored in surface water (SCDHS, 2016), no microbial survey has previously been reported from eastern Long Island's shallow coastal groundwater. This study was also motivated in part by concerns following Hurricane Sandy in October 2012 related to potential damage to OWDS from coastal flooding and saltwater inundation. The goals of this study were to: 1) characterize the extent of FIB in groundwater downgradient of coastal communities using OWDS; 2) compare concentrations of FIB in groundwater to the concentrations of inorganic forms of nitrogen and pharmaceutical compounds found in samples collected in parallel as reported by Fisher et al. (2016); 3) examine ARB abundance, taxonomic diversity, and potential for influence by this septic discharge in shallow groundwater; and 4) compare the concentrations of FIB in coastal groundwater with those reported from publicly available water-quality monitoring data from adjacent

2. Methods 2.1. Groundwater sampling sites and extraction Between the months of September and December in 2013, groundwater was sampled once at each of 15 locations in the shallow aquifer on eastern Long Island where wastewater-influenced groundwater discharges into adjacent surface waters are either known or suspected (Fig. 1; Fisher et al., 2016). These 15 locations were characterized primarily as mixed use/medium density residential with two to four dwellings per 0.4 ha by Fisher et al. (2016) and comprised four regional groupings: Peconic; Forge River; Fire Island; Oakdale. Publicly available monitoring data from adjacent surface waters, collected during an overlapping period from our groundwater samples in this study, show that surface waters adjacent to these groundwater sites are often degraded by elevated FIB (data available from Suffolk County Department of Health Services, Office of Ecology), providing some of the justification for site selection. The surface water monitoring data were publicly available, but the analyses described in the results section are novel both in their spatial comparisons and the use of these data for comparison to groundwater samples. Despite the surface water quality data being publicly available, the patterns described have not been previously reported. Temporary wells were installed using a drive point sampler (Gas Vapor Probe Kit; AMS, Inc.) at 12 locations; monitoring wells were sampled at three other locations. Depth to water table varied by location with eight feet being our deepest sampling depth. A battery-operated peristaltic pump was used to pump groundwater at a rate of approximately 0.5 L per minute. When samples were collected from monitoring wells, purging was conducted as per U.S. Geological Survey National Field Manual for the Collection of Water-Quality Data (USGS, variously dated) prior to sampling. A multiparameter sonde (YSI, Inc.) and turbidity meter were used to measure physical constituents including: temperature, pH, conductivity, dissolved oxygen, turbidity. Groundwater sampling began after stabilization of physical constituents as per USGS National Field Manual (USGS, variously dated). Immediately before groundwater was collected for the biological analyses presented in this study, split groundwater samples were collected for chemical analyses, including nutrients, pharmaceuticals, and wastewater-indicator compounds - methods for collection and results of the chemical analyses are presented in Fisher et al. (2016). 2.2. Surface water fecal bacterial data For the purpose of evaluating groundwater as a potential fecal bacterial source to the adjacent coastal embayments, FIB data from surface waters, in the nearshore and mid-channel of coastal waterways, were gathered from the Suffolk County Department of Health Services (SCDHS), Office of Ecology. EPA methods SM 9221B-2006 and SM 9221C, E−2006 were used for enumeration of total coliforms and fecal coliforms, respectively. Total and fecal coliform concentrations 2

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Fig. 1. Map of Eastern Long Island showing groundwater sampling sites and surface water sites. Fecal indicator bacteria (FIB) data from Suffolk County Department of Health Services (SCDHS), Office of Ecology were analyzed to show significant differences in nearshore compared to mid-channel surface water sites and for comparison to groundwater sampling results.

Bacterial quality of groundwater in eastern Long Island, New York) on Open Science Framework database (https://osf.io/b69sh/). Samples for enumeration of HET and ARB were serially diluted 10fold into autoclaved and 0.22 μm filtered groundwater and 100 μl plated on solid R2A agar (Reasoner and Geldreich, 1985), with or without the addition of antibiotics. Methods similar to Munir et al. (2011) and Kim et al. (2010) were used for enumeration of ARB on R2A agar amended with 50 mg/L sulfamethoxazole, 50 mg/L ampicillin, or 10 mg/L tetracycline. Inoculated plates were incubated at 28 °C for five days and bacterial colonies were enumerated on plates with the lowest dilution displaying growth of less than 300 colonies. Dilution water was plated on all media types and processed in parallel with samples as a negative control. Results for HET and ARB are reported as Colony Forming Units per ml (CFUs/ml) of water.

detected between January 2011 and December 2014 in samples collected monthly from the South Shore Bays, Peconic Estuary, and Forge River were analyzed to highlight spatial patterns and allow comparison to FIB levels in adjacent groundwater within the four study regions identified in the previous section. Fire Island and Oakdale were both located adjacent to the Great South Bay (Fig. 1) and therefore, the same mid-channel site was used for comparison to FIB in nearshore and groundwater sites in these regions. Nearshore sites in the Peconic included three coastal and two tributaries for comparison to mid-channel and groundwater sites. 2.3. Groundwater bacterial enumeration Samples for enumeration of total coliforms and E. coli were collected in sterile 120 ml vessels with sodium thiosulfate and processed within 6 h of collection (EPA method 9223B) using the IDEXX Colilert methodology. Enterococci samples were collected using the same approach and processed within 6 h of collection (EPA method 1600) using the IDEXX Enterolert methodology. The results of these FIB assays are reported as a Most Probable Number per 100 ml (MPN/100 ml), to allow for direct comparison to water quality standards. Samples for enumeration of total heterotrophic (HET) and antibiotic resistant bacteria (ARB) were collected in sterile 15 ml centrifuge tubes and processed within 12 h of collection. Collection containers were immediately stored on ice and protected from sunlight until processing. Sterilized water was brought into the field and transferred into sterile 120 ml vessels at each sampling site as controls to identify any contamination during sample collection, handling, and lab processing. Controls were processed in parallel with samples and yielded no detectable growth in all cases. Triplicate samples for enumeration of HET and ARB were collected at three locations for quality control. The mean coefficient of variation was 19% for HET and 42% for ARB. Groundwater bacteria raw data are available in a publicly-accessible project (project title:

2.4. Taxonomic identification of groundwater antibiotic resistant isolates Isolated colonies were picked off the antibiotic-amended R2A media and suspended in 30 μl of nuclease-free (HyClone) water for molecular analysis. Tubes containing bacterial biomass were heated to 95 °C for 5 min to lyse cells and frozen at −20 °C until further processing could be completed. PCR was used to amplify 16 S rRNA genes using universal bacterial primers 8F and 1492R (Teske et al., 2002). Controls, including HyClone water as an amplification template, were included to identify any contamination during the process of sample picking and amplification, but negative controls did not amplify. Amplicons were visualized using gel electrophoresis and sent to Eton Bioscience Inc. (Union, NJ) for single pass Sanger sequencing with the same 8F primer. The resulting sequences were grouped into three libraries containing sequences from sulfamethoxazole, ampicillin, and tetracycline resistant isolates. Sequence files were manually edited to include only high quality reads of at least 500 basepairs using Geneious software (https:// www.geneious.com/) and uploaded in FASTA format to the Ribosomal 3

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coliforms have been detected at levels exceeding EPA guidelines for recreational water in eastern Long Island's coastal waters (Table 2). Between 2011 and 2014, total coliforms were detected in 59% of samples collected from nearshore surface water sites (3.5 × 102 MPN/ 100 ml mean concentration; > 1.6 × 104 MPN/100 ml maximum concentration), and in 13% of samples collected from mid-channel surface water sites (9.1 MPN/100 ml mean concentration; 7.0 × 102 MPN/ 100 ml maximum concentration). During the same time period, fecal coliforms were detected in 39% of samples collected from nearshore surface water sites (1.5 × 102 MPN/100 ml mean concentration; 1.6 × 104 MPN/100 ml maximum concentration), and in 6% of samples collected from mid-channel surface water sites (4.8 MPN/100 ml mean concentration; 3 × 102 MPN/100 ml maximum concentration). In the Forge River, fecal coliforms were detected at significantly greater concentrations (Kruskal-Wallis H = 11.7, p < 0.05) at upriver sites than in the discharge area along the south shore (Fig. 2). Overall, in surface water, fecal coliforms were detected at significantly greater concentrations (Mann-Whitney U = 0, p < 0.05) in samples collected from nearshore sites than in samples collected from mid-channel sites (Fig. 3). Total coliforms were detected at significantly greater concentrations (Kruskal-Wallis H = 9.33, p < 0.01; Dunn's multiple comparison mean rank difference = −7.75, p < 0.01) in samples collected from nearshore surface water sites than in samples collected from groundwater sites (Fig. 4).

Database Project webserver (RDP Release 11, Update 5) (https://rdp. cme.msu.edu/) for alignment and classification using the RDP Naive Bayesian rRNA Classifier Version 2.11 (classifier query run on March 21, 2018) at the 95% confidence level. DNA sequences have been deposited in the National Center for Biotechnology Information's GenBank database under accession numbers MG951844-MG952226. 2.5. Statistical analyses Prism statistical analysis software was used to perform non-parametric tests to evaluate our data because microbial and geochemical data commonly do not meet the assumptions of parametric statistics. Kruskal-Wallis analysis of variance was used to evaluate the significance of differences of total coliform concentrations in groundwater and surface water environments. Fecal coliform concentrations in surface water were analyzed for significance of differences using a MannWhitney U test. Spearman's rank correlation was used to examine the association between bacterial and chemical contaminant concentrations. For bacterial data, values of zero were replaced with values of 0.1 or 1, depending on the minimum detection limit of the assay, when calculating geometric means or graphing data on logarithmic scales. Statistical analyses were considered significant at p values less than 0.05. 3. Results

3.3. Abundance, identification, and diversity of ARB in groundwater

3.1. FIB in groundwater

ARB were detected in 10 of the 15 (67%) groundwater samples at concentrations spanning more than three orders of magnitude with extensive spatial variability across our study area (Table 3; Fig. 5). Sulfamethoxazole resistant bacteria (SMX) were detected in 67% of samples with a mean and maximum concentration of 1.2 × 102 CFUs/ ml and 1.1 × 103 CFUs/ml, respectively. Ampicillin resistant bacteria (AMP) were detected in 47% of samples with a mean and maximum concentration of 20 CFUs/ml and 1.5 × 102 CFUs/ml, respectively. Tetracycline resistant bacteria (TET) were detected in 33% of samples with a mean and maximum concentration of 20 CFUs/ml and 2.4 × 102 CFUs/ml, respectively. ARB were not correlated with the total (summed) concentration of pharmaceuticals (PHARM) (PHARM vs. SMX rs = −0.057, p = 0.84; PHARM vs. AMP rs = 0.042, p = 0.88; PHARM vs. TET rs = −0.27, p = 0.33) and SMX resistant bacteria were not correlated with concentrations of sulfamethoxazole (rs = −0.018, p = 0.919). ARB were positively correlated with HET (HET vs. SMX rs = 0.92, p < 0.01; HET vs. AMP rs = 0.77, p < 0.01; HET vs. TET rs = 0.56, p < 0.05). HET and SMX did not differ significantly (Kruskal-Wallis H = 20.44, p < 0.01; Dunn's multiple comparison mean rank difference = 12.3, p = 0.27) and SMX were consistently more abundant than AMP and TET (Fig. 5). Proteobacteria, the most abundant phylum detected from 16 S rRNA gene sequences of 383 antibiotic resistant isolates, accounted for 72%

FIB were detected in 12 of the 15 (80%) groundwater samples and in all four regions of our study area. However, when detected, FIB were present at very low concentrations. Total coliforms were detected in 40% of groundwater samples with a mean and maximum concentration of 1.3 MPN/100 ml and 12 MPN/100 ml, respectively. E. coli were detected in 20% of groundwater samples with a mean and maximum concentration of 0.4 MPN/100 ml and 2 MPN/100 ml, respectively. Enterococci were detected in 53% of groundwater samples with a mean and maximum concentration of 0.6 MPN/100 ml and 2 MPN/100 ml, respectively (Table 1). No correlation was found between the abundance of total coliforms and inorganic forms of nitrogen including NH4+ (rs = 0.35, p = 0.22), NO3- + NO2- (rs = −0.16, p = 0.58) and total inorganic nitrogen (rs = 0.21, p = 0.46). Similarly, no correlation was found between the levels of total coliforms and the total concentration of pharmaceuticals (rs = −0.0026, p = 0.99) measured in parallel samples at the same collection location and time by Fisher et al. (2016). 3.2. Comparison of FIB in groundwater to surface water Water-quality data obtained from the SCDHS Office of Ecology's publicly available database show that total coliforms and fecal

Table 1 Fecal indicator bacteria (FIB) concentrations in groundwater. Data show percentage of groundwater samples in which total coliforms, E. coli, and enterococci were detected. Maximum concentrations are reported as a Most Probable Number per 100 ml (MPN/100 ml). Region

Peconic Forge River Fire Island Oakdale All sites

Number of sites

6 2 5 2 15

Total Coliforms

E. coli

Enterococci

% of samples detected

maximum concentration (MPN/100 ml)

% of samples detected

maximum concentration (MPN/100 ml)

% of samples detected

maximum concentration (MPN/100 ml)

17% 50% 80% 0% 40%

Not availablea 12 2 <1 12

17% 50% 20% 0% 20%

1 2 2 <1 2

50% 0% 60% 100% 53%

1 <1 1 2 2

a Total coliforms maximum concentration for the Peconic region is not available because the sample water at one of the sites positive for E. coli was too turbid to interpret the color change indicative of the presence of coliforms.

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Table 2 Fecal indicator bacteria (FIB) concentrations in nearshore and mid-channel surface waters based on monthly water quality monitoring data from Suffolk County Department of Health Services, Office of Ecology. Data show percentage of surface water samples in which total coliforms and fecal coliforms were detected, demonstrating increased occurrence in nearshore as compared to mid-channel environments. Maximum concentrations are reported as a Most Probable Number per 100 ml (MPN/100 ml). Region

Peconic Forge River Fire Island Oakdale All sites

Number of sites

nearshore mid-channel nearshore mid-channel nearshore mid-channel nearshore mid-channel nearshore mid-channel

5 2 1 1 1 1 1 1 8 4

Total coliforms

Fecal coliforms

% of samples detected

maximum concentration (MPN/ 100 ml)

% of samples detected

maximum concentration (MPN/ 100 ml)

56% 3% 60% 38% 54% 15% 68% 15% 59% 13%

> 16,000 20 800 90 3000 700 5000 700 > 16,000 700

39% 3% 40% 18% 41% 3% 35% 3% 39% 6%

16,000 20 500 70 1300 300 1100 300 16,000 300

Fig. 2. Fecal coliform annual (2011–2014) geometric mean (geomean) concentrations demonstrating the spatial distribution gradient observed in the Forge River. Box and whisker representation of the data include a central line representing the median and whiskers representing the full range of annual geomean concentrations.

Fig. 4. Total coliforms were detected at significantly greater concentrations (Kruskal-Wallis H = 9.33, p < 0.01; Dunn's multiple comparison mean rank difference = −7.75, p < 0.01) in nearshore surface waters than in groundwater. Bars represent mean and standard deviation, and ND indicates that total coliforms were not detected in groundwater in the Peconic and Oakdale regions.

accounted for 46% of sulfamethoxazole resistant sequences. Hydrogenophaga and Reyranella were the dominant genera in the ampicillin resistant sequence library and accounted for 40% of ampicillin resistant sequences. Afipia was the dominant genus in the tetracycline resistant isolate library. Genera commonly associated with sewage contamination, (e.g. Escherichia, Enterobacter, and Klebsiella) were not detected in the groundwater isolates screened, although they had been detected in prior studies of sewage impacted surface water using similar sampling effort and methods conducted in the same laboratory (Young et al., 2013).

4. Discussion Fig. 3. Fecal coliforms in surface water were detected at significantly greater concentrations (Mann-Whitney U = 0, p < 0.05) in nearshore than in midchannel environments. Bars represent mean and standard deviation.

4.1. Potential for groundwater to act as a source of FIB to coastal embayments Although some form of FIB was detected in 80% of groundwater samples and in all four regions of our study area, when detected, concentrations were extremely low. Total coliforms were significantly less abundant in groundwater than in nearshore surface water, suggesting that groundwater is likely not a major regional source of FIB to coastal embayments. However, FIB were present in surface water at

of all sequences, including 66% of sulfamethoxazole, 78% of ampicillin, and 77% of tetracycline resistant sequences. Dominant genera were variable across differing antibiotic resistant sequence libraries (Table 4). Parvibaculum was the dominant genus in the sulfamethoxazole resistant sequence library. Parvibaculum and Bacillus combined 5

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240 20 10 < 10 240 42 ( ± 97) 15 ( ± 7) 3 ( ± 4) 1 ( ± 0) 20 ( ± 61) 150 20 60 < 10 150

Fig. 5. Total heterotrophic bacteria (HET) and antibiotic resistant bacteria (ARB) abundance in groundwater spanned more than three orders of magnitude across our study area. HET and sulfamethoxazole resistant bacteria (SMX) concentrations did not differ significantly (Kruskal-Wallis H = 20.44, p < 0.01; Dunn's multiple comparison mean rank difference = 12.3, p = 0.27). SMX were consistently more abundant than ampicillin resistant bacteria (AMP) and tetracycline resistant bacteria (TET). Box and whisker representation of the data include a central line representing the median and whiskers representing the full range of concentrations.

significantly greater concentrations in nearshore than in mid-channel environments, suggesting that there are other, larger and likely surfacederived, sources contributing to elevated nearshore FIB abundance in embayment water. There has been increasing recognition of the importance of identifying the various sources and transport pathways of fecal microbes to improve management of coastal water quality (O'Mullan et al., 2017). While identifying those surface pollution sources is beyond the scope of this paper, recent studies in other regions have highlighted the importance of wild and domesticated animals (Jiang et al., 2007; Harwood et al., 2014), including geese (Green et al., 2012), gulls (Lu et al., 2011; Converse et al., 2012), and dogs (Kildare et al., 2007), as well as the importance of stormwater runoff in delivering FIB to aquatic ecosystems (Parker et al., 2010; Sauer et al., 2011; Sidhu et al., 2012), in both urban and suburban environments. Animal sources and stormwater delivery may be very important to the water quality of coastal embayments in Eastern Long Island, but both require additional research to constrain their potential importance as sources influencing coastal FIB concentrations in this region. It must be noted that the lack of elevated FIB in groundwater does not mean that there is no reason for concern about the influence of OWDS on adjacent surface water quality. For example, our study did not address viruses, which have different transport and survival characteristics than bacteria and may not be expected to correlate with FIB (Borchardt et al., 2003; Ferguson et al., 2012), especially following subsurface transport. Viral contamination has been associated with disease outbreak, such as acute gastrointestinal illness, in areas that extract groundwater as a source of drinking water (Brunkard et al., 2011; Borchardt et al., 2012). We must also acknowledge that our survey employed a single sampling event at each location and therefore did not provide data on seasonal variability. We sampled each location at a single depth near the water table and, as a result, may not have captured flow paths important for the transport of bacterial contaminants. It is unlikely, however, that critical flow paths were consistently missed because of the well-sorted nature of the shallow aquifer, consistently low FIB concentrations across our 15 sample sites, and the detection of pharmaceuticals in parallel samples collected by Fisher et al. (2016). In addition to microbial contamination, recent studies have demonstrated the transport of nutrients and contaminants of emerging concern, including pharmaceuticals, in groundwater downgradient of OWDS (Carrara et al., 2008; Del Rosario et al., 2014;

6 2 5 2 15 Peconic Forge River Fire Island Oakdale All sites

83% 100% 100% 50% 87%

874 ( ± 1781) 290 ( ± 256) 768 ( ± 591) 11 ( ± 13) 645 ( ± 1156)

4500 400 1370 20 4500

50% 100% 100% 0% 67%

204 ( ± 449) 75 ( ± 49) 94 ( ± 65) 1 ( ± 0) 123 ( ± 281)

1120 110 190 < 10 1120

33% 100% 60% 0% 47%

27 ( ± 60) 15 ( ± 7) 20 ( ± 25) 1 ( ± 0) 20 ( ± 39)

33% 100% 20% 0% 33%

TET max conc (CFUs/ml) TET mean, (SD) TET % samples detected AMP max conc (CFUs/ml) AMP mean, (SD) AMP % samples detected SMX max conc (CFUs/ml) SMX mean, (SD) SMX % samples detected HET max conc (CFUs/ml) HET mean, (SD) HET % samples detected Number of sites Region

Table 3 Total heterotrophic (HET), sulfamethoxazole resistant (SMX), ampicillin resistant (AMP), and tetracycline resistant (TET) assemblage abundance in groundwater. Data show percentage of groundwater samples in which HET and ARB were detected. Bacteria were enumerated on R2A agar with or without the addition of antibiotics. Mean ± standard deviation (SD) and maximum concentration (max conc) are reported as Colony Forming Units per ml (CFUs/ml) for HET, SMX, AMP, and TET.

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Table 4 Taxonomy of antibiotic resistant bacteria detected from 16 S rRNA gene sequences (n = 383) from SMX, AMP and TET isolates based on Ribosomal Database Project at 95% confidence unless otherwise noted. Plate type

Number of sequences

Phylum

Number of sequences

Genus

SMX (n = 192)

127

Proteobacteria

56 10 9 3 2

Parvibaculum Sphingomonasa Unclassified Comamonadaceae Rhizobium, Afipiac, Mesorhizobium, Devosiab, Pseudolabrys, Cupriavidus Stenotrophomonasa, Xanthomonas, Brevundimonas, Rhizomicrobiumb, Novosphingobium, Bradyrhizobium, Zoogloea, Simplicispiraa, Unclassified Rhodospirillaceae, Unclassified Bradyrhizobiaceae, Unclassified Phyllobacteriaceae Tolumonas, Pseudomonas, Acetobacter, Kaistia, Bosea, Hyphomicrobium, Aquabacterium, Undibacterium, Pelomonasa, Unclassified Sphingomonadaceae, Unclassified Beijerinckiaceaea, Unclassified Rhizobiales Bacillus Paenibacillus Paenisporosarcina, Rummeliibacillus, Planococcaceae incertae sedis, Staphylococcus, Brevibacillus, Unclassified Bacillales Leifsonia Microbacterium Arthrobacter, Flexivirga, Nocardioides, Unclassified Microbacteriaceae Terriglobusa Hydrogenophaga, Reyranella Sphingomonas Curvibacterd Caulobacter, Roseomonas, Unclassified Beta Proteobacteria Cupriavidus Xanthomonas, Pseudomonas, Acidovorax, Variovorax, Nitrospirillum, Roseococcusa, Azorhizobium, Parvibaculum, Bradyrhizobium, Bosea, Rhodopseudomonasa, Methylobacterium, Sphingobium, Unclassified Myxococcales, Unclassified Rhizobiales Terrabacter Streptomyces, Cellulosimicrobium Mycobacterium, Rudaibacter, Phycicoccus, Tetrasphaera, Unclassified Streptomycetaceae Chitinophaga Unclassified Chitinophagaceae Bacillus Paenibacillusa Unclassified Bacillales Terriglobus Afipiae, Unclassified Bradyrhizobiaceae Bradyrhizobiumc Unclassified Beta Proteobacteria Variovorax, Rhodoplanesa, Tardiphagaa, Nitrobacter, Rhodopseudomonasa, Unclassified Acetobacteraceae, Unclassified Rhizobiales Unclassified Solirubrobacteralesd Streptomyces Mycobacterium, Conexibacter Terriglobus Unclassified Acidobacteria Group 1

1

Amp (n = 130)

Tet (n = 61)

48

Firmicutes

33 9 1

15

Actinobacteria

2 101

Acidobacteria Proteobacteria

8 3 1 2 26 13 6 4 3 1

12

Actinobacteria

9

Bacteroidetes

7

Firmicutes

1 47

Acidobacteria Proteobacteria

11

Actinobacteria

3

Acidobacteria

3 2 1 6 3 4 2 1 1 14 8 4 1 7 2 1 2 1

a Indicates that one of the sequences was classified with 80–95% confidence; bindicates that two of the sequences were classified with 80–95% confidence; cindicates that three of the sequences were classified with 80–95% confidence; dindicates that five of the sequences were classified with 80–95% confidence; eindicates that six of the sequences were classified with 80–95% confidence; any sequence with a confidence level less than 80% for classification at the level of genus is listed as "unclassified" in the table.

intense coastal flooding, which inundated all of our groundwater sample sites. Coastal flooding associated with large scale storms is expected to increase in both frequency and intensity in the coming decades as a consequence of sea level rise and global climate change (Cooper et al., 2008; Tebaldi et al., 2012; Woodruff et al., 2013). While our data do not demonstrate large-scale transport of FIB from groundwater to coastal embayments, future circumstances, including lack of maintenance or storm events that cause damage to OWDS, could change the extent of FIB transport. There could also be isolated hotspots, not captured by our study locations, where groundwater FIB contamination is a consistent source of local contamination. This study should also motivate additional efforts to identify, and better inform management solutions related to, surface sources of FIB delivery to these coastal embayments.

Phillips et al., 2015; Fisher et al., 2016). Discharge of these contaminants into surface water can have substantial detrimental impacts on coastal ecosystems, including expansion of harmful algal blooms (Heisler et al., 2008) and increasing selection pressure for the evolution of antibiotic resistant bacteria (Martínez, 2008; Finley et al., 2013). However, FIB are the primary management tool used to assess fecal contamination in coastal surface water, and are found to be elevated in the nearshore of eastern Long Island's embayments (Fig. 3). Our data do not provide support for widespread groundwater FIB contamination or a major influence of OWDS on the FIB levels in these embayments, but given the limited number of total samples in this study, there would be value in additional sampling to confirm these patterns, as these data are of significant management relevance. This study was motivated in part by concerns after Hurricane Sandy in October 2012 related to storm surge and potential damage to OWDS from saltwater inundation. Much damage to eastern Long Island attributed to the storm did not result from precipitation, but rather from

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communities, including community succession and antibiotic resistance expansion, have the potential to influence biogeochemical activity and interact with related nutrient pollution management concerns. The influences of pharmaceuticals in sewage discharge on the evolution of environmental microbes, as well as their biogeochemical activity in situ, remains an important topic and may still have relevance to environmental management in Long Island's shallow groundwater.

4.2. Potential influence of OWDS discharge on ARB in groundwater Based on our FIB data, large scale or widespread groundwater transport of fecal bacterial contaminants, including ARB, is unlikely to be a major concern downgradient of OWDS in these residential areas. However, the detection of pharmaceuticals in parallel samples collected by Fisher et al. (2016) highlight the potential for resistance expansion in the native bacteria through selective pressures associated with exposure to antibiotics (Martínez, 2008; Finley et al., 2013). ARB abundance spanned more than three orders of magnitude across our study area with extensive spatial variability. Despite this, ARB were not correlated with the summed concentration of pharmaceuticals. The lack of fecal bacteria and sewage associated genera in the characterized resistant isolates suggests that resistance may be occurring primarily among the native groundwater bacteria rather than via direct transport of ARB from OWDS. By comparison, in a study on the influences of wastewater discharge on the abundance and diversity of ARB in New York City's waterways, Young et al. (2013) found a greater representation of fecal bacteria and genera containing known pathogenic taxa, as detected in 16 S rRNA gene sequences of resistant isolates from that system and resistant organisms in the family Enterobacteriaceae, a group of bacteria associated with the gastrointestinal tract, were associated with sewage overflow events. Although there appears to be a strong linkage between wastewater discharge and the delivery of ARB to other surface water environments, a clear connection was not observed in the shallow groundwater of our study area on Eastern Long Island where combined sewer systems are not present but where OWDS are common. Dominant genera identified by 16 S rRNA gene sequences of resistant isolates in our groundwater samples included Parvibaculum and Bacillus (SMX), Hydrogenophaga and Reyranella (AMP), and Afipia (TET) (Table 4). Bacillus, one of the largest and most intensively studied bacterial genera, can be very widely distributed and includes organisms isolated from soil (Logan, 2012), plant tissue (Reva et al., 2002), and the gastrointestinal tracts of animals (Fakhry et al., 2008), but its presence is not considered to be evidence for fecal contamination. Parvibaculum has been isolated from natural environments including deepsea waters (Lai et al., 2011; Rosario-Passapera et al., 2012), and environments characterized by hydrocarbon enrichment. Bacillus and Afipia have both been isolated from groundwater, including a nuclear legacy site with elevated levels of NO3- (Venkatramanan et al., 2013). Hydrogenophaga has been isolated from soil and water (Willems et al., 1989), including wastewater (Du et al., 2015), although it is not typically considered an indicator of sewage contamination. Reyranella, a genus that has been isolated using an amoeba co-culture technique (Pagnier et al., 2011), has been collected from freshwater (Pagnier et al., 2011; Cui et al., 2017) and soil (Kim et al., 2013). These genera occur in very diverse habitats and their presence would not be considered evidence for sewage contamination. While we do not have evidence for major fecal or pathogenic resistance, and these patterns are consistent with the low levels of FIB detected from the same samples, the discharges from OWDS may still contribute to expanded resistance in the broader or native groundwater microbial community. Interestingly, SMX did not differ significantly from HET, and SMX were consistently more abundant than AMP and TET. Sulfamethoxazole is a commonly prescribed folic acid antagonist and is one of the most frequently detected antibiotics in groundwater and surface water influenced by anthropogenic disturbances (Barnes et al., 2008; Segura et al., 2009; Underwood et al., 2011). Sulfamethoxazole was detected in two of the fifteen groundwater samples, but concentrations were not correlated with the abundance of SMX resistant bacteria. In a study on the effects of sulfamethoxazole on groundwater bacterial NO3- reduction, Underwood et al. (2011) found that increasing sulfamethoxazole concentrations in denitrifying microcosms resulted in a shift in species composition toward organisms that were not able to reduce NO3-. Therefore, chemically driven alterations to existing microbial

5. Conclusions The results of this study did not support our hypothesis that FIB would be detected in groundwater at high concentrations downgradient of communities that utilize OWDS. However, water-quality data compiled from the SCDHS Office of Ecology monitoring program, demonstrate that significantly greater concentrations of FIB occur in nearshore compared to mid-channel embayment surface water environments. This provides evidence that sources other than groundwater, such as wildlife, surface runoff, or illicit sewage discharges, are contributing to elevated bacterial contamination in these coastal embayments. The low levels of FIB, both in terms of detection frequency (e.g. 20% for E. coli) and concentration (e.g. maximum of 2 MPN/100 ml for E. coli) in groundwater, despite the widespread detection of pharmaceuticals (described by Fisher et al., 2016) in parallel samples, demonstrates the importance of measuring emerging contaminants, and not just traditional bacterial indicators, when assessing sewage pollution from OWDS. Although the patterns of ARB may be altered by exposure to the pharmaceuticals transported downgradient of OWDS, the lack of ARB genera strongly associated with wastewater or fecal contamination suggests that resistance may be occurring in the native groundwater bacteria, rather than direct transport of resistant bacteria from OWDS. Declaration of competing interest None. Acknowledgements: The authors would like to thank Tristen Tagliaferri and Kaitlyn Colella, both from the USGS New York Water Science Center, for assistance in sample collection. Funding for isolate sequencing in this study was provided by a Junior Faculty Research Award in Science and Engineering from the City University of New York and the Sloan Foundation to Gregory O'Mullan. USGS field collections were supported through the Disaster Relief Appropriations Act of 2013 (PL 113-2). The managers of the funding sources did not participate in the design or interpretation of the study. References Ahn, J.H., Grant, S.B., Surbeck, C.Q., DiGiacomo, P.M., Nezlin, N.P., Jiang, S., 2005. Coastal water quality impact of stormwater runoff from an urban watershed in southern California. Environ. Sci. Technol. 39 (16), 5940–5953. Allen, H.K., Donato, J., Wang, H.H., Cloud-Hansen, K.A., Davies, J., Handelsman, J., 2010. Call of the wild: antibiotic resistance genes in natural environments. Nat. Rev. Microbiol. 8 (4), 251–259. Arnold, B.F., Wade, T.J., Benjamin-Chung, J., Schiff, K.C., Griffith, J.F., Dufour, A.P., Weisberg, S.B., Colford Jr., J.M., 2016. Acute gastroenteritis and recreational water: highest burden among young US children. Am. J. Public Health 106 (9), 1690–1697. Baquero, F., Martínez, J.L., Cantón, R., 2008. Antibiotics and antibiotic resistance in water environments. Curr. Opin. Biotechnol. 19 (3), 260–265. Barnes, K.K., Kolpin, D.W., Furlong, E.T., Zaugg, S.D., Meyer, M.T., Barber, L.B., 2008. A national reconnaissance of pharmaceuticals and other organic wastewater contaminants in the United States—I) Groundwater. Sci. Total Environ. 402 (2), 192–200. Bellin, C.A., Rao, P.S.C., 1993. Impact of bacterial biomass on contaminant sorption and transport in a subsurface soil. Appl. Environ. Microbiol. 59 (6), 1813–1820. Bennett, P.M., 2008. Plasmid encoded antibiotic resistance: acquisition and transfer of antibiotic resistance genes in bacteria. Br. J. Pharmacol. 153 (S1), S347–S357. Boehm, A.B., Shellenbarger, G.G., Paytan, A., 2004. Groundwater discharge: potential association with fecal indicator bacteria in the surf zone. Environ. Sci. Technol. 38 (13), 3558–3566.

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