Report Contaminants of Emerging Concern in Seneca Lake, NY and Public Perception of the Issues

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Report Prepared by:

Syracuse University1

Upstate Freshwater Institute2

SUNY College of Environmental Science and Forestry3

Submitted to:

Seneca Lake Pure Waters Association

April 24, 2019

1Dr. T. Zeng, Depart. of Civil and Environmental Engineering, Syracuse University, Syracuse, NY 13244 2MaryGail Perkins, Upstate Freshwater Institute, 224 Midler Park Drive, Syracuse, NY 13206 3Dr. Sharon Moran, Depart. Environmental Studies, SUNY College of Environmental Science and Forestry, 1 Forestry Drive, Syracuse, NY 13210

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Project Background

Contaminants of emerging concern (CECs) comprise a wide array of synthetic (i.e., man-made) and naturally occurring organic compounds that are found with increasing frequency at low levels in the aquatic environment and are thought to have the potential to cause adverse ecological health impacts. CECs include, but are not limited to, pharmaceuticals, personal care products, pesticides, and commercial, household and industrial chemicals. While the majority of CECs are man-made, some naturally produced organic compounds are also considered as CECs. The primary example in lakes is algal toxins which are produced by some types of cyanobacteria (also known as “blue-green algae”).

CECs present a challenge to lake monitoring and management programs mainly because available data on their occurrence, fate, transport, and adverse effects is relatively limited. Further complicating this issue, some CECs, once released into the lakes, may be transformed by natural sunlight or microorganisms to generate the so-called transformation products that are even less studied. This research is being conducted to survey the occurrence of contaminants of emerging concern in lakes in New York State. The results of this study will be used by researchers and other stakeholders to evaluate the nature and spatial distribution of these contaminants in NY's surface waters. This study is for information gathering and university research purposes only.

This project brought citizen scientists who participate in the Citizens Statewide Lake Assessment Program (CSLAP) together with professional scientists from Syracuse University (SU), SUNY-College of Environmental Science and Forestry (ESF), and the Upstate Freshwater Institute (UFI). The CSLAP volunteers from 18 participating lakes worked collaboratively with UFI and SU researchers by using strip tests for on-site monitoring of microcystin and atrazine and collecting near surface lake water samples for further analyses of CECs at SU’s laboratory. Surface water samples (1.5 m depth) were analyzed from a deep water site at the southern end of the lake for a pre-determined “screening” using a list of 217 CECs that have been typically found in other surface water bodies.

Water samples were collected by CSLAP volunteers at approximately two week intervals from mid-July through early October to document any changes in the composition and concentration of CECs over time. Eight water samples for water quality and seven samples for CEC analyses were collected from Seneca Lake. Results of the CEC monitoring are outlined below. In addition, we also demonstrate how participation in this CEC monitoring program contributed to individual citizen scientist’s understanding of their lake’s water quality issues and engagement with their local community.

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Understanding Nanogram per Liter (ng/L) Concentrations

Contaminant concentrations are commonly determined by measuring the mass (weight) of a contaminant per unit volume of water. Most water quality measurements (e.g., dissolved oxygen, total suspended solids, dissolved organic carbon) are recorded in milligrams per liter (mg/L), or the number of milligrams of a contaminant present in one liter of water. CECs though are typically measured in the nanogram per liter (ng/L) concentration range.

In the instance of mg/L, there are 1,000,000 milligrams in one liter of water. A microgram per liter (µg/L) concentration is even smaller; one one-thousandth (0.001) of a milligram per liter (mg/L). Nanogram per liter concentrations are even smaller still – one one-thousandth (0.001) of a µg/L. Concentrations measured in µg/L and ng/L are so small that they are difficult to visualize. The following three examples illustrate the relationship of the above concentrations to each other:

Example 1:

A typical US dollar bill weighs 1 gram. Cut the dollar bill into 1,000 equal pieces and each piece will weigh 1 milligram (mg). Cut one of those smaller pieces into 1,000 more pieces and each of those pieces will weigh one microgram (µg). Cut one of those tiny pieces into another thousand pieces and that piece will weigh 1 nanogram (ng). To summarize, a whole US dollar bill weights 1 gram, 1 thousandth of a dollar bill weighs 1 mg, 1 millionth of a dollar bill weighs 1 µg, and 1 billionth of a dollar bill weighs 1 ng.

Example 2:

1 gram (g) = 1 US dollar bill

1 milligram (mg) = the weight of a brain of a worker honeybee

1 microgram (µg) = the weight of an eyelash

1 nanogram (ng) = the weight of a human cell

Example 3: Visual representation of relative sizes (not to exact scale)

1 g

1 mg 1 µg 1 ng

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Seneca Lake and its Watershed

The Finger Lakes of central New York consist of 11, elongated, north-south oriented glacially formed lakes. Seneca Lake (42.7777° N, 76.9348° W), located in the geographic center of the Finger Lakes region of central New York, is the second largest (only slightly smaller surface area than Cayuga Lake), is the deepest and has the largest volume of the eleven Finger Lakes. Seneca Lake is classified as a AA drinking water supply. The 2017 data suggests that the north site of Seneca Lake is typical of mesotrophic (moderately productive) lakes, based on intermediate water clarity (Secchi disk), nutrient (TP) and algae levels (chlorophyll-a) (NYSDEC, 2017). Water quality conditions suggest a moderate susceptibility to algae blooms (NYSDEC, 2017).

The Seneca Lake watershed is a complex network of 29 sub-basins and encompasses 42 municipalities and five counties. Keuka Lake Outlet enters Seneca Lake in the middle of the western shore. The lake has not completely frozen over in any year since 1912. In general on a watershed-wide basis, agricultural land has been on a steady decline, forests and developed areas have increased, and the category of idle land has been on the increase. A significant percentage of agricultural land is located on the northern (particularly the western side) of the lake (Seneca Lake Watershed Management Plan, 2012).

Table 1: Seneca Lake Morphometric and Watershed Characteristics Lake Characteristics Value

Trophic Status Mesotrophic 2 Lake Area 172.59 km Lake Depth 133 m

Mean Lake Depth 89 m 9 3 Volume 15.5 × 10 m Retention Time 16.70 yrs Watershed Characteristics1 2 Watershed Area 1,844 km Watershed Area/Lake Area 11 Lake and Wetlands 15.4% Agriculture 40.1%

Forest, Shrubs, Grasslands 38.0% Residential 6.3%

Urban 0.1% 1 Primary land-use is Agriculture (particularly on the north end of the Figure 1: Seneca Lake with lake). sampling location shown (circle) Page 4 of 24

NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Strip Test Results

CSLAP volunteers used two different Abraxis test strips to test for the presence/absence of the algal toxin microcystin and the herbicide atrazine, respectively, in water samples. Results of the microcystin strip tests are shown below. If laboratory analyses for total chlorophyll (via FluoroProbe III) indicated a total chlorophyll concentration greater than 10 μg/L (as per NYSDEC protocol) in any given water sample, then UFI analyzed that sample for total microcystins by the ELISA test, a standard test protocol certified by the NYS Department of Health’s Environmental Laboratory Accreditation Program (ELAP) and adopted by the New York State DEC.

Microcystin Strip Test Results

Sample Date MC Strip Test Total Chl-aFP ELISA MC 7/8/2018 1-10 μgMC/L 5.0 µg/L NA 7/25/2018 1-10 μgMC/L 4.5 µg/L NA 8/6/2018 1-10 μgMC/L 9.8 µg/L NA 8/19/2018 1-10 μgMC/L 7.9 µg/L NA 9/3/2018 Inconclusive 5.3 µg/L NA 9/18/2018 1-10 μgMC/L 5.1 µg/L NA 10/1/2018 1-10 μgMC/L 8.5 µg/L NA MC = Microcystin, Total Chl-aFP = total chlorophyll-a measured by FluoroProbe III, ELISA = Enzyme-linked immunosorbent assay, NA = sample didn’t meet chlorophyll criteria to be run for ELISA test

In the case of Seneca Lake, the open water chlorophyll concentrations in all samples were always below 10 µg/L and didn’t meet the threshold for conducting an ELISA microcystin analysis. Therefore, no ELISA data were available to further verify the strip test results, although the probability for actual microcystin concentrations in these samples to exceed the minimum reporting limit of 0.3 µg/L is likely low given the low chlorophyll concentrations (Hollister and Kreakie , 2016). The EPA recommended value for recreational criteria and swimming advisory limits is 4 µg/L. Overall, the Abraxis Microcystin test strips did not perform as expected based on experiences with some other participating lakes. Oftentimes strip test results were not in agreement with the ELISA analysis or gave inconclusive results. For example, strip tests indicated microcystin levels greater than 1 µg/L in many water samples which actually had rather low chlorophyll concentrations. While we are not entirely certain why the strip tests did not perform as well as hoped, it’s possible that the test result was impacted by degradation of the antibody/enzymes during shipping/subsequent storage (i.e., not under ideal laboratory conditions for temperature or humidity).

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Given the significant amount of time (~45 min) it requires to perform the Microcystin strip test and the uncertainty of test results, it appears that the Microcystin strip test is a less than ideal test for citizen scientists to use for on-site monitoring of microcystins.

Atrazine Strip Test Results

Results of the Abraxis atrazine strip tests are summarized below. The strip test has a method detection limit of 3,000 ng/L.

Sample Date Atrazine Strip Test Atrazine LC-HRMS1 7/8/2018 < 3,000 ng/L 74.3 7/25/2018 < 3,000 ng/L 65.2 8/6/2018 < 3,000 ng/L 74.3 8/19/2018 < 3,000 ng/L 82.7 9/3/2018 < 3,000 ng/L 76.5 9/18/2018 < 3,000 ng/L 87.9 10/1/2018 < 3,000 ng/L 87.8

1LC-HRMS = Liquid chromatography-High Resolution Mass Spectrometry

The Abraxis atrazine test strip results indicated that the concentrations of atrazine were below the detection limit for the strip test. Laboratory screening for atrazine using liquid chromatography-high resolution mass spectrometry at SU indicated a relatively consistent concentration of atrazine over the study period with an average concentration of 78.4 ng/L, well below the detection limit of the strip test. In this instance the Atrazine strip test appeared to have given a correct result but was not sensitive enough to detect the ambient concentration of atrazine in the lake at this site.

Water Quality Indicators

Lakes are dynamic and complex systems that are affected by inputs from the surrounding watershed. The water quality of a lake determines to a large extent the suitability of the lake for various uses and activities. Lake depth, volume, temperature, watershed land use characteristics, and transport of nutrients and other chemicals to the lake from surrounding streams all have the potential to impact how a lake can be used by the community and how lakes may be perceived by the surrounding community with regards to aesthetics. Nutrient poor lakes are categorized as oligotrophic while nutrient rich lakes are categorized as eutrophic. Lakes falling in-between oligotrophic and eutrophic conditions have intermediate levels of nutrients and biological productivity. These lakes are categorized as mesotrophic lakes. Human

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

activities that impact a lake’s trophic state (i.e., increased nutrient loading from watershed activities such as agriculture or industrial waste water discharges) are termed cultural eutrophication.

Figures 2 and 3 are “Box and Whisker” plots. These plots are useful for summarizing a lot of information into a simple graphic. There is a “box and whisker” for each lake. The boundary of the gray box closest to the bottom of the graph indicates the 25th percentile of the measurements, the solid line in the box indicates the average of the measurements, the dotted line indicates the median (50th percentile), the top of the gray box indicates the 75th percentile and the whiskers represent the 10th (bottom whisker) and 90th (top whisker) percentiles of the measurements.

Several common indicators of water quality are summarized in Figures 2 and 3 for Seneca Lake (red arrows) and compared to the other lakes in this study. All samples collected were from the open waters of the lake. Water temperature (Fig. 2a) was collected at a depth of 1.5 m. Temperatures vary among lakes due to a variety of factors including surface area and depth of the lake, how sheltered a lake is, and by inputs from the surrounding watershed. Specific conductance (SC; Fig. 2b) is a measure of water’s capability to conduct electrical flow. This capability is directly related to the concentration of ions in the water. These ions come from dissolved salts and inorganic materials such as carbonate compounds. Higher SC can be an indicator of potential contamination due to road salt use. pH is a measure of how acidic or alkaline a lake is. At a pH less than 7 the lake is “acidic” while a pH greater than 7 indicate “alkaline” conditions. Most lake pH values are between 6 and 9 which is considered an acceptable range for most aquatic organisms. The pH of lakes in this study were all within this range although their median concentration varied depending on location.

Phosphorus, nitrogen, and chlorophyll-a concentrations are summarized in Figure 3. Phosphorus is a common component of agricultural fertilizers, manure, and organic wastes in sewage and wastewater discharges. Phosphorus is an important nutrient for algae and high concentrations in lakes can promote excessive algal growth leading to nuisance algae blooms. Figure 3a summarizes the concentrations of phosphorus over the study period for the lakes in this study. In most lakes, total nitrogen concentrations are usually less than 1,000 µg/L. Nitrogen is common in our environment, but elevated levels of nitrogen in lakes may result from various sources in the watershed that runoff into streams and subsequently enter a lake. Nitrogen can originate from agricultural activities where it’s used to encourage crop growth, from storage and/or application of manure near water bodies, or from sewage treatment plant discharges. Figure 3b summarizes the nitrogen concentrations in the 18 lakes over the study period. The quantity of algae in a lake can be estimated by using chlorophyll-a (chl-a; a pigment found in all algae) as an indicator of algae biomass. Chl-a concentrations greater than 10 µg/L

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

often indicate excessive algae growth. The chl-a concentrations for the lakes in this study are summarized in Fig. 3c.

Figure 4 illustrates the concentration over time (time-series) of total phosphorus (TP; Fig. 4a),

total chl-a determined by FluoroProbe measurements (chl-aFP; Fig. 4b), cyanobacteria or blue-

green algae concentration determined by FluoroProbe measurements (BGAFP; Fig. 4c), and percent contribution by BGA to total chl (%BGA; Fig. 4d). The dynamics and interactions between nutrients (phosphorus and nitrogen) and the algal community and how they impact the concentrations of cyanobacteria in a lake are complex and not well understood. In general, higher nutrient concentrations and warmer temperatures will favor the growth of cyanobacteria over other types of algae (e.g., green algae, diatoms).

Seneca Lake was sampled at the northern end of the lake. The average temperature (Fig. 2a) and pH (Fig. 2c) were similar to the other lakes in the study, while the average specific conductance was the highest of all the lakes (Fig. 2b; related to industrial activities in/around local salt mining facilities). Seneca Lake had both the highest and most variable average total phosphorus concentrations (Fig. 3a). Average total nitrogen (Fig. 3b) and chlorophyll-a (Fig. 3c) concentrations were similar to the other lakes in this study. Changes in total phosphorus, chlorophyll-a, and cyanobacteria (blue-green algae) over the course of this study are shown in Figure 4. Total phosphorus was quite variable over time (Fig. 3a, 4a). Blue-green algae accounted for ~ 1% of the algae population (determined by fluorescence) during July and August. During the month of September, the contribution of blue-greens had a modest increase (~6%; Fig. 3d) but overall concentrations remained low (Fig. 3c).

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

40 (a)

25 20

Temperature (°C) Temperature 15

10 (b)

600

S/cm)

 400 SC( 200

0 (c) 8.5

8.0

7.5 pH 7.0

6.5

6.0

Tully Lake

Song Lake Guilford Lake Canada LakeCanada Lake Moraine Crooked Lake Hadlock Pond LakeCazenovia DeRuyter Reservoir Plymouth Reservoir Otisco Lake - Site 1 Otisco Lake - Site 2 Cayuga Lake - Site 3 Seneca Lake - Site 1 Owasco Lake - Site 1

Skaneateles Lake - Site 2 Silver Lake (Wyoming County) Lake George - Diamond Island

Second Lake (Fulton Lakes) Chain of

Figure 2: 2018 time-series of surface (1.5m) water quality parameters and select indicators of contaminants of emerging concern: (a) water temperature, (b) specific conductance, and (c) pH. Box plot shows 10%, 25%, median, mean (dotted line), 75%, and 90% of samples values. Red arrows indicate Seneca Lake results.

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

100 (a)

40 TP (µg/L) TP

0 (b) 1200

800

TN (µg/L) TN 400

0 (c)

g/L) 30 

20 Chl-a(

Tully Lake Song Lake Guilford Lake Canada LakeCanada Lake Moraine Crooked Lake Hadlock Pond LakeCazenovia DeRuyter Reservoir Plymouth Reservoir Otisco Lake - Site 1 Otisco Lake - Site 2 Cayuga Lake - Site 3 Seneca Lake - Site 1 Owasco Lake - Site 1

Skaneateles Lake - Site 2

Lake George - Diamond Island Silver Lake (Wyoming County)

Second Lake (Fulton Lakes) Chain of

Figure 3: 2018 time-series of surface (1.5m) water quality parameters and select indicators of contaminants of emerging concern: (a) total phosphorus, (b) total nitrogen, and (c) chlorophyll-a (fluorometric). Box plot shows 10%, 25%, median, mean (dotted line), 75%, and 90% of samples values. Red arrows indicate Seneca Lake results.

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes Seneca Lake 80 (a)

g/L)

 40

TP( 20

0 (b) 10 g/L)

 8 ( FP 6

Tot.Chl-a 2

0 (c) 0.30

0.25 g/L)

 0.20 ( FP 0.15

BGA 0.10

0.05 0.00 (d) 6

5 FP 4

%BGA 2

1 0 Jun Jul Aug Sep Oct Nov 2018 Figure 4: 2018 time-series of (a) total phosphorus, (b) total chlorophyll-a by FluoroProbe, (c) blue-green algae by FluoroProbe, and (d) % contribution of bluegreen algae to total chlorophyll- a in Seneca Lake – Site 1. Box plot shows 10%, 25%, median, mean (dotted line), 75%, and 90% of samples values. Red arrows indicate Owasco Lake results.

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

CECs in Seneca Lake

Figure 5 is a “violin” plot that shows the concentration ranges of CECs detected in Seneca Lake. Violin plots are similar to “Box-and-Whisker” plots (e.g., Figures 2 and 3) except that they also display the frequency distribution of the data: the thicker part means the data in that section of the violin exhibits higher frequency, whereas the thinner part implies lower frequency. Blue violins in Figure 5 represent pharmaceuticals, green violins represent pesticides, and orange violins represent household chemicals. Each violin extends from the minimum to the maximum CEC concentrations, with the grey bar in the middle marking the median and the two red lines bracketing 75th and 25th percentiles. Each data point (representing the average of duplicate CEC measurements) is shown as a black dot superimposed on the corresponding violin.

Of the 217 CECs screened for by Syracuse University (see Appendix 1), 19 were identified and quantified. Table 2 summarizes the concentrations of CECs measured for each sample collected from Seneca Lake. Five human-use pharmaceuticals were detected in the lake at varying frequencies. Metformin (a medication for the treatment of type 2 diabetes) and gabapentin (an anticonvulsant medication and pain reliever) were detected throughout the sampling season. Azelaic acid (an acne medication) and sitagliptin (another anti-diabetic medication) were detected at lower frequencies. Seven pesticides or their transformation products were detected in the lake. Atrazine and metolachlor (two herbicides commonly used for control of annual grasses and broadleaf weeds) were detected at relatively consistent concentrations over the sampling season along with their transformation products (e.g., atrazine-desisopropyl, atrazine-desethyl, atrazine-2-hydroxy, metolachlor OA, metolachlor ESA). Atrazine-2-hydroxy, atrazine-desethyl, and atrazine-desisopropyl were detected at lower concentrations than their parent compound atrazine. In contrast, both metolachlor ESA and metolachlor OA were detected at higher concentrations than their parent compound metolachlor. Seven household chemicals were detected in the lake. Benzothiazole (a vulcanization accelerator found in rubber products such as tires) was detected at the highest average concentration among all 19 CECs throughout the sampling season, while its derivative 2-hydroxybenzothiazole was only detected three times at considerably lower levels. DEET (a common active ingredient in bug spray) and melamine (a compound commonly found in laminates, coatings, glues, and plastic dinnerware) were detected at the second and third highest average concentrations, respectively, among CECs. Oxybenzone (a UV blockers used in sunscreens) and 5-methyl-1H- benzotriazole (a corrosion inhibitor used to prevent the corrosion of copper and brass) were also detected throughout the sampling season. Lastly, sucralose (an artificial sweetener) and caffeine (a stimulant), the two indicator compounds of sewage impacts, were frequently detected, suggesting potential sewage inputs into the lake (e.g., from sewage treatment facilities or septic systems).

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Figure 5: Violin plot of the concentrations of 19 CECs detected in Seneca Lake. Blue violins represent pharmaceuticals, green violins represent pesticides, and orange violins represent household chemicals. For each CEC, the violin extends from the minimum to the maximum concentrations. The grey bar in the middle of each violin is the median. The two red lines bracket 75th and 25th percentiles. The shape of the violin depicts the empirical distribution of the data. Each data point (representing the average of duplicate CEC measurements) is shown as a black dot superimposed on the corresponding violin. CECs not detected in the lake were also labeled on the x-axis (see Figure 7 for comparison with all participating lakes).

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Table 2: Summary of CECs with their minimum, average, and maximum average concentrations measured in Seneca Lake (2018). Sample analysis conducted in duplicate on 7 water samples for all CECs. The method detection limit (MDL) is defined as the lowest concentration of a CEC in a sample that can be detected but not necessarily quantified. The method quantification limit (MQL) is defined as the lowest concentration of a CEC in a sample that can be determined with acceptable precision and accuracy.

Min. Avg. Max. CEC Common Use 7/08/2018 7/25/2018 8/06/2018 8/19/2018 9/03/2018 9/18/2018 10/01/2018 (ng/L) (ng/L) (ng/L)

Azelaic Acid Pharmaceuticals

Caffeine Pharmaceuticals

Gabapentin Pharmaceuticals 36.8 37.6 37.2 23.6 25.8 26.2 26.6 23.6 30.6 37.6

Metformin Pharmaceuticals 58.2 57.5 57.5 55.0 68.4 108.6 202.7 55.0 86.8 202.7

Sitagliptin Pharmaceuticals

Atrazine Pesticides 74.3 65.2 74.3 82.7 76.5 87.9 87.8 65.2 78.4 87.9

Atrazine-desisopropyl Pesticide TPs 20.5 24.1 27.6 33.3 34.1 39.7 40.5 20.5 31.4 40.5

Atrazine-desethyl Pesticide TPs 25.2 27.0 30.9 40.3 46.1 54.7 57.3 25.2 40.2 57.3

Atrazine-2-hydroxy Pesticide TPs 54.5 41.4 56.3 54.3 43.6 62.6 58.5 41.4 53.0 62.6

Metolachlor Pesticides 12.5 11.6 12.1 16.1 16.2 16.7 16.7 11.6 14.5 16.7

Metolachlor OA Pesticide TPs 21.5* 20.3* 17.0* 28.6 19.1* 34.8 29.3 17.0* 24.4 34.8

Metolachlor ESA Pesticide TPs 185.3 223.4 238.0 84.2 39.1 237.9 286.9 39.1 185.0 286.9

Melamine Plasticizers 376.0 121.8

Benzothiazole Vulcanization Accelerators 455.9 560.5 295.8 300.2 491.2 145.1 806.3 145.1 436.4 806.3

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes 2-Hydroxybenzothiazole Vulcanization Accelerators

5-Methyl-1H- Corrosion Inhibitors benzotriazole 20.2 18.9 22.5 20.9 25.9 29.8 32.7 18.9 24.4 32.7

Oxybenzone UV Blockers 15.4 11.9 11.8 17.4 13.5 23.5 26.2 11.8 17.1 26.2

DEET Insect Repellents 586.3 128.6 460.0 134.3 361.6 123.2 763.6 123.2 365.4 763.6

Sucralose Artificial Sweeteners 73.7 86.8 82.0 79.1 80.2 93.8 67.0 67.0 80.4 93.8

1. “*” = Concentrations below the method quantification limits (MQLs) but above the method detection limits (MDLs). 2. TPs = Transformation Products. 3. A total of 7 water samples were collected and analyzed in duplicate. 4. Water samples were screened for a total number of 217 CECs (see Appendix 1).

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Figure 6 summarizes the detection frequency of CECs in all 18 lakes participating in this study. Blue bars in Figure 6 represent pharmaceuticals, green bars represent pesticides, and orange bars represent household chemicals. During our study period (June – October 2018), a total of 115 water samples were collected by CSLAP volunteers from 18 participating lakes. In total, 33 different CECs were identified and quantified in these 115 water samples, including 12 human- use pharmaceuticals, 10 pesticides or their transformation products, and 11 household chemicals. Overall, pesticides and H=household chemicals were more frequently detected than pharmaceuticals in participating lakes over the 2018 sampling season. The detection frequency of 9 CECs exceeded 50%, including caffeine (a stimulant), atrazine (a herbicide), atrazine-2- hydroxy (a transformation product of atrazine), metolachlor (another herbicide), metolachlor OA (a transformation product of metolachlor), DEET (an insect repellent), oxybenzone (a UV blocker), benzothiazole (a vulcanization accelerator), and benzophenone (another UV blocker).

Figure 6: Bar plot of the detection frequency of all 33 CECs detected in 18 participating lakes. Blue bars represent pharmaceuticals, green bars represent pesticides, and orange bars represent household chemicals. The detection frequency for each CEC was calculated as the number of detections divided by the total number of water samples (i.e., 115).

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Figure 7 is a violin plot that shows the concentration ranges of CECs detected in all 18 participating lakes. Generally, the spectrum of CECs found in participating lakes is similar to those reported for surface waters with recreational usage. The concentrations of CECs varied by 2 orders of magnitude from <10 ng/L up to 1,000 ng/L across lakes. Summed concentrations of CECs in individual participating lakes ranged from ~600 ng/L to ~2,900 ng/L. On average, household chemicals accounted for 66.5±26.2% (by mass) of CECs quantified in the lakes, followed by pesticides (27.5±25.1%) and pharmaceuticals (6.1±11.2%), respectively.

Figure 7: Violin plot of the concentrations of 33 CECs detected in 18 participating lakes. Blue violins represent pharmaceuticals, green violins represent pesticides, and orange violins represent household chemicals. For each CEC, the violin extends from the minimum to the maximum concentrations. The grey bar in the middle of each violin is the median. The two red lines bracket 75th and 25th percentiles. The shape of the violin depicts the empirical distribution of the data. Each data point (representing the average of duplicate CEC measurements) is shown as a black dot superimposed on the corresponding violin.

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Social Science Findings

In addition to measuring specific substances using different analytical methods, our project had a focus on the human dimensions of monitoring as well. The project included an exploration of the ways people thought about the watersheds they monitor, including their knowledge, awareness, and engagement. Using pre- and post-surveys, as well as face-to-face interviews, we explored people’s thoughts and concerns.

The perceptions of people doing the monitoring work are important because their work helps develop the information base that regulators and community members rely on. In fact, many scientists (including limnologists) in New York make regular use of CSLAP data. Another reason to explore people's knowledge and awareness is to help clarify the extent to which they understand the dynamics of contamination in their lake watersheds. Furthermore, investigating the impact of the innovations in monitoring that were piloted in the summer of 2018 also helps document both people's areas of uncertainty, as well as the knowledge gains associated with training and outreach done.

The surveys and interviews had several findings. First, most people doing lake monitoring have fairly high levels of knowledge about factors affecting the health of their watersheds and lake dynamics. Second, in terms of public opinion, the participants appear fairly similar to the general public in terms of their overall perspectives on government. Third, the people who participated in our training and ran the pilot tests demonstrated higher levels of knowledge and awareness about contamination, specifically in connection with wastewater as a source of pollutants for their lake. Finally, some respondents did mention interest in additional attention toward wastewater sources, such a septic systems, entering lake watersheds. In sum, the surveys and interviews helped to: demonstrate the levels of knowledge held by lake monitors, support the importance of their contributions to better lake management, and document the impact of the training and piloting conducted relative to knowledge, awareness, and engagement.

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Acknowledgements

This work was funded by a National Science Foundation Grant EAGER grant (#1743988). We would like to acknowledge Seneca Lake CSLAP volunteer A. Mason for his time and effort in collecting samples and running strip tests to support this research effort.

Additional Resources

Additional information regarding this project and the role of CECs in our environment can be found at the project website: http://monitoringcecs.org/

Human subject protection

This project has been approved by the Syracuse University Institutional Review Board (IRB), Office of Research Integrity and Protections.

• IRB Protocol Number: 17-255

• Protocol Approval Date: 07/19/17

• Protocol Expiration Date: 07/18/22

References

Hollister JW and Kreakie BJ. 2016. Associations between chlorophyll a and various microcystin health advisory concentrations [version 2; referees: 1 approved, 2 approved with reservations] F1000Research 2016, 5:151doi:10.12688/f1000research.7955.2

NYSDEC, 2017. Seneca Lake CSLAP Report, 2017. Prepared by NYSDEC, Albany, NY. 8pp.

Seneca Lake Watershed Management Plan: Characterization and Subwatershed. May 2012. Written by Hobart and William Smith Colleges, Finger Lakes Institute at Hobart & William Smith Colleges, Genesee/Finger Lake Regional Planning Council, Southern Tier Central Regional Planning and Development Board Evaluation. 155pp.

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Appendix 1: Listing of CECs targeted for analysis by Syracuse University in this study

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Appendix Table 1: List of 217 CECs targeted for analysis by Syracuse University in this project Molecular Molecular Compound Name Common Use Compound Name Common Use Formula Formula Pharmaceutical Transformation 2,4-D C8H6Cl2O3 Pesticides Benzoylecgonine C16H19NO4 Products 2-Hydroxybenzothiazole C7H5NOS Vulcanization Accelerators Betamethasone C22H29FO5 Pharmaceuticals (Human use) 4-Methyl-1H-benzotriazole C7H7N3 Corrosion Inhibitors Betaxolol C18H29NO3 Pharmaceuticals (Human use) 4-Methylethcathinone C12H17NO Pharmaceuticals (Drugs of abuse) Bifenazate C17H20N2O3 Pesticides 5-Methyl-1H-benzotriazole C7H7N3 Corrosion Inhibitors Bisoprolol C18H31NO4 Pharmaceuticals (Human use) Abacavir C14H18N6O Pharmaceuticals (Human use) Bromacil C9H13BrN2O2 Pesticides Acetaminophen C8H9NO2 Pharmaceuticals (Human use) Bupivacaine C18H28N2O Pharmaceuticals (Human use) Acetamiprid C10H11ClN4 Pesticides Bupropion C13H18ClNO Pharmaceuticals (Human use) Albuterol C13H21NO3 Pharmaceuticals (Human use) Butalbital C11H16N2O3 Pharmaceuticals (Human use) Aldicarb C7H14N2O2S Pesticides Butyl paraben C11H14O3 Personal Care Products Aliskiren C30H53N3O6 Pharmaceuticals (Human use) Butylbenzyl phthalate C19H20O4 Plasticizers Amantadine C10H17N Pharmaceuticals (Human use) Butylone C12H15NO3 Pharmaceuticals (Drugs of abuse) Ametryn C9H17N5S Pesticides Caffeine C8H10N4O2 Pharmaceuticals (Human use) Amitriptyline C20H23N Pharmaceuticals (Human use) Carbamazepine C15H12N2O Pharmaceuticals (Human use) Carbamazepine Pharmaceutical Transformation Androstenedione C19H26O2 Pharmaceuticals (Animal use) C15H12N2O2 epoxide Products Androsterone C19H30O2 Pharmaceuticals (Animal use) Carbaryl C12H11NO2 Pesticides Antipyrine C11H12N2O Pharmaceuticals (Human use) Carbendazim C9H9N3O2 Pesticides Artemisinin C15H22O5 Pharmaceuticals (Human use) Carbofuran C12H15NO3 Pesticides Atazanavir C38H52N6O7 Pharmaceuticals (Human use) Carbofuran-3-hydroxy C12H15NO4 Pesticides Atenolol C14H22N2O3 Pharmaceuticals (Human use) Celecoxib C17H14F3N3O2S Pharmaceuticals (Human use) Atrazine C8H14ClN5 Pesticides Cetirizine C21H25ClN2O3 Pharmaceuticals (Human use) Atrazine-2-hydroxy C8H15N5O Pesticide Transformation Products Cimetidine C10H16N6S Pharmaceuticals (Human use) Atrazine-desethyl C6H10ClN5 Pesticide Transformation Products Citalopram C20H21FN2O Pharmaceuticals (Human use) Atrazine-desisopropyl C5H8ClN5 Pesticide Transformation Products Clarithromycin C38H69NO13 Pharmaceuticals (Human use) Azelaic acid C9H16O4 Pharmaceuticals (Human use) Clindamycin C18H33ClN2O5S Pharmaceuticals (Human use) Azithromycin C38H72N2O12 Pharmaceuticals (Human use) Clothianidin C6H8ClN5O2S Pesticides Bentazon C10H12N2O3S Pesticides Codeine C18H21NO3 Pharmaceuticals (Human use) Pharmaceutical Transformation Benzophenone C13H10O Personal Care Products Cotinine C10H12N2O Products Benzophenone-3 C14H12O3 Personal Care Products Cycloheximide C15H23NO4 Pesticides Benzothiazole C7H5NS Vulcanization Accelerators Cyclopentolate C17H25NO3 Pharmaceuticals (Human use) Benzotriazole C6H5N3 Corrosion Inhibitors Darunavir C27H37N3O7S Pharmaceuticals (Human use)

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Compound Name Molecular Formula Common Use Compound Name Molecular Formula Common Use DEET C12H17NO Pesticides Fluconazole C13H12F2N6O Pharmaceuticals (Human use) Desomorphine C17H21NO2 Pharmaceuticals (Drugs of abuse) Fluometuron C10H11F3N2O Pesticides Detomidine C12H14N2 Pharmaceuticals (Animal use) Fluoxetine C17H18F3NO Pharmaceuticals (Human use) Dexpanthenol C9H19NO4 Pharmaceuticals (Human use) Flurandrenolide C24H33FO6 Pharmaceuticals (Human use) Dextromethorphan C18H25NO Pharmaceuticals (Human use) Gabapentin C9H17NO2 Pharmaceuticals (Human use) Diazepam C16H13ClN2O Pharmaceuticals (Human use) Galaxolidone C18H24O2 Personal Care Products Dichlorvos C4H7Cl2O4P Pesticides Gemfibrozil C15H22O3 Pharmaceuticals (Human use) Diclofenac C14H11Cl2NO2 Pharmaceuticals (Human use) Glutethimide C13H15NO2 Pharmaceuticals (Drugs of abuse) Dienogest C20H25NO2 Pharmaceuticals (Human use) Griseofulvin C17H17ClO6 Pharmaceuticals (Human use) Diethyl phthalate C12H14O4 Plasticizers Guaifenesin C10H14O4 Pharmaceuticals (Human use) Diltiazem C22H26N2O4S Pharmaceuticals (Human use) Hydrocortisone C21H30O5 Pharmaceuticals (Human use) Dimethachlor C13H18ClNO2 Pesticide Transformation Products Hydroxyprogesterone C21H30O3 Pharmaceuticals (Human use) Dimethyl phthalate C10H10O4 Plasticizers Ibuprofen C13H18O2 Pharmaceuticals (Human use) Dinoprostone C20H32O5 Pharmaceuticals (Human use) Imazapyr C13H15N3O3 Pesticides Dinotefuran C7H14N4O3 Pesticides Imidacloprid C9H10ClN5O2 Pesticides Diphenhydramine C17H21NO Pharmaceuticals (Human use) Indole-3-butyric acid C12H13NO2 Pesticides Diuron C9H10Cl2N2O Pesticides Irbesartan C25H28N6O Pharmaceuticals (Human use) Dopamine C8H11NO2 Pharmaceuticals (Human use) Triclosan C12H7Cl3O2 Personal Care Products Doxylamine C17H22N2O Pharmaceuticals (Human use) Isopropyl paraben C10H12O3 Personal Care Products Drospirenone C24H30O3 Pharmaceuticals (Human use) Isoproturon C12H18N2O Pesticides Efavirenz C14H9ClF3NO2 Pharmaceuticals (Human use) Ketamine C13H16ClNO Pharmaceuticals (Human use) Ephedrine C10H15NO Pharmaceuticals (Human use) Labetalol C19H24N2O3 Pharmaceuticals (Human use) Epitestosterone C19H28O2 Pharmaceuticals (Human use) Lamotrigine C9H7Cl2N5 Pharmaceuticals (Human use) Erythromycin C37H67NO13 Pharmaceuticals (Human use) Levamisole C11H12N2S Pharmaceuticals (Animal use) Estriol C18H24O3 Pharmaceuticals (Animal use) Levetiracetam C8H14N2O2 Pharmaceuticals (Human use) Ethyl paraben C9H10O3 Personal Care Products Levonorgestrel C21H28O2 Pharmaceuticals (Human use) Eugenol C10H12O2 Personal Care Products Levorphanol C17H23NO Pharmaceuticals (Human use) Fenamidone C17H17N3OS Pesticides Lidocaine C14H22N2O Pharmaceuticals (Human use) Fexofenadine C32H39NO4 Pharmaceuticals (Human use) Lopinavir C37H48N4O5 Pharmaceuticals (Human use) Fipronil C12H4Cl2F6N4OS Pesticides Losartan C22H23ClN6O Pharmaceuticals (Human use) Flecainide C17H20F6N2O3 Pharmaceuticals (Human use) Lovastatin C24H36O5 Pharmaceuticals (Human use)

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Molecular Molecular Compound Name Common Use Compound Name Common Use Formula Formula Malathion C10H19O6PS2 Pesticides Oxcarbazepine C15H12N2O2 Pharmaceuticals (Human use) Maprotiline C20H23N Pharmaceuticals (Human use) Paliperidone C23H27FN4O3 Pharmaceuticals (Human use) MCPA Acid C9H9ClO3 Pesticides Pentazocine C19H27NO Pharmaceuticals (Human use) Mebendazole C16H13N3O3 Pharmaceuticals (Human use) Pentedrone C12H17NO Pharmaceuticals (Drugs of abuse) Mecoprop-p C10H11ClO3 Pesticides Pentobarbital C11H18N2O3 Pharmaceuticals (Human use) Medroxyprogesterone C22H32O3 Pharmaceuticals (Human use) Phendimetrazine C12H17NO Pharmaceuticals (Human use) Melamine C3H6N6 Plasticizers Phenylephrine C9H13NO2 Pharmaceuticals (Human use) Melatonin C13H16N2O2 Pharmaceuticals (Animal use) Phenytoin C15H12N2O2 Pharmaceuticals (Human use) Memantine C12H21N Pharmaceuticals (Human use) Pilocarpine C11H16N2O2 Pharmaceuticals (Human use) Meperidine C15H21NO2 Pharmaceuticals (Human use) Pirimicarb C11H18N4O2 Pesticides Metalaxyl C15H21NO4 Pesticides Pirlimycin C17H31ClN2O5S Pharmaceuticals (Animal use) Metaxalone C12H15NO3 Pharmaceuticals (Human use) Prasterone C19H28O2 Pharmaceuticals (Human use) Metformin C4H11N5 Pharmaceuticals (Human use) Pregabalin C8H17NO2 Pharmaceuticals (Human use) Methadone C21H27NO Pharmaceuticals (Human use) Prilocaine C13H20N2O Pharmaceuticals (Human use) Methocarbamol C11H15NO5 Pharmaceuticals (Human use) Primidone C12H14N2O2 Pharmaceuticals (Human use) Metolachlor C15H22ClNO2 Pesticides Prometon C10H19N5O Pesticides Metolachlor ESA C15H23NO5S Pesticide Transformation Products Prometryn C10H19N5S Pesticides Metolachlor OA C15H21NO4 Pesticide Transformation Products Propamocarb C9H20N2O2 Pesticides Metoprolol C15H25NO3 Pharmaceuticals (Human use) Propazine C9H16ClN5 Pesticides Molindone C16H24N2O2 Pharmaceuticals (Human use) Propoxyphene C22H29NO2 Pharmaceuticals (Human use) Morphine C17H19NO3 Pharmaceuticals (Human use) Propranolol C16H21NO2 Pharmaceuticals (Human use) Mycophenolic acid C17H20O6 Pharmaceuticals (Human use) Propyl paraben C10H12O3 Personal Care Products Pharmaceutical Transformation N4-Acetylsulfamethoxazole C12H13N3O4S Pyrazon C10H8ClN3O Pharmaceuticals (Human use) Products Nadolol C17H27NO4 Pharmaceuticals (Human use) Pyrimethamine C12H13ClN4 Pharmaceuticals (Human use) Napropamide C17H21NO2 Pesticides Pyrovalerone C16H23NO Pharmaceuticals (Drugs of abuse) Naproxen C14H14O3 Pharmaceuticals (Human use) Ranitidine C13H22N4O3S Pharmaceuticals (Human use) Nevirapine C15H14N4O Pharmaceuticals (Human use) Rimantadine C12H21N Pharmaceuticals (Human use) Pharmaceutical Transformation Nicotine C10H14N2 Pharmaceuticals (Human use) Ritalinic acid C13H17NO2 Products Nitenpyram C11H15ClN4O2 Pharmaceuticals (Animal use) Ropivacaine C17H26N2O Pharmaceuticals (Human use) Desvenlafaxine C16H25NO2 Pharmaceuticals (Human use) Secobarbital C12H18N2O3 Pharmaceuticals (Human use) Oxamyl C7H13N3O3S Pesticides Sertraline C17H17Cl2N Pharmaceuticals (Human use)

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NSF Grant: Citizen Science-Based Monitoring Framework for Contaminants of Emerging Concern in New York State Lakes

Compound Name Molecular Formula Common Use Siduron C14H20N2O Pesticides Simazine C7H12ClN5 Pesticides Sitagliptin C16H15F6N5O Pharmaceuticals (Human use) Sotalol C12H20N2O3S Pharmaceuticals (Human use) Stanolone C19H30O2 Pharmaceuticals (Animal use) Stavudine C10H12N2O4 Pharmaceuticals (Human use) Sucralose C12H19Cl3O8 Artificial Sweeteners Sulfadoxine C12H14N4O4S Pharmaceuticals (Human use) Sulfamethazine C12H14N4O2S Pharmaceuticals (Animal use) Sulfamethoxazole C10H11N3O3S Pharmaceuticals (Human use) Sulfapyridine C11H11N3O2S Pharmaceuticals (Human use) Sulfisomidin C12H14N4O2S Pharmaceuticals (Human use) Sulfoxaflor C10H10F3N3OS Pesticides Tapentadol C14H23NO Pharmaceuticals (Human use) Telmisartan C33H30N4O2 Pharmaceuticals (Human use) Terbuthylazine C9H16ClN5 Pesticides Testosterone C19H28O2 Pharmaceuticals (Human use) Theophylline C7H8N4O2 Pharmaceuticals (Human use) Thiabendazole C10H7N3S Pesticides Thiacloprid C10H9ClN4S Pesticides Thiamethoxam C8H10ClN5O3S Pesticides Tranexamic acid C8H15NO2 Pharmaceuticals (Human use) Trenbolone C18H22O2 Pharmaceuticals (Animal use) Tributyl phosphate (CH3(CH2)3O)3PO Plasticizers Trimethoprim C14H18N4O3 Pharmaceuticals (Human use) TDCPP C9H15Cl6O4P Flame Retardants TCEP C6H12Cl3O4P Flame Retardants Valsartan C24H29N5O3 Pharmaceuticals (Human use) Varenicline C13H13N3 Pharmaceuticals (Human use) Venlafaxine C17H27NO2 Pharmaceuticals (Human use) Zidovudine C10H13N5O4 Pharmaceuticals (Human use)

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