Organic Micropollutants in New York Lakes: A Statewide Citizen Science Occurrence Study

Abstract

The widespread occurrence of organic micropollutants (OMPs) is a challenge for aquatic ecosystem management, and closing the gaps in risk assessment of OMPs requires a data-driven approach. One promising tool for increasing the spatiotemporal coverage of OMP data sets is through the active involvement of citizen volunteers to expand the scale of OMP monitoring. Working collaboratively with volunteers from the Citizens Statewide Lake Assessment Program (CSLAP), we conducted the first statewide study on OMP occurrence in surface waters of New York lakes. Samples collected by CSLAP volunteers were analyzed for OMPs by a suspect screening method based on mixed-mode solid-phase extraction and liquid chromatography-high resolution mass spectrometry. Sixty-five OMPs were confirmed and quantified in samples from 111 lakes across New York. Hierarchical clustering of OMP occurrence data revealed the relevance of 11 most frequently detected OMPs for classifying the contamination status of lakes. Partial least squares regression and multiple linear regression analyses prioritized three water quality parameters linked to agricultural and developed land uses (i.e., total dissolved nitrogen, specific conductance, and a wastewater-derived fluorescent organic matter component) as the best combination of predictors that partly explained the interlake variability in OMP occurrence. Lastly, the exposure-activity ratio approach identified the potential for biological effects associated with detected OMPs that warrant further biomonitoring studies. Overall, this work demonstrated the feasibility of incorporating citizen science approaches into the regional impact assessment of OMPs.

High-throughput wastewater analysis for substance use assessment in central New York during the COVID-19 pandemic

“…High-resolution accurate mass screening was performed using positive and negative electrospray ionization in separate runs following instrumental settings described in our recent work. 60 The total analysis time for each sample was 36 minutes.…”

Coupling Suspect and Nontarget Screening with Mass Balance Modeling to Characterize Organic Micropollutants in the Onondaga Lake–Three Rivers System

“…Samples were extracted by mixed-mode solid-phase extraction (SPE) and analyzed by liquid chromatography–HRMS (LC–HRMS) as described in our previous work. 41 Within 24 h of collection, duplicate samples (500 mL each) were adjusted to pH 6.8 ± 0.1 with ammonium acetate and formic acid, spiked with a mixture of isotope-labeled internal standards (200 ng/L each; Table S1 ), and filtered through precombusted 0.7 μm glass fiber filters. Samples were then passed through preconditioned dual SPE cartridges containing 200 mg of Sepra ZT (Phenomenex), 100 mg of Sepra ZT-SAX (Phenomenex), 100 mg of Sepra ZT-SCX (Phenomenex), and 150 mg of ISOLUTE ENV+ (Biotage) sorbents as the top layer and 200 mg of Enviro-Clean graphitized nonporous carbon (United Chemical Technologies) as the bottom layer.…”

“…Suspect screening was conducted in TraceFinder 4.1 (Thermo Scientific) using a custom suspect database containing compound-specific information for 3308 OMPs, including 2135 pharmaceuticals, pesticides, personal care products, and household and industrial chemicals as well as 1173 TPs. 41 Full scan mass spectra were processed by TraceFinder using predefined peak detection and isotopic pattern parameters ( Table S6 ) for suspect database matching. Only peaks fulfilling a mass accuracy tolerance of 5 ppm and an isotopic pattern fit threshold of >50% were selected for dd-MS2 spectra acquisition.…”

Coupling Suspect and Nontarget Screening with Mass Balance Modeling to Characterize Organic Micropollutants in the Onondaga LakeThree Rivers System

“…Fluorescence excitation-emission matrices (EEMs) and UV-visible absorbance spectra were simultaneously measured in a Starna Cells 3-Q-10 quartz cuvette (1-cm pathlength) using a Horiba Scientific Aqualog spectrofluorometer as described in our previous work. 3 Optical indices, such as Napierian absorption coefficients (a) 4 at 254 nm, 280 nm, and 440 nm, [5][6][7] E2:E3 (the ratio of absorption coefficients at 250 and 365 nm; an inverse proxy of dissolved organic matter (DOM) molecular size), 8 spectral slope ratio (SR; the ratio of spectral slope coefficient S275-295 to spectral slope coefficient S290-350; an indicator of environmental processing of DOM), 9 CDOM250-450 (the integrated absorption of chromophoric DOM from 250 to 450 nm), 9, 10 SUVA254 (the specific UV absorbance at 254 nm; a proxy of DOM aromaticity), 11 fluorescence index (FI; an indicator of the source of DOM, which is either microbially derived from bacteria and algae or terrestrially derived from plant litter and soil), 12,13 humification index (HIX; an indicator of the degree of DOM humification), [14][15][16] freshness index (β:α; an indicator of the contribution of recently plant-derived or autochthonous DOM, where β represents more recently produced DOM and α represents more decomposed DOM), [17][18][19] and FDOM (the integrated volumetric fluorescence intensity of fluorescent DOM with excitation wavelengths from 240 to 550 nm and emission wavelengths from 248.2 to 600.9 nm normalized to the Raman peak area of ultrapure water), 20,21 were extracted from the absorbance and fluorescence data using MATLAB R2019a.…”

“…Overall, suspect and nontarget screening prioritized 385 compounds. For each suspect or nontarget compound, the absolute SPE recovery, ion suppression, matrix factor, and limits of quantification (LOQs) were determined as detailed in our previous work 3 and summarized in Table S9 and Figure S9. Target analysis was performed retrospectively to quantify the concentrations of confirmed OMPs in the samples.…”