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

Shiru Wang a, Hyatt C. Green b, Maxwell L. Wilder b, Qian Du c, Brittany L. Kmush d, Mary B. Collins e, David A. Larsen d and Teng Zeng *a
aDepartment of Civil and Environmental Engineering, Syracuse University, Syracuse, NY 13244, USA. E-mail: tezeng@syr.edu; Tel: +1-315-443-1099
bDepartment of Environmental and Forest Biology, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA
cQuadrant Biosciences Inc., Syracuse, NY 13210, USA
dDepartment of Public Health, Syracuse University, Syracuse, NY 13244, USA
eDepartment of Environmental Studies, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA

Received 28th August 2020 , Accepted 14th October 2020

First published on 15th October 2020


Wastewater entering sewer networks represents a unique source of pooled epidemiological information. In this study, we coupled online solid-phase extraction with liquid chromatography-high resolution mass spectrometry to achieve high-throughput analysis of health and lifestyle-related substances in untreated municipal wastewater during the coronavirus disease 2019 (COVID-19) pandemic. Twenty-six substances were identified and quantified in influent samples collected from six wastewater treatment plants during the COVID-19 pandemic in central New York. Over a 12 week sampling period, the mean summed consumption rate of six major substance groups (i.e., antidepressants, antiepileptics, antihistamines, antihypertensives, synthetic opioids, and central nervous system stimulants) correlated with disparities in household income, marital status, and age of the contributing populations as well as the detection frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater and the COVID-19 test positivity in the studied sewersheds. Nontarget screening revealed the covariation of piperine, a nontarget substance, with SARS-CoV-2 RNA in wastewater collected from one of the sewersheds. Overall, this proof-of-the-concept study demonstrated the utility of high-throughput wastewater analysis for assessing the population-level substance use patterns during a public health crisis such as COVID-19.

Environmental significance

Wastewater surveillance has been increasingly implemented worldwide for monitoring population-level substance use trends. Our study demonstrates the application of online solid-phase extraction and liquid chromatography-high resolution mass spectrometry for high-throughput screening of pharmaceuticals and lifestyle chemicals in municipal wastewater for substance use assessment in sewersheds experiencing the coronavirus pandemic.


Wastewater-based epidemiology (WBE) is an evidence-based approach for monitoring infectious disease outbreaks,1–3 substance use trends,4–6 and antimicrobial resistance spread at the community scale.7,8 Since the onset of the coronavirus pandemic in 2019, research groups worldwide have demonstrated the feasibility and scalability of monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater or sludge to track and/or predict the transmission and control of SARS-CoV-2 in sewered communities.9–24 Given the widespread efforts on wastewater sampling brought by these SARS-CoV-2 surveillance platforms, co-analysis of human biomarkers in wastewater for substance use assessment represents an attractive strategy to gain additional insights into population behavior and health status underlying the susceptibility to coronavirus disease 2019 (COVID-19) and its adverse outcomes. Indeed, recent reviews and meta-analyses have highlighted common risk factors for COVID-19 and substance use.25–27 For example, comorbidities associated with substance use disorders (e.g., cardiovascular, metabolic, pulmonary, and respiratory diseases) are known to exacerbate COVID-19 mortality and related health outcomes.28–31 Meanwhile, socioeconomic disparities in health exert a disproportionate impact on the severity of COVID-19 in certain racial and ethnic minority populations (e.g., African-American)32–34 and disadvantaged communities,35–37 particularly those having a higher prevalence of substance use disorders.25 Furthermore, COVID-related non-pharmaceutical interventions (e.g., lockdowns) and pharmacologic treatments may in turn contribute to changes in substance use behaviors.38–41 For instance, emergency medical services data in Kentucky and Virginia showed increases in opioid overdose rates during the early months of the pandemic,42,43 while longitudinal wastewater data in South Australia suggested a decline in alcohol consumption due to social distancing and isolation measures.44 Collectively, these studies underscore the importance of substance use assessment by means of wastewater analysis to complement clinical and sociodemographic data for characterizing risk drivers of COVID-19 at the population level.

In this proof-of-concept study, we combined online solid-phase extraction (online SPE) with liquid chromatography-high resolution mass spectrometry (LC-HRMS) to enable high-throughput screening of pharmaceuticals and lifestyle chemicals in untreated municipal wastewater sampled from central New York during the COVID-19 pandemic. Online SPE automates wastewater extraction, preconcentration, and large-volume injections, and has been applied for the rapid analysis of illicit and prescription drugs in several WBE studies.45–49 LC-HRMS streamlines target screening of known substances as well as wide-scope screening of emerging or unknown substances in wastewater.50,51 For example, recent WBE studies have implemented data-driven prioritization strategies based on suspect screening, and to a lesser extent, nontarget screening to identify new psychoactive substances, widely consumed illicit drugs, and their unknown metabolites in wastewater.52–56 Our specific objectives of this study were to (i) estimate the population-level consumption rates of common health and lifestyle-related substances in untreated wastewater collected from six sewersheds in central New York; (ii) explore the relationships between the consumption rates of representative substance groups and sociodemographics and COVID-19 prevalence in the studied sewersheds; and (iii) develop a nontarget screening workflow to prioritize unknown substances that covaried with SARS-CoV-2 RNA in wastewater for follow-up investigations.

Materials and methods

Chemicals and reagents

Methanol (LC-MS grade), water (LC-MS grade), acetonitrile (LC-MS grade), and formic acid solution (LC-MS grade) were supplied by Fisher Scientific (Waltham, MA). Unlabeled reference standards and isotope-labeled internal standards were purchased from Sigma-Aldrich (St. Louis, MO), Toronto Research Chemicals (Toronto, Ontario), C/D/N Isotopes (Pointe-Claire, Quebec), and Cambridge Isotope Laboratories (Tewksbury, MA) as high-purity substances or concentrated solutions and stored following manufacturers' recommendations.

Wastewater sampling

Over the course of this study, twelve batches of 24 h flow-proportional composite influent wastewater samples were collected weekly between April 29 and July 15, 2020 from six municipal wastewater treatment plants (WWTPs A–F; Table 1) in central New York under dry weather conditions. Our sampling followed the rising and falling COVID-19 prevalence in the study region where the daily number of laboratory-confirmed positive COVID-19 cases peaked in late May 2020.57 Note that samples were only collected in the middle of the week due to the lack of personnel availability and laboratory accessibility on weekends. WWTPs A–F connect to sewer networks that primarily consist of gravity sewers. The average sewer transit times for these sewer networks ranged from 1.2 to 4.4 h with a mean of 2.6 ± 1.5 h, which resembled the estimated median transit time of 3.3 h for WWTPs of varying sizes across the U.S.58 Together, these six WWTPs serve a total population of ∼396[thin space (1/6-em)]300 in urban and suburban areas of central New York. Upon collection, samples were first transported on ice to Biosafety Level 3 laboratories at SUNY-Upstate Medical University for SARS-CoV-2 RNA analysis as detailed in Green et al.59 Split samples (∼40 mL) were frozen in the dark at −20 °C and processed at Syracuse University for online SPE-LC-HRMS analysis as soon as practically possible. General operational (e.g., average daily flow rates) and wastewater quality parameters (e.g., pH and temperature) were provided by WWTP personnel when applicable. Nine 24 h composite influent wastewater samples collected from WWTP A over the year of 2018 and archived at −20 °C were also analyzed in this study for comparison with the 2020 samples.
Table 1 Sewershed and sociodemographic characteristics
a Calculated using the linear distance method as described by Kapo et al.58 by dividing the average distances between the population density polygon centroids and the coordinate of a given WWTP by an average sewage flow velocity of 0.6 m s−1 (personal communication with the sewer maintenance superintendent118). b Extracted from the 2014–2018 American Community Survey as estimates ± margins of error. c Including American Indian and Alaska Native alone, Native Hawaiian and other Pacific Islander alone, and some other race alone. d Including Master's degree, Doctorate degree, and professional school.
WWTP average capacity (million gallons per day) 84.2 9.0 7.0 3.0 10.0 6.5
Sewershed area (km2) 347.3 125.6 40.5 80.8 155.8 127.4
Sewer transit timea (hours) 2.5 ± 1.3 (0.5–7.5) 1.8 ± 0.9 (0.5–3.1) 1.2 ± 0.4 (0.7–1.7) 4.4 ± 2.3 (0.8–7.8) 3.5 ± 1.3 (1.1–5.3) 2.5 ± 1.3 (0.2–4.5)
Sewered populationb 239[thin space (1/6-em)]032 ± 3739 30[thin space (1/6-em)]058 ± 1352 26[thin space (1/6-em)]138 ± 1132 11[thin space (1/6-em)]849 ± 812 50[thin space (1/6-em)]084 ± 1486 39[thin space (1/6-em)]123 ± 1409
[thin space (1/6-em)]
Male 47.6 ± 0.9% 49.7 ± 2.8% 49.9 ± 2.7% 49.4 ± 3.9% 48.1 ± 1.8% 47.9 ± 2.2%
Female 52.4 ± 1.0% 50.3 ± 2.5% 50.1 ± 2.5% 50.6 ± 3.9% 51.9 ± 1.7% 52.1 ± 2.2%
[thin space (1/6-em)]
Age group (years)
<18 20.8 ± 1.6% 19.9 ± 4.8% 22.1 ± 5.0% 23.5 ± 7.5% 21.3 ± 3.4% 20.6 ± 3.9%
18–24 14.2 ± 1.2% 6.0 ± 2.5% 7.1 ± 2.8% 5.9 ± 3.4% 6.6 ± 1.8% 9.8 ± 2.6%
25–34 14.6 ± 0.9% 12.8 ± 2.7% 13.3 ± 2.7% 13.0 ± 4.1% 13.8 ± 1.9% 9.3 ± 1.8%
35–44 10.5 ± 0.7% 12.3 ± 2.7% 12.1 ± 2.5% 13.0 ± 4.2% 12.2 ± 1.7% 11.3 ± 2.0%
45–54 12.2 ± 0.8% 15.0 ± 2.8% 14.6 ± 2.6% 16.4 ± 4.2% 15.7 ± 1.9% 12.9 ± 2.1%
55–64 12.3 ± 0.9% 15.5 ± 3.2% 13.7 ± 2.8% 16.4 ± 4.9% 14.2 ± 2.0% 15.8 ± 2.7%
65–69 4.7 ± 0.5% 6.1 ± 1.5% 5.9 ± 1.6% 4.9 ± 2.1% 5.5 ± 1.1% 6.2 ± 1.4%
≥70 10.7 ± 1.0% 12.4 ± 3.3% 11.2 ± 3.1% 6.9 ± 3.6% 10.7 ± 2.1% 14.1 ± 3.0%
[thin space (1/6-em)]
White 70.9 ± 1.3% 95.3 ± 4.5% 92.5 ± 4.3% 96.3 ± 6.8% 92.3 ± 2.9% 84.5 ± 3.4%
Black or African American 17.8 ± 0.9% 1.9 ± 1.5% 2.8 ± 1.1% 1.4 ± 1.0% 2.9 ± 1.1% 6.3 ± 1.6%
Asian 4.6 ± 0.4% 0.8 ± 0.5% 1.8 ± 0.7% 1.2 ± 1.0% 1.8 ± 0.5% 5.5 ± 1.3%
Otherc 2.3 ± 0.3% 0.4 ± 0.4% 0.6 ± 0.5% 0.0 ± 0.5% 0.8 ± 0.4% 1.6 ± 0.6%
Non-hispanic/latino 93.5 ± 1.5% 97.4 ± 4.4% 96.6 ± 4.4% 97.7 ± 6.3% 97.1 ± 2.9% 96.9 ± 3.5%
Hispanic/Latino 6.5 ± 0.5% 2.6 ± 1.1% 3.4 ± 1.2% 2.3 ± 2.1% 2.9 ± 0.8% 3.1 ± 0.8%
[thin space (1/6-em)]
Marital status
Never married 44.3 ± 1.4% 26.7 ± 3.4% 29.7 ± 3.4% 24.0 ± 4.5% 28.0 ± 2.3% 28.3 ± 2.9%
Now married 39.0 ± 1.0% 56.7 ± 3.2% 55.2 ± 3.2% 62.2 ± 5.5% 54.7 ± 2.2% 56.1 ± 2.8%
Widowed 6.4 ± 0.4% 5.4 ± 1.3% 4.7 ± 1.2% 5.1 ± 1.7% 5.8 ± 0.9% 6.9 ± 1.1%
Divorced 10.3 ± 0.6% 11.2 ± 1.9% 10.4 ± 1.8% 8.7 ± 2.6% 11.5 ± 1.4% 8.7 ± 1.4%
[thin space (1/6-em)]
Education attainment
Below/some high school 12.0 ± 1.5% 5.1 ± 3.0% 5.6 ± 3.3% 5.8 ± 4.6% 5.8 ± 2.4% 5.5 ± 2.9%
High school 26.9 ± 1.1% 26.6 ± 2.9% 25.8 ± 3.0% 25.1 ± 4.7% 27.0 ± 2.1% 16.3 ± 2.0%
Some college 18.5 ± 0.9% 19.2 ± 3.0% 16.5 ± 2.5% 22.1 ± 4.9% 17.6 ± 1.8% 14.8 ± 2.0%
Associate's 11.3 ± 0.5% 14.5 ± 1.8% 13.9 ± 1.7% 14.7 ± 2.5% 14.3 ± 1.2% 8.5 ± 1.2%
Bachelor's 17.7 ± 0.7% 19.7 ± 1.9% 22.1 ± 2.0% 18.3 ± 2.9% 22.6 ± 1.5% 24.9 ± 1.9%
Graduate/professionald 13.6 ± 0.6% 14.9 ± 1.8% 16.1 ± 1.8% 14.0 ± 2.4% 12.7 ± 1.1% 30.0 ± 2.2%
[thin space (1/6-em)]
Household income
Below/near poverty 21.3 ± 1.4% 9.0 ± 3.0% 8.4 ± 2.9% 14.2 ± 6.3% 9.7 ± 2.2% 10.6 ± 2.8%
Low income 24.5 ± 1.9% 21.8 ± 5.5% 19.2 ± 5.5% 15.9 ± 9.1% 17.9 ± 3.6% 16.4 ± 4.3%
Middle class 47.0 ± 2.9% 56.3 ± 9.6% 61.0 ± 9.9% 54.7 ± 14.6% 60.3 ± 6.9% 51.7 ± 7.9%
High income 7.2 ± 0.7% 12.9 ± 2.8% 11.4 ± 2.5% 15.2 ± 4.1% 12.1 ± 1.8% 21.3 ± 2.9%

Sample analysis

Prior to instrumental analysis, 10 mL of raw wastewater samples were spiked with a mixture of isotope-labeled chemicals as internal standards (200 ng L−1 each) and filtered by 0.22 μm Millex-GP polyethersulfone syringe filters into Chromacol autosampler vials. Samples were then analyzed in duplicate by a Thermo Scientific EQuan MAX Plus online SPE system hyphenated with a Vanquish Flex ultra-high-performance liquid chromatograph and an LTQ-Orbitrap XL hybrid ion trap-Orbitrap high resolution mass spectrometer. Briefly, 1 mL of each sample was loaded from a 5 mL stainless steel sample loop onto a Hypersil GOLD aQ C18 trap column (20 × 2.1 mm i.d., 12 μm particle size) for analyte preconcentration. The trap column was washed with acidified water (amended with 0.1% v/v formic acid) to remove inorganics and subsequently eluted with the analytical pump gradient. The eluted analytes were transferred to a Hypersil GOLD C18 analytical column (100 × 2.1 mm i.d., 3 μm particle size) running acidified water and methanol as the mobile phases (both amended with 0.1% v/v formic acid) for chromatographic separation. The trap and analytical columns were then re-equilibrated to their starting conditions prior to the next injection. 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.

Suspect screening was performed in TraceFinder 4.1 (Thermo Scientific) with predefined peak filtering criteria using an in-house database containing compound-specific information for most frequently used pharmaceuticals and lifestyle chemicals (including their stable metabolites) in the study region. Full scan triggered data-dependent tandem mass (dd-MS2) spectra of suspect compounds were interrogated against reference spectra in the mzCloud mass spectral library61 using Compound Discoverer 3.1 (Thermo Scientific). Suspect compounds with an mzCloud match factor of >30 were selected for further confirmation. Twenty-six substances were confirmed in 100% wastewater samples based on the match of their chromatographic retention times and dd-MS2 spectra to those of authentic reference standards. Target quantification of these 26 confirmed compounds was performed using 14-point calibration curves (i.e., 0.1–5000 ng L−1) with reference to their isotope-labeled analogues (Table 2). Good linearity (R2 = 0.992–0.999) was established for the calibration curves of all target compounds. Limits of quantification for target compounds were determined in pooled wastewater (i.e., prepared by mixing equal volumes of WWTP A–F samples) to account for matrix effects (Table 2). Calibration standards were run with each sample sequence to evaluate within- and between-run accuracy and precision. Solvent blanks were run to check for potential cross-contamination following the analysis of highly concentrated standards.

Table 2 Online SPE-LC-HRMS method performance for target substances
Target substance CAS Adduct Exact mass (m/z) Diagnostic fragment (m/z) RTa (min) LOQb (ng L−1) ILISc
a Retention time. b Limit of quantification determined in pooled wastewater (i.e., prepared by mixing equal volumes of WWTP A–F samples). c Isotope-labeled internal standard. d 2-Ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine. e Nontarget substance.
Acetaminophen 103-90-2 [M + H]+ 152.0706 126.0100 4.74 0.3 Acetaminophen-d3
Amphetamine 300-62-9 [M + H]+ 136.1121 119.0859 6.22 1.2 Amphetamine-d10
Atenolol 29122-68-7 [M + H]+ 267.1703 190.0851 4.35 12 Atenolol-d7
Caffeine 58-08-2 [M + H]+ 195.0877 138.0656 6.58 1.1 Caffeine-d9
Carbamazepine 298-46-4 [M + H]+ 237.1022 194.0955 11.79 6.6 Carbamazepine-d10
Cimetidine 51481-61-9 [M + H]+ 253.1230 159.0690 4.57 7.5 Cimetidine-d3
Diphenhydramine 58-73-1 [M + H]+ 256.1696 224.0828 10.44 6.8 Diphenhydramine-d3
Ephedrine 299-42-3 [M + H]+ 166.1226 148.1156 5.42 15 Ephedrine-d3
Gabapentin 60142-96-3 [M + H]+ 172.1332 154.1221 6.15 0.4 Gabapentin-d10
Lamotrigine 84057-84-1 [M + H]+ 256.0151 210.9816 8.44 4.0 Lamotrigine-13C, 15N4
Levetiracetam 102767-28-2 [M + H]+ 171.1128 126.0908 5.62 8.0 Levetiracetam-d6
Lidocaine 137-58-6 [M + H]+ 235.1805 234.1846 6.93 1.0 Lidocaine-d10
Methamphetamine 537-46-2 [M + H]+ 150.1277 91.0538 6.44 4.3 Methamphetamine-d8
Methocarbamol 532-03-6 [M + H]+ 242.1023 163.0748 8.75 32 Methocarbamol-d3
Metoprolol 51384-51-1 [M + H]+ 268.1907 191.1058 8.00 2.2 Metoprolol-d7
Phentermine 122-09-8 [M + H]+ 150.1277 133.1006 7.28 7.6 Phentermine-d5
Pregabalin 148553-50-8 [M + H]+ 160.1332 142.1221 6.13 8.9 Pregabalin-d6
Sulfamethoxazole 723-46-6 [M + H]+ 254.0594 156.0107 7.44 6.6 Sulfamethoxazole-d4
Tramadol 27203-92-5 [M + H]+ 264.1958 246.1840 7.57 3.4 Tramadol-13C, d3
Trimethoprim 738-70-5 [M + H]+ 291.1452 230.1152 6.26 9.2 Trimethoprim-d9
Venlafaxine 93413-69-5 [M + H]+ 278.2115 260.2000 9.78 3.0 Venlafaxine-d6
Benzoylecgonine 519-09-5 [M + H]+ 290.1387 168.1012 7.63 2.9 Benzoylecgonine-d3
EDDPd 30223-73-5 [M + H]+ 278.1903 249.1502 10.30 4.5 EDDP-d3
Hydroxybupropion 92264-81-8 [M + H]+ 256.1099 238.0985 8.76 3.6 Hydroxybupropion-d6
Ritalinic acid 19395-41-6 [M + H]+ 220.1332 84.0804 7.52 1.0 Ritalinic acid-d10
Sucralose 56038-13-2 [M + FA] 441.0128 395.0071 7.15 25 Sucralose-d6
Piperinee 94-62-2 [M + H]+ 286.1438 201.0539 15.41 4.4 Metolachlor-d6

Nontarget screening was conducted with WWTP A wastewater samples exhibiting a characteristic time trend of SARS-CoV-2 RNA abundance (i.e., first increased and then decreased over the sampling period). Peak profile preprocessing (e.g., retention time alignment, unknown compound detection and grouping, background subtraction, molecular formula assignment, intensity normalization) and differential analysis (i.e., peak area ratio filtering) were performed in Compound Discoverer 3.1 to filter mass spectral features that showed elevated through-time peak intensities over the same time period during which higher SARS-CoV-2 RNA concentrations were detected in corresponding samples. Tucker's congruence coefficients (TCCs) were then computed using the multiway package62 in R 4.0.2 to evaluate the shape similarity between the time trends of filtered nontarget features and SARS-CoV-2 RNA. Typically, a TCC value above 0.95 indicates good shape similarity between two profiles.63 Lastly, hierarchical cluster analysis (HCA) was performed using the factoextra package64 in R to further prioritize clusters of high-TCC nontarget features that showed the closest time trend similarity with SARS-CoV-2 RNA detection. Full scan triggered dd-MS2 spectra of HCA-prioritized nontarget features were searched on mzCloud61 and further confirmed (or rejected) by authentic reference standards. Target quantification of a newly confirmed nontarget compound (i.e., piperine) was performed retrospectively.

Data analysis

Following instrumental analysis, the mass loads and consumption rates of individual pharmaceuticals and lifestyle chemicals were estimated using the following equations:65
image file: d0em00377h-t1.tif(1)
image file: d0em00377h-t2.tif(2)
where mass load represents the daily mass load of a substance entering a WWTP (g d−1), CW is the aqueous concentration of a substance in wastewater (ng L−1), Q is the average daily flow rate of wastewater measured at the inlet of a WWTP (m3 d−1), stability is the in-sewer stability change of a substance (%; assuming <10% according to previous WBE studies66–68 given the relatively short sewer transit times in the studied sewersheds), consumption rate is the collective consumption of a substance (i.e., consumed and disposed) by a given population (mg per d per 1000 people), excretion is the average excretion rate of a parent compound or its metabolite reported in the pharmacokinetic literature and WBE studies,65,68–79 MWParent is the molecular weight of a parent compound, MWMetabolite is the molecular weight of a stable metabolite when present, and population is the number of residents served by a given WWTP (assuming no substantial variations due to travel restrictions80). Note that the mass loads and substance consumption rates calculated herein only represent best estimates with acknowledged uncertainties.68,81

The detection frequency of SARS-CoV-2 RNA in wastewater was determined by dividing the number of SARS-CoV-2 RNA occurrence (i.e., at least one positive hit out of three reverse transcription quantitative polymerase chain reactions) by the total number of samples analyzed for each sewershed over the sampling period.59 The daily COVID-19 case counts were retrieved from the New York State Statewide COVID-19 Testing database and geocoded by research staff from the New York State Department of Health (NYSDOH) to the address points within each sewershed.57 The COVID-19 test positivity for each sewershed was calculated by dividing the total number of positive tests by the total number of tests conducted over the sampling period. Sociodemographic attributes of the population in each sewershed (i.e., 5 year estimates and associated margins of error for age groups, race, ethnicity, marital status, educational attainment, household income, employment status) were extracted from the U.S. Census Bureau's 2014–2018 American Community Survey at the block-group level using the tidycensus82 package and georeferenced using the tigris83 package in R. Statistical analyses (e.g., Spearman correlation matrix analysis and Mann–Whitney test) were performed using GraphPad Prism 8.4.

Results and discussion

Estimation of population-level substance consumption rates

Overall, twenty-six health and lifestyle-related substances with 100% detection frequency in wastewater samples were quantified by online SPE-LC-HRMS, including 22 parent compounds and 4 metabolites (i.e., 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (a metabolite of methadone), benzoylecgonine (a metabolite of cocaine), hydroxybupropion (a metabolite of bupropion), and ritalinic acid (a metabolite of methylphenidate)). Several other compounds prioritized by suspect screening (i.e., buprenorphine, codeine, norfentanyl, norhydrocodone, oxycodone) were also confirmed in wastewater samples but not quantified due to their limited detection frequency. Most compounds occurred at ng L−1-μg L−1 levels in wastewater samples and can be broadly categorized as antibacterials (i.e., sulfamethoxazole and trimethoprim), antidepressants (i.e., bupropion and venlafaxine), antiepileptics (i.e., carbamazepine, gabapentin, lamotrigine, levetiracetam, pregabalin), antihypertensives (i.e., atenolol and metoprolol), antihistamines (i.e., cimetidine and diphenhydramine), synthetic opioids (i.e., methadone and tramadol), and central nervous system stimulants (i.e., amphetamine, cocaine, ephedrine, methamphetamine, methylphenidate). Other compounds included acetaminophen (analgesic), lidocaine (local anesthetic), methocarbamol (muscle relaxant), phentermine (appetite suppressant), as well as caffeine and sucralose. Many of these compounds have been established as health and lifestyle biomarkers for substance use assessment in previous WBE studies.84

With a few exceptions, the mean consumptions rates of 26 target pharmaceuticals and lifestyle chemicals were close to previous estimates via the analysis of wastewater sampled from other northeastern U.S. sewersheds.45,85–87 On average, the consumption rates of these substances spanned over five orders of magnitude, with acetaminophen and trimethoprim being the most and least consumed substance, respectively (Fig. 1). The consumption rate of caffeine varied from 6.91 ± 2.57 × 104 to 1.85 ± 1.05 × 105 mg per d per 1000 people across sewersheds, which bracketed the average daily beverage caffeine intakes estimated for the U.S. population (i.e., 165 ± 1 mg per d per person)88 and overlapped with the range of values reported for major European cities via wastewater analysis (i.e., 86–263 mg per d per person).71 Likewise, the mean consumption rate of sucralose, the most widely consumed artificial sweetener in the U.S.,89 was 1.49 ± 0.70 × 104 mg per d per 1000 people, which was similar to the average value previously reported for the Albany area of New York based on wastewater analysis (i.e., 18.5 ± 4.4 g per d per 1000 people).90 The fact that the consumption rates of caffeine and sucralose estimated in this work matched literature-reported values provides confidence in the validity of our analysis. Furthermore, the mean consumption rates of antidepressants, antiepileptics, antihistamines, antihypertensives, opioids, and stimulants correlated with each other in most cases (ρ = 0.886–1.000; p = 0.003–0.033), which qualitatively agreed with prior WBE work showing correlations among the population-level consumption rates of antidepressants, antiepileptics, antihypertensives, and opioids.72,91 Such covariations among the consumption rates of these representative substance groups pointed to common underlying determinants of substance use patterns in the contributing populations.

image file: d0em00377h-f1.tif
Fig. 1 Consumption rates of 26 substances in sewersheds A–F between April 29 and July 15, 2020 (n = 72). The box extends from the 25th to 75th percentiles. The whiskers extend down to the 5th percentile and up to the 95th. The centerline in each box represents the median, while the plus sign “+” represents the mean. Note that the consumption rate of methadone was estimated via its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), the consumption rate of cocaine was estimated via its metabolite benzoylecgonine, the consumption rate of bupropion was estimated via its metabolite hydroxybupropion, and the consumption rate of methylphenidate was estimated via its metabolite ritalinic acid.

Substance consumption in the context of sociodemographic heterogeneity and COVID-19 prevalence

Over the 12 week sampling period, the mean summed consumption rate of 26 substances varied across sewersheds, ranging from 9.65 ± 3.97 × 105 to 2.21 ± 0.86 × 106 mg per d per 1000 people. Such differences in substance consumption rates likely resulted from disparities in the sociodemographic characteristics of the contributing populations as suggested by previous WBE studies.72,91,92 For example, a nationwide WBE study in Australia highlighted opioids, antidepressants, antiepileptics, and antihypertensives as proxies of socioeconomic distress and inequalities.91 Similarly, wastewater surveillance in Greece confirmed a concomitant increase in the consumption of psychoactive drugs and mental illnesses as a consequence of adverse socioeconomic changes.72 To the best of our knowledge, however, no U.S.-based WBE studies have examined the associations between sewershed-specific substance consumption rates and sociodemographics. Of the major sociodemographic variables extracted from the 5 year American Community Survey, the ratio of low income to high income population, the ratio of not married to married population, and the ratio of age 18–34 to age 35–69 population showed statistically significant positive correlations with the mean summed consumption rate of 26 substances (ρ = 0.886–0.943; p = 0.017–0.033), suggesting that disparities in household income, marital status, and age all affected substance consumption by the contributing populations. Specifically, low-income households (i.e., annual income < $45[thin space (1/6-em)]000), not married adults (including never married, widowed, and divorced), and individuals in their early adulthood (i.e., age 18–34) likely consumed a higher amount of antidepressants, antiepileptics, antihistamines, antihypertensives, opioids, and stimulants compared to high-income households, married adults, and individuals in their later adulthood (Fig. 2). In contrast, sociodemographic heterogeneity had no apparent effect on the consumption of other substances such as antibacterials, which was also in line with earlier findings.91 Presumably, predictive regression models could be developed for the consumption rates of specific substances or substance groups and sociodemographic determinants as demonstrated by a recent study in Australia,93 but the relatively small sample size in this study precluded model training and cross-validation.
image file: d0em00377h-f2.tif
Fig. 2 Spearman correlations between the mean summed consumption rate of six substance groups and sociodemographics in sewersheds A–F between April 29 and July 15, 2020: (a) correlation between the mean summed consumption rate of six substance groups (i.e., the sum of antidepressants, antiepileptics, antihistamines, antihypertensives, synthetic opioids, and central nervous system stimulants) and the ratio of low income (including below/near poverty) to high income population. (b) Correlation between the mean summed consumption rate of six substance groups and the ratio of not married to married population. (c) Correlation between the mean summed consumption rate of six substance groups and the ratio of age 18–34 to age 35–69 population. Vertical error bars represent the standard deviation of the mean summed consumption rates of six substance groups (n = 12 for each sewershed). Horizontal error bars represent the margins of error of sociodemographic data extracted from the 5 year American Community Survey (2014–2018).

Over the sampling period, the mean summed consumption rate of 26 substances also exhibited positive correlations (ρ = 0.943; p = 0.017) with the detection frequency of SARS-CoV-2 RNA in wastewater samples (i.e., ranging from 50.0% to 91.7%) and the overall COVID-19 test positivity in the studied sewersheds (i.e., ranging from 1.57% to 4.04% based on COVID-19 case data provided by NYSDOH). Closer examination of the data revealed that the detection frequency of SARS-CoV-2 RNA and the overall COVID-19 test positivity showed strong correlations with the consumption rates of antidepressants, antiepileptics, antihypertensives, antihistamines, opioids, and stimulants (Fig. 3), which were not unexpected given that COVID-19 and substance use likely share common sociodemographic risk factors. However, generalization of these relationships would require expanding our pilot wastewater surveillance program to collect and analyze samples with a wider geographic and temporal coverage. The degree to which the consumption rates of specific substances or substance groups are indicative of population susceptibility to COVID-19 in any given sewershed also warrants further investigation with rigorous uncertainty assessment.

image file: d0em00377h-f3.tif
Fig. 3 Spearman correlations between the mean consumption rates of specific substance groups and the SARS-CoV-2 RNA detection frequency in wastewater samples or the COVID-19 test positivity in sewersheds A–F between April 29 and July 15, 2020: (a) correlation between the mean consumption rate of antidepressants (i.e., the sum of bupropion and venlafaxine) and the SARS-CoV-2 RNA detection frequency in wastewater samples (n = 12 for each sewershed). (b) Correlation between the mean consumption rate of antiepileptics (i.e., the sum of carbamazepine, gabapentin, lamotrigine, levetiracetam, and pregabalin) and the SARS-CoV-2 RNA detection frequency in wastewater samples (n = 12 for each sewershed). (c) Correlation between the mean consumption rate of antihistamines (i.e., the sum of cimetidine and diphenhydramine) and the SARS-CoV-2 RNA detection frequency in wastewater samples (n = 12 for each sewershed). (d) Correlation between the mean consumption rate of antihypertensives (i.e., the sum of atenolol and metoprolol) and the SARS-CoV-2 RNA detection frequency in wastewater samples (n = 12 for each sewershed). (e) Correlation between the mean consumption rate of synthetic opioids (i.e., the sum of methadone and tramadol) and the SARS-CoV-2 RNA detection frequency in wastewater samples (n = 12 for each sewershed). (f) Correlation between the mean consumption rate of central nervous system stimulants (i.e., the sum of amphetamine, cocaine, ephedrine, methamphetamine, and methylphenidate) and the SARS-CoV-2 RNA detection frequency in wastewater samples (n = 12 for each sewershed). (g) Correlation between the mean consumption rate of antidepressants and the COVID-19 test positivity in each sewershed. (h) Correlation between the mean consumption rate of antiepileptics and the COVID-19 test positivity in each sewershed. (i) Correlation between the mean consumption rate of antihistamines and the COVID-19 test positivity in each sewershed. (j) Correlation between the mean consumption rate of antihypertensives and the COVID-19 test positivity in each sewershed. (k) Correlation between the mean consumption rate of synthetic opioids and the COVID-19 test positivity in each sewershed. (l) Correlation between the mean consumption rate of central nervous system stimulants and the COVID-19 test positivity in each sewershed. Error bars represent the standard deviation of the mean consumption rates of specific substance groups (n = 12 for each sewershed).

Nontarget screening of unknown substances in wastewater

Nontarget screening filtered clusters of mass spectral features characterized by normalized peak intensities that tracked with SARS-CoV-2 RNA concentrations in WWTP A samples. Small relative standard deviations (<13%) in the absolute peak intensities of isotope-labeled internal standards spiked in WWTP A samples suggested that temporal variations in the peak intensities of these mass spectral features were likely not driven by matrix effects or instrumental drift, although such possibilities could not be completely ruled out.94,95 Few commonalities existed among the filtered mass spectral features as they occurred across a wide range of mass-to-charge ratios and retention times. Out of the 595 filtered mass spectral features, the normalized peak intensity profiles of 43 showed a TCC value of >0.95 (i.e., good similarity) with the concentration profile of SARS-CoV-2 RNA in corresponding wastewater samples. Further hierarchical clustering revealed that 11 of these high-TCC mass spectral features showed the closest similarity in temporal dynamics with SARS-CoV-2 RNA between April 29 and June 24, 2020 (Fig. 4). For example, one of the HCA-prioritized mass spectral features, m/z 286.1438, had a chromatographic retention time of 15.43 min and a dd-MS2 spectrum match factor of 91.5 to the mzCloud reference spectrum of piperine. This feature was subsequently confirmed as piperine by comparing its chromatographic retention time and MS2 spectrum in wastewater samples to those of piperine reference standard (Fig. 5). Piperine is a natural alkaloid in black pepper that possesses important therapeutic properties96 and has been proposed as a potential inhibitor of SARS-CoV-2 RNA-dependent RNA polymerase on the basis of molecular docking simulations.97 Only two previous studies reported the occurrence of piperine in wastewater98 or wastewater-impacted surface waters,99 but neither of them quantified its actual concentration. Like other 26 substances identified by suspect screening, piperine was ubiquitously present in wastewater sampled from the studied sewersheds (i.e., 100% detection frequency) based on a retrospective screening. Since no commercially available isotope-labeled analogue existed for piperine at the time of this study, the concentration of piperine in wastewater samples was determined semi-quantitatively (ranging from 22 to 2020 ng L−1) with reference to metolachlor-d6 considering its closest chromatographic retention time to piperine. Notably, the concentration of piperine in WWTP A samples collected between April 29 and June 24, 2020 correlated with the concentration of SARS-CoV-2 RNA (ρ = 0.970; p = 0.0006), as expected from the time trend of piperine peak intensities observed during this period. Nevertheless, this correlation does not imply causality and should not be extrapolated beyond the scope of this work without a mechanistic understanding of the covariation of piperine and SARS-CoV-2 RNA in wastewater other than the fact that both are excreted in feces.100,101 Follow-up in silico fragmentation efforts are also needed to provide meaningful annotations for other HCA-prioritized mass spectral features with time trends similar to that of SARS-CoV-RNA. Once confirmed with the authentic reference standards, extensive sampling and analytical efforts are required to evaluate whether any of these nontarget substances may serve as a proxy of SARS-CoV-2 RNA abundance in wastewater especially when evaluated against SARS-CoV-2 structural proteins.102 In short, nontarget screening holds good promise as a hypothesis-generating method for prioritizing unknown substances as possible wastewater biomarkers of SARS-CoV-2 RNA prevalence, although gaps in metabolomics and informatics approaches should be addressed in order to identify the most relevant candidates for in-depth investigations.
image file: d0em00377h-f4.tif
Fig. 4 Time trends of SARS-CoV-2 RNA and HCA-prioritized nontarget mass spectral features in a subset of WWTP A wastewater samples (n = 9): (a) concentration profile of SARS-CoV-2 RNA measured in WWTP A samples collected between April 29 and June 24, 2020. Note that no SARS-CoV-2 RNA was detectable in April 29 sample or samples collected beyond June 24, 2020. SARS-CoV-2 RNA was detectable in June 10, 17, and 24 samples at levels below the limit of quantification (i.e., the LOQ was 5 gene copies per mL at the time of this study), so the concentrations were assigned half of the LOQ (i.e., 2.5 gene copies per mL) for the purpose of time trend analysis. (b) Normalized intensity profiles of 11 nontarget mass spectral features, including m/z 286.1438, in WWTP A samples collected between April 29 and June 24, 2020.

image file: d0em00377h-f5.tif
Fig. 5 Identification of piperine in wastewater: (a) extracted ion chromatogram of piperine in wastewater (May 13, 2020 sample from WWTP A). (b) Extracted ion chromatogram of piperine reference standard (200 ng L−1 in LC-MS grade methanol). (c) Head-to-tail plot of piperine MS2 spectra in wastewater and reference standard (acquired at a normalized collision energy of 30% using higher energy collision-induced dissociation).

Limitations and implications

The results of this proof-of-concept study should be interpreted with several limitations in mind. First, our online SPE-LC-HRMS method was less prone to supply chain disruptions (e.g., shortage of cartridges, sorbents, and solvents) during the pandemic but relied on the use of a C18 polar-endcapped reversed phase trap column, which could not effectively retain highly polar substances of potential interest. Complementing the current method with the use of mixed-bed multilayer trap columns103 is crucial for expanding the analytical window to cover a broader chemical space. Second, our mass load and substance use calculations did not explicitly account for the sorption of substances to suspended particulate matter or changes in the in-sewer fate and transport of substances due to shifts in sewer network characteristics (e.g., pH/temperature fluctuations, redox conditions, biofilm growth).66,81,85,104–108 Concurrent measurements of endogenous biomarkers (e.g., catecholamine metabolites109–112) may improve the characterization of near-real-time population dynamics but still require a calibration of wastewater-derived population data against census-based estimates (e.g., via the ongoing 2020 U.S. Census).113 Third, our sampling plan focused on centralized wastewater treatment systems in urban areas and did not include onsite or clustered treatment systems in rural or coastal communities (e.g., ∼22% of households in New York State are on septic systems114). Furthermore, only weekday samples were collected in this work despite known within-week variability in wastewater flow and substance consumption rates.115,116 Continuous and close-to-source sampling design (e.g., at specific sewer network nodes) should be implemented in the future to minimize biased data interpretation along spatial and temporal gradients. Lastly, our wastewater surveillance platform was not established until after the onset of the COVID-19 pandemic, which prevented us from evaluating the potential impact of COVID-19 on substance use patterns through wastewater analysis. For comparison purposes, nine archived wastewater samples collected from WWTP A during 2018 were analyzed along with samples collected during this study. Interestingly, the mass loads of antibacterials, antidepressants, antiepileptics, antihypertensives, antihistamines, opioids, and stimulants estimated using these archived samples were consistently lower than those estimated using the 2020 samples (Mann–Whitney test p < 0.0001). Such differences did not necessarily provide support for the hypothesis of increased substance consumption during the pandemic because long-term storage and freeze–thaw cycles might have led to the loss of analytes to varying degrees.117 Only wastewater samples collected and analyzed over the long term until immediately before the pandemic may provide a robust baseline assessment of substance use in the pre-COVID era.44

Despite the abovementioned limitations, our study demonstrated the versatility of high-throughput wastewater analysis for substance use assessment during the COVID-19 pandemic. Our analysis showed that the mean summed consumption rate of six major substance groups (i.e., antidepressants, antiepileptics, antihistamines, antihypertensives, synthetic opioids, and central nervous system stimulants) correlated with census-derived sociodemographic variables reflecting disparities in household income, marital status, and age distribution in the studied sewersheds. Our analysis also provided the first evidence that the mean summed consumption rate of these substance groups correlated with the detection frequency of SARS-CoV-2 RNA in wastewater as well as the COVID-19 test positivity during the sampling period, although the observed relationships were likely specific to the study region. Lastly, our nontarget screening workflow proved efficient in identifying unknown substances (i.e., piperine as an example) that covaried with SARS-CoV-2 RNA in wastewater for follow-up studies. Overall, preliminary findings from this study support the necessity of establishing regional and nationwide wastewater surveillance initiatives and the prospect of integrating wastewater analytics with epidemiological modeling to yield actionable public health insights.

Conflicts of interest

There are no conflicts to delcare.


We gratefully acknowledge wastewater treatment plant operators for their assistance in wastewater sampling despite severe logistical constraints during the pandemic. We thank Pruthvi Kilaru (Department of Public Health, Syracuse University) and Ariana Fenty (Department of Environmental and Forest Biology, SUNY-ESF) for coordinating sample delivery and processing. We also thank other team members of the SARS2 Early Warning Wastewater Surveillance Platform (SARS2-EWSP) for their support. We further thank the editor and anonymous reviewers for their constructive feedback. This work was supported by the Collaboration for Unprecedented Success and Excellence (CUSE) Grant Program administered by Syracuse University's Office of Research and the Faculty Fellows Program administered by the Syracuse Center of Excellence for Environmental and Energy Systems (SyracuseCoE) through an award from the New York State Department of Economic Development under Award Number #C150183.


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