High-throughput wastewater analysis for
substance use assessment in central New York
during the COVID-19 pandemic
Shiru Wang, aHyatt C. Green,bMaxwell L. Wilder,bQian Du,cBrittany L. Kmush,d Mary B. Collins,eDavid A. Larsendand Teng Zeng *a
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 sew-ersheds. 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.
Introduction
Wastewater-based epidemiology (WBE) is an evidence-based approach for monitoring infectious disease outbreaks,1–3 substance use trends,4–6and antimicrobial resistance spread at
the community scale.7,8 Since the onset of the coronavirus pandemic in 2019, research groups worldwide have demon-strated 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 wide-spread efforts on wastewater sampling brought by these
SARS-CoV-2 surveillance platforms, co-analysis of human
biomarkers in wastewater for substance use assessment repre-sents an attractive strategy to gain additional insights into population behavior and health status underlying the suscep-tibility 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.25–27For 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.28–31Meanwhile, socio-economic 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,35–37particularly those having a higher prevalence of substance use disorders.25Furthermore, COVID-related non-pharmaceutical interventions (e.g., lockdowns) and pharmaco-logic 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
a
Department of Civil and Environmental Engineering, Syracuse University, Syracuse, NY 13244, USA. E-mail: [email protected]; 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 Cite this:Environ. Sci.: Processes Impacts, 2020, 22, 2147 Received 28th August 2020 Accepted 14th October 2020 DOI: 10.1039/d0em00377h rsc.li/espi Environmental signicance
Wastewater surveillance has been increasingly implemented worldwide for monitoring population-level substance use trends. Our study demon-strates 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.
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while longitudinal wastewater data in South Australia suggested a decline in alcohol consumption due to social distancing and isolation measures.44Collectively, these studies underscore the importance of substance use assessment by means of waste-water analysis to complement clinical and sociodemographic data for characterizing risk drivers of COVID-19 at the pop-ulation 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 chromatography- high-throughput screening of pharmaceuticals and lifestyle chem-icals in untreated municipal wastewater sampled from central New York during the COVID-19 pandemic. Online SPE auto-mates 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.50,51For example, recent WBE studies have imple-mented 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.52–56Our specic 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 sew-ersheds 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 workow to prioritize unknown substances that covaried with SARS-CoV-2 RNA in wastewater for follow-up investigations.
Materials and methods
Chemicals and reagents
Methanol MS grade), water MS grade), acetonitrile (LC-MS grade), and formic acid solution (LC-(LC-MS grade) were supplied by Fisher Scientic (Waltham, MA). Unlabeled refer-ence 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 (Tewks-bury, 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 ow-proportional composite inuent 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-conrmed 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 acces-sibility 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.58Together, these six WWTPs serve a total population of 396 300 in urban and suburban areas of central New York. Upon collection, samples were rst 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.59Split samples (40 mL) were frozen in the dark at 20C and processed at Syracuse University for online SPE-LC-HRMS analysis as soon as practically possible. General operational (e.g., average dailyow rates) and wastewater quality parame-ters (e.g., pH and temperature) were provided by WWTP personnel when applicable. Nine 24 h composite inuent wastewater samples collected from WWTP A over the year of 2018 and archived at20C were also analyzed in this study for comparison with the 2020 samples.
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 L1each) andltered by 0.22 mm Millex-GP polyethersulfone syringe lters into Chromacol autosampler vials. Samples were then analyzed in duplicate by a Thermo Scientic 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. Briey, 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 mm particle size) for analyte preconcentration. The trap column was washed with acidied 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 mm particle size) running acidied 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.60The total analysis time for each sample was 36 minutes.
Suspect screening was performed in TraceFinder 4.1 (Thermo Scientic) with predened peak ltering criteria using an in-house database containing compound-specic 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 library61using Compound Discoverer 3.1 (Thermo Scientic). Suspect compounds with an
mzCloud match factor of >30 were selected for further conr-mation. Twenty-six substances were conrmed in 100% waste-water samples based on the match of their chromatographic retention times and dd-MS2 spectra to those of authentic
reference standards. Target quantication of these 26
conrmed compounds was performed using 14-point calibra-tion curves (i.e., 0.1–5000 ng L1) with reference to their
isotope-labeled analogues (Table 2). Good linearity (R2¼ 0.992–0.999) was established for the calibration curves of all target compounds. Limits of quantication 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
Table 1 Sewershed and sociodemographic characteristics
A B C D E F
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 239 032 3739 30 058 1352 26 138 1132 11 849 812 50 084 1486 39 123 1409 Genderb 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%
Age groupb(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% Race/ethnicityb 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% Marital statusb 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% Education attainmentb
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% Household incomeb 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%
aCalculated using the linear distance method as described by Kapo et al.58by dividing the average distances between the population density
polygon centroids and the coordinate of a given WWTP by an average sewageow velocity of 0.6 m s1(personal communication with the
sewer maintenance superintendent118). bExtracted from the 2014–2018 American Community Survey as estimates margins of error.
cIncluding American Indian and Alaska Native alone, Native Hawaiian and other Pacic Islander alone, and some other race alone.dIncluding
Master's degree, Doctorate degree, and professional school.
and precision. Solvent blanks were run to check for potential cross-contamination following the analysis of highly concen-trated standards.
Nontarget screening was conducted with WWTP A waste-water samples exhibiting a characteristic time trend of SARS-CoV-2 RNA abundance (i.e.,rst increased and then decreased over the sampling period). Peak prole preprocessing (e.g., retention time alignment, unknown compound detection and grouping, background subtraction, molecular formula assign-ment, intensity normalization) and differential analysis (i.e., peak area ratioltering) were performed in Compound Discov-erer 3.1 to lter 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 package62in R 4.0.2 to evaluate the shape similarity between the time trends of ltered nontarget features and SARS-CoV-2 RNA. Typically, a TCC value above 0.95 indicates good shape similarity between two proles.63 Lastly, hierarchical cluster analysis (HCA) was performed using the factoextra package64in R to further prior-itize 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 mzCloud61and further conrmed (or
rejected) by authentic reference standards. Target
quantication of a newly conrmed 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
Mass load ¼ CW Q 1 1 þ stability 1 1000 (1)
Consumption rate ¼ mass load 1000 1 excretion MWParent MWMetabolite 1000 population (2) where mass load represents the daily mass load of a substance entering a WWTP (g d1), CWis the aqueous concentration of
a substance in wastewater (ng L1), Q is the average dailyow rate of wastewater measured at the inlet of a WWTP (m3d1),
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 sew-ersheds), 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
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 L1) ILISc
Acetaminophen 103-90-2 [M + H]+ 152.0706 126.0100 4.74 0.3 Acetaminophen-d 3 Amphetamine 300-62-9 [M + H]+ 136.1121 119.0859 6.22 1.2 Amphetamine-d 10 Atenolol 29122-68-7 [M + H]+ 267.1703 190.0851 4.35 12 Atenolol-d 7 Caffeine 58-08-2 [M + H]+ 195.0877 138.0656 6.58 1.1 Caffeine-d 9 Carbamazepine 298-46-4 [M + H]+ 237.1022 194.0955 11.79 6.6 Carbamazepine-d 10 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-d 7 Phentermine 122-09-8 [M + H]+ 150.1277 133.1006 7.28 7.6 Phentermine-d 5 Pregabalin 148553-50-8 [M + H]+ 160.1332 142.1221 6.13 8.9 Pregabalin-d 6 Sulfamethoxazole 723-46-6 [M + H]+ 254.0594 156.0107 7.44 6.6 Sulfamethoxazole-d 4 Tramadol 27203-92-5 [M + H]+ 264.1958 246.1840 7.57 3.4 Tramadol-13C, d 3 Trimethoprim 738-70-5 [M + H]+ 291.1452 230.1152 6.26 9.2 Trimethoprim-d 9 Venlafaxine 93413-69-5 [M + H]+ 278.2115 260.2000 9.78 3.0 Venlafaxine-d 6 Benzoylecgonine 519-09-5 [M + H]+ 290.1387 168.1012 7.63 2.9 Benzoylecgonine-d 3 EDDPd 30223-73-5 [M + H]+ 278.1903 249.1502 10.30 4.5 EDDP-d 3 Hydroxybupropion 92264-81-8 [M + H]+ 256.1099 238.0985 8.76 3.6 Hydroxybupropion-d 6
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
aRetention time.bLimit of quantication determined in pooled wastewater (i.e., prepared by mixing equal volumes of WWTP A–F samples).
cIsotope-labeled internal standard.d2-Ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine.eNontarget substance.
rate of a parent compound or its metabolite reported in the pharmacokinetic literature and WBE studies,65,68–79MWParentis
the molecular weight of a parent compound, MWMetaboliteis 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 acknowl-edged 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.57The 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 pop-ulation 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 quanti-ed 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), benzoy-lecgonine (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 conrmed in wastewater samples but not quantied due to their limited detection frequency. Most compounds occurred at ng L1-mg L1levels in wastewater samples and can be broadly categorized as
Fig. 1 Consumption rates of 26 substances in sewersheds A–F between April 29 and July 15, 2020 (n ¼ 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 estimatedvia its metabolite benzoylecgonine, the consumption rate of
bupropion was estimatedvia its metabolite hydroxybupropion, and the consumption rate of methylphenidate was estimated via its metabolite
ritalinic acid.
antibacterials (i.e., sulfamethoxazole and trimethoprim), anti-depressants (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), phenter-mine (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 overve 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 104to
1.85 1.05 105mg 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)88and 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 articial sweetener in the U.S.,89was 1.49 0.70 104mg 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).90The fact that the consumption rates of caffeine and sucralose estimated in this work matched literature-reported values provides condence in the validity of our analysis. Furthermore, the mean consumption rates of
antidepressants, antiepileptics, antihistamines, antihyperten-sives, opioids, and stimulants correlated with each other in most cases (r ¼ 0.886–1.000; p ¼ 0.003–0.033), which qualita-tively agreed with prior WBE work showing correlations among the population-level consumption rates of antidepressants, antiepileptics, antihypertensives, and opioids.72,91Such covari-ations among the consumption rates of these representative substance groups pointed to common underlying determinants of substance use patterns in the contributing populations.
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 106mg 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, anti-epileptics, and antihypertensives as proxies of socioeconomic distress and inequalities.91Similarly, wastewater surveillance in Greece conrmed a concomitant increase in the consumption of psychoactive drugs and mental illnesses as a consequence of adverse socioeconomic changes.72To the best of our knowledge, however, no U.S.-based WBE studies have examined the asso-ciations between sewershed-specic 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 signicant positive correlations with the mean summed consumption rate of 26 substances (r ¼ 0.886–0.943; p ¼ 0.017–0.033), suggesting that disparities in household income, marital status, and age all
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 ¼ 12 for each sewershed). Horizontal error bars represent the margins of error of
sociodemo-graphic data extracted from the 5 year American Community Survey (2014–2018).
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 ¼ 12 for each sewershed). (b) Correlation between the mean consumption rate of antiepileptics (i.e., the sum of
car-bamazepine, gabapentin, lamotrigine, levetiracetam, and pregabalin) and the SARS-CoV-2 RNA detection frequency in wastewater samples (n ¼
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 ¼ 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 ¼ 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 ¼ 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
affected substance consumption by the contributing pop-ulations. Specically, low-income households (i.e., annual income < $45 000), 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, antihyperten-sives, 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 nd-ings.91 Presumably, predictive regression models could be developed for the consumption rates of specic substances or substance groups and sociodemographic determinants as demonstrated by a recent study in Australia,93but the relatively small sample size in this study precluded model training and cross-validation.
Over the sampling period, the mean summed consumption rate of 26 substances also exhibited positive correlations (r ¼ 0.943; p¼ 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 corre-lations with the consumption rates of antidepressants, antiep-ileptics, antihypertensives, antihistamines, opioids, and stimulants (Fig. 3), which were not unexpected given that COVID-19 and substance use likely share common sociodemo-graphic risk factors. However, generalization of these
relation-ships 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 specic substances or substance groups are indicative of population susceptibility to COVID-19 in any given sewershed also warrants further investigation with rigorous uncertainty assessment.
Nontarget screening of unknown substances in wastewater Nontarget screeningltered 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 dri, although such
possibilities could not be completely ruled out.94,95 Few
commonalities existed among the ltered mass spectral
features as they occurred across a wide range of mass-to-charge ratios and retention times. Out of the 595ltered mass spectral features, the normalized peak intensity proles of 43 showed a TCC value of >0.95 (i.e., good similarity) with the concentra-tion prole 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 chro-matographic retention time of 15.43 min and a dd-MS2 spec-trum match factor of 91.5 to the mzCloud reference specspec-trum of piperine. This feature was subsequently conrmed 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 properties96and 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 occur-rence of piperine in wastewater98 or wastewater-impacted surface waters,99 but neither of them quantied its actual concentration. Like other 26 substances identied 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 L1) with reference to metola-chlor-d6considering 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 (r ¼ 0.970; p¼ 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 extrap-olated 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.100,101Follow-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 conrmed with the authentic reference standards, extensive sampling and analytical efforts are required to evaluate whether any of these nontarget
methylphenidate) and the SARS-CoV-2 RNA detection frequency in wastewater samples (n ¼ 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 ¼ 12 for
each sewershed).
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 priori-tizing 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.
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 shis in sewer network characteristics (e.g., pH/temperature uctuations, redox conditions, biolm growth).66,81,85,104–108 Concurrent measurements of endogenous biomarkers (e.g., catecholamine metabolites109–112) may improve the character-ization 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 ow and substance consumption rates.115,116 Continuous and close-to-source sampling design (e.g., at specic sewer network nodes) should be implemented in the future to minimize biased data interpretation along spatial and temporal gradients. Lastly, our wastewater surveillance
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 ¼ 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,
includingm/z 286.1438, in WWTP A samples collected between April
29 and June 24, 2020.
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 L1in 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).
platform was not established until aer the onset of the COVID-19 pandemic, which prevented us from evaluating the potential impact of COVID-19 on substance use patterns through water analysis. For comparison purposes, nine archived waste-water 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 demon-strated 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, antiepi-leptics, antihistamines, antihypertensives, synthetic opioids, and central nervous system stimulants) correlated with census-derived sociodemographic variables reecting disparities in household income, marital status, and age distribution in the studied sewersheds. Our analysis also provided the rst 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 specic to the study region. Lastly, our nontarget screening workow 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 ndings from this study support the necessity of establishing regional and nationwide wastewater surveillance initiatives and the prospect of integrating waste-water analytics with epidemiological modeling to yield action-able public health insights.
Con
flicts of interest
There are no conicts to delcare.
Acknowledgements
We gratefully acknowledge wastewater treatment plant opera-tors 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 adminis-tered 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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