Wastewater-based epidemiology: estimation of community consumption of drugs and diets

Wastewater-Based Epidemiology: Estimation of Community Consumption of Drugs and Diets......Page 2
 Wastewater-Based Epidemiology: Estimation of Community Consumption of Drugs and Diets......Page 4
  Library of Congress Cataloging-in-Publication Data......Page 5
Foreword......Page 6
Utilizing Wastewater-Based Epidemiology To Determine Temporal Trends in Illicit Stimulant Use in Seattle, Washington......Page 8
Subject Index......Page 9
Preface......Page 10
Methodology......Page 12
 Drugs of Abuse—A Global Concern......Page 14
 Conventional Estimation of the Prevalence of Substance Use......Page 15
 Applications for the Determination of the Prevalence of Substance Abuse......Page 17
  Figure 3. Per capita consumption rates of illicit drugs in a community in Western Kentucky during special events. Standard errors representing daily variation could not be presented because single-day special events were monitored. THC-COOH: (±)-11-nor-9-carboxy-Δ9-tetrahydrocannabinol; THC-COOH is reported as THC-COOH/100. Source: Data from reference 12.......Page 18
 Methodological Challenges......Page 19
 Strategic Challenges Associated with WBE......Page 21
 WBE—Potential Tool for an Early Warning System for Drugs and Narcotics or Psychoactive Substances......Page 22
 WBE—Potential Tool for an Early Warning System for Public Health Biomarkers......Page 24
 WBE—Complementary to the Conventional Survey-Based Approaches......Page 25
 References......Page 26
 Introduction......Page 34
 Stability of Collected Samples......Page 35
  Figure 1. Flowchart of the main analytical steps in wastewater analysis.......Page 36
 Extraction......Page 38
 Direct Injection Methods......Page 47
 Gas Chromatography–Mass Spectrometry (GC-MS)......Page 48
 Liquid Chromatography–Tandem Mass Spectrometry (LC-MSMS)......Page 49
 Liquid Chromatography–High-Resolution Mass Spectrometry (LC-HRMS)......Page 50
 Method Validation and Quality Assurance/Quality Control......Page 52
 Chiral Analysis......Page 53
 Conclusion......Page 54
 References......Page 55
 Introduction......Page 62
 Wastewater—Validated Methods......Page 63
 Sample Preparation and Preconcentration......Page 73
  Figure 1. Schematic showing the three principal derivatization pathways (silylation, acylation, and esterification/carbamation) for an aliphatic amine group, as used in studies covered in this review. Amphetamine is used as a representative drug biomarker.......Page 74
 Phenethylamine Stimulants......Page 75
 Cannabis......Page 79
 Chromatographic Separation......Page 80
 Comparison with LC-Based Techniques......Page 81
 Conclusion......Page 84
 References......Page 85
 Introduction......Page 90
 Sample Collection......Page 92
 Containers......Page 93
 Stability of Drug Residues in Wastewater......Page 94
  Figure 2. Stability (percentage change) of select drugs in wastewater at different conditions (24 h, 4 °C, pH 7.5; 12 h, 20 °C, pH 7.5; 12 h, 2 °C, pH 7.4; 24 h, 2 °C, pH 7.4; 12 h, 19 °C, pH 7.4; 24 h, 19 °C, pH 7.4; 12 h, 2 °C, pH 7.4, filtered; 24 h, 2 °C, pH 7.4, filtered; 12 h, 19 °C, pH 7.4, filtered; 24 h, 19 °C, pH 7.4, filtered) (14232731). THC-COOH: 11-nor-9-carboxy-Δ 9-tetrahydrocannnabinol. Circles represent data points ≥50%.......Page 95
 Analysis of Drugs in SPM......Page 96
 Need for Best-Practice Analytical Protocols......Page 97
 Uncertainties with Analytical Data Treatment......Page 98
 Uncertainties with Population Dynamics......Page 99
  Figure 4. Structure of select chemical markers used for the near-accurate estimation of population in WWTP catchments. Sol: solubility at 25 °C estimate from Log Kow; t1/2: environmental half-life estimated from a fugacity model. (Source: ChemSpider).......Page 100
 Uncertainties with Pharmacokinetic Measures......Page 101
  Figure 5. The variation in percentage excretion rates of parent illicit drugs or metabolites in urine with the different routes, forms, or doses of administration (1257). I.M.: intramuscular; I.V.: intravenous. Smoked* indicates use of a high-dose delivery apparatus; Smoked** indicates use as cigarettes. Reference lines represent an average or a range of percentage excretion without considering variable routes, forms, or doses of administered drugs 13.......Page 102
 Insights and Future Perspectives......Page 103
 References......Page 104
Wastewater-Based Epidemiological Engineering—Modeling Illicit Drug Biomarker Fate in Sewer Systems as a Means To Back-Calculate Urban Chemical Consumption Rates......Page 110
 Introduction......Page 111
  Figure 1. Overview of back-calculating chemical consumption rates in WBE engineering. Masses of biomarkers are quantified and used as input to simulation models to back-calculate chemical ingestion rates and population sizes in urban areas. Biomarkers contained in sewage samples (representing a combined urine sample) are collected at the influent of a given WWTP. Areas shown with red are those addressed in detail in this chapter.......Page 112
 Laboratory-Scale Batch Experiments—With In-Sewer Suspended Solids......Page 113
 Suspended Solids......Page 114
  Figure 2. Diffusion and sorption of trace xenobiotic chemicals into biofilms. Biofilm shown as porous medium that can be simulated using spatially discretized simulation models. Chemical transport through diffusion in the bulk and through the boundary layer (1), and within biofilm pores (2), and sorption onto biofilm solids (3) 22.......Page 115
  Figure 3. Relative abiotic transformation and biotransformation rates obtained under aerobic and anaerobic conditions. Unidentified transformation rate values are indicated with an asterisk (*). T. P.: transformation product(s) 19.......Page 116
 Modeling Biomarker Fate in Sewer Biofilm......Page 117
  Figure 5. Values of biotransformation rate coefficients for drug biomarkers in raw wastewater 19 including COE and NCOE transfromations 23 and in sewer biofilm 20 under aerobic (a) and anaerobic (b) conditions using pseudo-first-order kinetic equations. Error bars identify the upper bound of the 95% credibility interval of estimated parameters.......Page 118
  Figure 7. Values of effective diffusivity coefficient (f) estimated from experimental data obtained using biofilm grown with fixed thickness of 50, 200, and 500 μm (symbols), calculated (lines) using Eq. 1 for eight positively charged pharmaceutical trace organics, and plotted as a function of biofilm thickness (three different levels) and corresponding log KOW values 24.......Page 119
  Figure 8. A comparison of chemical transformation pathway identification methods for HER and COE biomarkers. Posterior distribution of estimated parameter values (histograms) were obtained under abiotic and biotic conditions using the methodology proposed—Method 1 (in red), Method 2 (dotted blue line, upper X axis), and Method 3 (solid black line). T. P.: unknown transformation product. Solid arrows are pathways identified from the literature according to human metabolic pathways, and new identified pathways are shown with dashed arrows 23.......Page 120
  Figure 9. Chemical transformation pathway identification for (a) HER–6-MAM and (b) MORG–MOR biomarkers considering human metabolism as prior knowledge. Simulation results are demonstrated for highlighted chemicals using calibration Methods 1 through 3. Posterior parameter probability distribution calculated using Method 1 (in red), Method 2 (dotted blue line plotted on upper X axis), and Method 3 (solid black line). T. P.: unknown transformation product. (c) Measured and simulated biomarker concentration data with uncertainty bands obtained using Methods 1 through 3. Markers are measured data, and lines are simulation results. The shaded area reflects the 95% confidence interval of model prediction (red area and full line: Method 1; grey area and dashed line: Method 2; blue area and dotted line: Method 3) 23.......Page 121
  Figure 10. Values of the estimated transformation rate coefficient k (d−1) (full and empty dots, collected from literature) plotted as a function of temperature (°C) and approximated with the Arrhenius equation. Line denotes best fit, and the shaded band is the 95% confidence interval of the prediction. Values of k are estimated at standard temperature (25 °C), and Arrhenius coefficients θ are estimated with the 95% confidence interval.......Page 122
 Outlook and Perspectives on the Back-Calculation of Chemical Consumption in Urban Areas......Page 123
 References......Page 124
Applications......Page 128
 Introduction......Page 130
 Sample Collection and Analysis......Page 132
  Figure 1. Mean influent loads of METH in major Chinese cities between 2014 and 2016.......Page 135
 METH and KET Use in Beijing and Shenzhen (2012–2016)......Page 137
  Figure 3. Mean influent loads of METH in Beijing and Shenzhen from 2012 to 2016.......Page 138
  Figure 4. Mean influent loads of KET in Shenzhen from 2012 to 2016.......Page 139
 WBE Monitoring by Drug Control Authorities in China......Page 140
 Conclusions......Page 142
 References......Page 143
 Background......Page 148
 Section 2—WBE Estimation of Cannabis Consumption—A Pilot Test......Page 149
 Area-Frame Design......Page 150
 Sampling Strategy......Page 151
  Figure 1. Daily total flow (blue line) and hourly peak flow (orange line) from June to December, 2017, for one of the participating sites.......Page 152
 Section 3—Supplemental Research Questions......Page 153
 Chemical Analysis......Page 154
 Counterintuitive Findings......Page 155
  Figure 2. Weekly load of THC-COOH per inhabitant over four consecutive months for the whole population included in the study (8.4 million people).......Page 156
  Figure 4. Indexed flow and THC-COOH concentration over six consecutive months for two large sewersheds (100 = average monthly flow or concentration).......Page 157
  Figure 5. Average THC-COOH load per week for each site over the period of March to August, 2018. Data adapted with permission from 18.......Page 158
 Using Wastewater Drug Loads to Estimate Consumption......Page 159
 1 Wastewater Sampling Techniques......Page 161
 Acknowledgments......Page 162
 References......Page 163
 Introduction......Page 166
 Materials......Page 167
 Chemical Analysis......Page 168
 QA/QC......Page 169
 Scope of Illicit Drugs in Seattle Wastewater......Page 170
  Figure 1. The weekly average of illicit drug loads for all weeks sampled with day of week mass load averages for all analytes in (A) and expanded MDMA in (B). Each day had between n=8 and n=11. Uncertainty bars are represented by the 95% confidence interval around the mean day of week loads.......Page 171
  Figure 2. Mass load of MDMA in Seattle wastewater during Pride weekends compared to other (non-Pride) weeks. Uncertainty bars are represented by the 95% confidence interval for the non-Pride week averages.......Page 172
  Figure 3. Mass load of BZE over Pride Week. The mass load of BZE to represent COC use during Seattle Pride weekend is compared to the mass load of a non-Pride weekends (2015–2018). Error bars are represented by the 95% confidence interval.......Page 173
 Conclusion......Page 174
 References......Page 175
Detection in Sewage and Community Consumption of Stimulant Drugs in Northeastern United States......Page 178
 Introduction......Page 179
 Sample Collection......Page 180
 Sample Extraction and Analysis......Page 181
 Wastewater-Based Epidemiology Back Calculation......Page 182
 Wastewater Characteristics and Population Estimates......Page 183
  Figure 1. Measured concentration of stimulant drugs at the WWTPs.......Page 184
 References......Page 189
WBE Applications beyond Drugs......Page 196
Assessing the Potential To Monitor Plant-Based Diet Trends in Communities Using a Wastewater-Based Epidemiology Approach......Page 198
 Introduction......Page 199
 Sewage Samples......Page 200
 Quality Assurance and Quality Control......Page 201
 Concentration and Mass Loading of Phytoestrogens in Influent Wastewater from Two U.S. Cities......Page 202
 Estimated per Capita Phytoestrogen Consumption in Two U.S. Cities......Page 204
  Figure 2. Estimated per capita consumption of phytoestrogens in Cities 1 and 2.......Page 205
 Conclusions......Page 206
 References......Page 207
 Bommanna G. Loganathan......Page 210
Indexes......Page 212
Author Index......Page 214
 M......Page 216
 W......Page 217