اپیدمیولوژی مبتنی بر فاضلاب: برآورد مصرف داروها و رژیمهای غذایی در جامعه
Wastewater-based epidemiology : estimation of community consumption of drugs and diets
معرفی کتاب «اپیدمیولوژی مبتنی بر فاضلاب: برآورد مصرف داروها و رژیمهای غذایی در جامعه» (با عنوان لاتین Wastewater-based epidemiology : estimation of community consumption of drugs and diets) نوشتهٔ American Chemical Society. Division of Environmental Chemistry.; Burgard, Daniel A.; Loganathan, Bommanna G.; Subedi, Bikram، منتشرشده توسط نشر American Chemical Society Inc در سال 1319. این کتاب در فرمت pdf، زبان انگلیسی ارائه شده است.
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
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