A Novel Method to Characterise Levels of Pharmaceutical Pollution in Large-Scale Aquatic Monitoring Campaigns
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Substances
2.2. Sampling Kits and Water Collection Protocol
2.3. HPLC-MS/MS Protocol
2.4. Quality Control
2.5. Method Validation
2.5.1. Evaluation of Chemical Stability
2.5.2. Interlaboratory Assessment
3. Results and Discussion
3.1. Method Validation
3.2. Evaluation of Chemical Stability during Shipment
3.3. Interlaboratory Assessment
3.4. Concentrations of Studied APIs in Muddy Creek
3.5. Implication for Future Research
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Therapeutic Class | Compound | Associated Internal Standard | |
---|---|---|---|
Analgesic | Lidocaine | Lidocaine D6 | |
Naproxen | Naproxen D3 | ||
Paracetamol | Paracetamol D4 | ||
Anti-epileptic | Carbamazepine | Carbamazepine D10 | |
Gabapentin | Gabapentin D10 | ||
Pregabalin | Atrazine D5 | ||
Antibiotics | Fluoroquinolones | Ciprofloxacin | Ciprofloxacin D8 |
Enrofloxacin * | Atrazine D5 | ||
Lincosamide | Lincomyacin * | Atrazine D5 | |
Macrolides | Clarithromycin | Atrazine D5 | |
Erythromycin | Atrazine D5 | ||
Tylosin * | Atrazine D5 | ||
Nitroimidazole | Metronidazole | Metronidazole D3 | |
Penicillin | Cloxacillin | Atrazine D5 | |
Sulfonamides | Sulfadiazine * | Atrazine D5 | |
Sulfamethoxazole | Sulfamethoxazole D4 | ||
Trimethoprim | Trimethoprim D9 | ||
Tetracyclines | Oxytetracycline | Atrazine D5 | |
Tetracycline | Atrazine D5 | ||
Antidepressant | Amitriptyline | Amitriptyline D3 | |
Citalopram | Citalopram D6 | ||
Desvenlafaxine | Desvenlafaxine D6 | ||
Fluoxetine | Atrazine D5 | ||
Sertraline | Sertraline D3 | ||
Venlafaxine | Venlafaxine D6 | ||
Norfluoxetine | Norfluoxetine D6 | ||
Antifungal | Clotrimazole | Atrazine D5 | |
Fluconazole | Atrazine D5 | ||
Itraconazole | Itraconazole D4 | ||
Ketoconazole | Atrazine D5 | ||
Miconazole | Atrazine D5 | ||
Thiabendazole | Atrazine D5 | ||
Antihistamine | Cetirizine | Atrazine D6 | |
Diphenhydramine | Diphenhydramine D3 | ||
Fexofenadine | Atrazine D5 | ||
Ketotifen | Atrazine D5 | ||
Loratadine | Atrazine D5 | ||
Antihyperglycemic | Metformin | Metformin D6 | |
Sitagliptin | Sitagliptin D4 | ||
Antimalarial | Artemisinin | Atrazine D5 | |
Antiviral/-retroviral | Nevirapine | Atrazine D5 | |
Oseltamivir | Atrazine D5 | ||
Benzodiazepine | Diazepam | Diazepam D5 | |
Oxazepam | Oxazepam D5 | ||
Temazepam | Temazepam D5 | ||
Beta-blocker | Atenolol | Atenolol D7 | |
Propranolol | Propranolol D7 | ||
Calcium channel blocker | Diltiazem | Diltiazem D3 | |
Verapamil | Verapamil D7 | ||
Diuretic | Triamterene | Triamterene D5 | |
Histamine H2 receptor antagonist | Cimetidine | Atrazine D5 | |
Ranitidine | Atrazine D5 | ||
Opioid pain medication | Codeine | Codeine D6 | |
Hydrocodone | Hydrocodone D3 | ||
Tramadol | Atrazine D5 | ||
Oral contraceptive | Noreistherone | Atrazine D5 | |
Selective estrogen receptor modulator | Raloxifene | Ralixifene D4 | |
Stimulant | Caffeine | Atrazine D5 | |
Cotinine | Cotinine D3 | ||
Nicotine | Atrazine D5 | ||
β2 adrenergic receptor agonist (anti-asthma) | Salbutamol | Salbutamol D9 |
Validation Parameter | Mean ± SD | Within Acceptable Range * | |
---|---|---|---|
Linearity | r2 | 0.984 ± 0.02 | |
Intra-day Repeatability (%) | 10 ng/L | 29 ± 19 | 46% |
100 ng/L | 11 ± 9 | 82% | |
1000 ng/L | 8 ± 6 | 95% | |
Intermediate/Inter-day Precision (%) | 10 ng/L | 42 ± 41 | 34% |
100 ng/L | 16 ± 12 | 72% | |
1000 ng/L | 9 ± 6 | 93% | |
Analyte Response in LCMS-grade water (%) | 10 ng/L | 95 ± 32 | 75% |
100 ng/L | 104 ± 22 | 90% | |
1000 ng/L | 94 ± 15 | 97% | |
Analyte Response in tap water (%) | 10 ng/L | 110 ± 27 | 59% |
100 ng/L | 101 ± 31 | 80% | |
1000 ng/L | 106 ± 28 | 90% | |
Analyte Response in surface water (%) | 10 ng/L | 101 ± 88 | 56% |
100 ng/L | 106 ± 26 | 82% | |
1000 ng/L | 106 ± 18 | 92% | |
Analyte Response in WWTP effluent (%) | 10 ng/L | 195 ± 212 | 39% |
100 ng/L | 117 ± 38 | 59% | |
1000 ng/L | 108 ± 18 | 92% | |
Analyte Response WWTP influent (%) | 10 ng/L | 465 ± 992 | 20% |
100 ng/L | 168 ± 81 | 38% | |
1000 ng/L | 145 ± 48 | 54% | |
Limit of Detection (ng/L) | LCMS-grade water (ng/L) | 9.16 | |
Drinking water (ng/L) | 9.72 | ||
Surface water (ng/L) | 11.79 | ||
WWTP effluent (ng/L) | 20.22 | ||
WWTP influent (ng/L) | 54.45 | ||
Limit of Quantification (ng/L) | LCMS-grade water (ng/L) | 18.32 | |
Drinking water (ng/L) | 19.44 | ||
Surface water (ng/L) | 23.57 | ||
WWTP effluent (ng/L) | 40.43 | ||
WWTP influent (ng/L) | 108.89 |
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Wilkinson, J.L.; Boxall, A.B.A.; Kolpin, D.W. A Novel Method to Characterise Levels of Pharmaceutical Pollution in Large-Scale Aquatic Monitoring Campaigns. Appl. Sci. 2019, 9, 1368. https://doi.org/10.3390/app9071368
Wilkinson JL, Boxall ABA, Kolpin DW. A Novel Method to Characterise Levels of Pharmaceutical Pollution in Large-Scale Aquatic Monitoring Campaigns. Applied Sciences. 2019; 9(7):1368. https://doi.org/10.3390/app9071368
Chicago/Turabian StyleWilkinson, John L., Alistair B.A. Boxall, and Dana W. Kolpin. 2019. "A Novel Method to Characterise Levels of Pharmaceutical Pollution in Large-Scale Aquatic Monitoring Campaigns" Applied Sciences 9, no. 7: 1368. https://doi.org/10.3390/app9071368
APA StyleWilkinson, J. L., Boxall, A. B. A., & Kolpin, D. W. (2019). A Novel Method to Characterise Levels of Pharmaceutical Pollution in Large-Scale Aquatic Monitoring Campaigns. Applied Sciences, 9(7), 1368. https://doi.org/10.3390/app9071368