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Updated: Nov 29, 2025

Characterization of Aquatic Biofilms with Flow Cytometry
Published on: June 6, 2018
Application of multi-parameter population model based on endogenous population biomarkers and flow volume in
Chenzhi Hou1, Tingting Chu1, Mengyi Chen2
1School of Pharmacy, China Pharmaceutical University, No. 24 Tongjiaxiang Road, Nanjing 210009, China; China National Narcotics Control Commission - China Pharmaceutical University Joint Laboratory on Key Technologies of Narcotics Control, No. 24 Tongjiaxiang Road, Nanjing 210009, China.
Abstract:
The consumption or prevalence of acesulfame, caffeine, paracetamol and amantadine was estimated by wastewater-based epidemiology based on a multi-parameter population model in 20 sewage treatment plants (STPs) in Hebei province, China. To minimize the uncertainties contributed by population estimation in WBE, a multi-parameter population model was established based on the population biomarkers equivalent population and flow volume-population with the weight factors calculated by the analytic hierarchy process (AHP). 4-Pyridoxic acid (4-PA), cotinine, trans-3'-hydroxycotinine (trans-3'-OH-Cot) and 1,4-methylimidazole acetic acid (MIAA) were selected as population biomarkers. The estimated model population showed the highest correlations (r2 = 0.97, p < 0.01) and lowest variation (one way-ANOVA, p = 0.82, mean variation: -0.1%) comparing to the census data, suggestion better population estimation. The estimated consumption of acesulfame, caffeine, paracetamol and amantadine was 6.7 ± 2.4 mg/day/inh, 50.5 ± 38.5 mg/day/inh, 61.5 ± 52.7 mg/day/inh and 0.52 ± 0.33 mg/day/inh, respectively. Meanwhile, the prevalence of paracetamol and amantadine was calculated to be 5.3% ± 4.5% and 0.28% ± 0.18%, respectively. The estimated results were consistent with that of previous researches in China and were also in accordance with the consumption calculated by sales data (acesulfame and paracetamol). Moreover, uncertainty study showed decrease in population-associated uncertainties by using a multi-parameter population model. The results demonstrated that the multi-parameter population model constructed in this research is feasible to apply in WBE and might lead to lower uncertainties in population estimation.
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