A model to estimate the population contributing to the wastewater using samples collected on census day

Area of Science:

• Environmental Science
• Public Health
• Analytical Chemistry

Background:

• Accurate estimation of de facto population size is crucial for wastewater-based epidemiology.
• Current methods for population estimation in wastewater analysis lack standardized uncertainty assessment.

Purpose of the Study:

• To develop and validate a model for estimating the de facto population size contributing to wastewater samples.
• To utilize wastewater analysis on a census day for improved population estimation.

Main Methods:

• Collected wastewater samples from ten sewage treatment plants on a census day.
• Quantified mass loads of pharmaceuticals and personal care products.
• Developed linear models using chemical mass loads to estimate population size.
• Employed Bayesian inference to update prior population knowledge with chemical data.

Main Results:

• Fourteen chemicals, particularly acesulfame and gabapentin, showed strong linear correlations with population size.
• Bayesian inference using chemical mass loads accurately estimated large populations.
• Prior population knowledge for small populations showed limited improvement with chemical data.

Conclusions:

• Wastewater analysis on census day provides a robust method for estimating large populations for sewage epidemiology.
• Chemical mass loads are effective predictors of population size in wastewater.
• Further research is needed to improve population estimation for smaller populations.