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

O'Brien, Jake W., Thai, Phong K., Eaglesham, Geoff, Ort, Christoph, Scheidegger, Andreas, Carter, Steve, Lai, Foon Yin and Mueller, Jochen F. (2014) A model to estimate the population contributing to the wastewater using samples collected on census day. Environmental Science and Technology, 48 1: 517-525. doi:10.1021/es403251g

Author O'Brien, Jake W.
Thai, Phong K.
Eaglesham, Geoff
Ort, Christoph
Scheidegger, Andreas
Carter, Steve
Lai, Foon Yin
Mueller, Jochen F.
Title A model to estimate the population contributing to the wastewater using samples collected on census day
Journal name Environmental Science and Technology   Check publisher's open access policy
ISSN 0013-936X
Publication date 2014-01-07
Year available 2013
Sub-type Article (original research)
DOI 10.1021/es403251g
Open Access Status
Volume 48
Issue 1
Start page 517
End page 525
Total pages 9
Place of publication Washington, DC United States
Publisher American Chemical Society
Collection year 2014
Language eng
Subject 1600 Chemistry
2304 Environmental Chemistry
Abstract An important uncertainty when estimating per capita consumption of, for example, illicit drugs by means of wastewater analysis (sometimes referred to as "sewage epidemiology") relates to the size and variability of the de facto population in the catchment of interest. In the absence of a day-specific direct population count any indirect surrogate model to estimate population size lacks a standard to assess associated uncertainties. Therefore, the objective of this study was to collect wastewater samples at a unique opportunity, that is, on a census day, as a basis for a model to estimate the number of people contributing to a given wastewater sample. Mass loads for a wide range of pharmaceuticals and personal care products were quantified in influents of ten sewage treatment plants (STP) serving populations ranging from approximately 3500 to 500 000 people. Separate linear models for population size were estimated with the mass loads of the different chemical as the explanatory variable: 14 chemicals showed good, linear relationships, with highest correlations for acesulfame and gabapentin. De facto population was then estimated through Bayesian inference, by updating the population size provided by STP staff (prior knowledge) with measured chemical mass loads. Cross validation showed that large populations can be estimated fairly accurately with a few chemical mass loads quantified from 24-h composite samples. In contrast, the prior knowledge for small population sizes cannot be improved substantially despite the information of multiple chemical mass loads. In the future, observations other than chemical mass loads may improve this deficit, since Bayesian inference allows including any kind of information relating to population size.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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