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, & Mueller, Jochen F. (2014) A Model to Estimate the Population Contributing to the Wastewater Using Samples Collected on Census Day. Environmental Science & Technology, 48(1), pp. 517-525.

View at publisher

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.

Impact and interest:

16 citations in Scopus
Search Google Scholar™
15 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 88985
Item Type: Journal Article
Refereed: No
DOI: 10.1021/es403251g
ISSN: 0013-936X
Deposited On: 13 Oct 2015 23:15
Last Modified: 26 Oct 2015 00:48

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page