Demographic-Driven Wastewater-based Epidemiology: Refining Population Estimates for Enhanced Chemical Monitoring

Kishore Kumar Jagadeesan; Megan Robertson; John Bagnall; Barbara Kasprzyk-Hordern

doi:10.1021/acs.est.5c08178

Demographic-Driven Wastewater-based Epidemiology: Refining Population Estimates for Enhanced Chemical Monitoring

Kishore Kumar Jagadeesan
, Megan Robertson
, John Bagnall
, Barbara Kasprzyk-Hordern

Wessex Water Services Ltd
Wessex Water Services Ltd

Research output: Contribution to journal › Article › peer-review

3 Citations (SciVal)

Abstract

Wastewater-based epidemiology (WBE) is a vital tool for public health and environmental monitoring, yet accurate population estimation and demographic profiles within wastewater treatment plant (WWTP) catchments remain a challenge. This study integrates high-resolution 2021 UK Census data, NHS patient registration records, and WWTP chemical parameters to refine population estimates and assess demographic influences on wastewater composition. We compared four statistical models: Simple Approach (SA), Density-Adjusted Bootstrapping (BtD), Density-Adjusted Bootstrapping with Overlap (BtDO), and Bayesian Hierarchical Modeling (ByD), across four diverse UK catchments (12,000–960,000). The BtDO method provided the most consistent estimates, with relative uncertainties as low as 1.5% in larger catchments. Demographic analysis revealed that older populations and higher deprivations correlated with elevated pharmaceutical load (e.g., analgesics, r = 0.92, p < 0.01). Traditional load-based indicators (Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD)) significantly overestimated population due to nonhuman contributions, particularly in agricultural areas. This framework, transferable to regions with spatially referenced data, enhances WBE precision for public health surveillance and environmental risk assessment.

Original language	English
Pages (from-to)	25356-25367
Number of pages	12
Journal	Environmental Science & Technology
Volume	59
Issue number	47
Early online date	18 Nov 2025
DOIs	https://doi.org/10.1021/acs.est.5c08178
Publication status	Published - 2 Dec 2025

Funding

The support of EPSRC Impact Acceleration Account (EP/R51164X/1, ENTRUST IAA), Wessex Water Innovative Pathway Control Project, GCRF EWS-C19 (EP/V028499/1), and UKRI NERC PACIFIC Project (NE/X015947/1) is greatly appreciated. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Open Access funding enabled and organized by University of Bath

Funders	Funder number
University of Bath
Engineering and Physical Sciences Research Council	EP/R51164X/1
Wessex Water Innovative Pathway Control Project	GCRF EWS-C19, EP/V028499/1
Natural Environment Research Council	NE/X015947/1

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bayesian modeling
chemical biomarkers
environmental risk
NHS registration
population estimation
wastewater-based epidemiology

ASJC Scopus subject areas

General Chemistry
Environmental Chemistry

Access to Document

10.1021/acs.est.5c08178Licence: CC BY

Cite this

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title = "Demographic-Driven Wastewater-based Epidemiology: Refining Population Estimates for Enhanced Chemical Monitoring",

abstract = "Wastewater-based epidemiology (WBE) is a vital tool for public health and environmental monitoring, yet accurate population estimation and demographic profiles within wastewater treatment plant (WWTP) catchments remain a challenge. This study integrates high-resolution 2021 UK Census data, NHS patient registration records, and WWTP chemical parameters to refine population estimates and assess demographic influences on wastewater composition. We compared four statistical models: Simple Approach (SA), Density-Adjusted Bootstrapping (BtD), Density-Adjusted Bootstrapping with Overlap (BtDO), and Bayesian Hierarchical Modeling (ByD), across four diverse UK catchments (12,000–960,000). The BtDO method provided the most consistent estimates, with relative uncertainties as low as 1.5\% in larger catchments. Demographic analysis revealed that older populations and higher deprivations correlated with elevated pharmaceutical load (e.g., analgesics, r = 0.92, p < 0.01). Traditional load-based indicators (Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD)) significantly overestimated population due to nonhuman contributions, particularly in agricultural areas. This framework, transferable to regions with spatially referenced data, enhances WBE precision for public health surveillance and environmental risk assessment.",

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author = "Jagadeesan, \{Kishore Kumar\} and Megan Robertson and John Bagnall and Barbara Kasprzyk-Hordern",

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AU - Kasprzyk-Hordern, Barbara

PY - 2025/12/2

Y1 - 2025/12/2

N2 - Wastewater-based epidemiology (WBE) is a vital tool for public health and environmental monitoring, yet accurate population estimation and demographic profiles within wastewater treatment plant (WWTP) catchments remain a challenge. This study integrates high-resolution 2021 UK Census data, NHS patient registration records, and WWTP chemical parameters to refine population estimates and assess demographic influences on wastewater composition. We compared four statistical models: Simple Approach (SA), Density-Adjusted Bootstrapping (BtD), Density-Adjusted Bootstrapping with Overlap (BtDO), and Bayesian Hierarchical Modeling (ByD), across four diverse UK catchments (12,000–960,000). The BtDO method provided the most consistent estimates, with relative uncertainties as low as 1.5% in larger catchments. Demographic analysis revealed that older populations and higher deprivations correlated with elevated pharmaceutical load (e.g., analgesics, r = 0.92, p < 0.01). Traditional load-based indicators (Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD)) significantly overestimated population due to nonhuman contributions, particularly in agricultural areas. This framework, transferable to regions with spatially referenced data, enhances WBE precision for public health surveillance and environmental risk assessment.

AB - Wastewater-based epidemiology (WBE) is a vital tool for public health and environmental monitoring, yet accurate population estimation and demographic profiles within wastewater treatment plant (WWTP) catchments remain a challenge. This study integrates high-resolution 2021 UK Census data, NHS patient registration records, and WWTP chemical parameters to refine population estimates and assess demographic influences on wastewater composition. We compared four statistical models: Simple Approach (SA), Density-Adjusted Bootstrapping (BtD), Density-Adjusted Bootstrapping with Overlap (BtDO), and Bayesian Hierarchical Modeling (ByD), across four diverse UK catchments (12,000–960,000). The BtDO method provided the most consistent estimates, with relative uncertainties as low as 1.5% in larger catchments. Demographic analysis revealed that older populations and higher deprivations correlated with elevated pharmaceutical load (e.g., analgesics, r = 0.92, p < 0.01). Traditional load-based indicators (Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD)) significantly overestimated population due to nonhuman contributions, particularly in agricultural areas. This framework, transferable to regions with spatially referenced data, enhances WBE precision for public health surveillance and environmental risk assessment.

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KW - chemical biomarkers

KW - environmental risk

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KW - population estimation

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ER -

Demographic-Driven Wastewater-based Epidemiology: Refining Population Estimates for Enhanced Chemical Monitoring

Abstract

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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