Apportioning sources of chemicals of emerging concern along an urban river with inverse modelling

Kajetan Chrapkiewicz; Alex G Lipp; Leon P Barron; Richard Barnes; Gareth G Roberts

doi:10.1016/j.scitotenv.2024.172827

Apportioning sources of chemicals of emerging concern along an urban river with inverse modelling

Sci Total Environ. 2024 Jul 10:933:172827. doi: 10.1016/j.scitotenv.2024.172827. Epub 2024 May 1.

Authors

Kajetan Chrapkiewicz¹, Alex G Lipp², Leon P Barron³, Richard Barnes⁴, Gareth G Roberts⁵

Affiliations

¹ Department of Earth Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. Electronic address: k.chrapkiewicz17@imperial.ac.uk.
² Merton College, University of Oxford, Merton Street, Oxford OX1 4JD, Oxfordshire, UK.
³ MRC Centre for Environment and Health, Environment Research Group, School of Public Health, Imperial College London, Wood Lane, London W12 0BZ, UK.
⁴ Lawrence Berkeley National Laboratory, Wang Hall, Berkeley, CA 94720, USA.
⁵ Department of Earth Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. Electronic address: gareth.roberts@imperial.ac.uk.

PMID: 38701930
DOI: 10.1016/j.scitotenv.2024.172827

Abstract

Concentrations of chemicals in river water provide crucial information for assessing environmental exposure and risks from fertilisers, pesticides, heavy metals, illicit drugs, pathogens, pharmaceuticals, plastics and perfluorinated substances, among others. However, using concentrations measured along waterways (e.g., from grab samples) to identify sources of contaminants and understand their fate is complicated by mixing of chemicals downstream from diverse diffuse and point sources (e.g., agricultural runoff, wastewater treatment plants). To address this challenge, a novel inverse modelling approach is presented. Using waterway network topology, it quantifies locations and concentrations of contaminant sources upstream by inverting concentrations measured in water samples. It is computationally efficient and quantifies uncertainty. The approach is demonstrated for 13 contaminants of emerging concern (CECs) in an urban stream, the R. Wandle (London, UK). Mixing (the forward problem) was assumed to be conservative, and the location of sources and their concentrations were treated as unknowns to be identified. Calculated CEC source concentrations, which ranged from below detection limit (a few ng/L) up to 1μg/L, were used to predict concentrations of chemicals downstream. Using this approach, >90% of data were predicted within observational uncertainty. Principal component analysis of calculated source concentrations revealed signatures of two distinct chemical sources. First, pharmaceuticals and insecticides were associated with a subcatchment containing a known point source of treated effluent from a wastewater treatment plant. Second, illicit drugs and salicylic acid were associated with multiple sources, interpreted as input from untreated sewage including Combined Sewer Overflows (CSOs), misconnections, runoff and direct disposal throughout the catchment. Finally, a simple algorithmic approach that incorporates network topology was developed to design sampling campaigns to improve resolution of source apportionment. Inverse modelling of contaminant measurements can provide objective means to apportion sources in waterways from spot samples in catchments on a large scale.

Keywords: Contaminants of emerging concern; Inverse modelling; Pharmaceutical and illicit drugs; River mixing; Source apportionment; Thames drainage basin; Water pollution.