Toggle Main Menu Toggle Search

Open Access padlockePrints

Developing Surrogate Markers for Predicting Antibiotic Resistance “Hot Spots” in Rivers Where Limited Data Are Available

Lookup NU author(s): Amelie Ott, Dr Greg O'Donnell, Dr Andrew Zealand, Dr Michaela Goodson, Professor David Graham



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Pinpointing environmental antibiotic resistance (AR) hot spots in low-and middle-income countries (LMICs) is hindered by a lack of available and comparable AR monitoring data relevant to such settings. Addressing this problem, we performed a comprehensive spatial and seasonal assessment of water quality and AR conditions in a Malaysian river catchment to identify potential “simple” surrogates that mirror elevated AR. We screened for resistant coliforms, 22 antibiotics, 287 AR genes and integrons, and routine water quality parameters, covering absolute concentrations and mass loadings. To understand relationships, we introduced standardized “effect sizes” (Cohen’s D) for AR monitoring to improve comparability of field studies. Overall, water quality generally declined and environmental AR levels increased as one moved down the catchment without major seasonal variations, except total antibiotic concentrations that were higher in the dry season (Cohen’s D > 0.8, P < 0.05). Among simple surrogates, dissolved oxygen (DO) most strongly correlated (inversely) with total AR gene concentrations (Spearman’s ρ 0.81, P < 0.05). We suspect this results from minimally treated sewage inputs, which also contain AR bacteria and genes, depleting DO in the most impacted reaches. Thus, although DO is not a measure of AR, lower DO levels reflect wastewater inputs, flagging possible AR hot spots. DO measurement is inexpensive, already monitored in many catchments, and exists in many numerical water quality models (e.g., oxygen sag curves). Therefore, we propose combining DO data and prospective modeling to guide local interventions, especially in LMIC rivers with limited data.

Publication metadata

Author(s): Ott A, ODonnell G, Tran NH, Haniffah MRM, Su JQ, Zealand AM, Gin KYH, Goodson ML, Zhu YG, Graham DW

Publication type: Article

Publication status: Published

Journal: Environmental, Science & Technology

Year: 2021

Volume: 55

Issue: 11

Pages: 7466-7478

Print publication date: 01/06/2021

Online publication date: 17/05/2021

Acceptance date: 03/05/2021

Date deposited: 28/05/2021

ISSN (print): 0013-936X

ISSN (electronic): 1520-5851

Publisher: American Chemical Society


DOI: 10.1021/acs.est.1c00939


Altmetrics provided by Altmetric


Find at Newcastle University icon    Link to this publication