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|Title:||Monitoring and modelling antibiotic resistance in Southeast Asian rivers|
|Abstract:||Pinpointing environmental antibiotic resistance (AR) hotspots in rivers 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, a comprehensive spatial and seasonal assessment of water quality and AR conditions in a Malaysian river catchment was preformed to identify potential 'simple' surrogates that mirror elevated AR. This included screening for β-lactam resistant coliforms, 22 antibiotics, 287 AR genes and integrons, and routine water quality parameters, covering absolute concentrations and mass loadings. Novel approaches were developed and applied to advance environmental microbiome and resistome research. To investigate relationships, standardised 'effect sizes' (Cohen's D) were introduced for AR monitoring to improve comparability of field studies. Quantitative microbiome profiling (QMP) was applied to overcome biases caused by relative taxa abundance data. In addition, Hill numbers were introduced as a unified diversity framework for environmental microbiome research. Overall, water quality generally declined, and environmental AR levels increased as one moved downstream 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). This is suspected to result 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, relatively lower DO levels reflect wastewater inputs, flagging possible AR hot spots. Furthermore, DO is easy-to-measure and inexpensive, already monitored in many catchments, and exists in many numerical water quality models (e.g., oxygen sag curves). Therefore, combining DO data and prospective modelling (e.g., with the watershed model HSPF) could guide local interventions, especially in LMIC rivers with limited data.|
|Appears in Collections:||School of Engineering|
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|Ott A 2021.pdf||Thesis||7.9 MB||Adobe PDF||View/Open|
|dspacelicence.pdf||Licence||43.82 kB||Adobe PDF||View/Open|
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