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.

In most practical cases, density-driven currents flow over surfaces that are not smooth; however, the effects of bottom roughness on these currents have not been fully understood yet. Hence, this study aims to examine the velocity structure of density currents while propagating over rough beds. To this end, alterations in the vertical velocity profiles within the body of these currents were investigated in the presence of different bottom roughness configurations. Initially, laboratory experiments were carried out for density currents flowing over a smooth surface to provide a baseline for comparison. Thereafter, seven bottom roughness configurations were tested, encompassing both dense and sparse bottom roughness. The bottom roughness consisted of repeated arrays of square cross-section beams covering the full channel width and perpendicular to the flow direction. The primary results indicate that the bottom roughness decelerated the currents and modified the shape of velocity profiles, particularly in the region close to the bed. Additionally, a critical spacing of the roughness elements was detected for which the currents demonstrated the lowest velocities. For the spacings above the critical value, increasing the distance between the roughness elements had little impact on controlling the velocity of these currents. Moreover, using dimensional analysis, equations were developed for estimating the mean velocities of the currents flowing over various configurations of the bottom roughness. The findings of this research could contribute towards better parameterization and improved knowledge of density currents flowing over rough beds. This can lead to a better prediction of the evolution of these currents in many practical cases as well as improved planning and design measures for the control of such currents.

Predicting the impact of climate change and human activities on river systems is imperative for effective management of aquatic ecosystems. Unique information can be derived that is critical to the survival of aquatic species under dynamic environmental conditions. Therefore, the response of a tropical river system under climate and land-use changes from the aspects of water temperature and dissolved oxygen concentration were evaluated. Nine designed projected climate change scenarios and three future land-use scenarios were integrated into the Hydrological Simulation Program FORTRAN (HSPF) model to determine the impact of climate change and land-use on water temperature and dissolved oxygen (DO) concentration using basin-wide simulation of river system in Malaysia. The model performance coefficients showed a good correlation between simulated and observed streamflow, water temperature, and DO concentration in a monthly time step simulation. The Nash-Sutcliffe Efficiency for streamflow was 0.88 for the calibration period and 0.82 for validation period. For water temperature and DO concentration, data from three stations were calibrated and the Nash-Sutcliffe Efficiency for both water temperature and DO ranged from 0.53 to 0.70. The output of the calibrated model under climate change scenarios show that increased rainfall and air temperature do not affects DO concentration and water temperature as much as the condition of a decrease in rainfall and increase in air temperature. The regression model on changes in streamflow, DO concentration, and water temperature under the climate change scenarios illustrates that scenarios that produce high to moderate streamflow, produce small predicted change in water temperatures and DO concentrations compared with the scenarios that produced a low streamflow. It was observed that climate change slightly affects the relationship between water temperatures and DO concentrations in the tropical rivers that we include in this study. This study demonstrates the potential impact of climate and future land-use changes on tropical rivers and how they might affect the future ecological systems. Most rivers in suburban areas will be ecologically unsuitable to some aquatic species. In comparison, rivers surrounded by agricultural and forestlands are less affected by the projected climate and land-uses changes. The results from this study provide a basis in which resource management and mitigation actions can be developed.

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Background Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks. Results Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation. Conclusions Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.