Organic micropollutants (OMPs) facilitate the spread of antibiotic resistance genes (ARGs). Ammonia-oxidizing microorganisms (AOMs) are crucial for OMP degradation during riverbank filtration (RBF) and significantly influenced by NH4+-N concentrations. However, the effect of NH4+-N on OMP removal and ARG occurrence in RBF remains unclear. This study aimed to examine the effects of low (∼0.1 mg/L) and high (∼2.2 mg/L) NH4+-N concentrations on OMP removal, ARG occurrence, and microbial communities. NH4+-N addition had no significant effect on the removal of 108 out of 128 OMPs, suggesting that other factors primarily govern the removal process. Notably, NH4+-N addition enhanced the removal of 20 OMPs by 3–70%, including three quinolones (e.g., flumequine), indicating its promotion of specific OMP removals. This effect may primarily result from NH4+-N enhancing OMP biotransformation through the stimulation of AOMs (particularly AOA and comammox) and heterotrophs (e.g., Bradyrhizobium). Furthermore, NH4+-N addition significantly reduced the abundance of eight ARGs, including quinolone ARGs, likely due to its inhibition of antibiotic-resistant bacteria. Additionally, we hypothesize that NH4+-N alleviates OMP selective pressure on microorganisms by promoting OMP conversion through AOMs. This study enhances the understanding of microbe-mediated OMP removal in the presence of NH4+-N and its impact on ARG occurrence during RBF.

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline that characterizes charts by their properties and similarities to each other, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.