Despite its emerging significant public health concern, the presence of antibiotic resistance genes (ARGs) in urban air has not received significant attention. Here, we profiled relative abundances (as a fraction, normalized by 16S rRNA gene) of 30 ARG subtypes resistant to seven common classes of antibiotics, which are quinolones, β-lactams, macrolides, tetracyclines, sulfonamides, aminoglycosides, and vancomycins, in ambient total particulate matter (PM) using a novel protocol across 19 world cities. In addition, their longitudinal changes in PM samples in Xi'an, China as an example were also studied. Geographically, the ARGs were detected to vary by nearly 100-fold in their abundances, for example, from 0.07 (Bandung, Indonesia) to 5.6 (San Francisco, USA). The β-lactam resistance gene blaTEM was found to be most abundant, seconded by quinolone resistance gene qepA; and their corresponding relative abundances have increased by 178% and 26%, respectively, from 2004 to 2014 in Xi'an. Independent of cities, gene network analysis indicates that airborne ARGs were differentially contributed by bacterial taxa. Results here reveal that urban air is being polluted by ARGs, and different cities are challenged with varying health risks associated with airborne ARG exposure. This work highlights the threat of urban airborne transmission of ARGs and the need of redefining our current air quality standards in terms with public health.

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Influenza epidemics worldwide result in substantial economic and human costs annually. However, rapid and reliable flu diagnosis methods are significantly lacking. Here we have demonstrated the selective detection of influenza A viruses down to 29 viruses/μL in clinical exhaled breath condensate (EBC) samples (diluted by 100-fold) within minutes using silicon nanowire (SiNW) sensor devices. For 90% of the cases, we have observed that EBC samples tested positive or negative by gold standard method RT-qPCR generated corresponding positive or negative SiNW sensor responses. High selectivity of SiNW sensing was also demonstrated using H1N1 viruses, 8 iso PGF 2a, and inert nanoparticles. Finally, magnetic beads were shown capable of enhancing SiNW sensing directly for low level viruses and 8 iso PGF 2a. When calibrated by virus standards and EBC controls, our work suggests that the SiNW sensor device can be reliably applied to the diagnosis of flu in a clinical setting with 2 orders of magnitude less time compared to the gold standard method RT-qPCR.