A new method enables near real-time identification of airborne bacteria

A team from the Barcelona Institute for Global Health (ISGlobal), a centre supported by the ”la Caixa” Foundation, has demonstrated for the first time that it is possible to identify bacteria present in the air almost instantly, without the need to collect samples or process them in the laboratory. The study, published in the journal Atmospheric Measurement Techniques, shows that a portable device can recognise different types of bacteria in aerosols by combining an ultraviolet laser that induces fluorescence in their components with an artificial intelligence system capable of interpreting that signal.

Microorganisms present in the atmosphere—the so-called aerobiome—play a key role in ecosystems, climate and human health. However, their study has been limited by technical challenges: air samples usually contain extremely low amounts of bacterial DNA, which slows down sequencing-based approaches. Automated bioaerosol detectors have made significant progress in pollen identification, but until now there has been no effective method to distinguish microbial particles in real time, a limitation that hinders environmental surveillance and rapid response to biological threats or bioaerosol-related pollution events.

To achieve this, the team adapted a commercial device known as Rapid-E, replacing its original laser with a 266 nm laser capable of exciting—i.e. inducing fluorescence in—compounds characteristic of bacteria. They then generated laboratory aerosols containing five bacterial species commonly found in urban environments, simulating particles that may be present in ambient air. The device analysed each particle by measuring how it scattered light and the type of fluorescence it emitted—a kind of optical “fingerprint” for each microorganism—and these data were fed into machine learning models trained to recognise species-specific patterns.

Accuracy of 96.7% in distinguising between bacterial from non-bacterial particles

The results show that the system can distinguish between bacterial and non-bacterial particles with an accuracy of 96.7%, and identify specific bacterial species with an average accuracy of 69.2%, a remarkable achievement given the small size and complexity of microbial particles. The study also found that fluorescence lifetime is the most informative feature for classification, outperforming both fluorescence spectra and light scattering.

According to Alejandro Fontal, first author of the study and researcher at ISGlobal: “We have demonstrated that it is possible to recognise airborne bacteria in near real time without the need to collect and process samples. This opens up new possibilities for environmental surveillance and for the study of the aerobiome, an ecosystem that has so far been largely unexplored.”

Xavier Rodó, senior author of the study and ICREA researcher at ISGlobal, adds that “the improvements introduced in this device could in the future enable continuous and more refined monitoring of bioaerosols, helping to anticipate health risks, study the dispersion of pathogens, or better understand microbial dynamics at a global scale.”

The study represents an important step towards environmental monitoring systems capable of detecting airborne microorganisms rapidly, automatically and without laboratory-based procedures. In the future, this technology could be extended to fungi, viruses and other biological components of the air, as well as support early-warning systems for aerosol-borne pathogens such as SARS-CoV-2. It could also complement climate and air-quality studies, as it allows the differential separation of hydrocarbons emitted by vehicle combustion. Other potential applications include improved monitoring in urban environments, hospitals and critical infrastructures. The authors stress, however, that the prototype and the methodology will need to be validated under real-world conditions and with complex particle mixtures that are more representative of ambient air.

Reference
Fontal, A., Borràs, S., Cañas, L., Pozdniakova, S., and Rodó, X. Laser-Induced Fluorescence coupled with Machine Learning as an effective approach for real-time identification of bacteria in bioaerosols. Atmospheric Measurement Techniques. 2025;18:7297–7313. https://doi.org/10.5194/amt-18-7297-2025