Pathogens on fire: a scoping review of smoke-borne pathogen ecology in the One Health framework

Wildland fires are increasing in both frequency and severity in many areas globally. Smoke from wildland fires (wildfires and prescribed burns), as well as agricultural burning, releases not only pollutants but also viable microorganisms, including pathogens capable of long-distance dispersal, potentially posing unrecognized risks to human, animal, and plant health.

This scoping review synthesizes knowledge about pathogenic microbial dispersal in smoke from wildland fires, identifies gaps in pathogen ecology and epidemiology, and outlines research priorities in a One Health framework.

This review followed the Arksey & O'Malley framework with PRISMA-ScR guidance, using systematic searches in PubMed, Google Scholar, and grey literature sources (USDA Forest Service, World Health Organization, U.S. Environmental Protection Agency). After screening and applying inclusion criteria, 36 studies were retained that addressed microbial transport, viability, and disease associated with wildland fire smoke.

There is evidence that wildland fire smoke can aerosolize diverse microbial assemblages, including pathogenic fungi such as Coccidioides and Puccinia, and bacteria capable of forming heat-resistant spores. If microbes can remain viable in smoke across greater distances, there would be the possibility of long-distance dispersal while suspended in smoke plumes. However, data about infection outcomes, dose-response relationships, and host susceptibility are lacking. Current wildland fire smoke surveillance focuses almost exclusively on abiotic pollutants, leaving microbial risks largely ignored.

A One Health approach integrates fire ecology, aerobiology, microbiology, and epidemiology across host species. After determining how important the role of dispersal in smoke is for human, animal, and plant health, priority actions may include improving pathogen viability sampling, incorporating microbial monitoring into smoke surveillance networks, and developing predictive models to assess health and ecological risks.