As domestic and international travel has expanded, the risk of the cross-border dissemination of infectious pathogens has increased, as clearly evidenced during the COVID-19 pandemic. Aircraft- and airport-based wastewater surveillance can provide timely, aggregate evidence of pathogen circulation among mobile populations and may complement clinical, genomic, and syndromic surveillance at points of entry. The strongest evidence for this approach comes from SARS-CoV-2 RNA detection and variant monitoring in aircraft lavatory wastewater, airport terminal wastewater, and airport-associated wastewater systems. However, aviation wastewater signals require cautious interpretation because detectability depends on pathogen shedding dynamics, passenger lavatory use, flight duration, sampling matrix, sample volume, assay sensitivity, sequencing depth, and the availability of epidemiological metadata. This structured narrative review synthesizes current evidence on aircraft and airport wastewater surveillance and examines the scientific determinants that influence surveillance validity and interpretation. Key methodological considerations include sampling design, sample preservation, nucleic acid extraction, molecular detection, sequencing, bioinformatics, inhibition assessment, recovery controls, assay sensitivity, and quality assurance. The review emphasizes that wastewater signals are aggregate and matrix-dependent and should not be interpreted as individual-level diagnosis, direct evidence of transmission, or definitive attribution to a specific flight, route, country, or population. Although SARS-CoV-2 surveillance is comparatively well-documented, multi-pathogen surveillance for enteric viruses, respiratory viruses, vaccine-preventable infections, emerging pathogens, and antimicrobial resistance markers requires pathogen-specific validation and clearer interpretation criteria. Aircraft and airport wastewater surveillance is therefore best viewed as an early-warning and situational-awareness approach whose value depends on converting technical signals into scientifically interpretable evidence.
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Vaidya et al. (2026) studied this question.
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