Bioaerosol sampling is a critical step toward characterizing airborne pathogen exposure risks for many respiratory infectious diseases in human-to-human or human-to-animal outbreaks. The integrated performance-parameter design and sampling efficiency of aerosol-to-hydrosol samplers (cyclones) has been rarely published or studied. This review focuses on the determination of cyclone performance-parameter design and impact factors of the sampling efficiency, discusses their mechanisms of collection and describes their advantages and drawbacks. The crucial design parameters of cyclones in different applications are then discussed in detail. We also discuss how the aerosol-to-hydrosol sampling efficiency is intrinsically related to the performance parameters for bioaerosol cyclone design. In the final section of this review, scientific questions and challenges for future work are presented to encourage high collection efficiency design and high-quality cyclones.

Aerosol-to-hydrosol biological samplers have a low particle collection rate and aerosol-to-liquid efficiency owing to their airflow rate and geometry. The study develops a method to optimize the design of an aerosol-to-hydrosol bioaerosol sampler for better particle-collection efficiency. Four important performance parameters-particle size, nozzle angle, liquid container shape, and distance between nozzle and liquid surface-were optimized using the orthogonal design method. These parameters, along with the particle-capturing characteristics, were predicted using an approach based on computational fluid dynamics. Subsequently, this approach was used to test the bioaerosol's capture efficiency. A prototype based on the optimized geometrical design was 3D-printed and tested in a field experiment. The most optimal parameters were particle size = 10 mu m; inverted cone shape; liquid-level-nozzle distance = 10 mm; and angle = 45 degrees. Copyright (c) 2020 American Association for Aerosol Research

Current respiratory pathogen surveillance is largely based on case reporting, sentinel surveillance, and environmental sampling. This study developed an autonomous pathogen detection system (APDS) integrating real-time bioaerosol sensing, intelligent triggering, aerosol collection, nucleic acid extraction, real-time PCR, and automated data reporting for respiratory pathogen surveillance. System performance was evaluated in a 20 m³ aerosol chamber and through 52 weeks of continuous monitoring in a hospital outpatient hall in Beijing. Laboratory experiments showed that the developed platform enables the detection of airborne influenza A virus at concentrations as low as 3.75 × 10⁴ 50% tissue culture infectious dose/m³ and can detect airborne Pseudomonas aeruginosa at concentrations as low as 1.80 × 10⁴ copies/m³. The platform can detect low concentrations of airborne pathogen nucleic acids that are undetectable by surface sampling. During field monitoring, APDS autonomously triggered 381 tests and recorded 46 pathogen-positive detections. Respiratory syncytial virus showed significant correlations when APDS signals preceded national surveillance data by 1–3 weeks, with the strongest correlation at a 3-week lead (ρ = 0.487). Given the spatial mismatch between single-site APDS monitoring and national surveillance data, this association should be considered exploratory. These findings demonstrate the feasibility of APDS for long-term automated aerosol surveillance and suggest its potential as a complementary tool for respiratory pathogen monitoring.