A Pandemic Early Warning System Decision Analysis Concept Utilizing a Distributed Network of Air Samplers via Electrostatic Air Precipitation

Abstract

The COVID-19 pandemic has highlighted the need for improved airborne infectious disease monitoring capability. A key challenge is to develop a technology that captures pathogens for identification from ambient air. While pathogenic species vary significantly in size and shape, for effective airborne pathogen detection the target species must be selectively captured from aerosolized droplets. Captured pathogens must then be separated from the remaining aerosolized droplet content and characterized in real-time. While improvements have been made with clinical laboratory automated sorting in culture media based on morphological characteristics of cells, this application has not extended to aerosol samples containing bacteria, viruses, spores, or prions. This manuscript presents a strategy and a model for the development of an airborne pandemic early warning system using aerosol sampling.

Numerical Simulation and Analytical Evaluation of the Collection Efficiency of the Particles in a Gas by the Wire-Plate Electrostatic Precipitators

“…The harmful emissions of micro and nanoparticles to the environment inevitably increase with technological development. These contaminants significantly affect human health as well as the environment and organisms [1][2][3][4]. There are various contaminant filtering technologies; among them, electrostatic precipitation has the advantages of lowpressure drop in the main fluid flow and relatively good performance in the collection efficiency [5][6][7].…”