Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparative Study of Computational Models for Reducing Air Pollution Through the Generation of Negative Ions

Version 1 : Received: 7 May 2021 / Approved: 10 May 2021 / Online: 10 May 2021 (14:02:23 CEST)

How to cite: Ortiz-Grisales, P.; Patiño-Murillo, J.; Duque-Grisales, E. Comparative Study of Computational Models for Reducing Air Pollution Through the Generation of Negative Ions. Preprints 2021, 2021050195 (doi: 10.20944/preprints202105.0195.v1). Ortiz-Grisales, P.; Patiño-Murillo, J.; Duque-Grisales, E. Comparative Study of Computational Models for Reducing Air Pollution Through the Generation of Negative Ions. Preprints 2021, 2021050195 (doi: 10.20944/preprints202105.0195.v1).

Abstract

Today, air quality is one of the global concerns that governments are facing. One of the main air pollutants is the particulate matter (PM) that affects human health. This article presents the modeling of a purification system by means of negative air ions (NAIs) for air pollutant removal, using computational intelligence methods. The system uses a high voltage booster output to ionize air molecules from stainless steel electrodes; its particle-capturing efficiency reaches up to 97%. With two devices (5 x 2 x 2.5 cm), 2 trillion negative ions are produced per second, and the particulate matter (PM 2.5) can be reduced from 999 to 0 mg / m3 in a period of approximately 5 to 7 minutes (in a 40 x 40 x 40 cm acrylic chamber). This negative ion generator is a viable and sustainable alternative to reduce polluting emissions, with beneficial effects on human health.

Subject Areas

Environmental pollution; air purification; negative ion generators; particulate matter.

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