Mathematical Estimation of Particulate Air Pollution Levels by Aerosols Tomography

Abstract

Air pollution control and mitigation are important factors in wellbeing and sustainability. To this end, air pollution monitoring has a significant role. Today, air pollution monitoring is mainly done by standardized stations. The spread of those stations is sparse and their cost hinders the option of adding more. Thus, arises the need for cheaper and available means to assess air pollution. In this article, a mathematical method to solve the inverse problem of aerosols tomography is proposed. The suggested method applies filtered back-projection method on a pixel-wise blur estimation. Using the method, particles' concentrations in a 3D space is reconstructed from photos taken from different angles. The proposed method is shown to be very effective for assessing air pollution levels by means of multi angle imaging. Specifically, estimating images' blur as an indication for Particulate Matter (PM) ambient levels. The results of the research point towards strong correlation between image blur and pollution level in the medium and the ability to reconstruct the aerosols distribution in space.