10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
Abstract View
Extracting Air Quality from Photographs
Batsal Pudasaini, Joseph Skufca, Sumona Mondal, Natasha Banerjee, Jan Scrimgeour, Mark Kanaparthi, SURESH DHANIYALA, Clarkson University
Abstract Number: 1407 Working Group: Low-Cost and Portable Sensors
Abstract Understanding the effects of aerosol particles on human health is made challenging by the lack of high-resolution air quality data. Globally, PM2.5 monitoring sites are limited in number, as they require extensive manpower and equipment to operate. This research takes a physics-based approach in estimating PM2.5 by analyzing photographs from different locations. A background image is impacted by the presence of aerosol, because of a combination of particle scattering and extinction of light. We develop a governing equation that relates camera signal to the properties of aerosol, the incident light, and the image being captured. From inversion of this integral equation, we establish an expression for turbidity and estimate PM2.5 from these measurements. Using 3-years’ worth of images captured from a camera at a fixed location (downtown Chicago), the calculated turbidity is compared against actual PM2.5 data from nearby EPA monitoring sites. It is observed that on average, turbidity and PM2.5 have a statistically significant positive linear correlation. However, turbidity on its own can prove insufficient in predicting PM2.5 value due to several aerosol characteristics affecting both the predictor and response variable. A new model is created by combining turbidity and meteorological conditions in order to estimate PM2.5. We will present our theoretical approach and the details of our prediction capabilities of PM2.5 in our presentation.