AAAR 32nd Annual Conference
September 30 - October 4, 2013
Oregon Convention Center
Portland, Oregon, USA
Abstract View
Assessing PM Concentrations at Urban Spatiotemporal Scale by Image Analysis Based on the Image Effective Bandwidth Measure
YAEL ETZION, David M. Broday, Barak Fishbain, Technion - Israel Institute of Technology
Abstract Number: 202 Working Group: Portable and Inexpensive Sensor Technology for Air Quality Monitoring
Abstract Size and concentration of airborne particulate matter (PM) are important indicators of air pollution events and public health risks. However, the important efforts of monitoring size resolved PM concentrations in ambient air are hindered by the highly dynamic spatiotemporal variations of the PM concentrations. Satellite remote sensing is a common approach for gathering spatiotemporal data regarding aerosol events but its current spatial resolution is limited to a large grid that does not fit high varying urban areas. Moreover, satellite-borne remote sensing has limited revisit periods and it measures along vertical atmospheric columns. Thus, linking satellite-borne aerosol products to ground PM measurements is extremely challenging. In the last two decades visibility analysis is used by the US Environmental Protection Agency (US-EPA) to obtain quantitative representation of air quality in rural areas by horizontal imaging. However, significantly fewer efforts have been given to utilize the acquired scene characteristics (color, contrast, etc.) for quantitative parametric modeling of PM concentrations. We suggest utilizing quantitative measures of image characteristics, mainly related to contrast, for predicting PM concentrations. In particular, we examined an innovative measure, called image effective bandwidth (IEB) that tallies the image blurriness. The method was validated by assembling and analyzing a large data set of time-series images, capturing a selected urban scene by horizontal imaging, versus PM concentrations and meteorological data (wind direction and velocity, relative humidity, etc.) that were simultaneously measured from air quality monitoring stations located in the imaged scene and its neighborhood. Quantitative and qualitative statistical evaluation of the suggested method shows that dynamic changes of PM concentrations can be inferred from the acquired images.