Assessing exposure to ambient air pollution at the urban temporal scale by analysis of image color and effective bandwidth

摘要

Airborne particulate matter (PM) attributes have a key role in assessing its health effects, since exposure to PM, especially the fine particle fraction (with diameter smaller than 2.5 urn, PM2.5), has been associated with adverse effects such as cardiovascular and respiratory diseases. PM2.5 has also longer atmospheric residence time in comparison to the coarser fraction (PM2.5-10), which increases its potential exposure risk. It is therefore important to monitor PM2.5 spatiotemporal variations near the ground at an urban-scale resolution. Satellite remote sensing is a common approach for gathering spatiotemporal data regarding aerosol events but its current spatial resolution is limited to a relatively large grid that does not fit highly varying urban conditions. 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 still extremely challenging. In the last two decades, visibility analysis of horizontal imaging is used by the US Environmental Protection Agency (US-EPA) to obtain quantitative representation of the air quality in rural areas. 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 the image effective bandwidth, a quantitative measure of image characteristics, for predicting PM concentrations. For validating the suggested method, we have assembled a large dataset that consists of time series imaging as well as measurements from air quality monitoring stations located in the study area, that report PM concentrations and meteorological data (wind direction and velocity, relative humidity, etc.) Quantitative and qualitative statistical evaluation of the suggested method shows that dynamic changes of PM concentrations can be inferred from the acquired images.