Analysis of image color and effective bandwidth as a tool for assessing air pollution at urban spatiotemporal scale

Abstract

Size and concentration of airborne particulate matter (PM) are important indicators of air pollution events and public health risks. It is therefore important to monitor size resolved PM concentrations in the ambient air. This task, however, is 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 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.

Framework for Analyzing Environmental Indicators Measurements Acquired by Wireless Distributed Sensory Network – Air Pollution Showcase

“…Recent sensory and communication technological developments have led to the emergence of Wireless Distributed Environmental Sensing Networks (WDESNs) that consist of Micro-Sensory-Units (MSUs), mainly in air and atmospheric assessments (Mead et al 2013;Chen 2008;Dutta et al 2009;Williams et al 2013;Etzion et al 2013;Broday et al 2013) and aquatic systems (Kroll and King 2007;Hall et al 2007;Pickard et al 2011;Research and Development, National Homeland Security Research Center 2005;Kramer 2009;Storey et al 2011). These have greatly enhanced monitoring of complex infrastructure and the natural environment, allowing the study of fundamental processes in the environment, as well as hazard warnings, such as flood (Hart and Martinez 2006;Martinez and Hart 2004;Zhou and Roure 2007) and pollution alerts (Hochbaum and Fishbain 2011), in a new way.…”

Mathematical Estimation of Particulate Air Pollution Levels by Multi-angle Imaging

“…Instruments such as Multi-angle Imaging SpectroRadiometer (MISR) and Airborne Multi-angle SpectroPolarimetric Imager (AirMSPI) are currently applied for remote aerosols sensing [9,10]. However, these methods have low temporal resolution, as it takes a satellite few hours (or few days) to complete their orbit (latitude dependent), but more importantly, these devices measure the full vertical atmospheric column, which makes it less relevant to the amount of pollution at ground level that can indicate health hazards [11]. On top of these, this method is expensive and cannot be easily deployed.…”

“…The underlying assumption here is that due to the particles' optical properties of scattering, different concentrations of particles will cause the light field to scatter differently [11]. The higher the concentration of scatterers is, the more refractions the light will go through.…”

Mathematical Estimation of Particulate Air Pollution Levels by Aerosols Tomography

“…This research aims at improving this notion, by looking at a 3D volume with changing concentrations at each voxel (as opposed to the homogeneous atmosphere assumption made by Graves and Newsam), attaining a quantitative measurement for PM concentration at each voxel (volume element). The underlying assumption here is that due to the particles' optical properties of scattering, different concentrations of particles will cause the light field to scatter differently [14]. The higher the concentration of scatterers, the more refractions the light will go through.…”