Powering an Egyptian Air Quality Information System with the Bayesian Maximum Entropy Space/Time Analysis Toolbox: Results From the Cairo Baseline Year Study

Abstract

The main goal of this work is to provide an efficient and powerful mapping analysis tool for a Temporal Geographic Information System (TGIS) of air quality in Cairo. There are currently several monitoring stations collecting air quality data, such as Particulate Matter (PM) and Lead concentrations, in the greater Cairo area. The analysis of this data needs to address several critical issues including (a) the high variability of data in space and time, (b) the combination of hard data (accurate measurements) and soft information (such as uncertain measurement values, etc). The Bayesian Maximum Entropy (BME) is a novel and powerful stochastic estimation method that addresses these issues. The implementation of the BME method for space/time mapping of the air quality in Cairo is demonstrated On the PM and Lead data collected during one year at 34 monitoring stations in Cairo. The case study presented demonstrates the usefulness of this mapping analysis tool in order to generate the most accurate and informative maps available to date on the air quality in Cairo. Hence this work presents the framework to use the BME method as a useful space/time analysis tool for a TGIS of air quality in the greater Cairo area.