An exercise in selecting low-cost air quality sensor placements within an urban environment

Abstract

Air pollution poses the most important environmental health risk to citizens of major cities all over the world. The high cost of current monitoring programs means that enforcement of current regulation, such as the United States Environmental Protection Agency's ambient air quality standards, can be lacking at the individual level. Because of their low cost, sensor networks offer the benefit of providing detailed, high resolution pollutant exposure maps which can inform a number of community and government initiatives aimed at tackling air pollution. The question then arises, what is the optimal configuration of low-cost sensors to measure air pollution within an urban environment? Due to the large number of potential locations in which to measure data, there are difficulties in defining where to place a limited number of sensors. This thesis outlines a proven decision method from spatial statistics: optimal experimental design, and applies the method to a test case in the city of London.