Protocol for hunting PM2.5 emission hot spots in cities

Abstract

Particulate Matter (PM) is a major air pollutant that has the potential for adversely affecting human health. Actionable data on the spatial distribution of temporal variability of PM2.5 emission hot spots in large cities are sparse. The main objective of this research is to provide a protocol for using search agents to hunt for PM2.5 emission hot spots in urban environments. We propose short range identification of variability of harmful PM2.5 concentrations can be achieved using IoT devices mounted on a mobile platform. We propose that long range identification the PM2.5 emission hot spots can attained by searching through the city on different days. We applied this approach to Hyderabad, India by fixing a mobile platform on a street car. We corrected the IoT device measurement errors by calibrating the sensing component data against a reference instrument co-located on the mobile platform. We identified that random forest regression was the most suitable technique to reduce the variability between the IoT devices. The spatial variability of PM2.5 harmful emission hot spots at industrial settings and congested roads were identified. The temporal variability based on image processing shows a weak correlation between PM2.5 concentrations and number of vehicles, and PM2.5 and visibility. The Hyderabad PM2.5 emission hot spots findings demonstrate a clear need to inform people with heart and lung conditions when it is unhealthy to be outside; and when it is unhealthy for children and elderly people to be outside for prolonged periods. Our emission hunting approach can be applied to any mobile platform carried by people walking, cycling or by drones and robots in any city.