Enabling Large-Scale Urban Air Quality Monitoring with Mobile Sensor Nodes

Urban air pollution is a major concern in many cities worldwide. Atmospheric pollutants are responsible for health problems ranging from asthma to cancer. Air pollution also causes environmental damages. Monitoring airborne pollutants is of utmost importance to reliably assess the impact of air pollution on the human health, enable urban planners to craft and accurately evaluate new policies, and increase public awareness. Nowadays, air pollution is monitored by networks of highly accurate but fixed measurement stations. Hence, the gathered data has a low spatial resolution and can not be used to assess the spatial variability of pollutants in detail. As a result, little is known about the spatial distribution of air pollutants in urban environments. In this thesis, we tackle this challenge and derive fine-grained intraurban pollution maps valuable for a range of applications. We use compact low-cost sensors installed on top of public transport vehicles to obtain a high spatial measurement resolution within a large urban area. We develop algorithms that allow us to accurately monitor the phenomena of interest despite using noisy, low-cost sensors. Finally, we use the measurements to derive air pollution maps with a high spatial and temporal resolution. The main contributions of this thesis are: • We build a mobile air quality monitoring network by equipping public transport vehicles with low-cost air quality sensor nodes collecting spatially resolved measurements. It is the first mobile air pollution monitoring network operating for over three years by now. • We are the first to study multi-hop calibration of mobile sensor networks, with respect to a reference signal, in detail. We develop a new calibration algorithm to accurately calibrate networks of lowcost sensors by highly reducing error propagation in the network. Further, we assess the quality of the measurements by integrating generic models for the phenomena monitored and the sensors used. • We describe a new modeling approach and use the measurements collected with our mobile network to derive accurate urban pollution maps with an unprecedented spatial and temporal resolution. The maps open up many new application opportunities. For example, we introduce a novel route planning service, which helps urban dwellers to reduce their exposure to airborne pollutants.