The effect of ego-motion on environmental monitoring.

Air pollution has a proven impact on public health. Currently, pollutant levels are obtained by high-priced, sizeable, stationary Air Quality Monitoring (AQM) stations. Recent developments in sensory and communication technologies have made relatively low-cost, micro-sensing units (MSUs) feasible. Their lower power consumption and small size enable mobile sensing, deploying single or multiple units simultaneously. Recent studies have reported on measurements acquired by mobile MSUs, mounted on cars, bicycles and pedestrians. While these modes of transportation inherently present different velocity and acceleration regimes, the effect of the sensors' varying movement characteristics have not been previously accounted for. This research assesses the impact of sensor's motion on its functionality through laboratory measurements and a field campaign. The laboratory setup consists of a wind tunnel to assess the effect of air flow on the measurements of nitrogen dioxide and ozone at different velocities in a controlled environment, while the field campaign is based on three cars mounted with MSUs, measuring pollutants and environmental variables at different traveling speeds. In both experimental designs we can regard the MSUs as a moving object in the environment, i.e. having a distinct ego-motion. The results show that MSU's behavior is highly affected by variation in speed and sensor placement with respect to direction of movement, mainly due to the physical properties of installed sensors. This strongly suggests that any future design of MSU must account for the speed effect from the design stage all the way through deployment and results analysis. This is the first report examining the influence of airflow variations on MSU's ability to accurately measure pollutant levels.

On the feasibility of measuring urban air pollution by wireless distributed sensor networks.

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150 m apart. We demonstrate the network's capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the system's performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution.