(145d) Optimizing Real-Time Spatiotemporal Sensor Placement for Monitoring Air Pollutant for Health Impact Assessment

Air pollution exposure assessment, which involves the monitoring of pollutant species concentrations in the atmosphere, is an important step in assessing the health impact of air pollution. Currently, the air pollutants are often monitored via stationary monitoring stations. Due to the high cost of maintaining a monitoring network, the monitors can only be installed in limited number of locations. The sparse spatial coverage of monitors can lead to errors in estimating the actual exposure of pollutants in individuals. The recent advancement of cheaper sensors can help address the limitations of using stationary monitors. One approach is dynamic sensing – a new monitoring approach that adjusts the locations of portable sensors in real time to measure the dynamic changes in air quality. The key challenge in dynamic sensing is to develop algorithms to identify the optimal sensor locations in real time in the face of inherent uncertainties in the fate and transport of the air pollutants. The objective of this paper is to develop an algorithmic framework to address the challenge of sensor placement in real time. We demonstrate the capability of our algorithmic framework in a case study in Atlanta. Our real-time sensor placement optimization algorithm will allow, for the first time, the assessment of spatial-temporal variability of pollution which cannot be accomplished by a stationary monitoring station. Besides air pollution exposure assessment, our algorithm can have broader applications in improving sensor placement in drone-based monitoring for water bodies, water security networks, or advanced power systems.