Real-time optimal spatiotemporal sensor placement for monitoring air pollutants

Abstract

Air pollution exposure assessment involves monitoring of pollutant species concentrations in the atmosphere along with their health impact assessment on the population. Often air pollutants are monitored via stationary monitoring stations. Due to the cost of sensors and land for the installation of the sensors within an urban area as well as maintenance of a monitoring network, sensors can only be installed at a limited number of locations. The sparse spatial coverage of immobile monitors can lead to errors in estimating the actual exposure of pollutants. One approach to address these limitations is dynamic sensing, a new monitoring technique 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 emissions estimates and the fate and transport of air pollutants. In this paper, we present an algorithmic framework to address the challenge of sensor placement in real time, given those uncertainties. Uncertainty in the system includes location and amount of pollutants as well as meteorology leading to a stochastic optimization problem. We use the novel better optimization of nonlinear uncertain systems (BONUS) algorithm to solve these problems. Fisher information (FI) is used as the objective of the optimization. We demonstrate the capability of our novel algorithm using a case study in Atlanta, Georgia. Our real-time sensor placement algorithm allows, for the first time, determination of the optimal location of sensors under the spatial–temporal variability of pollutants, which cannot be accomplished by a stationary monitoring station. We present the dynamic locations of sensors for observing concentrations of pollutants as well as for observing the impacts of these pollutants on populations.