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.

Graphic abstract

中文翻译：

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实时最佳时空传感器放置以监测空气污染物

空气污染暴露评估包括监测大气中污染物的浓度以及对人群的健康影响评估。通常，空气污染物通过固定的监测站进行监测。由于传感器的成本和用于在市区内安装传感器以及维护监控网络的土地成本，传感器只能安装在有限的位置。固定式监视器的空间稀疏，可能会导致估算污染物实际暴露量时出现错误。解决这些局限性的一种方法是动态感应，这是一种新的监视技术，可以实时调整便携式传感器的位置，以测量空气质量的动态变化。动态感测的关键挑战是开发算法，以面对排放估算以及空气污染物的归宿和传输固有的不确定性，实时识别最佳传感器位置。在本文中，鉴于这些不确定性，我们提出了一种算法框架来实时解决传感器放置的挑战。系统的不确定性包括污染物的位置和数量以及导致随机优化问题的气象学。我们使用新颖的更好的非线性不确定系统（BONUS）算法来解决这些问题。Fisher信息（FI）被用作优化的目标。我们使用佐治亚州亚特兰大市的案例研究证明了我们新颖算法的功能。我们的实时传感器放置算法首次允许 在污染物的时空变化下确定传感器的最佳位置，这是固定的监测站无法实现的。我们介绍了传感器的动态位置，用于观察污染物的浓度以及观察这些污染物对人口的影响。

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