Faculty of Civil & Environmental Engineering, Technion-Israeli Institute of Technology, Haifa 3200003, Israel.
Environmental Physics Department, Israel Institute for Biological Research, 24 Lerer St., Ness Ziona 7410001, Israel.
Sensors (Basel). 2022 May 17;22(10):3808. doi: 10.3390/s22103808.
Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance of the network heavily depends on optimal deployment of the sensors. The latter is known as the location-allocation problem. Here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions. The sensors are then placed in those locations identified as the most informative. Specifically, entropy is used to quantify the locations' informativity. This entropy method is compared to two commonly used heuristics for solving the location-allocation problem. In the first, sensors are randomly deployed; in the second, the sensors are placed according to maximal cumulative pollution levels (i.e., hot spots). Two simulated scenarios were evaluated: one containing point sources and buildings and the other containing line sources (i.e., roads). The entropy method resulted in superior sensor deployment in terms of source apportionment and dense pollution field reconstruction from the sparse sensors' network measurements.
空气污染是城市化和工业化带来的主要不利环境后果之一。减轻空气污染的第一步是监测和识别其来源。传感器阵列的部署总是涉及成本和性能之间的权衡。网络的性能在很大程度上取决于传感器的最佳部署。后者被称为位置分配问题。在这里，提出了一种新的基于信息论的方法，该方法使用拉格朗日大气扩散模型在不同的气象条件下计算不同位置的空气污染水平。然后将传感器放置在被认为最具信息量的位置。具体来说，熵用于量化位置的信息量。将该熵方法与解决位置分配问题的两种常用启发式方法进行了比较。第一种方法是随机部署传感器；第二种方法是根据最大累积污染水平（即热点）放置传感器。评估了两种模拟场景：一种包含点源和建筑物，另一种包含线源（即道路）。在源分配和从稀疏传感器网络测量重建密集污染场方面，熵方法导致了更好的传感器部署。
