Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Korea.
Department of Information and Measurement Systems, Moscow State University of Geodesy and Cartography, Moscow 105064, Russia.
Sensors (Basel). 2022 Jun 24;22(13):4767. doi: 10.3390/s22134767.
Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.
空气污染已成为所有特大城市的严重问题。有必要对大气状态进行持续监测，但使用固定站接收的污染数据不足以准确评估空气的气溶胶污染水平。移动测量设备可以显著提高接收数据的时空分辨率。不幸的是，移动、低成本传感器的读数质量明显低于固定传感器。这使得有必要根据所使用的移动传感器的特性来评估监测系统的各种特性。本文提出了一种方法，其中污染检测时间被视为随机变量。据我们所知，我们首次推导出了污染检测时间的累积分布函数，该函数取决于监测系统的特征。所得到的分布函数使得在智慧城市中优化某些空气污染检测系统的特性成为可能。
