Ali Sharafat, Alam Fakhrul, Potgieter Johan, Arif Khalid Mahmood
Department of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand.
Department of Electrical & Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand.
Sensors (Basel). 2024 May 4;24(9):2930. doi: 10.3390/s24092930.
Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.
低成本环境传感器已被视为一种很有前景的技术，可用于以高时空分辨率监测空气污染。然而，这些经济高效的传感器捕获的污染物数据不如传统传感器准确，需要仔细校准以提高其准确性和可靠性。在本文中，我们建议利用时间信息，例如传感器的部署时长以及读数获取的时间，以改进低成本传感器的校准。此信息很容易获取，且迄今为止尚未在已发表的文献中用于经济高效的环境气体污染物传感器的校准。我们利用了世界各地研究团队收集的三个数据集，这些团队从与精确参考传感器共置的现场部署低成本一氧化碳和一氧化氮传感器收集数据。我们的研究表明，将时间信息用作协变量可显著提高基于机器学习的常见校准技术（如随机森林和长短期记忆）的准确性。
