Yavari Ali, Mirza Irfan Baig, Bagha Hamid, Korala Harindu, Dia Hussein, Scifleet Paul, Sargent Jason, Tjung Caroline, Shafiei Mahnaz
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia.
6G Research and Innovation Lab, Swinburne University of Technology, Melbourne, VIC 3122, Australia.
Sensors (Basel). 2023 Sep 19;23(18):7971. doi: 10.3390/s23187971.
Greenhouse gas (GHG) emissions reporting and sustainability are increasingly important for businesses around the world. Yet the lack of a single standardised method of measurement, when coupled with an inability to understand the true state of emissions in complex logistics activities, presents enormous barriers for businesses to understanding the extent of their emissions footprint. One of the traditional approaches to accurately capturing and monitoring gas emissions in logistics is through using gas sensors. However, connecting, maintaining, and operating gas sensors on moving vehicles in different road and weather conditions is a large and costly challenge. This paper presents the development and evaluation of a reliable and accurate sensing technique for GHG emissions collection (or monitoring) in real-time, employing the Internet of Things (IoT) and Artificial Intelligence (AI) to eliminate or reduce the usage of gas sensors, using reliable and cost-effective solutions.
温室气体（GHG）排放报告和可持续发展对全球企业而言愈发重要。然而，缺乏单一标准化的测量方法，再加上无法了解复杂物流活动中的真实排放状况，给企业了解其排放足迹的范围带来了巨大障碍。精确捕捉和监测物流中气体排放的传统方法之一是使用气体传感器。然而，在不同道路和天气条件下的移动车辆上连接、维护和操作气体传感器是一项艰巨且成本高昂的挑战。本文介绍了一种用于实时收集（或监测）温室气体排放的可靠且准确的传感技术的开发与评估，该技术利用物联网（IoT）和人工智能（AI），通过可靠且经济高效的解决方案来消除或减少气体传感器的使用。
