Low-cost air quality sensors: The good, the bad, and the ugly. Preliminary findings from the QUANT study

Stuart E Lacy,Sebastian Diez,Thomas J Bannan,Michael Flynn, Nathan Watson,Nicholas Marsden, Max Priestman, Pete M Edwards

crossref(2023)

引用 0|浏览4
暂无评分
摘要
<p>Low-cost air pollution sensors (LCS) have a potentially vital role to play in tackling air pollution. Their affordability facilitates the creation of dense networks, which coupled with their intrinsic high time resolution offers a vast spatio-temporal coverage unlike that possible with conventional instrumentation, offering a paradigm shift in the way we measure key pollutants, evaluate health impacts of air pollution exposure and assess clean air policies. However, despite the availability of numerous commercial LCS products, there is limited current understanding as to their suitability for these tasks.</p> <p>&#160;</p> <p>The QUANT project aims to address this deficit in order to enable the use of LCS to support UK clean air ambitions. Over a period of three years, 52<strong> </strong>commercial LCS instruments from 14<strong> </strong>companies have been measuring real world air quality data in a range of UK urban environments, collocated with reference grade instruments. With the data collection period having ended in November 2022, there now exists a comprehensive dataset of measurements from multiple pollutants across a variety of LCS systems, differing in both the underlying sensor technology as well as the software layer that converts the noisy raw signals into meaningful concentrations.&#160;</p> <p>&#160;</p> <p>This talk summarises the initial conclusions emerging from this study, highlighting the various factors that should be taken into account when considering LCS for a specific task, for example: the robustness of the calibration algorithm, variability between devices from the same company, how well the measurements transfer between sites, and differing response to meteorological conditions. To help make this knowledge accessible to a wider audience, a web-app is presented that can allow researchers and decision makers to interactively explore the dataset to assess the applicability of devices to their particular requirements.</p>
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要