Hasan Md Hasibul, Yu Haofei, Ivey Cesunica, Pillarisetti Ajay, Yuan Ziyang, Do Khanh, Li Yi
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida32816, United States.
Department of Civil and Environmental Engineering, The University of California, Berkeley, Berkeley, California94720, United States.
ACS Omega. 2023 Jan 31;8(6):5917-5924. doi: 10.1021/acsomega.2c07734. eCollection 2023 Feb 14.
Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO) and ozone (O) were deployed in six cities in the United States (Atlanta, GA; New York City, NY; Sacramento, CA; Riverside, CA; Portland, OR; Phoenix, AZ) to evaluate the impacts of different climatic and geographical conditions on their performance and calibration. Three calibration methods were applied, including regression via linear and polynomial models and random forest methods. When signals from carbon monoxide (CO) sensors were included in the calibration models for NO and O sensors, model performance generally increased, with pronounced improvements in selected cities such as Riverside and New York City. Such improvements may be due to (1) temporal co-variation between concentrations of CO and NO and/or between CO and O; (2) different performance levels of low-cost CO, NO, and O sensors; and (3) different impacts of environmental conditions on sensor performance. The results showed an innovative approach for improving the calibration of NO and O sensors by including CO sensor signals into the calibration models. Community users of LCAQ sensors may be able to apply these findings further to enhance the data quality of their deployed NO and O monitors.
低成本空气质量（LCAQ）传感器正越来越多地用于社区空气质量监测。然而，低成本传感器收集的数据包含大量噪声，对这些传感器进行适当校准仍是一个广泛讨论但尚未完全解决的关注领域。在本研究中，在美国的六个城市（佐治亚州亚特兰大市；纽约州纽约市；加利福尼亚州萨克拉门托市；加利福尼亚州里弗赛德市；俄勒冈州波特兰市；亚利桑那州菲尼克斯市）部署了多个测量二氧化氮（NO）和臭氧（O）的LCAQ传感器，以评估不同气候和地理条件对其性能和校准的影响。应用了三种校准方法，包括线性和多项式模型回归以及随机森林方法。当一氧化碳（CO）传感器的信号被纳入NO和O传感器的校准模型时，模型性能通常会提高，在里弗赛德市和纽约市等选定城市有显著改善。这种改善可能是由于：（1）CO与NO浓度之间和/或CO与O浓度之间的时间协变；（2）低成本CO、NO和O传感器的不同性能水平；（3）环境条件对传感器性能的不同影响。结果表明了一种通过将CO传感器信号纳入校准模型来改进NO和O传感器校准的创新方法。LCAQ传感器的社区用户或许能够进一步应用这些发现来提高其部署的NO和O监测仪的数据质量。
