Koziel Slawomir, Pietrenko-Dabrowska Anna, Wojcikowski Marek, Pankiewicz Bogdan
Engineering Optimization and Modeling Center, Reykjavik University, 102, Reykjavik, Iceland.
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland.
Sci Rep. 2024 Apr 21;14(1):9152. doi: 10.1038/s41598-024-59993-6.
Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.
空气污染是一项重大的现代挑战，影响着生活质量、环境和经济。它由各种污染物组成，如气体、颗粒物、生物分子等，来源包括车辆排放、工业生产、农业活动和自然事件。在这些有害气体中，二氧化氮（NO）在人口密集的城市地区尤为普遍。鉴于其对健康和环境的不利影响，准确监测NO水平对于制定有效的风险缓解策略至关重要。然而，精确测量NO存在挑战，因为传统方法依赖于昂贵且笨重的设备。这促使人们开发更经济实惠的替代方案，尽管其可靠性往往存疑。本文的目的是介绍一种开创性的方法，用于精确校准具有成本效益的NO传感器。该技术涉及对低成本传感器读数进行统计预处理，使其分布与参考数据对齐。这种校准的核心是一个人工神经网络（ANN）代理，旨在预测传感器校正系数。它利用环境变量（温度、湿度、大气压力），参考辅助NO传感器，并纳入主传感器先前读数的短时间序列。这些方法通过全局数据缩放得到补充。通过使用定制设计的具有成本效益的监测平台和5个月内收集的高精度公共参考站数据进行验证，我们校准框架的每个组件都被证明至关重要，从而使其具有卓越的准确性（与参考数据的相关系数接近0.95，均方根误差低于2.4µg/m）。这种性能水平使校准后的传感器成为传统监测方法可行且具有成本效益的替代方案。
