Koziel Slawomir, Pietrenko-Dabrowska Anna, Wójcikowski Marek, Pankiewicz Bogdan
Engineering Optimization & Modeling Center, Reykjavik University, 102 Reykjavik, Iceland.
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland.
Sensors (Basel). 2025 Apr 8;25(8):2352. doi: 10.3390/s25082352.
Air quality significantly impacts the environment and human living conditions, with direct and indirect effects on the economy. Precise and prompt detection of air pollutants is crucial for mitigating risks and implementing strategies to control pollution within acceptable thresholds. One of the common pollutants is nitrogen dioxide (NO), high concentrations of which are detrimental to the human respiratory system and may lead to serious lung diseases. Unfortunately, reliable NO detection requires sophisticated and expensive apparatus. Although cheap sensors are now widespread, they lack accuracy and stability and are highly sensitive to environmental conditions. The purpose of this study is to propose a novel approach to precise calibration of the low-cost NO sensors. It is illustrated using a custom-developed autonomous platform for cost-efficient NO monitoring. The platform utilizes various sensors alongside electronic circuitry, control and communication units, and drivers. The calibration strategy leverages comprehensive data from multiple reference stations, employing neural network (NN) and kriging interpolation metamodels. These models are built using diverse environmental parameters (temperature, pressure, humidity) and cross-referenced data gathered by surplus NO sensors. Instead of providing direct outputs of the calibrated sensor, our approach relies on predicting affine correction coefficients, which increase the flexibility of the correction process. Additionally, a calibration stage incorporating global correlation enhancement is developed and applied. Demonstrative experiments extensively validate this approach, affirming the platform and calibration methodology's practicality for reliable and cost-effective NO monitoring, especially keeping in mind that the predictive power of the enhanced sensor (correlation coefficient nearing 0.9 against reference data, RMSE < 3.5 µg/m) is close to that of expensive reference equipment.
空气质量对环境和人类生活条件有重大影响，对经济产生直接和间接影响。精确且及时地检测空气污染物对于降低风险以及实施将污染控制在可接受阈值内的策略至关重要。二氧化氮（NO）是常见污染物之一，高浓度的二氧化氮对人体呼吸系统有害，可能导致严重的肺部疾病。不幸的是，可靠的NO检测需要复杂且昂贵的设备。尽管廉价传感器如今已广泛应用，但它们缺乏准确性和稳定性，并且对环境条件高度敏感。本研究的目的是提出一种针对低成本NO传感器进行精确校准的新方法。使用一个定制开发的用于经济高效的NO监测的自主平台对此进行了说明。该平台除了电子电路、控制和通信单元以及驱动程序外，还利用了各种传感器。校准策略利用来自多个参考站的综合数据，采用神经网络（NN）和克里金插值元模型。这些模型是使用不同的环境参数（温度、压力、湿度）以及由多余的NO传感器收集的交叉参考数据构建的。我们的方法不是提供校准传感器的直接输出，而是依赖于预测仿射校正系数，这增加了校正过程的灵活性。此外，还开发并应用了一个包含全局相关性增强的校准阶段。示范性实验广泛验证了这种方法，证实了该平台和校准方法对于可靠且经济高效的NO监测的实用性，尤其要注意增强型传感器的预测能力（与参考数据的相关系数接近0.9，均方根误差<3.5μg/m）接近昂贵的参考设备。
