Koziel Slawomir, Pietrenko-Dabrowska Anna, Wojcikowski Marek, Pankiewicz Bogdan
Engineering Optimization & Modeling Center, Reykjavik University, 102, Reykjavík, Iceland.
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdańsk, Poland.
Sci Rep. 2024 Oct 30;14(1):26120. doi: 10.1038/s41598-024-77214-y.
Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO detectors. The methodology was showcased using a purpose-built platform housing NO and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO monitoring.
准确跟踪有害气体浓度对于迅速有效地采取措施减轻与空气污染相关的风险至关重要，特别是在减少其对生活条件、环境和经济的影响方面。城市环境中一种常见的污染物是二氧化氮（NO），它由汽车发动机、商业制造和食品加工中的化石燃料燃烧产生。其浓度升高会对人体呼吸系统产生不利影响，加剧哮喘并可能导致各种肺部疾病。然而，精确监测NO需要复杂且昂贵的设备，这促使人们需要更经济实惠且可靠的替代方案。本文介绍了一种新方法，通过将机器学习与神经网络替代模型、全局数据缩放以及一组扩展的校正模型输入相结合，可靠地校准具有成本效益的NO传感器。这些输入包括环境参数的差异（如温度、湿度、大气压力），以及来自主要和辅助低成本NO探测器的读数。该方法通过一个专门构建的平台进行展示，该平台包含NO和环境传感器、电子控制单元、驱动器以及用于数据传输的无线通信模块。对比实验利用了在波兰格但斯克进行的为期五个月的测量活动中从三个独立的高精度参考站获取的NO数据，以及在相同地点由便携式测量平台收集的低成本传感器数据。数值实验使用了几种校准场景，使用了各种校准输入集，以及启用/禁用差异、全局数据缩放和来自主要传感器的NO读数。结果验证了显著的校正质量，与参考数据的相关系数超过0.9，均方根误差低于3.2µg/m³。这种性能水平使校准后的传感器成为用于NO监测的昂贵固定设备的可靠且经济高效的替代方案。
