Innovation Center, School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia.
School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia.
Sensors (Basel). 2021 May 11;21(10):3338. doi: 10.3390/s21103338.
Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO and PM10 particles, with promising results and an achieved R2 of 0.93-0.97, 0.82-0.94 and 0.73-0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.
现有的政府空气质量监测网络由静态测量站组成，这些测量站非常可靠，能够准确测量广泛的空气污染物，但它们体积庞大、成本高昂，需要大量的维护。作为一种有前途的解决方案，低成本传感器被引入作为补充性的空气质量监测站。然而，由于精度较低、生命周期短以及相应的校准问题，这些传感器并不可靠。最近的研究表明，低成本传感器会受到相对湿度和温度的影响。在本文中，我们探索了使用机器学习方法来进一步改进校准算法的方法，旨在考虑温度和湿度对读数的影响，从而提高测量精度。我们对线性回归、人工神经网络和随机森林算法进行了详细的比较分析，分析了它们在 CO、NO 和 PM10 粒子测量上的性能，结果表明，对于每种污染物，分别在一年中观测期的不同，这些算法的 R2 值达到了 0.93-0.97、0.82-0.94 和 0.73-0.89。本文还提供了关于如何将低成本传感器用作参考监测站的补充监测站，以提高空间和时间测量分辨率的综合分析和建议。
