Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors

Nenad Gligoric¹  Ivan Vajs²  Ilija Radovanovic²  Dejan Drajic²  Ivan Popovic³ 

[1] DunavNET, DNET Labs, Trg Oslobodjenja 127, 21000 Novi Sad, Serbia;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;

关键词: air pollution measurements;    low-cost sensors;    calibration;    machine learning;    artificial neural network;    temperature and relative humidity;   

DOI  :  10.3390/s21103338

来源: DOAJ

【 摘 要 】

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, NO₂ and PM10 particles, with promising results and an achieved $R^2$ 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.

【 授权许可】

Unknown