A Deep Calibration Method for Low-Cost Air Monitoring Sensors With Multilevel Sequence Modeling

Air pollution is growing ever more serious as a result of rising consumption of energy and other natural resources. Generally, governmental static monitoring stations provide accurate air pollution data, but they are sparsely distributed in the space. In contrast, microstations as a kind of low-cost air monitoring equipment can be distributed densely though their accuracy is relatively low. This article proposes a deep calibration method (DeepCM) for low-cost air monitoring sensors equipped in the microstations, which consists of an encoder and a decoder. In the encoding stage, multilevel time-series features are extracted, including local, global, and periodic time-series features. These features can not only capture local, global, and periodic trend information, but also benefit to alleviating cross-interference and noise effect. In the decoding stage, a final feature extracted by the encoder along with initial features of the moment to be calibrated are fed into the decoder to obtain a calibrated result. The proposed method is evaluated on two real-world datasets. The experimental results demonstrate that our method yields the best performance by comparison with eight baseline methods.