Developing an online air quality warning system based on streaming data for dynamic environmental management

Abstract

Obtaining valuable information from streaming data of air pollutants concentration is of considerable significance to facilitate dynamic environmental management. However, the complex nonlinear characteristics, especially concept drift, in the streaming data renders the study to be considerably challenging, which increases the necessity of developing an online air quality warning system (AQWS). Aiming to accurately forecast future trends and uncertainty of the streaming data and provide a scientific evaluation of air quality, a novel online AQWS, including the modules of data preprocessing, forecasting, performance assessment, and air quality evaluation, is established in this study, which can be considered a contribution to the field. In the proposed online AQWS, an advanced denoising algorithm is developed in the module of data preprocessing to purify the studied streaming data, which is conducive to ensuring the generalization of the forecasting module; further, a probabilistic and nonparametric Bayesian method, i.e., the sparse spectrum Gaussian process regression, is developed in the module of forecasting to perform the deterministic forecasting and uncertainty quantification of the streaming data; finally, this study also constructs an online air quality evaluation model based on a cloud model in the module of air quality evaluation, which can model the fuzziness and randomness involved in the evaluation process simultaneously. In particular, an improved super scale weight method based on the theory of entropy is proposed in the cloud model to determine the time-varying weight of air pollutants dynamically, which can constitute an important contribution of the study. To validate the effectiveness and feasibility of the proposed online AQWS, two case studies based on the streaming data from Zhengzhou city and Changchun city in China are carried out, and corresponding results in the modules of performance assessment and air quality evaluation demonstrate that the online AQWS can yield more accurate prediction results compared to the benchmarks considered and has great potential for application to dynamic environmental management.