Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model

In recent decades, particulate pollution in the air has caused severe health problems. Therefore, it has become a hot research topic to accurately predict particulate concentrations. Particle concentration has a strong spatial-temporal correlation due to pollution transportation between regions, making it important to understand how to utilize these features to predict particulate concentration. In this paper, Pearson Correlation Coefficients (PCCs) are used to compare the particle concentrations at the target site with those at other locations. The models based on bi-directional gated recurrent units (Bi-GRUs) and PCCs are proposed to predict particle concentrations. The proposed model has the advantage of requiring fewer samples and can forecast particulate concentrations in real time within the next six hours. As a final step, several Beijing air quality monitoring stations are tested for pollutant concentrations hourly. Based on the correlation analysis and the proposed prediction model, the prediction error within the first six hours is smaller than those of the other three models. The model can help environmental researchers improve the prediction accuracy of fine particle concentrations and help environmental policymakers implement relevant pollution control policies by providing tools. With the correlation analysis between the target site and adjacent sites, an accurate pollution control decision can be made based on the internal relationship.