A grey spatiotemporal incidence model with application to factors causing air pollution

Area of Science:

• Environmental Science
• Data Science
• Spatial Analysis

Background:

• Air pollution in China is a significant concern, yet existing grey incidence models for identifying causal factors lack stability.
• Traditional grey incidence models produce unreliable results when the order of panel data changes, hindering accurate pollution factor identification.

Purpose of the Study:

• To develop a novel grey incidence model that improves the reliability and uniformity of air pollution factor identification.
• To introduce the grey spatiotemporal incidence (GSTI) model, designed to address the limitations of existing grey incidence models.

Main Methods:

• Defined spatiotemporal data to represent spatial relationships between objects, moving beyond three-dimensional panel data.
• Incorporated two procedures: trend coefficient for measuring data sequence connections and measurement coefficient for calculating grey incidence degree.
• Discussed five properties of the GSTI model and demonstrated its application using monthly air pollution data from South Jiangsu province in 2018.

Main Results:

• The proposed GSTI model demonstrated superior applicability and accuracy in identifying the main factors causing air pollution in South Jiangsu province.
• The GSTI model proved more reliable and uniform compared to existing grey incidence models, especially when dealing with spatial data.
• The study confirmed the practical utility of the GSTI model for constructing robust grey incidence models.

Conclusions:

• The grey spatiotemporal incidence (GSTI) model offers a significant advancement in identifying air pollution factors by effectively utilizing spatiotemporal data.
• The GSTI model provides a more stable and reliable approach for analyzing complex environmental data compared to traditional methods.
• This research highlights the GSTI model's potential for accurate environmental monitoring and policy development in pollution control efforts.