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Human migration-based graph convolutional network for PM2.5 forecasting in post-COVID-19 pandemic age.
Choujun Zhan1,2, Wei Jiang2, Hu Min2
1School of Computer, South China Normal University, Guangzhou, Guangdong China.
Neural Computing & Applications
|December 5, 2022
Summary
Human migration significantly impacts urban air pollution, particularly fine particulate matter (PM2.5). This study introduces a novel graph convolutional network model that accurately forecasts PM2.5 concentrations by incorporating human migration data.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Data Science
Background:
- The coronavirus disease 2019 (COVID-19) pandemic led to non-pharmaceutical interventions impacting human migration patterns.
- Previous research indicates a strong correlation between human migration and air pollution levels.
- Understanding these dynamics is crucial for effective environmental management and public health strategies.
Purpose of the Study:
- To investigate the role of human migration as a factor in forecasting particulate matter (PM2.5) concentrations in the post-pandemic era.
- To analyze PM2.5 variations and compare them with migration trends in Hubei province.
- To develop and evaluate a novel model for PM2.5 forecasting that integrates human migration data.
Main Methods:
- Analysis of PM2.5 concentration data in 11 Hubei cities from 2015 to 2020.
- Comparison of PM2.5 trends with Hubei province's migration trends in 2020.
- Development of a migration attentive graph convolutional network (MAGCN) model utilizing migration flow data between areas.
Main Results:
- Human migration was found to indirectly influence urban PM2.5 concentrations.
- The proposed MAGCN model effectively integrated migration data to capture spatial-temporal dependencies.
- Experimental results demonstrated the high accuracy of the MAGCN model in forecasting PM2.5 concentrations.
Conclusions:
- Human migration is a significant, previously underutilized factor for PM2.5 forecasting.
- The MAGCN model offers a promising approach for improving air quality prediction by incorporating migration dynamics.
- This research provides valuable insights for environmental policy and urban planning in the context of population mobility.
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