Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data
Zheng Cao1,2, Guanhua Guo1,2, Zhifeng Wu1,2
1School of Geographical Sciences Guangzhou University Guangzhou China.
Geohealth
|November 17, 2021
Summary
This study introduces a novel method to assess PM2.5 exposure risk by combining social media and population data. The findings provide crucial insights for developing effective PM2.5 pollution mitigation strategies and supporting eco-health.
Area of Science:
- Environmental Science
- Public Health
- Data Science
Background:
- Assessing PM2.5 exposure risk is vital for mitigating adverse health effects.
- Traditional population survey data lack high spatiotemporal detail.
- Social media data offer high-resolution PM2.5 exposure insights but lack sample data for specific groups like older adults.
Purpose of the Study:
- To develop a reliable method for mapping total PM2.5 exposure risk by integrating social media and population survey data.
- To quantify PM2.5 exposure risk at high spatiotemporal resolutions.
Main Methods:
- Developed hourly exceedance PM2.5 exposure risk indicators using population modeling (HEPEpmd) and social media data (HEPEsm).
- Combined social media data with population survey-derived data to map total PM2.5 exposure risk.
- Analyzed daily accumulative HEPEsm and HEPEpsd, temporal peaks, and spatial distribution shifts.
Main Results:
- Daily accumulative HEPEsm ranged from 0 to 0.009, and HEPEpsd from 0 to 0.026.
- Observed three daily peaks for both indicators at 13:00, 18:00, and 22:00.
- HEPEsm showed an expanding spatial area 1.5 times larger than HEPEpsd, with HEPEpsd concentrating in the old downtown area.
Conclusions:
- The integrated approach provides an easier and reliable method for mapping total exceedance PM2.5 exposure risk.
- Results offer a foundation for developing PM2.5 pollution mitigation strategies.
- Supports scientific advancements for sustainability and eco-health achievement.
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