Using Multisource Data to Assess PM 2.5 Exposure and Spatial Analysis of Lung Cancer in Guangzhou, China

Elevated air pollution, along with rapid urbanization, have imposed higher health risks and a higher disease burden on urban residents. To accurately assess the increasing exposure risk and the spatial association between PM and lung cancer incidence, this study integrated PM data from the National...

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Bibliographic Details
Published inInternational journal of environmental research and public health Vol. 19; no. 5
Main Authors Fan, Wenfeng, Xu, Linyu, Zheng, Hanzhong
Format Journal Article
LanguageEnglish
Published Switzerland 24.02.2022
Subjects
Air Pollutants - analysis
Air Pollution - adverse effects
Air Pollution - analysis
China - epidemiology
Environmental Monitoring
Humans
Lung Neoplasms - epidemiology
Lung Neoplasms - etiology
Particulate Matter - analysis
Spatial Analysis
China
ordinary kriging interpolation model
health risk
PM2.5 pollution exposure
spatial correlation analysis
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Summary:Elevated air pollution, along with rapid urbanization, have imposed higher health risks and a higher disease burden on urban residents. To accurately assess the increasing exposure risk and the spatial association between PM and lung cancer incidence, this study integrated PM data from the National Air Quality Monitoring Platform and location-based service (LBS) data to introduce an improved PM exposure model for high-precision spatial assessment of Guangzhou, China. In this context, the spatial autocorrelation method was used to evaluate the spatial correlation between lung cancer incidence and PM . The results showed that people in densely populated areas suffered from higher exposure risk, and the spatial distribution of population exposure risk was highly consistent with the dynamic distribution of the population. In addition, areas with PM roughly overlapped with areas with high lung cancer incidence, and the lung cancer incidence in different locations was not randomly distributed, confirming that lung cancer incidence was significantly associated with PM exposure. Therefore, dynamic population distribution has a great impact on the accurate assessment of environmental exposure and health burden, and it is necessary to use LBS data to improve the exposure assessment model. More mitigation controls are needed in highly populated and highly polluted areas.
ISSN:1660-4601