Spatiotemporal variability in long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across the Yangtze River Delta (YRD) region over 2010–2016: A multistage approach.

The Yangtze River Delta region (YRD) is one of the most densely populated regions in the world, and is frequently influenced by fine particulate matter (PM 2.5). Specifically, lung cancer mortality has been recognized as a major health burden associated with PM 2.5. Therefore, this study developed a multistage approach 1) to first create dasymetric population data with moderate resolution (1 km) by using a random forest algorithm, brightness reflectance of nighttime light (NTL) images, a digital elevation model (DEM), and a MODIS-derived normalized difference vegetation index (NDVI), and 2) to apply the improved population dataset with a MODIS-derived PM 2.5 dataset to estimate the association between spatiotemporal variability of long-term population exposure to PM 2.5 and lung cancer mortality attributable to PM 2.5 across YRD during 2010–2016 for microscale planning. The created dasymetric population data derived from a coarse census unit (administrative unit) were fairly matched with census data at a fine spatial scale (street block), with R2 and RMSE of 0.64 and 27,874.5 persons, respectively. Furthermore, a significant urban-rural difference of population exposure was found. Additionally, population exposure in Shanghai was 2.9–8 times higher than the other major cities (7-year average: 192,000 μg·people/m3·km2). More importantly, the relative risks of lung cancer mortality in high-risk areas were 28%–33% higher than in low-risk areas. There were 12,574–14,504 total lung cancer deaths attributable to PM 2.5 , and lung cancer deaths in each square kilometer of urban areas were 7–13 times higher than for rural areas. These results indicate that moderate-resolution information can help us understand the spatiotemporal variability of population exposure and related health risk in a high-density environment. • Data fusion with a multistage approach to estimate long-term population exposure • Machine learning algorithm to improve the dasymetric population mapping • High spatiotemporal variability of lung cancer mortality attributable to PM 2.5 • Urban exposure driven by density while rural exposure driven by ambient pollution • Significant urban-rural difference of lung-cancer mortality across YRD [ABSTRACT FROM AUTHOR]