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Article: 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

TitleSpatiotemporal 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
Authors
KeywordsPopulation exposure
Premature mortality
Spatiotemporal variability
Dasymetric population
Random forest model
Issue Date2020
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/chemosphere
Citation
Chemosphere, 2020, v. 257, p. article no. 127153 How to Cite?
AbstractThe Yangtze River Delta region (YRD) is one of the most densely populated regions in the world, and is frequently influenced by fine particulate matter (PM2.5). Specifically, lung cancer mortality has been recognized as a major health burden associated with PM2.5. 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 PM2.5 dataset to estimate the association between spatiotemporal variability of long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 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 PM2.5, 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.
Persistent Identifierhttp://hdl.handle.net/10722/283336
ISSN
2019 Impact Factor: 5.778
2015 SCImago Journal Rankings: 1.536

 

DC FieldValueLanguage
dc.contributor.authorWang, H-
dc.contributor.authorLi, J-
dc.contributor.authorGao, M-
dc.contributor.authorChan, TC-
dc.contributor.authorGao, Z-
dc.contributor.authorZhang, M-
dc.contributor.authorLi, Y-
dc.contributor.authorGu, Y-
dc.contributor.authorChen, A-
dc.contributor.authorYang, Y-
dc.contributor.authorHo, HC-
dc.date.accessioned2020-06-22T02:55:10Z-
dc.date.available2020-06-22T02:55:10Z-
dc.date.issued2020-
dc.identifier.citationChemosphere, 2020, v. 257, p. article no. 127153-
dc.identifier.issn0045-6535-
dc.identifier.urihttp://hdl.handle.net/10722/283336-
dc.description.abstractThe Yangtze River Delta region (YRD) is one of the most densely populated regions in the world, and is frequently influenced by fine particulate matter (PM2.5). Specifically, lung cancer mortality has been recognized as a major health burden associated with PM2.5. 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 PM2.5 dataset to estimate the association between spatiotemporal variability of long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 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 PM2.5, 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.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/chemosphere-
dc.relation.ispartofChemosphere-
dc.subjectPopulation exposure-
dc.subjectPremature mortality-
dc.subjectSpatiotemporal variability-
dc.subjectDasymetric population-
dc.subjectRandom forest model-
dc.titleSpatiotemporal 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-
dc.typeArticle-
dc.identifier.emailHo, HC: hcho21@hku.hk-
dc.identifier.authorityHo, HC=rp02482-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.chemosphere.2020.127153-
dc.identifier.pmid32531486-
dc.identifier.scopuseid_2-s2.0-85085911365-
dc.identifier.hkuros310546-
dc.identifier.volume257-
dc.identifier.spagearticle no. 127153-
dc.identifier.epagearticle no. 127153-
dc.publisher.placeUnited Kingdom-

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