Revisiting the estimations of PM 2.5 -attributable mortality with advancements in PM 2.5 mapping and mortality statistics

Ying Liu, Naizhuo Zhao, Jennifer K. Vanos, Guofeng Cao

Research output: Contribution to journalArticle

Abstract

With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM 2.5 ) and mortality statistics are increasingly used to examine the health effects of PM 2.5 . Choices of these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM 2.5 -attributable mortality by taking advantage of recent advancements in high resolution mapping of PM 2.5 concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM 2.5 -mortality for the years of 2000, 2005, 2010 and 2015 using three PM 2.5 concentration datasets [Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR)] at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM 2.5 -mortality from the 10 km CTM-derived PM 2.5 dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM 2.5 datasets. The estimated PM 2.5 -mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM 2.5 concentrations reduction, the uses of the two newly developed 1 km resolution PM 2.5 datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM 2.5 concentration is the primary factor that leads to the PM 2.5 -attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM 2.5 -attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. Capsule: We revisited the estimations of PM 2.5 -attributable mortality with advancements in PM 2.5 mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.

Original languageEnglish (US)
Pages (from-to)499-507
Number of pages9
JournalScience of the Total Environment
Volume666
DOIs
StatePublished - May 20 2019
Externally publishedYes

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Statistics
mortality
kriging
Particulate Matter
statistics
Capsules
Health
particulate matter
spatial variation

Keywords

  • Geospatial uncertainty
  • GIS
  • Mortality
  • PM
  • Public health

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

Cite this

Revisiting the estimations of PM 2.5 -attributable mortality with advancements in PM 2.5 mapping and mortality statistics . / Liu, Ying; Zhao, Naizhuo; Vanos, Jennifer K.; Cao, Guofeng.

In: Science of the Total Environment, Vol. 666, 20.05.2019, p. 499-507.

Research output: Contribution to journalArticle

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abstract = "With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM 2.5 ) and mortality statistics are increasingly used to examine the health effects of PM 2.5 . Choices of these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM 2.5 -attributable mortality by taking advantage of recent advancements in high resolution mapping of PM 2.5 concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM 2.5 -mortality for the years of 2000, 2005, 2010 and 2015 using three PM 2.5 concentration datasets [Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR)] at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM 2.5 -mortality from the 10 km CTM-derived PM 2.5 dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM 2.5 datasets. The estimated PM 2.5 -mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM 2.5 concentrations reduction, the uses of the two newly developed 1 km resolution PM 2.5 datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM 2.5 concentration is the primary factor that leads to the PM 2.5 -attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM 2.5 -attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. Capsule: We revisited the estimations of PM 2.5 -attributable mortality with advancements in PM 2.5 mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.",
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