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First close insight into global daily gapless 1 km PM_2.5 pollution, variability, and health impact.

ABSTRACT: Here we retrieve global daily 1 km gapless PM_2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth's populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.

SUBMITTER: Wei J

PROVIDER: S-EPMC10724144 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact.

Wei Jing J Li Zhanqing Z Lyapustin Alexei A Wang Jun J Dubovik Oleg O Schwartz Joel J Sun Lin L Li Chi C Liu Song S Zhu Tong T

Nature communications 20231215 1

Here we retrieve global daily 1 km gapless PM<sub>2.5</sub> concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth's populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks a ...[more]

PMID: 38102117

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Project description:The Denver Aerosol Sources and Health (DASH) study was designed to evaluate associations between PM2.5 species and sources and adverse human health effects. The DASH study generated a five-year (2003-2007) time series of daily speciated PM2.5 concentration measurements from a single, special-purpose monitoring site in Denver, CO. To evaluate the ability of this site to adequately represent the short term temporal variability of PM2.5 concentrations in the five county Denver metropolitan area, a one year supplemental set of PM2.5 samples was collected every sixth day at the original DASH monitoring site and concurrently at three additional sites. Two of the four sites, including the original DASH site, were located in residential areas at least 1.9 km from interstate highways. The other two sites were located within 0.3 km of interstate highways. Concentrations of elemental carbon (EC), organic carbon (OC), and 58 organic molecular markers were measured at each site. To assess spatial variability, site pairs were compared using the Pearson correlation coefficient (r) and coefficient of divergence (COD), a statistic that provides information on the degree of uniformity between monitoring sites. Biweekly co-located samples collected from July 2004 to September 2005 were also analyzed and used to estimate the uncertainty associated with sampling and analytical measurement for each species. In general, the two near-highway sites exhibited higher concentrations of EC, OC, polycyclic aromatic hydrocarbons (PAHs), and steranes than did the more residential sites. Lower spatial heterogeneity based on r and COD was inferred for all carbonaceous species after considering their divergence and lack of perfect correlations in co-located samples. Ratio-ratio plots combined with available gasoline- and diesel-powered motor vehicle emissions profiles for the region suggested a greater impact to high molecular weight (HMW) PAHs from diesel-powered vehicles at the near-highway sites and a more uniformly distributed impact to ambient hopanes from gasoline-powered motor vehicles at all four sites.

Dataset Information

First close insight into global daily gapless 1 km PM_2.5 pollution, variability, and health impact.

Publications

First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact.

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First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact.

Publications

First close insight into global daily gapless 1 km PM&lt;sub&gt;2.5&lt;/sub&gt; pollution, variability, and health impact.

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OmicsDI is part of the ELIXIR infrastructure

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First close insight into global daily gapless 1 km PM_2.5 pollution, variability, and health impact.

First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact.