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Cluster‐Enhanced Ensemble Learning for Mapping Global Monthly Surface Ozone From 2003 to 2019
Geophysical Research Letters ; 49(11):1-13, 2022.
Article in English | Academic Search Complete | ID: covidwho-1900433
ABSTRACT
Surface ozone is damaging to human health and crop yields. When evaluating global air pollution risk, gridded datasets with high accuracy are desired to reflect the local variations in air pollution concentrations. Here, a cluster‐enhanced ensemble machine learning method was used to develop a new 0.5‐degree monthly surface ozone data set during 2003–2019 by combining numerous informative variables. The overall accuracy of our data set is 91.5% (90.8% for space and 92.3% for time). Historically, populations in South Asia, North Africa and Middle‐East, and High‐income North America are exposed to the highest ozone concentrations. Globally, the population weighted ozone concentration in the peak season is 47.07 ppbv. Our results highlight that ozone pollution is intensifying in some regions, and implicate air quality management is crucial to secure human health from air pollution. Plain Language

Summary:

Surface ozone is one of the most hazardous air pollutants to human health and plants. However, estimation of global surface ozone is still limited. Here, by using state‐of‐the‐art machine learning techniques, we fuse satellite, chemical transport model outputs, atmospheric reanalyses, and emission data with surface observations to construct a full coverage and long‐time period surface ozone data set. We demonstrate that surface population weighted ozone concentration in North America and Europe has decreased from 2003 to 2019, while ozone pollution in East Asia has intensified during 2016–2019. We also show at least 37% of the world's population is exposed to ozone greater than the World Health Organization's interim target one of 50 ppbv (MDA8) in the peak season. Our results could help identify the key regions for improving global air quality and offers an insightful data set for human health assessments and air quality management. Key Points A cluster‐enhanced ensemble machine learning method can predict global surface ozone with high accuracyPopulations in South Asia, North Africa and Middle‐East, and High‐income North America are exposed to the highest MDA8 during 2003–2019At least 37% of world's population is exposed to greater than 50 ppbv MDA8 in peak seasons [ FROM AUTHOR] Copyright of Geophysical Research Letters is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Language: English Journal: Geophysical Research Letters Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Language: English Journal: Geophysical Research Letters Year: 2022 Document Type: Article