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• W4213228834 abstract "Abstract Ultrahigh‐resolution air quality models that resolve sharp gradients of pollutant concentrations benefit the assessment of human health impacts. Mitigating fine particulate matter (PM 2.5 ) concentrations over the past decade has triggered ozone (O 3 ) deterioration in China. Effective control of both pollutants remains poorly understood from an ultrahigh‐resolution perspective. We propose a regional‐to‐local model suitable for quantitatively mitigating pollution pathways at various resolutions. Sensitivity scenarios for controlling nitrogen oxide (NO x ) and volatile organic compound (VOC) emissions are explored, focusing on traffic and industrial sectors. The results show that concurrent controls on both sectors lead to reductions of 17%, 5%, and 47% in NO x , PM 2.5 , and VOC emissions, respectively. The reduced traffic scenario leads to reduced NO 2 and PM 2.5 but increased O 3 concentrations in urban areas. Guangzhou is located in a VOC‐limited O 3 formation regime, and traffic is a key factor in controlling NO x and O 3 . The reduced industrial VOC scenario leads to reduced O 3 concentrations throughout the mitigation domain. The maximum decrease in median hourly NO 2 is >11 μg/m³, and the maximum increase in the median daily maximum 8‐hr rolling O 3 is >10 μg/m³ for the reduced traffic scenario. When controls on both sectors are applied, the O 3 increase reduces to <7 μg/m³. The daily averaged PM 2.5 decreases by <2 μg/m³ for the reduced traffic scenario and varies little for the reduced industrial VOC scenario. An O 3 episode analysis of the dual‐control scenario leads to O 3 decreases of up to 15 μg/m³ (8‐hr metric) and 25 μg/m³ (1‐hr metric) in rural areas." @default.
• W4213228834 created "2022-02-24" @default.
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• W4213228834 date "2022-03-01" @default.
• W4213228834 modified "2023-10-01" @default.
• W4213228834 title "Implications of Mitigating Ozone and Fine Particulate Matter Pollution in the Guangdong‐Hong Kong‐Macau Greater Bay Area of China Using a Regional‐To‐Local Coupling Model" @default.
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• W4213228834 doi "https://doi.org/10.1029/2021gh000506" @default.
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• W4213228834 hasPublicationYear "2022" @default.
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