2017
DOI: 10.1016/j.atmosres.2016.12.008
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Model analysis of soil dust impacts on the boundary layer meteorology and air quality over East Asia in April 2015

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Cited by 24 publications
(25 citation statements)
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“…Dust storms are a typical natural weather event in northern China in spring (March–May). During the spring 2015 atmospheric deposition sampling campaign, China's National Weather Bureau recorded two major dust episodes, which circulated over northern China and the BS on 28–31 March and 15–16 April, respectively (Chen et al, ; Wang et al, ). During these two events, the PM concentrations in Bohai Rim were extremely high.…”
Section: Discussionmentioning
confidence: 99%
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“…Dust storms are a typical natural weather event in northern China in spring (March–May). During the spring 2015 atmospheric deposition sampling campaign, China's National Weather Bureau recorded two major dust episodes, which circulated over northern China and the BS on 28–31 March and 15–16 April, respectively (Chen et al, ; Wang et al, ). During these two events, the PM concentrations in Bohai Rim were extremely high.…”
Section: Discussionmentioning
confidence: 99%
“…Based on observations conducted in Beijing, ~150 km upwind from the nearest BS coastline (Figure a), during the first dust event on 28 March, mass concentrations of particles with an aerodynamic diameter ≤10 μm (PM 10 ) reached a maximum of 800 μg/m 3 . The hourly PM 10 mass concentration exceeded 1,000 μg/m 3 during the second dust event on 16 April, which was regarded as the worst sandstorm event over the past decade, despite persisting for only 2 days (Chen et al, ).…”
Section: Discussionmentioning
confidence: 99%
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“…For instance, UHI based on land surface temperature (LST) measurements can be obtained from remote sensors (Lazzarini et al, ; Owen et al, ; Shen et al, ; Zhou et al, ). However, coarse temporal resolutions, classification uncertainties in underlying surface types, and impacts of clouds and aerosols during rainy seasons or over heavy polluted regions may generate large biases (Chen, Zhang, Zhu, et al, ; Reynolds et al, ; Xie et al, ).…”
Section: Introductionmentioning
confidence: 99%