English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. The explosive growth of PM 2.5 mass usually results in extreme PM 2.5 levels and severe hazepollution in eastern China, and is generally underestimated by currentatmospheric chemistry models. Based on one such model, GRAPES_CUACE, threesensitivity experiments – a “background” experiment (EXP1), an “onlineaerosol feedback” experiment (EXP2), and an “80 % decrease in theturbulent diffusion coefficient of chemical tracers” experiment, based onEXP2 (EXP3) – were designed to study the contributions of theaerosol–radiation feedback (AF) and the decrease in the turbulent diffusioncoefficient to the explosive growth of PM 2.5 during a “red alert”heavy haze event in China's Jing–Jin–Ji (Beijing–Tianjin–Hebei) region.The results showed that the turbulent diffusion coefficient calculated byEXP1 was about 60–70 m −2  s −1 on a clear day and30–35 m −2  s −1 on a haze day. This difference in the diffusioncoefficient was not enough to distinguish between the unstable atmosphere onthe clear day and the extremely stable atmosphere during the PM 2.5 explosive growth stage. Furthermore, the inversion calculated by EXP1 wasobviously weaker than the actual inversion from sounding observations on thehaze day. This led to a 40 %–51 % underestimation of PM 2.5 byEXP1; the AF decreased the diffusion coefficient by about 43 %–57 %during the PM 2.5 explosive growth stage, which obviously strengthenedthe local inversion. In addition, the local inversion indicated by EXP2 wasmuch closer to the sounding observations than that indicated by EXP1. Thisresulted in a 20 %–25 % reduction of PM 2.5 negative errors inthe model, with errors as low as −16  % to −11  % in EXP2. However,the inversion produced by EXP2 was still weaker than the actual observations,and the AF alone could not completely explain the PM 2.5 underestimation.Based on EXP2, the 80 % decrease in the turbulent diffusion coefficientof chemical tracers in EXP3 resulted in near-zero turbulent diffusion,referred to as a “turbulent intermittence” atmospheric state, whichsubsequently resulted in a further 14 %–20 % reduction of thePM 2.5 underestimation; moreover, the negative PM 2.5 errors werereduced to −11  % to 2 %. The combined effects of the AF and thedecrease in the turbulent diffusion coefficient explained over 79 % ofthe underestimation of the explosive growth of PM 2.5 in this study. Theresults show that online calculation of the AF is essential for theprediction of PM 2.5 explosive growth and peaks during severe haze inChina's Jing–Jin–Ji region. Furthermore, an improvement in the planetaryboundary layer scheme with respect to extremely stable atmosphericstratification is essential for a reasonable description of local “turbulentintermittence” and a more accurate prediction of PM 2.5 explosive growthduring severe haze in this region of China.

atmospheric chemistry and physics

Contributions to the explosive growth of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; mass due to aerosol–radiation feedback and decrease in turbulent diffusion during a red alert heavy haze in Beijing–Tianjin–Hebei, China