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首页> 外文期刊>Atmospheric chemistry and physics >Contributions to the explosive growth of PMsub2.5/sub mass due to aerosol–radiation feedback and decrease in turbulent diffusion during a red alert heavy haze in Beijing–Tianjin–Hebei, China
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Contributions to the explosive growth of PMsub2.5/sub mass due to aerosol–radiation feedback and decrease in turbulent diffusion during a red alert heavy haze in Beijing–Tianjin–Hebei, China

机译：北京-天津-河北地区红色预警严重雾霾期间气溶胶-辐射反馈导致PM 2.5 物质爆炸性增长的原因和湍流扩散的减少

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The explosive growth of PM sub2.5/sub mass usually results in extreme PM sub2.5/sub levels and severe haze pollution in eastern China, and is generally underestimated by current atmospheric chemistry models. Based on one such model, GRAPES_CUACE, three sensitivity experiments – a “background” experiment (EXP1), an “online aerosol feedback” experiment (EXP2), and an “80?% decrease in the turbulent diffusion coefficient of chemical tracers” experiment, based on EXP2 (EXP3) – were designed to study the contributions of the aerosol–radiation feedback (AF) and the decrease in the turbulent diffusion coefficient to the explosive growth of PM sub2.5/sub 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 by EXP1 was about 60–70?m sup?2/sup s sup?1/sup on a clear day and 30–35?m sup?2/sup s sup?1/sup on a haze day. This difference in the diffusion coefficient was not enough to distinguish between the unstable atmosphere on the clear day and the extremely stable atmosphere during the PM sub2.5/sub explosive growth stage. Furthermore, the inversion calculated by EXP1 was obviously weaker than the actual inversion from sounding observations on the haze day. This led to a 40?%–51?% underestimation of PM sub2.5/sub by EXP1; the AF decreased the diffusion coefficient by about 43?%–57?% during the PM sub2.5/sub explosive growth stage, which obviously strengthened the local inversion. In addition, the local inversion indicated by EXP2 was much closer to the sounding observations than that indicated by EXP1. This resulted in a 20?%–25?% reduction of PM sub2.5/sub negative errors in the 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 sub2.5/sub underestimation. Based on EXP2, the 80?% decrease in the turbulent diffusion coefficient of chemical tracers in EXP3 resulted in near-zero turbulent diffusion, referred to as a “turbulent intermittence” atmospheric state, which subsequently resulted in a further 14?%–20?% reduction of the PM sub2.5/sub underestimation; moreover, the negative PM sub2.5/sub errors were reduced to ?11 % to 2?%. The combined effects of the AF and the decrease in the turbulent diffusion coefficient explained over 79?% of the underestimation of the explosive growth of PM sub2.5/sub in this study. The results show that online calculation of the AF is essential for the prediction of PM sub2.5/sub explosive growth and peaks during severe haze in China's Jing–Jin–Ji region. Furthermore, an improvement in the planetary boundary layer scheme with respect to extremely stable atmospheric stratification is essential for a reasonable description of local “turbulent intermittence” and a more accurate prediction of PM sub2.5/sub explosive growth during severe haze in this region of China.
机译：PM 2.5 物质的爆炸性增长通常导致中国东部PM 2.5 的极端水平和严重的霾污染，通常被当前的大气化学模型低估。基于一个这样的模型GRAPES_CUACE，进行了三个灵敏度实验–“背景”实验（EXP1），“在线气溶胶反馈”实验（EXP2）和“化学示踪剂的湍流扩散系数降低了80％”，基于EXP2（EXP3）的研究旨在研究“红色预警”期间气溶胶辐射反馈（AF）的贡献以及湍流扩散系数的降低对PM 2.5 爆炸性增长的影响中国京津冀（北京-天津-河北）地区发生的大雾霾事件。结果表明，在晴天，EXP1计算得到的湍流扩散系数约为60-70？m ？2 s ？1 ，而在30-35？m 阴霾天的？2 s ？1 。扩散系数的这种差异不足以区分晴天的不稳定气氛和PM 2.5 爆炸物生长阶段的极端稳定气氛。此外，由EXP1计算出的反演明显比在阴霾天的测深观测要弱。这导致EXP1低估了PM 2.5 的40 %% – 51％。 AF在 2.5 PM爆炸阶段使扩散系数降低了43％〜57％，明显增强了局部反演。此外，由EXP2表示的局部反演比由EXP1表示的更接近测深观测。这导致模型中PM 2.5 的负误差减少了20 %%-25％，而EXP2中的误差低至16％至11％。但是，EXP2产生的反演仍比实际观测结果弱，仅凭AF不能完全解释PM 2.5 的低估。根据EXP2，EXP3中化学示踪剂的湍流扩散系数降低80％，导致湍流扩散接近零，称为“湍流间歇”大气状态，随后又导致14 %%-20％的湍流扩散。 PM 2.5 低估的百分比降低;此外，负的PM 2.5 误差降低到了11％到2％。 AF和湍流扩散系数降低的共同作用解释了本研究中PM 2.5 爆炸性增长低估的79％以上。结果表明，AF的在线计算对于预测京津冀地区PM 2.5 爆炸物的增长和严重雾霾时的峰值至关重要。此外，对于极端稳定的大气分层，行星边界层方案的改进对于合理描述局部“湍流间歇”和更准确地预测严重雾霾期间PM 2.5 爆炸性增长至关重要。中国这个地区。

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