中国不同区域大气边界层与pm2.5的关系

Mengyun Lou, Qing Zhou, J. Jin, Yanfen Peng, Rui Dai, Yong Zhang, Jianping Guo
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引用次数: 0

摘要

行星边界层与气溶胶的相互作用是导致大气质量恶化的主要原因之一。阐明污染与pbl之间的关系对于改善空气质量的预测至关重要。利用2014 - 2017年的地面空气质量和气象资料,结合l波段高分辨率(1秒)探空测量,研究了中国不同地区边界层的精细结构,以及边界层高度(BLH)与PM2.5的相关性。不同地区的PBL-PM2.5相互作用存在显著差异。在年内时间尺度上,华北平原(NCP)的负相关最强,其次是长江三角洲(YRD)。与此同时,青藏高原(TBP)空气质量相对清洁,负相关系数最低。NCP、YRD和TBP的相关系数分别为0.34、-0.25和-0.18。在重污染地区,污染物-多氯联苯-气象关系密切,相互作用最为明显。在清洁条件下,TBP中BLH与PM2.5之间没有明显的相互作用,气象条件对PBL的发展影响较大。pbl与污染的相互作用取决于污染程度和blh的背景值,PM2.5与blh的关系是非线性的,在污染物浓度较高或blh较低时更为明显。当然,这种关系也受到其他气象变量的显著影响。
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The Linkages between Atmospheric Boundary Layer and PM 2.5 from Different Region in China
Planetary boundary layer (PBL) – aerosol interaction is one of a major causes for the deterioration of air quality. Elucidating the relationship between pollution–PBL becomes essential for improving the prediction of air quality. Ground-based air quality and meteorological data, in combination with L-band high resolution (1-sec) radiosonde measurements from 2014 to 2017, were used to study the fine structures of PBL, and the correlations between boundary layer height (BLH) and PM2.5 from different region in China.There is a significant difference in the PBL–PM2.5 interaction in different regions. On the inner-annual timescale, the strongest negative correlation is observed over the North China Plain (NCP) with highly polluted conditions, followed by the Yangtze River Delta (YRD). Meanwhile, the air quality of Qinghai-Tibet Plateau (TBP) is relatively clean, where it shows the lowest negative correlation coefficient. The correlation coefficient are 0.34, -0.25 and -0.18 for the NCP, the YRD and the TBP. In the heavily polluted region, the pollutant–PBL–meteorology is closely related, indicating that their interaction is most obvious. Under clean conditions, no distinct interaction can be found between BLH and PM2.5 in the TBP, whereas the meteorological conditions show a greater impact on the development of PBL.The PBL-pollution interaction depends in the degree of pollution and the background value of BLHs, and the relationship between PM2.5 and BLHs is nonlinear, which is more obvious with high concentration of pollutants or the low BLHs. Of course, this relationship is also significantly affected by other meteorological variables.
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