基于AMDAR及综合观测数据研究大气边界层结构变化与重污染过程相互影响

The regional heavy pollution with high atmospheric PM2.5 concentration takes place frequently in China, which seriously threat to human health. In order to identify the formation mechanism of heavy pollution and control the occurrence of heavy pollution effectively, the mutual effect between the structure variation of atmospheric boundary layer and the atmospheric aerosol during the heavy pollution course should be estimated. However, because of the limitation of conventional meteorological observation technology and the difficulty of integrating multi disciplines, such as environment and meteorology, the interaction mechanism between structure variation of atmospheric boundary layer and atmospheric aerosol during the heavy pollution course was not clear. To solve this problem, this project takes the North China as the study areas and firstly applies the AMDAR (Aircraft Meteorological Data Relay) data in the study of atmospheric boundary layer structural change by proposing a new AMDAR analysis technology based on data mining. The AMDAR was with high temporal-spatial resolution, accurate, reliable and as a result could reflect the atmospheric boundary layer structure variation continuously as compared with conventional meteorological observation technology. Then the atmospheric boundary structure variation characteristics will be investigated based on in-depth analysis of sufficient heavy pollution processes, by comprehensively utilizing AMDAR, routine meteorological observation, intensive sounding observation data and the numerical simulation technology. The physical-chemical characteristics of atmospheric environment would also be analyzed based on comprehensive three-dimensional observation results, including PM2.5 chemical composition, particle size distribution and vertical distribution of particulate matter obtained by laser radar monitoring. Then, the key scientific issues in the mutual effect between structure variation of atmospheric boundary layer and atmospheric aerosol during heavy pollution course will be further studied and the interaction mechanism will be revealed. The outputs of this project could also provide scientific basis for the promotion of heavy pollution forecast and warning technology, the determination of atmospheric environmental capacity and the making of optimal control scheme for air quality improvement in Beijing-Tianjin-Hebei and surrounding areas.

我国大范围高浓度重污染时常发生，严重威胁人体健康。为识别大气重污染形成机制及更有效的防控重污染发生，亟需弄清大气边界层结构变化与重污染过程的相互影响。由于常规气象探测技术的局限性与环境、气象等多学科交叉融合的难度，大气边界层结构变化与重污染相互影响机制尚不明确。本项目以华北地区为研究对象，在大量重污染过程深入分析的基础上，建立基于数据挖掘的飞机AMDAR（Aircraft Meteorological Data Relay）资料分析技术，综合利用AMDAR、气象探空观测及加密观测数据，结合数值模拟研究大气边界层结构变化规律。基于PM2.5化学组分、粒径分布、激光雷达等综合立体观测结果，分析重污染过程的污染特征，揭示大气边界层结构变化与重污染过程的相互作用，研究解决其关键科学问题。研究结果可为华北地区重污染预测预警技术的提升、大气环境承载力的确定与空气污染优化控制方案的制定提供重要科学依据。

大气边界层结构变化是影响大气重污染形成的重要原因。本研究以华北地区六个主要城市（北京、天津、石家庄、太原、济南和郑州）为研究对象，基于高时间分辨率的AMDAR数据深度挖掘及地面气象和空气质量综合观测数据综合分析，采用数值模拟、数理统计等多种技术手段，研究大气PM2.5与大气边界层时空分布特征，以及大气边界层结构变化对重污染过程的相互影响。探讨了气溶胶及其重要组分对大气边界层结构变化与近地面PM2.5浓度的反馈贡献。.研究结果表明：（1）基于AMDAR数据深度挖掘和数值模型模拟相结合方法，研究各有关城市大气边界层高度（PBLH）及特征：夏季（1191 m）>春季（1095 m）>秋季（1017 m）>冬季（936 m），且日变化呈现单峰特征。PBLH与边界层内的风速呈同向变化趋势，六城市PBLH每下降100 m地面风速的协同变化率在约为-0.3~ -0.5 m/s（秋冬季）和-0.2~ -0.3 m/s（春夏季）；而与边界层内的湿度呈反向变化趋势，PBLH每下降100 m地面湿度的协同变化率约为+2.9~+3.5%（秋冬季）和+2.9~+4.1%（春夏季），表明不利的物理扩散条件与不利化学反应条件的叠加效应，导致华北地区PM2.5污染的形成；（2）基于当前区域排放状况，分析了华北六城市气象条件对PM2.5污染的影响，在有利的气象扩散条件下（PBLH>800 m和风速>4 m/s）冬季PM2.5浓度约为夏季的2.7~3.2倍，在不利的扩散条件下（PBLH<200 m和风速<2 m/s）约为4.2~4.6倍，揭示了季节排放差异和季节背景差异的重要影响；（3）基于AMDAR数据深度挖掘、地面环境与气象综合观测、数值模拟、统计回归等综合分析研究，构建了典型城市大气边界层结构与重污染过程的相互关系模型；（4）研究探讨了气溶胶颗粒物主要组分、有关气象要素对太阳辐射量、PBLH等的影响与相互作用。本研究成果可为大气污染控制策略制定提供重要的科技支撑。.

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