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大气多环芳烃区域迁移转化模型比较与关键影响因素:以京津冀地区为例
摘要点击 641  全文点击 127  投稿时间:2021-08-20  修订日期:2021-11-04
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中文关键词  CMAQ模型  BETR模型  多环芳烃(PAHs)  XGBoost模型  SHAP值
英文关键词  CMAQ model  BETR model  polycyclic aromatic hydrocarbons(PAHs)  XGBoost model  SHAP value
作者单位E-mail
张馨露 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012 13656435287@163.com 
刘世杰 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012 liusj@craes.org.cn 
韩美丽 北京中科三清环境技术有限公司, 北京 101500  
苏超 山西大学黄土高原研究所, 太原 030006  
张志鹏 中国石油大学(华东)理学院, 青岛 266580  
马琳琳 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012  
李洋 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012  
程苗苗 中国环境科学研究院环境基准与风险评估国家重点实验室, 北京 100012  
中文摘要
      针对芘(Pyr)和苯并[a]芘(BaP),以京津冀地区2014年为例分别构建CMAQ和BETR模型系统开展数值模拟,对比评估两种模型对PAHs大气迁移转化的模拟效果,并利用XGBoost模型识别CMAQ中影响PAHs环境行为的关键大气物理化学过程和参数.结果表明,BETR和CMAQ模拟年均值与实测年均值比值基本在1/2~2之间,且CMAQ模拟值和实测值季节变化趋势相同,验证了两类模型结果的可靠性.同时,将CMAQ模型9 km网格模拟浓度平均至27 km网格并和BETR模拟浓度的对比结果显示,BETR模型Pyr和BaP模拟浓度平均分别约为CMAQ年均模拟浓度的1.59倍和1.38倍,两类模型在年均浓度水平和空间分布方面具有较好的可比性.基于XGBoost模型的SHAP变量重要性分析表明,边界层高是对Pyr和BaP迁移转化影响最大的气象因素,其重要性在所有因素中占比高达22%~35%,在部分城市和污染物中对浓度变化的贡献甚至超过排放量,且和两种PAHs浓度呈显著负相关;PAHs浓度水平其次受风速影响最大,且风速和PAHs浓度呈负相关关系;风向对不同城市污染物浓度的影响则各不相同.
英文摘要
      In this study, taking the Beijing-Tianjin-Hebei region as an example, CMAQ and BETR models were constructed to carry out numerical simulation for the pyrene (Pyr) and benzo[a] pyrene (BaP) in 2014. The model results were compared and evaluated for the atmospheric transportation and transformation of PAHs. Additionally, the XGBoost model was used to identify the key atmospheric physicochemical processes and parameters that affect the environmental behavior of PAHs in the CMAQ. The results showed that the ratio of the simulated value of BETR and annual average value of CMAQ to the measured annual average value was between 1/2 and 2, and the seasonal trend of the simulated concentrations of Pyr and BaP from the CMAQ model were basically consistent with the measured values, which verified the reliability of the two types of models. At the same time, the simulated concentration of the CMAQ model averaged from 9 km grid to 27 km grid and was comparable to the BETR concentration. The results showed that the average concentrations of Pyr and BaP in the BETR model were approximately 1.59 and 1.38 times those of the CMAQ simulation concentrations, respectively, indicating that the two models had good comparability in terms of average annual concentration level and spatial distribution. The SHAP-based variable importance on the XGBoost model showed that boundary layer height was the most significant meteorological factor affecting the transportation and transformation of Pyr and BaP, accounting for 22%-35% of all factors, and sometimes even exceeded the emissions for certain cities and pollutants. The boundary layer height was significantly negatively correlated with the concentration of PAHs. Wind speed was a secondary factor affecting the concentration of PAHs and was negatively correlated with the PAHs, whereas the influence of wind direction on the concentration of PAHs varied.

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