| 2018~2022年北京市PM2.5污染过程时空、气象及传输特征 |
| 摘要点击 2409 全文点击 356 投稿时间：2024-03-19 修订日期：2024-05-15 |
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| 中文关键词 PM2.5 时空变化 气象条件 区域传输 北京市 |
| 英文关键词 PM2.5 spatio-temporal variation meteorological condition regional transport Beijing |
| 作者 | 单位 | E-mail | | 郭元喜 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | guoyuanxi@126.com | | 刘保献 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | | | 李云婷 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | lee_yunting@163.com | | 沈秀娥 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | | | 王书肖 | 清华大学环境学院环境模拟与污染控制国家重点联合实验室, 北京 100084
国家环境保护大气复合污染来源与控制重点实验室, 北京 100084 | | | 宋倩 | 清华大学环境学院环境模拟与污染控制国家重点联合实验室, 北京 100084
国家环境保护大气复合污染来源与控制重点实验室, 北京 100084 | | | 陈晨 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | | | 孙峰 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | | | 陈阳 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | | | 孙瑞雯 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | | | 李倩 | 北京市生态环境监测中心大气颗粒物监测技术北京市重点实验室, 北京 100048 | | | 尹德嘉 | 清华大学环境学院环境模拟与污染控制国家重点联合实验室, 北京 100084
国家环境保护大气复合污染来源与控制重点实验室, 北京 100084 | | | 姜越琪 | 清华大学环境学院环境模拟与污染控制国家重点联合实验室, 北京 100084
国家环境保护大气复合污染来源与控制重点实验室, 北京 100084 | | | 董赵鑫 | 清华大学环境学院环境模拟与污染控制国家重点联合实验室, 北京 100084
国家环境保护大气复合污染来源与控制重点实验室, 北京 100084 | |
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| 中文摘要 |
| 基于PM2.5监测数据、气象观测数据以及CMAQ-ISAM空气质量模型,分析了2018~2022年北京市108次PM2.5污染过程的时空变化、气象条件以及区域传输情况. 结果表明,PM2.5污染过程的过程次数、过程平均浓度、过程峰值浓度均呈现明显改善趋势,并且,中重度污染过程的改善程度显著优于轻度污染过程. 分季节来看,夏季PM2.5污染过程基本消失,其他季节的污染程度则有待进一步改善. 从日变化来看,重度污染过程的浓度改善主要源于白天至前半夜时段. 近5 a来,过程浓度平均值东南高、西北低. 其中,经济技术开发区浓度最高,延庆区浓度最低. 从空间演变来看,2018~2022年,各区过程浓度平均值全部大幅下降,下降幅度为13~31 μg·m-3,下降率为11%~25%；各区污染天数全部大幅改善,改善幅度为35~52 d,改善率为56%~68%. 平均风速、平均相对湿度和西北风频率是污染过程期间对北京市PM2.5浓度影响最大的3个气象要素,污染天的平均风速为1.6 m·s-1,平均相对湿度为62.4%,西北风频率为3%. 北京市污染过程大多始于东部和南部,东南边界站早于全市12 h发生污染,南部、西南和东部边界站分别早于全市10、8和5 h发生污染. PM2.5污染过程发生期间,北京市本地贡献率为34%,区域传输贡献率为66%. 区域上,河北省对北京市的传输贡献率最大,为33%. 分城市来看,近周边保定、廊坊、天津和唐山对北京市传输影响最突出,传输贡献率分别为9%、6%、5%和5%. 分传输通道来看,东南通道贡献率为24%,西南通道贡献率为23%,建立区域联防联控机制十分必要. |
| 英文摘要 |
| Based on PM2.5 monitoring data, meteorological observation data, and a CMAQ-ISAM air quality model, the spatio-temporal variation, meteorological conditions, and regional transport characteristics of 108 PM2.5 pollution events in Beijing from 2018 to 2022 were analyzed. The results showed that the frequency, the mean concentration, and the peak concentration of PM2.5 pollution events demonstrated a significant decrease and the decrease degree of moderate and heavy pollution events was significantly larger than that of light pollution events. From the seasonal perspective, the PM2.5 pollution events nearly disappeared in summer, however, were severe in other seasons. According to the diurnal variation curves, PM2.5 concentration during heavy pollution events decreased significantly from daytime to the first half of night and did not decrease notably in the second half of the night. In the past five years, the mean concentration of PM2.5 pollution events was higher in the southeast and lower in the northwest, with the highest in the Beijing Economic and Technological Development Area and the lowest in Yanqing District. From the perspective of spatial evolution, the mean concentration in all districts significantly decreased from 2018 to 2022, with a decrease range of 13-31 μg·m-3 and a decrease percentage of 11%-25%； the frequency in all districts also decreased significantly, with a decrease range of 35-52 days and a decrease percentage of 56%-68%. The mean wind speed, mean relative humidity, and northwest wind frequency were the three most important meteorological factors that had an impact on PM2.5 concentration in Beijing. For polluted days, the mean wind speed was 1.6 m·s-1, the mean relative humidity was 62.4%, and the frequency of northwest wind was 3%. Most of the pollution events began from Eastern Beijing and Southern Beijing. Compared to those throughout the whole city, PM2.5 pollution events usually occurred 12 hours earlier at the southeast border station and 10, 8, and 5 hours earlier at the southern, southwest, and eastern border stations, respectively. During PM2.5 pollution events in Beijing, the local contribution was 34%, and the transport contribution was 66%. Out of all nearby provinces, Hebei had the biggest contribution of 33%. With regard to cities, Baoding, Langfang, Tianjin, and Tangshan had the most prominent impact on Beijing's PM2.5 pollution events, accounting for 9%, 6%, 5%, and 5% of the contribution, respectively. When it comes to transport pathways, the southeast pathway contributed 24% and the southwest pathway contributed 23%. The government must establish a regional joint prevention and control mechanism. |
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