| 2000~2020年天津PM2.5质量浓度演变及驱动因子分析 |
| 摘要点击 3814 全文点击 950 投稿时间：2021-08-17 修订日期：2021-08-27 |
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| 中文关键词 细颗粒物 大气扩散条件 环境模式 驱动因子 天津 |
| 英文关键词 fine particles atmospheric diffusion condition environmental model driving factor Tianjin |
| 作者 | 单位 | E-mail | | 蔡子颖 | 天津市环境气象中心, 天津 300074
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | 120078030@163.com | | 郝囝 | 天津市气象科学研究所, 天津 300074 | | | 韩素芹 | 天津市环境气象中心, 天津 300074
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | | | 唐颖潇 | 天津市环境气象中心, 天津 300074
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | | | 杨旭 | 天津市环境气象中心, 天津 300074
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | | | 樊文雁 | 天津市环境气象中心, 天津 300074
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | | | 姚青 | 天津市环境气象中心, 天津 300074
中国气象局-南开大学大气环境与健康研究联合实验室, 天津 300074 | | | 邱晓滨 | 天津市气象科学研究所, 天津 300074 | |
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| 中文摘要 |
| 基于中国大气成分实时追踪数据集、天津气象局和生态环境局长序列PM2.5质量浓度和气象观测，结合MEIC排放清单和环境模式构建的细颗粒气象条件扩散指数，研究2000~2020年天津地区PM2.5质量浓度演变规律及驱动因子，以期更科学地分析气象对大气环境影响，为"十四五"期间深度环境治理提供支撑.结果表明，2000~2020年天津PM2.5质量浓度呈现3个阶段变化，第一阶段2000~2007年，呈现持续地上升，其变化速率为4.58 μg·（m3·a）-1，该阶段排放量的快速增加是主导因素，其作用是气象条件年际波动影响的4倍，排放量增加使得PM2.5质量浓度增加45.3%；第二阶段为2007~2013年，该阶段PM2.5质量浓度呈现波动变化，出现了两个浓度峰值年（2007年和2013年），该阶段排放稳定，气象条件年际波动对PM2.5质量浓度年际波动产生重要影响，两者相关系数0.81；第三阶段为2013~2020年，PM2.5质量浓度呈现快速地下降，其变化速率为-6.9 μg·（m3·a）-1，排放量的下降作用是决定性的，使得PM2.5下降了43.9%，气象扩散条件转好也提供正贡献，约使得PM2.5下降8.7%.从近20年数据分析，气象条件年际变化导致的大气扩散条件年际变化2003~2004年和2013~2015年为谷值，2008~2010年和2018~2020年为峰值，峰峰相距和谷谷相距是11 a左右；气象条件年际变化导致的大气扩散条件年际波动平均强度是4%，可解释近20年PM2.5质量浓度年际变化的25%~50%，峰谷相差16%，气象扩散条件年际波动对未来PM2.5目标制定和应对措施设计有重要影响. |
| 英文摘要 |
| Based on real-time tracking data, PM2.5 mass concentration, and meteorological observations of the Tianjin Meteorological Bureau and the Ecological Environment Bureau, combined with the fine particle meteorological condition diffusion index constructed using the environmental model, the change and driving factors of the PM2.5 mass concentration in Tianjin from 2000 to 2020 were studied to analyze the impact of meteorology on the atmospheric environment. The study showed that change in PM2.5 mass concentration in Tianjin took place in three stages from 2000 to 2020; the first stage showed a continuous increase from 2000 to 2007. The rapid increase in emissions in this stage was the dominant factor, and its effect was four times that of the annual fluctuation in meteorological conditions. The second stage was from 2007 to 2013, in which the PM2.5 mass concentration fluctuated, with two peak years (2007 and 2013). The emissions were stable in this stage. The annual fluctuation of meteorological conditions had an important influence on the annual fluctuation in PM2.5 mass concentration. The third stage was from 2013 to 2020; the PM2.5 mass concentration decreased rapidly, and the decline in emissions was decisive, which reduced the PM2.5 mass concentration by 40% to 50%. The improvement in the meteorological diffusion conditions also provided a positive contribution, which reduced the PM2.5 mass concentration by approximately 10%. Based on the analysis of the data over the past 20 years, the annual variation in atmospheric diffusion conditions caused by the annual variation in meteorological conditions was periodic, with trough values from 2003 to 2004 and 2013 to 2015 and peaks from 2008 to 2010 and 2018 to 2020; the distance between peaks and valleys was approximately 11 years. It was estimated that the next atmospheric diffusion condition valley stage will occur circa 2025. The average intensity of the annual fluctuation in atmospheric diffusion conditions caused by the annual variation in meteorological conditions was 4%, which can explain 25%-50% of the annual variation in PM2.5 mass concentration over the past 20 years, with a difference between peaks and valleys of 16%. The periodic fluctuations in meteorological diffusion conditions have an important impact on the future PM2.5 target setting and corresponding measures design. |
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