| 2001~2019年气象条件对江苏省PM2.5分布的影响 |
| 摘要点击 2990 全文点击 848 投稿时间：2021-04-27 修订日期：2021-07-19 |
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| 中文关键词 气象条件 PM2.5 区域在线双向耦合模式(WRF-Chem) 江苏 统计分析 |
| 英文关键词 meteorological conditions PM2.5 weather research and forecasting model coupled with chemistry (WRF-Chem) Jiangsu Province statistical analysis |
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| 中文摘要 |
| 利用区域在线空气质量模式WRF-Chem模拟研究了2001~2019年气象条件对江苏省PM2.5浓度分布的影响.在排放源不变的情况下，气象条件引起的江苏省PM2.5年均浓度的最强正、负异常分别出现在2008和2001年，它们的异常值相对于多年平均值分别占比10.5%和-14.3%，表明气象条件对PM2.5浓度年际变化有明显影响.经验正交函数分解的结果表明，气象条件对江苏省PM2.5浓度的空间分布的影响具有一致性.边界层高度、温度、相对湿度、风速和降水整体上都与江苏省PM2.5浓度呈现显著负相关关系.以上气象因子所构建的线性回归方程能较好地描述PM2.5浓度和气象条件之间的关系，其拟合值与模拟值相关性为0.73，通过了99%的信度水平检验. |
| 英文摘要 |
| The weather research and forecasting model coupled with chemistry (WRF-Chem) was used to investigate the impact of meteorological conditions on PM2.5 in Jiangsu Province from 2001 to 2019. Under the condition of constant emission sources, the strongest positive and negative anomalies of annual PM2.5 concentration caused by meteorological conditions occurred in 2008 and 2001, respectively. Furthermore, their anomalies respectively accounted for 10.5% and -14.3% relative to the long-time averaged annual PM2.5 concentration, indicating that meteorological conditions are an important factor causing the interannual variation in PM2.5 concentration in Jiangsu Province. The empirical orthogonal function decomposition results show that the influence of meteorological conditions on the spatial distribution of PM2.5 concentration in Jiangsu Province is consistent under this mode. Additionally, the boundary layer height, temperature, relative humidity, wind speed, and precipitation all have significant negative correlations with the PM2.5 concentration in Jiangsu Province. Meanwhile, the linear regression equation constructed by the above meteorological factors can characterize the relationship between PM2.5 concentration and meteorological conditions well. Moreover, the correlation between the fitting value and the simulated value was 0.73, which was statistically significant at a 99% confidence level according to a student's t test. |
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