2001~2019年气象条件对江苏省PM2.5分布的影响

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PM_2.5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PM_2.5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PM_2.5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PM_2.5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PM_2.5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PM_2.5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.

Impact of Meteorological Conditions on PM2.5 in Jiangsu Province from 2001 to 2019

The weather research and forecasting model coupled with chemistry (WRF-Chem) was used to investigate the impact of meteorological conditions on PM_2.5 in Jiangsu Province from 2001 to 2019. Under the condition of constant emission sources, the strongest positive and negative anomalies of annual PM_2.5 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 PM_2.5 concentration, indicating that meteorological conditions are an important factor causing the interannual variation in PM_2.5 concentration in Jiangsu Province. The empirical orthogonal function decomposition results show that the influence of meteorological conditions on the spatial distribution of PM_2.5 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 PM_2.5 concentration in Jiangsu Province. Meanwhile, the linear regression equation constructed by the above meteorological factors can characterize the relationship between PM_2.5 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.