北京秋冬季一次重污染过程PM2.5来源数值模拟研究

利用WRF模式(The Weather Research and Forecasting Model)和嵌套网格空气质量模式(NAQPMS)对2016年11月发生在京津冀地区一次PM_(2.5)污染事件进行模拟研究并分析污染过程中的天气形势变化.结果表明,均压场、低空逆温层和偏南暖湿气流输送的存在为北京地区PM_(2.5)形成提供了有利条件,NAQPMS模式能够合理的再现北京大气污染物时空变化,细颗粒物PM_(2.5)和可吸入颗粒物PM_(10)模拟与观测数据相关系数达0.71,模拟数据在观测数据两倍范围内占比(FAC2)达65%.源解析结果表明,在不考虑临时实施减控措施下,11月18日区域外输送对北京PM_(2.5)浓度贡献为55.25%,区域内输送贡献为44.75%,北京东北区域PM_(2.5)外地源主要为河北中部、河北南部、天津和山东,所占贡献为9.67%、9.01%、7.90%和7.99%.污染物主要来源为生活源、交通源和工业源,分别占比39.6%、34.6%和20.0%.而实际上北京在唐山、保定采取一系列控制措施后仍在研究时段内出现高PM_(2.5)浓度,意味着在同样天气形势下需要对河北中部、河北南部、天津和山东等浓度贡献占比大的城市加强减排管控才能有效减缓高PM_(2.5)浓度的出现.

Numerical simulation study on source apportionment of PM2.5 in a heavy winter pollution event over Beijing area

In this study, the Weather Research and Forecasting Model (WRF) and the Nested Air Quality Prediction Model System (NAQPMS) were employed to simulate an extreme severe haze event over Beijing-Tianjin-Hebei area in November 2016. The synoptic patterns in this episode was also analyzed. Results show that large-scale high pressure, temperature-inversion layer in the boundary layer and southerly warm-humid airflow accelerated the accumulation of pollutants. The model validation by comparing with observations showed that NAQPMS reproduces the temporal and spatial distribution of pollutants in this episode. The correlation coefficients of PM_2.5 and PM₁₀ between simulations and observations reached 0.71. 65% of simulated data were in the twice range (FAC2) of observed data. the results of the source-tagging calculation suggested that local emission accounting for 44.75% of the surface PM_2.5 concentrations in Beijing on the 18^th November, while emissions in its surrounding areas contributed 55.25%. Emissions from the central Hebei and southern Hebei and Tianjin, Shandong had significant contribution to the PM_2.5 pollutions in Beijing northwest area with the contributions 9.67%,9.01%,7.90% and 7.99%, respectively. The main sources of pollutants were living sources, transportation sources and industrial sources, accounting for 39.6%, 34.6% and 20.0%, respectively.