不同人为源排放清单对大气污染物浓度数值模拟的影响:以浙江省为例

应用大气化学模式WRF-Chem(Weather Research and Forecast-Chemistry),分别选用亚洲排放源清单INTEX-B(Intercontinental Chemical Transport Experiment-Phase B)、REASv2.1(Regional Emission inventory in Asia version 2.1)以及全球排放源清单HTAP_v2(Hemispheric Transport of Air Pollution version 2),对浙江省2013年12月进行模拟,分别记为IN、RE和HT试验,研究人为源排放清单对大气污染物浓度数值模拟的影响。结果表明,3组试验合理的反映出PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)、PM10(空气动力学当量直径小于等于10μm的颗粒物,即可吸入颗粒物)和NO_2近地面浓度的时空分布特征,相关系数为0.5~0.8,85%以上的模拟值落在观测值的0.5~2倍范围内,但对SO_2近地面浓度模拟较差。IN、RE、HT试验对PM2.5和PM10的模拟偏差均成递减趋势,约为30%、16%和6%,HT试验的模拟值更加接近观测。INTEX-B清单中PM2.5的一次排放与二次气溶胶前提物SO_2均高于REAS与HTAP清单,因此会导致更多的硫酸盐生成,从而进一步增加PM2.5浓度。HTAP_v2清单中较低的NH3排放会抑制硝酸盐的生成,从而有助于降低PM2.5浓度。3个清单的基准年与模拟年的差异对SO_2浓度模拟的准确性影响更大,INTEX-B清单中SO_2排放量明显高于REASv2.1与HTAP_v2清单,尤其在浙北和沿海工业发达地区,导致IN试验模拟的SO_2在这些地区存在明显高估。3组试验模拟的NO_2浓度偏差最小且更为接近(-8%~4%),主要原因是3个清单在浙江省的NOx排放十分一致。从3组试验结果之间的差异程度来看,浙江省范围内PM2.5、PM10、SO_2和NO_2逐日浓度模拟值之间的平均差异程度分别约为14%、15%、51%和16%,最大差异程度分别为69%、78%、137%和132%。月均浓度与逐日浓度的平均差异程度基本一致,但最大差异程度明显更低。总体来看3组试验模拟的PM2.5、PM10与NO_2的差异程度明显低于SO_2。

Effects of Different Anthropogenic Emission Inventories on Simulated Air Pollutants Concentrations: A Case Study in Zhejiang Province

The effects of anthropogenic emission inventories on simulated air pollutants concentrations in Zheijiang Province have been analyzed using the WRF-Chem (Weather Research Forecast-Chemistry) model. Three independent emission inventories, i.e. INTEX-B (Intercontinental Chemical Transport Experiment-Phase B), REASv2.1 (Regional Emission Inventory in Asia version 2.1), and HTAP_v2 (Hemispheric Transport of Air Pollution version 2), are used for model simulations during December 2013. The three experiments are denoted by IN, RE, and HT, respectively. Compared with in situ measurements, the three experiments can reasonably reproduce the temporal and spatial characteristics of PM2.5, PM10, and NO₂ surface concentrations with correlation coefficients ranging from 0.5 to 0.8. More than 85% of simulated values are within the range of 0.5 to 2 times of observational values. However, all of them have a poor performance on simulation of SO₂ concentration. The relative biases of PM2.5 and PM10 concentrations simulated by IN, RE, and HT are about 30%, 16%, and 6%, respectively, and the best performance is obtained by HT. The PM2.5 primary emissions and the secondary aerosol precursor SO₂ emissions of INTEX-B are significantly higher than those of REASv2.1 and HTAP_v2 emission inventories, which results in more sulfate aerosols and subsequently increases the PM2.5 concentration. The obviously lower NH₃ emission of HTAP_v2 compared to that in the other two emission inventories inhibits the formation of nitrate aerosols, which helps to reduce the PM2.5 concentration. Differences between the base year of emission inventories and the simulation year have greater impacts on the accuracy of simulated SO₂ concentrations than that of PM2.5, PM10, and NO_x. SO₂ emissions of INTEX-B are significantly higher than those of REASv2.1 and HTAP_v2 emission inventories, especially for the northern part of Zhejiang Province and the coastal industrialized areas, which is the primary reason for the obvious overestimation of SO₂ using the INTEX-B inventory. NO_x emissions of the three emission inventories are very consistent over Zhejiang Province, which could be the main reason for the similar modeled values and small relative biases (-8%-4%) of NO₂ in the three experiments. Furthermore, the simulations with different anthropogenic emission inventories do differ in their predictions of daily PM2.5, PM10, SO₂, and NO₂ concentrations with mean variations of 14%, 15%, 51%, and 16%, and maximum variations of 69%, 78%, 137%, and 132% over Zhejiang Province. The variations of monthly average concentrations of pollutants are basically consistent with those of daily average values of pollutants, but the maximum variations of simulated monthly values are obviously lower than those of simulated daily values. Generally, the variability of PM2.5, PM10, and NO₂ in the three simulations is significantly smaller than that of SO₂.