Improvement of the Summer Ozone Simulation over Key Cite-Clusters in China through Emission Inversion Method
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摘要: 在嵌套网格空气质量预报模式系统（NAQPMS）的基础上，采用污染源反演方法优化以中国多尺度排放清单（MEIC）为主的先验排放清单中臭氧（O3）前体物排放量估计。分析时段为2019年6～8月，重点评估了污染源反演对我国“2+26”城市、长三角、珠三角、成渝4个重点城市群O3模拟的改进效果。评估结果表明，污染源反演获得的“2+26”城市、长三角、珠三角的氮氧化物（NOx）排放速率整体低于先验清单的排放速率约0.6 μg m−2 s−1，但反演的挥发性有机物（VOCs）排放速率在“2+26”城市整体上高于先验清单的排放速率约0.5 μg m−2 s−1。利用反演的NOx和VOCs排放量和NAQPMS模式对4个城市群O3进行模拟，发现反演排放数据可以显著改进夏季O3模拟性能，使得O3日最大8小时平均值（MDA8-O3）模拟的均方根误差（RMSE）从40～60 μg/m³降低至20～30 μg/m³，模拟值与观测值的相关系数从0.6～0.7提升至0.8以上，模拟和观测O3浓度日变化峰值差异从2～50 μg/m³缩小到2～20 μg/m³。本文结果表明基于地面观测数据的污染源反演可以有效改进重点城市群的O3模拟性能，反演的O3前体物排放量与先验清单的排放量差异可为先验清单效验和评估提供参考。Abstract: Based on the Nested Grid Air Quality Prediction Model System (NAQPMS), the emission source inversion method is utilized to optimize the estimation of ozone (O3) precursor in the emission a priori inventory dominated by China Multi-Scale Emission Inventory (MEIC). From June to August 2019, the effect of improving O3 simulation by employing source inversion emission inventory is mostly examined in “2+26” Cities, Yangtze River Delta, Pearl River Delta, and Chengdu−Chongqing urban agglomerations with severe O3 pollution from June to August 2019 (summer). The evaluation results show that the nitrogen oxide (NOx) emission rate obtained by source inversion is lower than the a priori inventory emission rate of about 0.6 μg m−2 s−1, but the volatile organic compounds (VOCs) emission rate of inversion is higher than the a priori inventory emission rate of about 0.5 μg m−2 s−1 in “2+26” cities. The source inversion emission inventory is used to simulate O3 in four urban agglomerations, and the simulated performance of O3 in summer could be significantly improved by inversion emission data, which reduces the root-mean-square error (RMSE) of the maximum eight-hour mean of O3 (MDA8-O3) from 40–60 μg/m³ to 20–30 μg/m³ and increases the correlation coefficient from 0.6–0.7 to more than 0.8. The discrepancy between the simulated and observed diurnal variation peaks of O3 narrowed from 2–50 μg/m³ to 2–20 μg/m³. The results of this study show that pollution source inversion based on ground observation data may effectively improve the performance of O3 simulation in the key urban agglomeration, and the difference between the emissions of O3 precursor inversion emissions and the a priori inventory may provide a reference for the effectiveness and evaluation of the a priori inventory.
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Key words:
- Emission source inversion /
- Ozone /
- Numerical simulation /
- “2+26” cities
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图 1 本文所选择的城市群分布与观测站点（绿色圆点）
Figure 1. Distribution of urban agglomerations and observation sites (green dots) in this study
图 2 2019年6～8月（a）长三角、（b）“2+26”城市、（c）珠三角和（d）成渝先验模拟（黑线）、反演模拟（灰线）与观测（圆点）的O3日最大8小时滑动平均浓度（MDA8-O3）逐日变化
Figure 2. A priori simulation (black line), inversion simulation (grey line), and observation (dot) of the daily variation of the maximum ozone day 8-h moving average (MDA8-O3) in the (a) Yangtze River Delta, (b) “2+26” Cities, (c) Pearl River Delta, and (d) Chengdu−Chongqing from Jun to Aug 2019
图 3 2019年6～8月源反演前后（a）NOx和（b）VOCs的站点排放速率变化（源反演后减去源反演前），这里的VOCs包含了C2H6、HCHO、ALD2、PAR、ETH、OLET、OLEI、TOL、XYL、ISOP （Zaveri and Peters, 1999）
Figure 3. Variations of (a) NOx and (b) VOCs in site emission rates before and after source inversion from Jun to Aug 2019 (value after inversion minus value before inversion), where VOCs include C2H6, HCHO, ALD2, PAR, ETH, OLET, OLEI, TOL, XYL, and ISOP (Zaveri and Peters, 1999)
图 4 2019年6～8月（a）北京、（b）杭州、（c）广州和（d）重庆先验模拟（虚线）、反演模拟（实线）与观测（圆点为O3，三角为NO2）浓度的平均日变化
Figure 4. A priori simulation (dotted line), inversion simulation (solid line), and observation (dot for O3 and triangle for NO2) of mean diurnal variation in (a) Beijing, (b) Hangzhou, (c) Guangzhou, and (d) Chongqing from Jun to Aug 2019
图 5 2019年6～8月“2+26”城市、长三角、珠三角和成渝的日间（浅色）、夜间（深色）O3浓度观测值和模式模拟值之间的（a）相关系数和（b）均方根误差（RMSE），基于O3浓度小时值计算
Figure 5. (a) Correlation coefficient and (b) root-mean-square error (RMSE) of daytime (lignt) and nocturnal (dark) O3 concentration between observations and simultions in the Yangtze River Delta, “2+26” Cities, Pearl River Delta, and Chengdu−Chongqing from Jun to Aug 2019. The calculation is based on the hourly O3 concentration
表 1 本文在研究中涉及的城市群所包含的城市
Table 1. Cities of the urban agglomerations involved in the study
城市群 所含城市 “2+26”城市 安阳、保定、滨州、沧州、德州、邯郸、菏泽、鹤壁、衡水、济南、济宁、焦作、晋城、开封、廊坊、聊城、濮阳、阳泉、石家庄、太原、唐山、天津、新乡、邢台、长治、郑州、淄博、北京 长三角 上海、南京、无锡、常州、苏州、南通、盐城、扬州、镇江、泰州、杭州、宁波、嘉兴、湖州、绍兴、金华、舟山、台州、合肥、芜湖、马鞍山、铜陵、安庆、滁州、池州、宣城 珠三角 广州、佛山、肇庆、深圳、东莞、惠州、珠海、中山、江门 成渝 成都、重庆、自贡、泸州、德阳、绵阳、遂宁、内江、乐山、达州、南充、宜宾、雅安、资阳、眉山、广安 
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表 2 反演前的先验排放清单
Table 2. Prior emission inventory before source inversion
清单名称 参考文献 HTAP v2.2全球人为源清单 Janssens-Maenhout et al.（2015） GFED4生物质燃烧排放清单 Randerson et al.（2017）; van der Werf et al.（2010） MEGAN-MACC生物 VOC源排放清单 Sindelarova et al.（2014） POET海洋 VOC 源排放清单 Granier et al.（2005） NOx 土壤源排放清单 Yan et al.（2003） NOx 闪电源排放清单 Price et al.（1997）
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表 3 基于2019年6～8月的“2+26”城市、长三角、珠三角和成渝MDA8-O3源反演前后模拟O3污染日数与观测O3污染日数的比值
Table 3. Ratio of simulated O3 polluted days to observed O3 polluted days before and after source inversion in “2+26” Cities, Yangtze River Delta, Pearl River Delta, and Chengdu−Chongqing from Jun to Aug 2019
O3污染城市群 比值（轻度污染） 比值（中度污染及以上） 源反演前 源反演后 源反演前 源反演后 “2+26”城市 0.48 0.98 0 0.63 长三角 0.47 0.74 0 / 珠三角 0.5 1 / / 成渝 0.83 0.92 / / 注：比值越接近1，表示模式能够模拟出O3污染日的性能越高。“/”表示观测O3未达到这一污染等级。轻度污染时O3浓度为160～215 μg/m3，中度污染及以上＞215 μg/m3
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表 4 2019年6～8月“2+26”城市、长三角、珠三角和成渝模拟小时O3浓度观测值和模式模拟值之间的相关系数和均方根误差
Table 4. Correlation coefficient and RMSE of hourly O3 concentration between observations and simultions in the Yangtze River Delta, “2+26” Cities, Pearl River Delta, and Chengdu−Chongqing from Jun to Aug 2019
O3污染城市群 相关系数 均方根误差/μg m−3 源反演前 源反演后 源反演前 源反演后 “2+26”城市 0.64 0.82 60.2 37.2 长三角 0.61 0.82 49.6 32.7 珠三角 0.72 0.83 36.6 27.1 成渝 0.61 0.8 61.8 36.4
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