Numerical Simulation of Summertime OH Concentrations in China Since the Implementation of the Air Pollution Prevention and Control Action Plan
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摘要: OH自由基是对流层中主要的氧化剂，是大气氧化性的重要表征。文章利用GEOS-Chem模式量化了2014～2017年“大气污染防治行动计划”执行以来，人为排放和气象因素变化对中国夏季大气OH浓度变化的贡献。模拟结果表明，2014～2017年间夏季整个中国OH浓度呈现上升趋势，最大上升出现在30°N附近的华南地区。在华北平原地区，OH浓度也呈明显的上升趋势（0.1×106 molecules cm−3 a−1），而OH浓度比较高的珠江三角洲地区的OH变化趋势较小。敏感性试验结果表明，气象和人为排放变化都对2014～2017年华北平原OH浓度上升有促进作用，但人为排放的贡献（OH增加10.0%）远大于气象的贡献（OH增加1.5%）；OH浓度变化最大的南方地区主要是气象条件控制。进一步对气象因素分析发现，影响全国OH 变化最重要的气象要素是太阳短波辐射，决定了2014～2017年中国OH浓度增长趋势最大的区域。但在华北地区，2014～2017年短波辐射略微减少的影响被边界层高度明显降低带来的OH增加所抵消。
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关键词:
- OH自由基 /
- GEOS-Chem模式 /
- 气象 /
- 人为排放
Abstract: The OH radical is the primary tropospheric oxidant, accounting for the oxidation capacity of the atmosphere. The GEOS-Chem model was used to examine the impact of anthropogenic emission and meteorological parameter changes on summertime OH concentrations in China since the implementation of the Air Pollution Prevention and Control Action Plan. Our modeling results for the years 2014–2017 demonstrate that the summertime OH concentrations in China exhibited an overall upward trend with the fastest increase occurring around 30°N over eastern China; the North China Plain was also simulated to have an obvious upward OH concentration trend of 0.1 × 106 molecules cm−3 a−1 while the Pearl River Delta experienced a weak downward trend. Further sensitivity experiment simulations showed that changes in both meteorology and anthropogenic emissions over the years 2014–2017 contributed to the increases in OH concentrations in the North China Plain, wherein the contribution of anthropogenic emissions was significantly larger than that of meteorology (10% vs. 1.5%). Meteorology played a major role in OH concentration increase around 30°N over eastern China. Further meteorological analysis demonstrated that the meteorological variable with the greatest contribution was solar shortwave radiation, which can explain the changes in the OH concentrations over a large fraction of China during 2014–2017. However, the role of solar shortwave radiation was offset by the boundary layer height in impacting the changes in OH concentrations during 2014–2017 in the North China Plain.-
Key words:
- OH radical /
- GEOS-Chem model /
- Meteorology /
- Anthropogenic emissions
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图 1 2014～2017年中国夏季OH浓度均值模拟结果的空间分布。绿色方框分别代表华北平原（NCP）和珠江三角洲地区（PRD）
Figure 1. Spatial distributions of simulated mean summertime OH concentrations in China from 2014 to 2017. Green rectangles denote the North China Plain (NCP) and Pearl River Delta (PRD)
图 2 2017年夏季重点城市群OH浓度（C）日变化。华北平原（NCP）：35°～41°N，113.75°～118.75°E；长江三角洲（YRD）：30°～33°N，118°～122°E；珠江三角洲（PRD）：22°～23°N，112°～115°E；四川盆地（SCB）：28.5°～31.5°N，103.5°～107°E
Figure 2. Daily variation of summertime OH concentrations in the four megacity clusters in 2017. North China Plain (NCP): 35–41°N, 113.75–118.75°E; Yangtze River Delta (YRD): 30–33°N, 118–122°E; Pearl River Delta (PRD): 22–23°N, 112–115°E; Sichuan Basin (SCB): 28.5–31.5°N, 103.5–107°E
图 3 2014～2017年（a）中国年夏季Base模拟的OH浓度年际变化趋势；（b）气象和人为排放变化对中国夏季OH浓度的影响（2017年的Base模拟与2014年Base模拟的差值）；（c）气象变化对中国夏季OH浓度的影响（MET17_EM14敏感性试验结果与2014年Base模拟的差值）；（d）人为排放变化对中国夏季OH浓度的影响（EM17_MET14敏感性试验结果与2014年Base模拟的差值）
Figure 3. (a) Linear trends of summertime OH concentrations during 2014–2017 in China from the Expt Base simulation; (b) The impact of changes in both meteorology and anthropogenic emissions on the summertime OH concentrations during 2014–2017 in China (2017 Base simulation minus 2014 Base simulation); (c) The impact of changes in meteorology on the summertime OH concentrations during 2014–2017 in China (Expt MET17_EM14 minus 2014 Base simulation); (d) The impact of changes in anthropogenic emissions on the summertime OH concentrations during 2014–2017 in China (Expt EM17_MET14 minus 2014 Base simulation)
图 4 2017年相比于2014年人为排放和气象参数对华北平原和珠江三角洲OH浓度贡献的季节变化（红色代表排放的贡献，蓝色代表气象的贡献）：对（a）华北平原和（b）珠江三角洲的绝对贡献；对（c）华北平原和（d）珠江三角洲的相对贡献
Figure 4. Seasonal variation of the contributions (contr) of anthropogenic emissions and meteorology to the OH concentrations during 2014–2017 in the North China Plain and Pearl River Delta (red and blue represent the contributions of emissions and meteorology, respectively). Absolute contribution of (a) North China Plain and (b) Pearl River Delta; Relative contribution of (c) North China Plain and (d) Pearl River Delta
图 5 LMG方法估算的气象要素对2014～2017年期间华北平原和珠江三角洲夏季OH浓度变化的相对重要性（不同的颜色代表着不同的气象要素，每个色块上方插入的值是每个气象要素贡献的百分比）
Figure 5. LMG method of estimating the relative contributions of the dominant meteorological variables to the summertime OH concentration changes during 2014–2017 in the North China Plain and Pearl River Delta (different colors represent the different meteorological variables, the value in each color block is the percentage contribution of each meteorological variable). SWGDN: surface incoming shortwave flux; PBLH.daytime: daytime planetary boundary layer height; T2: 2-meter-height air temperature; SLP: sea level pressure; RH1000: relative humidity at 1000 hPa. SWGDN: surface incoming shortwave flux; PBLH.daytime: daytime planetary boundary layer height; T2: 2-meter-height air temperature; SLP: sea level pressure; RH1000: relative humidity at 1000 hPa
图 6 2017年与2014年中国夏季短波辐射（SWDGN）、白天边界层高度（PBLH.daytime）、地面2 m温度（T2）以及海平面气压（SLP）的差值分布
Figure 6. Differences in summertime (a) shortwave radiation (SWDGN), (b) daytime planetary boundary layer height (PBLH.daytime), (c) 2-m air temperature (T2), and (d) sea level pressure (SLP) between 2017 and 2014 (2017 minus 2014) in China
表 1 GEOS-Chem敏感性试验设计
Table 1. Configurations of the GEOS-Chem experiments
敏感性试验名称 气象场年份 人为排放年份 Base 2014～2017年 2014～2017年 MET17_EM14 2017年 2014年 EM17_MET14 2014年 2017年 
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表 2 OH自由基浓度的观测结果与GEOS-Chem模式模拟结果
Table 2. The observed and GEOS-Chem modeled OH radical concentrations
观测点位置 观测时间 观测OH浓度/106 molecules cm−3 模拟OH浓度/106 molecules cm−3 观测值参考文献 河北望都（38.7°N，115.2°E） 2014年夏季 峰值5～15 日均值2.4 Tan et al.（2017） 北京IAP（39.6°N，116.2°E） 2017年夏季 均值5.82 日均值2.4 Woodward-Massey et al.（2020） 北京PKU（40°N, 116.3°E) 2017年冬季 峰值1.5～2.0 日均值0.3 Ma et al.（2019） 广东广州（23.5°N，113.0°E） 2006年夏季 峰值15～26 日均值3.9* Lu et al.（2013） 广东鹤山（22.7°N，112.9°E） 2014年秋季 日最大中值4.5 日均值2.9 Tan et al.（2019） 四川成都(30. 4°N, 103.8°E) 2019年夏季 峰值10～20 日均值3.5* Yang et al.（2021） *2006年与2019年不在本文模式模拟的时间范围之内，分别选择了与观测时间较近的2014和2017年的模拟与观测进行对比
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