The Mitigation of SOA and O3 by VOCs Emission Reduction
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摘要:
挥发性有机物（VOCs）是臭氧（O3）和二次有机气溶胶（SOA）的重要前体物.然而，目前关于VOCs排放控制对SOA和O3缓解作用的研究仍然不足.本研究总结了四大排放源的精细化VOCs减排潜力，进一步利用WRF-Chem模型量化了中国主要大气污染区污染事件期间VOCs减排对SOA和O3缓解的效益.结果表明，通过精细化VOCs减排，华北、长三角、华中、珠三角和四川盆地的SOA浓度分别降低了89.2%、81.2%、74.5%、72.0%和77.3%.其中，工业VOCs减排是SOA缓解的主要贡献因素.然而，由于O3及其前体物的非线性光化学过程，这种VOCs减排只能使O3浓度降低不到10%.华北、长三角、华中、珠三角和四川盆地的O3浓度分别降低了7.55、9.05、7.29、4.31和3.15 μg/m3.减少工业、交通和居民VOCs排放分别使臭氧平均浓度降低了3.6%（3.48 μg/m3）、2.2%（2.07 μg/m3）和1.1%（0.98 μg/m3）.
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关键词:
- 臭氧 /
- 二次有机气溶胶 /
- 挥发性有机物(VOCs) /
- 大气污染缓解 /
- VOCs减排 /
- WRF-Chem模拟
Abstract:Volatile organic compounds (VOCs) are the important precursors of ozone (O3) and secondary organic aerosols (SOA). However, current research on the mitigation of SOA and O3 by VOCs emission reduction is still insufficient. In this study, based on previous research and policies, a refined VOCs emission reduction potential covering four major emission sources was summarized. The benefits of VOCs emission reduction on SOA and ozone mitigation during the pollution events for five major air pollution regions in China were quantified using the WRF-Chem model. The results showed that the refined VOCs emission reduction strategy could reduce the SOA concentrations by 89.2%, 81.2%, 74.5%, 72.0%, and 77.3% in the North China Plain region, Yangtze River Delta, Central China, Pearl River Delta and Sichuan Basin, respectively. The reduction potential of industrial VOCs emissions is the main contributor to the SOA mitigation in these five regions. Nevertheless, such VOCs emission reduction could only reduce ozone concentration by less than 10%, due to the nonlinear photochemical processes of ozone and its precursors. The refined VOCs emission reduction strategy could reduce the O3 concentrations by 7.55, 9.05, 7.29, 4.31, and 3.15 μg/m3 in five areas, respectively. VOCs emission reduction of industry, transportation and residential could reduce the ozone concentration by 3.6% (3.48 μg/m3), 2.2% (2.07 μg/m3) and 1.1% (0.98 μg/m3), respectively, on average in the five main air pollution regions.
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图 1 2017年1月至3月五个主要区域的核心城市PM2.5平均观测值（a）；2017年6月至8月五个主要区域的核心城市O3平均观测值（b）
图中橙色、红色和紫色表示污染物超标
Fig. 1. The average observed values of PM2.5 in five urban areas from January to March 2017 (a); Average O3 observations for five urban areas from June to August 2017 (b)
图 2 5个城市地区PM2.5（a~e）及O3（f~j）模拟结果与观测的相关性及平均偏差
图a、f对应NCP地区，图b、g对应YRD地区，图c、h对应CC地区，图d、i对应PRD地区，图e、j对应SCB地区；（a~e）模拟时间段为2017年2月11日至17日，（f~j）模拟时间段为2017年6月1日至9日；图中横坐标是PM2.5和O3的观测浓度值，纵坐标是模拟浓度值，单位为μg/m3
Fig. 2. The correlation and average deviation between observation and simulation for PM2.5 (a-e) and O3 (f-j) in 5 urban areas
图 3 CON和REAL实验SOA（a）、PM2.5（b）和O3（c）浓度的差异
Fig. 3. Difference in SOA (a), PM2.5 (b) and O3 (c) concentration of CON and REAL experimental
图 4 敏感性试验与控制实验SOA浓度的空间差异；模拟时间段为2017年2月11日至17日
Fig. 4. Spatial difference of SOA concentration between sensitivity test and control experiment; The simulation period was February 11-17, 2017
图 5 敏感性试验与控制实验O3浓度的空间差异；模拟时间段为2017年6月1日至9日
Fig. 5. Spatial difference of O3 concentration between sensitivity test and control experiment; The simulation period was June 1-9, 2017
表 1 WRF-Chem物理和化学方案的配置
Table 1. Configuration of the WRF-Chem physical and chemical program
参数方案 WRF-Chem具体选项 参考来源 微物理 Lin微物理过程方案 Lin et al., 1983 长波辐射 RRTM长波辐射方案 Gallus and Bresch, 2006 短波辐射 Goddard短波辐射方案 Chou and Suarez, 1999 地表参数化方案 MM5地表参数化方案 Jiménez et al., 2012 陆面模型 Noah陆面模型 Ek et al., 2003 边界层方案 YSU边界层方案 Noh et al., 2003 积云参数化方案 Grell 3D积云参数化方案 Grell and Dévényi, 2002 化学方案 CB05机制和SAPRC-99机制 Sarwar et al., 2008和Carter, 1990 
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表 2 WRF-Chem模型敏感性试验的描述
Table 2. Description of sensitivity test of WRF-Chem model
减排方案 描述 CON 应用原始的排放情况 C20 全国人为VOCs污染源排放量减少20% C40 全国人为VOCs污染源排放量减少40% C60 全国人为VOCs污染源排放量减少60% C80 全国人为VOCs污染源排放量减少80% C100 全国人为VOCs污染源排放量设置为零 AGR 农业VOCs排放量设置为零 IND 工业VOCs排放量设置为零 POW 发电部门VOCs排放量设置为零 TRA 交通运输VOCs排放量设置为零 RES 居民住宅VOCs排放量设置为零 REAL 根据实际文献和政策总结计算出的减排比例
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表 3 各行业VOCs减排策略和减排潜力
Table 3. VOCs emission reduction strategies and emission reduction potential
部门 行业 VOCs排放控制策略 VOCs减排潜力 参考文献 工业 石油化工 对失控排放的有效控制（包括泄漏检测与修复系统（LDAR）、过程控制、源头排放控制） 40% Simayi et al., 2021和Wang et al., 2023a 溶剂 油漆 绿色涂料替代 70%~80% Shi et al., 2023 工业粘合剂 实施更严格的VOCs排放限制 60% Gao et al., 2021b 印刷 使用VOCs含量低的原材料 46%~66% You et al., 2023 农药 吸附/冷凝分离/催化燃烧 70%~90% Zheng et al., 2017 干洗 冷凝分离 70%~85% Zheng et al., 2017 制药行业 使用LDAR技术 55%~72% Zhang et al., 2021 交通运输 道路车辆 摩托车：安装排放控制装置 45%~88% Dhital et al., 2019 乘用车：加强排放监测和执法，并结合车辆数量的监管限制 51% Wu et al., 2023 非道路车辆 船舶：更换燃料 67% Wu et al., 2019 居民住宅 固体燃料（煤、生物质燃料等） 清洁供暖技术 90% He et al., 2023和Sun et al., 2019
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表 4 气象因子模拟结果的对比结果，包括观测和模拟结果的平均偏差（MB）、平均归一化偏差（NMB）、平均误差（ME）和平均归一化误差（NME）
Table 4. Detailed statistics of meteorological factors. Mean bias (MB), normalized mean bias (NMB), mean error (ME) and normalized mean error (NME) of observation and simulation results.
R MB ME NMB（%） NME（%） PM2.5模拟时段（2017年2月11日至17日） U10 0.80 0.02（m/s） 0.82（m/s） 1.28 2.21 V10 0.86 ‒0.45（m/s） 1.05（m/s） 0.78 0.80 T2 0.95 0.90（K） 1.79（K） ‒0.002 0.007 RH2 0.86 ‒5.18（%） 11.67（%） ‒0.06 0.21 O3模拟时段（2017年6月1日至9日） U10 0.80 ‒0.28（m/s） 0.85（m/s） 0.31 ‒2.93 V10 0.87 ‒0.02（m/s） 0.96（m/s） 2.19 ‒1.79 T2 0.90 ‒0.15（K） 1.21（K） ‒0.000 6 0.004 RH2 0.89 ‒2.52（%） 5.91（%） ‒0.04 0.08
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