VOCs排放控制对SOA和O₃的减缓作用

The Mitigation of SOA and O₃ by VOCs Emission Reduction

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）.

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.

Graphical abstract

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.

Key words

ozone / secondary organic aerosols / volatile organic compounds (VOCs) / pollution mitigation / VOCs emission reduction / WRF⁃Chem simulation

0 引言

当前，中国面临着细颗粒物（PM_2.5）污染严重和臭氧（O₃）污染日益突出的双重问题.自2013年《大气污染防治行动计划》发布以来，华北平原（NCP）、长江三角洲（YRD）和珠江三角洲（PRD）等主要污染地区的PM_2.5污染持续缓解（ Zhai et al.， 2019； Shen et al.， 2021）.然而，在秋冬季节，严重的PM_2.5污染仍在全国主要城市地区频繁发生.2013年至2022年，北京经历了多次极其严重和持续的PM_2.5污染事件（Batterman et al.， 2016； Wang et al.， 2023b； Yin et al.， 2025）.与此同时，2013-2022年，中国地表臭氧水平呈上升趋势（Gao et al.， 2021a； Li et al.， 2021； Wei et al.， 2022a； Pan et al.， 2023）.

挥发性有机化合物（VOCs）是地表O₃的关键前体物（Liu et al.， 2021； Ma et al.， 2022），也是参与大气化学反应生成二次有机气溶胶（SOA）的重要物质（Guo et al.， 2017； Wang et al.， 2017； Wu et al.， 2017），部分VOCs对人类健康有害（祁士华等， 2001）.据估计，对流层中约90%的臭氧是由VOCs氧化形成的（ Moeller， 2004； Stevenson et al.， 2006； Young et al.， 2013； Veld et al.， 2024）.在中国，城市地区的O₃光化学生成对VOCs高度敏感（ Wang et al.， 2023c； Li et al.， 2024）.近年来O₃浓度的增加可能与城市区域在VOCs限制条件下NO _x 的减少有关.此外，SOA是大气中挥发性和半挥发性有机化合物反应的产物（Hallquist et al.， 2009； Ziemann and Atkinson， 2012； Zhu et al.， 2017），约占有机气溶胶总量的50%~85%（Jimenez et al.， 2009）.在中国主要污染地区，雾霾发生时SOA对PM_2.5的贡献为30%~77%（Huang et al.， 2014）.因此，如何约束VOCs排放以保证控制NO _x 在降低PM_2.5和O₃方面的有效性已成为政策制定中的重要挑战.

以往许多观测和模拟研究都讨论了VOCs减排及其对SOA和O₃浓度的影响.例如，Wei et al. （2022b）开展的WRF⁃Chem敏感性实验结果表明，VOCs减少30%将导致北京地区O₃浓度的总体下降，中部下降幅度较大，南北部下降幅度较小，但下降幅度都小于2%.张新宇等（2023）对保定市的臭氧污染研究结果显示，在郊区减少35%的VOCs排放能使臭氧浓度最大下降7.0 μg/m³，而在城区相同的减排能使臭氧浓度最大下降44.9 μg/m³.周德荣等（2023）利用WRF⁃CMAQ研究中国东部地区臭氧污染发现，在设置减少40%的VOCs和20%的NO _x 的减排情景下能较好改善臭氧污染情况，各城市臭氧浓度下降5.4~20.2 μg/m³.潘瑞欣等（2024）利用与前者相同的方法对我国工业城市开展模拟研究，结果表明通过设置减少高达80%的人为VOCs排放才使臭氧最大8 h（MDA8 O₃）浓度最大降低6.5 μg/m³，而需要人为VOCs和NO _x 协同减排才能明显降低MDA8 O₃浓度，最大减少75.4 μg/m³. Sharma et al. （2016）结合WRF⁃CMAQ对印度的臭氧污染研究发现，减少50%的人为VOCs排放导致臭氧浓度小幅下降2%.此外，Li et al. （2022a）利用区域空气质量模型系统（RAQMS）开展的华东地区SOA研究表明，减少全部人为VOCs排放后可使京津冀各城市的SOA减少39%~55%. Azmi and Sharma（2022）利用WRF⁃Chem对印度地区的生物SOA减排研究表明，减少25%~75%人为VOCs排放，其对生物SOA的控制只有1/10的有效性.这些研究旨在寻找VOCs减排对SOA和O₃的最大效益，而忽略了减排的实际可行性.准确评估一个地区人为VOCs的减排潜力及其协同治理SOA和O₃的效果，是一个迫切需要深入研究的大气环境科学问题.这对未来中国区域大气污染综合治理决策具有重要意义.

此外，VOCs具有种类繁多、时空变异性强、寿命短、测量难度高等特点（Mo et al.， 2016； Zhu et al.， 2019； Hui et al.， 2020； Li et al.， 2022b； Duan et al.， 2023； Epping and Koch， 2023）.因此，不同地区VOCs的来源和物理化学过程存在显著差异，这对VOCs减排研究的区域代表性提出了挑战.中国主要城市群的交通、工业、住宅和农业活动中大量人为挥发性有机化合物的排放（Wei et al.， 2011，2019； Huang and Hsieh， 2019； Xuan et al.， 2021； Simayi et al.， 2022）使大气SOA和O₃的来源变得复杂.本研究基于中国更精细、更现实、更全面的VOCs减排策略，构建一套涉及工业过程源、固定燃烧源、道路移动源和溶剂产品使用源等人为源VOCs综合减排情景；定量人为源VOCs排放对我国PM_2.5和O₃污染形成的贡献，并评估人为源VOCs减排对PM_2.5与O₃协同治理的有效性.

1 数据来源和方法

1.1　数据来源

地面PM_2.5和O₃逐时浓度数据（2017年）来源于中国生态环境部（https：//data.cma. cn/）.根据环境空气质量国家Ⅱ级标准，日最大8 h平均值（MDA8 O₃）超过160 μg/m³则为臭氧污染日.PM_2.5污染日的定义是PM_2.5的24 h平均浓度超过75 μg/m³.

笔者使用的逐小时气象数据来自第五代欧洲中期天气预报中心再分析业务全球分析数据（ERA5；水平分辨率：0.25°×0.25°；时间分辨率：1 h；https：//www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5）.

1.2　模型参数介绍

WRF⁃Chem模型是由NOAA的预测系统实验室开发的新一代区域空气质量模型，完全在线耦合了天气预报模型（WRF）和化学模型（Chem）.在模拟实验中，气象数据使用NCEP⁃FNL（https：//rda.ucar.edu/），为模拟提供侧边界条件和初始气象场，FNL数据采用1°×1°水平分辨率和6 h时间间隔.所选区域的水平网格为172×216，网格大小为25 km×25 km，垂直分辨率为28层（从地表到100 hPa）.主要分析中国污染严重的城市集中地区，包括NCP、YRD、华中地区（CC）、PRD和四川盆地（SCB）.

物理和化学参数方案的主要选择列于表1.用于模拟SOA浓度的气相化学机制是CB05并附加了氯化物反应（Sarwar et al.， 2008）.该机制已通过Kinetic PreProcessor（KPP）纳入WRF⁃Chem，包括97种大气成分，191个反应（其中涉及60多种挥发性有机化合物和120多个相关反应）.与RACM和MOZART机制相比，CB05机制能够模拟更复杂的VOCs反应过程，能够更好地表征冬季条件下SOA和PM_2.5的形成.此外，对于O₃的模拟，笔者使用了SAPRC⁃99气相化学机制（Carter， 1990）.该机制包括78种物质和211个化学方程，SAPRC⁃99的VOCs机制能够重现几乎在所有环境实验中观察到的VOCs氧化形成的O₃.在该机制中，VOCs在对流层的消耗过程主要与OH自由基发生反应.

1.3　减排情景设置

笔者利用2017年中国多分辨率排放清单（MEIC，1.3版，0.25°×0.25°）作为WRF⁃Chem的人为排放文件输入. MEIC清单的人为排放分为五大排放源：农业、工业、居民住宅、发电部门和交通运输.生物源排放使用MEGAN模型第2版进行在线计算.生物质燃烧排放输入使用NCAR（FINN）的火灾清单（Wiedinmyer et al.， 2011）.

一般情况下，PM_2.5超标事件一般发生在冬季，而臭氧超标事件多发生在夏季.因此，笔者选取模型模拟2017年2月11日至17日（PM_2.5超标时段）和6月1日至9日（O₃超标时段）全国范围内的气象场和污染物时空分布.在这两个时期，五个主要区域的核心城市都处于污染状态（图1）.模型考虑了一周的启动时间，以确保消除初始不稳定性和不确定性.为了分析VOCs减排对SOA和O₃浓度的影响，分别在人为VOCs排放总量减少20%、40%、60%、80%和100%的情况下各进行了5次敏感性模拟（表2）.此外，笔者还设计了另外5个VOCs排放情景，分别关闭农业、工业、居民住宅、发电部门和交通运输的VOCs排放，以计算每个部门对SOA和O₃贡献的相对重要性.

对于REAL敏感性试验，笔者从文献中总结了每个源的减排潜力（表3），并基于MEIC排放清单的5个源设置了VOCs减排比例（Li et al.， 2017； Zheng et al.， 2018）.基于表3的总结，本文的减排方案设置为工业VOCs总排放量减少约55%，交通VOCs总排放量减少53%，居民VOCs总排放量减少90%.根据Simayi et al.（2021）和Wang et al. （2023a）的研究，控制石油化工行业的无组织排放可以使VOCs排放减少约40%.其中无组织排放的控制方法包括：（1）使用不含VOCs的原料和低VOCs含量的产品；（2）改进现有工艺，减少生产过程中挥发性有机化合物的逸出；（3）利用泄漏检测与修复系统定期检测VOCs泄漏点.在MEIC清单中，石油化工是VOCs的主要来源，水泥厂、钢铁厂和工业锅炉的排放量都相对较小（Xue et al.， 2016； Wang et al.， 2022； Bai et al.， 2023）.

溶剂行业主要包括用于汽车和建筑墙壁的工业涂料、工业粘合剂、印刷、脱脂、农药使用、制药和干洗.根据Shi et al. （2023）以及Jiménez⁃López and Hincapié⁃Llanos （2022）的研究，用绿色涂料替代传统涂料可以减少高达70%至80%的VOCs排放.Gao et al. （2021b）认为实施更严格的VOCs限制措施可以将工业粘合剂的VOCs排放量减少60%.此外，在印刷行业，减少VOCs的排放可以通过使用低VOCs含量的原材料，增加生产过程的气密性，并实施废物收集处理措施（减少46%至66%的排放量）来实现（You et al.， 2023）.Zheng et al.（2017）研究发现，吸附/冷凝分离/催化燃烧的应用可使农药排放的VOCs量减少70%~90%，而冷凝分离也可使干洗过程中产生的VOCs减少70%~85%.此外，利用泄漏检测与修复系统可以将制药过程中VOCs的产生减少55%至72%（Zhang et al.， 2021）.

居民住宅挥发性有机化合物的主要来源是使用固体燃料，包括煤和生物质燃料.He et al.（2023）和Sun et al.（2019）的研究表明，用半气化炉和煤球组合代替传统的炉子和燃料可以减少高达90%的VOCs排放.此外，交通运输通常分为两类：道路交通和非道路交通.在道路交通方面，Wu et al. （2023）认为，加强排放监测和执行更严格的法律规定措施，可以有效遏制51%的乘用车VOCs排放.为摩托车安装排放控制装置可以减少45%~88%的VOCs排放（Dhital et al.， 2019）.在非道路运输中，船舶是VOCs的主要来源，约占67%.Wu et al. （2019）明确表示，用清洁燃料替代传统燃料可以减少船舶VOCs排放高达67%.

2 结果与讨论

2.1　模拟结果对比验证

为了更好地评价模型，首先比较了气象因子的模拟结果（表4）.模型能较好地再现各气象要素的时空分布，全国范围各气象要素平均相关系数为0.80~0.95，NMB和NME小于3.0%.风速和风向在整个模拟期间符合Emery et al. （2017）提出的性能基准.

同时，WRF⁃Chem模型能够较好地模拟PM_2.5和O₃的时空演变，观测数据与模拟结果的相关系数分别为0.51~0.74（PM_2.5）和0.70~0.88（O₃），如图2所示.WRF⁃Chem模式略微高估了NCP和YRD地区PM_2.5浓度（MB分别为2 μg/m³和1 μg/m³），低估了CC、PRD和SCB地区的PM_2.5，特别是PRD地区（MB=-16 μg/m³）.前人的研究也普遍高估NCP地区的PM_2.5浓度（Yang et al.， 2020），低估南方地区和SCB的PM_2.5浓度（Liu et al.， 2017； Wang et al.， 2020）.然而，WRF⁃Chem模式显著高估了臭氧浓度，MB可达41 μg/m³.Gao et al.（2020）报告称， WRF⁃Chem模型会倾向于将O₃浓度预测得更高.

2.2　REAL减排情景下SOA、PM_2.5和O₃污染的缓解

图3a和3b显示了2月11日至17日REAL实验与CON实验SOA和PM_2.5浓度差异的空间分布.在REAL减排情景下，我国整个中东部地区和南部地区的SOA浓度有明显的下降.其中浓度下降最大的是SCB地区，成都地区最为明显，SOA浓度下降最大约6.34 μg/m³.其次是华中地区、珠三角和长三角地区，SOA浓度降低的最大值分别是3.31 μg/m³、2.92 μg/m³和2.61 μg/m³.在模拟时间段内，根据可实施的减排措施设置VOCs减排，REAL减排设置可使SCB、CC、PRD、YRD和NCP地区SOA平均浓度分别显著降低2.80 μg/m³（77.3%）、 2.04 μg/m³（74.5%）、1.49 μg/m³（72.0%）、1.37 μg/m³（80.6%）和0.73 μg/m³（88.0%）.VOCs对PM_2.5的减排效果与SOA的结果相似，SCB地区浓度下降最大，最大值为34.58 μg/m³.SCB、CC、PRD、YRD和NCP地区PM_2.5平均浓度分别显著降低14.94 μg/m³、10.55 μg/m³、7.80 μg/m³、5.51 μg/m³和3.92 μg/m³.这说明VOCs减排措施在我国中东部地区和南部地区对减少SOA和PM_2.5浓度具有显著效果，特别是在SCB地区效果最为明显，且减排效果在不同区域之间存在差异.

图3b显示了6月2日至9日O₃浓度从CON到REAL的平均变化.在REAL减排情景下，O₃浓度有明显的下降，其中浓度下降最大的是长三角地区，O₃浓度下降最大约16.74 μg/m³.此外，华中地区O₃浓度也有明显的降低，河南的东南部、湖北的东部地区和安徽地区O₃浓度值最大可减少11.87 μg/m³.平均而言，在模拟时间段内，REAL减排设置可使YRD、NCP、CC、PRD和SCB地区平均O₃浓度分别降低6.11 μg/m³（5.1%）、4.05 μg/m³（3.6%）、 4.00 μg/m³（3.2%）、3.50 μg/m³（4.9%）和1.87 μg/m³（1.5%）.Wei et al. （2018）对北京地区设置的减少30%VOCs敏感性试验表明，在工业区和农村地区O₃浓度有一定程度的升高（5×10^-9），在城市地区有少量的下降（2×10^-9）.当VOCs排放设置为0时，工业区臭氧贡献12.7%，而对市中心和农村地区贡献仅1.9%和0.4%.Li et al. （2020）利用WRF⁃Chem对兰州地区臭氧减排研究表明，在臭氧平均浓度高于150 μg/m³的背景下，减少10%的TVOC排放，O₃浓度下降了5%.在他们模拟的时间段内，兰州处于VOCs限制，使臭氧浓度明显降低的方案仍是减少NO _x .要使O₃得到最大程度的改善，需要NO _x 和VOCs协同减排.

2.3　人为源VOCs排放对SOA、PM_2.5和O₃浓度的贡献

为了定量计算人为源VOCs排放对SOA、PM_2.5和O₃浓度的贡献，图4和附图1显示了不同VOCs排放量设置下，SOA和PM_2.5平均浓度与控制实验的差异.VOCs减排比例越大，SOA和PM_2.5浓度下降越多（附图2和附图3），其中SCB地区减少最为显著（SOA：1.31~3.45 μg/m³；PM_2.5：6.46~18.95 μg/m³），这与前面的REAL试验显示出了类似的结果.通过完全消除人为VOCs排放，可使模拟时间段内5个地区绝大部分的SOA显著降低（NCP：97.5%、YRD：95.8%、CC：93.8%、PRD：92.3%和SCB：95.3%）.除NCP地区外，其他城市地区PM_2.5浓度值可减少13.0%~22.1%（NCP下降约3.5%）.SOA只贡献部分PM_2.5，所以VOCs对PM_2.5的贡献更少.对于特定行业的减排结果（附图2和附图3），减少工业VOCs排放实现了最显著的SOA和PM_2.5减缓，使SOA浓度平均降低了1.74 μg/m³，PM_2.5浓度平均降低了8.78 μg/m³.下降最明显的地区是SCB地区（SOA：2.90 μg/m³；PM_2.5：15.33 μg/m³）.其次是居民住宅和交通运输，他们对污染物的贡献相似，可分别使SOA平均浓度降低 0.66 μg/m³和0.70 μg/m³，PM_2.5平均浓度降低3.13 μg/m³和3.33 μg/m³.另外值得注意的是，与其他城市地区有所差异的是，珠三角地区交通运输行业排放的VOCs比居民住宅对SOA （0.67 μg/m³，0.45 μg/m³）的贡献更大.由此可见，通过对工业、交通运输和居民住宅VOCs排放的控制，可以明显改善我国城市地区SOA的污染情况.

类似地，图5显示了不同VOCs排放量设置下，O₃平均浓度与控制实验的差异.结果显示：VOCs减排比例越大，可使5个城市地区O₃平均浓度依次降低0.49~7.29 μg/m³（附图4）.其中减少最明显的是YRD地区，臭氧浓度减少最大值为8.42~26.14 μg/m³.通过完全消除人为VOCs排放，5个城市地区臭氧浓度有一定程度的下降（NCP：7.55 μg/m³、YRD：9.05 μg/m³、CC： 7.29 μg/m³、PRD：4.31 μg/m³和SCB：3.15 μg/m³）.对于特定行业的减排（附图4），结果表明减少工业排放VOCs实现了最显著的O₃减排，5个地区O₃浓度平均降低3.28 μg/m³，下降最明显的地区是YRD地区（最大减少16.00 μg/m³）.其次居民住宅可使臭氧平均浓度降低2.07 μg/m³（约2.1%）.然后是交通运输，可以导致臭氧平均浓度减少0.98 μg/m³（约1.0%）.值得注意的是，由于臭氧光化学生成的非线性效应，在ARG和POW减排情景下，部分地区的臭氧浓度略有增加.此外，在MEIC排放清单的总结中，农业和发电这两个部门对VOCs的贡献较少，其中农业对VOCs的贡献为0.总的来说，仅通过减少VOCs排放，对臭氧污染的控制效果不佳，根据前人的经验，可能还需考虑氮氧化物联合制定减排方案.

3 结论

为了清晰全面地了解VOCs减排潜力，本研究建立了一套完善的VOCs减排策略，实施2017年中国五大工业排放源VOCs减排设定.同时，建立不同比例、不同行业的VOCs减排情景，与精细化的情景进行对比验证.使用WRF⁃Chem模型定量分析了VOCs控制策略对SOA和O₃的缓解作用.基于PM_2.5和O₃的空气环境质量的国家二级标准，选取PM_2.5超标和O₃超标两个时间段进行WRF⁃Chem模拟，评估VOCs减排对SOA和O₃的影响.

根据MEIC排放清单的总结，工业（工业+溶剂）、交通和居民是人为挥发性有机化合物的主要来源.精细化的VOCs减排策略可使工业VOCs总排放量减少约55%，交通VOCs总排放量减少53%，居民VOCs总排放量减少90%.在此减排配置下，NCP地区的SOA可减少约88.0%，YRD、CC、PRD和SCB地区的SOA浓度可分别减少约80.6%、74.5%、72.0%和77.3%.SCB、CC、PRD、YRD和NCP地区PM_2.5平均浓度可分别降低 14.94 μg/m³、10.55 μg/m³、7.80 μg/m³、5.51 μg/m³和3.92 μg/m³.然而，本文的实验结果显示VOCs对O₃的影响相对较小.在相同的减排情景下，各城市地区O₃浓度下降幅度均小于10%，但在量级尺度上，可使O₃浓度平均降低3.91 μg/m³，最大减少约 16.74μg/m³，说明控制城市VOCs排放值得人们关注.

为了验证精细化VOCs减排方案的可靠性，笔者另设置了10个敏感性试验进行对比验证.当设置的VOCs减排比例越大时，SOA和PM_2.5浓度降低越多.其中，四川盆地减少最为明显.此外，工业排放的VOCs是SOA和PM_2.5的主要贡献者，可让5个城市地区的SOA浓度降低0.74~2.90 μg/m³，PM_2.5浓度降低3.93~15.33 μg/m³.其次，交通运输和居民住宅排放可以分别减少约0.70 μg/m³和0.66 μg/m³的SOA，以及3.13 μg/m³和3.33 μg/m³的PM_2.5.但VOCs对O₃的控制效果不佳，VOCs减排比例越大时，O₃浓度降低越明显.工业VOCs减排对O₃的缓解效果最明显，可使城市地区O₃平均浓度降低约3.48 μg/m³.其次是交通运输和居民住宅，平均浓度分别降低约2.07 μg/m³和0.98 μg/m³.

本次研究结果表明，通过减少VOCs的排放可以一定程度地减少SOA和O₃.此外，VOCs会影响大气中原有的光化学平衡，产生·OH等自由基，进而促进臭氧和二次颗粒物的生成.所以，控制VOCs排放对于减少O₃和PM_2.5污染具有关键作用.但由于VOCs和NO _x 的减排效果之间存在一定的非线性关系，单一减排VOCs可能无法达到预期的效果.且VOCs的排放源复杂多样，使得全面控制和减少VOCs排放具有一定的难度.NO _x 和VOCs是O₃和PM_2.5的共同前体物，在很大程度上决定了O₃和PM_2.5的浓度.此外，NO _x 和VOCs与O₃浓度呈非线性关系，在NO _x 限制区减少VOCs的排放可能引起臭氧浓度的升高.NO _x 和VOCs的协同减排，可以在两者之间找到平衡点，实现最佳减排效果.因此，为了进一步控制SOA及PM_2.5和O₃浓度，除了控制氮氧化物和颗粒物的排放外，还必须制定减少VOCs排放的策略，本研究结果可为其提供科学依据.

附图见https://doi.org/10.3799/dqkx.2024.090.

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基金资助

国家自然科学基金面上项目(42475186)