VOCs排放控制对SOA和O<sub>3</sub>的减缓作用

马静; 燕莹莹; 孔少飞; 王五科; 童芷萱

doi:10.3799/dqkx.2024.090

VOCs排放控制对SOA和O₃的减缓作用

doi: 10.3799/dqkx.2024.090

马静^1, ,,
燕莹莹^{1, 2, ,},
孔少飞^{1, 2},
王五科¹,
童芷萱¹

1.
中国地质大学(武汉)环境学院, 湖北武汉 430078
2.
湖北省大气复合污染研究中心, 湖北武汉 430074

基金项目:

国家自然科学基金面上项目 42475186

详细信息

作者简介:
马静（1998-），男，博士研究生，主要研究方向大气环境、臭氧污染成因和来源. ORCID：0009-0002-0003-166X. E-mail：2426410516@qq.com

通讯作者:
燕莹莹, E-mail: yanyingying@cug.edu.cn

中图分类号: P404
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出版历程
- 收稿日期: 2024-07-29
- 网络出版日期: 2025-10-10
- 刊出日期: 2025-09-25

The Mitigation of SOA and O₃ by VOCs Emission Reduction

Ma Jing^{1
,
,},
Yan Yingying^{1, 2
, ,},
Kong Shaofei^{1, 2},
Wang Wuke¹,
Tong Zhixuan¹

1.
School of Environmental Studies, China University of Geosciences (Wuhan), Wuhan 430078, China
2.
Research Center for Complex Air Pollution of Hubei Province, Wuhan 430074, China

摘要

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

HTML全文

图 1 2017年1月至3月五个主要区域的核心城市PM_2.5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 PM_2.5 in five urban areas from January to March 2017 (a); Average O₃ observations for five urban areas from June to August 2017 (b)

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图 2 5个城市地区PM_2.5（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PM_2.5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 PM_2.5 (a-e) and O₃ (f-j) in 5 urban areas

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图 3 CON和REAL实验SOA（a）、PM_2.5（b）和O₃（c）浓度的差异

Fig. 3. Difference in SOA (a), PM_2.5 (b) and O₃ (c) concentration of CON and REAL experimental

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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

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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

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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

下载: 导出CSV

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
	道路车辆	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

下载: 导出CSV

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（%）
PM_2.5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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