2013～2020年北京市城区PM<sub>2.5</sub>及其化学组分正增长机制研究

江琪; 张碧辉; 赵有龙; 王飞; 孙业乐

doi:10.3878/j.issn.1006-9895.2110.21142

2013～2020年北京市城区PM_2.5及其化学组分正增长机制研究

doi: 10.3878/j.issn.1006-9895.2110.21142

江琪^{1, 2,},
张碧辉^1, ,,
赵有龙³,
王飞⁴,
孙业乐⁵

1.
国家气象中心, 北京100081
2.
中国气象局交通气象重点实验室, 南京210008
3.
河北省邯郸市气象局, 邯郸056001
4.
中国气象局云降水物理与人工影响天气重点开放实验室, 北京 100081
5.
中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室, 北京100029

基金项目: 国家重点研发计划项目2019YFC0214602，国家自然科学基金项目41875181，中国气象局气象预报业务关键技术发展专项 YBGJXM(2019)02-02，国家气象中心预报员专项CMAYBY2021-092，南京气象科技创新研究院北极阁基金BJG202106，中国气象局大气化学重点开放实验室开放课题2020B05，北京春季天气预报短期攻关CXFZ2022J013

详细信息

作者简介:
江琪，女，1989年出生，高工，主要从事大气环境预报和气溶胶相关研究工作。E-mail: Jiangqi89@163.com

通讯作者:
张碧辉，E-mail: bihui_zhang@qq.com

中图分类号: P402
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-04
- 录用日期: 2021-12-21
- 网络出版日期: 2021-12-29
- 刊出日期: 2023-03-15

Growth Mechanism of PM_2.5 and Its Chemical Components in Beijing’ s Urban Area from 2013 to 2020

Qi JIANG^{1, 2
,},
Bihui ZHANG^{1
, ,},
Youlong ZHAO³,
fei WANG⁴,
Yele SUN⁵

1.
National Meteorological Centre, Beijing 100081
2.
Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing 210008
3.
Handan Meteorological Bureau of Hebei Province, Handan 056001
4.
Cloud−Precipitation Physics and Weather Modification Key Laboratory, China Meteorological Administration, Beijing 100081
5.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Funds: National Key Research and Development Program (Grant 2019YFC0214602), National Natural Science Foundation of China (Grant 41875181), China Meteorological Administration’ s Special Project for the Development of Key Technologies for Meteorological Forecasting (Grant YBGJXM(2019)02-02), National Meteorological Center Forecaster Special Project (Grant CMAYBY2018-092), Arctic Pavilion Fund of Nanjing Institute of Meteorological Science and Technology Innovation (Grant BJG202106), Key Open Laboratory of Atmospheric Chemistry, China Meteorological Administration (Grant 2020B05), Short-Term Tackling of Beijing Spring Weather Forecast Program（Grant CXFZ2022J013）

摘要

摘要: 2013年以来，北京市城区细颗粒物（PM_2.5）质量浓度年均值呈逐年降低趋势，但PM_2.5重污染事件仍旧频发，污染出现快速甚至爆发增长的成因和理化机制仍存在诸多不确定性。基于北京市城区2013～2020年常规气象要素、PM_2.5及其化学组分观测资料，分析了PM_2.5在缓慢、快速和爆发三种增长机制下的颗粒物浓度和组分的阈值及其与气象条件的相关关系。结果表明，2013～2020年，北京市城区PM_2.5在增长时段（1～24 h间隔）中平均累积速率呈逐年放缓的趋势，大气污染累积阶段中缓慢增长的比重逐年升高。在判别标准逐渐严苛的前提下，爆发增长的比重逐年变化不大（4%～7%）。2013～2016年爆发增长的PM_2.5浓度阈值为62 µg m⁻³，2017年后，该阈值严苛至45 µg m⁻³。82 µg m⁻³为2018年以来极易出现PM_2.5爆发增长的界限值，高于此值后爆发增长的概率将大幅提升。有机物（Org）在爆发增长中起到了至关重要的作用。同一时间间隔Org在亚微米气溶胶（PM₁）增长浓度中的贡献缓慢增长＜快速增长＜爆发增长，其中一次有机气溶胶（POA）在快速和爆发增长中对Org增长浓度的贡献平均超过50%，高于研究时段40%的平均占比。无机组分中，硫酸盐（SO₄²⁻）在PM₁增长浓度中的贡献爆发增长（13%）＞快速增长（11.8%）＞缓慢增长（11.1%），硝酸盐（NO₃⁻）的贡献相反。二次气溶胶（SPM）在累积阶段的贡献高于一次气溶胶（PPM），但爆发增长中，PPM在污染增长中贡献（最高达45%）明显高于平均时段的33%，PPM在爆发增长中的贡献不可小觑。秋冬季的爆发增长开始后，温度和气压均有所降低，而湿度明显升高。北京城区爆发增长中主要的气团来向为偏南向（三个高度占比分布为69%～82%），其次为偏东方向（12%～20%）。
- 爆发增长 /
- 化学组分 /
- 阈值 /
- 气象要素 /
- PM_2.5 /
- 污染累积
Abstract: Since 2013, the annual average mass concentration of PM_2.5 in Beijing’s urban area has been decreasing annually, but heavy PM_2.5 pollution incidents have continued to occur frequently. Moreover, there exist many uncertainties regarding the causes as well as the physical and the chemical mechanisms of the rapid or even the explosive growth of the pollution. This study analyzes the thresholds of conventional meteorological elements, PM_2.5, and its chemical components under the three growth mechanisms, slow, rapid, and explosive, as well as the correlation between the changes in meteorological elements and increase in the pollutant concentration from 2013 to 2020 in Beijing’s urban area. The results showed that from 2013 to 2020, the average accumulation rate of PM_2.5 had a slowing trend, and the proportion of slow growth in the accumulation phase of PM_2.5 increased annually in Beijing. Under the premise that the criterion is gradually strict, the proportion of explosive growth has not shown a drastic change yearly (4%–7%). The PM_2.5 concentration threshold for an explosive increase from 2013 to 2016 was 62 µg m⁻³. After 2017, the threshold was strict to 45 µg m⁻³, which has become 82 µg m⁻³ since 2018. After this value, the probability of explosive growth will increase significantly. Organic aerosol (Org) played a vital role in the explosive growth. In the same time interval, the order of the contribution of Org to the growth concentration of submicron aerosol species (PM₁) is slow growth < fast growth < explosive growth. The contribution of primary OA (POA) in the rapid and burst growth to the Org growth concentration on average exceeds 50%, which is higher than the average proportion of 40% during the study period. Among the inorganic components, the contribution of SO₄²⁻ in the increasing concentration of PM₁ shows the order of explosive growth (13%) > fast growth (11.8%) > slow growth (11.1%). Meanwhile, the opposite is observed for the contribution of NO₃⁻. The contribution of secondary particulate matter (SPM) in the cumulative phase is higher than that of the primary particulate matter (PPM). However, the contribution of PPM to the pollution increase (up to 45%) in the explosive growth is significantly higher than 33% in the average period, indicating that the contribution of PPM to the explosive growth cannot be underestimated. After the explosive growth began, the temperature and pressure both decreased, while the humidity increased significantly in autumn and winter. The main air mass in the explosive growth is southward (the three heights account for 69%–82%), followed by the eastward direction (12%–20%) in Beijing’s urban area.
- Explosive growth /
- Chemical composition /
- Threshold /
- Meteorological element /
- PM_2.5 /
- Pollution accumulation

HTML全文

图 1 2013～2020年（纵坐标左轴）中1～24 h 不同时间间隔内（横坐标表示时间间隔为1 h、2 h、···、24 h）+ΔPM_2.5年均值（图中刷色表）以及该间隔+ΔPM_2.5的8年均值（图中圆点，数值对应右轴和颜色标尺）

Figure 1. Annual average of +ΔPM_2.5 (color table in the figure) and the 8-year average of +ΔPM_2.5 (dots in the figure, value corresponds to the right axis and the color bar) from 2013 to 2020 (left axis of the ordinate) within the 1–24-h interval (abscissa is divided into intervals of 1 h, 2 h, ···, 24 h)

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图 2 2013～2020年1～24 h间隔中采用（a–c）方案1和（d–f）方案2两种判别条件下缓慢（HM）、快速（KS）和爆发（BF）增长在总增长次数中所占比重

Figure 2. Proportion of slow (HM), rapid (KS), and explosive (BF) growth in the total number of growth under two discrimination conditions of (a–c) Plan 1and (d−f) Plan 2 in the 1–24-h interval from 2013 to 2020

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图 3 2013～2020年1～24 h间隔中采用方案1（下）和方案2（上）两种判别条件下爆发增长发生前PM_2.5浓度值的下四分位（25P）和上四分位数（75P）

Figure 3. Lower (25P) and upper quartiles (75P) of the PM_2.5 concentration before the explosive growth occurs under scheme 1 (bottom) and scheme 2 (top), respectively, in the 1–24-h interval from 2013 to 2020

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图 4 2012年7月至2013年5月1～24 h间隔内缓慢（HM）、快速（KS）和爆发（BF）增长中PM₁各组分增长值在PM₁总增长值中所占比重以及三种增长方式次数在所有增长时段中所占的比重

Figure 4. Growth rate of each component of PM₁ in slow (HM), rapid (KS), and explosive (BF) growth and the proportion of the three growth methods in all growth periods (purple circle) in the 1–24-h interval from July 2012 to May 2013

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图 5 三种增长方式中HOA和OOA的增长浓度在Org增长值中所占比重

Figure 5. Proportion of the growth concentration of hydrocarbon-like organic aerosol (HOA) and oxygenated organic aerosol (OOA) in the growth value of organic aerosol (Org) in the three growth methods

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图 6 三种增长方式中PPM和SPM的增长浓度在PM₁增长值中所占比重

Figure 6. Proportion of the growth concentration of PPM (primary particulate matter) and SPM (secondary particulate matter) in the growth value of PM₁ among the three growth methods.

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图 7 1 h间隔时PM₁各组分在爆发增长发生前的质量浓度统计图, 圆点为平均值，竖线分别为第10百分位（下）和第90百分位数（上），横线自上而下分别为第25、50和75百分位数（下图同）

Figure 7. Statistical graph of the mass concentration of each component of PM1 before the explosive growth occurred in the 1-h interval. The dot is the average, the vertical line is the 10th (bottom) and 90th percentile (top), and the horizontal line is the 25th, 50th, and 75th percentile from top to bottom (the picture below is the same).

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图 8 1h间隔时PM₁各组分在全部增长阶段（GP）、缓慢（HM）、快速（KS）和爆发增长（BF）中的增长浓度统计值。

Figure 8. Statistical values of the growth concentration of each component of PM₁ in all growth stages (GP), slow (HM), fast (KS), and explosive (BF), at the 1-h interval.

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图 9 秋冬季1～24 h间隔时爆发、快速和缓慢三种方式发生前温度（Temp）、气压（Pres）、风速（WS）、湿度（RH）和混合层高度（MLH）统计图

Figure 9. Statistic graphs of temperature (Temp), pressure (Pres), wind speed (WS), humidity (RH), and mixed layer height (MLH) before the explosive, fast, and slow growth at the 1–24-h interval in autumn and winter

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图 10 爆发增长中100 m、300 m和500 m高度中72 h污染物后向轨迹叠加图

Figure 10. Overlay of backward trajectories of pollutants at heights of 100, 300, and 500 m during the explosive growth.

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表 1 秋冬季三种增长阶段1～24 h间隔中各气象要素的平均变化值

Table 1. Average change value of each meteorological element in the 1–24-h interval of the three growth stages in autumn and winter

间隔	相对湿度			温度/°C			混合层高度/m			风速/m s⁻¹			气压/hPa
间隔	HM	KS	BF	HM	KS	BF	HM	KS	BF	HM	KS	BF	HM	KS	BF
1 h	0.2%	0.7%	3.5%	0.1	0.0	−0.2	−2.0	−1.8	0.1	0.0	0.0	−0.1	−0.1	−0.1	−0.1
2 h	1.6%	1.9%	5.0%	−0.1	0.0	−0.4	−0.5	−1.5	−4.0	0.0	0.0	−0.1	0.0	−0.1	−0.1
3 h	2.4%	3.1%	6.2%	−0.3	−0.1	−0.6	4.7	0.9	−5.7	−0.1	0.0	−0.1	0.0	−0.1	−0.1
4 h	3.4%	4.2%	7.4%	−0.4	−0.2	−0.7	12.3	5.1	12.2	−0.1	−0.1	−0.1	−0.1	−0.1	−0.2
5 h	4.3%	5.0%	9.1%	−0.5	−0.2	−1.0	20.3	10.8	18.4	−0.1	−0.1	−0.1	−0.1	−0.3	−0.2
6 h	5.1%	6.2%	9.8%	−0.6	−0.4	−1.1	27.7	20.3	32.4	−0.2	−0.1	−0.2	−0.2	−0.3	−0.3
7 h	5.9%	7.1%	10.2%	−0.7	−0.4	−1.1	41.3	17.5	43.6	−0.2	−0.1	−0.2	−0.3	−0.5	−0.4
8 h	6.6%	7.8%	11.3%	−0.7	−0.4	−1.2	53.6	21.5	48.3	−0.2	−0.2	−0.2	−0.4	−0.6	−0.6
9 h	7.0%	8.1%	11.4%	−0.8	−0.3	−1.1	59.6	16.4	33.1	−0.2	−0.2	−0.2	−0.5	−0.8	−0.8
10 h	7.5%	8.7%	11.8%	−0.8	−0.4	−1.1	59.0	20.5	16.6	−0.2	−0.2	−0.2	−0.6	−0.9	−1.0
11 h	7.9%	9.1%	11.8%	−0.8	−0.3	−0.9	57.7	15.8	−15.8	−0.2	−0.2	−0.3	−0.6	−1.0	−1.2
12 h	8.2%	9.2%	12.1%	−0.7	−0.2	−0.8	52.1	−2.0	−19.0	−0.3	−0.2	−0.3	−0.7	−1.2	−1.4
13 h	8.3%	9.4%	11.7%	−0.6	−0.2	−0.6	38.8	−5.1	−40.6	−0.3	−0.2	−0.3	−0.8	−1.3	−1.5
14 h	8.3%	9.8%	11.4%	−0.6	−0.1	−0.4	27.6	−15.9	−39.1	−0.2	−0.2	−0.3	−0.8	−1.4	−1.6
15 h	8.2%	9.8%	11.9%	−0.6	−0.1	−0.4	10.6	−14.8	−40.2	−0.2	−0.2	−0.2	−0.9	−1.5	−1.7
16 h	8.1%	10.1%	11.7%	−0.5	0.0	−0.2	−5.3	−13.5	−42.8	−0.2	−0.2	−0.2	−0.9	−1.6	−1.9
17 h	7.8%	10.4%	12.2%	−0.4	0.0	−0.2	−19.5	−13.3	−40.0	−0.2	−0.2	−0.2	−1.0	−1.7	−2.0
18 h	7.6%	10.5%	12.3%	−0.3	0.1	−0.2	−34.2	−6.8	−38.0	−0.2	−0.2	−0.2	−1.1	−1.9	−2.1
19 h	7.5%	10.6%	12.7%	−0.2	0.2	−0.2	−45.0	−13.2	−27.9	−0.2	−0.2	−0.2	−1.1	−2.0	−2.3
20 h	7.3%	10.8%	13.1%	−0.1	0.2	−0.3	−51.8	−4.1	−26.5	−0.2	−0.2	−0.2	−1.2	−2.2	−2.4
21 h	7.2%	11.0%	13.9%	0.1	0.3	−0.2	−56.1	−8.9	−10.4	−0.2	−0.2	−0.1	−1.3	−2.3	−2.6
22 h	7.1%	11.3%	14.4%	0.1	0.3	−0.2	−60.0	−9.5	−8.3	−0.2	−0.2	−0.1	−1.3	−2.4	−2.9
23 h	7.1%	11.5%	15.6%	0.2	0.3	−0.2	−60.2	−19.6	2.4	−0.2	−0.2	−0.2	−1.4	−2.6	−3.0
24 h	7.1%	11.7%	16.6%	0.2	0.4	−0.3	−63.4	−28.7	23.0	−0.2	−0.2	−0.2	−1.4	−2.7	−3.1

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参考文献(30)

[1]	Cai S Y, Wang Y J, Zhao B, et al. 2017. The impact of the “Air Pollution Prevention and Control Action Plan” on PM_2.5 concentrations in Jing–Jin–Ji region during 2012–2020 [J]. Science of the Total Environment, 580: 197−209. doi: 10.1016/j.scitotenv.2016.11.188
[2]	Guo S, Hu M, Wang Z B, et al. 2010. Size-resolved aerosol water-soluble ionic compositions in the summer of Beijing: Implication of regional secondary formation [J]. Atmospheric Chemistry and Physics, 10(3): 947−959. doi: 10.5194/acp-10-947-2010
[3]	Huang K, Zhuang G, Lin Y, et al. 2012. Typical types and formation mechanisms of haze in an eastern Asia megacity, Shanghai [J]. Atmospheric Chemistry and Physics, 12(1): 105−124. doi: 10.5194/acp-12-105-2012
[4]	江琪, 孙业乐, 王自发, 等. 2013. 应用颗粒物化学组分监测仪(ACSM)实时在线测定致霾细粒子无机和有机组分 [J]. 科学通报, 58(36): 3818−3828. doi: 10.1360/972013-501 Jiang Qi, Sun Yele, Wang Zifa, et al. 2013. Real-time online measurements of the inorganic and organic composition of haze fine particles with an Aerosol Chemical Speciation Monitor (ACSM) [J]. Chinese Science Bulletin (in Chinese), 58(36): 3818−3828. doi: 10.1360/972013-501
[5]	Jiang Q, Sun Y L, Wang Z, et al. 2015. Aerosol composition and sources during the Chinese Spring Festival: Fireworks, secondary aerosol, and holiday effects [J]. Atmospheric Chemistry and Physics, 15(11): 6023−6034. doi: 10.5194/acp-15-6023-2015
[6]	Lei L, Xie C H, Wang D W, et al. 2020. Fine particle characterization in a coastal city in China: Composition, sources, and impacts of industrial emissions [J]. Atmospheric Chemistry and Physics, 20(5): 2877−2890. doi: 10.5194/acp-20-2877-2020
[7]	Liu L K, Zhang X Y, Zhong J T, et al. 2019. The ‘two-way feedback mechanism’ between unfavorable meteorological conditions and cumulative PM_2.5 mass existing in polluted areas south of Beijing [J]. Atmos. Environ., 208: 1−9. doi: 10.1016/j.atmosenv.2019.02.050
[8]	Liu X H, Zhu B, Kang H Q, et al. 2021. Stable and transport indices applied to winter air pollution over the Yangtze River Delta, China [J]. Environmental Pollution, 272: 115954. doi: 10.1016/j.envpol.2020.115954
[9]	吕梦瑶, 张恒德, 王继康, 等. 2019. 2015年冬季京津冀两次重污染天气过程气象成因 [J]. 中国环境科学, 39(7): 2748−2757. doi: 10.3969/j.issn.1000-6923.2019.07.007 Lü Mengyao, Zhang Hengde, Wang Jikang, et al. 2019. Analysis of meteorological causes of two heavily polluted weather processes in Beijing–Tianjin–Hebei region in winter of 2015 [J]. China Environmental Science (in Chinese), 39(7): 2748−2757. doi: 10.3969/j.issn.1000-6923.2019.07.007
[10]	Ng N L, Herndon S C, Trimborn A, et al. 2011. An aerosol chemical speciation monitor (ACSM) for routine monitoring of the composition and mass concentrations of ambient aerosol [J]. Aerosol Science and Technology, 45(7): 780−794. doi: 10.1080/02786826.2011.560211
[11]	Sun Y L, Wang Z F, Dong H B, et al. 2012. Characterization of summer organic and inorganic aerosols in Beijing, China with an aerosol chemical speciation monitor [J]. Atmos. Environ., 51: 250−259. doi: 10.1016/j.atmosenv.2012.01.013
[12]	Sun Y L, Jiang Q, Wang Z F, et al. 2014. Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013 [J]. J. Geophys. Res., 119(7): 4380−4398. doi: 10.1002/2014JD021641
[13]	Sun Y L, Du W, Fu P Q, et al. 2016. Primary and secondary aerosols in Beijing in winter: Sources, variations and processes [J]. Atmospheric Chemistry and Physics, 16(13): 8309−8329. doi: 10.5194/acp-16-8309-2016
[14]	Ulbrich I M, Canagaratna M R, Zhang Q, et al. 2009. Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data [J]. Atmospheric Chemistry and Physics, 9(9): 2891−2918. doi: 10.5194/acp-9-2891-2009
[15]	Wang Z F, Li J, Wang Z, et al. 2014. Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control [J]. Science China Earth Sciences, 57(1): 3−13. doi: 10.1007/s11430-013-4793-0
[16]	Wang D F, Zhou B, Fu Q Y, et al. 2016. Intense secondary aerosol formation due to strong atmospheric photochemical reactions in summer: Observations at a rural site in eastern Yangtze River Delta of China [J]. Science of the Total Environment, 571: 1454−1466. doi: 10.1016/j.scitotenv.2016.06.212
[17]	Wang Y J, Bao S W, Wang S X, et al. 2017. Local and regional contributions to fine particulate matter in Beijing during heavy haze episodes [J]. Science of the Total Environment, 580: 283−296. doi: 10.1016/j.scitotenv.2016.12.127
[18]	Wu P, Ding Y H, Liu Y J. 2017. Atmospheric circulation and dynamic mechanism for persistent haze events in the Beijing–Tianjin–Hebei region [J]. Advances in Atmospheric Sciences, 34(4): 429−440. doi: 10.1007/s00376-016-6158-z
[19]	Xu W Q, Chen C, Qiu Y M, et al. 2021. Organic aerosol volatility and viscosity in the North China Plain: Contrast between summer and winter [J]. Atmospheric Chemistry and Physics, 21(7): 5463−5476. doi: 10.5194/acp-21-5463-2021
[20]	Zhang Y H, Hu M, Zhong L J, et al. 2008. Regional integrated experiments on air quality over Pearl River Delta 2004 (PRIDE-PRD2004): Overview [J]. Atmos. Environ., 42(25): 6157−6173. doi: 10.1016/j.atmosenv.2008.03.025
[21]	Zhang Q H, Zhang J P, Xue H W. 2010. The challenge of improving visibility in Beijing [J]. Atmospheric Chemistry and Physics, 10(16): 7821−7827. doi: 10.5194/acp-10-7821-2010
[22]	Zhang X Y, Wang Y Q, Niu T, et al. 2012. Atmospheric aerosol compositions in China: Spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols [J]. Atmospheric Chemistry and Physics, 12(2): 779−799. doi: 10.5194/acp-12-779-2012
[23]	Zhang X Y, Wang J Z, Wang Y Q, et al. 2015. Changes in chemical components of aerosol particles in different haze regions in China from 2006 to 2013 and contribution of meteorological factors [J]. Atmospheric Chemistry and Physics, 15(22): 12935−12952. doi: 10.5194/acp-15-12935-2015
[24]	Zhang X Y, Zhong J T, Wang J Z, et al. 2018. The interdecadal worsening of weather conditions affecting aerosol pollution in the Beijing area in relation to climate warming [J]. Atmospheric Chemistry and Physics, 18(8): 5991−5999. doi: 10.5194/acp-18-5991-2018
[25]	张小曳, 徐祥德, 丁一汇, 等. 2020. 2013～2017年气象条件变化对中国重点地区PM_2.5质量浓度下降的影响 [J]. 中国科学: 地球科学, 50(4): 483–500. Zhang Xiaoye, Xu Xiangde, Ding Yihui, et al. 2019. The impact of meteorological changes from 2013 to 2017 on PM_2.5 mass reduction in key regions in China [J]. Science China Earth Sciences, 62(12): 1885–1902. doi:10.1007/s11430-019-9343-3
[26]	Zheng G J, Duan F K, Su H, et al. 2015. Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions [J]. Atmospheric Chemistry and Physics, 15(6): 2969−2983. doi: 10.5194/acp-15-2969-2015
[27]	Zhong J T, Zhang X Y, Wang Y Q, et al. 2017. Relative contributions of boundary-layer meteorological factors to the explosive growth of PM_2.5 during the red-alert heavy pollution episodes in Beijing in December 2016 [J]. Journal of Meteorological Research, 31(5): 809−819. doi: 10.1007/s13351-017-7088-0
[28]	Zhong J T, Zhang X Y, Dong Y S, et al. 2018. Feedback effects of boundary-layer meteorological factors on cumulative explosive growth of PM_2.5 during winter heavy pollution episodes in Beijing from 2013 to 2016 [J]. Atmospheric Chemistry and Physics, 18(1): 247−258. doi: 10.5194/acp-18-247-2018
[29]	Zhong J T, Zhang X Y, Wang Y Q. 2019. Reflections on the threshold for PM_2.5 explosive growth in the cumulative stage of winter heavy aerosol pollution episodes (HPEs) in Beijing [J]. Tellus B:Chemical and Physical Meteorology, 71(1): 1528134. doi: 10.1080/16000889.2018.1528134
[30]	Zhu X W, Tang G Q, Guo J P, et al. 2018. Mixing layer height on the North China Plain and meteorological evidence of serious air pollution in southern Hebei [J]. Atmospheric Chemistry and Physics, 18(7): 4897−4910. doi: 10.5194/acp-18-4897-2018