Aircraft Measurements of Aerosol Vertical Distributions and Its Activation Efficiency over the Pearl River Delta

Duan Jing; Lou Xiaofeng; Chen Yong; Gao Yang; Li Xia; Zhou Rongbin; Mao Hui; Lu Guangxian; Wang Hui; Lin Junjun

doi:10.11898/1001-7313.20190604

Vol.30, NO.6, 2019

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Article Navigation > Journal of Applied Meteorological Science > 2019 > 30(6): 677-689

Duan Jing, Lou Xiaofeng, Chen Yong, et al. Aircraft measurements of aerosol vertical distributions and its activation efficiency over the Pearl River Delta. J Appl Meteor Sci, 2019, 30(6): 677-689. DOI: 10.11898/1001-7313.20190604.

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Duan Jing, Lou Xiaofeng, Chen Yong, et al. Aircraft measurements of aerosol vertical distributions and its activation efficiency over the Pearl River Delta. J Appl Meteor Sci, 2019, 30(6): 677-689. DOI: 10.11898/1001-7313.20190604.

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Aircraft Measurements of Aerosol Vertical Distributions and Its Activation Efficiency over the Pearl River Delta

DOI: 10.11898/1001-7313.20190604

1.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
2.
Key Laboratory for Cloud Physics of China Meteorological Administration, Beijing 100081
3.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
4.
Guangdong Meteorological Bureau, Guangzhou 510080
5.
Jiangmen Meteorological Bureau of Guangdong Province, Jiangmen 529000
6.
Shenzhen Meteorological Bureau of Guangdong Province, Shenzhen 518040

Received Date: 2019-07-15
Rev Recd Date: 2019-09-30
Publish Date: 2019-11-30

Abstract

Abstract

Based on airborne observations over the Pearl River Delta from 6 flights during 14-27 September 2017, characteristics of low-level vertical distributions (below 3 km) of aerosol and cloud condensation nuclei (CCN) in northern and southern parts of Shenzhen (22 km) is studied. The vertical distribution of aerosol and CCN number concentration and aerosol number concentration spectrum are calculated and analyzed. The weather situation, meteorological conditions and air mass backward trajectory model are used to investigate the aerosol spatial features. Combined with CCN data under different supersaturations conditions, the activation characteristic of aerosol is obtained. Results show that low-level aerosol in Shenzhen varies from 500 to 9000 cm^-3. About 50% sample points (≤ 1.5 km) of the aerosol average number concentration are lower than 1000 cm^-3. As a coastal city, Shenzhen's aerosol concentration is lower than inland cities under some weather conditions, with a uniform aerosol distribution within the boundary layer. Three aerosol types in Shenzhen are summarized according to the main impacting factors, which is ocean (Ⅰ), land (Ⅲ) and combined ocean-land (Ⅱ). Comparing with type Ⅲ of aerosol, type I has less number concentration and larger size. The aerosol spectrum of pattern I is bimodal distribution, while type Ⅲ is trimodal distribution. The number concentration of type Ⅲ is highest and the particle size type Ⅲ is small. The number concentration of type Ⅱ is between the type Ⅰ and Ⅲ, with a bimodal distribution. Aerosol in northern Shenzhen is higher than that in southern, as northern Shenzhen closed to city cluster of the Pearl River Delta suffering more anthropogenic aerosol impact than that of southern Shenzhen. Activation spectrums of CCN are fitted by 3-flight data including clean and polluted aerosol patterns. Parameters C and k of the empirical function N_CCN(S)=CS^k are 695 and 0.65 under clean conditions (23 September 2017), and their counterparts under polluted conditions are 4108 and 1.11 (27 September 2017). The aerosol activation efficiency which is the ratio of number concentration between CCN and aerosol is calculated under different supersaturations conditions.
- aerosol number concentration;
- vertical distribution;
- cloud condensation nuclei;
- activation spectrum;
- activation efficiency

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References(46)

Figures and Tables

Fig. 1 Six-flight paths around Shenzhen Airport in Sep 2017

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Fig. 2 The vertical profile of number concentration and radius for six flights around Shenzhen Airport in Sep 2017

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Fig. 3 24-hour backward tracks for six flights at 400 m, 1200 m and 2000 m heights in Sep 2017

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Fig. 4 The low-level number concentration distribution for six flights around Shenzhen Airport in Sep 2017

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Fig. 5 The aerosol number concentration spectrum (below 1.5 km height) of six flights around Shenzhen Airport in Sep 2017

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Fig. 6 The vertical profile of aerosol number concentration of six flights in southern and northern parts of Shenzhen in Sep 2017

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Fig. 7 The surface PM_2.5 mass concentration(a) and low-level (100 m) aerosol number concentration(b) for six flights in southern and northern parts of Shenzhen in Sep 2017

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Fig. 8 Activation spectrums of CCN in 100 m height in Shenzhen in Sep 2017

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Fig. 9 Ratio of number concentration between CCN and aerosol in different supersaturation in Shenzhen in Sep 2017

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Table 1 Synoptic situation and surface weather condition for six flights in Sep 2017

飞行日期	垂直探测时间	天气形势	温度/℃	风向	风速/(m·s^-1)	相对湿度/%
09-14	13:34—13:45 17:27—17:33	副高南侧，热带气旋北侧偏东气流	28.4	东风	5.1	66
09-18	12:05—12:13 14:04—14:11	高空东北气流，地面弱高压后部	27.0	东东南	5.1	80
09-23	15:15—15:31 17:58—18:08	副高西侧东南气流，地面低槽	27.2	东东南	5.1	87
09-24	11:52—12:21 13:59—14:05	副高西侧东南气流，地面高压后部	25.0	东东南	11.8	92
09-26	13:28—13:44 15:22—15:28	副高南侧偏东气流，地面弱低压场	25.0	东东南	1.5	89
09-27	11:25—11:31 13:07—13:18	副高南侧偏东气流，地面弱脊控制	24.8	西南	1.0	94

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Table 2 Mean value and standard deviation of aerosol number concentration and its radius (below 1.5 km height) in Sep 2017

飞行日期	气溶胶数浓度/cm^-3	有效半径/μm	体积半径/μm
09-14	2306±465	0.151±0.011	0.182±0.037
09-18	5807±1454	0.143±0.010	0.166±0.027
09-23	927±314	0.136±0.009	0.185±0.025
09-24	353±307	0.174±0.032	0.315±0.116
09-26	919±313	0.120±0.012	0.169±0.030
09-27	5352±3246	0.148±0.010	0.167±0.021

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Table 3 Comparison of parameters C and k for different continental atmospheric regimes

观测地点	时间	C/cm^-3	k	类型	出处
澳大利亚帕克斯	1958年春	2000	0.4	大陆型	文献[39]
美国布法罗		3500	0.9	城市型	文献[40]
美国圣迭戈	1976年秋	2500	0.7	污染	文献[41]
巴西亚马逊	2002年秋	2220	1.28	污染	文献[42]
中国石家庄	2005—2007年	16821	0.71	污染	文献[43]
韩国济州岛	2006—2009年	4194	0.47	岛屿	文献[44]
中国内蒙古	2010年	1615	1.42	污染	文献[45]
中国黄山	2012年秋	8895	0.41		文献[46]
中国深圳	2017-09-18	3236	1.04		本文
中国深圳	2017-09-23	695	0.65		本文
中国深圳	2017-09-27	4108	1.11		本文

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