2020年长沙市PM2.5组分特征及来源解析

doi:10.3969/j.issn.1673-503X.2024.05.007

Abstract

Abstract:

Based on the online and offline daily monitoring data of seven sites in Changsha City in 2020, the chemical component pollution characteristics of PM_2.5 were analyzed.The positive matrix factorization (PMF) model was used to analyze the source apportionments of PM_2.5.The results show that the water-soluble ions in PM_2.5 has the highest mass proportion, accounting for 64.3%, followed by organic matter (OM), accounting for 26.6%, SO₄^2-, NO₃^- and NH₄⁺ are the main components in water-soluble ions.The PMF model identified five sources of PM_2.5, vehicle exhaust emission source (36%), secondary formation source (31%), industrial emission source (16%), biomass burning source (9%), and dust source (8%).The highest proportion at Changsha Environmental Protection College are secondary formation and motor vehicle emissions, reaching 38% and 37%; the contribution of motor vehicle emissions at Mapoling site is the highest, 42%; the sharing rates of secondary formation, motor vehicle emissions, and industrial emissions at Environmental Protection Bureau of the High-tech Zone are relatively similar, 28%, 26%, and 25%.In order to further control PM_2.5 pollution, it is recommended that Changsha could focus on controlling vehicle exhaust emissions, accelerating the elimination of old motor vehicles, and promoting the electrification of fuel vehicles; meanwhile, differentiated control measures should be implemented for different seasons, with focus on controlling vehicle exhaust emissions in autumn and winter; in summer, it is recommended to prioritize controlling oxidants such as ozone to reduce the secondary formation rate of PM_2.5.

Key words: PM_2.5, PMF model, Component characteristics, Source apportionment

CLC Number:

Maoqing FAN,Qiao WU,Huiwen GU,Huimin HUANG,Ming ZENG. Component characteristics and source apportionments of ambient PM_2.5 during 2020 in Changsha[J]. Journal of Meteorology and Environment, 2024, 40(5): 57-64.

Figures/Tables 9

Fig.1

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

References 25

1	崔金梦, 丛春华, 郑怡, 等. 2013—2021年山东大气污染特征及气象条件分析[J]. 气象与环境学报, 2024, 40 (3): 46- 54.
2	李中杰, 严长安, 李亚丽, 等. 昆明PM_2.5质量浓度演变特征与潜在源扩散模拟分析[J]. 气象与环境学报, 2024, 40 (3): 26- 36.
3	张克兴. 基于分布滞后非线性模型分析PM_2.5对循环系统疾病寿命损失影响[D]. 北京: 中国疾病预防控制中心, 2018.
4	中华人民共和国生态环境部. 中国生态环境状况公报[EB/OL]. (2023-02-09). https://www.mee.gov.cn/hjzl/sthjzk/.
5	王跃思, 姚利, 王莉莉, 等. 2013年元月我国中东部地区强霾污染成因分析[J]. 中国科学: 地球科学, 2014, 44 (1): 15- 26.
6	朱佳雷, 王体健, 邢莉, 等. 江苏省一次重霾污染天气的特征和机理分析[J]. 中国环境科学, 2011, 31 (12): 1943- 1950.
7	王璟煦, 杜谨宏, 续鹏, 等. 临汾市人为源一次PM_2.5排放特征及钢铁、焦化等重点行业管控对策[J]. 工程科学学报, 2024, 46 (7): 1324- 1335.
8	朱淑贞, 佟洁, 鲍丰, 等. 廊坊市秋冬季大气细颗粒物污染特征及来源解析[J]. 环境科学, 2023, 44 (1): 20- 29.
9	史建武, 吴小彤, 韩新宇, 等. 临沧市城区大气PM_2.5组分特征及受境外生物质燃烧传输的影响[J/OL]. 环境化学, 2024: 1-13[2024-06-06]. http://kns.cnki.net/kcms/detail/11.1844.X.20240514.0916.002.html.
10	Koch S , Winterhalter R , Uherek E , et al. Formation of new particles in the gas-phase ozonolysis of monoterpenes[J]. Atmospheric Environment, 2000, 34 (23): 4031- 4042. doi: 10.1016/S1352-2310(00)00133-3
11	Zhang X , Zhang Z Z , Xiao Z S , et al. Heavy haze pollution during the COVID-19 lockdown in the Beijing-Tianjin-Hebei region, China[J]. Journal of Environmental Sciences, 2022, 114, 170- 178. doi: 10.1016/j.jes.2021.08.030
12	贾小芳, 李杨, 孙晓晴, 等. 2015年华北地区上甸子大气本底站PM_2.5化学组分特征及来源分析[J]. 气象与环境学报, 2023, 39 (6): 87- 95.
13	饶永才, 冯春莉, 邓国庆, 等. 2019—2021年徐州市大气细颗粒物化学组成特征及来源解析[J]. 中国环境监测, 2023, 39 (2): 158- 168.
14	谢文豪, 张强, 杨方社, 等. 运城市2020—2021年采暖季PM_2.5污染成因分析[J]. 环境科学学报, 2023, 43 (11): 182- 195.
15	林楚雄, 姚沛廷, 彭杏, 等. 深圳市城区冬季大气PM_2.5在线来源解析[J]. 中国环境科学, 2023, 43 (2): 506- 513.
16	USEPA. EPA positive matrix factorization(PMF)5.0 fundamentals and user guide[R]. Washington: U.S. Environmental Protection Agency Office of Research and Development, 2014.
17	Cao J J , Shen Z X , Chow J C , et al. Seasonal variations and sources of mass and chemical composition for PM₁₀ aerosol in Hangzhou, China[J]. Particuology, 2009, 7 (3): 161- 168. doi: 10.1016/j.partic.2009.01.009
18	Turpin B J , Cary R A , Huntzicker J J . An in situ, Time-resolved analyzer for aerosol organic and elemental carbon[J]. Aerosol Science and Technology, 1990, 12 (1): 161- 171. doi: 10.1080/02786829008959336
19	Chow J C , Watson J G , Lu Z Q , et al. Descriptive analysis of PM_2.5 and PM₁₀ at regionally representative locations during SJVAQS/AUSPEX[J]. Atmospheric Environment, 1996, 30 (12): 2079- 2112. doi: 10.1016/1352-2310(95)00402-5
20	Schhauer J J , Kleemen M J , Cass G R , et al. Measurement of emissions from air pollution sources.2.C1 through C30 organic compounds from medium duty diesel trucks[J]. Environmental Science & Technology, 1999, 33 (10): 1578- 1587.
21	Yamasoe M A , Artaxo P , Miguel A H , et al. Chemical composition of aerosol particles from direct emissions of vegetation fires in the Amazon Basin: water-soluble species and trace elements[J]. Atmospheric Environment, 2000, 34 (10): 1641- 1653. doi: 10.1016/S1352-2310(99)00329-5
22	胡偲豪. 华北典型工业城市颗粒物污染特征和来源解析——以运城、邯郸市为例[D]. 邯郸: 河北工程大学, 2023.
23	顾家伟. 我国城市大气颗粒物无机元素污染研究进展与趋势[J]. 地球与环境, 2019, 47 (3): 385- 396.
24	Yu S Y , Liu W J , Xu Y S , et al. Characteristics and oxidative potential of atmospheric PM_2.5 in Beijing: source apportionment and seasonal variation[J]. Science of the Total Environment, 2019, 650, 277- 287. doi: 10.1016/j.scitotenv.2018.09.021
25	Li Y Y , Chang M , Ding S S , et al. Monitoring and source apportionment of trace elements in PM_2.5: implications for local air quality management[J]. Journal of Environmental Management, 2017, 196, 16- 25.

采样日期	天气情况	气温/℃	RH/(%)	风向	风速/(m·s^-1)	AQI指数	首要污染物	空气质量级别	PM_2.5浓度/(μg·m^-3)
1月14日	小雨	5~8	85	N	2.3	103	PM_2.5	轻度污染	77
1月15日	中雨	2~7	100	N	3.4	64	PM_2.5	良	46
3月10日	晴	7~17	72	N	4.0	56	O_3-8h	良	39
3月11日	阵雨	10~15	86	SE	2.0	67	PM_2.5	良	48
3月12日	小雨	11~16	99	N	1.8	53	PM_2.5	良	37
3月13日	阴	10~16	82	N	3.5	75	PM_2.5	良	55
5月27日	多云	21~29	83	N	1.2	86	O_3-8h	良	37
5月28日	多云	21~30	75	S	1.8	137	O_3-8h	轻度污染	45
5月29日	小雨	21~24	95	NE	2.6	50	-	优	35
5月30日	多云	20~30	84	SE	1.4	86	O_3-8h	良	18
7月24日	晴	27~35	59	SW	3	69	O_3-8h	良	31
7月25日	晴	26~34	61	NW	2.5	82	O_3-8h	良	52
7月26日	晴	25~34	68	S	3	73	O_3-8h	良	48
7月27日	小雨	24~26	99	S	5.5	29	-	优	9
10月13日	晴	17~24	78	NW	3	118	PM_2.5	轻度污染	89
10月14日	阴	16~20	91	N	5.4	104	PM_2.5	轻度污染	78
10月15日	小雨	14~16	95	NW	5.2	30	-	优	12
10月16日	小雨	14~16	96	NW	5.5	27	-	优	9

[1]	Zhongjie LI,Chang'an YAN,Yali LI,Dawei ZHANG,Jianwu SHI. Study of PM_2.5 mass concentration evolution and potential source diffusion simulation in Kunming [J]. Journal of Meteorology and Environment, 2024, 40(3): 26-36.
[2]	Jinmeng CUI,Chunhua CONG,Yi ZHENG,Jinmin CHEN,Ming YANG,Jikang WANG,Hongli LIU,Xuguang DONG. Analysis of air pollution characteristics and meteorological conditions in Shandong province during 2013-2021 [J]. Journal of Meteorology and Environment, 2024, 40(3): 46-54.
[3]	Wen-li LÜ,Xiao-meng SHI,Kai ZHANG. Visibility characteristics and influencing factors of a fog-haze process in Qingdao [J]. Journal of Meteorology and Environment, 2023, 39(3): 47-55.
[4]	Xu-ting ZHANG, Hui LIU, Rui-fang LIU, Fei JU, Jia-hui-min LIU, Xing-xing GAO, Shao-ni HUANG, Nan WANG. Study on hourly PM_2.5 concentration forecast based on XGBoost method in Xi'an city [J]. Journal of Meteorology and Environment, 2023, 39(1): 44-54.
[5]	Yuan-jun XIAO,Bao-shan LI,Wen-dan SONG,Yong-xiang CHENG,Jing-feng HUANG. The impact of concentration change of PM_2.5 on air temperature in China during 1998-2016 [J]. Journal of Meteorology and Environment, 2022, 38(3): 85-92.
[6]	Mei LIU,Xing-jie JI,Li TIAN,Ya-lei DING,Xuan ZUO. Analysis of variation characteristics of atmospheric self-purification capability in the main urban area of Zhengzhou from 1961 to 2020 [J]. Journal of Meteorology and Environment, 2022, 38(3): 93-100.
[7]	Xue-qiang WANG,Hui-jie DONG. Local dust weather process caused by a mesoscale convective storm in Inner Mongolia Autonomous Region [J]. Journal of Meteorology and Environment, 2021, 37(6): 11-19.
[8]	Hao YIN, Jun-ling TAN, Ju-yong WANG, Hong-quan LI, Lei XI, Bin WU, Ji-dong QIU. Analysis of PM_2.5 concentration changes and transmission characteristics in Huzhou city [J]. Journal of Meteorology and Environment, 2021, 37(5): 20-26.
[9]	Yu-tian DAI, Xue-bin YANG. Analysis of air pollution characteristics and influencing factors in the winter of 2018 in Northwest Shandong province [J]. Journal of Meteorology and Environment, 2021, 37(4): 40-47.
[10]	Zhao-peng LUAN,Hui-chao LU,Tian LI,Xiang-qian CUI,Tian-liang ZHAO,Qing-rui ZHU. Impacts of precipitation and wind on PM_2.5 concentration and its region transport in Tai'an [J]. Journal of Meteorology and Environment, 2021, 37(3): 33-39.
[11]	Feng-juan GUO,Chun-hua LI,Chun-ling DOU. Distribution of PM_2.5 mass concentration over Karamay and its influencing factors [J]. Journal of Meteorology and Environment, 2020, 36(4): 52-58.
[12]	Xiu-ling WANG,Jia-jia HUA,Xuan LI,Qian-jin MA,Guan WANG,Heng LI,Xiao-xia CAO. Meteorological conditions for formation and dissipation of PM_2.5 heavy pollution in Tangshan from 2015 to 2017 [J]. Journal of Meteorology and Environment, 2020, 36(4): 45-51.
[13]	Zhen WANG,Wei-fen YANG,Xiang YE,Yan-ping LI,Jing XIA,Chun-yu LI,Tao HE. Impacts of meteorological factors on PM_2.5 concentration in urban regions of Changzhou [J]. Journal of Meteorology and Environment, 2020, 36(3): 26-32.
[14]	Xin-yi LI, Feng-kai YANG, Yu-lei ZHU, Chang-jian NI. Evolution characteristics and formation mechanism of a hazing process in Chengdu [J]. Journal of Meteorology and Environment, 2020, 36(2): 28-33.
[15]	TANG Ying-xiao, HAN Su-qin, CAI Zi-ying, YANG Xu, FENG Jin. Feature analysis of atmospheric self-purification capability and atmospheric particulate concentration in Tianjin during 2013-2017 [J]. Journal of Meteorology and Environment, 2019, 35(6): 55-66.

Component characteristics and source apportionments of ambient PM_2.5 during 2020 in Changsha

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 25

Related Articles 15

Metrics

Comments

Recommended 10

Component characteristics and source apportionments of ambient PM2.5 during 2020 in Changsha

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 25

Related Articles 15

Metrics

Comments

Recommended 10

Component characteristics and source apportionments of ambient PM_2.5 during 2020 in Changsha