昆明PM2.5质量浓度演变特征与潜在源扩散模拟分析

doi:10.3969/j.issn.1673-503X.2024.03.004

Abstract

Abstract:

Based on the hourly monitoring data of near-surface PM_2.5 mass concentration and the meteorological data of the same period in Kunming area from 2016 to 2020, the correlation between the variation characteristics of PM_2.5 mass concentration and meteorological factors was analyzed, and the seasonal characteristics of PM_2.5 transport path and pollution track and the distribution of potential source areas were studied by using the backward trajectory model. The results showed that the mass concentration of PM_2.5 increased first and then decreased and fluctuated peak-valley throughout the year. The overall concentration was 57.4% higher in spring and winter than in autumn and summer, and relatively stable at night, while the variation range was large in daytime, and the concentration at night was significantly higher than that in daytime (22.6%). The mass concentration of PM_2.5 was negatively correlated with air temperature and wind speed, the correlation coefficients were -0.60 and -0.13, respectively, and was positively correlated with air pressure (R=0.44, P < 0.01). There were seasonal differences in the source direction of airflow trajectories in Kunming area. The sources of spring and winter trajectories were concentrated, mainly from the southwest, and scattered in summer and autumn centered on the south. Except for all the airflows arriving in Kunming area in summer and autumn are clean airflow, the transmission path in spring is far and the contribution source area is large. The central part of Yunnan province (Yuxi city), the southwest Yunnan province (Chuxiong prefecture, Puer city) and parts of northwestern Bangladesh and northern Myanmar are the most important contributors to PM_2.5 quality concentrations in Kunming area, and the coverage and contribution degree of high values in winter are smaller than those in spring, and are only distributed in dots in south-central Yunnan province (Yuxi city, Honghe prefecture).

Key words: PM_2.5, Backward trajectory, Transport pathway, Potential source

CLC Number:

X169
X513

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.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

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Fig.5

Table 1

Transmission path of backward trajectory and PM2.5 concentration range from 2016 to 2020 in Kunming area"

季节	聚类编号	各聚类占总气流比例	途经区域	各聚类气流PM_2.5质量浓度/(μg·m^-3)		各聚类中污染气流占比	污染气流PM_2.5质量浓度/(μg·m^-3)
季节	聚类编号	各聚类占总气流比例	途经区域	均值	标准偏差	各聚类中污染气流占比	均值	标准偏差
春季	1	26.8%	中国云南省西南部(玉溪市、普洱市)	37.43	15.68	1.0%	90.00	16.73
	2	16.0%	缅甸东部、中国云南省中西部(楚雄州、普洱市、临沧市)	40.49	17.32	5.1%	85.80	7.34
	3	13.2%	中国云南省东部(曲靖市)	29.89	13.75	0.0%
	4	14.0%	缅甸西中东部、中国云南省中西部(楚雄州、大理州、临沧市、保山市、德宏州)	36.32	13.50	1.2%	88.33	10.12
	5	30.1%	缅甸东部、中国云南省中西部(楚雄州、大理州、临沧市、保山市、德宏州)	33.32	12.44	0.5%	76.33	0.58
	总	100.0%		35.49	14.87	1.4%	85.12	9.91
夏季	1	14.5%	中国广西壮族自治区西北部(百色市)、中国云南省东部(曲靖市)	20.53	8.55	—	—	—
	2	20.7%	中国云南省中南部(玉溪市、红河州)	18.17	8.23	—	—	—
	3	15.5%	中国贵州省西部(毕节市、六盘水市)、中国云南省东部(曲靖市)	20.80	9.02	—	—	—
	4	4.7%	缅甸东部、中国云南省西南部(楚雄州、普洱市、临沧市)	12.17	4.77	—	—	—
	5	17.0%	中国云南省东南部(玉溪市、文山州)	23.48	11.00	—	—	—
	6	27.7%	中国云南省西南部(玉溪市、普洱市、西双版纳州)	14.95	7.60	—	—	—
	总	100.0%		18.35	9.26
秋季	1	42.5%	中国云南省中南部(玉溪市、红河州)	23.27	12.05	0.5%	79.50	2.38
	2	20.0%	中国云南省东南部(玉溪市、文山州)	27.56	13.91	0.8%	81.40	7.23
	3	18.1%	中国贵州省西部(毕节市、六盘水市)、云南省东部(曲靖市)	23.24	12.84	0.3%	84.33	—
	4	19.5%	中国云南省西南部(玉溪市、普洱市、临沧市)	22.98	9.99	0.3%	80.75	—
	总	100.0%		24.26	12.34	0.5%	81.50	4.43
冬季	1	38.1%	中国云南省中南部(玉溪市、红河州)	35.25	19.24	4.2%	93.19	19.23
	2	20.6%	缅甸东部、中国云南省西南部(楚雄州、普洱市、临沧市)	27.32	14.63	1.2%	96.00	13.34
	3	13.2%	中国云南省东部(曲靖市)	45.85	23.01	9.3%	93.35	24.59
	4	24.9%	中国云南省西南部(玉溪市、普洱市、临沧市)	29.08	15.14	1.8%	94.13	16.63
	5	3.2%	孟加拉国西北部、缅甸北部、中国云南省中西部(楚雄州、大理州、临沧市、保山市、德宏州)	25.86	11.68	0.0%	—	—
	总	100.0%		32.67	18.69	3.5%	94.17	20.18

Table 1

Fig.6

Fig.7

Fig.8

References 38

1	Chen Z , Wang J N , Ma G X , et al. China tackles the health effects of air pollution[J]. The Lancet, 2013, 382 (9909): 1959- 1960. doi: 10.1016/S0140-6736(13)62064-4
2	张小曳, 孙俊英, 王亚强, 等. 我国雾-霾成因及其治理的思考[J]. 科学通报, 2013, 58 (13): 1178- 1187.
3	Tian S L , Pan Y P , Liu Z R , et al. Size-resolved aerosol chemical analysis of extreme haze pollution events during early 2013 in urban Beijing, China[J]. Journal of Hazardous Materials, 2014, 279, 452- 460. doi: 10.1016/j.jhazmat.2014.07.023
4	李勇, 廖琴, 赵秀阁, 等. PM_2.5污染对我国健康负担和经济损失的影响[J]. 环境科学, 2021, 42 (4): 1688- 1695.
5	王燕丽, 薛文博, 雷宇, 等. 京津冀区域PM_2.5污染相互输送特征[J]. 环境科学, 2017, 38 (12): 4897- 4904.
6	余钟奇, 瞿元昊, 周广强, 等. 2018年秋冬季长江三角洲区域PM_2.5污染来源数值研究[J]. 中国环境科学, 2020, 40 (10): 4237- 4246.
7	王毓铮, 黄志炯, 肖笑, 等. 珠三角典型城市大气污染减排措施的PM_2.5改善评估研究[J]. 环境科学学报, 2021, 41 (7): 2530- 2539.
8	Maji K J , Ye W F , Arora M , et al. PM_2.5-related health and economic loss assessment for 338 Chinese cities[J]. Environment International, 2018, 121, 392- 403.
9	Yin P , Brauer M , Cohen A J , et al. The effect of air pollution on deaths, disease burden, and life expectancy across China and its provinces, 1990-2017:an analysis for the global burden of disease study 2017[J]. The Lancet Planetary Health, 2020, 4 (9): e386- e398.
10	杨书申, 孙珍全, 邵龙义. 城市大气细颗粒物PM_2.5的研究进展[J]. 中原工学院学报, 2006, 17 (1): 1-5, 25.
11	Martinelli N , Girelli D , Cigolini D , et al. Access rate to the emergency department for venous thromboembolism in relationship with coarse and fine particulate matter air pollution[J]. PLoS One, 2012, 7 (4): e34831.
12	Brook R D , Rajagopalan S , Pope C A , et al. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association[J]. Circulation, 2010, 121 (21): 2331- 2378.
13	成莹菲, 侯雪伟, 朱彬, 等. 稳定天气形势下京津冀和长三角地区重度污染过程气象成因解析[J]. 环境科学学报, 2020, 40 (5): 1575- 1586.
14	王芳龙, 李忠勤, 杨宏, 等. 天水市大气颗粒物输送通道及污染源区研究[J]. 环境化学, 2020, 39 (9): 2371- 2383.
15	吕炜, 李金凤, 王雪松, 等. 长距离污染传输对珠江三角洲区域空气质量影响的数值模拟研究[J]. 环境科学学报, 2015, 35 (1): 30- 41.
16	严宇, 王雪松, 周家斌. 福州市区冬春季PM_2.5污染特征与来源差异性分析[J]. 环境科学学报, 2019, 39 (4): 1049- 1056.
17	牛司平, 朱旭, 朱兰兰, 等. 皖南地区铜陵市大气颗粒物污染特征及潜在源研究[J]. 环境科学学报, 2022, 42 (5): 60- 73.
18	陈云波, 徐峻, 何友江, 等. 北京市冬季典型重污染时段PM_2.5污染来源模式解析[J]. 环境科学研究, 2016, 29 (5): 627- 636.
19	刘娜, 余晔, 张莉燕, 等. 2016—2018年西宁市颗粒物来源及输送差异分析[J]. 环境科学学报, 2021, 41 (10): 4212- 4227.
20	薛文博, 付飞, 王金南, 等. 中国PM_2.5跨区域传输特征数值模拟研究[J]. 中国环境科学, 2014, 34 (6): 1361- 1368.
21	李颜君, 安兴琴, 范广洲. 北京地区大气颗粒物输送路径及潜在源分析[J]. 中国环境科学, 2019, 39 (3): 915- 927.
22	Hao T Y , Cai Z Y , Chen S C , et al. Transport pathways and potential source regions of PM_2.5 on the west coast of Bohai Bay during 2009-2018[J]. Atmosphere, 2019, 10 (6): 345.
23	段时光, 姜楠, 杨留明, 等. 郑州市冬季大气PM_2.5传输路径和潜在源分析[J]. 环境科学, 2019, 40 (1): 86- 93.
24	毕丽玫. 高原城市昆明大气PM_2.5污染特征及与气象条件相关性分析研究[D]. 昆明: 昆明理工大学, 2015.
25	张朝能, 王梦华, 胡振丹, 等. 昆明市PM_2.5浓度时空变化特征及其与气象条件的关系[J]. 云南大学学报(自然科学版), 2016, 38 (1): 90- 98.
26	李洁, 刘芝芹, 彭明俊, 等. 昆明市PM_2.5和PM₁₀质量浓度变化特征分析[J]. 环境科学导刊, 2020, 39 (2): 37- 41.
27	杨健, 丁祥, 刘寅, 等. 高原城市昆明PM_2.5中碳组分污染特征及来源分析[J]. 环境化学, 2017, 36 (2): 257- 264.
28	韩林洁, 黄俊, 韩新宇, 等. 昆明市典型干季大气PM_2.5中重金属污染特征与来源研究[J]. 昆明理工大学学报(自然科学版), 2019, 44 (2): 99- 110.
29	盛涛. 昆明市大气PM₁₀和PM_2.5比值特征及来源研究[D]. 昆明市: 昆明理工大学, 2014.
30	Draxler R R, Hess G D. Description of the HYSPLIT_4 modeling system[R]. Sliver Spring, Maryland: Air Resources Laboratory, 1997.
31	Wang Y Q , Zhang X Y , Draxler R R . TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data[J]. Environmental Modelling & Software, 2009, 24 (8): 938- 939.
32	Salvador P , Artíñano B , Alonso D G , et al. Identification and characterisation of sources of PM₁₀ in Madrid (Spain) by statistical methods[J]. Atmospheric Environment, 2004, 38 (3): 435- 447.
33	Liu N , Yu Y , He J J , et al. Integrated modeling of urban-scale pollutant transport: application in a Semi-arid Urban Valley, northwestern China[J]. Atmospheric Pollution Research, 2013, 4 (3): 306- 314.
34	曾静, 王美娥, 张红星. 北京市夏秋季大气PM_2.5浓度与气象要素的相关性[J]. 应用生态学报, 2014, 25 (9): 2695- 2699.
35	高睿娜, 孙银川, 左河疆, 等. 银川市主要污染物变化特征及其关系研究[J]. 气象与环境学报, 2022, 38 (1): 33- 39.
36	尹浩, 郯俊岭, 王巨勇, 等. 湖州市PM_2.5浓度变化及传输特征分析[J]. 气象与环境学报, 2021, 37 (5): 20- 26.
37	常嘉成. 云南高原典型城市空气质量变化特征及大气环境容量评估研究[D]. 南京: 南京信息工程大学, 2019.
38	郭晓. 昆明市呈贡区及主城区干季大气PM_2.5的化学特征和来源对比[D]. 昆明: 云南师范大学, 2020.

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[15]	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.

Study of PM_2.5 mass concentration evolution and potential source diffusion simulation in Kunming

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