新疆大气PM2.5来源与潜在贡献源分析

doi:10.13866/j.azr.2023.06.03

摘要/Abstract

摘要：

利用2021年3月—2022年2月新疆空气质量数据分析PM_2.5浓度演化特征及其控制因素，结合因子分析和NO₂、SO₂与CO的来源特性辨别物质排放源，并借助基于Hysplit模式的MeteoInfo软件包确定PM_2.5输送路径和潜在贡献源区分布状况。结果表明：（1）新疆PM_2.5浓度显著偏高，尤其是冬季平均高达86.16 μg·m^-3。其中，天山北坡经济带PM_2.5来源主要受周围油气田作业排放及其输送过程中大风扬尘的支配，而其他地区的PM_2.5主要源于大风扬尘，辅以石油与天然气燃烧排放。（2）天山北坡经济带经油气田作业区气流输送PM_2.5浓度虽然仅为局地路径的50%，但路径占比达50%，因此，应是区域PM_2.5来源的重要通道，且PM_2.5浓度变异系数高达103.6%，是导致雾霾甚至浮尘天气形成的关键因素。哈密盆地与塔里木盆地物质补给路径虽然存在差异，但二者PM_2.5潜在贡献源区均主要分布在孔雀河流域和罗布泊等地。（3）外源气流受盆地地形作用而演化成辐合/辐散气流，辅以（类）山谷风促进污染物混合，应是天山北坡经济带和塔里木盆地内PM_2.5演化趋势类似的成因之一。

关键词: PM_2.5, 聚类分析, Hysplit模式, 潜在贡献源区, 新疆

Abstract:

Using the air pollutant mass concentration data in Xinjiang from March 2021 to February 2022, the evolution characteristics of PM_2.5 concentration and its potential contribution source area were analyzed. The results showed that: (1) The concentration of PM_2.5 in Xinjiang was significantly high, especially in winter, the average was as high as 86.16 μg·m^-3, which had certain potential risks to human health. Influenced by the near-surface air stability, the PM_2.5 concentration was higher at nighttime and lower in the daytime, but supplemented by (similar) valley winds, the PM_2.5 concentration exhibited a significant double-peak pattern in the Hami Basin and the Tarim Basin. (2) The PM_2.5 in the economic belt of the northern slope of the Tianshan Mountains was mainly dominated by the emission from the surrounding oil and gas fields and the dust from strong winds during the transportation process, while the source of PM_2.5 in other areas, where human activities were relatively weak, was mainly controlled by the dust from strong winds, supplemented by the combustion of oil and natural gas. (3) The overall low concentration of PM_2.5 in each path in the source region of the Irtysh River indicates that the impact of pollutants on environmental quality could be ignored. The high concentration of PM_2.5 in the NB area was mainly affected by the local atmospheric circulation, but the airflow passing through the oil and gas industry area was a key factor leading to the formation of haze and floating dust weather. Although there are differences in the material supply paths between the Hami Basin and the Tarim Basin, their PM_2.5 potential contribution source areas were all mainly distributed in the Kongqi River Basin and Lop Nur on the eastern edge of the Taklimakan Desert. (4) Exogenous airflow evolved into convergent/divergent airflow due to the topography of the basin, supplemented by (quasi) valley winds to promote the mixing of pollutants, which could be the key factor for the similar evolution trend of PM_2.5 in the economic belt of the northern slope of the Tianshan Mountains and Tarim Basin.

Key words: PM_2.5, cluster analysis, Hybrid single-particle lagrangian integrated trajectory (Hysplit) model, potential contribution source area, Xinjiang

许君利, 韩海东, 王建. 新疆大气PM_2.5来源与潜在贡献源分析[J]. 干旱区研究, 2023, 40(6): 874-884.

XU Junli, HAN Haidong, WANG Jian. Recharge sources and potential source areas of atmospheric PM_2.5 in Xinjiang[J]. Arid Zone Research, 2023, 40(6): 874-884.

图/表 8

图1

表1

图2

表2

图3

表3

图4

图5

参考文献 42

[1]	Almeida S M, Manousakas M, Diapouli E, et al. Ambient particulate matter source apportionment using receptor modelling in European and Central Asia urban areas[J]. Environmental Pollution, 2020, 266: 115199. doi: 10.1016/j.envpol.2020.115199
[2]	EEA. Air Quality in Europe 2018 Report[R]. European Environment Agency, 2018: 1977-8449.
[3]	Atkinson R W, Cohen A, Mehta S, et al. Systematic review and meta-analysis of epidemiological time-series studies on outdoor air pollution and health in Asia[J]. Air Quality Atmosphere and Health, 2012, 5: 383-391. doi: 10.1007/s11869-010-0123-2
[4]	张书源, 程全国, 邢红彬. 基于数据挖掘技术的PM_2.5污染与居民死亡人数的暴露-反应关系[J]. 沈阳大学学报(自然科学版), 2022, 34(1): 17-23.
	[Zhang Shuyuan, Cheng Quanguo, Xing Hongbin. Exposure-response relationship between PM_2.5 pollution and death toll of residents based on data mining technology[J]. Journal of Shenyang University (Natural Science), 2022, 34(1): 17-23.]
[5]	Stanaway J D, Afshin A, Gakidou E, et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet, 2018, 392(10159): 1923-1994. doi: S0140-6736(18)32225-6 pmid: 30496105
[6]	亚力昆江·吐尔逊, 迪丽努尔·塔力甫, 阿不力克木·阿布力孜, 等. 乌鲁木齐市采暖期大气PM_2.5、PM_10-2.5中重金属污染水平评价[J]. 新疆大学学报(自然科学版), 2010, 27(3): 338-342.
	[Yalkunjan Tursun, Dilnur Talifu, Ablikim Ablizi, et al. Pollution level of heavy metals in PM_2.5 and PM_10-2.5 during winter in Urumqi[J]. Journal of Xinjiang University (Natural Science Edition), 2010, 27(3): 338-342.]
[7]	Yu H, Yang W, Wang X H, et al. A seriously sand storm mixed air-polluted area in the margin of Tarim Basin: Temporal-spatial distribution and potential sources[J]. Science of the Total Environment, 2019, 676: 436-446. doi: 10.1016/j.scitotenv.2019.04.298
[8]	王建, 韩海东, 许君利, 等. 天山科其喀尔冰川末端降水化学特征及控制因素[J]. 干旱区研究, 2022, 39(2): 347-358.
	[Wang Jian, Han Haidong, Xu Junli, et al. Chemical characteristics and their influencing factors of precipitation at the end of the Koxkar Glacier, Tianshan Mountains[J]. Arid Zone Research, 2022, 39(2): 347-358.]
[9]	Hu H, Zhao X Y, Wang J, et al. Chemical characteristics of road dust PM_2.5 fraction in oasis cities at the margin of Tarim Basin[J]. Journal of Environmental Sciences, 2020, 95: 217-224. doi: 10.1016/j.jes.2020.03.030
[10]	刘琳, 张正勇, 刘芬, 等. 天山北坡经济带城市 PM_{2. 5}质量浓度时空分布及模拟分析[J]. 环境科学研究, 2018, 31(11): 1849-1857.
	[Liu Lin, Zhang Zhengyong, Liu Fen, et al. Spatial-temporal distribution and simulation analysis of PM_2.5 concentration of the cities in the northern slope economic zone of Tianshan Mountain[J]. Research of Environmental Sciences, 2018, 31(11): 1849-1857.]
[11]	Liu Y X, Teng Y, Liang S, et al. Establishment of PM₁₀ and PM_2.5 emission inventories from wind erosion source and simulation of its environmental impact based on WEPS-Models 3 in southern Xinjiang, China[J]. Atmospheric Environment, 2021, 248: 118222. doi: 10.1016/j.atmosenv.2021.118222
[12]	Meng L, Yang X H, Zhao T L, et al. Modeling study on three-dimensional distribution of dust aerosols during a dust storm over the Tarim Basin, Northwest China[J]. Atmospheric Research, 2019, 218: 285-295. doi: 10.1016/j.atmosres.2018.12.006
[13]	马明杰, 何清, 杨兴华, 等. 塔克拉玛干沙漠北缘沙尘天气起沙量的贡献研究[J]. 干旱区资源与环境, 2022, 36(7): 133-138.
	[Ma Mingjie, He Qing, Yang Xinghua, et al. Contributions of dusty weather to dust emission amounts at the northern argin of the Taklimakan Desert[J]. Journal of Arid Land Resources and Environment, 2022, 36(7): 133-138.]
[14]	Ge Y X, Abuduwaili J, Ma L, et al. Potential transport pathways of dust emanating from the playa of Ebinur Lake, Xinjiang, in arid Northwest China[J]. Atmospheric Research, 2016, 178: 196-206.
[15]	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.
	[Duan Shiguang, Jiang Nan, Yang Liuming, et al. Transport pathways and potential sources of PM_2.5 during the winter in Zhengzhou[J]. Environmental Science, 2019, 40(1): 86-93.]
[16]	Huang W, Chang S Q, Xie C L, et al. Moisture sources of extreme summer precipitation events in north Xinjiang and their relation-ship with atmospheric circulation[J]. Advances in Climate Change Research, 2017, 8(1): 12-17. doi: 10.1016/j.accre.2017.02.001
[17]	高卫东. 新疆土壤元素含量特征及其对沙尘气溶胶贡献分析[J]. 干旱区资源与环境, 2008, 22(8): 155-158.
	[Gao Weidong. Analysis on element content in Xinjiang soil and contribution to dust aerosol[J]. Journal of Arid Land Resources and Environment, 2008, 22(8): 155-158.]
[18]	Salmabadi H, Saeedi M. Determination of the transport routes of and the areas potentially affected by SO₂ emanating from Khatoonabad copper smelter (KCS), Kerman province, Iran using HYSPLIT[J]. Atmospheric Pollution Research, 2019, 10(1): 321-333. doi: 10.1016/j.apr.2018.08.008
[19]	Zhang L, Shen F Z, Gao J L, et al. Characteristics and potential sources of black carbon particles in suburban Nanjing, China[J]. Atmospheric Pollution Research, 2020, 11(5): 981-991. doi: 10.1016/j.apr.2020.02.011
[20]	Rupakheti D, Rupakheti M, Abdullaev S F, et al. Columnar aerosol properties and radiative effects over Dushanbe, Tajikistan in Central Asia[J]. Environmental Pollution, 2020, 265: 114872. doi: 10.1016/j.envpol.2020.114872
[21]	Fedkin N M, Li C, Dickerson R R, et al. Linking improvements in sulfur dioxide emissions to decreasing sulfate wet deposition by combining satellite and surface observations with trajectory analysis[J]. Atmospheric Environment, 2019, 199: 210-223 doi: 10.1016/j.atmosenv.2018.11.039
[22]	刘新春, 钟玉婷, 何清, 等. 塔克拉玛干沙漠腹地及周边地区 PM₁₀时空变化特征及影响因素分析[J]. 中国沙漠, 2011, 31(2): 323-330.
	[Liu Xinchun, Zhong Yuting, He Qing, et al. Spatio-temporal pattern of PM₁₀ concentration and impact factors in the hinterland and surrounding area of Taklimakan Desert[J]. Journal of Desert Research, 2011, 31(2): 323-330.]
[23]	熊欢欢, 梁龙武, 曾赠, 等. 中国城市PM_2.5时空分布的动态比较分析[J]. 资源科学, 2017, 39(1): 136-146. doi: 10.18402/resci.2017.01.14
	[Xiong Huanhuan, Liang Longwu, Zeng Zeng, et al. Dynamic analysis of PM_2.5 spatial-temporal characteristics in China[J]. Resources Science, 2017, 39(1): 136-146.] doi: 10.18402/resci.2017.01.14
[24]	金莉莉, 李振杰, 何清, 等. 乌鲁木齐市城区和郊区近地层风速廓线[J]. 中国沙漠, 2017, 37(4): 755-769.
	[Jin Lili, Li Zhenjie, He Qing, et al. Surface layer wind speed profiles in center and suburbs of Urumqi, China[J]. Journal of Desert Research, 2017, 37(4): 755-769.]
[25]	陶健红. 西北地区沙尘天气的时空特征及影响因素分析[J]. 中国沙漠, 2009, 29(2): 327-334.
	[Tao Jianhong. Spatial-temporal characteristics of sand-dust events and influencing factors in Northwest China[J]. Journal of Desert Research, 2009, 29(2): 327-334.]
[26]	马勇刚, 黄粤. 基于1982—2013年NDVI数据的新疆30年植被状况季节与年际趋势分析[J]. 气候与环境研究, 2018, 23(1): 26-36.
	[Ma Yonggang, Huang Yue. Interannual and seasonal trend analysis of vegetation condition in Xinjiang based on 1982-2013 NDVI data[J]. Climatic and Environmental Research, 2018, 23(1): 26-36.]
[27]	陈春艳, 王建捷, 唐冶, 等. 新疆夏季降水日变化特征[J]. 应用气象学报, 2017, 28(1): 72-85.
	[Chen Chunyan, Wang Jianjie, Tang Ye, et al. Diurnal variations of summer precipitation in Xinjiang[J]. Journal of Applied Meteorological Science, 2017, 28(1): 72-85.]
[28]	Bahtebay J, Zhang F, Ariken M, et al. Evaluation of the coordinated development of urbanization-resources-environment from the incremental perspective of Xinjiang, China[J]. Journal of Cleaner Production, 2021, 325: 129309. doi: 10.1016/j.jclepro.2021.129309
[29]	Gu H H, Wang G H, Zhu W, et al. Gusty wind disturbances and large-scale turbulent structures in the neutral atmospheric surface layer[J]. Science China (Physics, Mechanics & Astronomy), 2019, 62(11): 114711.
[30]	杨梅, 李岩瑛, 张春燕, 等. 河西走廊中东部春季沙尘暴变化特征及其典型个例分析[J]. 干旱区地理, 2021, 44(5): 1339-1349.
	[Yang Mei, Li Yanying, Zhang Chuanyan, et al. Variation characteristics of spring sandstorm and its typical case analysis in the middle east of Hexi Corridor[J]. Arid Land Geography, 2021, 44(5): 1339-1349.]
[31]	徐祥德, 王寅钧, 魏文寿, 等. 特殊大地形背景下塔里木盆地夏季降水过程及其大气水分循环结构[J]. 沙漠与绿洲气象, 2014, 8(2): 1-11.
	[Xu Xiangde, Wang Yinjun, Wei Wenshou, et al. Summertime precipitation process and atmospheric water cycle over Tarim Basin under the specific large terrain background[J]. Desert and Oasis Meteorology, 2014, 8(2): 1-11.]
[32]	Zhao Z Y, Cao F, Fan M Y, et al. Nitrate aerosol formation and source assessment in winter at different regions in Northeast China[J]. Atmospheric Environment, 2021, 267: 118767. doi: 10.1016/j.atmosenv.2021.118767
[33]	宋友桂, 宗秀兰, 李越, 等. 中亚黄土沉积与西风区末次冰期快速气候变化[J]. 第四纪研究, 2019, 39(3): 535-548.
	[Song Yougui, Zong Xiulan, Li Yue, et al. Loess sediments and rapid climate oscillation during the last glacial period in the westerlies-dominated Central Asia[J]. Quaternary Sciences, 2019, 39(3): 535-548.]
[34]	张喆, 丁建丽, 王瑾杰. 艾比湖地区气溶胶光学特性分析[J]. 环境科学, 2020, 41(8): 3484-3491.
	[Zhang Zhe, Ding Jianli, Wang Jinjie. Aerosol optical properties over the Ebinur region[J]. Environmental Science, 2020, 41(8): 3484-3491.]
[35]	刘子龙, 代斌, 崔卓彦, 等. 大气污染物浓度变化特征及潜在源分析——以乌鲁木齐为例[J]. 干旱区研究, 2021, 38(2): 562-569.
	[Liu Zilong, Dai Bin, Cui Zhuoyan, et al. Concentration characteristics and potential source of atmospheric pollutants: A case study in Urumqi[J]. Arid Zone Research, 2021, 38(2): 562-569.]
[36]	杨晓玲, 李岩瑛, 陈静, 等. 河西走廊罕见强沙尘天气传输及其过程持续特征[J]. 干旱区地理, 2022, 45(5): 1415-1425.
	[Yang Xiaoling, Li Yanying, Chen Jing, et al. Transmission of rare strong dust and its process continuous characteristics in Hexi Corridor[J]. Arid Land Geography, 2022, 45(5): 1415-1425.]
[37]	Chen X L, Song Y G, Li Y, et al. Provenance of sub-aerial surface sediments in the Tarim Basin, Western China[J]. Catena, 2021, 198: 105014. doi: 10.1016/j.catena.2020.105014
[38]	Zhou C L, Yang F, Mamtimin A, et al. Wind erosion events at different wind speed levels in the Tarim Basin[J]. Geomorphology, 2020, 369: 107386. doi: 10.1016/j.geomorph.2020.107386
[39]	Carvalho A C, Carvalho A, Gelpi I, et al. Influence of topography and land use on pollutants dispersion in the Atlantic coast of Iberian Peninsula[J]. Atmospheric Environment, 2006, 40: 3969-3982. doi: 10.1016/j.atmosenv.2006.02.014
[40]	Li X, Xia X G, Zhong S Y, et al. Shallow foehn on the northern leeside of Tianshan Mountains and its influence on atmospheric boundary layer over Urumqi, China: A climatological study[J]. Atmospheric Research, 2020, 240: 104940. doi: 10.1016/j.atmosres.2020.104940
[41]	Wang W, Samat A, Abuduwaili J, et al. Temporal characterization of sand and dust storm activity and its climatic and terrestrial drivers in the Aral Sea region[J]. Atmospheric Research, 2022, 275: 106242. doi: 10.1016/j.atmosres.2022.106242
[42]	Sun W T, Gao X, Lei J Q. Shaping effects of sand flow channels on aeolian geomorphology-a case study of the Badain Jaran, Tengger, and Ulan Buh Deserts, northern China[J]. Catena, 2022, 214: 106255. doi: 10.1016/j.catena.2022.106255

	PM_2.5浓度均值/(μg·m^-3)					浓度变异系数/%
	全疆	NA区	NB区	NH区	S区	全疆	NA区	NB区	NH区	S区
春季	40.3	9.18	26.69	32.16	79.47	63.27	42.30	63.71	150.10	105.57
夏季	18.57	6.90	13.94	15.3	32.2	23.95	26.32	18.42	51.84	47.38
秋季	39.32	7.91	28.07	28.26	73.03	52.12	24.09	57.62	65.59	69.44
冬季	86.16	12.24	96.12	37.16	74.67	28.75	63.43	38.52	34.90	37.22
全年	46.13	9.07	41.2	28.28	65.01	69.64	54.55	94.41	99.78	84.55

	新疆			S区			NA区			NB区			NH区
	F1	F2	F3	F1_S	F2_S	F3_S	F1_NA	F2_NA	F3_NA	F1_NB	F2_NB	F3_NB	F1_NH	F2_NH	F3_NH
PM_2.5	0.44	0.79	0.30	0.15	0.97	0.10	0.20	0.83	0.05	0.91	0.31	0.15	0.18	0.96	0.06
NO₂	0.88	0.22	0.29	0.88	0.02	0.39	0.74	0.15	0.41	0.78	0.48	0.22	0.93	0.03	-0.02
O₃	-0.92	-0.18	-0.03	-0.94	-0.20	-0.12	-0.84	-0.03	-0.14	-0.20	-0.94	0.03	-0.91	-0.12	-0.09
CO	0.76	0.37	0.39	0.73	0.07	0.57	0.74	0.13	-0.18	0.76	0.56	0.16	0.83	0.06	0.10
SO₂	0.21	0.13	0.95	0.33	0.01	0.93	0.07	0.14	0.93	0.18	0.06	0.98	0.09	0.05	0.99
PM₁₀	0.15	0.95	0.04	0.05	0.98	-0.06	0.01	0.86	0.14	0.90	0.19	0.13	-0.01	0.98	0.01
解释方差/%	41.03	29.41	20.51	38.74	32.46	22.70	30.98	24.75	18.49	41.58	32.16	17.95	40.49	31.63	16.82

路径	NA区			NB区			NH区			S区
路径	路径占比 /%	平均值 /(μg·m^-3)	变异系数 /%	路径占比 /%	平均值 /(μg·m^-3)	变异系数 /%	路径占比 /%	平均值 /(μg·m^-3)	变异系数 /%	路径占比 /%	平均值 /(μg·m^-3)	变异系数 /%
1	13.64%	8.93	82.64%	21.08%	26.25	86.13%	25.18%	27.57	147.26%	23.15%	51.34	68.60%
2	46.46%	9.78	85.89%	50.83%	33.85	103.60%	41.97%	28.35	124.09%	40.11%	63.43	57.28%
3	20.71%	8.71	75.55%	11.44%	62.77	60.71%	17.90%	33.92	59.11%	17.24%	70.24	77.22%
4	19.19%	10.2	75.59%	16.65%	67.9	75.68%	14.95%	22.78	67.95%	19.50%	80.67	130.00%