黄河流域城市PM<sub>2.5</sub>时空分异特征及污染物解析

徐雯丽; 陈强强

doi:10.13198/j.issn.1001-6929.2022.10.06

黄河流域城市PM_2.5时空分异特征及污染物解析

doi: 10.13198/j.issn.1001-6929.2022.10.06

徐雯丽,
陈强强^,

甘肃农业大学财经学院，甘肃兰州　730070

基金项目: 国家社会科学基金项目(No.21BJY117)

详细信息

作者简介:
徐雯丽（1998-），女，河北张家口人，xuwenli1628@163.com

通讯作者:
陈强强(1979-)，男，甘肃陇西人，教授，硕士，主要从事区域经济研究，jjglxy666@126.com

中图分类号: X513
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出版历程
- 收稿日期: 2022-07-31
- 修回日期: 2022-09-26

Spatial-Temporal Variation Characteristics and Pollutants Analysis of Urban PM_2.5in the Yellow River Basin

XU Wenli,
CHEN Qiangqiang^,

College of Finance and Economics, Gansu Agricultural University, Lanzhou 730070, China

Funds: National Social Science Foundation of China (No.21BJY117)

摘要

摘要: 分析揭示黄河流域城市PM_2.5时空分异特征，对打赢大气污染防治攻坚战，推动黄河流域空气污染跨区域协同治理机制的建立和完善，以及流域绿色高质量发展具有重要意义. 本文以中国空气质量在线监测分析平台456个监测站点的PM_2.5浓度监测数据为基础，运用莫兰指数和标准差椭圆方法分析黄河流域70个城市2015—2021年PM_2.5的时空分异特征、演变格局，并基于皮尔逊相关系数分析法对其污染源进行解析. 结果表明：①PM_2.5浓度的月度、季节变化特征明显. 月均浓度呈底部宽缓的“U”型分布，12月或1月达到最大值；冬季平均浓度最高、春秋季次之、夏季最低，冬季浓度是夏季的1.9~2.6倍；年均PM_2.5浓度整体趋降，且表现为下游>中游>上游的空间分异性. ②PM_2.5的空间聚集表现为上游“低—低”集聚、下游“高—高”集聚、中游城市的空间聚集特征不显著，空间正相关集聚的城市数量以先增后减的趋势变化，负相关集聚特征的城市较少. ③PM_2.5空间分布格局总体呈“西北—东南”的地理空间走向，其浓度的分布在地理空间上分散化，但分布范围趋于缩减. ④上游城市PM_2.5污染源复杂多样，主要有PM₁₀、NO₂、CO和SO₂，中游城市PM_2.5污染主要来源于PM₁₀、NO₂和CO，而下游城市为PM₁₀、CO. 研究显示，黄河流域城市PM_2.5浓度的时空分异性明显，上下游存在显著的空间正相关聚集，城市污染联防联控对流域空气质量的改善发挥重要作用.
- PM_2.5 /
- 时空分异特征 /
- 黄河流域城市 /
- 相关性
Abstract: Analyzing and revealing the spatial and temporal characteristics of PM_2.5 in cities of the Yellow River Basin is of great significance for winning the battle against air pollution and promoting the establishment and improvement of the cross-regional cooperative control mechanism of air pollution and the green high-quality development. Based on the PM_2.5 concentration monitoring data of 456 monitoring stations, this paper analyzed the spatial and temporal variation characteristics and evolution pattern of PM_2.5 in 70 cities in the Yellow River Basin from 2015 to 2021 by using the Moran index and standard deviation ellipse method, and identified the pollution sources by Pearson correlation coefficient analysis method. The results showed that: (1) PM_2.5 concentration reflected monthly and seasonal changes. The monthly average concentration showed a ‘U-shaped’ distribution with a wide and slow bottom, reaching the maximum in December or January. In terms of seasons, the average concentration in winter was the highest (1.9-2.6 times that of summer), followed by spring and autumn, and the lowest in summer. The annual average concentration decreased in general, showing a spatial differentiation of downstream > midstream > upstream. (2) The spatial aggregation of PM_2.5 was characterized by ‘low-low’ agglomeration in the upstream, ‘high-high’ agglomeration in the downstream, and insignificant spatial aggregation in the midstream. The number of cities with positive spatial correlation agglomeration increased first and then decreased, while the number of cities with negative spatial correlation agglomeration was relatively small. (3) The spatial distribution pattern of PM_2.5 generally followed a ‘northwest-southeast’ geographical spatial trend, and the concentration distribution showed the characteristics of geographical spatial decentralization and gradual reduction of distribution range. (4) PM_2.5 pollution in upstream cities was complex and diverse, mainly including PM₁₀, NO₂, CO and SO₂. PM₁₀, NO₂ and CO were the main sources in midstream cities, and PM₁₀ and CO were the main sources in downstream cities. The spatial heterogeneity of PM_2.5 concentration in cities of the Yellow River Basin was obvious, and there was a significant positive spatial correlation agglomeration in the upstream and downstream. Collaborative prevention and control in cities will help to further improve air quality in the Yellow River Basin.
- PM_2.5 /
- spatial-temporal variation characteristics /
- cities of the Yellow River Basin /
- correlation

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图 1 黄河流域范围及监测站点分布

注：1—果洛藏族自治州；2—玉树藏族自治州；3—海北藏族自治州；4—海东市；5—海南藏族自治州；6—海西蒙古族藏族自治州；7—黄南藏族自治州；8—西宁市；9—白银市；10—定西市；11—甘南藏族自治州；12—兰州市；13—临夏回族自治州；14—武威市；15—阿拉善盟；16—包头市；17—巴彦淖尔市；18—乌海市；19—固原市；20—石嘴山市；21—吴忠市；22—银川市；23—中卫市；24—甘孜藏族自治州；25—阿坝藏族羌族自治州；26—陇南市；27—平凉市；28—庆阳市；29—天水市；30—焦作市；31—济源市；32—洛阳市；33—三门峡市；34—呼和浩特市；35—鄂尔多斯市；36—乌兰察布市；37—宝鸡市；38—商洛市；39—铜川市；40—渭南市；41—西安市；42—咸阳市；43—延安市；44—榆林市；45—长治市；46—大同市；47—晋城市；48—晋中市；49—临汾市；50—吕梁市；51—朔州市；52—太原市；53—忻州市；54—阳泉市；55—运城市；56—安阳市；57—鹤壁市；58—开封市；59—濮阳市；60—新乡市；61—郑州市；62—滨州市；63—德州市；64—东营市；65—菏泽市；66—济南市；67—济宁市；68—聊城市；69—泰安市；70—淄博市.

Figure 1. The range of Yellow River and distribution of monitoring sites

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图 2 2015—2021年黄河流域城市PM_2.5浓度月变化特征

Figure 2. Monthly variation of PM_2.5 concentration in cities of the Yellow River Basin from 2015 to 2021

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图 3 黄河流域城市PM_2.5浓度季节变化特征

Figure 3. Seasonal variation of PM_2.5 concentration in cities of the Yellow River Basin

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图 4 2015—2021黄河流域城市PM_2.5浓度年变化特征

Figure 4. Annual variation of PM_2.5 concentration in cities of the Yellow River Basin from 2015 to 2021

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图 5 2015—2021年黄河流域城市PM_2.5污染等级天数占比

Figure 5. Annual statistics of the proportion days covered by PM_2.5 pollution in the Yellow River Basin from 2015 to 2021

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图 6 黄河流域城市PM_2.5浓度的空间分布

Figure 6. Spatial distribution of PM_2.5 concentration in cities of the Yellow River Basin

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图 7 黄河流域城市PM_2.5局部空间自相关LISA图

Figure 7. LISA distribution of PM_2.5 in Yellow River Basin cities

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图 8 2015—2021年黄河流域城市PM_2.5浓度时空演变格局

Figure 8. Spatial and temporal patterns of PM_2.5 concentration in cities of the Yellow River Basin from 2015 to 2021

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表 1 黄河流域省份城市区域划分

Table 1. Division of provinces and cities in the Yellow River Basin

流域划分	省份	地级市(或自治州)
上游(25个)	青海省(8个)	玉树藏族自治州、果洛藏族自治州、海北藏族自治州、黄南藏族自治州、西宁市、海东市、海西蒙古族藏族自治州、海南藏族自治州
	甘肃省(6个)	甘南藏族自治州、临夏回族自治州、武威市、兰州市、定西市、白银市
	四川省(2个)	阿坝藏族羌族自治州、甘孜藏族自治州
	宁夏回族自治区(5个)	石嘴山市、固原市、吴忠市、中卫市、银川市
	内蒙古自治区(4个)	乌海市、包头市、阿拉善盟、巴彦淖尔市
中游(30个)	甘肃省(4个)	庆阳市、平凉市、陇南市、天水市
	内蒙古自治区(3个)	鄂尔多斯市、呼和浩特市、乌兰察布市
	陕西省(8个)	榆林市、铜川市、渭南市、延安市、咸阳市、西安市、宝鸡市、商洛市
	山西省(11个)	朔州市、忻州市、太原市、长治市、晋城市、阳泉市、晋中市、吕梁市、临汾市、运城市、大同市
	河南省(4个)	济源市、焦作市、洛阳市、三门峡市
下游(15个)	河南省(6个)	濮阳市、鹤壁市、安阳市、郑州市、开封市、新乡市
下游(15个)	山东省(9个)	菏泽市、济宁市、聊城市、泰安市、德州市、济南市、淄博市、滨州、东营市

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表 2 2015—2021年黄河流域城市PM_2.5全局Moran′s I

Table 2. Global spatial Moran index of PM_2.5 in the Yellow River Basin from 2015 to 2021

年份	Moran′s I值	Z值	P值
2015	0.798	10.614 4	0.001
2016	0.760	10.331 5	0.001
2017	0.727	9.827 9	0.001
2018	0.820	11.137 9	0.001
2019	0.793	10.800 5	0.001
2020	0.746	10.018 1	0.001
2021	0.734	9.804 4	0.001

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表 3 2015—2021年黄河流域城市PM_2.5浓度空间聚集城市统计

Table 3. Statistics of PM_2.5 spatial concentration in cities of the Yellow River Basin from 2015 to 2021 个

年份	上游		中游		下游		总计
年份	“高—高”集聚	“低—低”集聚	“高—高”集聚	“低—低”集聚	“高—高”集聚	“低—低”集聚	“高—高”集聚	“低—低”集聚
2015	0	18	7	5	15	0	22	23
2016	0	22	10	2	15	0	25	24
2017	0	22	13	2	15	0	28	24
2018	0	24	12	3	15	0	27	27
2019	0	21	11	2	15	0	26	23
2020	0	17	11	2	15	0	26	19
2021	0	19	11	3	15	0	26	22

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表 4 黄河流域城市PM_2.5浓度的标准差椭圆参数

Table 4. Standard deviation elliptic parameters of PM_2.5 concentration in cities of the Yellow River Basin

年份	方位角/(°)	重心坐标	重心所在地区	椭圆面积/km²	长轴标准差/km	短轴标准差/km	长短轴差值/km
2015	88.64	110.51°E、36.42°N	临汾市	637229.85	756.08	268.32	487.76
2016	88.22	110.52°E、36.38°N	临汾市	606788.41	725.15	266.49	458.66
2017	87.95	110.53°E、36.43°N	临汾市	606141.32	716.21	269.43	446.78
2018	88.16	110.82°E、36.35°N	临汾市	575232.88	695.06	263.47	431.59
2019	89.09	110.72°E、36.40°N	临汾市	582801.08	693.71	267.46	426.25
2020	89.60	110.62°E、36.50°N	临汾市	595855.86	694.44	273.16	421.28
2021	88.76	110.59°E、36.41°N	临汾市	597499.40	710.41	267.76	442.65

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表 5 黄河流域城市不同季节的PM_2.5浓度与其他空气污染物的Pearson相关系数

Table 5. Seasonal variation of Pearson correlation coefficients between PM_2.5 and gaseous pollutants in the cities of the Yellow River Basin

项目		PM₁₀浓度	NO₂浓度	SO₂浓度	CO浓度	O₃浓度
上游城市PM_2.5浓度	春季	0.428^*	0.589^**	−0.009	0.243	0.161
	夏季	0.826^**	0.520^**	0.331	0.449^*	0.661^**
	秋季	0.871^**	0.763^**	0.346	0.630^**	0.102
	冬季	0.923^**	0.835^**	0.421^*	0.024	−0.382
	年均值	0.950^**	0.772^**	0.570^**	0.758^**	0.338
中游城市PM_2.5浓度	春季	0.686^**	0.364^*	0.111	0.690^**	0.546^**
	夏季	0.791^**	0.278	0.125	0.879^**	0.867^**
	秋季	0.767^**	0.429^*	−0.110	0.705^**	0.427^*
	冬季	0.878^**	0.589^**	−0.156	−0.182	−0.425^*
	年均值	0.950^**	0.662^**	0.295	0.639^**	0.243
下游城市PM_2.5浓度	春季	0.015	−0.034	−0.273	0.190	−0.408
	夏季	0.444	0.572^*	0.151	0.382	0.262
	秋季	0.861^**	0.327	−0.219	0.535^*	−0.521^*
	冬季	0.614^*	−0.290	−0.373	−0.079	0.222
	年均值	0.825^**	0.265	−0.097	0.607^*	−0.271
注：*表示在0.01(双侧)水平上显著相关；表示在0.05(双侧)水平上显著相关.

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