2015—2020年山西省空气质量时空变化特征及其驱动因素分析

何向阳; 张凤英; 何立环; 马广文; 林兰钰; 方德昆

doi:10.13198/j.issn.1001-6929.2023.08.03

2015—2020年山西省空气质量时空变化特征及其驱动因素分析

doi: 10.13198/j.issn.1001-6929.2023.08.03

何向阳^{1, 2,},
张凤英²,
何立环²,
马广文²,
林兰钰²,
方德昆^2, ,

1.
中国环境科学研究院环境健康风险评估与研究中心，北京　100012
2.
中国环境监测总站，北京　100012

基金项目: 国家重点研发计划项目(No.2022YFC3702604)

详细信息

作者简介:
何向阳（1998-），男，山西临汾人，hexyes@outlook.com

通讯作者:
方德昆(1990-)，男，北京人，工程师，硕士，主要从事生态环境综合分析研究，fangdk@cnemc.cn

中图分类号: X823
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-15
- 修回日期: 2023-07-26
- 网络出版日期: 2023-08-11

Spatiotemporal Variation Characteristics and Driving Factors of Air Quality in Shanxi Province from 2015 to 2020

1.
Center for Environmental Health Risk Assessment and Research, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
2.
China National Environmental Monitoring Centre, Beijing 100012, China

Funds: National Key Research and Development Program of China (No.2022YFC3702604)

摘要

摘要: 山西省是近年来我国空气质量较差的地区之一，本文基于山西省11个地级市2015—2020年空气质量监测结果、气象数据以及2020年的社会经济数据，综合地理加权回归(GWR)模型、多尺度地理加权回归(MGWR)模型以及小波分析等方法开展该地区空气质量时空变化和驱动因素研究. 结果表明：①近年来，山西省SO₂、CO和PM_2.5浓度均呈明显下降趋势，但NO₂和O₃污染加重导致山西省空气污染防治形势严峻. ②冬季山西省空气质量空间差异性明显，11个城市中大同市的空气质量最好，临汾市空气质量最差. ③山西省不同社会经济因子与空气污染物相关关系的空间异质性不同，人均GDP和城市绿化覆盖率对SO₂浓度影响的空间异质性较高，人均GDP和人口密度对NO₂浓度影响的空间异质性较高. ④临汾市、大同市气象因子与PM_2.5浓度的相关性一致，两市平均气温与O₃浓度均呈显著正相关，风速、降水量和平均相对湿度与O₃浓度相关性均较弱，且在两市中存在明显差异. ⑤小波分析结果表明，临汾市、大同市PM_2.5浓度与风速在短周期中多呈负相关关系，长周期中PM_2.5浓度变化滞后于风速；O₃浓度与风速在不同短周期中的相关性不同，长周期中风速变化滞后于O₃浓度变化. 研究显示，山西省空气质量持续改善，不同经济因素和气象因素对空气质量时空特征的影响存在明显差异.
- 山西省 /
- 空气质量 /
- 时空特征 /
- 地理加权回归 /
- 小波分析
Abstract: In recent years, Shanxi Province became one of the regions with the worst air quality in China. This research uses the air quality monitoring data, meteorological data, and socio-economic data of 11 cities in Shanxi Province from 2015 and 2020 to study the spatial-temporal changes and driving factors of air quality. It employs comprehensive geographically weighted regression (GWR), multiscale geographically weighted regression (MGWR), and wavelet analysis methods. The results showed that: (1) In recent years, SO₂, CO and PM_2.5 concentrations in Shanxi Province showed a downward trend, but the worsening levels of NO₂ and O₃ created a serious challenge for air pollution prevention and control in the region. (2) During the winter, there were significant spatial differences of air quality among 11 cities in Shanxi Province. The air quality of Datong City was better than other cities, and the air quality of Linfen City was worse. (3) The spatial heterogeneity of the relationship between different socioeconomic factors and air pollutants in Shanxi Province was different. The spatial heterogeneity of the impact of per capita GDP and urban greening rate on SO₂ was high. The spatial heterogeneity of the impact of per capita GDP and population density on NO₂ was high. (4) The correlation between meteorological factors and PM_2.5 in Linfen city and Datong city was consistent, with a significant positive correlation between the average temperature and O_3. The correlations between wind speed, precipitation, and average relative humidity and O₃ were weak, and there were significant differences between the two cities. (5) The wavelet analysis showed that the PM_2.5 concentration in the two cities was negatively correlated with wind speed in short cycles, while the change of PM_2.5 concentration lagged behind wind speed in the long cycle. The correlation between O₃ concentration and wind speed in different short cycles was inconsistent, and the change of wind speed lagged behind the change of O₃ concentration in long cycles. In summary, the air quality in Shanxi Province continuously improved, and there were obvious differences in the impact of different economic and meteorological factors on the spatial and temporal characteristics of air quality.
- Shanxi Province /
- air quality /
- spatiotemporal characteristics /
- geographically weighted regression /
- wavelet analysis

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图 1 山西省海拔及空气质量监测点位

Figure 1. Air quality monitoring points and altitude in Shanxi Province

下载: 全尺寸图片幻灯片

图 2 2015—2020年山西省六项污染物浓度逐日变化情况

Figure 2. Daily concentration of six pollutants in Shanxi Province from 2015 to 2020

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图 3 山西省六项污染物浓度逐年变化特征以及2020年与2015年污染物年均浓度差值

Figure 3. Annual characteristics of six pollutant and the difference in annual average concentration difference in Shanxi Province between 2020 and 2015

下载: 全尺寸图片幻灯片

图 4 2015—2020年山西省11个地级市的AQI值逐日变化特征

Figure 4. Daily characteristics of AQI values in 11 cities of Shanxi Province from 2015 to 2020

下载: 全尺寸图片幻灯片

图 5 2015—2020年山西省11个地级市空气质量等级分布情况

Figure 5. Air quality grade distribution of 11 cities in Shanxi Province from 2015 to 2020

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图 6 MGWR模型回归系数分布

Figure 6. Distribution of MGWR regression coefficient

下载: 全尺寸图片幻灯片

图 7 临汾市和大同市PM_2.5浓度、O₃浓度、风速的小波图

注：黑实线包围的黄色部分表示相关性通过95%显著检验，阴影部分受到边界效应影响，其功率谱不予考虑^[33]. 下同.

Figure 7. Wavelet diagram of PM_2.5 concentration, O₃ concentration and wind speed in Linfen City and Datong City

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图 8 临汾市和大同市PM_2.5浓度、O₃浓度与风速的交叉小波图、小波相干图

注：箭头方向朝左表示二者反相位变化，为负相关性，反之亦然.

Figure 8. Cross wavelet and wavelet coherence diagram of PM_2.5 concentration, O₃ concentration and wind speed in Linfen City and Datong City

下载: 全尺寸图片幻灯片

表 1 OLS和GWR模型分析结果

Table 1. Analysis results of OLS and GWR models

因变量	自变量	OLS模型			GWR模型
因变量	自变量	回归系数	P	VIF值	回归系数最小值	回归系数中值	回归系数最大值
SO₂浓度	人口密度	−1.289^***	0.005	2.867	−1.312	−1.279	−1.260
	城镇化率	1.370^***	0.002	2.443	1.351	1.354	1.377
	人均GDP	−0.046	0.808	1.852	−0.062	−0.037	−0.027
	工业用电量	−0.095	0.584	1.261	−0.106	−0.092	−0.083
	城市绿化覆盖率	0.301	0.111	1.480	0.290	0.297	0.308
	AICc¹⁾	11.699			33.681
	R²	0.895			0.906
	调整R²	0.790			0.741
	F-statistic²⁾	8.505^**	0.017 4
	带宽				17
NO₂浓度	人口密度	0.336	0.427	2.867	0.299	0.343	0.379
	城镇化率	0.203	0.570	2.443	0.183	0.206	0.225
	人均GDP	0.298	0.304	1.852	0.265	0.282	0.319
	工业用电量	−0.151	0.545	1.261	−0.151	−0.145	−0.137
	城市绿化覆盖率	−0.526^*	0.065	1.480	−0.525	−0.520	−0.512
	AICc¹⁾	19.717			41.626
	R²	0.759			0.785
	调整R²	0.518			0.411
	F-statistic²⁾	3.146	0.117
	带宽				17
注：1)AICc值是衡量模型优良度的一种标准，值越小，模型拟合优度越高. 2) F -statistic用于检验模型是否具有显著性. *、、*分别表示在1%、5%、10%水平上的显著性. 下同.

下载: 导出CSV

表 2 MGWR模型分析结果

Table 2. Analysis results of MGWR model

因变量	自变量	MGWR
因变量	自变量	回归系数最小值	回归系数中值	回归系数最大值	带宽
SO₂浓度	人口密度	−1.318	−1.314	−1.313	10
	城镇化率	1.314	1.320	1.340	10
	人均GDP	−0.072	0.024	0.110	7
	工业用电量	−0.116	−0.103	−0.096	10
	城市绿化覆盖率	0.256	0.302	0.337	8
	AICc	47.603
	R²	0.934
	调整R²	0.766
NO₂浓度	人口密度	0.420	0.463	0.477	9
	城镇化率	0.132	0.158	0.188	10
	人均GDP	0.163	0.210	0.295	9
	工业用电量	−0.200	−0.137	−0.104	10
	城市绿化覆盖率	−0.540	−0.530	−0.515	10
	AICc	49.500
	R²	0.813
	调整R²	0.404

下载: 导出CSV

表 3 2015—2020年临汾市和大同市PM_2.5、O₃浓度与气象因子的Pearson相关系数

Table 3. Pearson correlation coefficient between PM_2.5, O₃ and meteorological factors in Linfen City and Datong City from 2015 to 2020

城市	气象因子	Pearson相关系数
城市	气象因子	PM_2.5浓度	O₃浓度
临汾市	风速	−0.338 9^***	0.170 8^***
	平均气温	−0.486 5^***	0.744 5^***
	降水量	−0.157 5^***	−0.048 3
	平均相对湿度	0.050 2	−0.102 1^***
大同市	风速	−0.335 8^***	0.006 9
	平均气温	−0.312 6^***	0.779 8^***
	降水量	−0.163 1^***	0.119 5^***
	平均相对湿度	0.074 0^**	0.050 7
注：表示P<0.002，*表示P<0.001.

下载: 导出CSV

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