2015—2020年中国三大城市群臭氧浓度时空变化特征及影响因子

叶深; 王鹏; 折远洋; 丁明军

doi:10.12153/j.issn.1674-991X.20221094

2015—2020年中国三大城市群臭氧浓度时空变化特征及影响因子

doi: 10.12153/j.issn.1674-991X.20221094

叶深^{1, 2,},
王鹏^{1, 2, ,},
折远洋^{1, 2},
丁明军^{1, 2}

1.
江西师范大学地理与环境学院
2.
江西师范大学, 鄱阳湖湿地与流域研究教育部重点实验室

基金项目: 中国科学院战略性先导科技专项资助(XDA20040201)

详细信息

作者简介:
叶深（1996—），男，硕士，主要研究方向为大气环境，ys1996120@outlook.com

通讯作者:
王鹏（1982—），男，教授，博士，主要从事鄱阳湖流域水环境研究工作，wangpengjlu@jxnu.edu.cn

中图分类号: X511
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-02
- 录用日期: 2023-02-20
- 修回日期: 2022-12-05
- 网络出版日期: 2023-07-19

Spatio-temporal variation characteristics and influencing factors of ozone in three major urban agglomerations in China from 2015 to 2020

YE Shen^{1, 2
,},
WANG Peng^{1, 2
, ,},
SHE Yuanyang^{1, 2},
DING Mingjun^{1, 2}

1.
School of Geography and Environment, Jiangxi Normal University
2.
Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University

摘要

摘要:
针对中国京津冀、长三角、珠三角三大城市群，分析了2015—2020年三大城市群的臭氧浓度时空变化特征，基于随机森林模型和地理探测器模型分别研究了影响其时间变化和空间变化的主要因子。结果表明：1）2015—2020年三大城市群臭氧浓度整体呈逐年升高的时空演变特征。其臭氧变化率存在中部向南北递减的趋势，即长三角（3.4%）>京津冀（2.9%）>珠三角（2.1%）；臭氧浓度平均值呈北高南低的空间变化特征，即京津冀（98.3 μg/m³）>长三角（96.7 μg/m³）>珠三角（90.5 μg/m³）。2）温度、风速、人均GDP和能源消耗量不仅是影响三大城市群臭氧浓度时间变化的主要因子，而且与臭氧浓度存在着阈值效应。3）能源消耗量和人均GDP是影响三大城市群臭氧浓度空间变化的主要因子，其对臭氧浓度空间变化的解释率均超过36%。今后关于城市群臭氧的防控应更关注经济发达地区，并通过重点监测和预警高耗能区等手段，达到城市群臭氧防治效果。
- 城市群 /
- 臭氧 /
- 机器学习 /
- 地理探测器 /
- 时空变化
Abstract:
The spatio-temporal variation characteristics of ozone concentration in the three major urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta in China from 2015 to 2020 were analyzed, and the main factors affecting temporal and spatial changes were studied based on random forest model and geographical detector model. The results showed that: 1) From 2015 to 2020, the temporal and spatial evolution characteristics of ozone concentration values of the three urban agglomerations showed an increasing trend year by year, and the ozone variation rate showed a trend of "decreasing from the central to the south": Yangtze River Delta (3.4%) > Beijing-Tianjin-Hebei (2.9%) > Pearl River Delta (2.1%). The spatial variation characteristics of the average ozone concentration were "high in the north and low in the south": Beijing-Tianjin-Hebei (98.3 μg/m³) > Yangtze River Delta (96.7 μg/m³) > Pearl River Delta (90.5 μg/m³). 2) Temperature, wind speed, GDP, and energy consumption were not only the main factors affecting the temporal variation of ozone in the three urban agglomerations, but also had a threshold effect on ozone concentration. 3) Energy consumption and GDP were the main factors affecting the spatial change of ozone concentration in the three urban agglomerations, and their interpretation rates were more than 36%. Therefore, for ozone prevention and control in urban agglomerations more attention should be paid to economically developed areas, and key monitoring and early warning should be carried out in high energy consuming areas to achieve the effectiveness of ozone prevention and control in urban agglomerations.
- urban agglomerations /
- ozone /
- machine learning /
- geographical detector /
- spatio-temporal changes

HTML全文

图 1 三大城市群的研究区域

Figure 1. Map of the study area of the three major urban agglomerations

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图 2 三大城市群臭氧浓度随机森林模型示意

Figure 2. Schematic diagram of ozone random forest model in the three major urban agglomerations

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图 3 2015—2020年三大城市群臭氧浓度时间变化

Figure 3. Temporal changes of ozone in the three major urban agglomerations from 2015 to 2020

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图 4 2015—2020年三大城市群臭氧浓度空间变化

Figure 4. Spatial changes of ozone in the three major urban agglomerations from 2015 to 2020

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图 5 多元线性回归与随机森林回归拟合

Figure 5. Fitting with multiple linear regression and random forest regression

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图 6 三大城市群臭氧浓度时间变化影响因子重要性排序

Figure 6. Ranking of the importance of ozone impact factors in the three major urban agglomerations

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图 7 三大城市群臭氧浓度时间变化影响因子阈值分析

Figure 7. Threshold analysis of ozone concentration influencing factors in the three major urban agglomerations

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表 1 地理探测器交互探测

Table 1. Geographical detector interactive detection

判断依据	交互作用
q(x₁∩x₂)<min[q(x₁)，q(x₂)]	非线性减弱
min[q(x₁)，q(x₂)]<q(x₁∩x₂)<max[q(x₁)，q(x₂)]	单因子分线性减弱
q(x₁∩x₂)>max[q(x₁)，q(x₂)]	互相增强
q(x₁∩x₂)=q(x₁)+q(x₂)	独立
q(x₁∩x₂)>q(x₁)+q(x₂)	非线性增强

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表 2 三大城市群臭氧浓度空间变化因子探测

Table 2. Factor detection analysis of spatial ozone changes in the three major urban agglomerations

影响因子	京津冀城市群		长三角城市群		珠三角城市群
影响因子	q	排序	q	排序	q	排序
温度	0.508^***	3	0.214^***	5	0.399^***	3
相对湿度	0.476^***	4	0.150^***	7	0.218^***	7
风速	0.291^***	5	0.205^***	6	0.104^***	8
降水量	0.290^***	6	0.356^***	3	0.257^***	6
人均GDP	0.546^***	1	0.359^***	2	0.589^***	1
产业结构	0.186^***	8	0.081^***	8	0.271^***	5
能源消耗量	0.527^***	2	0.506^***	1	0.564^***	2
私家车保有量	0.192^***	7	0.290^***	4	0.279^***	4
注：***表示在0.001水平显著相关。

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表 3 三大城市群臭氧浓度空间变化因子交互作用探测

Table 3. Detection of the interaction of spatial ozone changes in the three major urban agglomerations

地区	影响因子	温度	相对湿度	风速	降水量	人均GDP	产业结构	能源消耗量	私家车保有量
京津冀城市群	温度	0.508
	相对湿度	0.928	0.476
	风速	0.969	0.956	0.291
	降水量	0.905	0.798	0.899	0.289
	人均GDP	0.870	0.955	0.882	0.862	0.546
	产业结构	0.897	0.926	0.674	0.483	0.790	0.186
	能源消耗量	0.952	0.849	0.718	0.855	0.926	0.983	0.527
	私家车保有量	0.978	0.980	0.429	0.893	0.665	0.472	0.723	0.192
长三角城市群	温度	0.214
	相对湿度	0.730	0.150
	风速	0.504	0.788	0.205
	降水量	0.676	0.762	0.647	0.360
	人均GDP	0.893	0.950	0.595	0.838	0.356
	产业结构	0.399	0.477	0.355	0.609	0.683	0.081
	能源消耗量	0.832	0.941	0.666	0.755	0.788	0.714	0.505
	私家车保有量	0.439	0.552	0.577	0.553	0.792	0.539	0.719	0.291
珠三角城市群	温度	0.399
	相对湿度	0.634	0.218
	风速	0.488	0.409	0.104
	降水量	0.589	0.759	0.545	0.257
	人均GDP	0.819	0.698	0.669	0.884	0.589
	产业结构	0.685	0.922	0.547	0.382	0.728	0.271
	能源消耗量	0.619	0.598	0.643	0.661	0.780	0.764	0.564
	私家车保有量	0.611	0.429	0.337	0.584	0.683	0.629	0.624	0.279

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表 4 三大城市群臭氧浓度空间变化主要影响因子交互机制

Table 4. Interaction mechanism of main influencing factors of ozone spatial changes in the three major urban agglomerations

京津冀城市群			长三角城市群			珠三角城市群
双因子交互作用	交互值	交互关系	双因子交互作用	交互值	交互关系	双因子交互作用	交互值	交互关系
人均GDP∩温度	0.870	↑↑	能源消耗量∩温度	0.619	↑	人均GDP∩温度	0.819	↑↑
人均GDP∩相对湿度	0.955	↑↑	能源消耗量∩相对湿度	0.598	↑	人均GDP∩相对湿度	0.698	↑↑
人均GDP∩风速	0.882	↑	能源消耗量∩风速	0.711	↑↑	人均GDP∩风速	0.669	↑↑
人均GDP∩降水量	0.862	↑	能源消耗量∩降水量	0.862	↑↑	人均GDP∩降水量	0.884	↑
人均GDP∩产业结构	0.790	↑	能源消耗量∩人均GDP	0.865	↑↑	人均GDP∩产业结构	0.728	↑↑
人均GDP∩能源消耗量	0.926	↑↑	能源消耗量∩产业结构	0.764	↑	人均GDP∩能源消耗量	0.780	↑↑
人均GDP∩私家车保有量	0.665	↑↑	能源消耗量∩私家车保有量	0.624	↑↑	人均GDP∩私家车保有量	0.683	↑↑
注：↑表示非线性增强；↑↑表示互相增强。

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