| 中原城市群PM2.5浓度驱动因子联动效应及非线性影响 |
| 摘要点击 1810 全文点击 1245 投稿时间：2022-01-05 修订日期：2022-04-07 |
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| 中文关键词 PM2.5 非线性影响 驱动因子 联动效应 中原城市群 |
| 英文关键词 PM2.5 nonlinear effect driving factor linkage effect Central Plains Urban Agglomeration |
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| 中文摘要 |
| 城市群是中国PM2.5污染与防治的核心区.为探究中原城市群PM2.5浓度驱动因子的作用机制，基于多源遥感数据和统计数据，采用空间自相关、参数最优地理探测器以及系统动态面板回归模型等方法，量化了PM2.5浓度的驱动因子及因子间的联动效应，进一步分析了社会经济因素对PM2.5浓度的非线性影响并给出相应的治霾建议.结果表明：①2012~2018年，中原城市群PM2.5浓度下降程度存在空间分异，北部较南部地区污染减弱更为显著.②PM2.5浓度高值聚集有从中原城市群北部向东部转移的趋势，而低值聚集情况相对稳定.③高程对PM2.5浓度的解释力最强，因子间联动效应对PM2.5污染的解释力均表现为增强，其中高程与降水交互后解释力最强.④人均GDP、人口密度、夜间灯光、外商直接投资和第二产业占比均与PM2.5浓度之间存在非线性关系.结合中原城市群现状，加大污染治理投入、调整城市结构形态、加强基础设施建设、调整人口分布与产业结构、保持较高的城市活跃水平、设定严格的环境法规和引入高质量外商投资等有助于治理PM2.5污染. |
| 英文摘要 |
| Urban agglomerations are the core areas of PM2.5 pollution and prevention in China. In order to explore the mechanism of PM2.5 concentration driving factors in the Central Plains Urban Agglomeration, we took multi-source remote sensing data and statistical data as the foundation and used spatial autocorrelation, an optimal parameters-based geographical detector, and a system dynamic panel regression model to quantify the driving factors of PM2.5 concentrations and the linkage between the factor effects. Furthermore, we analyzed the nonlinear influence of social and economic factors on PM2.5 concentration and gave corresponding suggestions for haze control. The results showed that:① from 2012 to 2018, the degree of PM2.5 concentration decline in the Central Plains Urban Agglomeration showed spatial differentiation, and pollution reduction in the northern region was more significant than that in the southern region. ② The high concentration of PM2.5 had a tendency to shift from the north to the east of the Central Plains Urban Agglomeration, whereas the low concentration was relatively stable. ③ Elevation had the strongest explanatory power for PM2.5 concentration, and the explanatory power of the inter-factor linkage effect on PM2.5 pollution was enhanced, among which the interaction between elevation and precipitation had the strongest explanatory power. ④ PM2.5 concentration had a nonlinear relationship with per capita GDP, population density, night lighting, foreign direct investment, and the proportion of secondary industry. According to the status quo of the Central Plains Urban Agglomeration, increasing investment in pollution control, adjusting urban structure and form, strengthening infrastructure construction, adjusting population distribution and industrial structure, maintaining a high level of urban activity, setting strict environmental regulations, and introducing high-quality foreign investment can help control PM2.5 pollution. |
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