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中国城市群中 PM 和臭氧浓度的时空特征。

Spatiotemporal characteristics of PM and ozone concentrations in Chinese urban clusters.

机构信息

School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China.

Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of China), Key Laboratory of Applied Statistics and Data Science, School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan, 410081, PR China.

出版信息

Chemosphere. 2022 May;295:133813. doi: 10.1016/j.chemosphere.2022.133813. Epub 2022 Jan 31.

Abstract

Despite China's public commitment to emphasise air pollution investigation and control, trends in PM and ozone concentrations in Chinese urban clusters remain unclear. This study quantifies the spatiotemporal variations in PM and surface ozone at the scale of Chinese urban clusters by using a long-term integrated dataset from 2015 to 2020. Nonlinear Granger causality testing was used to explore the spatial association patterns of PM and ozone pollution in five megacity cluster regions. The results show a significant downward trend in annual mean PM concentrations from 2015 to 2020, with a decline rate of 2.8 μg m yr. By contrast, surface ozone concentrations increased at a rate of 2.1 μg m yr over the 6 years. The annual mean PM concentrations in urban clusters show significant spatial clustering characteristics, mainly in Beijing-Tianjin-Hebei (BTH), Fenwei Plain (FWP), Northern slope of Tianshan Mountains urban cluster (NSTM), Sichuan Basin urban cluster (SCB), and Yangtze River Delta (YRD). Surface ozone shows severe summertime pollution and distributional variability, with increased ozone pollution in major urban clusters. The highest increases were observed in BTH, Yangtze River midstream urban cluster (YRMR), YRD, and Pearl River Delta (PRD). Nonlinear Granger causality tests showed that PM was a nonlinear Granger cause of ozone, further supporting the literature's findings that PM reduction promoted photochemical reaction rates and stimulated ozone production. The nonlinear test statistic passed the significance test in magnitude and statistical significance. FWP was an exception, with no significant long-term nonlinear causal link between PM and ozone. This study highlights the challenges of compounded air pollution caused primarily by ozone and secondary PM. These results have implications for the design of synergistic pollution abatement policies for coupled urban clusters.

摘要

尽管中国公开承诺要加强对空气污染的调查和控制,但中国城市群中 PM 和臭氧浓度的趋势仍不清楚。本研究通过使用 2015 年至 2020 年的长期综合数据集,量化了中国城市群中 PM 和地面臭氧的时空变化。使用非线性格兰杰因果检验来探索五个特大城市群地区的 PM 和臭氧污染的空间关联模式。结果表明,2015 年至 2020 年,年平均 PM 浓度呈显著下降趋势,下降率为 2.8μg m yr。相比之下,6 年来,地面臭氧浓度以每年 2.1μg m yr 的速度增加。城市群的年平均 PM 浓度表现出显著的空间聚类特征,主要集中在北京-天津-河北(BTH)、汾渭平原(FWP)、天山北坡城市群(NSTM)、四川盆地城市群(SCB)和长江三角洲(YRD)。地面臭氧表现出严重的夏季污染和分布变化,主要城市群的臭氧污染增加。增幅最大的是 BTH、长江中游城市群(YRMR)、YRD 和珠江三角洲(PRD)。非线性格兰杰因果检验表明,PM 是臭氧的非线性格兰杰原因,进一步支持了文献中 PM 减排促进光化学反应速率并刺激臭氧生成的发现。非线性检验统计量在幅度和统计显著性上都通过了显著性检验。FWP 是一个例外,PM 和臭氧之间没有显著的长期非线性因果关系。本研究强调了主要由臭氧和二次 PM 引起的复合型空气污染的挑战。这些结果对设计协同城市群污染减排政策具有启示意义。

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