Spatial and temporal distribution characteristics of PM2.5 concentration in global typical countries based on remote sensing products

doi:10.3969/j.issn.1004-9479.2024.07.20222375

Abstract

Abstract:

In the context of globalization, PM_2.5 pollution has become a global phenomenon. Understanding the spatial and temporal differential distribution of PM_2.5 concentrations in different countries has become an increasingly important topic for governments. Taking 27 typical countries in the world as the research objects, Ensemble Empirical Mode Decomposition (EEMD), GIS map representation, and comparative research methods were used to analyze the spatial and temporal variation characteristics of PM_2.5 pollution from 1998 to 2019. The results show that: ① The trend fluctuation of PM_2.5 in the global typical countries in the study area was divided into four types: 'continuous rise', 'continuous fall', 'first rise and then fall' and 'first fall and then rise. 70% of the countries showed a trend of improving PM_2.5 pollution during the study period; ② Except for Africa, the monthly changes of countries in the same regions are similar; ③ Compared with North America, Europe, and Oceania, Asia, the Middle East, and Africa have larger seasonal changes in PM_2.5 concentrations; ④ From the perspective of the average annual concentration, Pakistan, India, Bangladesh, Iran, Egypt, Saudi Arabia, Turkey and Nigeria had higher PM_2.5concentration in the past 20 years, which also have exceeded the PM_2.5 transition target I pollution level(35μg/m³) proposed by WHO. The comparative study of spatial and temporal differences of PM_2.5pollution under long time series can provide scientific reference for countries with different levels of economic development, and complement the global national macro scale PM_2.5 concentration characteristics.

Key words: remote sensing products, Eensemble Empirical Mode Decomposition, global typical countries, PM_2.5, spatiotemporal distribution

摘要：

在全球化背景下，PM_2.5污染已成为一种全球现象，梳理不同国家PM_2.5浓度的时空差异化分布，已越来越成为学术界的重要课题。以全球27个典型国家为研究对象，采用集合经验模态分解、GIS地图表达、比较研究法，对1998—2019年PM_2.5污染的时空变化特征进行分析。研究结果显示：①研究期内全球27个典型国家的PM_2.5波动趋势划分为持续上升、持续下降、先升后降、先降后升4种类型，70%的国家在研究时段内表现出PM_2.5污染转好趋势；②除非洲区域典型国家外，其余同一区域的国家月度变化具有相似性；③与北美、欧洲、大洋洲区域的典型国家相比，亚洲、中东及非洲地区典型国家的PM_2.5浓度季节变化的幅度要更大；④从20年的年均浓度上来看，巴基斯坦、印度、孟加拉国、伊朗、埃及、沙特阿拉伯、土耳其和尼日利亚的PM_2.5浓度更高，且已超过WHO提出的PM_2.5过渡目标Ⅰ污染等级（35 μg/m³）。长时间序列下PM_2.5污染时空差异特征的比较研究可补充全球国家宏观尺度的PM_2.5浓度特征研究，为不同经济发展水平国家的污染治理提供科学参考。

关键词: 遥感产品, 集合经验模态分解, 全球典型国家, PM_2.5, 时空分布

Tianhui TAO, Yishao SHI, Jiaqi LI, Zhihong PENG. Spatial and temporal distribution characteristics of PM_2.5 concentration in global typical countries based on remote sensing products[J]. World Regional Studies, 2024, 33(7): 18-32.

陶天慧, 石忆邵, 李嘉琪, 彭志宏. 基于遥感产品的全球典型国家PM_2.5 浓度时空分布特征[J]. 世界地理研究, 2024, 33(7): 18-32.

Figures/Tables 9

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区域	国家	陆地面积	区域	国家	陆地面积	区域	国家	陆地面积
欧洲	俄罗斯	1 709.82	东亚	中国	960	东南亚	越南	32.95
	英国	24.41		韩国	10.33		菲律宾	29.97
	法国	55		日本	37.8		泰国	51.30
	意大利	30.13	南亚	巴基斯坦	79.61		马来西亚	33
	西班牙	50.60		印度	298		印度尼西亚	191.35
中东	伊朗	16.45		孟加拉国	14.76	北美洲	加拿大	998
	埃及	100.15	南美洲	巴西	851.49		美国	937
	沙特阿拉伯	225	非洲	南非	121.91		墨西哥	196.43
	土耳其	78.36	非洲	尼日利亚	92.38	大洋洲	澳大利亚	769.2

区域	国家	陆地面积	区域	国家	陆地面积	区域	国家	陆地面积
欧洲	俄罗斯	1 709.82	东亚	中国	960	东南亚	越南	32.95
	英国	24.41		韩国	10.33		菲律宾	29.97
	法国	55		日本	37.8		泰国	51.30
	意大利	30.13	南亚	巴基斯坦	79.61		马来西亚	33
	西班牙	50.60		印度	298		印度尼西亚	191.35
中东	伊朗	16.45		孟加拉国	14.76	北美洲	加拿大	998
	埃及	100.15	南美洲	巴西	851.49		美国	937
	沙特阿拉伯	225	非洲	南非	121.91		墨西哥	196.43
	土耳其	78.36	非洲	尼日利亚	92.38	大洋洲	澳大利亚	769.2

国家分类	人均国民收入/现价美元
低收入	≦1 025
中低收入	1 026~4 035
中高收入	4 036~12 475
高收入	>12 476

国家分类	人均国民收入/现价美元
低收入	≦1 025
中低收入	1 026~4 035
中高收入	4 036~12 475
高收入	>12 476

区域	典型国家	周期（月）	趋势类型	趋势斜率	转折时间
东亚	中国	3、12、20、66、88、264、264	先升后降	0.50、－0.47	2009.04
	韩国	3、6、13、26、88、132、264	先升后降	0.37、－0.27	2010.07
	日本	3、9、13、38、88、264、264	先升后降	0.11、－0.16	2007.03
东南亚	越南	3、9、15、33、88、264、264	先升后降	0.34、－0.29	2009.10
	菲律宾	3、7、13、26、53、132、264	先升后降	0.27、－0.21	2010.05
	泰国	3、7、13、29、53、132、264	先升后降	0.44、－0.34	2010.05
	马来西亚	3、6、14、26、53、88、264	上升	0.21	—
	印度尼西亚	3、7、13、24、53、132、264	上升	0.57	—
南亚	印度	3、13、22、53、132、264、264	上升	0.77	—
	巴基斯坦	3、6、13、30、66、132、264	先升后降	0.39、－0.10	2015.04
	孟加拉国	4、13、14、33、66、264、264	上升	1.23	—
中东	伊朗	3、11、16、38、66、264、264	先升后降	0.64、－0.20	2014.10
	埃及	3、7、13、29、44、132、264	上升	0.28	—
	沙特阿拉伯	3、8、13、29、53、132、264	先升后降	1.03、－0.33	2014.08
	土耳其	3、9、13、33、66、132、264	上升	0.18	—
欧洲	俄罗斯	3、7、13、26、66、132、132	先降后升	－0.08、0.10	2007.06
	英国	3、8、13、29、53、132、264	下降	－0.10	—
	法国	3、9、15、29、132、264、264	下降	－0.28	—
	意大利	3、9、14、33、66、264、264	下降	－0.26	—
	西班牙	3、7、12、24、53、264、264	下降	－0.15	—
非洲	尼日利亚	3、13、20、53、88、264	下降	－0.48	—
非洲	南非共和国	3、6、12、26、53、132、264	先升后降	0.22、－0.12	2012.03
北美洲	加拿大	4、7、12、33、66、132、132	下降	－0.03	—
	美国	3、6、14、24、53、264、264	下降	－0.13	—
	墨西哥	3、7、13、26、66、132、264	先降后升	－0.34、0.03	2017.09
南美洲	巴西	4、9、13、29、66、264、264	先升后降	0.47、－0.08	2016.08
大洋洲	澳大利亚	3、7、14、29、66、264、264	上升	0.06	—