2000—2022年珠江三角洲城市群PM<sub>2.5</sub>浓度的时空变化分析

吴楚冰; 肖福安; 邓玉娇; 吴志峰

doi:10.16032/j.issn.1004-4965.2026.006

2000—2022年珠江三角洲城市群PM_2.5浓度的时空变化分析

doi: 10.16032/j.issn.1004-4965.2026.006

吴楚冰¹,
肖福安^{1, 2, ,},
邓玉娇³,
吴志峰¹

1.
广州大学地理科学与遥感学院, 广东广州 510006
2.
南方海洋科学与工程广东省实验室(珠海), 广东珠海 519082
3.
广东省生态气象中心, 广东广州 510641

基金项目:

广州市基础研究计划市校(院)企联合资助项目 2024A03J0086

国家自然科学基金 42476021

国家自然科学基金重大研究计划重点项目 92158204

广东省基础与应用基础研究基金自然科学基金项目 2025A1515010956

详细信息

通讯作者:
肖福安，男，安徽省人，副教授，主要从事物理海洋和气候变化研究。E-mail：fuanxiao@gzhu.edu.cn

中图分类号: X513
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- 文章访问数: 2
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- 被引次数: 0
出版历程
- 收稿日期: 2024-07-01
- 修回日期: 2025-07-18
- 网络出版日期: 2026-03-14
- 刊出日期: 2026-02-20

Temporal and Spatial Variation of PM_2.5 Concentration in the Pearl River Delta Urban Agglomeration from 2000 to 2022

1.
School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
2.
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, China
3.
Guangdong Ecological Meteorology Center, Guangzhou 510641, China

摘要

摘要: 过往基于观测站点对PM_2.5浓度时空特征的揭示并不完整，本文评估了珠江三角洲城市群PM_2.5遥感反演数据的准确性，并基于遥感反演数据进一步探讨了PM_2.5年均、季均和月均浓度的时空格局变化及影响因素。2015—2022年的校验结果表明，遥感反演数据在珠三角城市群的准确度较高(R²：0.964~0.998)，通过分段线性回归分析发现，2000—2022年PM_2.5年均浓度在时间上呈现“上升-停滞-下降”的阶段特征，转折点为2004年和2013年(R²=0.943 6)，主要影响因素为政府政策；空间上呈现“中心高，四周低”的分异规律，广佛(广州佛山)交界处基本为PM_2.5污染的重心，污染最轻的区域为珠海、惠州东南部和江门南部。此外，扩展经验正交函数(EEOF)和小波分析结果表明，PM_2.5浓度还具有显著的季节变化规律，具体表现为“秋冬高，春夏低”。以高污染时期(2003—2014年)为例，PM_2.5月均浓度值在全年内呈现“先降后升”的趋势，1月最高(62.55 μg·m^-3)，7月最低(22.53 μg·m^-3)，降水是其主要影响因素；空间上，PM_2.5浓度高的月份(10月—次年1月)，污染范围向南扩散至江门和珠海，且不同地区的污染浓度差距较大。该研究结果可以为城市群的大气污染联防联控提供理论支撑。
- PM_2.5 /
- 遥感数据 /
- 时空分布 /
- 珠江三角洲
Abstract: Previous studies based on observation stations have not fully revealed the spatiotemporal characteristics of PM_2.5 concentrations. This paper evaluates the accuracy of PM_2.5 remote sensing inversion data in the Pearl River Delta (PRD) urban agglomeration and further explores the spatiotemporal patterns and influencing factors of annual, seasonal, and monthly average PM_2.5 concentrations based on the remote sensing inversion data. Validation from 2015 to 2022 confirms the high accuracy of the remote sensing data (R²: 0.964-0.998). Analysis based on the inversion data from 2000 to 2022 reveals a "rise-stagnation-decline" trend in annual average PM_2.5 concentrations, with key turning points in 2004 and 2013 (R²=0.943 6). The primary influencing factors are government policies. Spatially, PM_2.5 concentrations exhibit a distinct"high in the center, low in the periphery"pattern. The Guangzhou-Foshan border forms the core pollution zone for PM_2.5, while the regions with the lowest pollution levels are identified in Zhuhai, southeastern Huizhou, and southern Jiangmen. Seasonally, PM_2.5 concentrations follow a"high in autumn and winter and low in spring and summer"pattern. Taking the high pollution period (2003-2014) as an example, monthly average concentrations of PM_2.5 showed a trend of"falling first and then rising" throughout the year, with the highest in January (62.55 μg·m^-3) and the lowest in July (22.53 μg·m^-3), with precipitation being the main influencing factor. Spatially, from October to January, the area of high PM_2.5 concentrations expands southward to encompass Jiangmen and Zhuhai, with significant regional variations. The findings of this study provide theoretical basis for the joint prevention and control of air pollution in urban agglomerations.
- PM_2.5 /
- remote sensing data /
- spatial and temporal distribution /
- Pearl River Delta

HTML全文

图 1 研究区域及站点分布图

注：基于审图号GS(2020)4630号(自然资源部监制)制图，底图无修改。

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图 2 2015—2022年PM_2.5日均浓度站点观测值与数据集反演值的散点图

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图 3 2015—2022年各站点PM_2.5日均浓度观测值与数据集反演值的拟合结果

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图 4 2000—2022年PM_2.5年均浓度

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图 5 PM_2.5年均浓度时空变化

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图 6 2000—2022年PM_2.5季均浓度EEOF分析的第一模态结果

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图 7 2000—2022年PM_2.5季均浓度EEOF分析的第二模态结果

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图 8 2000—2022年珠三角PM_2.5季均浓度EEOF第二模态的时间系数的小波分析结果

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图 9 珠三角各城市PM_2.5月均浓度变化

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图 10 2003—2014年PM_2.5月均浓度时空分布

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