2019－2023年粤港澳大湾区NO2浓度变化的自然主控因子解析

doi:10.16258/j.cnki.1674-5906.2025.04.004

生态环境学报 ›› 2025, Vol. 34 ›› Issue (4): 534-547.DOI: 10.16258/j.cnki.1674-5906.2025.04.004

• 研究论文【环境科学】 • 上一篇下一篇

2019－2023年粤港澳大湾区NO₂浓度变化的自然主控因子解析

郭铭彬¹(), 龚建周¹, 王丽娟²^,^*(), 王时宽¹

1.广州大学地理科学与遥感学院，广东广州 510006
2.浙江省农业科学院农村发展研究所，浙江杭州 310021

收稿日期:2024-11-16 出版日期:2025-04-18 发布日期:2025-04-24
通讯作者: ^*王丽娟。E-mail: wanglj1981@126.com
作者简介:郭铭彬（2002年生），男，硕士研究生，主要研究方向为生态遥感与大气环境。E-mail: guomb61127@126.com
基金资助:
国家自然科学基金重点资助项目(4243000531);广东乡村地域系统野外科学观测研究站(2021B1212050026)

Analysis of the Natural Dominant Factors Driving NO₂ Concentration Changes in the Guangdong-Hong Kong-Macao Greater Bay Area from 2019 to 2023

GUO Mingbin¹(), GONG Jianzhou¹, WANG Lijuan²^,^*(), WANG Shikuan¹

1. School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, P. R. China
2. Institute of Rural Development, Zhejiang Academy of Agriculture Science, Hangzhou 310021, P. R. China

Received:2024-11-16 Online:2025-04-18 Published:2025-04-24

摘要/Abstract

摘要： 基于Sentinel-5P卫星提供的二氧化氮对流层柱浓度数据（NRTI/L3_NO₂），结合气象数据、NDVI和陆表温度数据，采用Sen趋势分析、Mann-Kendall检验等方法，并辅以地理探测器与时空地理加权回归模型（GTWR），解析2019－2023年粤港澳大湾区NO₂柱浓度时空变化与自然驱动机制。结果显示：1）年际变化上，2021年NO₂柱浓度达到峰值，2020年为最低，季节性变化上冬季浓度最高，夏季最低，空间分布呈“中间高、四周低”的特点；2）Sen年趋势分析表明，广佛交界、深圳西部、肇庆等地NO₂浓度上升，珠海、江门、澳门等地下降；Mann-Kendall检验显示，广州北部与肇庆为显著增长区；3）地理探测器分析表明，风速、温度、湿度和气压是主要影响因子，降水和太阳辐射影响较弱；湿度与风速、湿度与温度的交互作用显著，非线性增强效应表现在气压、降水与其他因子的交互中；4）GTWR模型分析显示，风速、温度和陆表温度对NO₂浓度存在正向影响，广佛与深圳尤为显著；气压、湿度与植被指数对其存在负向影响，江门与珠海更为明显；降水与太阳辐射的影响复杂，空间差异较大。该研究可为理解大湾区NO₂污染的时空变化及自然驱动机制提供参考，助力空气质量管理和污染控制策略的制定。

关键词: NO₂柱浓度, 时空变化特征, 谷歌地球引擎（GEE云平台）, 影响因素, 粤港澳大湾区

Abstract:

To investigate the recent changes in NO₂ column concentrations and their natural dominant factors in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), this study utilized near real-time tropospheric NO₂ column concentration data (NRTI/L3_NO₂) provided by the Sentinel-5P atmospheric monitoring satellite, along with meteorological data, NDVI data, and land surface temperature data. Methods such as Sen’s trend analysis, Mann-Kendall tests, geographical detector models, and the Geographically and Temporally Weighted Regression (GTWR) model were employed to analyze the spatiotemporal variations and natural driving mechanisms of NO₂ column concentrations in GBA from 2019 to 2023. The main findings are as follows: 1) At the annual scale, NO₂ column concentrations peaked in 2021 and were lowest in 2020. Seasonally, the concentrations were the highest in winter and the lowest in summer. Spatially, a “high in the center and low in the periphery was observed. 2) Sen’s trend analysis indicated an increasing trend in NO₂ concentrations in areas such as the Guangzhou-Foshan border, western Shenzhen, and Zhaoqing, while decreasing trends were observed in Zhuhai, Jiangmen, Macao, and parts of Zhongshan. The Mann-Kendall test results showed that significant increases were mainly concentrated in northern Guangzhou and Zhaoqing, while other areas exhibited minimal changes, although seasonal differences were pronounced. 3) The geographical detector analysis revealed that wind speed, temperature, humidity, and air pressure were the primary explanatory factors, whereas precipitation and solar radiation had weaker effects. The interaction between humidity and wind speed, as well as between humidity and temperature, was the most significant, with nonlinear enhancement effects primarily reflected in interactions involving air pressure, precipitation, and other factors. 4) The GTWR model analysis showed that wind speed, temperature, and land surface temperature had positive impacts on NO₂ concentrations, particularly in Guangzhou-Foshan and Shenzhen. Conversely, air pressure, humidity, and NDVI have negative effects, particularly in Jiangmen and Zhuhai. The effects of precipitation and solar radiation are more complex, with significant spatial variations. This study provides insights into the natural driving mechanisms of NO₂ pollution and its spatiotemporal variations in the GBA over the past five years, offering valuable support for the formulation of targeted air quality management and pollution control strategies.

Key words: NO₂ column concentration, temporal and spatial change characteristics, Google Earth Engine (GEE Cloud platform), Influencing factors, Guangdong-Hong Kong-Macao Greater Bay Area

中图分类号:

郭铭彬, 龚建周, 王丽娟, 王时宽. 2019－2023年粤港澳大湾区NO₂浓度变化的自然主控因子解析[J]. 生态环境学报, 2025, 34(4): 534-547.

GUO Mingbin, GONG Jianzhou, WANG Lijuan, WANG Shikuan. Analysis of the Natural Dominant Factors Driving NO₂ Concentration Changes in the Guangdong-Hong Kong-Macao Greater Bay Area from 2019 to 2023[J]. Ecology and Environment, 2025, 34(4): 534-547.

图/表 21

图1 粤港澳大湾区区位图基于自然资源部标准地图服务网站下载的审图号为GS（2024）0650号标准地图制作；底图边界无修改

Figure 1 Location map of Guangdong-Hong Kong-Macau Greater Bay Area (GBA)

表1 数据主要信息、来源及相应产品介绍

Table 1 Data main information, sources and corresponding product descriptions

数据		分辨率	数据来源	产品介绍
NO₂数据	对流层柱浓度	1 km	GEE云平台（https://earthengine.google.com/）	选用Sentinel-5P Near Real-Time NO₂数据集，该影像集提供每天（近实时）的大气污染数据
气象数据	年平均风速（WSD）	10 km		选用ERA5-land数据集，该影像集通过重演 ECMWF ERA5气候再分析的陆地部分生成，拥有更高的空间分辨率
	年平均气温（TEMP）
	年平均湿度（RH）
	年平均气压（PRE）
	年平均降水（PRCP）
	年平均太阳辐射度（DNI）
植被数据	归一化植被指数（NDVI）	500 m		选用MODIS产品中MOD13A2 16d影像集，该影像集将16 d每个像素位置的植被指数（NDVI）进行合成而得
下垫面状况	陆表温度（LST）	1 km		选用MODIS产品中的MOD11A2 V6影像集，为8 d平均地表温度合成数据

表2 两个自变量对因子变量交互作用类型

Table 2 Type of interaction between two independent variables and factor variables

判据	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)]	单因子非线性减弱
q(X₁∩X₂)>Max[q(X₁), q(X₂)]	双因子增强
q(X₁∩X₂)=q(X₁)+q(X₂)	独立
q(X₁∩X₂)>q(X₁)+q(X₂)	非线性增强

图2 粤港澳大湾区9市NO2地面浓度与NO2柱浓度的相关性分析

Figure 2 The correlation analysis between surface NO2 concentration and NO2 column density in the GBA’s 9 cities

图3 粤港澳大湾区各区域NO2柱浓度均值逐年变化规律

Figure 3 Yearly variation of NO2 column concentrations in different regions of the GBA

图4 粤港澳大湾区NO2柱浓度空间分布年际变化

Figure 4 Annual variation in spatial distribution of NO2 column concentration in the GBA

图5 粤港澳大湾区各区域NO2柱浓度均值月变化规律

Figure 5 Monthly variation of NO2 column concentrations in different regions of the GBA

图6 粤港澳大湾区NO2柱浓度空间分布月际变化

Figure 6 Monthly variation in spatial distribution of NO2 column concentration in the GBA

图7 粤港澳大湾区NO2柱浓度年际变化趋势

Figure 7 Trend of NO2 column concentration in the GBA

图8 粤港澳大湾区NO2浓度与社会经济因子的空间关联

Figure 8 Spatial correlation between NO2 concentration and socioeconomic factors in GBA

表3 因子探测逐年q值变化

Table 3 The factor detects the change of q value year by year

年份	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈
2019	0.684	0.672	0.297	0.603	0.183	0.466	0.444	0.167
2020	0.600	0.632	0.490	0.588	0.143	0.523	0.402	0.211
2021	0.630	0.675	0.378	0.613	0.131	0.539	0.358	0.215
2022	0.638	0.665	0.194	0.611	0.114	0.513	0.488	0.249
2023	0.607	0.624	0.804	0.595	0.123	0.444	0.460	0.181
多年均值	0.645	0.683	0.613	0.619	0.100	0.519	0.405	0.233

图9 因子交互探测逐年变化图上述因子含义与2.4.1相同

Figure 9 The factor detects the change of q value year by year

表4 各因子协方差值

Table 4 Covariance of each factor

因子	WSD	TEMP	RH	PRE	PRCP	NDVI	LST	DNI
VIF	2.02	6.60	1.40	5.44	1.34	2.43	3.07	2.28

图10 风速回归系数空间分布图

Figure 10 The spatial distribution map of wind speed regression coefficients

图11 温度回归系数空间分布图

Figure 11 The spatial distribution map of temperature coefficients

图12 湿度回归系数空间分布图

Figure 12 The spatial distribution map of humidity regression coefficients

图13 气压回归系数空间分布图

Figure 13 The spatial distribution map of air pressure regression coefficients

图14 降水回归系数空间分布图

Figure 14 The spatial distribution map of precipitation regression coefficients

图15 太阳辐射回归系数空间分布图

Figure 15 The spatial distribution map of solar radiation regression coefficients

图16 归一化植被指数回归系数空间分布图

Figure 16 The spatial distribution map of normalized vegetation index regression coefficients

图17 陆表温度回归系数空间分布图

Figure 17 The spatial distribution map of land surface temperature regression coefficients

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2019－2023年粤港澳大湾区NO₂浓度变化的自然主控因子解析

Analysis of the Natural Dominant Factors Driving NO₂ Concentration Changes in the Guangdong-Hong Kong-Macao Greater Bay Area from 2019 to 2023

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