Exploring Air Pollution Characteristics from Spatio-Temporal Perspective: A Case Study of the Top 10 Urban Agglomerations in China

Jul 9, 2026

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Rapid urbanization has intensified air pollution, posing a major public health risk. Many cities monitor six pollutants, and earlier work used PCA with cluster analysis to find similar city patterns, yet ignored spatial and temporal effects. This study integrates GTWPCA and STCA to capture spatio-temporal heterogeneity, applying the method to air pollution data for China’s top 10 urban agglomerations during 2016–2021. Results show GTWPCA improves local interpretation, STCA emphasizes spatial–temporal coupling, and the combined approach supports spatio-temporal characterization for control actions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages1

Reading Time3 min

Introduction

Background and public health impact

Conventional PCA–CA framework

Need to incorporate spatial and temporal effects

Proposed spatio-temporal approach

Environmental Research 224 (2023) 115512 Contents lists available at ScienceDirect Environmental Research journal [homepage: www.elsevier.com/locate/envres](homepage: www.elsevier.com/locate/envres) | Exploring air pollution characteristics from spatio-temporal perspective: A case study of the top 10 urban agglomerations in China Jiakuan Hana, Yi Yanga, Xiaoyue Yanga, *, Dongchao Wang b, Xiaolong Wang a, Pengqi Sun a a School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang, 222005, China b College of Geography and Environment, Shandong Normal University, Jinan, 250000, China | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Handling Editor: Aijie Wang | | Air pollution has become a global public health risk factor as rapid urbanization advances. To observe the air pollution situation, air monitoring stations have been established in many cities, which record six air pollutants. Previous studies have identified cities exhibiting similar air pollution characteristics by combining principal component analysis (PCA) with cluster analysis (CA). However, spatial and temporal effects were neglected. In this paper, we focus on the combination of GTWPCA and STCA, which fully incorporates spatio-temporal effects. It is then applied to air pollution data from the top 10 urban agglomerations in China during 2016–2021. Key experimental findings include: 1. GTWPCA provides a more detailed interpretation of local variation than PCA. 2. Compared with CA, STCA highlights the coupling effect in the spatial and temporal dimensions. 3. The combination of GTWPCA and STCA captures similar air pollution characteristics from spatio-temporal perspectives, which has the potential to help environmental authorities take further action to control air pollution. | | Keywords: GTWPCA STCA Air pollution Spatiotemporal analysis Urban agglomerations | | | 1. Introduction Air pollution is a complex mixture of multiple components. Some studies have shown that exposure to air pollution increases mortality and morbidity from cardiovascular and respiratory diseases and lung cancer, and shortens life expectancy (Boogaard et al., 2019; Brunekreef and Holgate, 2002). With the unprecedented global urbanization and rapid industrialization, many cities around the world are suffering from serious air pollution (Baldasano et al., 2003; Mohtar et al., 2018). In recent decades, China’s economy has grown explosively in recent decades, and cities have developed extremely rapidly to form more mature urban agglomerations. The severity of air pollution in urban agglomerations is accentuated by the concentration of larger populations and economic activities. Over the years, China has been taking a number of proactive measures to improve air quality. For example, air quality standards have been promulgated, a legal system for environmental protection has been established, etc (Yue et al., 2020; Zhang et al., 2016). To improve air quality with targeted measures, many scholars in China and abroad have done a lot of relevant research. The commonly adopted research methodological framework is to combine principal component analysis (PCA) and cluster analysis (CA). In particular, PCA is a statistical technique that simplifies the data set by reducing the dimensionality. The components extracted by PCA are then analyzed to identify the main air pollutants. On the other hand, CA is a technique that captures the inherently similar features of data, which achieve the classification of air pollution with similar characteristics. Various researches have been carried out based on a research framework combining PCA and CA (Lau et al., 2009; Nazir et al., 2011; Zhong et al., 2014). In particular, Pires discriminates similar air pollution behavior by PCA and CA for the concentrations of SO2, PM10, CO, NO2 and O3 (Pires et al., 2008a, 2008b). However, the above studies have neither considered spatial diff

FAQs

Why is air pollution research framed as a spatio-temporal problem in this study?

Air pollution varies across locations and changes over time, and traditional PCA and CA frameworks do not account for either spatial differences or temporal evolution.

What is the main methodological contribution of this paper?

The study combines GTWPCA and STCA to incorporate spatio-temporal effects, enabling more detailed interpretation and capturing coupling in space and time.

How is the proposed approach validated or demonstrated?

It is applied to air pollution data from the top 10 urban agglomerations in China for 2016–2021, with findings comparing GTWPCA to PCA and STCA to CA.

Jul 9, 2026

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