Elsevier

Journal of Cleaner Production

Volume 232, 20 September 2019, Pages 692-704
Journal of Cleaner Production

Quantifying the spatial heterogeneity influences of natural and socioeconomic factors and their interactions on air pollution using the geographical detector method: A case study of the Yangtze River Economic Belt, China

https://doi.org/10.1016/j.jclepro.2019.05.342Get rights and content

Highlights

  • The relations between air pollution and natural and economic factors are found.

  • The novel geographical detector method is applied in this study.

  • PM2.5 factor has the biggest q value, implying the primary pollutant.

  • Interaction of factors plays a more important role in affecting air pollution.

Abstract

This study takes the Yangtze River Economic Belt as a study area and analyzes the impacts of natural and socioeconomic factors on air pollution based on a dataset of urban air quality monitoring data in 2015 and meteorological and economic statistical data. We first apply the grey relational degree to test for the quantitative relationships between the natural and socioeconomic factors and air pollution. We then employ a novel method, specifically, the geographical detector, from the perspective of spatial stratified heterogeneity to reveal the potential impacts and interaction impacts of the natural and socioeconomic factors on air pollution. The results are as follows. (1) The grey relational degree results reveal that all factors in the topographical and meteorological layer, pollution sources layer, economic development layer, and urbanization layer have high relational degrees, indicating that these factors are closely correlated with air pollution. (2) The factor detector analysis reveals that the PM2.5 factor has the biggest q value, indicating that it is the primary contributor to air pollution, followed by PM10 and elevation. (3) The interaction detector analysis reveals that the interaction of two factors plays a more important role in influencing air pollution than does each factor individually. Moreover, the interactions between pair factors of pollution sources are the strongest. (4) The risk detector analysis reveals that elevation and precipitation are negatively correlated with air pollution, whereas pollution and urbanization factors are positively correlated with air pollution. (5) Finally, two leading impact areas for atmospheric pollution, namely, the Yangtze River Delta urban agglomeration and the Wuhan metropolitan area are predominantly attributed to the combination of natural and urbanization factors, whereas Yunnan and Guizhou are the least impact areas for atmospheric pollution because of their topographical and meteorological factors.

Introduction

China has experienced large-scale industrialization and rapid urbanization in recent years. As a consequence, China has suffered from serious environmental degradation problems, notably, air pollution characterized by extensive haze-fog (Huang et al., 2014). For example, in 2013, northern China was attacked by a persistent and extensive haze-fog pollution, affecting more than 800 million people. It is regarded as one of the most severe air pollution events since the last century (Wang et al., 2014), causing huge economic losses valued at approximately 23 billion yuan.

The Yangtze River Economic Belt, one of the most important ecological zones, connects to three national urban agglomerations, namely, Sichuan-Chongqing urban agglomeration, the middle reaches of the Yangtze River delta urban agglomeration, and the lower reaches of the Yangtze River delta urban agglomeration, including Jiangsu province, Zhejiang province and Shanghai, crossing east and west China. The Yangtze River Economic Belt is also one of the core economic zones, accounting for more than 40% of GDP and population of China. However, it has been heavily influenced by anthropogenic and industrial activities (Chen et al., 2014). Hence, the environmental and ecological protection of the Yangtze River is the top priority for China's governments. In 2014, the State Council implemented a national outlook to protect the sustainable development of the region. Recently, President Xi re-emphasized the work to protect the environmental and ecological conditions of the Yangtze River Economic Belt. Hence, it has a very important geographical position and is of a great strategic significance. However, the Yangtze River Economic Belt has also faced a big challenge of the severe air pollution. Specifically, the days of haze-fog events occurring in the lower-middle reaches of the Yangtze River Delta River amount to more than 100, implying that environmental quality has degraded greatly so far (Wang et al., 2015). The worsening air pollution not only has posed a great threat to health of local people, but also has extremely affected industrial production, transportation, and socio-economic sustainable development (Guan et al., 2014; Tian et al., 2018).

To address the severe air pollution problem, a set of questions must urgently be answered. Specifically, what type of spatial pattern does air pollution along the Yangtze River Economic Belt exhibit? What natural and socioeconomic factors degrade local air quality? Of these factors, which is the primary contributor to the degradation of air quality and which has a great interaction effect? The answers to the aforementioned questions are not only the primary objectives of this study but are also the keys to solving worsening air quality of the Yangtze River Economic Belt. Air pollution has been hotly debated in China. There are a growing number of studies aimed at understanding the factors that contribute to China's air pollution problem. Regarding natural factors, a number of studies report that meteorological factors play important roles in affecting air pollution (Liu et al., 2017), including temperature, precipitation, humidity, atmospheric pressure, and wind speed, etc. (Li et al., 2014; Sabetghadam and Ahmadi-Givi, 2014; Wang et al., 2016a, Wang et al., 2016b; Lu et al., 2017). In addition, some studies conclude that topographical conditions, for example, altitude affects the diffusion and clustering of atmospheric pollutants (Alvarez et al., 2013). Regarding anthropogenic activities related to socioeconomic factors, the existing literature states that population density, industrial production activities, fossil fuel consumption, and automobile exhaust are the primary sources of air pollution (Hixson et al., 2012; Lin and Wang, 2016; Ma et al., 2016; Wu et al., 2016; Chen et al., 2017; Jiang et al., 2018). Moreover, urbanization levels, economic development levels, industrial structure, energy use mix, energy efficiency, and land use type, etc., can also affect urban air quality (Fang et al., 2015; Yang et al., 2018a, Yang et al., 2018b; Yuan et al., 2015; Chen et al., 2014). These existing studies offer many insights into understanding what determines air pollution of the Yangtze River Economic Belt from the perspective of natural and socioeconomic factors.

Although many studies discuss the spatiotemporal variation of air pollution and its influencing factors, very few researchers have analyzed the influences on air pollution from the perspective of meteorological factors, pollution sources and socioeconomic factors (Liu et al., 2017). On the other hand, most studies have focused more on the time series analysis of air pollution than spatial analysis from a geography perspective. Although some studies considered the spatial dependence of air pollution (Liu et al., 2017; Cheng et al., 2017), they still ignored the spatial differentiation of air pollution at urban agglomeration levels, and they lacked the analysis of the interaction effects of many factors, which may have resulted in biased and incomplete conclusions. Regarding the environmental-related problems of the Yangtze River Economic Belt, existing studies focused more on research from a macro perspective, including sustainable development research and environmental quality evaluation systems (see, among others, Xie et al., 2016; Guo et al., 2017; He et al., 2017). Moreover, some studies discussed the spatiotemporal variations of specific cities, for example, Wuhan or the Yangtze River Delta urban agglomeration (Wang et al., 2013; Yang et al., 2017). However, very few studies took the Yangtze River Economic Belt as the study area to analyze air pollution from the perspective of natural and socioeconomic factors. Although some studies, based on a spatial dependence perspective, applied a spatial lag model to analyze the socioeconomic factors of air pollution of the Yangtze River Economic Belt (Yang and Wang., 2017), these might also suffer from two potential disadvantages. One is that the study may lack the analysis of spatial differentiation and interaction effects. Specifically, because air pollution of the Yangtze River Economic Belt varies from city to city, it is of great significance to discover to what extent potential factors might explain this spatial differentiation and the interaction effects. Classical econometrics posits that space is homogeneous, which could result in biased conclusions.

Hence, we attempt to fill the mentioned-above gaps in this study. The contribution of our work may be threefold. (1) The first is to introduce a newly-developed method, the geographical detector method, to analyze air pollution of the Yangtze River Economic Belt, since the novel method has a few advantages. Specifically, it is capable of handling the dependent variable and independent variables and finding their similarity in the space, and even of detecting whether the interaction effect happens between two factors (Wang et al., 2010; Yang et al., 2018a). (2) The second is that with respect of the measurement of air pollution, most studies focus on a single pollutant, for example, PM2.5. However, the air pollution of the Yangtze River Economic Belt may be attributed to other pollutants. This is because the primary pollutant is not merely PM2.5, but varies with time and place. On the other hand, people are usually exposed to multiple pollutants (Zhan et al., 2018). To conclude, a single pollutant, for example, PM2.5, SO2, and NO2 cannot be a good indicator to comprehensively describe the air quality of the Yangtze River Economic Belt. We hence consider a comprehensive indicator, air quality index (AQI), which is newly-developed by the ministry of environmental protection of China, to evaluate its air quality. (3) To the best of our knowledge, little attention of the analysis of the effects of natural factors, pollution sources and socio-economic factors on air quality of the Yangtze River Economic Belt has been received. The third contribution is that we consider all possible influencing factors to quantify their impacts on air pollution.

Overall, this study takes 110 cities of the Yangtze River Economic Belt as the study area, employs AQI to evaluate its air quality, and analyzes its natural meteorological factors and socio-economic factors. Technically speaking, we first apply the grey relational degree method to analyze the correlation coefficients between air pollution and its natural and socio-economic factors, and then employs the geographical detector method to find the main contributors to the air quality index of the Yangtze River Economic Belt. Environmental protection of the Yangtze River Economic Belt has been the top priority in China. Otherwise, it may cause ecological disasters, which may have a great impact on large area of China. Hence, the study is not only of great significance for meeting the demand for air quality improvements of the Yangtze River Economic Belt, but also provides a new insight into implementing effective and efficient urban air pollution prevention and control policies.

Section snippets

Methods and data sources

This section first introduces an indicator for air pollution, namely, air quality index (AQI), and the natural meteorological and socioeconomic air pollution influencing factors. Then, it presents the main analysis methods, namely, the grey relational degree and the geographical detector method. Lastly, data sources are presented.

Empirical results and discussions

This section first presents the results of the grey relational degree analysis, and then analyzes the results of the geographical detector model, as well as discusses the differences between our results and previous studies.

Conclusions and policy implications

Air pollution has become one of the great challenges for sustainable development in China, having received much attention. Hence, the primary objective of this study is to analyze the impacts of natural and socioeconomic factors and their interactions on air pollution in the Yangtze River Economic Belt by means of a novel spatial stratified heterogeneity analysis method, the geographical detector method. This method has a few advantages compared with the classical linear regression. Empirical

Acknowledgements:

This research was funded by the National Natural Science Foundation of China [41761021, 41871155], the Natural Science Foundation of Guangdong Province [2018B030312004], and the Humanities and Social Science Research Program of the Ministry of Education [17YJC790061], and the Zhejiang Provincial Natural Science Foundation of China [LY19G030013].

References (50)

  • D. Maddison

    Environmental Kuznets curves: a spatial econometric approach

    Environ. Econ. Manag.

    (2006)
  • F. Song et al.

    What drives the change in China's energy intensity: combining decomposition analysis and econometric analysis at the provincial

    Energy Policy

    (2012)
  • X. Tian et al.

    Economic impacts from PM2.5 pollution-related health effects in China's road transport sector: a provincial-level analysis

    Environ. Int.

    (2018)
  • H. Wang et al.

    Chemical composition of PM2.5 and meteorological impact among three years in urban Shanghai, China

    J. Clean. Prod.

    (2016)
  • H. Wang et al.

    A study of the meteorological causes of a prolonged and severe haze episode in January 2013 over central-eastern China

    Atmos. Environ.

    (2014)
  • J. Wang et al.

    Contamination characteristics and possible sources of PM10 and PM2.5 in different functional areas of Shanghai, China. Atmos

    Environ. Times

    (2013)
  • S. Wang et al.

    Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: an empirical analysis based on provincial panel data

    Renew. Sustain. Energy Rev.

    (2016)
  • Z. Wang et al.

    Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim urban agglomeration

    Chemosphere

    (2016)
  • Y.Y. Xie et al.

    Spatiotemporal variations of PM2.5 and PM10 concentrations between 31 Chinese cities and their relationships with SO2, NO2, CO and O3

    Particuology

    (2015)
  • H. Xie et al.

    Measuring the sustainable performance of industrial land utilization in major industrial zones of China

    Technol. Forecast. Soc. Change

    (2016)
  • S.C. Xu et al.

    Regional differences in impacts of economic growth and urbanization on air pollutants in China based on provincial panel estimation

    J. Clean. Prod.

    (2019)
  • X. Xu et al.

    Impacts of urbanization and air pollution on building energy demands—beijing case study

    Appl. Energy

    (2018)
  • D. Yang et al.

    Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China

    Environ. Pollut.

    (2018)
  • D. Yang et al.

    Yang, H. Global distribution and evolvement of urbanization and PM2.5 (1998–2015)

    Atmos. Environ.

    (2018)
  • D. Zhan et al.

    The driving factors of air quality index in China

    J. Clean. Prod.

    (2018)
  • Cited by (0)

    View full text