Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities

Jin, Jie-Qi; Du, Yue; Xu, Lijun; Chen, Zhaoyue; Chen, Jin-Jian; Wu, Ying; Ou, Chun Quan

doi:10.1016/j.envpol.2019.113023

Cited by 31 publications

(11 citation statements)

References 36 publications

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“…Moreover, our results found that with the increasing level of NO 2 in northern cities, concentrations of O 3 first decreased due to the titration reaction and then increased when concentrations of NO 2 became high. The future work mainly includes two directions: 1) in this study, only data on air pollutants from 31 municipalities and provincial cities in China were used; thus, in future work, it is recommended that data from individual sites across provinces could also be collected to enrich the case study dataset before being processed by the triclustering method; 2) since spatio-temporal heterogeneity also exists in relationships among air pollutants and their driving forces, e.g., meteorological and socio-economic factors (Zhao et al, 2018;Jin et al, 2019), in future work, it is thus recommended to apply the tri-clustering based analysis to the visual exploration of the spatio-temporal heterogeneity of air pollutants and their driving factors.…”

Section: Discussionmentioning

confidence: 99%

Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

2022

Front. Earth Sci.

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Severe air pollution in China has become a challenging issue because of its adverse health effects. The distribution of air pollutants and their relationships exhibits spatio-temporal heterogeneity due to influences by meteorological and socioeconomic factors. Investigation of spatio-temporal variations of criteria air pollutants and their relationships, thus, helps understand the current status and further assist pollution prevention and control. Even though many studies have been conducted, relationships among pollutants are non-linear due to complicated chemical reactions and were difficult to model by linear analyses in previous studies. Here, we presented a tri-clustering–based method, the Bregman cuboid average tri-clustering algorithm with I-divergence (BCAT_I), to explore spatio-temporal heterogeneity of air pollutants and their relationships in China. Concentrations of PM2.5, PM10, CO, SO2, NO2, and O3 in 31 provincial cities in 2021 were used as the case study dataset. Results showed that air pollutants except O3 exhibited spatial and seasonal variations, i.e., low in summer in southern cities and high in winter in northern cities. Variations of PMs were more similar to those of CO than other pollutants in southern cities in 2021. Results also found that relationships among these air pollutants were heterogeneous in different regions and time periods in China. Moreover, with the increasing level of NO2 from summer to winter in northern cities, concentrations of O3 first decreased and then increased. This is because the response of O3 to NO2 was negative at the low pollution level due to the titration reaction, which, however, changed to positive when concentrations of NO2 became high.

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Section: Discussionmentioning

confidence: 99%

Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

2022

Front. Earth Sci.

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“…Precision ( PR ): The precipitation process is an important way to remove particulate pollutants [ 45 ]. In this paper, the average value of annual precipitation recorded by urban meteorological monitoring stations is used to measure urban precipitation.…”

Section: Methodology and Materialsmentioning

confidence: 99%

Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China

Zhang

Xuan

2021

IJERPH

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PM2.5 pollution has produced adverse effects all over the world, especially in fast-developing China. PM2.5 pollution in China is widespread and serious, which has aroused widespread concern of the government, the public and scholars. This paper evaluates the evolution trend and spatial pattern of PM2.5 pollution in China based on the data of 281 prefecture-level cities in China from 2007 to 2017, and reveals the pollution situation of PM2.5 and its relationship with industrial restructuring and technological progress by using spatial dynamic panel model. The results show that China’s PM2.5 pollution has significant path dependence and spatial correlation, and the industrial restructuring and technological progress have significant positive effects on alleviating PM2.5 pollution. As a decomposition item of technological progress, technical change effectively alleviates PM2.5 pollution. Another important discovery is that the interaction between industrial restructuring and technological progress will aggravate PM2.5 pollution. Finally, in order to effectively improve China’s air quality, while advocating the Chinese government to pursue high-quality development, this paper puts forward a regional joint prevention mechanism.

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“…Coal burning was positively correlated with PM 2.5 concentrations [47]. Some scholars had discovered that the proportion of the secondary industry and the total emissions of industrial sulfur also contributed to the PM 2.5 concentrations [55]. Although the progress of production technology, the adjustment of industrial structure and the improvement of energy intensity can reduce PM 2.5 pollution [32], [56]; the production technology of many cities is relatively backward, the industrial structure is dominated by heavy industry, and the energy efficiency is not high, all of which need to be improved.…”

Section: A Explanation For Direct and Indirect Effects Of Multi-dimementioning

confidence: 99%

The Impact of Multi-Dimensional Urbanization on PM₂.₅ Concentrations in 261 Cities of China

Yang

Zhu

et al. 2020

IEEE Access

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Rapid urbanization has caused serious PM 2.5 pollution. The existing researches mainly focus on the contribution of one aspect of urbanization to PM 2.5 concentrations. However, few studies have comprehensively considered the direct and indirect effects of multi-dimensional urbanization on PM 2.5 concentrations. In order to identify the impact of multi-dimensional urbanization on PM 2.5 concentrations, the entropy-right method, the spatial lag model, spatial error model and spatial Durbin model were used to investigate the relationships between multi-dimensional urbanization and PM 2.5 concentrations in 261 cities of China in 2016. Results demonstrated that the spillover effect of PM 2.5 concentrations between different cities was significant. Economic urbanization had the strongest direct effect on PM 2.5 concentrations. Social urbanization and ecological urbanization had a significantly negative impact on PM 2.5 concentrations in local cities. However, the biggest indirect effect on PM 2.5 concentrations in adjacent cities was social urbanization, followed by land urbanization. The direct and indirect effects of population urbanization on PM 2.5 concentrations were non-significant and the least. The findings from this result analysis and discussion can be used as a theoretical basis to provide some policy suggestions for China's air governance and sustainable urban development. INDEX TERMS PM 2.5 concentrations, multi-dimensional urbanization, spillover effects, spatial regression model.

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Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities

Cited by 31 publications

References 36 publications

Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China

The Impact of Multi-Dimensional Urbanization on PM₂.₅ Concentrations in 261 Cities of China

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