Routes and clustering features of PM2.5 spillover within the Jing-Jin-Ji region at multiple timescales identified using complex network-based methods

Li, Huajiao; Qi, Yajie; Li, Chao; Liu, Xueyong

doi:10.1016/j.jclepro.2018.10.284

Cited by 44 publications

(17 citation statements)

References 34 publications

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“…Among the few works that concern the co-movement rather than its extreme co-movement of the haze, the spatial dependence of PM2.5 among cities was explored via spatial autocorrelation analysis. 35–37 It demonstrates that the period with severe haze (e.g. winter) also shows strong spatial dependence, so does the cities with severe haze (Shijiazhuang and Hengshui).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…34 The spatiotemporal variations of PM2.5 level, especially its spatial autocorrelation and spillover behavior at multiple timescales or different areas, are analyzed. 35–37…”

Section: Introductionmentioning

confidence: 99%

“…[29][30][31] Researchers have studied the variation, nonlinearity, and spatial correlation of PM2.5 concentration or its adjusted value in China. [32][33][34][35][36][37] For example, the longterm variation trend in satellite-derived PM2.5 concentrations and how it was related to pollutant emissions and meteorological parameters are investigated. 32 PM2.5 concentration from 2010 to 2015 is adjusted with respect to meteorological factors using statistical measures to quantify the severity of PM2.5 pollution in Beijing.…”

Section: Introductionmentioning

confidence: 99%

“…34 The spatiotemporal variations of PM2.5 level, especially its spatial autocorrelation and spillover behavior at multiple timescales or different areas, are analyzed. [35][36][37] Moreover, PM2.5 level and emission are closely related to China's economic development. The heterogeneous effect of democracy, political globalization, urbanization, and other socioeconomic factors on PM2.5 concentrations are studied using cross city or country data.…”

Section: Introductionmentioning

confidence: 99%

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The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

Zhang

2020

Science Progress

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Extreme haze was often observed at many locations in Beijing–Tianjin–Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014–2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing–Tianjin–Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze’s appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.

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Section: Conclusion and Discussionmentioning

confidence: 99%

“…34 The spatiotemporal variations of PM2.5 level, especially its spatial autocorrelation and spillover behavior at multiple timescales or different areas, are analyzed. 35–37…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

Zhang

2020

Science Progress

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“…rough complex networks, we can portray the consecutive transmission process and explore the dynamic transmission mechanisms of the correlation between investor attention and stock price [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

Guo

Feng

2019

Complexity

Self Cite

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e relationship between investor attention and stock prices has been a topic of interest in economics. Previous studies have shown that the correlation relationship between the two changes with time. However, there are few studies to explore the timevarying evolution of the relationship, as well as the transmission characteristics under important cycles. us, this paper is dedicated to discover the dynamic transmission characteristics of the correlation between investor attention and stock price. We selected the typical stocks of China's energy industry, PetroChina and Sinopec, as the research objects, as they occupy a large market share and are representative. And the transaction data and attention data are used to build investor attention indicator. In order to reproduce the dynamic transmission process of correlation at di erent cycles, sliding time window and complex network are applied. e results show that PetroChina and Sinopec stocks have a weakly negative correlation between investor attention and stock price from 2017 to 2018. However, from the perspective of di erent cycles, the correlation has time-varying characteristics. As the cycle grows, the types of transmission patterns of the ve consecutive days of correlation between the two become less, but the transmission intensity between the modes increases and the transition becomes more regular and inclined. In addition, by mining the important transmission modes and main transmission paths under important periods, we nd that the series modes of uncorrelated or weakly positive correlation for ve consecutive days dominate the transition of modes in the networks. Also, the closed loop formed by these two important modes and related modes is the main transmission path. ese ndings can reveal the rules of the typical stock market in China's energy industry and help investors with di erent investment cycle preferences make sound decisions.

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Regional‐level prediction model with difference equation model and fine particulate matter (PM_2.5) concentration data

Han

Lei

2021

Math Methods in App Sciences

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Accurate reporting and prediction of PM2.5 concentration is very important for improving public health. In this article, we use spectral clustering algorithm to cluster 15 cities in the Pearl River Delta. On this basis, we propose a special difference equation model, especially the use of nonlinear diffusion equations to characterize the temporal and spatial dynamic characteristics of PM2.5 propagation between and within clusters for real‐time prediction. For example, through the analysis of PM2.5 concentration data for 91 consecutive days in the Pearl River Delta, and according to different accuracy definitions, the average prediction accuracy of the difference equation model in all city clusters is 97% or 88%. The mean absolute error (MAE) of the forecast data for each urban agglomeration is within 7 units ( ). Experimental results show that the difference equation model can effectively reduce the prediction time and improve the prediction accuracy. Therefore, based on the spectral clustering algorithm and the difference equation model, the fastest prediction speed and the best prediction result can be obtained, and the problem of PM2.5 concentration prediction can be effectively solved. The research can provide decision support for local air pollution early warning and urban integrated management.

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Routes and clustering features of PM2.5 spillover within the Jing-Jin-Ji region at multiple timescales identified using complex network-based methods

Cited by 44 publications

References 34 publications

The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

Regional‐level prediction model with difference equation model and fine particulate matter (PM_2.5) concentration data

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Routes and clustering features of PM2.5 spillover within the Jing-Jin-Ji region at multiple timescales identified using complex network-based methods

Cited by 44 publications

References 34 publications

The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

Regional‐level prediction model with difference equation model and fine particulate matter (PM2.5) concentration data

Contact Info

Product

Resources

About

Regional‐level prediction model with difference equation model and fine particulate matter (PM_2.5) concentration data