Spatial Association Effect of Haze Pollution in Cheng-Yu Urban Agglomeration

Zhang, Degang; Yuan-quan, LU; Tian, Yuan

doi:10.1038/s41598-020-66665-8

Cited by 21 publications

(11 citation statements)

References 26 publications

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“…The complex network theory has recently been widely adopted to study inter-regional air pollution spillovers (Zhang D. et al, 2020;Yu et al, 2021;Zhang Q. et al, 2021). From the complex network perspective, the cities are regarded as nodes, and the air pollutant spillovers between cities are regarded as edges; thus, the cities in the whole region of China can be linked to form a network capturing the direct and indirect interaction of air pollutants comprehensively (Carmona-Cabezas et al, 2019).…”

Section: Open Access Edited Bymentioning

confidence: 99%

Multi-timescale analysis of air pollution spreaders in Chinese cities based on a transfer entropy network

Tan²,

Liu³

et al. 2022

Front. Environ. Sci.

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Cross-regional air pollutant spillovers aggravate air pollution in China. To mitigate air pollution, identifying and monitoring air pollution spreaders (APS) is a vital strategy that helps locate the source of air pollution and guides the Joint Prevention and Control of Air Pollution. In this paper, we define an APS as a city with a high spillover impact (CHSI) of air pollution and propose a transfer entropy network to investigate the APS from a multi-timescale analysis perspective. Taking the time series of PM2.5 concentration of 358 Chinese cities from 1 January 2015 to 31 December 2020 as the sample, they are decomposed into short, medium, and long timescales, corresponding to an average period of 12, 111, and 530 days, respectively. Then, we use transfer entropy networks to analyze APS’s spatial distribution and temporal variation patterns on each timescale. The results demonstrate that air pollution spillover widely exists in Chinese cities, and the short-term air pollution spillover dominates all spillovers. The CHSIs form large agglomeration areas in Central and East China on short and medium timescales, while the results of the undecomposed data show a more discrete distribution. In addition, the cities’ air pollution spillover impact is usually high in winter and spring and low in summer. Moreover, the spillover impacts of half of the cities have a lead-lag relationship between short and medium timescales. All results suggest that combining short-term controls and longer-term strategies helps China mitigate air pollution and develop sustainably.

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Section: Open Access Edited Bymentioning

confidence: 99%

Multi-timescale analysis of air pollution spreaders in Chinese cities based on a transfer entropy network

Tan²,

Liu³

et al. 2022

Front. Environ. Sci.

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show abstract

“…Li et al constructed spillover networks at multiple timescales to show the interactions of PM 2.5 spillover behavior within Jing-Jin-Ji region ( Li et al, 2019 ). Zhang et al examined the spatial effects on pollution transport in Cheng-Yu urban agglomeration using complex network in which spatial associations of haze pollution were displayed ( Zhang et al, 2020 ). Rafael et al converted tropospheric ozone (O 3 ) time series into complex network and applied centrality metrics to differentiate the ozone dynamics between rural and urban regions ( Rafael et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal evolution of NO2 diffusion in Beijing in response to COVID-19 lockdown using complex network

Zhang

Yang

et al. 2022

Chemosphere

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The COVID-19 pandemic and the corresponding lockdown measures have been confirmed to reduce the air pollution in major megacities worldwide. Especially at some monitoring hotspots, NO 2 has been verified to show a significant decrease. However, the diffusion pattern of these hotspots in responding to COVID-19 is not clearly understood at present stage. Hence, we selected Beijing, a typical megacity with the strictest lockdown measures during COVID-19 period, as the studied city and attempted to discover the NO 2 diffusion process through complex network method. The improved metrics derived from the topological structure of the network were adopted to describe the performance of diffusion. Primarily, we found evidences that COVID-19 had significant effects on the spatial diffusion distribution due to combined effect of changed human activities and meteorological conditions. Besides, to further quantify the impacts of disturbance caused by different lockdown measures, we discussed the evolutionary diffusion patterns from lockdown period to recovery period. The results displayed that the difference between normal operation and pandemic operation firstly increased at the cutoff of lockdown measures but then declined after the implement of recovery measures. The source areas had greater vulnerability and lower resilience than receptors areas. Furthermore, based on the conclusion that the diffusion pattern changed during different periods, we explored the key stations on the path of diffusion process to further gain information. These findings could provide references for comprehending spatiotemporal pattern on city scale, which might be help for high-resolution air pollution mapping and prediction.

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“…Although air quality has been improved in recent years, the pressure of smog control is still present [ 43 ]. Most of the existing studies focus on analysis within a certain urban agglomeration [ 37 , 44 , 45 ], while those covering the entire YREB suffer from a short period of analysis [ 46 ] and single model selection [ 37 , 43 ]; this results in poor interpretation ability of PM2.5 pollution in a long time series and a large space in the Yangtze River Economic Belt. Therefore, a comprehensive study of PM2.5 based on a long-term multi-spatial scale, multi-impact index, and multi-analysis model of the YREB is needed.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

Yan

Tao

Zhang

et al. 2021

IJERPH

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As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

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Spatial Association Effect of Haze Pollution in Cheng-Yu Urban Agglomeration

Cited by 21 publications

References 26 publications

Multi-timescale analysis of air pollution spreaders in Chinese cities based on a transfer entropy network

Multi-timescale analysis of air pollution spreaders in Chinese cities based on a transfer entropy network

Spatiotemporal evolution of NO2 diffusion in Beijing in response to COVID-19 lockdown using complex network

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

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