Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China
Bibliographic Data
| ID | 22031712 |
|---|---|
| Authors | Shuaiqian Zhang (0000-0001-6224-1991, Nantong University), Fei Tao (0000-0002-9020-0633, Hong Kong Polytechnic University), Qi Wu (0000-0001-5487-3278, Nantong University), Qile Han (Nantong University), Yu Wang (0009-0005-4222-2626, Nantong University), Tong Zhou (0000-0003-3901-3477, Nantong University, corresponding author) |
| Year | 2022 |
| Volume | 11 |
| Issue | 4 |
| Pages | 267 |
| Publication date | 2022-04-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi11040267 |
| OpenAlex | W4224130387 |
| Language | EN |
| Citations received | 1 |
| References cited | 73 |
The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors
Beijing · China · Geography · Metropolitan area · Regional science · Transport engineering · Air Quality and Health Impacts · Energy, Environment, Economic Growth · Engineering · Urban Transport and Accessibility
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| Citations per year | 0.33 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |