Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR
A case study of Henan Province
Bibliographic Data
| ID | 22089400 |
|---|---|
| Authors | Hua Wang (0000-0001-8396-809X, Zhengzhou University of Light Industry), Mingcheng Zhang (0000-0002-6786-4693, Zhengzhou University of Light Industry), Jiqiang Niu (0000-0002-3315-2970, Xinyang Normal University, corresponding author), Xiaoyun Zheng (0000-0001-6518-6932, Ministry of Natural Resources) |
| Year | 2023 |
| Volume | 11 |
| Pages | 1295468-1295468 |
| Publication date | 2023-12-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.1295468 |
| PMID | 38115845 |
| OpenAlex | W4389345269 |
| Language | EN |
| Citations received | 3 |
| References cited | 61 |
Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM 2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO 2 emissions) on PM 2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM 2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter “M”, and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM 2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM 2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM 2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM 2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM 2.5
China · Climatology · Common spatial pattern · Driving factors · Geographically Weighted Regression · Geography · Meteorology · Physical geography · Remote sensing · Spatial analysis · Spatial distribution · Spatial ecology · Spatial heterogeneity · Spatiotemporal pattern · Statistics · Air Quality and Health Impacts · Environmental Science · Impact of Light on Environment and Health · Mathematics · Urban Transport and Accessibility · Ecology · Geology
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Multiscale Geographically Weighted Regression (MGWR)
| Unique citing works | 3 |
|---|---|
| Citations per year | 1.5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |