Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy
<p>PM<sub>2.5</sub> pollution significantly impedes sustainable development in large urban agglomerations. Understanding its driving mechanisms allows the development of targeted management strategies. However, focusing on static factors alone limits the ability to regulate the pol...
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2025
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| _version_ | 1849927644671377408 |
|---|---|
| author | Tingting Kang (4497127) |
| author2 | Junyi Yang (602557) Zeng Li (688702) Han Wang (254423) Renrong Xiao (22674891) Pengjun Zhao (1360821) |
| author2_role | author author author author author |
| author_facet | Tingting Kang (4497127) Junyi Yang (602557) Zeng Li (688702) Han Wang (254423) Renrong Xiao (22674891) Pengjun Zhao (1360821) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tingting Kang (4497127) Junyi Yang (602557) Zeng Li (688702) Han Wang (254423) Renrong Xiao (22674891) Pengjun Zhao (1360821) |
| dc.date.none.fl_str_mv | 2025-11-24T13:40:24Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.30695061.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Nonlinear_driving_mechanism_of_PM_sub_2_5_sub_and_spatial_governance_considering_time_series_information_entropy/30695061 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Infectious Diseases Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Driving mechanism PM2.5 pollution MGWR temporal information entropy decoupling effect |
| dc.title.none.fl_str_mv | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>PM<sub>2.5</sub> pollution significantly impedes sustainable development in large urban agglomerations. Understanding its driving mechanisms allows the development of targeted management strategies. However, focusing on static factors alone limits the ability to regulate the pollution reduction pace. Achieving stable and efficient PM<sub>2.5</sub> reduction in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is challenging. This study employed time series information entropy, random forest, partial dependency plot, and multiscale geographically weighted regression (MGWR) to explore the nonlinearity and spatial heterogeneity of static and dynamic influences on PM<sub>2.5</sub>. We then applied K-means clustering to design region-specific control strategies. Results revealed that temperature (46.39%) and wind speed (44.12%) primarily drove static PM<sub>2.5</sub> intensity, while changes in gross domestic product (GDP; 47.50%) influenced its evolution. Enhancing road networks and building density fosters compact cities that steadily reduce PM<sub>2.5</sub>. Minor fluctuations in population density and nighttime lighting significantly reduced PM<sub>2.5</sub>, indicating that stable demographic and economic transitions support sustained reductions. At the subregional level, cooling strategies should be prioritized in the central GBA, while regulating population growth, GDP, and road construction is key in peripheral areas. This study clarifies PM<sub>2.5</sub>,s dynamic driving mechanisms and offers actionable insights for managing both the level and timing of reductions.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_50de2f0bd97a6b68e04e3ddb47eb2a0f |
| identifier_str_mv | 10.6084/m9.figshare.30695061.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30695061 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropyTingting Kang (4497127)Junyi Yang (602557)Zeng Li (688702)Han Wang (254423)Renrong Xiao (22674891)Pengjun Zhao (1360821)EcologyInfectious DiseasesSpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedDriving mechanismPM2.5 pollutionMGWRtemporal information entropydecoupling effect<p>PM<sub>2.5</sub> pollution significantly impedes sustainable development in large urban agglomerations. Understanding its driving mechanisms allows the development of targeted management strategies. However, focusing on static factors alone limits the ability to regulate the pollution reduction pace. Achieving stable and efficient PM<sub>2.5</sub> reduction in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is challenging. This study employed time series information entropy, random forest, partial dependency plot, and multiscale geographically weighted regression (MGWR) to explore the nonlinearity and spatial heterogeneity of static and dynamic influences on PM<sub>2.5</sub>. We then applied K-means clustering to design region-specific control strategies. Results revealed that temperature (46.39%) and wind speed (44.12%) primarily drove static PM<sub>2.5</sub> intensity, while changes in gross domestic product (GDP; 47.50%) influenced its evolution. Enhancing road networks and building density fosters compact cities that steadily reduce PM<sub>2.5</sub>. Minor fluctuations in population density and nighttime lighting significantly reduced PM<sub>2.5</sub>, indicating that stable demographic and economic transitions support sustained reductions. At the subregional level, cooling strategies should be prioritized in the central GBA, while regulating population growth, GDP, and road construction is key in peripheral areas. This study clarifies PM<sub>2.5</sub>,s dynamic driving mechanisms and offers actionable insights for managing both the level and timing of reductions.</p>2025-11-24T13:40:24ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.30695061.v1https://figshare.com/articles/dataset/Nonlinear_driving_mechanism_of_PM_sub_2_5_sub_and_spatial_governance_considering_time_series_information_entropy/30695061CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306950612025-11-24T13:40:24Z |
| spellingShingle | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy Tingting Kang (4497127) Ecology Infectious Diseases Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Driving mechanism PM2.5 pollution MGWR temporal information entropy decoupling effect |
| status_str | publishedVersion |
| title | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy |
| title_full | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy |
| title_fullStr | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy |
| title_full_unstemmed | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy |
| title_short | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy |
| title_sort | Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy |
| topic | Ecology Infectious Diseases Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Driving mechanism PM2.5 pollution MGWR temporal information entropy decoupling effect |