Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approach
Abstract Mapping air pollution at high spatial resolution is essential for understanding, managing, and mitigating the adverse impacts of air pollution. Current air pollution monitoring approaches suffer from limited spatial coverage and resolution. Artificial intelligence holds great promise for ta...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-01044-6 |
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| author | Ting Zhang Bo Zheng Ruqi Huang |
| author_facet | Ting Zhang Bo Zheng Ruqi Huang |
| author_sort | Ting Zhang |
| collection | DOAJ |
| description | Abstract Mapping air pollution at high spatial resolution is essential for understanding, managing, and mitigating the adverse impacts of air pollution. Current air pollution monitoring approaches suffer from limited spatial coverage and resolution. Artificial intelligence holds great promise for tackling these challenges, yet its application in air pollution monitoring remains nascent, facing limited transferability regarding low-quality labeled and non-uniform spread data. Here, we introduce Height-Field Signed Distance Function (HF-SDF), an innovative 3D implicit representation, to reconstruct air pollution concentration maps from coarse, incomplete data, which achieves both extensive spatial coverage and fine-scale results with powerful transferability. HF-SDF learns a continuous and transferable mapping model that integrates an auto-decoder network with a geometric constraint, offering flexible resolution. The evaluation uses reanalysis data and satellite observations, reaching accuracy rates of 96% and 91%, respectively. HF-SDF reveals immense promise in advancing air pollution monitoring by offering insights into the spatial heterogeneity of pollution distributions. |
| format | Article |
| id | doaj-art-cfa100b0c813400f92d12279f1af1200 |
| institution | DOAJ |
| issn | 2397-3722 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-cfa100b0c813400f92d12279f1af12002025-08-20T03:10:16ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-05-01811910.1038/s41612-025-01044-6Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approachTing Zhang0Bo Zheng1Ruqi Huang2Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua UniversityInstitute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua UniversityInstitute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua UniversityAbstract Mapping air pollution at high spatial resolution is essential for understanding, managing, and mitigating the adverse impacts of air pollution. Current air pollution monitoring approaches suffer from limited spatial coverage and resolution. Artificial intelligence holds great promise for tackling these challenges, yet its application in air pollution monitoring remains nascent, facing limited transferability regarding low-quality labeled and non-uniform spread data. Here, we introduce Height-Field Signed Distance Function (HF-SDF), an innovative 3D implicit representation, to reconstruct air pollution concentration maps from coarse, incomplete data, which achieves both extensive spatial coverage and fine-scale results with powerful transferability. HF-SDF learns a continuous and transferable mapping model that integrates an auto-decoder network with a geometric constraint, offering flexible resolution. The evaluation uses reanalysis data and satellite observations, reaching accuracy rates of 96% and 91%, respectively. HF-SDF reveals immense promise in advancing air pollution monitoring by offering insights into the spatial heterogeneity of pollution distributions.https://doi.org/10.1038/s41612-025-01044-6 |
| spellingShingle | Ting Zhang Bo Zheng Ruqi Huang Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approach npj Climate and Atmospheric Science |
| title | Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approach |
| title_full | Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approach |
| title_fullStr | Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approach |
| title_full_unstemmed | Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approach |
| title_short | Adaptive high-resolution mapping of air pollution with a novel implicit 3D representation approach |
| title_sort | adaptive high resolution mapping of air pollution with a novel implicit 3d representation approach |
| url | https://doi.org/10.1038/s41612-025-01044-6 |
| work_keys_str_mv | AT tingzhang adaptivehighresolutionmappingofairpollutionwithanovelimplicit3drepresentationapproach AT bozheng adaptivehighresolutionmappingofairpollutionwithanovelimplicit3drepresentationapproach AT ruqihuang adaptivehighresolutionmappingofairpollutionwithanovelimplicit3drepresentationapproach |