High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data
This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Te...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/15/3878 |
| _version_ | 1797586037563719680 |
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| author | Chunhui Liu Sensen Wu Zhen Dai Yuanyuan Wang Zhenhong Du Xingyu Liu Chunxia Qiu |
| author_facet | Chunhui Liu Sensen Wu Zhen Dai Yuanyuan Wang Zhenhong Du Xingyu Liu Chunxia Qiu |
| author_sort | Chunhui Liu |
| collection | DOAJ |
| description | This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region. |
| first_indexed | 2024-03-11T00:17:34Z |
| format | Article |
| id | doaj.art-90cb0c4e63d84fd1bd153ff344ba9559 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:17:34Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-90cb0c4e63d84fd1bd153ff344ba95592023-11-18T23:32:05ZengMDPI AGRemote Sensing2072-42922023-08-011515387810.3390/rs15153878High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI DataChunhui Liu0Sensen Wu1Zhen Dai2Yuanyuan Wang3Zhenhong Du4Xingyu Liu5Chunxia Qiu6College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaChina Mobile (Zhejiang) Innovation Research Institute Co., Ltd., Hangzhou 310016, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaThis study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.https://www.mdpi.com/2072-4292/15/15/3878Beijing–Tianjin–Hebei regionTROPOMIGTNNWRground-level NO2high resolution |
| spellingShingle | Chunhui Liu Sensen Wu Zhen Dai Yuanyuan Wang Zhenhong Du Xingyu Liu Chunxia Qiu High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data Remote Sensing Beijing–Tianjin–Hebei region TROPOMI GTNNWR ground-level NO2 high resolution |
| title | High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data |
| title_full | High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data |
| title_fullStr | High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data |
| title_full_unstemmed | High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data |
| title_short | High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data |
| title_sort | high resolution daily spatiotemporal distribution and evaluation of ground level nitrogen dioxide concentration in the beijing tianjin hebei region based on tropomi data |
| topic | Beijing–Tianjin–Hebei region TROPOMI GTNNWR ground-level NO2 high resolution |
| url | https://www.mdpi.com/2072-4292/15/15/3878 |
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