Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques
Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropag...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/15/3/287 |
| _version_ | 1797242141699735552 |
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| author | Yanyu Li Meng Zhang Guodong Ma Haoyuan Ren Ende Yu |
| author_facet | Yanyu Li Meng Zhang Guodong Ma Haoyuan Ren Ende Yu |
| author_sort | Yanyu Li |
| collection | DOAJ |
| description | Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the “optimal” estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the “optimal” estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, and SO<sub>2</sub> in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O<sub>3</sub> slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area. |
| first_indexed | 2024-04-24T18:34:30Z |
| format | Article |
| id | doaj.art-71d2b7af8f4f40cdae37eed7947cb611 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-04-24T18:34:30Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-71d2b7af8f4f40cdae37eed7947cb6112024-03-27T13:20:36ZengMDPI AGAtmosphere2073-44332024-02-0115328710.3390/atmos15030287Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning TechniquesYanyu Li0Meng Zhang1Guodong Ma2Haoyuan Ren3Ende Yu4School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaAccurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the “optimal” estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the “optimal” estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, and SO<sub>2</sub> in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O<sub>3</sub> slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area.https://www.mdpi.com/2073-4433/15/3/287machine learningmultispectral satellite dataaerosol optical depthsurface reflectanceair pollutantspatiotemporal distribution |
| spellingShingle | Yanyu Li Meng Zhang Guodong Ma Haoyuan Ren Ende Yu Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques Atmosphere machine learning multispectral satellite data aerosol optical depth surface reflectance air pollutant spatiotemporal distribution |
| title | Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques |
| title_full | Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques |
| title_fullStr | Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques |
| title_full_unstemmed | Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques |
| title_short | Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques |
| title_sort | analysis of primary air pollutants spatiotemporal distributions based on satellite imagery and machine learning techniques |
| topic | machine learning multispectral satellite data aerosol optical depth surface reflectance air pollutant spatiotemporal distribution |
| url | https://www.mdpi.com/2073-4433/15/3/287 |
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