A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
The diameter of PM2.5 is less than that of 2.5 μg/m<sup>3</sup> particulate matter; PM2.5 is small enough to enter the body through the alveolar microvasculature and has a major impact on human health. Therefore, people are interested in the establishment of air quality monitoring and fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/4971 |
| _version_ | 1797532258907717632 |
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| author | Eric Hsueh-Chan Lu Chia-Yu Liu |
| author_facet | Eric Hsueh-Chan Lu Chia-Yu Liu |
| author_sort | Eric Hsueh-Chan Lu |
| collection | DOAJ |
| description | The diameter of PM2.5 is less than that of 2.5 μg/m<sup>3</sup> particulate matter; PM2.5 is small enough to enter the body through the alveolar microvasculature and has a major impact on human health. Therefore, people are interested in the establishment of air quality monitoring and forecasting. The historical and current air quality indices (AQI) can now be easily obtained from air quality sensors. However, people are more likely to need the PM2.5 forecasting information. Based on the literature, air quality varies because of a variety of factors, such as the meteorology in urban areas. In this paper, a spatial-temporal approach is proposed to forecast PM2.5 for 48 h using temporal and spatial features. From the temporal perspective, it is considered that the AQI in a few hours may be very similar because AQI is continuous. In addition, this research reveals the relationship between weather similarities and PM2.5 similarity. It is found that the more similar the weather is, the more similar the PM2.5 value is. From a spatial perspective, it is also considered that the air quality may be similar to that of the adjacent monitoring stations. Finally, the experimental results, based on AirBox data, show that the proposed approach outperforms the two methods based on well-established measurements in terms of the PM2.5 forecast error. |
| first_indexed | 2024-03-10T10:56:33Z |
| format | Article |
| id | doaj.art-5ed930b323824a61874c88e3f43a8aa5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:56:33Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5ed930b323824a61874c88e3f43a8aa52023-11-21T21:48:20ZengMDPI AGApplied Sciences2076-34172021-05-011111497110.3390/app11114971A Spatial-Temporal Approach for Air Quality Forecast in Urban AreasEric Hsueh-Chan Lu0Chia-Yu Liu1Department of Geomatics, National Cheng Kung University, Tainan City 701, TaiwanDepartment of Geomatics, National Cheng Kung University, Tainan City 701, TaiwanThe diameter of PM2.5 is less than that of 2.5 μg/m<sup>3</sup> particulate matter; PM2.5 is small enough to enter the body through the alveolar microvasculature and has a major impact on human health. Therefore, people are interested in the establishment of air quality monitoring and forecasting. The historical and current air quality indices (AQI) can now be easily obtained from air quality sensors. However, people are more likely to need the PM2.5 forecasting information. Based on the literature, air quality varies because of a variety of factors, such as the meteorology in urban areas. In this paper, a spatial-temporal approach is proposed to forecast PM2.5 for 48 h using temporal and spatial features. From the temporal perspective, it is considered that the AQI in a few hours may be very similar because AQI is continuous. In addition, this research reveals the relationship between weather similarities and PM2.5 similarity. It is found that the more similar the weather is, the more similar the PM2.5 value is. From a spatial perspective, it is also considered that the air quality may be similar to that of the adjacent monitoring stations. Finally, the experimental results, based on AirBox data, show that the proposed approach outperforms the two methods based on well-established measurements in terms of the PM2.5 forecast error.https://www.mdpi.com/2076-3417/11/11/4971air quality forecastspatial-temporalurban computingdata miningAirBox |
| spellingShingle | Eric Hsueh-Chan Lu Chia-Yu Liu A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas Applied Sciences air quality forecast spatial-temporal urban computing data mining AirBox |
| title | A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas |
| title_full | A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas |
| title_fullStr | A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas |
| title_full_unstemmed | A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas |
| title_short | A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas |
| title_sort | spatial temporal approach for air quality forecast in urban areas |
| topic | air quality forecast spatial-temporal urban computing data mining AirBox |
| url | https://www.mdpi.com/2076-3417/11/11/4971 |
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