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PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China

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State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
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Collaborative Innovation Centre of Geospatial Technology, Wuhan University, Wuhan 430079, China
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 31; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010031
Received: 15 November 2020 / Revised: 4 January 2021 / Accepted: 11 January 2021 / Published: 13 January 2021
The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small. View Full-Text
Keywords: PM2.5; MAIAC 1 km AOD; MODIS; modified support vector regression; meteorological data PM2.5; MAIAC 1 km AOD; MODIS; modified support vector regression; meteorological data
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MDPI and ACS Style

Chen, N.; Yang, M.; Du, W.; Huang, M. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China. ISPRS Int. J. Geo-Inf. 2021, 10, 31. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010031

AMA Style

Chen N, Yang M, Du W, Huang M. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China. ISPRS International Journal of Geo-Information. 2021; 10(1):31. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010031

Chicago/Turabian Style

Chen, Nengcheng, Meijuan Yang, Wenying Du, and Min Huang. 2021. "PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China" ISPRS International Journal of Geo-Information 10, no. 1: 31. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010031

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