Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM<sub>2.5</sub> Estimation
The increase in atmospheric pollution dominated by particles with an aerodynamic diameter smaller than 2.5 μm (PM<sub>2.5</sub>) has become one of the most serious environmental hazards worldwide. The geographically weighted regression (GWR) model is a vital method to estimate the spatia...
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MDPI AG
2021-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/6/413 |
| _version_ | 1797530085954158592 |
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| author | Weihao Xuan Feng Zhang Hongye Zhou Zhenhong Du Renyi Liu |
| author_facet | Weihao Xuan Feng Zhang Hongye Zhou Zhenhong Du Renyi Liu |
| author_sort | Weihao Xuan |
| collection | DOAJ |
| description | The increase in atmospheric pollution dominated by particles with an aerodynamic diameter smaller than 2.5 μm (PM<sub>2.5</sub>) has become one of the most serious environmental hazards worldwide. The geographically weighted regression (GWR) model is a vital method to estimate the spatial distribution of the ground-level PM<sub>2.5</sub> concentration. Wind information reflects the directional dependence of the spatial distribution, which can be abstracted as a combination of spatial and directional non-stationarity components. In this paper, a GWR model considering directional non-stationarity (GDWR) is proposed. To assess the efficacy of our method, monthly PM<sub>2.5</sub> concentration estimation was carried out as a case study from March 2015 to February 2016 in the Yangtze River Delta region. The results indicate that the GDWR model attained the best fitting effect (0.79) and the smallest error fluctuation, the ordinary least squares (OLS) (0.589) fitting effect was the worst, and the GWR (0.72) and directionally weighted regression (DWR) (0.74) fitting effects were moderate. A non-stationarity hypothesis test was performed to confirm directional non-stationarity. The distribution of the PM<sub>2.5</sub> concentration in the Yangtze River Delta is also discussed here. |
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| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T10:23:56Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-eb0683c2fb5c4962b467b9a30ad11f8e2023-11-22T00:15:09ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-06-0110641310.3390/ijgi10060413Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM<sub>2.5</sub> EstimationWeihao Xuan0Feng Zhang1Hongye Zhou2Zhenhong Du3Renyi Liu4School of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou 310027, ChinaThe increase in atmospheric pollution dominated by particles with an aerodynamic diameter smaller than 2.5 μm (PM<sub>2.5</sub>) has become one of the most serious environmental hazards worldwide. The geographically weighted regression (GWR) model is a vital method to estimate the spatial distribution of the ground-level PM<sub>2.5</sub> concentration. Wind information reflects the directional dependence of the spatial distribution, which can be abstracted as a combination of spatial and directional non-stationarity components. In this paper, a GWR model considering directional non-stationarity (GDWR) is proposed. To assess the efficacy of our method, monthly PM<sub>2.5</sub> concentration estimation was carried out as a case study from March 2015 to February 2016 in the Yangtze River Delta region. The results indicate that the GDWR model attained the best fitting effect (0.79) and the smallest error fluctuation, the ordinary least squares (OLS) (0.589) fitting effect was the worst, and the GWR (0.72) and directionally weighted regression (DWR) (0.74) fitting effects were moderate. A non-stationarity hypothesis test was performed to confirm directional non-stationarity. The distribution of the PM<sub>2.5</sub> concentration in the Yangtze River Delta is also discussed here.https://www.mdpi.com/2220-9964/10/6/413GWRnon-stationaritywindPM<sub>2.5</sub> concentrationslocally varying anisotropy |
| spellingShingle | Weihao Xuan Feng Zhang Hongye Zhou Zhenhong Du Renyi Liu Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM<sub>2.5</sub> Estimation ISPRS International Journal of Geo-Information GWR non-stationarity wind PM<sub>2.5</sub> concentrations locally varying anisotropy |
| title | Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM<sub>2.5</sub> Estimation |
| title_full | Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM<sub>2.5</sub> Estimation |
| title_fullStr | Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM<sub>2.5</sub> Estimation |
| title_full_unstemmed | Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM<sub>2.5</sub> Estimation |
| title_short | Improving Geographically Weighted Regression Considering Directional Nonstationary for Ground-Level PM<sub>2.5</sub> Estimation |
| title_sort | improving geographically weighted regression considering directional nonstationary for ground level pm sub 2 5 sub estimation |
| topic | GWR non-stationarity wind PM<sub>2.5</sub> concentrations locally varying anisotropy |
| url | https://www.mdpi.com/2220-9964/10/6/413 |
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