Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM<sub>2.5</sub> Concentrations in China
Numerous studies have revealed that the sparse spatiotemporal distributions of ground-level PM<sub>2.5</sub> measurements affect the accuracy of PM<sub>2.5</sub> simulation, especially in large geographical regions. However, the high precision and stability of ground-level PM...
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MDPI AG
2021-02-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/12/2/272 |
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| author | Ning Li Junli Xu Xianqing Lv |
| author_facet | Ning Li Junli Xu Xianqing Lv |
| author_sort | Ning Li |
| collection | DOAJ |
| description | Numerous studies have revealed that the sparse spatiotemporal distributions of ground-level PM<sub>2.5</sub> measurements affect the accuracy of PM<sub>2.5</sub> simulation, especially in large geographical regions. However, the high precision and stability of ground-level PM<sub>2.5</sub> measurements make their role irreplaceable in PM<sub>2.5</sub> simulations. This article applies a dynamically constrained interpolation methodology (DCIM) to evaluate sparse PM<sub>2.5</sub> measurements captured at scattered monitoring sites for national-scale PM<sub>2.5</sub> simulations and spatial distributions. The DCIM takes a PM<sub>2.5</sub> transport model as a dynamic constraint and provides the characteristics of the spatiotemporal variations of key model parameters using the adjoint method to improve the accuracy of PM<sub>2.5</sub> simulations. From the perspective of interpolation accuracy and effect, kriging interpolation and orthogonal polynomial fitting using Chebyshev basis functions (COPF), which have been proved to have high PM<sub>2.5</sub> simulation accuracy, were adopted to make a comparative assessment of DCIM performance and accuracy. Results of the cross validation confirm the feasibility of the DCIM. A comparison between the final interpolated values and observations show that the DCIM is better for national-scale simulations than kriging or COPF. Furthermore, the DCIM presents smoother spatially interpolated distributions of the PM<sub>2.5</sub> simulations with smaller simulation errors than the other two methods. Admittedly, the sparse PM<sub>2.5</sub> measurements in a highly polluted region have a certain degree of influence on the interpolated distribution accuracy and rationality. To some extent, adding the right amount of observations can improve the effectiveness of the DCIM around existing monitoring sites. Compared with the kriging interpolation and COPF, the results show that the DCIM used in this study would be more helpful for providing reasonable information for monitoring PM<sub>2.5</sub> pollution in China. |
| first_indexed | 2024-03-09T00:48:03Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-09T00:48:03Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj.art-ddd9edcf7a344d39823018c3101cfa9e2023-12-11T17:26:24ZengMDPI AGAtmosphere2073-44332021-02-0112227210.3390/atmos12020272Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM<sub>2.5</sub> Concentrations in ChinaNing Li0Junli Xu1Xianqing Lv2School of Science, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266100, ChinaPhysical Oceanography Laboratory, Qingdao Collaborative Innovation Center of Marine Science and Technology (CIMST), Ocean University of China, Qingdao 266100, ChinaNumerous studies have revealed that the sparse spatiotemporal distributions of ground-level PM<sub>2.5</sub> measurements affect the accuracy of PM<sub>2.5</sub> simulation, especially in large geographical regions. However, the high precision and stability of ground-level PM<sub>2.5</sub> measurements make their role irreplaceable in PM<sub>2.5</sub> simulations. This article applies a dynamically constrained interpolation methodology (DCIM) to evaluate sparse PM<sub>2.5</sub> measurements captured at scattered monitoring sites for national-scale PM<sub>2.5</sub> simulations and spatial distributions. The DCIM takes a PM<sub>2.5</sub> transport model as a dynamic constraint and provides the characteristics of the spatiotemporal variations of key model parameters using the adjoint method to improve the accuracy of PM<sub>2.5</sub> simulations. From the perspective of interpolation accuracy and effect, kriging interpolation and orthogonal polynomial fitting using Chebyshev basis functions (COPF), which have been proved to have high PM<sub>2.5</sub> simulation accuracy, were adopted to make a comparative assessment of DCIM performance and accuracy. Results of the cross validation confirm the feasibility of the DCIM. A comparison between the final interpolated values and observations show that the DCIM is better for national-scale simulations than kriging or COPF. Furthermore, the DCIM presents smoother spatially interpolated distributions of the PM<sub>2.5</sub> simulations with smaller simulation errors than the other two methods. Admittedly, the sparse PM<sub>2.5</sub> measurements in a highly polluted region have a certain degree of influence on the interpolated distribution accuracy and rationality. To some extent, adding the right amount of observations can improve the effectiveness of the DCIM around existing monitoring sites. Compared with the kriging interpolation and COPF, the results show that the DCIM used in this study would be more helpful for providing reasonable information for monitoring PM<sub>2.5</sub> pollution in China.https://www.mdpi.com/2073-4433/12/2/272ground-level PM<sub>2.5</sub> simulationdynamically constrained interpolation methodologyPM<sub>2.5</sub> transport modelkriging interpolationorthogonal polynomial fittingchebyshev basis functions |
| spellingShingle | Ning Li Junli Xu Xianqing Lv Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM<sub>2.5</sub> Concentrations in China Atmosphere ground-level PM<sub>2.5</sub> simulation dynamically constrained interpolation methodology PM<sub>2.5</sub> transport model kriging interpolation orthogonal polynomial fitting chebyshev basis functions |
| title | Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM<sub>2.5</sub> Concentrations in China |
| title_full | Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM<sub>2.5</sub> Concentrations in China |
| title_fullStr | Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM<sub>2.5</sub> Concentrations in China |
| title_full_unstemmed | Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM<sub>2.5</sub> Concentrations in China |
| title_short | Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM<sub>2.5</sub> Concentrations in China |
| title_sort | application of dynamically constrained interpolation methodology in simulating national scale spatial distribution of pm sub 2 5 sub concentrations in china |
| topic | ground-level PM<sub>2.5</sub> simulation dynamically constrained interpolation methodology PM<sub>2.5</sub> transport model kriging interpolation orthogonal polynomial fitting chebyshev basis functions |
| url | https://www.mdpi.com/2073-4433/12/2/272 |
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