期刊
SCIENTIFIC REPORTS
卷 5, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/srep08698
关键词
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资金
- National Natural Science Foundation of China [41201384, 41471377, 41101431]
- Opening Project of Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3)
- Hunan Provincial Natural Science Foundation of China [12JJ3034]
- Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping [201228, 2014J07]
- State Key Laboratory of Resources and Environmental Information System
- NieYing Talent Program of Central South University
Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs 'information entropy', an area-based statistic, along with traditional point-based statistics (e.g. error rate, RMSE) to evaluate the performance of LUR model and OK interpolation in mapping PM2.5 concentrations in Houston from a multidimensional perspective. The point-based validation reveals significant differences between LUR and OK at different test sites despite the similar end-result accuracy (e.g. error rate 6.13% vs. 7.01%). Meanwhile, the area-based validation demonstrates that the PM2.5 concentrations simulated by the LUR model exhibits more detailed variations than those interpolated by the OK method (i.e. information entropy, 7.79 vs. 3.63). Results suggest that LUR modeling could better refine the spatial distribution scenario of PM2.5 concentrations compared to OK interpolation. The significance of this study primarily lies in promoting the integration of point- and area-based statistics for model performance evaluation in air pollution mapping.
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