Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective
文献类型：SCI/SSCI论文
| 作者 | Zou B.; Luo, Y. Q.; Wan, N.; Zheng, Z.; Sternberg, T.; Liao, Y. L. |
| 发表日期 | 2015 |
| 关键词 | land-use regression air-pollution exposure fine particulate matter geographic information-systems escape project models areas gis dioxide europe |
| 英文摘要 | 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. |
| 出处 | Scientific Reports |
| 卷 | 5 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 2045-2322 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/38541]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Zou B.,Luo, Y. Q.,Wan, N.,et al. Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective. 2015. |
入库方式： OAI收割
来源：地理科学与资源研究所
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