Abstract
Accurate, fine-grained monitoring and estimation of traffic pollution are essential for preventing people from the health issues caused by air pollution. In this paper, we illustrated the concept of conducting fine-grained spatial interpolation of near-road traffic pollution distribution with mobile monitoring data. Different spatial interpolation techniques, including Kriging, Natural Neighbor Tessellation (NNT), and Inverse Distance Weighted (IDW) were compared. Results show NNT outperforms others especially in the cases of sparse data. This conclusion contributes to the monitoring and estimation of traffic pollution in smart cities.
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Acknowledgements
The work is supported by the National Natural Science Foundation of China (NSFC) via grant No. 71701173 and the Science and Technology Project of Chengdu via grant No. 2020-RK00-00208-ZF. Any conclusions, opinions, findings, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors.
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Peng, X., Sun, Z., Liu, R., Yang, F. (2023). Fine-Grained Traffic Pollution Monitoring and Estimation: A Case Study in Chengdu. In: Yang, Z. (eds) Environmental Science and Technology: Sustainable Development. ICEST 2022. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-27431-2_17
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DOI: https://doi.org/10.1007/978-3-031-27431-2_17
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