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A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM 2.5 Removal Modes on a Micro-Temporal Scale

Author

Listed:
  • Tingchen Wu

    (Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
    Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
    Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Xiao Xie

    (Key Lab for Environmental Computation and Sustainability of Liaoning Province, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China)

  • Bing Xue

    (Key Lab for Environmental Computation and Sustainability of Liaoning Province, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China)

  • Tao Liu

    (Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
    Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
    Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

Abstract

PM 2.5 is unanimously considered to be an important indicator of air quality. Sustained rainfall is a kind of typical but complex rainfall process in southern China with an uncertain duration and intervals. During sustained rainfall, the variation of PM 2.5 concentrations in hour-level time series is diverse and complex. However, existing analytical methods mainly examine overall removals at the annual/monthly time scale, missing a quantitative analysis mode that applies micro-scale time data to describe the removal phenomenon. In order to further achieve air quality prediction and prevention in the short term, it is necessary to analyze its micro-temporal removal effect for atmospheric environment quality forecasting. This paper proposed a quantitative modeling and prediction method for sustained rainfall-PM 2.5 removal modes on a micro-temporal scale. Firstly, a set of quantitative modes for sustained rainfall-PM 2.5 removal mode in a micro-temporal scale were constructed. Then, a mode-constrained prediction of the sustained rainfall-PM 2.5 removal effect using the factorization machines (FM) was proposed to predict the future sustained rainfall removal effect. Moreover, the historical observation data of Nanjing city at an hourly scale from 2016 to January 2020 were used for mode modeling. Meanwhile, the whole 2020 year observation data were used for the sustained rainfall-PM 2.5 removal phenomenon prediction. The experiment shows the reasonableness and effectiveness of the proposed method.

Suggested Citation

  • Tingchen Wu & Xiao Xie & Bing Xue & Tao Liu, 2021. "A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM 2.5 Removal Modes on a Micro-Temporal Scale," Sustainability, MDPI, vol. 13(19), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:11022-:d:649998
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    References listed on IDEAS

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    1. Yingcheng Li & Kai Zhu, 2017. "Spatial dependence and heterogeneity in the location processes of new high-tech firms in Nanjing, China," Papers in Regional Science, Wiley Blackwell, vol. 96(3), pages 519-535, August.
    2. Panbo Guan & Hanyu Zhang & Zhida Zhang & Haoyuan Chen & Weichao Bai & Shiyin Yao & Yang Li, 2021. "Assessment of Emission Reduction and Meteorological Change in PM 2.5 and Transport Flux in Typical Cities Cluster during 2013–2017," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    3. Wentao Yang & Zhanjun He & Huikun Huang & Jincai Huang, 2021. "A Clustering Framework to Reveal the Structural Effect Mechanisms of Natural and Social Factors on PM 2.5 Concentrations in China," Sustainability, MDPI, vol. 13(3), pages 1-15, January.
    4. Keyu Zhai & Xing Gao & Yuerong Zhang & Meiling Wu, 2019. "Perceived Sustainable Urbanization Based on Geographically Hierarchical Data Structures in Nanjing, China," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    5. Hao Wu & Xinwei Gao, 2021. "Multimodal Data Based Regression to Monitor Air Pollutant Emission in Factories," Sustainability, MDPI, vol. 13(5), pages 1-17, March.
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