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

Tingchen Wu, Xiao Xie, Bing Xue and Tao Liu
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Tingchen Wu: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, 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

Sustainability, 2021, vol. 13, issue 19, 1-17

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.

Keywords: sustained rainfall; PM 2.5 removal mode; micro-temporal scale; quantitative modeling; mode prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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