Prediction of daily PM2.5 concentration in China using partial differential equations

PLoS One. 2018 Jun 6;13(6):e0197666. doi: 10.1371/journal.pone.0197666. eCollection 2018.

Abstract

Accurate reporting and forecasting of PM2.5 concentration are important for improving public health. In this paper, we propose a partial differential equation (PDE) model, specially, a linear diffusive equation, to describe the spatial-temporal characteristics of PM2.5 in order to make short-term prediction. We analyze the temporal and spatial patterns of a real dataset from China's National Environmental Monitoring and validate the PDE-based model in terms of predicting the PM2.5 concentration of the next day by the former days' history data. Our experiment results show that the PDE model is able to characterize and predict the process of PM2.5 transport. For example, for 300 continuous days of 2016, the average prediction accuracy of the PDE model over all city-regions is 93% or 83% based on different accuracy definitions. To our knowledge, this is the first attempt to use PDE-based model to study PM2.5 prediction in both temporal and spatial dimensions.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Air Pollutants / toxicity*
  • Air Pollution / adverse effects*
  • China
  • Cities
  • Environmental Monitoring*
  • Forecasting
  • Humans
  • Particle Size
  • Particulate Matter / toxicity*
  • Social Networking

Substances

  • Air Pollutants
  • Particulate Matter

Associated data

  • figshare/10.6084/m9.figshare.6243254

Grants and funding

This work was supported by the Major Research Plan of the National Natural Science Foundation of China, (91430108) to SHC; the National Basic Research Program (2012CB955804) and the National Natural Science Foundation of China (11171251, 11771322, 11571324) to SHC; the National Science Foundation (DMS-1737861) to HYW; the Major Program of Tianjin University of Finance and Economics (ZD1302) to SHC; and Science Research Project of Tianjin Education Commission (2017SK108) to YFW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.