TY - JOUR
T1 - Prediction of daily PM2.5 concentration in China using partial differential equations
AU - Wang, Yufang
AU - Wang, Haiyan
AU - Chang, Shuhua
AU - Avram, Adrian
N1 - Funding Information:
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.
Publisher Copyright:
© 2018 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018/6
Y1 - 2018/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85048149425&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048149425&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0197666
DO - 10.1371/journal.pone.0197666
M3 - Article
C2 - 29874245
AN - SCOPUS:85048149425
SN - 1932-6203
VL - 13
JO - PloS one
JF - PloS one
IS - 6
M1 - e0197666
ER -