Estimation of Ground PM2.5 Concentrations using a DEM-assisted Information Diffusion Algorithm: A Case Study in China

• Published in: Scientific reports Vol. 7; no. 1; pp. 1 - 12
• Main Authors: Ma, Lei, Gao, Yu, Fu, Tengyu, Cheng, Liang, Chen, Zhenjie, Li, Manchun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.11.2017; Nature Publishing Group
• Subjects: 704/106/35/823; 704/172/4081; Air pollution; Algorithms; Diffusion; Humanities and Social Sciences; multidisciplinary; Particulate matter; Pollutants; Science; Science (multidisciplinary); Spatial distribution
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-14197-z

When estimating national PM 2.5 concentrations, the results of traditional interpolation algorithms are unreliable due to a lack of monitoring sites and heterogeneous spatial distributions. PM 2.5 spatial distribution is strongly correlated to elevation, and the information diffusion algorithm has been shown to be highly reliable when dealing with sparse data interpolation issues. Therefore, to overcome the disadvantages of traditional algorithms, we proposed a method combining elevation data with the information diffusion algorithm. Firstly, a digital elevation model (DEM) was used to segment the study area into multiple scales. Then, the information diffusion algorithm was applied in each region to estimate the ground PM 2.5 concentration, which was compared with estimation results using the Ordinary Kriging and Inverse Distance Weighted algorithms. The results showed that: (1) reliable estimate at local area was obtained using the DEM-assisted information diffusion algorithm; (2) the information diffusion algorithm was more applicable for estimating daily average PM 2.5 concentrations due to the advantage in noise data; (3) the information diffusion algorithm required less supplementary data and was suitable for simulating the diffusion of air pollutants. We still expect a new comprehensive model integrating more factors would be developed in the future to optimize the interpretation accuracy of short time observation data.