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

Lei Ma,Manchun Li,Zhenjie Chen,Tengyu Fu,Yu Gao,Liang Cheng

doi:10.1038/s41598-017-14197-z

Abstract

When estimating national PM2.5 concentrations, the results of traditional interpolation algorithms are unreliable due to a lack of monitoring sites and heterogeneous spatial distributions. PM2.5 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 PM2.5 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 PM2.5 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.

Highlights

The spatial interpolation algorithm can be used to overcome the issues relating to data acquisition and model development when estimating ground PM2.5 concentrations
The multi-scale segmentation algorithm was combined with the information diffusion algorithm to estimate ground PM2.5 concentrations in the study area using ground-measured PM2.5 data, based on the assumption that ground PM2.5 spatial distribution is highly correlated with elevation
Using the information diffusion algorithm, this study considered the impact of elevation on PM2.5 spatial distribution, and segmented the study area based on elevation variations

Summary

Introduction

The spatial interpolation algorithm can be used to overcome the issues relating to data acquisition and model development when estimating ground PM2.5 concentrations. It is difficult to combine the factors affecting PM2.5 spatial distribution with the algorithms These shortcomings restrict the accuracy of traditional interpolation algorithms in ground PM2.5 concentration estimation. The information diffusion algorithm transforms the original information to value samples of the fuzzy set, and assigns information of single-value samples to different discrete points based on the diffusion functions[26,27] In this way, reliable results can be produced even if the underlying physical processes are not fully understood[28]. This study used the information diffusion algorithm to estimate ground PM2.5 concentrations by assuming the segmentation results of a digital elevation model (DEM) as the interpolation window widths. In the past few years, the air quality has Altitude (m)

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Discussion

Conclusion