科研之友
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摘要
The frequent hazy weather has brought enormous threaten to human health, especially in developing countries. Modeling a fine-grained distribution of PM2.5 (Particulate matter with diameters less than 2.5 mu m) concentrations is of great importance for industrial and environmental applications. The contradiction between the need of fine-grained distribution and the approach of coarse-grained collection has to be addressed immediately. In this paper, we focus on solving the problem of inferring PM2.5 concentration of unobserved areas based on data samples collected by mobile sensors. We propose a Probabilistic Concentration Estimation Method (PCEM) for a regional fine-grained PM2.5 distribution considering the nature of particle motion. It simulates particles transport in an open airflow field referring the concept of random walk. In the meantime, quadratic programming and heuristic function are also constructed to optimize the algorithm accuracy and robustness. We employ several mobile sensors to collect original data randomly in a region of HangZhou City for certain period of time and utilize the proposed PCEM to generate the real time fine-grained PM2.5 concentration distribution. The results can demonstrate up to 100 times higher resolution level of the PM2.5 concentration distribution than the traditional approaches based on monitoring sites. The degree of correlation between estimated PM2.5 concentration and real measured data is up to 0.9735. The average calculation error of PCEM can be reduced about 41.0% compared with widely used artificial network (ANN). Furthermore, PCEM can easily adapt to other PM distribution inference with different built-in sensors, which could help with a deeper understanding of an informed air quality monitoring system in the future.
- 出版日期2016-1
- 单位杭州电子科技大学
