An estimated method of urban concentration distribution for a mobile sensing system

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• 作者：Yidan Hu ^{hydagnes@163.com" class="auth_mail" title="E-mail the corresponding author} ; Jin Fan ; ^{fanjin@hdu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Hua Zhang ^{zhangh@hdu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Xinxin Chen ^{indifferentkks@163.com" class="auth_mail" title="E-mail the corresponding author} ; Guojun Dai ^{daigj@hdu.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fine-grained PM2.5 concentration distribution ; Particle transport modeling ; Mobile sensing ; Random walk
• 刊名：Pervasive and Mobile Computing
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：25
• 期：Complete
• 页码：88-103
• 全文大小：1746 K

文摘

The frequent hazy weather has brought enormous threaten to human health, especially in developing countries. Modeling a fine-grained distribution of class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S157411921500111X&_mathId=si10.gif&_user=111111111&_pii=S157411921500111X&_rdoc=1&_issn=15741192&md5=ac34e51e1e63c7eec5fb6f02f0bfc948">class="imgLazyJSB inlineImage" height="15" width="37" alt="View the MathML source" style="margin-top: -5px; vertical-align: middle" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S157411921500111X-si10.gif">class="mathContainer hidden">class="mathCode">${\mathrm{PM}}_{2.5}$ (Particulate matter with diameters less than class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S157411921500111X&_mathId=si9.gif&_user=111111111&_pii=S157411921500111X&_rdoc=1&_issn=15741192&md5=07bb6761c22a610ab8233e452c5a5c66">class="imgLazyJSB inlineImage" height="14" width="47" alt="View the MathML source" style="margin-top: -5px; vertical-align: middle" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S157411921500111X-si9.gif">class="mathContainer hidden">class="mathCode">) 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 class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S157411921500111X&_mathId=si10.gif&_user=111111111&_pii=S157411921500111X&_rdoc=1&_issn=15741192&md5=ac34e51e1e63c7eec5fb6f02f0bfc948">class="imgLazyJSB inlineImage" height="15" width="37" alt="View the MathML source" style="margin-top: -5px; vertical-align: middle" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S157411921500111X-si10.gif">class="mathContainer hidden">class="mathCode">${\mathrm{PM}}_{2.5}$ 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 class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S157411921500111X&_mathId=si119.gif&_user=111111111&_pii=S157411921500111X&_rdoc=1&_issn=15741192&md5=1830bfcee0adf96c58fa6c6439f48b89">class="imgLazyJSB inlineImage" height="15" width="38" alt="View the MathML source" style="margin-top: -5px; vertical-align: middle" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S157411921500111X-si119.gif">class="mathContainer hidden">class="mathCode">${\mathrm{PM}}_{2.5}$ distribution considering the nature of particle motion. It simulates particles transport in an open air-flow 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 class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S157411921500111X&_mathId=si10.gif&_user=111111111&_pii=S157411921500111X&_rdoc=1&_issn=15741192&md5=ac34e51e1e63c7eec5fb6f02f0bfc948">class="imgLazyJSB inlineImage" height="15" width="37" alt="View the MathML source" style="margin-top: -5px; vertical-align: middle" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S157411921500111X-si10.gif">class="mathContainer hidden">class="mathCode">${\mathrm{PM}}_{2.5}$ concentration distribution. The results can demonstrate up to 100 times higher resolution level of the class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S157411921500111X&_mathId=si119.gif&_user=111111111&_pii=S157411921500111X&_rdoc=1&_issn=15741192&md5=1830bfcee0adf96c58fa6c6439f48b89">class="imgLazyJSB inlineImage" height="15" width="38" alt="View the MathML source" style="margin-top: -5px; vertical-align: middle" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S157411921500111X-si119.gif">class="mathContainer hidden">class="mathCode">${\mathrm{PM}}_{2.5}$ concentration distribution than the traditional approaches based on monitoring sites. The degree of correlation between estimated class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S157411921500111X&_mathId=si10.gif&_user=111111111&_pii=S157411921500111X&_rdoc=1&_issn=15741192&md5=ac34e51e1e63c7eec5fb6f02f0bfc948">class="imgLazyJSB inlineImage" height="15" width="37" alt="View the MathML source" style="margin-top: -5px; vertical-align: middle" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S157411921500111X-si10.gif">class="mathContainer hidden">class="mathCode">${\mathrm{PM}}_{2.5}$ 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.