TY - JOUR
T1 - Particle Pollution Estimation Based on Image Analysis
AU - Liu, Chenbin
AU - Tsow, Francis
AU - Zou, Yi
AU - Tao, Nongjian
N1 - Publisher Copyright:
© 2016 Liu 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 - 2016/2/1
Y1 - 2016/2/1
N2 - Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction.
AB - Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction.
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U2 - 10.1371/journal.pone.0145955
DO - 10.1371/journal.pone.0145955
M3 - Article
C2 - 26828757
AN - SCOPUS:84958787370
SN - 1932-6203
VL - 11
JO - PloS one
JF - PloS one
IS - 2
M1 - e0145955
ER -