Particle Pollution Estimation Based on Image Analysis

• Published in: PloS one Vol. 11; no. 2; p. e0145955
• Main Authors: Liu, Chenbin, Tsow, Francis, Zou, Yi, Tao, Nongjian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-02-2016; Public Library of Science (PLoS)
• Subjects: Air pollution; Air Pollution - analysis; Analysis; Atmospheric pollution; Bioinformatics; Biosensors; Chemical engineering; China; Color; Digital cameras; Earth Sciences; Engineering and Technology; Exposure; Feature extraction; Humidity; Image analysis; Image processing; Image Processing, Computer-Assisted; Light; Methods; Particulate emissions; Particulate matter; Particulate Matter - analysis; Particulates; Pattern recognition; Physical Sciences; Pollution; Principal Component Analysis; Principal components analysis; Public awareness; Regression Analysis; Research and Analysis Methods; Smartphones; Sun; Support Vector Machine; Weather; Weather conditions; Weather forecasting; China; Beijing China; United States > US; Arizona
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0145955

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.