title: Particle Pollution Estimation Based on Image Analysis.
abstract: 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.
authors.affiliation: School of Chemistry & Chemical Engineering, Nanjing University, Nanjing, JiangSu, China.
authors.forename: Chenbin
authors.initials: C
authors.lastname: Liu
authors.affiliation: Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America.
authors.forename: Francis
authors.initials: F
authors.lastname: Tsow
authors.affiliation: Beijing Kinto Investment Management Co., Ltd, Beijing, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zou
authors.affiliation: School of Chemistry & Chemical Engineering, Nanjing University, Nanjing, JiangSu, China.|Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America.
authors.forename: Nongjian
authors.initials: N
authors.lastname: Tao
mesh_terms: D000397:Air Pollution / Q000032:analysis*; D002681:China; D003116:Color; D006813:Humidity; D007091:Image Processing, Computer-Assisted; D008027:Light; D052638:Particulate Matter / Q000032:analysis*; D025341:Principal Component Analysis; D012044:Regression Analysis; D060388:Support Vector Machine; D014887:Weather
chemical_list: D052638:Particulate Matter
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