Discussion on Potential Influential Factors of PM2 5 by Using Deep Learning

  • 曾 建元

Student thesis: Master's Thesis

Abstract

There are many factors that influence PM2 5 reducing the emission of PM2 5 is one of the subjects of the world's interest However it is indicated recently that one of the sources of the secondary PM2 5 is the complex chemical reaction of NH3 and the exhaust gases emitted from factories Therefore this study collects the open data provided by the government including the weather data of air monitoring stations the air pollution data of registered industrial discharge and stock raising data There are two subjects to be discussed Subject 1 is to use Apache Spark as Cloud computing platform to study the effect of controlled air pollution source data and size of animal nearby the air monitoring station on the annual mean PM2 5 Subject 2 is to study the open data of controlled air pollution sources from the Environmental Protection Administration and the open data of stock raising from the Council of Agriculture the Tensorflow is used to study deep learning model to discuss whether the related derived factors affect the PM2 5 concentration or not The experimental results show that the Spearman's correlation coefficient of the air pollution data of industrial discharge and the size of animal nearby the air monitoring station for the annual mean PM2 5 is 0 to 1 representing positive correlation The deep learning experiment shows that the MSE classification accuracy of PM2 5 concentration of the deep learning model with industrial data + stock raising data + weather data is 0 75 whereas the MSE classification accuracy of PM2 5 concentration of the deep learning model only with weather data is 1 5 Therefore the meteorological factor industrial factor and stock raising factor may influence the PM2 5 concentration in the study area hoping to provide references for the government bodies to make decisions and to install related air monitors in factories and livestock farms to analyze the air quality data so as to improve the environment reduce the emission of PM2 5 and reduce the probability of suffering from cardiovascular disease
Date of Award2017 May 2
Original languageEnglish
SupervisorJui-Hung Chang (Supervisor)

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