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PM2.5 CONCENTRATION CHANGE TREND FORECAST:
TAKING AN ADMINISTRATIVE REGION OF KAOHSIUNG CITY
AS AN EXAMPLE

Chun-I Chen*
Department of Industrial Management, I-Shou University, Taiwan, R.O.C.
*corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Chia-Chin Hsu
Department of Industrial Management, I-Shou University, Taiwan, R.O.C.

Wan-Chin Chen
Department of Industrial Management, I-Shou University, Taiwan, R.O.C.

Ting-Yu Chen
Department of Industrial Management, I-Shou University, Taiwan, R.O.C.

Wan-Ting Chen
Department of Industrial Management, I-Shou University, Taiwan, R.O.C.

 

Abstract

PM2.5 pollution has large negative influence on the human body, and the concentration of PM2.5 will change over time under the influences of the emissions of different pollution sources, precipitation, wind speed, and other atmosphere diffusion conditions. With PM2.5 concentration data provided by the survey station of the Environmental Protection Administration as the data used for construction of the model, this research adopted the Moving Average Method, Exponential Smoothing and Grey Forecasting Method to construct the model to forecast PM2.5 concentration. The research results show that it has the best performance when Smoothing coefficient α is equal to 0.8.

Keywords: Fine Particulate Matters, Simple Moving Average Method, Exponential Smoothing, Grey Forecasting Method

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