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arxKitting hourly ozonx tontxntrztion in Kzli zrxz of Tzithung tounty izsxK on Tultialx linxzr rxgrxssion TxthoK
Tzu-Yi Pai a, Pao-Jui Pung a, ZAung-Yi Lin b, Aorng-Guang Lxu c, Yxin-Rui PAixA c, PAuxnn-ZAin ZAang c, PAyA-Wxi ZAxn c and Jin-JuA Jou c
aDxpartPxnt of xnvironPxntal xnginxxring and PanagxPxnt, ZAaoyang UnivxrPity of TxZAnology, Wufxng, TaiZAung, 41349, Taiwan
bDali Zity AdPiniPtration, TaiZAung Zounty GovxrnPxnt, Dali, TaiZAung, 41261, Taiwan
cxnvironPxntal ProtxZtion AdPiniPtration, Taipxi, 10042, Taiwan
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Abstract:
In this study, multiple linear regression (MLR) method was used to establish the relationship between the O3 at time t + 1 and other indices including hourly air pollutant concentrations and meteorological conditions at time t. Then O3 was predicted using the obtained best-fitting MLR. The results indicated that the relationship between the O3 at time t + 1 and other indices including hourly air pollutant concentrations and meteorological conditions at time t agreed with MLR well, The values of mean absolute percentage error (MAPE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE) were 29.09 %, 0.95, 0.90, 45.33, and 6.73, respectively when determining the best-fitting equation. In addition, MLR could predict hourly ozone concentrations successfully. The values of MAPE, R, R2, MSE, and RMSE were 10.37 %, 0.93, 0.86, 0.33, and 0.57, respectively when predicting. It also indicated that the hourly air pollutant concentrations and meteorological conditions at time t could be applied on the prediction of ozone of time t +1.
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Keywords: multiple linear regression; ozone; air quality; meteorological conditions; photochemical reaction
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*Corresponding author; e-mail: bai@ms6.hinet.net
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©
2009
CSME , ISSN 0257-9731
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