Modeling Ground Level Ozone (O3) of Air Pollution Using Regression Technique
Introduction: Ground-level ozone (O3) was a secondary pollutant involving several types of reactions arising from complicated atmospheric chemistry. This research utilized statistical equations to discern the complex influence of meteorological parameters and precursor contaminants influencing O3 ch...
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Universiti Putra Malaysia Press
2023
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| 總結: | Introduction: Ground-level ozone (O3) was a secondary pollutant involving several types of reactions arising from complicated atmospheric chemistry. This research utilized statistical equations to discern the complex influence of meteorological parameters and precursor contaminants influencing O3 chemistry and concentrations. The goal of this study was to predict ozone (O3) concentrations in Nilai, Negeri Sembilan. Methods: Data were collected from 1 January 2016 until 31 December 2018 that including ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), temperature, and relative humidity (RH). The data were analyzed by using Multiple Linear Regression (MLR) in predicting the next hours of O3 concentration. Results: O3 concentration reached its peak during 15:00 hours and lower at night time (20:00 hours) due to the absence of sunlight and redox reactions. There exists strong significant correlation between O3 and temperature (r= 0.729, p<0.01), relative humidity (r= -0.732, p<0.01), NOx (r= -0.654, p<0.01), NO (r= -0.630, p<0.01) and NO2 (r= -0.535, p<0.01). Meanwhile, MLR models executed high accuracy for O3,t+1 (R2= 0.5565), O3,t+2 (R2= 0.5326) and O3,t+3 (R2= 0.5197). Conclusion: In conclusion, the MLR model is suitable for the next hours O concentration prediction. � 2022 UPM Press. All rights reserved. |
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