Forecasting of ozone concentration using frequency MA-OWA model

Air pollution can cause the human health, plants growth and daily mortality in numerous studies over the past decade. Therefore, forecasting and analysis of air quality are important topics of research today. The causes of poor air quality are global warming, greenhouse effects, acid rain, etc. Air pollution problems are related to the emissions of sulfur dioxides (SO₂), nitrogen dioxide (NO₂), suspended particulates (PM₁₀), ozone (O₃), carbon monoxide (CO), and unburned hydrocarbons (HC) and so on. O₃ adverse effects on human health has received intensively concern in recent years, It has been recognized as one of the principal pollutants that degrades air quality, thus we uses O ₃ attribute to evaluate air quality. This study proposed a frequency moving average - order weight average (MA-OWA) model to forecast air quality by daily O₃ concentration. Due to O₃ data is belong to time series pattern, MA can simple calculation and OWA operator can aggregate multiple lag periods into single aggregated value by different situation parameters a. Frequency MA-OWA based time series model can efficiently and accurately forecast O₃. To demonstrate of the proposed, air quality monitoring the urban sites of Hsinchu (Taiwan), is selected for the numerical experiment from 2007 was utilized. From the results, the proposed methods outperform the listing methods in RMSE and MAPE.