A rolling forecast approach for next six-hour air quality index track

Abstract

Current daily AQI forecasting results do not help people making decision to undertake outdoor activities in the coming hours effectively, because the air quality index (AQI) varies greatly at different times of the day. In this study, an approach to providing a rolling forecast of the next six-hour AQI in the seven air quality areas of Taiwan by using big data analysis is proposed. The segmented fitted characteristic curves for the intervals midnight, early morning, morning, afternoon, evening, and night by using the piecewise cubic spline method was potted based on the monitoring data of the past three years to establish the rolling forecast model, which can fully match the local environmental and climatic characteristics. The principle of the proposed rolling forecast track is as follows: (1) replace the constant coefficient with the current AQI in the characteristic curves to form the prediction formula, (2) use the sum of the predicted values plus or minus half of the mean standard deviations to set upper or lower boundaries, respectively, of the forecast range, (3) increase the range of boundaries with the forecast hour, and (4) repeat steps (1)–(3) when entering the next hour. The step (1) is an important step for the rolling forecast, which ensures that the deviation of the predicted value is minimized. The average mean absolute normalized gross error ranges of the rolling forecasts were all lower than 9%. These results demonstrate that the proposed rolling forecast model established using monthly characteristic curves can yield highly accurate forecasts. Therefore, the proposed rolling forecast approach can be used by governments to update people about the AQI over the next 6 h.