An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China

In order to investigate the effect of meteorological factors on submicron particle (ultrafine particle (UFP) and particulate matter 1.0 (PM1.0)) concentrations under busy traffic conditions, a model study was conducted in Hangzhou, a city with a rapid increase of on-road vehicle fleet in China. A statistical model, Autoregressive Integrated Moving Average (ARIMA), was used for this purpose. ARIMA results indicated that barometric pressure and wind velocity were anti-correlated and temperature and relative humidity were positively correlated with UFP number concentrations and PM1.0 mass concentrations (p < 0.05). These data suggest that meteorological factors are significant predictors in forecasting roadside atmospheric concentrations of submicron particles. The findings provide baseline information on the potential effect of meteorological factors on UFP and PM1.0 levels on a busy viaduct with heavy traffic most of the day. This study also provides a framework that may be applied in future studies, with large scale time series data, to predict the impact of meteorological factors on submicron particle concentrations in fast-developing cities, in China.

This is the first study that focused on forecasting roadside submicron particles from meteorological factors in a Southeast China city, Hangzhou. By using Autoregressive Integrated Moving Average models we found that barometric pressure, wind speed, temperature and relative humidity were significant predictors for UFP number concentration and PM1.0 mass concentration.Figure optionsDownload as PowerPoint slideHighlights
► ARIMA models were developed for predicting UFP and PM1.0 levels in urban atmosphere.
► Pressure, wind speed, temperature and humidity are significant predictors.
► Models developed in this study may be useful in future large scale studies in China.