Monitoring fine and ultrafine particles in the atmosphere of a Southeast Chinese city

Predicting submicron air pollution indicators: a machine learning approach

The regulation of air pollutant levels is rapidly becoming one of the most important tasks for the governments of developing countries, especially China. Submicron particles, such as ultrafine particles (UFP, aerodynamic diameter ≤ 100 nm) and particulate matter ≤ 1.0 micrometers (PM1.0), are an unregulated emerging health threat to humans, but the relationships between the concentration of these particles and meteorological and traffic factors are poorly understood. To shed some light on these connections, we employed a range of machine learning techniques to predict UFP and PM1.0 levels based on a dataset consisting of observations of weather and traffic variables recorded at a busy roadside in Hangzhou, China. Based upon the thorough examination of over twenty five classifiers used for this task, we find that it is possible to predict PM1.0 and UFP levels reasonably accurately and that tree-based classification models (Alternating Decision Tree and Random Forests) perform the best for both these particles. In addition, weather variables show a stronger relationship with PM1.0 and UFP levels, and thus cannot be ignored for predicting submicron particle levels. Overall, this study has demonstrated the potential application value of systematically collecting and analysing datasets using machine learning techniques for the prediction of submicron sized ambient air pollutants.

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 (PM(1.0))) 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 PM(1.0) 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 PM(1.0) 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.