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Forecasting of Hourly PM2.5 in South-West Zone in Santiago de Chile

Category: Air Pollution Modeling

Article In Press
DOI: 10.4209/aaqr.2018.01.0029
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Patricio Perez 1, Camilo Menares2

  • 1 Departamento de Fisica, Universidad de Santiago de Chile, Santiago, Chile
  • 2 Departamento de Geofisica, Facultad de Ciencias Fisicas y Matematicas, Universidad de Chile, Santiago, Chile

Highlights

The performance of statistical hourly PM2.5 forecasting model is shown.
Accuracy achieved with a neural network model is good.
High concentrations episodes are correctly forecasted.
Daily averages from hourly forecasted values are captured.


Abstract

We present the results of a neural network model designed for the forecasting of hourly PM2.5 concentrations in Santiago, Chile. The study focuses on the observed values at two of the monitoring stations, which are located in the south-west zone of the city and are among the stations that register the highest concentrations during the period between April and August. This is the season when air quality is very often in ranges that are harmful to the population and some restrictions on emissions become useful.

The forecasting model is a multilayer neural network. The input variables are observed values of hourly PM10 and PM2.5 concentrations measured at the station of interest and at a neighboring station at 7 PM of the present day and some observed and forecasted meteorological variables. NO2 concentrations during the morning and afternoon hours, which may be associated with secondary particle formation, are also used as input. The implemented models are trained with 2014 and 2015 data and tested with 2016 values. Information is collected until 7 PM of the present day, and the largest forecasting error up to 21 hours in advance is 32%.

The accuracy of this forecasting is better than that obtained with a neural model previously used for the forecasting of hourly PM2.5 concentrations in the north-west zone in Santiago. Our neural models show better results than those obtained with linear models with the same input variables. The developed models provide a tool for anticipating episodes in Santiago and other cities with similarly unfavorable conditions for pollutant dispersion.

Keywords

Air quality forecasting Particulate matter PM2.5 Neural networks Meteorology forecast


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