Patricio Perez 1, Camilo Menares2


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



Received: January 25, 2018
Revised: April 25, 2018
Accepted: May 13, 2018
Download Citation: ||https://doi.org/10.4209/aaqr.2018.01.0029  

  • Download: PDF


Cite this article:
Perez, P. and Menares, C. (2018). Forecasting of Hourly PM2.5 in South-West Zone in Santiago de Chile. Aerosol Air Qual. Res. 18: 2666-2679. https://doi.org/10.4209/aaqr.2018.01.0029


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

 



Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

7.3
2022CiteScore
 
 
77st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

The Future Environment and Role of Multiple Air Pollutants

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.