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Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes
https://orcid.org/0000-0002-6787-7355
Abstract We present the results of an hourly PM2.5 concentrations forecasting model in Santiago, Chile. The study concentrates on the comparison between model and observed values at the monitoring station with the highest concentrations (Cerro Navia station) for the time period between April and August, which is the season when high concentration episodes are frequent. The forecasting model is a feed forward neural network, The input variables are past values of hourly PM10 and PM2.5 concentrations measured at the city station with the highest values during episodes, concentrations from a neighboring station and some observed and forecasted meteorological variables. Training is performed with 2010 and 2011 data and the model is tested with 2012 values. Information is collected until 7 PM of the present day and percent error forecasting up to 15 h in advance, starting at 8 PM of the present day, is of the order of 30%. Accuracy of forecasting is significantly better than different forms of persistence and may be considered as a useful tool for anticipating episodes.
Highlights The performance of statistical hourly PM2.5 forecasting model is shown. Accuracy achieved with a neural network model is better than persistence. High concentrations episodes are correctly forecasted. Daily averages from hourly forecasted values are captured.
Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes
https://orcid.org/0000-0002-6787-7355
Abstract We present the results of an hourly PM2.5 concentrations forecasting model in Santiago, Chile. The study concentrates on the comparison between model and observed values at the monitoring station with the highest concentrations (Cerro Navia station) for the time period between April and August, which is the season when high concentration episodes are frequent. The forecasting model is a feed forward neural network, The input variables are past values of hourly PM10 and PM2.5 concentrations measured at the city station with the highest values during episodes, concentrations from a neighboring station and some observed and forecasted meteorological variables. Training is performed with 2010 and 2011 data and the model is tested with 2012 values. Information is collected until 7 PM of the present day and percent error forecasting up to 15 h in advance, starting at 8 PM of the present day, is of the order of 30%. Accuracy of forecasting is significantly better than different forms of persistence and may be considered as a useful tool for anticipating episodes.
Highlights The performance of statistical hourly PM2.5 forecasting model is shown. Accuracy achieved with a neural network model is better than persistence. High concentrations episodes are correctly forecasted. Daily averages from hourly forecasted values are captured.
Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes
https://orcid.org/0000-0002-6787-7355
Abstract We present the results of an hourly PM2.5 concentrations forecasting model in Santiago, Chile. The study concentrates on the comparison between model and observed values at the monitoring station with the highest concentrations (Cerro Navia station) for the time period between April and August, which is the season when high concentration episodes are frequent. The forecasting model is a feed forward neural network, The input variables are past values of hourly PM10 and PM2.5 concentrations measured at the city station with the highest values during episodes, concentrations from a neighboring station and some observed and forecasted meteorological variables. Training is performed with 2010 and 2011 data and the model is tested with 2012 values. Information is collected until 7 PM of the present day and percent error forecasting up to 15 h in advance, starting at 8 PM of the present day, is of the order of 30%. Accuracy of forecasting is significantly better than different forms of persistence and may be considered as a useful tool for anticipating episodes.
Highlights The performance of statistical hourly PM2.5 forecasting model is shown. Accuracy achieved with a neural network model is better than persistence. High concentrations episodes are correctly forecasted. Daily averages from hourly forecasted values are captured.
Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes
Perez, Patricio (author) / Gramsch, Ernesto (author)
Atmospheric Environment ; 124 ; 22-27
2015-11-07
6 pages
Article (Journal)
Electronic Resource
English
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