A Model For PM2.5 Forecasting In
Santiago, Chile
Author(s)
P. Perez & P. Rodriguez
Abstract
Given the evidence that concentrations of the fine fraction of particulate matter
in the atmosphere are more strongly associated with health problems than
concentrations of the coarse fraction, it seems very likely that in the near future
air quality in large cities will be defined more on the basis of PM2.5
concentrations rather than PM10. We have developed a forecasting model for the
maximum of the 24 hour moving average of PM2.5. The model is based on the
neural network technique. Training is performed with historical data from four
monitoring stations located in Santiago Chile and with relevant meteorological
information in the city. Cross correlations between several candidate variables
for input allowed the selection of a reduced number of them which are used in
the final model. Results show that the developed model can be used as an
operational tool for air quality management.
Keywords: air pollution, forecasting, PM2.5, neural networks.
1 Introduction
At present, air quality in Chile is defined in terms of the 24 hour moving average
(24MA) of concentrations of particulate matter with diameter smaller than 10
microns (PM10). Although these particles are small enough as to penetrate the
respiratory tract of humans, associations of long-term ambient PM10
concentrations with mortality have indicated that the most significant harm can
be attributed to the effect of the fine fraction of PM10 (PM2.5, particles with
diameter less than 2.5 microns) [1]. Environmental policies in the recent years in
Santiago, Chile, as for example more strict control of emissions in industry, and
replacement of old buses by new cleaner vehicles in public transportation have
produced significant improvements in air quality in the city. However, this city
Keywords
air pollution, forecasting, PM2.5, neural networks.
Related Book
Other papers in this volume
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