Air Quality Forecasting In A Large City
Author(s)
P. Perez
Abstract
We describe the different air pollution statistical forecasting models that have
been used in Santiago, Chile during the fall/winter period for the last ten years.
Effort has been concentrated on particulate matter PM10 for which a standard of
150 µg/m3 for the 24 h average is currently established. Inputs to the models are
concentrations measured at several monitoring stations distributed throughout
the city and meteorological information in the region. Outputs are the expected
maxima concentrations for the following day at the site of the same monitoring
stations. Forecast values using neural network models are compared with the
results obtained with linear models and persistence. Recently, a clustering
algorithm has appeared as a potentially useful tool to detect high concentration
episodes in advance.
Keywords: particulate matter forecasting, neural networks, linear models.
1 Introduction
Air pollution has been a major concern in the metropolitan area of Santiago, the
capital of Chile during the last 15 years. Together with Sao Paulo, Mexico City,
and some Chinese cities it is considered as one of the most polluted in world.
Several factors concur to create unfavorable conditions for air pollutant
dispersion. The city is located in a valley that has an extension between 70 and
80 km in the north-south direction and approximately 40 km in the east-west
direction. To the west we find the Andes Mountains and to the east a coastal
range. Some elevations to the north and south trap the air and air pollutants in a
region of poor air circulation, which is enhanced during fall and winter when
strong thermal inversions prevent vertical dispersion. During this period of the
year, the 24 hour moving average (24MA) of PM10 is used as an indicator of air
quality.
Keywords
particulate matter forecasting, neural networks, linear models.
Related Book
Other papers in this volume
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