Prediction Of Ground Level Ozone Concentration Using Artificial
Neural Network Modeling
Author(s)
P.O. Scarlatos & M. Crumiere
Abstract
Prediction of ground level ozone concentration
using artificial neural network modeling
P.D. Scarlatos and M. Crumiere
Department of Ocean Engineering,
Florida Atlantic University, U.S.A.
Abstract
Ozone is a toxic and reactive air pollutant that is hazardous to human health and
is detrimental to many living organisms and inorganic substances. Ozone is a
secondary pollutant that depends on other air pollutants and meteorological
conditions. Prediction of anticipated ozone concentrations is very important for
an effective air quality management. Timely warnings of unhealthy ozone
concentrations can reduce the associated risks to human health and to sensitive
ecosystems. The standard for maximum ozone concentration for hourly
exposure is 0.120 parts per million. Recently, a new 8-hour standard for ozone
concentrations of 0.080 parts per million was set by the US Environmental
Protection Agency. A variety of physically based and statisitcal models have
been developed and applied for
Keywords
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