This paper presents a new ground-level Ozone (O3) forecasting model that considers key factors affecting its concentration, which include precursor emissions and meteorological conditions. Increased surface temperature, due to built-up areas produces heat-island effect, which is known to influence O3 level. A methodology to create a surface temperature variable using remote sensing imageries for representing the heat-island effect is explained in this paper. The surface temperature predictor variable is included in the O3 forecasting model for rural areas in Mississippi. The developed multiple regression model has a multiple correlation R of +0.732. The validation results affirm reasonableness of the model. This model can produce O3 forecasts for locations that do not have monitoring stations since it does not require the prior day's O3 value. It can also be used to identify candidate locations for establishing an air quality monitoring station, and/or used in air quality trend analysis. The inclusion of the surface temperature variable in the model allows users to evaluate the adverse impact of built-up areas on air quality degradation, as demonstrated by the before/after analysis of new developments in Oxford, Mississippi. It shows that the 14.4% increase in built-up areas replacing natural grass and wooded surfaces could result in 3.9% higher weighted average surface temperature. This surface temperature rise coupled with a traffic growth over the three-year period could cause a 16.7% higher O3 concentration in the area under the same climate conditions. As demonstrated, the model can serve as a decision-making tool in landuse and transportation planning for local agencies.
Abstract