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This paper addresses the problem of air pollutant profile estimation by using measurements collected from different weather stations. An algorithm is developed, based on an Extended Kalman Filter to handle missing temporal data and using the statistical Kriging method to interpolate spatial data. Combination of extended Kalman filtering with Matérn(More)
Models for predicting vehicular emissions of carbon dioxide (CO<sub>2</sub>) are usually insensitive to vehicle modes of operation (such as cruise, acceleration, deceleration, and idling) as they are based on the average speed of motor vehicles. In the present study, real world on-road second-by-second data are used to improve the accuracy of air quality(More)
Emissions from motor vehicles need to be predicted fairly accurately to ensure an appropriate air quality management plan. This research work explores the use of a nonparametric regression algorithm known as the multivariate adaptive regression splines (MARS) in comparison with the artificial neural networks (ANN) for the purpose of best approximation of(More)
It is essential to maintain air quality standards and inform people when air pollutant concentrations exceed permissible limits. For example, ground-level ozone, a harmful gas formed by NO<sub>x</sub> and VOCs emitted from various sources, can be estimated through integration of observation data obtained from measurement sites and effective air-quality(More)
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