Event № 860
Advisor: Prof. Barak Fishbain
Abstract: Air pollution is one of the most prominent environmental health risks and pathogen generator. Many air pollution studies are based on data collected from air quality monitoring stations (AQMS). AQMS are the “gold standard” for the air pollution data measurements. Yet, due to their high costs they are scattered sparingly. As the number of measuring sites is limited, the AQMS data is generalized through mathematical methods. Here we introduce two methods to improve the spatiotemporal coverage.
The first method deals with the spatial coverage expansion. The method consists of two stages. At the first stage, the method finds sources’ locations and emission rates in the model’s parameters space ("source term"). At the second stage, the method uses the source term as an input and generates dense pollution maps using the dispersion model. The suggested algorithm is model invariant to the gas dispersion model, hence it is applicable for a wide range of applications in which different gas dispersion model are used. Simulation for an industrial-area shows that the suggested scheme generates more accurate maps than the state-of-the-art technique. The resulted air pollution dens map may serve as a valuable tool for mitigation acts and regulatory agencies.
Extending the temporal coverage of the measuring array is achieved through long‐term forecasting.While short-termforecasting, a few days into the future, is a well-established research domain, there is no method for long-term forecasting (e.g., the pollution level distribution in the upcoming months or years). Here we introduce and define long-term air pollution forecasting, where long-term refers to estimating pollution levels in the next few months or years. A Discrete-Time-Markov-based model for forecasting ambient nitrogen oxides patterns is presented. The modelaccurately forecasts overall pollution level distributions, and the expectancy for tomorrow’s pollution level given today’s level, based on longitudinal historical data. It thus characterizes the temporal behavior of pollution. The model was applied to five distinctive regions in Israel and Australia and was compared against several forecasting methods and was shown to provide better results with a relatively lower total error rate.