Introducing an integrated software tool to help industrial plants assess the impact of their emissions on air quality in surrounding areas, based on air quality forecasts. It is a tool for mapping the expected exceedances of the EU Air Quality Directive over a large area centered on the industrial plants and the contribution of different emission sources. The system allows identifying in time and space the percentage of inmission concentrations due to industrial plants. The system uses the state-of-the-art of Eulerian models. The air quality forecasting system is highly automated, generating air quality prediction and impact results by each morning for the day following. The zero emission methodology (ON/OFF) has been used. To implement the numerical forecast system, scripts were developed in a Linux environment to automatically execute the various data preprocessor, postprocessors and model codes. Models results are converted to the visualization required format. We present the results of several simulations with 1 km of spatial resolution over Spain. The simulations demonstrate that differences (OFF-ON) show heterogeneity patterns at spatial and temporal scales due to significant topographic diversity and meteorological variations at short distances. The magnitude of these changes in concentration is potentially significant and illustrates the accuracy of the modelling tool and how it can be used in forecasting mode to provide meaningful and relevant information to stakeholders. The results show that the modeling system is capable of determining the impact of emission sources in real time and in forecast mode.
Roberto San Jose, Technical University of Madrid, Spain
Juan Luis Perez Camaño, Technical University of Madrid, Spain
Libia Pérez, Technical University of Madrid, Spain
Rosa M. Gonzalez-Barras, Complutense University, Spain
Stream: Environmental Sustainability & Environmental Management: Atmosphere and Air
This paper is part of the ACSEE2019 Conference Proceedings (View)
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