RIO: A NOVEL APPROACH FOR AIR POLLUTION MAPPING

Real-time assessment of the ambient air quality has gained an increased interest in recent years. To give support to this evolution, the statistical air pollution interpolation model RIO is developed. Due to the very low computational cost this interpolation model is an efficient tool for an environment agency when performing real-time air quality assessments. Beside this, a reliable interpolation model can be used to produce analysed maps of historical data records as well. RIO is an interpolation model that can be classified as a detrended Kriging model. In a first step the local character of the air pollution sampling values is removed in a detrending procedure. Subsequently, the site-independent data is interpolated by an Ordinary Kriging scheme. Finally, in a retrending step a local bias is added to the Kriging interpolation results. As spatially resolved driving force in the detrending process, a land use indicator is developed based on the CORINE land cover data set. The indicator is optimized independently for the three pollutants O3, NO2 and PM10. As a result, the RIO model is able to account for the local character of the air pollution phenomenon at locations where no monitoring stations are available. Through a cross-validation procedure the superiority of the RIO model over standard interpolation techniques, such as the Ordinary Kriging is demonstrated. Air quality maps are presented for the three pollutants mentioned and compared to maps based on standard interpolation techniques

RIO: A NOVEL APPROACH FOR AIR POLLUTION MAPPING

Real-time assessment of the ambient air quality has gained an increased interest in recent years. To give support to this evolution, the statistical air pollution interpolation model RIO is developed. Due to the very low computational cost this interpolation model is an efficient tool for an environment agency when performing real-time air quality assessments. Beside this, a reliable interpolation model can be used to produce analysed maps of historical data records as well. RIO is an interpolation model that can be classified as a detrended Kriging model. In a first step the local character of the air pollution sampling values is removed in a detrending procedure. Subsequently, the site-independent data is interpolated by an Ordinary Kriging scheme. Finally, in a retrending step a local bias is added to the Kriging interpolation results. As spatially resolved driving force in the detrending process, a land use indicator is developed based on the CORINE land cover data set. The indicator is optimized independently for the three pollutants O3, NO2 and PM10. As a result, the RIO model is able to account for the local character of the air pollution phenomenon at locations where no monitoring stations are available. Through a cross-validation procedure the superiority of the RIO model over standard interpolation techniques, such as the Ordinary Kriging is demonstrated. Air quality maps are presented for the three pollutants mentioned and compared to maps based on standard interpolation techniques

Toekomstverkenning MIRA 2009 Wetenschappelijk rapport verzuring

In dit wetenschappelijk rapport worden de jaargemiddelde deposities, sectorale bijdragen en de overschrijding van kritische lasten voor verzuring bepaald in Vlaanderen, en dit voor twee MIRA-scenario’s, met name REF- scenario en EUR-scenario, voor de potentieel verzurende stoffen (NHx, NOy en SOx) voor de periode 2010 t.e.m. 2030. Het jaar 2006 werd beschouwd als referentiejaar. Er wordt ook bepaald wanneer herstel van bodemverzuring kan verwacht worden voor Vlaamse bosbodems

Exploring the Full Potential of Reversible Deactivation Radical Polymerization Using Pareto-Optimal Fronts

The use of Pareto-optimal fronts to evaluate the full potential of reversible deactivation radical polymerization (RDRP) using multi-objective optimization (MOO) is illustrated for the first time. Pareto-optimal fronts are identified for activator regenerated electron transfer atom transfer radical polymerization (ARGET ATRP) of butyl methacrylate and nitroxide mediated polymerization (NMP) of styrene. All kinetic and diffusion parameters are literature based and a variety of optimization paths, such as temperature and fed-batch addition programs, are considered. It is shown that improvements in the control over the RDRP characteristics are possible beyond the capabilities of batch or isothermal RDRP conditions. Via these MOO-predicted non-classical polymerization procedures, a significant increase of the degree of microstructural control can be obtained with a limited penalty on the polymerization time; specifically, if a simultaneous variation of various polymerization conditions is considered. The improvements are explained based on the relative importance of the key reaction rates as a function of conversion