Neural modelling of the spatial distribution of air pollutants |
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| Authors: | H Pfeiffer G Baumbach L Sarachaga-Ruiz S Kleanthous O Poulida E Beyaz |
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| Institution: | 1. Insitute of Process Engineering and Power Plant Technology (IVD), Department of Air Quality Control, Universitaet Stuttgart, Pfaffenwaldring 23, 70569 Stuttgart, Germany;2. Greek Cypriot Community, Nicosia, Cyprus;3. Turkish Cypriot Community, Nicosia, Cyprus;1. ExxonMobil Upstream Research Company, P.O. Box 2189, Houston, TX, USA;2. SPT Group, 11490 Westheimer Rd., Suite 500, Houston, TX, USA;1. Energy, Environment and Water Research Centre (EEWRC), The Cyprus Institute, Nicosia, 2121, Cyprus;2. Computation-based Science and Technology Research Centre (CaSToRC), The Cyprus Institute, Nicosia, 2121, Cyprus;3. Max Planck Institute for Chemistry, Mainz, 55128, Germany;1. Rio de Janeiro State University, Institute of Chemistry, Rua São Francisco Xavier, 524, Maracanã, Rio de Janeiro 20550-013, Brazil;2. Rio de Janeiro State University, Faculty of Technology, Rodovia Presidente Dutra Km 298, Pólo Industrial, Resende, Rio de Janeiro 27537-000, Brazil;1. State Key Laboratory of Organic Geochemistry, Guangdong Key Laboratory of Environment and Resources, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China;2. Graduate School of the Chinese Academy of Sciences, Beijing 100039, China;1. School of Metallurgy and Materials Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China;2. Chongqing Key Laboratory of Nano/Micro Composite Materials and Devices, Chongqing, 401331, China;3. School of Applied Science, Taiyuan University of Science and Technology, Taiyuan, 030024, China;4. National Institute of Metrology, Beijing, 100029, China;1. Department of Civil and Environmental Engineering, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand;2. School of Population Health, The University of Auckland, Private Bag 92019, Auckland, New Zealand;3. School of Environment, The University of Auckland, Private Bag 92019, Auckland, New Zealand;4. Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand |
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| Abstract: | In this paper, a new method to calculate the average spatial distribution of air pollutants based on diffusive sampling measurements and artificial neural networks evaluation is presented. Most established methods like interpolation algorithms are inflexible or limited in considering important distribution parameters such as emission sources or land use. Of special interest are air quality measurements since they provide a direct view on the actual pollutant level. With diffusive samplers, the average concentration of many gaseous species over a large area can be determined simultaneously. During a project in Cyprus, NO2 diffusive samplers were exposed at 270 sites in six month-long campaigns throughout one year providing the database for the model described in this paper. A multilayer perceptron was trained with the NO2 measurement data and distribution parameters like population density and meteorological parameters using a 1 × 1 km grid covering Cyprus. The best fit could be achieved with an emissions inventory including previously simulated concentration plumes and population density data as input nodes for the neural network, resulting in realistic maps of the annual average distribution of NO2 in Cyprus. |
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