Analysis of the distribution of airborne pollution using genetic algorithms |
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| Institution: | 1. Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA 19141, USA;2. Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, D-33098 Paderborn, Germany;3. Young Researchers and Elite Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran;4. Department of Mathematics and Statistics, York University, Toronto M3J 1P3, Canada;5. Polo Tecnologico IISS G. Galilei, Via Cadorna 14, 39100 Bolzano, Italy;6. Department of Industrial Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran;1. School of Materials Science and Engineering, Sichuan University of Science and Engineering, Zigong, 643000, Sichuan, PR China;2. Key Laboratory of Material Corrosion and Protection of Sichuan Province, Zigong, 643000, Sichuan, PR China;3. CAS Key Laboratory of Nuclear Materials and Safety Assessment, Institute of Metal Research, CAS, Shenyang, 110016, PR China;4. Corrosion and Electrochemistry Research Group, Department of Pure and Applied Chemistry, University of Calabar, P.M.B. 1115, Calabar, Nigeria;5. School of Chemistry and Environmental Engineering, Sichuan University of Science and Engineering, Zigong, 643000, Sichuan, PR China;6. School of Chemical Engineering, Institute of Pharmaceutical Engineering Technology and Application, Key Laboratory of Green Chemistry of Sichuan Institutes of Higher Education, Sichuan University of Science and Engineering, Zigong, 643000, Sichuan, PR China;1. Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education; Institute for Clean Energy & Advanced Materials, School of Materials & Energy, Southwest University; Chongqing Key Laboratory for Advanced Materials and Technologies of Clean Energies, Chongqing 400715, China;2. School of Physical Science and Technology, Southwest University, Chongqing 400715, China;3. Key Laboratory of Laser Technology and Optoelectronic Functional Materials of Hainan Province, College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, China;1. Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA 02139, USA;2. School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA;3. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA;4. Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USA |
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| Abstract: | Increasing environmental awareness has been an important factor behind the development of receptor models, which attempt to identify the sources of airborne pollution reaching a monitoring station by analysis of the profile of pollutants collected. Despite the central role of such models in understanding the movement of air pollution, they yield results which contain significant uncertainty. This paper reports the application of the Genetic Algorithm (GA) to this source apportionment problem. Implementation of a matrix formulation for the GA allows it to tackle the many-source/many-receptor problem, with results which suggest the GA approach is a significant advance over current models. |
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