A particle swarm optimization methodology to design an effective air quality monitoring network

A. Sangeetha () and T. Amudha ()
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A. Sangeetha: Bharathiar University
T. Amudha: Bharathiar University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 11, No 4, 15739-15763

Abstract: Abstract Good air quality is highly essential to the well-being of mankind, all living organisms and the environment. The quality of air is degrading at a faster pace through natural and anthropogenic activities, on a global scale. The transportation sector is attributed as one of the main pollution sources, with vehicles emitting enormous volumes of pollutants such as nitrogen oxide, carbon monoxide and particulate matter into the environment. Establishing an Air Quality Monitoring Network (AQMN) to effectively monitor and assess the atmospheric air quality is the need of the hour for every city. This research focuses on designing an optimal AQMN in Coimbatore, a city in Tamil Nadu, India, based on the emissions from automobile sources. AQMN is aimed to measure the concentration of pollutants at various locations by means of automobile emissions and meteorological factors. An air dispersion model called General Finite Line Source Model is employed in the estimation of pollutant concentration from emission and dispersion levels. The design of AQMN is a combinatorial optimization problem, and the optimum locations to establish an AQMN are identified using the bio-inspired algorithm which is Particle Swarm Optimization (PSO). A utility function that comprises pattern score and violation score of various specified sites in the study area is used in the assessment of the fitness of the optimal locations for AQMN. Assessment of pollution concentration at sample stations in the study area has clearly indicated the gradual increase in air pollution over the years, contributed by increase in vehicle traffic as well as the changes in climatic conditions. PSO algorithm has given the best optimal locations to establish monitoring stations for the design of AQMN to effectively monitor the level of pollutants emitted from automobile sources. The outcome of this work is a scalable AQMN prototype which can well serve as a guideline to the Central Pollution Control Board in improving the air quality monitoring infrastructure in Coimbatore city.

Keywords: Air pollution; Air quality monitoring network; Automobile emissions; Bio-Inspired computing; Dispersion model; Particle swarm optimization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-021-01312-4

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