An improved particle swarm optimization (PSO): method to enhance modeling of airborne particulate matter (PM10)
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ORIGINAL PAPER
An improved particle swarm optimization (PSO): method to enhance modeling of airborne particulate matter (PM10) B. Ordóñez‑De León1 · M. A. Aceves‑Fernandez1 · S. M. Fernandez‑Fraga2 · J. M. Ramos‑Arreguín1 · E. Gorrostieta‑Hurtado1 Received: 25 July 2018 / Accepted: 2 January 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Nowadays, it is of paramount importance for human health the monitoring and modelling of air quality. Among the different pollutants, there are some that are considerable more difficult to model due to their chemical composition. Some of these are particulate matter (particles ≤ 10 microns, P M10, and particles ≤ 2.5 microns, P M2.5), which can cause respiratory diseases or even cause premature deaths. Furthermore, There are several models that can be used to evaluate air quality. In this contribution, the combination of a neuro-fuzzy based method with particle swarm optimization is proposed to crucially increase accuracy when dealing with the non-linear behavior of airborne particulate matter (PM10). Several experiments were carried out to show the feasibility of the proposed method and to show that even when the nature of the data, that has dynamic behavior, variance in the spread of data, the present of outliers, climatic conditions, among other factors, the modeling of this particular set of data may be made accurately, showing the robustness and feasibility of the proposed method. Keywords Environmental pollution · Air quality · Particle swarm optimization (PSO) · ANFIS
1 Introduction 1.1 Airborne pollution The environment has been affected by the presence of pollutants such as ozone ( O3), nitrogen oxide ( NO2), carbon monoxide (CO), sulfur dioxide (SO2), particles smaller than 10 microns (PM10) and particles smaller than 2.5 microns (PM2.5). For this reason, pollutant monitoring has been necessary in large cities with high concentration of population and industries (Peel et al. 2011; Mandel et al. 2015). There are many studies that show a relationship between an increasing of airborne pollution with respiratory diseases or even deaths (Peel et al. 2011; Vanos et al. 2014; Mandel et al. 2015). For this reason, in this contribution a * M. A. Aceves‑Fernandez [email protected] 1
Faculty of Engineering, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, 76000 Querétaro, Mexico
Department of Computer Systems, Instituto Tecnológico de Querétaro, Av. Tecnológico s/n, Centro, 76000 Santiago de Querétaro, Mexico
2
neurofuzzy model optimized with a particle swarm model is proposed to increase the accuracy of the model for airborne particulate matter (PM10) in Mexico City.
1.2 PM10 particles Particulate matter (PM) is one of the main airborne pollution issues, which represent significant economic and human health implications (Vega et al. 2009). This is especially so for large cities, unfavorable geographical and meteorological conditions, and a high number of emission sources, including uncontrolled sources, which is t
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