Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities
LEPABE—Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr Roberto Frias, 4200-465 Porto, Portugal
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Sustainability 2019, 11(21), 6019; https://doi.org/10.3390/su11216019
Received: 27 September 2019 / Revised: 25 October 2019 / Accepted: 25 October 2019 / Published: 29 October 2019
(This article belongs to the Special Issue Sustainable City: Innovative Technologies for Air Quality Monitoring and Assessment)
Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is associated with adverse effects on human health (e.g., fatal cardiovascular and respiratory diseases), and environmental concerns (e.g., visibility impairment and damage in ecosystems). This study aimed to evaluate temporal and spatial trends and behaviors of PM2.5 concentrations in different European locations. Statistical threshold models using Artificial Neural Networks (ANN) defined by Genetic Algorithms (GA) were also applied for an urban centre site in Istanbul, to evaluate the influence of meteorological variables and PM10 concentrations on PM2.5 concentrations. Lower PM2.5 concentrations were observed in northern Europe. The highest values were found at traffic-related sites. PM2.5 concentrations were usually higher during the winter and tended to present strong increases during rush hours. PM2.5/PM10 ratios were slightly higher at background sites and the lower values were found in northern Europe (Helsinki and Stockholm). Ratios were usually higher during cold months and during the night. The statistical model (ANN + GA) allowed evaluating the combined effect of different explanatory variables (temperature, wind speed, relative humidity, air pressure and PM10 concentrations) on PM2.5 concentrations, under different regimes defined by relative humidity (threshold value of 79.1%). Important information about the temporal and spatial trends and behaviors related to PM2.5 concentrations in different European locations was developed.
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Keywords:
air pollution; artificial neural network; genetic algorithm; particulate matter; spatial variation; temporal variation
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MDPI and ACS Style
Adães, J.; Pires, J.C.M. Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities. Sustainability 2019, 11, 6019. https://doi.org/10.3390/su11216019
AMA Style
Adães J, Pires JCM. Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities. Sustainability. 2019; 11(21):6019. https://doi.org/10.3390/su11216019
Chicago/Turabian StyleAdães, José, and José C.M. Pires 2019. "Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities" Sustainability 11, no. 21: 6019. https://doi.org/10.3390/su11216019
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