Analysis and Modelling of PM 2.5 Temporal and Spatial Behaviors in European Cities

José Adães and José C. M. Pires
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José Adães: LEPABE—Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr Roberto Frias, 4200-465 Porto, Portugal
José C. M. Pires: LEPABE—Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr Roberto Frias, 4200-465 Porto, Portugal

Sustainability, 2019, vol. 11, issue 21, 1-26

Abstract: Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM 2.5 ) 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 PM 2.5 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 PM 10 concentrations on PM 2.5 concentrations. Lower PM 2.5 concentrations were observed in northern Europe. The highest values were found at traffic-related sites. PM 2.5 concentrations were usually higher during the winter and tended to present strong increases during rush hours. PM 2.5 /PM 10 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 PM 10 concentrations) on PM 2.5 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 PM 2.5 concentrations in different European locations was developed.

Keywords: air pollution; artificial neural network; genetic algorithm; particulate matter; spatial variation; temporal variation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
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