Estimating airborne pollutant concentrations in vegetated urban sites using statistical models with microclimate and urban geometry parameters as predictor variables: A case study in the city of Athens Greece

Abstract

The present study demonstrates the efficiency of applying statistical models to estimate airborne pollutant concentrations in urban vegetation by using as predictor variables readily available or easily accessible data. Results revealed that airborne cadmium concentrations in vegetation showed a predictable response to wind conditions and to various urban landscape features such as the distance between the vegetation and the adjacent street, the mean height of the adjacent buildings, the mean density of vegetation between vegetation and the adjacent street and the mean height of vegetation. An artificial neural network (ANN) model was found to have superiority in terms of accuracy with an R(2) value on the order of 0.9. The lowest R(2) value (on the order of 0.7) was associated with the linear model (SMLR model). The linear model with interactions (SMLRI model) and the tree regression (RTM) model gave similar results in terms of accuracy with R(2) values on the order of 0.8. The improvement of the results with the use of the non-linear models (RTM and ANN) and the inclusion of interaction terms in the SMLRI model implied the nonlinear relationships of pollutant concentrations to the selected predictors and showed the importance of the interactions between the various predictor variables. Despite the limitations of the models, some of them appear to be promising alternatives to multimedia-based simulation modeling approaches in urban areas with vegetation, where the application of typical deposition models is sometimes limited.

Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India

“…In addition, the CART model is widely used to solve regression problems in domains as diverse as engineering, medicine and the environment [28, 29, 30]. For example, A. Troncoso has used the CART model to predict wind speeds for a wind farm in northern Guadalajara, Spain [25].…”

Planning, Designing, and Managing Green Roofs and Green Walls for Public Health – An Ecosystem Services Approach

“…Vegetation has the potential to mitigate air pollution. The density, height, thickness, and coverage of the vegetation, as determined by relative tree cover, tree size, and density, are the main characteristics determining the effect (Escobedo and Nowak, 2009;Tiwary et al, 2009;Tsiros et al, 2009;Dzierżanowski et al, 2011;Tallis et al, 2011;Nowak et al, 2013;Baldauf, 2017). However, there are differences between species (Benjamin et al, 1996).…”

Espacios verdes urbanos como componente de un ecosistema. Funciones, servicios, usuarios, participación de la comunidad, iniciativas y acciones

“…All types of green space are associated with heat stress, urban heat islands and air pollution reductions. Green space density as the relative tree cover affects the relationship between green space and the mitigation of air pollution (Baik, Kwak, Park & Ryu, 2012;Tsiros, Dimopoulos, Chronopoulos & Chronopoulos, 2009).…”

“…Communities green spaces are associated with reduced household pollution material exposure (Dadvand et al, 2012). Vegetation density in green spaces is associated with pollution mitigation (Escobedo & Nowak, 2009;Nowak et al, 2014;Nowak, Greenfield, Hoehn & Lapoint, 2013;Tallis, Taylor, Sinnett & Freer-Smith, 2011;Tiwary et al, 2009;Tsiros et al, 2009;Yin, Shen, Zhou, Zou, Che & Wang, 2011). Bushes instead of trees may retain more pollution particles and reduce concentrations (Wania, Bruse, Blond & Weber, 2012).…”