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

Tsiros, IX; Dimopoulos, IF; Chronopoulos, KI; Chronopoulos, G

dc.contributor.author	Tsiros, IX	en
dc.contributor.author	Dimopoulos, IF	en
dc.contributor.author	Chronopoulos, KI	en
dc.contributor.author	Chronopoulos, G	en
dc.date.accessioned	2014-03-01T01:30:25Z
dc.date.available	2014-03-01T01:30:25Z
dc.date.issued	2009	en
dc.identifier.issn	1093-4529	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19569
dc.subject	Artificial neural networks	en
dc.subject	Environmental management	en
dc.subject	Estimation	en
dc.subject	Model	en
dc.subject	Prediction	en
dc.subject	Regression tree model	en
dc.subject	Urban green	en
dc.subject	Urban microclimate	en
dc.subject	Urban vegetation	en
dc.subject.classification	Engineering, Environmental	en
dc.subject.classification	Environmental Sciences	en
dc.subject.other	cadmium	en
dc.subject.other	air pollutant	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	chemistry	en
dc.subject.other	city	en
dc.subject.other	climate	en
dc.subject.other	Cynodon	en
dc.subject.other	environmental monitoring	en
dc.subject.other	Greece	en
dc.subject.other	methodology	en
dc.subject.other	statistical model	en
dc.subject.other	Air Pollutants	en
dc.subject.other	Cadmium	en
dc.subject.other	Cities	en
dc.subject.other	Climate	en
dc.subject.other	Cynodon	en
dc.subject.other	Environmental Monitoring	en
dc.subject.other	Greece	en
dc.subject.other	Linear Models	en
dc.subject.other	Models, Statistical	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Adjacent buildings	en
dc.subject.other	Airborne pollutants	en
dc.subject.other	Artificial neural network models	en
dc.subject.other	Deposition models	en
dc.subject.other	Interaction term	en
dc.subject.other	Mean density	en
dc.subject.other	Non-linear model	en
dc.subject.other	Non-linear relationships	en
dc.subject.other	Pollutant concentration	en
dc.subject.other	Predictor variables	en
dc.subject.other	Regression tree models	en
dc.subject.other	Statistical models	en
dc.subject.other	Tree regression	en
dc.subject.other	Urban areas	en
dc.subject.other	Urban geometry	en
dc.subject.other	Urban green	en
dc.subject.other	Urban landscape	en
dc.subject.other	Urban microclimate	en
dc.subject.other	Urban site	en
dc.subject.other	Urban vegetation	en
dc.subject.other	Wind conditions	en
dc.subject.other	Cadmium	en
dc.subject.other	Computer simulation	en
dc.subject.other	Environmental management	en
dc.subject.other	Estimation	en
dc.subject.other	Forecasting	en
dc.subject.other	Forestry	en
dc.subject.other	Mathematical models	en
dc.subject.other	Models	en
dc.subject.other	Neural networks	en
dc.subject.other	Vegetation	en
dc.title	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	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/10934520903263256	en
heal.identifier.secondary	http://dx.doi.org/10.1080/10934520903263256	en
heal.language	English	en
heal.publicationDate	2009	en
heal.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 R2 value on the order of 0.9. The lowest R2 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 R2 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. Copyright © Taylor & Francis Group, LLC.	en
heal.publisher	TAYLOR & FRANCIS INC	en
heal.journalName	Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering	en
dc.identifier.doi	10.1080/10934520903263256	en
dc.identifier.isi	ISI:000271611300002	en
dc.identifier.volume	44	en
dc.identifier.issue	14	en
dc.identifier.spage	1496	en
dc.identifier.epage	1502	en