TY - JOUR
T1 - Applying machine learning methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon
AU - Šimić, Iva
AU - Lovrić, Mario
AU - Godec, Ranka
AU - Kröll, Mark
AU - Bešlić, Ivan
PY - 2020/8
Y1 - 2020/8
N2 - Narrow city streets surrounded by tall buildings are favorable to inducing a general effect of a “canyon” in which pollutants strongly accumulate in a relatively small area because of weak or inexistent ventilation. In this study, levels of nitrogen-oxide (NO2), elemental carbon (EC) and organic carbon (OC) mass concentrations in PM10 particles were determined to compare between seasons and different years. Daily samples were collected at one such street canyon location in the center of Zagreb in 2011, 2012 and 2013. By applying machine learning methods we showed seasonal and yearly variations of mass concentrations for carbon species in PM10 and NO2, as well as their covariations and relationships. Furthermore, we compared the predictive capabilities of five regressors (Lasso, Random Forest, AdaBoost, Support Vector Machine and Partials Least squares) with Lasso regression being the overall best performing algorithm. By showing the feature importance for each model, we revealed true predictors per target. These measurements and application of machine learning of pollutants were done for the first time at a street canyon site in the city of Zagreb, Croatia.
AB - Narrow city streets surrounded by tall buildings are favorable to inducing a general effect of a “canyon” in which pollutants strongly accumulate in a relatively small area because of weak or inexistent ventilation. In this study, levels of nitrogen-oxide (NO2), elemental carbon (EC) and organic carbon (OC) mass concentrations in PM10 particles were determined to compare between seasons and different years. Daily samples were collected at one such street canyon location in the center of Zagreb in 2011, 2012 and 2013. By applying machine learning methods we showed seasonal and yearly variations of mass concentrations for carbon species in PM10 and NO2, as well as their covariations and relationships. Furthermore, we compared the predictive capabilities of five regressors (Lasso, Random Forest, AdaBoost, Support Vector Machine and Partials Least squares) with Lasso regression being the overall best performing algorithm. By showing the feature importance for each model, we revealed true predictors per target. These measurements and application of machine learning of pollutants were done for the first time at a street canyon site in the city of Zagreb, Croatia.
KW - AdaBoost
KW - EC
KW - Lasso regression
KW - NO
KW - Random forest regression
UR - http://www.scopus.com/inward/record.url?scp=85083699183&partnerID=8YFLogxK
U2 - 10.1016/j.envpol.2020.114587
DO - 10.1016/j.envpol.2020.114587
M3 - Article
AN - SCOPUS:85083699183
SN - 0269-7491
VL - 263
JO - Environmental Pollution
JF - Environmental Pollution
M1 - 114587
ER -