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Analyzing Particulate Matters (PM10, PM2.5, PM1) Emitted from Vehicles in
Two Different Altitude of Street Canyon in Malaysia
Nur Syahirah Binti Che Hamid
Supervisor: Dr. Hajah Noor Zaitun Binti Yahaya
Bachelor of Technology (Environment)
School of Ocean Engineering
Universiti Malaysia Terengganu
Street canyon is a relatively narrow street with building lining up along the both sides
creating a canyon-like environment. Transportation has emitted hazardous particles
which could give harmful effect to human health and environment. This study was
conducted to predict the interactions of Particulate Matters with traffic flow and
meteorological condition. Boosted Regression Trees (BRT) algorithm is a technique
which capable to explain the complexity of air pollutant relationship among variable.
The PM10, PM2.5 and PM1 were trained by BRT of learning rate 0.001, 0.05 and 0.1.
The tree complexity of data are 1, 2 and 2 while number of tree estimated by using
cross-validation were 4996, 255, and 604 which quantify the pollutants concentration
using performance indicator such as FAC2, RMSE and R 2 . The R 2 values for PM10, PM2.5
and PM1 were 0.74, 0.76 and 0.80 respectively. This proved that BRT is a useful
technique to identify relationships and main factors influencing the pollutants in street
canyon.
76 | U M T U N D E R G R A D U A T E R E S E A R C H D A Y 2019