  The construction industry is one of the main producers of greenhouse gasses
(GHG). Quantifying the amount of air pollutants including GHG emissions during
a construction project has become an additional project objective to
traditional metrics such as time, cost, and safety in many parts of the world.
A major contributor to air pollution during construction is the use of heavy
equipment and thus their efficient operation and management can substantially
reduce the harm to the environment. Although the on-road vehicle emission
prediction is a widely researched topic, construction equipment emission
measurement and reduction have received very little attention. This paper
describes the development and deployment of a novel framework that uses machine
learning (ML) methods to predict the level of emissions from heavy construction
equipment monitored via an Internet of Things (IoT) system comprised of
accelerometer and gyroscope sensors. The developed framework was validated
using an excavator performing real-world construction work. A portable emission
measurement system (PEMS) was employed along with the inertial sensors to
record data including the amount of CO, NOX, CO2, SO2, and CH4 pollutions
emitted by the equipment. Different ML algorithms were developed and compared
to identify the best model to predict emission levels from inertial sensors
data. The results showed that Random Forest with the coefficient of
determination (R2) of 0.94, 0.91 and 0.94 for CO, NOX, CO2, respectively was
the best algorithm among different models evaluated in this study.
